diff --git a/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375.mmd b/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ae19a9dbeb334a63c137795610b964c6c8731fc3 --- /dev/null +++ b/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375.mmd @@ -0,0 +1,613 @@ + +# 5'-tRNAGly(GCC) halves generated by IRE1α are linked to ER stress response + +Ji-Hyun Yeom Chung- Ang University + +Eunkyoung Shin Chung- Ang University + +Yoonjie Ha Chung- Ang University https://orcid.org/0000- 0002- 6506- 9338 + +Minju Joo Chung- Ang University + +Hanyong Jin Yanbian University + +Haifeng Liu Chung- Ang University + +Daeyoung Kim Chung- Ang University + +Yong- Hak Kim Daegu Catholic University School of Medicine https://orcid.org/0000- 0001- 6192- 5996 + +Hak Kyun Kim Chung- Ang University + +Jeongkyu Kim Chung- Ang University + +Hong- Man Kim NES Biotechnology + +Minkyung Ryu NES Biotechnology + +Keun Pil Kim Chung- Ang University + +Yoonsoo Hahn Chung- Ang University https://orcid.org/0000- 0003- 4273- 9842 + +Jeehyeon Bae School of Pharmacy, Chung- Ang University https://orcid.org/0000- 0003- 1995- 1378 + +Kangseok Lee ( kangseok@cau.ac.kr) Chung- Ang University https://orcid.org/0000- 0002- 0060- 6884 + +<--- Page Split ---> + +## Article + +Keywords: IRE1α, alternative splicing, tRNAGly(GCC), ER stress, HNRNPM, HNRNPH2, tRNA halves + +Posted Date: August 3rd, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1464849/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +1 5'-tRNAGly(GCC) halves generated by IRE1α are linked to ER stress response + +<--- Page Split ---> + +## 4 Summary + +Transfer RNA (tRNA) halves (tRHs) have various biological functions. However, the biogenesis of specific \(5^{\prime}\) - tRHs under certain conditions remains unknown. Here, we report that inositol- requiring enzyme \(1\alpha\) (IRE1α) cleaves the anticodon stem- loop region of tRNA \(^{\mathrm{Gly(GCC)}}\) to produce \(5^{\prime}\) - tRHs ( \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) ) with highly selective target discrimination upon endoplasmic reticulum (ER) stress. We observed IRE1α expression- dependent \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) production in human cancer cells. Levels of \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) were positively correlated with the degree of cancer cell proliferation both in vitro and in vivo; this effect required co- expression of two nuclear ribonucleoproteins, HNRNPM and HNRNPH2, which we identified as binding proteins of \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) . In addition, \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) modulated mRNA isoform biogenesis. Furthermore, under ER stress in vivo, we observed simultaneous induction of IRE1α and \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) expression in mouse organs and a distantly related organism, Cryptococcus neoformans. Thus, collectively, our findings indicate an evolutionarily conserved function for IRE1α- generated \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) in cellular adaptation upon ER stress. + +Key words: IRE1α, alternative splicing, tRNA \(^{\mathrm{Gly(GCC)}}\) , ER stress, HNRNPM, HNRNPH2, tRNA halves + +<--- Page Split ---> + +## Introduction + +Transfer RNA- derived fragments (tRFs) or transfer RNA- derived small RNAs (tsRNAs) have been recognised as functional small non- coding RNAs (ncRNAs) present in most organisms1. Multiple classes of tRFs have been identified in various cell types1. In particular, 31–40 nucleotide (nt) long tRFs generated by specific cleavage in the anticodon loop of mature tRNAs are referred to as tRNA halves (tRHs). Other tRFs are 14–40 nt in length and primarily correspond to the ends of mature tRNA (5'- tRFs and 3'- CCA tRFs) or pre- tRNA (3'- U tRFs)1. + +In mammalian cells, limited information exists regarding the enzymes that generate tRFs. Angiogenin (ANG), a member of the RNase A superfamily, produces tRHs under certain stress conditions2- 5. In the case of RNase Z, it cleaves pre- tRNAs and generates 3'- U tRFs containing a stretch of U residues6. Additionally, dicer induces cleavage in the D loop and T loop of tRNAs, producing 5'- tRFs and 3'- CCA tRFs, respectively7, 8. Furthermore, recent deep sequencing data suggest that dicer processes tRFs in specific tRNAs and cell types9. + +Functional roles of identified tRFs in biological processes include translational regulation of gene expression10- 13, gene silencing, and regulation of ribosome synthesis6, 14. tRHs affect cell proliferation4, 14- 18, apoptosis5, and epigenetic inheritance19, 20. For instance, changes in the profiles of a subset of sperm tRFs, including 5'- tRHs of tRNAGly(GCC) (5'- tRH- GlyGCC), were reported in mice fed a high- fat diet20. Moreover, protein restriction in mice increases 5'- tRH- GlyGCC levels19. Additionally, 5'- tRH- GlyGCC, induced by alkB homologue 3, \(\alpha\) - ketoglutarate dependent dioxygenase (ALKBH3)—a tRNA demethylase—benefits the growth and progression of cervical carcinoma16. 5'- tRH- GlyGCC levels were also upregulated in papillary thyroid carcinoma18. Although 5'- tRH- GlyGCC appears to play various roles in cellular physiology, it remains unclear which enzyme generates these tRHs. + +A structural analysis of IRE1α revealed that the catalytic residues between the tRNA + +<--- Page Split ---> + +endonuclease and IRE1α contain functional groups with a shared chemical nature and spatial disposition21. IRE1α—a key regulator of signalling in the unfolded protein response (UPR)—is a conserved ER- localised transmembrane protein with ribonuclease activity22. Upon ER stress, IRE1α becomes activated and cleaves specific sites in the mRNA that encodes the transcription factor X- box- binding protein 1 (XBP1)23, 24. IRE1α also participates in regulated IRE1α- dependent decay, i.e., the degradation of multiple mRNAs and miRNAs under ER stress in an XBP1- independent manner25- 27. In particular, a consensus sequence (5'- CH(U or A or C)GCM(A or C)R(G or A)- 3')) accompanied by a stem- loop structure was proposed as an IRE1α cleavage site in mRNA28. + +Herein, we observed that several tRNAs bear the consensus element for IRE1α cleavage in their anticodon loop region. Considering that tRNAGly(GCC) is one such tRNA, we hypothesised that IRE1α may participate in producing 5'- tRHs from tRNAGly(GCC). To test the hypothesis, we aimed to investigate the direct involvement of IRE1α in the production of 5'- tRHs from tRNAGly(GCC), as well as their physiological function under ER stress. + +## Results + +## 5'-tRH accumulation by IRE1α upregulation + +To explore whether IRE1α can cleave tRNAs and produce 5'- tRHs, we compared tRF profiles in human ovarian cancer- derived KGN cells endogenously expressing IRE1α (KGN) with those in the same cells exogenously overproducing IRE1α (KGN- IRE1αoe) using small RNA- sequencing (small RNA- seq). We selected human ovarian cancer cells, as 5'- tRH- GlyGCC reportedly functions in reproductive cells16, 20. The relative abundance of 5'- tRFs from tRNAGly(GCC) species markedly increased when IRE1α was overexpressed (Fig. 1a and Supplementary Table 1). Additionally, 5'- tRFs from tRNACys(GCA) appeared to accumulate, + +<--- Page Split ---> + +albeit at much lower levels compared to those from tRNA \(\mathrm{Gly(GCC)}\) in KGN- IRE1 \(\alpha^{\mathrm{oe}}\) cells (Fig. 1a and Supplementary Table 1). These results support the notion that among tRNA species, only tRNA \(\mathrm{Gly(GCC)}\) and tRNA \(\mathrm{Cys(GCA)}\) bear the consensus element for IRE1 \(\alpha\) cleavage in their anticodon stem- loop region. + +Among three different tRNA \(\mathrm{Gly}\) isoacceptors, containing GCC, UCC, and CCC anticodons, 5'- tRFs with their 3'- end corresponding to position 33 of tRNA \(\mathrm{Gly(GCC)}\) were most abundant and enriched in KGN- IRE1 \(\alpha^{\mathrm{oe}}\) compared to KGN cells (Fig. 1a,b and Supplementary Table 1). In addition, high levels of 5'- tRFs, with their 3'- end corresponding to positions 31 and 32 of tRNA \(\mathrm{Gly(GCC)}\) , were observed when IRE1 \(\alpha\) was overexpressed (Fig. 1b and Supplementary Table 1). These 5'- tRFs from tRNA \(\mathrm{Gly(GCC)}\) occupied approximately \(89\%\) of the total 5'- tRFs from IRE1 \(\alpha^{\mathrm{oe}}\) cells (Fig. 1b), indicating that IRE1 \(\alpha\) overexpression primarily generates 5'- tRFs from tRNA \(\mathrm{Gly(GCC)}\) . In the case of tRNA \(\mathrm{Cys(GCA)}\) , high levels of 5'- tRFs, with their 3'- end corresponding to positions 33 and 34, were observed when IRE1 \(\alpha\) was overexpressed (Fig. 1a, b and Supplementary Table 1). We also observed enrichment of 5'- tRFs with their 3'- end corresponding to position 33 of tRNA \(\mathrm{Gly(GCC)}\) in IRE1 \(\alpha\) - overexpressing cells; however, these 5'- tRFs accounted for only \(\sim 2\%\) of the total (Fig. 1a, b and Supplementary Table 1). + +To validate the small RNA- seq results, tRNA fragments were analysed via northern blotting with specific probes for the 5' upstream regions of the tRNA \(\mathrm{Gly(GCC)}\) , tRNA \(\mathrm{Cys(GCA)}\) , tRNA \(\mathrm{Gly(TCC)}\) , and tRNA \(\mathrm{Lys(CTT)}\) anticodon stem- loops. An IRE1 \(\alpha\) expression- dependent increase was observed in the levels of 5'- tRFs from tRNA \(\mathrm{Gly(GCC)}\) and tRNA \(\mathrm{Cys(GCA)}\) (Fig. 1c). The relative abundances of these 5'- tRFs were \(\sim 1.2\%\) and \(\sim 0.1\%\) of full- length tRNA \(\mathrm{Gly(GCC)}\) and tRNA \(\mathrm{Cys(GCA)}\) , respectively. The length of these 5'- tRFs corresponded to 32 and 33 nt- long synthetic RNAs containing sequences of 5'- tRFs from tRNA \(\mathrm{Gly(GCC)}\) (Fig. 1c). The relative abundances of other 5'- tRFs from tRNA \(\mathrm{Gly(TCC)}\) and tRNA \(\mathrm{Lys(CTT)}\) were not significantly + +<--- Page Split ---> + +changed upon IRE1α overexpression, which agreed with the small RNA- seq results. Moreover, overexpression of a catalytically inactive form of IRE1α (K599A) \(^{29}\) did not significantly impact the levels of these 5'- tRFs (Extended Data Fig. 1a), indicating IRE1α cleavage activity- dependent production of 5'- tRH- Gly \(^{GCC}\) . + +To assess the size of tRFs from tRNA \(^{\mathrm{Gly(GCC)}}\) , we performed primer extension analysis on the samples used for northern blot analysis. Primer extension targeting for tRNA \(^{\mathrm{Gly(GCC)}}\) produced one distinct cDNA band in reactions prepared with RNA samples from KGN- IRE1α \(^{\mathrm{oe}}\) cells. This cDNA band was synthesized from the 3'- tRH of tRNA \(^{\mathrm{Gly(GCC)}}\) , whose 5'- end corresponded to position 34 (Fig. 1d). This 3'- tRH of tRNA \(^{\mathrm{Gly(GCC)}}\) can be generated by IRE1α cleavage of tRNA \(^{\mathrm{Gly(GCC)}}\) between positions 33 and 34 within its anticodon stem- loop region (C \(^{31}\) UG \(^{1}\) CCAC \(^{37}\) ). This cleavage can also generate a 33- nt long 5'- tRH of tRNA \(^{\mathrm{Gly(GCC)}}\) , with the same 3'- end that mapped the highest in KGN- IRE1α \(^{\mathrm{oe}}\) cells in small RNA seq analysis (Fig. 1a, b). We were not able to detect cDNA bands corresponding 31 and 32 nt- long tRFs that were indicated in small RNA- seq and northern blot analyses (Fig. 1b,c). Overexpression of angiogenin (ANG)—a ribonuclease that produces tRHs by cleaving the anticodon loop region of tRNAs \(^{4, 5}\) — resulted in high levels of 5'- tRFs from all tRNA species (tRNA \(^{\mathrm{Gly(GCC)}}\) , tRNA \(^{\mathrm{Lys(CTT)}}\) , and tRNA \(^{\mathrm{Val(TAC)}}\) ; Extended Data Fig. 1b). Collectively, these results indicate that IRE1α activity is primarily responsible for generation of 5'- tRHs from tRNA \(^{\mathrm{Gly(GCC)}}\) . + +## Selective cleavage of tRNA \(^{\mathrm{Gly(GCC)}}\) by IRE1α + +To assess whether IRE1α is solely responsible for the production of 5'- tRHs from tRNA \(^{\mathrm{Gly(GCC)}}\) , we isolated tRNAs from KGN cell total RNA and isolated tRNAs by size fractionation. Purified tRNAs were then incubated with human IRE1α. IRE1α was found to selectively cleave tRNA \(^{\mathrm{Gly(GCC)}}\) in vitro (Fig. 2a). Specifically, IRE1α- mediated cleavage of tRNA \(^{\mathrm{Gly(GCC)}}\) + +<--- Page Split ---> + +generated one major and two minor \(5^{\prime}\) - tRNAGly(GCC) fragments (Fig. 2a). When the 3'- end of these fragments was mapped by primer extension analysis, seven distinct cDNA bands (Fig. 2b) were detected. The cDNA band was most prominent and could be synthesised from the 3'- tRH of tRNAGly(GCC) with a 5'- end corresponding to position 34 (Fig. 2b, labelled as b). This 3'- tRH could be generated by IRE1α cleavage of tRNAGly(GCC) between positions 33 and 34 within its anticodon stem- loop region (C \(^{31}\) UG \(_{1}\) CCAC \(^{37}\) ). This cleavage site corresponded to the 3'- end of the most abundant 5'- tRFs from tRNAGly(GCC) that were identified by small RNA- seq (Fig. 1b). Moreover, this cDNA band is identical to the distinct cDNA detected in the primer extension assay of tRNAGly(GCC) fragments in KGN cells following IRE1α overexpression (Fig. 1d). Another distinct band (labelled as a) also corresponded to the 3'- end of the second most abundant 5'- tRFs from tRNAGly(GCC) identified in small RNA- seq (Fig. 1b, 2b). Meanwhile, the cleavage sites deduced from other cDNA bands were not observed in the small RNA- seq analysis (Fig. 1b) or in the primer extension of in vivo generated fragments of tRNAGly(GCC) (Fig. 1d). + +To further biochemically verify the ability of IRE1α to cleave tRNAGly(GCC), we conducted an in vitro IRE1α cleavage reaction using purified tRNAGly(GCC) as a substrate (Extended Data Fig. 2a–c). Two major and five minor cleavage products appeared to be dependent on IRE1α (Fig. 2c). Among them, one major cleavage product (labelled as 5) corresponded to an IRE1α cleavage product of tRNAGly(GCC) between positions 33 and 34 (Fig. 2c). This cleavage site corresponded to the 3'- end of the most abundant 5'- tRFs from tRNAGly(GCC) identified via small RNA-seq (Fig. 1b) and was the only cDNA detected in the primer extension assay of tRNAGly(GCC) in vivo generated fragments (Fig. 1d) when IRE1α was overexpressed. Five other products also corresponded to IRE1α cleavage products of tRNAGly(GCC) at sites generated by in vitro IRE1α cleavage of total tRNAs (Fig. 2b). An + +<--- Page Split ---> + +additional cleavage product (labelled as 7) was also detected in small RNA- seq (Fig. 1b). However, several tRFs identified from in vitro cleavage of tRNAGly(GCC) were not detected in the small RNA- seq analyses (Fig. 1a,b) or primer extension assay of tRNAGly(GCC) fragments generated in vivo (Fig. 1d). These tRFs might have resulted from decreased IRE1α stringency in the sequence- specific cleavage of tRNAGly(GCC) in vitro, or from tRNAGly(GCC) structural alterations induced during purification or incubation. It is also possible that they arose from fragmentation of tRNAGly(GCC) cleavage products. + +These clearly show the ability of IRE1α to selectively cleave tRNAGly(GCC) within the anticodon stem- loop region. Furthermore, the cleavage site (C31UG\CCAC37) deduced from both small RNA- seq (Fig. 1a,b) and primer extension analyses of in vivo generated tRNAGly(GCC) fragments (Fig. 1d) corresponded with the major in vitro IRE1α cleavage product of tRNAGly(GCC) (Fig. 2b,c). Based on these results, we designated 33- nt long 5'- tRHs generated from the cleavage of tRNAGly(GCC) at the overlapping site (C31UG\CCAC37) as 5'- tRH- GlyGCC. + +## Induction of 5'- tRH-GlyGCC generation upon ER stress + +Considering that IRE1α is an ER stress- activated endonuclease, we hypothesised that ER stress- induced activation of IRE1α may cause generation of 5'- tRH- GlyGCC from tRNAGly(GCC) cleavage. To test this hypothesis, we induced ER stress in KGN cells using thapsigargin (TG) or tunicamycin (TM). Western blot analysis and an XBP1 splicing assay confirmed that these agents stimulated the expression and ribonucleolytic activity of IRE1α (Fig. 3a). + +Next, ER stress- induced IRE1α activation on tRNA cleavage was examined via northern blot analysis on tRNAs. In agreement with the effect of IRE1α overexpression on the generation of tRHs from tRNAGly(GCC) (Fig. 1c), 5'- tRH- GlyGCC was generated while distinct tRFs from other tRNAs were not detected following ER stress- induced IRE1α expression (Fig. + +<--- Page Split ---> + +3a and Extended Data Fig. 3a). Moreover, we found that the cleavage pattern of these 5'- tRHs resembled those generated by IRE1α, which cleaves tRNAGly(GCC) between positions 33 and 34 within the anticodon stem- loop (C31UG↓CCAC37) (Fig. 3b). Furthermore, TG- or TM- induced production of 5'- tRHs was not observed in IRE1α knock out cells (IRE1α-/-; Fig. 3c and Extended Data Fig. 3b, 4). Hence, ER stress induces 5'- tRH- GlyGCC production via IRE1α- dependent tRNAGly(GCC) cleavage in KGN cells. + +To investigate whether the production of 5'- tRHs from tRNAGly(GCC) is commonly coupled with ER stress in human cells, we induced ER stress in HeLa cells with TG or TM. ER stress- dependent selective generation of 5'- tRFs from tRNAGly(GCC) was also observed in HeLa cells (Extended Data Fig. 3c, d). + +## Proteins interacting with 5'- tRH-GlyGCC + +To investigate the functional role of 5'- tRH- GlyGCC, we characterised proteins bound to 5'- tRH- GlyGCC in KGN cells via biotinylation of the tRH 5'- and 3'- ends. Specifically, 33- nt long 5'- tRHs of tRNAGly(GCC) (5'- tRH- GlyGCC mimic) were used. To assess non- specific protein binding of biotinylated RNA with a streptavidin coated microplate, 5'- biotin- oligo A8 RNA and 3'- biotin- tRH- GlyGCC were used as controls. Two protein bands near 70 kDa and 55 kDa appeared to specifically bind to a 5'- biotin- tRH- GlyGCC in both samples of TG- treated and - untreated cells but did not bind 3'- biotin- tRH- GlyGCC or 5'- biotin- oligo A8 RNA (Extended Data Fig. 5a, right panel). The comparative tandem mass spectrometry analysis of proteins interacting with biotinylated RNA showed that the heterogeneous nuclear ribonucleoprotein M isoform b (HNRNPM) and heterogeneous nuclear ribonucleoprotein H isoform X11 (HNRNPH2) in TG- treated and - untreated samples were enriched at similar levels in the microplate containing 5'- biotin- tRH- GlyGCC (Extended Data Fig. 5b and Supplementary Table 2). These nuclear proteins + +<--- Page Split ---> + +may be potential binding partners of the 5'- tRHs of tRNAGly(GCC). We were also able to detect a moderate amount of HNRNPF, while HNRNPH1 was not detected. Orthologues of HNRNP proteins have been previously identified as binding proteins of tRHGly(GCC) in mouse cells30. + +We further assessed the physical interaction between 5'- tRH- GlyGCC and HNRNP proteins via electrophoretic mobility shift assay (EMSA) using purified HNRNPM and HNRNPH2 recombinant proteins and 5'- P32- labelled synthetic 5'- tRH- GlyGCC and 5'- tRH- LysCTT. These proteins bind 5'- tRH- GlyGCC with much higher affinity than 5'- tRH- LysCTT (Extended Data Fig. 6a), providing evidence of specific interactions between 5'- tRH- GlyGCC and HNRNP proteins. + +We further tested physical interaction of 5'- tRH- GlyGCC or 3'- tRH- GlyGCC with HNRNP proteins (HNRNPM and HNRNPH2) by using the surface plasmon resonance (SPR). In SPR assay, both 5'- tRH- GlyGCC and 3'- tRH- GlyGCC showed dose- dependent binding signal to the immobilized HNRNPM and HNRNPH2 (Extended Data Fig. 6b). However, kinetic analysis indicated that 5'- tRH- GlyGCC has about \(10 \sim 35\) times higher affinities for HNRNPM and HNRNPH2 with \(K_D\) of \(86.30 \mathrm{nM}\) and \(27.07 \mathrm{nM}\) , compared to 3'- tRH- GlyGCC, which showed affinities for HNRNPM and HNRNPH2 with \(K_D\) of \(0.96 \mu \mathrm{M}\) and \(0.94 \mu \mathrm{M}\) , respectively (Extended Data Fig. 6c). These results provide clear evidence for specific and strong interaction between 5'- tRH- GlyGCC and HNRNP proteins. + +## Roles of ER stress-induced 5'- tRH-GlyGCC + +To investigate functional roles for 5'- tRH- GlyGCC in cancer cells that produced these 5'- tRHs upon ER stress, KGN and HeLa cells were treated with synthetic 5'- tRH- GlyGCC (5'- tRH- GlyGCC mimic) and two other control tRH mimics (5'- tRH- LysCTT and 3'- tRH- GlyGCC). Treatment with 5'- tRH- GlyGCC mimic promoted cell survival in a manner dependent on mimic + +<--- Page Split ---> + +concentrations, which increased cell survival of KGN and HeLa cells by \(34\%\) and \(25\%\) , respectively, at the highest concentration of the mimic used (Fig. 4a,b). In contrast, the two control mimics did not significantly affect cell viability with the highest concentration of \(3'\) - tRH- Gly \(^{\mathrm{GCC}}\) inducing only a moderate increase in the viability of KGN cells (Fig. 4a,b). Blocking the \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) mimic with complementary antisense DNA oligos significantly reduced the positive effects on cell viability (Extended Data Fig. 7a). The enhancement of cell viability by \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) mimic occurred due to increased proliferation of KGN cells, as no effect on apoptosis was observed (Extended Data Fig. 7b,c). In addition, tRH mimic transfection did not affect the migration capability of KGN cells (Extended Data Fig. 7d). These results suggested that \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) functions to control cancer cell proliferation. + +Next, we investigated whether HNRNPM and HNRNPH2 participate in \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) - mediated cell survival. The \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) mimic- induced promotion of cell survival in KGN and HeLa cells was abolished following HNRNPM or HNRNPH2 knockdown (Fig. 4c,d). Transfection of the \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) mimics in HNRNPM- depleted KGN (Fig. 4c) or HNRNPH2- depleted HeLa (Fig. 4d) cells further reduced cell survival compared to those treated with control mimics, suggesting that HNRNPM and HNRNPH2 might have a cell line- specific role in \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) - mediated cell survival. Hence, \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) - mediated cell survival depends on its interaction with HNRNP proteins in these cancer cells. Considering that we observed analogous results following knockdown of HNRNPF/H1/H2 (Extended Data Fig. 7e,f), the \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) might interact with multiple nuclear ribonucleoprotein for cellular function. + +We further examined whether IRE1α- dependent \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) generation mediates the ER stress- induced effect on cell survival. Treatment with antisense DNA oligos targeting endogenous \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) (anti- \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) ) significantly promoted TG- induced cell + +<--- Page Split ---> + +death in WT KGN cells compared to those treated with 5'- tRH- LysCTT (anti- 5'- tRH- LysCTT) (Fig. 4e). In contrast, anti- 5'- tRH- GlyGCC did not elicit such an effect in IRE1α- /- cells (Fig. 4e). Thus, IRE1α cleavage- generated 5'- tRH- GlyGCC contributes to cellular adaptation upon ER stress. + +To investigate in vivo 5'- tRH- GlyGCC function, we silenced the endogenous 5'- tRH- GlyGCC or 5'- tRH- LysCTT by delivering antisense DNA oligos against them using a functionalized gold nanoparticle (AuNP)- based delivery system (AuNPdT) in a xenograft mouse model. As shown in Figure. 4f, tumour growth in mice treated with AuNPdT loaded with anti- 5'- tRH- GlyGCC was prominently inhibited compared with that treated with AuNPdT alone or AuNPdT loaded with anti- 5'- tRH- LysCTT (Fig. 4f). Consistent with anti- proliferative response observed in cancer cells treated with anti- 5'- tRH- GlyGCC in vitro (Extended Data Fig. 7g), proliferating cell nuclear antigen (PCNA) expression in tumours decreased by \(\sim 44\%\) upon anti- 5'- tRH- GlyGCC treatment in xenografted tumours (Fig. 4g). + +## Effect of 5'- tRH-GlyGCC in alternative splicing + +To dissect the relevance of 5'- tRH- GlyGCC functioning, we performed total transcriptome analysis on KGN cells transfected with 5'- tRH- mimics. The RNA abundance of 66 genes was altered more than 1.5- fold in cells transfected with 5'- tRH- GlyGCC mimics compared to those with 5'- tRH- LysCTT mimics (Fig. 5a and Supplementary Table 3). Functional annotation analysis further indicated that most genes were enriched in alternative splicing and phosphoproteins (Fig. 5b and Supplementary Table 4). Based on these results, and the fact that 5'- tRH- GlyGCC interacts with multiple nuclear proteins functioning in RNA splicing, we hypothesised that 5'- tRH- GlyGCC modulates alternative splicing of a target gene subset. For this + +<--- Page Split ---> + +reason, we further analysed isoforms of total transcripts using nanopore sequencing and FLAIR (full- length alternative isoform analysis of RNA) modules31. + +We analysed four main types of alternative splicing events (alternative 3'- and 5'- splicing, intron retention, and exon skipping events) associated with isoform formation. Compared to the control group (5'- tRH- LysCTT), we identified 19 differential isoforms from the 17 genes in the 5'- tRH- GlyGCC- treated group, where one or more of their junctions exhibited alternative 5'/3' splice site selection or exon skipping (Supplementary Table 5). These genes had multiple alternative splicing events within their transcripts, except CFDP1 (exon skipping), PRDX4 (alternative 5'- splicing), and MAGED2 (alternative 3'- splicing) (Supplementary Table 5). Among them, the isoform usage of ELOB (Fig. 5c, upper panel) and PMSB5 (Fig. 5c, lower panel) was significantly altered between 5'- tRH- LysCTT- and 5'- tRH- GlyGCC- treated groups. These results were confirmed by RT- qPCR using isoform specific primers (Fig. 5d). In addition, sequestering of 5'- tRH- GlyGCC by antisense DNA oligos in xenografted tumours resulted in a shift in mRNA isoform composition in an opposite direction (Fig. 5e) compared to what we observed with 5'- tRH- GlyGCC mimics treatment in cancer cells (Fig. 5d). Treatment of tumours with anti 5'- tRH- LysCTT did not affect isoform composition of these genes (Fig. 5e). Hence, 5'- tRH- GlyGCC levels affect alternative splicing events, leading to alterations in the transcript isoform profile. + +## Nucleus localisation of tRH-GlyGCC + +Our results showing an interaction between tRH- GlyGCC and nuclear proteins (Extended Data Fig. 6), as well as the effect of tRH- GlyGCC mimics on transcript isoform profiles (Fig. 5), suggest that tRH- GlyGCC functions within the nucleus. Thus, to determine the subcellular distribution of ER stress- induced 5'- tRHs of tRNAGly(GCC), we conducted a fluorescent in situ + +<--- Page Split ---> + +hybridisation assay (FISH), with a probe designed to recognise \(5'\) - tRHs of tRNAGly(GCC). This assay was performed under conditions designed to avoid the denaturation of stable mature tRNAs and hybridisation of the probe to full- length tRNAs. Fluorescent signals, obtained with the probe recognising the \(5'\) - tRHs of tRNAGly(GCC), displayed a nucleus- associated localisation pattern with higher signal intensity following TG- induced ER stress, compared to treatment with dimethylsulphoxide (DMSO; Extended Data Fig. 8a). To confirm the specificity of the hybridisation probe, we performed a series of experiments under non- denaturing or denaturing conditions using an additional control probe. This control probe (anticodon bridging probe) bridged the \(5'\) - and \(3'\) - regions spanning the nucleotides that encompass the anticodon and was designed to detect only intact full- length tRNAs with minimal complementarity for \(5'\) - tRHs of tRNAGly(GCC). The anticodon bridging probe showed a fluorescent signal under denaturing FISH conditions, while no signal was observed under non- denaturing conditions (Extended Data Fig. 8a). Hence, \(5'\) - tRHs of tRNAGly(GCC) were definitively recognised with the specific probe used under our experimental conditions. In addition, measurement of tRH- GlyGCC distribution by TaqMan assay showed that \(5'\) - tRH- GlyGCC levels were elevated by \(\sim 25\%\) in the nuclear fraction of KGN cells following TG treatment over the 6 h period (Extended Data Fig. 8b). These results indicate that \(5'\) - tRHs of tRNAGly(GCC) localise to the nucleus when cells are subjected to ER stress. + +## IRE1α-dependent \(5'\) -tRHGhy(GCC) cleavage in other organisms + +To investigate whether IRE1α- mediated generation of \(5'\) - tRH- GlyGCC upon ER stress occurs in other eukaryotic species, we analysed selective generation of \(5'\) - tRH- GlyGCC in an acute ER stress murine model32 and ER- stressed yeast species, Cryptococcus neoformans. IRE1 + +<--- Page Split ---> + +homologues of mouse and yeast show \(94.40\%\) and \(40.15\%\) sequence similarity, respectively to the protein kinase and kinase- extension nuclease domains of human IRE1α. + +We observed prominent induction of IRE1α expression in the ovary, liver, epididymis, kidney, and pancreas of ER- stressed mice (Extended Data Fig. 9a). Northern blot analysis showed an increased abundance of \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) fragments in the ovary (Fig. 6a), liver (Extended Data Fig. 9b), and epididymis (Extended Data Fig. 9c) in samples taken from ER- stressed mice compared to control mice samples, while other tRNA fragments, the size of which were similar to \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) , were not detected (Extended Data Fig. 9b, d). Moreover, primer extension analysis indicates that \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) fragments in the ovary resulted from the overlapping IRE1α cleavage site identified in KGN cells, which generates a 33- nt long \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) (Fig. 6b). These results indicate that ER stress induces selective generation of \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) in mice in an IRE1α expression- dependent manner. + +We also observed that high levels of \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) coincided with enhanced IRE1 expression in \(C\) . neoformans when ER stress was induced by TM treatment (Fig. 6c and Extended Data Fig. 9e). Primer extension analysis indicate that these \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) resulted from Ire1 cleavage at the site corresponding to the overlapping site identified in KGN cells and mouse ovary (Fig. 6d). A minor band, which was slightly longer than \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) , was detected in the ire1- deletion strain when treated with TM (Fig. 6c and Extended Data Fig. 9e). These results indicate the existence of an additional unknown activity for tRNA \(^{\mathrm{Gly(GCC)}}\) cleavage under ER stress in this yeast species. Taken together, IRE1α- dependent selective generation of \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) under ER stress appears to be widely conserved in eukaryotic organisms. + +## Discussion + +<--- Page Split ---> + +This study highlights that IRE1α- mediated selective cleavage of tRNAGly(GCC) and 5'- tRH generation upon ER stress is conserved in human, mice, and a distantly related yeast species, C. neoformans. In fact, within these organisms, IRE1α homologues selectively cleave tRNAGly(GCC) species at the same site. These results raise the question of why these organisms evolutionarily retain this biological event in response to ER stress. Perhaps, as we found in the case of human cancer cells (Fig. 4e), 5'- tRH- Gly(GCC) contributes to cellular adaptation upon ER stress. + +Dicer and ANG generate different types of tRFs for multiple roles in cellular processes33. However, ANG is the only identified enzyme associated with the generation of 5'- tRHs by cleaving the anticodon stem- loop of mature tRNAs in mammalian cells2-5, 16, 34. Additional enzymes responsible for the generation of certain 5'- tRHs have not been identified under specific conditions, such as metabolic diseases, cancer, and reproductive cell maturation18-20. One such example is 5'- tRH from tRNAGly(GCC) produced in mouse sperm, which reportedly suppresses the expression of genes associated with endogenous retroelement MERVL in embryonic stem cells and embryos by regulating gene expression from specific regions of the genome19, 20. This 5'- tRH from tRNAGly(GCC) was shown to be upregulated in papillary thyroid carcinoma18. Although an increasing number of reports have revealed that tRNA- derived fragments are involved in various biological processes, its biogenesis is remains largely unknown. Here, we show that under ER stress conditions, IRE1α cleaves the anticodon stem- loop of tRNAGly(GCC) to produce 5'- tRH in human cancer cells. Moreover, generation of 5'- tRHs from tRNAGly(GCC) appears to be ER stress- specific, as it was not observed under other stress conditions tested in this study (Extended Data Fig. 10a,b). + +Colicins and ANG generate tRHs by cleaving target tRNAs and the anticodon loop of most tRNAs, respectively, thereby inhibiting protein synthesis35, 36. In the case of IRE1α- + +<--- Page Split ---> + +mediated generation of tRHs from tRNAGly(GCC), it is unlikely that 5'- tRH- GlyGCC affects protein synthesis efficiency, as IRE1α appears to cleave a small portion of tRNAGly(GCC) upon ER stress, and thus, does not significantly reduce the pool of mature tRNAGly(GCC) (Fig 1c and 3a). Consistent with this notion, overexpression of IRE1α did not affect expression levels of two glycine-rich proteins, which contain a high proportion of the GGC codon in their mRNA (Extended Data Fig. 10c, Supplementary Table 6). + +Although the detailed modes of action for most tRFs and tRHs remain unclear, several studies indicate that they can regulate the expression and translational efficiency of endogenous target genes by interacting with binding partners, including cytochrome c, YBX1, PIWI, and the AGO family \(^{5, 10, 37 - 40}\) . In the case of 5'- tRH- GlyGCC, we found that they interact with two nuclear proteins, HNRNPM and HNRNPH2, and these interactions are required for 5'- tRH- GlyGCC to influence cancer cell survival (Fig. 4c,d). Knockdown of HNRNPF or HNRNPH1, other interacting proteins identified by Boskovic et al \(^{30}\) , showed similar effects on cancer cell survival, suggesting that 5'- tRH- GlyGCC interacts with multiple nuclear proteins to exert these effects. A recent report identified RBM17, a splicing- related RNA- binding protein, as a binding protein of 5'- tRH- GlyGCC \(^{18}\) , supporting our notion that 5'- tRH- GlyGCC functions in the nucleus. + +Additionally, treatment with 5'- tRH- GlyGCC mimics altered the expression of genes primarily associated with alternative splicing, which overlaps with the function of HNRNPP proteins and RBM17 identified as 5'- tRH- GlyGCC binding proteins. Moreover, analyses of isoforms within the total transcriptome data using nanopore sequencing, together with the alternative splicing assay results, indicate that 5'- tRH- GlyGCC affects the profiles of a subset of transcript isoforms (Fig. 5). Consistent with these nuclear phenomena, we observed nuclear localisation of 5'- tRH- GlyGCC following ER stress. However, further studies are needed to elucidate the detailed mechanisms underlying the role of 5'- tRH- GlyGCC in alternative splicing + +<--- Page Split ---> + +events (Fig. 6e). + +Aberrant expression of tRNA fragments is reported in various human disease conditions, providing potential targets for disease detection and therapeutics. Our data showed that antisense DNA oligos- mediated \(5^{\prime}\) - tRH- Gly \(^{\text{GCC}}\) suppression inhibited tumour proliferation in a xenograft mouse model (Fig. 4f,g). Thus, we believe that understanding the regulatory role of \(5^{\prime}\) - tRH- Gly \(^{\text{GCC}}\) can be used as a novel biomarker and potential therapeutic target in cancer cells. + +## References and Notes + +1. Kim, H.K., Yeom, J.H. & Kay, M.A. Transfer RNA-Derived Small RNAs: Another Layer of Gene Regulation and Novel Targets for Disease Therapeutics. Mol Ther 28, 2340-2357 (2020). +2. Fu, H. et al. Stress induces tRNA cleavage by angiogenin in mammalian cells. 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J Exp Clin Cancer Res 40, 222 (2021). +19. Sharma, U. et al. Biogenesis and function of tRNA fragments during sperm maturation and fertilization in mammals. Science (New York, N.Y.) 351, 391-396 (2016). +20. Chen, Q. et al. Sperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder. Science (New York, N.Y.) 351, 397-400 (2016). +21. Lee, K.P. et al. Structure of the dual enzyme Ire1 reveals the basis for catalysis and regulation in nonconventional RNA splicing. Cell 132, 89-100 (2008). +22. Cox, J.S., Sham, C.E. & Walter, P. Transcriptional induction of genes encoding endoplasmic reticulum resident proteins requires a transmembrane protein kinase. Cell 73, 1197-1206 (1993). +23. Calfon, M. et al. IRE1 couples endoplasmic reticulum load to secretory capacity by processing the XBP-1 mRNA. Nature 415, 92-96 (2002). +24. Yoshida, H., Matsui, T., Yamamoto, A., Okada, T. & Mori, K. XBP1 mRNA is induced by ATF6 and spliced by IRE1 in response to ER stress to produce a highly active transcription factor. Cell 107, 881-891 (2001). +25. Coelho, D.S. et al. Xbp1-independent Ire1 signaling is required for photoreceptor differentiation and rhabdomere morphogenesis in Drosophila. Cell reports 5, 791-801 (2013). +26. Hollien, J. et al. Regulated Ire1-dependent decay of messenger RNAs in mammalian cells. The Journal of cell biology 186, 323-331 (2009). +27. Upton, J.P. et al. IRE1α cleaves select microRNAs during ER stress to derepress translation of proapoptotic Caspase-2. Science (New York, N.Y.) 338, 818-822 (2012). +28. Oikawa, D., Tokuda, M., Hosoda, A. & Iwawaki, T. Identification of a consensus element + +<--- Page Split ---> + +451 recognized and cleaved by IRE1 alpha. Nucleic acids research 38, 6265- 6273 (2010). 452 29. Tirasophon, W., Welihinda, A.A. & Kaufman, R.J. A stress response pathway from the endoplasmic reticulum to the nucleus requires a novel bifunctional protein kinase/endoribonuclease (Ire1p) in mammalian cells. Genes & development 12, 1812- 1824 (1998). 456 30. Boskovic, A., Bing, X.Y., Kaymak, E. & Rando, O.J. Control of noncoding RNA production and histone levels by a 5' tRNA fragment. Genes & development 34, 118- 131 (2020). 457 31. Tang, A.D. et al. Full- length transcript characterization of SF3B1 mutation in chronic lymphocytic leukemia reveals downregulation of retained introns. Nature communications 11, 1438 (2020). 461 32. Abdullahi, A., Stanojcic, M., Parousis, A., Patsouris, D. & Jeschke, M.G. Modeling Acute ER Stress in Vivo and in Vitro. Shock (Augusta, Ga.) 47, 506- 513 (2017). 463 33. Kumar, P., Kuscu, C. & Dutta, A. Biogenesis and Function of Transfer RNA- Related Fragments (tRFs). Trends in biochemical sciences 41, 679- 689 (2016). 465 34. Emara, M.M. et al. Angiogenin- induced tRNA- derived stress- induced RNAs promote stress- induced stress granule assembly. The Journal of biological chemistry 285, 10959- 10968 (2010). 468 35. Ogawa, T. et al. A cytotoxic ribonuclease targeting specific transfer RNA anticodons. Science (New York, N.Y.) 283, 2097- 2100 (1999). 470 36. Tomita, K., Ogawa, T., Uozumi, T., Watanabe, K. & Masaki, H. A cytotoxic ribonuclease which specifically cleaves four isoaccepting arginine tRNAs at their anticodon loops. Proceedings of the National Academy of Sciences of the United States of America 97, 8278- 8283 (2000). 473 37. Kumar, P., Anaya, J., Mudunuri, S.B. & Dutta, A. Meta- analysis of tRNA derived RNA fragments reveals that they are evolutionarily conserved and associate with AGO proteins to recognize specific RNA targets. BMC biology 12, 78 (2014). 476 38. Goodarzi, H. et al. Endogenous tRNA- Derived Fragments Suppress Breast Cancer Progression via YBX1 Displacement. Cell 161, 790- 802 (2015). 478 39. Couvillion, M.T., Bounova, G., Purdom, E., Speed, T.P. & Collins, K. A Tetrahymena Piwi bound to mature tRNA 3' fragments activates the exonuclease Xrn2 for RNA processing in the nucleus. Molecular cell 48, 509- 520 (2012). 481 40. Martinez, G., Choudury, S.G. & Slotkin, R.K. tRNA- derived small RNAs target transposable element transcripts. Nucleic acids research 45, 5142- 5152 (2017). + +## Methods + +## Cell culture and reagents + +Cell lines used in this study are described in Supplementary Table 7. DMSO, TG, TM, and + +<--- Page Split ---> + +STF083010 were purchased from Sigma- Aldrich (St Louis, MO, USA). + +## Oligonucleotides + +Synthetic oligonucleotides used in this study are listed in Supplementary Table 8. + +## Plasmid construction and transfection + +Plasmids used in this study are listed in Supplementary Table 9. The myc- tagged IRE1α (pCMV- IRE1α) and ANG were produced by PCR amplification. The PCR products were digested with KpnI and NotI for IRE1α and EcoRI and SalI for ANG (Takara Bio, Shiga, Japan) and then ligated into pCMV- myc empty vector (Clontech, Mountain View, CA, USA). KGN cells were transfected with plasmids for IRE1α, IRE1α (K599A) and ANG using Neon transfection system (Invitrogen, Carlsbad, CA, USA) as described previously41. + +## Small RNA sequencing analysis + +tRNA was sequenced from two biological replicate samples. Total RNA from the KGN and KGN- IRE1αee cells were isolated and treated with T4 polynucleotide kinase (T4 PNK; New England Biolabs) and incubated at 37 °C for 30 min. Samples were separated on a 12% polyacrylamide gel containing 8 M urea to excise the 18- 40 nt region and were visualised with SYBR Gold (Thermo Fisher Scientific). RNAs were eluted from the acrylamide bands overnight in 0.3 M NaCl and then precipitated in ethanol/glycogen. Small RNA libraries were constructed using a SMARTER® smRNA- Seq Kit for Illumina® (Takara Bio) according to the manufacturer's guidelines. Sequencing libraries were generated according to the MiSeq reagent kit v3 and single end sequencing manufacturer instructions. Small RNA- seq reads were trimmed with the cutadapt programme42 with parameters recommended by the SMARTER + +<--- Page Split ---> + +smRNA- Seq Kit manual. Trimmed sequences with read- lengths ranging from 15 to 42 bp were collected and mapped to the human genome and non- redundant mature tRNA sequences using the bowtie2 program43 implemented in the tRAX software package (http://trna.ucsc.edu/tRAX/). Reads mapped to tRNAs were extracted and their aligned positions were obtained using the bam2bed program of the BEDOPS suite44. The final position of a read was considered a cleavage site. Number of reads ending at each position of tRNAs was calculated. When a read was mapped to multiple tRNAs, fractional counts were allocated to all mapped tRNAs. The resulting read counts were subjected to differential cleavage analysis using the DESeq2 package45. Read mapping to a single isodecoder set was assigned to individual tRF, and those mapping to multiple tRFs with identical sequences were assigned to a single tRF considering mapped read counts of their 3'- tRFs. We used a tRNA gene annotation format, such as 'W- X- Y:Z' (W: amino- acid; X: anticodon; Y: unique gene identifier; Z: cleavage site) in Gly- GCC- 1:33. + +## Northern blot analysis + +The procedure for northern blot analysis has been described previously46. RNA was transferred to an Immobilon Hybond- XL membrane (GE Healthcare Life Sciences, Amersham, Buckinghamshire, UK) and then hybridised with a \(^{32}\mathrm{P}\) - 5'- end- labeled probe specific for the tRNAGGly(GCC). The northern blot membranes were then stripped and reprobed with a radiolabelled probe specific for the tRNACys(GCA), tRNAGGly(TCC), tRNALys(CTT), tRNAVal(TAC), or 5.8S rRNA. 5.8S rRNA was used as a loading control. + +## Western blot analysis + +Total proteins were extracted and analysed by western blotting as described previously47. Total + +<--- Page Split ---> + +protein from yeast cells were analysed according to the method described by Bahn et al48. The antibodies used in western blot analysis are listed in Supplementary table 10. + +## Primer extension analysis + +Three micrograms of total RNA from KGN cells were used in primer extension reactions. The Gly- GCC- R primer was labelled at the \(5^{\prime}\) - end with \((\gamma - ^{32}\mathrm{P})\) ATP and T4 polynucleotide kinase (New England Biolabs, Ipswich, MA, USA). RNA and the labelled primers were denatured at \(70^{\circ}\mathrm{C}\) for 5 min and then annealed by cooling to \(37^{\circ}\mathrm{C}\) for 90 min. They were then extended at \(42^{\circ}\mathrm{C}\) for 1 h with 5 units (U) of avian myeloblastosis virus reverse transcriptase (AMV RTase; New England Biolabs). The products were separated on \(10\%\) polyacrylamide gel containing 8 M urea. Sequencing ladders were generated using \(5\mu \mathrm{g}\) of the PCR product amplified from the cDNA of tRNAGly(GCC). Images were analysed in a Bio- Rad phosphorimager using Quantity One software (Bio- Rad Laboratories). + +## tRNA purification + +Unfractionated tRNAs (tRNAMix) were purified from total RNA by gel purification. In brief, total RNA from KGN cells was separated on \(10\%\) polyacrylamide gel containing 8 M urea. The tRNA fraction was eluted from the gel in RNA extraction buffer [0.5 M ammonium acetate, \(0.2\%\) sodium dodecyl sulphate, and \(0.1\mathrm{mM}\) EDTA (pH 8.0)]. The eluted tRNAMix was purified by phenol/chloroform extraction and ethanol precipitation. For further isolation of tRNAGly(GCC), oligo DNA- immobilised beads were prepared according to the method described by Yokogawa et al49. + +<--- Page Split ---> + +## Cleavage analysis and site mapping + +Purified total tRNAMix (1 \(\mu \mathrm{g}\) ) and \(5^{\prime}\) - end \(^{32}\mathrm{P}\) - labelled tRNAGly(GCC) were incubated with 10 pmol of recombinant IRE1α (OriGene Technologies, Rockville, MD, USA) in \(20~\mu \mathrm{L}\) of cleavage buffer [0.2 M HEPES pH 7.6, 0.5 M K(OAC), \(10\mathrm{mM}\mathrm{Mg(OAC)}_2\) , \(0.5\%\) Triton X- 100, \(10\mathrm{mM}\) DTT, and \(10\mathrm{mM}\) ATP] at \(37^{\circ}\mathrm{C}\) for 30 or 120 min. The cleaved products from purified tRNAMix were recovered and used for northern blot and primer extension assays. A hydrolysis ladder was then created by incubating 2 pmol of tRNAGly(GCC) in hydrolysis buffer (50 mM \(\mathrm{NaCO_3}\) pH 9.2 and \(1\mathrm{mM}\) EDTA pH 8.0) at \(95^{\circ}\mathrm{C}\) for 10 min. RNase T1 ladder was created by incubating 2 pmol of tRNAGly(GCC) with RNase T1 (Fermentas, Waltham, MA, USA) at \(37^{\circ}\mathrm{C}\) for 2 min in reaction buffer (30 mM Tris- HCl pH 7.9, \(10\mathrm{mM}\mathrm{MgCl}_2\) , \(160\mathrm{mM}\mathrm{NaCl}\) , \(0.1\mathrm{mM}\) DTT, and \(0.1\mathrm{mM}\) EDTA pH 8.0). The cleaved products from radiolabelled tRNAGly(GCC) were separated on a \(10\%\) polyacrylamide gel containing \(8\mathrm{M}\) urea, and images were analysed in a Bio- Rad phosphorimager using the Quantity One software (Bio- Rad Laboratories). + +## Semi-quantitative RT-PCR and Reverse transcription–quantitative real-time PCR (RT- qPCR) + +To amplify the spliced and unspliced XBP1 mRNA, a pair of primers (XBP1 splicing- F and XBP1 splicing- R) were used to flank the splicing site and yield 473 bp and 447 bp product sizes of XBP1u and XBP1s, respectively. Products were resolved on \(2.5\%\) agarose gel. Alternative splicing was detected using isoform specific primers. To amplify the spliced and unspliced HXL1 mRNA, a pair of primers, C. deutero- F and C. deutero- R, were used as described previously \(^{55}\) , yielding PCR product sizes of 475 bp and 419 bp for HXL1u and HXL1s, respectively. These PCR products were electrophoresed on \(2.5\%\) agarose gel. ACT1 + +<--- Page Split ---> + +was used as a loading control. Samples for RT- qPCR were prepared and analysed as previously described47. Gene expression levels were quantified using the \(\Delta \Delta \mathrm{Ct}\) method. + +## Construction of IRE1α KO cell line + +IRE1α KO cells were generated as described previously53. To generate plasmids targeting IRE1α, pX458 (D10A)- IRE1α (GuideA) or pX458 (D10A)- IRE1α (GuideB) dual- guide oligonucleotide primers were cloned into the vector pX458 (D10A). Targeted IRE1α genomic DNA fragments (679 bp) were amplified using primers gIRE1α (F) and gIRE1α (R). Allelic deletion was confirmed by TOPcloner™ TA core Kit (Enzynomics, Korea) and DNA sequencing (Cosmogenetech, Korea). + +## Oligonucleotide pull-down assay + +KGN cells were treated for 6 h with 0.1 μM TG; untreated cells were included as controls. After washing twice with \(1 \times\) PBS, harvested cells were disrupted with ice- cold cell lysis buffer [4% CHAPS, 100 mM NaCl, 2 mM EDTA and a \(1 \times\) protease inhibition cocktail (Roche, Mannheim, Germany) in 50 mM Tris/HCl buffer, pH 7.2]. The cellular levels of IRE1α and \(\beta\) - actin were determined by western blotting. To capture proteins bound to the enriched \(5'\) - tRHGly(GCC) from TG- treated cells, 34 nucleotide RNA baits (200 nM each), designed via biotinylation of the \(5'\) - or \(3'\) - end were utilised in a Thermo Scientific Pierce streptavidin- coated microplate containing 200 μg protein per well and an RNase inhibitor (Promega). A \(5'\) - biotin- oligo A8 RNA was included in control wells. Following incubation for 1 h at 4 °C, the contents of the protein extract were aspirated and washed with 100 mM, 300 mM, and 500 mM NaCl in 25 mM Tris/HCl (pH 7.2). The bound proteins were eluted with SDS- PAGE loading buffer + +<--- Page Split ---> + +containing \(1\%\) (w/v) SDS and \(5\%\) (v/v) 2-mercaptoethanol, as well as \(10\%\) (v/v) glycerol in 25 mM Tris/HCl (pH 6.8). + +## Tandem mass spectrometry analysis + +Protein bands detected via SDS- PAGE, following the biotin- streptavidin method, were excised, destained, and reduced with \(50~\mathrm{mM}\) dithiothreitol at \(60^{\circ}\mathrm{C}\) for \(15\mathrm{min}\) . The reduced cysteine residues were alkylated with \(100~\mathrm{mM}\) iodoacetamide at room temperature for \(1\mathrm{h}\) in the dark. The gel pieces were then washed thrice with deionised water and dehydrated twice in acetonitrile (ACN). The dried gels were soaked in \(10~\mathrm{mM}\) ammonium bicarbonate with \(20~\mu \mathrm{g / mL}\) trypsin (Promega, Madison WI, USA) on ice. Proteins in gel were digested for \(24\mathrm{h}\) at \(37^{\circ}\mathrm{C}\) and treated again with \(20~\mu \mathrm{L}\) of trypsin solution for another \(24\mathrm{h}\) . The digested peptides were extracted from the gel pieces and analysed on an nLC Velos Pro mass instrument equipped with a PicoFrit™ column ( \(100~\mathrm{mm}\) , packed with \(5\mu \mathrm{m}\) Biobasic® C18) and an EASY- Column™ ( \(2\mathrm{cm}\) , packed with \(5\mu \mathrm{m}\) C18; Thermo Fisher Scientific). The LC conditions were as follows: \(0.3~\mu \mathrm{L / min}\) was a 45- min linear gradient from \(5\%\) to \(40\%\) ACN in a \(0.1\%\) formic acid buffer solution, followed by a \(10\mathrm{min}\) column wash with \(80\%\) ACN and \(20\mathrm{min}\) re- equilibration to the initial buffer condition. Full mass (MS1) scan was performed in the \(m / z\) 300- 2000 range in a positive ion mode. Data- dependent MS2 scans of the seven most intense ions were performed from the full scan with the options of \(1.5m / z\) isolation width, \(35\%\) normalised collision energy, and \(30~\mathrm{s}\) dynamic exclusion duration. The acquired MS2 data were primarily analysed by SEQUEST search against a human reference protein database from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/genome) and common protein contaminants in the common Repository of Adventitious Proteins (https://www.thegpm.org/crap/) with the following options: maximum miscleavage of 1, + +<--- Page Split ---> + +precursor mass tolerance 0.8 Da, fragment mass tolerance 1.0 Da, dynamic modification of methionine oxidation, and static modification of cysteine with iodoacetamide. The identified proteins with unique peptides are reported in Supplementary Table 2. + +## Electrophoretic mobility shift assay + +The synthetic 5'- mimics of tRHs were purchased from Bioneer (Daejon, Korea). The 5'- end of tRH mimics were radiolabelled using \(\gamma\) - \(^{32}\mathrm{P}\) - ATP and T4 polynucleotide kinase (Takara Bio) and purified with an Illustra MicroSpin G- 25 column (GE Healthcare Life Sciences). Prior to use, labelled and unlabelled tRH mimics were heated at \(65^{\circ}\mathrm{C}\) for 10 min and slowly cooled to room temperature. Next, \(100\mathrm{ng}\) of BSA (Takara Bio), or HNRNPM (Mybiosource, San Diego, USA) or HNRNPH2 recombinant proteins (Mybiosource), were incubated with binding buffer [10 mM Tris- HCl (pH 8.0), \(150\mathrm{mM}\) KCl, \(0.5\mathrm{mM}\) EDTA, \(0.1\%\) Triton X- 100, \(0.02\mathrm{mM}\) DTT, \(12.5\%\) glycerol] and \(3.3\mathrm{pmol}\) of cold probes for each 5'- tRH mimic for 10 min at room temperature. Samples were then incubated with \(0.033\mathrm{pmol}\) of 5'- labelled tRHs for 10 min at room temperature. Native loading dye [100 mM Tris- HCl (pH 8.0), \(8.33\%\) glycerol, \(0.002\%\) brilliant blue G] and \(8\%\) polyacrylamide gels were used to load the samples. Vacuum dried gels were exposed to an intensifying screen and images were analysed in a Bio- Rad phosphorimager using Quantity One software (Bio- Rad Laboratories). + +## HNRNPM and HNRNPH2 binding constants + +The binding affinities of purified HNRNPM and HNRNPH2 (Mybiosource, San Diego, USA) to synthetic 5'- tRH- Gly \(^{GCC}\) or 3'- tRH- Gly \(^{GCC}\) (Bioneer, Daejon, Korea) were measured using BIAcore T200 instrument and CM5 sensorchip (GE Healthcare Life Sciences) at \(25^{\circ}\mathrm{C}\) . Activation, immobilization, deactivation and preparation of the mock- coupled flow cell were + +<--- Page Split ---> + +performed according to the manufacturer's instructions. The binding signals were generated by subtracting the signal for the mock- coupled flow cell from that for the HNRNPM- or HNRNPH2- immobilized flow cells. Calculation of equilibrium dissociation constant \((K_{D})\) from the sensorgrams were done with BIAccore T200 Evaluation software version 3.2 (GE Healthcare Life Sciences) by fitting the data to a 1:1 binding model. + +## Cell viability assay + +All tRH mimics were purchased from were supplied by Genolution Pharmaceuticals, Inc. (Seoul, Korea). Antisense DNA oligos were purchased from BIONICS (Seoul, Korea). Cell- viability assays were performed as previously \(^{47}\) . + +## RNA interference + +All siRNAs were purchased from Bioneer (Seoul, Korea). The siRNA transfection method has been described previously \(^{47}\) . + +## Cell proliferation assay + +KGN or HeLa cells \((1 \times 10^{4})\) were seeded in 96- well plates for \(24 \mathrm{~h}\) ; the cells were then transfected with increasing amounts of \(5'\) - or \(3'\) - tRH mimics using the lipofectamine 2000 reagent (Invitrogen). HeLa cells \((1 \times 10^{4})\) were seeded in 96- well plates for \(24 \mathrm{~h}\) ; the cells were then transfected with increasing amounts of antisense DNA oligos complementary to the \(5'\) - tRH- Gly \(^{GCC}\) or \(5'\) - tRH- Lys \(^{CTT}\) loaded onto functionalized AuNP. After a 48- h transfection, cell proliferation was measured using the Cell Proliferation ELISA, BrdU (colorimetric) kit (Sigma- Aldrich) according to the manufacturer's instructions. + +<--- Page Split ---> + +## Flow cytometry analysis + +To detect apoptotic cells, KGN cells \((1 \times 10^{6})\) were transfected with the indicated \(5'\) - or \(3'\) - tRHs mimic and \(48 \mathrm{~h}\) post- transfection stained with the FITC Annexin V Apoptosis Detection Kit (BD Pharmingen, San Diego, CA, USA) according to the manufacturer's instructions. + +## Cell migration assay + +Cell migration was assessed based on the protocol described in our previous study47. Briefly, KGN cells \((1 \times 10^{6})\) were transfected with the indicated \(5'\) - or \(3'\) - tRH mimics for \(48 \mathrm{~h}\) . Images of migrated cells were captured at \(\times 100\) magnification under a bright- field microscope (Olympus CKX41, Tokyo, Japan). + +## Total transcriptome analysis + +RNA was sequenced from two biological replicate samples of KGN cells transfected with the \(5'\) - tRHs mimic for \(48 \mathrm{~h}\) . In brief, total RNA samples were converted into cDNA libraries using the TruSeq Stranded mRNA Sample Prep Kit (Illumina). Starting with \(1 \mu \mathrm{g}\) of total RNA, poly- . adenylated RNA (primarily mRNA) was selected and purified using oligo- dT- conjugated magnetic beads. This mRNA was physically fragmented and converted into single- stranded cDNA using reverse transcriptase and random hexamer primers, with the addition of actinomycin D to the FSA (First Strand Synthesis Act D Mix) to suppress DNA- dependent synthesis of the second strand. Double- stranded cDNA was created by removing the RNA template and synthesising the second strand in the presence of dUTP (deoxyribouridine triphosphate) in place of dTTP (deoxythymidine triphosphate). A single A base was added to the \(3'\) end to facilitate ligation of the sequencing adapters, which contained a single T base + +<--- Page Split ---> + +overhang. Adapter- ligated cDNA was amplified by PCR to increase the amount of sequence- ready library. During this amplification the polymerase stalls when it encounters a U base, rendering the second strand a poor template. Accordingly, amplified material uses the first strand as a template, thereby preserving the strand information. Final cDNA libraries were analysed for size distribution using an Agilent Bioanalyzer (DNA 1000 kit; Agilent), quantitated by qPCR (Kapa Library Quant Kit; Kapa Biosystems, Wilmington, MA), and normalised to 2 nmol/L in preparation for total transcriptome analysis. + +## Alternative splicing analysis + +Purified mRNA was sequenced from three biological triplicate samples of KGN cells transfected with the 5'- tRHs mimic for 48 h. Briefly, Direct RNA sequencing was performed using the Direct RNA sequencing protocol (SQK- PCS109 kit) for the MinION. All steps were followed according to the manufacturer's specification. The constructed library was loaded on a FLO- MIN106D R9.4 flow cell and sequenced on a MinION device (Oxford Nanopore Technologies). The sequencing run was terminated after 48 h. Analyses of differential isoform usage using FLAIR modules has been described previously31. + +## Induction of acute ER stress in vivo + +Acute ER stress was induced in vivo using a mouse model as described previously32. Briefly, immunodeficiency female or male BALB/c nu/nu mice (7- weeks- old) were purchased from Saeron Bio Inc (Uiwang, Korea) and rested for 3- 5 days. BALB/c mice were injected intraperitoneally with TG solution (1 μg/g body weight) or TM solution (0.5 μg/g body weight) as described previously. As controls, mice were injected intraperitoneally with control buffer (1× PBS containing 2% DMSO). Mice were euthanized by cervical dislocation and, major + +<--- Page Split ---> + +organs were harvested at \(6\mathrm{h}\) , \(12\mathrm{h}\) , and \(24\mathrm{h}\) post- treatment, and the samples were prepared for western blot and northern blot analyses. All animal protocols were approved by the Chung- Ang University Institutional Animal Case and Use committee (IRB# CAU202000115). For \(C\) . neoformans, WT and ire1- deletion (ire1 \(\Delta\) ) strains in early log phase were treated with TM (5 \(\mu \mathrm{g / ml}\) ) and DTT (2 mM) at \(30^{\circ}\mathrm{C}\) for \(2\mathrm{h}\) . + +## Fluorescence in situ hybridisation + +Cells were cultured under conditions of normal growth or subjected to ER stress by treating with \(0.1\mu \mathrm{M}\) TG for \(6\mathrm{h}\) . After culture, the cells were washed thrice in PBS, fixed with \(4\%\) paraformaldehyde in PBS for \(15\mathrm{min}\) at room temperature, and washed thrice with PBS. Cells were permeabilised with \(0.2\%\) Triton X- 100 in PBS for \(15\mathrm{min}\) at room temperature and washed twice with PBS. Slides were then blocked and prehybridised for \(2\mathrm{h}\) at \(37^{\circ}\mathrm{C}\) in hybridisation buffer ( \(2\%\) bovine serum albumin, \(5\times\) Denhardt's solution, \(4\times\) SSC, and \(35\%\) deionised formamide). Hybridisation was performed overnight in a humid dark chamber at \(37^{\circ}\mathrm{C}\) in the presence of \(1\mathrm{ng / mL}\) of the indicated oligonucleotide conjugated to cyanine 3 dye (Cy3). FISH assays were also performed under denaturing conditions by heating the slides at \(75^{\circ}\mathrm{C}\) for \(5\mathrm{min}\) , immediately before the hybridisation step. After hybridisation, cells were washed once in \(2\times\) SSC containing \(50\%\) deionised formamide, once in \(2\times\) SSC, and once in \(1\times\) SSC. Cells were mounted on slides using a mounting solution containing DAPI. Fluorescence was detected with a laser scanning confocal microscope (Carl Zeiss ZEN 2011, Germany). Relative fluorescence intensities were assessed using ImageJ software (NIH, USA). + +## TaqMan assay for \(5^{\prime}\) -tRH-GlyGCC + +The TaqMan assay was performed as described previously \(^{47}\) . KGN cells were treated with \(0.1\) + +<--- Page Split ---> + +\(\mu \mathrm{M}\) of TG for the indicated times, and fractionation of nuclear and cytosolic RNA was isolated using a Cytoplasmic and Nuclear RNA Purification Kit (Norgen Biotek, Thorold, Canada), according to the manufacturer's instructions. \(5^{\prime}\) tRH- Gly \(^{GC C}\) and U6 snRNA quantification was conducted using custom designed TaqMan microRNA assays according to manufacturer's recommended protocols (Applied Biosystems, Foster City, CA, USA). + +## Mouse xenograft experiment + +HeLa cells \((1 \times 10^{6})\) were subcutaneously injected into 7- week- old BALB/c nu/nu immunodeficiency mice (Saeron Bio Inc), whose weights ranged between 18 and 20 g. We randomly allocated mice to three groups. Once the HeLa cells formed tumours (tumour volume: \(\sim 0.1 \mathrm{cm}^3\) ), \(\mathrm{AuNP}^{\mathrm{dT}}\) , \(\mathrm{AuNP}^{\mathrm{dT}}\) - anti- 5'- tRH- Lys \(^{\mathrm{CTT}}\) or \(\mathrm{AuNP}^{\mathrm{dT}}\) - anti- 5'- tRH- Gly \(^{\mathrm{GC C}}\) suspended in PBS were directly injected into the tumour sites every two days. \(\mathrm{AuNP}^{\mathrm{dT}}\) - anti- 5'- tRH- Lys \(^{\mathrm{CTT}}\) and \(\mathrm{AuNP}^{\mathrm{dT}}\) - anti- 5'- tRH- Gly \(^{\mathrm{GC C}}\) were prepared by mixing \(\mathrm{AuNP}^{\mathrm{dT}}\) (NES Biotechnology, Seoul, Korea) with polyadenylated antisense DNA oligos as previously described \(^{50}\) . + +Mice were weighed and sizes of the tumours were measured every other day. The volume \((\mathrm{cm}^3)\) of each tumour ((length \(\times\) width \(^2 \times \pi\) )/6) was determined over 30 days period after xenotransplantation. Tumour- bearing mice were euthanized by cervical dislocation 18 days after the first injection of the functionalized AuNP composites, and tumours were excised. The samples were prepared for RT- qPCR and western blot analyses. + +## Statistical analysis + +Multiple- comparison analyses of values were performed using the Student- Newman- Keuls test, and Student's \(t\) - test was used for comparisons with control samples, using SAS version 9.2 (SAS Institute, Cary, NC, USA) and SigmaPlot (Systat Software, San Jose, CA, USA). The + +<--- Page Split ---> + +data are presented as mean \(\pm\) standard error of the mean (SEM); \(P < 0.05\) was considered statistically significant. + +## Data availability + +Small RNA- seq data was deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA772059. The mass spectrometry data have been deposited in the ProteomeXchange Consortium via the PRIDE \(^{51}\) partner repository with the dataset identifier PXD013798. + +## References + +Kim, J.H. et al. Differential apoptotic activities of wild- type FOXL2 and the adult- type granulosa cell tumor- associated mutant FOXL2 (C134W). Oncogene 30, 1653- 1663 (2011). Martin, M. Cutadapt removes adapter sequences from high- throughput sequencing reads. EMBnet/journal; Vol 17, No 1: Next Generation Sequencing Data Analysis (2011). Langmead, B. & Salzberg, S.L. Fast gapped- read alignment with Bowtie 2. Nature methods 9, 357- 359 (2012). Neph, S. et al. BEDOPS: high- performance genomic feature operations. Bioinformatics (Oxford, England) 28, 1919- 1920 (2012). Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA- seq data with DESeq2. Genome biology 15, 550 (2014). (!! INVALID CITATION !!! (Lee et al., 2002)). Shin, E. et al. An alternative miRISC targets a cancer- associated coding sequence mutation in FOXL2. The EMBO journal 39, e104719 (2020). Bahn, Y.S., Kojima, K., Cox, G.M. & Heitman, J. Specialization of the HOG pathway and its impact on differentiation and virulence of Cryptococcus neoformans. Mol Biol Cell 16, 2285- 2300 (2005). Yokogawa, T., Kitamura, Y., Nakamura, D., Ohno, S. & Nishikawa, K. Optimization of the hybridization- based method for purification of thermostable tRNAs in the presence of tetraalkylammonium salts. Nucleic acids research 38, e89 (2010). Kim, J.H. et al. A functionalized gold nanoparticles- assisted universal carrier for antisense DNA. Chem Commun (Camb) 46, 4151- 4153 (2010). + +<--- Page Split ---> + +803 51. Perez- Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: 804 improving support for quantification data. Nucleic acids research 47, D442- d450 (2019). 805 + +<--- Page Split ---> + +## Figure legends + +Fig. 1. Small RNA- seq analysis of IRE1α- induced tRFs in vivo. a, Total tRF or 5'- tRF mapped read counts in KGN- WT and IRE1α- overexpressing (KGN- IRE1αoe) cells. Hatched line: reads mapped to tRNAGly(GCC). b, (Left) Volcano plot depicting differentially expressed 5'- tRFs in KGN- WT and KGN- IRE1αoe cells. Red dots: 5'- tRFs from tRNAGly(GCC); blue dots: 5'- tRFs from tRNACys(GCA); black dot: 5'- tRFs from tRNAGly(GCC) expressed at higher levels in KGN- IRE1αoe cells (red box: Log2 Fold Change \(>1.5\) ; \(p < 0.001\) ). (Right) Based on small RNA- seq analysis, cleavage sites at the anticodon loop in the secondary human tRNAGly(GCC) and tRNACys(GCA) structures. Red: acceptor stem at 5'- end; Purple: D loop; light green: anticodon loop; dark green: anticodon; yellow: T loop; blue: CCA tail at 3'- end. Numbering in the anticodon indicates the 3'- end positions of the tRFs; percent indicates the proportion of 5'- tRFs in total 5'- tRFs (Log2 Fold Change \(>1.5\) ; \(p < 0.001\) ). c, Northern blot analysis of tRNA fragments in KGN cells following IRE1α overexpression KGN cells were transfected with plasmid encoding myc- tagged IRE1α for 24 h, total RNA was extracted for analysis of 5'- tRNA fragments by northern blotting. The expression of IRE1α and GAPDH (loading control) was analysed by western blotting. Ribonucleolytic activity of IRE1α was confirmed XBP1 splicing assay using RT- PCR analysis of unspliced/spliced (u/s) XBP1. Red arrow: 5'- tRFs from tRNAGly(GCC) generated by IRE1α. M: size marker. Percentage of 5'- tRF compared to full- length tRNA are shown. The data are presented as the mean \(\pm\) SEM from three independent experiments. The asterisk indicated statistically significant differences (**\(p < 0.01\) , *\(p < 0.05\) ; paired student's \(t\) - test). ns, not significant. d, (Left) Primer extension analysis of 5'- end of tRNAGly(GCC) fragment in KGN cells. KGN cells were transfected with a plasmid encoding IRE1α or kinase defected mutant (IRE1α- K599A). (Right) Secondary structure of mature tRNAGly(GCC) and IRE1α cleavage sites at anticodon. Numbering in the anticodon indicates the + +<--- Page Split ---> + +positions of mature tRNA nucleotides. Red arrow: prominent cleaved products of the tRNAGly(GCC) generated by IRE1α. + +Fig. 2. IRE1α- specific cleavage of tRNAGly(GCC) in vitro. a, Northern blotting results for tRNAGly(GCC) and tRNALys(CTT). Red arrow: prominent cleaved products of the tRNAGly(GCC) generated by IRE1α. b, Primer extension assay on tRNAGly(GCC) cleavage products in the presence of IRE1α in vitro. IRE1α cleavage sites in the tRNAGly(GCC) are denoted by different letters (a–g). c, In vitro cleavage of tRNAGly(GCC) by IRE1α. Secondary structure of mature tRNAGly(GCC) and IRE1α cleavage sites (a–g from Fig. 2b and 1–7 from Fig. 2c). Black arrows: position of the tRNAGly(GCC) cleavage site generated by IRE1α. Red arrow: major cleavage site by IRE1α. + +Fig. 3. ER stress induces 5'- tRHs cleavage by tRNAGly(GCC). a, Northern blot analysis of tRNAGly(GCC)- derived fragments in KGN cells upon ER stress. KGN Cells were treated with 0.1% DMSO, TG (0.1 μM) or TM (1 μg/ml) and harvested at the indicated times. Total RNA was isolated and probed with a probe specific for the tRNAGly(GCC). Red arrow: tRHs from tRNAGly(GCC) cleaved by IRE1α. b, (Upper panel) 5'- end of tRNAGly(GCC) fragment detected in Fig. 3a (at 6 h) determined by primer extension analysis. (Lower panel) Secondary structure of mature tRNAGly(GCC) and IRE1α cleavage sites at anticodon stem loop. Red arrow: major IRE1α cleavage site. c, Northern blot analysis of tRNAGly(GCC) fragments in control KGN (WT) or IRE1α knockout-KGN cells (IRE1α-/-). Cells were treated with DMSO, TG (0.1 μM) or TM (1 μg/ml) for 6 h and harvested. Total RNA was isolated and probed with a probe specific for the tRNAGly(GCC). Relative amount of 5'- tRH-Gly(GCC) is presented in the lower panel of Fig. 3a and c, respectively. The data are presented as the mean ± SEM from three independent experiments. + +<--- Page Split ---> + +The expression of IRE1α and \(\beta\) - actin (loading control) was analysed by western blotting. Ribonucleolytic activity of IRE1α was confirmed XBP1 splicing assay using RT- PCR analysis of unspliced/spliced (u/s) XBP1. Different letters denote statistically significant differences \((p\) \(< 0.0001\) ; Student- Newman- Keuls test). + +Fig. 4. Functional roles of 5'- tRFs of tRNAGlu(GCC). a,b, Cell viability of KGN (a) and HeLa (b) cells following transfection with tRH mimics (5'- tRH- LysCTT, 5'- tRH- GlyGCC, or 3'- tRH- GlyGCC). c,d, Cell viability of KGN (c) and HeLa (d) cells following transfection with small interfering RNAs (siRNAs) for HNRNPM or HNRNPH2 (200 nM) and tRH mimics (50 nM) (5'- tRH- LysCTT, 5'- tRH- GlyGCC, or 3'- tRH- GlyGCC) (upper panel). Knockdown efficiency of HNRNPM or HNRNPH2 proteins (lower panel). e, Cell viability of WT and IRE1α-/- KGN cells following transfection with antisense DNA oligos (50 nM) targeting endogenous 5'- tRFs (anti-5'- tRH- LysCTT or anti-5'- tRH- GlyGCC) in the absence or presence of TG (0.1 μM). (a-e) Data are presented as the mean ± SEM of three independent experiments performed in triplicate. Different letters denote statistically significant differences \((p < 0.0001\) ; Student- Newman- Keuls test). f, Volumes of tumours from mice injected with either the AuNPdT only (vehicle) as a control (n = 18), AuNPdT- anti-5'- tRH- LysCTT (anti-5'- tRH- LysCTT, n = 18), or AuNPdT- anti-5'- tRH- GlyGCC (anti-5'- tRH- GlyGCC, n = 18) were measured. g, Representative immunoblots and quantified data for tumours from each group are presented. (f-g) The asterisk indicated statistically significant differences (\\*p < 0.05, \\*\\*p < 0.01, \\*\\*\\*p < 0.001; paired student's t- test). ns, not significant. + +Fig. 5. 5'- tRH- GlyGCC mediate alternative splicing events. a, Volcano plot of differentially expressed protein- coding genes in KGN cells transfected with 5'- tRH- GlyGCC mimic and + +<--- Page Split ---> + +control KGN cells transfected with \(5^{\prime}\) - tRH- LysCTT mimic. Blue dots: significant upregulation of target genes; red dots: significant downregulation of target genes. b, DAVID functional analysis of genes with transcript abundance altered by more than 1.5- fold. c, Differential isoform usage (left) and major isoforms (right) from the ELOB (upper panel) and PMSB5 (lower panel). Red box: alternative splicing region. d, Validation of alternative splicing events from ELOB and PMSB5 in KGN cells transfected with \(5^{\prime}\) - tRH- LysCTT or \(5^{\prime}\) - tRH- GlyGCC mimics by RT- qPCR. e, Inhibitory effects of AuNP- conjugated antisense DNA oligos (anti- \(5^{\prime}\) - tRH- LysCTT or anti- \(5^{\prime}\) - tRH- GlyGCC) were confirmed by validation of alternative splicing events from ELOB and PMSB5. (d- e) The asterisk indicated statistically significant differences ( \(*p < 0.05\) , \(**p < 0.01\) , \(***p < 0.001\) , \(****p < 0.0001\) ; paired student's \(t\) - test). + +Fig. 6. ER stress induces generation of \(5^{\prime}\) - tRHs from tRNAGly(GCC) in mouse and C. neoformans. a, Northern blot analysis of tRNAGly(GCC)- derived fragments in the ovary from ER stress- induced mouse. 5.8S rRNA was used as the loading control. The expression of IRE1α and β- actin (loading control) was analysed by western blotting. b, \(5^{\prime}\) - end of tRNAGly(GCC) fragment as determined by primer extension assay using total RNA isolated from ovaries after treatment with 0.1% DMSO or TG (left panel). Secondary structure of mouse mature tRNAGly(GCC) and IRE1α cleavage sites at anticodon stem loop (right panel). Red arrow: TG- induced IRE1α cleavage sites c, Northern blot analysis of tRNAGly(GCC) fragments in C. neoformans. 5.8S rRNA was used as the loading control. The expression of IRE1 and GAPDH (loading control) was analysed by western blotting. Ribonucleolytic activity of IRE1α was confirmed HXL1 splicing assay using RT- PCR analysis of unspliced/spliced (u/s) HXL1. Arrows: TM- induced IRE1 cleavage sites. d, Primer extension analysis of tRNAGly(GCC) fragments in C. neoformans. Secondary structure of C. neoformans mature tRNAGly(GCC) and + +<--- Page Split ---> + +IRE1 cleavage sites at anticodon stem loop are illustrated (right panel). Red arrow: TM- induced IRE1 cleavage sites e, Proposed model for the IRE1α selective generation of 5'- tRH- GlyGCC that contributes to cellular adaptation upon ER stress presented in diverse eukaryotic organisms from yeast to humans. + +## Extended data figure legends + +Extended Data Fig. 1. Production of 5'- tRHs from tRNAGly(GCC) in vivo via an IRE1α activity- dependent manner. a, Northern blot analysis of tRNA fragments in KGN cells. KGN cells were transfected with an empty vector (pCMV- myc) or a plasmid encoding IRE1α kinase defected mutant (IRE1α- K599A). Total RNA was isolated from the transfected cells and northern blot was performed with a \(^{32}\mathrm{P}\) - 5'- end- labeled probe specific for the tRNAGly(GCC). 5.8S rRNA was probed as a loading control. The expression of IRE1α and GAPDH (loading control) was analysed by western blotting. The northern blot membranes were then stripped and reprobed with a \(^{32}\mathrm{P}\) - 5'- end- labeled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). Ribonucleolytic activity of IRE1α was confirmed XBP1 splicing assay using RT- PCR analysis of unspliced/spliced (u/s) XBP1. M, size marker. Red arrow: 5'- tRFs from tRNAGly(GCC) generated by IRE1α- K599A. b, Northern blots for tRNAGly(GCC) fragments in KGN cells after transfection with 2.5 μg of pCMV- myc control plasmid or plasmid encoding myc- tagged ANG. 5.8S rRNA was used as the loading control. Blue arrows: prominent cleaved products of the tRNAGly(GCC) generated by ANG. + +Extended Data Fig. 2. Purification and cleavage of tRNAGly(GCC) in vitro. a, Scheme diagram of isolation of tRNAGly(GCC) species from purified total tRNA in vitro using the biotinylated antisense oligo DNA- conjugated streptavidin C1 beads (left panel). Red text and + +<--- Page Split ---> + +lines indicate biotinylated antisense oligo DNA complementary to the secondary structure of tRNAGly(GCC) (right panel). b, Loading capacity of biotinylated antisense oligo DNA on streptavidin C1 beads in EtBr stained gel. Approximately 8 pmol of oligo DNAs were loaded onto 1 mg of streptavidin C1 beads. c, Quality of isolated tRNAGly(GCC) species determined by EtBr staining and northern blot assay. + +Extended Data Fig. 3. Cleavage of tRFs induced by ER stress. a, Northern blot analysis of tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT)- derived fragments in KGN cells upon ER stress. The northern blot membranes used in Fig. 3a were stripped and re-probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). b, Northern blot analysis of tRNA fragments in control KGN (WT) or IRE1α knockout-KGN cells (IRE1α/- ). The northern blot membranes used in Fig. 3c were stripped and re-probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). c, Northern blot analysis of tRNAGly(GCC) fragments in HeLa cells. Red arrow: tRHs from tRNAGly(GCC) cleaved by IRE1α. Right panel: relative amount of \(5'\) - tRH-Gly(GCC). The data are presented as the mean \(\pm\) SEM from three independent experiments. \(^{**}p < 0.01\) ; paired Student's \(t\) - test. d, Northern blot membranes used in Extended data Fig. 3c were stripped and re-probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). + +Extended Data Fig. 4. Generation of IRE1α- knockout (IRE1α/- ) KGN cells using the CRISPR/Cas9 system. a, Schematic representation of the CRISPR/Cas9- nicksase strategy for IRE1α knockout in KGN cells. A pair of guide RNAs [sgIRE1α (A) and (B)] was designed by targeting the PAM sequence at the catalytic region of IRE1α. The sgRNA sequences and PAM sequences are shown in blue and red, respectively. Triangle indicates the possible cleavage sites. + +<--- Page Split ---> + +b, Knockout of IRE1α validated using western blot analysis. c, T7E1 assay results to detect CRISPR/Cas9- induced modification in IRE1α-/-. Arrows indicate the positions of the expected DNA bands cleaved by T7E1. d, DNA sequences of the targeting sites in IRE1α-/- KGN cells. The regions of IRE1α sequences are shown in pink. The number of mutated nucleotides is indicated on the right. Actual chromatograms from sequencing analysis are shown. + +Extended Data Fig. 5. Identification of proteins interacting with 5'- tRH- GlyGCC. a, SDS- PAGE of whole- cell lysates and 3'- /5'- biotinylated tRH- Gly(GCC)- bound proteins captured in a streptavidin microplate containing samples from KGN cells treated (+) and untreated (-) with 0.1 μM thapsigargin (TG) for 6 h. Different protein bands with molecular weights (MWs) near 70 kDa and 55 kDa in samples bound to 5'- biotin- tRH- GlyGCC are shown in an SDS- PAGE gel. b, Venn diagrams of identified proteins from the excised gel pieces at MWs near 70 kDa and 55 kDa. Normalised counts of the peptide- to- spectrum matches of identified proteins with > 2 unique peptides from tandem mass spectrometry data are compared. Tandem mass spectrometry results from each sample are shown in Supplementary Table 2. + +Extended Data Fig. 6. Interaction between tRHs with HNRNP proteins. a, Electrophoretic mobility shift assay (EMSA) results for synthetic 5'- tRH- GlyGCC or 5'- tRH- LysCTT. Synthetic 5'- tRH- LysCTT was used as a control. b, Sensorgrams of the interaction between the immobilized HNRNP proteins (HNRNPM or HNRNPH2) and the purified 5'- tRH- GlyGCC used as analyte. Purified 5'- tRH- GlyGCC were delivered from the lowest to the highest concentration (30, 100, 300, 1000, 3000, 10000 nM). c, Summary of surface plasmon resonance (SPR) kinetic and affinity measurements using BIAcore T200. 5'- tRH- GlyGCC or 3'- tRH- GlyGCC binding to HNRNPM and HNRNPH2 measured by SPR. The equilibrium dissociation constant + +<--- Page Split ---> + +\((K_{D})\) , the association constant \((k_{a})\) and the dissociation constant \((k_{d})\) are presented. + +# Extended Data Fig. 7. Effects of transfecting tRHs mimics or antisense DNA oligos against + +tRHs on physiology of KGN cells. a, Cell viability of KGN cells following transfection with tRHs mimics and antisense oligos to each of the tRHs mimics (mimic + antisense treated). b, Proliferation of KGN cells following transfection with tRHs mimics. Different letters denote statistically significant differences \((p < 0.0001\) ; Student- Newman- Keuls test). Data are presented as the mean \(\pm\) SEM of three independent experiments performed in triplicate. Effects of tRHs mimics on apoptosis (c) and (d) migration of KGN cells. ns, not significant. Scale bar \(= 100 \mu \mathrm{m}\) . e, f, Upper panel: cell viability of KGN (e) and HeLa (f) cells following transfection with siRNAs for HNRNPF or HNRNPH1 and tRH mimics from tRNA. Knockdown efficiency of HNRNP proteins (lower panel). Different letters denote statistically significant differences \((p < 0.0001\) ; Student- Newman- Keuls test). g, Proliferation of HeLa cells following transfection with antisense DNA oligos of tRHs. Different letters denote statistically significant differences \((p < 0.0001\) ; Student- Newman- Keuls test). Data are presented as the mean \(\pm\) SEM of three independent experiments performed in triplicate. + +Extended Data Fig. 8. Subcellular localisation of 5'- tRHs from tRNAGly(GCC) during ER stress. a, Red fluorescence (Cy3): subcellular distribution of 5'- tRHs, from tRNAGly(GCC) in unstressed (right panel) and stressed cells (left panel) following TG treatment. DNA was stained with DAPI. Anticodon bridging probes were designed to anneal with the anticodon loop, and they recognised only the anticodon loop of whole tRNA to avoid significant hybridisation signals with 5'- tRHs of tRNAGly(GCC). Scale bar \(= 20 \mu \mathrm{m}\) . b, 5'- tRH- Gly(GCC) enrichment following transfection of KGN cells with 0.1 \(\mu \mathrm{M}\) of TG. The data (mean \(\pm\) SEM) are presented + +<--- Page Split ---> + +as the fold enrichment calculated from three independent experiments. \(**p < 0.01\) , ns, not significant. + +# Extended Data Fig. 9. Effects of ER stress-inducing agents on IRE1α expression in mouse + +and C. neoformans. a, IRE1α and \(\beta\) - actin (loading control) protein expression in the organs of BALB/c mice injected with TM and TG. b, Northern blot analysis of tRNAGly(GCC)- derived fragments in the liver from ER stress- induced mice. 5.8S rRNA was used as the loading control. Red arrow: prominent cleaved products of the tRNAGly(GCC) generated by IRE1α. M, size marker from KGN cells. c, Northern blot analysis of tRNAGly(GCC)- derived fragments in the epididymis from ER stress- induced mouse. 5.8S rRNA was used as the loading control. The expression of IRE1α and \(\beta\) - actin (loading control) was analysed by western blotting. M, size marker from KGN cells. d, The northern blot membranes used in Fig. 6a were stripped and re- probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT), respectively. e, Northern blot analysis of tRNACys (GCA), tRNAGly(TCC), or tRNALys(CTT)- derived fragments in C. neoformans. The northern blot membranes used in Fig. 6c were stripped and re- probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). + +# Extended Data Fig. 10. Effects of other stresses on 5'-tRH-GlyGCC production and IRE1α overexpression on expression of GCC codon-rich genes. a, b, Northern blot analysis of KGN + +cells treated with sodium arsenite (SA) (a) and Cobalt chloride (CoCl2) (b) (upper panel). IRE1α, ANG, and \(\beta\) - actin (loading control) expression (lower panel). c, Expression of FOXL2, + +Loricrin, IRE1α, and \(\beta\) - actin (loading control) in KGN cells transfected with plasmid encoding + +myc- tagged IRE1α and treated with STF083010 (inhibitor of the endonuclease activity of + +<--- Page Split ---> + +1022 IRE1α). + +1023 + +<--- Page Split ---> +![](images/Figure_1.jpg) + + + +
Figure 1
+ +![](images/Figure_5.jpg) + + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + + +<--- Page Split ---> +![](images/Figure_unknown_1.jpg) + + +<--- Page Split ---> +![](images/Figure_unknown_2.jpg) + + +<--- Page Split ---> +![PLACEHOLDER_50_0] + + +<--- Page Split ---> +![PLACEHOLDER_51_0] + + +<--- Page Split ---> +![PLACEHOLDER_52_0] + + +<--- Page Split ---> +![PLACEHOLDER_53_0] + + +<--- Page Split ---> +![PLACEHOLDER_54_0] + + +<--- Page Split ---> +![PLACEHOLDER_55_0] + + + +![PLACEHOLDER_55_1] + + + +![PLACEHOLDER_55_2] + + +<--- Page Split ---> +![PLACEHOLDER_56_0] + +
Extended Data Figure 5
+ +![PLACEHOLDER_56_1] + + +<--- Page Split ---> +![PLACEHOLDER_57_0] + + + +
b
+ +![PLACEHOLDER_57_1] + + + +
C
+ +
5'-tRH-GlyGCC3'-tRH-GlyGCC
\(K_D\) (nM)\(K_a\) (M-1s-1)\(K_d\) (s-1)\(K_D\) (nM)\(K_a\) (M-1s-1)\(K_d\) (s-1)
HNRNPM86.301.04 x 1048.96 x 10-49592.74 x 1032.63 x 10-3
HNRNPH227.072.92 x 1047.90 x 10-49384.90 x 1034.60 x 10-3
+ +<--- Page Split ---> +![PLACEHOLDER_58_0] + + +<--- Page Split ---> +![PLACEHOLDER_59_0] + + + +
b
+ +![PLACEHOLDER_59_1] + + +<--- Page Split ---> +![PLACEHOLDER_60_0] + + +<--- Page Split ---> +![PLACEHOLDER_61_0] + + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryTable110DB.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375_det.mmd b/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..fa98efea8478784b3d24cf1e565a6aff64aaa812 --- /dev/null +++ b/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375_det.mmd @@ -0,0 +1,747 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 875, 175]]<|/det|> +# 5'-tRNAGly(GCC) halves generated by IRE1α are linked to ER stress response + +<|ref|>text<|/ref|><|det|>[[44, 195, 245, 238]]<|/det|> +Ji-Hyun Yeom Chung- Ang University + +<|ref|>text<|/ref|><|det|>[[44, 243, 245, 284]]<|/det|> +Eunkyoung Shin Chung- Ang University + +<|ref|>text<|/ref|><|det|>[[44, 290, 600, 330]]<|/det|> +Yoonjie Ha Chung- Ang University https://orcid.org/0000- 0002- 6506- 9338 + +<|ref|>text<|/ref|><|det|>[[44, 335, 245, 377]]<|/det|> +Minju Joo Chung- Ang University + +<|ref|>text<|/ref|><|det|>[[44, 382, 220, 423]]<|/det|> +Hanyong Jin Yanbian University + +<|ref|>text<|/ref|><|det|>[[44, 429, 245, 470]]<|/det|> +Haifeng Liu Chung- Ang University + +<|ref|>text<|/ref|><|det|>[[44, 475, 245, 516]]<|/det|> +Daeyoung Kim Chung- Ang University + +<|ref|>text<|/ref|><|det|>[[44, 521, 810, 562]]<|/det|> +Yong- Hak Kim Daegu Catholic University School of Medicine https://orcid.org/0000- 0001- 6192- 5996 + +<|ref|>text<|/ref|><|det|>[[44, 567, 245, 608]]<|/det|> +Hak Kyun Kim Chung- Ang University + +<|ref|>text<|/ref|><|det|>[[44, 613, 245, 654]]<|/det|> +Jeongkyu Kim Chung- Ang University + +<|ref|>text<|/ref|><|det|>[[44, 660, 225, 700]]<|/det|> +Hong- Man Kim NES Biotechnology + +<|ref|>text<|/ref|><|det|>[[44, 706, 225, 747]]<|/det|> +Minkyung Ryu NES Biotechnology + +<|ref|>text<|/ref|><|det|>[[44, 752, 245, 793]]<|/det|> +Keun Pil Kim Chung- Ang University + +<|ref|>text<|/ref|><|det|>[[44, 799, 600, 840]]<|/det|> +Yoonsoo Hahn Chung- Ang University https://orcid.org/0000- 0003- 4273- 9842 + +<|ref|>text<|/ref|><|det|>[[44, 845, 785, 886]]<|/det|> +Jeehyeon Bae School of Pharmacy, Chung- Ang University https://orcid.org/0000- 0003- 1995- 1378 + +<|ref|>text<|/ref|><|det|>[[44, 890, 600, 932]]<|/det|> +Kangseok Lee ( kangseok@cau.ac.kr) Chung- Ang University https://orcid.org/0000- 0002- 0060- 6884 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 45, 102, 63]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 82, 888, 103]]<|/det|> +Keywords: IRE1α, alternative splicing, tRNAGly(GCC), ER stress, HNRNPM, HNRNPH2, tRNA halves + +<|ref|>text<|/ref|><|det|>[[44, 120, 310, 140]]<|/det|> +Posted Date: August 3rd, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 159, 474, 179]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1464849/v1 + +<|ref|>text<|/ref|><|det|>[[42, 197, 910, 240]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[78, 99, 770, 118]]<|/det|> +1 5'-tRNAGly(GCC) halves generated by IRE1α are linked to ER stress response + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[78, 100, 207, 117]]<|/det|> +## 4 Summary + +<|ref|>text<|/ref|><|det|>[[113, 130, 884, 580]]<|/det|> +Transfer RNA (tRNA) halves (tRHs) have various biological functions. However, the biogenesis of specific \(5^{\prime}\) - tRHs under certain conditions remains unknown. Here, we report that inositol- requiring enzyme \(1\alpha\) (IRE1α) cleaves the anticodon stem- loop region of tRNA \(^{\mathrm{Gly(GCC)}}\) to produce \(5^{\prime}\) - tRHs ( \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) ) with highly selective target discrimination upon endoplasmic reticulum (ER) stress. We observed IRE1α expression- dependent \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) production in human cancer cells. Levels of \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) were positively correlated with the degree of cancer cell proliferation both in vitro and in vivo; this effect required co- expression of two nuclear ribonucleoproteins, HNRNPM and HNRNPH2, which we identified as binding proteins of \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) . In addition, \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) modulated mRNA isoform biogenesis. Furthermore, under ER stress in vivo, we observed simultaneous induction of IRE1α and \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) expression in mouse organs and a distantly related organism, Cryptococcus neoformans. Thus, collectively, our findings indicate an evolutionarily conserved function for IRE1α- generated \(5^{\prime}\) - tRH- Gly \(^{\mathrm{GCC}}\) in cellular adaptation upon ER stress. + +<|ref|>text<|/ref|><|det|>[[115, 622, 880, 675]]<|/det|> +Key words: IRE1α, alternative splicing, tRNA \(^{\mathrm{Gly(GCC)}}\) , ER stress, HNRNPM, HNRNPH2, tRNA halves + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 100, 231, 117]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[115, 131, 881, 312]]<|/det|> +Transfer RNA- derived fragments (tRFs) or transfer RNA- derived small RNAs (tsRNAs) have been recognised as functional small non- coding RNAs (ncRNAs) present in most organisms1. Multiple classes of tRFs have been identified in various cell types1. In particular, 31–40 nucleotide (nt) long tRFs generated by specific cleavage in the anticodon loop of mature tRNAs are referred to as tRNA halves (tRHs). Other tRFs are 14–40 nt in length and primarily correspond to the ends of mature tRNA (5'- tRFs and 3'- CCA tRFs) or pre- tRNA (3'- U tRFs)1. + +<|ref|>text<|/ref|><|det|>[[115, 327, 883, 511]]<|/det|> +In mammalian cells, limited information exists regarding the enzymes that generate tRFs. Angiogenin (ANG), a member of the RNase A superfamily, produces tRHs under certain stress conditions2- 5. In the case of RNase Z, it cleaves pre- tRNAs and generates 3'- U tRFs containing a stretch of U residues6. Additionally, dicer induces cleavage in the D loop and T loop of tRNAs, producing 5'- tRFs and 3'- CCA tRFs, respectively7, 8. Furthermore, recent deep sequencing data suggest that dicer processes tRFs in specific tRNAs and cell types9. + +<|ref|>text<|/ref|><|det|>[[115, 524, 883, 840]]<|/det|> +Functional roles of identified tRFs in biological processes include translational regulation of gene expression10- 13, gene silencing, and regulation of ribosome synthesis6, 14. tRHs affect cell proliferation4, 14- 18, apoptosis5, and epigenetic inheritance19, 20. For instance, changes in the profiles of a subset of sperm tRFs, including 5'- tRHs of tRNAGly(GCC) (5'- tRH- GlyGCC), were reported in mice fed a high- fat diet20. Moreover, protein restriction in mice increases 5'- tRH- GlyGCC levels19. Additionally, 5'- tRH- GlyGCC, induced by alkB homologue 3, \(\alpha\) - ketoglutarate dependent dioxygenase (ALKBH3)—a tRNA demethylase—benefits the growth and progression of cervical carcinoma16. 5'- tRH- GlyGCC levels were also upregulated in papillary thyroid carcinoma18. Although 5'- tRH- GlyGCC appears to play various roles in cellular physiology, it remains unclear which enzyme generates these tRHs. + +<|ref|>text<|/ref|><|det|>[[160, 853, 880, 872]]<|/det|> +A structural analysis of IRE1α revealed that the catalytic residues between the tRNA + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 97, 882, 380]]<|/det|> +endonuclease and IRE1α contain functional groups with a shared chemical nature and spatial disposition21. IRE1α—a key regulator of signalling in the unfolded protein response (UPR)—is a conserved ER- localised transmembrane protein with ribonuclease activity22. Upon ER stress, IRE1α becomes activated and cleaves specific sites in the mRNA that encodes the transcription factor X- box- binding protein 1 (XBP1)23, 24. IRE1α also participates in regulated IRE1α- dependent decay, i.e., the degradation of multiple mRNAs and miRNAs under ER stress in an XBP1- independent manner25- 27. In particular, a consensus sequence (5'- CH(U or A or C)GCM(A or C)R(G or A)- 3')) accompanied by a stem- loop structure was proposed as an IRE1α cleavage site in mRNA28. + +<|ref|>text<|/ref|><|det|>[[115, 394, 882, 546]]<|/det|> +Herein, we observed that several tRNAs bear the consensus element for IRE1α cleavage in their anticodon loop region. Considering that tRNAGly(GCC) is one such tRNA, we hypothesised that IRE1α may participate in producing 5'- tRHs from tRNAGly(GCC). To test the hypothesis, we aimed to investigate the direct involvement of IRE1α in the production of 5'- tRHs from tRNAGly(GCC), as well as their physiological function under ER stress. + +<|ref|>sub_title<|/ref|><|det|>[[118, 591, 185, 607]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[118, 622, 496, 641]]<|/det|> +## 5'-tRH accumulation by IRE1α upregulation + +<|ref|>text<|/ref|><|det|>[[115, 654, 882, 871]]<|/det|> +To explore whether IRE1α can cleave tRNAs and produce 5'- tRHs, we compared tRF profiles in human ovarian cancer- derived KGN cells endogenously expressing IRE1α (KGN) with those in the same cells exogenously overproducing IRE1α (KGN- IRE1αoe) using small RNA- sequencing (small RNA- seq). We selected human ovarian cancer cells, as 5'- tRH- GlyGCC reportedly functions in reproductive cells16, 20. The relative abundance of 5'- tRFs from tRNAGly(GCC) species markedly increased when IRE1α was overexpressed (Fig. 1a and Supplementary Table 1). Additionally, 5'- tRFs from tRNACys(GCA) appeared to accumulate, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 97, 881, 217]]<|/det|> +albeit at much lower levels compared to those from tRNA \(\mathrm{Gly(GCC)}\) in KGN- IRE1 \(\alpha^{\mathrm{oe}}\) cells (Fig. 1a and Supplementary Table 1). These results support the notion that among tRNA species, only tRNA \(\mathrm{Gly(GCC)}\) and tRNA \(\mathrm{Cys(GCA)}\) bear the consensus element for IRE1 \(\alpha\) cleavage in their anticodon stem- loop region. + +<|ref|>text<|/ref|><|det|>[[115, 229, 883, 610]]<|/det|> +Among three different tRNA \(\mathrm{Gly}\) isoacceptors, containing GCC, UCC, and CCC anticodons, 5'- tRFs with their 3'- end corresponding to position 33 of tRNA \(\mathrm{Gly(GCC)}\) were most abundant and enriched in KGN- IRE1 \(\alpha^{\mathrm{oe}}\) compared to KGN cells (Fig. 1a,b and Supplementary Table 1). In addition, high levels of 5'- tRFs, with their 3'- end corresponding to positions 31 and 32 of tRNA \(\mathrm{Gly(GCC)}\) , were observed when IRE1 \(\alpha\) was overexpressed (Fig. 1b and Supplementary Table 1). These 5'- tRFs from tRNA \(\mathrm{Gly(GCC)}\) occupied approximately \(89\%\) of the total 5'- tRFs from IRE1 \(\alpha^{\mathrm{oe}}\) cells (Fig. 1b), indicating that IRE1 \(\alpha\) overexpression primarily generates 5'- tRFs from tRNA \(\mathrm{Gly(GCC)}\) . In the case of tRNA \(\mathrm{Cys(GCA)}\) , high levels of 5'- tRFs, with their 3'- end corresponding to positions 33 and 34, were observed when IRE1 \(\alpha\) was overexpressed (Fig. 1a, b and Supplementary Table 1). We also observed enrichment of 5'- tRFs with their 3'- end corresponding to position 33 of tRNA \(\mathrm{Gly(GCC)}\) in IRE1 \(\alpha\) - overexpressing cells; however, these 5'- tRFs accounted for only \(\sim 2\%\) of the total (Fig. 1a, b and Supplementary Table 1). + +<|ref|>text<|/ref|><|det|>[[115, 622, 883, 870]]<|/det|> +To validate the small RNA- seq results, tRNA fragments were analysed via northern blotting with specific probes for the 5' upstream regions of the tRNA \(\mathrm{Gly(GCC)}\) , tRNA \(\mathrm{Cys(GCA)}\) , tRNA \(\mathrm{Gly(TCC)}\) , and tRNA \(\mathrm{Lys(CTT)}\) anticodon stem- loops. An IRE1 \(\alpha\) expression- dependent increase was observed in the levels of 5'- tRFs from tRNA \(\mathrm{Gly(GCC)}\) and tRNA \(\mathrm{Cys(GCA)}\) (Fig. 1c). The relative abundances of these 5'- tRFs were \(\sim 1.2\%\) and \(\sim 0.1\%\) of full- length tRNA \(\mathrm{Gly(GCC)}\) and tRNA \(\mathrm{Cys(GCA)}\) , respectively. The length of these 5'- tRFs corresponded to 32 and 33 nt- long synthetic RNAs containing sequences of 5'- tRFs from tRNA \(\mathrm{Gly(GCC)}\) (Fig. 1c). The relative abundances of other 5'- tRFs from tRNA \(\mathrm{Gly(TCC)}\) and tRNA \(\mathrm{Lys(CTT)}\) were not significantly + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 216]]<|/det|> +changed upon IRE1α overexpression, which agreed with the small RNA- seq results. Moreover, overexpression of a catalytically inactive form of IRE1α (K599A) \(^{29}\) did not significantly impact the levels of these 5'- tRFs (Extended Data Fig. 1a), indicating IRE1α cleavage activity- dependent production of 5'- tRH- Gly \(^{GCC}\) . + +<|ref|>text<|/ref|><|det|>[[115, 228, 882, 675]]<|/det|> +To assess the size of tRFs from tRNA \(^{\mathrm{Gly(GCC)}}\) , we performed primer extension analysis on the samples used for northern blot analysis. Primer extension targeting for tRNA \(^{\mathrm{Gly(GCC)}}\) produced one distinct cDNA band in reactions prepared with RNA samples from KGN- IRE1α \(^{\mathrm{oe}}\) cells. This cDNA band was synthesized from the 3'- tRH of tRNA \(^{\mathrm{Gly(GCC)}}\) , whose 5'- end corresponded to position 34 (Fig. 1d). This 3'- tRH of tRNA \(^{\mathrm{Gly(GCC)}}\) can be generated by IRE1α cleavage of tRNA \(^{\mathrm{Gly(GCC)}}\) between positions 33 and 34 within its anticodon stem- loop region (C \(^{31}\) UG \(^{1}\) CCAC \(^{37}\) ). This cleavage can also generate a 33- nt long 5'- tRH of tRNA \(^{\mathrm{Gly(GCC)}}\) , with the same 3'- end that mapped the highest in KGN- IRE1α \(^{\mathrm{oe}}\) cells in small RNA seq analysis (Fig. 1a, b). We were not able to detect cDNA bands corresponding 31 and 32 nt- long tRFs that were indicated in small RNA- seq and northern blot analyses (Fig. 1b,c). Overexpression of angiogenin (ANG)—a ribonuclease that produces tRHs by cleaving the anticodon loop region of tRNAs \(^{4, 5}\) — resulted in high levels of 5'- tRFs from all tRNA species (tRNA \(^{\mathrm{Gly(GCC)}}\) , tRNA \(^{\mathrm{Lys(CTT)}}\) , and tRNA \(^{\mathrm{Val(TAC)}}\) ; Extended Data Fig. 1b). Collectively, these results indicate that IRE1α activity is primarily responsible for generation of 5'- tRHs from tRNA \(^{\mathrm{Gly(GCC)}}\) . + +<|ref|>sub_title<|/ref|><|det|>[[117, 721, 486, 740]]<|/det|> +## Selective cleavage of tRNA \(^{\mathrm{Gly(GCC)}}\) by IRE1α + +<|ref|>text<|/ref|><|det|>[[115, 752, 882, 872]]<|/det|> +To assess whether IRE1α is solely responsible for the production of 5'- tRHs from tRNA \(^{\mathrm{Gly(GCC)}}\) , we isolated tRNAs from KGN cell total RNA and isolated tRNAs by size fractionation. Purified tRNAs were then incubated with human IRE1α. IRE1α was found to selectively cleave tRNA \(^{\mathrm{Gly(GCC)}}\) in vitro (Fig. 2a). Specifically, IRE1α- mediated cleavage of tRNA \(^{\mathrm{Gly(GCC)}}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 97, 883, 545]]<|/det|> +generated one major and two minor \(5^{\prime}\) - tRNAGly(GCC) fragments (Fig. 2a). When the 3'- end of these fragments was mapped by primer extension analysis, seven distinct cDNA bands (Fig. 2b) were detected. The cDNA band was most prominent and could be synthesised from the 3'- tRH of tRNAGly(GCC) with a 5'- end corresponding to position 34 (Fig. 2b, labelled as b). This 3'- tRH could be generated by IRE1α cleavage of tRNAGly(GCC) between positions 33 and 34 within its anticodon stem- loop region (C \(^{31}\) UG \(_{1}\) CCAC \(^{37}\) ). This cleavage site corresponded to the 3'- end of the most abundant 5'- tRFs from tRNAGly(GCC) that were identified by small RNA- seq (Fig. 1b). Moreover, this cDNA band is identical to the distinct cDNA detected in the primer extension assay of tRNAGly(GCC) fragments in KGN cells following IRE1α overexpression (Fig. 1d). Another distinct band (labelled as a) also corresponded to the 3'- end of the second most abundant 5'- tRFs from tRNAGly(GCC) identified in small RNA- seq (Fig. 1b, 2b). Meanwhile, the cleavage sites deduced from other cDNA bands were not observed in the small RNA- seq analysis (Fig. 1b) or in the primer extension of in vivo generated fragments of tRNAGly(GCC) (Fig. 1d). + +<|ref|>text<|/ref|><|det|>[[112, 556, 883, 872]]<|/det|> +To further biochemically verify the ability of IRE1α to cleave tRNAGly(GCC), we conducted an in vitro IRE1α cleavage reaction using purified tRNAGly(GCC) as a substrate (Extended Data Fig. 2a–c). Two major and five minor cleavage products appeared to be dependent on IRE1α (Fig. 2c). Among them, one major cleavage product (labelled as 5) corresponded to an IRE1α cleavage product of tRNAGly(GCC) between positions 33 and 34 (Fig. 2c). This cleavage site corresponded to the 3'- end of the most abundant 5'- tRFs from tRNAGly(GCC) identified via small RNA-seq (Fig. 1b) and was the only cDNA detected in the primer extension assay of tRNAGly(GCC) in vivo generated fragments (Fig. 1d) when IRE1α was overexpressed. Five other products also corresponded to IRE1α cleavage products of tRNAGly(GCC) at sites generated by in vitro IRE1α cleavage of total tRNAs (Fig. 2b). An + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 315]]<|/det|> +additional cleavage product (labelled as 7) was also detected in small RNA- seq (Fig. 1b). However, several tRFs identified from in vitro cleavage of tRNAGly(GCC) were not detected in the small RNA- seq analyses (Fig. 1a,b) or primer extension assay of tRNAGly(GCC) fragments generated in vivo (Fig. 1d). These tRFs might have resulted from decreased IRE1α stringency in the sequence- specific cleavage of tRNAGly(GCC) in vitro, or from tRNAGly(GCC) structural alterations induced during purification or incubation. It is also possible that they arose from fragmentation of tRNAGly(GCC) cleavage products. + +<|ref|>text<|/ref|><|det|>[[115, 328, 884, 512]]<|/det|> +These clearly show the ability of IRE1α to selectively cleave tRNAGly(GCC) within the anticodon stem- loop region. Furthermore, the cleavage site (C31UG\CCAC37) deduced from both small RNA- seq (Fig. 1a,b) and primer extension analyses of in vivo generated tRNAGly(GCC) fragments (Fig. 1d) corresponded with the major in vitro IRE1α cleavage product of tRNAGly(GCC) (Fig. 2b,c). Based on these results, we designated 33- nt long 5'- tRHs generated from the cleavage of tRNAGly(GCC) at the overlapping site (C31UG\CCAC37) as 5'- tRH- GlyGCC. + +<|ref|>sub_title<|/ref|><|det|>[[116, 558, 576, 577]]<|/det|> +## Induction of 5'- tRH-GlyGCC generation upon ER stress + +<|ref|>text<|/ref|><|det|>[[115, 589, 881, 741]]<|/det|> +Considering that IRE1α is an ER stress- activated endonuclease, we hypothesised that ER stress- induced activation of IRE1α may cause generation of 5'- tRH- GlyGCC from tRNAGly(GCC) cleavage. To test this hypothesis, we induced ER stress in KGN cells using thapsigargin (TG) or tunicamycin (TM). Western blot analysis and an XBP1 splicing assay confirmed that these agents stimulated the expression and ribonucleolytic activity of IRE1α (Fig. 3a). + +<|ref|>text<|/ref|><|det|>[[115, 754, 881, 872]]<|/det|> +Next, ER stress- induced IRE1α activation on tRNA cleavage was examined via northern blot analysis on tRNAs. In agreement with the effect of IRE1α overexpression on the generation of tRHs from tRNAGly(GCC) (Fig. 1c), 5'- tRH- GlyGCC was generated while distinct tRFs from other tRNAs were not detected following ER stress- induced IRE1α expression (Fig. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 281]]<|/det|> +3a and Extended Data Fig. 3a). Moreover, we found that the cleavage pattern of these 5'- tRHs resembled those generated by IRE1α, which cleaves tRNAGly(GCC) between positions 33 and 34 within the anticodon stem- loop (C31UG↓CCAC37) (Fig. 3b). Furthermore, TG- or TM- induced production of 5'- tRHs was not observed in IRE1α knock out cells (IRE1α-/-; Fig. 3c and Extended Data Fig. 3b, 4). Hence, ER stress induces 5'- tRH- GlyGCC production via IRE1α- dependent tRNAGly(GCC) cleavage in KGN cells. + +<|ref|>text<|/ref|><|det|>[[115, 295, 881, 413]]<|/det|> +To investigate whether the production of 5'- tRHs from tRNAGly(GCC) is commonly coupled with ER stress in human cells, we induced ER stress in HeLa cells with TG or TM. ER stress- dependent selective generation of 5'- tRFs from tRNAGly(GCC) was also observed in HeLa cells (Extended Data Fig. 3c, d). + +<|ref|>sub_title<|/ref|><|det|>[[117, 459, 455, 478]]<|/det|> +## Proteins interacting with 5'- tRH-GlyGCC + +<|ref|>text<|/ref|><|det|>[[114, 490, 882, 872]]<|/det|> +To investigate the functional role of 5'- tRH- GlyGCC, we characterised proteins bound to 5'- tRH- GlyGCC in KGN cells via biotinylation of the tRH 5'- and 3'- ends. Specifically, 33- nt long 5'- tRHs of tRNAGly(GCC) (5'- tRH- GlyGCC mimic) were used. To assess non- specific protein binding of biotinylated RNA with a streptavidin coated microplate, 5'- biotin- oligo A8 RNA and 3'- biotin- tRH- GlyGCC were used as controls. Two protein bands near 70 kDa and 55 kDa appeared to specifically bind to a 5'- biotin- tRH- GlyGCC in both samples of TG- treated and - untreated cells but did not bind 3'- biotin- tRH- GlyGCC or 5'- biotin- oligo A8 RNA (Extended Data Fig. 5a, right panel). The comparative tandem mass spectrometry analysis of proteins interacting with biotinylated RNA showed that the heterogeneous nuclear ribonucleoprotein M isoform b (HNRNPM) and heterogeneous nuclear ribonucleoprotein H isoform X11 (HNRNPH2) in TG- treated and - untreated samples were enriched at similar levels in the microplate containing 5'- biotin- tRH- GlyGCC (Extended Data Fig. 5b and Supplementary Table 2). These nuclear proteins + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 97, 881, 185]]<|/det|> +may be potential binding partners of the 5'- tRHs of tRNAGly(GCC). We were also able to detect a moderate amount of HNRNPF, while HNRNPH1 was not detected. Orthologues of HNRNP proteins have been previously identified as binding proteins of tRHGly(GCC) in mouse cells30. + +<|ref|>text<|/ref|><|det|>[[115, 196, 882, 380]]<|/det|> +We further assessed the physical interaction between 5'- tRH- GlyGCC and HNRNP proteins via electrophoretic mobility shift assay (EMSA) using purified HNRNPM and HNRNPH2 recombinant proteins and 5'- P32- labelled synthetic 5'- tRH- GlyGCC and 5'- tRH- LysCTT. These proteins bind 5'- tRH- GlyGCC with much higher affinity than 5'- tRH- LysCTT (Extended Data Fig. 6a), providing evidence of specific interactions between 5'- tRH- GlyGCC and HNRNP proteins. + +<|ref|>text<|/ref|><|det|>[[114, 392, 882, 675]]<|/det|> +We further tested physical interaction of 5'- tRH- GlyGCC or 3'- tRH- GlyGCC with HNRNP proteins (HNRNPM and HNRNPH2) by using the surface plasmon resonance (SPR). In SPR assay, both 5'- tRH- GlyGCC and 3'- tRH- GlyGCC showed dose- dependent binding signal to the immobilized HNRNPM and HNRNPH2 (Extended Data Fig. 6b). However, kinetic analysis indicated that 5'- tRH- GlyGCC has about \(10 \sim 35\) times higher affinities for HNRNPM and HNRNPH2 with \(K_D\) of \(86.30 \mathrm{nM}\) and \(27.07 \mathrm{nM}\) , compared to 3'- tRH- GlyGCC, which showed affinities for HNRNPM and HNRNPH2 with \(K_D\) of \(0.96 \mu \mathrm{M}\) and \(0.94 \mu \mathrm{M}\) , respectively (Extended Data Fig. 6c). These results provide clear evidence for specific and strong interaction between 5'- tRH- GlyGCC and HNRNP proteins. + +<|ref|>sub_title<|/ref|><|det|>[[117, 721, 470, 740]]<|/det|> +## Roles of ER stress-induced 5'- tRH-GlyGCC + +<|ref|>text<|/ref|><|det|>[[115, 752, 882, 872]]<|/det|> +To investigate functional roles for 5'- tRH- GlyGCC in cancer cells that produced these 5'- tRHs upon ER stress, KGN and HeLa cells were treated with synthetic 5'- tRH- GlyGCC (5'- tRH- GlyGCC mimic) and two other control tRH mimics (5'- tRH- LysCTT and 3'- tRH- GlyGCC). Treatment with 5'- tRH- GlyGCC mimic promoted cell survival in a manner dependent on mimic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 97, 882, 415]]<|/det|> +concentrations, which increased cell survival of KGN and HeLa cells by \(34\%\) and \(25\%\) , respectively, at the highest concentration of the mimic used (Fig. 4a,b). In contrast, the two control mimics did not significantly affect cell viability with the highest concentration of \(3'\) - tRH- Gly \(^{\mathrm{GCC}}\) inducing only a moderate increase in the viability of KGN cells (Fig. 4a,b). Blocking the \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) mimic with complementary antisense DNA oligos significantly reduced the positive effects on cell viability (Extended Data Fig. 7a). The enhancement of cell viability by \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) mimic occurred due to increased proliferation of KGN cells, as no effect on apoptosis was observed (Extended Data Fig. 7b,c). In addition, tRH mimic transfection did not affect the migration capability of KGN cells (Extended Data Fig. 7d). These results suggested that \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) functions to control cancer cell proliferation. + +<|ref|>text<|/ref|><|det|>[[115, 426, 882, 774]]<|/det|> +Next, we investigated whether HNRNPM and HNRNPH2 participate in \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) - mediated cell survival. The \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) mimic- induced promotion of cell survival in KGN and HeLa cells was abolished following HNRNPM or HNRNPH2 knockdown (Fig. 4c,d). Transfection of the \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) mimics in HNRNPM- depleted KGN (Fig. 4c) or HNRNPH2- depleted HeLa (Fig. 4d) cells further reduced cell survival compared to those treated with control mimics, suggesting that HNRNPM and HNRNPH2 might have a cell line- specific role in \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) - mediated cell survival. Hence, \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) - mediated cell survival depends on its interaction with HNRNP proteins in these cancer cells. Considering that we observed analogous results following knockdown of HNRNPF/H1/H2 (Extended Data Fig. 7e,f), the \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) might interact with multiple nuclear ribonucleoprotein for cellular function. + +<|ref|>text<|/ref|><|det|>[[115, 787, 881, 874]]<|/det|> +We further examined whether IRE1α- dependent \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) generation mediates the ER stress- induced effect on cell survival. Treatment with antisense DNA oligos targeting endogenous \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) (anti- \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) ) significantly promoted TG- induced cell + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 97, 881, 216]]<|/det|> +death in WT KGN cells compared to those treated with 5'- tRH- LysCTT (anti- 5'- tRH- LysCTT) (Fig. 4e). In contrast, anti- 5'- tRH- GlyGCC did not elicit such an effect in IRE1α- /- cells (Fig. 4e). Thus, IRE1α cleavage- generated 5'- tRH- GlyGCC contributes to cellular adaptation upon ER stress. + +<|ref|>text<|/ref|><|det|>[[115, 228, 883, 512]]<|/det|> +To investigate in vivo 5'- tRH- GlyGCC function, we silenced the endogenous 5'- tRH- GlyGCC or 5'- tRH- LysCTT by delivering antisense DNA oligos against them using a functionalized gold nanoparticle (AuNP)- based delivery system (AuNPdT) in a xenograft mouse model. As shown in Figure. 4f, tumour growth in mice treated with AuNPdT loaded with anti- 5'- tRH- GlyGCC was prominently inhibited compared with that treated with AuNPdT alone or AuNPdT loaded with anti- 5'- tRH- LysCTT (Fig. 4f). Consistent with anti- proliferative response observed in cancer cells treated with anti- 5'- tRH- GlyGCC in vitro (Extended Data Fig. 7g), proliferating cell nuclear antigen (PCNA) expression in tumours decreased by \(\sim 44\%\) upon anti- 5'- tRH- GlyGCC treatment in xenografted tumours (Fig. 4g). + +<|ref|>sub_title<|/ref|><|det|>[[117, 558, 505, 576]]<|/det|> +## Effect of 5'- tRH-GlyGCC in alternative splicing + +<|ref|>text<|/ref|><|det|>[[115, 589, 883, 840]]<|/det|> +To dissect the relevance of 5'- tRH- GlyGCC functioning, we performed total transcriptome analysis on KGN cells transfected with 5'- tRH- mimics. The RNA abundance of 66 genes was altered more than 1.5- fold in cells transfected with 5'- tRH- GlyGCC mimics compared to those with 5'- tRH- LysCTT mimics (Fig. 5a and Supplementary Table 3). Functional annotation analysis further indicated that most genes were enriched in alternative splicing and phosphoproteins (Fig. 5b and Supplementary Table 4). Based on these results, and the fact that 5'- tRH- GlyGCC interacts with multiple nuclear proteins functioning in RNA splicing, we hypothesised that 5'- tRH- GlyGCC modulates alternative splicing of a target gene subset. For this + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 99, 880, 152]]<|/det|> +reason, we further analysed isoforms of total transcripts using nanopore sequencing and FLAIR (full- length alternative isoform analysis of RNA) modules31. + +<|ref|>text<|/ref|><|det|>[[115, 164, 884, 675]]<|/det|> +We analysed four main types of alternative splicing events (alternative 3'- and 5'- splicing, intron retention, and exon skipping events) associated with isoform formation. Compared to the control group (5'- tRH- LysCTT), we identified 19 differential isoforms from the 17 genes in the 5'- tRH- GlyGCC- treated group, where one or more of their junctions exhibited alternative 5'/3' splice site selection or exon skipping (Supplementary Table 5). These genes had multiple alternative splicing events within their transcripts, except CFDP1 (exon skipping), PRDX4 (alternative 5'- splicing), and MAGED2 (alternative 3'- splicing) (Supplementary Table 5). Among them, the isoform usage of ELOB (Fig. 5c, upper panel) and PMSB5 (Fig. 5c, lower panel) was significantly altered between 5'- tRH- LysCTT- and 5'- tRH- GlyGCC- treated groups. These results were confirmed by RT- qPCR using isoform specific primers (Fig. 5d). In addition, sequestering of 5'- tRH- GlyGCC by antisense DNA oligos in xenografted tumours resulted in a shift in mRNA isoform composition in an opposite direction (Fig. 5e) compared to what we observed with 5'- tRH- GlyGCC mimics treatment in cancer cells (Fig. 5d). Treatment of tumours with anti 5'- tRH- LysCTT did not affect isoform composition of these genes (Fig. 5e). Hence, 5'- tRH- GlyGCC levels affect alternative splicing events, leading to alterations in the transcript isoform profile. + +<|ref|>sub_title<|/ref|><|det|>[[118, 722, 417, 740]]<|/det|> +## Nucleus localisation of tRH-GlyGCC + +<|ref|>text<|/ref|><|det|>[[115, 753, 883, 871]]<|/det|> +Our results showing an interaction between tRH- GlyGCC and nuclear proteins (Extended Data Fig. 6), as well as the effect of tRH- GlyGCC mimics on transcript isoform profiles (Fig. 5), suggest that tRH- GlyGCC functions within the nucleus. Thus, to determine the subcellular distribution of ER stress- induced 5'- tRHs of tRNAGly(GCC), we conducted a fluorescent in situ + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 95, 883, 678]]<|/det|> +hybridisation assay (FISH), with a probe designed to recognise \(5'\) - tRHs of tRNAGly(GCC). This assay was performed under conditions designed to avoid the denaturation of stable mature tRNAs and hybridisation of the probe to full- length tRNAs. Fluorescent signals, obtained with the probe recognising the \(5'\) - tRHs of tRNAGly(GCC), displayed a nucleus- associated localisation pattern with higher signal intensity following TG- induced ER stress, compared to treatment with dimethylsulphoxide (DMSO; Extended Data Fig. 8a). To confirm the specificity of the hybridisation probe, we performed a series of experiments under non- denaturing or denaturing conditions using an additional control probe. This control probe (anticodon bridging probe) bridged the \(5'\) - and \(3'\) - regions spanning the nucleotides that encompass the anticodon and was designed to detect only intact full- length tRNAs with minimal complementarity for \(5'\) - tRHs of tRNAGly(GCC). The anticodon bridging probe showed a fluorescent signal under denaturing FISH conditions, while no signal was observed under non- denaturing conditions (Extended Data Fig. 8a). Hence, \(5'\) - tRHs of tRNAGly(GCC) were definitively recognised with the specific probe used under our experimental conditions. In addition, measurement of tRH- GlyGCC distribution by TaqMan assay showed that \(5'\) - tRH- GlyGCC levels were elevated by \(\sim 25\%\) in the nuclear fraction of KGN cells following TG treatment over the 6 h period (Extended Data Fig. 8b). These results indicate that \(5'\) - tRHs of tRNAGly(GCC) localise to the nucleus when cells are subjected to ER stress. + +<|ref|>sub_title<|/ref|><|det|>[[117, 721, 626, 740]]<|/det|> +## IRE1α-dependent \(5'\) -tRHGhy(GCC) cleavage in other organisms + +<|ref|>text<|/ref|><|det|>[[117, 753, 881, 840]]<|/det|> +To investigate whether IRE1α- mediated generation of \(5'\) - tRH- GlyGCC upon ER stress occurs in other eukaryotic species, we analysed selective generation of \(5'\) - tRH- GlyGCC in an acute ER stress murine model32 and ER- stressed yeast species, Cryptococcus neoformans. IRE1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 99, 880, 150]]<|/det|> +homologues of mouse and yeast show \(94.40\%\) and \(40.15\%\) sequence similarity, respectively to the protein kinase and kinase- extension nuclease domains of human IRE1α. + +<|ref|>text<|/ref|><|det|>[[115, 164, 883, 479]]<|/det|> +We observed prominent induction of IRE1α expression in the ovary, liver, epididymis, kidney, and pancreas of ER- stressed mice (Extended Data Fig. 9a). Northern blot analysis showed an increased abundance of \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) fragments in the ovary (Fig. 6a), liver (Extended Data Fig. 9b), and epididymis (Extended Data Fig. 9c) in samples taken from ER- stressed mice compared to control mice samples, while other tRNA fragments, the size of which were similar to \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) , were not detected (Extended Data Fig. 9b, d). Moreover, primer extension analysis indicates that \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) fragments in the ovary resulted from the overlapping IRE1α cleavage site identified in KGN cells, which generates a 33- nt long \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) (Fig. 6b). These results indicate that ER stress induces selective generation of \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) in mice in an IRE1α expression- dependent manner. + +<|ref|>text<|/ref|><|det|>[[115, 491, 883, 806]]<|/det|> +We also observed that high levels of \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) coincided with enhanced IRE1 expression in \(C\) . neoformans when ER stress was induced by TM treatment (Fig. 6c and Extended Data Fig. 9e). Primer extension analysis indicate that these \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) resulted from Ire1 cleavage at the site corresponding to the overlapping site identified in KGN cells and mouse ovary (Fig. 6d). A minor band, which was slightly longer than \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) , was detected in the ire1- deletion strain when treated with TM (Fig. 6c and Extended Data Fig. 9e). These results indicate the existence of an additional unknown activity for tRNA \(^{\mathrm{Gly(GCC)}}\) cleavage under ER stress in this yeast species. Taken together, IRE1α- dependent selective generation of \(5'\) - tRH- Gly \(^{\mathrm{GCC}}\) under ER stress appears to be widely conserved in eukaryotic organisms. + +<|ref|>sub_title<|/ref|><|det|>[[117, 853, 211, 869]]<|/det|> +## Discussion + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 97, 882, 315]]<|/det|> +This study highlights that IRE1α- mediated selective cleavage of tRNAGly(GCC) and 5'- tRH generation upon ER stress is conserved in human, mice, and a distantly related yeast species, C. neoformans. In fact, within these organisms, IRE1α homologues selectively cleave tRNAGly(GCC) species at the same site. These results raise the question of why these organisms evolutionarily retain this biological event in response to ER stress. Perhaps, as we found in the case of human cancer cells (Fig. 4e), 5'- tRH- Gly(GCC) contributes to cellular adaptation upon ER stress. + +<|ref|>text<|/ref|><|det|>[[115, 327, 882, 808]]<|/det|> +Dicer and ANG generate different types of tRFs for multiple roles in cellular processes33. However, ANG is the only identified enzyme associated with the generation of 5'- tRHs by cleaving the anticodon stem- loop of mature tRNAs in mammalian cells2-5, 16, 34. Additional enzymes responsible for the generation of certain 5'- tRHs have not been identified under specific conditions, such as metabolic diseases, cancer, and reproductive cell maturation18-20. One such example is 5'- tRH from tRNAGly(GCC) produced in mouse sperm, which reportedly suppresses the expression of genes associated with endogenous retroelement MERVL in embryonic stem cells and embryos by regulating gene expression from specific regions of the genome19, 20. This 5'- tRH from tRNAGly(GCC) was shown to be upregulated in papillary thyroid carcinoma18. Although an increasing number of reports have revealed that tRNA- derived fragments are involved in various biological processes, its biogenesis is remains largely unknown. Here, we show that under ER stress conditions, IRE1α cleaves the anticodon stem- loop of tRNAGly(GCC) to produce 5'- tRH in human cancer cells. Moreover, generation of 5'- tRHs from tRNAGly(GCC) appears to be ER stress- specific, as it was not observed under other stress conditions tested in this study (Extended Data Fig. 10a,b). + +<|ref|>text<|/ref|><|det|>[[115, 820, 881, 872]]<|/det|> +Colicins and ANG generate tRHs by cleaving target tRNAs and the anticodon loop of most tRNAs, respectively, thereby inhibiting protein synthesis35, 36. In the case of IRE1α- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 95, 883, 283]]<|/det|> +mediated generation of tRHs from tRNAGly(GCC), it is unlikely that 5'- tRH- GlyGCC affects protein synthesis efficiency, as IRE1α appears to cleave a small portion of tRNAGly(GCC) upon ER stress, and thus, does not significantly reduce the pool of mature tRNAGly(GCC) (Fig 1c and 3a). Consistent with this notion, overexpression of IRE1α did not affect expression levels of two glycine-rich proteins, which contain a high proportion of the GGC codon in their mRNA (Extended Data Fig. 10c, Supplementary Table 6). + +<|ref|>text<|/ref|><|det|>[[115, 295, 883, 610]]<|/det|> +Although the detailed modes of action for most tRFs and tRHs remain unclear, several studies indicate that they can regulate the expression and translational efficiency of endogenous target genes by interacting with binding partners, including cytochrome c, YBX1, PIWI, and the AGO family \(^{5, 10, 37 - 40}\) . In the case of 5'- tRH- GlyGCC, we found that they interact with two nuclear proteins, HNRNPM and HNRNPH2, and these interactions are required for 5'- tRH- GlyGCC to influence cancer cell survival (Fig. 4c,d). Knockdown of HNRNPF or HNRNPH1, other interacting proteins identified by Boskovic et al \(^{30}\) , showed similar effects on cancer cell survival, suggesting that 5'- tRH- GlyGCC interacts with multiple nuclear proteins to exert these effects. A recent report identified RBM17, a splicing- related RNA- binding protein, as a binding protein of 5'- tRH- GlyGCC \(^{18}\) , supporting our notion that 5'- tRH- GlyGCC functions in the nucleus. + +<|ref|>text<|/ref|><|det|>[[115, 621, 884, 872]]<|/det|> +Additionally, treatment with 5'- tRH- GlyGCC mimics altered the expression of genes primarily associated with alternative splicing, which overlaps with the function of HNRNPP proteins and RBM17 identified as 5'- tRH- GlyGCC binding proteins. Moreover, analyses of isoforms within the total transcriptome data using nanopore sequencing, together with the alternative splicing assay results, indicate that 5'- tRH- GlyGCC affects the profiles of a subset of transcript isoforms (Fig. 5). Consistent with these nuclear phenomena, we observed nuclear localisation of 5'- tRH- GlyGCC following ER stress. However, further studies are needed to elucidate the detailed mechanisms underlying the role of 5'- tRH- GlyGCC in alternative splicing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 100, 252, 118]]<|/det|> +events (Fig. 6e). + +<|ref|>text<|/ref|><|det|>[[115, 132, 883, 283]]<|/det|> +Aberrant expression of tRNA fragments is reported in various human disease conditions, providing potential targets for disease detection and therapeutics. Our data showed that antisense DNA oligos- mediated \(5^{\prime}\) - tRH- Gly \(^{\text{GCC}}\) suppression inhibited tumour proliferation in a xenograft mouse model (Fig. 4f,g). Thus, we believe that understanding the regulatory role of \(5^{\prime}\) - tRH- Gly \(^{\text{GCC}}\) can be used as a novel biomarker and potential therapeutic target in cancer cells. + +<|ref|>sub_title<|/ref|><|det|>[[70, 329, 305, 346]]<|/det|> +## References and Notes + +<|ref|>text<|/ref|><|det|>[[115, 361, 884, 877]]<|/det|> +1. Kim, H.K., Yeom, J.H. & Kay, M.A. Transfer RNA-Derived Small RNAs: Another Layer of Gene Regulation and Novel Targets for Disease Therapeutics. Mol Ther 28, 2340-2357 (2020). +2. Fu, H. et al. Stress induces tRNA cleavage by angiogenin in mammalian cells. FEBS letters 583, 437-442 (2009). +3. Yamasaki, S., Ivanov, P., Hu, G.F. & Anderson, P. Angiogenin cleaves tRNA and promotes stress-induced translational repression. The Journal of cell biology 185, 35-42 (2009). +4. Honda, S. et al. Sex hormone-dependent tRNA halves enhance cell proliferation in breast and prostate cancers. Proceedings of the National Academy of Sciences of the United States of America 112, E3816-3825 (2015). +5. Saikia, M. et al. Angiogenin-cleaved tRNA halves interact with cytochrome c, protecting cells from apoptosis during osmotic stress. Molecular and cellular biology 34, 2450-2463 (2014). +6. Haussecker, D. et al. Human tRNA-derived small RNAs in the global regulation of RNA silencing. RNA (New York, N.Y.) 16, 673-695 (2010). +7. Cole, C. et al. Filtering of deep sequencing data reveals the existence of abundant Dicer-dependent small RNAs derived from tRNAs. RNA (New York, N.Y.) 15, 2147-2160 (2009). +8. Maute, R.L. et al. tRNA-derived microRNA modulates proliferation and the DNA damage response and is down-regulated in B cell lymphoma. Proceedings of the National Academy of Sciences of the United States of America 110, 1404-1409 (2013). +9. Li, Z. et al. Extensive terminal and asymmetric processing of small RNAs from rRNAs, snoRNAs, snRNAs, and tRNAs. 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Lee, Y.S., Shibata, Y., Malhotra, A. & Dutta, A. A novel class of small RNAs: tRNA-derived RNA fragments (tRFs). Genes & development 23, 2639-2649 (2009). +16. Chen, Z. et al. Transfer RNA demethylase ALKBH3 promotes cancer progression via induction of tRNA-derived small RNAs. Nucleic acids research 47, 2533-2545 (2019). +17. Shao, Y. et al. tRF-Leu-CAG promotes cell proliferation and cell cycle in non-small cell lung cancer. Chemical biology & drug design 90, 730-738 (2017). +18. Han, L. et al. A 5'-tRNA halve, tRNA-Gly promotes cell proliferation and migration via binding to RBM17 and inducing alternative splicing in papillary thyroid cancer. J Exp Clin Cancer Res 40, 222 (2021). +19. Sharma, U. et al. Biogenesis and function of tRNA fragments during sperm maturation and fertilization in mammals. Science (New York, N.Y.) 351, 391-396 (2016). +20. Chen, Q. et al. Sperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder. Science (New York, N.Y.) 351, 397-400 (2016). +21. Lee, K.P. et al. Structure of the dual enzyme Ire1 reveals the basis for catalysis and regulation in nonconventional RNA splicing. Cell 132, 89-100 (2008). +22. Cox, J.S., Sham, C.E. & Walter, P. Transcriptional induction of genes encoding endoplasmic reticulum resident proteins requires a transmembrane protein kinase. Cell 73, 1197-1206 (1993). +23. Calfon, M. et al. IRE1 couples endoplasmic reticulum load to secretory capacity by processing the XBP-1 mRNA. Nature 415, 92-96 (2002). +24. Yoshida, H., Matsui, T., Yamamoto, A., Okada, T. & Mori, K. XBP1 mRNA is induced by ATF6 and spliced by IRE1 in response to ER stress to produce a highly active transcription factor. Cell 107, 881-891 (2001). +25. Coelho, D.S. et al. Xbp1-independent Ire1 signaling is required for photoreceptor differentiation and rhabdomere morphogenesis in Drosophila. Cell reports 5, 791-801 (2013). +26. Hollien, J. et al. Regulated Ire1-dependent decay of messenger RNAs in mammalian cells. The Journal of cell biology 186, 323-331 (2009). +27. Upton, J.P. et al. IRE1α cleaves select microRNAs during ER stress to derepress translation of proapoptotic Caspase-2. Science (New York, N.Y.) 338, 818-822 (2012). +28. Oikawa, D., Tokuda, M., Hosoda, A. & Iwawaki, T. Identification of a consensus element + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 100, 884, 763]]<|/det|> +451 recognized and cleaved by IRE1 alpha. Nucleic acids research 38, 6265- 6273 (2010). 452 29. Tirasophon, W., Welihinda, A.A. & Kaufman, R.J. A stress response pathway from the endoplasmic reticulum to the nucleus requires a novel bifunctional protein kinase/endoribonuclease (Ire1p) in mammalian cells. Genes & development 12, 1812- 1824 (1998). 456 30. Boskovic, A., Bing, X.Y., Kaymak, E. & Rando, O.J. Control of noncoding RNA production and histone levels by a 5' tRNA fragment. Genes & development 34, 118- 131 (2020). 457 31. Tang, A.D. et al. Full- length transcript characterization of SF3B1 mutation in chronic lymphocytic leukemia reveals downregulation of retained introns. Nature communications 11, 1438 (2020). 461 32. Abdullahi, A., Stanojcic, M., Parousis, A., Patsouris, D. & Jeschke, M.G. Modeling Acute ER Stress in Vivo and in Vitro. Shock (Augusta, Ga.) 47, 506- 513 (2017). 463 33. Kumar, P., Kuscu, C. & Dutta, A. Biogenesis and Function of Transfer RNA- Related Fragments (tRFs). Trends in biochemical sciences 41, 679- 689 (2016). 465 34. Emara, M.M. et al. Angiogenin- induced tRNA- derived stress- induced RNAs promote stress- induced stress granule assembly. The Journal of biological chemistry 285, 10959- 10968 (2010). 468 35. Ogawa, T. et al. A cytotoxic ribonuclease targeting specific transfer RNA anticodons. Science (New York, N.Y.) 283, 2097- 2100 (1999). 470 36. Tomita, K., Ogawa, T., Uozumi, T., Watanabe, K. & Masaki, H. A cytotoxic ribonuclease which specifically cleaves four isoaccepting arginine tRNAs at their anticodon loops. Proceedings of the National Academy of Sciences of the United States of America 97, 8278- 8283 (2000). 473 37. Kumar, P., Anaya, J., Mudunuri, S.B. & Dutta, A. Meta- analysis of tRNA derived RNA fragments reveals that they are evolutionarily conserved and associate with AGO proteins to recognize specific RNA targets. BMC biology 12, 78 (2014). 476 38. Goodarzi, H. et al. Endogenous tRNA- Derived Fragments Suppress Breast Cancer Progression via YBX1 Displacement. Cell 161, 790- 802 (2015). 478 39. Couvillion, M.T., Bounova, G., Purdom, E., Speed, T.P. & Collins, K. A Tetrahymena Piwi bound to mature tRNA 3' fragments activates the exonuclease Xrn2 for RNA processing in the nucleus. Molecular cell 48, 509- 520 (2012). 481 40. Martinez, G., Choudury, S.G. & Slotkin, R.K. tRNA- derived small RNAs target transposable element transcripts. Nucleic acids research 45, 5142- 5152 (2017). + +<|ref|>sub_title<|/ref|><|det|>[[118, 789, 196, 805]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[118, 822, 330, 840]]<|/det|> +## Cell culture and reagents + +<|ref|>text<|/ref|><|det|>[[115, 855, 880, 874]]<|/det|> +Cell lines used in this study are described in Supplementary Table 7. DMSO, TG, TM, and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 99, 700, 118]]<|/det|> +STF083010 were purchased from Sigma- Aldrich (St Louis, MO, USA). + +<|ref|>sub_title<|/ref|><|det|>[[118, 166, 261, 183]]<|/det|> +## Oligonucleotides + +<|ref|>text<|/ref|><|det|>[[118, 198, 775, 216]]<|/det|> +Synthetic oligonucleotides used in this study are listed in Supplementary Table 8. + +<|ref|>sub_title<|/ref|><|det|>[[118, 263, 439, 280]]<|/det|> +## Plasmid construction and transfection + +<|ref|>text<|/ref|><|det|>[[115, 295, 882, 479]]<|/det|> +Plasmids used in this study are listed in Supplementary Table 9. The myc- tagged IRE1α (pCMV- IRE1α) and ANG were produced by PCR amplification. The PCR products were digested with KpnI and NotI for IRE1α and EcoRI and SalI for ANG (Takara Bio, Shiga, Japan) and then ligated into pCMV- myc empty vector (Clontech, Mountain View, CA, USA). KGN cells were transfected with plasmids for IRE1α, IRE1α (K599A) and ANG using Neon transfection system (Invitrogen, Carlsbad, CA, USA) as described previously41. + +<|ref|>sub_title<|/ref|><|det|>[[118, 526, 386, 544]]<|/det|> +## Small RNA sequencing analysis + +<|ref|>text<|/ref|><|det|>[[115, 558, 882, 872]]<|/det|> +tRNA was sequenced from two biological replicate samples. Total RNA from the KGN and KGN- IRE1αee cells were isolated and treated with T4 polynucleotide kinase (T4 PNK; New England Biolabs) and incubated at 37 °C for 30 min. Samples were separated on a 12% polyacrylamide gel containing 8 M urea to excise the 18- 40 nt region and were visualised with SYBR Gold (Thermo Fisher Scientific). RNAs were eluted from the acrylamide bands overnight in 0.3 M NaCl and then precipitated in ethanol/glycogen. Small RNA libraries were constructed using a SMARTER® smRNA- Seq Kit for Illumina® (Takara Bio) according to the manufacturer's guidelines. Sequencing libraries were generated according to the MiSeq reagent kit v3 and single end sequencing manufacturer instructions. Small RNA- seq reads were trimmed with the cutadapt programme42 with parameters recommended by the SMARTER + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 98, 883, 512]]<|/det|> +smRNA- Seq Kit manual. Trimmed sequences with read- lengths ranging from 15 to 42 bp were collected and mapped to the human genome and non- redundant mature tRNA sequences using the bowtie2 program43 implemented in the tRAX software package (http://trna.ucsc.edu/tRAX/). Reads mapped to tRNAs were extracted and their aligned positions were obtained using the bam2bed program of the BEDOPS suite44. The final position of a read was considered a cleavage site. Number of reads ending at each position of tRNAs was calculated. When a read was mapped to multiple tRNAs, fractional counts were allocated to all mapped tRNAs. The resulting read counts were subjected to differential cleavage analysis using the DESeq2 package45. Read mapping to a single isodecoder set was assigned to individual tRF, and those mapping to multiple tRFs with identical sequences were assigned to a single tRF considering mapped read counts of their 3'- tRFs. We used a tRNA gene annotation format, such as 'W- X- Y:Z' (W: amino- acid; X: anticodon; Y: unique gene identifier; Z: cleavage site) in Gly- GCC- 1:33. + +<|ref|>sub_title<|/ref|><|det|>[[118, 560, 307, 576]]<|/det|> +## Northern blot analysis + +<|ref|>text<|/ref|><|det|>[[115, 590, 883, 774]]<|/det|> +The procedure for northern blot analysis has been described previously46. RNA was transferred to an Immobilon Hybond- XL membrane (GE Healthcare Life Sciences, Amersham, Buckinghamshire, UK) and then hybridised with a \(^{32}\mathrm{P}\) - 5'- end- labeled probe specific for the tRNAGGly(GCC). The northern blot membranes were then stripped and reprobed with a radiolabelled probe specific for the tRNACys(GCA), tRNAGGly(TCC), tRNALys(CTT), tRNAVal(TAC), or 5.8S rRNA. 5.8S rRNA was used as a loading control. + +<|ref|>sub_title<|/ref|><|det|>[[118, 821, 297, 838]]<|/det|> +## Western blot analysis + +<|ref|>text<|/ref|><|det|>[[115, 853, 880, 872]]<|/det|> +Total proteins were extracted and analysed by western blotting as described previously47. Total + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 880, 152]]<|/det|> +protein from yeast cells were analysed according to the method described by Bahn et al48. The antibodies used in western blot analysis are listed in Supplementary table 10. + +<|ref|>sub_title<|/ref|><|det|>[[118, 208, 336, 225]]<|/det|> +## Primer extension analysis + +<|ref|>text<|/ref|><|det|>[[115, 238, 882, 522]]<|/det|> +Three micrograms of total RNA from KGN cells were used in primer extension reactions. The Gly- GCC- R primer was labelled at the \(5^{\prime}\) - end with \((\gamma - ^{32}\mathrm{P})\) ATP and T4 polynucleotide kinase (New England Biolabs, Ipswich, MA, USA). RNA and the labelled primers were denatured at \(70^{\circ}\mathrm{C}\) for 5 min and then annealed by cooling to \(37^{\circ}\mathrm{C}\) for 90 min. They were then extended at \(42^{\circ}\mathrm{C}\) for 1 h with 5 units (U) of avian myeloblastosis virus reverse transcriptase (AMV RTase; New England Biolabs). The products were separated on \(10\%\) polyacrylamide gel containing 8 M urea. Sequencing ladders were generated using \(5\mu \mathrm{g}\) of the PCR product amplified from the cDNA of tRNAGly(GCC). Images were analysed in a Bio- Rad phosphorimager using Quantity One software (Bio- Rad Laboratories). + +<|ref|>sub_title<|/ref|><|det|>[[118, 569, 270, 586]]<|/det|> +## tRNA purification + +<|ref|>text<|/ref|><|det|>[[115, 599, 882, 817]]<|/det|> +Unfractionated tRNAs (tRNAMix) were purified from total RNA by gel purification. In brief, total RNA from KGN cells was separated on \(10\%\) polyacrylamide gel containing 8 M urea. The tRNA fraction was eluted from the gel in RNA extraction buffer [0.5 M ammonium acetate, \(0.2\%\) sodium dodecyl sulphate, and \(0.1\mathrm{mM}\) EDTA (pH 8.0)]. The eluted tRNAMix was purified by phenol/chloroform extraction and ethanol precipitation. For further isolation of tRNAGly(GCC), oligo DNA- immobilised beads were prepared according to the method described by Yokogawa et al49. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 100, 417, 118]]<|/det|> +## Cleavage analysis and site mapping + +<|ref|>text<|/ref|><|det|>[[115, 129, 882, 545]]<|/det|> +Purified total tRNAMix (1 \(\mu \mathrm{g}\) ) and \(5^{\prime}\) - end \(^{32}\mathrm{P}\) - labelled tRNAGly(GCC) were incubated with 10 pmol of recombinant IRE1α (OriGene Technologies, Rockville, MD, USA) in \(20~\mu \mathrm{L}\) of cleavage buffer [0.2 M HEPES pH 7.6, 0.5 M K(OAC), \(10\mathrm{mM}\mathrm{Mg(OAC)}_2\) , \(0.5\%\) Triton X- 100, \(10\mathrm{mM}\) DTT, and \(10\mathrm{mM}\) ATP] at \(37^{\circ}\mathrm{C}\) for 30 or 120 min. The cleaved products from purified tRNAMix were recovered and used for northern blot and primer extension assays. A hydrolysis ladder was then created by incubating 2 pmol of tRNAGly(GCC) in hydrolysis buffer (50 mM \(\mathrm{NaCO_3}\) pH 9.2 and \(1\mathrm{mM}\) EDTA pH 8.0) at \(95^{\circ}\mathrm{C}\) for 10 min. RNase T1 ladder was created by incubating 2 pmol of tRNAGly(GCC) with RNase T1 (Fermentas, Waltham, MA, USA) at \(37^{\circ}\mathrm{C}\) for 2 min in reaction buffer (30 mM Tris- HCl pH 7.9, \(10\mathrm{mM}\mathrm{MgCl}_2\) , \(160\mathrm{mM}\mathrm{NaCl}\) , \(0.1\mathrm{mM}\) DTT, and \(0.1\mathrm{mM}\) EDTA pH 8.0). The cleaved products from radiolabelled tRNAGly(GCC) were separated on a \(10\%\) polyacrylamide gel containing \(8\mathrm{M}\) urea, and images were analysed in a Bio- Rad phosphorimager using the Quantity One software (Bio- Rad Laboratories). + +<|ref|>sub_title<|/ref|><|det|>[[115, 590, 880, 641]]<|/det|> +## Semi-quantitative RT-PCR and Reverse transcription–quantitative real-time PCR (RT- qPCR) + +<|ref|>text<|/ref|><|det|>[[115, 654, 882, 871]]<|/det|> +To amplify the spliced and unspliced XBP1 mRNA, a pair of primers (XBP1 splicing- F and XBP1 splicing- R) were used to flank the splicing site and yield 473 bp and 447 bp product sizes of XBP1u and XBP1s, respectively. Products were resolved on \(2.5\%\) agarose gel. Alternative splicing was detected using isoform specific primers. To amplify the spliced and unspliced HXL1 mRNA, a pair of primers, C. deutero- F and C. deutero- R, were used as described previously \(^{55}\) , yielding PCR product sizes of 475 bp and 419 bp for HXL1u and HXL1s, respectively. These PCR products were electrophoresed on \(2.5\%\) agarose gel. ACT1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 880, 150]]<|/det|> +was used as a loading control. Samples for RT- qPCR were prepared and analysed as previously described47. Gene expression levels were quantified using the \(\Delta \Delta \mathrm{Ct}\) method. + +<|ref|>sub_title<|/ref|><|det|>[[118, 198, 417, 216]]<|/det|> +## Construction of IRE1α KO cell line + +<|ref|>text<|/ref|><|det|>[[115, 238, 882, 423]]<|/det|> +IRE1α KO cells were generated as described previously53. To generate plasmids targeting IRE1α, pX458 (D10A)- IRE1α (GuideA) or pX458 (D10A)- IRE1α (GuideB) dual- guide oligonucleotide primers were cloned into the vector pX458 (D10A). Targeted IRE1α genomic DNA fragments (679 bp) were amplified using primers gIRE1α (F) and gIRE1α (R). Allelic deletion was confirmed by TOPcloner™ TA core Kit (Enzynomics, Korea) and DNA sequencing (Cosmogenetech, Korea). + +<|ref|>sub_title<|/ref|><|det|>[[118, 470, 393, 488]]<|/det|> +## Oligonucleotide pull-down assay + +<|ref|>text<|/ref|><|det|>[[115, 500, 882, 852]]<|/det|> +KGN cells were treated for 6 h with 0.1 μM TG; untreated cells were included as controls. After washing twice with \(1 \times\) PBS, harvested cells were disrupted with ice- cold cell lysis buffer [4% CHAPS, 100 mM NaCl, 2 mM EDTA and a \(1 \times\) protease inhibition cocktail (Roche, Mannheim, Germany) in 50 mM Tris/HCl buffer, pH 7.2]. The cellular levels of IRE1α and \(\beta\) - actin were determined by western blotting. To capture proteins bound to the enriched \(5'\) - tRHGly(GCC) from TG- treated cells, 34 nucleotide RNA baits (200 nM each), designed via biotinylation of the \(5'\) - or \(3'\) - end were utilised in a Thermo Scientific Pierce streptavidin- coated microplate containing 200 μg protein per well and an RNase inhibitor (Promega). A \(5'\) - biotin- oligo A8 RNA was included in control wells. Following incubation for 1 h at 4 °C, the contents of the protein extract were aspirated and washed with 100 mM, 300 mM, and 500 mM NaCl in 25 mM Tris/HCl (pH 7.2). The bound proteins were eluted with SDS- PAGE loading buffer + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 880, 152]]<|/det|> +containing \(1\%\) (w/v) SDS and \(5\%\) (v/v) 2-mercaptoethanol, as well as \(10\%\) (v/v) glycerol in 25 mM Tris/HCl (pH 6.8). + +<|ref|>sub_title<|/ref|><|det|>[[118, 199, 420, 216]]<|/det|> +## Tandem mass spectrometry analysis + +<|ref|>text<|/ref|><|det|>[[115, 225, 883, 884]]<|/det|> +Protein bands detected via SDS- PAGE, following the biotin- streptavidin method, were excised, destained, and reduced with \(50~\mathrm{mM}\) dithiothreitol at \(60^{\circ}\mathrm{C}\) for \(15\mathrm{min}\) . The reduced cysteine residues were alkylated with \(100~\mathrm{mM}\) iodoacetamide at room temperature for \(1\mathrm{h}\) in the dark. The gel pieces were then washed thrice with deionised water and dehydrated twice in acetonitrile (ACN). The dried gels were soaked in \(10~\mathrm{mM}\) ammonium bicarbonate with \(20~\mu \mathrm{g / mL}\) trypsin (Promega, Madison WI, USA) on ice. Proteins in gel were digested for \(24\mathrm{h}\) at \(37^{\circ}\mathrm{C}\) and treated again with \(20~\mu \mathrm{L}\) of trypsin solution for another \(24\mathrm{h}\) . The digested peptides were extracted from the gel pieces and analysed on an nLC Velos Pro mass instrument equipped with a PicoFrit™ column ( \(100~\mathrm{mm}\) , packed with \(5\mu \mathrm{m}\) Biobasic® C18) and an EASY- Column™ ( \(2\mathrm{cm}\) , packed with \(5\mu \mathrm{m}\) C18; Thermo Fisher Scientific). The LC conditions were as follows: \(0.3~\mu \mathrm{L / min}\) was a 45- min linear gradient from \(5\%\) to \(40\%\) ACN in a \(0.1\%\) formic acid buffer solution, followed by a \(10\mathrm{min}\) column wash with \(80\%\) ACN and \(20\mathrm{min}\) re- equilibration to the initial buffer condition. Full mass (MS1) scan was performed in the \(m / z\) 300- 2000 range in a positive ion mode. Data- dependent MS2 scans of the seven most intense ions were performed from the full scan with the options of \(1.5m / z\) isolation width, \(35\%\) normalised collision energy, and \(30~\mathrm{s}\) dynamic exclusion duration. The acquired MS2 data were primarily analysed by SEQUEST search against a human reference protein database from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/genome) and common protein contaminants in the common Repository of Adventitious Proteins (https://www.thegpm.org/crap/) with the following options: maximum miscleavage of 1, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 99, 883, 184]]<|/det|> +precursor mass tolerance 0.8 Da, fragment mass tolerance 1.0 Da, dynamic modification of methionine oxidation, and static modification of cysteine with iodoacetamide. The identified proteins with unique peptides are reported in Supplementary Table 2. + +<|ref|>sub_title<|/ref|><|det|>[[118, 231, 415, 249]]<|/det|> +## Electrophoretic mobility shift assay + +<|ref|>text<|/ref|><|det|>[[115, 262, 883, 675]]<|/det|> +The synthetic 5'- mimics of tRHs were purchased from Bioneer (Daejon, Korea). The 5'- end of tRH mimics were radiolabelled using \(\gamma\) - \(^{32}\mathrm{P}\) - ATP and T4 polynucleotide kinase (Takara Bio) and purified with an Illustra MicroSpin G- 25 column (GE Healthcare Life Sciences). Prior to use, labelled and unlabelled tRH mimics were heated at \(65^{\circ}\mathrm{C}\) for 10 min and slowly cooled to room temperature. Next, \(100\mathrm{ng}\) of BSA (Takara Bio), or HNRNPM (Mybiosource, San Diego, USA) or HNRNPH2 recombinant proteins (Mybiosource), were incubated with binding buffer [10 mM Tris- HCl (pH 8.0), \(150\mathrm{mM}\) KCl, \(0.5\mathrm{mM}\) EDTA, \(0.1\%\) Triton X- 100, \(0.02\mathrm{mM}\) DTT, \(12.5\%\) glycerol] and \(3.3\mathrm{pmol}\) of cold probes for each 5'- tRH mimic for 10 min at room temperature. Samples were then incubated with \(0.033\mathrm{pmol}\) of 5'- labelled tRHs for 10 min at room temperature. Native loading dye [100 mM Tris- HCl (pH 8.0), \(8.33\%\) glycerol, \(0.002\%\) brilliant blue G] and \(8\%\) polyacrylamide gels were used to load the samples. Vacuum dried gels were exposed to an intensifying screen and images were analysed in a Bio- Rad phosphorimager using Quantity One software (Bio- Rad Laboratories). + +<|ref|>sub_title<|/ref|><|det|>[[118, 722, 500, 740]]<|/det|> +## HNRNPM and HNRNPH2 binding constants + +<|ref|>text<|/ref|><|det|>[[115, 753, 883, 871]]<|/det|> +The binding affinities of purified HNRNPM and HNRNPH2 (Mybiosource, San Diego, USA) to synthetic 5'- tRH- Gly \(^{GCC}\) or 3'- tRH- Gly \(^{GCC}\) (Bioneer, Daejon, Korea) were measured using BIAcore T200 instrument and CM5 sensorchip (GE Healthcare Life Sciences) at \(25^{\circ}\mathrm{C}\) . Activation, immobilization, deactivation and preparation of the mock- coupled flow cell were + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 250]]<|/det|> +performed according to the manufacturer's instructions. The binding signals were generated by subtracting the signal for the mock- coupled flow cell from that for the HNRNPM- or HNRNPH2- immobilized flow cells. Calculation of equilibrium dissociation constant \((K_{D})\) from the sensorgrams were done with BIAccore T200 Evaluation software version 3.2 (GE Healthcare Life Sciences) by fitting the data to a 1:1 binding model. + +<|ref|>sub_title<|/ref|><|det|>[[118, 306, 277, 323]]<|/det|> +## Cell viability assay + +<|ref|>text<|/ref|><|det|>[[117, 337, 880, 420]]<|/det|> +All tRH mimics were purchased from were supplied by Genolution Pharmaceuticals, Inc. (Seoul, Korea). Antisense DNA oligos were purchased from BIONICS (Seoul, Korea). Cell- viability assays were performed as previously \(^{47}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 470, 268, 487]]<|/det|> +## RNA interference + +<|ref|>text<|/ref|><|det|>[[117, 501, 880, 553]]<|/det|> +All siRNAs were purchased from Bioneer (Seoul, Korea). The siRNA transfection method has been described previously \(^{47}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 599, 315, 617]]<|/det|> +## Cell proliferation assay + +<|ref|>text<|/ref|><|det|>[[115, 631, 882, 848]]<|/det|> +KGN or HeLa cells \((1 \times 10^{4})\) were seeded in 96- well plates for \(24 \mathrm{~h}\) ; the cells were then transfected with increasing amounts of \(5'\) - or \(3'\) - tRH mimics using the lipofectamine 2000 reagent (Invitrogen). HeLa cells \((1 \times 10^{4})\) were seeded in 96- well plates for \(24 \mathrm{~h}\) ; the cells were then transfected with increasing amounts of antisense DNA oligos complementary to the \(5'\) - tRH- Gly \(^{GCC}\) or \(5'\) - tRH- Lys \(^{CTT}\) loaded onto functionalized AuNP. After a 48- h transfection, cell proliferation was measured using the Cell Proliferation ELISA, BrdU (colorimetric) kit (Sigma- Aldrich) according to the manufacturer's instructions. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 133, 323, 150]]<|/det|> +## Flow cytometry analysis + +<|ref|>text<|/ref|><|det|>[[117, 164, 881, 250]]<|/det|> +To detect apoptotic cells, KGN cells \((1 \times 10^{6})\) were transfected with the indicated \(5'\) - or \(3'\) - tRHs mimic and \(48 \mathrm{~h}\) post- transfection stained with the FITC Annexin V Apoptosis Detection Kit (BD Pharmingen, San Diego, CA, USA) according to the manufacturer's instructions. + +<|ref|>sub_title<|/ref|><|det|>[[118, 297, 291, 314]]<|/det|> +## Cell migration assay + +<|ref|>text<|/ref|><|det|>[[117, 328, 881, 445]]<|/det|> +Cell migration was assessed based on the protocol described in our previous study47. Briefly, KGN cells \((1 \times 10^{6})\) were transfected with the indicated \(5'\) - or \(3'\) - tRH mimics for \(48 \mathrm{~h}\) . Images of migrated cells were captured at \(\times 100\) magnification under a bright- field microscope (Olympus CKX41, Tokyo, Japan). + +<|ref|>sub_title<|/ref|><|det|>[[118, 494, 356, 510]]<|/det|> +## Total transcriptome analysis + +<|ref|>text<|/ref|><|det|>[[115, 525, 882, 872]]<|/det|> +RNA was sequenced from two biological replicate samples of KGN cells transfected with the \(5'\) - tRHs mimic for \(48 \mathrm{~h}\) . In brief, total RNA samples were converted into cDNA libraries using the TruSeq Stranded mRNA Sample Prep Kit (Illumina). Starting with \(1 \mu \mathrm{g}\) of total RNA, poly- . adenylated RNA (primarily mRNA) was selected and purified using oligo- dT- conjugated magnetic beads. This mRNA was physically fragmented and converted into single- stranded cDNA using reverse transcriptase and random hexamer primers, with the addition of actinomycin D to the FSA (First Strand Synthesis Act D Mix) to suppress DNA- dependent synthesis of the second strand. Double- stranded cDNA was created by removing the RNA template and synthesising the second strand in the presence of dUTP (deoxyribouridine triphosphate) in place of dTTP (deoxythymidine triphosphate). A single A base was added to the \(3'\) end to facilitate ligation of the sequencing adapters, which contained a single T base + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 315]]<|/det|> +overhang. Adapter- ligated cDNA was amplified by PCR to increase the amount of sequence- ready library. During this amplification the polymerase stalls when it encounters a U base, rendering the second strand a poor template. Accordingly, amplified material uses the first strand as a template, thereby preserving the strand information. Final cDNA libraries were analysed for size distribution using an Agilent Bioanalyzer (DNA 1000 kit; Agilent), quantitated by qPCR (Kapa Library Quant Kit; Kapa Biosystems, Wilmington, MA), and normalised to 2 nmol/L in preparation for total transcriptome analysis. + +<|ref|>sub_title<|/ref|><|det|>[[118, 362, 356, 380]]<|/det|> +## Alternative splicing analysis + +<|ref|>text<|/ref|><|det|>[[115, 393, 883, 609]]<|/det|> +Purified mRNA was sequenced from three biological triplicate samples of KGN cells transfected with the 5'- tRHs mimic for 48 h. Briefly, Direct RNA sequencing was performed using the Direct RNA sequencing protocol (SQK- PCS109 kit) for the MinION. All steps were followed according to the manufacturer's specification. The constructed library was loaded on a FLO- MIN106D R9.4 flow cell and sequenced on a MinION device (Oxford Nanopore Technologies). The sequencing run was terminated after 48 h. Analyses of differential isoform usage using FLAIR modules has been described previously31. + +<|ref|>sub_title<|/ref|><|det|>[[118, 657, 419, 674]]<|/det|> +## Induction of acute ER stress in vivo + +<|ref|>text<|/ref|><|det|>[[115, 688, 883, 870]]<|/det|> +Acute ER stress was induced in vivo using a mouse model as described previously32. Briefly, immunodeficiency female or male BALB/c nu/nu mice (7- weeks- old) were purchased from Saeron Bio Inc (Uiwang, Korea) and rested for 3- 5 days. BALB/c mice were injected intraperitoneally with TG solution (1 μg/g body weight) or TM solution (0.5 μg/g body weight) as described previously. As controls, mice were injected intraperitoneally with control buffer (1× PBS containing 2% DMSO). Mice were euthanized by cervical dislocation and, major + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 250]]<|/det|> +organs were harvested at \(6\mathrm{h}\) , \(12\mathrm{h}\) , and \(24\mathrm{h}\) post- treatment, and the samples were prepared for western blot and northern blot analyses. All animal protocols were approved by the Chung- Ang University Institutional Animal Case and Use committee (IRB# CAU202000115). For \(C\) . neoformans, WT and ire1- deletion (ire1 \(\Delta\) ) strains in early log phase were treated with TM (5 \(\mu \mathrm{g / ml}\) ) and DTT (2 mM) at \(30^{\circ}\mathrm{C}\) for \(2\mathrm{h}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 296, 405, 314]]<|/det|> +## Fluorescence in situ hybridisation + +<|ref|>text<|/ref|><|det|>[[115, 325, 883, 775]]<|/det|> +Cells were cultured under conditions of normal growth or subjected to ER stress by treating with \(0.1\mu \mathrm{M}\) TG for \(6\mathrm{h}\) . After culture, the cells were washed thrice in PBS, fixed with \(4\%\) paraformaldehyde in PBS for \(15\mathrm{min}\) at room temperature, and washed thrice with PBS. Cells were permeabilised with \(0.2\%\) Triton X- 100 in PBS for \(15\mathrm{min}\) at room temperature and washed twice with PBS. Slides were then blocked and prehybridised for \(2\mathrm{h}\) at \(37^{\circ}\mathrm{C}\) in hybridisation buffer ( \(2\%\) bovine serum albumin, \(5\times\) Denhardt's solution, \(4\times\) SSC, and \(35\%\) deionised formamide). Hybridisation was performed overnight in a humid dark chamber at \(37^{\circ}\mathrm{C}\) in the presence of \(1\mathrm{ng / mL}\) of the indicated oligonucleotide conjugated to cyanine 3 dye (Cy3). FISH assays were also performed under denaturing conditions by heating the slides at \(75^{\circ}\mathrm{C}\) for \(5\mathrm{min}\) , immediately before the hybridisation step. After hybridisation, cells were washed once in \(2\times\) SSC containing \(50\%\) deionised formamide, once in \(2\times\) SSC, and once in \(1\times\) SSC. Cells were mounted on slides using a mounting solution containing DAPI. Fluorescence was detected with a laser scanning confocal microscope (Carl Zeiss ZEN 2011, Germany). Relative fluorescence intensities were assessed using ImageJ software (NIH, USA). + +<|ref|>sub_title<|/ref|><|det|>[[118, 820, 396, 839]]<|/det|> +## TaqMan assay for \(5^{\prime}\) -tRH-GlyGCC + +<|ref|>text<|/ref|><|det|>[[115, 852, 877, 872]]<|/det|> +The TaqMan assay was performed as described previously \(^{47}\) . KGN cells were treated with \(0.1\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 882, 250]]<|/det|> +\(\mu \mathrm{M}\) of TG for the indicated times, and fractionation of nuclear and cytosolic RNA was isolated using a Cytoplasmic and Nuclear RNA Purification Kit (Norgen Biotek, Thorold, Canada), according to the manufacturer's instructions. \(5^{\prime}\) tRH- Gly \(^{GC C}\) and U6 snRNA quantification was conducted using custom designed TaqMan microRNA assays according to manufacturer's recommended protocols (Applied Biosystems, Foster City, CA, USA). + +<|ref|>sub_title<|/ref|><|det|>[[118, 296, 361, 313]]<|/det|> +## Mouse xenograft experiment + +<|ref|>text<|/ref|><|det|>[[115, 327, 884, 544]]<|/det|> +HeLa cells \((1 \times 10^{6})\) were subcutaneously injected into 7- week- old BALB/c nu/nu immunodeficiency mice (Saeron Bio Inc), whose weights ranged between 18 and 20 g. We randomly allocated mice to three groups. Once the HeLa cells formed tumours (tumour volume: \(\sim 0.1 \mathrm{cm}^3\) ), \(\mathrm{AuNP}^{\mathrm{dT}}\) , \(\mathrm{AuNP}^{\mathrm{dT}}\) - anti- 5'- tRH- Lys \(^{\mathrm{CTT}}\) or \(\mathrm{AuNP}^{\mathrm{dT}}\) - anti- 5'- tRH- Gly \(^{\mathrm{GC C}}\) suspended in PBS were directly injected into the tumour sites every two days. \(\mathrm{AuNP}^{\mathrm{dT}}\) - anti- 5'- tRH- Lys \(^{\mathrm{CTT}}\) and \(\mathrm{AuNP}^{\mathrm{dT}}\) - anti- 5'- tRH- Gly \(^{\mathrm{GC C}}\) were prepared by mixing \(\mathrm{AuNP}^{\mathrm{dT}}\) (NES Biotechnology, Seoul, Korea) with polyadenylated antisense DNA oligos as previously described \(^{50}\) . + +<|ref|>text<|/ref|><|det|>[[115, 557, 882, 707]]<|/det|> +Mice were weighed and sizes of the tumours were measured every other day. The volume \((\mathrm{cm}^3)\) of each tumour ((length \(\times\) width \(^2 \times \pi\) )/6) was determined over 30 days period after xenotransplantation. Tumour- bearing mice were euthanized by cervical dislocation 18 days after the first injection of the functionalized AuNP composites, and tumours were excised. The samples were prepared for RT- qPCR and western blot analyses. + +<|ref|>sub_title<|/ref|><|det|>[[118, 755, 275, 772]]<|/det|> +## Statistical analysis + +<|ref|>text<|/ref|><|det|>[[115, 787, 882, 870]]<|/det|> +Multiple- comparison analyses of values were performed using the Student- Newman- Keuls test, and Student's \(t\) - test was used for comparisons with control samples, using SAS version 9.2 (SAS Institute, Cary, NC, USA) and SigmaPlot (Systat Software, San Jose, CA, USA). The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 98, 881, 150]]<|/det|> +data are presented as mean \(\pm\) standard error of the mean (SEM); \(P < 0.05\) was considered statistically significant. + +<|ref|>sub_title<|/ref|><|det|>[[118, 199, 264, 216]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[118, 230, 881, 348]]<|/det|> +Small RNA- seq data was deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA772059. The mass spectrometry data have been deposited in the ProteomeXchange Consortium via the PRIDE \(^{51}\) partner repository with the dataset identifier PXD013798. + +<|ref|>sub_title<|/ref|><|det|>[[118, 394, 215, 411]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[115, 430, 884, 860]]<|/det|> +Kim, J.H. et al. Differential apoptotic activities of wild- type FOXL2 and the adult- type granulosa cell tumor- associated mutant FOXL2 (C134W). Oncogene 30, 1653- 1663 (2011). Martin, M. Cutadapt removes adapter sequences from high- throughput sequencing reads. EMBnet/journal; Vol 17, No 1: Next Generation Sequencing Data Analysis (2011). Langmead, B. & Salzberg, S.L. Fast gapped- read alignment with Bowtie 2. Nature methods 9, 357- 359 (2012). Neph, S. et al. BEDOPS: high- performance genomic feature operations. Bioinformatics (Oxford, England) 28, 1919- 1920 (2012). Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA- seq data with DESeq2. Genome biology 15, 550 (2014). (!! INVALID CITATION !!! (Lee et al., 2002)). Shin, E. et al. An alternative miRISC targets a cancer- associated coding sequence mutation in FOXL2. The EMBO journal 39, e104719 (2020). Bahn, Y.S., Kojima, K., Cox, G.M. & Heitman, J. Specialization of the HOG pathway and its impact on differentiation and virulence of Cryptococcus neoformans. Mol Biol Cell 16, 2285- 2300 (2005). Yokogawa, T., Kitamura, Y., Nakamura, D., Ohno, S. & Nishikawa, K. Optimization of the hybridization- based method for purification of thermostable tRNAs in the presence of tetraalkylammonium salts. Nucleic acids research 38, e89 (2010). Kim, J.H. et al. A functionalized gold nanoparticles- assisted universal carrier for antisense DNA. Chem Commun (Camb) 46, 4151- 4153 (2010). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 101, 880, 140]]<|/det|> +803 51. Perez- Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: 804 improving support for quantification data. Nucleic acids research 47, D442- d450 (2019). 805 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 100, 247, 117]]<|/det|> +## Figure legends + +<|ref|>text<|/ref|><|det|>[[110, 125, 884, 875]]<|/det|> +Fig. 1. Small RNA- seq analysis of IRE1α- induced tRFs in vivo. a, Total tRF or 5'- tRF mapped read counts in KGN- WT and IRE1α- overexpressing (KGN- IRE1αoe) cells. Hatched line: reads mapped to tRNAGly(GCC). b, (Left) Volcano plot depicting differentially expressed 5'- tRFs in KGN- WT and KGN- IRE1αoe cells. Red dots: 5'- tRFs from tRNAGly(GCC); blue dots: 5'- tRFs from tRNACys(GCA); black dot: 5'- tRFs from tRNAGly(GCC) expressed at higher levels in KGN- IRE1αoe cells (red box: Log2 Fold Change \(>1.5\) ; \(p < 0.001\) ). (Right) Based on small RNA- seq analysis, cleavage sites at the anticodon loop in the secondary human tRNAGly(GCC) and tRNACys(GCA) structures. Red: acceptor stem at 5'- end; Purple: D loop; light green: anticodon loop; dark green: anticodon; yellow: T loop; blue: CCA tail at 3'- end. Numbering in the anticodon indicates the 3'- end positions of the tRFs; percent indicates the proportion of 5'- tRFs in total 5'- tRFs (Log2 Fold Change \(>1.5\) ; \(p < 0.001\) ). c, Northern blot analysis of tRNA fragments in KGN cells following IRE1α overexpression KGN cells were transfected with plasmid encoding myc- tagged IRE1α for 24 h, total RNA was extracted for analysis of 5'- tRNA fragments by northern blotting. The expression of IRE1α and GAPDH (loading control) was analysed by western blotting. Ribonucleolytic activity of IRE1α was confirmed XBP1 splicing assay using RT- PCR analysis of unspliced/spliced (u/s) XBP1. Red arrow: 5'- tRFs from tRNAGly(GCC) generated by IRE1α. M: size marker. Percentage of 5'- tRF compared to full- length tRNA are shown. The data are presented as the mean \(\pm\) SEM from three independent experiments. The asterisk indicated statistically significant differences (**\(p < 0.01\) , *\(p < 0.05\) ; paired student's \(t\) - test). ns, not significant. d, (Left) Primer extension analysis of 5'- end of tRNAGly(GCC) fragment in KGN cells. KGN cells were transfected with a plasmid encoding IRE1α or kinase defected mutant (IRE1α- K599A). (Right) Secondary structure of mature tRNAGly(GCC) and IRE1α cleavage sites at anticodon. Numbering in the anticodon indicates the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 880, 152]]<|/det|> +positions of mature tRNA nucleotides. Red arrow: prominent cleaved products of the tRNAGly(GCC) generated by IRE1α. + +<|ref|>text<|/ref|><|det|>[[115, 197, 882, 446]]<|/det|> +Fig. 2. IRE1α- specific cleavage of tRNAGly(GCC) in vitro. a, Northern blotting results for tRNAGly(GCC) and tRNALys(CTT). Red arrow: prominent cleaved products of the tRNAGly(GCC) generated by IRE1α. b, Primer extension assay on tRNAGly(GCC) cleavage products in the presence of IRE1α in vitro. IRE1α cleavage sites in the tRNAGly(GCC) are denoted by different letters (a–g). c, In vitro cleavage of tRNAGly(GCC) by IRE1α. Secondary structure of mature tRNAGly(GCC) and IRE1α cleavage sites (a–g from Fig. 2b and 1–7 from Fig. 2c). Black arrows: position of the tRNAGly(GCC) cleavage site generated by IRE1α. Red arrow: major cleavage site by IRE1α. + +<|ref|>text<|/ref|><|det|>[[115, 491, 883, 872]]<|/det|> +Fig. 3. ER stress induces 5'- tRHs cleavage by tRNAGly(GCC). a, Northern blot analysis of tRNAGly(GCC)- derived fragments in KGN cells upon ER stress. KGN Cells were treated with 0.1% DMSO, TG (0.1 μM) or TM (1 μg/ml) and harvested at the indicated times. Total RNA was isolated and probed with a probe specific for the tRNAGly(GCC). Red arrow: tRHs from tRNAGly(GCC) cleaved by IRE1α. b, (Upper panel) 5'- end of tRNAGly(GCC) fragment detected in Fig. 3a (at 6 h) determined by primer extension analysis. (Lower panel) Secondary structure of mature tRNAGly(GCC) and IRE1α cleavage sites at anticodon stem loop. Red arrow: major IRE1α cleavage site. c, Northern blot analysis of tRNAGly(GCC) fragments in control KGN (WT) or IRE1α knockout-KGN cells (IRE1α-/-). Cells were treated with DMSO, TG (0.1 μM) or TM (1 μg/ml) for 6 h and harvested. Total RNA was isolated and probed with a probe specific for the tRNAGly(GCC). Relative amount of 5'- tRH-Gly(GCC) is presented in the lower panel of Fig. 3a and c, respectively. The data are presented as the mean ± SEM from three independent experiments. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 216]]<|/det|> +The expression of IRE1α and \(\beta\) - actin (loading control) was analysed by western blotting. Ribonucleolytic activity of IRE1α was confirmed XBP1 splicing assay using RT- PCR analysis of unspliced/spliced (u/s) XBP1. Different letters denote statistically significant differences \((p\) \(< 0.0001\) ; Student- Newman- Keuls test). + +<|ref|>text<|/ref|><|det|>[[115, 260, 883, 775]]<|/det|> +Fig. 4. Functional roles of 5'- tRFs of tRNAGlu(GCC). a,b, Cell viability of KGN (a) and HeLa (b) cells following transfection with tRH mimics (5'- tRH- LysCTT, 5'- tRH- GlyGCC, or 3'- tRH- GlyGCC). c,d, Cell viability of KGN (c) and HeLa (d) cells following transfection with small interfering RNAs (siRNAs) for HNRNPM or HNRNPH2 (200 nM) and tRH mimics (50 nM) (5'- tRH- LysCTT, 5'- tRH- GlyGCC, or 3'- tRH- GlyGCC) (upper panel). Knockdown efficiency of HNRNPM or HNRNPH2 proteins (lower panel). e, Cell viability of WT and IRE1α-/- KGN cells following transfection with antisense DNA oligos (50 nM) targeting endogenous 5'- tRFs (anti-5'- tRH- LysCTT or anti-5'- tRH- GlyGCC) in the absence or presence of TG (0.1 μM). (a-e) Data are presented as the mean ± SEM of three independent experiments performed in triplicate. Different letters denote statistically significant differences \((p < 0.0001\) ; Student- Newman- Keuls test). f, Volumes of tumours from mice injected with either the AuNPdT only (vehicle) as a control (n = 18), AuNPdT- anti-5'- tRH- LysCTT (anti-5'- tRH- LysCTT, n = 18), or AuNPdT- anti-5'- tRH- GlyGCC (anti-5'- tRH- GlyGCC, n = 18) were measured. g, Representative immunoblots and quantified data for tumours from each group are presented. (f-g) The asterisk indicated statistically significant differences (\\*p < 0.05, \\*\\*p < 0.01, \\*\\*\\*p < 0.001; paired student's t- test). ns, not significant. + +<|ref|>text<|/ref|><|det|>[[117, 820, 881, 871]]<|/det|> +Fig. 5. 5'- tRH- GlyGCC mediate alternative splicing events. a, Volcano plot of differentially expressed protein- coding genes in KGN cells transfected with 5'- tRH- GlyGCC mimic and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 98, 883, 415]]<|/det|> +control KGN cells transfected with \(5^{\prime}\) - tRH- LysCTT mimic. Blue dots: significant upregulation of target genes; red dots: significant downregulation of target genes. b, DAVID functional analysis of genes with transcript abundance altered by more than 1.5- fold. c, Differential isoform usage (left) and major isoforms (right) from the ELOB (upper panel) and PMSB5 (lower panel). Red box: alternative splicing region. d, Validation of alternative splicing events from ELOB and PMSB5 in KGN cells transfected with \(5^{\prime}\) - tRH- LysCTT or \(5^{\prime}\) - tRH- GlyGCC mimics by RT- qPCR. e, Inhibitory effects of AuNP- conjugated antisense DNA oligos (anti- \(5^{\prime}\) - tRH- LysCTT or anti- \(5^{\prime}\) - tRH- GlyGCC) were confirmed by validation of alternative splicing events from ELOB and PMSB5. (d- e) The asterisk indicated statistically significant differences ( \(*p < 0.05\) , \(**p < 0.01\) , \(***p < 0.001\) , \(****p < 0.0001\) ; paired student's \(t\) - test). + +<|ref|>text<|/ref|><|det|>[[110, 457, 883, 872]]<|/det|> +Fig. 6. ER stress induces generation of \(5^{\prime}\) - tRHs from tRNAGly(GCC) in mouse and C. neoformans. a, Northern blot analysis of tRNAGly(GCC)- derived fragments in the ovary from ER stress- induced mouse. 5.8S rRNA was used as the loading control. The expression of IRE1α and β- actin (loading control) was analysed by western blotting. b, \(5^{\prime}\) - end of tRNAGly(GCC) fragment as determined by primer extension assay using total RNA isolated from ovaries after treatment with 0.1% DMSO or TG (left panel). Secondary structure of mouse mature tRNAGly(GCC) and IRE1α cleavage sites at anticodon stem loop (right panel). Red arrow: TG- induced IRE1α cleavage sites c, Northern blot analysis of tRNAGly(GCC) fragments in C. neoformans. 5.8S rRNA was used as the loading control. The expression of IRE1 and GAPDH (loading control) was analysed by western blotting. Ribonucleolytic activity of IRE1α was confirmed HXL1 splicing assay using RT- PCR analysis of unspliced/spliced (u/s) HXL1. Arrows: TM- induced IRE1 cleavage sites. d, Primer extension analysis of tRNAGly(GCC) fragments in C. neoformans. Secondary structure of C. neoformans mature tRNAGly(GCC) and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 216]]<|/det|> +IRE1 cleavage sites at anticodon stem loop are illustrated (right panel). Red arrow: TM- induced IRE1 cleavage sites e, Proposed model for the IRE1α selective generation of 5'- tRH- GlyGCC that contributes to cellular adaptation upon ER stress presented in diverse eukaryotic organisms from yeast to humans. + +<|ref|>sub_title<|/ref|><|det|>[[118, 263, 371, 281]]<|/det|> +## Extended data figure legends + +<|ref|>text<|/ref|><|det|>[[115, 295, 883, 741]]<|/det|> +Extended Data Fig. 1. Production of 5'- tRHs from tRNAGly(GCC) in vivo via an IRE1α activity- dependent manner. a, Northern blot analysis of tRNA fragments in KGN cells. KGN cells were transfected with an empty vector (pCMV- myc) or a plasmid encoding IRE1α kinase defected mutant (IRE1α- K599A). Total RNA was isolated from the transfected cells and northern blot was performed with a \(^{32}\mathrm{P}\) - 5'- end- labeled probe specific for the tRNAGly(GCC). 5.8S rRNA was probed as a loading control. The expression of IRE1α and GAPDH (loading control) was analysed by western blotting. The northern blot membranes were then stripped and reprobed with a \(^{32}\mathrm{P}\) - 5'- end- labeled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). Ribonucleolytic activity of IRE1α was confirmed XBP1 splicing assay using RT- PCR analysis of unspliced/spliced (u/s) XBP1. M, size marker. Red arrow: 5'- tRFs from tRNAGly(GCC) generated by IRE1α- K599A. b, Northern blots for tRNAGly(GCC) fragments in KGN cells after transfection with 2.5 μg of pCMV- myc control plasmid or plasmid encoding myc- tagged ANG. 5.8S rRNA was used as the loading control. Blue arrows: prominent cleaved products of the tRNAGly(GCC) generated by ANG. + +<|ref|>text<|/ref|><|det|>[[115, 787, 881, 871]]<|/det|> +Extended Data Fig. 2. Purification and cleavage of tRNAGly(GCC) in vitro. a, Scheme diagram of isolation of tRNAGly(GCC) species from purified total tRNA in vitro using the biotinylated antisense oligo DNA- conjugated streptavidin C1 beads (left panel). Red text and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 883, 250]]<|/det|> +lines indicate biotinylated antisense oligo DNA complementary to the secondary structure of tRNAGly(GCC) (right panel). b, Loading capacity of biotinylated antisense oligo DNA on streptavidin C1 beads in EtBr stained gel. Approximately 8 pmol of oligo DNAs were loaded onto 1 mg of streptavidin C1 beads. c, Quality of isolated tRNAGly(GCC) species determined by EtBr staining and northern blot assay. + +<|ref|>text<|/ref|><|det|>[[115, 295, 883, 675]]<|/det|> +Extended Data Fig. 3. Cleavage of tRFs induced by ER stress. a, Northern blot analysis of tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT)- derived fragments in KGN cells upon ER stress. The northern blot membranes used in Fig. 3a were stripped and re-probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). b, Northern blot analysis of tRNA fragments in control KGN (WT) or IRE1α knockout-KGN cells (IRE1α/- ). The northern blot membranes used in Fig. 3c were stripped and re-probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). c, Northern blot analysis of tRNAGly(GCC) fragments in HeLa cells. Red arrow: tRHs from tRNAGly(GCC) cleaved by IRE1α. Right panel: relative amount of \(5'\) - tRH-Gly(GCC). The data are presented as the mean \(\pm\) SEM from three independent experiments. \(^{**}p < 0.01\) ; paired Student's \(t\) - test. d, Northern blot membranes used in Extended data Fig. 3c were stripped and re-probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). + +<|ref|>text<|/ref|><|det|>[[115, 722, 883, 872]]<|/det|> +Extended Data Fig. 4. Generation of IRE1α- knockout (IRE1α/- ) KGN cells using the CRISPR/Cas9 system. a, Schematic representation of the CRISPR/Cas9- nicksase strategy for IRE1α knockout in KGN cells. A pair of guide RNAs [sgIRE1α (A) and (B)] was designed by targeting the PAM sequence at the catalytic region of IRE1α. The sgRNA sequences and PAM sequences are shown in blue and red, respectively. Triangle indicates the possible cleavage sites. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 249]]<|/det|> +b, Knockout of IRE1α validated using western blot analysis. c, T7E1 assay results to detect CRISPR/Cas9- induced modification in IRE1α-/-. Arrows indicate the positions of the expected DNA bands cleaved by T7E1. d, DNA sequences of the targeting sites in IRE1α-/- KGN cells. The regions of IRE1α sequences are shown in pink. The number of mutated nucleotides is indicated on the right. Actual chromatograms from sequencing analysis are shown. + +<|ref|>text<|/ref|><|det|>[[115, 295, 883, 578]]<|/det|> +Extended Data Fig. 5. Identification of proteins interacting with 5'- tRH- GlyGCC. a, SDS- PAGE of whole- cell lysates and 3'- /5'- biotinylated tRH- Gly(GCC)- bound proteins captured in a streptavidin microplate containing samples from KGN cells treated (+) and untreated (-) with 0.1 μM thapsigargin (TG) for 6 h. Different protein bands with molecular weights (MWs) near 70 kDa and 55 kDa in samples bound to 5'- biotin- tRH- GlyGCC are shown in an SDS- PAGE gel. b, Venn diagrams of identified proteins from the excised gel pieces at MWs near 70 kDa and 55 kDa. Normalised counts of the peptide- to- spectrum matches of identified proteins with > 2 unique peptides from tandem mass spectrometry data are compared. Tandem mass spectrometry results from each sample are shown in Supplementary Table 2. + +<|ref|>text<|/ref|><|det|>[[115, 622, 883, 872]]<|/det|> +Extended Data Fig. 6. Interaction between tRHs with HNRNP proteins. a, Electrophoretic mobility shift assay (EMSA) results for synthetic 5'- tRH- GlyGCC or 5'- tRH- LysCTT. Synthetic 5'- tRH- LysCTT was used as a control. b, Sensorgrams of the interaction between the immobilized HNRNP proteins (HNRNPM or HNRNPH2) and the purified 5'- tRH- GlyGCC used as analyte. Purified 5'- tRH- GlyGCC were delivered from the lowest to the highest concentration (30, 100, 300, 1000, 3000, 10000 nM). c, Summary of surface plasmon resonance (SPR) kinetic and affinity measurements using BIAcore T200. 5'- tRH- GlyGCC or 3'- tRH- GlyGCC binding to HNRNPM and HNRNPH2 measured by SPR. The equilibrium dissociation constant + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 99, 781, 118]]<|/det|> +\((K_{D})\) , the association constant \((k_{a})\) and the dissociation constant \((k_{d})\) are presented. + +<|ref|>title<|/ref|><|det|>[[115, 164, 880, 184]]<|/det|> +# Extended Data Fig. 7. Effects of transfecting tRHs mimics or antisense DNA oligos against + +<|ref|>text<|/ref|><|det|>[[115, 196, 881, 610]]<|/det|> +tRHs on physiology of KGN cells. a, Cell viability of KGN cells following transfection with tRHs mimics and antisense oligos to each of the tRHs mimics (mimic + antisense treated). b, Proliferation of KGN cells following transfection with tRHs mimics. Different letters denote statistically significant differences \((p < 0.0001\) ; Student- Newman- Keuls test). Data are presented as the mean \(\pm\) SEM of three independent experiments performed in triplicate. Effects of tRHs mimics on apoptosis (c) and (d) migration of KGN cells. ns, not significant. Scale bar \(= 100 \mu \mathrm{m}\) . e, f, Upper panel: cell viability of KGN (e) and HeLa (f) cells following transfection with siRNAs for HNRNPF or HNRNPH1 and tRH mimics from tRNA. Knockdown efficiency of HNRNP proteins (lower panel). Different letters denote statistically significant differences \((p < 0.0001\) ; Student- Newman- Keuls test). g, Proliferation of HeLa cells following transfection with antisense DNA oligos of tRHs. Different letters denote statistically significant differences \((p < 0.0001\) ; Student- Newman- Keuls test). Data are presented as the mean \(\pm\) SEM of three independent experiments performed in triplicate. + +<|ref|>text<|/ref|><|det|>[[115, 655, 884, 871]]<|/det|> +Extended Data Fig. 8. Subcellular localisation of 5'- tRHs from tRNAGly(GCC) during ER stress. a, Red fluorescence (Cy3): subcellular distribution of 5'- tRHs, from tRNAGly(GCC) in unstressed (right panel) and stressed cells (left panel) following TG treatment. DNA was stained with DAPI. Anticodon bridging probes were designed to anneal with the anticodon loop, and they recognised only the anticodon loop of whole tRNA to avoid significant hybridisation signals with 5'- tRHs of tRNAGly(GCC). Scale bar \(= 20 \mu \mathrm{m}\) . b, 5'- tRH- Gly(GCC) enrichment following transfection of KGN cells with 0.1 \(\mu \mathrm{M}\) of TG. The data (mean \(\pm\) SEM) are presented + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 98, 881, 150]]<|/det|> +as the fold enrichment calculated from three independent experiments. \(**p < 0.01\) , ns, not significant. + +<|ref|>title<|/ref|><|det|>[[115, 198, 881, 216]]<|/det|> +# Extended Data Fig. 9. Effects of ER stress-inducing agents on IRE1α expression in mouse + +<|ref|>text<|/ref|><|det|>[[115, 230, 883, 641]]<|/det|> +and C. neoformans. a, IRE1α and \(\beta\) - actin (loading control) protein expression in the organs of BALB/c mice injected with TM and TG. b, Northern blot analysis of tRNAGly(GCC)- derived fragments in the liver from ER stress- induced mice. 5.8S rRNA was used as the loading control. Red arrow: prominent cleaved products of the tRNAGly(GCC) generated by IRE1α. M, size marker from KGN cells. c, Northern blot analysis of tRNAGly(GCC)- derived fragments in the epididymis from ER stress- induced mouse. 5.8S rRNA was used as the loading control. The expression of IRE1α and \(\beta\) - actin (loading control) was analysed by western blotting. M, size marker from KGN cells. d, The northern blot membranes used in Fig. 6a were stripped and re- probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT), respectively. e, Northern blot analysis of tRNACys (GCA), tRNAGly(TCC), or tRNALys(CTT)- derived fragments in C. neoformans. The northern blot membranes used in Fig. 6c were stripped and re- probed with a \(^{32}\mathrm{P} - 5'\) - end- labelled probe specific for the tRNACys(GCA), tRNAGly(TCC), or tRNALys(CTT). + +<|ref|>title<|/ref|><|det|>[[115, 690, 881, 739]]<|/det|> +# Extended Data Fig. 10. Effects of other stresses on 5'-tRH-GlyGCC production and IRE1α overexpression on expression of GCC codon-rich genes. a, b, Northern blot analysis of KGN + +<|ref|>text<|/ref|><|det|>[[115, 752, 883, 801]]<|/det|> +cells treated with sodium arsenite (SA) (a) and Cobalt chloride (CoCl2) (b) (upper panel). IRE1α, ANG, and \(\beta\) - actin (loading control) expression (lower panel). c, Expression of FOXL2, + +<|ref|>text<|/ref|><|det|>[[115, 815, 883, 836]]<|/det|> +Loricrin, IRE1α, and \(\beta\) - actin (loading control) in KGN cells transfected with plasmid encoding + +<|ref|>text<|/ref|><|det|>[[115, 850, 883, 870]]<|/det|> +myc- tagged IRE1α and treated with STF083010 (inhibitor of the endonuclease activity of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[51, 101, 184, 115]]<|/det|> +1022 IRE1α). + +<|ref|>text<|/ref|><|det|>[[52, 135, 90, 147]]<|/det|> +1023 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[133, 144, 860, 333]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[800, 103, 875, 120]]<|/det|> +
Figure 1
+ +<|ref|>image<|/ref|><|det|>[[152, 360, 850, 675]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 152, 810, 373]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[152, 139, 707, 515]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[137, 135, 852, 732]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[142, 128, 737, 682]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 152, 825, 825]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[168, 150, 789, 460]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[144, 175, 800, 640]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[135, 153, 863, 580]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 170, 671, 300]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[177, 310, 666, 444]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[140, 468, 686, 630]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[150, 145, 844, 410]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[614, 107, 837, 123]]<|/det|> +
Extended Data Figure 5
+ +<|ref|>image<|/ref|><|det|>[[130, 444, 864, 644]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[150, 156, 730, 325]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[150, 348, 171, 362]]<|/det|> +
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+ +<|ref|>image<|/ref|><|det|>[[170, 364, 757, 580]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[150, 598, 171, 611]]<|/det|> +
C
+ +<|ref|>table<|/ref|><|det|>[[170, 613, 755, 682]]<|/det|> +
5'-tRH-GlyGCC3'-tRH-GlyGCC
\(K_D\) (nM)\(K_a\) (M-1s-1)\(K_d\) (s-1)\(K_D\) (nM)\(K_a\) (M-1s-1)\(K_d\) (s-1)
HNRNPM86.301.04 x 1048.96 x 10-49592.74 x 1032.63 x 10-3
HNRNPH227.072.92 x 1047.90 x 10-49384.90 x 1034.60 x 10-3
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 130, 870, 660]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[161, 175, 740, 411]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[135, 444, 154, 458]]<|/det|> +
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+ +<|ref|>image<|/ref|><|det|>[[180, 465, 365, 603]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[137, 140, 866, 747]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[135, 170, 777, 530]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 358, 150]]<|/det|> +SupplementaryTable110DB.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/images_list.json b/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a376fb527bfa5ef49e81bb7659dd8de9d7538513 --- /dev/null +++ b/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Overview of the proposed data-driven EMS framework. (a) Comparison of three EMS paradigms: Traditional rule-based EMS relies on expert knowledge and calibration based on fixed driving cycles. Simulation-based EMS requires high-precision models and entails transitioning from simulation analysis to real-world deployment, resulting in a gap between simulated and real-world performance (sim-to-real gap). In contrast, the proposed real-world data-driven EMS learns directly from actual data. (b) China has established a three-tier EV monitoring and management system involving the state, local governments, and enterprises. The National Monitoring and Management Platform collects real-time operational data from over 20 million EVs [25]. (c) The ORL agent works with an existing EMS and continuously collects EV data to improve the EMS. (d) Overall diagram of the proposed data-driven EMS methodology.", + "footnote": [], + "bbox": [ + [ + 112, + 68, + 880, + 608 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Comparison for different datasets. (a) The simulation model for the fuel cell hybrid electric vehicle (FCEV), depicts the powertrain layout and energy flow topology. (b) The principle of EMS involves the allocation of energy flow among hybrid energy systems (fuel cell and battery) to achieve predefined objectives based on driving conditions. Here, real driving conditions are gathered and then use the simulated FCEV model to generate energy cost data for training the ORL agent. (c) Distribution of encoded actions for the four datasets, each action is normalized to the [-1,1]. D1 represents data generated by an expert policy, D2 and D3 denote suboptimal data generated by a combination of expert and random policies, D4 comprises entirely random data. (d) The states distribution of four datasets.", + "footnote": [], + "bbox": [ + [ + 120, + 72, + 861, + 625 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: The ORL agent learning performance. (a) Learning curves of ORL agent for the four different datasets. (b) The comparison of absolute rewards (original rewards are negative) under three validation conditions. \"D1,\" \"D2,\" \"D3,\" and \"D4\" correspond to the best rewards achieved by ORL after learning on each respective dataset. Notably, ORL agent on various datasets closely approximate or exceed the expert policy. (c) The comparison of energy costs between the original EMS and the optimized EMS using ORL shows a notable reduction in energy costs through the data-driven learning process. (d) The action (FC power slope) distributions of optimized EMS using ORL", + "footnote": [], + "bbox": [ + [ + 66, + 230, + 920, + 747 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4: Performance analysis comparing different methods. (a) The comparison between two data-driven EMS methods. The matrix numbers represent the relative reward rates of ORL and BC compared to the expert EMS (PPO) under the same conditions, emphasizing the minimal influence of data quality on ORL's performance. (b) Comprehensive performance of different algorithms on the WTVC condition, DP representing the globally optimal EMS. (c) Comprehensive performance on the CHTC condition. (d) Comprehensive performance on the FTP condition. (e) The efficiency distribution of fuel cell power demonstrates that ORL learns a superior EMS, ensuring that fuel cell system remains within the high-efficiency range. (f) FC degradation costs under three conditions", + "footnote": [], + "bbox": [ + [ + 68, + 67, + 930, + 604 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5: ORL continuously learning and improving from data. (a) ORL for continuous learning from data in three scenarios. (b) Driving data collected from the real-world route. (c) Speed trajectories of three ZBDC conditions. (d) Comparing the total cost under ZBDC-No1, the total cost comprises hydrogen consumption, battery cost, and cell degradation cost. (e) Comparing the total cost under ZBDC-No2. (f) Comparing the total cost under ZBDC-No3. (g) Fuel cell power distribution cloud chart of Baseline EMS. (h) Fuel cell power distribution cloud chart following one data update. (i) Fuel cell power distribution cloud chart following two data updates.", + "footnote": [], + "bbox": [ + [ + 70, + 75, + 940, + 776 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6: Performance with more data. (a) Performance of PPO for 4 training data and 12 testing data. (b) The comprehensive performance comparison of different EMS methods reveals that ORL can significantly mitigate the performance degradation observed in the testing phase of PPO. (c) Performance of ORL for 12 testing data. (d) Speed distribution of four conditions. (e) The demand power of HWDC represents an extreme condition. The red dashed line indicates the maximum output power of FC system. (f) Overall performance of the 4 cases as the training data increase under test driving cycle CLTC; (g) Performance on GCDC; (h) Performance on ZNDC; (I) Performance on HWDC indicates that ORL ultimately learns a reasonable EMS in extreme driving conditions. (j) Battery SOC trajectories of different EMS under the test driving cycle HWDC.", + "footnote": [], + "bbox": [ + [ + 68, + 70, + 945, + 753 + ] + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a.mmd b/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..af2adf0b2daeb8a435645a67bedc265260ec58b2 --- /dev/null +++ b/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a.mmd @@ -0,0 +1,337 @@ + +# Learning Superior Energy Management from Electric Vehicle Data + +Hongwen He hwhbit@bit.edu.cn + +Beijing Institute of Technology Yong Wang Beijing Institute of Technology Jingda Wu Nanyang Technological University https://orcid.org/0000- 0002- 7336- 4492 Zhongbao Wei Beijing Institute of Technology https://orcid.org/0000- 0003- 0051- 5648 Fengchun Sun Beijing Institute of Technology + +## Article + +Keywords: Energy management, Electric vehicle data, Reinforcement learning, Fuel cell vehicles, Data- driven + +Posted Date: July 3rd, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4523312/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on March 22nd, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 58192- 9. + +<--- Page Split ---> + +# Learning Superior Energy Management from Electric Vehicle Data + +Yong Wang \(^{a,b}\) , Jingda Wu \(^{c}\) , Hongwen He \(^{a,b}\) , Zhongbao Wei \(^{a}\) and Fengchun Sun \(^{a}\) + +\(^{a}\) School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China \(^{b}\) National Key Laboratory of Advanced Vehicle Integration and Control, Beijing Institute of Technology, Beijing, 100081, China \(^{c}\) The Hong Kong Polytechnic University, Hung Hom, Hong Kong + +## ARTICLE INFO + +## ABSTRACT + +Keywords: Energy management Electric vehicle data Reinforcement learning Fuel cell vehicles Data- driven + +Despite the promising potential of energy management technologies in optimizing electric vehicle (EV) performance and fostering global energy sustainability, the extensive research conducted over the past decade has yet to translate into practical applications. This discrepancy arises primarily from the reliance of existing methodologies on simulation- based development paradigms, leading to a significant disparity between simulated results and real- world efficacy. Herein, we present a pioneering real- world data- driven energy management strategies (EMS) approach that utilizes an innovative offline reinforcement learning (ORL) framework. This paradigm enables EMS to learn from diverse real- world data, obviating the need for explicit rule design or high- fidelity simulators, and allowing for seamless application of the proposed method to any existing EMS. Moreover, it continuously enhances performance even after deployment in actual energy management systems. We evaluate the proposed ORL method on fuel cell EVs, training the ORL agent to optimize energy consumption and system degradation. The EV monitoring and management platform in China provides real- world data for validating our methodology. The results demonstrate that ORL consistently learns superior EMS in various conditions. With increasing data availability, its performance improves significantly, from \(88\%\) to \(98.6\%\) relative to theoretical optimality after two data updates. After training with more than 60 million kilometers of data, the ORL agent can learn a general EMS that adapts to unseen and corner- case conditions. These results highlight the effectiveness of integrating the data- driven method with established EMS techniques to enhance performance and underline its potential to utilize large- scale data to improve vehicle energy efficiency and longevity. + +## 1. Introduction + +The automotive industry is undergoing a significant transformation, primarily due to the global focus on sustainability and environmental conservation. Electric vehicles (EVs) are leading this shift, playing a key role in mitigating environmental challenges and advancing sustainable transportation solutions [1, 2]. Concurrently, the emergence of hybrid energy systems (HES) within the EV powertrain represents an emerging trend, offering superior solutions over single energy systems [3]. By integrating multiple energy sources such as batteries, fuel cells, and internal combustion engines, HES improves overall efficiency, sustainability, and reliability, while also providing adaptability to a wide range of driving conditions [4]. Propelled by rapid technological advancements and supportive policies, EVs equipped with HES, including Hybrid EVs (HEV), Plug- in hybrid EVs (PHEV), and Fuel cell EVs (FCEV), are gaining traction worldwide [3]. For example, BYD, a prominent EV manufacturer, achieved sales of 3.02 million EVs in 2023, and PHEVs represent \(47.9\%\) of total sales. In the first quarter of 2024, PHEVs accounted for a notable \(51.6\%\) of total sales, indicating their growing popularity. This trend is mainly attributed to advancements in HES energy management, which enhance energy efficiency and overall performance. + +The energy management strategy (EMS), responsible for allocating energy flow among HES to achieve predefined objectives, performs several vital functions essential for EVs:(1) EMS optimizes system efficiency by intelligently allocating energy flow based on driving conditions, reducing energy consumption and extending driving range [5]. (2) EMS optimizes power delivery based on driver demand, improving acceleration and responsiveness, while also ensuring smooth power delivery for improved driving experience. (3) By considering the characteristics of different power sources, EMS extends the lifespan of the HES, thus enhancing system reliability and safety [6]. Early EMS solutions utilize various rule- based approaches to achieve energy- saving objectives. Rule- based EMS involves the design of predefined rules and parameters tailored to specific driving conditions and vehicle characteristics, relying on + +<--- Page Split ---> + +expert knowledge with iterative refinement based on testing feedback [7]. However, this process can be labor- intensive and time- consuming, requiring manual expertise for rule creation and extensive experimentation for parameter calibration. Moreover, its static nature and inability to adapt to dynamic driving scenarios limit its effectiveness in maximizing energy savings and overall performance. + +Recently, there has been a surge in advanced algorithms for energy management of HES, including dynamic programming (DP), model predictive control (MPC), reinforcement learning (RL), and others. A common feature across these methods is the use of optimal control or machine learning (ML) algorithms to compute optimal EMS based on high- fidelity simulators. Collectively, these approaches are referred to as simulation- based EMS in our study. Although DP and MPC provide elegant solutions based on future driving data, accurately forecasting future driving conditions using historical speed and dynamic traffic information remains a significant challenge [8]. Additionally, prediction models and optimization algorithms often result in considerable computational complexity, leading to suboptimal real- time performance [3]. Consequently, applying optimal control methods in real- world vehicle settings poses considerable challenges at present. In contrast, learning- based models, exemplified by Deep RL (DRL), do not rely on knowledge of future conditions and present promising alternatives for EMS [9]. DRL involves learning optimal actions in an environment through trial and error, where the DRL agent interacts with the environment to maximize cumulative rewards over time [10]. DRL- based EMS has been a subject of active research in recent years, with recent developments in online DRL algorithms such as Deep Deterministic Policy Gradient (DDPG) [11], Soft Actor- Critic (SAC) [12], Proximal Policy Optimization (PPO) [13], and others, leading to remarkable results. However, the application of DRL- based EMS faces challenges as an online learning paradigm, particularly concerning the interaction between the DRL agent and the EV simulator, which raises safety concerns. Moreover, while existing literature assumes that simulation models accurately represent real- world conditions, the construction of high- fidelity models that encompass vehicle dynamics, powertrain, traffic scenarios, and driver behavior [14] remains a challenge [15]. This discrepancy can cause the "sim- to- real" problem, where EMS learned in simulators may not be effectively transferred to real vehicles, which leads to the complexity of EMS development [16]. + +In recent years, data- driven methodologies, propelled by advancements in ML techniques and the availability of large datasets, have become essential in addressing key challenges in the EV field [17]. With support from data collection platforms and open- access laboratory data, these data- driven approaches have revolutionized various aspects of battery management systems (BMS). This includes automatic discovery of complex battery aging mechanisms [18], prediction of battery safety envelopes [19], evaluation of safety conditions [20], estimation of battery state of health [21], and even enhancing battery lifetime prediction models with unlabeled data [22]. Notably, innovations in feature extraction and supervised ML techniques tailored for time- series data have greatly enhanced prediction accuracy. This success has sparked interest in exploring data- driven methods for sequential decision- making tasks, including improving energy management systems. A common approach to implementing a data- driven EMS involves using supervised learning, where the ML model is employed to capture the complex and non- linear relationships between input features and corresponding control outputs. In [23], recurrent neural networks are trained offline using a substantial amount of training data obtained from the global optimization strategy DP, yielding a sub- optimal EMS that closely approximates the DP. It is essential to recognize the differences from the prediction tasks in BMS applications, as EMS entails sequential decision- making. Although supervised learning can mimic the policy of EMS through imitation learning, its heavy reliance on expert data may result in limited generalization to new and diverse scenarios. Hence, it's crucial to explore alternative methods for learning EMS from non- expert data, which is common in automotive applications. + +In this paper, we present a novel data- driven EMS paradigm, addressing key challenges discussed above by leveraging offline data generated by existing EMS strategies, without the need for explicit rule design or online interaction. Our approach integrates DRL and supervised learning techniques, departing from traditional rule- based EMS and simulation- based methods to enhance EMS capabilities, as visually depicted in Figure 1(a). Notably, our proposed method can be directly integrated with any EMS algorithm and continuously improves through the collection data. We term this approach the offline reinforcement learning (ORL) agent [24], which is trained by combining real- world data as well as carefully filtered and processed simulation data through a novel exploration scheme. To evaluate this paradigm, we focus on a fuel cell electric vehicle (FCEV) as the subject and collect data for learning power allocation strategies. The EV monitoring and management platform in China offers real- world data to validate our methodology (Figure 1(b)). Overall, the ORL agent introduced herein exhibits four key features (Figure 1(c)): + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Overview of the proposed data-driven EMS framework. (a) Comparison of three EMS paradigms: Traditional rule-based EMS relies on expert knowledge and calibration based on fixed driving cycles. Simulation-based EMS requires high-precision models and entails transitioning from simulation analysis to real-world deployment, resulting in a gap between simulated and real-world performance (sim-to-real gap). In contrast, the proposed real-world data-driven EMS learns directly from actual data. (b) China has established a three-tier EV monitoring and management system involving the state, local governments, and enterprises. The National Monitoring and Management Platform collects real-time operational data from over 20 million EVs [25]. (c) The ORL agent works with an existing EMS and continuously collects EV data to improve the EMS. (d) Overall diagram of the proposed data-driven EMS methodology.
+ +1) Purely data-driven EMS: By autonomously learning and optimizing from collected offline datasets, our approach facilitates the development of advanced EMS without necessitating expert knowledge or the construction of high-fidelity EV simulators. This data-driven process significantly simplifies EMS development workflows. + +2) Learning from non-optimal data: Our research indicates that ORL can learn near-optimal EMS from non-optimal data and even can derive superior policies from sub-optimal. Our approach is less reliant on data quality allowing for effective learning. This practicality allows us to utilize raw data generated by actual vehicles, which is common in automotive applications. + +3) Enhancement with increased data: The performance of ORL improves as more training data is utilized, demonstrating its ability to continuously adapt and enhance EMS performance. Through training across diverse + +<--- Page Split ---> + +datasets, it can potentially adapt to new driving conditions and yield favorable results, even in corner- case conditions. When sufficient data are available, ORL can learn a generalized EMS. + +4) Compatibility with existing EMS: Our approach seamlessly integrates with established rule-based or simulation-based EMS methods, leveraging data from onboard controllers to augment EMS performance. This ensures that baseline performance is preserved while facilitating further enhancement using ORL, making it a valuable extension to conventional EMS methodologies. + +## 2. Results + +### 2.1. The overview of data-driven EMS + +In Figure 1(d), the framework overview of ORL for EMS is illustrated. We introduce the three phases of applying the proposed ORL algorithm to the EMS problem, which include data collection, offline learning, and evaluation. ORL is an innovative subset of RL methods rooted in data- driven approaches. Unlike most simulation- based EMS scheduling methods, the data- based learning process does not require the building of an EV simulation environment. + +In the data collection phase, an available dataset \((D)\) is gathered from the existing EMS policy \((\pi_{\beta})\) . It is crucial to record various parameters such as velocity, acceleration, hydrogen consumption, electricity consumption, fuel cell degradation, battery state of charge (SOC), and others during vehicle operation. However, due to limitations in real- world data quality and privacy concerns associated with collecting data from a large number of actual vehicles, we developed an FCEV simulation model to generate laboratory datasets 2(a). This FCEV model enables the generation of extensive, high- fidelity data based on real- world driving conditions 2(b). Subsequently, we employ a tailored data processing model to encode control and state variables into standardized data formats. The data encoding process converts these parameters into a transition dataset \(D = \left(s,r,a,s^{\prime}\right)_{i}\) . Here, \(s\) , \(a\) , and \(r\) represent the state, action, and reward as described in the Methods section, respectively, while \(s^{\prime}\) denotes the subsequent state, and \(i\) indexes a transition in the dataset. These meticulously curated datasets are stored in the buffer and serve as input for the ORL agent. + +In the offline learning phase, our proposed ORL agent is designed based on the Actor- Critic network and incorporates Behavior Cloning (BC) and Discriminator Blend (DB) mechanisms. In each training step, mini- batches of transitions \((s,r,a,s^{\prime})\) stored in a data buffer are sampled from the replay buffer to update the ORL networks. The Actor network, responsible for determining the action \(\mathbf{a}_{t}\sim \pi_{\beta}\left(\cdot \mid \mathbf{s}_{t}\right)\) , is updated to maximize the expected return as estimated by the Critic network. BC regularization is applied to the policy update step to encourage the policy to prioritize actions present in the dataset. Moreover, the DB component is introduced to allow actions beyond the dataset distribution similar to those included in the dataset. Through training, the agent learns to optimize its actions based on observed states and rewards. The details of the ORL agent will be elaborated upon in the Methods section in detail. + +After completing the training phase, the neural network parameters representing the EMS policy of the ORL agent are saved for future use. Subsequently, the trained agent is evaluated to gauge its effectiveness and performance. Utilizing the FCEV environment established during the data collection phase, our subsequent experiments employ three standard driving cycles (WTVC, FTP, and CHTC) to evaluate energy cost. Additionally, we incorporate various real- world driving scenarios to comprehensively assess the trained EMS policy. Following the evaluation, adjustments to the agent hyperparameters or training process may be considered to improve its performance. This iterative process of training, evaluation, and refinement continues until the desired level of EMS performance is attained. Upon achieving satisfactory performance, the trained agent becomes eligible for deployment in real- world scenarios, where it can be utilized to efficiently optimize energy management systems. + +### 2.2. Data for learning and analysis + +We select the Proximal Policy Optimization (PPO), which demonstrates the best performance among online DRL algorithms in our EMS problem, as the expert EMS. Using PPO, we generate datasets denoted as \(D^{E}\) comprising 300K time steps. Additionally, we employ a random agent that samples actions randomly, generating datasets denoted as \(D^{R}\) , which represent poor performance. To create settings with varying levels of data quality in the suboptimal offline dataset, we combine transitions from the expert datasets \(D^{E}\) and the random datasets \(D^{R}\) in different ratios. Specifically, we consider four different dataset compositions, denoted as D1, D2, D3, and D4. These settings are defined as follows: D1 (Data- 1): Consists solely of transitions from the expert dataset \(D^{E}\) , representing the expert policy. D2 (Data- 2): Contains two- thirds of transitions from the expert dataset \(D^{E}\) and one- third of transitions from the random dataset \(D^{R}\) , representing suboptimal data. D3 (Data- 3): Comprises one- third of transitions from the expert dataset + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Comparison for different datasets. (a) The simulation model for the fuel cell hybrid electric vehicle (FCEV), depicts the powertrain layout and energy flow topology. (b) The principle of EMS involves the allocation of energy flow among hybrid energy systems (fuel cell and battery) to achieve predefined objectives based on driving conditions. Here, real driving conditions are gathered and then use the simulated FCEV model to generate energy cost data for training the ORL agent. (c) Distribution of encoded actions for the four datasets, each action is normalized to the [-1,1]. D1 represents data generated by an expert policy, D2 and D3 denote suboptimal data generated by a combination of expert and random policies, D4 comprises entirely random data. (d) The states distribution of four datasets.
+ +\(D^{E}\) and two- thirds of transitions from the random dataset \(D^{R}\) , representing another suboptimal data. D4 (Data- 4): Comprises solely of transitions from the random dataset \(D^{R}\) , representing the random policy. + +Figure 2(a) depicts the action distributions for the four datasets. Significant differences can be observed among the four EMS policies, with the action range of D1 falling within (- 0.2, 0.5), resulting in relatively stable variations in FC power. As random policy data is introduced, the action ranges in the other datasets all fall within (- 1, 1), particularly for D4 where the action distribution is uniformly spread across (- 1, 1). This implies that this policy is noisy, denoted as a poor EMS. Figure 2(b) illustrates the state distributions for the four datasets. Note that all states have undergone post- processing and scaling to the (0, 1) interval. By comparing the box plots of the four datasets, it is evident that the SOC of D1 falls within a reasonable range (0.38- 0.7), meeting the EMS constraints regarding the battery SOC. + +<--- Page Split ---> + +However, the SOC of the other datasets falls into unreasonable ranges, such as SOC in the range (0.2, 1) for D3. Additionally, with the increase in \(D^R\) data, the FC power distribution ranges of D3 and D4 become wider. Since the conditions of the four datasets are derived from fixed segments of standard driving cycles, the velocity distribution remains the same across all datasets. + +Creating challenging datasets is practical because generating sub- optimal or random data is more cost- effective than collecting expert- level data from real vehicles. Therefore, an effective data- driven EMS method must be able to effectively handle and learn from these suboptimal offline datasets. + +### 2.3. Learning superior EMS from non-optimal data + +![](images/Figure_3.jpg) + +
Figure 3: The ORL agent learning performance. (a) Learning curves of ORL agent for the four different datasets. (b) The comparison of absolute rewards (original rewards are negative) under three validation conditions. "D1," "D2," "D3," and "D4" correspond to the best rewards achieved by ORL after learning on each respective dataset. Notably, ORL agent on various datasets closely approximate or exceed the expert policy. (c) The comparison of energy costs between the original EMS and the optimized EMS using ORL shows a notable reduction in energy costs through the data-driven learning process. (d) The action (FC power slope) distributions of optimized EMS using ORL
+ +We first study the performance of the ORL agent with different datasets. To ensure a fair comparison, the algorithm employs uniform experimental settings and network parameters across four datasets. Figure 3(a) illustrates the average reward of the training process on the four datasets. This average, computed as the mean reward over every 1000 episodes, undergoes validation across 10 iterations using three standard driving cycles: WTVC, CHTC, and FTP. + +<--- Page Split ---> + +Training involves utilizing a buffer comprising 300,000 samples, with the ORL agent randomly selecting 256 data points for each training iteration, accumulating to one million training epochs. For D1, which comprises exclusively expert data, convergence is evident after approximately 210e3 episode. However, ORL exhibits a slower convergence speed during iterative learning on the other three datasets, converging at around 330e3, 600e3, and 360e3 epochs, respectively. This suggests that the data distribution influences the learning speed, but ultimately, ORL learns an effective EMS. + +Figure 3(b) presents the reward performance of trained ORL across three driving cycles. It notes that the absolute reward value of ORL decreases in D1, from 323.4 to 297.3 on the CHTC, representing an improvement of \(8.8\%\) . Surprisingly, even on suboptimal datasets D2 and D3, ORL outperforms the expert strategy, with reward increases of \(1.8\%\) and \(3.4\%\) , respectively. Similarly, ORL still outperforms on the WTVC and FTP conditions, learning superior strategies from the suboptimal datasets D2 and D3. The exception is D3 on the WTVC condition, possibly due to the high- speed nature of the WTVC condition, leading to larger reward values for SOC. Despite this, the final results of the energy consumption remain within rational bounds. Particularly noteworthy is the excellent performance on the random dataset D4, where ORL closely approaches expert results across all three validation conditions, achieving rewards of 402, 325, and 395. Compared to the original average reward of 2637 for the D4 dataset, ORL has reduced the reward by \(85.8\%\) . + +Figure 3(c) provides a detailed comparison of the energy costs between ORL and the original datasets. The blue dots represent the mean costs of the four original EMS datasets, accompanied by error bars indicating the maximum and minimum costs. In contrast, the red dots depict the energy costs incurred by ORL in the corresponding datasets. Despite the inclusion of random data that leads to a degradation in cost performance, ORL consistently maintains lower costs across all data sets. For instance, in the WTVC condition, the cost escalates from the initial 90 RMB in dataset D1 to 163 RMB in dataset D4. However, ORL consistently maintains costs within the range of 90- 95 RMB. In particular, the minimum cost values of the original D4 dataset exceed those achieved by ORL significantly, with ORL achieving reductions in costs that exceed \(40\%\) in all three conditions. This observation underscores ORL's ability not only to glean superior results from expert EMS but also to consistently yield excellent outcomes from progressively suboptimal datasets. Remarkably, ORL even attains expert- level EMS performance when trained solely on noisy datasets. + +To elucidate the rationale behind the performance enhancements, Figure 3(d) illustrates the action distributions of optimized EMS using ORL. As different EMS policies can be reflected by the actions taken, in the context of the FCEV considered here, this pertains to the FC power slope under the same driving cycle. Comparing Figures 2(c) and 3(d), we notice significant changes in D2, D3, and D4 compared to Figure 2. In D2, D3, and D4, the action distributions closely resemble those of expert data in D1, concentrating within the range of [-0.3, 0.3], as opposed to the wider range of [-1, 1] observed in Figure 2. The change is particularly pronounced in D4, where the absence of expert data results in slight differences in the action distributions compared to D1, D2 and D3. However, all ORL policies tend to learn FC power variations with smaller ranges, ensuring smoother FC power output while satisfying power demand requirements. + +In conclusion, through experimentation on three validation conditions and four datasets, our ORL agent not only learns better EMS strategies from expert strategies but also demonstrates the ability to learn near- expert strategies from entirely noisy datasets and even achieves superior results from datasets containing a mixture of expert and noisy data. This observed convergence underscores ORL's ability to enhance and optimize the original EMS through learning from data obtained from any EMS. + +### 2.4. Performance by comparative evaluation + +To demonstrate the superior performance of ORL, we contrast it with simulation- based and imitation learning EMS approaches. Since imitation learning and ORL are closely related, both involve learning EMS from data. We first compare the performance between ORL and BC. It's important to note that BC typically employs a supervised learning paradigm, learning from expert data, while ORL incorporates reinforcement learning with exploration mechanisms. This distinctive learning mechanism results in significant performance differences. + +In Figure 4(a), we compare the testing reward in the WTVC, CHTC and FTP driving cycles, and calculate the percentage of ORL and BC costs relative to the expert EMS (PPO). In D1, both ORL and BC achieve favorable results, with ORL surpassing the original expert data by a maximum of \(9.1\%\) , while BC remains comparable to the expert. In D2, ORL maintains superiority over expert- based EMS, while BC experiences significant cost degradation (ranging from \(6\%\) to \(70\%\) ). In D3 and D4, ORL continues to outperform or closely match the expert, while BC, limited by data + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4: Performance analysis comparing different methods. (a) The comparison between two data-driven EMS methods. The matrix numbers represent the relative reward rates of ORL and BC compared to the expert EMS (PPO) under the same conditions, emphasizing the minimal influence of data quality on ORL's performance. (b) Comprehensive performance of different algorithms on the WTVC condition, DP representing the globally optimal EMS. (c) Comprehensive performance on the CHTC condition. (d) Comprehensive performance on the FTP condition. (e) The efficiency distribution of fuel cell power demonstrates that ORL learns a superior EMS, ensuring that fuel cell system remains within the high-efficiency range. (f) FC degradation costs under three conditions
+ +quality, fails to learn an optimal EMS. This underscores ORL's capability to learn superior EMS from non- expert data, while imitation learning demonstrates poorer performance and struggles to learn favorable EMS with non- expert data. + +Figures 4(b- d) present detailed results of different methods under the WTVC, CHTC, and FTP conditions, with the red lines representing the percentage of cost compared to DP. It is evident that ORL learns an optimal EMS policy on the D1 dataset, achieving percentages close to \(99.9\%\) , \(99.4\%\) , and \(97.6\%\) of DP, respectively. As for PPO, a benchmark expert policy, its cost results are \(98.6\%\) , \(98.0\%\) , and \(97.6\%\) of DP, respectively. Thus, BC learns similar expert EMS in D1, but its performance significantly deteriorates on suboptimal D2 data. Another online DRL method, TD3, also demonstrates satisfactory performance, however, its overall costs are lower than those of PPO and ORL. + +In Figure 4(e), the FC power distribution of the EMS learned by ORL on the D1 dataset is depicted, with the green curve representing the efficiency curve of the FC system. ORL demonstrates a superior EMS, ensuring that the + +<--- Page Split ---> + +power distribution remains within the high- efficiency range, resulting in reduced hydrogen consumption. Additionally, a narrower power variation range, as shown in Figure 3(d), minimizes FC degradation costs. As illustrated in Figure 3(f), ORL incurs minimal FC degradation costs in the three conditions, with costs of 2.3, 1.4, and 1.4, respectively. Furthermore, examination of Figures 3(b- d) indicates that the battery SOC remains within a reasonable range. These findings collectively affirm that ORL has successfully learned a superior EMS. + +### 2.5. Continuous learning with growing data + +We have demonstrated in previous experiments that ORL can learn optimal EMS strategies from data and outperforms other methods. In this section, we further showcase ORL for continuous learning from data. We conduct experiments in three cases depicted in Figure 5(a), collecting real- vehicle data in different driving scenarios including urban road, highway, and downtown road for the three cases (Figure 5(b)). + +#### 2.5.1. Case 1: Continuous learning from historical data + +Take the example of driving a bus on fixed routes to illustrate Case 1. We collected real electric bus driving data in Zhengzhou, China, over three consecutive days. Figure 5(c) shows the speed trajectories of three days, labeled as ZBDC- No1, ZBDC- No2, and ZBDC- No3, respectively. Noticeably, there are variations in the speed trajectories along the same route over different days. Figures 5(d- f) show the total cost of different EMS strategies in the three scenarios, which include hydrogen consumption, battery cost, and fuel cell degradation. The baseline is the original EMS of FCEV, and using the baseline data under ZBDC- No1 driving cycle to train the ORL agent as the ORL(Z1) strategy, which is then applied to the new condition ZBDC- No2. Furthermore, we train a new ORL(Z2) EMS using data from both the baseline on ZBDC- No1 and the ORL(Z1) on ZBDC- No2, which is then validated on ZBDC- No3. + +It can be observed that the baseline EMS performs poorly on the first day (ZBDC- No1), with a cost only \(88.0\%\) compared to DP. The corresponding FC power and power slope distributions are shown in Figures 5(g). On the second day as depicted in Figure 5(e) and (h), the performance of ORL(Z1) achieves a significant improvement by learning from the previous data, reaching \(96.4\%\) of the cost compared to DP on the ZBDC- No2. On the third day, continuously learning from more data, the ORL(Z2) achieves a cost of \(98.6\%\) compared to DP on the ZBDC- No3, as shown in Figure 5(f) and (i). By comparing the three power distribution, it is evident that ORL(Z1) and ORL(Z2) distribute more FC output power in the high- efficient range, resulting in lower overall energy consumption, and the smaller power slope also leads to lower system degradation costs. + +In conclusion, with data updates, new data can be utilized to train ORL, leading to the evolution of batter EMS strategies. This demonstrates the ability of ORL to continuously learn and improve from historical data. Additionally, our method integrates seamlessly with established EMS methods, using real- time data from onboard controllers to increase EMS performance. This ensures that baseline performance is preserved while facilitating further enhancement using ORL, making it a valuable extension to conventional EMS methodologies. + +#### 2.5.2. Case 2: Improving from simulated data + +In Case 2, we address the challenge of improving EMS performance from simulated data, where historical data is absent and driving conditions are unknown. Despite the ability of simulation- based methods (such as DRL) to derive ideal EMS strategies from simulated EV models, deploying these strategies onto real vehicles often leads to performance degradation due to the stochastic and unknown nature of real- world driving conditions, a problem known as the sim- to- real gap, extensively studied in the fields of RLL. + +As illustrated in Figure 6(a), during the simulation phase, the PPO algorithm is trained on the standardized driving cycle (WTVC) and three specific conditions ZBDC (Figure 5(c)) to obtain an ideal EMS, denoted as PPO (Train). Subsequently, the EMS is validated on 12 local driving conditions denoted as PPO (Test). As depicted in Figure 6(b), the cost difference between PPO (Train) and DP across the four training conditions is minimal, with an average difference of \(3.16\%\) . However, when tested on the 12 new conditions(DC- 1 to DC- 12), the average cost difference between PPO (Test) and DP rises to \(12.75\%\) . This indicates a significant performance degradation of DRL- based methods when transitioning from simulation to real- world conditions. + +To mitigate the sim- to- real problem, our proposed ORL method leverages data from PPO (Test) for further learning. As shown in Figure 6(c), the ORL approach achieves a substantially lower cost across the 12 local operating conditions compared to the PPO (Train) strategy. The average cost difference between ORL and DP is merely \(1.42\%\) (Figure 6(b)). In summary, our experiments demonstrate that ORL can effectively learn from simulated data to enhance the performance of the original EMS, addressing the sim- to- real problem inherent in traditional simulation- based methods. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5: ORL continuously learning and improving from data. (a) ORL for continuous learning from data in three scenarios. (b) Driving data collected from the real-world route. (c) Speed trajectories of three ZBDC conditions. (d) Comparing the total cost under ZBDC-No1, the total cost comprises hydrogen consumption, battery cost, and cell degradation cost. (e) Comparing the total cost under ZBDC-No2. (f) Comparing the total cost under ZBDC-No3. (g) Fuel cell power distribution cloud chart of Baseline EMS. (h) Fuel cell power distribution cloud chart following one data update. (i) Fuel cell power distribution cloud chart following two data updates.
+ +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 6: Performance with more data. (a) Performance of PPO for 4 training data and 12 testing data. (b) The comprehensive performance comparison of different EMS methods reveals that ORL can significantly mitigate the performance degradation observed in the testing phase of PPO. (c) Performance of ORL for 12 testing data. (d) Speed distribution of four conditions. (e) The demand power of HWDC represents an extreme condition. The red dashed line indicates the maximum output power of FC system. (f) Overall performance of the 4 cases as the training data increase under test driving cycle CLTC; (g) Performance on GCDC; (h) Performance on ZNDC; (I) Performance on HWDC indicates that ORL ultimately learns a reasonable EMS in extreme driving conditions. (j) Battery SOC trajectories of different EMS under the test driving cycle HWDC.
+ +<--- Page Split ---> + +#### 2.5.3. Case 3: Learning a general EMS with large-scale data + +To assess the generalization of the ORL model, especially in extreme conditions, the ORL agent trained on huge amounts of data and tested its performance on four new driving conditions. Figure 6(d) illustrates the speed distribution for these four conditions. Among these, the China Light- Duty Vehicle Test Cycle (CLTC) stands as the standard cycle, GCDC is obtained from an EV operating in a downtown road, ZBDC represents a new condition collected in Zhengzhou, and HWDC is collected from a fuel vehicle traveling on a highway. The four conditions have distinct characteristics and originate from different road and vehicle types. Particularly for HWDC, where the demand power exceeds \(250\mathrm{kW}\) . As depicted in Figure 6(e), the power demand exceeds the \(100\mathrm{kW}\) maximum output power of FC system, indicating that HWDC is an extreme condition for the FCEV studied in this work. + +Firstly, we establish datasets of four different scales: 4e4 (ORL- 4), 20e4 (ORL- 20), 100e4 (ORL- 100), and 500e4 (ORL- 500) samples, excluding data from the four validation conditions obtained in previous experiments (Cases 1 and 2). The results on the four validation conditions are depicted in Figures 6(f- i). It is evident from Figure 6(f), (g), and (h) that both the reward and cost exhibit a gradual decrease with an increase in training data. With more training data, the ORL model consistently enhances its performance. Notably, the rate of performance improvement diminishes after reaching the 100e4 sample mark, with minimal disparity observed between the ORL- 100 and ORL- 500. At approximately 20e4 samples, the ORL model outperforms the DRL- TD3 algorithm (as indicated by the dashed lines in the figures). It is essential to highlight that, under the extreme HWDC condition, although ORL- 20 achieves the lowest cost (Figure 6(i)), its reward absolute value is not the lowest. This phenomenon is because the ORL- 20 prefers battery consumption during high power demands, exceeding the maximum output of the FC system, violating the SOC constraint. This phenomenon arises because ORL- 20 tends to prioritize battery consumption during periods of high power demand, the FC system fails to achieve its maximum power output. Consequently, the strategy fails to maintain the SOC within the desired range, rendering it ineffective as an EMS. Conversely, ORL- 500 demonstrates superior performance, with lower reward and cost, and SOC maintained within a reasonable range, as illustrated in Figure 6(j). Overall, after learning from 5 million data points (equivalent to over 60 million kilometers), the ORL agent can learn a general EMS adaptable to unseen and even corner- case conditions. + +This result highlights two advantages of the ORL agent: First, its performance surpasses that of the original policy; second, it demonstrates that with increased data availability, learning performance improves. The ORL model can learn a general EMS from a large amount of EV data. + +## 3. Methods + +### 3.1. EV Environment + +In this work, we evaluate EMS performance using a fuel cell hybrid electric vehicle (FCEV) within a simulation environment. Figure. 1(e) illustrates the schematic diagram of the FCEV and its components, which include a fuel cell (FC) system, a hydrogen storage tank, an electric motor (EM), and a Lithium- ion battery (LIB) pack. The FC stack serves as the primary power source to meet the energy requirements of the vehicle. The diagram also depicts the energy flow from the hydrogen storage tank to the motor. The FC system converts hydrogen energy into electricity, which then collaborates with the LIB through the high- voltage bus. This electric energy is subsequently utilized to power a single electric motor, connected to the driving wheel via a fixed- ratio final gear. According to the vehicle driving resistance equation, the driving power demand is determined by the speed and acceleration of the FCEV, and can be expressed as follows: + +\[P_{d} = \frac{1}{3.6\cdot\eta_{\mathrm{me}}}\left(m g C_{f}\upsilon_{t} + m\delta \upsilon_{t}a_{t} + m g s i n(i) + \frac{C_{D}A}{21.15}\upsilon_{t}^{3}\right) \quad (1)\] + +where \(\eta_{\mathrm{me}}\) is the efficiency of the vehicle drivetrain; \(m\) is the vehicle mass; \(g\) is the gravitational constant; \(C_{f}\) is the rolling resistance coefficient; \(\delta\) is the rotational mass conversion coefficient; \(C_{D}\) is the air resistance coefficient, \(A\) is the frontal area; \(\upsilon_{t}\) is the longitudinal velocity at the time step \(t\) , \(a_{t}\) is the acceleration, \(i\) is the angle of slope of the road. The power demand is provided by the FC system and the battery pack, the power balance of the FCEV can be formulated as: + +\[P_{d} = \left(P_{f c}\cdot \eta_{D C / D C} + P_{b a t}\right)\cdot \eta_{D C / A C}\cdot \eta_{E M} \quad (2)\] + +where \(P_{f c}\) and \(P_{b a t}\) respectively denote the output power of the fuel cell system and the LIB pack; \(\eta_{D C / D C},\eta_{D C / A C}\) and \(\eta_{\mathrm{EM}}\) represent the efficiency of the DC/DC converter, DC/AC inverter, and the electric motor, respectively. The + +<--- Page Split ---> + +battery pack is modeled using an equivalent circuit model in Equation (3): + +\[\left\{ \begin{array}{l l}{P_{b a t}(t) = V_{o c}(t) - R_{0}\cdot I^{2}(t)}\\ {I(t) = \frac{V_{o c}(t) - \sqrt{V_{o c}^{2}(t) - 4\cdot R_{0}\cdot P_{b a t}(t)}}{2R_{0}}}\\ {S O C(t) = \frac{Q_{0} - \int_{0}^{t}I(t)dt}{Q}} \end{array} \right. \quad (3)\] + +where \(S O C\) is the battery state of charge, \(V_{o c}\) is the open- circuit voltage, \(I_{t}\) is the current at time \(t\) , \(R_{0}\) is the internal resistance, \(P_{b a t}\) is the output power in the charge- discharge cycles, \(Q_{0}\) is the initial battery capacity, \(Q\) is the nominal battery capacity. + +According to the battery aging model in [12]. The degradation rate of battery operation \(\gamma_{\mathrm{bat}}\) is affected by the charge/discharge rate ( \(C_{rate}\) ). The relationship between the battery aging correction factor and \(C_{rate}\) can be fitted from experiment data: + +\[\gamma_{\mathrm{bat}} = \mu_{1}\left|C_{\mathrm{rate}}\right|^{2} + \mu_{2}\left|C_{\mathrm{rate}}\right| + \mu_{3} \quad (4)\] + +where \(\mu_{1}, \mu_{2}, \mu_{3}\) are the curve- fitting coefficients. LIB can operate for about 5000 full cycles in a lifetime. The battery degradation cost \(C_{bat,degr}\) can be calculated by: + +\[C_{bat,degr} = \int_{0}^{t}\gamma_{bat}^{-1}P_{bat}dt\cdot PR_{bat} / (5000\cdot 3600) \quad (5)\] + +where \(PR_{\mathrm{bat}}\) is the battery price per kWh that is 1500RMB/kWh. + +The fuel cell system efficiency under different power conditions is obtained from experiment data. Thus, the mass flow rate of the hydrogen consumption can be calculated by: + +\[\dot{m}_{H_{2}} = P_{f c s} / \left(\eta_{f c s}\cdot \mathrm{LHV}_{H_{2}}\right) \quad (6)\] + +where \(\eta_{f c s}\) is the fuel cell system efficiency; \(P_{f c s}\) is the fuel cell system output power; \(\mathrm{LHV}_{H_{2}}\) is the hydrogen low calorific value. The fuel cell hydrogen cost can be calculated by: + +\[C_{f c s,H_{2}} = PR_{H_{2}}\cdot \int_{0}^{t}\dot{m}_{H_{2}}dt \quad (7)\] + +where \(PR_{H_{2}}\) is the hydrogen price per kilogram(60RMB/kg). + +The fuel cell degrades rapidly under four typical conditions: load changing, start/stop, low power, and high power conditions. We assume that the fuel cell system continues operating until the vehicle power system is shut down, thus the start/stop condition is not considered in the EMS. The fuel cell voltage degradation rate, denoted as \(\gamma_{\mathrm{fcs}}\) , can be calculated by: + +\[\gamma_{\mathrm{fcs}} = \kappa_{\mathrm{low}}\cdot T_{\mathrm{low}} + \kappa_{\mathrm{high}}\cdot T_{\mathrm{high}} + \kappa_{\mathrm{cha}}\cdot \Delta P_{\mathrm{fcs}} \quad (8)\] + +where \(\kappa_{\mathrm{low}}\) is the degradation rate under low power condition; \(T_{\mathrm{low}}\) is the low power condition duration; \(\kappa_{\mathrm{high}}\) is the degradation rate under high power condition; \(T_{\mathrm{high}}\) is the high power condition duration; \(\kappa_{cha}\) is the degradation rate under load changing condition; \(\Delta P_{f c s}\) is the fuel cell power slope. Fuel cell is considered to reach the end of its life when \(10\%\) of voltage at rated power has been lost. The fuel cell operation degradation cost can be calculated by: + +\[C_{f c s,degr} = k_{f c s}\cdot \gamma_{f c s}\cdot P_{f c s,\mathrm{rate}}\cdot PR_{f c s} / \left(V_{f c s,\mathrm{end}}\cdot 1000\right) \quad (9)\] + +where \(k_{f c s}\) is the fuel cell life correction factor; \(V_{f c s,\mathrm{end}}\) is fuel cell voltage drop at the end- of- life; \(P_{f c}\) , rate is the rated power of the fuel cell; \(PR_{f c s}\) is the fuel cell price per kilowatt(4000RMB/kW). + +<--- Page Split ---> + +### 3.2. Problem modeling + +In this work, the EMS of electric vehicles is modeled as a long- term sequential decision process objective to minimize total energy cost while maintaining battery SOC within reasonable limits. The optimization objective can be formulated as: + +\[J_{EMS} = \min \sum_{t = 0}^{T} cost(t) + \alpha f_{s}(SOC(t)) \quad (10)\] + +where \(T\) is the total length of the driving cycle, \(cost(t)\) is the energy cost including hydrogen consumption, the cost of battery, and fuel cell degradation, \(f_{s}(SOC(t))\) is the SOC maintaining function, \(\alpha\) the tradeoff between energy cost and SOC. + +To tackle the sequential decision, the energy management problem is formulated as a Markov Decision Process (MDP), which is a framework for learning the optimal EMS from interaction to minimize total energy cost. The MDP defined by a tuple \((S,A,P,R,\rho_{0},\gamma)\) , where \(S\) denotes the state space, \(A\) denotes the action space, \(P\left(s^{\prime}\mid s,a\right)\) denotes the transition distribution, \(\rho_{0}(s)\) denotes the initial state distribution, \(R(s,a)\) denotes the reward function, and \(\gamma \in (0,1)\) denotes the discount factor. The goal is to find a policy \(\pi (a\mid s)\) that maximizes the expected cumulative discounted rewards \(J(\pi) = E_{\pi ,P,\rho_{0}}\left[\sum_{t = 0}^{\infty}\gamma^{t}R\left(s_{t},a_{t}\right)\right]\) . To use this formulation for FCEV. The state space at time point \(t\) for the FCEV is defined as: + +\[S = \left\{v_{t},acc_{t},SOC_{t},P_{fcs}^{t}\right\} \quad (11)\] + +where \(v_{t},a_{t},P_{fcs},SOC_{t}\) are the vehicle speed, acceleration, fuel cell power, and battery SOC. The action represents the control variable, defined as allocating power to the energy sources of the vehicle. In the context of the FCEV, the action is defined as the FC power slope, denoted as \(\Delta P_{fcs}\) . The continuous action can be described as follow: + +\[A = \left\{\Delta P_{fcs} = P_{fcs}^{t} - P_{fcs}^{t - 1},\Delta P_{fcs}\in [-10\mathrm{kW},10\mathrm{kW}]\right\} \quad (12)\] + +The reward function \(R\) describes the reward \(R(s_{t + 1};s_{t};a_{t})\) associated with transitioning from state \(s_{t}\) to state \(s_{t} + 1\) using action \(a_{t}\) . The design of the reward function is pivotal in the learning process. For the FCEV, multiple objectives are taken into account, including hydrogen consumption, FC degradation, and battery- related costs such as electricity consumption and degradation. Additionally, it is essential to maintain the battery SOC. Therefore, the reward function is defined as the sum of energy costs while ensuring that the battery charge- sustaining constraints are maintained. + +\[R = -\left\{C_{fcs,H_2} + C_{fcs,degr} + C_{bat,eh_2} + C_{bat,degr} + \alpha \left[SOC_{ref} - SOC(t)\right]^2\right\} \quad (13)\] + +The battery electricity consumption \(C_{bat,eh_2}\) is calculated according to the battery charge/discharge efficiency and converted into price cost: + +\[C_{bat,eh_2} = \int_0^t \left[P_{bat} / \left(\eta_{d / c}\eta_{DC / DC},\mathrm{LHV}_{H_2}\right)\right]dt\cdot PR_{H_2} \quad (14)\] + +where \(\eta_{d / c}\) is the battery discharge/charge efficiency. + +### 3.3. Offline RL algorithm + +We employ the offline reinforcement learning (ORL) paradigm to address the MDP problem described above. The goal is to learn a policy \(\pi \sim a_{t}(\pi :S\to A)\) that maximizes the expectation of the sum of discounted rewards \(J(\pi)\) Each policy \(\pi\) has a corresponding state- action value function (also known as Q function), which denotes the expected return \(Q(s,a)\) when following the policy \(\pi\) after taking an action \(a\) in state \(s\) + +\[Q(s,a) = \mathbb{E}\left[\sum_{t = t}^{\infty}\gamma^{i - t}R_{i}\mid s_{t} = s,a_{t} = a\right] \quad (15)\] + +Here, we approximate the Q function \(Q(s,a)\) using deep neural networks by minimizing the squared Bellman error. The ORL algorithm utilized in our proposal is an extension of the twin delayed deep deterministic policy gradient + +<--- Page Split ---> + +algorithm (TD3) [26]. TD3 is a state- of- the- art online DRL algorithm implemented under the actor- critic framework that learns a deterministic policy. For the actor part, it learns a deterministic target policy by mapping states to a specific action. The update of the Actor network of TD3 algorithm aims to maximize the estimation of the current policy by the critic network. + +When offline logged datasets are available, it is reasonable to push the policy towards favoring actions contained in the dataset D. Hence, our proposed ORL algorithm augments the standard policy update step in TD3 with Behavior Cloning (BC) regularization to reinforce the policy's focus on the behaviors observed in the dataset D [27]. This regularization term encourages the policy to mimic the demonstrated behaviors more closely, leading to improved generalization and performance. Furthermore, in pursuit of improving the policy, Discriminator Blend (DB) regularization [28] is employed to enhance the flexibility of the policy constraint. This is achieved by integrating a discriminator using Generative Adversarial Networks (GANs) [29]. By incorporating a discriminator, the policy is enabled to explore actions that may not be included in the dataset \(D\) , leading to a more diverse and adaptive policy. This involves utilizing a neural network as an approximator of the policy function \(\pi\) : + +\[\pi = \arg \max_{\pi}\mathbb{E}_{(s,a)\sim \mathbf{D}}\left[\lambda Q(s,\pi (s)) - (1 - \beta)(\pi (s) - a)^2 +\beta \log (D(s,\pi (s)))\right] \quad (16)\] + +\(\mathbb{E}()\) is the mathematical expectation. The parameter \(\beta\) (range of 0 to 1) adjusts the balance between BC and DB constraints. The DB is trained to assess whether a given action \(a\) and state \(s\) pair belongs to the dataset \(a_{D}\) or is generated by the policy \(\pi_{\theta}\) , which acts as the generator \(G\) in GANs. This enables BD to effectively regulate the policy learning process by encouraging the policy to explore actions beyond the dataset while ensuring that these actions are plausible according to the discriminator's perception. The \(\lambda\) is a normalization term based on the average absolute value of \(Q\) to control the balance between RL and imitation, defined as: + +\[\lambda = \frac{\alpha}{\frac{1}{N}\sum_{(s_i,a_i)}\left|Q(s_i,a_i)\right|} \quad (17)\] + +The parameter \(\alpha\) is used to control the strength of the regularize where the larger \(\alpha\) will make the algorithm approach more RL, and \(N\) represents the number of transitions in the dataset. To normalize the characteristics of each state in the provided dataset. Let \(s_i\) be the \(i\) th feature of the state \(s\) in the dataset, let \(\mu_i,\sigma_i\) be the mean and standard deviation ( \(\eta\) is a constant value to avoid division by zero.): + +\[s_i = \frac{s_i - \mu_i}{\sigma_i + \eta} \quad (18)\] + +The critic part estimates the Q- value of a state- action pair. TD3 employs two critic networks to mitigate overestimation bias, with each critic having a corresponding target network. Each critic network \(Q_{1}\left(s,a\mid \theta^{Q_{1}}\right)\) and \(Q_{2}\left(s,a\mid \theta^{Q_{2}}\right)\) corresponds to a target network \(Q_{1}^{\prime}\left(s,a\mid \theta^{Q_{1}^{\prime}}\right)\) and \(Q_{2}^{\prime}\left(s,a\mid \theta^{Q_{2}^{\prime}}\right)\) respectively. The minimum Q- value among the two critics is used as the target Q- value during training. The critic network is updated by minimizing the loss function: + +\[L\left(\theta^{Q_{i}}\right) = \mathbb{E}\left[\left(y_{t} - Q_{i}\left(s_{t},a_{t}\mid \theta^{Q_{i}}\right)\mid a_{t} = \mu \left(s_{t}\mid \theta^{\mu}\right)\right)^{2}\right] \quad (19)\] + +where \(\theta^{Q}\) denotes the weights of the critic network. The target Q- value \(y\) is evaluated by taking the minimum of the estimates from the two Q- functions as follows: + +\[y_{t} = r\left(s_{t},a_{t}\right) + \gamma \min_{i = 1,2}Q_{i}^{\prime}\left(s_{t + 1},a_{t + 1}\mid \theta^{Q_{i}^{\prime}}\right) \quad (20)\] + +where \(a_{t + 1}\sim \pi_{\phi^{\prime}}\left(s_{t + 1}\right) + \epsilon ,\quad \epsilon \sim \mathrm{clip}(\mathcal{N}(0,\tilde{\sigma}), - c,c)\) is the exploration noise to smooth the value estimates and improve robustness of the learned \(Q\) functions, \(r\) is the instantaneous one- step reward, \(\gamma\) is the discounting factor. + +### 3.4. Baseline Methods + +We use a series of baseline EMS methods for comparatively evaluating the ORL method. The inputs and outputs of all baselines are the same as those of the proposed method. + +<--- Page Split ---> + +Dynamic Programming (DP) [30]: In optimization control methods, the EMS problem is formulated as a nonlinearly constrained optimization problem, aiming to minimize the objective function presented in Equation (10). DP is an optimization control method that operates by seeking the shortest path backward in time. Its objective is to derive the minimum cost function for each grid at every stage in reverse chronological order. In our study, DP is used as the benchmark EMS policy, representing the global optimum and providing upper limits for comparison. It's important to recognize that DP requires future information as input to achieve the optimization objective. + +Behavior Cloning (BC) [31]: BC, as a fundamental imitation learning approach, seeks to emulate the EMS policy by directly learning from the provided dataset, which is assumed to be generated by an expert policy or near- expert policy. It employs supervised learning techniques to train a model to map states to actions. Both BC and ORL involve learning from data for EMS applications. In this context, we establish BC as the benchmark and aim to showcase the superior performance of ORL. + +Proximal Policy Optimization (PPO) [32]: PPO is a state- of- the- art online DRL algorithm, which has been extensively applied in various applications requiring sophisticated decision- making in dynamic environments. PPO offers a robust and efficient approach to training agent by leveraging on- policy learning, effective use of data through mini- batch updates, stability through policy clipping, and adaptive learning rates. Leveraging the strengths of PPO, we utilize it to generate the dataset necessary for ORL, with its policy serving as an expert (near- optimal) strategy for comparison purposes. We provide it to explore the superiority of ORL compared to the online DRL. + +Twin Delayed Deep Deterministic Policy Gradient (TD3) [26]: TD3 is an advanced online DRL algorithm, stemming from the Actor- Critic framework. It has garnered significant attention due to its effectiveness in overcoming challenges associated with continuous action spaces and high- dimensional state spaces. TD3 employs twin critic networks to estimate the value of actions more accurately. By utilizing two critic networks, TD3 mitigates overestimation bias and enhances the robustness of value function estimation. We also provide it to explore the superiority of ORL compared to the online DRL. + +## 4. Discussion + +In conclusion, we have presented a novel data- driven EMS for hybrid energy systems in EVs. Leveraging an innovative offline reinforcement learning agent, our approach learns directly from driving data. Experimental results demonstrate that the ORL agent not only learns optimal EMS strategies from expert data but also exhibits the ability to learn superior EMS from datasets containing a mixture of expert and noisy data, and even achieves near- optimal strategies from entirely noisy datasets. Moreover, our approach demonstrates that with increased data availability, performance improves as the agent is trained with more data. + +This approach offers three notable benefits. Firstly, it is sufficiently simple, as it solely relies on collected data for automatic learning by the agent, unlike the traditional EMS development process, which often requires extensive expert knowledge and repeated measurements. Furthermore, the data used in our approach are non- expert data readily available from real vehicles. Secondly, our method ensures stable performance by integrating seamlessly with existing EMS without altering the original EMS performance lower bound. Our approach continuously improves upon the baseline EMS through data- driven enhancements leveraging the strengths of both technologies. For example, to address the performance shortcomings in rule- based EMS, ORL enables incremental learning, allowing for the continual enhancement of EMS performance with historical data. Similarly, ORL addresses the sim- to- real gap problem in simulation- based methods by enhancing pretrained EMS models, thereby ensuring their effectiveness in real- world deployment scenarios. Lastly, our approach exhibits versatility, as with sufficient data, it can learn a generalized EMS applicable to various EVs and operating conditions. This aligns with the current trend of large- scale language models and similar approaches in artificial intelligence, where a single large model with large- scale data can be trained to perform well across diverse tasks and domains. + +Overall, we believe that ORL could serve as a foundational framework for data- driven EMS, with potential applications extending beyond EVs to grid EMS, industrial energy management systems, and other vehicle control systems. However, a limitation of this work is that the ORL agent may require more data to further enhance its performance. Addressing this limitation could involve exploring methods to efficiently gather and utilize additional data for agent training, potentially improving its effectiveness in real- world applications. + +<--- Page Split ---> + +## Acknowledgements + +AcknowledgementsThis work was supported in part by the National Natural Science Foundation of China (Grant No. 52172377). + +## Author Contributions + +Y.W. designed the study and methodology; Y.W., J.Wu. and H.H. collected and analyzed data; Y.W. generated the figures; Y.W., J.Wu. and W.Z. wrote the manuscript; H.H., W.Z. and F.S. reviewed and edited the manuscript. All authors contributed to the paper. + +## References + +[1] B. Borlaug, M. Muratori, M. Gillern, D. Woody, W. Muston, T. Canada, A. Ingram, H. Gresham, C. McQueen, Heavy- duty truck electrification and the impacts of depot charging on electricity distribution systems, Nature Energy 6 (2021) 673- 682. [2] Y. Zhao, Z. Wang, Z.- J. M. Shen, F. 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Deng, X. Zhu, P. Xie, S. Zhang, et al., China's battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs, Green Energy and Intelligent Transportation 1 (2022) 100020. + +<--- Page Split ---> + +[26] S. Fujimoto, H. Hoof, D. Meger, Addressing function approximation error in actor- critic methods, in: International conference on machine learning, PMLR, 2018, pp. 1587- 1596. [27] S. Fujimoto, S. S. Gu, A minimalist approach to offline reinforcement learning, Advances in neural information processing systems 34 (2021) 20132- 20145. [28] S. Kidera, K. Shintani, T. Tsuneda, S. Yamane, Combined constraint on behavior cloning and discriminator in offline reinforcement learning, IEEE Access (2024).[29] A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A. A. Bharath, Generative adversarial networks: An overview, IEEE signal processing magazine 35 (2018) 53- 65. [30] P. Saiteja, B. Ashok, Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles, Renewable and Sustainable Energy Reviews 157 (2022) 112038. [31] A. Block, A. Jadbabaie, D. Pfrommer, M. Simchowitz, R. Tedrake, Provable guarantees for generative behavior cloning: Bridging low- level stability and high- level behavior, Advances in Neural Information Processing Systems 36 (2024).[32] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal policy optimization algorithms, arXiv preprint arXiv:1707.06347 (2017). + +<--- Page Split ---> diff --git a/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a_det.mmd b/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..7530f53b040252d66f3db5e4a4e4f893e76c4c61 --- /dev/null +++ b/preprint/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a/preprint__08ebe812ad76a570c3ee7e20f364f13b63c9040737d4f23cef0f561c4cb41a3a_det.mmd @@ -0,0 +1,463 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 955, 174]]<|/det|> +# Learning Superior Energy Management from Electric Vehicle Data + +<|ref|>text<|/ref|><|det|>[[44, 196, 257, 240]]<|/det|> +Hongwen He hwhbit@bit.edu.cn + +<|ref|>text<|/ref|><|det|>[[44, 268, 712, 476]]<|/det|> +Beijing Institute of Technology Yong Wang Beijing Institute of Technology Jingda Wu Nanyang Technological University https://orcid.org/0000- 0002- 7336- 4492 Zhongbao Wei Beijing Institute of Technology https://orcid.org/0000- 0003- 0051- 5648 Fengchun Sun Beijing Institute of Technology + +<|ref|>sub_title<|/ref|><|det|>[[44, 515, 103, 532]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 552, 928, 595]]<|/det|> +Keywords: Energy management, Electric vehicle data, Reinforcement learning, Fuel cell vehicles, Data- driven + +<|ref|>text<|/ref|><|det|>[[44, 613, 285, 633]]<|/det|> +Posted Date: July 3rd, 2024 + +<|ref|>text<|/ref|><|det|>[[42, 651, 475, 671]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4523312/v1 + +<|ref|>text<|/ref|><|det|>[[42, 689, 916, 732]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 750, 535, 770]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 805, 934, 848]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on March 22nd, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 58192- 9. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[68, 66, 921, 94]]<|/det|> +# Learning Superior Energy Management from Electric Vehicle Data + +<|ref|>text<|/ref|><|det|>[[70, 106, 832, 128]]<|/det|> +Yong Wang \(^{a,b}\) , Jingda Wu \(^{c}\) , Hongwen He \(^{a,b}\) , Zhongbao Wei \(^{a}\) and Fengchun Sun \(^{a}\) + +<|ref|>text<|/ref|><|det|>[[68, 140, 822, 186]]<|/det|> +\(^{a}\) School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China \(^{b}\) National Key Laboratory of Advanced Vehicle Integration and Control, Beijing Institute of Technology, Beijing, 100081, China \(^{c}\) The Hong Kong Polytechnic University, Hung Hom, Hong Kong + +<|ref|>sub_title<|/ref|><|det|>[[70, 205, 228, 220]]<|/det|> +## ARTICLE INFO + +<|ref|>sub_title<|/ref|><|det|>[[370, 207, 488, 221]]<|/det|> +## ABSTRACT + +<|ref|>text<|/ref|><|det|>[[69, 232, 210, 309]]<|/det|> +Keywords: Energy management Electric vehicle data Reinforcement learning Fuel cell vehicles Data- driven + +<|ref|>text<|/ref|><|det|>[[368, 232, 928, 488]]<|/det|> +Despite the promising potential of energy management technologies in optimizing electric vehicle (EV) performance and fostering global energy sustainability, the extensive research conducted over the past decade has yet to translate into practical applications. This discrepancy arises primarily from the reliance of existing methodologies on simulation- based development paradigms, leading to a significant disparity between simulated results and real- world efficacy. Herein, we present a pioneering real- world data- driven energy management strategies (EMS) approach that utilizes an innovative offline reinforcement learning (ORL) framework. This paradigm enables EMS to learn from diverse real- world data, obviating the need for explicit rule design or high- fidelity simulators, and allowing for seamless application of the proposed method to any existing EMS. Moreover, it continuously enhances performance even after deployment in actual energy management systems. We evaluate the proposed ORL method on fuel cell EVs, training the ORL agent to optimize energy consumption and system degradation. The EV monitoring and management platform in China provides real- world data for validating our methodology. The results demonstrate that ORL consistently learns superior EMS in various conditions. With increasing data availability, its performance improves significantly, from \(88\%\) to \(98.6\%\) relative to theoretical optimality after two data updates. After training with more than 60 million kilometers of data, the ORL agent can learn a general EMS that adapts to unseen and corner- case conditions. These results highlight the effectiveness of integrating the data- driven method with established EMS techniques to enhance performance and underline its potential to utilize large- scale data to improve vehicle energy efficiency and longevity. + +<|ref|>sub_title<|/ref|><|det|>[[70, 536, 217, 555]]<|/det|> +## 1. Introduction + +<|ref|>text<|/ref|><|det|>[[68, 559, 928, 753]]<|/det|> +The automotive industry is undergoing a significant transformation, primarily due to the global focus on sustainability and environmental conservation. Electric vehicles (EVs) are leading this shift, playing a key role in mitigating environmental challenges and advancing sustainable transportation solutions [1, 2]. Concurrently, the emergence of hybrid energy systems (HES) within the EV powertrain represents an emerging trend, offering superior solutions over single energy systems [3]. By integrating multiple energy sources such as batteries, fuel cells, and internal combustion engines, HES improves overall efficiency, sustainability, and reliability, while also providing adaptability to a wide range of driving conditions [4]. Propelled by rapid technological advancements and supportive policies, EVs equipped with HES, including Hybrid EVs (HEV), Plug- in hybrid EVs (PHEV), and Fuel cell EVs (FCEV), are gaining traction worldwide [3]. For example, BYD, a prominent EV manufacturer, achieved sales of 3.02 million EVs in 2023, and PHEVs represent \(47.9\%\) of total sales. In the first quarter of 2024, PHEVs accounted for a notable \(51.6\%\) of total sales, indicating their growing popularity. This trend is mainly attributed to advancements in HES energy management, which enhance energy efficiency and overall performance. + +<|ref|>text<|/ref|><|det|>[[68, 753, 928, 883]]<|/det|> +The energy management strategy (EMS), responsible for allocating energy flow among HES to achieve predefined objectives, performs several vital functions essential for EVs:(1) EMS optimizes system efficiency by intelligently allocating energy flow based on driving conditions, reducing energy consumption and extending driving range [5]. (2) EMS optimizes power delivery based on driver demand, improving acceleration and responsiveness, while also ensuring smooth power delivery for improved driving experience. (3) By considering the characteristics of different power sources, EMS extends the lifespan of the HES, thus enhancing system reliability and safety [6]. Early EMS solutions utilize various rule- based approaches to achieve energy- saving objectives. Rule- based EMS involves the design of predefined rules and parameters tailored to specific driving conditions and vehicle characteristics, relying on + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[68, 71, 928, 138]]<|/det|> +expert knowledge with iterative refinement based on testing feedback [7]. However, this process can be labor- intensive and time- consuming, requiring manual expertise for rule creation and extensive experimentation for parameter calibration. Moreover, its static nature and inability to adapt to dynamic driving scenarios limit its effectiveness in maximizing energy savings and overall performance. + +<|ref|>text<|/ref|><|det|>[[67, 135, 928, 459]]<|/det|> +Recently, there has been a surge in advanced algorithms for energy management of HES, including dynamic programming (DP), model predictive control (MPC), reinforcement learning (RL), and others. A common feature across these methods is the use of optimal control or machine learning (ML) algorithms to compute optimal EMS based on high- fidelity simulators. Collectively, these approaches are referred to as simulation- based EMS in our study. Although DP and MPC provide elegant solutions based on future driving data, accurately forecasting future driving conditions using historical speed and dynamic traffic information remains a significant challenge [8]. Additionally, prediction models and optimization algorithms often result in considerable computational complexity, leading to suboptimal real- time performance [3]. Consequently, applying optimal control methods in real- world vehicle settings poses considerable challenges at present. In contrast, learning- based models, exemplified by Deep RL (DRL), do not rely on knowledge of future conditions and present promising alternatives for EMS [9]. DRL involves learning optimal actions in an environment through trial and error, where the DRL agent interacts with the environment to maximize cumulative rewards over time [10]. DRL- based EMS has been a subject of active research in recent years, with recent developments in online DRL algorithms such as Deep Deterministic Policy Gradient (DDPG) [11], Soft Actor- Critic (SAC) [12], Proximal Policy Optimization (PPO) [13], and others, leading to remarkable results. However, the application of DRL- based EMS faces challenges as an online learning paradigm, particularly concerning the interaction between the DRL agent and the EV simulator, which raises safety concerns. Moreover, while existing literature assumes that simulation models accurately represent real- world conditions, the construction of high- fidelity models that encompass vehicle dynamics, powertrain, traffic scenarios, and driver behavior [14] remains a challenge [15]. This discrepancy can cause the "sim- to- real" problem, where EMS learned in simulators may not be effectively transferred to real vehicles, which leads to the complexity of EMS development [16]. + +<|ref|>text<|/ref|><|det|>[[67, 456, 928, 732]]<|/det|> +In recent years, data- driven methodologies, propelled by advancements in ML techniques and the availability of large datasets, have become essential in addressing key challenges in the EV field [17]. With support from data collection platforms and open- access laboratory data, these data- driven approaches have revolutionized various aspects of battery management systems (BMS). This includes automatic discovery of complex battery aging mechanisms [18], prediction of battery safety envelopes [19], evaluation of safety conditions [20], estimation of battery state of health [21], and even enhancing battery lifetime prediction models with unlabeled data [22]. Notably, innovations in feature extraction and supervised ML techniques tailored for time- series data have greatly enhanced prediction accuracy. This success has sparked interest in exploring data- driven methods for sequential decision- making tasks, including improving energy management systems. A common approach to implementing a data- driven EMS involves using supervised learning, where the ML model is employed to capture the complex and non- linear relationships between input features and corresponding control outputs. In [23], recurrent neural networks are trained offline using a substantial amount of training data obtained from the global optimization strategy DP, yielding a sub- optimal EMS that closely approximates the DP. It is essential to recognize the differences from the prediction tasks in BMS applications, as EMS entails sequential decision- making. Although supervised learning can mimic the policy of EMS through imitation learning, its heavy reliance on expert data may result in limited generalization to new and diverse scenarios. Hence, it's crucial to explore alternative methods for learning EMS from non- expert data, which is common in automotive applications. + +<|ref|>text<|/ref|><|det|>[[67, 731, 928, 894]]<|/det|> +In this paper, we present a novel data- driven EMS paradigm, addressing key challenges discussed above by leveraging offline data generated by existing EMS strategies, without the need for explicit rule design or online interaction. Our approach integrates DRL and supervised learning techniques, departing from traditional rule- based EMS and simulation- based methods to enhance EMS capabilities, as visually depicted in Figure 1(a). Notably, our proposed method can be directly integrated with any EMS algorithm and continuously improves through the collection data. We term this approach the offline reinforcement learning (ORL) agent [24], which is trained by combining real- world data as well as carefully filtered and processed simulation data through a novel exploration scheme. To evaluate this paradigm, we focus on a fuel cell electric vehicle (FCEV) as the subject and collect data for learning power allocation strategies. The EV monitoring and management platform in China offers real- world data to validate our methodology (Figure 1(b)). Overall, the ORL agent introduced herein exhibits four key features (Figure 1(c)): + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 68, 880, 608]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[67, 616, 930, 739]]<|/det|> +
Figure 1: Overview of the proposed data-driven EMS framework. (a) Comparison of three EMS paradigms: Traditional rule-based EMS relies on expert knowledge and calibration based on fixed driving cycles. Simulation-based EMS requires high-precision models and entails transitioning from simulation analysis to real-world deployment, resulting in a gap between simulated and real-world performance (sim-to-real gap). In contrast, the proposed real-world data-driven EMS learns directly from actual data. (b) China has established a three-tier EV monitoring and management system involving the state, local governments, and enterprises. The National Monitoring and Management Platform collects real-time operational data from over 20 million EVs [25]. (c) The ORL agent works with an existing EMS and continuously collects EV data to improve the EMS. (d) Overall diagram of the proposed data-driven EMS methodology.
+ +<|ref|>text<|/ref|><|det|>[[68, 770, 928, 820]]<|/det|> +1) Purely data-driven EMS: By autonomously learning and optimizing from collected offline datasets, our approach facilitates the development of advanced EMS without necessitating expert knowledge or the construction of high-fidelity EV simulators. This data-driven process significantly simplifies EMS development workflows. + +<|ref|>text<|/ref|><|det|>[[68, 820, 928, 885]]<|/det|> +2) Learning from non-optimal data: Our research indicates that ORL can learn near-optimal EMS from non-optimal data and even can derive superior policies from sub-optimal. Our approach is less reliant on data quality allowing for effective learning. This practicality allows us to utilize raw data generated by actual vehicles, which is common in automotive applications. + +<|ref|>text<|/ref|><|det|>[[68, 884, 928, 917]]<|/det|> +3) Enhancement with increased data: The performance of ORL improves as more training data is utilized, demonstrating its ability to continuously adapt and enhance EMS performance. Through training across diverse + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[68, 71, 925, 105]]<|/det|> +datasets, it can potentially adapt to new driving conditions and yield favorable results, even in corner- case conditions. When sufficient data are available, ORL can learn a generalized EMS. + +<|ref|>text<|/ref|><|det|>[[68, 103, 928, 170]]<|/det|> +4) Compatibility with existing EMS: Our approach seamlessly integrates with established rule-based or simulation-based EMS methods, leveraging data from onboard controllers to augment EMS performance. This ensures that baseline performance is preserved while facilitating further enhancement using ORL, making it a valuable extension to conventional EMS methodologies. + +<|ref|>sub_title<|/ref|><|det|>[[68, 193, 166, 209]]<|/det|> +## 2. Results + +<|ref|>sub_title<|/ref|><|det|>[[68, 215, 402, 233]]<|/det|> +### 2.1. The overview of data-driven EMS + +<|ref|>text<|/ref|><|det|>[[68, 233, 928, 299]]<|/det|> +In Figure 1(d), the framework overview of ORL for EMS is illustrated. We introduce the three phases of applying the proposed ORL algorithm to the EMS problem, which include data collection, offline learning, and evaluation. ORL is an innovative subset of RL methods rooted in data- driven approaches. Unlike most simulation- based EMS scheduling methods, the data- based learning process does not require the building of an EV simulation environment. + +<|ref|>text<|/ref|><|det|>[[68, 297, 928, 476]]<|/det|> +In the data collection phase, an available dataset \((D)\) is gathered from the existing EMS policy \((\pi_{\beta})\) . It is crucial to record various parameters such as velocity, acceleration, hydrogen consumption, electricity consumption, fuel cell degradation, battery state of charge (SOC), and others during vehicle operation. However, due to limitations in real- world data quality and privacy concerns associated with collecting data from a large number of actual vehicles, we developed an FCEV simulation model to generate laboratory datasets 2(a). This FCEV model enables the generation of extensive, high- fidelity data based on real- world driving conditions 2(b). Subsequently, we employ a tailored data processing model to encode control and state variables into standardized data formats. The data encoding process converts these parameters into a transition dataset \(D = \left(s,r,a,s^{\prime}\right)_{i}\) . Here, \(s\) , \(a\) , and \(r\) represent the state, action, and reward as described in the Methods section, respectively, while \(s^{\prime}\) denotes the subsequent state, and \(i\) indexes a transition in the dataset. These meticulously curated datasets are stored in the buffer and serve as input for the ORL agent. + +<|ref|>text<|/ref|><|det|>[[68, 473, 928, 604]]<|/det|> +In the offline learning phase, our proposed ORL agent is designed based on the Actor- Critic network and incorporates Behavior Cloning (BC) and Discriminator Blend (DB) mechanisms. In each training step, mini- batches of transitions \((s,r,a,s^{\prime})\) stored in a data buffer are sampled from the replay buffer to update the ORL networks. The Actor network, responsible for determining the action \(\mathbf{a}_{t}\sim \pi_{\beta}\left(\cdot \mid \mathbf{s}_{t}\right)\) , is updated to maximize the expected return as estimated by the Critic network. BC regularization is applied to the policy update step to encourage the policy to prioritize actions present in the dataset. Moreover, the DB component is introduced to allow actions beyond the dataset distribution similar to those included in the dataset. Through training, the agent learns to optimize its actions based on observed states and rewards. The details of the ORL agent will be elaborated upon in the Methods section in detail. + +<|ref|>text<|/ref|><|det|>[[68, 603, 928, 750]]<|/det|> +After completing the training phase, the neural network parameters representing the EMS policy of the ORL agent are saved for future use. Subsequently, the trained agent is evaluated to gauge its effectiveness and performance. Utilizing the FCEV environment established during the data collection phase, our subsequent experiments employ three standard driving cycles (WTVC, FTP, and CHTC) to evaluate energy cost. Additionally, we incorporate various real- world driving scenarios to comprehensively assess the trained EMS policy. Following the evaluation, adjustments to the agent hyperparameters or training process may be considered to improve its performance. This iterative process of training, evaluation, and refinement continues until the desired level of EMS performance is attained. Upon achieving satisfactory performance, the trained agent becomes eligible for deployment in real- world scenarios, where it can be utilized to efficiently optimize energy management systems. + +<|ref|>sub_title<|/ref|><|det|>[[68, 761, 370, 779]]<|/det|> +### 2.2. Data for learning and analysis + +<|ref|>text<|/ref|><|det|>[[68, 778, 928, 925]]<|/det|> +We select the Proximal Policy Optimization (PPO), which demonstrates the best performance among online DRL algorithms in our EMS problem, as the expert EMS. Using PPO, we generate datasets denoted as \(D^{E}\) comprising 300K time steps. Additionally, we employ a random agent that samples actions randomly, generating datasets denoted as \(D^{R}\) , which represent poor performance. To create settings with varying levels of data quality in the suboptimal offline dataset, we combine transitions from the expert datasets \(D^{E}\) and the random datasets \(D^{R}\) in different ratios. Specifically, we consider four different dataset compositions, denoted as D1, D2, D3, and D4. These settings are defined as follows: D1 (Data- 1): Consists solely of transitions from the expert dataset \(D^{E}\) , representing the expert policy. D2 (Data- 2): Contains two- thirds of transitions from the expert dataset \(D^{E}\) and one- third of transitions from the random dataset \(D^{R}\) , representing suboptimal data. D3 (Data- 3): Comprises one- third of transitions from the expert dataset + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 72, 861, 625]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[68, 629, 930, 735]]<|/det|> +
Figure 2: Comparison for different datasets. (a) The simulation model for the fuel cell hybrid electric vehicle (FCEV), depicts the powertrain layout and energy flow topology. (b) The principle of EMS involves the allocation of energy flow among hybrid energy systems (fuel cell and battery) to achieve predefined objectives based on driving conditions. Here, real driving conditions are gathered and then use the simulated FCEV model to generate energy cost data for training the ORL agent. (c) Distribution of encoded actions for the four datasets, each action is normalized to the [-1,1]. D1 represents data generated by an expert policy, D2 and D3 denote suboptimal data generated by a combination of expert and random policies, D4 comprises entirely random data. (d) The states distribution of four datasets.
+ +<|ref|>text<|/ref|><|det|>[[68, 767, 928, 802]]<|/det|> +\(D^{E}\) and two- thirds of transitions from the random dataset \(D^{R}\) , representing another suboptimal data. D4 (Data- 4): Comprises solely of transitions from the random dataset \(D^{R}\) , representing the random policy. + +<|ref|>text<|/ref|><|det|>[[68, 801, 928, 916]]<|/det|> +Figure 2(a) depicts the action distributions for the four datasets. Significant differences can be observed among the four EMS policies, with the action range of D1 falling within (- 0.2, 0.5), resulting in relatively stable variations in FC power. As random policy data is introduced, the action ranges in the other datasets all fall within (- 1, 1), particularly for D4 where the action distribution is uniformly spread across (- 1, 1). This implies that this policy is noisy, denoted as a poor EMS. Figure 2(b) illustrates the state distributions for the four datasets. Note that all states have undergone post- processing and scaling to the (0, 1) interval. By comparing the box plots of the four datasets, it is evident that the SOC of D1 falls within a reasonable range (0.38- 0.7), meeting the EMS constraints regarding the battery SOC. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[68, 71, 928, 137]]<|/det|> +However, the SOC of the other datasets falls into unreasonable ranges, such as SOC in the range (0.2, 1) for D3. Additionally, with the increase in \(D^R\) data, the FC power distribution ranges of D3 and D4 become wider. Since the conditions of the four datasets are derived from fixed segments of standard driving cycles, the velocity distribution remains the same across all datasets. + +<|ref|>text<|/ref|><|det|>[[68, 136, 928, 185]]<|/det|> +Creating challenging datasets is practical because generating sub- optimal or random data is more cost- effective than collecting expert- level data from real vehicles. Therefore, an effective data- driven EMS method must be able to effectively handle and learn from these suboptimal offline datasets. + +<|ref|>sub_title<|/ref|><|det|>[[68, 198, 518, 216]]<|/det|> +### 2.3. Learning superior EMS from non-optimal data + +<|ref|>image<|/ref|><|det|>[[66, 230, 920, 747]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[67, 748, 930, 840]]<|/det|> +
Figure 3: The ORL agent learning performance. (a) Learning curves of ORL agent for the four different datasets. (b) The comparison of absolute rewards (original rewards are negative) under three validation conditions. "D1," "D2," "D3," and "D4" correspond to the best rewards achieved by ORL after learning on each respective dataset. Notably, ORL agent on various datasets closely approximate or exceed the expert policy. (c) The comparison of energy costs between the original EMS and the optimized EMS using ORL shows a notable reduction in energy costs through the data-driven learning process. (d) The action (FC power slope) distributions of optimized EMS using ORL
+ +<|ref|>text<|/ref|><|det|>[[68, 862, 928, 928]]<|/det|> +We first study the performance of the ORL agent with different datasets. To ensure a fair comparison, the algorithm employs uniform experimental settings and network parameters across four datasets. Figure 3(a) illustrates the average reward of the training process on the four datasets. This average, computed as the mean reward over every 1000 episodes, undergoes validation across 10 iterations using three standard driving cycles: WTVC, CHTC, and FTP. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[68, 71, 928, 169]]<|/det|> +Training involves utilizing a buffer comprising 300,000 samples, with the ORL agent randomly selecting 256 data points for each training iteration, accumulating to one million training epochs. For D1, which comprises exclusively expert data, convergence is evident after approximately 210e3 episode. However, ORL exhibits a slower convergence speed during iterative learning on the other three datasets, converging at around 330e3, 600e3, and 360e3 epochs, respectively. This suggests that the data distribution influences the learning speed, but ultimately, ORL learns an effective EMS. + +<|ref|>text<|/ref|><|det|>[[68, 168, 929, 329]]<|/det|> +Figure 3(b) presents the reward performance of trained ORL across three driving cycles. It notes that the absolute reward value of ORL decreases in D1, from 323.4 to 297.3 on the CHTC, representing an improvement of \(8.8\%\) . Surprisingly, even on suboptimal datasets D2 and D3, ORL outperforms the expert strategy, with reward increases of \(1.8\%\) and \(3.4\%\) , respectively. Similarly, ORL still outperforms on the WTVC and FTP conditions, learning superior strategies from the suboptimal datasets D2 and D3. The exception is D3 on the WTVC condition, possibly due to the high- speed nature of the WTVC condition, leading to larger reward values for SOC. Despite this, the final results of the energy consumption remain within rational bounds. Particularly noteworthy is the excellent performance on the random dataset D4, where ORL closely approaches expert results across all three validation conditions, achieving rewards of 402, 325, and 395. Compared to the original average reward of 2637 for the D4 dataset, ORL has reduced the reward by \(85.8\%\) . + +<|ref|>text<|/ref|><|det|>[[68, 329, 928, 490]]<|/det|> +Figure 3(c) provides a detailed comparison of the energy costs between ORL and the original datasets. The blue dots represent the mean costs of the four original EMS datasets, accompanied by error bars indicating the maximum and minimum costs. In contrast, the red dots depict the energy costs incurred by ORL in the corresponding datasets. Despite the inclusion of random data that leads to a degradation in cost performance, ORL consistently maintains lower costs across all data sets. For instance, in the WTVC condition, the cost escalates from the initial 90 RMB in dataset D1 to 163 RMB in dataset D4. However, ORL consistently maintains costs within the range of 90- 95 RMB. In particular, the minimum cost values of the original D4 dataset exceed those achieved by ORL significantly, with ORL achieving reductions in costs that exceed \(40\%\) in all three conditions. This observation underscores ORL's ability not only to glean superior results from expert EMS but also to consistently yield excellent outcomes from progressively suboptimal datasets. Remarkably, ORL even attains expert- level EMS performance when trained solely on noisy datasets. + +<|ref|>text<|/ref|><|det|>[[68, 489, 928, 635]]<|/det|> +To elucidate the rationale behind the performance enhancements, Figure 3(d) illustrates the action distributions of optimized EMS using ORL. As different EMS policies can be reflected by the actions taken, in the context of the FCEV considered here, this pertains to the FC power slope under the same driving cycle. Comparing Figures 2(c) and 3(d), we notice significant changes in D2, D3, and D4 compared to Figure 2. In D2, D3, and D4, the action distributions closely resemble those of expert data in D1, concentrating within the range of [-0.3, 0.3], as opposed to the wider range of [-1, 1] observed in Figure 2. The change is particularly pronounced in D4, where the absence of expert data results in slight differences in the action distributions compared to D1, D2 and D3. However, all ORL policies tend to learn FC power variations with smaller ranges, ensuring smoother FC power output while satisfying power demand requirements. + +<|ref|>text<|/ref|><|det|>[[68, 634, 928, 715]]<|/det|> +In conclusion, through experimentation on three validation conditions and four datasets, our ORL agent not only learns better EMS strategies from expert strategies but also demonstrates the ability to learn near- expert strategies from entirely noisy datasets and even achieves superior results from datasets containing a mixture of expert and noisy data. This observed convergence underscores ORL's ability to enhance and optimize the original EMS through learning from data obtained from any EMS. + +<|ref|>sub_title<|/ref|><|det|>[[68, 728, 454, 746]]<|/det|> +### 2.4. Performance by comparative evaluation + +<|ref|>text<|/ref|><|det|>[[68, 746, 928, 826]]<|/det|> +To demonstrate the superior performance of ORL, we contrast it with simulation- based and imitation learning EMS approaches. Since imitation learning and ORL are closely related, both involve learning EMS from data. We first compare the performance between ORL and BC. It's important to note that BC typically employs a supervised learning paradigm, learning from expert data, while ORL incorporates reinforcement learning with exploration mechanisms. This distinctive learning mechanism results in significant performance differences. + +<|ref|>text<|/ref|><|det|>[[68, 826, 928, 907]]<|/det|> +In Figure 4(a), we compare the testing reward in the WTVC, CHTC and FTP driving cycles, and calculate the percentage of ORL and BC costs relative to the expert EMS (PPO). In D1, both ORL and BC achieve favorable results, with ORL surpassing the original expert data by a maximum of \(9.1\%\) , while BC remains comparable to the expert. In D2, ORL maintains superiority over expert- based EMS, while BC experiences significant cost degradation (ranging from \(6\%\) to \(70\%\) ). In D3 and D4, ORL continues to outperform or closely match the expert, while BC, limited by data + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[68, 67, 930, 604]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[67, 620, 930, 727]]<|/det|> +
Figure 4: Performance analysis comparing different methods. (a) The comparison between two data-driven EMS methods. The matrix numbers represent the relative reward rates of ORL and BC compared to the expert EMS (PPO) under the same conditions, emphasizing the minimal influence of data quality on ORL's performance. (b) Comprehensive performance of different algorithms on the WTVC condition, DP representing the globally optimal EMS. (c) Comprehensive performance on the CHTC condition. (d) Comprehensive performance on the FTP condition. (e) The efficiency distribution of fuel cell power demonstrates that ORL learns a superior EMS, ensuring that fuel cell system remains within the high-efficiency range. (f) FC degradation costs under three conditions
+ +<|ref|>text<|/ref|><|det|>[[68, 760, 928, 794]]<|/det|> +quality, fails to learn an optimal EMS. This underscores ORL's capability to learn superior EMS from non- expert data, while imitation learning demonstrates poorer performance and struggles to learn favorable EMS with non- expert data. + +<|ref|>text<|/ref|><|det|>[[68, 793, 928, 890]]<|/det|> +Figures 4(b- d) present detailed results of different methods under the WTVC, CHTC, and FTP conditions, with the red lines representing the percentage of cost compared to DP. It is evident that ORL learns an optimal EMS policy on the D1 dataset, achieving percentages close to \(99.9\%\) , \(99.4\%\) , and \(97.6\%\) of DP, respectively. As for PPO, a benchmark expert policy, its cost results are \(98.6\%\) , \(98.0\%\) , and \(97.6\%\) of DP, respectively. Thus, BC learns similar expert EMS in D1, but its performance significantly deteriorates on suboptimal D2 data. Another online DRL method, TD3, also demonstrates satisfactory performance, however, its overall costs are lower than those of PPO and ORL. + +<|ref|>text<|/ref|><|det|>[[68, 889, 928, 923]]<|/det|> +In Figure 4(e), the FC power distribution of the EMS learned by ORL on the D1 dataset is depicted, with the green curve representing the efficiency curve of the FC system. ORL demonstrates a superior EMS, ensuring that the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[68, 72, 928, 153]]<|/det|> +power distribution remains within the high- efficiency range, resulting in reduced hydrogen consumption. Additionally, a narrower power variation range, as shown in Figure 3(d), minimizes FC degradation costs. As illustrated in Figure 3(f), ORL incurs minimal FC degradation costs in the three conditions, with costs of 2.3, 1.4, and 1.4, respectively. Furthermore, examination of Figures 3(b- d) indicates that the battery SOC remains within a reasonable range. These findings collectively affirm that ORL has successfully learned a superior EMS. + +<|ref|>sub_title<|/ref|><|det|>[[68, 166, 448, 184]]<|/det|> +### 2.5. Continuous learning with growing data + +<|ref|>text<|/ref|><|det|>[[68, 184, 928, 249]]<|/det|> +We have demonstrated in previous experiments that ORL can learn optimal EMS strategies from data and outperforms other methods. In this section, we further showcase ORL for continuous learning from data. We conduct experiments in three cases depicted in Figure 5(a), collecting real- vehicle data in different driving scenarios including urban road, highway, and downtown road for the three cases (Figure 5(b)). + +<|ref|>title<|/ref|><|det|>[[68, 261, 516, 278]]<|/det|> +#### 2.5.1. Case 1: Continuous learning from historical data + +<|ref|>text<|/ref|><|det|>[[68, 278, 928, 406]]<|/det|> +Take the example of driving a bus on fixed routes to illustrate Case 1. We collected real electric bus driving data in Zhengzhou, China, over three consecutive days. Figure 5(c) shows the speed trajectories of three days, labeled as ZBDC- No1, ZBDC- No2, and ZBDC- No3, respectively. Noticeably, there are variations in the speed trajectories along the same route over different days. Figures 5(d- f) show the total cost of different EMS strategies in the three scenarios, which include hydrogen consumption, battery cost, and fuel cell degradation. The baseline is the original EMS of FCEV, and using the baseline data under ZBDC- No1 driving cycle to train the ORL agent as the ORL(Z1) strategy, which is then applied to the new condition ZBDC- No2. Furthermore, we train a new ORL(Z2) EMS using data from both the baseline on ZBDC- No1 and the ORL(Z1) on ZBDC- No2, which is then validated on ZBDC- No3. + +<|ref|>text<|/ref|><|det|>[[68, 405, 928, 535]]<|/det|> +It can be observed that the baseline EMS performs poorly on the first day (ZBDC- No1), with a cost only \(88.0\%\) compared to DP. The corresponding FC power and power slope distributions are shown in Figures 5(g). On the second day as depicted in Figure 5(e) and (h), the performance of ORL(Z1) achieves a significant improvement by learning from the previous data, reaching \(96.4\%\) of the cost compared to DP on the ZBDC- No2. On the third day, continuously learning from more data, the ORL(Z2) achieves a cost of \(98.6\%\) compared to DP on the ZBDC- No3, as shown in Figure 5(f) and (i). By comparing the three power distribution, it is evident that ORL(Z1) and ORL(Z2) distribute more FC output power in the high- efficient range, resulting in lower overall energy consumption, and the smaller power slope also leads to lower system degradation costs. + +<|ref|>text<|/ref|><|det|>[[68, 534, 928, 615]]<|/det|> +In conclusion, with data updates, new data can be utilized to train ORL, leading to the evolution of batter EMS strategies. This demonstrates the ability of ORL to continuously learn and improve from historical data. Additionally, our method integrates seamlessly with established EMS methods, using real- time data from onboard controllers to increase EMS performance. This ensures that baseline performance is preserved while facilitating further enhancement using ORL, making it a valuable extension to conventional EMS methodologies. + +<|ref|>title<|/ref|><|det|>[[68, 628, 434, 645]]<|/det|> +#### 2.5.2. Case 2: Improving from simulated data + +<|ref|>text<|/ref|><|det|>[[68, 645, 928, 726]]<|/det|> +In Case 2, we address the challenge of improving EMS performance from simulated data, where historical data is absent and driving conditions are unknown. Despite the ability of simulation- based methods (such as DRL) to derive ideal EMS strategies from simulated EV models, deploying these strategies onto real vehicles often leads to performance degradation due to the stochastic and unknown nature of real- world driving conditions, a problem known as the sim- to- real gap, extensively studied in the fields of RLL. + +<|ref|>text<|/ref|><|det|>[[68, 725, 928, 838]]<|/det|> +As illustrated in Figure 6(a), during the simulation phase, the PPO algorithm is trained on the standardized driving cycle (WTVC) and three specific conditions ZBDC (Figure 5(c)) to obtain an ideal EMS, denoted as PPO (Train). Subsequently, the EMS is validated on 12 local driving conditions denoted as PPO (Test). As depicted in Figure 6(b), the cost difference between PPO (Train) and DP across the four training conditions is minimal, with an average difference of \(3.16\%\) . However, when tested on the 12 new conditions(DC- 1 to DC- 12), the average cost difference between PPO (Test) and DP rises to \(12.75\%\) . This indicates a significant performance degradation of DRL- based methods when transitioning from simulation to real- world conditions. + +<|ref|>text<|/ref|><|det|>[[68, 838, 928, 919]]<|/det|> +To mitigate the sim- to- real problem, our proposed ORL method leverages data from PPO (Test) for further learning. As shown in Figure 6(c), the ORL approach achieves a substantially lower cost across the 12 local operating conditions compared to the PPO (Train) strategy. The average cost difference between ORL and DP is merely \(1.42\%\) (Figure 6(b)). In summary, our experiments demonstrate that ORL can effectively learn from simulated data to enhance the performance of the original EMS, addressing the sim- to- real problem inherent in traditional simulation- based methods. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[70, 75, 940, 776]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[67, 788, 930, 880]]<|/det|> +
Figure 5: ORL continuously learning and improving from data. (a) ORL for continuous learning from data in three scenarios. (b) Driving data collected from the real-world route. (c) Speed trajectories of three ZBDC conditions. (d) Comparing the total cost under ZBDC-No1, the total cost comprises hydrogen consumption, battery cost, and cell degradation cost. (e) Comparing the total cost under ZBDC-No2. (f) Comparing the total cost under ZBDC-No3. (g) Fuel cell power distribution cloud chart of Baseline EMS. (h) Fuel cell power distribution cloud chart following one data update. (i) Fuel cell power distribution cloud chart following two data updates.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[68, 70, 945, 753]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[67, 763, 930, 884]]<|/det|> +
Figure 6: Performance with more data. (a) Performance of PPO for 4 training data and 12 testing data. (b) The comprehensive performance comparison of different EMS methods reveals that ORL can significantly mitigate the performance degradation observed in the testing phase of PPO. (c) Performance of ORL for 12 testing data. (d) Speed distribution of four conditions. (e) The demand power of HWDC represents an extreme condition. The red dashed line indicates the maximum output power of FC system. (f) Overall performance of the 4 cases as the training data increase under test driving cycle CLTC; (g) Performance on GCDC; (h) Performance on ZNDC; (I) Performance on HWDC indicates that ORL ultimately learns a reasonable EMS in extreme driving conditions. (j) Battery SOC trajectories of different EMS under the test driving cycle HWDC.
+ +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[68, 72, 555, 90]]<|/det|> +#### 2.5.3. Case 3: Learning a general EMS with large-scale data + +<|ref|>text<|/ref|><|det|>[[68, 88, 929, 217]]<|/det|> +To assess the generalization of the ORL model, especially in extreme conditions, the ORL agent trained on huge amounts of data and tested its performance on four new driving conditions. Figure 6(d) illustrates the speed distribution for these four conditions. Among these, the China Light- Duty Vehicle Test Cycle (CLTC) stands as the standard cycle, GCDC is obtained from an EV operating in a downtown road, ZBDC represents a new condition collected in Zhengzhou, and HWDC is collected from a fuel vehicle traveling on a highway. The four conditions have distinct characteristics and originate from different road and vehicle types. Particularly for HWDC, where the demand power exceeds \(250\mathrm{kW}\) . As depicted in Figure 6(e), the power demand exceeds the \(100\mathrm{kW}\) maximum output power of FC system, indicating that HWDC is an extreme condition for the FCEV studied in this work. + +<|ref|>text<|/ref|><|det|>[[68, 216, 929, 475]]<|/det|> +Firstly, we establish datasets of four different scales: 4e4 (ORL- 4), 20e4 (ORL- 20), 100e4 (ORL- 100), and 500e4 (ORL- 500) samples, excluding data from the four validation conditions obtained in previous experiments (Cases 1 and 2). The results on the four validation conditions are depicted in Figures 6(f- i). It is evident from Figure 6(f), (g), and (h) that both the reward and cost exhibit a gradual decrease with an increase in training data. With more training data, the ORL model consistently enhances its performance. Notably, the rate of performance improvement diminishes after reaching the 100e4 sample mark, with minimal disparity observed between the ORL- 100 and ORL- 500. At approximately 20e4 samples, the ORL model outperforms the DRL- TD3 algorithm (as indicated by the dashed lines in the figures). It is essential to highlight that, under the extreme HWDC condition, although ORL- 20 achieves the lowest cost (Figure 6(i)), its reward absolute value is not the lowest. This phenomenon is because the ORL- 20 prefers battery consumption during high power demands, exceeding the maximum output of the FC system, violating the SOC constraint. This phenomenon arises because ORL- 20 tends to prioritize battery consumption during periods of high power demand, the FC system fails to achieve its maximum power output. Consequently, the strategy fails to maintain the SOC within the desired range, rendering it ineffective as an EMS. Conversely, ORL- 500 demonstrates superior performance, with lower reward and cost, and SOC maintained within a reasonable range, as illustrated in Figure 6(j). Overall, after learning from 5 million data points (equivalent to over 60 million kilometers), the ORL agent can learn a general EMS adaptable to unseen and even corner- case conditions. + +<|ref|>text<|/ref|><|det|>[[68, 473, 928, 523]]<|/det|> +This result highlights two advantages of the ORL agent: First, its performance surpasses that of the original policy; second, it demonstrates that with increased data availability, learning performance improves. The ORL model can learn a general EMS from a large amount of EV data. + +<|ref|>sub_title<|/ref|><|det|>[[68, 544, 180, 561]]<|/det|> +## 3. Methods + +<|ref|>sub_title<|/ref|><|det|>[[68, 568, 258, 585]]<|/det|> +### 3.1. EV Environment + +<|ref|>text<|/ref|><|det|>[[68, 585, 929, 730]]<|/det|> +In this work, we evaluate EMS performance using a fuel cell hybrid electric vehicle (FCEV) within a simulation environment. Figure. 1(e) illustrates the schematic diagram of the FCEV and its components, which include a fuel cell (FC) system, a hydrogen storage tank, an electric motor (EM), and a Lithium- ion battery (LIB) pack. The FC stack serves as the primary power source to meet the energy requirements of the vehicle. The diagram also depicts the energy flow from the hydrogen storage tank to the motor. The FC system converts hydrogen energy into electricity, which then collaborates with the LIB through the high- voltage bus. This electric energy is subsequently utilized to power a single electric motor, connected to the driving wheel via a fixed- ratio final gear. According to the vehicle driving resistance equation, the driving power demand is determined by the speed and acceleration of the FCEV, and can be expressed as follows: + +<|ref|>equation<|/ref|><|det|>[[113, 732, 925, 775]]<|/det|> +\[P_{d} = \frac{1}{3.6\cdot\eta_{\mathrm{me}}}\left(m g C_{f}\upsilon_{t} + m\delta \upsilon_{t}a_{t} + m g s i n(i) + \frac{C_{D}A}{21.15}\upsilon_{t}^{3}\right) \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[68, 779, 928, 860]]<|/det|> +where \(\eta_{\mathrm{me}}\) is the efficiency of the vehicle drivetrain; \(m\) is the vehicle mass; \(g\) is the gravitational constant; \(C_{f}\) is the rolling resistance coefficient; \(\delta\) is the rotational mass conversion coefficient; \(C_{D}\) is the air resistance coefficient, \(A\) is the frontal area; \(\upsilon_{t}\) is the longitudinal velocity at the time step \(t\) , \(a_{t}\) is the acceleration, \(i\) is the angle of slope of the road. The power demand is provided by the FC system and the battery pack, the power balance of the FCEV can be formulated as: + +<|ref|>equation<|/ref|><|det|>[[113, 868, 925, 890]]<|/det|> +\[P_{d} = \left(P_{f c}\cdot \eta_{D C / D C} + P_{b a t}\right)\cdot \eta_{D C / A C}\cdot \eta_{E M} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[68, 896, 928, 931]]<|/det|> +where \(P_{f c}\) and \(P_{b a t}\) respectively denote the output power of the fuel cell system and the LIB pack; \(\eta_{D C / D C},\eta_{D C / A C}\) and \(\eta_{\mathrm{EM}}\) represent the efficiency of the DC/DC converter, DC/AC inverter, and the electric motor, respectively. The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 71, 614, 90]]<|/det|> +battery pack is modeled using an equivalent circuit model in Equation (3): + +<|ref|>equation<|/ref|><|det|>[[111, 98, 926, 188]]<|/det|> +\[\left\{ \begin{array}{l l}{P_{b a t}(t) = V_{o c}(t) - R_{0}\cdot I^{2}(t)}\\ {I(t) = \frac{V_{o c}(t) - \sqrt{V_{o c}^{2}(t) - 4\cdot R_{0}\cdot P_{b a t}(t)}}{2R_{0}}}\\ {S O C(t) = \frac{Q_{0} - \int_{0}^{t}I(t)dt}{Q}} \end{array} \right. \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[67, 199, 928, 250]]<|/det|> +where \(S O C\) is the battery state of charge, \(V_{o c}\) is the open- circuit voltage, \(I_{t}\) is the current at time \(t\) , \(R_{0}\) is the internal resistance, \(P_{b a t}\) is the output power in the charge- discharge cycles, \(Q_{0}\) is the initial battery capacity, \(Q\) is the nominal battery capacity. + +<|ref|>text<|/ref|><|det|>[[68, 247, 928, 298]]<|/det|> +According to the battery aging model in [12]. The degradation rate of battery operation \(\gamma_{\mathrm{bat}}\) is affected by the charge/discharge rate ( \(C_{rate}\) ). The relationship between the battery aging correction factor and \(C_{rate}\) can be fitted from experiment data: + +<|ref|>equation<|/ref|><|det|>[[111, 306, 926, 333]]<|/det|> +\[\gamma_{\mathrm{bat}} = \mu_{1}\left|C_{\mathrm{rate}}\right|^{2} + \mu_{2}\left|C_{\mathrm{rate}}\right| + \mu_{3} \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[68, 340, 928, 376]]<|/det|> +where \(\mu_{1}, \mu_{2}, \mu_{3}\) are the curve- fitting coefficients. LIB can operate for about 5000 full cycles in a lifetime. The battery degradation cost \(C_{bat,degr}\) can be calculated by: + +<|ref|>equation<|/ref|><|det|>[[111, 385, 926, 425]]<|/det|> +\[C_{bat,degr} = \int_{0}^{t}\gamma_{bat}^{-1}P_{bat}dt\cdot PR_{bat} / (5000\cdot 3600) \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[93, 433, 586, 451]]<|/det|> +where \(PR_{\mathrm{bat}}\) is the battery price per kWh that is 1500RMB/kWh. + +<|ref|>text<|/ref|><|det|>[[68, 450, 928, 484]]<|/det|> +The fuel cell system efficiency under different power conditions is obtained from experiment data. Thus, the mass flow rate of the hydrogen consumption can be calculated by: + +<|ref|>equation<|/ref|><|det|>[[111, 492, 926, 525]]<|/det|> +\[\dot{m}_{H_{2}} = P_{f c s} / \left(\eta_{f c s}\cdot \mathrm{LHV}_{H_{2}}\right) \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[68, 532, 928, 567]]<|/det|> +where \(\eta_{f c s}\) is the fuel cell system efficiency; \(P_{f c s}\) is the fuel cell system output power; \(\mathrm{LHV}_{H_{2}}\) is the hydrogen low calorific value. The fuel cell hydrogen cost can be calculated by: + +<|ref|>equation<|/ref|><|det|>[[111, 575, 926, 614]]<|/det|> +\[C_{f c s,H_{2}} = PR_{H_{2}}\cdot \int_{0}^{t}\dot{m}_{H_{2}}dt \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[68, 622, 528, 640]]<|/det|> +where \(PR_{H_{2}}\) is the hydrogen price per kilogram(60RMB/kg). + +<|ref|>text<|/ref|><|det|>[[68, 639, 928, 705]]<|/det|> +The fuel cell degrades rapidly under four typical conditions: load changing, start/stop, low power, and high power conditions. We assume that the fuel cell system continues operating until the vehicle power system is shut down, thus the start/stop condition is not considered in the EMS. The fuel cell voltage degradation rate, denoted as \(\gamma_{\mathrm{fcs}}\) , can be calculated by: + +<|ref|>equation<|/ref|><|det|>[[111, 715, 926, 736]]<|/det|> +\[\gamma_{\mathrm{fcs}} = \kappa_{\mathrm{low}}\cdot T_{\mathrm{low}} + \kappa_{\mathrm{high}}\cdot T_{\mathrm{high}} + \kappa_{\mathrm{cha}}\cdot \Delta P_{\mathrm{fcs}} \quad (8)\] + +<|ref|>text<|/ref|><|det|>[[68, 745, 928, 812]]<|/det|> +where \(\kappa_{\mathrm{low}}\) is the degradation rate under low power condition; \(T_{\mathrm{low}}\) is the low power condition duration; \(\kappa_{\mathrm{high}}\) is the degradation rate under high power condition; \(T_{\mathrm{high}}\) is the high power condition duration; \(\kappa_{cha}\) is the degradation rate under load changing condition; \(\Delta P_{f c s}\) is the fuel cell power slope. Fuel cell is considered to reach the end of its life when \(10\%\) of voltage at rated power has been lost. The fuel cell operation degradation cost can be calculated by: + +<|ref|>equation<|/ref|><|det|>[[111, 821, 926, 846]]<|/det|> +\[C_{f c s,degr} = k_{f c s}\cdot \gamma_{f c s}\cdot P_{f c s,\mathrm{rate}}\cdot PR_{f c s} / \left(V_{f c s,\mathrm{end}}\cdot 1000\right) \quad (9)\] + +<|ref|>text<|/ref|><|det|>[[68, 853, 928, 889]]<|/det|> +where \(k_{f c s}\) is the fuel cell life correction factor; \(V_{f c s,\mathrm{end}}\) is fuel cell voltage drop at the end- of- life; \(P_{f c}\) , rate is the rated power of the fuel cell; \(PR_{f c s}\) is the fuel cell price per kilowatt(4000RMB/kW). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[68, 70, 270, 88]]<|/det|> +### 3.2. Problem modeling + +<|ref|>text<|/ref|><|det|>[[68, 87, 928, 137]]<|/det|> +In this work, the EMS of electric vehicles is modeled as a long- term sequential decision process objective to minimize total energy cost while maintaining battery SOC within reasonable limits. The optimization objective can be formulated as: + +<|ref|>equation<|/ref|><|det|>[[112, 142, 926, 191]]<|/det|> +\[J_{EMS} = \min \sum_{t = 0}^{T} cost(t) + \alpha f_{s}(SOC(t)) \quad (10)\] + +<|ref|>text<|/ref|><|det|>[[68, 198, 928, 248]]<|/det|> +where \(T\) is the total length of the driving cycle, \(cost(t)\) is the energy cost including hydrogen consumption, the cost of battery, and fuel cell degradation, \(f_{s}(SOC(t))\) is the SOC maintaining function, \(\alpha\) the tradeoff between energy cost and SOC. + +<|ref|>text<|/ref|><|det|>[[68, 246, 928, 362]]<|/det|> +To tackle the sequential decision, the energy management problem is formulated as a Markov Decision Process (MDP), which is a framework for learning the optimal EMS from interaction to minimize total energy cost. The MDP defined by a tuple \((S,A,P,R,\rho_{0},\gamma)\) , where \(S\) denotes the state space, \(A\) denotes the action space, \(P\left(s^{\prime}\mid s,a\right)\) denotes the transition distribution, \(\rho_{0}(s)\) denotes the initial state distribution, \(R(s,a)\) denotes the reward function, and \(\gamma \in (0,1)\) denotes the discount factor. The goal is to find a policy \(\pi (a\mid s)\) that maximizes the expected cumulative discounted rewards \(J(\pi) = E_{\pi ,P,\rho_{0}}\left[\sum_{t = 0}^{\infty}\gamma^{t}R\left(s_{t},a_{t}\right)\right]\) . To use this formulation for FCEV. The state space at time point \(t\) for the FCEV is defined as: + +<|ref|>equation<|/ref|><|det|>[[112, 365, 926, 397]]<|/det|> +\[S = \left\{v_{t},acc_{t},SOC_{t},P_{fcs}^{t}\right\} \quad (11)\] + +<|ref|>text<|/ref|><|det|>[[68, 403, 928, 455]]<|/det|> +where \(v_{t},a_{t},P_{fcs},SOC_{t}\) are the vehicle speed, acceleration, fuel cell power, and battery SOC. The action represents the control variable, defined as allocating power to the energy sources of the vehicle. In the context of the FCEV, the action is defined as the FC power slope, denoted as \(\Delta P_{fcs}\) . The continuous action can be described as follow: + +<|ref|>equation<|/ref|><|det|>[[112, 460, 926, 496]]<|/det|> +\[A = \left\{\Delta P_{fcs} = P_{fcs}^{t} - P_{fcs}^{t - 1},\Delta P_{fcs}\in [-10\mathrm{kW},10\mathrm{kW}]\right\} \quad (12)\] + +<|ref|>text<|/ref|><|det|>[[68, 501, 928, 585]]<|/det|> +The reward function \(R\) describes the reward \(R(s_{t + 1};s_{t};a_{t})\) associated with transitioning from state \(s_{t}\) to state \(s_{t} + 1\) using action \(a_{t}\) . The design of the reward function is pivotal in the learning process. For the FCEV, multiple objectives are taken into account, including hydrogen consumption, FC degradation, and battery- related costs such as electricity consumption and degradation. Additionally, it is essential to maintain the battery SOC. Therefore, the reward function is defined as the sum of energy costs while ensuring that the battery charge- sustaining constraints are maintained. + +<|ref|>equation<|/ref|><|det|>[[112, 590, 926, 623]]<|/det|> +\[R = -\left\{C_{fcs,H_2} + C_{fcs,degr} + C_{bat,eh_2} + C_{bat,degr} + \alpha \left[SOC_{ref} - SOC(t)\right]^2\right\} \quad (13)\] + +<|ref|>text<|/ref|><|det|>[[68, 630, 928, 666]]<|/det|> +The battery electricity consumption \(C_{bat,eh_2}\) is calculated according to the battery charge/discharge efficiency and converted into price cost: + +<|ref|>equation<|/ref|><|det|>[[112, 671, 926, 714]]<|/det|> +\[C_{bat,eh_2} = \int_0^t \left[P_{bat} / \left(\eta_{d / c}\eta_{DC / DC},\mathrm{LHV}_{H_2}\right)\right]dt\cdot PR_{H_2} \quad (14)\] + +<|ref|>text<|/ref|><|det|>[[68, 720, 457, 739]]<|/det|> +where \(\eta_{d / c}\) is the battery discharge/charge efficiency. + +<|ref|>sub_title<|/ref|><|det|>[[68, 750, 293, 767]]<|/det|> +### 3.3. Offline RL algorithm + +<|ref|>text<|/ref|><|det|>[[68, 767, 928, 833]]<|/det|> +We employ the offline reinforcement learning (ORL) paradigm to address the MDP problem described above. The goal is to learn a policy \(\pi \sim a_{t}(\pi :S\to A)\) that maximizes the expectation of the sum of discounted rewards \(J(\pi)\) Each policy \(\pi\) has a corresponding state- action value function (also known as Q function), which denotes the expected return \(Q(s,a)\) when following the policy \(\pi\) after taking an action \(a\) in state \(s\) + +<|ref|>equation<|/ref|><|det|>[[112, 838, 926, 888]]<|/det|> +\[Q(s,a) = \mathbb{E}\left[\sum_{t = t}^{\infty}\gamma^{i - t}R_{i}\mid s_{t} = s,a_{t} = a\right] \quad (15)\] + +<|ref|>text<|/ref|><|det|>[[68, 895, 928, 930]]<|/det|> +Here, we approximate the Q function \(Q(s,a)\) using deep neural networks by minimizing the squared Bellman error. The ORL algorithm utilized in our proposal is an extension of the twin delayed deep deterministic policy gradient + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[68, 70, 928, 137]]<|/det|> +algorithm (TD3) [26]. TD3 is a state- of- the- art online DRL algorithm implemented under the actor- critic framework that learns a deterministic policy. For the actor part, it learns a deterministic target policy by mapping states to a specific action. The update of the Actor network of TD3 algorithm aims to maximize the estimation of the current policy by the critic network. + +<|ref|>text<|/ref|><|det|>[[68, 135, 929, 283]]<|/det|> +When offline logged datasets are available, it is reasonable to push the policy towards favoring actions contained in the dataset D. Hence, our proposed ORL algorithm augments the standard policy update step in TD3 with Behavior Cloning (BC) regularization to reinforce the policy's focus on the behaviors observed in the dataset D [27]. This regularization term encourages the policy to mimic the demonstrated behaviors more closely, leading to improved generalization and performance. Furthermore, in pursuit of improving the policy, Discriminator Blend (DB) regularization [28] is employed to enhance the flexibility of the policy constraint. This is achieved by integrating a discriminator using Generative Adversarial Networks (GANs) [29]. By incorporating a discriminator, the policy is enabled to explore actions that may not be included in the dataset \(D\) , leading to a more diverse and adaptive policy. This involves utilizing a neural network as an approximator of the policy function \(\pi\) : + +<|ref|>equation<|/ref|><|det|>[[112, 287, 926, 320]]<|/det|> +\[\pi = \arg \max_{\pi}\mathbb{E}_{(s,a)\sim \mathbf{D}}\left[\lambda Q(s,\pi (s)) - (1 - \beta)(\pi (s) - a)^2 +\beta \log (D(s,\pi (s)))\right] \quad (16)\] + +<|ref|>text<|/ref|><|det|>[[68, 326, 928, 425]]<|/det|> +\(\mathbb{E}()\) is the mathematical expectation. The parameter \(\beta\) (range of 0 to 1) adjusts the balance between BC and DB constraints. The DB is trained to assess whether a given action \(a\) and state \(s\) pair belongs to the dataset \(a_{D}\) or is generated by the policy \(\pi_{\theta}\) , which acts as the generator \(G\) in GANs. This enables BD to effectively regulate the policy learning process by encouraging the policy to explore actions beyond the dataset while ensuring that these actions are plausible according to the discriminator's perception. The \(\lambda\) is a normalization term based on the average absolute value of \(Q\) to control the balance between RL and imitation, defined as: + +<|ref|>equation<|/ref|><|det|>[[112, 429, 926, 475]]<|/det|> +\[\lambda = \frac{\alpha}{\frac{1}{N}\sum_{(s_i,a_i)}\left|Q(s_i,a_i)\right|} \quad (17)\] + +<|ref|>text<|/ref|><|det|>[[68, 481, 928, 549]]<|/det|> +The parameter \(\alpha\) is used to control the strength of the regularize where the larger \(\alpha\) will make the algorithm approach more RL, and \(N\) represents the number of transitions in the dataset. To normalize the characteristics of each state in the provided dataset. Let \(s_i\) be the \(i\) th feature of the state \(s\) in the dataset, let \(\mu_i,\sigma_i\) be the mean and standard deviation ( \(\eta\) is a constant value to avoid division by zero.): + +<|ref|>equation<|/ref|><|det|>[[112, 553, 926, 589]]<|/det|> +\[s_i = \frac{s_i - \mu_i}{\sigma_i + \eta} \quad (18)\] + +<|ref|>text<|/ref|><|det|>[[68, 595, 928, 689]]<|/det|> +The critic part estimates the Q- value of a state- action pair. TD3 employs two critic networks to mitigate overestimation bias, with each critic having a corresponding target network. Each critic network \(Q_{1}\left(s,a\mid \theta^{Q_{1}}\right)\) and \(Q_{2}\left(s,a\mid \theta^{Q_{2}}\right)\) corresponds to a target network \(Q_{1}^{\prime}\left(s,a\mid \theta^{Q_{1}^{\prime}}\right)\) and \(Q_{2}^{\prime}\left(s,a\mid \theta^{Q_{2}^{\prime}}\right)\) respectively. The minimum Q- value among the two critics is used as the target Q- value during training. The critic network is updated by minimizing the loss function: + +<|ref|>equation<|/ref|><|det|>[[112, 707, 926, 741]]<|/det|> +\[L\left(\theta^{Q_{i}}\right) = \mathbb{E}\left[\left(y_{t} - Q_{i}\left(s_{t},a_{t}\mid \theta^{Q_{i}}\right)\mid a_{t} = \mu \left(s_{t}\mid \theta^{\mu}\right)\right)^{2}\right] \quad (19)\] + +<|ref|>text<|/ref|><|det|>[[68, 747, 928, 782]]<|/det|> +where \(\theta^{Q}\) denotes the weights of the critic network. The target Q- value \(y\) is evaluated by taking the minimum of the estimates from the two Q- functions as follows: + +<|ref|>equation<|/ref|><|det|>[[112, 788, 926, 822]]<|/det|> +\[y_{t} = r\left(s_{t},a_{t}\right) + \gamma \min_{i = 1,2}Q_{i}^{\prime}\left(s_{t + 1},a_{t + 1}\mid \theta^{Q_{i}^{\prime}}\right) \quad (20)\] + +<|ref|>text<|/ref|><|det|>[[68, 832, 928, 867]]<|/det|> +where \(a_{t + 1}\sim \pi_{\phi^{\prime}}\left(s_{t + 1}\right) + \epsilon ,\quad \epsilon \sim \mathrm{clip}(\mathcal{N}(0,\tilde{\sigma}), - c,c)\) is the exploration noise to smooth the value estimates and improve robustness of the learned \(Q\) functions, \(r\) is the instantaneous one- step reward, \(\gamma\) is the discounting factor. + +<|ref|>sub_title<|/ref|><|det|>[[68, 880, 263, 897]]<|/det|> +### 3.4. Baseline Methods + +<|ref|>text<|/ref|><|det|>[[68, 897, 928, 930]]<|/det|> +We use a series of baseline EMS methods for comparatively evaluating the ORL method. The inputs and outputs of all baselines are the same as those of the proposed method. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[68, 71, 928, 170]]<|/det|> +Dynamic Programming (DP) [30]: In optimization control methods, the EMS problem is formulated as a nonlinearly constrained optimization problem, aiming to minimize the objective function presented in Equation (10). DP is an optimization control method that operates by seeking the shortest path backward in time. Its objective is to derive the minimum cost function for each grid at every stage in reverse chronological order. In our study, DP is used as the benchmark EMS policy, representing the global optimum and providing upper limits for comparison. It's important to recognize that DP requires future information as input to achieve the optimization objective. + +<|ref|>text<|/ref|><|det|>[[68, 168, 928, 250]]<|/det|> +Behavior Cloning (BC) [31]: BC, as a fundamental imitation learning approach, seeks to emulate the EMS policy by directly learning from the provided dataset, which is assumed to be generated by an expert policy or near- expert policy. It employs supervised learning techniques to train a model to map states to actions. Both BC and ORL involve learning from data for EMS applications. In this context, we establish BC as the benchmark and aim to showcase the superior performance of ORL. + +<|ref|>text<|/ref|><|det|>[[68, 248, 928, 346]]<|/det|> +Proximal Policy Optimization (PPO) [32]: PPO is a state- of- the- art online DRL algorithm, which has been extensively applied in various applications requiring sophisticated decision- making in dynamic environments. PPO offers a robust and efficient approach to training agent by leveraging on- policy learning, effective use of data through mini- batch updates, stability through policy clipping, and adaptive learning rates. Leveraging the strengths of PPO, we utilize it to generate the dataset necessary for ORL, with its policy serving as an expert (near- optimal) strategy for comparison purposes. We provide it to explore the superiority of ORL compared to the online DRL. + +<|ref|>text<|/ref|><|det|>[[68, 344, 928, 443]]<|/det|> +Twin Delayed Deep Deterministic Policy Gradient (TD3) [26]: TD3 is an advanced online DRL algorithm, stemming from the Actor- Critic framework. It has garnered significant attention due to its effectiveness in overcoming challenges associated with continuous action spaces and high- dimensional state spaces. TD3 employs twin critic networks to estimate the value of actions more accurately. By utilizing two critic networks, TD3 mitigates overestimation bias and enhances the robustness of value function estimation. We also provide it to explore the superiority of ORL compared to the online DRL. + +<|ref|>sub_title<|/ref|><|det|>[[68, 466, 197, 484]]<|/det|> +## 4. Discussion + +<|ref|>text<|/ref|><|det|>[[68, 488, 928, 586]]<|/det|> +In conclusion, we have presented a novel data- driven EMS for hybrid energy systems in EVs. Leveraging an innovative offline reinforcement learning agent, our approach learns directly from driving data. Experimental results demonstrate that the ORL agent not only learns optimal EMS strategies from expert data but also exhibits the ability to learn superior EMS from datasets containing a mixture of expert and noisy data, and even achieves near- optimal strategies from entirely noisy datasets. Moreover, our approach demonstrates that with increased data availability, performance improves as the agent is trained with more data. + +<|ref|>text<|/ref|><|det|>[[68, 584, 928, 794]]<|/det|> +This approach offers three notable benefits. Firstly, it is sufficiently simple, as it solely relies on collected data for automatic learning by the agent, unlike the traditional EMS development process, which often requires extensive expert knowledge and repeated measurements. Furthermore, the data used in our approach are non- expert data readily available from real vehicles. Secondly, our method ensures stable performance by integrating seamlessly with existing EMS without altering the original EMS performance lower bound. Our approach continuously improves upon the baseline EMS through data- driven enhancements leveraging the strengths of both technologies. For example, to address the performance shortcomings in rule- based EMS, ORL enables incremental learning, allowing for the continual enhancement of EMS performance with historical data. Similarly, ORL addresses the sim- to- real gap problem in simulation- based methods by enhancing pretrained EMS models, thereby ensuring their effectiveness in real- world deployment scenarios. Lastly, our approach exhibits versatility, as with sufficient data, it can learn a generalized EMS applicable to various EVs and operating conditions. This aligns with the current trend of large- scale language models and similar approaches in artificial intelligence, where a single large model with large- scale data can be trained to perform well across diverse tasks and domains. + +<|ref|>text<|/ref|><|det|>[[68, 793, 928, 875]]<|/det|> +Overall, we believe that ORL could serve as a foundational framework for data- driven EMS, with potential applications extending beyond EVs to grid EMS, industrial energy management systems, and other vehicle control systems. However, a limitation of this work is that the ORL agent may require more data to further enhance its performance. Addressing this limitation could involve exploring methods to efficiently gather and utilize additional data for agent training, potentially improving its effectiveness in real- world applications. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[70, 75, 250, 92]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[92, 97, 900, 115]]<|/det|> +AcknowledgementsThis work was supported in part by the National Natural Science Foundation of China (Grant No. 52172377). + +<|ref|>sub_title<|/ref|><|det|>[[70, 138, 276, 155]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[70, 161, 928, 210]]<|/det|> +Y.W. designed the study and methodology; Y.W., J.Wu. and H.H. collected and analyzed data; Y.W. generated the figures; Y.W., J.Wu. and W.Z. wrote the manuscript; H.H., W.Z. and F.S. reviewed and edited the manuscript. 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Klimov, Proximal policy optimization algorithms, arXiv preprint arXiv:1707.06347 (2017). + +<--- Page Split ---> diff --git a/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7.mmd b/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7.mmd new file mode 100644 index 0000000000000000000000000000000000000000..e94e67eca091645031e21d1f8c2308346953fdfb --- /dev/null +++ b/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7.mmd @@ -0,0 +1,478 @@ + +# Mapping Decidualization Resistance in Former Severe Preeclampsia Patients at Multi-Omic Levels + +Tamara Garrido- Gómez tgarrideo@fundacioncarlossimon.com + +Carlos Simon Foundation - INCLIVA Health Research Institute Irene Muñoz- Blat Carlos Simon Foundation - INCLIVA Health Research Institute Raul Pérez- Moraga Carlos Simon Foundation - INCLIVA Health Research Institute https://orcid.org/0000- 0002- 9611- 9123 + +Nerea Castillo Marco Carlos Simon Foundation - INCLIVA Health Research Institute + +Nerea Castillo Marco Carlos Simon Foundation - INCLIVA Health Research Institute + +Ana Ochando Carlos Simon Foundation - INCLIVA Health Research Institute + +Sheila Ortega Carlos Simon Foundation - INCLIVA Health Research Institute + +Marcos Parras Carlos Simon Foundation - INCLIVA Health Research Institute + +Rogelio Monfort Hospital Universitario y Politecnico La Fe + +Elena Satorres- Perez Hospital Universitario y Politecnico La Fe + +Blanca Novillo Hospital Universitario y Politecnico La Fe + +Alfredo Perales Hospital Universitario La Fe, Valencia Spain. + +Matthew Gormley University California San Francisco + +Beatriz Roson Carlos Simon Foundation- INCLIVA Health Research Institute, 46012 Valencia, Spain https://orcid.org/0000- 0002- 9851- 2025 + +Susan Fisher University California San Francisco + +<--- Page Split ---> + +# Carlos Simon + +Carlos SimonCarlos Simon Foundation- INCLIVA Health Research Institute https://orcid.org/0000- 0003- 0902- 9531 + +## Article + +# Keywords: + +Posted Date: May 6th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4331532/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +## Additional Declarations: + +There is NO Competing Interest. + +Supplementary Figure 1 is not available with this version. + +Version of Record: A version of this preprint was published at Nature Medicine on January 7th, 2025. See the published version at https://doi.org/10.1038/s41591- 024- 03407- 7. + +<--- Page Split ---> + +# Mapping Decidualization Resistance in Former Severe Preeclampsia Patients at Multi-Omic Levels + +1 Mapping Decidualization Resistance in Former Severe Preeclampsia 2 Patients at Multi-Omic Levels 3 4 Irene Muñoz- Blat1,2,8, Raul Perez- Moraga1,3,8, Nerea Castillo- Marco1,2,8, Teresa Cordero1,2, Ana 5 Ochando1, Sheila Ortega1, Marcos Parras1,2, Rogelio Monfort4, Elena Satorres- Perez4, Blanca Novillo4, 6 Alfredo Perales4, Matthew Gormley5, Beatriz Roson1,2, Susan Fisher5, Carlos Simón1,6,7, Tamara 7 Garrido- Gómez1,2 8 9 1Carlos Simon Foundation, Valencia, Spain 10 2 INCLIVA Health Research Institute, Valencia, Spain 11 3 R&D Department, Igenomix, Valencia, Spain 12 4 Hospital Universitario y Politecnico La Fe, Valencia, Spain 13 5 University California San Francisco, San Francisco, CA, USA 14 6Department of Pediatrics, Obstetrics and Gynecology, University of Valencia, Valencia, Spain 15 7Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard Medical 16 School, Boston, MA, USA 17 8These authors contributed equally 18 # Correspondence; carlos.simon@uv.es; tgarrido@fundacioncarlossimon.com + +<--- Page Split ---> + +Endometrial decidualization resistance (DR) is implicated in various gynaecological and obstetric conditions. Employing a multi- omic strategy, we unraveled the cellular and molecular characteristics of DR in patients that have suffered severe preeclampsia (sPE). Morphological analysis unveiled significant glandular anatomical abnormalities, confirmed histologically. Single- cell RNA sequencing (scRNA- seq) of endometrial samples from sPE cases (n=11) and controls (n=12) revealed sPE- associated shifts in cell composition, manifesting as a stromal mosaic state characterized by proliferative stromal cells (MMP11, SFRP1+) alongside IGFBP1+ decidualized cells, with concurrent epithelial mosaicism and a dearth of epithelial- stromal transition associated with decidualization. Cell- cell communication network mapping underscored aberrant crosstalk among specific cell types, implicating crucial pathways such as endoglin and WNT. Spatial transcriptomics in a replication cohort validated DR- associated features. Laser capture microdissection/mass spectrometry in a second replication cohort corroborated several scRNA- seq findings, notably the absence of stromal to epithelial transition at a pathway level, indicating disrupted response to steroid hormones, particularly estrogens. These insights shed light on potential molecular mechanisms underpinning DR pathogenesis in the context of sPE. + +<--- Page Split ---> + +## Introduction + +Pregnancy health is shaped during the periconceptional period due to the interplay between the implanting embryo and the maternal endometrium (1). Decidualization entails the functional and morphological changes that occur within the endometrium transforming the maternal uterine lining to accommodate the invasive trophoblast (2- 4). The endometrial stromal cells (ESCs) transformation is hormonally regulated, driven by increasing progesterone levels and local cAMP production (5, 6), which stimulate the synthesis of a complex network of intracellular and secreted proteins (7). It starts in the early secretory phase of the menstrual cycle, independent of the presence of a conceptus, in areas adjacent to the uterine spiral arteries thereafter spreading throughout the endometrium (8). + +Decidualization resistance (DR) refers to the inability of the endometrium to undergo these specific changes and has been documented in reproductive disorders including endometriosis (9- 11), miscarriage (12), recurrent pregnancy loss (13, 14) and the great obstetrical syndromes. The latter include preeclampsia (PE) (15- 17), intrauterine growth restriction (IUGR) (18), and/or placenta accrete spectrum disorder (19). + +Preeclampsia is a major- obstetric complication affecting \(8\%\) of first- time pregnancies. It is characterized by the new onset of hypertension, proteinuria, and other signs such as vascular endothelial damage (20). The life- threatening condition known as severe PE (sPE) is diagnosed based on higher blood pressure criteria (systolic \(\geq 160 \mathrm{mm} \mathrm{Hg}\) or diastolic of \(\leq 100 \mathrm{mm} \mathrm{Hg}\) ), symptoms of central nervous system dysfunction, hepatic abnormalities, thrombocytopenia, renal abnormalities, and/or pulmonary edema (21). sPE is a placental insufficiency syndrome mediated by early shallow cytotrophoblast (CTB) invasion of uterine decidua and spiral arterioles, leading to incomplete endovascular invasion and altered uteroplacental perfusion (20, 22, 23). We provided evidence of a decidualization defect in women with sPE, detected at the time of delivery and persisting years after the affected pregnancy (15). Endometrial bulk RNA- seq results from affected women post- sPE revealed altered ovarian hormone receptor signaling pathways (16). + +Here, we initially observed gross and microscopic endometrial glandular defects in late secretory endometrium from former sPE patients. Then, we assemble a spatially resolved single- cell multionic characterization of the DR using sPE as a clinical model. Our objective is to gain insights into this pathological condition, combining different omics techniques including single- cell RNA sequencing (scRNA seq), spatial transcriptomics and laser capture microdissection coupled mass spectrometry [LCM- MS]) with different samples sets. Thus, we provide the first detailed description of the whole status of cells, cell communications, perturbations in specific cell communication pathways at scRNA- seq, spatial and proteomic resolution. + +<--- Page Split ---> + +## Results + +Decidualization resistance (DR) in former sPE patients: associated changes at gross, microscopic and single cell levels. + +Endometrial tissue was collected during the late secretory phase of the cycle from women with a previous sPE pregnancy (Fig. 1a- d) and control individuals who had normal obstetric outcomes (Fig. 1e- h). Initial examination of the samples (n=8/group) (Supplementary Table 1a) consistently showed distinct morphological features of the endometrium from cases that were not observed in control specimens. Specifically, the openings of the glands were dilated in the cases (Fig. 1a,b) vs. the control group (Fig. 1e,f). Histological analysis of H&E stained, longitudinal and sagittal tissue sections confirmed this finding and showed that the dilation involved the entire gland (compare Fig. 1c,d vs. Fig. 1g,h). We took these findings as preliminary evidence of DR endometrial defects, during the late secretory phase of the cycle, among the former sPE patients. + +Thus, we used a scRNA- seq approach to uncover the molecular correlates of the observed morphological differences. We profiled biopsies of late secretory phase endometrium from former sPE patients (n=11) as compared to equivalent samples from control individuals who had normal obstetric outcomes (n=12) (Supplementary Table 1b). The scRNA- seq workflow is outlined in Fig. 1i. Altogether we profiled 65,381 high quality cells (see Methods) and integrated the transcriptomes in a uniform manifold approximation and projection (UMAP). Of these 28,154 were from the sPE group (Fig. 1j) and 37,227 were from the control group (Fig. 1k). + +The cells were clustered using previously published scRNA- seq datasets and markers (2, 3, 24). Nine major cell types were identified: epithelium, stroma, cycling stroma, perivascular, endothelium, NK cells, T cells, macrophages, and B cells. The expression of canonical markers of each cell type is shown as a dotplot in Extended Data Fig. 1a. Global integration of the merged cases and control datasets enabled an initial comparison of the cellular composition of the endometrial biopsies from the two groups (Fig. 1l). Differences in the clustering of stromal and epithelial cells was immediately apparent, the samples from the former sPE patients lacked subpopulations that were more prominent in the controls, and vice versa. This finding prompted us to perform a detailed comparison of these cell types from the two sources. + +## Endometrial cell type-specific differentiation defects in former sPE patients: Stroma. + +Integration of the stromal and perivascular cell transcriptomes identified several subpopulations, including three types of decidual cells (Fig. 2a). Decidualized stroma 1 cells expressed genes related to ribosome activity (RPS29, RPL37A) and progesterone- associated endometrial protein (PAEP). + +<--- Page Split ---> + +Decidualized stroma 2 cells co- expressed markers of the pre- decidual and decidual states, \(EGR1\) and \(CXCL2\) (25, 26), respectively. Decidualized stroma 3 cells were characterized by well- known decidualization markers such as \(IGFBP1\) and \(TIMP3\) (27), \(ATF3\) (28), and transcription factors that are associated with late decidualization, including \(CSRNP1\) (3). + +We also identified a stroma EMT subpopulation (stromal transition), this cluster expressed \(PDGFRA\) that is involved in the transition that occurs during endometrial regeneration after menstruation and decidualization at the maternal- fetal interface (27). Another cluster is the proliferative stroma expressing markers of the proliferative phase of the menstrual cycle: \(SFRP4\) (29), \(MMP11\) (30, 31), \(DIO2\) (32), \(SFRP1\) (33) and \(PGRMC1\) (34). We also captured two distinct perivascular cell subpopulations that maintain a close relationship with stromal cells during decidualization. The perivascular 1 (STEAP4+) and perivascular 2 (MYH11+) subpopulations clustered near cells that regulate stromal angiogenesis, that expressed \(CCBE1\) , a regulator of \(VEGFC\) (35, 36). The complete set of stromal cell subtypes and their differential gene expression is shown in Extended data Fig. 1b. + +Mapping the identity of the cells enabled identification of subpopulations that were differentially abundant in sPE or control samples (Fig. 2b). Specifically, decidualized stroma 1 and 2 as well as the stromal transition subpopulation were enriched in control samples, whereas decidualized stroma 3 and the proliferative stroma were increased in endometrium from sPE samples. This mosaic state—proliferative stromal cells (MMP11, \(SFRP1+\) ) coexisting with \(IGFBP1+\) decidualized cells—was the hallmark of DR in sPE. We graphed the data as neighborhoods in which dots represent neighborhood sizes and the weight of the connecting lines depicts the number of cells shared among neighborhoods (Fig. 2c). The results confirmed a statistically significant increase in the number of cells that defined this mosaic state as well as the decrease of decidualized stromal 1, 2 and stromal transitioning cells in sPE samples. The neighborhood data were also visualized as a Beeswarm plot (Fig. 2d). + +Differential gene expression analysis revealed a significant number of dysregulated genes (DEGs) associated with DR in all the affected stromal subpopulations (Log2FC>0.5, and p. adjusted<0.05) (Extended Data Fig. 2a). In cases, decidualized stroma 1 and 2 cells upregulated \(TIMP3\) , and decidual stroma 3 cells upregulated \(IGFBP1\) and \(IGFBP6\) . Also in sPE cases, the proliferative stroma subpopulation was characterized by upregulation of \(SFRP4\) present in the proliferative phase (37) and \(FOS\) that is present in the endometrium of endometriosis patients (38). Additionally, in sPE cases endothelial cells showed an upregulation of \(C2CD4B\) (39), a marker of acute inflammatory response (Extended Data Fig. 2b). Consistently, immune cells from sPE, including macrophages, natural killer and B cells showed an aberrant transcriptomic profile indicative of inflammatory dysregulation, highlighted by higher expression of \(IL1B\) , \(CCL5\) (40), \(SCGB1D2\) (41), respectively (Extended Data Fig. 2c). These results point to an imbalance in stromal populations in sPE with a predominant aberrant stage of proliferating stromal cells coexisting with decidualised cells in a proinflammatory niche. + +<--- Page Split ---> + +## Endometrial cell type-specific differentiation defects in former sPE patients: Epithelia + +We integrated the epithelial cell transcriptomes, which identified the following epithelial cell subtypes (Fig. 2e): ciliated epithelium (which expressed TPP3, RSPH1, and C1orf194), additionally, these cells expressed FOXJ1 and PIFO as previously shown (3). Preciliated epithelial cells expressed CDC20B and CCNO. We also identified specific PDGFRA+ ciliated epithelial cells which were only present in former sPE patients (sPE ciliated epithelium). The population transitioning between epithelium and stroma (epithelial transition) expressed genes identified in the stromal cells, including TIMP3 and DCN. Luminal epithelial cells were identified by published markers: TGS1 and MSLN (3). Glandular secretory cells—the most abundant epithelial cell type—expressed PAEP, CXCL14, and SPP1. As with the stroma, we identified a subpopulation of epithelial cells expressing markers of the proliferative phase, such as IHH and EMID1. The complete set of epithelial cell subtypes from cases and controls and their differentially expressed genes is shown in Extended data Fig. 1c. + +Mapping the identity of the cells enabled identification of subpopulations that were differentially abundant in sPE or control samples (Fig. 2f). Specifically, the proliferative epithelium, ciliated epithelium and sPE ciliated epithelium subpopulations were more numerous in sPE. It has been reported that the numbers of ciliated epithelial cells increase during the proliferative phase (42), supporting the concept of abnormal epithelial differentiation in DR. Cell types that were enriched in the control group (glandular epithelial cells and epithelial transition) comprised a small fraction of the sPE samples. Next, we constructed neighbourhood graphs of the data (Fig. 2g). The results confirmed the statistically significant, differential abundances of the afore mentioned cell types. The neighbourhood data were also visualized as a Beeswarm plot (Fig. 2h). + +Analysis of gene expression revealed significant dysregulation associated with DR in the differentially abundant epithelial subpopulations (Log2FC \(>0.5\) and Spatial FDR \(< 0.1\) ) (Extended data Fig. 2d). VIM, PGR, and MMP7 were upregulated in the proliferative epithelium of samples from former sPE patients. The sPE epithelium subpopulation was characterized by upregulation of SRFP4 and IGF1, and downregulation of CXCL14, PAEP and TIMP3; the ciliated epithelial cells downregulated MT1G, CXCL14 and GPX3. As to the subpopulations that were less abundant in former sPE patients, the glandular epithelium cells from these individuals had dysregulated expression of several secretoglobins and reduced mRNA levels of the transcriptional regulator, MECOM. Also epithelium transition had higher expression of SERF4, FOS, JUN, and lower expression of CXCL14 and TIMP3, among other. + +Altogether, we confirm that there is a notable imbalance in epithelial populations in former sPE patients with a predominant aberrant stage of proliferative epithelium and ciliated epithelium coexisting with glandular secretory and luminal epithelium together with various aberrant epithelial cell types that might contribute to the epithelial phenotype observed macroscopically and microscopically (Fig 1a- h). + +<--- Page Split ---> + +## Endometrial cell type-specific differentiation defects in former sPE patients: Epithelia to Stroma Transition. + +We integrated the transcriptomes of the subpopulations that were either the precursors or the products of the epithelia to stroma transition zone (Fig. 3a; Extended data Fig. 3). This analysis included: stroma, epithelium, epithelium- stromal transition, ciliated epithelium and cycling cells. The complete set of epithelial cell subtypes from both sample groups and their differentially expressed genes is shown in Extended data Fig. 1d. Comparing both conditions (Fig. 3b) there was a notable absence of all the subtypes in sPE samples, including those in the transition zone. This conclusion was substantiated by neighbourhood analysis (Fig. 3c) and the data were visualized as a beeswarm plot (Fig. 3d). Thus, cells involved in the epithelia- to- mesenchymal transition (EMT), which were abundant in the control samples, were greatly reduced in the endometrium from former sPE patients. + +To delve deeper into understanding of the epithelial- to- stromal transition, we performed RNA velocity analysis followed by cellDancer correction of the combined datasets were impacted in late secretory phase endometrial biopsies from former sPE patients (Extended Data Fig. 3a). The results revealed a temporal dynamic transcriptional process originating from epithelial cells and extending to stromal cells. We depicted this dynamic using cellCondiments obtaining two lineages (Extended Data Fig. 3b). Linage 1, representing the most interesting progression alongside the epithelium, epithelium- stromal transition and stroma cell populations, was projected in both sPE and control samples (Fig. 3e). The differentiation vector map for lineage 1 shows a clear transition from secretory glandular epithelium to stroma describing an EMT associated with late secretory decidual state in controls. In contrast, we found significant density disturbances that are associated with DR in formerly sPE patients (Extended Data Fig. 3c and d). We identify genes whose expression patterns change along the pseudotime (Fig. 3f; Extended Data Fig. 4). Epithelial cells reported with the expression of genes (DUSP2, IL17C, and FTH1), transition zone expressed genes associated with the EMT (SNAI2 and NOTCH3) and finally stromal cells are identified by the expression of collagens (COL6A1, COL6A2), GRIA3 and B3GALT5. Finally, differential expression between conditions highlighted the substantial contribution of controls to the transition (e.g., IGFBP2 and ACTA2) (Fig. 3g). These results suggested not only a decrease but also a dysregulation of fundamental aspects of stromal cell transition to an epithelial phenotype in DR endometrial samples. + +## Cell-cell communications are dysregulated among endometrial cells in sPE + +A myriad of cell- cell communication (CCC) networks is essential for properly responding to changing hormone levels during decidualization. We used CellChat to infer the ligand- receptor pairs that were involved in endometrial CCC networks in the control vs. the sPE datasets with statistical significance. + +<--- Page Split ---> + +The relative and absolute flow of information, calculated using the total interaction probability among all cell subpopulations in both conditions, is depicted in Extended Data Fig. 5a,b. Signals that were stronger in samples from sPE samples included components of the NRG, BMP, CX3C, and EGF pathways. Signals that were stronger in control samples included components of the IL1 and MADCAM pathways. In general, there was a notable increase in signals flowing among cells obtained from former sPE patients. Next, we mapped the perturbations in communication networks to specific cell types (Extended Data Fig. 6). The affected cells included glandular secretory epithelium, stroma, stroma early secretory, and stromal transition. Consistent with the overall pattern of information flow (Extended Data Fig. 6a), many of the outgoing (Extended Data Fig. 6b) and incoming signals (Extended Data Fig. 6c) in cells that comprised the sPE samples were largely absent in the comparable cell subpopulations from the control samples. Overall, mapping of the affected networks to particular cell types reaffirmed the phase shift of specific stromal cell subpopulations in the endometrium from former sPE patients. + +Next, we investigated the ligand- receptor pairs contributing to the sPE- associated CCC network perturbations and the cell types that were involved. The most statistically significant pathways and those that play particularly important roles in decidualization are displayed as chord plots (Fig. 4a,c,e,g,i,j) with the relative contribution of specific ligand receptor pairs shown as bar graphs (Fig. 4b,d,f,h). + +In control samples, endothelin- mediated signals are directed between the endothelium and glandular secretory epithelium to the various types of stromal cell subtypes (Fig. 4a). In DR cases, however, the signals are mainly redistributed between epithelial early proliferative and different stromal cell subpopulations. This dispersion involves weakening of EDN1- EDNRA signals and the new appearance of signaling mediated by EDN3- EDNRB (Fig. 4b) in sPE. Canonical and non- canonical WNT (ncWNT) signals are key regulators of decidualization. As to the latter, in control samples, the stromal transition subpopulation is strongly autoregulating and communicating with endothelial cells and the perivascular subpopulations (Fig. 4c). In samples from sPE cases, DR signals emerging from the stromal transition subpopulation with endothelium and the stroma are diminished and autocrine stroma early secretory regulation emerged (Fig. 4c), also involving new ligand- receptor pairs such as WNT5A- FZD10 (Fig. 4d). In control samples, canonical WNT signalling primarily involves autocrine regulation of the stromal transition cell type and communication between this subpopulation and stromal decidual cells (Fig. 4e). In cases, stromal transition cells and early secretory stromal cells signal to undifferentiated stromal cells (Fig. 4e), involving at least 4 canonical WNT pathways that were not active in control samples (Fig. 4f). + +There were also obvious alterations in signaling via SEMA3A, which is highly expressed in the proliferative phase of the cycle (24). In control samples, the stromal transition subpopulation communicates with endothelial cells and stroma (Fig. 4g). In DR samples, signals had a very different pattern, primarily distributed between proliferative epithelial cells and numerous other types (e.g., + +<--- Page Split ---> + +decidual cells, proliferative stromal cells, and endothelial cells; Fig. 4g). The latter signals included many pathways that were not observed in control samples (Fig. 4h and Extended data Fig. 7 and 8). We also investigated communication pathways involving SPP1, which is highly expressed during the mid- late secretory phase by epithelial cells (43). In control samples, SPP1 signals were weakly distributed among numerous cell types (Fig. 4i). In samples from the DR cases, strong autocrine (glandular epithelium) and paracrine signals (between glandular epithelium and numerous stromal a decidual cell types; Fig. 4i) emerged. Finally, dramatic changes in signaling via tenascin, an extracellular matrix protein, were also evident (Fig. 4j). This molecule is highly express in endometriosis (44) and in endometrium during the proliferative phase (45). In controls, communication was primary between stromal decidual cell type 3 and the epithelial subtypes. In DR cases, new circuitry arose involving glandular secretory epithelium and stromal cells as well as the various subtypes of decidual cells. In conclusion, the data suggest that in the endometrium of the cases with DR there is a very significant rewiring of signaling pathways in terms of molecules and cell types. Altogether the proliferative phenotype of cells from the cases leads to aberrant decidualization. + +## Spatial Resolution of DR in sPE + +We spatially resolved the transcriptomic changes discovered by scRNA- seq by spatial transcriptomics on formalin- fixed paraffin- embedded endometrial tissue biopsied during the late secretory phase, analysing 95 regions of interest (ROIs) in sPE (n=8) and control (n=8) patients (Fig. 5). Those regions fell into three categories based on the endometrial tissue architecture: (i) enriched in stromal (VIM+) and endothelial cells (CD31+) (Fig. 5a), (ii) enriched in glandular epithelium (PanCK+) (Fig. 5c) and (iii) luminal epithelial regions enriched in luminal epithelium and stromal cells (PanCK+, VIM+) (Fig. 5e). Differential expression analysis (cases vs. controls) revealed: (i) 430 DEGs in stroma, (ii) 575 DEGs in glandular epithelium, and (iii) 456 DEGs in luminal ROIs. Heatmaps of the DEGs for each compartment were constructed by unsupervised hierarchical clustering based on Pearson distances of the normalized data z- scores. With a few exceptions, sPE and control samples clustered separately. + +We coalesced the DEGs from the single cell and spatial transcriptomics analyses (Extended data Fig. 9). In general, the DEGs had the same pattern of up- or downregulation in both analyses, which cross- validated the findings. In the enriched stromal ROIs (Fig. 5b and Extended data Fig. 9a), DEGs identified by both technologies included those involved in cytoskeletal organization. TPM1, an actin binding protein involved in contractility and cytoskeleton dynamics (46), was downregulated as were MYL9, which increases decidual contractility, and ANXA2 previously associated with DR (15). SEMA3, identified in our analysis of cell- cell communications, was upregulated as was KDM6b, which is involved in regulating decidual DNA methylation and modulating target gene expression (47). + +<--- Page Split ---> + +The glandular epithelium ROI genes (Fig. 5d and Extended data Fig. 9b) were upregulated in DR cases and shared with the single cell analysis of epithelium including TNFRSF21 and the TNF receptor. They are also upregulated in women with recurrent pregnancy loss (48). + +The luminal epithelium ROI genes (Fig. 5f and Extended data Fig. 9c) that were upregulated in DR cases and shared with the single cell analysis of stroma included LIMA, LIM domain and Actin Binding 1, a cytoskeleton- associated protein that inhibits actin filament depolymerization, is essential for proper mitochondrial function and a key effector mediating pluripotency (49). SCRAB1, a scavenger receptor involved in the removal of apoptotic cells in degenerated decidua tissue, was downregulated. The luminal epithelium ROI genes (Fig. 5f) that were upregulated in cases and shared with the single cell analysis of the epithelium included MMP7. In controls, its expression is associated with the proliferative phase of the menstrual cycle and downregulated during the late secretory phase (30). DUSP2 and ACADSB also shared this pattern. DUSP2 interacts with IL- 6 and its overexpression activates pathways that regulate inflammation and proliferation (50). ACADSB participates in cell migration, invasion and proliferation and cancer cells (51). + +Altogether, coalescing the single cell and spatial transcriptomics data, pointed to an imbalance of cells in the epithelial and stromal compartments of samples from the DR cases that was attributable to late secretory endometrium retaining characteristics of the proliferative phase of the cycle. More specifically, our results suggested dysregulation of fundamental aspects of epithelial cell transition to a stromal phenotype during decidualization. + +## Spatial proteomic mapping of DR in sPE by laser capture microdissection-mass spectrometry + +We used LCM- MS to analyze the glandular epithelium, luminal epithelium and stroma of endometrial biopsies obtained during the late secretory phase of the cycle from an independent cohort of donors (sPE, n=7; controls, n=10) (Fig. 6a). To decipher the underlying functions of the identified proteins, overrepresentation analysis was performed, and protein- protein interaction networks were built to identify perturbations in endometria from the cases. In addition, estrogen receptor 1 (ESR1) and progesterone receptor (PGR) were included in the network to assess their associations with the DR phenotype. + +The stromal compartment had the highest number of DE proteins. In this regard, 439 (43.9%) were specific to sPE samples and 53 (5.8%) were unique to control samples (Fig. 6b). Among proteins specific to sPE samples, was STAT3—which participate in decidualization downstream of PGR and is aberrantly increased in endometriosis (52, 53)—. Response to steroid hormones, particularly estrogens, is enriched in the sPE group, as is aging and cell growth (p.adjusted<0.05; Fig. 6c), consistent with increased proliferation and supported by the differential expression of markers of cell proliferation in + +<--- Page Split ---> + +tumorigenesis such as ARG1 and B2M. Controls group showed an enrichment for extracellular matrix organization and EMT. The analysis of the protein- protein interaction network identified ESR1 and STAT3 as hub proteins, supporting the central role of hormonal signaling in the sPE affected pathways (Fig. 6d). + +The glandular epithelium of control samples had 401 (27%) unique proteins while 103 (6.9%) were only present in sPE samples and 982 were common to both groups (Fig. 6b). The proteome from controls was enriched for hormone secretion (p.adjusted \(< 0.05\) ), whereas the sPE proteome was enriched for cell survival in response to ROS and epithelial cell differentiation (p.adjusted \(< 0.05\) ; Fig. 6c). The protein- protein interaction network revealed that these pathways are significantly interconnected with ESR1 and PGR (Fig. 6e). Thus, disrupted hormonal signaling could drive an imbalance in proliferation/differentiation, affecting the secretory function of endometrial glands by mechanisms that include the generation of ROS and defective cytoskeletal organization. Notable proteins related to this phenotype were specific to controls such as EGFR and LRP1. + +The majority of luminal epithelium proteins 562 (63.3%) were shared between cases and controls; 67 (7.6%) were unique to controls and 254 (28.8%) were specific to cases (Fig. 6b). This proteome was enriched for response to steroids, specifically estrogens (p.adjusted \(< 0.05\) ; Fig. 6c), the major hormone during the proliferative phase. Accordingly, proteins specific to sPE were involved in cell proliferation (e.g., NONO, TRIM25), increased metabolism (e.g., PSMD2, PSMD7) and, consequently, inhibition of apoptosis signaling (e.g. SOD2, WFS1). In contrast, proteins unique to controls were enriched for extracellular matrix organization (e.g. CAV1, LAMB2). Then, we built a network with proteins involved in representative features of these pathways that were significantly disturbed in sPE (Fig. 6f). The results supported strong interconnections among these pathways and the key role of ESR1 and PGR. + +Finally, we identified intersections between the biological processes identified by the scRNA- seq and LCM- MS analyses (Fig. 6g). There was substantial concordance between the two datasets in terms of those that were specific to sPE samples or controls. In many cases, there was also an overlap in the related biological processes that mapped to two or more compartments. The sPE group was characterized by processes/molecules involved in epidermal regulation, responses to oxidative stress/ROS and aging. The control group was characterized by processes such as protein secretion, cell growth, extracellular matrix organization, leukocyte adhesion and IL2 production. The sPE group was also notable for the absence of processes that were significantly represented in the control group such as gland development, response to LIF and EMT. + +Thus, by overlapping the proteomic- based pathways associated with sPE and control samples with the DEGs from the single- cell dataset, we confirmed the key molecular features that shape DR at a multi- omic level. Overall, the main features are an imbalance in proliferation/differentiation in various regions + +<--- Page Split ---> + +of the endometrium, associated with a disturbed response to steroid hormones, particularly estrogens, which could ultimately affect epithelial- stromal crosstalk and the secretory function of glands. Altogether, these results provide the deepest characterization of DR to date with possible clinical implications not only in the understanding of the pathogenesis of sPE but also in endometriosis and other pathological conditions related with DR. + +## Discussion + +Most pregnancy disorders originate from alterations in the periconeptional period, at the time where trophoectoderm invades the decidualized endometrium (1). Defective trophoblast invasion is the primary underlying cause of the great obstetrical syndromes such as preeclampsia, IUGR or preterm labor (54). Trying to answer why is placentation abnormal in these relevant pathologies our group and others have identified a decidualization defect in the endometrium (the soil) of these patients as a contributing factor in these obstetrical conditions (4), including sPE (15, 16, 55), IUGR or placenta. + +Our present study offers a comprehensive exploration of the DR condition in former sPE patients, shedding light on the associated changes at gross, microscopic, scRNA seq, spatial transcriptomics and proteomics by LCM- MS. Our findings underscore the intricate molecular landscape underlying DR, implicating not only alterations in stroma and epithelium but also in the epithelial- to- stromal transition. + +At the morphological level, distinct features including glandular dilation, hinted at underlying DR. Deeper investigation using scRNA- seq revealed significant alterations in cellular composition and differentiation patterns. At the stromal level, our analysis delineated specific subpopulations associated with different decidualization stages, revealing a shift in the balance leading to a mosaic of proliferative and decidualized stromal cells in former sPE patients, which causes cellular communications to be affected. Dysregulated gene expression signatures further corroborated the aberrant cellular states, highlighting the upregulation of markers indicative of proliferative activity and downregulation of those associated with decidualization. Additionally, endothelial and immune cells showed a disturbed transcriptome consistent with inflammatory dysregulation. + +Similarly, an imbalance was evident in the epithelial cell subpopulations, with increased abundance of proliferative and ciliated epithelial cells in DR samples from former sPE patients. Dysregulated gene expression patterns mirrored this imbalance, with upregulation of genes linked to proliferation and downregulation of those associated with secretory functions indicating a potential dysregulation in the normal differentiation and maturation processes of uterine epithelial cells. This feature is relevant because glandular epithelia is the source of the uterine fluid or "uterine milk" that will provide nutrients and antimicrobial protection for the implanting conceptus. + +<--- Page Split ---> + +Our analysis also uncovered perturbations in the epithelial- to- stromal transition, emphasizing a significant reduction in cells involved in this transition in former sPE patients. Furthermore, dysregulated cell- cell communication networks were evident, with notable alterations in signaling pathways critical for decidualization as WNT or SPP1, angiogenesis, and hormonal regulation. + +Spatial transcriptomics and proteomic analyses provided additional layers of insight, highlighting spatially specific dysregulation in both epithelial and stromal compartments, mainly related to proliferation/differentiation imbalance. Key pathways implicated in DR, including hormonal signaling —particularly response to estrogens—, oxidative stress response, and cytoskeletal organization, were identified, further corroborating findings from single- cell analyses. + +The comprehensive characterization of DR presented in this study not only advances our understanding of the pathogenesis of sPE but also has broader implications for other great obstetrical syndromes prevalent gynecological diseases such as endometriosis (9, 10), and recurrent miscarriage (13). The identified molecular signatures may serve as potential biomarkers for preconceptional detection of DR and intervention strategies before pregnancy is established. Preconceptional care is emerging as a key component of reproductive care not only to reduce perinatal morbidity and mortality, but to optimize health for mothers and children (56). + +In summary, our multi- omic approach offers a nuanced depiction of DR, revealing an imbalance in proliferation/differentiation within the endometrium, disrupted response to steroid hormones—especially estrogens—and potential impacts on epithelial- stromal communication and glandular secretory function. These findings provide valuable insights into molecular underpinnings of DR and highlighting potential preconceptional therapeutic targets for mitigating the occurrence of the great obstetric syndromes. + +## Material and Methods + +## Study design + +A total of 23 non- pregnant women who had a previous pregnancy were enrolled in this study for single- cell RNA- Sequencing analysis. From those, 12 were women with a healthy previous pregnancy as control cases and 11 were women diagnosed with Severe Preeclampsia in their last pregnancy clinically classified based the ACOG guidelines: high blood pressure (systolic \(>160\) or diastolic \(>100 \mathrm{mmHg}\) ) or thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema, or the inset cerebral or visual disturbances. Endometrial biopsies were collected during the late secretory phase of the menstrual cycle (1 to 3 days before menstruation) and tissue were segmented in different portions, one of the were embedded in paraffin to obtain histological sections for the spatial transcriptomics approach, other section were included in OCT for the laser capture and mass spectrometry approach + +<--- Page Split ---> + +and another portion was processed to obtain the cell isolation that will be used to the single- cell RNA sequencing using 10X genomics technologies. The maternal parameters were recruited in the Supplementary table 1. A two tailed Student's T- test was applied between sPE and Control variables based on the normal distribution of data. + +## Sample collection + +All human endometrial samples of this study were collected in University and Polytechnic La Fe Hospital, Valencia, Spain and pass their Clinical Research Ethics Committee. Samples were collected from women aged 18- 40 without any medical condition who had been pregnant 1- 8 years earlier. All donors had regular menstrual cycles (25- 31 days) without underlying pathological condition and had no received hormonal therapy in the 3 months preceding sample collection. Endometrial biopsies were obtained by pipeline catheter (Genetics Hamont- Achel, Belgium) under sterile conditions and were maintained in a preservation solution HypoThermosol® FRS (Stemcell Technologies) at \(4^{\circ}\mathrm{C}\) until their processing. + +## Sample Processing + +Endometrial biopsies were washed with PBS and mince. Afterwards, samples were enzymatically digested with a solution containing \(1\mathrm{mg / mL}\) Collagenase V (C9263, Sigma- Aldrich), \(100\mu \mathrm{g / mL}\) DNase Type I (03724751103, Roche) and \(10\%\) inactivated FBS in RPMI media for \(45\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) and \(175\mathrm{rpm}\) . The enzymatic reaction was inactivated by adding 1 volume of \(10\%\) inactivated FBS in RPMI media (Complete medium), the solution filtered through a \(100 - \mu \mathrm{m}\) cell strainer, and the strainer washed with \(5\mathrm{mL}\) of complete medium. The flow- through solution contained the stromal fraction, whereas the undigested tissue retained in the cell strainer we the epithelial fraction of the endometrium. + +The stromal fraction was centrifuged \(5\mathrm{min}\) at \(2000\mathrm{rpm}\) , and the pellet was washed with PBS. 1x RBC Lysis Buffer (eBioscience, ThermoFisher Scientific) \(5\mathrm{min}\) at RT was applied to eliminate blood from the sample following manufacturer recommendation. The cell fraction was washed with PBS centrifuged \(5\mathrm{min}\) at \(2000\mathrm{rpm}\) and the pellet resuspended with \(200\mu \mathrm{L}\) of \(0.04\%\) BSA in PBS (w/v). Finally, sample was filtered using a \(40 - \mu \mathrm{m}\) flowmi filter (BAH136800040- 50EA, Merk) to obtain a single cell suspension of the stromal fraction. + +Regarding the epithelial fraction, the \(100 - \mu \mathrm{m}\) cell strainer mentioned above contained the epithelial cells. They were recovered by flushing \(15\mathrm{mL}\) PBS to the inverted cell strainer into a \(50\mathrm{- mL}\) falcon tube and centrifuged \(5\mathrm{min}\) at \(2000\mathrm{rpm}\) . For digestion of the epithelial tissue, the pellet was incubated with \(5\mathrm{mL}\) of \(100\mu \mathrm{g / mL}\) DNase Type I (03724751103, Roche) in \(0.25\%\) Trypsin- EDTA solution (25200072, Life Technologies) \(10\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) and \(175\mathrm{rpm}\) shacking. After inactivation with complete media, the suspension was filtered through a \(100 - \mu \mathrm{m}\) cell strainer, the filter was washed with \(5\mathrm{mL}\) PBS and the solution centrifuged \(5\mathrm{min}\) at \(2000\mathrm{rpm}\) . If needed, the pellet was treated with RBC lysis solution as + +<--- Page Split ---> + +mentioned before, washed again with PBS, resuspended with \(200~\mu \mathrm{L}\) of \(0.04\%\) BSA in PBS (w/v) and filtered using \(40 - \mu \mathrm{m}\) flowmi filter. + +An aliquot of stromal and epithelial cell suspensions was stained with Tripan Blue dye and counted for alive cell concentration in an automatic cell counter (EVETM, NanoEnTek). Same number of both stromal and epithelial cells will be mixed and a total of 17,000 cells were loaded a 10X Chromium as explained in the following section. + +## Single cell processing + +scRNA- seq analysis of the endometrial processed samples were performed using the 10X Chromium technology (10X Genomics, Pleasanton, CA, USA). As mentioned, 17,000 cells were loaded onto a 10X G Chip to obtain Gel Bead- in- emulsions (GEMs) containing an individual cell. GEMs were used to generate barcoded cDNA libraries following the manufacturer's instructions (Single Cell 3' Reagent Kit v3.1, 10X Genomics) and quantified using the TapeStation High Sensitivity D5000 kit (Agilent, Germany). Following, cDNA (1- 100 ng) was obtained to construct gene expression libraries that were quantified using the TapeStation High Sensitivity D1000 kit (Agilent, Germany) determining the average fragment size and library concentration. Libraries were normalized, diluted, and sequenced on the Illumina NovaSeq 6000 system (Illumina, USA) according to the manufacturer's instructions. + +## scRNA-seq data processing and filtering + +Raw sequences were demultiplexed, aligned, and counted using the CellRanger software suite (v 6.0.2) for whole cell gene expression calculations, which takes advantage of intronic reads to improve sensitivity and sequencing depth (human reference genome GRCh38- 2020- A). Low- quality droplets and barcodes were filtered out in four quality control- based consecutive steps throughout the analysis: (i) low UMI- count barcode removal using an EmptyDrops- based method; (ii) cells marked as doublets by DoubletFinder (2.0.3) and scds (1.6.0) tools – the hybrid approach from the scds R package was used to avoid removing false- positive doublets; (iii) cells with median absolute deviation (MAD) \(>3\) in two of three basic quality control metrics: number of detected features, number of counts and mitochondrial ratio. These cell- to- count matrices were integrated and corrected using Seurat and scVI functions, as described below. A final filtering step, (iv), was applied alongside different rounds of clustering, where the obtained clustered cells with less than 750 features/cell, more than \(25\%\) mitochondrial ratio, and/or showing a pattern of high doublet- scoring plus no gene marker associated expression (during manual cell type annotations), were also removed (Supplementary figure 1). A total of 65,381 high quality cells were integrated within the uniform manifold approximation and projection (UMAP). From those 28,154 cells formed the sPE group and 37,227 cells the control group. + +## Integration of single cells across conditions and clustering + +<--- Page Split ---> + +As a first clustering analysis approach, read count matrices per sample were merged and processed following Seurat's default pipeline (package version 4.1.3). After normalization, the first thirty principal components on the 4,000 highly variable genes were used for dimensional reduction; cells were clustered and projected onto the UMAP. FindNeighbors and FindClusters functions were then applied for graph- based clustering by constructing a KNN graph using Euclidean distance in the principal component analysis space, which was then defined into clusters using the Louvain algorithm to optimize the standard modularity function. cluster (R package v0.4.4) was applied to select the most stable clustering resolution. The first output of sample distribution in clusters and cluster marker genes was then explored to evaluate biases from our data batches. Next, the scVI python package (v0.19.0) was used to remove patient origin inter- individual biases. The top thirty scVI components were used to embed and plot cells in the new reduced dimension space. These matrices were then input into Seurat's clustering and differential expression protocol. The clustering of different primary cell types for fine- grain cell type annotations was equally computed, following all the above- described steps. + +## Tissue cell composition analysis and annotation of scRNA-seq datasets + +The identification and labeling of major cell types based on differentially expressed genes per cluster compared to the remaining clusters using the Wilcoxon Rank Sum test and adjusted p- values for multiple comparisons with the false discovery rate (FDR) method (57). The primary cell populations were labeled by revising the expression of reported canonical markers from each cluster. Cell clustering was conducted under single- cell published datasets(2, 3, 24), manual curation of canonical markers published in human protein atlas (HPA)(58). The main cell types were then subset separately: epithelium, stromal fibroblast, perivascular, cycling stroma, endothelium, macrophages, Becells, Tecells, Nk cells. A new clustering was performed on each to create the different zoom- ins that describe the contained sub- populations. The clustered zoom- ins were manually annotated by an extensive review of differentially expressed genes in the Human Protein Atlas database, single- cell atlases, and scientific literature(51). Genes with strong cluster- specificity, as determined by a p- value below 0.01, and the highest rank fold change and percentage of expressing cells were considered. + +## Cell trajectories on the transcriptional pseudotimes + +The inference of cell trajectories was based on the predicted velocity of mRNA synthesis in each cell (RNA velocity). The method for calculating RNA velocity begins with obtaining the spliced/unspliced matrix using the Velocytool (59). RNA velocities were separately obtained for each zoom- in clustering. As already stated by(60), calculations using Velocytool are sensitive to genes that undergo sudden large changes in their expression patterns, described as Transcription Bursts. To avoid this issue, velocities computed by scVelo (v0.2.5) were corrected using CellDancer (v1.17). In scVelo, we filtered the genes with fewer than 20 counts shared by cells in the filter genes function and used only the top + +<--- Page Split ---> + +2000 most variable genes in the filter_gene_s_dispersion. We used the dynamic mode to compute cellular and velocity dynamics. In CellDancer (61), we computed the velocities and projected the vector field predictions computed by the model onto the previously computed UMAP embedding space. After identifying the start and ends of the trajectories, we used Slingshot (62) to compute the genes related to changes across pseudotime in the inferred trajectories. + +## Analysis of differential cell abundances and differential gene expression + +To identify differential abundance in cell populations between sPE and control endometria we used MiloR (version 1.6.0) tool described by Dann et al.(63). This approach supports the grouping of cells on a k- nearest neighbor graph and evaluates the change in cell abundance between conditions, p values were adjusted using spatial FDR of \(< 0.1\) . Further differential gene expression analysis was performed using the Model- based Analysis of Single Cell Transcriptomics (MAST), where a contrast test was established for each cell type (64). + +## Analysis of cell-to-cell communication networks + +In this study, we employed the CellChat R package (v1.1.3) to discern potential cell interactions between distinct cell populations within both control and preeclampsia samples. This tool employs a curated database to infer the overall interaction probability and communication information flows based on the expression of specific ligand- receptor pairs. To elucidate, the total interaction probability measures the likelihood of communication between two cell types, where one serves as the sender and the other as the receiver. This estimation is based on the number of interactor molecules expressed (i.e., ligand- receptor pairs) and the strength of this interaction (expression level). + +Subsequently, the cumulative communication probability of all pairs in a pathway network is employed to compute the communication information flow. To ensure robust results, pathways with fewer than ten cells were excluded from the analysis ( \(10 <\) cells per subpopulation). Additionally, we accounted for the impact of varying cell population sizes by enabling the 'population.size' argument in the 'computeCommunProb' function, which properly scales the communication weight of each subpopulation (set to TRUE). + +Finally, we conducted a differential CCC analysis between control and preeclampsia samples using the 'ranknet' function, applying a significance threshold of 0.05. + +## Spatial Transcriptomics sample processing + +To achieve this objective, Nanostring® technology was utilized, specifically employing the GeoMx® Human Whole Transcriptome Atlas panel. This panel comprehensively covers protein- coding + +<--- Page Split ---> + +genes, allowing the spatial analysis of any tissue within a chosen biological area. In this study, specific biological regions were selected to capture the higher cellular diversity commonly found in the late secretory phase of the endometrium, encompassing various cell types such as stromal, endothelial, luminal epithelial, and glandular epithelial cells, among others. + +We followed established experimental procedures of Nanostring with some modifications, as detailed below. In summary, we prepared \(5\mu \mathrm{m}\) thick FFPE sections from 16 specimens. These consecutive sections were then subjected to H&E and WTA (Whole Transcriptome Analysis). + +The H&E slides was used as guide to select the ROIs. A total of 95 ROI, distributed equally in 16 patients were selected covering the main cell types of the endometrium. + +For the WTA, the slides were first incubated at \(60^{\circ}\mathrm{C}\) for an hour, then deparaffinized using limonene (Merck Chemicals And Life Science, S.A.U.), and subsequently rehydrated. Antigen retrieval was performed by subjecting the slides to \(1\mathrm{x}\) Tris- EDTA/pH 9 in a steamer for \(20\mathrm{min}\) at \(100^{\circ}\mathrm{C}\) . Next, proteinase K (Thermo Fisher Scientific, AM2548) was used to digest the samples at a concentration of \(1\mathrm{Xg / ml}\) for \(15\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) . Following that, the samples were postfixed in neutral- buffered formalin for \(5\mathrm{min}\) . + +Then samples underwent hybridization with a UV- photocleavable barcode- conjugated RNA in situ hybridization probe set, targeting 18,269 genes WTA. This hybridization process was conducted overnight at \(37^{\circ}\mathrm{C}\) . Afterward, the samples were thoroughly washed to eliminate any non- specific probes, and morphology markers were then applied to facilitate counterstaining. This counterstaining step was carried out for \(1\mathrm{h}\) at room temperature. + +Then, morphology markers utilized were a combination of fluorescent agents: 1:10 SYTO13 (Nanostring), 1:50 anti- panCK- Alexa Fluor 532 (Nanostring), 1:500 anti- vimentin Alexa Fluor 594 (Santa Cruz Biotechnology; cat.no: sc- 373717 AF594), 1:50 anti- CD31 Alexa Fluor 647 (Abcam; cat.no: ab215912) in blocking buffer W (NanoString). These markers were applied in blocking buffer W (NanoString). The immunofluorescence images, region of interest (ROI) selection, marker- specific area of interest (AOI) segmentation, and spatially indexed barcode cleavage and collection were performed using a GeoMx DSP instrument (NanoString). The typical exposure times were \(50\mathrm{ms}\) for SYTO13, \(200\mathrm{ms}\) for anti- panCK AF532, \(200\mathrm{ms}\) for anti- vimentin AF 594, and \(200\mathrm{ms}\) for anti- CD31 AF647. Approximately 96 ROIs were collected per specimen. + +For library preparation, the manufacturer's instructions were followed, involving PCR amplification to add Illumina adapter sequences and unique dual sample indexes. Each single molecule in the sequencing library corresponded to one cluster on the flow cell. The paired- end read strategy recommended by NanoString resulted in two paired- end reads, forward and reverse, per cluster, which can be described as one read pair. To achieve a minimum sequencing depth of 150- 200 reads per square + +<--- Page Split ---> + +micron of illumination area, all WTA AOIs were sequenced on a NextSeq 2000 (Read type: Paired- end, read length: read 1: 27bp, read 2: 27bp, index 1 (i7): 8bp, index 2 (i7): 8bp). + +## QC Of Spatial transcriptomics + +Inn every ROI, in segments with \(< 1000\) raw reads are removed, as well as segments with less than \(80\%\) of alignment, trimmed or stitched were removed. Regarding the \(\%\) of sequencing saturation defined as ([1- deduplicated reads/aligned reads] \(\%\) ) ROIs with less that \(50\%\) of this parameter are also removed. The geometric mean of several unique negative probes included in the GeoMx panel is calculated and it stablished the background count level per segment, + +The No Template Control (NTC) count: values \(>1000\) could indicate contamination for the segments associated with this NTC; however, in cases where the NTC count is between 1000- 10000, the segments may be used if the NTC data is uniformly low (e.g. 0- 2 counts for all probes). Quality control for nuclei: \(>100\) nuclei per segment is generally recommended;. All segments of the study pass those qc filters. + +We also remove outlier negative control probes from the data to refine our estimation of background and downstream gene detection. We remove outlier probes either entirely from the study (global) or from specific segments (local). There are 18807 probes that passed QC. 1 Global outlier and 7 Local outliers were detected + +Regarding the limit of quantification (LOQ) per ROI/AOI segment based on the negative control probes to guide the filtering of segments and genes with low signal relative to background. The formula for calculating the LOQ in the \(\mathrm{i}^{\mathrm{th}}\) segment at n standard deviations ( \(\mathrm{n} = 2\) for this study) is: + +\[LOQ_{i} = g e o m e a n(N e g P r o b e_{i})*g e o S D(N e g P r o b e_{i})^{n}\] + +In this dataset, we choose to remove segments with fewer than \(10\%\) of the genes detected. As a result, 0 segments were flagged and removed. In total, 0 segments were removed from the study from either segment QC or filtering. + +10,239 targets were detected above LOQ in \(10\%\) or more of the segments. Including the genes of interest list, 10,489 targets in total were analyzed further. We filtered down to this number of targets + +## Normalization of spatial transcriptomics + +Two common methods for normalization of GeoMx® WTA/CTA data are i) Quartile 3 (Q3) or ii) background. Both methods estimate a normalization factor per ROI/AOI segment to bring the segment data distributions together. Q3 is typically the preferred approach. High correlation between the geometric mean of the negative control probe counts and 75th quantile (Q3) of expression. Q3 normalization was used downstream for analysis. + +<--- Page Split ---> + +## Contrasting groups with Linear Mixed Models in Spatial transcriptomics + +Ia given contrast, we are interested in comparing the magnitude of differences between groups and quantifying the significance. In many GeoMx experiments, multiple areas of illumination (AOIs) or regions of interest (ROIs) are sampled in a given slide. To account for the nested, non- independent sampling, we often use a linear mixed effect model (LMM) with slide as a random effect. The LMM accounts for the subsampling per tissue, allowing us to adjust for the fact that the multiple regions of interest placed per tissue section are not independent observations, as is the assumption with other traditional statistical tests. + +Overall, there are two main types of the LMM models when used with GeoMx data: A) with random slope and B) without random slope. + +When comparing features that co- exist in a given tissue section, a random slope is included in the LMM model. When comparing features that are mutually exclusive in a given tissue section the LMM model does not require a random slope. + +## Spatial proteomics: laser capture microdissection; protein isolation/digestion and mass spectrometry. + +Three regions of each endometrial biopsy were isolated by laser capture microdissection (LMD): glandular epithelium, luminal epithelium and stroma. They were separately processed and analyzed using methods we previously published (65). Briefly, Frozen blocks of the tissue were sectioned \((- 20^{\circ}\mathrm{C})\) using a Leica CM3050 cryostat. Sections \((20\mu \mathrm{m})\) were mounted on Director slides (Expression Pathology; Hembrough et al., 2012). Slides with sections were kept under dry ice until LMD the same day. Sections on slides were manually defrosted in room air \((30\mathrm{~s})\) , immersed in PBS until the OCT was completely removed \((\sim 2\mathrm{~min})\) , dipped in \(0.1\%\) Toluidine Blue for \(30\mathrm{~s}\) , washed in ice- cold PBS, dehydrated (30 s/treatment) in a graded ethanol series \((75\% , 95\% , 100\%)\) , then rapidly dried with compressed nitrogen. + +Following capture, the samples were incubated in an alkaline surfactant- containing solution at \(60^{\circ}\mathrm{C}\) with rigorous vortexing every 15 min for 1 h. Iodoacetamide (Thermo Fisher Scientific) was added to \(15\mathrm{mM}\) and incubated at room temperature in the dark for \(30\mathrm{min}\) . Proteins were digested overnight at \(37^{\circ}\mathrm{C}\) with trypsin \((20\mathrm{ng / \mu l}\) , Pierce), then centrifuged at \(16,000\mathrm{g}\) (Eppendorf) for \(10\mathrm{min}\) . Trifluoroacetic acid (Pierce) was added to the supernatant such that the final concentration was \(0.5\%\) . Duplicate technical replicates were performed along with a control that consisted of a blank DIRECTOR slide that was carried through the entire sample preparation protocol. + +Samples were analyzed by reverse- phase HPLC- ESI- MS/MS using an Eksigent Ultra Plus nano- LC 2D HPLC system directly connected to an orthogonal quadrupole time- of- flight SCIEX TripleTOF 6600 mass spectrometer (SCIEX). Peptide and protein identifications were determined using the Paragon + +<--- Page Split ---> + +algorithm within the ProteinPilot search engine (v.5.0.2, SCIEX) against the corresponding proteome FASTA files obtained from UniProt. + +## Enrichment analysis + +Gene Ontology (GO) analyses were conducted to obtain biological processes using enrichGO function from clusterProfiler R package (v. 4.2.2). The input proteins were those described for both sPE and control groups per endometrial region (glandular epithelium, luminal epithelium, and stroma). Specific pathways of a group refer to enriched pathways that are present in the controls or in the sPE, but are not present in the other group. The input genes were the differential expressed genes grouped by endometrial region. The p- value adjustment method was FDR with a cutoff of 0.05. + +## Protein-protein interaction network + +The protein- protein interaction networks were created using the functional analysis suit String and visualized using Cytoscape software. Hub genes were extracted using the maximal clique centrality (MCC) and maximum neighborhood component (MNC) of the cytoHubba plugin. + +Data availability + +The single- cell RNA- sequencing data generated for this manuscript has been uploaded to GEO under accession number GSE265862. The uploaded data includes i) H5ad files containing the aggregated count matrices and metadata of each cell studied in the major cell populations and subpopulations; ii) Raw count matrices processed by Cell Ranger. The raw sequences are not publicly available due to privacy concerns. However, they are available from the corresponding authors (C.S, carlos.simon@uv.es; TG, tgarrido@fundacioncarlosimon.com) upon reasonable request and with permission of the Institutional Review Board of the Spanish hospitals involved. + +# Figures + +<--- Page Split ---> +![](images/Figure_2.jpg) + + +<--- Page Split ---> + +Figure 1. Morphological features in decidualization resistance reflected in single cell atlas in sPE and control condition. (a) Representative endometrial tissue collected during late secretory phase from women with a previous sPE \((n = 7)\) . (b) Zoom - in of the macroscopical glands of the endometrial tissue of figure 1 a. (c) Representative H&E slides staining of cross-section endometrial tissue of an sPE sample. (d) Representative H&E slides staining of longitudinal-section endometrial tissue of an sPE sample. (e) Representative endometrial tissue collected during late secretory phase from control women \((n = 10)\) . (f) Zoom - in of the macroscopical glands of the endometrial tissue of fig. 1c. (g) Representative H&E slides staining of cross-section endometrial tissue of a control sample. (h) Representative H&E slides staining of longitudinal-section endometrial tissue of a control sample. (i) Single-cell sequencing workflow. (j) Uniform manifold approximation and projection (UMAP) of single-cell integration of high-quality cells sPE cells (28,154) of the major cell types of the endometrium during late secretory phase. (k) UMAP of single-cell integration of high-quality cells control cells (37,227) of the major cell types of the endometrium during late secretory phase. (l) UMAP of the 65,381 high quality cells of types of merged cells of both sPE (blue) and control (red) samples. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 2. Altered stromal and epithelial cell differentiation states of DR in sPE condition. (a) UMAP of cell subpopulation identification of stromal and perivascular fraction of endometrium in late secretory phase. (b) UMAP of stromal merged cells of both sPE (blue) and control (red) samples. (c) Neighbourhood graph represents the differential abundance of stromal cell in late secretory endometrium. Dot size represents neighbourhoods, while edges thickness (weight) depicts the
+ +<--- Page Split ---> + +number of cells shared between neighbourhoods. Neighbourhoods coloured in blue represent those with a significant decrease in cell abundance in sPE and red highlight cells enriched in sPE. (d) Beeswarm plot of differential cell abundance by stromal cell subtypes. X-axis represents the log2-fold change in abundance of sPE. Each dot represents a neighbourhood; neighbourhoods coloured in blue represent those with a significant decrease in cell abundance in sPE condition while red dots are enriched in sPE samples. (e) Cell subpopulation identification of epithelial fraction of endometrium in late secretory phase. (f) UMAP of epithelial merged cells of both sPE (blue) and control (red) samples. (g) Neighbourhood graph represents the differential abundance of epithelial cell in late secretory endometrium. Dot size represents neighbourhoods, while edges depict the number of cells shared between neighbourhoods. Neighbourhoods coloured in blue represent those with a significant decrease in cell abundance in sPE and red highlight cells enriched in sPE. (h) Beeswarm plot of differential cell abundance by epithelial cell subtypes. X-axis represents the log2-fold change in abundance of sPE. Each dot represents a neighbourhood; neighbourhoods coloured in blue represent those with a significant decrease in cell abundance in sPE condition while red dots are enriched in sPE samples. + +<--- Page Split ---> +![PLACEHOLDER_27_0] + + +<--- Page Split ---> + +Figure 3. Absence of EMT in endometria with DR from sPE condition. (a) Zoom in of EMT and cell subpopulation identification of endometrium in late secretory phase. Circle highlights the epithelial- to- stroma transition subpopulation. (b) UMAP of EMT merged cells of both sPE (blue) and control (red) samples. (c) Neighborhood graph represents the differential cell abundance of MET cells in late secretory endometrium. Dot size represents neighborhoods, while edges depict the number of cells shared between neighborhoods. Neighborhoods colored in blue represent those with a significant decrease in cell abundance in sPE and red highlight cells enriched in sPE. (d) Beeswarm plot of differential cell abundance by MET cell subtypes. X- axis represents the log2- fold change in abundance of sPE. Each dot represents a neighborhood; neighborhoods colored in blue represent those with a significant decrease in cell abundance in sPE condition while red dots are enriched in sPE samples. (e) RNA velocity generated with scVelo of sPE and control samples of epithelial transition, epithelium- to- stromal transition and stromal transition subpopulations. Ciliated cell subtype was removed from trajectory inferences downstream analysis. Arrows represents the cell trajectories across clusters inferring differentiation cell trajectories. (f) Pattern of gene expression along the pseudotime from epithelium to stroma in the inferred trajectory. (g) Dotplot of differentially expressed genes in sPE vs controls identified across pseudotime associated to stroma, epithelial- to- stroma transition, and epithelium (color represents the average expression and dot size refers to the percentage of cells of each cluster expressing each marker). + +<--- Page Split ---> +![PLACEHOLDER_29_0] + + +<--- Page Split ---> + +Figure 4. Dysfunctional cell- to- cell communication networks associated with altered cell composition in DR. (a, c, e, g, i, j) Chords plots displaying the CCC network of Endoglin (EDN), noncanonical WNT (ncWNT), canonical WNT, Semaphorin (SEMA3), SPP1 and TENASCIN in sPE and control. Each coloured dot represents a cell subtype. Colour arrow represents de incoming signalling and the thickness of the lines refers to the strength of the signal between cell subtypes. (b, d, f, h) Barplot of each ligand- receptor pair contributing to the CCC of Endoglin, ncWNT, WNT and SEMA3. Red colour represents sPE expression pair and blue the Control. + +<--- Page Split ---> +![PLACEHOLDER_31_0] + +
Figure 5. Decidualization resistance in sPE confirmed with spatial transcriptomics. (a) Immunofluorescence of enriched stromal ROIs selected of one representative sPE sample (PanCK in green, Vimentin in yellow, CD31 in red and nucli in blue) and unsupervised hierarchical clustering based on Pearson distances of the normalized data z-scores of the top genes of enriched stromal ROIs. (b) Volcano plots depicting DEGs between sPE and controls within stromal ROIs. (c) Immunofluorescence of enriched glandular epithelial ROIs selected of one representative sPE sample and unsupervised hierarchical clustering based on Pearson distances of the normalized data z-scores of the top genes of enriched glandular epithelium ROIs. (d) Volcano plots depicting DEGs between sPE and controls within Glandular epithelial ROIs. (e) Immunofluorescence of enriched luminal epithelial
+ +<--- Page Split ---> + +761 ROI selected of one representative sPE sample and unsupervised hierarchical clustering based on 762 Pearson distances of the normalized data z-scores of the top genes of enriched luminal epithelium ROIs. 763 (f) Volcano plots depicting DEGs between sPE and controls within luminar epithelial ROIs. Heatmap 764 legend reflects info of group (sPE in red and control in blue), and colours represented the ROI of each 765 participant. Volcano plot legend represent significant genes ( \(\mathrm{p}< 0.05\) ) and non-significant genes (NS < 766 0). + +<--- Page Split ---> +![PLACEHOLDER_33_0] + + +![PLACEHOLDER_33_1] + + +![PLACEHOLDER_33_2] + + +![PLACEHOLDER_33_3] + + +![PLACEHOLDER_33_4] + + +<--- Page Split ---> + +Figure 6. Differential endometrial proteome associated with decidualization resistance in sPE compared to controls. (a) Endometrial section before laser capture microdissection, and after isolating the regions of interest (glandular epithelium, luminal epithelium, and stromal compartment). (b) Venn diagrams showing the total number of proteins identified between controls and sPE in the stromal compartment, glandular epithelium, and luminal epithelium, respectively. (c) Differential expressed pathways specific to controls and sPE per region analysed. Color gradient shows the protein ratio (%), which refers to what proportion of all the proteins detected in the region are proteins involved in the pathway. All pathways included are significantly enriched (adjusted. \(\mathrm{P< 0.05}\) ). (d) Protein-protein interaction network including those proteins involved in highlighted pathways in the stromal compartment. Colour shows the specificity of proteins (red, proteins unique to sPE; blue, proteins unique to controls; purple, proteins shared by the two groups; green, ESR1 and PGR). Shape shows the pathway (circle, response to steroids; hexagon, aging; rhombus, cell growth; rectangle, extracellular matrix organization). (e) Protein-protein interaction network including those proteins involved in highlighted pathways in the glandular epithelium. Colour shows the specificity of proteins (red, proteins unique to sPE; blue, proteins unique to controls; purple, proteins shared by the two groups; green, ESR1 and PGR). Shape shows the pathway (circle, hormone secretion; hexagon, response to reactive oxygen species (ROS); square, cell survival in response to ROS; rhombus, epidermal cell differentiation; rectangle, regulation of actin cytoskeleton organization. (f) Protein-protein interaction network including those proteins involved in highlighted pathways in the luminal epithelium. Colour shows the specificity of proteins (red, proteins unique to sPE; blue, proteins unique to controls; purple, proteins shared by the two groups; green, ESR1 and PGR). Shape shows the pathway (circle, response to steroids; hexagon, negative regulation of apoptotic signaling pathway; rhombus, proteasomal protein catabolic process; rectangle, extracellular matrix organization) (PPI enrichment refers to protein-protein interaction enrichment). (g) Dot plot showing the functional enrichment coincides between the spatial proteome and the differentially expressed genes at single-cell resolution. Left panel, sPE-specific pathways. Right panel, controls-specific pathways. Pink, scRNA-seq data; purple, LCM-MS. + +## References + +1. Moreno I, Capalbo A, Mas A, Garrido-Gomez T, Roson B, Poli M, et al. The human periconceptional maternal-embryonic space in health and disease. Physiol Rev. 2023;103(3):1965-2038. Epub 20230216. doi: 10.1152/physrev.00050.2021. PubMed PMID: 36796099. +2. Wang W, Vilella F, Alama P, Moreno I, Mignardi M, Isakova A, et al. Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle. 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Brosens I, Pijnenborg R, Vercruysse L, Romero R. The "Great Obstetrical Syndromes" are 964 associated with disorders of deep placentation. American Journal of Obstetrics and Gynecology. 965 2011;204(3):193- 201. doi: 10.1016/j.ajog.2010.08.009. 966 55. Garrido- Gómez T, Dominguez F, Quiñonero A, Estella C, Vilella F, Pellicer A, et al. Annexin A2 967 is critical for embryo adhesiveness to the human endometrium by RhoA activation through F- actin 968 regulation. FASEB J. 2012;26(9):3715- 27. Epub 2012/05/29. doi: 10.1096/fj.12- 204008. PubMed 969 PMID: 22645245. 970 56. Fleming TP, Watkins AJ, Velazquez MA, Mathers JC, Prentice AM, Stephenson J, et al. Origins 971 of lifetime health around the time of conception: causes and consequences. The Lancet. 972 2018;391(10132):1842- 52. doi: 10.1016/s0140- 6736(18)30312- x. 973 57. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful 974 Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 975 1995;57(1):289- 300. 976 58. Karlsson M, Zhang C, Mear L, Zhong W, Digre A, Katona B, et al. A single- cell type 977 transcriptomics map of human tissues. Science Advances. 2021;7(31):eabh2169. doi: 978 10.1126/sciadv.abh2169. 979 59. La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V, et al. RNA velocity of 980 single cells. Nature. 2018;560(7719):494- 8. Epub 20180808. doi: 10.1038/s41586- 018- 0414- 6. 981 PubMed PMID: 30089906; PubMed Central PMCID: PMC6130801. 982 60. Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. Generalizing RNA velocity to transient cell states 983 through dynamical modeling. Nat Biotechnol. 2020;38(12):1408- 14. Epub 20200803. doi: 984 10.1038/s41587- 020- 0591- 3. PubMed PMID: 32747759. 985 61. Li S, Zhang P, Chen W, Ye L, Brannan KW, Le N- T, et al. A relay velocity model infers cell- 986 dependent RNA velocity. Nature Biotechnology. 2024;42(1):99- 108. doi: 10.1038/s41587- 023- 01728- 987 5. 988 62. Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, et al. Slingshot: cell lineage and 989 pseudotime inference for single- cell transcriptomics. BMC Genomics. 2018;19(1). doi: 990 10.1186/s12864- 018- 4772- 0. 991 63. Dann E, Henderson NC, Teichmann SA, Morgan MD, Marioni JC. Differential abundance testing 992 on single- cell data using k- nearest neighbor graphs. Nature Biotechnology. 2022;40(2):245- 53. doi: 993 10.1038/s41587- 021- 01033- z. 994 64. Finak G, Mcdavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: a flexible statistical 995 framework for assessing transcriptional changes and characterizing heterogeneity in single- cell RNA 996 sequencing data. Genome Biology. 2015;16(1). doi: 10.1186/s13059- 015- 0844- 5. 997 65. Gormley M, Oliverio O, Kapidzic M, Ona K, Hall S, Fisher SJ. RNA profiling of laser 998 microdissected human trophoblast subtypes at mid- gestation reveals a role for cannabinoid signaling 999 in invasion. Development. 2021;148(20). doi: 10.1242/dev.199626. + +<--- Page Split ---> + +# 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. + +<--- Page Split ---> diff --git a/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7_det.mmd b/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..29ce2d23435ab6066c71542697ad594bb67f0a33 --- /dev/null +++ b/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7_det.mmd @@ -0,0 +1,598 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 930, 175]]<|/det|> +# Mapping Decidualization Resistance in Former Severe Preeclampsia Patients at Multi-Omic Levels + +<|ref|>text<|/ref|><|det|>[[44, 195, 422, 240]]<|/det|> +Tamara Garrido- Gómez tgarrideo@fundacioncarlossimon.com + +<|ref|>text<|/ref|><|det|>[[44, 268, 940, 360]]<|/det|> +Carlos Simon Foundation - INCLIVA Health Research Institute Irene Muñoz- Blat Carlos Simon Foundation - INCLIVA Health Research Institute Raul Pérez- Moraga Carlos Simon Foundation - INCLIVA Health Research Institute https://orcid.org/0000- 0002- 9611- 9123 + +<|ref|>text<|/ref|><|det|>[[44, 364, 940, 406]]<|/det|> +Nerea Castillo Marco Carlos Simon Foundation - INCLIVA Health Research Institute + +<|ref|>text<|/ref|><|det|>[[44, 411, 590, 454]]<|/det|> +Nerea Castillo Marco Carlos Simon Foundation - INCLIVA Health Research Institute + +<|ref|>text<|/ref|><|det|>[[44, 459, 590, 501]]<|/det|> +Ana Ochando Carlos Simon Foundation - INCLIVA Health Research Institute + +<|ref|>text<|/ref|><|det|>[[44, 506, 590, 549]]<|/det|> +Sheila Ortega Carlos Simon Foundation - INCLIVA Health Research Institute + +<|ref|>text<|/ref|><|det|>[[44, 554, 590, 597]]<|/det|> +Marcos Parras Carlos Simon Foundation - INCLIVA Health Research Institute + +<|ref|>text<|/ref|><|det|>[[44, 602, 590, 645]]<|/det|> +Rogelio Monfort Hospital Universitario y Politecnico La Fe + +<|ref|>text<|/ref|><|det|>[[44, 650, 414, 692]]<|/det|> +Elena Satorres- Perez Hospital Universitario y Politecnico La Fe + +<|ref|>text<|/ref|><|det|>[[44, 698, 414, 740]]<|/det|> +Blanca Novillo Hospital Universitario y Politecnico La Fe + +<|ref|>text<|/ref|><|det|>[[44, 745, 440, 788]]<|/det|> +Alfredo Perales Hospital Universitario La Fe, Valencia Spain. + +<|ref|>text<|/ref|><|det|>[[44, 805, 360, 845]]<|/det|> +Matthew Gormley University California San Francisco + +<|ref|>text<|/ref|><|det|>[[44, 851, 787, 915]]<|/det|> +Beatriz Roson Carlos Simon Foundation- INCLIVA Health Research Institute, 46012 Valencia, Spain https://orcid.org/0000- 0002- 9851- 2025 + +<|ref|>text<|/ref|><|det|>[[44, 920, 360, 960]]<|/det|> +Susan Fisher University California San Francisco + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[44, 42, 162, 60]]<|/det|> +# Carlos Simon + +<|ref|>text<|/ref|><|det|>[[50, 64, 940, 84]]<|/det|> +Carlos SimonCarlos Simon Foundation- INCLIVA Health Research Institute https://orcid.org/0000- 0003- 0902- 9531 + +<|ref|>sub_title<|/ref|><|det|>[[44, 125, 103, 143]]<|/det|> +## Article + +<|ref|>title<|/ref|><|det|>[[44, 163, 135, 181]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 201, 285, 220]]<|/det|> +Posted Date: May 6th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 239, 474, 259]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4331532/v1 + +<|ref|>text<|/ref|><|det|>[[42, 277, 914, 320]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>sub_title<|/ref|><|det|>[[44, 338, 253, 356]]<|/det|> +## Additional Declarations: + +<|ref|>text<|/ref|><|det|>[[44, 361, 323, 380]]<|/det|> +There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 398, 537, 418]]<|/det|> +Supplementary Figure 1 is not available with this version. + +<|ref|>text<|/ref|><|det|>[[42, 467, 954, 510]]<|/det|> +Version of Record: A version of this preprint was published at Nature Medicine on January 7th, 2025. See the published version at https://doi.org/10.1038/s41591- 024- 03407- 7. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[75, 84, 808, 125]]<|/det|> +# Mapping Decidualization Resistance in Former Severe Preeclampsia Patients at Multi-Omic Levels + +<|ref|>text<|/ref|><|det|>[[70, 139, 880, 625]]<|/det|> +1 Mapping Decidualization Resistance in Former Severe Preeclampsia 2 Patients at Multi-Omic Levels 3 4 Irene Muñoz- Blat1,2,8, Raul Perez- Moraga1,3,8, Nerea Castillo- Marco1,2,8, Teresa Cordero1,2, Ana 5 Ochando1, Sheila Ortega1, Marcos Parras1,2, Rogelio Monfort4, Elena Satorres- Perez4, Blanca Novillo4, 6 Alfredo Perales4, Matthew Gormley5, Beatriz Roson1,2, Susan Fisher5, Carlos Simón1,6,7, Tamara 7 Garrido- Gómez1,2 8 9 1Carlos Simon Foundation, Valencia, Spain 10 2 INCLIVA Health Research Institute, Valencia, Spain 11 3 R&D Department, Igenomix, Valencia, Spain 12 4 Hospital Universitario y Politecnico La Fe, Valencia, Spain 13 5 University California San Francisco, San Francisco, CA, USA 14 6Department of Pediatrics, Obstetrics and Gynecology, University of Valencia, Valencia, Spain 15 7Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard Medical 16 School, Boston, MA, USA 17 8These authors contributed equally 18 # Correspondence; carlos.simon@uv.es; tgarrido@fundacioncarlossimon.com + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 133, 881, 468]]<|/det|> +Endometrial decidualization resistance (DR) is implicated in various gynaecological and obstetric conditions. Employing a multi- omic strategy, we unraveled the cellular and molecular characteristics of DR in patients that have suffered severe preeclampsia (sPE). Morphological analysis unveiled significant glandular anatomical abnormalities, confirmed histologically. Single- cell RNA sequencing (scRNA- seq) of endometrial samples from sPE cases (n=11) and controls (n=12) revealed sPE- associated shifts in cell composition, manifesting as a stromal mosaic state characterized by proliferative stromal cells (MMP11, SFRP1+) alongside IGFBP1+ decidualized cells, with concurrent epithelial mosaicism and a dearth of epithelial- stromal transition associated with decidualization. Cell- cell communication network mapping underscored aberrant crosstalk among specific cell types, implicating crucial pathways such as endoglin and WNT. Spatial transcriptomics in a replication cohort validated DR- associated features. Laser capture microdissection/mass spectrometry in a second replication cohort corroborated several scRNA- seq findings, notably the absence of stromal to epithelial transition at a pathway level, indicating disrupted response to steroid hormones, particularly estrogens. These insights shed light on potential molecular mechanisms underpinning DR pathogenesis in the context of sPE. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 230, 101]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[115, 116, 883, 293]]<|/det|> +Pregnancy health is shaped during the periconceptional period due to the interplay between the implanting embryo and the maternal endometrium (1). Decidualization entails the functional and morphological changes that occur within the endometrium transforming the maternal uterine lining to accommodate the invasive trophoblast (2- 4). The endometrial stromal cells (ESCs) transformation is hormonally regulated, driven by increasing progesterone levels and local cAMP production (5, 6), which stimulate the synthesis of a complex network of intracellular and secreted proteins (7). It starts in the early secretory phase of the menstrual cycle, independent of the presence of a conceptus, in areas adjacent to the uterine spiral arteries thereafter spreading throughout the endometrium (8). + +<|ref|>text<|/ref|><|det|>[[115, 297, 881, 405]]<|/det|> +Decidualization resistance (DR) refers to the inability of the endometrium to undergo these specific changes and has been documented in reproductive disorders including endometriosis (9- 11), miscarriage (12), recurrent pregnancy loss (13, 14) and the great obstetrical syndromes. The latter include preeclampsia (PE) (15- 17), intrauterine growth restriction (IUGR) (18), and/or placenta accrete spectrum disorder (19). + +<|ref|>text<|/ref|><|det|>[[115, 410, 883, 654]]<|/det|> +Preeclampsia is a major- obstetric complication affecting \(8\%\) of first- time pregnancies. It is characterized by the new onset of hypertension, proteinuria, and other signs such as vascular endothelial damage (20). The life- threatening condition known as severe PE (sPE) is diagnosed based on higher blood pressure criteria (systolic \(\geq 160 \mathrm{mm} \mathrm{Hg}\) or diastolic of \(\leq 100 \mathrm{mm} \mathrm{Hg}\) ), symptoms of central nervous system dysfunction, hepatic abnormalities, thrombocytopenia, renal abnormalities, and/or pulmonary edema (21). sPE is a placental insufficiency syndrome mediated by early shallow cytotrophoblast (CTB) invasion of uterine decidua and spiral arterioles, leading to incomplete endovascular invasion and altered uteroplacental perfusion (20, 22, 23). We provided evidence of a decidualization defect in women with sPE, detected at the time of delivery and persisting years after the affected pregnancy (15). Endometrial bulk RNA- seq results from affected women post- sPE revealed altered ovarian hormone receptor signaling pathways (16). + +<|ref|>text<|/ref|><|det|>[[115, 658, 883, 833]]<|/det|> +Here, we initially observed gross and microscopic endometrial glandular defects in late secretory endometrium from former sPE patients. Then, we assemble a spatially resolved single- cell multionic characterization of the DR using sPE as a clinical model. Our objective is to gain insights into this pathological condition, combining different omics techniques including single- cell RNA sequencing (scRNA seq), spatial transcriptomics and laser capture microdissection coupled mass spectrometry [LCM- MS]) with different samples sets. Thus, we provide the first detailed description of the whole status of cells, cell communications, perturbations in specific cell communication pathways at scRNA- seq, spatial and proteomic resolution. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 85, 184, 101]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[117, 118, 880, 158]]<|/det|> +Decidualization resistance (DR) in former sPE patients: associated changes at gross, microscopic and single cell levels. + +<|ref|>text<|/ref|><|det|>[[117, 172, 881, 371]]<|/det|> +Endometrial tissue was collected during the late secretory phase of the cycle from women with a previous sPE pregnancy (Fig. 1a- d) and control individuals who had normal obstetric outcomes (Fig. 1e- h). Initial examination of the samples (n=8/group) (Supplementary Table 1a) consistently showed distinct morphological features of the endometrium from cases that were not observed in control specimens. Specifically, the openings of the glands were dilated in the cases (Fig. 1a,b) vs. the control group (Fig. 1e,f). Histological analysis of H&E stained, longitudinal and sagittal tissue sections confirmed this finding and showed that the dilation involved the entire gland (compare Fig. 1c,d vs. Fig. 1g,h). We took these findings as preliminary evidence of DR endometrial defects, during the late secretory phase of the cycle, among the former sPE patients. + +<|ref|>text<|/ref|><|det|>[[116, 386, 881, 540]]<|/det|> +Thus, we used a scRNA- seq approach to uncover the molecular correlates of the observed morphological differences. We profiled biopsies of late secretory phase endometrium from former sPE patients (n=11) as compared to equivalent samples from control individuals who had normal obstetric outcomes (n=12) (Supplementary Table 1b). The scRNA- seq workflow is outlined in Fig. 1i. Altogether we profiled 65,381 high quality cells (see Methods) and integrated the transcriptomes in a uniform manifold approximation and projection (UMAP). Of these 28,154 were from the sPE group (Fig. 1j) and 37,227 were from the control group (Fig. 1k). + +<|ref|>text<|/ref|><|det|>[[116, 553, 881, 750]]<|/det|> +The cells were clustered using previously published scRNA- seq datasets and markers (2, 3, 24). Nine major cell types were identified: epithelium, stroma, cycling stroma, perivascular, endothelium, NK cells, T cells, macrophages, and B cells. The expression of canonical markers of each cell type is shown as a dotplot in Extended Data Fig. 1a. Global integration of the merged cases and control datasets enabled an initial comparison of the cellular composition of the endometrial biopsies from the two groups (Fig. 1l). Differences in the clustering of stromal and epithelial cells was immediately apparent, the samples from the former sPE patients lacked subpopulations that were more prominent in the controls, and vice versa. This finding prompted us to perform a detailed comparison of these cell types from the two sources. + +<|ref|>sub_title<|/ref|><|det|>[[117, 799, 783, 816]]<|/det|> +## Endometrial cell type-specific differentiation defects in former sPE patients: Stroma. + +<|ref|>text<|/ref|><|det|>[[116, 831, 881, 892]]<|/det|> +Integration of the stromal and perivascular cell transcriptomes identified several subpopulations, including three types of decidual cells (Fig. 2a). Decidualized stroma 1 cells expressed genes related to ribosome activity (RPS29, RPL37A) and progesterone- associated endometrial protein (PAEP). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 169]]<|/det|> +Decidualized stroma 2 cells co- expressed markers of the pre- decidual and decidual states, \(EGR1\) and \(CXCL2\) (25, 26), respectively. Decidualized stroma 3 cells were characterized by well- known decidualization markers such as \(IGFBP1\) and \(TIMP3\) (27), \(ATF3\) (28), and transcription factors that are associated with late decidualization, including \(CSRNP1\) (3). + +<|ref|>text<|/ref|><|det|>[[115, 183, 882, 381]]<|/det|> +We also identified a stroma EMT subpopulation (stromal transition), this cluster expressed \(PDGFRA\) that is involved in the transition that occurs during endometrial regeneration after menstruation and decidualization at the maternal- fetal interface (27). Another cluster is the proliferative stroma expressing markers of the proliferative phase of the menstrual cycle: \(SFRP4\) (29), \(MMP11\) (30, 31), \(DIO2\) (32), \(SFRP1\) (33) and \(PGRMC1\) (34). We also captured two distinct perivascular cell subpopulations that maintain a close relationship with stromal cells during decidualization. The perivascular 1 (STEAP4+) and perivascular 2 (MYH11+) subpopulations clustered near cells that regulate stromal angiogenesis, that expressed \(CCBE1\) , a regulator of \(VEGFC\) (35, 36). The complete set of stromal cell subtypes and their differential gene expression is shown in Extended data Fig. 1b. + +<|ref|>text<|/ref|><|det|>[[115, 394, 882, 615]]<|/det|> +Mapping the identity of the cells enabled identification of subpopulations that were differentially abundant in sPE or control samples (Fig. 2b). Specifically, decidualized stroma 1 and 2 as well as the stromal transition subpopulation were enriched in control samples, whereas decidualized stroma 3 and the proliferative stroma were increased in endometrium from sPE samples. This mosaic state—proliferative stromal cells (MMP11, \(SFRP1+\) ) coexisting with \(IGFBP1+\) decidualized cells—was the hallmark of DR in sPE. We graphed the data as neighborhoods in which dots represent neighborhood sizes and the weight of the connecting lines depicts the number of cells shared among neighborhoods (Fig. 2c). The results confirmed a statistically significant increase in the number of cells that defined this mosaic state as well as the decrease of decidualized stromal 1, 2 and stromal transitioning cells in sPE samples. The neighborhood data were also visualized as a Beeswarm plot (Fig. 2d). + +<|ref|>text<|/ref|><|det|>[[115, 629, 882, 896]]<|/det|> +Differential gene expression analysis revealed a significant number of dysregulated genes (DEGs) associated with DR in all the affected stromal subpopulations (Log2FC>0.5, and p. adjusted<0.05) (Extended Data Fig. 2a). In cases, decidualized stroma 1 and 2 cells upregulated \(TIMP3\) , and decidual stroma 3 cells upregulated \(IGFBP1\) and \(IGFBP6\) . Also in sPE cases, the proliferative stroma subpopulation was characterized by upregulation of \(SFRP4\) present in the proliferative phase (37) and \(FOS\) that is present in the endometrium of endometriosis patients (38). Additionally, in sPE cases endothelial cells showed an upregulation of \(C2CD4B\) (39), a marker of acute inflammatory response (Extended Data Fig. 2b). Consistently, immune cells from sPE, including macrophages, natural killer and B cells showed an aberrant transcriptomic profile indicative of inflammatory dysregulation, highlighted by higher expression of \(IL1B\) , \(CCL5\) (40), \(SCGB1D2\) (41), respectively (Extended Data Fig. 2c). These results point to an imbalance in stromal populations in sPE with a predominant aberrant stage of proliferating stromal cells coexisting with decidualised cells in a proinflammatory niche. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 792, 101]]<|/det|> +## Endometrial cell type-specific differentiation defects in former sPE patients: Epithelia + +<|ref|>text<|/ref|><|det|>[[115, 115, 882, 362]]<|/det|> +We integrated the epithelial cell transcriptomes, which identified the following epithelial cell subtypes (Fig. 2e): ciliated epithelium (which expressed TPP3, RSPH1, and C1orf194), additionally, these cells expressed FOXJ1 and PIFO as previously shown (3). Preciliated epithelial cells expressed CDC20B and CCNO. We also identified specific PDGFRA+ ciliated epithelial cells which were only present in former sPE patients (sPE ciliated epithelium). The population transitioning between epithelium and stroma (epithelial transition) expressed genes identified in the stromal cells, including TIMP3 and DCN. Luminal epithelial cells were identified by published markers: TGS1 and MSLN (3). Glandular secretory cells—the most abundant epithelial cell type—expressed PAEP, CXCL14, and SPP1. As with the stroma, we identified a subpopulation of epithelial cells expressing markers of the proliferative phase, such as IHH and EMID1. The complete set of epithelial cell subtypes from cases and controls and their differentially expressed genes is shown in Extended data Fig. 1c. + +<|ref|>text<|/ref|><|det|>[[115, 373, 882, 576]]<|/det|> +Mapping the identity of the cells enabled identification of subpopulations that were differentially abundant in sPE or control samples (Fig. 2f). Specifically, the proliferative epithelium, ciliated epithelium and sPE ciliated epithelium subpopulations were more numerous in sPE. It has been reported that the numbers of ciliated epithelial cells increase during the proliferative phase (42), supporting the concept of abnormal epithelial differentiation in DR. Cell types that were enriched in the control group (glandular epithelial cells and epithelial transition) comprised a small fraction of the sPE samples. Next, we constructed neighbourhood graphs of the data (Fig. 2g). The results confirmed the statistically significant, differential abundances of the afore mentioned cell types. The neighbourhood data were also visualized as a Beeswarm plot (Fig. 2h). + +<|ref|>text<|/ref|><|det|>[[115, 589, 882, 787]]<|/det|> +Analysis of gene expression revealed significant dysregulation associated with DR in the differentially abundant epithelial subpopulations (Log2FC \(>0.5\) and Spatial FDR \(< 0.1\) ) (Extended data Fig. 2d). VIM, PGR, and MMP7 were upregulated in the proliferative epithelium of samples from former sPE patients. The sPE epithelium subpopulation was characterized by upregulation of SRFP4 and IGF1, and downregulation of CXCL14, PAEP and TIMP3; the ciliated epithelial cells downregulated MT1G, CXCL14 and GPX3. As to the subpopulations that were less abundant in former sPE patients, the glandular epithelium cells from these individuals had dysregulated expression of several secretoglobins and reduced mRNA levels of the transcriptional regulator, MECOM. Also epithelium transition had higher expression of SERF4, FOS, JUN, and lower expression of CXCL14 and TIMP3, among other. + +<|ref|>text<|/ref|><|det|>[[115, 800, 881, 886]]<|/det|> +Altogether, we confirm that there is a notable imbalance in epithelial populations in former sPE patients with a predominant aberrant stage of proliferative epithelium and ciliated epithelium coexisting with glandular secretory and luminal epithelium together with various aberrant epithelial cell types that might contribute to the epithelial phenotype observed macroscopically and microscopically (Fig 1a- h). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 84, 879, 123]]<|/det|> +## Endometrial cell type-specific differentiation defects in former sPE patients: Epithelia to Stroma Transition. + +<|ref|>text<|/ref|><|det|>[[115, 139, 882, 338]]<|/det|> +We integrated the transcriptomes of the subpopulations that were either the precursors or the products of the epithelia to stroma transition zone (Fig. 3a; Extended data Fig. 3). This analysis included: stroma, epithelium, epithelium- stromal transition, ciliated epithelium and cycling cells. The complete set of epithelial cell subtypes from both sample groups and their differentially expressed genes is shown in Extended data Fig. 1d. Comparing both conditions (Fig. 3b) there was a notable absence of all the subtypes in sPE samples, including those in the transition zone. This conclusion was substantiated by neighbourhood analysis (Fig. 3c) and the data were visualized as a beeswarm plot (Fig. 3d). Thus, cells involved in the epithelia- to- mesenchymal transition (EMT), which were abundant in the control samples, were greatly reduced in the endometrium from former sPE patients. + +<|ref|>text<|/ref|><|det|>[[115, 352, 882, 752]]<|/det|> +To delve deeper into understanding of the epithelial- to- stromal transition, we performed RNA velocity analysis followed by cellDancer correction of the combined datasets were impacted in late secretory phase endometrial biopsies from former sPE patients (Extended Data Fig. 3a). The results revealed a temporal dynamic transcriptional process originating from epithelial cells and extending to stromal cells. We depicted this dynamic using cellCondiments obtaining two lineages (Extended Data Fig. 3b). Linage 1, representing the most interesting progression alongside the epithelium, epithelium- stromal transition and stroma cell populations, was projected in both sPE and control samples (Fig. 3e). The differentiation vector map for lineage 1 shows a clear transition from secretory glandular epithelium to stroma describing an EMT associated with late secretory decidual state in controls. In contrast, we found significant density disturbances that are associated with DR in formerly sPE patients (Extended Data Fig. 3c and d). We identify genes whose expression patterns change along the pseudotime (Fig. 3f; Extended Data Fig. 4). Epithelial cells reported with the expression of genes (DUSP2, IL17C, and FTH1), transition zone expressed genes associated with the EMT (SNAI2 and NOTCH3) and finally stromal cells are identified by the expression of collagens (COL6A1, COL6A2), GRIA3 and B3GALT5. Finally, differential expression between conditions highlighted the substantial contribution of controls to the transition (e.g., IGFBP2 and ACTA2) (Fig. 3g). These results suggested not only a decrease but also a dysregulation of fundamental aspects of stromal cell transition to an epithelial phenotype in DR endometrial samples. + +<|ref|>sub_title<|/ref|><|det|>[[118, 800, 707, 817]]<|/det|> +## Cell-cell communications are dysregulated among endometrial cells in sPE + +<|ref|>text<|/ref|><|det|>[[115, 831, 881, 891]]<|/det|> +A myriad of cell- cell communication (CCC) networks is essential for properly responding to changing hormone levels during decidualization. We used CellChat to infer the ligand- receptor pairs that were involved in endometrial CCC networks in the control vs. the sPE datasets with statistical significance. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 882, 349]]<|/det|> +The relative and absolute flow of information, calculated using the total interaction probability among all cell subpopulations in both conditions, is depicted in Extended Data Fig. 5a,b. Signals that were stronger in samples from sPE samples included components of the NRG, BMP, CX3C, and EGF pathways. Signals that were stronger in control samples included components of the IL1 and MADCAM pathways. In general, there was a notable increase in signals flowing among cells obtained from former sPE patients. Next, we mapped the perturbations in communication networks to specific cell types (Extended Data Fig. 6). The affected cells included glandular secretory epithelium, stroma, stroma early secretory, and stromal transition. Consistent with the overall pattern of information flow (Extended Data Fig. 6a), many of the outgoing (Extended Data Fig. 6b) and incoming signals (Extended Data Fig. 6c) in cells that comprised the sPE samples were largely absent in the comparable cell subpopulations from the control samples. Overall, mapping of the affected networks to particular cell types reaffirmed the phase shift of specific stromal cell subpopulations in the endometrium from former sPE patients. + +<|ref|>text<|/ref|><|det|>[[116, 363, 881, 450]]<|/det|> +Next, we investigated the ligand- receptor pairs contributing to the sPE- associated CCC network perturbations and the cell types that were involved. The most statistically significant pathways and those that play particularly important roles in decidualization are displayed as chord plots (Fig. 4a,c,e,g,i,j) with the relative contribution of specific ligand receptor pairs shown as bar graphs (Fig. 4b,d,f,h). + +<|ref|>text<|/ref|><|det|>[[115, 462, 882, 797]]<|/det|> +In control samples, endothelin- mediated signals are directed between the endothelium and glandular secretory epithelium to the various types of stromal cell subtypes (Fig. 4a). In DR cases, however, the signals are mainly redistributed between epithelial early proliferative and different stromal cell subpopulations. This dispersion involves weakening of EDN1- EDNRA signals and the new appearance of signaling mediated by EDN3- EDNRB (Fig. 4b) in sPE. Canonical and non- canonical WNT (ncWNT) signals are key regulators of decidualization. As to the latter, in control samples, the stromal transition subpopulation is strongly autoregulating and communicating with endothelial cells and the perivascular subpopulations (Fig. 4c). In samples from sPE cases, DR signals emerging from the stromal transition subpopulation with endothelium and the stroma are diminished and autocrine stroma early secretory regulation emerged (Fig. 4c), also involving new ligand- receptor pairs such as WNT5A- FZD10 (Fig. 4d). In control samples, canonical WNT signalling primarily involves autocrine regulation of the stromal transition cell type and communication between this subpopulation and stromal decidual cells (Fig. 4e). In cases, stromal transition cells and early secretory stromal cells signal to undifferentiated stromal cells (Fig. 4e), involving at least 4 canonical WNT pathways that were not active in control samples (Fig. 4f). + +<|ref|>text<|/ref|><|det|>[[115, 810, 881, 896]]<|/det|> +There were also obvious alterations in signaling via SEMA3A, which is highly expressed in the proliferative phase of the cycle (24). In control samples, the stromal transition subpopulation communicates with endothelial cells and stroma (Fig. 4g). In DR samples, signals had a very different pattern, primarily distributed between proliferative epithelial cells and numerous other types (e.g., + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 394]]<|/det|> +decidual cells, proliferative stromal cells, and endothelial cells; Fig. 4g). The latter signals included many pathways that were not observed in control samples (Fig. 4h and Extended data Fig. 7 and 8). We also investigated communication pathways involving SPP1, which is highly expressed during the mid- late secretory phase by epithelial cells (43). In control samples, SPP1 signals were weakly distributed among numerous cell types (Fig. 4i). In samples from the DR cases, strong autocrine (glandular epithelium) and paracrine signals (between glandular epithelium and numerous stromal a decidual cell types; Fig. 4i) emerged. Finally, dramatic changes in signaling via tenascin, an extracellular matrix protein, were also evident (Fig. 4j). This molecule is highly express in endometriosis (44) and in endometrium during the proliferative phase (45). In controls, communication was primary between stromal decidual cell type 3 and the epithelial subtypes. In DR cases, new circuitry arose involving glandular secretory epithelium and stromal cells as well as the various subtypes of decidual cells. In conclusion, the data suggest that in the endometrium of the cases with DR there is a very significant rewiring of signaling pathways in terms of molecules and cell types. Altogether the proliferative phenotype of cells from the cases leads to aberrant decidualization. + +<|ref|>sub_title<|/ref|><|det|>[[118, 440, 372, 456]]<|/det|> +## Spatial Resolution of DR in sPE + +<|ref|>text<|/ref|><|det|>[[115, 471, 883, 693]]<|/det|> +We spatially resolved the transcriptomic changes discovered by scRNA- seq by spatial transcriptomics on formalin- fixed paraffin- embedded endometrial tissue biopsied during the late secretory phase, analysing 95 regions of interest (ROIs) in sPE (n=8) and control (n=8) patients (Fig. 5). Those regions fell into three categories based on the endometrial tissue architecture: (i) enriched in stromal (VIM+) and endothelial cells (CD31+) (Fig. 5a), (ii) enriched in glandular epithelium (PanCK+) (Fig. 5c) and (iii) luminal epithelial regions enriched in luminal epithelium and stromal cells (PanCK+, VIM+) (Fig. 5e). Differential expression analysis (cases vs. controls) revealed: (i) 430 DEGs in stroma, (ii) 575 DEGs in glandular epithelium, and (iii) 456 DEGs in luminal ROIs. Heatmaps of the DEGs for each compartment were constructed by unsupervised hierarchical clustering based on Pearson distances of the normalized data z- scores. With a few exceptions, sPE and control samples clustered separately. + +<|ref|>text<|/ref|><|det|>[[115, 711, 883, 888]]<|/det|> +We coalesced the DEGs from the single cell and spatial transcriptomics analyses (Extended data Fig. 9). In general, the DEGs had the same pattern of up- or downregulation in both analyses, which cross- validated the findings. In the enriched stromal ROIs (Fig. 5b and Extended data Fig. 9a), DEGs identified by both technologies included those involved in cytoskeletal organization. TPM1, an actin binding protein involved in contractility and cytoskeleton dynamics (46), was downregulated as were MYL9, which increases decidual contractility, and ANXA2 previously associated with DR (15). SEMA3, identified in our analysis of cell- cell communications, was upregulated as was KDM6b, which is involved in regulating decidual DNA methylation and modulating target gene expression (47). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 147]]<|/det|> +The glandular epithelium ROI genes (Fig. 5d and Extended data Fig. 9b) were upregulated in DR cases and shared with the single cell analysis of epithelium including TNFRSF21 and the TNF receptor. They are also upregulated in women with recurrent pregnancy loss (48). + +<|ref|>text<|/ref|><|det|>[[115, 164, 882, 409]]<|/det|> +The luminal epithelium ROI genes (Fig. 5f and Extended data Fig. 9c) that were upregulated in DR cases and shared with the single cell analysis of stroma included LIMA, LIM domain and Actin Binding 1, a cytoskeleton- associated protein that inhibits actin filament depolymerization, is essential for proper mitochondrial function and a key effector mediating pluripotency (49). SCRAB1, a scavenger receptor involved in the removal of apoptotic cells in degenerated decidua tissue, was downregulated. The luminal epithelium ROI genes (Fig. 5f) that were upregulated in cases and shared with the single cell analysis of the epithelium included MMP7. In controls, its expression is associated with the proliferative phase of the menstrual cycle and downregulated during the late secretory phase (30). DUSP2 and ACADSB also shared this pattern. DUSP2 interacts with IL- 6 and its overexpression activates pathways that regulate inflammation and proliferation (50). ACADSB participates in cell migration, invasion and proliferation and cancer cells (51). + +<|ref|>text<|/ref|><|det|>[[115, 427, 881, 536]]<|/det|> +Altogether, coalescing the single cell and spatial transcriptomics data, pointed to an imbalance of cells in the epithelial and stromal compartments of samples from the DR cases that was attributable to late secretory endometrium retaining characteristics of the proliferative phase of the cycle. More specifically, our results suggested dysregulation of fundamental aspects of epithelial cell transition to a stromal phenotype during decidualization. + +<|ref|>sub_title<|/ref|><|det|>[[115, 555, 855, 573]]<|/det|> +## Spatial proteomic mapping of DR in sPE by laser capture microdissection-mass spectrometry + +<|ref|>text<|/ref|><|det|>[[115, 591, 881, 744]]<|/det|> +We used LCM- MS to analyze the glandular epithelium, luminal epithelium and stroma of endometrial biopsies obtained during the late secretory phase of the cycle from an independent cohort of donors (sPE, n=7; controls, n=10) (Fig. 6a). To decipher the underlying functions of the identified proteins, overrepresentation analysis was performed, and protein- protein interaction networks were built to identify perturbations in endometria from the cases. In addition, estrogen receptor 1 (ESR1) and progesterone receptor (PGR) were included in the network to assess their associations with the DR phenotype. + +<|ref|>text<|/ref|><|det|>[[115, 762, 881, 893]]<|/det|> +The stromal compartment had the highest number of DE proteins. In this regard, 439 (43.9%) were specific to sPE samples and 53 (5.8%) were unique to control samples (Fig. 6b). Among proteins specific to sPE samples, was STAT3—which participate in decidualization downstream of PGR and is aberrantly increased in endometriosis (52, 53)—. Response to steroid hormones, particularly estrogens, is enriched in the sPE group, as is aging and cell growth (p.adjusted<0.05; Fig. 6c), consistent with increased proliferation and supported by the differential expression of markers of cell proliferation in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 170]]<|/det|> +tumorigenesis such as ARG1 and B2M. Controls group showed an enrichment for extracellular matrix organization and EMT. The analysis of the protein- protein interaction network identified ESR1 and STAT3 as hub proteins, supporting the central role of hormonal signaling in the sPE affected pathways (Fig. 6d). + +<|ref|>text<|/ref|><|det|>[[115, 187, 882, 387]]<|/det|> +The glandular epithelium of control samples had 401 (27%) unique proteins while 103 (6.9%) were only present in sPE samples and 982 were common to both groups (Fig. 6b). The proteome from controls was enriched for hormone secretion (p.adjusted \(< 0.05\) ), whereas the sPE proteome was enriched for cell survival in response to ROS and epithelial cell differentiation (p.adjusted \(< 0.05\) ; Fig. 6c). The protein- protein interaction network revealed that these pathways are significantly interconnected with ESR1 and PGR (Fig. 6e). Thus, disrupted hormonal signaling could drive an imbalance in proliferation/differentiation, affecting the secretory function of endometrial glands by mechanisms that include the generation of ROS and defective cytoskeletal organization. Notable proteins related to this phenotype were specific to controls such as EGFR and LRP1. + +<|ref|>text<|/ref|><|det|>[[115, 404, 882, 602]]<|/det|> +The majority of luminal epithelium proteins 562 (63.3%) were shared between cases and controls; 67 (7.6%) were unique to controls and 254 (28.8%) were specific to cases (Fig. 6b). This proteome was enriched for response to steroids, specifically estrogens (p.adjusted \(< 0.05\) ; Fig. 6c), the major hormone during the proliferative phase. Accordingly, proteins specific to sPE were involved in cell proliferation (e.g., NONO, TRIM25), increased metabolism (e.g., PSMD2, PSMD7) and, consequently, inhibition of apoptosis signaling (e.g. SOD2, WFS1). In contrast, proteins unique to controls were enriched for extracellular matrix organization (e.g. CAV1, LAMB2). Then, we built a network with proteins involved in representative features of these pathways that were significantly disturbed in sPE (Fig. 6f). The results supported strong interconnections among these pathways and the key role of ESR1 and PGR. + +<|ref|>text<|/ref|><|det|>[[115, 621, 882, 820]]<|/det|> +Finally, we identified intersections between the biological processes identified by the scRNA- seq and LCM- MS analyses (Fig. 6g). There was substantial concordance between the two datasets in terms of those that were specific to sPE samples or controls. In many cases, there was also an overlap in the related biological processes that mapped to two or more compartments. The sPE group was characterized by processes/molecules involved in epidermal regulation, responses to oxidative stress/ROS and aging. The control group was characterized by processes such as protein secretion, cell growth, extracellular matrix organization, leukocyte adhesion and IL2 production. The sPE group was also notable for the absence of processes that were significantly represented in the control group such as gland development, response to LIF and EMT. + +<|ref|>text<|/ref|><|det|>[[115, 839, 881, 901]]<|/det|> +Thus, by overlapping the proteomic- based pathways associated with sPE and control samples with the DEGs from the single- cell dataset, we confirmed the key molecular features that shape DR at a multi- omic level. Overall, the main features are an imbalance in proliferation/differentiation in various regions + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 191]]<|/det|> +of the endometrium, associated with a disturbed response to steroid hormones, particularly estrogens, which could ultimately affect epithelial- stromal crosstalk and the secretory function of glands. Altogether, these results provide the deepest characterization of DR to date with possible clinical implications not only in the understanding of the pathogenesis of sPE but also in endometriosis and other pathological conditions related with DR. + +<|ref|>sub_title<|/ref|><|det|>[[118, 239, 212, 255]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 264, 882, 418]]<|/det|> +Most pregnancy disorders originate from alterations in the periconeptional period, at the time where trophoectoderm invades the decidualized endometrium (1). Defective trophoblast invasion is the primary underlying cause of the great obstetrical syndromes such as preeclampsia, IUGR or preterm labor (54). Trying to answer why is placentation abnormal in these relevant pathologies our group and others have identified a decidualization defect in the endometrium (the soil) of these patients as a contributing factor in these obstetrical conditions (4), including sPE (15, 16, 55), IUGR or placenta. + +<|ref|>text<|/ref|><|det|>[[115, 444, 881, 531]]<|/det|> +Our present study offers a comprehensive exploration of the DR condition in former sPE patients, shedding light on the associated changes at gross, microscopic, scRNA seq, spatial transcriptomics and proteomics by LCM- MS. Our findings underscore the intricate molecular landscape underlying DR, implicating not only alterations in stroma and epithelium but also in the epithelial- to- stromal transition. + +<|ref|>text<|/ref|><|det|>[[115, 544, 882, 742]]<|/det|> +At the morphological level, distinct features including glandular dilation, hinted at underlying DR. Deeper investigation using scRNA- seq revealed significant alterations in cellular composition and differentiation patterns. At the stromal level, our analysis delineated specific subpopulations associated with different decidualization stages, revealing a shift in the balance leading to a mosaic of proliferative and decidualized stromal cells in former sPE patients, which causes cellular communications to be affected. Dysregulated gene expression signatures further corroborated the aberrant cellular states, highlighting the upregulation of markers indicative of proliferative activity and downregulation of those associated with decidualization. Additionally, endothelial and immune cells showed a disturbed transcriptome consistent with inflammatory dysregulation. + +<|ref|>text<|/ref|><|det|>[[115, 756, 882, 911]]<|/det|> +Similarly, an imbalance was evident in the epithelial cell subpopulations, with increased abundance of proliferative and ciliated epithelial cells in DR samples from former sPE patients. Dysregulated gene expression patterns mirrored this imbalance, with upregulation of genes linked to proliferation and downregulation of those associated with secretory functions indicating a potential dysregulation in the normal differentiation and maturation processes of uterine epithelial cells. This feature is relevant because glandular epithelia is the source of the uterine fluid or "uterine milk" that will provide nutrients and antimicrobial protection for the implanting conceptus. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 169]]<|/det|> +Our analysis also uncovered perturbations in the epithelial- to- stromal transition, emphasizing a significant reduction in cells involved in this transition in former sPE patients. Furthermore, dysregulated cell- cell communication networks were evident, with notable alterations in signaling pathways critical for decidualization as WNT or SPP1, angiogenesis, and hormonal regulation. + +<|ref|>text<|/ref|><|det|>[[115, 183, 881, 291]]<|/det|> +Spatial transcriptomics and proteomic analyses provided additional layers of insight, highlighting spatially specific dysregulation in both epithelial and stromal compartments, mainly related to proliferation/differentiation imbalance. Key pathways implicated in DR, including hormonal signaling —particularly response to estrogens—, oxidative stress response, and cytoskeletal organization, were identified, further corroborating findings from single- cell analyses. + +<|ref|>text<|/ref|><|det|>[[115, 305, 881, 459]]<|/det|> +The comprehensive characterization of DR presented in this study not only advances our understanding of the pathogenesis of sPE but also has broader implications for other great obstetrical syndromes prevalent gynecological diseases such as endometriosis (9, 10), and recurrent miscarriage (13). The identified molecular signatures may serve as potential biomarkers for preconceptional detection of DR and intervention strategies before pregnancy is established. Preconceptional care is emerging as a key component of reproductive care not only to reduce perinatal morbidity and mortality, but to optimize health for mothers and children (56). + +<|ref|>text<|/ref|><|det|>[[115, 472, 881, 602]]<|/det|> +In summary, our multi- omic approach offers a nuanced depiction of DR, revealing an imbalance in proliferation/differentiation within the endometrium, disrupted response to steroid hormones—especially estrogens—and potential impacts on epithelial- stromal communication and glandular secretory function. These findings provide valuable insights into molecular underpinnings of DR and highlighting potential preconceptional therapeutic targets for mitigating the occurrence of the great obstetric syndromes. + +<|ref|>sub_title<|/ref|><|det|>[[118, 646, 313, 662]]<|/det|> +## Material and Methods + +<|ref|>sub_title<|/ref|><|det|>[[118, 680, 222, 696]]<|/det|> +## Study design + +<|ref|>text<|/ref|><|det|>[[115, 710, 881, 909]]<|/det|> +A total of 23 non- pregnant women who had a previous pregnancy were enrolled in this study for single- cell RNA- Sequencing analysis. From those, 12 were women with a healthy previous pregnancy as control cases and 11 were women diagnosed with Severe Preeclampsia in their last pregnancy clinically classified based the ACOG guidelines: high blood pressure (systolic \(>160\) or diastolic \(>100 \mathrm{mmHg}\) ) or thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema, or the inset cerebral or visual disturbances. Endometrial biopsies were collected during the late secretory phase of the menstrual cycle (1 to 3 days before menstruation) and tissue were segmented in different portions, one of the were embedded in paraffin to obtain histological sections for the spatial transcriptomics approach, other section were included in OCT for the laser capture and mass spectrometry approach + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 168]]<|/det|> +and another portion was processed to obtain the cell isolation that will be used to the single- cell RNA sequencing using 10X genomics technologies. The maternal parameters were recruited in the Supplementary table 1. A two tailed Student's T- test was applied between sPE and Control variables based on the normal distribution of data. + +<|ref|>sub_title<|/ref|><|det|>[[118, 184, 259, 200]]<|/det|> +## Sample collection + +<|ref|>text<|/ref|><|det|>[[115, 215, 882, 392]]<|/det|> +All human endometrial samples of this study were collected in University and Polytechnic La Fe Hospital, Valencia, Spain and pass their Clinical Research Ethics Committee. Samples were collected from women aged 18- 40 without any medical condition who had been pregnant 1- 8 years earlier. All donors had regular menstrual cycles (25- 31 days) without underlying pathological condition and had no received hormonal therapy in the 3 months preceding sample collection. Endometrial biopsies were obtained by pipeline catheter (Genetics Hamont- Achel, Belgium) under sterile conditions and were maintained in a preservation solution HypoThermosol® FRS (Stemcell Technologies) at \(4^{\circ}\mathrm{C}\) until their processing. + +<|ref|>sub_title<|/ref|><|det|>[[118, 406, 268, 422]]<|/det|> +## Sample Processing + +<|ref|>text<|/ref|><|det|>[[115, 437, 882, 590]]<|/det|> +Endometrial biopsies were washed with PBS and mince. Afterwards, samples were enzymatically digested with a solution containing \(1\mathrm{mg / mL}\) Collagenase V (C9263, Sigma- Aldrich), \(100\mu \mathrm{g / mL}\) DNase Type I (03724751103, Roche) and \(10\%\) inactivated FBS in RPMI media for \(45\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) and \(175\mathrm{rpm}\) . The enzymatic reaction was inactivated by adding 1 volume of \(10\%\) inactivated FBS in RPMI media (Complete medium), the solution filtered through a \(100 - \mu \mathrm{m}\) cell strainer, and the strainer washed with \(5\mathrm{mL}\) of complete medium. The flow- through solution contained the stromal fraction, whereas the undigested tissue retained in the cell strainer we the epithelial fraction of the endometrium. + +<|ref|>text<|/ref|><|det|>[[115, 603, 881, 733]]<|/det|> +The stromal fraction was centrifuged \(5\mathrm{min}\) at \(2000\mathrm{rpm}\) , and the pellet was washed with PBS. 1x RBC Lysis Buffer (eBioscience, ThermoFisher Scientific) \(5\mathrm{min}\) at RT was applied to eliminate blood from the sample following manufacturer recommendation. The cell fraction was washed with PBS centrifuged \(5\mathrm{min}\) at \(2000\mathrm{rpm}\) and the pellet resuspended with \(200\mu \mathrm{L}\) of \(0.04\%\) BSA in PBS (w/v). Finally, sample was filtered using a \(40 - \mu \mathrm{m}\) flowmi filter (BAH136800040- 50EA, Merk) to obtain a single cell suspension of the stromal fraction. + +<|ref|>text<|/ref|><|det|>[[115, 748, 882, 902]]<|/det|> +Regarding the epithelial fraction, the \(100 - \mu \mathrm{m}\) cell strainer mentioned above contained the epithelial cells. They were recovered by flushing \(15\mathrm{mL}\) PBS to the inverted cell strainer into a \(50\mathrm{- mL}\) falcon tube and centrifuged \(5\mathrm{min}\) at \(2000\mathrm{rpm}\) . For digestion of the epithelial tissue, the pellet was incubated with \(5\mathrm{mL}\) of \(100\mu \mathrm{g / mL}\) DNase Type I (03724751103, Roche) in \(0.25\%\) Trypsin- EDTA solution (25200072, Life Technologies) \(10\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) and \(175\mathrm{rpm}\) shacking. After inactivation with complete media, the suspension was filtered through a \(100 - \mu \mathrm{m}\) cell strainer, the filter was washed with \(5\mathrm{mL}\) PBS and the solution centrifuged \(5\mathrm{min}\) at \(2000\mathrm{rpm}\) . If needed, the pellet was treated with RBC lysis solution as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 880, 123]]<|/det|> +mentioned before, washed again with PBS, resuspended with \(200~\mu \mathrm{L}\) of \(0.04\%\) BSA in PBS (w/v) and filtered using \(40 - \mu \mathrm{m}\) flowmi filter. + +<|ref|>text<|/ref|><|det|>[[115, 137, 881, 221]]<|/det|> +An aliquot of stromal and epithelial cell suspensions was stained with Tripan Blue dye and counted for alive cell concentration in an automatic cell counter (EVETM, NanoEnTek). Same number of both stromal and epithelial cells will be mixed and a total of 17,000 cells were loaded a 10X Chromium as explained in the following section. + +<|ref|>sub_title<|/ref|><|det|>[[118, 238, 288, 254]]<|/det|> +## Single cell processing + +<|ref|>text<|/ref|><|det|>[[115, 269, 882, 468]]<|/det|> +scRNA- seq analysis of the endometrial processed samples were performed using the 10X Chromium technology (10X Genomics, Pleasanton, CA, USA). As mentioned, 17,000 cells were loaded onto a 10X G Chip to obtain Gel Bead- in- emulsions (GEMs) containing an individual cell. GEMs were used to generate barcoded cDNA libraries following the manufacturer's instructions (Single Cell 3' Reagent Kit v3.1, 10X Genomics) and quantified using the TapeStation High Sensitivity D5000 kit (Agilent, Germany). Following, cDNA (1- 100 ng) was obtained to construct gene expression libraries that were quantified using the TapeStation High Sensitivity D1000 kit (Agilent, Germany) determining the average fragment size and library concentration. Libraries were normalized, diluted, and sequenced on the Illumina NovaSeq 6000 system (Illumina, USA) according to the manufacturer's instructions. + +<|ref|>sub_title<|/ref|><|det|>[[118, 483, 439, 500]]<|/det|> +## scRNA-seq data processing and filtering + +<|ref|>text<|/ref|><|det|>[[115, 519, 882, 852]]<|/det|> +Raw sequences were demultiplexed, aligned, and counted using the CellRanger software suite (v 6.0.2) for whole cell gene expression calculations, which takes advantage of intronic reads to improve sensitivity and sequencing depth (human reference genome GRCh38- 2020- A). Low- quality droplets and barcodes were filtered out in four quality control- based consecutive steps throughout the analysis: (i) low UMI- count barcode removal using an EmptyDrops- based method; (ii) cells marked as doublets by DoubletFinder (2.0.3) and scds (1.6.0) tools – the hybrid approach from the scds R package was used to avoid removing false- positive doublets; (iii) cells with median absolute deviation (MAD) \(>3\) in two of three basic quality control metrics: number of detected features, number of counts and mitochondrial ratio. These cell- to- count matrices were integrated and corrected using Seurat and scVI functions, as described below. A final filtering step, (iv), was applied alongside different rounds of clustering, where the obtained clustered cells with less than 750 features/cell, more than \(25\%\) mitochondrial ratio, and/or showing a pattern of high doublet- scoring plus no gene marker associated expression (during manual cell type annotations), were also removed (Supplementary figure 1). A total of 65,381 high quality cells were integrated within the uniform manifold approximation and projection (UMAP). From those 28,154 cells formed the sPE group and 37,227 cells the control group. + +<|ref|>sub_title<|/ref|><|det|>[[118, 871, 575, 888]]<|/det|> +## Integration of single cells across conditions and clustering + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 372]]<|/det|> +As a first clustering analysis approach, read count matrices per sample were merged and processed following Seurat's default pipeline (package version 4.1.3). After normalization, the first thirty principal components on the 4,000 highly variable genes were used for dimensional reduction; cells were clustered and projected onto the UMAP. FindNeighbors and FindClusters functions were then applied for graph- based clustering by constructing a KNN graph using Euclidean distance in the principal component analysis space, which was then defined into clusters using the Louvain algorithm to optimize the standard modularity function. cluster (R package v0.4.4) was applied to select the most stable clustering resolution. The first output of sample distribution in clusters and cluster marker genes was then explored to evaluate biases from our data batches. Next, the scVI python package (v0.19.0) was used to remove patient origin inter- individual biases. The top thirty scVI components were used to embed and plot cells in the new reduced dimension space. These matrices were then input into Seurat's clustering and differential expression protocol. The clustering of different primary cell types for fine- grain cell type annotations was equally computed, following all the above- described steps. + +<|ref|>sub_title<|/ref|><|det|>[[118, 391, 674, 408]]<|/det|> +## Tissue cell composition analysis and annotation of scRNA-seq datasets + +<|ref|>text<|/ref|><|det|>[[115, 426, 882, 693]]<|/det|> +The identification and labeling of major cell types based on differentially expressed genes per cluster compared to the remaining clusters using the Wilcoxon Rank Sum test and adjusted p- values for multiple comparisons with the false discovery rate (FDR) method (57). The primary cell populations were labeled by revising the expression of reported canonical markers from each cluster. Cell clustering was conducted under single- cell published datasets(2, 3, 24), manual curation of canonical markers published in human protein atlas (HPA)(58). The main cell types were then subset separately: epithelium, stromal fibroblast, perivascular, cycling stroma, endothelium, macrophages, Becells, Tecells, Nk cells. A new clustering was performed on each to create the different zoom- ins that describe the contained sub- populations. The clustered zoom- ins were manually annotated by an extensive review of differentially expressed genes in the Human Protein Atlas database, single- cell atlases, and scientific literature(51). Genes with strong cluster- specificity, as determined by a p- value below 0.01, and the highest rank fold change and percentage of expressing cells were considered. + +<|ref|>sub_title<|/ref|><|det|>[[118, 712, 526, 729]]<|/det|> +## Cell trajectories on the transcriptional pseudotimes + +<|ref|>text<|/ref|><|det|>[[115, 748, 882, 902]]<|/det|> +The inference of cell trajectories was based on the predicted velocity of mRNA synthesis in each cell (RNA velocity). The method for calculating RNA velocity begins with obtaining the spliced/unspliced matrix using the Velocytool (59). RNA velocities were separately obtained for each zoom- in clustering. As already stated by(60), calculations using Velocytool are sensitive to genes that undergo sudden large changes in their expression patterns, described as Transcription Bursts. To avoid this issue, velocities computed by scVelo (v0.2.5) were corrected using CellDancer (v1.17). In scVelo, we filtered the genes with fewer than 20 counts shared by cells in the filter genes function and used only the top + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 191]]<|/det|> +2000 most variable genes in the filter_gene_s_dispersion. We used the dynamic mode to compute cellular and velocity dynamics. In CellDancer (61), we computed the velocities and projected the vector field predictions computed by the model onto the previously computed UMAP embedding space. After identifying the start and ends of the trajectories, we used Slingshot (62) to compute the genes related to changes across pseudotime in the inferred trajectories. + +<|ref|>sub_title<|/ref|><|det|>[[117, 211, 684, 228]]<|/det|> +## Analysis of differential cell abundances and differential gene expression + +<|ref|>text<|/ref|><|det|>[[115, 246, 881, 378]]<|/det|> +To identify differential abundance in cell populations between sPE and control endometria we used MiloR (version 1.6.0) tool described by Dann et al.(63). This approach supports the grouping of cells on a k- nearest neighbor graph and evaluates the change in cell abundance between conditions, p values were adjusted using spatial FDR of \(< 0.1\) . Further differential gene expression analysis was performed using the Model- based Analysis of Single Cell Transcriptomics (MAST), where a contrast test was established for each cell type (64). + +<|ref|>sub_title<|/ref|><|det|>[[117, 397, 498, 414]]<|/det|> +## Analysis of cell-to-cell communication networks + +<|ref|>text<|/ref|><|det|>[[115, 428, 881, 581]]<|/det|> +In this study, we employed the CellChat R package (v1.1.3) to discern potential cell interactions between distinct cell populations within both control and preeclampsia samples. This tool employs a curated database to infer the overall interaction probability and communication information flows based on the expression of specific ligand- receptor pairs. To elucidate, the total interaction probability measures the likelihood of communication between two cell types, where one serves as the sender and the other as the receiver. This estimation is based on the number of interactor molecules expressed (i.e., ligand- receptor pairs) and the strength of this interaction (expression level). + +<|ref|>text<|/ref|><|det|>[[115, 595, 881, 726]]<|/det|> +Subsequently, the cumulative communication probability of all pairs in a pathway network is employed to compute the communication information flow. To ensure robust results, pathways with fewer than ten cells were excluded from the analysis ( \(10 <\) cells per subpopulation). Additionally, we accounted for the impact of varying cell population sizes by enabling the 'population.size' argument in the 'computeCommunProb' function, which properly scales the communication weight of each subpopulation (set to TRUE). + +<|ref|>text<|/ref|><|det|>[[115, 740, 880, 780]]<|/det|> +Finally, we conducted a differential CCC analysis between control and preeclampsia samples using the 'ranknet' function, applying a significance threshold of 0.05. + +<|ref|>sub_title<|/ref|><|det|>[[117, 828, 458, 845]]<|/det|> +## Spatial Transcriptomics sample processing + +<|ref|>text<|/ref|><|det|>[[115, 860, 881, 899]]<|/det|> +To achieve this objective, Nanostring® technology was utilized, specifically employing the GeoMx® Human Whole Transcriptome Atlas panel. This panel comprehensively covers protein- coding + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 169]]<|/det|> +genes, allowing the spatial analysis of any tissue within a chosen biological area. In this study, specific biological regions were selected to capture the higher cellular diversity commonly found in the late secretory phase of the endometrium, encompassing various cell types such as stromal, endothelial, luminal epithelial, and glandular epithelial cells, among others. + +<|ref|>text<|/ref|><|det|>[[115, 182, 881, 247]]<|/det|> +We followed established experimental procedures of Nanostring with some modifications, as detailed below. In summary, we prepared \(5\mu \mathrm{m}\) thick FFPE sections from 16 specimens. These consecutive sections were then subjected to H&E and WTA (Whole Transcriptome Analysis). + +<|ref|>text<|/ref|><|det|>[[115, 260, 880, 300]]<|/det|> +The H&E slides was used as guide to select the ROIs. A total of 95 ROI, distributed equally in 16 patients were selected covering the main cell types of the endometrium. + +<|ref|>text<|/ref|><|det|>[[115, 313, 881, 445]]<|/det|> +For the WTA, the slides were first incubated at \(60^{\circ}\mathrm{C}\) for an hour, then deparaffinized using limonene (Merck Chemicals And Life Science, S.A.U.), and subsequently rehydrated. Antigen retrieval was performed by subjecting the slides to \(1\mathrm{x}\) Tris- EDTA/pH 9 in a steamer for \(20\mathrm{min}\) at \(100^{\circ}\mathrm{C}\) . Next, proteinase K (Thermo Fisher Scientific, AM2548) was used to digest the samples at a concentration of \(1\mathrm{Xg / ml}\) for \(15\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) . Following that, the samples were postfixed in neutral- buffered formalin for \(5\mathrm{min}\) . + +<|ref|>text<|/ref|><|det|>[[115, 459, 881, 567]]<|/det|> +Then samples underwent hybridization with a UV- photocleavable barcode- conjugated RNA in situ hybridization probe set, targeting 18,269 genes WTA. This hybridization process was conducted overnight at \(37^{\circ}\mathrm{C}\) . Afterward, the samples were thoroughly washed to eliminate any non- specific probes, and morphology markers were then applied to facilitate counterstaining. This counterstaining step was carried out for \(1\mathrm{h}\) at room temperature. + +<|ref|>text<|/ref|><|det|>[[115, 580, 881, 779]]<|/det|> +Then, morphology markers utilized were a combination of fluorescent agents: 1:10 SYTO13 (Nanostring), 1:50 anti- panCK- Alexa Fluor 532 (Nanostring), 1:500 anti- vimentin Alexa Fluor 594 (Santa Cruz Biotechnology; cat.no: sc- 373717 AF594), 1:50 anti- CD31 Alexa Fluor 647 (Abcam; cat.no: ab215912) in blocking buffer W (NanoString). These markers were applied in blocking buffer W (NanoString). The immunofluorescence images, region of interest (ROI) selection, marker- specific area of interest (AOI) segmentation, and spatially indexed barcode cleavage and collection were performed using a GeoMx DSP instrument (NanoString). The typical exposure times were \(50\mathrm{ms}\) for SYTO13, \(200\mathrm{ms}\) for anti- panCK AF532, \(200\mathrm{ms}\) for anti- vimentin AF 594, and \(200\mathrm{ms}\) for anti- CD31 AF647. Approximately 96 ROIs were collected per specimen. + +<|ref|>text<|/ref|><|det|>[[115, 793, 881, 902]]<|/det|> +For library preparation, the manufacturer's instructions were followed, involving PCR amplification to add Illumina adapter sequences and unique dual sample indexes. Each single molecule in the sequencing library corresponded to one cluster on the flow cell. The paired- end read strategy recommended by NanoString resulted in two paired- end reads, forward and reverse, per cluster, which can be described as one read pair. To achieve a minimum sequencing depth of 150- 200 reads per square + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 880, 125]]<|/det|> +micron of illumination area, all WTA AOIs were sequenced on a NextSeq 2000 (Read type: Paired- end, read length: read 1: 27bp, read 2: 27bp, index 1 (i7): 8bp, index 2 (i7): 8bp). + +<|ref|>sub_title<|/ref|><|det|>[[118, 139, 363, 156]]<|/det|> +## QC Of Spatial transcriptomics + +<|ref|>text<|/ref|><|det|>[[115, 170, 881, 279]]<|/det|> +Inn every ROI, in segments with \(< 1000\) raw reads are removed, as well as segments with less than \(80\%\) of alignment, trimmed or stitched were removed. Regarding the \(\%\) of sequencing saturation defined as ([1- deduplicated reads/aligned reads] \(\%\) ) ROIs with less that \(50\%\) of this parameter are also removed. The geometric mean of several unique negative probes included in the GeoMx panel is calculated and it stablished the background count level per segment, + +<|ref|>text<|/ref|><|det|>[[115, 292, 881, 378]]<|/det|> +The No Template Control (NTC) count: values \(>1000\) could indicate contamination for the segments associated with this NTC; however, in cases where the NTC count is between 1000- 10000, the segments may be used if the NTC data is uniformly low (e.g. 0- 2 counts for all probes). Quality control for nuclei: \(>100\) nuclei per segment is generally recommended;. All segments of the study pass those qc filters. + +<|ref|>text<|/ref|><|det|>[[115, 392, 881, 477]]<|/det|> +We also remove outlier negative control probes from the data to refine our estimation of background and downstream gene detection. We remove outlier probes either entirely from the study (global) or from specific segments (local). There are 18807 probes that passed QC. 1 Global outlier and 7 Local outliers were detected + +<|ref|>text<|/ref|><|det|>[[115, 491, 870, 555]]<|/det|> +Regarding the limit of quantification (LOQ) per ROI/AOI segment based on the negative control probes to guide the filtering of segments and genes with low signal relative to background. The formula for calculating the LOQ in the \(\mathrm{i}^{\mathrm{th}}\) segment at n standard deviations ( \(\mathrm{n} = 2\) for this study) is: + +<|ref|>equation<|/ref|><|det|>[[193, 580, 742, 604]]<|/det|> +\[LOQ_{i} = g e o m e a n(N e g P r o b e_{i})*g e o S D(N e g P r o b e_{i})^{n}\] + +<|ref|>text<|/ref|><|det|>[[115, 622, 880, 686]]<|/det|> +In this dataset, we choose to remove segments with fewer than \(10\%\) of the genes detected. As a result, 0 segments were flagged and removed. In total, 0 segments were removed from the study from either segment QC or filtering. + +<|ref|>text<|/ref|><|det|>[[115, 700, 880, 741]]<|/det|> +10,239 targets were detected above LOQ in \(10\%\) or more of the segments. Including the genes of interest list, 10,489 targets in total were analyzed further. We filtered down to this number of targets + +<|ref|>sub_title<|/ref|><|det|>[[115, 755, 441, 772]]<|/det|> +## Normalization of spatial transcriptomics + +<|ref|>text<|/ref|><|det|>[[115, 785, 881, 893]]<|/det|> +Two common methods for normalization of GeoMx® WTA/CTA data are i) Quartile 3 (Q3) or ii) background. Both methods estimate a normalization factor per ROI/AOI segment to bring the segment data distributions together. Q3 is typically the preferred approach. High correlation between the geometric mean of the negative control probe counts and 75th quantile (Q3) of expression. Q3 normalization was used downstream for analysis. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 84, 707, 101]]<|/det|> +## Contrasting groups with Linear Mixed Models in Spatial transcriptomics + +<|ref|>text<|/ref|><|det|>[[115, 115, 881, 268]]<|/det|> +Ia given contrast, we are interested in comparing the magnitude of differences between groups and quantifying the significance. In many GeoMx experiments, multiple areas of illumination (AOIs) or regions of interest (ROIs) are sampled in a given slide. To account for the nested, non- independent sampling, we often use a linear mixed effect model (LMM) with slide as a random effect. The LMM accounts for the subsampling per tissue, allowing us to adjust for the fact that the multiple regions of interest placed per tissue section are not independent observations, as is the assumption with other traditional statistical tests. + +<|ref|>text<|/ref|><|det|>[[115, 282, 880, 322]]<|/det|> +Overall, there are two main types of the LMM models when used with GeoMx data: A) with random slope and B) without random slope. + +<|ref|>text<|/ref|><|det|>[[115, 336, 880, 400]]<|/det|> +When comparing features that co- exist in a given tissue section, a random slope is included in the LMM model. When comparing features that are mutually exclusive in a given tissue section the LMM model does not require a random slope. + +<|ref|>sub_title<|/ref|><|det|>[[115, 414, 803, 453]]<|/det|> +## Spatial proteomics: laser capture microdissection; protein isolation/digestion and mass spectrometry. + +<|ref|>text<|/ref|><|det|>[[115, 468, 881, 666]]<|/det|> +Three regions of each endometrial biopsy were isolated by laser capture microdissection (LMD): glandular epithelium, luminal epithelium and stroma. They were separately processed and analyzed using methods we previously published (65). Briefly, Frozen blocks of the tissue were sectioned \((- 20^{\circ}\mathrm{C})\) using a Leica CM3050 cryostat. Sections \((20\mu \mathrm{m})\) were mounted on Director slides (Expression Pathology; Hembrough et al., 2012). Slides with sections were kept under dry ice until LMD the same day. Sections on slides were manually defrosted in room air \((30\mathrm{~s})\) , immersed in PBS until the OCT was completely removed \((\sim 2\mathrm{~min})\) , dipped in \(0.1\%\) Toluidine Blue for \(30\mathrm{~s}\) , washed in ice- cold PBS, dehydrated (30 s/treatment) in a graded ethanol series \((75\% , 95\% , 100\%)\) , then rapidly dried with compressed nitrogen. + +<|ref|>text<|/ref|><|det|>[[115, 680, 881, 833]]<|/det|> +Following capture, the samples were incubated in an alkaline surfactant- containing solution at \(60^{\circ}\mathrm{C}\) with rigorous vortexing every 15 min for 1 h. Iodoacetamide (Thermo Fisher Scientific) was added to \(15\mathrm{mM}\) and incubated at room temperature in the dark for \(30\mathrm{min}\) . Proteins were digested overnight at \(37^{\circ}\mathrm{C}\) with trypsin \((20\mathrm{ng / \mu l}\) , Pierce), then centrifuged at \(16,000\mathrm{g}\) (Eppendorf) for \(10\mathrm{min}\) . Trifluoroacetic acid (Pierce) was added to the supernatant such that the final concentration was \(0.5\%\) . Duplicate technical replicates were performed along with a control that consisted of a blank DIRECTOR slide that was carried through the entire sample preparation protocol. + +<|ref|>text<|/ref|><|det|>[[115, 848, 881, 911]]<|/det|> +Samples were analyzed by reverse- phase HPLC- ESI- MS/MS using an Eksigent Ultra Plus nano- LC 2D HPLC system directly connected to an orthogonal quadrupole time- of- flight SCIEX TripleTOF 6600 mass spectrometer (SCIEX). Peptide and protein identifications were determined using the Paragon + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 880, 123]]<|/det|> +algorithm within the ProteinPilot search engine (v.5.0.2, SCIEX) against the corresponding proteome FASTA files obtained from UniProt. + +<|ref|>sub_title<|/ref|><|det|>[[118, 140, 283, 155]]<|/det|> +## Enrichment analysis + +<|ref|>text<|/ref|><|det|>[[115, 170, 881, 300]]<|/det|> +Gene Ontology (GO) analyses were conducted to obtain biological processes using enrichGO function from clusterProfiler R package (v. 4.2.2). The input proteins were those described for both sPE and control groups per endometrial region (glandular epithelium, luminal epithelium, and stroma). Specific pathways of a group refer to enriched pathways that are present in the controls or in the sPE, but are not present in the other group. The input genes were the differential expressed genes grouped by endometrial region. The p- value adjustment method was FDR with a cutoff of 0.05. + +<|ref|>sub_title<|/ref|><|det|>[[118, 316, 404, 332]]<|/det|> +## Protein-protein interaction network + +<|ref|>text<|/ref|><|det|>[[118, 347, 880, 409]]<|/det|> +The protein- protein interaction networks were created using the functional analysis suit String and visualized using Cytoscape software. Hub genes were extracted using the maximal clique centrality (MCC) and maximum neighborhood component (MNC) of the cytoHubba plugin. + +<|ref|>text<|/ref|><|det|>[[118, 424, 244, 440]]<|/det|> +Data availability + +<|ref|>text<|/ref|><|det|>[[115, 456, 881, 608]]<|/det|> +The single- cell RNA- sequencing data generated for this manuscript has been uploaded to GEO under accession number GSE265862. The uploaded data includes i) H5ad files containing the aggregated count matrices and metadata of each cell studied in the major cell populations and subpopulations; ii) Raw count matrices processed by Cell Ranger. The raw sequences are not publicly available due to privacy concerns. However, they are available from the corresponding authors (C.S, carlos.simon@uv.es; TG, tgarrido@fundacioncarlosimon.com) upon reasonable request and with permission of the Institutional Review Board of the Spanish hospitals involved. + +<|ref|>title<|/ref|><|det|>[[118, 723, 186, 738]]<|/det|> +# Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 100, 850, 840]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 83, 883, 394]]<|/det|> +Figure 1. Morphological features in decidualization resistance reflected in single cell atlas in sPE and control condition. (a) Representative endometrial tissue collected during late secretory phase from women with a previous sPE \((n = 7)\) . (b) Zoom - in of the macroscopical glands of the endometrial tissue of figure 1 a. (c) Representative H&E slides staining of cross-section endometrial tissue of an sPE sample. (d) Representative H&E slides staining of longitudinal-section endometrial tissue of an sPE sample. (e) Representative endometrial tissue collected during late secretory phase from control women \((n = 10)\) . (f) Zoom - in of the macroscopical glands of the endometrial tissue of fig. 1c. (g) Representative H&E slides staining of cross-section endometrial tissue of a control sample. (h) Representative H&E slides staining of longitudinal-section endometrial tissue of a control sample. (i) Single-cell sequencing workflow. (j) Uniform manifold approximation and projection (UMAP) of single-cell integration of high-quality cells sPE cells (28,154) of the major cell types of the endometrium during late secretory phase. (k) UMAP of single-cell integration of high-quality cells control cells (37,227) of the major cell types of the endometrium during late secretory phase. (l) UMAP of the 65,381 high quality cells of types of merged cells of both sPE (blue) and control (red) samples. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[135, 90, 888, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 786, 881, 894]]<|/det|> +
Figure 2. Altered stromal and epithelial cell differentiation states of DR in sPE condition. (a) UMAP of cell subpopulation identification of stromal and perivascular fraction of endometrium in late secretory phase. (b) UMAP of stromal merged cells of both sPE (blue) and control (red) samples. (c) Neighbourhood graph represents the differential abundance of stromal cell in late secretory endometrium. Dot size represents neighbourhoods, while edges thickness (weight) depicts the
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 881, 395]]<|/det|> +number of cells shared between neighbourhoods. Neighbourhoods coloured in blue represent those with a significant decrease in cell abundance in sPE and red highlight cells enriched in sPE. (d) Beeswarm plot of differential cell abundance by stromal cell subtypes. X-axis represents the log2-fold change in abundance of sPE. Each dot represents a neighbourhood; neighbourhoods coloured in blue represent those with a significant decrease in cell abundance in sPE condition while red dots are enriched in sPE samples. (e) Cell subpopulation identification of epithelial fraction of endometrium in late secretory phase. (f) UMAP of epithelial merged cells of both sPE (blue) and control (red) samples. (g) Neighbourhood graph represents the differential abundance of epithelial cell in late secretory endometrium. Dot size represents neighbourhoods, while edges depict the number of cells shared between neighbourhoods. Neighbourhoods coloured in blue represent those with a significant decrease in cell abundance in sPE and red highlight cells enriched in sPE. (h) Beeswarm plot of differential cell abundance by epithelial cell subtypes. X-axis represents the log2-fold change in abundance of sPE. Each dot represents a neighbourhood; neighbourhoods coloured in blue represent those with a significant decrease in cell abundance in sPE condition while red dots are enriched in sPE samples. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 90, 884, 856]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 883, 485]]<|/det|> +Figure 3. Absence of EMT in endometria with DR from sPE condition. (a) Zoom in of EMT and cell subpopulation identification of endometrium in late secretory phase. Circle highlights the epithelial- to- stroma transition subpopulation. (b) UMAP of EMT merged cells of both sPE (blue) and control (red) samples. (c) Neighborhood graph represents the differential cell abundance of MET cells in late secretory endometrium. Dot size represents neighborhoods, while edges depict the number of cells shared between neighborhoods. Neighborhoods colored in blue represent those with a significant decrease in cell abundance in sPE and red highlight cells enriched in sPE. (d) Beeswarm plot of differential cell abundance by MET cell subtypes. X- axis represents the log2- fold change in abundance of sPE. Each dot represents a neighborhood; neighborhoods colored in blue represent those with a significant decrease in cell abundance in sPE condition while red dots are enriched in sPE samples. (e) RNA velocity generated with scVelo of sPE and control samples of epithelial transition, epithelium- to- stromal transition and stromal transition subpopulations. Ciliated cell subtype was removed from trajectory inferences downstream analysis. Arrows represents the cell trajectories across clusters inferring differentiation cell trajectories. (f) Pattern of gene expression along the pseudotime from epithelium to stroma in the inferred trajectory. (g) Dotplot of differentially expressed genes in sPE vs controls identified across pseudotime associated to stroma, epithelial- to- stroma transition, and epithelium (color represents the average expression and dot size refers to the percentage of cells of each cluster expressing each marker). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 99, 900, 900]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 881, 235]]<|/det|> +Figure 4. Dysfunctional cell- to- cell communication networks associated with altered cell composition in DR. (a, c, e, g, i, j) Chords plots displaying the CCC network of Endoglin (EDN), noncanonical WNT (ncWNT), canonical WNT, Semaphorin (SEMA3), SPP1 and TENASCIN in sPE and control. Each coloured dot represents a cell subtype. Colour arrow represents de incoming signalling and the thickness of the lines refers to the strength of the signal between cell subtypes. (b, d, f, h) Barplot of each ligand- receptor pair contributing to the CCC of Endoglin, ncWNT, WNT and SEMA3. Red colour represents sPE expression pair and blue the Control. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 100, 884, 700]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 716, 881, 912]]<|/det|> +
Figure 5. Decidualization resistance in sPE confirmed with spatial transcriptomics. (a) Immunofluorescence of enriched stromal ROIs selected of one representative sPE sample (PanCK in green, Vimentin in yellow, CD31 in red and nucli in blue) and unsupervised hierarchical clustering based on Pearson distances of the normalized data z-scores of the top genes of enriched stromal ROIs. (b) Volcano plots depicting DEGs between sPE and controls within stromal ROIs. (c) Immunofluorescence of enriched glandular epithelial ROIs selected of one representative sPE sample and unsupervised hierarchical clustering based on Pearson distances of the normalized data z-scores of the top genes of enriched glandular epithelium ROIs. (d) Volcano plots depicting DEGs between sPE and controls within Glandular epithelial ROIs. (e) Immunofluorescence of enriched luminal epithelial
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 83, 883, 214]]<|/det|> +761 ROI selected of one representative sPE sample and unsupervised hierarchical clustering based on 762 Pearson distances of the normalized data z-scores of the top genes of enriched luminal epithelium ROIs. 763 (f) Volcano plots depicting DEGs between sPE and controls within luminar epithelial ROIs. Heatmap 764 legend reflects info of group (sPE in red and control in blue), and colours represented the ROI of each 765 participant. Volcano plot legend represent significant genes ( \(\mathrm{p}< 0.05\) ) and non-significant genes (NS < 766 0). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 110, 490, 309]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[580, 106, 833, 152]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[610, 164, 840, 309]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[130, 328, 911, 555]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[125, 590, 911, 899]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 84, 881, 667]]<|/det|> +Figure 6. Differential endometrial proteome associated with decidualization resistance in sPE compared to controls. (a) Endometrial section before laser capture microdissection, and after isolating the regions of interest (glandular epithelium, luminal epithelium, and stromal compartment). (b) Venn diagrams showing the total number of proteins identified between controls and sPE in the stromal compartment, glandular epithelium, and luminal epithelium, respectively. (c) Differential expressed pathways specific to controls and sPE per region analysed. Color gradient shows the protein ratio (%), which refers to what proportion of all the proteins detected in the region are proteins involved in the pathway. All pathways included are significantly enriched (adjusted. \(\mathrm{P< 0.05}\) ). (d) Protein-protein interaction network including those proteins involved in highlighted pathways in the stromal compartment. Colour shows the specificity of proteins (red, proteins unique to sPE; blue, proteins unique to controls; purple, proteins shared by the two groups; green, ESR1 and PGR). Shape shows the pathway (circle, response to steroids; hexagon, aging; rhombus, cell growth; rectangle, extracellular matrix organization). (e) Protein-protein interaction network including those proteins involved in highlighted pathways in the glandular epithelium. Colour shows the specificity of proteins (red, proteins unique to sPE; blue, proteins unique to controls; purple, proteins shared by the two groups; green, ESR1 and PGR). Shape shows the pathway (circle, hormone secretion; hexagon, response to reactive oxygen species (ROS); square, cell survival in response to ROS; rhombus, epidermal cell differentiation; rectangle, regulation of actin cytoskeleton organization. (f) Protein-protein interaction network including those proteins involved in highlighted pathways in the luminal epithelium. Colour shows the specificity of proteins (red, proteins unique to sPE; blue, proteins unique to controls; purple, proteins shared by the two groups; green, ESR1 and PGR). Shape shows the pathway (circle, response to steroids; hexagon, negative regulation of apoptotic signaling pathway; rhombus, proteasomal protein catabolic process; rectangle, extracellular matrix organization) (PPI enrichment refers to protein-protein interaction enrichment). (g) Dot plot showing the functional enrichment coincides between the spatial proteome and the differentially expressed genes at single-cell resolution. Left panel, sPE-specific pathways. Right panel, controls-specific pathways. Pink, scRNA-seq data; purple, LCM-MS. + +<|ref|>sub_title<|/ref|><|det|>[[68, 707, 206, 722]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[66, 737, 883, 897]]<|/det|> +1. Moreno I, Capalbo A, Mas A, Garrido-Gomez T, Roson B, Poli M, et al. The human periconceptional maternal-embryonic space in health and disease. Physiol Rev. 2023;103(3):1965-2038. Epub 20230216. doi: 10.1152/physrev.00050.2021. PubMed PMID: 36796099. +2. Wang W, Vilella F, Alama P, Moreno I, Mignardi M, Isakova A, et al. Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle. Nature Medicine. 2020;26(10):1644-53. doi: 10.1038/s41591-020-1040-z. +3. Garcia-Alonso L, Handfield LF, Roberts K, Nikolakopoulou K, Fernando RC, Gardner L, et al. 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Development. 2021;148(20). doi: 10.1242/dev.199626. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[0, 0, 997, 997]]<|/det|> +# 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. + +<--- Page Split ---> diff --git a/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/images_list.json b/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..67fa69f99e4561ae5cd2aebcb3c39354a6d0b952 --- /dev/null +++ b/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Landscape of somatic alterations in acral and sun-exposed melanomas. a-b, Genomic and clinical characterization of acral and sun-exposed melanoma samples sequenced in this work. a, The number of nonsynonymous SNVs and indels per melanoma exome (columns), cohort, age, frequently mutated genes (at least \\(5\\%\\) recurrence frequency in either melanoma subtype), nonsynonymous base substitution frequencies, and dominant COSMIC mutational signatures \\(^{92,93}\\) . Sig., signature; 5mC, 5-methylcytosine. b, The number of significant focal amplifications and deletions (GISTIC \\(Q < 0.05\\) ) per melanoma exome (columns), ordered identically to panel a. Cytobands with focal amplifications or deletions with at least \\(10\\%\\) recurrence frequency in either melanoma subtype are shown (GISTIC \\(Q < 10^{-5}\\) ), ordered by the relative difference in recurrence frequency in acral versus sun-exposed melanoma. c, Genes are plotted according to the fraction of acral (y-axis) or sun-exposed (x-axis) tumors where they are present with \\(4+\\) copies. Considering the genome-wide distribution of differences in recurrence frequencies between melanoma subtypes, genes are identified as significantly recurrent in acral or sun-exposed melanomas if their \\(|z\\text{-score}|\\geq 3\\) (dashed lines). Significantly recurrent genes are colored according to their cytoband location (inset). For clarity, a small amount of jitter was added to distinguish overlapping genes.", + "footnote": [], + "bbox": [ + [ + 113, + 90, + 884, + 536 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Focal amplifications in 22q11.21 are linked to shorter survival time, regional metastasis, and depletion of immunomodulatory programs in acral melanoma. a, Association between recurrent somatic alterations and overall survival (OS) in acral melanoma. Z-scores with positive and negative values indicated shorter and longer survival time, respectively \\((|Z| > 1.96\\) is significant at \\(P< 0.05\\) ; Methods). OS was calculated from the date of diagnosis (x-axis) and the date of tumor resection (y-axis). Shown are genes with a nonsynonymous mutation frequency of at least \\(5\\%\\) in either melanoma subtype and focal copy number events with at least \\(10\\%\\) recurrence frequency in each acral cohort (Supplementary Table 6). Focal amplifications in", + "footnote": [], + "bbox": [ + [ + 112, + 90, + 884, + 734 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Cell proliferation in response to suppression of LZTR1. a, Impact of LZTR1 knockdown on cell proliferation in nine primary melanoma cell lines and two normal human melanocyte lines (NBMEL). Key mutations are indicated (Supplementary Table 9). Bar plots depict fold change between the \\(3^{\\text{rd}}\\) and \\(6^{\\text{th}}\\) day after infection with LZTR1 shRNA (numbered), as compared to control (scrambled) shRNA ('C'). All shRNAs significantly reduced proliferation relative to control ( \\(P < 0.05\\) ; two-sided \\(t\\) test with unequal variance). b, Western blot showing the efficiency of LZTR1 knockdown in primary acral and sun-exposed melanoma cell lines, and in normal melanocytes, related to panel a. c, Cell proliferation (left) and LZTR1 expression (right) of a sun-exposed melanoma cell line that lost one LZTR1 allele in response to genomic modification by different CRISPR-Cas9 sgRNAs targeting", + "footnote": [], + "bbox": [ + [ + 190, + 95, + 812, + 707 + ] + ], + "page_idx": 18 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4: Changes in MAPK signaling in response to LZTR1 knockdown. a, b, Impact of LZTR1 loss on (a) RAS-GTP activity as measured by RAS-GTPase Activation ELISA assay, and (b) RAS levels, five days after shLZTR1 infection. c, Effect of LZTR1 loss on MAPK activity. Key somatic events are indicated below. WT, wildtype. d, e Increased levels of BRAF activity in response to shLZTR1 is associated with increase pERK. In panel e, cells were incubated with RAF the kinase inhibitors PLX4032 (500 nM) or LY3009120 (100 nM) for four hours at the end of treatment with shRNA. f, RAS translocation to the cytoplasm in response to shLZTR1; a-d, RAS is visualized by staining with magenta; Green (Cy2) indicates GM130 (a, b) and calnexin (c, d). Scale", + "footnote": [], + "bbox": [ + [ + 155, + 108, + 860, + 737 + ] + ], + "page_idx": 20 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5: Impact of LZTR1 knockdown on apoptosis, Rb signaling, and pigmentation. a, Gene Set Enrichment Analysis (GSEA)94 showing concordance in hallmark pathways among bulk acral melanoma tumors, acral melanoma single-cell", + "footnote": [], + "bbox": [ + [ + 112, + 90, + 835, + 840 + ] + ], + "page_idx": 22 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6: LZTR1 and CRKL confer properties consistent with malignant cell transformation and metastasis initiation. a, Morphological changes and spheroid formations in early passage normal human melanocytes (NBMEL C1220) overexpressing LZTR1 and/or CRKL. Top: Phase-contrast images of parental and infected cells in 2D culture. LZTR1 images were taken after two days induction with doxycycline (200 ng/ml), CRKL after three days of infection with PLX304-CRKL, and LZTR1+CRKL after six days infection of LZTR1 melanocytes with PLX304-CRKL and three days stimulation with doxycycline. Insert in LZTR1 shows that colonies were", + "footnote": [], + "bbox": [ + [ + 112, + 90, + 881, + 750 + ] + ], + "page_idx": 24 + } +] \ No newline at end of file diff --git a/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5.mmd b/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6276f27f25533a6db782f955f81e2b4a25623c8b --- /dev/null +++ b/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5.mmd @@ -0,0 +1,579 @@ + +# Integrative Molecular and Clinical Profiling of Acral Melanoma Identifies LZTR1 as a Key Tumor Promoter and Therapeutic Target + +Ruth Halaban ( \(\boxed{\bullet}\) ruth.halaban@yale.edu) Yale University School of Medicine https://orcid.org/0000- 0001- 8451- 1964 + +Aaron Newman Stanford University https://orcid.org/0000- 0002- 1857- 8172 + +Farshad Farshidfar Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, + +Cong Peng Xiangya Hospital, Central South University + +Chaya Levovitz IBM (United States) + +James Knight Yale University https://orcid.org/0000- 0003- 1166- 0437 + +Antonella Bacchiocchi Yale University + +Juan Su Xiangya Hospital, Central South University + +Kahn Rhissorrakrai IBM (United States) + +Mingzhu Yin Yale University School of Medicine + +Mario Sznol Yale University + +Stephan Ariyan Yale University + +James Clune Yale University School of Medicine + +Kelly Olino Yale University School of Medicine + +Laxmi Parida + +IBM Research - Thomas J. Watson Research Center https://orcid.org/0000- 0002- 7872- 5074 + +Joerg Nikolaus + +<--- Page Split ---> + +Yale University School of Medicine + +Meiling Zhang Yale University School of Medicine + +Shuang Zhao Xiangya Hospital, Central South University + +Yan Wang + +Department of Dermatologic Surgery Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College + +Gang Huang + +Department of Bone and Soft Tissue oncology, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University + +Miaojian Wan + +Department of Dermatology, The Third Affiliated Hospital, Sun Yat- sen University + +Xianan Li + +Department of Bone and Soft Tissue oncology, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University + +Jian Cao + +Rutgers Cancer Institute of New Jersey and the Department of Medicine, Robert Wood Johnson Medical School, Rutgers University + +Qin Yan + +Yale University https://orcid.org/0000- 0003- 4077- 453X + +Xiang Chen + +Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008 https://orcid.org/0000- 0001- 8187- 636X + +## Article + +Keywords: cytoband chr22q11.21, oncogene, metastasis + +Posted Date: September 30th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 110475/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on February 23rd, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28566- 4. + +<--- Page Split ---> + +# Integrative Molecular and Clinical Profiling of Acral Melanoma Identifies LZTR1 as a Key Tumor Promoter and Therapeutic Target + +Farshad Farshidfar \(^{1,2,14,\dagger}\) , Cong Peng \(^{3,\dagger}\) , Chaya Levovitz \(^{4}\) , James Knight \(^{5}\) , Antonella Bacchiocchi \(^{6}\) , Juan Su \(^{3}\) , Kahn Rhrisorrakrai \(^{4}\) , Mingzhu Yin \(^{3,7}\) , Mario Szmol \(^{8}\) , Stephan Ariyan \(^{9}\) , James Clune \(^{9}\) , Kelly Olino \(^{9}\) , Laxmi Parida \(^{4}\) , Joerg Nikolaus \(^{10}\) , Meiling Zhang \(^{7}\) , Shuang Zhao \(^{3}\) , Yan Wang \(^{11}\) , Gang Huang \(^{12}\) , Miaojian Wan \(^{13}\) , Xianan Li \(^{12}\) , Jian Cao \(^{7,15}\) , Qin Yan \(^{7}\) , Xiang Chen \(^{3,*}\) , Aaron M. Newman \(^{1,2,*}\) and Ruth Halaban \(^{6,*}\) + +\(^{1}\) Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. \(^{2}\) Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. \(^{3}\) Xiangya Hospital, Central South University, Changsha, China. \(^{4}\) IBM Research, Yorktown Heights, NY, USA. \(^{5}\) Yale Center for Genome Analysis, Yale University, New Haven, CT, 06520, USA. \(^{6}\) Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA. \(^{7}\) Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. \(^{8}\) Department of Internal Medicine, Section of Medical Oncology, Yale University School of Medicine, New Haven, CT, USA. \(^{9}\) Department of Surgery, Yale University School of Medicine, New Haven, CT, USA. \(^{10}\) Department of Molecular and Cellular Physiology, Yale University School of Medicine, New Haven, CT, USA. \(^{11}\) Department of Dermatologic Surgery Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, China. \(^{12}\) Department of Bone and Soft Tissue oncology, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University, Changsha, Hunan, China. \(^{13}\) Department of Dermatology, The Third Affiliated Hospital, Sun Yat- sen University, Guangzhou, China. \(^{14}\) Current address: Tenaya Therapeutics, South San Francisco, CA, USA. \(^{15}\) Current address: Rutgers Cancer Institute of New Jersey and the Department of Medicine, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA. \(^{\dagger}\) These authors contributed equally. + +\(^{\dagger}\) These authors contributed equally. + +\(^{*}\) Corresponding authors: Xiang Chen, Xiangya Hospital, Central South University, Changsha, China. Phone: 01186- 731- 84327303; E- mail: chenxiangck@126. com Aaron M. Newman, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. Phone: 650 724- 7270; E- mail: amnewman@stanford.edu Ruth Halaban, Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA. Phone: 203 785- 4352; E- mail: ruth.halaban@yale.edu + +<--- Page Split ---> + +## ABSTRACT + +Acral melanoma, the most common melanoma subtype among non- Caucasian individuals, is associated with poor prognosis. However, its key molecular drivers remain obscure. Here, we performed integrative genomic and clinical profiling of acral melanomas from a cohort of 104 patients treated in North America or China. We found that recurrent, late- arising amplifications of cytoband chr22q11.21 are a leading determinant of inferior survival, strongly associated with metastasis, and linked to downregulation of immunomodulatory genes associated with response to immune checkpoint blockade. Unexpectedly, LZTR1 – a known tumor suppressor in other cancers – is a key candidate oncogene in this cytoband. Silencing of LZTR1 in melanoma cell lines caused apoptotic cell death independent of major hotspot mutations or melanoma subtypes. Conversely, overexpression of LZTR1 in normal human melanocytes initiated processes associated with metastasis, including anchorage- independent growth, formation of spheroids, and increased levels of MAPK and SRC activities. Our results provide new insights into the etiology of acral melanoma and implicate LZTR1 as a key tumor promoter and therapeutic target. + +<--- Page Split ---> + +## INTRODUCTION + +Over the last two decades, a tremendous effort has been made to understand the genomic basis of melanoma. Collectively, these analyses have shown that sun- exposed melanomas harbor a large number of mutations and genomic rearrangements associated with ultraviolet (UV) radiation \(^{1 - 6}\) . In contrast, acral melanomas, originating from hairless skin such as palms and soles, display a lower mutational burden, a higher rate of structural alteration, and poorer survival outcomes \(^{2,4,7 - 17}\) . BRAF and NRAS are the most frequently affected oncogenes in acral melanomas but at a lower frequency compared to sun- exposed melanomas, whereas KIT mutations are more common in acral melanomas \(^{14,18 - 23}\) . Copy number variation (CNV) is a well- established feature of acral melanomas, contributing to aberrant regulation of several pathways affecting cell proliferation and gene expression. These include amplification of CDK4, CCND1, MAPK1 and NOTCH2; loss of CDKN2A (p16INK4) and NF1; inactivation of TP53; modifications of chromatin regulators (e.g., HDAC amplification and loss of ARID1A and ARID1B); and alterations in TERT \(^{11,13,14,20,23 - 25}\) . Despite these findings, attempts to treat acral melanoma with targeted inhibitors, such as CDK4/6 inhibitors, have failed \(^{12}\) . + +While most genomic studies of acral melanoma have been limited to relatively small clinical cohorts \(^{1,9,13,26,27}\) , a recent whole genome analysis of 87 patients – 90% of which were of European ancestry – further confirmed the importance of structural rearrangements and copy number aberrations in this disease \(^{15}\) . Given the predominance of acral melanoma in non- Caucasian populations \(^{28,29}\) and the lack of effective targeted treatment options, large- scale genomic surveys of acral melanomas from ethnically diverse populations are needed. + +Here, we applied whole exome (tumor/normal) and RNA sequencing to characterize acral melanomas from 104 patients treated in the United States ( \(n = 37\) ) and China ( \(n = 67\) ), most of whom had long- term follow- up data available. Through comparative genomic analysis with 157 sun- exposed melanomas, we identified novel molecular features of acral melanoma; generated the first prognostic map linking highly recurrent somatic aberrations in acral melanoma to risk of death; and found that later- arising focal amplifications in chr22q11.21 are associated with lymph node involvement and distant metastasis, leading to poor outcomes. Within chr22q11.21, we identified LZTR1 – a known tumor suppressor in other cancers – as a key candidate driver of metastasis. Our findings reveal novel molecular insights of acral melanoma pathogenesis and designate LZTR1 as a new therapeutic target. + +<--- Page Split ---> + +## RESULTS + +## Genomic characteristics of acral and sun-exposed melanoma + +To characterize the genomic landscape of acral melanoma across ethnically diverse patient populations, we analyzed 104 tumors, including 97 by whole exome sequencing (WES), from patients treated in North America ('Yale') or China ('CSU') (Table 1). Both cohorts spanned all disease stages, included long- term follow- up, and encompassed patients with distinct ethnic origins, including Caucasian ( \(n = 31\) ; Yale) and Asian ancestry ( \(n = 67\) ; CSU) (Supplementary Fig. 1a and Supplementary Table 1). We also applied WES to profile 134 tumors from patients with sun- exposed melanoma, including patients with stage I through IV disease (Table 1). Notably, sun- exposed patients showed longer survival time than acral melanoma patients, consistent with previous studies \(^{16,17}\) (Supplementary Fig. 1b). Peripheral blood leukocytes were analyzed as germline controls and whole- transcriptome sequencing (RNA- seq) was applied to 105 tumors, including 38 acral and 37 sun- exposed melanomas with matched WES data (Table 1 and Supplementary Table 1). Tumor purities, clinical follow- up, and median survival times were comparable between acral cohorts, supporting their combined assessment (Supplementary Fig. 1c,d; Supplementary Table 1). + +To verify key somatic lesions in acral melanoma, we began by performing a comparative genomics analysis. We observed striking variation in the prevalence of single nucleotide variants (SNVs) and insertions/deletions (indels) between melanoma subtypes, confirming a nearly ten- fold lower mutational burden in acral melanoma \(^{2,13}\) (median of 406 vs. 42 nonsynonymous variants per exome in sun- exposed vs. acral melanoma, respectively; \(P = 2.2 \times 10^{- 6}\) , two- sided Wilcoxon rank sum test; Fig. 1a, Supplementary Table 2, Supplementary Data). The most commonly mutated genes in acral melanomas were RAS family members (22% in NRAS, KRAS, and HRAS), followed by KIT (15%), CGREF1 (10%), BRAF (8%), and TP53 (4%). With the exception of CGREF1, which was limited to CSU patients, recurrence frequencies were similar between cohorts (Supplementary Table 2). Mutational signature analysis \(^{17}\) corroborated the prevalence of UV- induced mutagenesis in sun- exposed melanomas. In contrast, mutational signatures in acral melanomas were largely attributable to deamination of 5- methylcytosine (signature 1), which can arise from reactive oxygen species during melanin synthesis \(^{30}\) , as well as alkylating agents (signature 11) and APOBEC activity (signature 13) (Fig. 1a, bottom, and Supplementary Data). + +As expected, focal amplifications were a core feature of acral melanoma in both cohorts (median of 20 vs. 11 per exome in acral vs. sun- exposed melanoma, respectively; \(P = 1.24 \times 10^{- 10}\) , two- sided Wilcoxon rank sum test; Fig. 1b, top; Supplementary Figs. 1e and 2a; Supplementary Table 3). Among highly recurrent gene- level amplifications with at least four copies, those in chromosomes 4, 5, 8, 11, 12, and 22 were nearly exclusive to acral melanomas in our study (Fig. 1c; Supplementary + +<--- Page Split ---> + +Table 3). The most common amplifications enriched in acral melanoma were in cytobands 11q13.3 (47%), 5p15.33 (42%), 8q24.3 (42%), 22q13.1 (39%), and 22q11.21 (38%) (Fig. 1b; Supplementary Fig. 2a, Supplementary Table 3). Recurrent focal deletions, which included alterations in known genes such as CDKN2A (9p21.3) \(^{22,31,32}\) , were less prevalent than in sun- exposed cases (Fig.1b, bottom; Supplementary Fig. 2b, Supplementary Table 4). Amplification and deletion frequencies were largely maintained in both acral cohorts (Supplementary Tables 3 and 4). We also identified multiple fusion genes with potential roles in oncogenesis, including several not previously described in acral melanoma (Supplementary Table 5). + +Collectively, these results provide a comprehensive resource of somatic lesions in acral melanomas from genetically- distinct patient populations; corroborate and extend previous genomic studies \(^{2,4,7 - 15}\) , and demonstrate the integrity and high quality of our data for downstream clinical analysis. + +## Somatic determinants of risk in acral melanoma + +Having systematically cataloged somatic aberrations in nearly 100 acral melanomas, we next sought to evaluate their clinical significance. We began by focusing on amplification events owing to their unique prevalence in this disease (Fig. 1b). Starting with the most statistically- significant peaks detected by GISTIC \(^{33}\) in a pooled analysis of both acral cohorts ( \(Q < 10^{- 5}\) ), we identified several loci linked to adverse overall survival, including peaks involving cytobands 22q11.21 and 22q13.1. Among them, cytoband 22q11.21 was most strongly associated with inferior overall survival (adjusted \(P < 0.05\) , univariate Cox regression of time from tumor resection; Supplementary Fig. 3a, Supplementary Table 6). This result was maintained when expanding the analysis to include all focal events identified by GISTIC ( \(Q < 0.05\) ) with at least 10% recurrence frequency in both acral cohorts and all genes with a nonsynonymous mutation frequency of at least 5% in either melanoma subtype (Fig. 2a; Supplementary Table 6). We also considered focal amplifications identified from the largest cohort (CSU) and tested in each cohort separately (Fig. 2b; Supplementary Fig. 3b). Again, 22q11.21 amplification was a leading determinant of adverse survival. + +Given this observation, we sought to better understand 22q11.21 focal amplification and the factors underlying its clinical phenotype. We first tested whether 22q11.21 is a surrogate for advanced disease at the time of tumor resection. Intriguingly, 22q11.21 amplifications were observed across all stages except stage I disease ( \(P = 0.03\) , Chi \(X^{2}\) test; Fig. 2c, left). We verified this result in three independent acral melanoma cohorts, including an external dataset comprised of 33 patients for whom stage at presentation was known \(^{11}\) , demonstrating that 22q11.21 amplification is a recurrent late- arising event in acral melanoma (Supplementary Fig. 3c). Given this result, we reassessed survival associations using stage as a covariate. Regardless of + +<--- Page Split ---> + +whether we examined all patients or just those with stage II through IV disease, 22q11.21 amplifications remained significant after multivariate adjustment for stage \((P = 0.008\) and 0.024, respectively; Cox proportional hazards regression). This was also true for acral patients with advanced disease (III or IV) \((P = 0.03\) , Cox proportional hazards regression; Supplementary Fig. 3c), for whom stage alone did not significantly stratify outcomes. + +As a common late- arising event, we next tested if 22q11.21 amplifications might correlate with tumor progression. Indeed, in both acral cohorts, we observed a strong positive correlation between 22q11.21 amplification frequency and the number of positive lymph nodes (Fig. 2c, right; Supplementary Fig. 3e). Remarkably, nearly \(75\%\) of patients with \(>1\) positive lymph node harbored at least one gain of 22q11.21 (Fig. 2c, right). Reanalysis of WES data from an independent study11 confirmed this trend (Supplementary Fig. 3f). While this association was observed in both primary and metastatic tumor specimens, the latter showed a modest but consistent increase in amplification frequency after controlling for lymph node status (Supplementary Fig. 3g). No other associations with clinical indices were observed (Supplementary Fig. 3h, Supplementary Table 1). + +Taken together, these data reveal that 22q11.21 focal amplification is a conserved, late- arising somatic event linked to poor survival and regional metastasis in acral melanoma, independent of Caucasian or Asian ancestry. Accordingly, this event could represent a critical step in the initiation or maintenance of nodal metastasis. + +## Integrative genomics of 22q11.21 focal amplification + +To understand the biological significance of 22q11.21 amplification in acral melanoma, we next examined transcriptional hallmarks of 22q11.21- amplified tumors. By employing a linear model adjusted for stage (Methods), we rank- ordered genes by their differential expression in 22q11.21- amplified tumors and performed gene set enrichment analysis34 (Fig. 2d). Overall, 22q11.21- amplified melanomas were significantly enriched in canonical signaling pathways associated with tumorigenesis and metabolic activity, including MYC target genes, oxidative phosphorylation, and unfolded protein response35. In contrast, patients with non- amplified tumors showed higher expression of immunoreactive programs such as IL6/JAK/STAT and IFN- \(\gamma\) response pathways. We hypothesized that such patients might be superior candidates for existing or emerging immunotherapies (Fig. 2d). Consistent with this possibility, we observed a striking reciprocal relationship between 22q11.21- amplification and the expression of immunomodulatory genes, including key targets of immune checkpoint blockade (e.g., PDCD1, CTLA4) (Fig. 2e). Among patients with high expression of immunomodulatory genes, only \(12\%\) were amplified, whereas among patients with low expression, \(62\%\) were amplified (Fig. 2f). This result was highly significant \((P = 0.009\) , + +<--- Page Split ---> + +Fisher's exact test), indicating that 22q11.21- amplified and non- amplified tumors preferentially reflect "cold" and "hot" tumor microenvironments, respectively. + +To extend these observations to single cells, we applied single- cell RNA sequencing (scRNA- seq) to an acral melanoma tumor specimen with four additional copies of 22q11.21, as determined by WES (Supplementary Fig. 4a, Supplementary Data). Using canonical marker genes and copy number inference via CONICSmat36, 321 single- cell transcriptomes were confidently identified as melanoma cells (Supplementary Fig. 4b, Methods). We confirmed over- expression of genes on the 22q arm, consistent with WES (Supplementary Fig. 4c). However, 22q expression levels were heterogeneous across cells, indicating variability in the number of copies per cell (Supplementary Fig. 4c). By dividing malignant cells into two groups according to the median expression of 22q arm genes (selected from within the 22q11.21 peak identified by GISTIC), we observed the same amplification- enriched pathways identified in bulk tumors, including oxidative phosphorylation and MYC targets, confirming their malignant origin (Supplementary Fig. 4d). + +Immature cancer cells often display elevated metabolism via oxidative phosphorylation and MYC activity37 and stemness features in melanoma tumors have been linked to poor survival38- 41. To test whether 22q11.21- amplified cells exhibit an immature cellular phenotype, we employed CytoTRACE, a recently described in silico method for predicting developmental potential on the basis of single- cell transcriptional diversity42. Indeed, cells with higher relative copies of 22q11.21 were predicted to be less mature (Fig. 2g). Notably, this result was independent of genes physically located on 22q, implying that 22q11.21- amplified cells exhibit a more accessible genome, a hallmark of immature cells in normal tissues42 (Supplementary Fig. 4e). + +Finally, we leveraged the \(t\) - statistic to rank genes in 22q11.21 according to their expression in amplified versus non- amplified tumors (Fig. 2h, Supplementary Table 7). The top- ranking gene associated with amplification was LZTR1 (leucine zipper like transcription regulator 1). We were struck by this result because LZTR1, a member of the Kelch- like (KLHL) family and an adaptor for Cullin 3 (CUL3) ubiquitin ligase complexes43,44, is considered a tumor suppressor in schwannoma and glioblastoma43,45,46. Nevertheless, we found that high expression of LZTR1 is predictive of poor outcome, both in acral and sun- exposed melanomas from this study, and in 443 advanced sun- exposed melanomas profiled by TCGA (The Cancer Genome Atlas) (Supplementary Fig. 5, Methods). Beyond LZTR1, we noted that ZNF74, a zinc finger protein, and CRKL (CRK like proto- oncogene, adaptor protein), a recurrently amplified gene in multiple carcinomas47- 50, including non- small cell lung cancer (3% – 13% of cases)47- 49, were ranked 2nd and 3rd in our analysis, respectively. Given these results, we set out to characterize the biological functions of these genes to determine which, if any, underlie the observed clinical phenotype of 22q11.21 amplification. + +<--- Page Split ---> + +## Suppression of LZTR1 attenuates melanoma cell proliferation and induces apoptosis independent of Ras or MAPK activity + +We began by silencing several chr22q11.21- amplified genes using lentiviral delivery of short hairpin RNAs (shRNAs) (Supplementary Table 8), with the goal of determining the impact of targeted knockdowns on melanoma cell proliferation. Treatment of two acral melanoma cell lines with ZNF74 shRNA had a modest effect on cell proliferation (Supplementary Fig. 6a). Similarly, while downregulation of CRKL induced growth arrest, only one of three shRNAs against CRKL successfully downregulated CRKL, and only two of five tested cell lines were highly affected (Supplementary Fig. 6b). Conversely, silencing of LZTR1 consistently arrested cell proliferation. This was the case regardless of subtype (acral or sun- exposed) or mutations in BRAF or NRAS (Fig. 3a, b). In addition, we observed growth arrest in normal melanocytes derived from two independent foreskins (Fig. 3a, b). We ruled out off- target effects because six different LZTR1- directed shRNAs induced growth arrest, as did CRISPR- Cas9 sgRNA directed against LZTR1 (Fig. 3a- c; Supplementary Fig 5c; Supplementary Table 8). The observed phenotype had a long- term effect since LZTR1- null melanoma cells did not survive in vitro, whereas cells infected with control shRNA (scrambled) continued to proliferate. We also tested depletion of SNAP29 and THAP7, both of which are physically located on 22q11.21 but whose expression levels were not significantly linked to 22q11.21 amplification (Supplementary Table 7). Knockdown of these genes had little to no effect on proliferation (Supplementary Fig. 5d, e). + +Given these results, we sought to better understand the biological consequences of LZTR1 knockdown. Inactivating germline mutations in LZTR1 are associated with Noonan syndrome and functional studies have linked LZTR1 inactivation to RAS ubiquitination, increased RAS- MAPK signaling, and cell proliferation \(^{51 - 56}\) . Indeed, suppression of LZTR1 in melanoma cells increased the constitutive levels of GTP- bound RAS, an effect similar to that observed in growth factor- stimulated cells \(^{54,57}\) . RAS- GTP levels increased in NRAS- or BRAF- mutant melanoma cells without a change in total RAS protein (Fig. 4a, b). + +We also observed widespread changes in MAPK signaling following LZTR1 knockdown. For example, there was an increase in pERK in melanoma cells carrying BRAF \(^{V600E}\) , PDE4DIP- BRAF, or GOLG4A- RAF1 (Fig. 4c). In contrast, pERK decreased in NRAS \(^{Q61L/R}\) melanoma cells, PDE8A- RAF1 fusion- bearing melanoma cells, and normal human melanocytes (Fig. 4c). ERK activation was likely due to an increase in BRAF levels (Fig. 4d), enhancing BRAF activity. Treatment of melanoma cells with BRAF \(^{V600E/K}\) or pan- RAF inhibitors (PLX4032 or LY3009120) reduced shLZTR1- induced pERK activation (Fig. 4e), rendering further support for the role of BRAF kinase activity. + +<--- Page Split ---> + +On the other hand, ERK inhibition in shLZTR1- treated cells could potentially arise from RAS translocation to the cytoplasm (Fig. 4f), and the consequent disassociation from its membrane- bound mitogenic effectors, which are critical for \(NRAS^{Q61/L/R}\) mutant and WT cells lacking BRAF mutations. RAS translocation was not linked to de- ubiquitination, because loss of LZTR1 did not change the levels of ubiquitinated RAS (Supplementary Fig. 7a). RAF1 levels were diminished in most cell lines (Fig. 4d), reflecting a decrease in gene expression. Thus, downregulation of LZTR1 induces growth arrest independently of ERK activity, the presence of BRAF or NRAS oncogenes, and changes in RAS- GTP levels. + +We next explored if our in vitro melanoma systems effectively recapitulate key 22q11.21- related signaling pathways observed in vivo. To this end, we performed bulk RNA- sequencing of a melanoma cell line (YUSIK) to assess the impact of LZTR1 knockdown. Remarkably, depletion of LZTR1 induced transcriptome- wide changes that largely mirrored those observed in bulk tumors and single melanoma cells (Fig. 5a). + +We noticed that among altered transcriptional programs, apoptosis- related genes were elevated in cell lines and tumors with lower LZTR1 expression (Fig. 5a). These data are supported by an increase in caspase activity after treatment with LZTR1 shRNA or sgRNA (Fig. 5b, c), which led to the degradation of known caspase substrates58, including pRb, p53, PARP1, NFKB, and GOLGA4 (Fig. 5d). Notably, GOLGA4 localizes to the Golgi apparatus, the subcellular site of LZTR159. Moreover, shLZTR1- induced caspase activity was suppressed by the pan- caspase inhibitor IDN- 6556 (Emricasan), which also rescued several substrates, including LZTR1 (Fig. 5c, d). These data are consistent with a previous report showing that LZTR1 undergoes caspase- mediated degradation59. Furthermore, shLZTR1 led to disruptions of cellular organization, including actin depolymerization into irregular shapes (Fig. 5e, left), or formation of actin rings around the Golgi and nucleus (Fig. 5e, right). Such changes are characteristic of cells undergoing fast or slow apoptotic death, respectively60. + +Several cell cycle proteins were also downregulated, in line with pathway enrichment analyses (Fig. 5f and Supplementary Fig. 7b- e). In addition, retinoblastoma proteins (pRb and p130) were suppressed in the nine melanoma cell lines tested (Fig. 5f). This was likely due to ubiquitination and degradation61, as pRb was not rescued by the caspase inhibitor IDN- 6556 (Fig. 5d). Elimination of pRb may enhance mitochondrial- mediated apoptosis because it leads to reduced mitochondrial mass, reduced activity of the electron transport chain, and increased reactive oxygen species (ROS)62- 65. + +A major reason for growth arrest in some melanoma cell lines is downregulation of MITF, a lineage- specific transcription factor critical for melanocyte and melanoma cell proliferation66. MITF stability is reduced when phosphorylated by MAPK or KIT67,68, and this process was clearly observed in three out of four melanoma cell lines with + +<--- Page Split ---> + +increased ERK activity (Fig. 5g, as compared to Fig. 4c, e). Downregulation of MITF, as expected, is associated with decrease in tyrosinase (TYR), the key enzyme in melanin synthesis as well as cellular pigmentation (Fig. 5h, i). These results are consistent with our published observations using the same melanoma cell lines69. + +## Overexpression of LZTR1 in normal melanocytes confers properties of malignant transformation and metastasis + +We next evaluated the impact of overexpressing LZTR1 in normal melanocytes and compared the effects to overexpression of CRKL. The latter is a SH3/SH2 adaptor protein that promotes lung cancer cell invasion via ERK activation70 and epithelial- mesenchymal transition (EMT) in colorectal and pancreatic carcinomas71. Early passage human melanocytes (passage 4) were transduced with HA- tagged LZTR1 cloned into the pInd20 lentiviral vector, V5- tagged CRKL inserted into the PLX304 vector, or both constructs. Over- expression of these genes did not enhance the rate of cell proliferation; rather, melanocytes overexpressing CRKL grew slower compared to parental cells (Supplementary Fig. 8a). Nevertheless, within 2- 3 days after infection, we noticed a striking induction of anchorage- independent growth, observed as cells overexpressing LZTR1 or CRKL formed three- dimensional clusters in 2D and 3D collagen cultures (Fig. 6a, top and bottom rows, respectively). Moreover, this result – which was reminiscent of a malignant cell phenotype72 – was further enhanced when both genes were co- expressed (LZTR1+CRKL), leading to the formation of spheroids that detached from the surface of the dish (Fig. 6a). + +During metastasis, primary melanoma cells detach from the dermis and migrate to secondary sites through increased cell- cell interactions and promotion of cancer cell survival. We therefore examined changes in adhesion proteins affecting cell- matrix and cell- cell interactions known to mediate aggregation, the formation of spheroids72, and in vivo EMT73,74. Our data show that E- cadherin was downregulated whereas N- cadherin and integrin \(\beta 1\) were upregulated in response to increased expression of LZTR1 and CRKL, a process that was enhanced when both genes were co- expressed (Fig. 6b). Notably, our results with CRKL were consistent with HCT116 colon cancer cells, in which loss of CRKL was found to increase E- cadherin expression and shift the cells toward an epithelial phenotype71. Importantly, LZTR1 and CRKL, both alone and in combination, induced high levels of constitutively active ERK and SRC relative to parental cells (Fig. 6b, pERK and pSRC), functions that support viability and proliferation. We also identified downregulation of MITF in cells overexpressing CRKL as the possible cause for growth rate attenuation (Fig. 6b; Supplementary Fig. 8a). Consistent with this finding, while higher expression of MITF defines a proliferative subtype of melanoma (MITFhigh- AXLlow), lower expression is preferentially associated with invasion (MITFlow- AXLhigh)75. + +<--- Page Split ---> + +Based on these findings, we hypothesized that co- amplification of LZTR1 and CRKL might lead to increased rates of distant recurrence. Given that 22q11.21 amplification is a late- arising event in acral melanoma (Fig. 2c, left), we tested this hypothesis by examining acral melanoma patients diagnosed with stage II or III disease. Indeed, in patients for whom distant metastasis- free survival (DMFS) data were available, focal amplification of 22q11.21 was associated with earlier development of distant metastatic disease, with a median lead time of nearly 1 year (Fig. 6c). + +Finally, we investigated whether overexpression of LZTR1 or CRKL release normal human melanocytes from their dependency on growth factors, a common phenotype of metastatic melanoma cells76. While normal human melanocytes retained their growth factor dependency (Supplementary Fig. 8), LZTR1, but not CRKL, enabled immortalized mouse melanocytes to form colonies and divide in the absence of their only required growth factor, TPA (tetradecanoyl phorbol acetate)77 (Fig. 6d,e). This phenotype is likely the consequence of MAPK activation, as seen by the presence of phosphorylated ERK (Fig. 6f). + +Taken together, these results strongly implicate LZTR1 and CRKL in malignant transformation and the initiation of metastasis. While both genes showed similar phenotypes, the effects of overexpression were notably enhanced when LZTR1 and CRKL were co- expressed. However, only LZTR1 released immortalized mouse melanocytes from their dependency on growth factor, a characteristic shared by melanoma cells. + +## DISCUSSION + +Acral melanoma has high incidence among non- Caucasian populations, accounting for up to \(86\%\) of melanomas diagnosed in Asian patients as compared to \(\sim 10\%\) of Caucasians29,78- 84. Our work establishes common features of acral melanomas in cohorts from Asian and Caucasian populations. These include 1) the consistent association between specific focal amplifications and poor outcomes, and 2) the identification of LZTR1 as a key gene within 22q11.21, the most prognostic recurrent alteration identified in both acral cohorts. Based on these findings, we performed a comprehensive analysis of LZTR1 signaling pathways and obtained functional evidence for LZTR1 as a tumor promoter. + +LZTR1 is co- amplified with CRKL and downregulation of each gene inhibits melanoma cell proliferation, albeit to varying degrees. While CRKL has been linked to tumor growth as a candidate oncogene in several human malignancies, including lung adenocarcinoma47- 49, LZTR1 is generally considered a tumor suppressor. Germline mutations in LZTR1 are involved in Noonan syndrome53,85, schwannomatosis46 and glioblastoma43,86. Moreover, somatic loss- of- function mutations in LZTR1 occur in \(22\%\) + +<--- Page Split ---> + +of glioblastomas. These mutations drive self- renewal and growth of glioma spheres \(^{43}\) , consistent with a role in tumor suppression. However, despite these findings, LZTR1 is amplified in a subset of carcinomas (up to \(8.3\%\) ), including bladder, uterine, and lung cancers \(^{87,88}\) . These data, coupled with our results, suggest that LZTR1 could have tumor- promoting capabilities in multiple human malignancies. + +Unique aspects of our study include the broad range of tumor specimens analyzed and the utilization of cells harboring different oncogenes that modulate LZTR1 activity. For example, in NRAS- mutant melanoma, RAS mis- localized to the cytoplasm in response to shLZTR1 and caused MAPK inhibition. On the other hand, elimination of LZTR1 in BRAF- mutant cells increased BRAF levels, leading to ERK activation. In several cell lines, ERK activation induced growth arrest via MITF degradation, a process unique to melanocytes and the melanoma system \(^{67,68}\) . + +Importantly, our study demonstrates that LZTR1 and CRKL – two of the top three genes associated with ch22q11.2 amplification in acral melanoma – facilitate anchorage- independent growth in normal human melanocytes, likely by reducing E- cadherin, increasing N- cadherin, and activating integrin \(\beta 1\) . The reciprocal expression of E- cadherin and N- cadherin in early melanoma progression has been known for about two decades \(^{73,89}\) , but to our knowledge, our findings link these events to genomic modification of two specific genes for the first time. In addition, we observed activation of MAPK and SRC kinases, the likely consequences of integrin signaling \(^{90,91}\) . The ability of LZTR1 to convert immortalized mouse melanocytes to a growth- factor independent mode of proliferation, a major characteristic of melanoma cells in culture, further underscores its tumorigenic potential. These results agree with our genomic observation that ch22q11.21 amplification is a late- arising event associated with regional and distant metastasis. + +Separately, we identified a striking inverse relationship between immunomodulatory genes and 22q11.21 amplification. It is tempting to speculate that high levels of LZTR1 reduce the inflammatory response while protecting cells from stress- induced apoptosis, thereby facilitating metastasis. Conversely, patients with low levels of LZTR1 preferentially harbor a hot tumor microenvironment, which might provide benefit from immunotherapy. Future studies will be needed to explore these possibilities. + +In summary, we demonstrate that late- arising focal amplifications of cytoband 22q11.21 are a leading determinant of shorter survival time in acral melanoma. Our genomic and functional experiments provide critical new insights into the pathogenesis of this disease and strongly implicate LZTR1 as a novel tumor promoter and promising therapeutic target. + +<--- Page Split ---> + +## Acknowledgements + +AcknowledgementsThis work was supported by the Melanoma Research Alliance (R.H., Q.Y., J.C.), the Sokoloff- MRA Award (R.H.), the Yale SPORE in Skin Cancer (Bosenberg and Kluger), the Roslyn and Jeremy Meyer Award (R.H.), the National Cancer Institute (A.M.N., R00CA187192), the Stinehart- Reed foundation (A.M.N.), the Stanford Bio- X Interdisciplinary Initiatives Seed Grants Program (IIP) (A.M.N.), the Virginia and D.K. Ludwig Fund for Cancer Research (A.M.N.), the Natural Science Foundation of China Major Projects of International Cooperation and Exchanges grant 81620108024 (X.C.), the General Program grant 81874138 (M.Y.), a New Investigator Award provided by Rutgers Cancer Institute of New Jersey (State of NJ appropriation and National Institutes of Health grant P30CA072720, J.C.), and a Melanoma Research Foundation Career Development Award (J.C.). We wish to acknowledge the Yale Center for Genome Analysis (YCGA) for performing WES and RNA- seq, David Calderwood and Ben Turk for providing the short hairpin RNA lentiviral vectors, the West Campus Imaging Core for confocal microscopy and cell imaging, Junkun Liu for performing cell immunostaining, Robert Straub and Jenna Ollodart for technical assistance. We thank for Dr. Doug Brash for his insight regarding acral melanoma mutations and to Zoe Halaban for her critical questions and enthusiastic support during these studies. We dedicate this manuscript to the memory of our colleague Dr. Deepak Narayan, a surgeon- scientist who provided great insight into this work and who will be remembered for his legacy of unparalleled innovation in the face of complex problems. + +## Author Contributions + +A.M.N. and R.H. conceived the study, developed strategies for related experiments, and wrote the manuscript. X.C. initiated the collaborative studies, supervised sample collections from multi- clinical centers in China, exome and RNA- sequencing and some functional studies. F.F. co- wrote the paper and performed bioinformatics analyses with assistance from C.L., J.K., K.R., and L.P. R.H. performed key functional experiments with assistance from C.P., A.B., M.Y., M.Z., and J.N. A.B. performed experiments, tissue collection and processing (Yale cohort) and obtained clinical data. C.P. performed sample and clinical data collection and assisted with sequencing (CSU cohort). J.S. assisted with the collection of clinical data (CSU cohort). M.S. identified patients and collected clinical data (Yale cohort). S.A., D.N., J.C., K.O, S.Z., Y.W, G.H, M.W., and X.L contributed tumor specimens. J.C. and Q.Y. assisted with the conception of the study, performed experiments, and contributed to writing. All authors commented on the manuscript at all stages. A.M.N. and R.H. jointly supervised this work. + +## Competing Interests + +The authors declare no competing interests. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Landscape of somatic alterations in acral and sun-exposed melanomas. a-b, Genomic and clinical characterization of acral and sun-exposed melanoma samples sequenced in this work. a, The number of nonsynonymous SNVs and indels per melanoma exome (columns), cohort, age, frequently mutated genes (at least \(5\%\) recurrence frequency in either melanoma subtype), nonsynonymous base substitution frequencies, and dominant COSMIC mutational signatures \(^{92,93}\) . Sig., signature; 5mC, 5-methylcytosine. b, The number of significant focal amplifications and deletions (GISTIC \(Q < 0.05\) ) per melanoma exome (columns), ordered identically to panel a. Cytobands with focal amplifications or deletions with at least \(10\%\) recurrence frequency in either melanoma subtype are shown (GISTIC \(Q < 10^{-5}\) ), ordered by the relative difference in recurrence frequency in acral versus sun-exposed melanoma. c, Genes are plotted according to the fraction of acral (y-axis) or sun-exposed (x-axis) tumors where they are present with \(4+\) copies. Considering the genome-wide distribution of differences in recurrence frequencies between melanoma subtypes, genes are identified as significantly recurrent in acral or sun-exposed melanomas if their \(|z\text{-score}|\geq 3\) (dashed lines). Significantly recurrent genes are colored according to their cytoband location (inset). For clarity, a small amount of jitter was added to distinguish overlapping genes.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Focal amplifications in 22q11.21 are linked to shorter survival time, regional metastasis, and depletion of immunomodulatory programs in acral melanoma. a, Association between recurrent somatic alterations and overall survival (OS) in acral melanoma. Z-scores with positive and negative values indicated shorter and longer survival time, respectively \((|Z| > 1.96\) is significant at \(P< 0.05\) ; Methods). OS was calculated from the date of diagnosis (x-axis) and the date of tumor resection (y-axis). Shown are genes with a nonsynonymous mutation frequency of at least \(5\%\) in either melanoma subtype and focal copy number events with at least \(10\%\) recurrence frequency in each acral cohort (Supplementary Table 6). Focal amplifications in
+ +<--- Page Split ---> + +cytobands significantly associated with OS and recurrently mutated genes are labeled. Additional details are provided in Methods. b, Kaplan Meier curves showing overall survival of acral melanoma patients, stratified by 22. q11.21 amplification status and calculated from the date of tumor resection. Significance was assessed with the log- rank test. HR, hazard ratio. 95% HR confidence intervals are shown in brackets. c, Left: Acral melanoma tumor stage shown as a function of 22q11.21 amplification status. Statistical significance was evaluated by a Chi \(X^{2}\) test. Right: Fraction of 22q11.21- amplified melanomas stratified by the number of involved lymph nodes (N stage). d, Hallmark signaling pathways significantly enriched in 22q11.21- amplified vs. non- amplified acral melanomas, as determined by pre- ranked Gene Set Enrichment Analysis (GSEA). Genes were ranked by \(\log_{2}\) fold change adjusted for stage (Methods). OXPHOS, oxidative phosphorylation. e, Hierarchical clustering of 31 immunomodulatory genes (average linkage with Euclidean distance) in acral melanomas. CD3D and CD8A are included as lineage markers for T cells and CD8 T cells, respectively. f, Bar plot showing frequency of 22q11.21- amplified acral melanomas separated into high and low immunomodulatory expression groups, as defined by clustering in panel e. Statistical significance was determined by Fisher's exact test. g, Single- cell differentiation status, as imputed by CytoTRACE \(^{42}\) (top), versus the estimated relative number of 22q11.21 copies per cell (normalized between 0 and 1), as imputed by CONICSmat \(^{36}\) (bottom), in cancer cells from a 22q11.21- amplified acral melanoma tumor (Supplementary Fig. 4; Methods). Single- cell transcriptomes are visualized in the top subpanel by Uniform Manifold Approximation and Projection (UMAP). h, Association between gene expression and 22q11.21 amplification status in Yale and CSU acral melanoma cohorts. Only genes physically located on cytoband 22q11.21 are shown. Group comparisons were performed using a two- sided \(t\) - test with unequal variance. The top three genes are indicated. Amplified, \(3+\) copies. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: Cell proliferation in response to suppression of LZTR1. a, Impact of LZTR1 knockdown on cell proliferation in nine primary melanoma cell lines and two normal human melanocyte lines (NBMEL). Key mutations are indicated (Supplementary Table 9). Bar plots depict fold change between the \(3^{\text{rd}}\) and \(6^{\text{th}}\) day after infection with LZTR1 shRNA (numbered), as compared to control (scrambled) shRNA ('C'). All shRNAs significantly reduced proliferation relative to control ( \(P < 0.05\) ; two-sided \(t\) test with unequal variance). b, Western blot showing the efficiency of LZTR1 knockdown in primary acral and sun-exposed melanoma cell lines, and in normal melanocytes, related to panel a. c, Cell proliferation (left) and LZTR1 expression (right) of a sun-exposed melanoma cell line that lost one LZTR1 allele in response to genomic modification by different CRISPR-Cas9 sgRNAs targeting
+ +<--- Page Split ---> + +LZTR1. Bar plots depict fold change between the \(3^{\text{rd}}\) and \(6^{\text{th}}\) day after infection with sgRNA 5. Reduced cell proliferation is statistically significant ( \(P < 0.05\) ; two- sided \(t\) test with unequal variance). Bars in a, c represent the mean of triplicate or quadruplet wells and error bars indicate SEM. Actin levels in b, c show protein loading. Cell lines are indicated above all plots in a- c and colored according to their origin: acral melanoma (blue), sun- exposed melanoma (red), normal melanocyte (grey). NBMEL, newborn melanocyte cultured from Caucasian foreskin; WT, wildtype. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4: Changes in MAPK signaling in response to LZTR1 knockdown. a, b, Impact of LZTR1 loss on (a) RAS-GTP activity as measured by RAS-GTPase Activation ELISA assay, and (b) RAS levels, five days after shLZTR1 infection. c, Effect of LZTR1 loss on MAPK activity. Key somatic events are indicated below. WT, wildtype. d, e Increased levels of BRAF activity in response to shLZTR1 is associated with increase pERK. In panel e, cells were incubated with RAF the kinase inhibitors PLX4032 (500 nM) or LY3009120 (100 nM) for four hours at the end of treatment with shRNA. f, RAS translocation to the cytoplasm in response to shLZTR1; a-d, RAS is visualized by staining with magenta; Green (Cy2) indicates GM130 (a, b) and calnexin (c, d). Scale
+ +<--- Page Split ---> + +bar = 50 μm. Blue and red indicate acral and sun- exposed melanoma cell lines, respectively. shRNAs in a- e are indicated by numeric identifiers. C, scrambled shRNA control. Actin levels in a- e show protein loading. Cell lines are indicated above all plots in a- f and colored according to their origin: acral melanoma (blue), sun- exposed melanoma (red), normal melanocyte (grey). NBMEL, newborn melanocyte. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5: Impact of LZTR1 knockdown on apoptosis, Rb signaling, and pigmentation. a, Gene Set Enrichment Analysis (GSEA)94 showing concordance in hallmark pathways among bulk acral melanoma tumors, acral melanoma single-cell
+ +<--- Page Split ---> + +transcriptomes (scRNA- seq), and a primary melanoma cell line (LZTR1 vs. KD), in relation to high vs. low LZTR1 expression. Gold, high positive normalized enrichment score (NES); blue, high negative NES; KD, knockdown. b, LZTR1 shRNA and sgRNA (CRISPR- Cas9) induce apoptosis in melanoma cells. c, d, Effects of inhibiting caspase activity with IDN- 6556 (IDN, 2 μM, 3 days). As shown in c, IDN- 6556 suppressed shLZTR1- induced caspase activity. As shown in d, IDN- 6556 increased the levels of LZTR1 (known to be degraded by caspases) and rescued caspase substrates, such as GOLGA4, p53, and to a lesser extent, NF- \(\kappa \beta\) . e, Effect of shLZTR1 on cell morphology and actin filament organization. Actin filaments were visualized by staining with rhodamine- phalloidin (magenta) and the Golgi with anti- GM130 (green, Cy2). The nuclei are stained with DAPI (blue). Scale bar = 50 μm. f, shLZTR1 downregulates Rb and p130 (also known as RBL2). g- i, Impact of shLZTR1 on MITF (panel g), tyrosinase (TYR) (panel h), and pigmentation (panel i). shRNAs in b- d and f- i are indicated by unique numerical identifiers. C, scrambled shRNA control. Actin levels in b- d and f- h show protein loading. Of note, the actin control in f and h is identical because the same membrane was used to blot the proteins. Cell lines are indicated above all plots in b- g and colored according to their origin: acral melanoma (blue), sun- exposed melanoma (red). + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 6: LZTR1 and CRKL confer properties consistent with malignant cell transformation and metastasis initiation. a, Morphological changes and spheroid formations in early passage normal human melanocytes (NBMEL C1220) overexpressing LZTR1 and/or CRKL. Top: Phase-contrast images of parental and infected cells in 2D culture. LZTR1 images were taken after two days induction with doxycycline (200 ng/ml), CRKL after three days of infection with PLX304-CRKL, and LZTR1+CRKL after six days infection of LZTR1 melanocytes with PLX304-CRKL and three days stimulation with doxycycline. Insert in LZTR1 shows that colonies were
+ +<--- Page Split ---> + +evident without magnification. The non- induced LZTR1 cells grow as a monolayer, as seen for the parental non- transformed melanocytes (Parental). Scale bar = 100 μm. Bottom: Low magnification images showing 3D cultures of melanocytes seeded in 0.5% collagen for three days. LZTR1 and CRKL, both alone and in combination, induced aggregation and multicellular spheroids. Scale bar = 500 μm. b, Western blot showing critical changes in normal human melanocytes overexpressing LZTR1, CRKL, or both (as seen in the top two lanes) compared to parental (-). Cells were harvested after incubation in regular medium, or medium supplemented with doxycycline for two days when applicable (Dox, 200 ng/ml). Of note, an increase in LZTR1 produced by basal promoter activity was sufficient to induce constitutive MAPK and SRC activities. Actin levels show protein loading. c, Kaplan Meier plot showing differences in distant metastasis- free survival (DMFS) between acral melanoma patients stratified by 22q11.21- amplification status. Patients with stage II or III disease at diagnosis with available DMFS data are shown (Yale cohort). Statistical significance was assessed by a log- rank test. HR, hazard ratio. 95% HR confidence interval is shown in brackets. d, 2D cultures of spontaneously immortalized mouse melanocytes (C57BL) forming colonies in the absence of TPA in response to LZTR1 (Dox). The dark colonies are seen without magnification (top), and under phase- contrast microscope (bottom). e, Cell proliferation of parental and LZTR1 transformed C57BL mouse melanocytes. f, Western blots displaying LZTR1 expression and MAPK activation (pERK) in TPA- starved (-) mouse melanocytes in response to doxycycline (+200 ng/ml), compared to parental, non- transformed cells (P). + +<--- Page Split ---> + + +Table 1: Patient characteristics and sequencing data + +
CharacteristicsAcral
(n = 104)		Sun- exposed
(n = 157)
Age (years)
Median (range)62 (29 - 89)66 (20 - 94)
Sex, n (%)
Female42 (40)61 (39)
Male62 (60)96 (61)
Stage at tumor resection, n (%)
01 (1)0 (0)
110 (10)7 (4)
239 (38)22 (14)
328 (27)7 (4)
426 (25)121 (77)
Tumor sample site, n (%)
Primary81 (78)41 (26)
Metastatic23 (22)116 (74)
WES, n (%)
Yale University32 (33)134 (100)
Central South University65 (67)0 (0)
Bulk RNA-seq, total n (n with WES)
Yale University22 (17)60 (37)
Central South University23 (21)0
+ +Out of 104 acral melanoma patients, 97 were profiled by WES, 7 were profiled by bulk RNA-seq and not WES, and 45 were profiled by both. Of 157 sun-exposed melanoma patients, 134 were profiled by WES, 23 were profiled by bulk RNA-seq and not WES, and 37 were profiled by both. + +<--- Page Split ---> + +## Methods + +## Human subjects + +All clinical specimens in this study were collected with informed consent for research use and were approved by the Yale University and Central South University Institutional Review Boards in accordance with the Declaration of Helsinki. Melanoma tumor specimens were excised to alleviate tumor burden. CSU samples were collected from Xiangya Hospital, Hospital for Skin Diseases (Institute of Dermatology), Chinese Academy of Medical Sciences in Nanjin, Third Affiliated Hospital of Sun Yat- sen University, Hunan Provincial Tumor Hospital, Xiangya Hospital, Central South University, First Affiliated Hospital of Harbin Medical University and Wuhan Union Hospital. + +## Nucleic acids extraction + +Melanomas were sequenced from snap- frozen tumors (Yale and CSU cohorts) or low passage cell cultures (<4) as previously described2,4 (Supplementary Tables 1 and 8). DNA from melanoma cells and freshly frozen tumors was extracted with the DNeasy purification kit (Qiagen Inc., Valencia, CA). High melanin content was removed with OneStep™ PCR Inhibitor Removal Kit (Zymo Research Corporation, Irvine, CA). Direct- zol™ RNA MiniPrep w/ Zymo- Spin™ IIC Columns (Zymo cat # D4019) were used to extract RNA from tumors, and the RNeasy PowerLyzer Tissue & Cells Kit (Qiagen, CAT # 15055- 50) was used to extract RNA from peripheral blood mononuclear cells (PBMCs) and melanoma cells. + +## Whole exome sequencing + +Sample preparation: The quality of genomic DNA was determined by estimating the \(\mathrm{A}_{260} / \mathrm{A}_{280}\) and \(\mathrm{A}_{260} / \mathrm{A}_{230}\) ratios by nanodrop, both of which required to be \(>1.8\) , and by electrophoresis in \(1\%\) agarose gel in which high quality DNA migrates as a single high molecular weight band. One \(\mu \mathrm{g}\) of genomic DNA was sheared to a mean fragment length of about 220 bp using focused acoustic energy (Covaris E220). The size distribution of the fragmented sample was determined by using the Caliper LabChip GX system. The fragmented DNA samples were transferred to a 96- well plate and library construction was completed using a liquid handling robot. Following fragmentation, we added T4 DNA polymerase and T4 polynucleotide kinase that blunt end and phosphorylate the fragments. The large Klenow fragment then adds a single adenine residue to the 3' end of each fragment and custom adapters (IDT) are ligated using T4 DNA ligase. Magnetic AMPure XP beads (Beckman Coulter) were used to purify and size select the adapter- ligated DNA fragments. The adapter- ligated DNA fragments were then PCR amplified using custom- made primers (IDT). During PCR, a unique six base index was inserted at both ends of each DNA fragment. Sample concentration was determined by picogreen and the fragment length distribution using the Caliper LabChip GX system. Samples yielding at least \(1 \mu \mathrm{g}\) of amplified DNA were used for capture. + +Targeted capture and sequencing: For the CSU cohort, capture was performed using the NimbleGenSeqCap Med Exome 44M kit, followed by 151 bp paired- end sequencing on the Illumina HiSeq X 10 platform. TrimGalore (version 0.3.7) and FastQC (v0.11.2) + +<--- Page Split ---> + +were used to remove adapters and low- quality sequences from the raw data. For the Yale cohort, equal amounts of each sample were pooled prior to capture. Example: for 16 samples per lane 62.5 ng of each genomic DNA library was pooled (1 \(\mu \mathrm{g}\) total) and lyophilized with Cot- 1 DNA and universal adapter blocking oligos (IDT). The dried sample was reconstituted according to the manufacturer's protocol (IDT), heat- denatured, and mixed with biotinylated DNA probes produced by IDT (xGen Exome Panel). Hybridizations were performed at \(65^{\circ}\mathrm{C}\) for 16 hours. Once the capture was complete, the samples were mixed with streptavidin- coated beads and washed with a series of stringent buffers to remove non- specifically bound DNA fragments. The captured fragments were PCR amplified and purified with AMPure XP beads. Samples were quantified by qRT- PCR using a commercially available kit (KAPA Biosystems) and insert size distribution determined with the LabChip GX. Samples with a yield of \(\geq 0.5\) ng/ul were used for sequencing. Sample concentrations were normalized to 2 nM and loaded onto Illumina NovaSeq 6000 flow cells at a concentration that yields at least 600Gbp of passing filter data per lane. Samples were sequenced using 101 bp paired- end sequencing reads according to Illumina protocols. + +## Bulk and single-cell RNA sequencing + +Bulk and single- cell RNA sequencingBulk RNA- seq: For the CSU cohort, total RNA was depleted of rRNA using the Ribo- Zero rRNA removal kit, namely, 1 \(\mu \mathrm{g}\) of total RNA was used as input for rRNA removal. Sequencing libraries were generated using the TruSeq RNA sample prep kit (Illumina). The libraries were sequenced as 151 bp paired- end reads using an Illumina HiSeq X Ten platform. For the Yale cohort, rRNA was depleted starting from 25- 1000ng of total RNA using the Kapa RNA HyperPrep Kit with RiboErase (KR1351). Indexed libraries that met appropriate cut- offs for both quantity and quality were quantified by qRT- PCR using a commercially available kit (KAPA Biosystems) and insert size distribution was determined with the LabChip GX or Agilent Bioanalyzer. Samples with a yield of \(\geq 0.5\) ng/ul were used for sequencing. Samples were run on a combination of Illumina HiSeq 2500, HiSeq 4000, and NovaSeq instruments, and multiplexed using unique dual barcode indexes (to avoid sample contamination or barcode hopping). + +scRNA- seq: To obtain a single- cell transcriptional portrait of a chr22q11.21- amplified tumor, we analyzed a primary acral melanoma specimen (YUJASMIN, Yale cohort) with 6 focal copies of 22q11.21, as determined by WES (Supplementary Table 1). The 10x Chromium 5' expression profiling platform with V1 chemistry was applied to a cryopreserved tumor cell suspension from YUJASMIN sorted for viable singlets to target 10,000 cells. Cells were sorted in the following ratios prior to library preparation: 50% CD3+CD45+ T cells: 25% CD3- CD45+ non- T immune cells: 25% CD45- stromal/cancer cells. Cell viability was assessed by the LIVE/DEAD™ Fixable Red Dead Cell Stain Kit (catalog #L34971, Thermo Fisher). The following antibodies were used: Alexa Fluor® 488 anti- human CD45 antibody (clone H130, catalog #304019, BioLegend); APC anti- human CD3 antibody (clone HIT3a, catalog #300319, BioLegend). The 10x library was sequenced on an Illumina HiSeq 2500 instrument. + +<--- Page Split ---> + +## Tumor genotyping from whole exome sequencing data + +Sequencing reads from exome- captured samples were analyzed with a combination of germline and somatic variant calling, permitting the identification of somatic variants, loss- of- heterozygosity (LOH) regions and copy- number variation (CNV) regions. + +SNVs and indels: BAM files of aligned reads were created for each sample by aligning the sequencing reads to the GRCh37 human reference with decoy sequences (the "hs37d5" reference) using BWA MEM95, marking duplicates using Picard MarkDuplicates (http://broadinstitute.github.io/picard), and then performing indel realignment and base quality score recalibration using GATK v3.296. Then, variants were called using the tumor/normal bam files in three ways: 1) a joint variant call using GATK HaplotypeCaller, GenotypeGVCFs and hard filtering following GATK 3.2 best practices; 2) somatic SNP variant calls using MuTect with options "max_alt_alleles_in_normal_count=6", "max_alt_allele_in_normal_fraction=0.1" and "max_alt_alleles_in_normal_qscore_sum=200"; 3) somatic indel variant calls using Indelocator with options "minCoverage=6", "minNormalCoverage=4" and "minFraction=0.2". The output from the three variant callers were merged using inhouse scripts into a single VCF file, containing the union of GATK variants and MuTect/Indelocator somatic variants, marking variants called as somatic by MuTect or Indelocator as "somatic". + +Those variants were annotated using Annovar97 and VEP98, and then the somatic variants were filtered using the following criteria: 1) tumor alt depth \(\geq 4\) , 2) normal read depth \(\geq 4\) , 3) normal alt depth \(\leq 1\) or normal alt frequency less than 1/5 tumor alt frequency, 4) the maximum population frequency of the variant from ExAC99, NHLBI, 1000 Genomes, or Yale Exome database must be less than 2% for a cancer- related gene (any gene in the Oncomine or Foundation Medicine gene panels or COSMIC CG Census gene list) or 1% for any other gene. Also, only protein changing variants with a VEP impact of MEDIUM or HIGH, or variants within 15 bases of a protein coding splice site were reported in the final output. + +LOH: Loss- of- heterozygosity (LOH) regions were identified using the joint variant calls generated from GATK. For each variant that was called heterozygous in the normal and had a depth \(\geq 20\) in the normal, the allele frequency of the tumor and normal were subtracted ("abs (tumorAF - normalAF)"). Then the R loess and predict functions were used to smooth the allele frequency differences, and then any region with fitted values above 0.1 were identified as LOH. Tumor purity was estimated by taking the maximum mean of any LOH region, multiplied by 2 (as the tumorAF deviation, plus and minus, identifies the homozygous tumor proportion of the sample). + +CNVs: Gene- level copy- number variant (CNV) regions were identified by first calculating the mean read depth for each RefGene coding exon, for the tumor and normal samples. Normalized tumor/normal read depth ratios were computed for each exon (normalized by the mean read depth of the tumor and normal across the exome), and then, using a partitioning of the genome into 20kb bins, a mean ratio for each 20kb region of the + +<--- Page Split ---> + +genome, which contains an exon, was computed. Those mean ratios were de- noised and segmented by circular binary segmentation (CBS) using the DNAcopy library from R (http://bioconductor.org/packages/release/bioc/html/DNAcopy.html.) Regions with a value deviating from the expected 1.0 ratio were identified as CNVs. For each CNV, ploidy is calculated using a deviation step value of 0.5 for high tumor purity samples (purity \(\geq 80\%\) , a 0.4 step for purity between \(40\%\) and \(80\%\) , and 0.32 step for purity less than \(40\%\) ), with the ploidy equaling 2 plus or minus the number of deviation steps. + +Focal amplifications and deletions were identified with GISTIC2.0 (version 2.0.23, release date 27 Mar 2017) \(^{33}\) using the CBS segmentation files described above and the hg19 reference genome (GRCh37). No marker input file was provided. Parameters were specified according to the authors' recommended run profile: amplification and deletion thresholds were set to 0.1, the q value threshold was set to 0.1 with a confidence level of 0.95, and log2 ratios were capped at 1.5. Gene- level GISTIC analysis and broad analysis were also applied, with a focal length cutoff of 0.7. Wide peaks identified with a Q value less than 0.1 in each melanoma subtype were aggregated into a master list (Supplementary Tables 3 and 4), and genes within each wide peak were used to construct a copy number matrix. Of note, if two or more peaks were identified within the same cytoband, we appended a suffix to the cytoband name to denote the melanoma subtype in which the cytoband was identified (AC, acral; SE, sun- exposed). If more than one peak was identified within the same cytoband for a given melanoma subtype, the subtype acronym was followed by a numerical identifier (1, 2, etc.). For cases in which one peak completely encompassed another one and where both peaks had the same orientation (i.e., amplified or deleted), the shorter one was eliminated. + +For comparative genomics and survival analyses, we constructed a matrix containing the mean copy number per wide peak (rows) for each melanoma tumor sample (columns). The mean copy number per wide peak was calculated as the average gene- level copy number (estimated as described above) per wide peak. We subtracted 2 from all gene- level values so that copy number- neutral regions are equal to 0 + +(Supplementary Data). + +## Visualization of somatic alterations across patients + +Recurrently mutated genes and significant focal CNVs were visualized using the Oncoprint function in ComplexHeatmap \(^{100}\) . The default bar plot (top) was replaced with a bar plot showing the number of nonsynonymous SNVs and indels per patient. For CNV regions, amplifications and deletions were calculated by averaging the GISTIC- generated gene- level copy numbers for all genes within each wide peak. Peaks with an average copy number above 3 (one additional copy) were deemed amplified, whereas peaks with an average copy number below 1.4 were considered deleted. + +## Bulk RNA-seq analysis + +Raw RNA- seq reads were aligned with Salmon \(^{101}\) (version 0.99) to the GENCODE v.25 \(^{102}\) reference transcript assembly. Subsequently, the tximport \(^{103}\) was used to generate an expression matrix normalized to transcripts per million (TPM). Protein- + +<--- Page Split ---> + +coding genes were determined using Ensembl release 92 human annotation \(^{104}\) (GRCh38.p12, Apr 2018), extracted by biomaRt \(^{105}\) (version 2.40.5) and non- protein- coding genes were omitted. Expression values were renormalized to TPM after this step. For batch normalization, we applied ComBat from sva \(^{106}\) using a parametric adjustment for sequencing center and year of sequencing. Following batch correction, negative values were replaced with zero and the expression matrix was \(\log_{2}\) - transformed after adding a pseudo- count of 1. + +To delineate pathways associated with focal amplification of 22q11.21 (Fig. 2d), genes differentially expressed between 22q11.21 amplified and non- amplified acral melanomas were identified by constructing a linear model (Im function in R) to predict amplification status as a function of 1) gene expression ( \(\log_{2}\) adjusted) and 2) tumor stage (at the time of resection). The \(t\) value corresponding to the expression vector of each gene was used to rank- order the transcriptome. Pre- ranked gene set enrichment analysis (GSEA) \(^{107}\) was subsequently applied to the ranked- ordered transcriptome in order to assess HALLMARK pathways in MSigDB (version 7.2) \(^{108}\) . + +Related to Fig. 2e, we curated a list of immunomodulatory genes, including immune checkpoint molecules, and analyzed their expression in both acral cohorts using hierarchical clustering applied with Pearson correlation and Ward D2. + +## Gene fusion detection from RNA-seq data + +To identify fusion genes, we aligned the RNA- seq reads for each sample to the GRCh38 human reference genome using HISAT \(^{109}\) . Candidate fusion transcripts in the sequencing reads were identified with STAR- Fusion, employing the STAR aligner and FusionInspector annotator to identify the position of the chimeric RNA. For ease of manual review, the fusions were sub- grouped, with each fusion placed into the first group that either gene matched: 1) mitochondrial genes, 2) immunoglobulin genes, 3) protocadherin genes, 4) commonly expressed fusions using GTEx expression data, 5) fusions of neighboring/local rearrangement genes, 6) non- annotated genes, and 7) all others i.e., the rare, non- local fusions of annotated protein coding genes. + +## Survival analysis + +Cox proportional hazards regression was applied to estimate overall survival associations. Cases with an initial diagnosis preceding the sequenced tumor by more than 5 years were excluded from analysis \((n = 5)\) . To estimate stage- adjusted associations with overall survival, stage was added as a covariate. Kaplan- Meier plots for comparison of survival curves were generated either by the survminer \(^{110}\) package in R (version 0.4.5) or by Graphpad Prism (version 8). + +To determine survival associations of focal CNVs identified by GISTIC (Fig. 2a, Supplementary Fig. 3a, Supplementary Table 6), we applied Cox regression separately to each region within each acral melanoma cohort using the copy number matrix described above (see Copy number analysis). In all cases, we dichotomized each CNV by analyzing amplified \((>0)\) versus non- amplified \((\leq 0)\) and deleted \((\leq 0)\) + +<--- Page Split ---> + +versus non- deleted \((\geq 0)\) . Survival z- scores were combined across cohorts using Stouffer's method111, yielding an unweighted meta- z- score for each gene. + +To analyze SNV- and indel- related survival associations in acral melanomas, we examined genes harboring one or more nonsynonymous mutations with at least \(5\%\) recurrence frequency in either acral melanoma cohort. These data were used to create a binary matrix in which recurrently mutated genes were rows and patients were columns (1, at least one recurrent SNV or indel; 0, otherwise). Survival z- scores were combined across cohorts as indicated above. The following four genes were insufficiently recurrent in at least one cohort to run Cox regression: CGREF1, NF1, TP53, and ARID2. To calculate survival associations for these genes, we randomly up- sampled patients from the Yale cohort in order to match the size of the CSU cohort. We then generated a cross- cohort survival Z- score for each gene (Supplementary Table 6). + +To relate LZTR1 expression to overall survival, we dichotomized patients in each cohort by determining an expression threshold that discriminates 22q11.21- amplified from non- amplified patients at a defined specificity. This was done to link the threshold for dichotomization with 22q11.21 amplification without being confounded by the upper range of LZTR1 expression in non- amplified tumors. We used a specificity cutpoint of \(95\%\) for acral melanomas profiled in this study and sun- exposed melanomas profiled by TCGA. A specificity cutpoint of \(90\%\) was used for sun- exposed melanomas profiled in this work owing to a lack of evaluable samples in the 'high' group \((n = 1)\) at a specificity cutpoint of \(95\%\) . Notably, LZTR1 expression was also significantly associated with adverse outcomes when assessed as a continuous variable (i.e., without dichotomization) in sun- exposed melanomas profiled by TCGA \((Z = 2.86, P = 0.004)\) . Skin cutaneous melanoma (SKCM) expression, copy number, and survival data from TCGA were downloaded from cBioPortal87. + +## Single-cell RNA sequencing analysis + +Single- cell RNA- seq reads were mapped to the GRCh38 human reference assembly and barcode- deduplicated using Cell Ranger (version 3.0.2). In total, 7,551 cells were sequenced, yielding a median of 945 genes per cell. The expression matrix generated by Cell Ranger was converted to counts per million (CPM) and was log2 transformed after addition of a pseudo- count to every value. Cells with less than 500 expressed genes were removed before importing the data into Seurat112 (version 3.0.2). Seurat was applied for pre- processing (default settings), data normalization (default settings), identifying the most variable features, dimension reduction (PCA and UMAP), finding marker genes, and clustering the single- cell expression data. In finding variable features, the low and high mean cutoffs were set to 0.0125 and 3, respectively, and the dispersion cutoffs were respectively set to 0.5 and infinity, with 20 bins. PCA was generated on the most variable genes with the first 15 principal components. Neighbors were calculated from the first 13 PCA components (as determined by the JackStraw function), followed by cluster analysis (resolution = 0.5). + +<--- Page Split ---> + +Single- cell copy number analysis: To estimate large- scale copy number alterations from scRNA- seq data, we used CONICSmat (COpy- Number analysis In single- Cell RNA- Sequencing from an expression matrix)36 as implemented in the R package, CONICSmat. Per the authors' recommendations, raw read counts were divided by 10 and were \(\log_2\) - adjusted after adding one to each count value. Chromosome arm positions from GRCh38 were used to define arm coordinates. Briefly, genes expressed in 5 cells or less were filtered, a normalization factor for each cell was calculated, and a Gaussian mixture model was calculated based on the z- score of the average centered gene expression for each region across all cells. Melanoma cells \((n = 321)\) were split into two groups based on the estimated relative copy number in the 22q arm. Cells with a normalized copy number between 0 and 0.10 were used for normalization of the expression data. Histograms were generated using plotHistogram and by setting the z- score threshold to 4. Chromosomal alterations in each single cell were visualized by plotHistogramHeatmap with the authors' recommended parameters (window size = 120, expression threshold = 0.2, visualization threshold = 1). Of 12 genes identified by GISTIC within the chr22q11.21 wide peak (Supplementary Table 3), relative copies of 22q11.21 for each cell were calculated by averaging the estimated copy number for all 7 genes with detectable expression (CPM>0). + +To characterize the RNA expression profile associated with chr22q11.21 focal amplification, the median relative copy number (0.45) inferred by CONICSmat was used to split the cells into chr22q11.21 high and low groups. The \(\log_2\) - adjusted scRNA- seq dataset normalized by Census \(^{113}\) was compared between these two groups to identify differentially expressed genes using a two sided \(t\) - test with unequal variance, and the resulting \(t\) - statistics were used for ranking the gene list. This gene list was submitted to pre- ranked GSEA \(^{107}\) to interrogate 50 HALLMARK pathways (1,000 permutations, weighted enrichment statistics, MSigDB version 7.2 \(^{108}\) ). + +Single- cell differentiation status: To predict the relative differentiation status of each melanoma cell profiled by scRNA- seq, we used CytoTRACE \(^{42}\) , a computational framework for inferring developmental potential on the basis of transcriptional diversity. Each of the 321 acral melanoma cells from chr22q11.21 amplified tissue received a CytoTRACE score between 0 (more differentiated) and 1 (less differentiated). CytoTRACE scores were visualized by Uniform Manifold Approximation and Projection (UMAP). To determine whether CytoTRACE is influenced by genes located on 22q, we reran CytoTRACE after excluding all genes on the 22q arm (Supplementary Fig. 4e). + +<--- Page Split ---> + +## Short hairpin RNA (shRNA), CRISPR-Cas9 sgRNA, and cell viability tests + +We used puromycin- bearing MISSION lentiviral vectors pLKO.1 shRNA to test the effect of downregulation of target proteins on cell proliferation and signal transduction, employing scramble vector SH002 as a negative control (MISSION, Sigma- Aldrich, + +Supplementary Table 8), or scrambled RNA (Supplementary Table 8). + +LentiCRISPRv2 plasmid was obtained from Addgene (addgene.org). Guide sequences targeting LZTR1 (Supplementary Table 8) were designed using CHOPCHOP (https://chopchop.cbu.uib.no/) and cloned into LentiCRISPRv2 to generate single sgRNA carrying plasmids following a standard method114. A non- target sequence was included as the control (Supplementary Table 8). + +The plasmids were packaged in lentiviral vectors with ViraPower™ Lentiviral Packaging Mix kit (Thermo Fisher, cat # K497500), and transfected into 293T cells. The medium was collected and filtered with Millex- GV 33 mm PVDF filter (Millipore SLGV033RS) and then concentrated with Amicon Ultra- 15 centrifugal filters (Millipore UFC910024). Melanoma cells and normal human melanocytes were infected with the lentiviruses, medium was changed the following day, and the cells were then incubated with puromycin (2.5 μg/ml) for five days. Cells were collected and processed for western blotting with antibodies to target proteins. In addition, two days after infection the shRNA treated cells were seeded in 96- well plates in triplicate or quadruplet wells and tested for cell viability in the absence and presence of puromycin for 72 hrs with the CellTiter- Glo® Luminescent Cell Viability Assay, for apoptosis or RAS activity GTPase assay. + +Alternatively, GV298- U6- MCS- Ubiquitin- Cherry- IRES- puromycin lentiviral plasmids were purchased from GeneChem, China. The plasmids were co- transfected with packaging plasmids (pspAX2 and pMD2G) into 293T cells using Turbofect (Thermo Scientific) according to the manufacturer's instructions. Lentiviruses were collected after 48 and 72 hours and used to infect into acral melanoma cells. Infected cells were incubated in medium supplemented with puromycin (1 μg/ml), for two or three days, seeded in 96- well plates (2x103/well, five replicates) and cell viability was measured with Cell Counting Kit- 8 (CCK- 8) (Bimake.com, China). The CCK- 8 test was repeated every 24 hrs for three days. + +## CRKL and LZTR1 lentivirus vectors + +pDONR223- CRKL and pDONR223- LZTR1 were purchased from Addgene and DNASU, respectively. LZTR1 was transferred into plnducer20 vector115 (a gift from Dr. Thomas F. Westbrook, Baylor College of Medicine), and CRKL to PLX304 with the Gateway LR Clonase II Enzyme mix (Thermo Fisher 11791020). The identity of each vector was validated by targeted sequencing. Lentiviruses were produced as described above and the plnducer20 LZTR1 infected melanocytes were selected with 250 mg/ml Geneticin (G418) from American Bio (Canton, MA), CAT # AB05057- 05000), and PLX304CRKL with 2.5 μg/ml Blasticidin S HCl from Thermo Fisher Scientific (Waltham, MA), CAT # A1113903. + +<--- Page Split ---> + +## Cell proliferation and apoptosis + +Cell proliferation and apoptosisThe melanoma cells were grown in OptiMEM (Invitrogen, Carlsbad, CA) supplemented with \(5\%\) fetal calf serum and antibiotics. Normal human melanocytes (NBMEL) were grown from newborn foreskins in medium supplemented with bFGF, heparin, IBMX and dbcAMP76. Mouse melanocytes were grown from one- day old newborn pups in the presence of horse serum, TPA, melanotropin, isobutyl methyl xanthine, and placental extract. They became immortalized and were shifted to medium containing only TPA after \(\sim 20\) passages in cultures116. Some of the Yale melanoma cell lines were characterized by next- generation sequencing before2,4 (Supplementary Table 9). + +Cell proliferation was measured with the CellTiter- Glo® Luminescent Cell Viability Assay (Promega Corporation, Madison, WI). Melanoma cells were seeded in 96- well plates in triplicate or quadruplet wells after knockdown with hairpin lentivirus shRNA as indicated. Standard Error (SE) was calculated employing GraphPad Prism 7 software117. In addition, we seeded cells in 12- well plates (10- 15,000/well) and measured proliferation by counting the number of cells from triplicate wells over a period up to 7- 9 days with Beckman Cell Counter. For cell count by crystal violet, we seeded cells \((3 \times 10^{3} / \text{well})\) in 6- well plates and then incubated for 10 days. Following incubation, cells were immobilized with \(4\%\) paraformaldehyde in PBS for 15 min, stained with crystal violet for 10 min and then washed with PBS. A minimum of three random fields at 40X magnification were counted to determine cell numbers. Each sample had three replicates. + +The rate of apoptosis was measured using the Dead Cell Apoptosis Kit with Alexa Fluor® 488 annexin V and propidium iodide (Invitrogen, V13241) following manufacturer instructions. + +PLX4032 (500 nM, Plexxikon)117 or LY3009120 (100 nM, Selleck, Pittsburgh, PA, Catalog No.S7842) were added to the growth medium 4 hours before harvesting the cells for Western blotting. + +For 3D cultures, melanocytes were suspended in 1 ml medium and seeded on \(0.5\%\) collagen (Cultrex, R&D Systems, Minneapolis, MN, Cat # 3442- 050- 01), in 24 well plates for three days. + +## Microscopy + +Images were acquired using an inverted Nikon Eclipse Ti fluorescence microscope with a Plan Apochromat lambda 60X/1.40 Oil objective or a Plan Fluor 4X/0.13 objective for fluorescent images or DIC images, respectively, a CSU- W1 confocal spinning disk unit, an iXon Ultra 888 camera (Andor Technology), MLC 400B laser unit (Agilent Technologies) and NIS Elements software (Nikon). + +## Western blotting and antibodies + +We used western blots to identify the levels of proteins as previously described69. Cell extracts (20 µg/lane) were fractionated in \(3\% - 8\%\) or \(4 - 12\%\) tris- acetate gel (NP0006, NuPAGE Life Technologies). The membranes were probed with the primary antibodies + +<--- Page Split ---> + +described in Supplementary Table 10. All antibodies were used at the concentrations recommended by the manufacturers. + +## RAS activity assay + +The amount of GTP- bound RAS was determined using the Ras GTPase Chemi ELISA Kit (Active Motif North America, 1914 Palomar Oaks Way, Carlsbad, C 92008) following the manufacturer's protocol. Melanoma cells treated with control shRNA or shLZTR1 were collected five days after infection by scraping on ice, washed with cold PBS, lysed, centrifuged and \(50 \mu \mathrm{g}\) protein/assay, in triplicates, were used following the manufacturer instructions. + +## Immunostaining + +Cells were grown on the surface of 4- well slides, washed 2- 3 times with PBS, fixed with \(4\%\) paraformaldehyde for 15 min at room temperature, washed three times with PBS, permeabilize with \(0.2\%\) NP40 in PBS for 5 minutes, washed with PBS and incubate in PBS containing \(1\%\) BSA or (blocking buffer) for one hour. The cells were incubated with anti- GM130 antibody (clone 4A3 Millipore, Mouse), or calnexin (mouse mAb) for 1 hr at room temperature, and stained with secondary Alexa Fluor (Cy2) diluted in blocking buffer 1:1000 for 1 hr. They were washed 3X with PBS, incubated with rhodamine- phalloidin to stain actin and DAPI to stain the nucleus. + +## Statistical analysis + +Linear relationships were modeled by linear regression \((R^2)\) , and a \(t\) test was used to assess whether the result was significantly nonzero. When data were normally distributed, group comparisons were determined using a \(t\) test with unequal variance or a paired \(t\) test, as appropriate; otherwise, a Wilcoxon test was applied. Results with \(P < 0.05\) were considered significant. Data analyses were performed with R and Prism v7 (GraphPad Software, Inc.). The investigators were not blinded to allocation during experiments and outcome assessment. 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Single-cell mRNA quantification and differential analysis with Census. Nature methods 14, 309-315 (2017). +114. Ran, F.A. et al. Genome engineering using the CRISPR-Cas9 system. Nat Protoc 8, 2281-2308 (2013). +115. Meerbrey, K.L. et al. The plNDUCER lentiviral toolkit for inducible RNA interference in vitro and in vivo. Proc Natl Acad Sci U S A 108, 3665-70 (2011). + +<--- Page Split ---> + +116. Tamura, A. et al. Normal murine melanocytes in culture. In Vitro Cellular & Developmental Biology 23, 519-522 (1987).117. Halaban, R. et al. PLX4032, a selective BRAF(V600E) kinase inhibitor, activates the ERK pathway and enhances cell migration and proliferation of BRAF(WT) melanoma cells. Pigment Cell Melanoma Res 23, 190-200 (2010). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- LZTR1SuppFigs20201117v1.pdf- LZTR1SuppTables20201115v1.xlsb- LZTR1SuppData20201109v1.xlsb + +<--- Page Split ---> diff --git a/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5_det.mmd b/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..2ca8ffc7dafe3a5a8526da610574f4c15ec69932 --- /dev/null +++ b/preprint/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5/preprint__0917f47f7945ea8fabfadaf8a77e22df7840ae52c0729198350fb1b6dd32dbf5_det.mmd @@ -0,0 +1,728 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 928, 209]]<|/det|> +# Integrative Molecular and Clinical Profiling of Acral Melanoma Identifies LZTR1 as a Key Tumor Promoter and Therapeutic Target + +<|ref|>text<|/ref|><|det|>[[44, 228, 714, 270]]<|/det|> +Ruth Halaban ( \(\boxed{\bullet}\) ruth.halaban@yale.edu) Yale University School of Medicine https://orcid.org/0000- 0001- 8451- 1964 + +<|ref|>text<|/ref|><|det|>[[44, 276, 670, 317]]<|/det|> +Aaron Newman Stanford University https://orcid.org/0000- 0002- 1857- 8172 + +<|ref|>text<|/ref|><|det|>[[44, 323, 730, 365]]<|/det|> +Farshad Farshidfar Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, + +<|ref|>text<|/ref|><|det|>[[44, 370, 425, 411]]<|/det|> +Cong Peng Xiangya Hospital, Central South University + +<|ref|>text<|/ref|><|det|>[[44, 416, 226, 456]]<|/det|> +Chaya Levovitz IBM (United States) + +<|ref|>text<|/ref|><|det|>[[44, 463, 543, 504]]<|/det|> +James Knight Yale University https://orcid.org/0000- 0003- 1166- 0437 + +<|ref|>text<|/ref|><|det|>[[44, 509, 240, 549]]<|/det|> +Antonella Bacchiocchi Yale University + +<|ref|>text<|/ref|><|det|>[[44, 556, 425, 597]]<|/det|> +Juan Su Xiangya Hospital, Central South University + +<|ref|>text<|/ref|><|det|>[[44, 602, 226, 642]]<|/det|> +Kahn Rhissorrakrai IBM (United States) + +<|ref|>text<|/ref|><|det|>[[44, 648, 357, 689]]<|/det|> +Mingzhu Yin Yale University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 694, 186, 734]]<|/det|> +Mario Sznol Yale University + +<|ref|>text<|/ref|><|det|>[[44, 740, 186, 780]]<|/det|> +Stephan Ariyan Yale University + +<|ref|>text<|/ref|><|det|>[[44, 786, 357, 827]]<|/det|> +James Clune Yale University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 832, 357, 873]]<|/det|> +Kelly Olino Yale University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 879, 177, 898]]<|/det|> +Laxmi Parida + +<|ref|>text<|/ref|><|det|>[[44, 901, 860, 920]]<|/det|> +IBM Research - Thomas J. Watson Research Center https://orcid.org/0000- 0002- 7872- 5074 + +<|ref|>text<|/ref|><|det|>[[44, 926, 177, 944]]<|/det|> +Joerg Nikolaus + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 45, 357, 64]]<|/det|> +Yale University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 71, 357, 110]]<|/det|> +Meiling Zhang Yale University School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 116, 420, 157]]<|/det|> +Shuang Zhao Xiangya Hospital, Central South University + +<|ref|>text<|/ref|><|det|>[[44, 163, 135, 181]]<|/det|> +Yan Wang + +<|ref|>text<|/ref|><|det|>[[44, 184, 950, 226]]<|/det|> +Department of Dermatologic Surgery Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College + +<|ref|>text<|/ref|><|det|>[[44, 232, 155, 251]]<|/det|> +Gang Huang + +<|ref|>text<|/ref|><|det|>[[44, 253, 919, 296]]<|/det|> +Department of Bone and Soft Tissue oncology, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University + +<|ref|>text<|/ref|><|det|>[[44, 301, 166, 320]]<|/det|> +Miaojian Wan + +<|ref|>text<|/ref|><|det|>[[53, 322, 752, 343]]<|/det|> +Department of Dermatology, The Third Affiliated Hospital, Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[44, 348, 130, 366]]<|/det|> +Xianan Li + +<|ref|>text<|/ref|><|det|>[[44, 369, 919, 411]]<|/det|> +Department of Bone and Soft Tissue oncology, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University + +<|ref|>text<|/ref|><|det|>[[44, 416, 125, 434]]<|/det|> +Jian Cao + +<|ref|>text<|/ref|><|det|>[[44, 437, 950, 480]]<|/det|> +Rutgers Cancer Institute of New Jersey and the Department of Medicine, Robert Wood Johnson Medical School, Rutgers University + +<|ref|>text<|/ref|><|det|>[[44, 485, 115, 503]]<|/det|> +Qin Yan + +<|ref|>text<|/ref|><|det|>[[53, 506, 543, 526]]<|/det|> +Yale University https://orcid.org/0000- 0003- 4077- 453X + +<|ref|>text<|/ref|><|det|>[[44, 531, 145, 550]]<|/det|> +Xiang Chen + +<|ref|>text<|/ref|><|det|>[[53, 553, 840, 596]]<|/det|> +Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008 https://orcid.org/0000- 0001- 8187- 636X + +<|ref|>sub_title<|/ref|><|det|>[[44, 636, 102, 654]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 675, 533, 695]]<|/det|> +Keywords: cytoband chr22q11.21, oncogene, metastasis + +<|ref|>text<|/ref|><|det|>[[44, 712, 350, 732]]<|/det|> +Posted Date: September 30th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 750, 463, 770]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 110475/v1 + +<|ref|>text<|/ref|><|det|>[[44, 788, 910, 831]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 866, 944, 910]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on February 23rd, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28566- 4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[135, 89, 863, 131]]<|/det|> +# Integrative Molecular and Clinical Profiling of Acral Melanoma Identifies LZTR1 as a Key Tumor Promoter and Therapeutic Target + +<|ref|>text<|/ref|><|det|>[[125, 145, 875, 237]]<|/det|> +Farshad Farshidfar \(^{1,2,14,\dagger}\) , Cong Peng \(^{3,\dagger}\) , Chaya Levovitz \(^{4}\) , James Knight \(^{5}\) , Antonella Bacchiocchi \(^{6}\) , Juan Su \(^{3}\) , Kahn Rhrisorrakrai \(^{4}\) , Mingzhu Yin \(^{3,7}\) , Mario Szmol \(^{8}\) , Stephan Ariyan \(^{9}\) , James Clune \(^{9}\) , Kelly Olino \(^{9}\) , Laxmi Parida \(^{4}\) , Joerg Nikolaus \(^{10}\) , Meiling Zhang \(^{7}\) , Shuang Zhao \(^{3}\) , Yan Wang \(^{11}\) , Gang Huang \(^{12}\) , Miaojian Wan \(^{13}\) , Xianan Li \(^{12}\) , Jian Cao \(^{7,15}\) , Qin Yan \(^{7}\) , Xiang Chen \(^{3,*}\) , Aaron M. Newman \(^{1,2,*}\) and Ruth Halaban \(^{6,*}\) + +<|ref|>text<|/ref|><|det|>[[112, 252, 875, 707]]<|/det|> +\(^{1}\) Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. \(^{2}\) Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. \(^{3}\) Xiangya Hospital, Central South University, Changsha, China. \(^{4}\) IBM Research, Yorktown Heights, NY, USA. \(^{5}\) Yale Center for Genome Analysis, Yale University, New Haven, CT, 06520, USA. \(^{6}\) Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA. \(^{7}\) Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. \(^{8}\) Department of Internal Medicine, Section of Medical Oncology, Yale University School of Medicine, New Haven, CT, USA. \(^{9}\) Department of Surgery, Yale University School of Medicine, New Haven, CT, USA. \(^{10}\) Department of Molecular and Cellular Physiology, Yale University School of Medicine, New Haven, CT, USA. \(^{11}\) Department of Dermatologic Surgery Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, China. \(^{12}\) Department of Bone and Soft Tissue oncology, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University, Changsha, Hunan, China. \(^{13}\) Department of Dermatology, The Third Affiliated Hospital, Sun Yat- sen University, Guangzhou, China. \(^{14}\) Current address: Tenaya Therapeutics, South San Francisco, CA, USA. \(^{15}\) Current address: Rutgers Cancer Institute of New Jersey and the Department of Medicine, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA. \(^{\dagger}\) These authors contributed equally. + +<|ref|>text<|/ref|><|det|>[[115, 722, 425, 740]]<|/det|> +\(^{\dagger}\) These authors contributed equally. + +<|ref|>text<|/ref|><|det|>[[115, 757, 876, 899]]<|/det|> +\(^{*}\) Corresponding authors: Xiang Chen, Xiangya Hospital, Central South University, Changsha, China. Phone: 01186- 731- 84327303; E- mail: chenxiangck@126. com Aaron M. Newman, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. Phone: 650 724- 7270; E- mail: amnewman@stanford.edu Ruth Halaban, Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA. Phone: 203 785- 4352; E- mail: ruth.halaban@yale.edu + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 226, 108]]<|/det|> +## ABSTRACT + +<|ref|>text<|/ref|><|det|>[[113, 113, 877, 414]]<|/det|> +Acral melanoma, the most common melanoma subtype among non- Caucasian individuals, is associated with poor prognosis. However, its key molecular drivers remain obscure. Here, we performed integrative genomic and clinical profiling of acral melanomas from a cohort of 104 patients treated in North America or China. We found that recurrent, late- arising amplifications of cytoband chr22q11.21 are a leading determinant of inferior survival, strongly associated with metastasis, and linked to downregulation of immunomodulatory genes associated with response to immune checkpoint blockade. Unexpectedly, LZTR1 – a known tumor suppressor in other cancers – is a key candidate oncogene in this cytoband. Silencing of LZTR1 in melanoma cell lines caused apoptotic cell death independent of major hotspot mutations or melanoma subtypes. Conversely, overexpression of LZTR1 in normal human melanocytes initiated processes associated with metastasis, including anchorage- independent growth, formation of spheroids, and increased levels of MAPK and SRC activities. Our results provide new insights into the etiology of acral melanoma and implicate LZTR1 as a key tumor promoter and therapeutic target. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 89, 267, 108]]<|/det|> +## INTRODUCTION + +<|ref|>text<|/ref|><|det|>[[114, 117, 879, 416]]<|/det|> +Over the last two decades, a tremendous effort has been made to understand the genomic basis of melanoma. Collectively, these analyses have shown that sun- exposed melanomas harbor a large number of mutations and genomic rearrangements associated with ultraviolet (UV) radiation \(^{1 - 6}\) . In contrast, acral melanomas, originating from hairless skin such as palms and soles, display a lower mutational burden, a higher rate of structural alteration, and poorer survival outcomes \(^{2,4,7 - 17}\) . BRAF and NRAS are the most frequently affected oncogenes in acral melanomas but at a lower frequency compared to sun- exposed melanomas, whereas KIT mutations are more common in acral melanomas \(^{14,18 - 23}\) . Copy number variation (CNV) is a well- established feature of acral melanomas, contributing to aberrant regulation of several pathways affecting cell proliferation and gene expression. These include amplification of CDK4, CCND1, MAPK1 and NOTCH2; loss of CDKN2A (p16INK4) and NF1; inactivation of TP53; modifications of chromatin regulators (e.g., HDAC amplification and loss of ARID1A and ARID1B); and alterations in TERT \(^{11,13,14,20,23 - 25}\) . Despite these findings, attempts to treat acral melanoma with targeted inhibitors, such as CDK4/6 inhibitors, have failed \(^{12}\) . + +<|ref|>text<|/ref|><|det|>[[114, 424, 870, 564]]<|/det|> +While most genomic studies of acral melanoma have been limited to relatively small clinical cohorts \(^{1,9,13,26,27}\) , a recent whole genome analysis of 87 patients – 90% of which were of European ancestry – further confirmed the importance of structural rearrangements and copy number aberrations in this disease \(^{15}\) . Given the predominance of acral melanoma in non- Caucasian populations \(^{28,29}\) and the lack of effective targeted treatment options, large- scale genomic surveys of acral melanomas from ethnically diverse populations are needed. + +<|ref|>text<|/ref|><|det|>[[114, 572, 877, 792]]<|/det|> +Here, we applied whole exome (tumor/normal) and RNA sequencing to characterize acral melanomas from 104 patients treated in the United States ( \(n = 37\) ) and China ( \(n = 67\) ), most of whom had long- term follow- up data available. Through comparative genomic analysis with 157 sun- exposed melanomas, we identified novel molecular features of acral melanoma; generated the first prognostic map linking highly recurrent somatic aberrations in acral melanoma to risk of death; and found that later- arising focal amplifications in chr22q11.21 are associated with lymph node involvement and distant metastasis, leading to poor outcomes. Within chr22q11.21, we identified LZTR1 – a known tumor suppressor in other cancers – as a key candidate driver of metastasis. Our findings reveal novel molecular insights of acral melanoma pathogenesis and designate LZTR1 as a new therapeutic target. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 210, 108]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[115, 117, 689, 137]]<|/det|> +## Genomic characteristics of acral and sun-exposed melanoma + +<|ref|>text<|/ref|><|det|>[[114, 144, 879, 445]]<|/det|> +To characterize the genomic landscape of acral melanoma across ethnically diverse patient populations, we analyzed 104 tumors, including 97 by whole exome sequencing (WES), from patients treated in North America ('Yale') or China ('CSU') (Table 1). Both cohorts spanned all disease stages, included long- term follow- up, and encompassed patients with distinct ethnic origins, including Caucasian ( \(n = 31\) ; Yale) and Asian ancestry ( \(n = 67\) ; CSU) (Supplementary Fig. 1a and Supplementary Table 1). We also applied WES to profile 134 tumors from patients with sun- exposed melanoma, including patients with stage I through IV disease (Table 1). Notably, sun- exposed patients showed longer survival time than acral melanoma patients, consistent with previous studies \(^{16,17}\) (Supplementary Fig. 1b). Peripheral blood leukocytes were analyzed as germline controls and whole- transcriptome sequencing (RNA- seq) was applied to 105 tumors, including 38 acral and 37 sun- exposed melanomas with matched WES data (Table 1 and Supplementary Table 1). Tumor purities, clinical follow- up, and median survival times were comparable between acral cohorts, supporting their combined assessment (Supplementary Fig. 1c,d; Supplementary Table 1). + +<|ref|>text<|/ref|><|det|>[[113, 452, 880, 774]]<|/det|> +To verify key somatic lesions in acral melanoma, we began by performing a comparative genomics analysis. We observed striking variation in the prevalence of single nucleotide variants (SNVs) and insertions/deletions (indels) between melanoma subtypes, confirming a nearly ten- fold lower mutational burden in acral melanoma \(^{2,13}\) (median of 406 vs. 42 nonsynonymous variants per exome in sun- exposed vs. acral melanoma, respectively; \(P = 2.2 \times 10^{- 6}\) , two- sided Wilcoxon rank sum test; Fig. 1a, Supplementary Table 2, Supplementary Data). The most commonly mutated genes in acral melanomas were RAS family members (22% in NRAS, KRAS, and HRAS), followed by KIT (15%), CGREF1 (10%), BRAF (8%), and TP53 (4%). With the exception of CGREF1, which was limited to CSU patients, recurrence frequencies were similar between cohorts (Supplementary Table 2). Mutational signature analysis \(^{17}\) corroborated the prevalence of UV- induced mutagenesis in sun- exposed melanomas. In contrast, mutational signatures in acral melanomas were largely attributable to deamination of 5- methylcytosine (signature 1), which can arise from reactive oxygen species during melanin synthesis \(^{30}\) , as well as alkylating agents (signature 11) and APOBEC activity (signature 13) (Fig. 1a, bottom, and Supplementary Data). + +<|ref|>text<|/ref|><|det|>[[114, 779, 881, 900]]<|/det|> +As expected, focal amplifications were a core feature of acral melanoma in both cohorts (median of 20 vs. 11 per exome in acral vs. sun- exposed melanoma, respectively; \(P = 1.24 \times 10^{- 10}\) , two- sided Wilcoxon rank sum test; Fig. 1b, top; Supplementary Figs. 1e and 2a; Supplementary Table 3). Among highly recurrent gene- level amplifications with at least four copies, those in chromosomes 4, 5, 8, 11, 12, and 22 were nearly exclusive to acral melanomas in our study (Fig. 1c; Supplementary + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 877, 270]]<|/det|> +Table 3). The most common amplifications enriched in acral melanoma were in cytobands 11q13.3 (47%), 5p15.33 (42%), 8q24.3 (42%), 22q13.1 (39%), and 22q11.21 (38%) (Fig. 1b; Supplementary Fig. 2a, Supplementary Table 3). Recurrent focal deletions, which included alterations in known genes such as CDKN2A (9p21.3) \(^{22,31,32}\) , were less prevalent than in sun- exposed cases (Fig.1b, bottom; Supplementary Fig. 2b, Supplementary Table 4). Amplification and deletion frequencies were largely maintained in both acral cohorts (Supplementary Tables 3 and 4). We also identified multiple fusion genes with potential roles in oncogenesis, including several not previously described in acral melanoma (Supplementary Table 5). + +<|ref|>text<|/ref|><|det|>[[114, 277, 883, 356]]<|/det|> +Collectively, these results provide a comprehensive resource of somatic lesions in acral melanomas from genetically- distinct patient populations; corroborate and extend previous genomic studies \(^{2,4,7 - 15}\) , and demonstrate the integrity and high quality of our data for downstream clinical analysis. + +<|ref|>sub_title<|/ref|><|det|>[[114, 393, 562, 412]]<|/det|> +## Somatic determinants of risk in acral melanoma + +<|ref|>text<|/ref|><|det|>[[113, 420, 880, 720]]<|/det|> +Having systematically cataloged somatic aberrations in nearly 100 acral melanomas, we next sought to evaluate their clinical significance. We began by focusing on amplification events owing to their unique prevalence in this disease (Fig. 1b). Starting with the most statistically- significant peaks detected by GISTIC \(^{33}\) in a pooled analysis of both acral cohorts ( \(Q < 10^{- 5}\) ), we identified several loci linked to adverse overall survival, including peaks involving cytobands 22q11.21 and 22q13.1. Among them, cytoband 22q11.21 was most strongly associated with inferior overall survival (adjusted \(P < 0.05\) , univariate Cox regression of time from tumor resection; Supplementary Fig. 3a, Supplementary Table 6). This result was maintained when expanding the analysis to include all focal events identified by GISTIC ( \(Q < 0.05\) ) with at least 10% recurrence frequency in both acral cohorts and all genes with a nonsynonymous mutation frequency of at least 5% in either melanoma subtype (Fig. 2a; Supplementary Table 6). We also considered focal amplifications identified from the largest cohort (CSU) and tested in each cohort separately (Fig. 2b; Supplementary Fig. 3b). Again, 22q11.21 amplification was a leading determinant of adverse survival. + +<|ref|>text<|/ref|><|det|>[[113, 728, 880, 907]]<|/det|> +Given this observation, we sought to better understand 22q11.21 focal amplification and the factors underlying its clinical phenotype. We first tested whether 22q11.21 is a surrogate for advanced disease at the time of tumor resection. Intriguingly, 22q11.21 amplifications were observed across all stages except stage I disease ( \(P = 0.03\) , Chi \(X^{2}\) test; Fig. 2c, left). We verified this result in three independent acral melanoma cohorts, including an external dataset comprised of 33 patients for whom stage at presentation was known \(^{11}\) , demonstrating that 22q11.21 amplification is a recurrent late- arising event in acral melanoma (Supplementary Fig. 3c). Given this result, we reassessed survival associations using stage as a covariate. Regardless of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 880, 208]]<|/det|> +whether we examined all patients or just those with stage II through IV disease, 22q11.21 amplifications remained significant after multivariate adjustment for stage \((P = 0.008\) and 0.024, respectively; Cox proportional hazards regression). This was also true for acral patients with advanced disease (III or IV) \((P = 0.03\) , Cox proportional hazards regression; Supplementary Fig. 3c), for whom stage alone did not significantly stratify outcomes. + +<|ref|>text<|/ref|><|det|>[[114, 217, 881, 438]]<|/det|> +As a common late- arising event, we next tested if 22q11.21 amplifications might correlate with tumor progression. Indeed, in both acral cohorts, we observed a strong positive correlation between 22q11.21 amplification frequency and the number of positive lymph nodes (Fig. 2c, right; Supplementary Fig. 3e). Remarkably, nearly \(75\%\) of patients with \(>1\) positive lymph node harbored at least one gain of 22q11.21 (Fig. 2c, right). Reanalysis of WES data from an independent study11 confirmed this trend (Supplementary Fig. 3f). While this association was observed in both primary and metastatic tumor specimens, the latter showed a modest but consistent increase in amplification frequency after controlling for lymph node status (Supplementary Fig. 3g). No other associations with clinical indices were observed (Supplementary Fig. 3h, Supplementary Table 1). + +<|ref|>text<|/ref|><|det|>[[115, 444, 872, 525]]<|/det|> +Taken together, these data reveal that 22q11.21 focal amplification is a conserved, late- arising somatic event linked to poor survival and regional metastasis in acral melanoma, independent of Caucasian or Asian ancestry. Accordingly, this event could represent a critical step in the initiation or maintenance of nodal metastasis. + +<|ref|>sub_title<|/ref|><|det|>[[115, 560, 603, 580]]<|/det|> +## Integrative genomics of 22q11.21 focal amplification + +<|ref|>text<|/ref|><|det|>[[114, 588, 870, 908]]<|/det|> +To understand the biological significance of 22q11.21 amplification in acral melanoma, we next examined transcriptional hallmarks of 22q11.21- amplified tumors. By employing a linear model adjusted for stage (Methods), we rank- ordered genes by their differential expression in 22q11.21- amplified tumors and performed gene set enrichment analysis34 (Fig. 2d). Overall, 22q11.21- amplified melanomas were significantly enriched in canonical signaling pathways associated with tumorigenesis and metabolic activity, including MYC target genes, oxidative phosphorylation, and unfolded protein response35. In contrast, patients with non- amplified tumors showed higher expression of immunoreactive programs such as IL6/JAK/STAT and IFN- \(\gamma\) response pathways. We hypothesized that such patients might be superior candidates for existing or emerging immunotherapies (Fig. 2d). Consistent with this possibility, we observed a striking reciprocal relationship between 22q11.21- amplification and the expression of immunomodulatory genes, including key targets of immune checkpoint blockade (e.g., PDCD1, CTLA4) (Fig. 2e). Among patients with high expression of immunomodulatory genes, only \(12\%\) were amplified, whereas among patients with low expression, \(62\%\) were amplified (Fig. 2f). This result was highly significant \((P = 0.009\) , + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 814, 129]]<|/det|> +Fisher's exact test), indicating that 22q11.21- amplified and non- amplified tumors preferentially reflect "cold" and "hot" tumor microenvironments, respectively. + +<|ref|>text<|/ref|><|det|>[[113, 137, 879, 398]]<|/det|> +To extend these observations to single cells, we applied single- cell RNA sequencing (scRNA- seq) to an acral melanoma tumor specimen with four additional copies of 22q11.21, as determined by WES (Supplementary Fig. 4a, Supplementary Data). Using canonical marker genes and copy number inference via CONICSmat36, 321 single- cell transcriptomes were confidently identified as melanoma cells (Supplementary Fig. 4b, Methods). We confirmed over- expression of genes on the 22q arm, consistent with WES (Supplementary Fig. 4c). However, 22q expression levels were heterogeneous across cells, indicating variability in the number of copies per cell (Supplementary Fig. 4c). By dividing malignant cells into two groups according to the median expression of 22q arm genes (selected from within the 22q11.21 peak identified by GISTIC), we observed the same amplification- enriched pathways identified in bulk tumors, including oxidative phosphorylation and MYC targets, confirming their malignant origin (Supplementary Fig. 4d). + +<|ref|>text<|/ref|><|det|>[[114, 404, 872, 585]]<|/det|> +Immature cancer cells often display elevated metabolism via oxidative phosphorylation and MYC activity37 and stemness features in melanoma tumors have been linked to poor survival38- 41. To test whether 22q11.21- amplified cells exhibit an immature cellular phenotype, we employed CytoTRACE, a recently described in silico method for predicting developmental potential on the basis of single- cell transcriptional diversity42. Indeed, cells with higher relative copies of 22q11.21 were predicted to be less mature (Fig. 2g). Notably, this result was independent of genes physically located on 22q, implying that 22q11.21- amplified cells exhibit a more accessible genome, a hallmark of immature cells in normal tissues42 (Supplementary Fig. 4e). + +<|ref|>text<|/ref|><|det|>[[113, 592, 879, 892]]<|/det|> +Finally, we leveraged the \(t\) - statistic to rank genes in 22q11.21 according to their expression in amplified versus non- amplified tumors (Fig. 2h, Supplementary Table 7). The top- ranking gene associated with amplification was LZTR1 (leucine zipper like transcription regulator 1). We were struck by this result because LZTR1, a member of the Kelch- like (KLHL) family and an adaptor for Cullin 3 (CUL3) ubiquitin ligase complexes43,44, is considered a tumor suppressor in schwannoma and glioblastoma43,45,46. Nevertheless, we found that high expression of LZTR1 is predictive of poor outcome, both in acral and sun- exposed melanomas from this study, and in 443 advanced sun- exposed melanomas profiled by TCGA (The Cancer Genome Atlas) (Supplementary Fig. 5, Methods). Beyond LZTR1, we noted that ZNF74, a zinc finger protein, and CRKL (CRK like proto- oncogene, adaptor protein), a recurrently amplified gene in multiple carcinomas47- 50, including non- small cell lung cancer (3% – 13% of cases)47- 49, were ranked 2nd and 3rd in our analysis, respectively. Given these results, we set out to characterize the biological functions of these genes to determine which, if any, underlie the observed clinical phenotype of 22q11.21 amplification. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 116, 810, 156]]<|/det|> +## Suppression of LZTR1 attenuates melanoma cell proliferation and induces apoptosis independent of Ras or MAPK activity + +<|ref|>text<|/ref|><|det|>[[113, 163, 872, 565]]<|/det|> +We began by silencing several chr22q11.21- amplified genes using lentiviral delivery of short hairpin RNAs (shRNAs) (Supplementary Table 8), with the goal of determining the impact of targeted knockdowns on melanoma cell proliferation. Treatment of two acral melanoma cell lines with ZNF74 shRNA had a modest effect on cell proliferation (Supplementary Fig. 6a). Similarly, while downregulation of CRKL induced growth arrest, only one of three shRNAs against CRKL successfully downregulated CRKL, and only two of five tested cell lines were highly affected (Supplementary Fig. 6b). Conversely, silencing of LZTR1 consistently arrested cell proliferation. This was the case regardless of subtype (acral or sun- exposed) or mutations in BRAF or NRAS (Fig. 3a, b). In addition, we observed growth arrest in normal melanocytes derived from two independent foreskins (Fig. 3a, b). We ruled out off- target effects because six different LZTR1- directed shRNAs induced growth arrest, as did CRISPR- Cas9 sgRNA directed against LZTR1 (Fig. 3a- c; Supplementary Fig 5c; Supplementary Table 8). The observed phenotype had a long- term effect since LZTR1- null melanoma cells did not survive in vitro, whereas cells infected with control shRNA (scrambled) continued to proliferate. We also tested depletion of SNAP29 and THAP7, both of which are physically located on 22q11.21 but whose expression levels were not significantly linked to 22q11.21 amplification (Supplementary Table 7). Knockdown of these genes had little to no effect on proliferation (Supplementary Fig. 5d, e). + +<|ref|>text<|/ref|><|det|>[[114, 572, 880, 732]]<|/det|> +Given these results, we sought to better understand the biological consequences of LZTR1 knockdown. Inactivating germline mutations in LZTR1 are associated with Noonan syndrome and functional studies have linked LZTR1 inactivation to RAS ubiquitination, increased RAS- MAPK signaling, and cell proliferation \(^{51 - 56}\) . Indeed, suppression of LZTR1 in melanoma cells increased the constitutive levels of GTP- bound RAS, an effect similar to that observed in growth factor- stimulated cells \(^{54,57}\) . RAS- GTP levels increased in NRAS- or BRAF- mutant melanoma cells without a change in total RAS protein (Fig. 4a, b). + +<|ref|>text<|/ref|><|det|>[[114, 739, 880, 900]]<|/det|> +We also observed widespread changes in MAPK signaling following LZTR1 knockdown. For example, there was an increase in pERK in melanoma cells carrying BRAF \(^{V600E}\) , PDE4DIP- BRAF, or GOLG4A- RAF1 (Fig. 4c). In contrast, pERK decreased in NRAS \(^{Q61L/R}\) melanoma cells, PDE8A- RAF1 fusion- bearing melanoma cells, and normal human melanocytes (Fig. 4c). ERK activation was likely due to an increase in BRAF levels (Fig. 4d), enhancing BRAF activity. Treatment of melanoma cells with BRAF \(^{V600E/K}\) or pan- RAF inhibitors (PLX4032 or LY3009120) reduced shLZTR1- induced pERK activation (Fig. 4e), rendering further support for the role of BRAF kinase activity. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 881, 270]]<|/det|> +On the other hand, ERK inhibition in shLZTR1- treated cells could potentially arise from RAS translocation to the cytoplasm (Fig. 4f), and the consequent disassociation from its membrane- bound mitogenic effectors, which are critical for \(NRAS^{Q61/L/R}\) mutant and WT cells lacking BRAF mutations. RAS translocation was not linked to de- ubiquitination, because loss of LZTR1 did not change the levels of ubiquitinated RAS (Supplementary Fig. 7a). RAF1 levels were diminished in most cell lines (Fig. 4d), reflecting a decrease in gene expression. Thus, downregulation of LZTR1 induces growth arrest independently of ERK activity, the presence of BRAF or NRAS oncogenes, and changes in RAS- GTP levels. + +<|ref|>text<|/ref|><|det|>[[114, 277, 875, 377]]<|/det|> +We next explored if our in vitro melanoma systems effectively recapitulate key 22q11.21- related signaling pathways observed in vivo. To this end, we performed bulk RNA- sequencing of a melanoma cell line (YUSIK) to assess the impact of LZTR1 knockdown. Remarkably, depletion of LZTR1 induced transcriptome- wide changes that largely mirrored those observed in bulk tumors and single melanoma cells (Fig. 5a). + +<|ref|>text<|/ref|><|det|>[[114, 384, 880, 644]]<|/det|> +We noticed that among altered transcriptional programs, apoptosis- related genes were elevated in cell lines and tumors with lower LZTR1 expression (Fig. 5a). These data are supported by an increase in caspase activity after treatment with LZTR1 shRNA or sgRNA (Fig. 5b, c), which led to the degradation of known caspase substrates58, including pRb, p53, PARP1, NFKB, and GOLGA4 (Fig. 5d). Notably, GOLGA4 localizes to the Golgi apparatus, the subcellular site of LZTR159. Moreover, shLZTR1- induced caspase activity was suppressed by the pan- caspase inhibitor IDN- 6556 (Emricasan), which also rescued several substrates, including LZTR1 (Fig. 5c, d). These data are consistent with a previous report showing that LZTR1 undergoes caspase- mediated degradation59. Furthermore, shLZTR1 led to disruptions of cellular organization, including actin depolymerization into irregular shapes (Fig. 5e, left), or formation of actin rings around the Golgi and nucleus (Fig. 5e, right). Such changes are characteristic of cells undergoing fast or slow apoptotic death, respectively60. + +<|ref|>text<|/ref|><|det|>[[114, 652, 861, 812]]<|/det|> +Several cell cycle proteins were also downregulated, in line with pathway enrichment analyses (Fig. 5f and Supplementary Fig. 7b- e). In addition, retinoblastoma proteins (pRb and p130) were suppressed in the nine melanoma cell lines tested (Fig. 5f). This was likely due to ubiquitination and degradation61, as pRb was not rescued by the caspase inhibitor IDN- 6556 (Fig. 5d). Elimination of pRb may enhance mitochondrial- mediated apoptosis because it leads to reduced mitochondrial mass, reduced activity of the electron transport chain, and increased reactive oxygen species (ROS)62- 65. + +<|ref|>text<|/ref|><|det|>[[114, 820, 880, 900]]<|/det|> +A major reason for growth arrest in some melanoma cell lines is downregulation of MITF, a lineage- specific transcription factor critical for melanocyte and melanoma cell proliferation66. MITF stability is reduced when phosphorylated by MAPK or KIT67,68, and this process was clearly observed in three out of four melanoma cell lines with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 857, 170]]<|/det|> +increased ERK activity (Fig. 5g, as compared to Fig. 4c, e). Downregulation of MITF, as expected, is associated with decrease in tyrosinase (TYR), the key enzyme in melanin synthesis as well as cellular pigmentation (Fig. 5h, i). These results are consistent with our published observations using the same melanoma cell lines69. + +<|ref|>sub_title<|/ref|><|det|>[[115, 204, 874, 245]]<|/det|> +## Overexpression of LZTR1 in normal melanocytes confers properties of malignant transformation and metastasis + +<|ref|>text<|/ref|><|det|>[[114, 252, 870, 553]]<|/det|> +We next evaluated the impact of overexpressing LZTR1 in normal melanocytes and compared the effects to overexpression of CRKL. The latter is a SH3/SH2 adaptor protein that promotes lung cancer cell invasion via ERK activation70 and epithelial- mesenchymal transition (EMT) in colorectal and pancreatic carcinomas71. Early passage human melanocytes (passage 4) were transduced with HA- tagged LZTR1 cloned into the pInd20 lentiviral vector, V5- tagged CRKL inserted into the PLX304 vector, or both constructs. Over- expression of these genes did not enhance the rate of cell proliferation; rather, melanocytes overexpressing CRKL grew slower compared to parental cells (Supplementary Fig. 8a). Nevertheless, within 2- 3 days after infection, we noticed a striking induction of anchorage- independent growth, observed as cells overexpressing LZTR1 or CRKL formed three- dimensional clusters in 2D and 3D collagen cultures (Fig. 6a, top and bottom rows, respectively). Moreover, this result – which was reminiscent of a malignant cell phenotype72 – was further enhanced when both genes were co- expressed (LZTR1+CRKL), leading to the formation of spheroids that detached from the surface of the dish (Fig. 6a). + +<|ref|>text<|/ref|><|det|>[[114, 560, 874, 901]]<|/det|> +During metastasis, primary melanoma cells detach from the dermis and migrate to secondary sites through increased cell- cell interactions and promotion of cancer cell survival. We therefore examined changes in adhesion proteins affecting cell- matrix and cell- cell interactions known to mediate aggregation, the formation of spheroids72, and in vivo EMT73,74. Our data show that E- cadherin was downregulated whereas N- cadherin and integrin \(\beta 1\) were upregulated in response to increased expression of LZTR1 and CRKL, a process that was enhanced when both genes were co- expressed (Fig. 6b). Notably, our results with CRKL were consistent with HCT116 colon cancer cells, in which loss of CRKL was found to increase E- cadherin expression and shift the cells toward an epithelial phenotype71. Importantly, LZTR1 and CRKL, both alone and in combination, induced high levels of constitutively active ERK and SRC relative to parental cells (Fig. 6b, pERK and pSRC), functions that support viability and proliferation. We also identified downregulation of MITF in cells overexpressing CRKL as the possible cause for growth rate attenuation (Fig. 6b; Supplementary Fig. 8a). Consistent with this finding, while higher expression of MITF defines a proliferative subtype of melanoma (MITFhigh- AXLlow), lower expression is preferentially associated with invasion (MITFlow- AXLhigh)75. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 880, 230]]<|/det|> +Based on these findings, we hypothesized that co- amplification of LZTR1 and CRKL might lead to increased rates of distant recurrence. Given that 22q11.21 amplification is a late- arising event in acral melanoma (Fig. 2c, left), we tested this hypothesis by examining acral melanoma patients diagnosed with stage II or III disease. Indeed, in patients for whom distant metastasis- free survival (DMFS) data were available, focal amplification of 22q11.21 was associated with earlier development of distant metastatic disease, with a median lead time of nearly 1 year (Fig. 6c). + +<|ref|>text<|/ref|><|det|>[[114, 237, 881, 397]]<|/det|> +Finally, we investigated whether overexpression of LZTR1 or CRKL release normal human melanocytes from their dependency on growth factors, a common phenotype of metastatic melanoma cells76. While normal human melanocytes retained their growth factor dependency (Supplementary Fig. 8), LZTR1, but not CRKL, enabled immortalized mouse melanocytes to form colonies and divide in the absence of their only required growth factor, TPA (tetradecanoyl phorbol acetate)77 (Fig. 6d,e). This phenotype is likely the consequence of MAPK activation, as seen by the presence of phosphorylated ERK (Fig. 6f). + +<|ref|>text<|/ref|><|det|>[[114, 404, 863, 524]]<|/det|> +Taken together, these results strongly implicate LZTR1 and CRKL in malignant transformation and the initiation of metastasis. While both genes showed similar phenotypes, the effects of overexpression were notably enhanced when LZTR1 and CRKL were co- expressed. However, only LZTR1 released immortalized mouse melanocytes from their dependency on growth factor, a characteristic shared by melanoma cells. + +<|ref|>sub_title<|/ref|><|det|>[[115, 560, 239, 578]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[114, 588, 880, 766]]<|/det|> +Acral melanoma has high incidence among non- Caucasian populations, accounting for up to \(86\%\) of melanomas diagnosed in Asian patients as compared to \(\sim 10\%\) of Caucasians29,78- 84. Our work establishes common features of acral melanomas in cohorts from Asian and Caucasian populations. These include 1) the consistent association between specific focal amplifications and poor outcomes, and 2) the identification of LZTR1 as a key gene within 22q11.21, the most prognostic recurrent alteration identified in both acral cohorts. Based on these findings, we performed a comprehensive analysis of LZTR1 signaling pathways and obtained functional evidence for LZTR1 as a tumor promoter. + +<|ref|>text<|/ref|><|det|>[[114, 775, 867, 895]]<|/det|> +LZTR1 is co- amplified with CRKL and downregulation of each gene inhibits melanoma cell proliferation, albeit to varying degrees. While CRKL has been linked to tumor growth as a candidate oncogene in several human malignancies, including lung adenocarcinoma47- 49, LZTR1 is generally considered a tumor suppressor. Germline mutations in LZTR1 are involved in Noonan syndrome53,85, schwannomatosis46 and glioblastoma43,86. Moreover, somatic loss- of- function mutations in LZTR1 occur in \(22\%\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 870, 190]]<|/det|> +of glioblastomas. These mutations drive self- renewal and growth of glioma spheres \(^{43}\) , consistent with a role in tumor suppression. However, despite these findings, LZTR1 is amplified in a subset of carcinomas (up to \(8.3\%\) ), including bladder, uterine, and lung cancers \(^{87,88}\) . These data, coupled with our results, suggest that LZTR1 could have tumor- promoting capabilities in multiple human malignancies. + +<|ref|>text<|/ref|><|det|>[[114, 197, 881, 337]]<|/det|> +Unique aspects of our study include the broad range of tumor specimens analyzed and the utilization of cells harboring different oncogenes that modulate LZTR1 activity. For example, in NRAS- mutant melanoma, RAS mis- localized to the cytoplasm in response to shLZTR1 and caused MAPK inhibition. On the other hand, elimination of LZTR1 in BRAF- mutant cells increased BRAF levels, leading to ERK activation. In several cell lines, ERK activation induced growth arrest via MITF degradation, a process unique to melanocytes and the melanoma system \(^{67,68}\) . + +<|ref|>text<|/ref|><|det|>[[114, 345, 883, 605]]<|/det|> +Importantly, our study demonstrates that LZTR1 and CRKL – two of the top three genes associated with ch22q11.2 amplification in acral melanoma – facilitate anchorage- independent growth in normal human melanocytes, likely by reducing E- cadherin, increasing N- cadherin, and activating integrin \(\beta 1\) . The reciprocal expression of E- cadherin and N- cadherin in early melanoma progression has been known for about two decades \(^{73,89}\) , but to our knowledge, our findings link these events to genomic modification of two specific genes for the first time. In addition, we observed activation of MAPK and SRC kinases, the likely consequences of integrin signaling \(^{90,91}\) . The ability of LZTR1 to convert immortalized mouse melanocytes to a growth- factor independent mode of proliferation, a major characteristic of melanoma cells in culture, further underscores its tumorigenic potential. These results agree with our genomic observation that ch22q11.21 amplification is a late- arising event associated with regional and distant metastasis. + +<|ref|>text<|/ref|><|det|>[[114, 613, 870, 753]]<|/det|> +Separately, we identified a striking inverse relationship between immunomodulatory genes and 22q11.21 amplification. It is tempting to speculate that high levels of LZTR1 reduce the inflammatory response while protecting cells from stress- induced apoptosis, thereby facilitating metastasis. Conversely, patients with low levels of LZTR1 preferentially harbor a hot tumor microenvironment, which might provide benefit from immunotherapy. Future studies will be needed to explore these possibilities. + +<|ref|>text<|/ref|><|det|>[[114, 761, 870, 861]]<|/det|> +In summary, we demonstrate that late- arising focal amplifications of cytoband 22q11.21 are a leading determinant of shorter survival time in acral melanoma. Our genomic and functional experiments provide critical new insights into the pathogenesis of this disease and strongly implicate LZTR1 as a novel tumor promoter and promising therapeutic target. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[116, 90, 300, 108]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[114, 112, 870, 462]]<|/det|> +AcknowledgementsThis work was supported by the Melanoma Research Alliance (R.H., Q.Y., J.C.), the Sokoloff- MRA Award (R.H.), the Yale SPORE in Skin Cancer (Bosenberg and Kluger), the Roslyn and Jeremy Meyer Award (R.H.), the National Cancer Institute (A.M.N., R00CA187192), the Stinehart- Reed foundation (A.M.N.), the Stanford Bio- X Interdisciplinary Initiatives Seed Grants Program (IIP) (A.M.N.), the Virginia and D.K. Ludwig Fund for Cancer Research (A.M.N.), the Natural Science Foundation of China Major Projects of International Cooperation and Exchanges grant 81620108024 (X.C.), the General Program grant 81874138 (M.Y.), a New Investigator Award provided by Rutgers Cancer Institute of New Jersey (State of NJ appropriation and National Institutes of Health grant P30CA072720, J.C.), and a Melanoma Research Foundation Career Development Award (J.C.). We wish to acknowledge the Yale Center for Genome Analysis (YCGA) for performing WES and RNA- seq, David Calderwood and Ben Turk for providing the short hairpin RNA lentiviral vectors, the West Campus Imaging Core for confocal microscopy and cell imaging, Junkun Liu for performing cell immunostaining, Robert Straub and Jenna Ollodart for technical assistance. We thank for Dr. Doug Brash for his insight regarding acral melanoma mutations and to Zoe Halaban for her critical questions and enthusiastic support during these studies. We dedicate this manuscript to the memory of our colleague Dr. Deepak Narayan, a surgeon- scientist who provided great insight into this work and who will be remembered for his legacy of unparalleled innovation in the face of complex problems. + +<|ref|>sub_title<|/ref|><|det|>[[116, 488, 315, 506]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[114, 510, 880, 738]]<|/det|> +A.M.N. and R.H. conceived the study, developed strategies for related experiments, and wrote the manuscript. X.C. initiated the collaborative studies, supervised sample collections from multi- clinical centers in China, exome and RNA- sequencing and some functional studies. F.F. co- wrote the paper and performed bioinformatics analyses with assistance from C.L., J.K., K.R., and L.P. R.H. performed key functional experiments with assistance from C.P., A.B., M.Y., M.Z., and J.N. A.B. performed experiments, tissue collection and processing (Yale cohort) and obtained clinical data. C.P. performed sample and clinical data collection and assisted with sequencing (CSU cohort). J.S. assisted with the collection of clinical data (CSU cohort). M.S. identified patients and collected clinical data (Yale cohort). S.A., D.N., J.C., K.O, S.Z., Y.W, G.H, M.W., and X.L contributed tumor specimens. J.C. and Q.Y. assisted with the conception of the study, performed experiments, and contributed to writing. All authors commented on the manuscript at all stages. A.M.N. and R.H. jointly supervised this work. + +<|ref|>sub_title<|/ref|><|det|>[[116, 767, 305, 785]]<|/det|> +## Competing Interests + +<|ref|>text<|/ref|><|det|>[[116, 793, 498, 810]]<|/det|> +The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 90, 884, 536]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 560, 876, 859]]<|/det|> +
Figure 1: Landscape of somatic alterations in acral and sun-exposed melanomas. a-b, Genomic and clinical characterization of acral and sun-exposed melanoma samples sequenced in this work. a, The number of nonsynonymous SNVs and indels per melanoma exome (columns), cohort, age, frequently mutated genes (at least \(5\%\) recurrence frequency in either melanoma subtype), nonsynonymous base substitution frequencies, and dominant COSMIC mutational signatures \(^{92,93}\) . Sig., signature; 5mC, 5-methylcytosine. b, The number of significant focal amplifications and deletions (GISTIC \(Q < 0.05\) ) per melanoma exome (columns), ordered identically to panel a. Cytobands with focal amplifications or deletions with at least \(10\%\) recurrence frequency in either melanoma subtype are shown (GISTIC \(Q < 10^{-5}\) ), ordered by the relative difference in recurrence frequency in acral versus sun-exposed melanoma. c, Genes are plotted according to the fraction of acral (y-axis) or sun-exposed (x-axis) tumors where they are present with \(4+\) copies. Considering the genome-wide distribution of differences in recurrence frequencies between melanoma subtypes, genes are identified as significantly recurrent in acral or sun-exposed melanomas if their \(|z\text{-score}|\geq 3\) (dashed lines). Significantly recurrent genes are colored according to their cytoband location (inset). For clarity, a small amount of jitter was added to distinguish overlapping genes.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 90, 884, 734]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 743, 879, 904]]<|/det|> +
Figure 2: Focal amplifications in 22q11.21 are linked to shorter survival time, regional metastasis, and depletion of immunomodulatory programs in acral melanoma. a, Association between recurrent somatic alterations and overall survival (OS) in acral melanoma. Z-scores with positive and negative values indicated shorter and longer survival time, respectively \((|Z| > 1.96\) is significant at \(P< 0.05\) ; Methods). OS was calculated from the date of diagnosis (x-axis) and the date of tumor resection (y-axis). Shown are genes with a nonsynonymous mutation frequency of at least \(5\%\) in either melanoma subtype and focal copy number events with at least \(10\%\) recurrence frequency in each acral cohort (Supplementary Table 6). Focal amplifications in
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 881, 545]]<|/det|> +cytobands significantly associated with OS and recurrently mutated genes are labeled. Additional details are provided in Methods. b, Kaplan Meier curves showing overall survival of acral melanoma patients, stratified by 22. q11.21 amplification status and calculated from the date of tumor resection. Significance was assessed with the log- rank test. HR, hazard ratio. 95% HR confidence intervals are shown in brackets. c, Left: Acral melanoma tumor stage shown as a function of 22q11.21 amplification status. Statistical significance was evaluated by a Chi \(X^{2}\) test. Right: Fraction of 22q11.21- amplified melanomas stratified by the number of involved lymph nodes (N stage). d, Hallmark signaling pathways significantly enriched in 22q11.21- amplified vs. non- amplified acral melanomas, as determined by pre- ranked Gene Set Enrichment Analysis (GSEA). Genes were ranked by \(\log_{2}\) fold change adjusted for stage (Methods). OXPHOS, oxidative phosphorylation. e, Hierarchical clustering of 31 immunomodulatory genes (average linkage with Euclidean distance) in acral melanomas. CD3D and CD8A are included as lineage markers for T cells and CD8 T cells, respectively. f, Bar plot showing frequency of 22q11.21- amplified acral melanomas separated into high and low immunomodulatory expression groups, as defined by clustering in panel e. Statistical significance was determined by Fisher's exact test. g, Single- cell differentiation status, as imputed by CytoTRACE \(^{42}\) (top), versus the estimated relative number of 22q11.21 copies per cell (normalized between 0 and 1), as imputed by CONICSmat \(^{36}\) (bottom), in cancer cells from a 22q11.21- amplified acral melanoma tumor (Supplementary Fig. 4; Methods). Single- cell transcriptomes are visualized in the top subpanel by Uniform Manifold Approximation and Projection (UMAP). h, Association between gene expression and 22q11.21 amplification status in Yale and CSU acral melanoma cohorts. Only genes physically located on cytoband 22q11.21 are shown. Group comparisons were performed using a two- sided \(t\) - test with unequal variance. The top three genes are indicated. Amplified, \(3+\) copies. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[190, 95, 812, 707]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 710, 872, 903]]<|/det|> +
Figure 3: Cell proliferation in response to suppression of LZTR1. a, Impact of LZTR1 knockdown on cell proliferation in nine primary melanoma cell lines and two normal human melanocyte lines (NBMEL). Key mutations are indicated (Supplementary Table 9). Bar plots depict fold change between the \(3^{\text{rd}}\) and \(6^{\text{th}}\) day after infection with LZTR1 shRNA (numbered), as compared to control (scrambled) shRNA ('C'). All shRNAs significantly reduced proliferation relative to control ( \(P < 0.05\) ; two-sided \(t\) test with unequal variance). b, Western blot showing the efficiency of LZTR1 knockdown in primary acral and sun-exposed melanoma cell lines, and in normal melanocytes, related to panel a. c, Cell proliferation (left) and LZTR1 expression (right) of a sun-exposed melanoma cell line that lost one LZTR1 allele in response to genomic modification by different CRISPR-Cas9 sgRNAs targeting
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 879, 213]]<|/det|> +LZTR1. Bar plots depict fold change between the \(3^{\text{rd}}\) and \(6^{\text{th}}\) day after infection with sgRNA 5. Reduced cell proliferation is statistically significant ( \(P < 0.05\) ; two- sided \(t\) test with unequal variance). Bars in a, c represent the mean of triplicate or quadruplet wells and error bars indicate SEM. Actin levels in b, c show protein loading. Cell lines are indicated above all plots in a- c and colored according to their origin: acral melanoma (blue), sun- exposed melanoma (red), normal melanocyte (grey). NBMEL, newborn melanocyte cultured from Caucasian foreskin; WT, wildtype. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[155, 108, 860, 737]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 749, 875, 908]]<|/det|> +
Figure 4: Changes in MAPK signaling in response to LZTR1 knockdown. a, b, Impact of LZTR1 loss on (a) RAS-GTP activity as measured by RAS-GTPase Activation ELISA assay, and (b) RAS levels, five days after shLZTR1 infection. c, Effect of LZTR1 loss on MAPK activity. Key somatic events are indicated below. WT, wildtype. d, e Increased levels of BRAF activity in response to shLZTR1 is associated with increase pERK. In panel e, cells were incubated with RAF the kinase inhibitors PLX4032 (500 nM) or LY3009120 (100 nM) for four hours at the end of treatment with shRNA. f, RAS translocation to the cytoplasm in response to shLZTR1; a-d, RAS is visualized by staining with magenta; Green (Cy2) indicates GM130 (a, b) and calnexin (c, d). Scale
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 874, 179]]<|/det|> +bar = 50 μm. Blue and red indicate acral and sun- exposed melanoma cell lines, respectively. shRNAs in a- e are indicated by numeric identifiers. C, scrambled shRNA control. Actin levels in a- e show protein loading. Cell lines are indicated above all plots in a- f and colored according to their origin: acral melanoma (blue), sun- exposed melanoma (red), normal melanocyte (grey). NBMEL, newborn melanocyte. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 90, 835, 840]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 846, 844, 901]]<|/det|> +
Figure 5: Impact of LZTR1 knockdown on apoptosis, Rb signaling, and pigmentation. a, Gene Set Enrichment Analysis (GSEA)94 showing concordance in hallmark pathways among bulk acral melanoma tumors, acral melanoma single-cell
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 405]]<|/det|> +transcriptomes (scRNA- seq), and a primary melanoma cell line (LZTR1 vs. KD), in relation to high vs. low LZTR1 expression. Gold, high positive normalized enrichment score (NES); blue, high negative NES; KD, knockdown. b, LZTR1 shRNA and sgRNA (CRISPR- Cas9) induce apoptosis in melanoma cells. c, d, Effects of inhibiting caspase activity with IDN- 6556 (IDN, 2 μM, 3 days). As shown in c, IDN- 6556 suppressed shLZTR1- induced caspase activity. As shown in d, IDN- 6556 increased the levels of LZTR1 (known to be degraded by caspases) and rescued caspase substrates, such as GOLGA4, p53, and to a lesser extent, NF- \(\kappa \beta\) . e, Effect of shLZTR1 on cell morphology and actin filament organization. Actin filaments were visualized by staining with rhodamine- phalloidin (magenta) and the Golgi with anti- GM130 (green, Cy2). The nuclei are stained with DAPI (blue). Scale bar = 50 μm. f, shLZTR1 downregulates Rb and p130 (also known as RBL2). g- i, Impact of shLZTR1 on MITF (panel g), tyrosinase (TYR) (panel h), and pigmentation (panel i). shRNAs in b- d and f- i are indicated by unique numerical identifiers. C, scrambled shRNA control. Actin levels in b- d and f- h show protein loading. Of note, the actin control in f and h is identical because the same membrane was used to blot the proteins. Cell lines are indicated above all plots in b- g and colored according to their origin: acral melanoma (blue), sun- exposed melanoma (red). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 90, 881, 750]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 760, 852, 900]]<|/det|> +
Figure 6: LZTR1 and CRKL confer properties consistent with malignant cell transformation and metastasis initiation. a, Morphological changes and spheroid formations in early passage normal human melanocytes (NBMEL C1220) overexpressing LZTR1 and/or CRKL. Top: Phase-contrast images of parental and infected cells in 2D culture. LZTR1 images were taken after two days induction with doxycycline (200 ng/ml), CRKL after three days of infection with PLX304-CRKL, and LZTR1+CRKL after six days infection of LZTR1 melanocytes with PLX304-CRKL and three days stimulation with doxycycline. Insert in LZTR1 shows that colonies were
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 879, 475]]<|/det|> +evident without magnification. The non- induced LZTR1 cells grow as a monolayer, as seen for the parental non- transformed melanocytes (Parental). Scale bar = 100 μm. Bottom: Low magnification images showing 3D cultures of melanocytes seeded in 0.5% collagen for three days. LZTR1 and CRKL, both alone and in combination, induced aggregation and multicellular spheroids. Scale bar = 500 μm. b, Western blot showing critical changes in normal human melanocytes overexpressing LZTR1, CRKL, or both (as seen in the top two lanes) compared to parental (-). Cells were harvested after incubation in regular medium, or medium supplemented with doxycycline for two days when applicable (Dox, 200 ng/ml). Of note, an increase in LZTR1 produced by basal promoter activity was sufficient to induce constitutive MAPK and SRC activities. Actin levels show protein loading. c, Kaplan Meier plot showing differences in distant metastasis- free survival (DMFS) between acral melanoma patients stratified by 22q11.21- amplification status. Patients with stage II or III disease at diagnosis with available DMFS data are shown (Yale cohort). Statistical significance was assessed by a log- rank test. HR, hazard ratio. 95% HR confidence interval is shown in brackets. d, 2D cultures of spontaneously immortalized mouse melanocytes (C57BL) forming colonies in the absence of TPA in response to LZTR1 (Dox). The dark colonies are seen without magnification (top), and under phase- contrast microscope (bottom). e, Cell proliferation of parental and LZTR1 transformed C57BL mouse melanocytes. f, Western blots displaying LZTR1 expression and MAPK activation (pERK) in TPA- starved (-) mouse melanocytes in response to doxycycline (+200 ng/ml), compared to parental, non- transformed cells (P). + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 110, 720, 720]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[114, 90, 608, 109]]<|/det|> +Table 1: Patient characteristics and sequencing data + +
CharacteristicsAcral
(n = 104)		Sun- exposed
(n = 157)
Age (years)
Median (range)62 (29 - 89)66 (20 - 94)
Sex, n (%)
Female42 (40)61 (39)
Male62 (60)96 (61)
Stage at tumor resection, n (%)
01 (1)0 (0)
110 (10)7 (4)
239 (38)22 (14)
328 (27)7 (4)
426 (25)121 (77)
Tumor sample site, n (%)
Primary81 (78)41 (26)
Metastatic23 (22)116 (74)
WES, n (%)
Yale University32 (33)134 (100)
Central South University65 (67)0 (0)
Bulk RNA-seq, total n (n with WES)
Yale University22 (17)60 (37)
Central South University23 (21)0
+ +<|ref|>table_footnote<|/ref|><|det|>[[114, 718, 863, 790]]<|/det|> +Out of 104 acral melanoma patients, 97 were profiled by WES, 7 were profiled by bulk RNA-seq and not WES, and 45 were profiled by both. Of 157 sun-exposed melanoma patients, 134 were profiled by WES, 23 were profiled by bulk RNA-seq and not WES, and 37 were profiled by both. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 198, 108]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 126, 268, 143]]<|/det|> +## Human subjects + +<|ref|>text<|/ref|><|det|>[[115, 143, 877, 300]]<|/det|> +All clinical specimens in this study were collected with informed consent for research use and were approved by the Yale University and Central South University Institutional Review Boards in accordance with the Declaration of Helsinki. Melanoma tumor specimens were excised to alleviate tumor burden. CSU samples were collected from Xiangya Hospital, Hospital for Skin Diseases (Institute of Dermatology), Chinese Academy of Medical Sciences in Nanjin, Third Affiliated Hospital of Sun Yat- sen University, Hunan Provincial Tumor Hospital, Xiangya Hospital, Central South University, First Affiliated Hospital of Harbin Medical University and Wuhan Union Hospital. + +<|ref|>sub_title<|/ref|><|det|>[[115, 317, 342, 334]]<|/det|> +## Nucleic acids extraction + +<|ref|>text<|/ref|><|det|>[[115, 334, 880, 490]]<|/det|> +Melanomas were sequenced from snap- frozen tumors (Yale and CSU cohorts) or low passage cell cultures (<4) as previously described2,4 (Supplementary Tables 1 and 8). DNA from melanoma cells and freshly frozen tumors was extracted with the DNeasy purification kit (Qiagen Inc., Valencia, CA). High melanin content was removed with OneStep™ PCR Inhibitor Removal Kit (Zymo Research Corporation, Irvine, CA). Direct- zol™ RNA MiniPrep w/ Zymo- Spin™ IIC Columns (Zymo cat # D4019) were used to extract RNA from tumors, and the RNeasy PowerLyzer Tissue & Cells Kit (Qiagen, CAT # 15055- 50) was used to extract RNA from peripheral blood mononuclear cells (PBMCs) and melanoma cells. + +<|ref|>sub_title<|/ref|><|det|>[[116, 508, 358, 525]]<|/det|> +## Whole exome sequencing + +<|ref|>text<|/ref|><|det|>[[115, 525, 878, 828]]<|/det|> +Sample preparation: The quality of genomic DNA was determined by estimating the \(\mathrm{A}_{260} / \mathrm{A}_{280}\) and \(\mathrm{A}_{260} / \mathrm{A}_{230}\) ratios by nanodrop, both of which required to be \(>1.8\) , and by electrophoresis in \(1\%\) agarose gel in which high quality DNA migrates as a single high molecular weight band. One \(\mu \mathrm{g}\) of genomic DNA was sheared to a mean fragment length of about 220 bp using focused acoustic energy (Covaris E220). The size distribution of the fragmented sample was determined by using the Caliper LabChip GX system. The fragmented DNA samples were transferred to a 96- well plate and library construction was completed using a liquid handling robot. Following fragmentation, we added T4 DNA polymerase and T4 polynucleotide kinase that blunt end and phosphorylate the fragments. The large Klenow fragment then adds a single adenine residue to the 3' end of each fragment and custom adapters (IDT) are ligated using T4 DNA ligase. Magnetic AMPure XP beads (Beckman Coulter) were used to purify and size select the adapter- ligated DNA fragments. The adapter- ligated DNA fragments were then PCR amplified using custom- made primers (IDT). During PCR, a unique six base index was inserted at both ends of each DNA fragment. Sample concentration was determined by picogreen and the fragment length distribution using the Caliper LabChip GX system. Samples yielding at least \(1 \mu \mathrm{g}\) of amplified DNA were used for capture. + +<|ref|>text<|/ref|><|det|>[[116, 844, 878, 897]]<|/det|> +Targeted capture and sequencing: For the CSU cohort, capture was performed using the NimbleGenSeqCap Med Exome 44M kit, followed by 151 bp paired- end sequencing on the Illumina HiSeq X 10 platform. TrimGalore (version 0.3.7) and FastQC (v0.11.2) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 876, 373]]<|/det|> +were used to remove adapters and low- quality sequences from the raw data. For the Yale cohort, equal amounts of each sample were pooled prior to capture. Example: for 16 samples per lane 62.5 ng of each genomic DNA library was pooled (1 \(\mu \mathrm{g}\) total) and lyophilized with Cot- 1 DNA and universal adapter blocking oligos (IDT). The dried sample was reconstituted according to the manufacturer's protocol (IDT), heat- denatured, and mixed with biotinylated DNA probes produced by IDT (xGen Exome Panel). Hybridizations were performed at \(65^{\circ}\mathrm{C}\) for 16 hours. Once the capture was complete, the samples were mixed with streptavidin- coated beads and washed with a series of stringent buffers to remove non- specifically bound DNA fragments. The captured fragments were PCR amplified and purified with AMPure XP beads. Samples were quantified by qRT- PCR using a commercially available kit (KAPA Biosystems) and insert size distribution determined with the LabChip GX. Samples with a yield of \(\geq 0.5\) ng/ul were used for sequencing. Sample concentrations were normalized to 2 nM and loaded onto Illumina NovaSeq 6000 flow cells at a concentration that yields at least 600Gbp of passing filter data per lane. Samples were sequenced using 101 bp paired- end sequencing reads according to Illumina protocols. + +<|ref|>sub_title<|/ref|><|det|>[[115, 389, 464, 407]]<|/det|> +## Bulk and single-cell RNA sequencing + +<|ref|>text<|/ref|><|det|>[[114, 406, 870, 615]]<|/det|> +Bulk and single- cell RNA sequencingBulk RNA- seq: For the CSU cohort, total RNA was depleted of rRNA using the Ribo- Zero rRNA removal kit, namely, 1 \(\mu \mathrm{g}\) of total RNA was used as input for rRNA removal. Sequencing libraries were generated using the TruSeq RNA sample prep kit (Illumina). The libraries were sequenced as 151 bp paired- end reads using an Illumina HiSeq X Ten platform. For the Yale cohort, rRNA was depleted starting from 25- 1000ng of total RNA using the Kapa RNA HyperPrep Kit with RiboErase (KR1351). Indexed libraries that met appropriate cut- offs for both quantity and quality were quantified by qRT- PCR using a commercially available kit (KAPA Biosystems) and insert size distribution was determined with the LabChip GX or Agilent Bioanalyzer. Samples with a yield of \(\geq 0.5\) ng/ul were used for sequencing. Samples were run on a combination of Illumina HiSeq 2500, HiSeq 4000, and NovaSeq instruments, and multiplexed using unique dual barcode indexes (to avoid sample contamination or barcode hopping). + +<|ref|>text<|/ref|><|det|>[[114, 631, 880, 841]]<|/det|> +scRNA- seq: To obtain a single- cell transcriptional portrait of a chr22q11.21- amplified tumor, we analyzed a primary acral melanoma specimen (YUJASMIN, Yale cohort) with 6 focal copies of 22q11.21, as determined by WES (Supplementary Table 1). The 10x Chromium 5' expression profiling platform with V1 chemistry was applied to a cryopreserved tumor cell suspension from YUJASMIN sorted for viable singlets to target 10,000 cells. Cells were sorted in the following ratios prior to library preparation: 50% CD3+CD45+ T cells: 25% CD3- CD45+ non- T immune cells: 25% CD45- stromal/cancer cells. Cell viability was assessed by the LIVE/DEAD™ Fixable Red Dead Cell Stain Kit (catalog #L34971, Thermo Fisher). The following antibodies were used: Alexa Fluor® 488 anti- human CD45 antibody (clone H130, catalog #304019, BioLegend); APC anti- human CD3 antibody (clone HIT3a, catalog #300319, BioLegend). The 10x library was sequenced on an Illumina HiSeq 2500 instrument. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[116, 90, 626, 109]]<|/det|> +## Tumor genotyping from whole exome sequencing data + +<|ref|>text<|/ref|><|det|>[[116, 125, 869, 178]]<|/det|> +Sequencing reads from exome- captured samples were analyzed with a combination of germline and somatic variant calling, permitting the identification of somatic variants, loss- of- heterozygosity (LOH) regions and copy- number variation (CNV) regions. + +<|ref|>text<|/ref|><|det|>[[113, 195, 867, 456]]<|/det|> +SNVs and indels: BAM files of aligned reads were created for each sample by aligning the sequencing reads to the GRCh37 human reference with decoy sequences (the "hs37d5" reference) using BWA MEM95, marking duplicates using Picard MarkDuplicates (http://broadinstitute.github.io/picard), and then performing indel realignment and base quality score recalibration using GATK v3.296. Then, variants were called using the tumor/normal bam files in three ways: 1) a joint variant call using GATK HaplotypeCaller, GenotypeGVCFs and hard filtering following GATK 3.2 best practices; 2) somatic SNP variant calls using MuTect with options "max_alt_alleles_in_normal_count=6", "max_alt_allele_in_normal_fraction=0.1" and "max_alt_alleles_in_normal_qscore_sum=200"; 3) somatic indel variant calls using Indelocator with options "minCoverage=6", "minNormalCoverage=4" and "minFraction=0.2". The output from the three variant callers were merged using inhouse scripts into a single VCF file, containing the union of GATK variants and MuTect/Indelocator somatic variants, marking variants called as somatic by MuTect or Indelocator as "somatic". + +<|ref|>text<|/ref|><|det|>[[114, 472, 870, 632]]<|/det|> +Those variants were annotated using Annovar97 and VEP98, and then the somatic variants were filtered using the following criteria: 1) tumor alt depth \(\geq 4\) , 2) normal read depth \(\geq 4\) , 3) normal alt depth \(\leq 1\) or normal alt frequency less than 1/5 tumor alt frequency, 4) the maximum population frequency of the variant from ExAC99, NHLBI, 1000 Genomes, or Yale Exome database must be less than 2% for a cancer- related gene (any gene in the Oncomine or Foundation Medicine gene panels or COSMIC CG Census gene list) or 1% for any other gene. Also, only protein changing variants with a VEP impact of MEDIUM or HIGH, or variants within 15 bases of a protein coding splice site were reported in the final output. + +<|ref|>text<|/ref|><|det|>[[114, 649, 877, 790]]<|/det|> +LOH: Loss- of- heterozygosity (LOH) regions were identified using the joint variant calls generated from GATK. For each variant that was called heterozygous in the normal and had a depth \(\geq 20\) in the normal, the allele frequency of the tumor and normal were subtracted ("abs (tumorAF - normalAF)"). Then the R loess and predict functions were used to smooth the allele frequency differences, and then any region with fitted values above 0.1 were identified as LOH. Tumor purity was estimated by taking the maximum mean of any LOH region, multiplied by 2 (as the tumorAF deviation, plus and minus, identifies the homozygous tumor proportion of the sample). + +<|ref|>text<|/ref|><|det|>[[115, 807, 880, 895]]<|/det|> +CNVs: Gene- level copy- number variant (CNV) regions were identified by first calculating the mean read depth for each RefGene coding exon, for the tumor and normal samples. Normalized tumor/normal read depth ratios were computed for each exon (normalized by the mean read depth of the tumor and normal across the exome), and then, using a partitioning of the genome into 20kb bins, a mean ratio for each 20kb region of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 872, 215]]<|/det|> +genome, which contains an exon, was computed. Those mean ratios were de- noised and segmented by circular binary segmentation (CBS) using the DNAcopy library from R (http://bioconductor.org/packages/release/bioc/html/DNAcopy.html.) Regions with a value deviating from the expected 1.0 ratio were identified as CNVs. For each CNV, ploidy is calculated using a deviation step value of 0.5 for high tumor purity samples (purity \(\geq 80\%\) , a 0.4 step for purity between \(40\%\) and \(80\%\) , and 0.32 step for purity less than \(40\%\) ), with the ploidy equaling 2 plus or minus the number of deviation steps. + +<|ref|>text<|/ref|><|det|>[[114, 230, 875, 528]]<|/det|> +Focal amplifications and deletions were identified with GISTIC2.0 (version 2.0.23, release date 27 Mar 2017) \(^{33}\) using the CBS segmentation files described above and the hg19 reference genome (GRCh37). No marker input file was provided. Parameters were specified according to the authors' recommended run profile: amplification and deletion thresholds were set to 0.1, the q value threshold was set to 0.1 with a confidence level of 0.95, and log2 ratios were capped at 1.5. Gene- level GISTIC analysis and broad analysis were also applied, with a focal length cutoff of 0.7. Wide peaks identified with a Q value less than 0.1 in each melanoma subtype were aggregated into a master list (Supplementary Tables 3 and 4), and genes within each wide peak were used to construct a copy number matrix. Of note, if two or more peaks were identified within the same cytoband, we appended a suffix to the cytoband name to denote the melanoma subtype in which the cytoband was identified (AC, acral; SE, sun- exposed). If more than one peak was identified within the same cytoband for a given melanoma subtype, the subtype acronym was followed by a numerical identifier (1, 2, etc.). For cases in which one peak completely encompassed another one and where both peaks had the same orientation (i.e., amplified or deleted), the shorter one was eliminated. + +<|ref|>text<|/ref|><|det|>[[115, 544, 881, 630]]<|/det|> +For comparative genomics and survival analyses, we constructed a matrix containing the mean copy number per wide peak (rows) for each melanoma tumor sample (columns). The mean copy number per wide peak was calculated as the average gene- level copy number (estimated as described above) per wide peak. We subtracted 2 from all gene- level values so that copy number- neutral regions are equal to 0 + +<|ref|>text<|/ref|><|det|>[[117, 630, 325, 647]]<|/det|> +(Supplementary Data). + +<|ref|>sub_title<|/ref|><|det|>[[115, 665, 595, 683]]<|/det|> +## Visualization of somatic alterations across patients + +<|ref|>text<|/ref|><|det|>[[115, 683, 874, 806]]<|/det|> +Recurrently mutated genes and significant focal CNVs were visualized using the Oncoprint function in ComplexHeatmap \(^{100}\) . The default bar plot (top) was replaced with a bar plot showing the number of nonsynonymous SNVs and indels per patient. For CNV regions, amplifications and deletions were calculated by averaging the GISTIC- generated gene- level copy numbers for all genes within each wide peak. Peaks with an average copy number above 3 (one additional copy) were deemed amplified, whereas peaks with an average copy number below 1.4 were considered deleted. + +<|ref|>sub_title<|/ref|><|det|>[[116, 823, 332, 841]]<|/det|> +## Bulk RNA-seq analysis + +<|ref|>text<|/ref|><|det|>[[116, 841, 835, 894]]<|/det|> +Raw RNA- seq reads were aligned with Salmon \(^{101}\) (version 0.99) to the GENCODE v.25 \(^{102}\) reference transcript assembly. Subsequently, the tximport \(^{103}\) was used to generate an expression matrix normalized to transcripts per million (TPM). Protein- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 861, 213]]<|/det|> +coding genes were determined using Ensembl release 92 human annotation \(^{104}\) (GRCh38.p12, Apr 2018), extracted by biomaRt \(^{105}\) (version 2.40.5) and non- protein- coding genes were omitted. Expression values were renormalized to TPM after this step. For batch normalization, we applied ComBat from sva \(^{106}\) using a parametric adjustment for sequencing center and year of sequencing. Following batch correction, negative values were replaced with zero and the expression matrix was \(\log_{2}\) - transformed after adding a pseudo- count of 1. + +<|ref|>text<|/ref|><|det|>[[115, 229, 870, 369]]<|/det|> +To delineate pathways associated with focal amplification of 22q11.21 (Fig. 2d), genes differentially expressed between 22q11.21 amplified and non- amplified acral melanomas were identified by constructing a linear model (Im function in R) to predict amplification status as a function of 1) gene expression ( \(\log_{2}\) adjusted) and 2) tumor stage (at the time of resection). The \(t\) value corresponding to the expression vector of each gene was used to rank- order the transcriptome. Pre- ranked gene set enrichment analysis (GSEA) \(^{107}\) was subsequently applied to the ranked- ordered transcriptome in order to assess HALLMARK pathways in MSigDB (version 7.2) \(^{108}\) . + +<|ref|>text<|/ref|><|det|>[[116, 385, 845, 438]]<|/det|> +Related to Fig. 2e, we curated a list of immunomodulatory genes, including immune checkpoint molecules, and analyzed their expression in both acral cohorts using hierarchical clustering applied with Pearson correlation and Ward D2. + +<|ref|>sub_title<|/ref|><|det|>[[115, 455, 507, 473]]<|/det|> +## Gene fusion detection from RNA-seq data + +<|ref|>text<|/ref|><|det|>[[115, 473, 879, 630]]<|/det|> +To identify fusion genes, we aligned the RNA- seq reads for each sample to the GRCh38 human reference genome using HISAT \(^{109}\) . Candidate fusion transcripts in the sequencing reads were identified with STAR- Fusion, employing the STAR aligner and FusionInspector annotator to identify the position of the chimeric RNA. For ease of manual review, the fusions were sub- grouped, with each fusion placed into the first group that either gene matched: 1) mitochondrial genes, 2) immunoglobulin genes, 3) protocadherin genes, 4) commonly expressed fusions using GTEx expression data, 5) fusions of neighboring/local rearrangement genes, 6) non- annotated genes, and 7) all others i.e., the rare, non- local fusions of annotated protein coding genes. + +<|ref|>sub_title<|/ref|><|det|>[[116, 648, 276, 665]]<|/det|> +## Survival analysis + +<|ref|>text<|/ref|><|det|>[[115, 666, 879, 770]]<|/det|> +Cox proportional hazards regression was applied to estimate overall survival associations. Cases with an initial diagnosis preceding the sequenced tumor by more than 5 years were excluded from analysis \((n = 5)\) . To estimate stage- adjusted associations with overall survival, stage was added as a covariate. Kaplan- Meier plots for comparison of survival curves were generated either by the survminer \(^{110}\) package in R (version 0.4.5) or by Graphpad Prism (version 8). + +<|ref|>text<|/ref|><|det|>[[115, 786, 852, 874]]<|/det|> +To determine survival associations of focal CNVs identified by GISTIC (Fig. 2a, Supplementary Fig. 3a, Supplementary Table 6), we applied Cox regression separately to each region within each acral melanoma cohort using the copy number matrix described above (see Copy number analysis). In all cases, we dichotomized each CNV by analyzing amplified \((>0)\) versus non- amplified \((\leq 0)\) and deleted \((\leq 0)\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 808, 127]]<|/det|> +versus non- deleted \((\geq 0)\) . Survival z- scores were combined across cohorts using Stouffer's method111, yielding an unweighted meta- z- score for each gene. + +<|ref|>text<|/ref|><|det|>[[115, 143, 875, 336]]<|/det|> +To analyze SNV- and indel- related survival associations in acral melanomas, we examined genes harboring one or more nonsynonymous mutations with at least \(5\%\) recurrence frequency in either acral melanoma cohort. These data were used to create a binary matrix in which recurrently mutated genes were rows and patients were columns (1, at least one recurrent SNV or indel; 0, otherwise). Survival z- scores were combined across cohorts as indicated above. The following four genes were insufficiently recurrent in at least one cohort to run Cox regression: CGREF1, NF1, TP53, and ARID2. To calculate survival associations for these genes, we randomly up- sampled patients from the Yale cohort in order to match the size of the CSU cohort. We then generated a cross- cohort survival Z- score for each gene (Supplementary Table 6). + +<|ref|>text<|/ref|><|det|>[[115, 352, 879, 579]]<|/det|> +To relate LZTR1 expression to overall survival, we dichotomized patients in each cohort by determining an expression threshold that discriminates 22q11.21- amplified from non- amplified patients at a defined specificity. This was done to link the threshold for dichotomization with 22q11.21 amplification without being confounded by the upper range of LZTR1 expression in non- amplified tumors. We used a specificity cutpoint of \(95\%\) for acral melanomas profiled in this study and sun- exposed melanomas profiled by TCGA. A specificity cutpoint of \(90\%\) was used for sun- exposed melanomas profiled in this work owing to a lack of evaluable samples in the 'high' group \((n = 1)\) at a specificity cutpoint of \(95\%\) . Notably, LZTR1 expression was also significantly associated with adverse outcomes when assessed as a continuous variable (i.e., without dichotomization) in sun- exposed melanomas profiled by TCGA \((Z = 2.86, P = 0.004)\) . Skin cutaneous melanoma (SKCM) expression, copy number, and survival data from TCGA were downloaded from cBioPortal87. + +<|ref|>sub_title<|/ref|><|det|>[[115, 596, 461, 614]]<|/det|> +## Single-cell RNA sequencing analysis + +<|ref|>text<|/ref|><|det|>[[115, 614, 874, 858]]<|/det|> +Single- cell RNA- seq reads were mapped to the GRCh38 human reference assembly and barcode- deduplicated using Cell Ranger (version 3.0.2). In total, 7,551 cells were sequenced, yielding a median of 945 genes per cell. The expression matrix generated by Cell Ranger was converted to counts per million (CPM) and was log2 transformed after addition of a pseudo- count to every value. Cells with less than 500 expressed genes were removed before importing the data into Seurat112 (version 3.0.2). Seurat was applied for pre- processing (default settings), data normalization (default settings), identifying the most variable features, dimension reduction (PCA and UMAP), finding marker genes, and clustering the single- cell expression data. In finding variable features, the low and high mean cutoffs were set to 0.0125 and 3, respectively, and the dispersion cutoffs were respectively set to 0.5 and infinity, with 20 bins. PCA was generated on the most variable genes with the first 15 principal components. Neighbors were calculated from the first 13 PCA components (as determined by the JackStraw function), followed by cluster analysis (resolution = 0.5). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 404]]<|/det|> +Single- cell copy number analysis: To estimate large- scale copy number alterations from scRNA- seq data, we used CONICSmat (COpy- Number analysis In single- Cell RNA- Sequencing from an expression matrix)36 as implemented in the R package, CONICSmat. Per the authors' recommendations, raw read counts were divided by 10 and were \(\log_2\) - adjusted after adding one to each count value. Chromosome arm positions from GRCh38 were used to define arm coordinates. Briefly, genes expressed in 5 cells or less were filtered, a normalization factor for each cell was calculated, and a Gaussian mixture model was calculated based on the z- score of the average centered gene expression for each region across all cells. Melanoma cells \((n = 321)\) were split into two groups based on the estimated relative copy number in the 22q arm. Cells with a normalized copy number between 0 and 0.10 were used for normalization of the expression data. Histograms were generated using plotHistogram and by setting the z- score threshold to 4. Chromosomal alterations in each single cell were visualized by plotHistogramHeatmap with the authors' recommended parameters (window size = 120, expression threshold = 0.2, visualization threshold = 1). Of 12 genes identified by GISTIC within the chr22q11.21 wide peak (Supplementary Table 3), relative copies of 22q11.21 for each cell were calculated by averaging the estimated copy number for all 7 genes with detectable expression (CPM>0). + +<|ref|>text<|/ref|><|det|>[[115, 418, 880, 562]]<|/det|> +To characterize the RNA expression profile associated with chr22q11.21 focal amplification, the median relative copy number (0.45) inferred by CONICSmat was used to split the cells into chr22q11.21 high and low groups. The \(\log_2\) - adjusted scRNA- seq dataset normalized by Census \(^{113}\) was compared between these two groups to identify differentially expressed genes using a two sided \(t\) - test with unequal variance, and the resulting \(t\) - statistics were used for ranking the gene list. This gene list was submitted to pre- ranked GSEA \(^{107}\) to interrogate 50 HALLMARK pathways (1,000 permutations, weighted enrichment statistics, MSigDB version 7.2 \(^{108}\) ). + +<|ref|>text<|/ref|><|det|>[[115, 577, 872, 718]]<|/det|> +Single- cell differentiation status: To predict the relative differentiation status of each melanoma cell profiled by scRNA- seq, we used CytoTRACE \(^{42}\) , a computational framework for inferring developmental potential on the basis of transcriptional diversity. Each of the 321 acral melanoma cells from chr22q11.21 amplified tissue received a CytoTRACE score between 0 (more differentiated) and 1 (less differentiated). CytoTRACE scores were visualized by Uniform Manifold Approximation and Projection (UMAP). To determine whether CytoTRACE is influenced by genes located on 22q, we reran CytoTRACE after excluding all genes on the 22q arm (Supplementary Fig. 4e). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 794, 108]]<|/det|> +## Short hairpin RNA (shRNA), CRISPR-Cas9 sgRNA, and cell viability tests + +<|ref|>text<|/ref|><|det|>[[115, 108, 882, 160]]<|/det|> +We used puromycin- bearing MISSION lentiviral vectors pLKO.1 shRNA to test the effect of downregulation of target proteins on cell proliferation and signal transduction, employing scramble vector SH002 as a negative control (MISSION, Sigma- Aldrich, + +<|ref|>text<|/ref|><|det|>[[115, 160, 740, 177]]<|/det|> +Supplementary Table 8), or scrambled RNA (Supplementary Table 8). + +<|ref|>text<|/ref|><|det|>[[115, 178, 870, 265]]<|/det|> +LentiCRISPRv2 plasmid was obtained from Addgene (addgene.org). Guide sequences targeting LZTR1 (Supplementary Table 8) were designed using CHOPCHOP (https://chopchop.cbu.uib.no/) and cloned into LentiCRISPRv2 to generate single sgRNA carrying plasmids following a standard method114. A non- target sequence was included as the control (Supplementary Table 8). + +<|ref|>text<|/ref|><|det|>[[113, 281, 883, 473]]<|/det|> +The plasmids were packaged in lentiviral vectors with ViraPower™ Lentiviral Packaging Mix kit (Thermo Fisher, cat # K497500), and transfected into 293T cells. The medium was collected and filtered with Millex- GV 33 mm PVDF filter (Millipore SLGV033RS) and then concentrated with Amicon Ultra- 15 centrifugal filters (Millipore UFC910024). Melanoma cells and normal human melanocytes were infected with the lentiviruses, medium was changed the following day, and the cells were then incubated with puromycin (2.5 μg/ml) for five days. Cells were collected and processed for western blotting with antibodies to target proteins. In addition, two days after infection the shRNA treated cells were seeded in 96- well plates in triplicate or quadruplet wells and tested for cell viability in the absence and presence of puromycin for 72 hrs with the CellTiter- Glo® Luminescent Cell Viability Assay, for apoptosis or RAS activity GTPase assay. + +<|ref|>text<|/ref|><|det|>[[115, 490, 880, 648]]<|/det|> +Alternatively, GV298- U6- MCS- Ubiquitin- Cherry- IRES- puromycin lentiviral plasmids were purchased from GeneChem, China. The plasmids were co- transfected with packaging plasmids (pspAX2 and pMD2G) into 293T cells using Turbofect (Thermo Scientific) according to the manufacturer's instructions. Lentiviruses were collected after 48 and 72 hours and used to infect into acral melanoma cells. Infected cells were incubated in medium supplemented with puromycin (1 μg/ml), for two or three days, seeded in 96- well plates (2x103/well, five replicates) and cell viability was measured with Cell Counting Kit- 8 (CCK- 8) (Bimake.com, China). The CCK- 8 test was repeated every 24 hrs for three days. + +<|ref|>sub_title<|/ref|><|det|>[[115, 666, 448, 682]]<|/det|> +## CRKL and LZTR1 lentivirus vectors + +<|ref|>text<|/ref|><|det|>[[115, 682, 880, 823]]<|/det|> +pDONR223- CRKL and pDONR223- LZTR1 were purchased from Addgene and DNASU, respectively. LZTR1 was transferred into plnducer20 vector115 (a gift from Dr. Thomas F. Westbrook, Baylor College of Medicine), and CRKL to PLX304 with the Gateway LR Clonase II Enzyme mix (Thermo Fisher 11791020). The identity of each vector was validated by targeted sequencing. Lentiviruses were produced as described above and the plnducer20 LZTR1 infected melanocytes were selected with 250 mg/ml Geneticin (G418) from American Bio (Canton, MA), CAT # AB05057- 05000), and PLX304CRKL with 2.5 μg/ml Blasticidin S HCl from Thermo Fisher Scientific (Waltham, MA), CAT # A1113903. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[116, 90, 411, 108]]<|/det|> +## Cell proliferation and apoptosis + +<|ref|>text<|/ref|><|det|>[[115, 108, 875, 247]]<|/det|> +Cell proliferation and apoptosisThe melanoma cells were grown in OptiMEM (Invitrogen, Carlsbad, CA) supplemented with \(5\%\) fetal calf serum and antibiotics. Normal human melanocytes (NBMEL) were grown from newborn foreskins in medium supplemented with bFGF, heparin, IBMX and dbcAMP76. Mouse melanocytes were grown from one- day old newborn pups in the presence of horse serum, TPA, melanotropin, isobutyl methyl xanthine, and placental extract. They became immortalized and were shifted to medium containing only TPA after \(\sim 20\) passages in cultures116. Some of the Yale melanoma cell lines were characterized by next- generation sequencing before2,4 (Supplementary Table 9). + +<|ref|>text<|/ref|><|det|>[[114, 263, 882, 473]]<|/det|> +Cell proliferation was measured with the CellTiter- Glo® Luminescent Cell Viability Assay (Promega Corporation, Madison, WI). Melanoma cells were seeded in 96- well plates in triplicate or quadruplet wells after knockdown with hairpin lentivirus shRNA as indicated. Standard Error (SE) was calculated employing GraphPad Prism 7 software117. In addition, we seeded cells in 12- well plates (10- 15,000/well) and measured proliferation by counting the number of cells from triplicate wells over a period up to 7- 9 days with Beckman Cell Counter. For cell count by crystal violet, we seeded cells \((3 \times 10^{3} / \text{well})\) in 6- well plates and then incubated for 10 days. Following incubation, cells were immobilized with \(4\%\) paraformaldehyde in PBS for 15 min, stained with crystal violet for 10 min and then washed with PBS. A minimum of three random fields at 40X magnification were counted to determine cell numbers. Each sample had three replicates. + +<|ref|>text<|/ref|><|det|>[[115, 490, 881, 542]]<|/det|> +The rate of apoptosis was measured using the Dead Cell Apoptosis Kit with Alexa Fluor® 488 annexin V and propidium iodide (Invitrogen, V13241) following manufacturer instructions. + +<|ref|>text<|/ref|><|det|>[[115, 559, 848, 612]]<|/det|> +PLX4032 (500 nM, Plexxikon)117 or LY3009120 (100 nM, Selleck, Pittsburgh, PA, Catalog No.S7842) were added to the growth medium 4 hours before harvesting the cells for Western blotting. + +<|ref|>text<|/ref|><|det|>[[115, 629, 847, 682]]<|/det|> +For 3D cultures, melanocytes were suspended in 1 ml medium and seeded on \(0.5\%\) collagen (Cultrex, R&D Systems, Minneapolis, MN, Cat # 3442- 050- 01), in 24 well plates for three days. + +<|ref|>sub_title<|/ref|><|det|>[[115, 700, 226, 717]]<|/det|> +## Microscopy + +<|ref|>text<|/ref|><|det|>[[115, 718, 874, 805]]<|/det|> +Images were acquired using an inverted Nikon Eclipse Ti fluorescence microscope with a Plan Apochromat lambda 60X/1.40 Oil objective or a Plan Fluor 4X/0.13 objective for fluorescent images or DIC images, respectively, a CSU- W1 confocal spinning disk unit, an iXon Ultra 888 camera (Andor Technology), MLC 400B laser unit (Agilent Technologies) and NIS Elements software (Nikon). + +<|ref|>sub_title<|/ref|><|det|>[[115, 822, 416, 839]]<|/det|> +## Western blotting and antibodies + +<|ref|>text<|/ref|><|det|>[[115, 840, 870, 891]]<|/det|> +We used western blots to identify the levels of proteins as previously described69. Cell extracts (20 µg/lane) were fractionated in \(3\% - 8\%\) or \(4 - 12\%\) tris- acetate gel (NP0006, NuPAGE Life Technologies). The membranes were probed with the primary antibodies + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 855, 125]]<|/det|> +described in Supplementary Table 10. All antibodies were used at the concentrations recommended by the manufacturers. + +<|ref|>sub_title<|/ref|><|det|>[[115, 144, 292, 161]]<|/det|> +## RAS activity assay + +<|ref|>text<|/ref|><|det|>[[115, 160, 880, 265]]<|/det|> +The amount of GTP- bound RAS was determined using the Ras GTPase Chemi ELISA Kit (Active Motif North America, 1914 Palomar Oaks Way, Carlsbad, C 92008) following the manufacturer's protocol. Melanoma cells treated with control shRNA or shLZTR1 were collected five days after infection by scraping on ice, washed with cold PBS, lysed, centrifuged and \(50 \mu \mathrm{g}\) protein/assay, in triplicates, were used following the manufacturer instructions. + +<|ref|>sub_title<|/ref|><|det|>[[115, 283, 268, 300]]<|/det|> +## Immunostaining + +<|ref|>text<|/ref|><|det|>[[115, 300, 879, 439]]<|/det|> +Cells were grown on the surface of 4- well slides, washed 2- 3 times with PBS, fixed with \(4\%\) paraformaldehyde for 15 min at room temperature, washed three times with PBS, permeabilize with \(0.2\%\) NP40 in PBS for 5 minutes, washed with PBS and incubate in PBS containing \(1\%\) BSA or (blocking buffer) for one hour. The cells were incubated with anti- GM130 antibody (clone 4A3 Millipore, Mouse), or calnexin (mouse mAb) for 1 hr at room temperature, and stained with secondary Alexa Fluor (Cy2) diluted in blocking buffer 1:1000 for 1 hr. They were washed 3X with PBS, incubated with rhodamine- phalloidin to stain actin and DAPI to stain the nucleus. + +<|ref|>sub_title<|/ref|><|det|>[[115, 456, 293, 473]]<|/det|> +## Statistical analysis + +<|ref|>text<|/ref|><|det|>[[115, 473, 874, 613]]<|/det|> +Linear relationships were modeled by linear regression \((R^2)\) , and a \(t\) test was used to assess whether the result was significantly nonzero. When data were normally distributed, group comparisons were determined using a \(t\) test with unequal variance or a paired \(t\) test, as appropriate; otherwise, a Wilcoxon test was applied. Results with \(P < 0.05\) were considered significant. Data analyses were performed with R and Prism v7 (GraphPad Software, Inc.). The investigators were not blinded to allocation during experiments and outcome assessment. No sample- size estimates were performed to ensure adequate power to detect a pre- specified effect size. + +<|ref|>sub_title<|/ref|><|det|>[[115, 630, 265, 647]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[115, 647, 824, 700]]<|/det|> +Raw sequencing data will be deposited in the Sequence Read Archive (SRA) and https://www.biosino.org/node/ and expression data will be deposited in the Gene Expression Omnibus (GEO). Accession numbers are pending. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 222, 107]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[110, 120, 880, 895]]<|/det|> +1. Curtin, J.A. et al. Distinct sets of genetic alterations in melanoma. N Engl J Med 353, 2135-47 (2005). +2. Krauthammer, M. et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat Genet 44, 1006-14 (2012). +3. Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251-63 (2012). +4. Krauthammer, M. et al. 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The plNDUCER lentiviral toolkit for inducible RNA interference in vitro and in vivo. Proc Natl Acad Sci U S A 108, 3665-70 (2011). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 872, 179]]<|/det|> +116. Tamura, A. et al. Normal murine melanocytes in culture. In Vitro Cellular & Developmental Biology 23, 519-522 (1987).117. Halaban, R. et al. PLX4032, a selective BRAF(V600E) kinase inhibitor, activates the ERK pathway and enhances cell migration and proliferation of BRAF(WT) melanoma cells. Pigment Cell Melanoma Res 23, 190-200 (2010). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 43, 311, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 398, 203]]<|/det|> +- LZTR1SuppFigs20201117v1.pdf- LZTR1SuppTables20201115v1.xlsb- LZTR1SuppData20201109v1.xlsb + +<--- Page Split ---> diff --git a/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/images_list.json b/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..70aa8c9ca88e618500c45cb062661bd04bfe154e --- /dev/null +++ b/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/images_list.json @@ -0,0 +1,115 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "FIG. 1: Patterns of transcriptional activity. A. Schematic of the model. Twenty TFs (pink) that switch between on/off states at rate \\(\\alpha = 10^{-5} \\tau_{\\mathrm{B}}^{-1}\\) (with \\(\\tau_{\\mathrm{B}}\\) the Brownian time, see SI, Supplementary Note 1) or \\(0.001 \\mathrm{~s}^{-1}\\) bind specifically to 39 TUs (red beads) randomly positioned along the chain, and non-specifically to other beads (blue). A TU is considered transcriptionally active if associated with a TF. B. Example conformation (TFs not shown). Some beads cluster and form loops; one TU not in a cluster (and not transcribed) is green, and another that is in a cluster (and transcribed) is yellow. Inset: zoom of boxed region. C. Transcriptional activity for each TU bead averaged over 1000 simulations (each lasting \\(10^{5} \\tau_{\\mathrm{B}}\\) ). TUs are grouped according to activity, with red, green and blue bars showing high \\((>70\\%)\\) , medium \\((20 - 70\\%)\\) and low \\((< 20\\%)\\) activity, respectively. This gives a population-level measure of activity. D. Variation of activity across simulations (reflecting cell-to-cell variation) for 3 representative TUs with high (red), medium (green), or low (blue) average activity (defined as in C).", + "footnote": [], + "bbox": [ + [ + 92, + 63, + 525, + 415 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "FIG. 2: Transcriptional bursting. A. Snapshots showing a 100-bead section of the simulated chain taken at different times. Initially, none of the 5 TUs (red) are in clusters and are inactive; later, 4 TUs join a cluster and are close to TFs – and so are transcribed. B. Kymograph where each row shows the changing transcription state of one TU during a simulation; pixels are colored red if the bead is associated with a TF and so transcribed, or black otherwise. White rectangle: example burst.", + "footnote": [], + "bbox": [ + [ + 90, + 61, + 530, + 312 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "FIG. 3: Regulatory networks formed by TU beads are percolating at low TF concentrations. Simulations (as Fig. 1, with \\(\\geq 800\\) simulations/condition) with different average numbers of active TFs \\((n)\\) and switching rate \\((\\alpha)\\) . Networks were constructed by calculating the Pearson correlation between the transcription time-series for all pairs of TUs; nodes represent each of the 39 TUs and edges are placed between nodes where there is a significant correlation \\((>0.15\\) in absolute value, corresponding to \\(p < 10^{-6}\\) ). A. Effect of TF concentration and switching. 39 nodes are shown around the perimeter, and thick black and grey lines denote positive and negative correlations between transcriptional activities of bead pairs. B. Effect of \\(n\\) on the fraction of nodes in the largest connected component.", + "footnote": [], + "bbox": [ + [ + 545, + 61, + 969, + 310 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "FIG. 4: Modelling SNPs and eQTL action. Sets of simulations ( \\(\\geq 800\\) simulations/condition) where each of the 39 TU beads is made non-binding in turn (to represent 39 different SNPs in regulatory elements) are compared with those with the \"wild-type\" chain (as Fig. 1). A. Chain with mutant (nonbinding) TU bead 930. (i) Snapshot. TFs not shown (inset: same structure without blue beads). (ii) Transcriptional rates of the 17 TUs with significantly different values in mutant first compared with the wild type one ( \\(p \\simeq 0.046\\) ; Students t-test). (iii) Regulatory network inferred from the matrix of Pearson correlations between activities of TUs. (iv) Change in Pearson correlation between TUs. B. Results from simulations where each TU bead is mutated in turn, and the \"transcriptional difference\" from the wild type (see text) determined. (i) Transcriptional difference versus position along the chain. (ii) Positive correlation of transcriptional difference with TU activity in wild-type. The plot shows that if we mutate a TU with high transcriptional activity, this leads to a larger difference.", + "footnote": [], + "bbox": [ + [ + 93, + 60, + 520, + 392 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "FIG. 5: Looping subtly affects transcriptional activity. Results of two sets of simulations are compared; one set as Fig. 1, in the other the chain contains 8 permanent loops (to represent convergent loops stabilized by cohesin/CTCF). A. Snapshot (beads within loops are magenta; TFs not shown; inset – same structure with only TUs shown). B. Average transcriptional activity for each TU in the looped chain (magenta bars – values for TUs in loops; magenta arcs – loop positions). C. Comparison between activity in wild-type and looped configuration for the 25 TUs with significantly different values in the two sets ( \\(p \\simeq 0.003\\) ; Students t-test). D. Regulatory network inferred from the matrix of Pearson correlations between expression of TUs (as Fig. 3A). E. Change in Pearson correlation between TUs due to loops.", + "footnote": [], + "bbox": [ + [ + 88, + 202, + 520, + 501 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "FIG. 6: Neighboring heterochromatin affects transcriptional activity. Results from two sets of simulations (at least 800 runs for each condition) are compared; one set as Fig. 1, in the other beads around TU beads 905, 907, 930 and 931 (from bead 901 to 940) are non-binding (to represent embedding the TU beads in heterochromatin). A. Snapshot with heterochromatic beads shown in gray (TFs not shown; inset – the same structure with only TUs). B. Average transcriptional activity for each TU. C. Comparison of average transcriptional activity with respect to wild-type for the 22 TUs with significantly different values in the two sets ( \\(p \\simeq 0.003\\) ; Students t-test). D. Regulatory network inferred from the matrix of Pearson correlations between activities of TUs (as Fig. 3A). E. Change in Pearson correlation between TUs due to heterochromatin.", + "footnote": [], + "bbox": [ + [ + 540, + 58, + 976, + 404 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "FIG. 7: Comparison of transcriptional activities of TUs on HSA14 in HUVECs determined using simulations and GRO-seq. A. Workflow (DHS model). Simulations (244 runs) involve a chain (35784 beads) representing HSA14, and 1700 switchable TFs confined in an ellipsoidal territory. Beads are classified as TUs (red, strong-binding), euchromatic (blue, weak-binding) or heterochromatic (grey, non-binding). Transcriptional activities from simulations are compared with those of GRO-seq data, by measuring the Spearman rank correlation. B. (i) Snapshot (TFs not shown). (ii, iii) TU beads and TFs in this configuration. C. Comparison of transcriptional activities of TUs from simulations and GRO-seq (ranked from \\(0 - 100\\%\\) , then binned in quintiles and showed as a heat map). A scatter plot of unbinned ranks of beads corresponding to SEs are superimposed (white circles). D. Comparison of transcriptional activities from simulations (for both DHS and HMM models) and GRO-seq for all 3 kb regions/beads, only TUs, and only connected patches of binding beads (see text). All correlations are significant \\((p < 10^{-6}\\) , indicated by grey lines). E (i-ii) Capture-HiC-like contact maps obtained from simulations and experiments [42] showing logarithm of number of contacts between 30 kbp bins which contain TUs.", + "footnote": [], + "bbox": [ + [ + 585, + 64, + 927, + 562 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_8.jpg", + "caption": "FIG. 8: Modelling effects of the DiGeorge deletion in HSA22. A. Workflow. Simulations (800 simulations/condition) for wild type (17102 beads) and deletion (16250 beads, where wild-type beads 6305 - 7156 are cut, corresponding to a deletion of chr22:18912231 - 21465672 in hg19). [Agreement between predicted transcriptional activity and GRO-seq in HSA22 is similar to that found for HSA1 (here, Spearman correlation is \\(r \\sim 0.29\\) , \\(p < 10^{-6}\\) ]. B. (i) Manhattan plot showing \\(-\\log_{10}\\) (p-value) as a function of genomic position along HSA22 (position given in Mbp), for changes in TU transcriptional activities between wild-type and deletion. (ii) Quantile-quantile plot showing expected versus observed values for \\(-\\log_{10}\\) (p-value) for the same data in (i). Expected values are computed from the normal distribution (these correspond to the null hypothesis according to which the change in transcriptional activities in the deletion is purely due to random variation). C. Regulatory networks of two 3 Mbp segments in chromosome 22 inferred from the Pearson correlation matrix. Edges show positive correlations \\(> 0.12\\) ( \\(p = 0.0007\\) ). Segments chosen have roughly the same number of nodes in 3 Mbp as the short fragment (Fig. 3Aii).", + "footnote": [], + "bbox": [], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0.mmd b/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0.mmd new file mode 100644 index 0000000000000000000000000000000000000000..a842c17875439be9f9a89c91b106ae4093cfcacb --- /dev/null +++ b/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0.mmd @@ -0,0 +1,269 @@ + +# Complex small-world regulatory networks emerge from the 3D organisation of the human genome + +Chris Brackley University of Edinburgh + +Nick Gilbert University of Edinburgh https://orcid.org/0000- 0003- 0505- 6081 + +Davide Michieletto University of Edinburgh + +Argyris Papantonis University of Göttingen + +Maria Pereira University of Edinburgh + +Peter Cook Oxford University https://orcid.org/0000- 0002- 6639- 188X Davide Marenduzzo (dmarendu@ph.ed.ac.uk) University of Edinburgh https://orcid.org/0000- 0003- 3974- 4915 + +## Article + +Keywords: regulatory networks, transcription factors, human genome + +Posted Date: June 12th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 566854/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on October 1st, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 25875-y. + +<--- Page Split ---> + +# Complex small-world regulatory networks emerge from the 3D organisation of the human genome + +C. +A. Brackley1, +N. Gilbert2, +D. Michieletto1,2, +A. Papantonis3, +M. +C. +F. Pereira1, +P. +R. Cook4, +D. Marenduzzo1 1SUPA, School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK 2MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK 3Institute of Pathology, University Medical Center, Georg-August University of Gottingen, 37075 Gottingen, Germany 4Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK + +The discovery that overexpressing one or a few critical transcription factors can switch cell state suggests that gene regulatory networks are relatively simple. In contrast, genome- wide association studies (GWAS) point to complex phenotypes being determined by hundreds of loci that rarely encode transcription factors and which individually have small effects. Here, we use computer simulations and a simple fitting- free polymer model of chromosomes to show that spatial correlations arising from 3D genome organisation naturally lead to stochastic and bursty transcription as well as complex small- world regulatory networks (where the transcriptional activity of each genomic region subtly affects almost all others). These effects require factors to be present at sub- saturating levels; increasing levels dramatically simplifies networks as more transcription units are pressed into use. Consequently, results from GWAS can be reconciled with those involving overexpression. We apply this pan- genomic model to predict patterns of transcriptional activity in whole human chromosomes, and, as an example, the effects of the deletion causing the diGeorge syndrome. + +## INTRODUCTION + +Transcription - the copying of DNA into RNA - is tightly regulated. Early insights into regulatory mechanisms came from work on binary on/off genetic switches controlled by one (or just a few) transcription factors such as the lambda and lac repressor in Escherichia coli [1]. Similar regulatory mechanisms are present in eukaryotes, albeit with additional complexity. For instance, a fibroblast cell can be reprogrammed into a muscle cell by a single master regulator (MYOD) [2, 3], or into pluripotent stem cells by four Yamanaka factors (Oct4, Sox2, c- Myc, Klf4) [4]. + +Genome- wide association studies (GWAS) lead to quite a different view: gene regulation is widely distributed and involves interactions between hundreds (perhaps thousands) of loci scattered around the genome [5, 6]. GWAS allow quantitative trait loci (QTLs) affecting any measurable genetic trait to be ranked in an unbiased way. With complex traits like human height, and diseases such as schizophrenia and type II diabetes, the top ten QTLs in the rank order combine to yield only modest effects, while the top one- hundred still account for less than half of the total genetic effect. Hundred more QTLs are expected to be identified as sample sizes and data resolution improve [5- 7]. Expression QTLs (eQTLs) are QTLs affecting transcription of other DNA regions. Perhaps surprisingly, these are rarely found in genes encoding transcription factors or other proteins; instead, they usually involve single- nucleotide changes in non- coding elements that bind transcription factors such as active enhancers and promot + +ers [8- 10]. + +Results from GWAS lead to the view that most generegulatory networks are incredibly complex, with the activity of a given gene being affected by a panoply of eQTLs, each having a tiny effect. This is captured by the "omnic gene" model, which is based on a set of gene- interaction equations [5, 6] such that the activity of almost any gene affects that of almost every other one. This model provides a useful and appealing framework to view GWAS results. However, it is difficult to compare its outputs with experimental data because it contains many parameters that are currently unknown and require fitting to training datasets. + +In general, existing models for gene regulation traditionally assume post- transcriptional and biochemically mediated interactions between different genes [11, 12], and disregard the role of 3D chromatin structure. Here we propose an alternative but complementary framework that links transcriptional regulation directly to 3D genome structure, deliberately neglecting downstream biochemical regulation to enable unambiguous interpretation of our results. This framework is motivated by experiments showing that chromatin folding can lead to contacts between enhancers and promoters affecting transcription, and that 3D structure changes in disease [13, 14]. Additionally, because our modelling is essentially fitting- free, its output can be directly compared to experiments. When the agreement is good, our model is validated; when poor, it points to some missing ingredient (such as biochemical feedback) that could be included in future models. + +Here, we use stochastic computer simulations of a polymer model for chromosome organization, in which a chain + +<--- Page Split ---> + +of beads represents a chromatin fibre, and a set of spheres complexes of transcription factors and RNA polymerases - which we will call "TFs" for short. Some chromatin beads are identified as transcription units (TUs), and we call them TU beads. They contain binding sites for TFs, and can be sites of transcriptional initiation (we do not discriminate between genic and non- genic promoters). As a simple starting point we only consider one type of TF that binds specifically and multivalently to TU beads, and non- specifically (i.e., with weak affinity) to every other bead. We perform 3D Brownian dynamics simulations that evolve the diffusive dynamics of the chain and associated factors. We previously showed that similar polymer models yield structures resembling those seen using chromosome- conformation- capture (3C) [15- 19] and microscopy [20]. Here, we link 3D structure to expression and transcriptional dynamics by measuring how often a TU bead is transcribed - which we do by computing the fraction of time it binds a TF. To establish the methodology, we model a 3 Mbp chromatin fragment, before going on to simulate whole human chromosomes. + +Our simulations capture many features of eukaryotic regulation. For example, transcription is stochastic and bursty (in agreement with single- cell transcriptomics data), and the predicted pattern of transcriptional activity in human chromosomes correlates significantly with that observed experimentally. We also find that small- world (percolating) networks that encapsulate much of the rich complexity observed in GWAS emerge through spatial effects alone. In other words, the activity of most (probably all) TUs in our model is affected by the activity of most (probably all) other segments in the genome. We find such pan- genomic regulation critically requires non- saturating concentrations of TFs - as normally found in vivo - and that increasing concentrations dramatically simplifies the networks. This enables us to reconcile the GWAS- based view that regulatory networks are complicated with the observation that overexpressing one or a few TFs can decisively alter cell state. + +## RESULTS + +We first consider a simple system where a 3 Mbp chromatin fragment is represented by a chain of 1000 beads (each \(30\mathrm{nm}\) in diameter, and corresponding to \(3\mathrm{kbp}\) ). We select at random \(N = 39\) beads and identify them as TUs (Fig. 1A; full details in SI, Supplementary Notes 1 and 2). The linear density of TU in the fragment is similar to that in human chromosome 22. Additionally, \(n\) spheres (also 30 nm in diameter) represent TFs (recall these are complexes of transcription factors and RNA polymerase II). TFs bind reversibly to TUs via a strong attractive interaction, and to all other beads weakly and non- specifically. An important feature is that TFs switch between active (binding) and inactive (non- binding) state at rate \(\alpha\) . Many factors switch like this in vivo (e.g., due to phosphorylation and de- phosphorylation), and switching is required to account for the rapid exchange of factors and polymerases between bound and free states seen in live- cell photobleaching experiments [21]. As \(\sim 7\) out of 8 polymerases attempting to initiate at promoters dissociate with a half- life of \(\sim 2.4\) s [22], our complexes generally behave like those in vivo. + +While our results refer to a single patterning of TUs along the fibre, they are representative of any arbitrary random positioning of TUs: in other words the qualitative trends we present below are robust and do not depend on the particular choice of the 1D pattern of TU along the fibre in any way. + +We say a TU bead is transcribed whenever a TF lies close to it (SI, Supplementary Note 1), and the transcriptional activity of a TU is then the fraction of time it is transcribed during a simulation. To reflect the situation in mammalian cells (Supplementary Note 4 and [23]), we typically assume there are fewer TFs than TU beads (i.e., \(n = 10\) TFs in the active binding state at any time, compared to 39 TUs). + +By interrogating TF- chromatin interactions at regular time intervals over hundreds of simulations we build up a population picture of transcription. A typical configuration of the 3 Mbp fragment is shown in Figure 1B. Strikingly, bound TFs spontaneously cluster, despite there being no attractive interactions between TUs or between TFs. Such clustering is driven by the "bridging- induced attraction" [16, 24, 25] that arises due to a positive feedback: when a TF forms a molecular bridge between two chromatin regions and forms a loop, the local chromatin concentration increases, making further TF binding more likely. Clusters then grow until limited by entropic costs of crowding (Fig. S1A). Most of the non- trivial phenomena described below result from such clustering. Clustering requires TF multivalency, as monovalent factors do not cluster [24]. However, the assumption of multivalency, which is common in the polymer physics literature [15], is well- founded. Several TFs are known to be bivalent or multivalent [26], and, more importantly, our spheres represent complexes of TFs and polymerases, so they will behave as multivalent binders even when the individual TFs in the complex are monovalent. Although clustering does not require any interactions between TFs, adding a weak attraction between them, as might arise for instance due to macromolecular crowding or electrostatic interactions between intrinsically disordered regions, should not qualitatively change any of the results discussed here (at least as long as TFs still microphase separate into clusters rather than undergoing macroscopic phase separation). + +The clusters we observe, and which emerge through the bridging- induced attraction, are qualitatively similar to those seen in vivo, which are variously described as transcriptional compartments, hubs, super- enhancer (SE) clusters, phase- separated droplets/condensates, and factories [7, 10, 27- 29]. They are also similar to the contact domains seen in microC [30], which are formed by acces + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
FIG. 1: Patterns of transcriptional activity. A. Schematic of the model. Twenty TFs (pink) that switch between on/off states at rate \(\alpha = 10^{-5} \tau_{\mathrm{B}}^{-1}\) (with \(\tau_{\mathrm{B}}\) the Brownian time, see SI, Supplementary Note 1) or \(0.001 \mathrm{~s}^{-1}\) bind specifically to 39 TUs (red beads) randomly positioned along the chain, and non-specifically to other beads (blue). A TU is considered transcriptionally active if associated with a TF. B. Example conformation (TFs not shown). Some beads cluster and form loops; one TU not in a cluster (and not transcribed) is green, and another that is in a cluster (and transcribed) is yellow. Inset: zoom of boxed region. C. Transcriptional activity for each TU bead averaged over 1000 simulations (each lasting \(10^{5} \tau_{\mathrm{B}}\) ). TUs are grouped according to activity, with red, green and blue bars showing high \((>70\%)\) , medium \((20 - 70\%)\) and low \((< 20\%)\) activity, respectively. This gives a population-level measure of activity. D. Variation of activity across simulations (reflecting cell-to-cell variation) for 3 representative TUs with high (red), medium (green), or low (blue) average activity (defined as in C).
+ +sible DNA sites clustering together in 3D space. Clustering arising through the bridging- induced attraction has recently been found in vitro for systems of DNA and cohesin (which binds multivalently to DNA) [31]. + +## Transcriptional activity varies along the chromatin fibre and is highly stochastic + +As TFs have the same affinity for all TUs, one might expect each TU to be bound with equal likelihood; however, + +transcriptional activity (the fraction of time a TU is transcribed) varies from \(\sim 10 - 90\%\) (Fig. 1C). What causes this variation? As TF copy number is limiting, and as bound TFs cluster, most transcription occurs in clusters – as is the case in vivo [7, 32–34]. Since TUs are positioned irregularly along the fragment, some have closer neighbours in 1D sequence space than others, and these are inevitably the ones most likely to cluster and be transcribed. Instead, those far from their neighbours are less likely to cluster and are less active. Accordingly, the transcriptional activity of a TU anticorrelates with distance to the nearest TU along the fibre (Fig. S1B; the Spearman correlation is \(r \simeq - 0.94\) , p- value \(p < 10^{- 12}\) ). + +Whilst Figure 1C pertains to population averages of 1000 simulations, it is informative to consider each simulation independently (as in single- cell transcriptomics). Such analysis shows that transcriptional activity is stochastic, varying substantially from simulation to simulation: a TU active in some simulations may be silent in others (Fig. 1D). + +## Transcriptional bursting + +During a simulation, chromatin conformation can change dramatically (Fig. 2A). Such changes often yield transcriptional "bursts" – periods of continued activity followed by silent periods (Fig. 2B) – as TUs with intermediate levels of activity repeatedly join a cluster to give a burst and then dissociate. Notably, TUs lying close to each other in sequence space often start and stop bursts coordinately due to the intrinsic positive feedback in the system (Fig. S1A). + +These results are consistent with experimental observations: single cell Hi- C [35] and transcriptomics [36] show that the structure and function of each individual cell is unique, and bursting is well documented [37–40] with nearby promoters often firing together [38]. + +## Local chromatin architecture creates small-world percolating transcription networks + +To investigate correlations between transcriptional activities of different TUs, we compute the Pearson correlation matrix between the activity of all possible TU pairs, and identify an emergent regulatory network in which TUs form nodes (Fig. 3A and Fig. S2). Specifically, we draw an edge between two TUs whenever there is a statistically significant positive or negative correlation between their transcriptional dynamics (Fig. 3A). This network arises only due to spatial interactions, as we assume no underlying biochemical regulation. + +The network shows a striking property. With \(n = 10\) active TFs, most nodes are connected (Fig. 3Aii), and the fraction of TUs participating in the largest connected com + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
FIG. 2: Transcriptional bursting. A. Snapshots showing a 100-bead section of the simulated chain taken at different times. Initially, none of the 5 TUs (red) are in clusters and are inactive; later, 4 TUs join a cluster and are close to TFs – and so are transcribed. B. Kymograph where each row shows the changing transcription state of one TU during a simulation; pixels are colored red if the bead is associated with a TF and so transcribed, or black otherwise. White rectangle: example burst.
+ +ponent is close to 1 (Fig. 3B). Such a network is said to be "percolating", which means that any two nodes are connected by a path along edges. Our percolating networks are also "small- world", which means that most nodes can be reached from every other node by a small number of steps [41] – we provide quantitative measurements of the small world- ness of our networks in the SI (Supplementary Note 5). The small- world phenomenology is consistent with the multitude of small- effect eQTLs detected by GWAS [5, 6]. Notably, the regulation we observe act at the transcriptional level, and not post- transcriptionally as envisaged by the omnigenic model [5, 6]. + +How might our simple model give rise to complex regulatory networks? By analysing simulation trajectories, we noted that TUs lying near each other in 1D sequence space often joined the same cluster in 3D. As a result, the activity of these clustered beads is highly positively correlated. At the same time, cluster formation sequesters TFs and so reduces the likelihood that another cluster forms elsewhere. As a result, most long- range correlations are negative (Fig. 3A). + +Crucially, these network properties depend on there being a low TF copy- number (as in vivo [23]) so TU beads do not become saturated. We therefore reasoned that increasing copy number should suppress correlations as more rarely- transcribed TUs are pressed into use. Indeed, increasing \(n\) reduces long- range negative correlations + +![](images/Figure_3.jpg) + +
FIG. 3: Regulatory networks formed by TU beads are percolating at low TF concentrations. Simulations (as Fig. 1, with \(\geq 800\) simulations/condition) with different average numbers of active TFs \((n)\) and switching rate \((\alpha)\) . Networks were constructed by calculating the Pearson correlation between the transcription time-series for all pairs of TUs; nodes represent each of the 39 TUs and edges are placed between nodes where there is a significant correlation \((>0.15\) in absolute value, corresponding to \(p < 10^{-6}\) ). A. Effect of TF concentration and switching. 39 nodes are shown around the perimeter, and thick black and grey lines denote positive and negative correlations between transcriptional activities of bead pairs. B. Effect of \(n\) on the fraction of nodes in the largest connected component.
+ +(Fig. 3Aiii,iv), and the fraction of nodes in the largest- connected component falls (Fig. 3B). Another way to think about this result is: if resources are plentiful, there is no need for sharing or competition, and all TUs can bind a TF independently of each other. If TFs do not switch and are permanently in the binding state (and \(n = 10\) ), the network becomes even more highly connected (Fig. 3Ai). + +## Modelling effect of mutations and SNPs in regulatory elements + +GWAS reveals that single- nucleotide polymorphisms (SNPs) in regulatory elements and TUs can lead to many small changes in transcriptional activity across the genome. To model this, we abrogate TF binding to one TU in the chain. Bead 930 is chosen first because it is usually highly active (Fig. 1C). This single "knock- out" affects in a statistically significant way the activity of almost half of the other TUs, both near and far away in sequence space (Fig. 4Aii). The immediately adjacent TU (i.e., bead 931) is down- regulated the most, while more distant ones are + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
FIG. 4: Modelling SNPs and eQTL action. Sets of simulations ( \(\geq 800\) simulations/condition) where each of the 39 TU beads is made non-binding in turn (to represent 39 different SNPs in regulatory elements) are compared with those with the "wild-type" chain (as Fig. 1). A. Chain with mutant (nonbinding) TU bead 930. (i) Snapshot. TFs not shown (inset: same structure without blue beads). (ii) Transcriptional rates of the 17 TUs with significantly different values in mutant first compared with the wild type one ( \(p \simeq 0.046\) ; Students t-test). (iii) Regulatory network inferred from the matrix of Pearson correlations between activities of TUs. (iv) Change in Pearson correlation between TUs. B. Results from simulations where each TU bead is mutated in turn, and the "transcriptional difference" from the wild type (see text) determined. (i) Transcriptional difference versus position along the chain. (ii) Positive correlation of transcriptional difference with TU activity in wild-type. The plot shows that if we mutate a TU with high transcriptional activity, this leads to a larger difference.
+ +up- regulated (due to loss of a strong competitor). This knock- out also rewires the whole network, even though it still retains its small- world character (Fig. 4Aiii). Both positive and negative interactions are affected along the whole chain, as shown by a heat map of the change in Pearson correlation between TU transcriptional activities (Fig. 4Aiv). + +We next systematically knock out each TU in turn. To quantify global effects, we define a "transcriptional difference" between the wild- type and each knock- out based on a standard Euclidian- distance metric (SI, Supplementary Note 3); the larger this quantity, the more different the two states are. This difference varies \(> 10\) - fold between different mutations (Fig. 4Bi). + +Together, these observations are reminiscent of the behaviour of SNPs and eQTLs. Thus, each TU mutant can be seen as a SNP underlying an eQTL; then, those with low and high transcriptional differences (Fig. 4Bi,ii) are low- and high- effect eQTLs (low- effect mutants are often isolated in sequence space), and those with wide effects (e.g., bead 930 in Fig. 4A) may be viewed as omnigenic. + +## Modelling loops, heterochromatin and euchromatin + +In mammalian genomes, promoter- enhancer pairs are often contained in loops stabilized by cohesin and the CCCTC- binding factor (CTCF) [42- 44]. To investigate how such loops might affect transcription, we incorporated eight permanent and non- overlapping loops at different positions in the chain (Fig. 5A, loops \(a - h\) ). In reality, such loops may arise from extrusion by cohesin halted at convergent CTCF loops [42]. Our assumption of stable, permanent loops is quantitatively accurate in the limit in which the interaction between cohesin and CTCF is strong and long- lived. However, we expect the trends to be qualitatively similar for more transient loops consistent with the loop extrusion model as in [19, 43]. + +The inclusion of stable loops has subtle effects. For example, loop \(h\) encompasses three TUs (beads 905, 907, 930), and expression of one is slightly boosted compared to the unlooped case (Figs. 5B,C). This is consistent with the idea that looping switches on some genes during development [45], and increases enhancer- promoter interactions [46]. However, up- regulation requires appropriate positioning of a TU within the loop. For instance, loop \(d\) encompasses two TUs (beads 396, 404), and has no effect on their activity. Broadly speaking, looping up- regulates activity, but not invariably so, and - perhaps surprisingly - two of the three most up- regulated TUs (beads 33, 886) are not contained in loops (Fig. 5C). Looping also extensively rewires the regulatory network (Fig. 5D,E). Globally, the increase in activity is modest, as incorporating all beads into closely- packed loops only increases total activity by \(\sim 10\%\) , with - once again - some TUs being down- as well as up- regulated (Fig. S3). This is consistent with experiments showing that the interplay between looping and expression is complex [47] but slight (e.g., knocking down human cohesin leaves expression of \(87\%\) genes unaffected, with global levels changing \(< 30\%\) [48]). + +In simulations thus far, TFs bind strongly to TU beads, and weakly to all others to model binding to open euchromatin [19, 49]. To investigate the effects of heterochromatin - which binds few TFs, carries few histone marks [50], and is gene poor and traditionally viewed as transcriptionally inert - we perform simulations where four of the most- active TUs (905, 907, 930 and 931) are embedded in a non- binding segment (running from bead 901 - 940). This has a dramatic effect (Figs. 6A- C): the activity of the TU beads now embedded in the non- binding + +<--- Page Split ---> + +island are at least halved, some nearby neighbors are downregulated, and more distant ones up- regulated (again due to a reduction in competition; Figs. 6B,C). The regulatory network is also rewired (Figs. 5D,E). + +Just as embedment in a non- binding segment downregulates a TU bead, embedment in a weak- binding (euchromatic) one up- regulates it (Fig. S4). This shows our model effectively captures position effects where the local chromatin context strongly influences activity [51]. + +![](images/Figure_5.jpg) + +
FIG. 5: Looping subtly affects transcriptional activity. Results of two sets of simulations are compared; one set as Fig. 1, in the other the chain contains 8 permanent loops (to represent convergent loops stabilized by cohesin/CTCF). A. Snapshot (beads within loops are magenta; TFs not shown; inset – same structure with only TUs shown). B. Average transcriptional activity for each TU in the looped chain (magenta bars – values for TUs in loops; magenta arcs – loop positions). C. Comparison between activity in wild-type and looped configuration for the 25 TUs with significantly different values in the two sets ( \(p \simeq 0.003\) ; Students t-test). D. Regulatory network inferred from the matrix of Pearson correlations between expression of TUs (as Fig. 3A). E. Change in Pearson correlation between TUs due to loops.
+ +## Modelling a whole human chromosome + +We next model a whole mid- sized human chromosome (HSA 14, length 107 Mbp; Fig. 7A), in a well- characterized and differentiated diploid cell (HUVEC, human umbilical vein endothelial cell). Now, multivalent and switchable TFs (20% active at any moment) at a non- saturating concentration bind to a string with 35784 beads. As chromosome territories are often ellipsoidal, simulations are performed in an ellipsoid of appropriate size[7, 52]; consequently, chromatin density is now higher than in simulations detailed above, with volume fractions comparable to those in vivo ( \(\sim 14\%\) ). + +![](images/Figure_6.jpg) + +
FIG. 6: Neighboring heterochromatin affects transcriptional activity. Results from two sets of simulations (at least 800 runs for each condition) are compared; one set as Fig. 1, in the other beads around TU beads 905, 907, 930 and 931 (from bead 901 to 940) are non-binding (to represent embedding the TU beads in heterochromatin). A. Snapshot with heterochromatic beads shown in gray (TFs not shown; inset – the same structure with only TUs). B. Average transcriptional activity for each TU. C. Comparison of average transcriptional activity with respect to wild-type for the 22 TUs with significantly different values in the two sets ( \(p \simeq 0.003\) ; Students t-test). D. Regulatory network inferred from the matrix of Pearson correlations between activities of TUs (as Fig. 3A). E. Change in Pearson correlation between TUs due to heterochromatin.
+ +Chromatin beads are classified using DNase- hypersensitivity data and ChIP- seq data for H3K27ac. DNase- hypersensitive sites (DHS) are excellent markers to locate promoters and enhancers (and so TF binding sites [19, 53]), whereas H3K27ac modifications strongly correlate with open chromatin [19]. Therefore, if the 3 kbp region corresponding to a chromatin bead has a DHS, then that bead is a TU; if it has H3K27ac, it is a euchromatin bead, and all other beads are non- binding (heterochromatic). We call this the "DHS" model. As properties of different chromatin segments have been catalogued using "hidden- Markov models" (HMMs) applied to many data sets [50], we alternatively classify beads + +<--- Page Split ---> + +according to HMM state; we call this the "HMM model" (Fig. S5). For more details, see Supplementary Note 4. + +Simulations using the DHS model again yield clusters enriched in TUs and TFs (Fig. 7B). As before, aggregating data from many simulations allows determination of transcriptional activities of every bead, which we compare with those of corresponding regions determined experimentally [54] by GRO- seq (global run- on sequencing [55]); activities of all 3 kbp regions are ranked from high to low, binned into quintiles, and compared. In Figure 7C, squares near the diagonal from bottom- left to top- right have high ranks (shown as red and yellow) compared to those off- diagonal (blue and purple) indicating good concordance between simulations and data. A specific sub- set of beads corresponding to super- enhancers (SEs) – which are highly active in vivo [56] – are also highly active in simulations (shown as white dots concentrated at top right). Plots showing the rank of transcriptional activities in simulations and experiments in selected genomic regions are shown in Fig. S6. Simulations yield patterns qualitatively closer to those obtained with GRO- seq than those given by poly(A) \(^+\) RNA- seq, as the latter only include gene transcription. + +Concordance between results from simulations and GRO- seq is confirmed by the Spearman rank correlation \((\sim 0.38\) for all beads; \(p< 10^{- 12}\) ; this measure is used because it is less sensitive to outliers; Fig. 7D). Restricting analysis just to TUs provides a more stringent comparison (as all TUs bind TFs with equal affinity); it still yields a significant correlation ( \(r\simeq 0.32\) , \(p< 10^{- 12}\) ; Fig. 7D). As neighbouring high- affinity regions tend to have roughly similar transcriptional rates in both simulations and data, we also average rates found in active "patches" (contiguous sets of beads which are either all TUs or all labelled as euchromatin), but found this has no significant effect (Fig. 7D). Concordance was confirmed using our HMM model (Fig. 7D, right, and Fig. S5). Adding cohesin- mediated looping to simulations involving the DHS model did not significantly change agreement with experimental data (e.g., for TUs only, \(r\simeq 0.33\) , \(p< 10^{- 12}\) ). Similar agreement with GRO- seq data was obtained from simulations applied to the H1 human embryonic stem- cell line (for TUs using the DHS model, \(r\simeq 0.29\) , \(p< 10^{- 12}\) ), and to the GM12878 cell line (DHS model, \(r\simeq 0.33\) , \(p< 10^{- 12}\) ). + +As in the chromosome fragment simulations (Fig. S1B), the transcriptional activity of a TU in our model anticorrelates with the distance to the nearest TU. In our HSA14 simulations, the presence of heterochromatin slightly reduces the absolute value of the correlation, which however remains highly significant (Spearman correlation \(r\simeq - 0.83\) , \(p< 10^{- 12}\) ). Interestingly, the experimental GRO- seq signal of a DHS also anticorrelates with the distance to the nearest DHS in a significant way, although more weakly than in simulations (Fig. S7; over the whole genome the Spearman correlation is \(r\sim - 0.23\) , \(p< 10^{- 12}\) ). + +## Networks inferred from simulations are qualitatively similar to experimental ones + +Regulatory networks emerging from our whole chromosome simulations are again small- world and highly connected (Fig. S8, and Supplementary Note 5). To facilitate comparison with previous results, we select four segments of HSA14 that have the same length as the one considered in Fig. 4 (i.e., 3 Mbp), and roughly the same density of TUs; all four segments again have highly- connected components (compare Figs. S8 and Fig. 4). However, patterns in real chromosomes and artificial fragments are quite different. In HSA14 networks, there are more positive interactions between sets of adjacent TUs and other sets that are \(>10\) beads distant in sequence space (black lines across the middle of circles in Fig. S8). + +Whole- chromosome networks also have the following statistical properties. First, their node- degree distribution decays exponentially (Fig. S9A) – as found in gene networks [57] but not in transcription factor interaction networks, which are often scale- free [58]. Second, they are modular (as clusters arising due to the bridging- induced attraction are the basic co- regulated building blocks) – again as found in gene [57] and eQTL [59] networks. [Modularity is apparent from the blocks visible in the correlation matrices, such as in Fig. S2.] Third, node degree broadly correlates with transcriptional activity (Spearman correlation 0.59, p- value \(< 10^{- 12}\) ) – as in gene coregulation networks [57]. + +## Contact maps found by simulations are qualitatively similar to Hi-C + +We previously showed [16] that simulations involving two different TFs (binding to active and inactive regions, respectively) yield contact maps much like those found with Hi- C [42]. Therefore, we expected the present simulations to reflect Hi- C data poorly as they involve only one TF binding to the minor (i.e., active) fraction of the genome, so contacts made by this structured minority would be obscured by those due to the unstructured majority. Even so, simulations yield contact maps broadly similar to those obtained by Hi- C (Fig. 7E). To measure the agreement, we use a comparison based on contact maps restricted to TUs as anchors – which may be considered as equivalent to interactions obtained by promoter- capture HiC [60]. These yield good concordance (Fig. 7E; Pearson coefficient \(r = 0.82\) ; \(r = 0.47\) when monitoring only long- range contacts between TUs at least 300 kbp away, \(p< 10^{- 6}\) in both cases). The exponent with which contact probability decays with 1D distance is \(\sim - 1.1\) in experiments, and \(\sim - 0.8\) in simulations (fitted for 1D distances between \(\sim 30\) kbp and 1.5 Mbp), both broadly consistent with the \(- 1\) value expected for a fractal globule [61]. The small discrepancy may point to our simulations slightly overestimating the weight of long- range contacts, perhaps + +<--- Page Split ---> + +because we do not include loop extrusion. + +Overall the results obtained in our HSA14 simulations show that a simple model based on 3D chromatin organisation captures much of the complexity in 3D structure and transcription of a whole human chromosome. + +## Modelling chromosome 22 carrying the diGeorge deletion + +Our approach can, in principle, be applied to study any chromosome providing appropriate genomic data are available (e.g., on DNase hypersensitivity and histone acetylation). As a proof of principle, we studied the effect of deleting \(\sim 2.55\) Mbp from HSA22 - an alteration which is associated with the diGeorge syndrome (Fig. 8A) [65]. This syndrome affects \(\sim 1\) in 4000 people, and the variable symptoms include congenital heart problems, frequent infections, developmental delays, and learning problems. + +We predict a multitude of small effects in TU activity, both near and far away from the deletion (see the Manhattan plot in Fig. 8Bi). In particular, most TUs are slightly up- regulated, as fewer TUs compete for the same number of factors, and the TUs which change the most have intermediate transcriptional activities in the wildtype (Fig. S10). The p- values associated with the change in transcriptional activities vary widely, and comparison of the observed distribution with the null hypothesis (indicating that changes in measured transcription are due to random variation) shows the observed is highly enriched in small p- values (Fig. 8Bii), as is generally the case with results from GWAS [5, 6]. The regulatory network is also re- wired (Fig. 8C). Results are consistent with measurements of differential gene expressions in patients, which showed both a large number of up- regulated and downregulated genes [62]. A more quantitative comparison between experiments and simulations would benefit from having GRO- seq data that include non- genic transcription. + +Clearly, this approach opens up a rich new field of study. For instance, while there may be processes which occur in vivo which are not represented in our model, it could still give an indication of the genes most likely to be affected by any chromosome rearrangement. + +## DISCUSSION AND CONCLUSIONS + +We have described a parsimonious 3D stochastic model for transcriptional dynamics based on multivalent binding of factors and polymerases (TFs) to genic and non- genic transcriptional units (TUs) in a chain representing a chromatin fibre. A distinctive feature of our framework is that it is fitting- free, which means the model is truly predictive and can provide a mechanistic understanding of the phenomena we observe. On the other hand, the absence of fitting renders it challenging to obtain a fully quantitative agreement between modelling and experiment. + +![](images/Figure_7.jpg) + +
FIG. 7: Comparison of transcriptional activities of TUs on HSA14 in HUVECs determined using simulations and GRO-seq. A. Workflow (DHS model). Simulations (244 runs) involve a chain (35784 beads) representing HSA14, and 1700 switchable TFs confined in an ellipsoidal territory. Beads are classified as TUs (red, strong-binding), euchromatic (blue, weak-binding) or heterochromatic (grey, non-binding). Transcriptional activities from simulations are compared with those of GRO-seq data, by measuring the Spearman rank correlation. B. (i) Snapshot (TFs not shown). (ii, iii) TU beads and TFs in this configuration. C. Comparison of transcriptional activities of TUs from simulations and GRO-seq (ranked from \(0 - 100\%\) , then binned in quintiles and showed as a heat map). A scatter plot of unbinned ranks of beads corresponding to SEs are superimposed (white circles). D. Comparison of transcriptional activities from simulations (for both DHS and HMM models) and GRO-seq for all 3 kb regions/beads, only TUs, and only connected patches of binding beads (see text). All correlations are significant \((p < 10^{-6}\) , indicated by grey lines). E (i-ii) Capture-HiC-like contact maps obtained from simulations and experiments [42] showing logarithm of number of contacts between 30 kbp bins which contain TUs.
+ +<--- Page Split ---> + +## A workflow + +HSA22 wild- type (16,250 beads) + +![](images/Figure_8.jpg) + + +B effects on transcription at other sites are: + +i ... widely scattered ii ... small + +![PLACEHOLDER_9_1] + + +![PLACEHOLDER_9_2] + +
FIG. 8: Modelling effects of the DiGeorge deletion in HSA22. A. Workflow. Simulations (800 simulations/condition) for wild type (17102 beads) and deletion (16250 beads, where wild-type beads 6305 - 7156 are cut, corresponding to a deletion of chr22:18912231 - 21465672 in hg19). [Agreement between predicted transcriptional activity and GRO-seq in HSA22 is similar to that found for HSA1 (here, Spearman correlation is \(r \sim 0.29\) , \(p < 10^{-6}\) ]. B. (i) Manhattan plot showing \(-\log_{10}\) (p-value) as a function of genomic position along HSA22 (position given in Mbp), for changes in TU transcriptional activities between wild-type and deletion. (ii) Quantile-quantile plot showing expected versus observed values for \(-\log_{10}\) (p-value) for the same data in (i). Expected values are computed from the normal distribution (these correspond to the null hypothesis according to which the change in transcriptional activities in the deletion is purely due to random variation). C. Regulatory networks of two 3 Mbp segments in chromosome 22 inferred from the Pearson correlation matrix. Edges show positive correlations \(> 0.12\) ( \(p = 0.0007\) ). Segments chosen have roughly the same number of nodes in 3 Mbp as the short fragment (Fig. 3Aii).
+ +In our simulations two types of fibres were considered: a 3 Mbp fragment with randomly- positioned TUs, which is useful to exemplify emerging trends, and human chromosomes 14 and 22 where TUs were appropriately positioned according to bioinformatic data. Despite deliberately excluding any explicit underlying network of biochemical regulation, our model nevertheless yields some notable results. These depend on having a low TF copy- number - a feature compatible with observations in vivo [23]. First, since TFs bind with the same affinity to all TUs, one might expect the latter to all be transcribed similarly, but they are not (Fig. 1). This is largely due to inter- TU spacing; TUs lying close together in 1D sequence space tend to be the most active (Fig. 1C) with positively- correlated dynamics reminiscent of transcriptional bursting (Fig. 2B). This is because they often cluster into structures which are analogous to the phase- separated transcription hubs/factories seen experimentally [7, 10], or to contact domains formed by accessible DNA sites found by high- resolution mapping of chromatin interactions by microC [30]. Second, switching off binding at any TU significantly affects the activity of many others, both near and far away in sequence space (Fig. 4). Third, introducing stable loops has subtle effects (Fig. 5), consistent with the modest changes in expression seen experimentally in cohesin knock- outs and degrons [48]. Fourth, transcriptional activity of a TU is strongly affected by the local environment in ways that are reminiscent of the silencing of a gene by incorporation into heterochromatin [51] (Fig. 6), or activation by embedment in euchromatin (Fig. S4). Fifth, the stochasticity seen in individual simulations reflects that detected by single- cell transcriptomics and single- cell Hi- C. Nevertheless, this variability does not prevent emergence of robust phenotypes in a cell population. Sixth, our simple fitting- free model predicts patterns of transcriptional activity in human chromosomes that promisingly and significantly correlate with experimental GRO- seq data (Fig. 7). This suggests that chromatin structure significantly constrains transcriptional activity. We hypothesise that additional downstream biochemical regulation, not included in our model, may provide a tool to adjust this underlying "structural" pattern of activity in a way which may be required for appropriate biological function. + +Finally, our results enable us to reconcile two conflicting sets of data, namely that regulatory networks are both complex (as GWAS shows that thousands of loci around the genome control complex phenotypes [5, 6]) and simple (as over- expressing just four Yamanaka factors switches cell fate [4]). Thus, our simulations reveal complex small- world networks of mutual up- and down- regulation (Figs. 3 and S8), consistent with GWAS results. However, increasing TF copy- number dramatically simplifies network structure (Fig. 3). We suggest such a simplification occurs when a fibroblast is reprogrammed into a pluripotent stem cell by over- expressing the Yamanaka factors; the high factor concentration simplifies the network so that the factors can combine to switch the phenotype (Fig. S11). + +Taken together, these results suggest the activity - or inactivity - of every genomic region affects that of every other region to some extent. We describe our framework as "pan- genomic" (Fig. S8). This is reminiscent of the omni- . genic model [5, 6] in the sense that many loci are involved, all having small effects. However, it differs as it provides an underlying mechanism for pangenomic effects, by posit + +<--- Page Split ---> + +ing a direct and immediate effect of structure on regulation at the transcriptional level, which contrasts with the nontrivial post- transcriptional pathways envisioned by the on- nigenic model. Additionally, our pangenomic model yields a natural framework to qualitatively understand mutually exclusive gene expression, when switching on one gene in a family turns off all others (as in developing olfactory neurons [63]). The current model to explain this phenomenon postulates a coupling between cis- acting up- regulation and trans- acting down- regulation. The pangenomic networks we find provide exactly this type of regulatory interactions (Fig. 3). On the other hand, it is challenging within our current model to account for local negative feedback mechanisms leading to noise reduction or oscillations [11], as these are more likely to arise biochemically (an example is the p53- Mdm2 system which achieves stabilisation of the cellular concentration of p53 via a negative feedback loop [64]). + +In conclusion, we have developed a framework that can be applied to predict the transcriptional activity of any genomic fragment in health or disease (Figs. 7, 8) providing appropriate experimental data is available. Predictive power can be enhanced by incorporating additional TFs, and more suitable datasets of histone marks. Other features which can improve correlations between experiments and simulations are a more accurate modelling of cohesin loop formation by loop extrusion, and of the heteromorphic nature of chromatin [19]. We hope to report on work incorporating the latter two features in the future. + +We thank the European Research Council (ERC CoG 648050 THREEDCELLPHYSICS) for support. + +## Code availability + +The code used for the simulation is LAMMPS, which is publicly available at https://lammps.sandia.gov/. Custom codes written to analyse data are available from the corresponding author upon request. + +## Data availability + +The datasets generated during and/or analysed during the current study are available from the corresponding author upon request. + +## Author contributions + +C. A. B., N. G., D. Mi., A. P., P. R. C., M. C. F. P., D. Ma. designed research; C. A. B., M. C. F. P., D. Ma. performed research; C. A. B., N. G., D. Mi., A. P., M. C. F. P., P. R. C., D. Ma. analysed the data and wrote the manuscript. + +[3] A. Dall'Agnese, L. Caputo, C. Nicoletti, J. di Iulio, A. Schmitt, S. Gatto, Y. Diao, Z. Ye, M. Forcato, R. Perera, et al., Mol. Cell 76, 453 (2019). [4] K. Takahashi, K. 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Levine, Oncogene 24, 2899 (2005).[65] https://dosage.clinicalgenome.org/clingen_region.cgi?id=ISCA-37446 + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Sl.pangenomic.revised.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0_det.mmd b/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..891dbeca3254e386e21267c1ac80295090cdceab --- /dev/null +++ b/preprint/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0/preprint__09855f388022d98f6d42aad5171260d00fc3df0376deedaa6931278a022350c0_det.mmd @@ -0,0 +1,356 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 912, 177]]<|/det|> +# Complex small-world regulatory networks emerge from the 3D organisation of the human genome + +<|ref|>text<|/ref|><|det|>[[44, 196, 260, 238]]<|/det|> +Chris Brackley University of Edinburgh + +<|ref|>text<|/ref|><|det|>[[44, 243, 615, 285]]<|/det|> +Nick Gilbert University of Edinburgh https://orcid.org/0000- 0003- 0505- 6081 + +<|ref|>text<|/ref|><|det|>[[44, 290, 260, 332]]<|/det|> +Davide Michieletto University of Edinburgh + +<|ref|>text<|/ref|><|det|>[[44, 337, 259, 378]]<|/det|> +Argyris Papantonis University of Göttingen + +<|ref|>text<|/ref|><|det|>[[44, 383, 260, 425]]<|/det|> +Maria Pereira University of Edinburgh + +<|ref|>text<|/ref|><|det|>[[44, 430, 616, 517]]<|/det|> +Peter Cook Oxford University https://orcid.org/0000- 0002- 6639- 188X Davide Marenduzzo (dmarendu@ph.ed.ac.uk) University of Edinburgh https://orcid.org/0000- 0003- 3974- 4915 + +<|ref|>sub_title<|/ref|><|det|>[[44, 558, 102, 575]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 594, 640, 614]]<|/det|> +Keywords: regulatory networks, transcription factors, human genome + +<|ref|>text<|/ref|><|det|>[[44, 633, 300, 652]]<|/det|> +Posted Date: June 12th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 670, 463, 690]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 566854/v1 + +<|ref|>text<|/ref|><|det|>[[44, 707, 910, 750]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 787, 925, 830]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 1st, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 25875-y. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[120, 59, 936, 93]]<|/det|> +# Complex small-world regulatory networks emerge from the 3D organisation of the human genome + +<|ref|>text<|/ref|><|det|>[[110, 105, 941, 210]]<|/det|> +C. +A. Brackley1, +N. Gilbert2, +D. Michieletto1,2, +A. Papantonis3, +M. +C. +F. Pereira1, +P. +R. Cook4, +D. Marenduzzo1 1SUPA, School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK 2MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK 3Institute of Pathology, University Medical Center, Georg-August University of Gottingen, 37075 Gottingen, Germany 4Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK + +<|ref|>text<|/ref|><|det|>[[191, 219, 864, 394]]<|/det|> +The discovery that overexpressing one or a few critical transcription factors can switch cell state suggests that gene regulatory networks are relatively simple. In contrast, genome- wide association studies (GWAS) point to complex phenotypes being determined by hundreds of loci that rarely encode transcription factors and which individually have small effects. Here, we use computer simulations and a simple fitting- free polymer model of chromosomes to show that spatial correlations arising from 3D genome organisation naturally lead to stochastic and bursty transcription as well as complex small- world regulatory networks (where the transcriptional activity of each genomic region subtly affects almost all others). These effects require factors to be present at sub- saturating levels; increasing levels dramatically simplifies networks as more transcription units are pressed into use. Consequently, results from GWAS can be reconciled with those involving overexpression. We apply this pan- genomic model to predict patterns of transcriptional activity in whole human chromosomes, and, as an example, the effects of the deletion causing the diGeorge syndrome. + +<|ref|>sub_title<|/ref|><|det|>[[227, 416, 373, 430]]<|/det|> +## INTRODUCTION + +<|ref|>text<|/ref|><|det|>[[88, 448, 513, 604]]<|/det|> +Transcription - the copying of DNA into RNA - is tightly regulated. Early insights into regulatory mechanisms came from work on binary on/off genetic switches controlled by one (or just a few) transcription factors such as the lambda and lac repressor in Escherichia coli [1]. Similar regulatory mechanisms are present in eukaryotes, albeit with additional complexity. For instance, a fibroblast cell can be reprogrammed into a muscle cell by a single master regulator (MYOD) [2, 3], or into pluripotent stem cells by four Yamanaka factors (Oct4, Sox2, c- Myc, Klf4) [4]. + +<|ref|>text<|/ref|><|det|>[[88, 606, 513, 859]]<|/det|> +Genome- wide association studies (GWAS) lead to quite a different view: gene regulation is widely distributed and involves interactions between hundreds (perhaps thousands) of loci scattered around the genome [5, 6]. GWAS allow quantitative trait loci (QTLs) affecting any measurable genetic trait to be ranked in an unbiased way. With complex traits like human height, and diseases such as schizophrenia and type II diabetes, the top ten QTLs in the rank order combine to yield only modest effects, while the top one- hundred still account for less than half of the total genetic effect. Hundred more QTLs are expected to be identified as sample sizes and data resolution improve [5- 7]. Expression QTLs (eQTLs) are QTLs affecting transcription of other DNA regions. Perhaps surprisingly, these are rarely found in genes encoding transcription factors or other proteins; instead, they usually involve single- nucleotide changes in non- coding elements that bind transcription factors such as active enhancers and promot + +<|ref|>text<|/ref|><|det|>[[543, 415, 616, 430]]<|/det|> +ers [8- 10]. + +<|ref|>text<|/ref|><|det|>[[543, 431, 966, 590]]<|/det|> +Results from GWAS lead to the view that most generegulatory networks are incredibly complex, with the activity of a given gene being affected by a panoply of eQTLs, each having a tiny effect. This is captured by the "omnic gene" model, which is based on a set of gene- interaction equations [5, 6] such that the activity of almost any gene affects that of almost every other one. This model provides a useful and appealing framework to view GWAS results. However, it is difficult to compare its outputs with experimental data because it contains many parameters that are currently unknown and require fitting to training datasets. + +<|ref|>text<|/ref|><|det|>[[543, 591, 966, 830]]<|/det|> +In general, existing models for gene regulation traditionally assume post- transcriptional and biochemically mediated interactions between different genes [11, 12], and disregard the role of 3D chromatin structure. Here we propose an alternative but complementary framework that links transcriptional regulation directly to 3D genome structure, deliberately neglecting downstream biochemical regulation to enable unambiguous interpretation of our results. This framework is motivated by experiments showing that chromatin folding can lead to contacts between enhancers and promoters affecting transcription, and that 3D structure changes in disease [13, 14]. Additionally, because our modelling is essentially fitting- free, its output can be directly compared to experiments. When the agreement is good, our model is validated; when poor, it points to some missing ingredient (such as biochemical feedback) that could be included in future models. + +<|ref|>text<|/ref|><|det|>[[543, 832, 965, 859]]<|/det|> +Here, we use stochastic computer simulations of a polymer model for chromosome organization, in which a chain + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 60, 513, 358]]<|/det|> +of beads represents a chromatin fibre, and a set of spheres complexes of transcription factors and RNA polymerases - which we will call "TFs" for short. Some chromatin beads are identified as transcription units (TUs), and we call them TU beads. They contain binding sites for TFs, and can be sites of transcriptional initiation (we do not discriminate between genic and non- genic promoters). As a simple starting point we only consider one type of TF that binds specifically and multivalently to TU beads, and non- specifically (i.e., with weak affinity) to every other bead. We perform 3D Brownian dynamics simulations that evolve the diffusive dynamics of the chain and associated factors. We previously showed that similar polymer models yield structures resembling those seen using chromosome- conformation- capture (3C) [15- 19] and microscopy [20]. Here, we link 3D structure to expression and transcriptional dynamics by measuring how often a TU bead is transcribed - which we do by computing the fraction of time it binds a TF. To establish the methodology, we model a 3 Mbp chromatin fragment, before going on to simulate whole human chromosomes. + +<|ref|>text<|/ref|><|det|>[[88, 360, 513, 614]]<|/det|> +Our simulations capture many features of eukaryotic regulation. For example, transcription is stochastic and bursty (in agreement with single- cell transcriptomics data), and the predicted pattern of transcriptional activity in human chromosomes correlates significantly with that observed experimentally. We also find that small- world (percolating) networks that encapsulate much of the rich complexity observed in GWAS emerge through spatial effects alone. In other words, the activity of most (probably all) TUs in our model is affected by the activity of most (probably all) other segments in the genome. We find such pan- genomic regulation critically requires non- saturating concentrations of TFs - as normally found in vivo - and that increasing concentrations dramatically simplifies the networks. This enables us to reconcile the GWAS- based view that regulatory networks are complicated with the observation that overexpressing one or a few TFs can decisively alter cell state. + +<|ref|>sub_title<|/ref|><|det|>[[260, 643, 340, 656]]<|/det|> +## RESULTS + +<|ref|>text<|/ref|><|det|>[[88, 674, 513, 858], [542, 61, 966, 162]]<|/det|> +We first consider a simple system where a 3 Mbp chromatin fragment is represented by a chain of 1000 beads (each \(30\mathrm{nm}\) in diameter, and corresponding to \(3\mathrm{kbp}\) ). We select at random \(N = 39\) beads and identify them as TUs (Fig. 1A; full details in SI, Supplementary Notes 1 and 2). The linear density of TU in the fragment is similar to that in human chromosome 22. Additionally, \(n\) spheres (also 30 nm in diameter) represent TFs (recall these are complexes of transcription factors and RNA polymerase II). TFs bind reversibly to TUs via a strong attractive interaction, and to all other beads weakly and non- specifically. An important feature is that TFs switch between active (binding) and inactive (non- binding) state at rate \(\alpha\) . Many factors switch like this in vivo (e.g., due to phosphorylation and de- phosphorylation), and switching is required to account for the rapid exchange of factors and polymerases between bound and free states seen in live- cell photobleaching experiments [21]. As \(\sim 7\) out of 8 polymerases attempting to initiate at promoters dissociate with a half- life of \(\sim 2.4\) s [22], our complexes generally behave like those in vivo. + +<|ref|>text<|/ref|><|det|>[[542, 163, 966, 246]]<|/det|> +While our results refer to a single patterning of TUs along the fibre, they are representative of any arbitrary random positioning of TUs: in other words the qualitative trends we present below are robust and do not depend on the particular choice of the 1D pattern of TU along the fibre in any way. + +<|ref|>text<|/ref|><|det|>[[542, 247, 966, 346]]<|/det|> +We say a TU bead is transcribed whenever a TF lies close to it (SI, Supplementary Note 1), and the transcriptional activity of a TU is then the fraction of time it is transcribed during a simulation. To reflect the situation in mammalian cells (Supplementary Note 4 and [23]), we typically assume there are fewer TFs than TU beads (i.e., \(n = 10\) TFs in the active binding state at any time, compared to 39 TUs). + +<|ref|>text<|/ref|><|det|>[[542, 348, 966, 757]]<|/det|> +By interrogating TF- chromatin interactions at regular time intervals over hundreds of simulations we build up a population picture of transcription. A typical configuration of the 3 Mbp fragment is shown in Figure 1B. Strikingly, bound TFs spontaneously cluster, despite there being no attractive interactions between TUs or between TFs. Such clustering is driven by the "bridging- induced attraction" [16, 24, 25] that arises due to a positive feedback: when a TF forms a molecular bridge between two chromatin regions and forms a loop, the local chromatin concentration increases, making further TF binding more likely. Clusters then grow until limited by entropic costs of crowding (Fig. S1A). Most of the non- trivial phenomena described below result from such clustering. Clustering requires TF multivalency, as monovalent factors do not cluster [24]. However, the assumption of multivalency, which is common in the polymer physics literature [15], is well- founded. Several TFs are known to be bivalent or multivalent [26], and, more importantly, our spheres represent complexes of TFs and polymerases, so they will behave as multivalent binders even when the individual TFs in the complex are monovalent. Although clustering does not require any interactions between TFs, adding a weak attraction between them, as might arise for instance due to macromolecular crowding or electrostatic interactions between intrinsically disordered regions, should not qualitatively change any of the results discussed here (at least as long as TFs still microphase separate into clusters rather than undergoing macroscopic phase separation). + +<|ref|>text<|/ref|><|det|>[[542, 760, 966, 858]]<|/det|> +The clusters we observe, and which emerge through the bridging- induced attraction, are qualitatively similar to those seen in vivo, which are variously described as transcriptional compartments, hubs, super- enhancer (SE) clusters, phase- separated droplets/condensates, and factories [7, 10, 27- 29]. They are also similar to the contact domains seen in microC [30], which are formed by acces + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[92, 63, 525, 415]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[88, 429, 515, 666]]<|/det|> +
FIG. 1: Patterns of transcriptional activity. A. Schematic of the model. Twenty TFs (pink) that switch between on/off states at rate \(\alpha = 10^{-5} \tau_{\mathrm{B}}^{-1}\) (with \(\tau_{\mathrm{B}}\) the Brownian time, see SI, Supplementary Note 1) or \(0.001 \mathrm{~s}^{-1}\) bind specifically to 39 TUs (red beads) randomly positioned along the chain, and non-specifically to other beads (blue). A TU is considered transcriptionally active if associated with a TF. B. Example conformation (TFs not shown). Some beads cluster and form loops; one TU not in a cluster (and not transcribed) is green, and another that is in a cluster (and transcribed) is yellow. Inset: zoom of boxed region. C. Transcriptional activity for each TU bead averaged over 1000 simulations (each lasting \(10^{5} \tau_{\mathrm{B}}\) ). TUs are grouped according to activity, with red, green and blue bars showing high \((>70\%)\) , medium \((20 - 70\%)\) and low \((< 20\%)\) activity, respectively. This gives a population-level measure of activity. D. Variation of activity across simulations (reflecting cell-to-cell variation) for 3 representative TUs with high (red), medium (green), or low (blue) average activity (defined as in C).
+ +<|ref|>text<|/ref|><|det|>[[88, 697, 513, 754]]<|/det|> +sible DNA sites clustering together in 3D space. Clustering arising through the bridging- induced attraction has recently been found in vitro for systems of DNA and cohesin (which binds multivalently to DNA) [31]. + +<|ref|>sub_title<|/ref|><|det|>[[102, 787, 499, 813]]<|/det|> +## Transcriptional activity varies along the chromatin fibre and is highly stochastic + +<|ref|>text<|/ref|><|det|>[[88, 830, 513, 858]]<|/det|> +As TFs have the same affinity for all TUs, one might expect each TU to be bound with equal likelihood; however, + +<|ref|>text<|/ref|><|det|>[[541, 60, 966, 247]]<|/det|> +transcriptional activity (the fraction of time a TU is transcribed) varies from \(\sim 10 - 90\%\) (Fig. 1C). What causes this variation? As TF copy number is limiting, and as bound TFs cluster, most transcription occurs in clusters – as is the case in vivo [7, 32–34]. Since TUs are positioned irregularly along the fragment, some have closer neighbours in 1D sequence space than others, and these are inevitably the ones most likely to cluster and be transcribed. Instead, those far from their neighbours are less likely to cluster and are less active. Accordingly, the transcriptional activity of a TU anticorrelates with distance to the nearest TU along the fibre (Fig. S1B; the Spearman correlation is \(r \simeq - 0.94\) , p- value \(p < 10^{- 12}\) ). + +<|ref|>text<|/ref|><|det|>[[541, 247, 966, 346]]<|/det|> +Whilst Figure 1C pertains to population averages of 1000 simulations, it is informative to consider each simulation independently (as in single- cell transcriptomics). Such analysis shows that transcriptional activity is stochastic, varying substantially from simulation to simulation: a TU active in some simulations may be silent in others (Fig. 1D). + +<|ref|>sub_title<|/ref|><|det|>[[660, 374, 850, 388]]<|/det|> +## Transcriptional bursting + +<|ref|>text<|/ref|><|det|>[[541, 402, 966, 532]]<|/det|> +During a simulation, chromatin conformation can change dramatically (Fig. 2A). Such changes often yield transcriptional "bursts" – periods of continued activity followed by silent periods (Fig. 2B) – as TUs with intermediate levels of activity repeatedly join a cluster to give a burst and then dissociate. Notably, TUs lying close to each other in sequence space often start and stop bursts coordinately due to the intrinsic positive feedback in the system (Fig. S1A). + +<|ref|>text<|/ref|><|det|>[[541, 533, 966, 604]]<|/det|> +These results are consistent with experimental observations: single cell Hi- C [35] and transcriptomics [36] show that the structure and function of each individual cell is unique, and bursting is well documented [37–40] with nearby promoters often firing together [38]. + +<|ref|>sub_title<|/ref|><|det|>[[561, 630, 945, 656]]<|/det|> +## Local chromatin architecture creates small-world percolating transcription networks + +<|ref|>text<|/ref|><|det|>[[541, 675, 966, 816]]<|/det|> +To investigate correlations between transcriptional activities of different TUs, we compute the Pearson correlation matrix between the activity of all possible TU pairs, and identify an emergent regulatory network in which TUs form nodes (Fig. 3A and Fig. S2). Specifically, we draw an edge between two TUs whenever there is a statistically significant positive or negative correlation between their transcriptional dynamics (Fig. 3A). This network arises only due to spatial interactions, as we assume no underlying biochemical regulation. + +<|ref|>text<|/ref|><|det|>[[541, 817, 966, 858]]<|/det|> +The network shows a striking property. With \(n = 10\) active TFs, most nodes are connected (Fig. 3Aii), and the fraction of TUs participating in the largest connected com + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[90, 61, 530, 312]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[88, 325, 515, 440]]<|/det|> +
FIG. 2: Transcriptional bursting. A. Snapshots showing a 100-bead section of the simulated chain taken at different times. Initially, none of the 5 TUs (red) are in clusters and are inactive; later, 4 TUs join a cluster and are close to TFs – and so are transcribed. B. Kymograph where each row shows the changing transcription state of one TU during a simulation; pixels are colored red if the bead is associated with a TF and so transcribed, or black otherwise. White rectangle: example burst.
+ +<|ref|>text<|/ref|><|det|>[[88, 468, 515, 640]]<|/det|> +ponent is close to 1 (Fig. 3B). Such a network is said to be "percolating", which means that any two nodes are connected by a path along edges. Our percolating networks are also "small- world", which means that most nodes can be reached from every other node by a small number of steps [41] – we provide quantitative measurements of the small world- ness of our networks in the SI (Supplementary Note 5). The small- world phenomenology is consistent with the multitude of small- effect eQTLs detected by GWAS [5, 6]. Notably, the regulation we observe act at the transcriptional level, and not post- transcriptionally as envisaged by the omnigenic model [5, 6]. + +<|ref|>text<|/ref|><|det|>[[88, 643, 515, 772]]<|/det|> +How might our simple model give rise to complex regulatory networks? By analysing simulation trajectories, we noted that TUs lying near each other in 1D sequence space often joined the same cluster in 3D. As a result, the activity of these clustered beads is highly positively correlated. At the same time, cluster formation sequesters TFs and so reduces the likelihood that another cluster forms elsewhere. As a result, most long- range correlations are negative (Fig. 3A). + +<|ref|>text<|/ref|><|det|>[[88, 775, 515, 858]]<|/det|> +Crucially, these network properties depend on there being a low TF copy- number (as in vivo [23]) so TU beads do not become saturated. We therefore reasoned that increasing copy number should suppress correlations as more rarely- transcribed TUs are pressed into use. Indeed, increasing \(n\) reduces long- range negative correlations + +<|ref|>image<|/ref|><|det|>[[545, 61, 969, 310]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[540, 335, 967, 510]]<|/det|> +
FIG. 3: Regulatory networks formed by TU beads are percolating at low TF concentrations. Simulations (as Fig. 1, with \(\geq 800\) simulations/condition) with different average numbers of active TFs \((n)\) and switching rate \((\alpha)\) . Networks were constructed by calculating the Pearson correlation between the transcription time-series for all pairs of TUs; nodes represent each of the 39 TUs and edges are placed between nodes where there is a significant correlation \((>0.15\) in absolute value, corresponding to \(p < 10^{-6}\) ). A. Effect of TF concentration and switching. 39 nodes are shown around the perimeter, and thick black and grey lines denote positive and negative correlations between transcriptional activities of bead pairs. B. Effect of \(n\) on the fraction of nodes in the largest connected component.
+ +<|ref|>text<|/ref|><|det|>[[541, 538, 966, 639]]<|/det|> +(Fig. 3Aiii,iv), and the fraction of nodes in the largest- connected component falls (Fig. 3B). Another way to think about this result is: if resources are plentiful, there is no need for sharing or competition, and all TUs can bind a TF independently of each other. If TFs do not switch and are permanently in the binding state (and \(n = 10\) ), the network becomes even more highly connected (Fig. 3Ai). + +<|ref|>sub_title<|/ref|><|det|>[[541, 672, 964, 699]]<|/det|> +## Modelling effect of mutations and SNPs in regulatory elements + +<|ref|>text<|/ref|><|det|>[[541, 717, 966, 858]]<|/det|> +GWAS reveals that single- nucleotide polymorphisms (SNPs) in regulatory elements and TUs can lead to many small changes in transcriptional activity across the genome. To model this, we abrogate TF binding to one TU in the chain. Bead 930 is chosen first because it is usually highly active (Fig. 1C). This single "knock- out" affects in a statistically significant way the activity of almost half of the other TUs, both near and far away in sequence space (Fig. 4Aii). The immediately adjacent TU (i.e., bead 931) is down- regulated the most, while more distant ones are + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[93, 60, 520, 392]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[88, 403, 515, 628]]<|/det|> +
FIG. 4: Modelling SNPs and eQTL action. Sets of simulations ( \(\geq 800\) simulations/condition) where each of the 39 TU beads is made non-binding in turn (to represent 39 different SNPs in regulatory elements) are compared with those with the "wild-type" chain (as Fig. 1). A. Chain with mutant (nonbinding) TU bead 930. (i) Snapshot. TFs not shown (inset: same structure without blue beads). (ii) Transcriptional rates of the 17 TUs with significantly different values in mutant first compared with the wild type one ( \(p \simeq 0.046\) ; Students t-test). (iii) Regulatory network inferred from the matrix of Pearson correlations between activities of TUs. (iv) Change in Pearson correlation between TUs. B. Results from simulations where each TU bead is mutated in turn, and the "transcriptional difference" from the wild type (see text) determined. (i) Transcriptional difference versus position along the chain. (ii) Positive correlation of transcriptional difference with TU activity in wild-type. The plot shows that if we mutate a TU with high transcriptional activity, this leads to a larger difference.
+ +<|ref|>text<|/ref|><|det|>[[88, 658, 513, 757]]<|/det|> +up- regulated (due to loss of a strong competitor). This knock- out also rewires the whole network, even though it still retains its small- world character (Fig. 4Aiii). Both positive and negative interactions are affected along the whole chain, as shown by a heat map of the change in Pearson correlation between TU transcriptional activities (Fig. 4Aiv). + +<|ref|>text<|/ref|><|det|>[[88, 760, 513, 859]]<|/det|> +We next systematically knock out each TU in turn. To quantify global effects, we define a "transcriptional difference" between the wild- type and each knock- out based on a standard Euclidian- distance metric (SI, Supplementary Note 3); the larger this quantity, the more different the two states are. This difference varies \(> 10\) - fold between different mutations (Fig. 4Bi). + +<|ref|>text<|/ref|><|det|>[[541, 61, 966, 162]]<|/det|> +Together, these observations are reminiscent of the behaviour of SNPs and eQTLs. Thus, each TU mutant can be seen as a SNP underlying an eQTL; then, those with low and high transcriptional differences (Fig. 4Bi,ii) are low- and high- effect eQTLs (low- effect mutants are often isolated in sequence space), and those with wide effects (e.g., bead 930 in Fig. 4A) may be viewed as omnigenic. + +<|ref|>sub_title<|/ref|><|det|>[[550, 189, 956, 202]]<|/det|> +## Modelling loops, heterochromatin and euchromatin + +<|ref|>text<|/ref|><|det|>[[541, 220, 966, 404]]<|/det|> +In mammalian genomes, promoter- enhancer pairs are often contained in loops stabilized by cohesin and the CCCTC- binding factor (CTCF) [42- 44]. To investigate how such loops might affect transcription, we incorporated eight permanent and non- overlapping loops at different positions in the chain (Fig. 5A, loops \(a - h\) ). In reality, such loops may arise from extrusion by cohesin halted at convergent CTCF loops [42]. Our assumption of stable, permanent loops is quantitatively accurate in the limit in which the interaction between cohesin and CTCF is strong and long- lived. However, we expect the trends to be qualitatively similar for more transient loops consistent with the loop extrusion model as in [19, 43]. + +<|ref|>text<|/ref|><|det|>[[541, 405, 966, 717]]<|/det|> +The inclusion of stable loops has subtle effects. For example, loop \(h\) encompasses three TUs (beads 905, 907, 930), and expression of one is slightly boosted compared to the unlooped case (Figs. 5B,C). This is consistent with the idea that looping switches on some genes during development [45], and increases enhancer- promoter interactions [46]. However, up- regulation requires appropriate positioning of a TU within the loop. For instance, loop \(d\) encompasses two TUs (beads 396, 404), and has no effect on their activity. Broadly speaking, looping up- regulates activity, but not invariably so, and - perhaps surprisingly - two of the three most up- regulated TUs (beads 33, 886) are not contained in loops (Fig. 5C). Looping also extensively rewires the regulatory network (Fig. 5D,E). Globally, the increase in activity is modest, as incorporating all beads into closely- packed loops only increases total activity by \(\sim 10\%\) , with - once again - some TUs being down- as well as up- regulated (Fig. S3). This is consistent with experiments showing that the interplay between looping and expression is complex [47] but slight (e.g., knocking down human cohesin leaves expression of \(87\%\) genes unaffected, with global levels changing \(< 30\%\) [48]). + +<|ref|>text<|/ref|><|det|>[[541, 718, 966, 859]]<|/det|> +In simulations thus far, TFs bind strongly to TU beads, and weakly to all others to model binding to open euchromatin [19, 49]. To investigate the effects of heterochromatin - which binds few TFs, carries few histone marks [50], and is gene poor and traditionally viewed as transcriptionally inert - we perform simulations where four of the most- active TUs (905, 907, 930 and 931) are embedded in a non- binding segment (running from bead 901 - 940). This has a dramatic effect (Figs. 6A- C): the activity of the TU beads now embedded in the non- binding + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 60, 513, 120]]<|/det|> +island are at least halved, some nearby neighbors are downregulated, and more distant ones up- regulated (again due to a reduction in competition; Figs. 6B,C). The regulatory network is also rewired (Figs. 5D,E). + +<|ref|>text<|/ref|><|det|>[[88, 120, 513, 190]]<|/det|> +Just as embedment in a non- binding segment downregulates a TU bead, embedment in a weak- binding (euchromatic) one up- regulates it (Fig. S4). This shows our model effectively captures position effects where the local chromatin context strongly influences activity [51]. + +<|ref|>image<|/ref|><|det|>[[88, 202, 520, 501]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[88, 512, 515, 688]]<|/det|> +
FIG. 5: Looping subtly affects transcriptional activity. Results of two sets of simulations are compared; one set as Fig. 1, in the other the chain contains 8 permanent loops (to represent convergent loops stabilized by cohesin/CTCF). A. Snapshot (beads within loops are magenta; TFs not shown; inset – same structure with only TUs shown). B. Average transcriptional activity for each TU in the looped chain (magenta bars – values for TUs in loops; magenta arcs – loop positions). C. Comparison between activity in wild-type and looped configuration for the 25 TUs with significantly different values in the two sets ( \(p \simeq 0.003\) ; Students t-test). D. Regulatory network inferred from the matrix of Pearson correlations between expression of TUs (as Fig. 3A). E. Change in Pearson correlation between TUs due to loops.
+ +<|ref|>sub_title<|/ref|><|det|>[[148, 729, 453, 742]]<|/det|> +## Modelling a whole human chromosome + +<|ref|>text<|/ref|><|det|>[[88, 759, 513, 858], [541, 614, 966, 671]]<|/det|> +We next model a whole mid- sized human chromosome (HSA 14, length 107 Mbp; Fig. 7A), in a well- characterized and differentiated diploid cell (HUVEC, human umbilical vein endothelial cell). Now, multivalent and switchable TFs (20% active at any moment) at a non- saturating concentration bind to a string with 35784 beads. As chromosome territories are often ellipsoidal, simulations are performed in an ellipsoid of appropriate size[7, 52]; consequently, chromatin density is now higher than in simulations detailed above, with volume fractions comparable to those in vivo ( \(\sim 14\%\) ). + +<|ref|>image<|/ref|><|det|>[[540, 58, 976, 404]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[540, 415, 966, 590]]<|/det|> +
FIG. 6: Neighboring heterochromatin affects transcriptional activity. Results from two sets of simulations (at least 800 runs for each condition) are compared; one set as Fig. 1, in the other beads around TU beads 905, 907, 930 and 931 (from bead 901 to 940) are non-binding (to represent embedding the TU beads in heterochromatin). A. Snapshot with heterochromatic beads shown in gray (TFs not shown; inset – the same structure with only TUs). B. Average transcriptional activity for each TU. C. Comparison of average transcriptional activity with respect to wild-type for the 22 TUs with significantly different values in the two sets ( \(p \simeq 0.003\) ; Students t-test). D. Regulatory network inferred from the matrix of Pearson correlations between activities of TUs (as Fig. 3A). E. Change in Pearson correlation between TUs due to heterochromatin.
+ +<|ref|>text<|/ref|><|det|>[[541, 672, 966, 859]]<|/det|> +Chromatin beads are classified using DNase- hypersensitivity data and ChIP- seq data for H3K27ac. DNase- hypersensitive sites (DHS) are excellent markers to locate promoters and enhancers (and so TF binding sites [19, 53]), whereas H3K27ac modifications strongly correlate with open chromatin [19]. Therefore, if the 3 kbp region corresponding to a chromatin bead has a DHS, then that bead is a TU; if it has H3K27ac, it is a euchromatin bead, and all other beads are non- binding (heterochromatic). We call this the "DHS" model. As properties of different chromatin segments have been catalogued using "hidden- Markov models" (HMMs) applied to many data sets [50], we alternatively classify beads + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 60, 513, 90]]<|/det|> +according to HMM state; we call this the "HMM model" (Fig. S5). For more details, see Supplementary Note 4. + +<|ref|>text<|/ref|><|det|>[[88, 91, 514, 387]]<|/det|> +Simulations using the DHS model again yield clusters enriched in TUs and TFs (Fig. 7B). As before, aggregating data from many simulations allows determination of transcriptional activities of every bead, which we compare with those of corresponding regions determined experimentally [54] by GRO- seq (global run- on sequencing [55]); activities of all 3 kbp regions are ranked from high to low, binned into quintiles, and compared. In Figure 7C, squares near the diagonal from bottom- left to top- right have high ranks (shown as red and yellow) compared to those off- diagonal (blue and purple) indicating good concordance between simulations and data. A specific sub- set of beads corresponding to super- enhancers (SEs) – which are highly active in vivo [56] – are also highly active in simulations (shown as white dots concentrated at top right). Plots showing the rank of transcriptional activities in simulations and experiments in selected genomic regions are shown in Fig. S6. Simulations yield patterns qualitatively closer to those obtained with GRO- seq than those given by poly(A) \(^+\) RNA- seq, as the latter only include gene transcription. + +<|ref|>text<|/ref|><|det|>[[88, 388, 514, 686]]<|/det|> +Concordance between results from simulations and GRO- seq is confirmed by the Spearman rank correlation \((\sim 0.38\) for all beads; \(p< 10^{- 12}\) ; this measure is used because it is less sensitive to outliers; Fig. 7D). Restricting analysis just to TUs provides a more stringent comparison (as all TUs bind TFs with equal affinity); it still yields a significant correlation ( \(r\simeq 0.32\) , \(p< 10^{- 12}\) ; Fig. 7D). As neighbouring high- affinity regions tend to have roughly similar transcriptional rates in both simulations and data, we also average rates found in active "patches" (contiguous sets of beads which are either all TUs or all labelled as euchromatin), but found this has no significant effect (Fig. 7D). Concordance was confirmed using our HMM model (Fig. 7D, right, and Fig. S5). Adding cohesin- mediated looping to simulations involving the DHS model did not significantly change agreement with experimental data (e.g., for TUs only, \(r\simeq 0.33\) , \(p< 10^{- 12}\) ). Similar agreement with GRO- seq data was obtained from simulations applied to the H1 human embryonic stem- cell line (for TUs using the DHS model, \(r\simeq 0.29\) , \(p< 10^{- 12}\) ), and to the GM12878 cell line (DHS model, \(r\simeq 0.33\) , \(p< 10^{- 12}\) ). + +<|ref|>text<|/ref|><|det|>[[88, 687, 514, 842]]<|/det|> +As in the chromosome fragment simulations (Fig. S1B), the transcriptional activity of a TU in our model anticorrelates with the distance to the nearest TU. In our HSA14 simulations, the presence of heterochromatin slightly reduces the absolute value of the correlation, which however remains highly significant (Spearman correlation \(r\simeq - 0.83\) , \(p< 10^{- 12}\) ). Interestingly, the experimental GRO- seq signal of a DHS also anticorrelates with the distance to the nearest DHS in a significant way, although more weakly than in simulations (Fig. S7; over the whole genome the Spearman correlation is \(r\sim - 0.23\) , \(p< 10^{- 12}\) ). + +<|ref|>sub_title<|/ref|><|det|>[[548, 62, 958, 88]]<|/det|> +## Networks inferred from simulations are qualitatively similar to experimental ones + +<|ref|>text<|/ref|><|det|>[[541, 105, 965, 290]]<|/det|> +Regulatory networks emerging from our whole chromosome simulations are again small- world and highly connected (Fig. S8, and Supplementary Note 5). To facilitate comparison with previous results, we select four segments of HSA14 that have the same length as the one considered in Fig. 4 (i.e., 3 Mbp), and roughly the same density of TUs; all four segments again have highly- connected components (compare Figs. S8 and Fig. 4). However, patterns in real chromosomes and artificial fragments are quite different. In HSA14 networks, there are more positive interactions between sets of adjacent TUs and other sets that are \(>10\) beads distant in sequence space (black lines across the middle of circles in Fig. S8). + +<|ref|>text<|/ref|><|det|>[[541, 291, 965, 476]]<|/det|> +Whole- chromosome networks also have the following statistical properties. First, their node- degree distribution decays exponentially (Fig. S9A) – as found in gene networks [57] but not in transcription factor interaction networks, which are often scale- free [58]. Second, they are modular (as clusters arising due to the bridging- induced attraction are the basic co- regulated building blocks) – again as found in gene [57] and eQTL [59] networks. [Modularity is apparent from the blocks visible in the correlation matrices, such as in Fig. S2.] Third, node degree broadly correlates with transcriptional activity (Spearman correlation 0.59, p- value \(< 10^{- 12}\) ) – as in gene coregulation networks [57]. + +<|ref|>sub_title<|/ref|><|det|>[[545, 490, 958, 515]]<|/det|> +## Contact maps found by simulations are qualitatively similar to Hi-C + +<|ref|>text<|/ref|><|det|>[[541, 532, 965, 858]]<|/det|> +We previously showed [16] that simulations involving two different TFs (binding to active and inactive regions, respectively) yield contact maps much like those found with Hi- C [42]. Therefore, we expected the present simulations to reflect Hi- C data poorly as they involve only one TF binding to the minor (i.e., active) fraction of the genome, so contacts made by this structured minority would be obscured by those due to the unstructured majority. Even so, simulations yield contact maps broadly similar to those obtained by Hi- C (Fig. 7E). To measure the agreement, we use a comparison based on contact maps restricted to TUs as anchors – which may be considered as equivalent to interactions obtained by promoter- capture HiC [60]. These yield good concordance (Fig. 7E; Pearson coefficient \(r = 0.82\) ; \(r = 0.47\) when monitoring only long- range contacts between TUs at least 300 kbp away, \(p< 10^{- 6}\) in both cases). The exponent with which contact probability decays with 1D distance is \(\sim - 1.1\) in experiments, and \(\sim - 0.8\) in simulations (fitted for 1D distances between \(\sim 30\) kbp and 1.5 Mbp), both broadly consistent with the \(- 1\) value expected for a fractal globule [61]. The small discrepancy may point to our simulations slightly overestimating the weight of long- range contacts, perhaps + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 62, 395, 75]]<|/det|> +because we do not include loop extrusion. + +<|ref|>text<|/ref|><|det|>[[87, 76, 515, 133]]<|/det|> +Overall the results obtained in our HSA14 simulations show that a simple model based on 3D chromatin organisation captures much of the complexity in 3D structure and transcription of a whole human chromosome. + +<|ref|>sub_title<|/ref|><|det|>[[108, 147, 494, 172]]<|/det|> +## Modelling chromosome 22 carrying the diGeorge deletion + +<|ref|>text<|/ref|><|det|>[[88, 189, 515, 312]]<|/det|> +Our approach can, in principle, be applied to study any chromosome providing appropriate genomic data are available (e.g., on DNase hypersensitivity and histone acetylation). As a proof of principle, we studied the effect of deleting \(\sim 2.55\) Mbp from HSA22 - an alteration which is associated with the diGeorge syndrome (Fig. 8A) [65]. This syndrome affects \(\sim 1\) in 4000 people, and the variable symptoms include congenital heart problems, frequent infections, developmental delays, and learning problems. + +<|ref|>text<|/ref|><|det|>[[88, 312, 515, 585]]<|/det|> +We predict a multitude of small effects in TU activity, both near and far away from the deletion (see the Manhattan plot in Fig. 8Bi). In particular, most TUs are slightly up- regulated, as fewer TUs compete for the same number of factors, and the TUs which change the most have intermediate transcriptional activities in the wildtype (Fig. S10). The p- values associated with the change in transcriptional activities vary widely, and comparison of the observed distribution with the null hypothesis (indicating that changes in measured transcription are due to random variation) shows the observed is highly enriched in small p- values (Fig. 8Bii), as is generally the case with results from GWAS [5, 6]. The regulatory network is also re- wired (Fig. 8C). Results are consistent with measurements of differential gene expressions in patients, which showed both a large number of up- regulated and downregulated genes [62]. A more quantitative comparison between experiments and simulations would benefit from having GRO- seq data that include non- genic transcription. + +<|ref|>text<|/ref|><|det|>[[88, 586, 515, 656]]<|/det|> +Clearly, this approach opens up a rich new field of study. For instance, while there may be processes which occur in vivo which are not represented in our model, it could still give an indication of the genes most likely to be affected by any chromosome rearrangement. + +<|ref|>sub_title<|/ref|><|det|>[[153, 683, 448, 696]]<|/det|> +## DISCUSSION AND CONCLUSIONS + +<|ref|>text<|/ref|><|det|>[[88, 714, 515, 854]]<|/det|> +We have described a parsimonious 3D stochastic model for transcriptional dynamics based on multivalent binding of factors and polymerases (TFs) to genic and non- genic transcriptional units (TUs) in a chain representing a chromatin fibre. A distinctive feature of our framework is that it is fitting- free, which means the model is truly predictive and can provide a mechanistic understanding of the phenomena we observe. On the other hand, the absence of fitting renders it challenging to obtain a fully quantitative agreement between modelling and experiment. + +<|ref|>image<|/ref|><|det|>[[585, 64, 927, 562]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[540, 577, 968, 851]]<|/det|> +
FIG. 7: Comparison of transcriptional activities of TUs on HSA14 in HUVECs determined using simulations and GRO-seq. A. Workflow (DHS model). Simulations (244 runs) involve a chain (35784 beads) representing HSA14, and 1700 switchable TFs confined in an ellipsoidal territory. Beads are classified as TUs (red, strong-binding), euchromatic (blue, weak-binding) or heterochromatic (grey, non-binding). Transcriptional activities from simulations are compared with those of GRO-seq data, by measuring the Spearman rank correlation. B. (i) Snapshot (TFs not shown). (ii, iii) TU beads and TFs in this configuration. C. Comparison of transcriptional activities of TUs from simulations and GRO-seq (ranked from \(0 - 100\%\) , then binned in quintiles and showed as a heat map). A scatter plot of unbinned ranks of beads corresponding to SEs are superimposed (white circles). D. Comparison of transcriptional activities from simulations (for both DHS and HMM models) and GRO-seq for all 3 kb regions/beads, only TUs, and only connected patches of binding beads (see text). All correlations are significant \((p < 10^{-6}\) , indicated by grey lines). E (i-ii) Capture-HiC-like contact maps obtained from simulations and experiments [42] showing logarithm of number of contacts between 30 kbp bins which contain TUs.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[103, 60, 218, 77]]<|/det|> +## A workflow + +<|ref|>text<|/ref|><|det|>[[105, 81, 319, 94]]<|/det|> +HSA22 wild- type (16,250 beads) + +<|ref|>image<|/ref|><|det|>[[130, 97, 515, 150]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[103, 164, 500, 179]]<|/det|> +B effects on transcription at other sites are: + +<|ref|>text<|/ref|><|det|>[[103, 180, 450, 195]]<|/det|> +i ... widely scattered ii ... small + +<|ref|>image<|/ref|><|det|>[[103, 198, 520, 360]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[106, 363, 520, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[87, 462, 513, 725]]<|/det|> +
FIG. 8: Modelling effects of the DiGeorge deletion in HSA22. A. Workflow. Simulations (800 simulations/condition) for wild type (17102 beads) and deletion (16250 beads, where wild-type beads 6305 - 7156 are cut, corresponding to a deletion of chr22:18912231 - 21465672 in hg19). [Agreement between predicted transcriptional activity and GRO-seq in HSA22 is similar to that found for HSA1 (here, Spearman correlation is \(r \sim 0.29\) , \(p < 10^{-6}\) ]. B. (i) Manhattan plot showing \(-\log_{10}\) (p-value) as a function of genomic position along HSA22 (position given in Mbp), for changes in TU transcriptional activities between wild-type and deletion. (ii) Quantile-quantile plot showing expected versus observed values for \(-\log_{10}\) (p-value) for the same data in (i). Expected values are computed from the normal distribution (these correspond to the null hypothesis according to which the change in transcriptional activities in the deletion is purely due to random variation). C. Regulatory networks of two 3 Mbp segments in chromosome 22 inferred from the Pearson correlation matrix. Edges show positive correlations \(> 0.12\) ( \(p = 0.0007\) ). Segments chosen have roughly the same number of nodes in 3 Mbp as the short fragment (Fig. 3Aii).
+ +<|ref|>text<|/ref|><|det|>[[88, 744, 513, 858], [541, 60, 966, 558]]<|/det|> +In our simulations two types of fibres were considered: a 3 Mbp fragment with randomly- positioned TUs, which is useful to exemplify emerging trends, and human chromosomes 14 and 22 where TUs were appropriately positioned according to bioinformatic data. Despite deliberately excluding any explicit underlying network of biochemical regulation, our model nevertheless yields some notable results. These depend on having a low TF copy- number - a feature compatible with observations in vivo [23]. First, since TFs bind with the same affinity to all TUs, one might expect the latter to all be transcribed similarly, but they are not (Fig. 1). This is largely due to inter- TU spacing; TUs lying close together in 1D sequence space tend to be the most active (Fig. 1C) with positively- correlated dynamics reminiscent of transcriptional bursting (Fig. 2B). This is because they often cluster into structures which are analogous to the phase- separated transcription hubs/factories seen experimentally [7, 10], or to contact domains formed by accessible DNA sites found by high- resolution mapping of chromatin interactions by microC [30]. Second, switching off binding at any TU significantly affects the activity of many others, both near and far away in sequence space (Fig. 4). Third, introducing stable loops has subtle effects (Fig. 5), consistent with the modest changes in expression seen experimentally in cohesin knock- outs and degrons [48]. Fourth, transcriptional activity of a TU is strongly affected by the local environment in ways that are reminiscent of the silencing of a gene by incorporation into heterochromatin [51] (Fig. 6), or activation by embedment in euchromatin (Fig. S4). Fifth, the stochasticity seen in individual simulations reflects that detected by single- cell transcriptomics and single- cell Hi- C. Nevertheless, this variability does not prevent emergence of robust phenotypes in a cell population. Sixth, our simple fitting- free model predicts patterns of transcriptional activity in human chromosomes that promisingly and significantly correlate with experimental GRO- seq data (Fig. 7). This suggests that chromatin structure significantly constrains transcriptional activity. We hypothesise that additional downstream biochemical regulation, not included in our model, may provide a tool to adjust this underlying "structural" pattern of activity in a way which may be required for appropriate biological function. + +<|ref|>text<|/ref|><|det|>[[542, 560, 966, 757]]<|/det|> +Finally, our results enable us to reconcile two conflicting sets of data, namely that regulatory networks are both complex (as GWAS shows that thousands of loci around the genome control complex phenotypes [5, 6]) and simple (as over- expressing just four Yamanaka factors switches cell fate [4]). Thus, our simulations reveal complex small- world networks of mutual up- and down- regulation (Figs. 3 and S8), consistent with GWAS results. However, increasing TF copy- number dramatically simplifies network structure (Fig. 3). We suggest such a simplification occurs when a fibroblast is reprogrammed into a pluripotent stem cell by over- expressing the Yamanaka factors; the high factor concentration simplifies the network so that the factors can combine to switch the phenotype (Fig. S11). + +<|ref|>text<|/ref|><|det|>[[542, 759, 966, 858]]<|/det|> +Taken together, these results suggest the activity - or inactivity - of every genomic region affects that of every other region to some extent. We describe our framework as "pan- genomic" (Fig. S8). This is reminiscent of the omni- . genic model [5, 6] in the sense that many loci are involved, all having small effects. However, it differs as it provides an underlying mechanism for pangenomic effects, by posit + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 60, 515, 319]]<|/det|> +ing a direct and immediate effect of structure on regulation at the transcriptional level, which contrasts with the nontrivial post- transcriptional pathways envisioned by the on- nigenic model. Additionally, our pangenomic model yields a natural framework to qualitatively understand mutually exclusive gene expression, when switching on one gene in a family turns off all others (as in developing olfactory neurons [63]). The current model to explain this phenomenon postulates a coupling between cis- acting up- regulation and trans- acting down- regulation. The pangenomic networks we find provide exactly this type of regulatory interactions (Fig. 3). On the other hand, it is challenging within our current model to account for local negative feedback mechanisms leading to noise reduction or oscillations [11], as these are more likely to arise biochemically (an example is the p53- Mdm2 system which achieves stabilisation of the cellular concentration of p53 via a negative feedback loop [64]). + +<|ref|>text<|/ref|><|det|>[[88, 321, 515, 476]]<|/det|> +In conclusion, we have developed a framework that can be applied to predict the transcriptional activity of any genomic fragment in health or disease (Figs. 7, 8) providing appropriate experimental data is available. Predictive power can be enhanced by incorporating additional TFs, and more suitable datasets of histone marks. Other features which can improve correlations between experiments and simulations are a more accurate modelling of cohesin loop formation by loop extrusion, and of the heteromorphic nature of chromatin [19]. We hope to report on work incorporating the latter two features in the future. + +<|ref|>text<|/ref|><|det|>[[88, 479, 515, 508]]<|/det|> +We thank the European Research Council (ERC CoG 648050 THREEDCELLPHYSICS) for support. + +<|ref|>sub_title<|/ref|><|det|>[[106, 512, 230, 526]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[88, 529, 515, 585]]<|/det|> +The code used for the simulation is LAMMPS, which is publicly available at https://lammps.sandia.gov/. Custom codes written to analyse data are available from the corresponding author upon request. + +<|ref|>sub_title<|/ref|><|det|>[[106, 589, 228, 602]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[88, 606, 515, 650]]<|/det|> +The datasets generated during and/or analysed during the current study are available from the corresponding author upon request. + +<|ref|>sub_title<|/ref|><|det|>[[106, 655, 260, 668]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[88, 671, 515, 744]]<|/det|> +C. 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Genet. 47, 598 (2015).[61] L. A. Mirny, Chromosome Res. 19, 37 (2011).[62] M. Jalbrzikowski, M. T. Lazaro, F. Gao, A. Huang, C. Chow, D. H. Geschwind, G. Coppola, and C. E. Bearden, PLoS ONE 10, e0132542 (2015).[63] A. K. Alsing and K. Sneppen, Nucleic Acids Res. 41, 4755 (2013).[64] S. L. Harris and A. J. Levine, Oncogene 24, 2899 (2005).[65] https://dosage.clinicalgenome.org/clingen_region.cgi?id=ISCA-37446 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 130, 317, 150]]<|/det|> +- Sl.pangenomic.revised.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/images_list.json b/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..306a798d017fb50330c786fb5b42557ff6c949b2 --- /dev/null +++ b/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | The schematical diagram of the presented BSTCM. The BSTCM system integrates STC metasurface into SSVEP-based BCI systems. The STC metasurface can correctly support the visual stimuli for the SSVEP-based BCI and facilitate the information interaction with the external environment. Based on the BSTCM system, high-security encrypted wireless communication systems are realized for the first time by combining with the variant HSKs and VSS method. In this way, smart devices can be controlled by the human mind with high security.", + "footnote": [], + "bbox": [ + [ + 115, + 85, + 880, + 394 + ] + ], + "page_idx": 20 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | The diagram of interaction and data processing procedures of the SSVEP-based BCI system. a. The interaction procedures for the SSVEP-based BCI involve the user wearing an EEG cap and focusing their attention on the intended target LED stimuli. Once the user's selected frequency is recognized, the interaction commands are transmitted to the STC metasurface. b. The feature extraction process includes the transformation of the input BCI signals into the frequency domain. The resulting Signal FFT is then subjected to outer product with pre-defined Template FFTs, yielding eight feature graphs. c. The lightweight classification model. Four convolutional and two fully connected layers were employed. d. An example of frequency responses of SSVEP signals for the four target frequencies, showing distinctive amplitude peaks corresponding to different frequencies.", + "footnote": [], + "bbox": [ + [ + 115, + 84, + 868, + 620 + ] + ], + "page_idx": 21 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | The details of the STC metasurface. a. The geometrical structure of the STC metasurface element. b, c. The reflective amplitude and phase of the element, respectively. d-f. The optimized STC matrices for different scattering angles at different harmonic frequencies. g-i. The far-field results corresponding to the optimized STC matrices. j-m. The encoding schemes for the transmitting symbol “0” or “1” for two users: the symbols “0” and “1” for Users 1 (j and k); the symbols “0” and “1” for Users 2 (l and m).", + "footnote": [], + "bbox": [ + [ + 123, + 96, + 880, + 555 + ] + ], + "page_idx": 22 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | The encrypted wireless communication system based on the BSTCM platform. a. The encrypted encoding scheme combining the harmonic-secret keys and the VSS method. b. The experimental scenarios of the encrypted wireless communication system, in which the operator equipped with EEG cap transmits the encrypted information to two users via the BSTCM platform, respectively. c. The receiving signals corresponding to the encoding scheme in Fig 3(j-m). d, e. The decoded information of VSK1 and VSK2 from the receiving signals based on the encrypted coding scheme. f. The correct transmitting information is decoded by the extracted VSK1 and VSK2.", + "footnote": [], + "bbox": [ + [ + 123, + 92, + 884, + 540 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 | Experiment on the wireless remote mind control of smart devices based on the BSTCM platform. a. The system architecture of wireless remote control. b. The experimental scenarios of the wireless remote-control system by the human brain. c, d. The theoretical and experimental results for far-filed patterns at \\(\\pm 2\\mathrm{th}\\) harmonic frequencies. f. The temporal waveform of the output voltages from four detectors when the operator lights up four devices in sequence. e. The variations between the input power of the detectors and output voltage with respect to the different distances between the receiving antenna and the metasurface center.", + "footnote": [], + "bbox": [ + [ + 128, + 88, + 880, + 428 + ] + ], + "page_idx": 24 + } +] \ No newline at end of file diff --git a/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663.mmd b/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663.mmd new file mode 100644 index 0000000000000000000000000000000000000000..05ac41e2343be74cb64c7bfcbe7d956e16cb76f8 --- /dev/null +++ b/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663.mmd @@ -0,0 +1,239 @@ + +# Secure wireless communication of brain-computer interface and mind control of smart devices enabled by space-time-coding metasurface + +Tie Jun Cui t.jcui@seu.edu.cn + +Southeast University https://orcid.org/0000- 0002- 5862- 1497 + +Qiang Xiao Southeast University + +Lin Han Fan Southeast University + +Qian Ma Southeast University https://orcid.org/0000- 0002- 4662- 8667 + +Yu Ming Ning Southeast University + +Ze Gu Southeast University + +Long Chen Southeast University https://orcid.org/0009- 0007- 1533- 0319 + +Lianlin Li peking university https://orcid.org/0000- 0001- 9394- 3638 + +Jian Wei You Southeast University https://orcid.org/0000- 0001- 5761- 9507 + +Ya Feng Niu Southeast University + +## Article + +Keywords: Space- time- coding metasurface, brain- computer interface, secure wireless communication, human- machine interactions + +Posted Date: August 23rd, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4860006/v1 + +<--- Page Split ---> + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on August 25th, 2025. See the published version at https://doi.org/10.1038/s41467-025-63326-0. + +<--- Page Split ---> + +# Secure wireless communication of brain-computer interface and mind control of smart devices enabled by space-time-coding metasurface + +Qiang Xiao \(^{1,\dagger}\) , Lin Han Fan \(^{2,\dagger}\) , Qian Ma \(^{1,\dagger}\) , \(*\) , Yu Ming Ning \(^{1}\) , Ze Gu \(^{1}\) , Long Chen \(^{1}\) , Lianlin Li \(^{3}\) , Jian Wei You \(^{1}\) , Ya Feng Niu \(^{2,\ast}\) and Tie Jun Cui \(^{1,\ast}\) + +\(^{1}\) State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China + +\(^{2}\) School of Mechanical Engineering, Southeast University, Nanjing 210096, China + +\(^{3}\) State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of + +Electronics, Peking University, 100871 Beijing, China + +\*E- mail: maqian@seu.edu.cn, nyf@seu.edu.cn, tjcui@seu.edu.cn + +\(^{\dagger}\) These authors contributed equally to this work + +## Abstract + +Brain- computer interface (BCI) provides an interconnected pathway between human brain and external devices and paves a potential route for mind manipulations. However, most of existing BCI technologies are based on simple signal transmission and independent of other interface devices, owing to the considerations of reliability and safety of human brain's information interaction in the complicated wireless environment. To address the formidable limitation, we present a brain space- time- coding metasurface (BSTCM) system to deeply fuse the visual stimulation and electromagnetic manipulation for reliable and secure information transfer between human brain and external devices. Here, we innovatively integrate the BCI flashing frame and electromagnetic encoding sequence in the BSTCM system, and the STC metasurface ensures the secure wireless communications by using the harmonic- encrypted beams. We design and fabricate a proof- of- principle demonstration system and experimentally show that the proposed wireless BCI scheme could establish a remote but safeguarded paradigm for human- machine interactions, intelligent metasurfaces, and potential applications in metaverse, as a prominently scrutinized domain in the future 6G wireless communications. + +KEYWORDS: Space- time- coding metasurface, brain- computer interface, secure wireless communication, human- machine interactions + +<--- Page Split ---> + +## Introduction + +Brain- computer interface (BCI) has emerged as a cutting- edge technology in human- machine interaction, and demonstrates promising applications such as metaverse interaction \(^1\) and smart homes \(^2\) . Electroencephalography (EEG) signals remain the predominant input signal modality in BCI systems, with notable implementations including motor imagery \(^3\) , P300 \(^4\) , and steady- state visually evoked potential (SSVEP) \(^5\) . The SSVEP- based BCI systems utilize the brain's SSVEP response to the fixed- frequency visual stimuli for mind recognition and interaction \(^6\) , offering a significant advantage of high information transfer rate (ITR). The BCIs have been widely used in various fields such as augmented reality (AR) \(^7,8\) , spelling input \(^9,10\) , medical rehabilitation and equipment control \(^11,12\) . Recently, some high- performance BCI systems have been continuously proposed \(^13,18\) . The advent and progression of 6G wireless communication technology substantially broaden the application prospects of BCIs, particularly in Internet of Things (IoT), virtual reality (VR) devices, and beyond \(^19,21\) . Hence, ensuring high security and privacy preservation \(^22\) becomes imperative when facilitating intelligent interactions within the constructed communication environment. + +However, most existing BCI systems lack in- depth research in terms of security. During the wireless transmissions of brain signals in a BCI system, there exists a vulnerability to theft and attack, potentially leading to the generation of inaccurate device control commands and unauthorized disclosure of personal privacy. Though some methodologies have been proposed to enhance the security and privacy of data in the BCI systems \(^{23 - 25}\) , there is a conspicuous absence to investigate the encryption mechanisms specifically tailored for the BCI systems. More importantly, the implementation of visual stimulation remains to be isolated from the back- end information processing systems, lacking deep information fusion and interaction. With the increasing demand for high security in the BCI systems, it is essential to develop intelligent interactions in the secure and reliable communication environment. The frequency- dependent SSVEP response and programmable harmonic characteristics of space- time- coding (STC) metasurfaces display notable similarities. Therefore, the STC metasurfaces can be used as a promising method that not only provides the visual stimulation but also ensures security of the BCI systems at the physical layer owing to their powerful ability to flexibly manipulate + +<--- Page Split ---> + +electromagnetic (EM) waves in both time and space domains26. + +The metasurfaces are composed of specific unit structures arranged in periodic or quasi- periodic arrays, which can flexibly control the EM waves at the subwavelength scale and yield a large number of unusual physical phenomena and novel devices27- 30. The proposal of digital coding and programmable metasurfaces has established a profound connection between the EM fields and digital information under the control of a high- speed field programmable gate array (FPGA)31. Recently, the exploration of STC metasurfaces has sparked a surge of research interest due to the excellent ability to manipulate the EM waves and process digital information in both temporal and spatial dimensions32- 37, resulting in many novel physical phenomena that cannot be realized by the traditional spatially modulated metasurfaces. More importantly, the utilization of STC metasurfaces holds the capability to precisely control the amplitudes, phases, and polarizations at different harmonic frequencies independently by specially designing the STC matrices38, which opens up avenues to develop advanced communication schemes with enhanced efficiency and reliability39- 45. Hence, the STC metasurface is a potential candidate for deep information modulation and interaction in the SSVEP- based BCI system owing to its capability to process information and interact with the frequency- dependent visual stimulation. + +In this article, we report a brain space- time- coding metasurface (BSTCM) system, which combines the human brain intelligence with the flexible capability of EM manipulations by the STC metasurface. To the best of our knowledge, this is the first instance to integrate the visual stimulation and EM encoding regulation deeply in a BCI system through metasurface. Here, the STC metasurface is used to effectively support visual stimulation for SSVEP- based BCIs while enabling the information interaction with the external environment. A lightweight deep- learning classification model is implemented to maximize the recognition performance. To enhance the BCI security, we propose a high- security encrypted wireless communication system based on the diverse harmonic regulation characteristics of BSTCM, which effectively combines the variant harmonic- based secret keys (HSKs) and visual secret sharing (VSS) method46- 48. In this way, decoding by eavesdroppers is only possible through the simultaneous interception of confidential information across all harmonic frequencies in the communication process and the acquisition of complex secret keys, which proves the system's exceptional + +<--- Page Split ---> + +levels of security. Finally, smart device control is presented by the BSTCM system, realizing intelligent human- machine interactions. The proposed BSTCM can establish a new paradigm of human machine interactions, metasurface- based wireless communications, and potential applications in metaverse. + +## System configuration + +The proposed BSTCM is schematically demonstrated in Fig. 1, which consists of an STC metasurface, a brain- computer interface based on SSVEP, and an FPGA control module. The metasurface element contains a meta- structure to regulate the EM wave, and a light- emitting diode (LED) stimulator for the SSVEP paradigm. The STC metasurface is divided into four LED flickering partitions, operating at different frequencies: \(8.5\mathrm{Hz}\) , \(10\mathrm{Hz}\) , \(11.5\mathrm{Hz}\) , and \(7\mathrm{Hz}\) . The corresponding SSVEP- based EEG signals can be accurately extracted by a head- worn EEG cap when the operator is exposed to the LED flickering stimulus at distinct frequencies with the assistance of FPGA. To enhance the efficiency and accuracy of the EEG signal identification, we propose a deep learning model to recognize the extracted EEG signals, as shown in Fig. 2c. Upon completion of the identification process, the recognized signals are transmitted to FPGA, enabling to control the STC metasurface to update the corresponding STC matrices in the four LED flickering frequencies without disrupting the EEG stimulation. The BSTCM system empowers individuals to customize and modulate the EM waves by the human mind. + +Leveraging the abundant harmonic modulation capabilities and the advantages of human brain intelligence, the BSTCM system can respectively implement a high- security encrypted wireless communication system and a smart device controlled by human mind. In the high- security encrypted wireless communications, the harmonic frequencies are adopted as the secret keys and the VSS method is employed to encrypt the transmitted information. Firstly, the information is encrypted into two visual secret ciphertexts that depend on the corresponding HSKs. As one of the interface devices in the metaverse, BCI plays a crucial role in enabling individuals to interact in the virtual world, and it requires high- security performance. The presented encrypted wireless communication is envisioned to fulfill the requirement for secure communication in the metaverse. For instance, in a metaverse scenario shown in Fig. 1, a legitimate transmitter (Alice) equipped with an EEG cap intentionally transfers the secret information to two legitimate receivers (Bob and Carol) at two harmonic frequencies based on the encrypted communication. The eavesdropper (Eve) cannot decrypt the transmitted + +<--- Page Split ---> + +information unless she simultaneously obtains the secret keys, two ciphertexts, and encryption mechanisms, which is nearly impossible. Hence, the proposed system ensures high security and concealment by generating a large number of HSKs by modulating the STC metasurface. Ingeniously camouflaged as an LED stimulator, the STC metasurface effectively obstructs the eavesdroppers from detecting the information interaction. On the other hand, the presented system also enables wireless control of smart devices in real environments, allowing for manipulating the devices directly based on the user's brain intention without the requirement of physical actions. Hence the BSTCM can serve as a stimulator for SSVEP, and effectively manipulate the EM waves for secure BCI wireless communications. + +## SSVEP signal recognition + +The EEG signals can be accurately recognized in a BCI system through the detection of SSVEP components. The target flickering stimuli with four frequencies are supported with the STC metasurface integrated with LEDs, and the commands can be output by simply fixating on the corresponding visual stimuli. The STC metasurface is composed of \(32 \times 32\) elements, and segmented into four partitions, with each region consisting of \(16 \times 16\) elements. In these partitions, LEDs operate at four distinct frequencies (8.5Hz, 10Hz, 11.5Hz, and 7Hz) and are implemented to evoke four distinct SSVEP signals. As depicted in Fig. 2a, the SSVEP- BCI is segmented into three distinct stages: preparation stage, signal acquisition stage, and signal recognition stage. During the preparation stage, the participant wears an EEG cap equipped with electrodes positioned over the O1 and O2 regions and focuses the attention on one of the regions on the STC metasurface. In the signal acquisition stage, the participant maintains their gaze while an EEG amplifier collects the EEG data (for details, see Supplementary Note 1). Fig. 2d illustrates the frequency distribution of the EEG signals for the four target frequencies after the FFT analysis (prefiltered using a Butterworth bandpass filter with a frequency range of 5- 40Hz). As illustrated in Fig. 2b, the signal undergoes an initial pre- processing procedure during the signal recognition stage, involving signal decomposition, spectrum calculation and weighted summation, output signal amplitude spectrum (SigSpec). Subsequently, an outer product operation is executed with the pre- calculated reference spectra (RefSpec), yielding eight feature graphs (comprising of twochannel and four target frequencies), each sized by \(160 \times 160\) pixels. A discernible square grid pattern emerges in the feature graph corresponding to the frequency of the anticipated classification result (for additional information, refer to Supplemental Information Notes 2- 5). The conventional SSVEP classification and recognition algorithms include canonical correlation analysis (CCA) \(^{49}\) , filter bank canonical correlation + +<--- Page Split ---> + +analysis (FBCCA) \(^{50}\) , task- related component analysis (TRCA) \(^{9}\) , support vector machine (SVM) \(^{51}\) , and convolutional neural network (CNN) \(^{52}\) . However, some limitations still exist in the above- mentioned algorithms. Here, we propose a deep learning classification algorithm that embeds the SSVEP components in the signal recognition stage (refer to Supplemental Information Note 6). The above- mentioned eight feature graphs are input into a lightweight CNN classification model containing four convolutional and two liner layers, as shown in Fig. 2c. The feature graphs are initially processed through a convolutional input layer. Subsequently, a MaxPooling operation with a \(2 \times 2\) kernel is applied following the activation of the data via the ReLU function. To facilitate the propagation of original data, a residual connection is strategically integrated between the second and third convolutional layers. Each convolutional layer is followed by the same \(2 \times 2\) MaxPooling operation and ReLU activation. Upon the extraction of features from the eight feature graphs, a series of two successive linear layers are deployed to categorize the features into four distinct target frequencies. Finally, an output layer with sigmoid activation is employed to reshape the output of the model into 4 confidence scores. Ultimately, the model achieved a classification accuracy of \(96.67\%\) on the validation set, with precision and recall rates surpassing \(90\%\) . Such results underscore the efficacy of the SSVEP component classification algorithm proposed in this work. + +## Design of STC metasurface + +Fig. 3a showcases the detailed configuration of the 1- bit STC metasurface element integrated with an LED, which consists of three metallic layers and multiple substrate layers. The top metallic layer has two slotted rectangular patches with a width of \(w = 7.3 \mathrm{mm}\) and a length of \(l = 11 \mathrm{mm}\) , in which the middle slot has a width of \(w_{1} = 1.5 \mathrm{mm}\) and a length of \(l_{1} = 7 \mathrm{mm}\) . The outer metal frame with a width of \(0.15 \mathrm{mm}\) is used to weaken the coupling effect between elements. The top F4B dielectric substrate has a relative permittivity of 2.65 and a loss tangent of 0.003 with the thickness \(h = 3 \mathrm{mm}\) and period \(p = 19 \mathrm{mm}\) . Two PIN diodes are serially loaded on the middle of two rectangular patches, and the two rectangular patches connected with metallic bars serve as positive and negative poles, in which two inductors are used as radio- frequency (RF) chokes to effectively isolate the RF signal from direct current (DC). An LED serving as the flash stimulation is placed outside the metallic patches to avoid affecting the scattering performance of the meta- atom and shares the same voltage with PIN diodes. To evaluate the performance of the 1- bit meta- atom, full- wave simulations are conducted using the commercial software CST Microwave Studio. As shown in Figs. 3b and 3c, the scattering characteristics of the meta- atom can be altered by switching the states of two PIN diodes + +<--- Page Split ---> + +corresponding to distinct RLC equivalent circuit models, hence realizing the 1- bit phase regulation. The diodes with OFF state are assigned as coding “0”, and ON state as coding “1”. When the meta-atom is subjected to normal illumination by a \(y\) - polarized plane wave, a phase difference of \(180^{\circ}\) between “0” and “1” states is observed at approximately \(6.9\mathrm{GHz}\) , and both states exhibit reflective amplitudes exceeding - 1 dB, indicating excellent performance of the 1- bit programmable meta-atom. Hence the designed meta-atom can be used to build the 1- bit STC metasurface. The detailed prototype design, modeling and characterization are given in Methods and Supplementary Note 9. + +To establish a dual- user encrypted wireless communication system, we implement an amplitude- shift keying modulation scheme using the STC metasurface, enabling simultaneous encrypted transmissions via space and frequency multiplexing. The control voltages, originating from FPGA, are applied to the PIN diodes, enabling periodic switching to the reflection phases of the 1- bit STC element with a time period of \(T_0 = 1 / f_0\) . Consequently, each STC element possesses a periodic time- coding sequence and constitutes part of an STC matrix, making it possible to flexibly control the EM waves in both space and time domains. The flexibility of STC matrices can generate a series of new harmonic spectra at the frequency interval of \(f_0\) , which are further optimized with specific requirements (see Supplementary Notes 7 and 8). The space- time modulation enables abundant harmonic characteristics to construct harmonic- based encrypted wireless communications. Using the 2D STC matrices, we can modulate the EM waves to steer to the desired directions for target users. The STC matrices encompass 16 rows of elements, each possessing periodic time- coding sequences with 11 intervals. As illustrative examples, three radiation patterns corresponding to three STC matrices (M0, M1, and M2) are shown in Fig. 3. The radiation pattern of the optimized STC matrix M1 in Fig. 3e is strategically manipulated to yield a main beam with a deflected angle of \(\theta = - 15^{\circ}\) at the \(+1\) st harmonic frequency, as showcased in Fig. 3h. Similarly, the radiation pattern of the STC matrix M2 (Fig. 3f) is optimized to direct the beam towards the angle \(\theta = +30^{\circ}\) , in correspondence with the - 1st harmonic frequency, as demonstrated in Fig. 3i. Furthermore, the time- invariant STC matrix M0 (Fig. 3d) facilitates the positioning of main beam towards a direction of \(\theta = 0^{\circ}\) , associated with the fundamental frequency, as illustrated in Fig. 3g. As an example of amplitude- shift keying modulation scheme, the left panels ( \(f_1 = 8.5\mathrm{Hz}\) and \(f_2 = 10\mathrm{Hz}\) ) of the STC metasurface are utilized to send digital symbols “0” and “1” to User1 at the position of \(\theta = - 15^{\circ}\) when the transmitting information is encoded as binary data streams. The right panels ( \(f_3 = 11.5\mathrm{Hz}\) and \(f_4 = 7\mathrm{Hz}\) ) of the STC metasurface are utilized to send digital + +<--- Page Split ---> + +symbols “0” and “1” to User2 at the position of \(\theta = +30^{\circ}\) . Experiments are carried out in a microwave anechoic chamber to measure the far- field radiation patterns of the STC metasurface, as illustrated in Supplementary Notes 10 and 11. + +Constrained by the requirement of the SSVEP- based BCI to gather data and detect EEG signals at a single frequency, a viable encoding strategy is proposed to enhance the symbol error rate (SER), as depicted in Fig. 3j- m. The transmission of the digital symbols “0/1” involves to encode a series of repeated data frames with a period of 4 frames as synchronous frames. In the encoding scheme, the first bit “1” serves as the start bit, signaling the initiation of the transmission process. The second bit signifies the harmonic frequency (+1st or - 1st) used for transmission, and the third bit corresponds to the transmission of the data itself. Finally, the fourth bit functions as the stop bit, indicating the end to transmit one data frame. Based on the encoding process, the digital symbol “0” or “1” for User1 is encoded as the repeated data frames “10001000” or “10101010...”, respectively. Similarly, the digital symbol “0/1” to User2 is encoded as repeated data frames “11001100...” or “11101110...”, respectively. Hence, the STC matrix M1 operating with a switching frequency \(f_0\) , is precisely injected into the region of high amplitude associated with the flicker frequencies \(f_1\) and \(f_2\) , as illustrated in Fig. 3j and 3k. The STC matrices are updated when the information is sent to User1 via the BSTCM system. Similarly, the STC matrix M2 with a period switch frequency \(f_0\) , undergoes injection into the high amplitude attributed to the flicker frequencies \(f_3\) and \(f_4\) , as displayed in Fig. 3j and 3k, representing the transmission of information to User2. Remarkably, the update of STC matrices does not affect the flickering frequency of LEDs used for SSVEP stimuli, ensuring that BCI signals can be synchronously detected and recognized. + +## Secure encrypted wireless communication system + +The conventional VSS method is employed to encrypt the individual pixels of confidential information, where each pixel is divided into two sub- pixels. It is worth noting that each share derived from this process remains devoid of any discernible information pertaining to the original image content. As a result, a secret message possesses two visual shared keys (VSKs), which effectively guarantee that the disclosure of any single VSK does not compromise the confidentiality of the original secret information. Here, we present a novel encryption strategy by combining the harmonic characteristics of STC metasurface with the VSS method, which is designed to realize high- security encrypted wireless communication directly controlled by the + +<--- Page Split ---> + +human mind. In the presented encryption strategy, two VSKs are not simply stacked with each other to recover the original information, but rely on HSKs. In the described encryption process, the confidential information to be transmitted is firstly encrypted to two random VSKs based on the presented encrypted encoding scheme. Then, two generated VSKs are further encoded into two random HSKs for the \(\pm 1\) harmonic frequencies, respectively. During decryption, two VSKs attached to each secret can be reconstructed with the pre- customized variant HSK sequences. The details of encryption and decryption methods can be found in Supplementary Note 12. The target image in question can be decrypted through the incoherent superposition of two extracted VSKs, aided by the presence of HSKs. By imbuing addressable shared information bits with the harmonic dynamics, a robust level of information security is achieved. As an illustrative example in Fig. 4a, the image “H”, which consists of \(5 \times 5\) grid of black or white pixels, is encrypted into two random VSK1 and VSK2 based on the presented encryption method. Two bitstream sequences of VSKs can be successively transmitted to two designated Users (Bob and Carol) by the BCI operator (Alice) using the presented BSTCM system. + +To validate the encrypted communication scheme, we experimentally set up an encrypted wireless communication system using the BSTCM, as demonstrated in Fig. 4b. The prototype of 1- bit STC metasurface is used to construct the encrypted communication system, as shown in Supplementary Figure 15. A commercial BCI device (actiCHamp of Brain Product GmbH company) composed of an EEG cap and amplifier is used to acquire the EEG signals when the operator stares at different partitions on the STC metasurface. The corresponding EEG signals can be accurately recognized by the proposed deep learning algorithm. The detailed description of the algorithm is provided in Supplementary Note 6. A linearly polarized horn antenna, connected to a signal generator (Keysight E8267D), is placed at a vertical distance of 4 m away from the metasurface center to excite a monochromatic plane wave at the frequency of 6.9 GHz. The receiving terminals are composed of two horn antennas (severed as two users) and a spectrum analyzer (Keysight N9040D), which is used to demodulate the received signal. Two receiving horn antennas are located at the angles of \(\theta = - 15^{\circ}\) and \(\theta = +30^{\circ}\) with relative to the metasurface normal, respectively. In the transmission process, the information undergoes encryption, resulting in the generation of two harmonic- based cipher texts according to the + +<--- Page Split ---> + +encryption scheme. The transmission process is achieved by ensuring that the BCI operator maintains direct visual focus on the STC metasurface in alignment with the binary cipher texts. In addition, the system enables prompt acquisition, recognition, and translation of EEG signals into appropriate control signals of FPGA, which in turn drive the STC metasurface. As a result, the accurate transmission of binary cipher texts is accomplished, facilitating reliable and efficient communications between the BCI operator and the intended recipients. + +In the receiving process, the radiated signals propagating through free space are received individually by the two horn antennas, and these signals are subsequently demodulated using the spectrum analyzer. As shown in Fig. 4c, the demodulated signals represent four encoding schemes that correspond to different digital symbols ("0" or "1") for the two users and then are processed to the decoded results shown in Fig. 4d by the FFT method. The decoded results clearly exhibit four repeated data frames, providing strong evidence to support the capability of the proposed system to recover the transmitting information successfully. Hence two binary cipher texts are transmitted to two specific users by the BCI operator based on the proposed wireless communication system. The transmitted information is received and the measured results are comprehensively showcased in Supplementary Note 13 and Supplementary Video 1. By employing the encoding method described earlier, the data streams corresponding to the specific users are extracted from the received signals, as depicted in Figs. 4e- f. Subsequently, these data streams are decrypted accurately by the method outlined in Supplementary Note 12. The deciphered information is displayed in Fig. 4g. The successful decryption and retrieval of the transmitted information affirm the robustness and efficacy of the proposed system in maintaining high levels of security and confidentiality. The system is not easily deciphered or cracked, proving its ability to protect sensitive information during transmissions. + +## Mind control to smart devices + +To experimentally verify the performance of the proposed BSTCM system, we conducted experiments to show the remote control capabilities of smart devices through human intention, which holds significant potential to enhance the quality of life for individuals with disabilities. + +<--- Page Split ---> + +The system flowchart depicted in Fig. 5a outlines the key steps involved in the experimental setup. Initially, the EEG signals are acquired and subsequently processed by the BSTCM system for command recognition. The recognized commands are then translated to update the STC matrices of metasurface by an FPGA. The four partitions of the STC metasurface are controlled by FPGA to generate different beams at four deflected angles corresponding to the four harmonic frequencies, enabling the remote control of smart devices. Notably, the main beam at the - 2nd harmonic frequency exhibits a pronounced peak in the direction of \(\theta = +10^{\circ}\) , corresponding to the device assigned to User 3. Conversely, the main beam at the +2nd harmonic frequency demonstrates a strong peak at \(\theta = - 45^{\circ}\) , corresponding to the device assigned to User 1. As displayed in Figs. 5c and d, the measured radiation patterns exhibit good consistency with theoretical predictions associated to the corresponding STC matrices. Additionally, the main beams were deflected at the other two angles (- 15° and 30°) using the +1st and - 1st harmonic frequencies (Users 2 and 4), as shown in Figs. 3h and i. The excellent harmonic beam manipulation of the STC metasurface lays a solid theory foundation for smart device control. The receiving terminals are composed of four horn antennas, four RF energy detectors (LMH2110), four Microcontroller Unit (MCU) modules (Arduino), and four LED modules (serve as smart devices), as depicted in Fig. 5b. The RF energy from harmonic beams captured by the detectors can be converted into DC voltages, which are subsequently fed into the MCU modules. Once the corresponding DC voltage is detected, the MCU modules send the control commands to activate the corresponding LED module, thereby implementing the remote control of the smart devices. The experimental setup is shown in Fig. 5b, where the STC metasurface is illuminated by a monochromatic frequency signal of 6.9GHz. + +To verify the robustness of the brain- control smart devices system, the position of the receiving antenna is sequentially placed at different distances from the metasurface center while maintaining specific harmonic direction angles with respect to the metasurface normal. The detection of RF energy from the emitted harmonic beams is carried out across varying distances and the corresponding DC voltages can be accurately converted by the RF detectors, as illustrated in Fig. 5e (see Supplementary Note 14 for details). We observe that the RF energies of four users relatively decrease and the converted DC voltages also decrease accordingly as + +<--- Page Split ---> + +the distance increases. To guarantee the attainment of the far- field region and maintain system stability, the four receiving antennas are positioned at a distance of approximately \(300\mathrm{cm}\) from the metasurface. Consequently, the detection voltage threshold is set as \(0.15\mathrm{V}\) , \(0.4\mathrm{V}\) , \(0.4\mathrm{V}\) , and \(0.2\mathrm{V}\) in MCU modules, respectively. We recorded a video of the brain- controlled multiple smart devices, as presented in Supplementary Movie 2. The sequence diagrams shown in Fig. 5f provide a clear visual representation of the sequential control process, where the smart devices (LED modules) are successively controlled through human intention using the BSTCM system. The experiment proves the feasibility of brain- controlled smart devices. By harnessing the power of BCI technology, individuals can exert control over smart devices without needing physical interaction. The BSTCM system enables seamless and intuitive manipulations of various smart devices, facilitating greater independence and convenience for people in daily life. The experimental results serve as a compelling validation supporting the significant benefits that the BSTCM can offer in improving the quality of life for individuals facing physical challenges. + +## Conclusions + +We presented a BSTCM system, which combines the human brain intelligence and flexible EM control capabilities of the STC metasurface. A series of visual stimuli encoded by specific coding schemes was employed on the metasurface to realize deep information processing and interaction in the EM field for reliable and stable communication environments. In addition, a lightweight deep- learning algorithm was proposed to accurately recognize the brain signals extracted by the BCI device. We further demonstrated a high- security encrypted wireless communication system based on diverse harmonic frequency characteristics, which combines the variant HSKs and VSS methods. The proposed system can enable the system to have high security. Finally, remote control of smart devices was demonstrated by the BSTCM system, realizing intelligent human- machine interactions. The proposed BSTCM, integrating the EM manipulation capability with the intelligence of the human brain, provides a new paradigm of interactions among human machine interface in metaverse, and future 6G wireless communication applications. + +<--- Page Split ---> + +## Methods and materials + +Details on STC metasurface. A metasurface prototype was fabricated by the standard printed circuit board (PCB) process. The entire STC metasurface is composed of \(32 \times 32\) digital elements and an effective size of \(608 \times 608 \mathrm{~mm}^2\) . For the convenience of processing and welding, the entire metasurface was disassembled into 8 sub- metasurfaces with \(8 \times 16\) digital elements and an effective size of \(152 \times 304 \mathrm{~mm}^2\) . A total number of \(2 \times 32 \times 32\) PIN diodes (SMP1320- 079LF from SKYWORKS) and RF inductors of \(10 \mathrm{nH}\) were elaborately welded on the metasurfaces with the surface mount technology, which is a mature engineering method to weld small components on PCB using the batch solder- reflow processes in a dedicated machine. Meanwhile, an LED is embedded into each metasurface element. + +The brain signal acquisition. As depicted in Fig. 2a, the SSVEP- BCI is delineated into three discrete distinct stages: the preparation stage, the signal acquisition stage, and the signal recognition stage. The EEG data in this study were acquired using the actiCHamp EEG amplifier manufactured by Brain Products GmbH. This amplifier offers a maximum capacity of 64 channels, 24- bit precision, and a sampling rate of \(100 \mathrm{kHz}\) . It exhibits a high common mode rejection ratio (CMRR) of 100, making it suitable for capturing and detecting SSVEP signals effectively. This study uses a standard 10- 20 EEG cap to obtain the signal. Since SSVEP is a local potential that predominantly originates from the occipital lobe \(^5\) , the O1 and O2 electrodes located on the occipital region were selected for EEG data acquisition, with a reference electrode placed at the vertex (Cz) region. The sample time in this study was set to four seconds. With a sampling rate of \(512 \mathrm{~Hz}\) and excluding the reference electrode data, each sample yielded 4096 sampling points (4 seconds \(\times 512 \mathrm{~Hz} \times 2\) channels). Square wave signals were utilized to present visual stimuli on the LEDs of the STC metasurface units, which emitted the red light, thereby ensuring adequate visual contrast. To minimize the harmonic interference, four target visual stimulus frequencies were: top- left at \(8.5 \mathrm{~Hz}\) , bottom- left at \(10 \mathrm{~Hz}\) , top- right at \(11.5 \mathrm{~Hz}\) , and bottom- right at \(7 \mathrm{~Hz}\) . To mitigate mutual interference between two closely spaced flickering stimuli pairs, the frequencies \(7 \mathrm{~Hz}\) and \(8.5 \mathrm{~Hz}\) , as well as \(10 \mathrm{~Hz}\) and \(11.5 \mathrm{~Hz}\) , + +<--- Page Split ---> + +were placed diagonally opposite to one another. The example of the collected brain wave signal data for four target frequencies is shown in Supplementary Figure 1. Data processing and model training were conducted in Python and PyTorch environments. Signal processing primarily involved utilizing scipy package for filtering and FFT calculations. Given that neither the absolute value of the EEG signal nor its temporal characteristics were pertinent to this study, all sampled signals were linearly transformed to have a mean of zero and a standard deviation of one. + +Classification model training. Initially, a dataset comprising 240 instances of raw SSVEP signals was collected from two participants. Subsequently, a subset consisting of 80 instances of randomly selected raw signals was employed to create RefSpec. The remaining 160 instances of raw signals were randomly selected to serve as training and validation datasets for classification model training. The two subsets have an equally divided number of signals across four stimuli frequencies. As for the training and validation dataset, a subset of 60 instances was extracted from the initial subset of 160 instances to serve as the validation set. The remaining 100 instances underwent a data augmentation process, which resulted in an expanded dataset of 1700 instances. These augmented instances constituted the training set, which was then utilized to train the model for 300 epochs. The validation operation was executed every two training epochs. + +## Acknowledgements + +The work is supported by the National Natural Science Foundation of China (62288101, 92167202, 72171044), the Major Project of Natural Science Foundation of Jiangsu Province (BK20212002), the State Key Laboratory of Millimeter Waves, Southeast University, China (K201924), the Fundamental Research Funds for the Central Universities (2242018R30001), the 111 Project (111- 2- 05), the China Postdoctoral Science Foundation (2021M700761), and ZhiShan Scholar Program of Southeast University (2242022R40004). + +## Author contributions + +<--- Page Split ---> + +T.J.C. suggested the designs and planned and supervised the work, in consultation with Q.M., Y.F.N., Q.X., and J.W.Y. conceived the idea and carried out the theoretical analysis and numerical simulations. Q.X., L.H.F., Y.M.N., and Z.G. built the system and performed the experimental measurements. Q.X., L.H.F. and L.C. performed the data analysis. Q.X. and L.H.F. wrote the manuscript. Q.M., L.L., J.W. Y, and T.J.C. reviewed the manuscript. All authors discussed the theoretical aspects and numerical simulations, interpreted the results and reviewed the manuscript. + +## Competing interests + +The authors declare no competing financial interest. + +## Data availability + +The data that support the findings of this study are available from the corresponding author upon request. + +## Code availability + +The code that supports the findings of this study are available from the corresponding author upon reasonable request. + +## References + +Wu, D. et al. Virtual-Reality Interpromotion Technology for Metaverse: A Survey. IEEE Internet of Things Journal 10, 15788- 15809, doi:10.1109/jiot.2023.3265848 (2023). Park, S., Ha, J., Park, J., Lee, K. & Im, C.- H. Brain- Controlled, AR- Based Home Automation System Using SSVEP- Based Brain- Computer Interface and EOG- Based Eye Tracker: A Feasibility Study for the Elderly End User. 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Fig. 1 | The schematical diagram of the presented BSTCM. The BSTCM system integrates STC metasurface into SSVEP-based BCI systems. The STC metasurface can correctly support the visual stimuli for the SSVEP-based BCI and facilitate the information interaction with the external environment. Based on the BSTCM system, high-security encrypted wireless communication systems are realized for the first time by combining with the variant HSKs and VSS method. In this way, smart devices can be controlled by the human mind with high security.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 | The diagram of interaction and data processing procedures of the SSVEP-based BCI system. a. The interaction procedures for the SSVEP-based BCI involve the user wearing an EEG cap and focusing their attention on the intended target LED stimuli. Once the user's selected frequency is recognized, the interaction commands are transmitted to the STC metasurface. b. The feature extraction process includes the transformation of the input BCI signals into the frequency domain. The resulting Signal FFT is then subjected to outer product with pre-defined Template FFTs, yielding eight feature graphs. c. The lightweight classification model. Four convolutional and two fully connected layers were employed. d. An example of frequency responses of SSVEP signals for the four target frequencies, showing distinctive amplitude peaks corresponding to different frequencies.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 | The details of the STC metasurface. a. The geometrical structure of the STC metasurface element. b, c. The reflective amplitude and phase of the element, respectively. d-f. The optimized STC matrices for different scattering angles at different harmonic frequencies. g-i. The far-field results corresponding to the optimized STC matrices. j-m. The encoding schemes for the transmitting symbol “0” or “1” for two users: the symbols “0” and “1” for Users 1 (j and k); the symbols “0” and “1” for Users 2 (l and m).
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 | The encrypted wireless communication system based on the BSTCM platform. a. The encrypted encoding scheme combining the harmonic-secret keys and the VSS method. b. The experimental scenarios of the encrypted wireless communication system, in which the operator equipped with EEG cap transmits the encrypted information to two users via the BSTCM platform, respectively. c. The receiving signals corresponding to the encoding scheme in Fig 3(j-m). d, e. The decoded information of VSK1 and VSK2 from the receiving signals based on the encrypted coding scheme. f. The correct transmitting information is decoded by the extracted VSK1 and VSK2.
+ +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5 | Experiment on the wireless remote mind control of smart devices based on the BSTCM platform. a. The system architecture of wireless remote control. b. The experimental scenarios of the wireless remote-control system by the human brain. c, d. The theoretical and experimental results for far-filed patterns at \(\pm 2\mathrm{th}\) harmonic frequencies. f. The temporal waveform of the output voltages from four detectors when the operator lights up four devices in sequence. e. The variations between the input power of the detectors and output voltage with respect to the different distances between the receiving antenna and the metasurface center.
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryMaterials.pdf SupplementaryVideos.zip + +<--- Page Split ---> diff --git a/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663_det.mmd b/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..87c36741341bbb476b2109b096e944252910c64e --- /dev/null +++ b/preprint/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663/preprint__0996185957de22ec96c13ddd4eeca9080a098f778fc62996a2333ee389185663_det.mmd @@ -0,0 +1,304 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 952, 208]]<|/det|> +# Secure wireless communication of brain-computer interface and mind control of smart devices enabled by space-time-coding metasurface + +<|ref|>text<|/ref|><|det|>[[44, 230, 235, 275]]<|/det|> +Tie Jun Cui t.jcui@seu.edu.cn + +<|ref|>text<|/ref|><|det|>[[44, 303, 598, 323]]<|/det|> +Southeast University https://orcid.org/0000- 0002- 5862- 1497 + +<|ref|>text<|/ref|><|det|>[[44, 328, 238, 368]]<|/det|> +Qiang Xiao Southeast University + +<|ref|>text<|/ref|><|det|>[[44, 374, 238, 414]]<|/det|> +Lin Han Fan Southeast University + +<|ref|>text<|/ref|><|det|>[[44, 420, 598, 460]]<|/det|> +Qian Ma Southeast University https://orcid.org/0000- 0002- 4662- 8667 + +<|ref|>text<|/ref|><|det|>[[44, 466, 238, 506]]<|/det|> +Yu Ming Ning Southeast University + +<|ref|>text<|/ref|><|det|>[[44, 512, 238, 551]]<|/det|> +Ze Gu Southeast University + +<|ref|>text<|/ref|><|det|>[[44, 558, 598, 598]]<|/det|> +Long Chen Southeast University https://orcid.org/0009- 0007- 1533- 0319 + +<|ref|>text<|/ref|><|det|>[[44, 604, 565, 644]]<|/det|> +Lianlin Li peking university https://orcid.org/0000- 0001- 9394- 3638 + +<|ref|>text<|/ref|><|det|>[[44, 650, 598, 690]]<|/det|> +Jian Wei You Southeast University https://orcid.org/0000- 0001- 5761- 9507 + +<|ref|>text<|/ref|><|det|>[[44, 696, 238, 736]]<|/det|> +Ya Feng Niu Southeast University + +<|ref|>sub_title<|/ref|><|det|>[[44, 780, 104, 798]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 818, 933, 860]]<|/det|> +Keywords: Space- time- coding metasurface, brain- computer interface, secure wireless communication, human- machine interactions + +<|ref|>text<|/ref|><|det|>[[44, 879, 322, 898]]<|/det|> +Posted Date: August 23rd, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 916, 475, 935]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4860006/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 916, 87]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 105, 535, 125]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 160, 936, 204]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on August 25th, 2025. See the published version at https://doi.org/10.1038/s41467-025-63326-0. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[67, 88, 872, 140]]<|/det|> +# Secure wireless communication of brain-computer interface and mind control of smart devices enabled by space-time-coding metasurface + +<|ref|>text<|/ref|><|det|>[[113, 155, 860, 195]]<|/det|> +Qiang Xiao \(^{1,\dagger}\) , Lin Han Fan \(^{2,\dagger}\) , Qian Ma \(^{1,\dagger}\) , \(*\) , Yu Ming Ning \(^{1}\) , Ze Gu \(^{1}\) , Long Chen \(^{1}\) , Lianlin Li \(^{3}\) , Jian Wei You \(^{1}\) , Ya Feng Niu \(^{2,\ast}\) and Tie Jun Cui \(^{1,\ast}\) + +<|ref|>text<|/ref|><|det|>[[115, 215, 872, 255]]<|/det|> +\(^{1}\) State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China + +<|ref|>text<|/ref|><|det|>[[115, 261, 707, 279]]<|/det|> +\(^{2}\) School of Mechanical Engineering, Southeast University, Nanjing 210096, China + +<|ref|>text<|/ref|><|det|>[[115, 284, 832, 302]]<|/det|> +\(^{3}\) State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of + +<|ref|>text<|/ref|><|det|>[[115, 307, 505, 323]]<|/det|> +Electronics, Peking University, 100871 Beijing, China + +<|ref|>text<|/ref|><|det|>[[115, 329, 582, 345]]<|/det|> +\*E- mail: maqian@seu.edu.cn, nyf@seu.edu.cn, tjcui@seu.edu.cn + +<|ref|>text<|/ref|><|det|>[[115, 352, 456, 368]]<|/det|> +\(^{\dagger}\) These authors contributed equally to this work + +<|ref|>sub_title<|/ref|><|det|>[[115, 400, 195, 415]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[113, 421, 884, 802]]<|/det|> +Brain- computer interface (BCI) provides an interconnected pathway between human brain and external devices and paves a potential route for mind manipulations. However, most of existing BCI technologies are based on simple signal transmission and independent of other interface devices, owing to the considerations of reliability and safety of human brain's information interaction in the complicated wireless environment. To address the formidable limitation, we present a brain space- time- coding metasurface (BSTCM) system to deeply fuse the visual stimulation and electromagnetic manipulation for reliable and secure information transfer between human brain and external devices. Here, we innovatively integrate the BCI flashing frame and electromagnetic encoding sequence in the BSTCM system, and the STC metasurface ensures the secure wireless communications by using the harmonic- encrypted beams. We design and fabricate a proof- of- principle demonstration system and experimentally show that the proposed wireless BCI scheme could establish a remote but safeguarded paradigm for human- machine interactions, intelligent metasurfaces, and potential applications in metaverse, as a prominently scrutinized domain in the future 6G wireless communications. + +<|ref|>text<|/ref|><|det|>[[115, 842, 881, 880]]<|/det|> +KEYWORDS: Space- time- coding metasurface, brain- computer interface, secure wireless communication, human- machine interactions + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 93, 247, 112]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[113, 123, 885, 505]]<|/det|> +Brain- computer interface (BCI) has emerged as a cutting- edge technology in human- machine interaction, and demonstrates promising applications such as metaverse interaction \(^1\) and smart homes \(^2\) . Electroencephalography (EEG) signals remain the predominant input signal modality in BCI systems, with notable implementations including motor imagery \(^3\) , P300 \(^4\) , and steady- state visually evoked potential (SSVEP) \(^5\) . The SSVEP- based BCI systems utilize the brain's SSVEP response to the fixed- frequency visual stimuli for mind recognition and interaction \(^6\) , offering a significant advantage of high information transfer rate (ITR). The BCIs have been widely used in various fields such as augmented reality (AR) \(^7,8\) , spelling input \(^9,10\) , medical rehabilitation and equipment control \(^11,12\) . Recently, some high- performance BCI systems have been continuously proposed \(^13,18\) . The advent and progression of 6G wireless communication technology substantially broaden the application prospects of BCIs, particularly in Internet of Things (IoT), virtual reality (VR) devices, and beyond \(^19,21\) . Hence, ensuring high security and privacy preservation \(^22\) becomes imperative when facilitating intelligent interactions within the constructed communication environment. + +<|ref|>text<|/ref|><|det|>[[113, 514, 885, 895]]<|/det|> +However, most existing BCI systems lack in- depth research in terms of security. During the wireless transmissions of brain signals in a BCI system, there exists a vulnerability to theft and attack, potentially leading to the generation of inaccurate device control commands and unauthorized disclosure of personal privacy. Though some methodologies have been proposed to enhance the security and privacy of data in the BCI systems \(^{23 - 25}\) , there is a conspicuous absence to investigate the encryption mechanisms specifically tailored for the BCI systems. More importantly, the implementation of visual stimulation remains to be isolated from the back- end information processing systems, lacking deep information fusion and interaction. With the increasing demand for high security in the BCI systems, it is essential to develop intelligent interactions in the secure and reliable communication environment. The frequency- dependent SSVEP response and programmable harmonic characteristics of space- time- coding (STC) metasurfaces display notable similarities. Therefore, the STC metasurfaces can be used as a promising method that not only provides the visual stimulation but also ensures security of the BCI systems at the physical layer owing to their powerful ability to flexibly manipulate + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 629, 107]]<|/det|> +electromagnetic (EM) waves in both time and space domains26. + +<|ref|>text<|/ref|><|det|>[[113, 115, 884, 528]]<|/det|> +The metasurfaces are composed of specific unit structures arranged in periodic or quasi- periodic arrays, which can flexibly control the EM waves at the subwavelength scale and yield a large number of unusual physical phenomena and novel devices27- 30. The proposal of digital coding and programmable metasurfaces has established a profound connection between the EM fields and digital information under the control of a high- speed field programmable gate array (FPGA)31. Recently, the exploration of STC metasurfaces has sparked a surge of research interest due to the excellent ability to manipulate the EM waves and process digital information in both temporal and spatial dimensions32- 37, resulting in many novel physical phenomena that cannot be realized by the traditional spatially modulated metasurfaces. More importantly, the utilization of STC metasurfaces holds the capability to precisely control the amplitudes, phases, and polarizations at different harmonic frequencies independently by specially designing the STC matrices38, which opens up avenues to develop advanced communication schemes with enhanced efficiency and reliability39- 45. Hence, the STC metasurface is a potential candidate for deep information modulation and interaction in the SSVEP- based BCI system owing to its capability to process information and interact with the frequency- dependent visual stimulation. + +<|ref|>text<|/ref|><|det|>[[112, 533, 884, 887]]<|/det|> +In this article, we report a brain space- time- coding metasurface (BSTCM) system, which combines the human brain intelligence with the flexible capability of EM manipulations by the STC metasurface. To the best of our knowledge, this is the first instance to integrate the visual stimulation and EM encoding regulation deeply in a BCI system through metasurface. Here, the STC metasurface is used to effectively support visual stimulation for SSVEP- based BCIs while enabling the information interaction with the external environment. A lightweight deep- learning classification model is implemented to maximize the recognition performance. To enhance the BCI security, we propose a high- security encrypted wireless communication system based on the diverse harmonic regulation characteristics of BSTCM, which effectively combines the variant harmonic- based secret keys (HSKs) and visual secret sharing (VSS) method46- 48. In this way, decoding by eavesdroppers is only possible through the simultaneous interception of confidential information across all harmonic frequencies in the communication process and the acquisition of complex secret keys, which proves the system's exceptional + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 883, 190]]<|/det|> +levels of security. Finally, smart device control is presented by the BSTCM system, realizing intelligent human- machine interactions. The proposed BSTCM can establish a new paradigm of human machine interactions, metasurface- based wireless communications, and potential applications in metaverse. + +<|ref|>sub_title<|/ref|><|det|>[[115, 221, 302, 238]]<|/det|> +## System configuration + +<|ref|>text<|/ref|><|det|>[[113, 251, 884, 567]]<|/det|> +The proposed BSTCM is schematically demonstrated in Fig. 1, which consists of an STC metasurface, a brain- computer interface based on SSVEP, and an FPGA control module. The metasurface element contains a meta- structure to regulate the EM wave, and a light- emitting diode (LED) stimulator for the SSVEP paradigm. The STC metasurface is divided into four LED flickering partitions, operating at different frequencies: \(8.5\mathrm{Hz}\) , \(10\mathrm{Hz}\) , \(11.5\mathrm{Hz}\) , and \(7\mathrm{Hz}\) . The corresponding SSVEP- based EEG signals can be accurately extracted by a head- worn EEG cap when the operator is exposed to the LED flickering stimulus at distinct frequencies with the assistance of FPGA. To enhance the efficiency and accuracy of the EEG signal identification, we propose a deep learning model to recognize the extracted EEG signals, as shown in Fig. 2c. Upon completion of the identification process, the recognized signals are transmitted to FPGA, enabling to control the STC metasurface to update the corresponding STC matrices in the four LED flickering frequencies without disrupting the EEG stimulation. The BSTCM system empowers individuals to customize and modulate the EM waves by the human mind. + +<|ref|>text<|/ref|><|det|>[[113, 581, 884, 895]]<|/det|> +Leveraging the abundant harmonic modulation capabilities and the advantages of human brain intelligence, the BSTCM system can respectively implement a high- security encrypted wireless communication system and a smart device controlled by human mind. In the high- security encrypted wireless communications, the harmonic frequencies are adopted as the secret keys and the VSS method is employed to encrypt the transmitted information. Firstly, the information is encrypted into two visual secret ciphertexts that depend on the corresponding HSKs. As one of the interface devices in the metaverse, BCI plays a crucial role in enabling individuals to interact in the virtual world, and it requires high- security performance. The presented encrypted wireless communication is envisioned to fulfill the requirement for secure communication in the metaverse. For instance, in a metaverse scenario shown in Fig. 1, a legitimate transmitter (Alice) equipped with an EEG cap intentionally transfers the secret information to two legitimate receivers (Bob and Carol) at two harmonic frequencies based on the encrypted communication. The eavesdropper (Eve) cannot decrypt the transmitted + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 299]]<|/det|> +information unless she simultaneously obtains the secret keys, two ciphertexts, and encryption mechanisms, which is nearly impossible. Hence, the proposed system ensures high security and concealment by generating a large number of HSKs by modulating the STC metasurface. Ingeniously camouflaged as an LED stimulator, the STC metasurface effectively obstructs the eavesdroppers from detecting the information interaction. On the other hand, the presented system also enables wireless control of smart devices in real environments, allowing for manipulating the devices directly based on the user's brain intention without the requirement of physical actions. Hence the BSTCM can serve as a stimulator for SSVEP, and effectively manipulate the EM waves for secure BCI wireless communications. + +<|ref|>sub_title<|/ref|><|det|>[[115, 319, 339, 337]]<|/det|> +## SSVEP signal recognition + +<|ref|>text<|/ref|><|det|>[[111, 345, 884, 914]]<|/det|> +The EEG signals can be accurately recognized in a BCI system through the detection of SSVEP components. The target flickering stimuli with four frequencies are supported with the STC metasurface integrated with LEDs, and the commands can be output by simply fixating on the corresponding visual stimuli. The STC metasurface is composed of \(32 \times 32\) elements, and segmented into four partitions, with each region consisting of \(16 \times 16\) elements. In these partitions, LEDs operate at four distinct frequencies (8.5Hz, 10Hz, 11.5Hz, and 7Hz) and are implemented to evoke four distinct SSVEP signals. As depicted in Fig. 2a, the SSVEP- BCI is segmented into three distinct stages: preparation stage, signal acquisition stage, and signal recognition stage. During the preparation stage, the participant wears an EEG cap equipped with electrodes positioned over the O1 and O2 regions and focuses the attention on one of the regions on the STC metasurface. In the signal acquisition stage, the participant maintains their gaze while an EEG amplifier collects the EEG data (for details, see Supplementary Note 1). Fig. 2d illustrates the frequency distribution of the EEG signals for the four target frequencies after the FFT analysis (prefiltered using a Butterworth bandpass filter with a frequency range of 5- 40Hz). As illustrated in Fig. 2b, the signal undergoes an initial pre- processing procedure during the signal recognition stage, involving signal decomposition, spectrum calculation and weighted summation, output signal amplitude spectrum (SigSpec). Subsequently, an outer product operation is executed with the pre- calculated reference spectra (RefSpec), yielding eight feature graphs (comprising of twochannel and four target frequencies), each sized by \(160 \times 160\) pixels. A discernible square grid pattern emerges in the feature graph corresponding to the frequency of the anticipated classification result (for additional information, refer to Supplemental Information Notes 2- 5). The conventional SSVEP classification and recognition algorithms include canonical correlation analysis (CCA) \(^{49}\) , filter bank canonical correlation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 81, 884, 496]]<|/det|> +analysis (FBCCA) \(^{50}\) , task- related component analysis (TRCA) \(^{9}\) , support vector machine (SVM) \(^{51}\) , and convolutional neural network (CNN) \(^{52}\) . However, some limitations still exist in the above- mentioned algorithms. Here, we propose a deep learning classification algorithm that embeds the SSVEP components in the signal recognition stage (refer to Supplemental Information Note 6). The above- mentioned eight feature graphs are input into a lightweight CNN classification model containing four convolutional and two liner layers, as shown in Fig. 2c. The feature graphs are initially processed through a convolutional input layer. Subsequently, a MaxPooling operation with a \(2 \times 2\) kernel is applied following the activation of the data via the ReLU function. To facilitate the propagation of original data, a residual connection is strategically integrated between the second and third convolutional layers. Each convolutional layer is followed by the same \(2 \times 2\) MaxPooling operation and ReLU activation. Upon the extraction of features from the eight feature graphs, a series of two successive linear layers are deployed to categorize the features into four distinct target frequencies. Finally, an output layer with sigmoid activation is employed to reshape the output of the model into 4 confidence scores. Ultimately, the model achieved a classification accuracy of \(96.67\%\) on the validation set, with precision and recall rates surpassing \(90\%\) . Such results underscore the efficacy of the SSVEP component classification algorithm proposed in this work. + +<|ref|>sub_title<|/ref|><|det|>[[115, 517, 355, 534]]<|/det|> +## Design of STC metasurface + +<|ref|>text<|/ref|><|det|>[[112, 547, 884, 912]]<|/det|> +Fig. 3a showcases the detailed configuration of the 1- bit STC metasurface element integrated with an LED, which consists of three metallic layers and multiple substrate layers. The top metallic layer has two slotted rectangular patches with a width of \(w = 7.3 \mathrm{mm}\) and a length of \(l = 11 \mathrm{mm}\) , in which the middle slot has a width of \(w_{1} = 1.5 \mathrm{mm}\) and a length of \(l_{1} = 7 \mathrm{mm}\) . The outer metal frame with a width of \(0.15 \mathrm{mm}\) is used to weaken the coupling effect between elements. The top F4B dielectric substrate has a relative permittivity of 2.65 and a loss tangent of 0.003 with the thickness \(h = 3 \mathrm{mm}\) and period \(p = 19 \mathrm{mm}\) . Two PIN diodes are serially loaded on the middle of two rectangular patches, and the two rectangular patches connected with metallic bars serve as positive and negative poles, in which two inductors are used as radio- frequency (RF) chokes to effectively isolate the RF signal from direct current (DC). An LED serving as the flash stimulation is placed outside the metallic patches to avoid affecting the scattering performance of the meta- atom and shares the same voltage with PIN diodes. To evaluate the performance of the 1- bit meta- atom, full- wave simulations are conducted using the commercial software CST Microwave Studio. As shown in Figs. 3b and 3c, the scattering characteristics of the meta- atom can be altered by switching the states of two PIN diodes + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 81, 883, 273]]<|/det|> +corresponding to distinct RLC equivalent circuit models, hence realizing the 1- bit phase regulation. The diodes with OFF state are assigned as coding “0”, and ON state as coding “1”. When the meta-atom is subjected to normal illumination by a \(y\) - polarized plane wave, a phase difference of \(180^{\circ}\) between “0” and “1” states is observed at approximately \(6.9\mathrm{GHz}\) , and both states exhibit reflective amplitudes exceeding - 1 dB, indicating excellent performance of the 1- bit programmable meta-atom. Hence the designed meta-atom can be used to build the 1- bit STC metasurface. The detailed prototype design, modeling and characterization are given in Methods and Supplementary Note 9. + +<|ref|>text<|/ref|><|det|>[[112, 285, 884, 904]]<|/det|> +To establish a dual- user encrypted wireless communication system, we implement an amplitude- shift keying modulation scheme using the STC metasurface, enabling simultaneous encrypted transmissions via space and frequency multiplexing. The control voltages, originating from FPGA, are applied to the PIN diodes, enabling periodic switching to the reflection phases of the 1- bit STC element with a time period of \(T_0 = 1 / f_0\) . Consequently, each STC element possesses a periodic time- coding sequence and constitutes part of an STC matrix, making it possible to flexibly control the EM waves in both space and time domains. The flexibility of STC matrices can generate a series of new harmonic spectra at the frequency interval of \(f_0\) , which are further optimized with specific requirements (see Supplementary Notes 7 and 8). The space- time modulation enables abundant harmonic characteristics to construct harmonic- based encrypted wireless communications. Using the 2D STC matrices, we can modulate the EM waves to steer to the desired directions for target users. The STC matrices encompass 16 rows of elements, each possessing periodic time- coding sequences with 11 intervals. As illustrative examples, three radiation patterns corresponding to three STC matrices (M0, M1, and M2) are shown in Fig. 3. The radiation pattern of the optimized STC matrix M1 in Fig. 3e is strategically manipulated to yield a main beam with a deflected angle of \(\theta = - 15^{\circ}\) at the \(+1\) st harmonic frequency, as showcased in Fig. 3h. Similarly, the radiation pattern of the STC matrix M2 (Fig. 3f) is optimized to direct the beam towards the angle \(\theta = +30^{\circ}\) , in correspondence with the - 1st harmonic frequency, as demonstrated in Fig. 3i. Furthermore, the time- invariant STC matrix M0 (Fig. 3d) facilitates the positioning of main beam towards a direction of \(\theta = 0^{\circ}\) , associated with the fundamental frequency, as illustrated in Fig. 3g. As an example of amplitude- shift keying modulation scheme, the left panels ( \(f_1 = 8.5\mathrm{Hz}\) and \(f_2 = 10\mathrm{Hz}\) ) of the STC metasurface are utilized to send digital symbols “0” and “1” to User1 at the position of \(\theta = - 15^{\circ}\) when the transmitting information is encoded as binary data streams. The right panels ( \(f_3 = 11.5\mathrm{Hz}\) and \(f_4 = 7\mathrm{Hz}\) ) of the STC metasurface are utilized to send digital + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 885, 151]]<|/det|> +symbols “0” and “1” to User2 at the position of \(\theta = +30^{\circ}\) . Experiments are carried out in a microwave anechoic chamber to measure the far- field radiation patterns of the STC metasurface, as illustrated in Supplementary Notes 10 and 11. + +<|ref|>text<|/ref|><|det|>[[113, 165, 884, 629]]<|/det|> +Constrained by the requirement of the SSVEP- based BCI to gather data and detect EEG signals at a single frequency, a viable encoding strategy is proposed to enhance the symbol error rate (SER), as depicted in Fig. 3j- m. The transmission of the digital symbols “0/1” involves to encode a series of repeated data frames with a period of 4 frames as synchronous frames. In the encoding scheme, the first bit “1” serves as the start bit, signaling the initiation of the transmission process. The second bit signifies the harmonic frequency (+1st or - 1st) used for transmission, and the third bit corresponds to the transmission of the data itself. Finally, the fourth bit functions as the stop bit, indicating the end to transmit one data frame. Based on the encoding process, the digital symbol “0” or “1” for User1 is encoded as the repeated data frames “10001000” or “10101010...”, respectively. Similarly, the digital symbol “0/1” to User2 is encoded as repeated data frames “11001100...” or “11101110...”, respectively. Hence, the STC matrix M1 operating with a switching frequency \(f_0\) , is precisely injected into the region of high amplitude associated with the flicker frequencies \(f_1\) and \(f_2\) , as illustrated in Fig. 3j and 3k. The STC matrices are updated when the information is sent to User1 via the BSTCM system. Similarly, the STC matrix M2 with a period switch frequency \(f_0\) , undergoes injection into the high amplitude attributed to the flicker frequencies \(f_3\) and \(f_4\) , as displayed in Fig. 3j and 3k, representing the transmission of information to User2. Remarkably, the update of STC matrices does not affect the flickering frequency of LEDs used for SSVEP stimuli, ensuring that BCI signals can be synchronously detected and recognized. + +<|ref|>sub_title<|/ref|><|det|>[[115, 650, 543, 668]]<|/det|> +## Secure encrypted wireless communication system + +<|ref|>text<|/ref|><|det|>[[115, 686, 884, 899]]<|/det|> +The conventional VSS method is employed to encrypt the individual pixels of confidential information, where each pixel is divided into two sub- pixels. It is worth noting that each share derived from this process remains devoid of any discernible information pertaining to the original image content. As a result, a secret message possesses two visual shared keys (VSKs), which effectively guarantee that the disclosure of any single VSK does not compromise the confidentiality of the original secret information. Here, we present a novel encryption strategy by combining the harmonic characteristics of STC metasurface with the VSS method, which is designed to realize high- security encrypted wireless communication directly controlled by the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 885, 469]]<|/det|> +human mind. In the presented encryption strategy, two VSKs are not simply stacked with each other to recover the original information, but rely on HSKs. In the described encryption process, the confidential information to be transmitted is firstly encrypted to two random VSKs based on the presented encrypted encoding scheme. Then, two generated VSKs are further encoded into two random HSKs for the \(\pm 1\) harmonic frequencies, respectively. During decryption, two VSKs attached to each secret can be reconstructed with the pre- customized variant HSK sequences. The details of encryption and decryption methods can be found in Supplementary Note 12. The target image in question can be decrypted through the incoherent superposition of two extracted VSKs, aided by the presence of HSKs. By imbuing addressable shared information bits with the harmonic dynamics, a robust level of information security is achieved. As an illustrative example in Fig. 4a, the image “H”, which consists of \(5 \times 5\) grid of black or white pixels, is encrypted into two random VSK1 and VSK2 based on the presented encryption method. Two bitstream sequences of VSKs can be successively transmitted to two designated Users (Bob and Carol) by the BCI operator (Alice) using the presented BSTCM system. + +<|ref|>text<|/ref|><|det|>[[113, 486, 885, 895]]<|/det|> +To validate the encrypted communication scheme, we experimentally set up an encrypted wireless communication system using the BSTCM, as demonstrated in Fig. 4b. The prototype of 1- bit STC metasurface is used to construct the encrypted communication system, as shown in Supplementary Figure 15. A commercial BCI device (actiCHamp of Brain Product GmbH company) composed of an EEG cap and amplifier is used to acquire the EEG signals when the operator stares at different partitions on the STC metasurface. The corresponding EEG signals can be accurately recognized by the proposed deep learning algorithm. The detailed description of the algorithm is provided in Supplementary Note 6. A linearly polarized horn antenna, connected to a signal generator (Keysight E8267D), is placed at a vertical distance of 4 m away from the metasurface center to excite a monochromatic plane wave at the frequency of 6.9 GHz. The receiving terminals are composed of two horn antennas (severed as two users) and a spectrum analyzer (Keysight N9040D), which is used to demodulate the received signal. Two receiving horn antennas are located at the angles of \(\theta = - 15^{\circ}\) and \(\theta = +30^{\circ}\) with relative to the metasurface normal, respectively. In the transmission process, the information undergoes encryption, resulting in the generation of two harmonic- based cipher texts according to the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 247]]<|/det|> +encryption scheme. The transmission process is achieved by ensuring that the BCI operator maintains direct visual focus on the STC metasurface in alignment with the binary cipher texts. In addition, the system enables prompt acquisition, recognition, and translation of EEG signals into appropriate control signals of FPGA, which in turn drive the STC metasurface. As a result, the accurate transmission of binary cipher texts is accomplished, facilitating reliable and efficient communications between the BCI operator and the intended recipients. + +<|ref|>text<|/ref|><|det|>[[113, 263, 885, 730]]<|/det|> +In the receiving process, the radiated signals propagating through free space are received individually by the two horn antennas, and these signals are subsequently demodulated using the spectrum analyzer. As shown in Fig. 4c, the demodulated signals represent four encoding schemes that correspond to different digital symbols ("0" or "1") for the two users and then are processed to the decoded results shown in Fig. 4d by the FFT method. The decoded results clearly exhibit four repeated data frames, providing strong evidence to support the capability of the proposed system to recover the transmitting information successfully. Hence two binary cipher texts are transmitted to two specific users by the BCI operator based on the proposed wireless communication system. The transmitted information is received and the measured results are comprehensively showcased in Supplementary Note 13 and Supplementary Video 1. By employing the encoding method described earlier, the data streams corresponding to the specific users are extracted from the received signals, as depicted in Figs. 4e- f. Subsequently, these data streams are decrypted accurately by the method outlined in Supplementary Note 12. The deciphered information is displayed in Fig. 4g. The successful decryption and retrieval of the transmitted information affirm the robustness and efficacy of the proposed system in maintaining high levels of security and confidentiality. The system is not easily deciphered or cracked, proving its ability to protect sensitive information during transmissions. + +<|ref|>sub_title<|/ref|><|det|>[[116, 784, 377, 801]]<|/det|> +## Mind control to smart devices + +<|ref|>text<|/ref|><|det|>[[115, 820, 883, 893]]<|/det|> +To experimentally verify the performance of the proposed BSTCM system, we conducted experiments to show the remote control capabilities of smart devices through human intention, which holds significant potential to enhance the quality of life for individuals with disabilities. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 85, 884, 696]]<|/det|> +The system flowchart depicted in Fig. 5a outlines the key steps involved in the experimental setup. Initially, the EEG signals are acquired and subsequently processed by the BSTCM system for command recognition. The recognized commands are then translated to update the STC matrices of metasurface by an FPGA. The four partitions of the STC metasurface are controlled by FPGA to generate different beams at four deflected angles corresponding to the four harmonic frequencies, enabling the remote control of smart devices. Notably, the main beam at the - 2nd harmonic frequency exhibits a pronounced peak in the direction of \(\theta = +10^{\circ}\) , corresponding to the device assigned to User 3. Conversely, the main beam at the +2nd harmonic frequency demonstrates a strong peak at \(\theta = - 45^{\circ}\) , corresponding to the device assigned to User 1. As displayed in Figs. 5c and d, the measured radiation patterns exhibit good consistency with theoretical predictions associated to the corresponding STC matrices. Additionally, the main beams were deflected at the other two angles (- 15° and 30°) using the +1st and - 1st harmonic frequencies (Users 2 and 4), as shown in Figs. 3h and i. The excellent harmonic beam manipulation of the STC metasurface lays a solid theory foundation for smart device control. The receiving terminals are composed of four horn antennas, four RF energy detectors (LMH2110), four Microcontroller Unit (MCU) modules (Arduino), and four LED modules (serve as smart devices), as depicted in Fig. 5b. The RF energy from harmonic beams captured by the detectors can be converted into DC voltages, which are subsequently fed into the MCU modules. Once the corresponding DC voltage is detected, the MCU modules send the control commands to activate the corresponding LED module, thereby implementing the remote control of the smart devices. The experimental setup is shown in Fig. 5b, where the STC metasurface is illuminated by a monochromatic frequency signal of 6.9GHz. + +<|ref|>text<|/ref|><|det|>[[115, 708, 884, 894]]<|/det|> +To verify the robustness of the brain- control smart devices system, the position of the receiving antenna is sequentially placed at different distances from the metasurface center while maintaining specific harmonic direction angles with respect to the metasurface normal. The detection of RF energy from the emitted harmonic beams is carried out across varying distances and the corresponding DC voltages can be accurately converted by the RF detectors, as illustrated in Fig. 5e (see Supplementary Note 14 for details). We observe that the RF energies of four users relatively decrease and the converted DC voltages also decrease accordingly as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 469]]<|/det|> +the distance increases. To guarantee the attainment of the far- field region and maintain system stability, the four receiving antennas are positioned at a distance of approximately \(300\mathrm{cm}\) from the metasurface. Consequently, the detection voltage threshold is set as \(0.15\mathrm{V}\) , \(0.4\mathrm{V}\) , \(0.4\mathrm{V}\) , and \(0.2\mathrm{V}\) in MCU modules, respectively. We recorded a video of the brain- controlled multiple smart devices, as presented in Supplementary Movie 2. The sequence diagrams shown in Fig. 5f provide a clear visual representation of the sequential control process, where the smart devices (LED modules) are successively controlled through human intention using the BSTCM system. The experiment proves the feasibility of brain- controlled smart devices. By harnessing the power of BCI technology, individuals can exert control over smart devices without needing physical interaction. The BSTCM system enables seamless and intuitive manipulations of various smart devices, facilitating greater independence and convenience for people in daily life. The experimental results serve as a compelling validation supporting the significant benefits that the BSTCM can offer in improving the quality of life for individuals facing physical challenges. + +<|ref|>sub_title<|/ref|><|det|>[[115, 518, 223, 535]]<|/det|> +## Conclusions + +<|ref|>text<|/ref|><|det|>[[113, 557, 884, 910]]<|/det|> +We presented a BSTCM system, which combines the human brain intelligence and flexible EM control capabilities of the STC metasurface. A series of visual stimuli encoded by specific coding schemes was employed on the metasurface to realize deep information processing and interaction in the EM field for reliable and stable communication environments. In addition, a lightweight deep- learning algorithm was proposed to accurately recognize the brain signals extracted by the BCI device. We further demonstrated a high- security encrypted wireless communication system based on diverse harmonic frequency characteristics, which combines the variant HSKs and VSS methods. The proposed system can enable the system to have high security. Finally, remote control of smart devices was demonstrated by the BSTCM system, realizing intelligent human- machine interactions. The proposed BSTCM, integrating the EM manipulation capability with the intelligence of the human brain, provides a new paradigm of interactions among human machine interface in metaverse, and future 6G wireless communication applications. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 117, 317, 134]]<|/det|> +## Methods and materials + +<|ref|>text<|/ref|><|det|>[[115, 152, 885, 395]]<|/det|> +Details on STC metasurface. A metasurface prototype was fabricated by the standard printed circuit board (PCB) process. The entire STC metasurface is composed of \(32 \times 32\) digital elements and an effective size of \(608 \times 608 \mathrm{~mm}^2\) . For the convenience of processing and welding, the entire metasurface was disassembled into 8 sub- metasurfaces with \(8 \times 16\) digital elements and an effective size of \(152 \times 304 \mathrm{~mm}^2\) . A total number of \(2 \times 32 \times 32\) PIN diodes (SMP1320- 079LF from SKYWORKS) and RF inductors of \(10 \mathrm{nH}\) were elaborately welded on the metasurfaces with the surface mount technology, which is a mature engineering method to weld small components on PCB using the batch solder- reflow processes in a dedicated machine. Meanwhile, an LED is embedded into each metasurface element. + +<|ref|>text<|/ref|><|det|>[[114, 429, 884, 895]]<|/det|> +The brain signal acquisition. As depicted in Fig. 2a, the SSVEP- BCI is delineated into three discrete distinct stages: the preparation stage, the signal acquisition stage, and the signal recognition stage. The EEG data in this study were acquired using the actiCHamp EEG amplifier manufactured by Brain Products GmbH. This amplifier offers a maximum capacity of 64 channels, 24- bit precision, and a sampling rate of \(100 \mathrm{kHz}\) . It exhibits a high common mode rejection ratio (CMRR) of 100, making it suitable for capturing and detecting SSVEP signals effectively. This study uses a standard 10- 20 EEG cap to obtain the signal. Since SSVEP is a local potential that predominantly originates from the occipital lobe \(^5\) , the O1 and O2 electrodes located on the occipital region were selected for EEG data acquisition, with a reference electrode placed at the vertex (Cz) region. The sample time in this study was set to four seconds. With a sampling rate of \(512 \mathrm{~Hz}\) and excluding the reference electrode data, each sample yielded 4096 sampling points (4 seconds \(\times 512 \mathrm{~Hz} \times 2\) channels). Square wave signals were utilized to present visual stimuli on the LEDs of the STC metasurface units, which emitted the red light, thereby ensuring adequate visual contrast. To minimize the harmonic interference, four target visual stimulus frequencies were: top- left at \(8.5 \mathrm{~Hz}\) , bottom- left at \(10 \mathrm{~Hz}\) , top- right at \(11.5 \mathrm{~Hz}\) , and bottom- right at \(7 \mathrm{~Hz}\) . To mitigate mutual interference between two closely spaced flickering stimuli pairs, the frequencies \(7 \mathrm{~Hz}\) and \(8.5 \mathrm{~Hz}\) , as well as \(10 \mathrm{~Hz}\) and \(11.5 \mathrm{~Hz}\) , + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 275]]<|/det|> +were placed diagonally opposite to one another. The example of the collected brain wave signal data for four target frequencies is shown in Supplementary Figure 1. Data processing and model training were conducted in Python and PyTorch environments. Signal processing primarily involved utilizing scipy package for filtering and FFT calculations. Given that neither the absolute value of the EEG signal nor its temporal characteristics were pertinent to this study, all sampled signals were linearly transformed to have a mean of zero and a standard deviation of one. + +<|ref|>text<|/ref|><|det|>[[113, 310, 885, 607]]<|/det|> +Classification model training. Initially, a dataset comprising 240 instances of raw SSVEP signals was collected from two participants. Subsequently, a subset consisting of 80 instances of randomly selected raw signals was employed to create RefSpec. The remaining 160 instances of raw signals were randomly selected to serve as training and validation datasets for classification model training. The two subsets have an equally divided number of signals across four stimuli frequencies. As for the training and validation dataset, a subset of 60 instances was extracted from the initial subset of 160 instances to serve as the validation set. The remaining 100 instances underwent a data augmentation process, which resulted in an expanded dataset of 1700 instances. These augmented instances constituted the training set, which was then utilized to train the model for 300 epochs. The validation operation was executed every two training epochs. + +<|ref|>sub_title<|/ref|><|det|>[[117, 645, 286, 662]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[114, 678, 884, 820]]<|/det|> +The work is supported by the National Natural Science Foundation of China (62288101, 92167202, 72171044), the Major Project of Natural Science Foundation of Jiangsu Province (BK20212002), the State Key Laboratory of Millimeter Waves, Southeast University, China (K201924), the Fundamental Research Funds for the Central Universities (2242018R30001), the 111 Project (111- 2- 05), the China Postdoctoral Science Foundation (2021M700761), and ZhiShan Scholar Program of Southeast University (2242022R40004). + +<|ref|>sub_title<|/ref|><|det|>[[117, 860, 301, 876]]<|/det|> +## Author contributions + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 885, 250]]<|/det|> +T.J.C. suggested the designs and planned and supervised the work, in consultation with Q.M., Y.F.N., Q.X., and J.W.Y. conceived the idea and carried out the theoretical analysis and numerical simulations. Q.X., L.H.F., Y.M.N., and Z.G. built the system and performed the experimental measurements. Q.X., L.H.F. and L.C. performed the data analysis. Q.X. and L.H.F. wrote the manuscript. Q.M., L.L., J.W. Y, and T.J.C. reviewed the manuscript. All authors discussed the theoretical aspects and numerical simulations, interpreted the results and reviewed the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[117, 265, 320, 285]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[117, 302, 538, 320]]<|/det|> +The authors declare no competing financial interest. + +<|ref|>sub_title<|/ref|><|det|>[[117, 352, 285, 371]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[117, 380, 881, 422]]<|/det|> +The data that support the findings of this study are available from the corresponding author upon request. + +<|ref|>sub_title<|/ref|><|det|>[[117, 451, 290, 470]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[117, 479, 881, 521]]<|/det|> +The code that supports the findings of this study are available from the corresponding author upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[117, 551, 228, 569]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[115, 579, 885, 911]]<|/det|> +Wu, D. et al. 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IEEE Transactions on Neural Systems and Rehabilitation Engineering 29, 2615- 2624, doi:10.1109/tnsre.2021.3132162 (2021). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 85, 880, 394]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 400, 883, 511]]<|/det|> +
Fig. 1 | The schematical diagram of the presented BSTCM. The BSTCM system integrates STC metasurface into SSVEP-based BCI systems. The STC metasurface can correctly support the visual stimuli for the SSVEP-based BCI and facilitate the information interaction with the external environment. Based on the BSTCM system, high-security encrypted wireless communication systems are realized for the first time by combining with the variant HSKs and VSS method. In this way, smart devices can be controlled by the human mind with high security.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 84, 868, 620]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 622, 881, 806]]<|/det|> +
Fig. 2 | The diagram of interaction and data processing procedures of the SSVEP-based BCI system. a. The interaction procedures for the SSVEP-based BCI involve the user wearing an EEG cap and focusing their attention on the intended target LED stimuli. Once the user's selected frequency is recognized, the interaction commands are transmitted to the STC metasurface. b. The feature extraction process includes the transformation of the input BCI signals into the frequency domain. The resulting Signal FFT is then subjected to outer product with pre-defined Template FFTs, yielding eight feature graphs. c. The lightweight classification model. Four convolutional and two fully connected layers were employed. d. An example of frequency responses of SSVEP signals for the four target frequencies, showing distinctive amplitude peaks corresponding to different frequencies.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 96, 880, 555]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 565, 881, 675]]<|/det|> +
Fig. 3 | The details of the STC metasurface. a. The geometrical structure of the STC metasurface element. b, c. The reflective amplitude and phase of the element, respectively. d-f. The optimized STC matrices for different scattering angles at different harmonic frequencies. g-i. The far-field results corresponding to the optimized STC matrices. j-m. The encoding schemes for the transmitting symbol “0” or “1” for two users: the symbols “0” and “1” for Users 1 (j and k); the symbols “0” and “1” for Users 2 (l and m).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 92, 884, 540]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 548, 881, 693]]<|/det|> +
Fig. 4 | The encrypted wireless communication system based on the BSTCM platform. a. The encrypted encoding scheme combining the harmonic-secret keys and the VSS method. b. The experimental scenarios of the encrypted wireless communication system, in which the operator equipped with EEG cap transmits the encrypted information to two users via the BSTCM platform, respectively. c. The receiving signals corresponding to the encoding scheme in Fig 3(j-m). d, e. The decoded information of VSK1 and VSK2 from the receiving signals based on the encrypted coding scheme. f. The correct transmitting information is decoded by the extracted VSK1 and VSK2.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 88, 880, 428]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 436, 883, 565]]<|/det|> +
Fig. 5 | Experiment on the wireless remote mind control of smart devices based on the BSTCM platform. a. The system architecture of wireless remote control. b. The experimental scenarios of the wireless remote-control system by the human brain. c, d. The theoretical and experimental results for far-filed patterns at \(\pm 2\mathrm{th}\) harmonic frequencies. f. The temporal waveform of the output voltages from four detectors when the operator lights up four devices in sequence. e. The variations between the input power of the detectors and output voltage with respect to the different distances between the receiving antenna and the metasurface center.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 43, 312, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 114]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 308, 177]]<|/det|> +SupplementaryMaterials.pdf SupplementaryVideos.zip + +<--- Page Split ---> diff --git a/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/images_list.json b/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..beb40e52e6c3a5325add9626030c35b7250e4b16 --- /dev/null +++ b/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/images_list.json @@ -0,0 +1,152 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 (a) U.S. automotive sector employment consists of assembly and parts manufacturing jobs, with assembly comprising \\(33\\%\\) of total automotive jobs. During periods of economic downturn, the percentage of jobs lost in the automotive sector exceeded that of the overall U.S. manufacturing sector, as seen during the recession of 2008 and the pandemic of 2019. (b) The transition from ICEV to BEV production will create shifts in the types and quantities of jobs in both assembly and parts manufacturing. BEVs will require workers to manufacture battery cells and battery packs instead of engines. Jobs categories are classified based on the North American Industrial Classification System (NAICS) codes. Assembly jobs: NAICS 3361. Engine parts jobs: NAICS 33631. Powertrain parts jobs: NAICS 33635. Other parts: NAICS codes 3363(x) according to the parenthesized values in (b).", + "footnote": [], + "bbox": [ + [ + 207, + 204, + 828, + 400 + ] + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 Transition plants: vehicle assembly plants that have fully transitioned from assembling ICEVs to BEVs. (a) Map of U.S. automotive vehicle assembly activity as of 2022. Colors show the number of workers classified under \"motor vehicle manufacturing\" (NAICS code 3361) for each state. Markers highlight major manufacturing plants in each state. Three \"transition plants\" have been identified as examples of complete ICEV to BEV production transformations: (1) Alameda County, CA, representing the transition of the former New United Motors Manufacturing, Inc. (NUMMI) vehicle assembly plant, which assembled ICEV passenger vehicles, to the Tesla plant which assembles BEVs; (2) Oakland County, Michigan, representing the transition from the production of General Motors ICEVs to the Chevy Bolt BEV over six years; and (3) McLean County, Illinois, representing the transition of the former Mitsubishi vehicle assembly plant to the Rivian plant which assembles electric pick-up trucks. (b) Possible trajectories of vehicle assembly workforce size that this work seeks to clarify.", + "footnote": [], + "bbox": [ + [ + 210, + 88, + 824, + 260 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 (a) Vehicle production history in Alameda County, California. Before 2010, the New United Motors Manufacturing Incorporated (NUMMI) factory produced ICEVs including the Corolla and the Tacoma. The factory has since been taken over by Tesla which has now been producing BEVs for over a decade. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via government NAICS 3361 data from the U.S. Quarterly Workforce Indicators (QWI), as well as via news reports. (d) Labor intensity in Alameda compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.", + "footnote": [], + "bbox": [ + [ + 208, + 87, + 832, + 450 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 (a) Vehicle production history in Oakland County, Michigan, home to the Orion Township assembly plant owned by General Motors. Before the 2008 recession, plants in the cities of Pontiac and Wixom were also actively producing vehicles, but both plants shut down in 2010, leaving the Orion plant as the sole operating plant in the county. In 2016, Orion began producing the Chevy Bolt BEV alongside GM ICEVs, before exclusively producing the Bolt as of 2021. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via averaging two government NAICS 3361 datasets: the U.S. Quarterly Workforce Indicators (QWI) and the U.S. Quarterly Census of Employment and Wages (QCEW). (d) Labor intensity in Oakland compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.", + "footnote": [], + "bbox": [ + [ + 205, + 85, + 832, + 450 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 (a) Vehicle production history in McLean County, Illinois. Before 2016, Mitsubishi owned an ICEV production plant in the town of Normal. The factory has since been purchased by Rivian, which began producing electric SUVs and pick-up trucks in 2021. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via news reports because government NAICS 3361 data was suppressed for McLean County. (d) Labor intensity in McLean compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.", + "footnote": [], + "bbox": [ + [ + 203, + 85, + 832, + 450 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Fig. A1 U.S.-level vehicle production (a), assembly workers (b), and labor intensity (c). Vehicle production data was obtained from the Automotive News Research & Data Center, while employment data was obtained from QCEW.", + "footnote": [], + "bbox": [ + [ + 283, + 128, + 666, + 545 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "Fig. A2 Battery cell and pack manufacturing increases the labor intensity of making BEVs. (a) Annual vehicle production volume in Alameda, CA. (b) Employment in Alameda, CA and Sparks, NV, based on news reports (see Table A2). Employment in Sparks, NV, reflects additional workers for battery cell and pack manufacturing at Tesla Gigafactory 1. Data is shown only when Alameda and Sparks data are available for the same production year. (c) Comparison of labor intensity (WPV) with and without including workers from Sparks, NV.", + "footnote": [], + "bbox": [ + [ + 234, + 102, + 792, + 416 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_2.jpg", + "caption": "Fig. A3 Comparison of vehicle sales prices (MSRP) in Alameda (a), Oakland (b), and McLean (c). Dollar values are inflation-adjusted to 2023 dollars based on the Consumer Price Index (CPI) for U.S. new vehicles [61]. MSRP for all available vehicle trims are shown.", + "footnote": [], + "bbox": [ + [ + 205, + 520, + 833, + 680 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_3.jpg", + "caption": "Fig. A4 Concept illustration: vertical integration creates more workforce co-location at the site of vehicle assembly.", + "footnote": [], + "bbox": [ + [ + 171, + 140, + 788, + 321 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_4.jpg", + "caption": "Fig. B5 Comparison of engine manufacturing parts jobs in 2022 against projected battery cell manufacturing jobs assuming 100% BEV uptake and under various assumptions of jobs per GWh.", + "footnote": [], + "bbox": [ + [ + 268, + 263, + 763, + 533 + ] + ], + "page_idx": 28 + } +] \ No newline at end of file diff --git a/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364.mmd b/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d674829fc795ea22f2d4767054ecaaea93a21b85 --- /dev/null +++ b/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364.mmd @@ -0,0 +1,435 @@ + +# "30% fewer workers for electric vehicle assembly": harbinger or myth? + +Andrew Weng + +asweng@umich.edu + +University of Michigan Omar Ahmed University of Michigan Gabriel Ehrlich University of Michigan Anna Stefanopoulou University of Michigan + +## Article + +Keywords: electric vehicle, manufacturing jobs, labor intensity + +Posted Date: April 12th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4237003/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on September 16th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 52435- x. + +<--- Page Split ---> + +# "30% fewer workers for electric vehicle assembly": harbinger or myth? + +Andrew Weng \(^{1\dagger}\) , Omar Y. Ahmed \(^{1\dagger}\) , Gabriel Ehrlich \(^{2}\) , Anna Stefanopoulou \(^{1*}\) + +\(^{1}\) Department of Mechanical Engineering, University of Michigan - Ann Arbor, 1231 Beal Ave, Ann Arbor, 48109, MI, U.S. \(^{2}\) Department of Economics, University of Michigan - Ann Arbor, 611 Tappan Ave, Ann Arbor, 48109, MI, U.S. + +\*Corresponding author(s). E- mail(s): annastef@umich.edu; Contributing authors: asweng@umich.edu; oyahmed@umich.edu; gehrlich@umich.edu; \(^{\dagger}\) These authors contributed equally to this work. + +## Abstract + +It has been widely hypothesized that the transition to battery electric vehicles will require \(30\%\) fewer assembly workers compared to internal combustion engine vehicles. This work uses publicly available datasets on vehicle production and employment to show that vehicle assembly plants in the U.S. that have previously assembled internal combustion vehicles but have since fully transitioned to assembling battery electric vehicles have required more, not fewer, workers to assemble the same number of vehicles. Our study suggests that widespread loss of employment at electric vehicle assembly sites is a smaller risk than many fear. Moreover, our study serves as a call for more regionally- focused analyses of the transition's effects on labor using data- driven and macro- level surveying approaches. + +Keywords: electric vehicle, manufacturing jobs, labor intensity + +<--- Page Split ---> + +## 1 Introduction + +The automotive industry employs 13 million workers in the U.S., including nearly 1 million in the manufacturing sector [1- 3] (Figure 1). Most of these workers are engaged in the production of internal combustion engine vehicles (ICEVs) today, but a rapid shift towards battery electric vehicles (BEVs) is underway as major automakers set ambitious targets to phase out ICEV production within the next two decades [4, 5]. + +![](images/Figure_1.jpg) + +
Fig. 1 (a) U.S. automotive sector employment consists of assembly and parts manufacturing jobs, with assembly comprising \(33\%\) of total automotive jobs. During periods of economic downturn, the percentage of jobs lost in the automotive sector exceeded that of the overall U.S. manufacturing sector, as seen during the recession of 2008 and the pandemic of 2019. (b) The transition from ICEV to BEV production will create shifts in the types and quantities of jobs in both assembly and parts manufacturing. BEVs will require workers to manufacture battery cells and battery packs instead of engines. Jobs categories are classified based on the North American Industrial Classification System (NAICS) codes. Assembly jobs: NAICS 3361. Engine parts jobs: NAICS 33631. Powertrain parts jobs: NAICS 33635. Other parts: NAICS codes 3363(x) according to the parenthesized values in (b).
+ +How will the transition to BEV production affect the overall number of jobs in the automotive sector? The answer to this question is at the core of a "Just Transition" which secures the future and livelihoods of workers and their communities in the transition to a low- carbon economy [6- 10]. For many U.S. auto workers, the possibility of job loss is not theoretical but experienced. During the 2008 global recession, automotive manufacturing employment declined by \(23\%\) within a year (Figure 1a). The overall U.S. manufacturing sector lost \(12\%\) of employment over the same period, suggesting that the automotive sector is particularly vulnerable to job losses during periods of economic downturn. Although some industry analysts note the potential for the transition to BEVs to create new U.S. jobs [11], the potential for reduced labor demand in the transition to BEV production has raised concerns among automotive labor groups that the transition may be disruptive for U.S. workers [6]. + +Despite the importance of understanding the BEV transition's effect on the number of automotive jobs, existing reports have been scarce and contradictory. A common + +<--- Page Split ---> + +narrative is that BEV powertrains have fewer parts compared to ICEV powertrains and thus take fewer workers to assemble, so the BEV transition will result in a net loss in automotive jobs. The claim of “30% fewer workers for EV assembly” entered public discourse as early as 2017 [12] and remains a central claim as part of ongoing debates on the effect of the BEV transition on jobs [6, 13–16]. The Economic Policy Institute found that, without policies promoting local production of electric vehicle powertrain components, 75,000 jobs could be lost in the U.S. by 2030. However, the same report also predicted that employment could rise by 150,000 jobs given local production [17]. A study by the Boston Consulting Group found that BEV labor requirements are about 1% less than those for ICEVs after accounting for all production process differences [18]. A study by Cotterman et al. (2022) found that the labor intensity required for BEV powertrain manufacturing can be more than twice as high as that for ICEVs if battery cell manufacturing is included and when industry shop floor data is used instead of academic models [19, 20]. The lack of consensus from these existing reports underscores the difficulty of estimating the future trajectory of BEV jobs based solely on technical assumptions [21] and expert judgment [22]. + +This work studies data from existing vehicle assembly plants based in the U.S. that have already fully transitioned from ICEV production to BEV production. By studying data from existing assembly plants, we show the effect of the ongoing BEV transition on automotive assembly jobs in the U.S. without making a priori assumptions of labor intensity, battery manufacturing location, and reliance on expert judgment. In all three assembly plants studied, we found that BEVs require more workers per vehicle produced than ICEVs. + +## 2 Identifying BEV transition plants + +For this work, we first identified U.S. counties in which there existed a single historic ICEV assembly plant that has since been converted to produce BEVs (Figure 2a). These sites, termed “transition plants,” provide the most direct comparison of labor intensity differences before, during, and after the BEV transition. Two attributes define transition plants. First, the plant must have fully transitioned from the assembly of ICEVs to BEVs. A full transition ensures that the latest labor intensity figures correspond to BEV production without considering the effect of simultaneous production of BEVs and ICEVs. Second, the plant must have been producing BEVs for at least two years and at a volume of more than 10,000 vehicles per year. This helps to exclude BEV assembly plants that are in the very early stages of production ramp- up, where production volumes are far from equilibrium conditions and with scarce data. + +To understand the effect of the BEV transition on the workforce size at each transition plant, we frame the transition as occurring in several stages, each of which bears consequences for the workforce size at the plant (Figure 2b). In the first stage, ICEV lines are retired, resulting in lower vehicle production volumes and a reduced workforce. In the second stage, assembly lines are re- tooled for BEV production, renewing the demand for workers. In the third and final stage, the BEV assembly plant approaches a steady- state in operational capabilities and production volumes. At this stage, the workforce size is expected to stabilize. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 Transition plants: vehicle assembly plants that have fully transitioned from assembling ICEVs to BEVs. (a) Map of U.S. automotive vehicle assembly activity as of 2022. Colors show the number of workers classified under "motor vehicle manufacturing" (NAICS code 3361) for each state. Markers highlight major manufacturing plants in each state. Three "transition plants" have been identified as examples of complete ICEV to BEV production transformations: (1) Alameda County, CA, representing the transition of the former New United Motors Manufacturing, Inc. (NUMMI) vehicle assembly plant, which assembled ICEV passenger vehicles, to the Tesla plant which assembles BEVs; (2) Oakland County, Michigan, representing the transition from the production of General Motors ICEVs to the Chevy Bolt BEV over six years; and (3) McLean County, Illinois, representing the transition of the former Mitsubishi vehicle assembly plant to the Rivian plant which assembles electric pick-up trucks. (b) Possible trajectories of vehicle assembly workforce size that this work seeks to clarify.
+ +We identified three transition plants for this study: Alameda County in California (Figure 3), Oakland County in Michigan (Figure 4), and McLean County in Illinois (Figure 5). Alameda was chosen as the site of the historic New United Motor Manufacturing Incorporated (NUMMI) plant, a joint venture between General Motors and Toyota, which closed in 2010 and has subsequently been owned and operated by Tesla to produce BEVs. Alameda represents a 'near- steady- state' BEV assembly case: Tesla has now been producing BEVs from this site for over a decade, and its annual production volume of BEVs now exceeds that of the NUMMI plant at its peak. Oakland was next identified as home to the General Motors (GM) Orion assembly plant, which began producing the Chevy Bolt BEV in 2016 concurrently with ICEVs [23]. As of 2021 the plant was exclusively making the Bolt, before ending its production in December 2023 [24]. Oakland thus provides a case study in which the same workforce transitioned from making ICEVs to making BEVs over a period of five years. Finally, McLean was identified as the home to a former ICEV plant owned by Mitsubishi, which has since been taken over by Rivian to produce mass- market electric light- duty trucks. McLean represents the case of a burgeoning BEV manufacturer at the early stages of vehicle production ramp- up. + +<--- Page Split ---> + +## 3 Understanding labor intensity through workers per vehicle + +At each transition plant, we study the labor intensity of vehicle assembly before, during, and after the transition to BEVs. Labor intensity is defined by the number of assembly workers needed to produce 1,000 vehicles (WPV) according to: + +\[\mathrm{WPV}(k) = \frac{W(k)}{V(k)}\times 1000, \quad (1)\] + +where \(W(k)\) is the number of auto assembly workers employed at the site averaged over four quarters of year \(k\) and \(V(k)\) is the total number of light- duty vehicles produced at the site during year \(k\) . WPV normalizes the workforce size by the production volume and is thus a measure of labor intensity. WPV can be converted to units of hours worked per vehicle by assuming a total annual hours worked per worker (see Section 8.3). However, since the hours worked are not publicly known at each transition plant, we chose to report labor intensity as WPV for this work. + +Vehicle production data is obtained from Automotive News Research & Data Center [25], which details vehicle production volumes per make, model, and assembly location (see Section 8.1). Automotive assembly worker data reflects employment under the North American Industrial Classification System (NAICS) code 3361, Motor Vehicle Manufacturing. Worker data is corroborated by combining data from from two publicly available government databases - Quarterly Census of Employment and Wages (QCEW) and Quarterly Workforce Indicators (QWI) - as well as from local news reports (see Section 8.2). National vehicle production and employment data (Figure A1) provides a reference estimate of average ICEV labor intensity, since BEV production in the U.S. had not surpassed 7% as of 2022. In the past two decades, national labor intensity has ranged between approximately 17 and 28 WPV. Labor intensity reached its lowest point of 17 WPV in 2015 before steadily climbing toward a high of 28 WPV in 2021. + +## 4 Alameda: high labor intensity despite a decade of BEV production + +Alameda County, California, is home to the vehicle assembly plant historically owned by North America Motor Manufacturing Incorporated (NUMMI) [26]. The plant was in operation from 1984 to 2010 as a joint venture between General Motors and Toyota, producing ca. 429,000 vehicles per year at its peak. The plant produced midsize economy passenger vehicles (Toyota Corolla and Pontiac Vibe) and light- duty trucks (Toyota Tacoma) (Figure 3a). The plant closed in 2010 after General Motors pulled out of the partnership in the aftermath of the global recession [27, 28]. The plant closure resulted in the direct loss of 4,700 manufacturing jobs [27] as well as the closure of 34 businesses in Alameda that supplied parts to the factory [29]. + +Following the NUMMI plant closure, Tesla purchased the factory and began to retool the lines, first to produce the Model S sedan and Model X SUV starting in 2012 + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 (a) Vehicle production history in Alameda County, California. Before 2010, the New United Motors Manufacturing Incorporated (NUMMI) factory produced ICEVs including the Corolla and the Tacoma. The factory has since been taken over by Tesla which has now been producing BEVs for over a decade. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via government NAICS 3361 data from the U.S. Quarterly Workforce Indicators (QWI), as well as via news reports. (d) Labor intensity in Alameda compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.
+ +and 2015, respectively, followed by the Model 3 and Model Y mass- market vehicles starting in 2017 and 2020, respectively. By 2019, the annual vehicle production volume surpassed that of the NUMMI plant at its peak, and by 2022, the plant was producing more than 450,000 units per year (Figure 3b). In the years between 2012 and 2022, Tesla has maintained a net hiring rate of ca. 2,500 manufacturing workers per year, totaling over 25,000 workers as of 2022 (Figure 3c). These employment numbers are corroborated by both government data and local news reports (see Methods section). + +The NUMMI factory reached peak labor efficiency, i.e. its lowest labor intensity, in 2006 at 15 WPV (Figure 3d). Since then, labor intensity has risen year- on- year as vehicle production volume declined. After Tesla acquired the plant in 2012, labor intensity stayed above 50 WPV over the next five years, coinciding with the production of Model S and Model X. Labor intensity then began to decrease starting in 2017, + +<--- Page Split ---> + +when Tesla began production of the Model 3, its first mass- market BEV. During the period between 2019 and 2022, labor intensity averaged at 51 WPV. Overall, during the decade since Tesla began to build BEVs, the labor intensity stayed above 45 WPV. + +To the best of our knowledge, the BEV labor force in Alameda includes labor for battery pack assembly for the Model S/X [30] but excludes battery pack assembly for the Model 3/Y which are reportedly manufactured off- site in Sparks, Nevada [31- 35]. This BEV labor force further excludes battery cell manufacturing since battery cells for the Model S and X are sourced from Panasonic in Japan [36], and cells for the Tesla Model 3 and Y are reportedly made in Sparks [31- 34]. Starting in 2017, Tesla also began to make Model 3 electric motors [35, 37] and battery packs in Sparks. The data after 2017 thus primarily reflects the labor intensity for assembling Model 3 and Y vehicles, excluding electric motor manufacturing, battery cell manufacturing, and battery pack manufacturing, with the exception of Model S/X battery packs which presumably continued to be made on- site. When battery manufacturing workers from Sparks, Nevada are included, labor intensity further increases to 67 WPV (Figure A2) in 2022, reflecting an additional 50% increase in labor intensity. + +Overall, Alameda highlights one example of an ICEV to BEV transition in which each BEV took more than twice as many workers to assemble, even before considering the additional labor needed to manufacture battery cells and electric motors. Tesla, now with more than a decade of BEV production, has reached annual production volumes exceeding its former ICEV counterparts. Yet, labor intensity between 2019 and 2022 (51 WPV) remained more than double that of the NUMMI plant during its peak productivity year in 2006 (16 WPV). + +## 5 Oakland: same plant owner, similar labor intensity + +Oakland County, Michigan, is the home to the Orion Assembly plant owned by General Motors (GM). Before the 2008 recession, the plant produced the Chevy Malibu and Pontiac G6 passenger sedans with a production rate peaking at ca. 456,000 per year in 2004 (Figure 4a). Production numbers declined in the proceeding years, eventually reaching zero in 2008 when the plant was idled as GM declared bankruptcy [38] (Figure 4b). As the economy recovered from the recession, the Orion plant re- opened to produce the Buick Verano and Chevy Sonic passenger sedans [39, 40]. In 2016, GM began to convert its assembly lines to produce the Chevy Bolt BEV [23]. By 2021, the plant had transitioned to exclusively assembling the Bolt, with the production rate peaking at ca. 42,000 units per year. GM ended production of the Bolt in December 2023 with plans to renovate the Orion plant to make electric trucks starting in 2025 [24]. Since 2016, vehicle assembly employment in Oakland County mirrored the vehicle production rate: as vehicle production declined, so did employment (Figure 4c). + +Data before 2016 reflects the labor intensity of ICEV assembly, which varied widely (Figure 4d). Before the 2008 recession, labor intensity varied between 24 WPV and 35 WPV. Following the factory shutdown in 2010, labor intensity decreased to 17 WPV. As the plant began to make the Chevy Bolt BEV in 2016, labor intensity began to rise. However, over the same period, the baseline national labor intensity (i.e. baseline + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 (a) Vehicle production history in Oakland County, Michigan, home to the Orion Township assembly plant owned by General Motors. Before the 2008 recession, plants in the cities of Pontiac and Wixom were also actively producing vehicles, but both plants shut down in 2010, leaving the Orion plant as the sole operating plant in the county. In 2016, Orion began producing the Chevy Bolt BEV alongside GM ICEVs, before exclusively producing the Bolt as of 2021. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via averaging two government NAICS 3361 datasets: the U.S. Quarterly Workforce Indicators (QWI) and the U.S. Quarterly Census of Employment and Wages (QCEW). (d) Labor intensity in Oakland compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.
+ +ICEV labor intensity) rose by a similar amount, which we attribute to a general market shift towards larger vehicle types [41]. + +Oakland thus represents a case in which the transition to BEV assembly did not appreciably change the labor intensity trajectory compared to the rest of the U.S., which continued to assemble mostly ICEVs during the same period. However, while the labor intensity remained similar, the total number of jobs declined due to the reduced vehicle production volumes (Figure 4b,c). Finally, we note that the labor intensity corresponding to Chevy Bolt BEV assembly excludes battery pack assembly labor to + +<--- Page Split ---> + +the best of our knowledge1. Battery cell manufacturing labor is also excluded since the battery cells are manufactured off- site2. The labor intensity for the Bolt is expected to increase if either battery pack assembly or cell manufacturing activity is included. + +## 6 McLean: ten-fold increase in labor intensity during BEV factory production ramp + +In this final case study, we turn to McLean County, Illinois, home to the former Mitsubishi vehicle assembly plant [44] (Figure 5). During Mitsubishi's ownership, vehicles produced included the Eclipse sedan and Outlander sport utility vehicle, with vehicle production plateauing ca. 69,000 vehicles in 2014. In the same year, the plant employed 1,250 workers. In 2015, Mitsubishi shut down operations due to global competitive pressures [45]. The plant was subsequently purchased by Rivian to be re- tooled for assembling electric pickup trucks [46]. In 2022, Rivian produced ca. 18,000 electric vehicles while employing 6,000 workers. + +We thus calculate labor intensity in McLean to be 18 WPV during ICEV production in 2014, compared with 316 WPV during BEV production in 2022. The high labor intensity seen in 2022 mirrors the similarly high levels seen during Alameda's first five years of BEV production (Figure 3d). Both cases represent periods during which fledgling BEV makers undergo rapid production ramp- up and in which production levels have not yet reached a steady state. Rivian also reportedly manufactures battery packs on- site [47], contributing to an additional use of labor which is included in the labor intensity calculation. Note, however, that battery cell manufacturing is not factored into the labor intensity calculation to the best of our knowledge since Rivian is reportedly using cells supplied by Samsung that are not manufactured on- site [47]. + +## 7 Discussion + +### 7.1 More workers for BEV assembly, not fewer + +The three case studies in Alameda, Oakland, and McLean counties collectively suggest that each BEV requires just as many, if not more, workers to assemble than each ICEV. We summarize this finding in Table 1, which compares the labor intensity before and after the BEV transition at each transition plant. For each comparison, we report the numbers corresponding to the year with the highest peak labor productivity (the inverse of labor intensity). In Alameda, labor intensity rose three- fold from 16 WPV to 46 WPV after the BEV transition. In Oakland, labor intensity rose two- fold from 17 WPV to 31 WPV. Finally, in McLean, labor intensity rose from 18 WPV to 316 WPV. In all three cases, the labor intensity increased following the BEV transition. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5 (a) Vehicle production history in McLean County, Illinois. Before 2016, Mitsubishi owned an ICEV production plant in the town of Normal. The factory has since been purchased by Rivian, which began producing electric SUVs and pick-up trucks in 2021. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via news reports because government NAICS 3361 data was suppressed for McLean County. (d) Labor intensity in McLean compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.
+ +Alameda highlighted a labor scenario for a maturing BEV assembly process. After ten years of BEV production, labor intensity in Alameda remained more than twice as high compared to its ICEV counterparts. McLean showed how labor intensity may increase ten- fold during the early years of vehicle production ramp towards volume production, especially for a new automaker. Oakland highlighted a case in which the labor intensity for making BEVs was no different from the baseline labor intensity for making ICEVs around the U.S. during the same time period. + +### 7.2 Explaining higher labor intensity in BEV assembly + +We postulate several factors that may influence the labor intensity of BEV assembly: the degree of investment in manufacturing technology development, the higher complexity of premium- class vehicles, and the extent of vertical integration. These factors + +<--- Page Split ---> + + +Table 1 More workers per vehicle needed for BEV assembly for all three counties studied. Production volume, employment numbers, and labor intensity correspond to the year of minimum labor intensity. The lists of vehicle models highlight certain high-volume production models and are not exhaustive. (\*) Battery pack assembly for Model S and X reportedly took place in Alameda [30]; Battery pack assembly for Model 3 and Y reportedly took place offsite in Gigafactory 1 located in Sparks, Nevada [31-35]. + +
ICEV AssemblyBEV Assembly
Alameda, CA
OwnerNUMMI
Vehicle modelsTacoma, Corolla, Vibe
Peak productivity year2006
Production volume429,000
Employment6,700
Labor intensity16 WPV
Includes pack assembly?—
Includes cell manuf.?—
Oakland, MI
OwnerGeneral Motors
Vehicle modelsSonic, Verano, Malibu
Peak productivity year2013
Production volume159,000
Employment2,600
Labor intensity17 WPV
Includes pack assembly?—
Includes cell manuf.?—
McLean, IL
OwnerMitsubishi
Vehicle modelsOutlander, Galant, Eclipse
Peak productivity year2014
Production volume69,000
Employment1,300
Labor intensity18 WPV
Includes pack assembly?—
Includes cell manuf.?—
+ +may explain why the labor intensity for BEV assembly today exceeds the labor intensity of past ICEV assembly, and, furthermore, why labor intensity outcomes are not uniform but vary by region. We discuss each of these factors in turn. + +Investment in manufacturing technology development. Increased levels of investment in technology development have the counter- intuitive effect of suppressing near- term labor productivity as companies hire a larger workforce to improve the technology. Such may be the case in Alameda, where Tesla's investment in BEV manufacturing technology [48] has resulted in a workforce of salaried engineers who are co- located within the assembly plant [49]. This claim is corroborated by government occupation data, which shows that engineering occupations accounted for \(7\%\) of total assembly employment in California in 2021 compared to \(5\%\) at the national level and \(3\%\) in Michigan (Table A1). Wages data tells a similar story: the average income of assembly workers in Alameda, CA, more than doubled from 2013 to 2021, indicating a growing presence of high- paid engineering occupations. Comparatively, in Oakland, MI, the average income increased by only \(20\%\) over the same period. We note that + +<--- Page Split ---> + +the presence of the engineering and other non- production workforce does not appear to be the major driver of the increase in labor intensity of BEV assembly compared to ICEV assembly in Alameda. Even under the extreme assumption that all workers in Alameda prior to the BEV transition were strictly in production roles, Alameda's BEV labor intensity in 2021 would remain over 30 WPV, twice as high as during peak ICEV production in 20063. + +Vehicle complexity. First- time BEV makers tend to produce and sell premium- class vehicles before they produce and sell economy- class vehicles. For example, the first mass- produced BEVs from Tesla (Model S) and Rivian (R1T) are both premium- class vehicles that sell for more than \$80,000 USD4. By comparison, before the BEV transition, the median sales price of ICEVs produced at the same plants was only \$28,000 USD, since production was dominated by mass- market vehicles such as the Toyota Tacoma and the Mitsubishi Outlander (Figure A3). The present- day bias towards more premium- class BEVs may thus partly explain the higher labor intensity observed for BEV assembly currently. As BEV manufacturers move towards offering more economy- class BEVs, labor intensity may yet decrease. This trend may already be seen in Oakland, where the Chevy Bolt EV's median sales price nearly matched that of its ICEV predecessors, as did labor intensity. + +Vertical integration. The rise in BEV labor productivity also parallels a growing industry trend towards consolidating workers who have historically worked off- site (i.e. at the site of parts suppliers) but who now work within the assembly plant (Figure A4). This consolidation is most clearly evident in Alameda, where Tesla has chosen to design and manufacture many vehicle components in- house, including electric motors [50], semiconductors [30], and seats [51]. In contrast, legacy automakers, over a period between the late 1990s and early 2000s, have opted for a strategy of outsourcing in which many vehicle components, such as the engine and transmission, are designed and manufactured off- site by parts suppliers [48, 52]. Consequently, parts manufacturing workers (e.g. engine and transmission manufacturing workers) that supported vehicle assembly tended not to be co- located at the site of vehicle assembly [53]. A transition to BEVs does not necessarily guarantee that an automaker will move towards greater vertical integration. For example, in the Oakland assembly plant, GM reportedly assembled battery packs off- site, so the workers for battery pack assembly are not included from our measure of labor intensity for the Chevy Bolt. + +### 7.3 Parts manufacturing + +While this study focused on the trajectory of automotive assembly jobs, the fate of automotive parts manufacturing jobs warrants further study. Parts manufacturing jobs comprised 66% of all auto manufacturing jobs in the U.S. in 2022 (Figure 1). For some parts manufacturing activities such as electrical, steering, suspension, brakes, seats, and interior trim, worker demand will persist within the context of BEV production. However, disruption in transmission and engine- related parts manufacturing jobs is expected since these components are simplified or absent in BEV powertrains + +<--- Page Split ---> + +[54]. Engine manufacturing jobs will especially be impacted, considering the lack of combustion engines in BEVs. + +[54]. Engine manufacturing jobs will especially be impacted, considering the lack of combustion engines in BEVs.In the U.S., engine manufacturing jobs accounted for \(7\%\) of all U.S. auto manufacturing jobs in 2022, or 56,486 workers. With the BEV transition, these workers face job losses but also the opportunity for re- employment in other parts of the BEV value chain. The most immediate source of worker re- employment is in battery cell manufacturing which accounts for up to \(75\%\) of the total labor intensity for producing a BEV powertrain [19]. Employment data on existing BEV OEMs also suggests that battery cell manufacturing including pack assembly can increase labor needed per BEV by over \(50\%\) (Figure A2 and Table A2) \(^{5}\) . Ultimately, whether new jobs in battery cell manufacturing will replace lost jobs in automotive parts manufacturing depends on the labor intensity needed to make battery cells, the geographical co- location of jobs, and skills [55- 58], which should be further investigated (Figure B5). + +### 7.4 Outlook + +Our study challenges the narrative that BEVs require \(30\%\) less labor to assemble than ICEVs. The observed data in three transition plants indicates that BEVs are more labor intensive to assemble than ICEVs, rather than less. These results suggest that the path towards greater BEV manufacturing will require a workforce size at the assembly plant that matches or exceeds the size of the ICEV workforce. The demand for workers at BEV assembly sites is spurred by a continued need to innovate and improve upon existing BEV manufacturing technology, a drive towards greater vertical integration, and the present- day tendency towards the production of higher- cost BEVs. Our analysis suggests that rapid, widespread job displacement during the BEV transition is a smaller risk than many fear. + +## 8 Methods + +### 8.1 Vehicle Production Data + +Vehicle production data \(V\) was obtained from the Automotive News Research & Data Center [25], which collates North American light- duty (i.e., passenger vehicles and pick- up trucks) vehicle sales, production, and inventory data on a monthly basis and organizes the data by automaker, vehicle make and model, and manufacturing plant location. Only vehicle production in the U.S. was considered for this work. Electric vehicle models were manually identified by cross- referencing publicly available lists of BEV makes and models. The counties in which manufacturing plants reside were manually identified using the counties' Federal Information Processing System (FIPS) codes to enable linking vehicle production data with county- level employment data (see Section 8.2). + +<--- Page Split ---> + +### 8.2 Employment Data + +County- level automotive manufacturing employment data \(W\) was sought from two government data sources, the Quarterly Census of Employment and Wages (QCEW) and the Quarterly Workforce Indicators (QWI), as well as from local news reports. + +The QCEW dataset, administered by the U.S. Bureau of Labor Statistics (BLS), comprises a quarterly count of employment and wages for workers covered by unemployment insurance programs, which totals more than \(95\%\) of U.S. workers [59]. The data is aggregated and classified by industry according to the North American Industry Classification System (NAICS), and provided for county, metropolitan statistical area (MSA), state, and national levels. This dataset provides employment data at the level of an "establishment", defined as a single physical worksite engaged predominantly in one type of economic activity, e.g., making automotive vehicles. + +The QWI dataset is administered by the U.S. Census Bureau and consists of administrative data from the Longitudinal Employer- Household Dynamics (LEHD) program, including Unemployment Insurance Wage Records, data from the Census Bureau, and data from the Office of Personnel Management (OPM). This allows the QWI dataset to provide both firm- level and worker- level data. The QWI data covers more than just those eligible for unemployment insurance benefits and includes all employers and their employees for which the administrative records are available. It provides detailed breakdowns by industry (using NAICS) and worker demographics (gender, age, education, race, and ethnicity), as well as earnings and various measures of job and worker flows. Unlike QCEW, which focuses on the establishment level, QWI produces insights into both the employer side (job creation, destruction, etc.) and employee side (turnover rates, accessions, separations) of labor dynamics. + +This work sought national, state- level, and county- level employment data from both QCEW and QWI under NAICS code 3361, which corresponds to Motor Vehicle Manufacturing and encompasses assembly workers. Parts manufacturing labor is excluded from these counts because it has its own distinct NAICS code. Occupation data from the Occupational Employment and Wage Statistics (OEWS) program of BLS was used to confirm that approximately \(60 - 80\%\) of NAICS 3361 workers are in production occupations (Standard Occupational Classification code 51- 0000, which includes assemblers and fabricators), depending on the region of the U.S. For reference, Table A1 summarizes occupation- industry data for NAICS 3361 in Michigan, California, and the U.S. + +As described in Section 8.3, NAICS 3361 data was sometimes suppressed at the county level from QCEW, QWI, or both databases. For this reason, local news reports, found via internet searches, provided another independent estimate of employment data for specific automotive factory sites. This data was used to substitute employment data if QCEW and QWI data were both suppressed, or to corroborate available QCEW and QWI data. Table 2 summarizes which data sources were used for each of the three counties. + +<--- Page Split ---> + + +
CountyQCEW (Gov)QWI (Gov)NewsNotes
Alameda✓✓QCEW data was suppressed
Oakland✓✓Average of QCEW and QWI data was used
McLean✓QCEW and QWI data were both suppressed
+ +Table 2 Summary of data sources used to study employment in the three transition counties. + +## 8.3 Limitations + +Regional (state- level and county- level) employment data for a particular industry is often suppressed by QCEW and QWI databases to maintain employer anonymity. This most often occurs when a single large employer comprises a majority of the data for a particular industry in a particular region. See Table 2 for a description of the suppression instances relevant to this work. Using two government databases as well as local news reports ensured a sufficient level of redundancy to circumvent these suppression instances. In most cases, the employment numbers from these data sources agree with each other, improving confidence in the reported numbers. + +Another limitation is that employment data corresponding to NAICS code 3361 covers both light- and heavy- duty vehicle manufacturing employment. The vehicle production data from Automotive News only covers light- duty vehicles, so combining the two datasets requires assuming a negligible volume of heavy- duty vehicle manufacturing presence within the selected counties. NAICS sub- code 33611 covers employment specifically for light- duty manufacturing, but that data is suppressed for the states and counties examined for this work. + +Additionally, there are discontinuities in the WPV calculations shown in Fig. 3- 5. These discontinuities correspond to periods of zero and extremely low vehicle production. For Alameda and McLean, periods of zero vehicle production occurred during ownership transition from their respective ICE- making to BEV- making companies. For Oakland, this period occurred during the Great Recession. We also observed that in each county, such a small number of vehicles were produced in the first year resuming production that the WPV metric disproportionately inflated for that year. Thus, for each county, we discard labor intensity calculation for the time period in which zero vehicles were produced, plus the first subsequent year that production resumed. + +We also acknowledge that another common metric for labor intensity is hours worked per vehicle. While the data on hours worked is available at the national level through surveys such as Current Employment Statistics (CES), county- level data is not made public by government agencies. For this reason, we chose to report labor intensity in units of workers per vehicle. From workers per vehicle, hours worked per vehicle can be approximated using the formula: + +\[\mathrm{HPV} = \frac{W\times t}{V} \quad (2)\] + +where HPV is the hours worked per vehicle, \(W\) is the number of workers, \(V\) is the total vehicles produced, and \(t\) is the annualized hours worked per worker. The time input \(t\) can be estimated to be 2,236 hours, assuming an average of 43 hours worked per week for vehicle manufacturing workers according to BLS [60]. + +<--- Page Split ---> + +## Resource Availability + +Resource AvailabilityFurther information and requests should be directed to and will be fulfilled by Anna Stefanopoulou (annastef@umich.edu). + +## Acknowledgements + +AcknowledgementsThe authors thank Kristin Dziczek for her insights and guidance on the auto manufacturing landscape in Michigan. The authors also thank Katelyn Freese and Katerina Freudenberg for their assistance with processing vehicle production data. We also appreciate help from Rebecca Gao for editing the manuscript. + +## Author Contributions + +Author ContributionsA.W.: conceptualization; methodology; writing - original draft; writing - review and editing; visualization. O.Y.A: methodology; software; investigation; data curation; visualization; writing - original draft; writing - review and editing. G.E.: methodology; writing - review and editing. A.S: conceptualization; writing - review and editing; funding acquisition; project administration. + +## Glossary of Terms + +Glossary of TermsBEV Battery Electric VehicleGM General MotorsICEV Internal Combustion Engine VehicleOEWS Occupational Employment and Wage StatisticsNAICS North American Industry Classification SystemNUMMI New United Motors Manufacturing Inc.QCEW Quarterly Census of Employment and WagesQWI Quarterly Workforce Indicators + +<--- Page Split ---> + +## References + +[1] International Labor Organization: COVID- 19 and the automotive industry. Technical report, International Labour Organization, Sectoral Policies Department (April 2020) + +[2] U.S. Bureau of Labor Statistics: Automotive Industry: Employment, Earnings, and Hours. https://www.bls.gov/iag/tgs/iagauto.htm. 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Accessed: 2023- 12- 4 (2019) + +[52] Chen, Y., Chowdhury, S.D., Donada, C.: Mirroring hypothesis and integrality: Evidence from tesla motors. J. Eng. Tech. Manage. 54, 41- 55 (2019) + +[53] Herrigel, G., Wittke, V.: Varieties of vertical disintegration: The global trend toward heterogeneous supply relations and the reproduction of difference in US and german manufacturing. Industry Studies Association 2004- 15 (2004) + +[54] Ruyter, A., Weller, S., Henry, I., Raimnie, A., Bentley, G., Nielsen, B.: Enabling a just transition in automotive: Evidence from the west midlands and south australia. Technical report, The British Academy (June 2022) + +[55] Krusemark, L., Ganguly, S., Harp, T., Kulicki, A., Smith, C., Prasad, K.V.: Examining workforce needs for north america: Battery industry education and training needs assessment (BIETNA). Technical report, Center for Automotive Research (2024) + +[56] Combemale, C., Whitefoot, K.S., Ales, L., Fuchs, E.R.H.: Not all technological change is equal: how the separability of tasks mediates the effect of technology change on skill demand. Ind Corp Change 30(6), 1361- 1387 (2022) + +[57] Weaver, A., Osterman, P.: Skill demands and mismatch in U.S. manufacturing. ILR Review 70(2), 275- 307 (2017) + +[58] Cotterman, T., Fuchs, E.R.H., Small, M.J., Whitefoot, K.: The Transition to Electrified Vehicles: Implications for the Future of Automotive Manufacturing and Worker Skills and Occupations (2022) + +[59] Sadeghi, A.: The births and deaths of business establishments in the united states. Mon. Labor Rev. December 2008(1), 1- 18 (2008) + +[60] U.S. Bureau of Labor Statistics: Automotive Industry: Employment, Earnings, and Hours. https://www.bls.gov/iag/tgs/iagauto.htm. Accessed: 2023- 10- 26 (2022) + +[61] Federal Reserve Bank of St. Louis: Consumer Price Index for All Urban Consumers: New Vehicles in U.S. City Average (2024) + +[62] Campagnol, N., Pfeiffer, A., Tryggestad, C.: Capturing the battery value- chain opportunity. Technical Report 1, McKinsey & Company (January 2022) + +<--- Page Split ---> + +[63] Lambert, F.: Tesla Gigafactory 1 now employs over 3,000 workers as it becomes biggest battery factory in the world. https://electrek.co/2018/08/21/tesla- gigafactory- 1- 3000- workers/. Accessed: 2023- 12- 11 (2018) + +[64] Knehr, K.W., Kubal, J.J., Nelson, P.A., Ahmed, S.: Battery performance and cost modeling for electric vehicles - a manual for BatPaC v5.0. Technical Report ANL/CSE- 22/1, Argonne National Laboratory (July 2022) + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Fig. A1 U.S.-level vehicle production (a), assembly workers (b), and labor intensity (c). Vehicle production data was obtained from the Automotive News Research & Data Center, while employment data was obtained from QCEW.
+ +<--- Page Split ---> +![](images/Figure_unknown_1.jpg) + +
Fig. A2 Battery cell and pack manufacturing increases the labor intensity of making BEVs. (a) Annual vehicle production volume in Alameda, CA. (b) Employment in Alameda, CA and Sparks, NV, based on news reports (see Table A2). Employment in Sparks, NV, reflects additional workers for battery cell and pack manufacturing at Tesla Gigafactory 1. Data is shown only when Alameda and Sparks data are available for the same production year. (c) Comparison of labor intensity (WPV) with and without including workers from Sparks, NV.
+ +![](images/Figure_unknown_2.jpg) + +
Fig. A3 Comparison of vehicle sales prices (MSRP) in Alameda (a), Oakland (b), and McLean (c). Dollar values are inflation-adjusted to 2023 dollars based on the Consumer Price Index (CPI) for U.S. new vehicles [61]. MSRP for all available vehicle trims are shown.
+ +<--- Page Split ---> +![](images/Figure_unknown_3.jpg) + +
Fig. A4 Concept illustration: vertical integration creates more workforce co-location at the site of vehicle assembly.
+ +
Location20132021
% of NAICS 3361 workers in productionCalifornia (State)66%62%
Michigan (State)74%81%
U.S.74%76%
% of NAICS 3361 workers in engineeringCalifornia (State)4%7%
Michigan (State)5%3%
U.S.5%5%
NAICS 3361 average monthly payAlameda, CA$6,243$16,462
Oakland, MI$7,557$8,907
U.S.$6,660$6,864
+ +Table A1 Proportion of NAICS 3361 workers in production (SOC code 51- 0000) and architecture/engineering occupations (SOC code 17- 0000), and average monthly pay of NAICS 3361 workers for California, Michigan, and the U.S. Occupation data was obtained from the Occupational Employment and Wage Statistics (OEWS). Income data for Alameda, CA was obtained from QWI. Income data for Oakland, MI was obtained from averaging QWI and QCEW data. Income data for the U.S. was obtained from QCEW. + +<--- Page Split ---> + + +
LocationDateNews SourceReported Employment
Tesla (Alameda)Jun 2012SFGATE1,000
Jul 2013Wired3,000
Jun 2016TheCountryCaller6,000
Oct 2017The Mercury News10,000
Mar 2019Forbes15,000
Jun 2022Tesla22,000
Tesla/PENA (Sparks)2016Electrek850
2017Electrek3,249
2018The Associated Press7,059
2022Tesla12,000
NUMMI (Alameda)Jan 2002SFGATE5,739
Mar 2006East Bay Times5,500
Apr 2010Recordnet.com4,700
Rivian (Normal)Oct 2021WGLT3,000
Apr 2022CIPROUD5,000
Jun 2022Energy News Network5,600
Jul 2022CIPROUD6,000
Mar 2023WGLT7,400
Mitsubishi (Normal)2004Chicago Tribune3,150
2014Local Wiki1,250
2015Chicago Tribune1,280
2016WQAD81,200
GM (Orion)2013CarGroup.org2,561
2022GM1,238
2023Wards Auto1,270
+ +Table A2 List of news reports used to corroborate factory employment numbers. PENA: Panasonic Energy of North America. + +<--- Page Split ---> + +## Appendix B Workers per GWh + +McKinsey reported that, on average, new battery factories add approximately 80 jobs for every GWh of capacity, i.e. 80 workers per GWh [62]. This number carries some uncertainty since differences in value- chain coverage, e.g. battery- cell production only versus local module and pack production or co- location of R&D facilities, are unclear. + +Tesla's Gigafactory 1 reportedly employed 3,249 people when the factory was producing 20 GWh of annual output [63]. Among these workers, 1,201 were employed by Panasonic, the main battery cell manufacturer, 93 are employed by Heitkamp & Thumann Group (H&T), a battery cell can supplier, and 1,955 were employed by Tesla. Assuming those employed by Panasonic and H&T are responsible for battery cell manufacturing, we infer that 1,294 workers are involved with producing 20 GWh of annual output, or 65 workers per GWh. If the employees from Tesla are included, then the calculation yields 162 workers per GWh. + +The BatPaC v5.0 baseline factory model reported an annual labor of 3,876,000 hours per year to produce 50 GWh of output [64]. Assuming each worker works 2,236 hours per year and (equivalent to a 43- hour work- week, the U.S. average for automotive manufacturing [60]), this amounts to 35 workers per GWh. + +Cotterman et al. [19] reported labor intensity per BEV powertrain assuming a 60kWh battery pack which varied depending on the data source and whether the labor was broken down between cell and pack/module assembly. For data sources where this breakdown was available, labor intensity ranged between 11 to 16 hours per 60kWh for industry data sources and 6 to 15 hours per 60kWh for public data sources. Assuming again 2,236 hours per year worked per worker, the range of labor demand is equivalent to 44 to 119 workers per GWh. + +<--- Page Split ---> +![](images/Figure_unknown_4.jpg) + +
Fig. B5 Comparison of engine manufacturing parts jobs in 2022 against projected battery cell manufacturing jobs assuming 100% BEV uptake and under various assumptions of jobs per GWh.
+ +<--- Page Split ---> diff --git a/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364_det.mmd b/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..00e7bc32732f04122872cd8fb913f45f7b35e8c3 --- /dev/null +++ b/preprint/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364/preprint__09af231471af1cc00448f4d0e910132c71d4ce10f09b6fc16f3a37da4472e364_det.mmd @@ -0,0 +1,585 @@ +<|ref|>title<|/ref|><|det|>[[43, 107, 912, 177]]<|/det|> +# "30% fewer workers for electric vehicle assembly": harbinger or myth? + +<|ref|>text<|/ref|><|det|>[[43, 196, 175, 214]]<|/det|> +Andrew Weng + +<|ref|>text<|/ref|><|det|>[[53, 223, 234, 240]]<|/det|> +asweng@umich.edu + +<|ref|>text<|/ref|><|det|>[[42, 269, 252, 426]]<|/det|> +University of Michigan Omar Ahmed University of Michigan Gabriel Ehrlich University of Michigan Anna Stefanopoulou University of Michigan + +<|ref|>sub_title<|/ref|><|det|>[[43, 469, 103, 486]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 506, 580, 526]]<|/det|> +Keywords: electric vehicle, manufacturing jobs, labor intensity + +<|ref|>text<|/ref|><|det|>[[44, 545, 300, 563]]<|/det|> +Posted Date: April 12th, 2024 + +<|ref|>text<|/ref|><|det|>[[42, 583, 475, 601]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4237003/v1 + +<|ref|>text<|/ref|><|det|>[[42, 619, 914, 662]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 680, 535, 700]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 736, 920, 779]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on September 16th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 52435- x. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[191, 157, 767, 208]]<|/det|> +# "30% fewer workers for electric vehicle assembly": harbinger or myth? + +<|ref|>text<|/ref|><|det|>[[234, 229, 718, 264]]<|/det|> +Andrew Weng \(^{1\dagger}\) , Omar Y. Ahmed \(^{1\dagger}\) , Gabriel Ehrlich \(^{2}\) , Anna Stefanopoulou \(^{1*}\) + +<|ref|>text<|/ref|><|det|>[[193, 273, 763, 339]]<|/det|> +\(^{1}\) Department of Mechanical Engineering, University of Michigan - Ann Arbor, 1231 Beal Ave, Ann Arbor, 48109, MI, U.S. \(^{2}\) Department of Economics, University of Michigan - Ann Arbor, 611 Tappan Ave, Ann Arbor, 48109, MI, U.S. + +<|ref|>text<|/ref|><|det|>[[216, 365, 737, 430]]<|/det|> +\*Corresponding author(s). E- mail(s): annastef@umich.edu; Contributing authors: asweng@umich.edu; oyahmed@umich.edu; gehrlich@umich.edu; \(^{\dagger}\) These authors contributed equally to this work. + +<|ref|>sub_title<|/ref|><|det|>[[443, 456, 512, 469]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[206, 473, 750, 604]]<|/det|> +It has been widely hypothesized that the transition to battery electric vehicles will require \(30\%\) fewer assembly workers compared to internal combustion engine vehicles. This work uses publicly available datasets on vehicle production and employment to show that vehicle assembly plants in the U.S. that have previously assembled internal combustion vehicles but have since fully transitioned to assembling battery electric vehicles have required more, not fewer, workers to assemble the same number of vehicles. Our study suggests that widespread loss of employment at electric vehicle assembly sites is a smaller risk than many fear. Moreover, our study serves as a call for more regionally- focused analyses of the transition's effects on labor using data- driven and macro- level surveying approaches. + +<|ref|>text<|/ref|><|det|>[[206, 615, 605, 628]]<|/det|> +Keywords: electric vehicle, manufacturing jobs, labor intensity + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[208, 83, 384, 101]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[207, 111, 831, 184]]<|/det|> +The automotive industry employs 13 million workers in the U.S., including nearly 1 million in the manufacturing sector [1- 3] (Figure 1). Most of these workers are engaged in the production of internal combustion engine vehicles (ICEVs) today, but a rapid shift towards battery electric vehicles (BEVs) is underway as major automakers set ambitious targets to phase out ICEV production within the next two decades [4, 5]. + +<|ref|>image<|/ref|><|det|>[[207, 204, 828, 400]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[206, 416, 832, 520]]<|/det|> +
Fig. 1 (a) U.S. automotive sector employment consists of assembly and parts manufacturing jobs, with assembly comprising \(33\%\) of total automotive jobs. During periods of economic downturn, the percentage of jobs lost in the automotive sector exceeded that of the overall U.S. manufacturing sector, as seen during the recession of 2008 and the pandemic of 2019. (b) The transition from ICEV to BEV production will create shifts in the types and quantities of jobs in both assembly and parts manufacturing. BEVs will require workers to manufacture battery cells and battery packs instead of engines. Jobs categories are classified based on the North American Industrial Classification System (NAICS) codes. Assembly jobs: NAICS 3361. Engine parts jobs: NAICS 33631. Powertrain parts jobs: NAICS 33635. Other parts: NAICS codes 3363(x) according to the parenthesized values in (b).
+ +<|ref|>text<|/ref|><|det|>[[207, 540, 831, 712]]<|/det|> +How will the transition to BEV production affect the overall number of jobs in the automotive sector? The answer to this question is at the core of a "Just Transition" which secures the future and livelihoods of workers and their communities in the transition to a low- carbon economy [6- 10]. For many U.S. auto workers, the possibility of job loss is not theoretical but experienced. During the 2008 global recession, automotive manufacturing employment declined by \(23\%\) within a year (Figure 1a). The overall U.S. manufacturing sector lost \(12\%\) of employment over the same period, suggesting that the automotive sector is particularly vulnerable to job losses during periods of economic downturn. Although some industry analysts note the potential for the transition to BEVs to create new U.S. jobs [11], the potential for reduced labor demand in the transition to BEV production has raised concerns among automotive labor groups that the transition may be disruptive for U.S. workers [6]. + +<|ref|>text<|/ref|><|det|>[[206, 712, 830, 740]]<|/det|> +Despite the importance of understanding the BEV transition's effect on the number of automotive jobs, existing reports have been scarce and contradictory. A common + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 87, 790, 316]]<|/det|> +narrative is that BEV powertrains have fewer parts compared to ICEV powertrains and thus take fewer workers to assemble, so the BEV transition will result in a net loss in automotive jobs. The claim of “30% fewer workers for EV assembly” entered public discourse as early as 2017 [12] and remains a central claim as part of ongoing debates on the effect of the BEV transition on jobs [6, 13–16]. The Economic Policy Institute found that, without policies promoting local production of electric vehicle powertrain components, 75,000 jobs could be lost in the U.S. by 2030. However, the same report also predicted that employment could rise by 150,000 jobs given local production [17]. A study by the Boston Consulting Group found that BEV labor requirements are about 1% less than those for ICEVs after accounting for all production process differences [18]. A study by Cotterman et al. (2022) found that the labor intensity required for BEV powertrain manufacturing can be more than twice as high as that for ICEVs if battery cell manufacturing is included and when industry shop floor data is used instead of academic models [19, 20]. The lack of consensus from these existing reports underscores the difficulty of estimating the future trajectory of BEV jobs based solely on technical assumptions [21] and expert judgment [22]. + +<|ref|>text<|/ref|><|det|>[[165, 316, 790, 414]]<|/det|> +This work studies data from existing vehicle assembly plants based in the U.S. that have already fully transitioned from ICEV production to BEV production. By studying data from existing assembly plants, we show the effect of the ongoing BEV transition on automotive assembly jobs in the U.S. without making a priori assumptions of labor intensity, battery manufacturing location, and reliance on expert judgment. In all three assembly plants studied, we found that BEVs require more workers per vehicle produced than ICEVs. + +<|ref|>sub_title<|/ref|><|det|>[[165, 429, 593, 450]]<|/det|> +## 2 Identifying BEV transition plants + +<|ref|>text<|/ref|><|det|>[[165, 458, 790, 614]]<|/det|> +For this work, we first identified U.S. counties in which there existed a single historic ICEV assembly plant that has since been converted to produce BEVs (Figure 2a). These sites, termed “transition plants,” provide the most direct comparison of labor intensity differences before, during, and after the BEV transition. Two attributes define transition plants. First, the plant must have fully transitioned from the assembly of ICEVs to BEVs. A full transition ensures that the latest labor intensity figures correspond to BEV production without considering the effect of simultaneous production of BEVs and ICEVs. Second, the plant must have been producing BEVs for at least two years and at a volume of more than 10,000 vehicles per year. This helps to exclude BEV assembly plants that are in the very early stages of production ramp- up, where production volumes are far from equilibrium conditions and with scarce data. + +<|ref|>text<|/ref|><|det|>[[165, 615, 790, 730]]<|/det|> +To understand the effect of the BEV transition on the workforce size at each transition plant, we frame the transition as occurring in several stages, each of which bears consequences for the workforce size at the plant (Figure 2b). In the first stage, ICEV lines are retired, resulting in lower vehicle production volumes and a reduced workforce. In the second stage, assembly lines are re- tooled for BEV production, renewing the demand for workers. In the third and final stage, the BEV assembly plant approaches a steady- state in operational capabilities and production volumes. At this stage, the workforce size is expected to stabilize. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[210, 88, 824, 260]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[206, 280, 833, 405]]<|/det|> +
Fig. 2 Transition plants: vehicle assembly plants that have fully transitioned from assembling ICEVs to BEVs. (a) Map of U.S. automotive vehicle assembly activity as of 2022. Colors show the number of workers classified under "motor vehicle manufacturing" (NAICS code 3361) for each state. Markers highlight major manufacturing plants in each state. Three "transition plants" have been identified as examples of complete ICEV to BEV production transformations: (1) Alameda County, CA, representing the transition of the former New United Motors Manufacturing, Inc. (NUMMI) vehicle assembly plant, which assembled ICEV passenger vehicles, to the Tesla plant which assembles BEVs; (2) Oakland County, Michigan, representing the transition from the production of General Motors ICEVs to the Chevy Bolt BEV over six years; and (3) McLean County, Illinois, representing the transition of the former Mitsubishi vehicle assembly plant to the Rivian plant which assembles electric pick-up trucks. (b) Possible trajectories of vehicle assembly workforce size that this work seeks to clarify.
+ +<|ref|>text<|/ref|><|det|>[[207, 423, 832, 666]]<|/det|> +We identified three transition plants for this study: Alameda County in California (Figure 3), Oakland County in Michigan (Figure 4), and McLean County in Illinois (Figure 5). Alameda was chosen as the site of the historic New United Motor Manufacturing Incorporated (NUMMI) plant, a joint venture between General Motors and Toyota, which closed in 2010 and has subsequently been owned and operated by Tesla to produce BEVs. Alameda represents a 'near- steady- state' BEV assembly case: Tesla has now been producing BEVs from this site for over a decade, and its annual production volume of BEVs now exceeds that of the NUMMI plant at its peak. Oakland was next identified as home to the General Motors (GM) Orion assembly plant, which began producing the Chevy Bolt BEV in 2016 concurrently with ICEVs [23]. As of 2021 the plant was exclusively making the Bolt, before ending its production in December 2023 [24]. Oakland thus provides a case study in which the same workforce transitioned from making ICEVs to making BEVs over a period of five years. Finally, McLean was identified as the home to a former ICEV plant owned by Mitsubishi, which has since been taken over by Rivian to produce mass- market electric light- duty trucks. McLean represents the case of a burgeoning BEV manufacturer at the early stages of vehicle production ramp- up. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[165, 81, 754, 121]]<|/det|> +## 3 Understanding labor intensity through workers per vehicle + +<|ref|>text<|/ref|><|det|>[[165, 130, 790, 174]]<|/det|> +At each transition plant, we study the labor intensity of vehicle assembly before, during, and after the transition to BEVs. Labor intensity is defined by the number of assembly workers needed to produce 1,000 vehicles (WPV) according to: + +<|ref|>equation<|/ref|><|det|>[[380, 185, 788, 218]]<|/det|> +\[\mathrm{WPV}(k) = \frac{W(k)}{V(k)}\times 1000, \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[165, 230, 790, 330]]<|/det|> +where \(W(k)\) is the number of auto assembly workers employed at the site averaged over four quarters of year \(k\) and \(V(k)\) is the total number of light- duty vehicles produced at the site during year \(k\) . WPV normalizes the workforce size by the production volume and is thus a measure of labor intensity. WPV can be converted to units of hours worked per vehicle by assuming a total annual hours worked per worker (see Section 8.3). However, since the hours worked are not publicly known at each transition plant, we chose to report labor intensity as WPV for this work. + +<|ref|>text<|/ref|><|det|>[[165, 330, 790, 515]]<|/det|> +Vehicle production data is obtained from Automotive News Research & Data Center [25], which details vehicle production volumes per make, model, and assembly location (see Section 8.1). Automotive assembly worker data reflects employment under the North American Industrial Classification System (NAICS) code 3361, Motor Vehicle Manufacturing. Worker data is corroborated by combining data from from two publicly available government databases - Quarterly Census of Employment and Wages (QCEW) and Quarterly Workforce Indicators (QWI) - as well as from local news reports (see Section 8.2). National vehicle production and employment data (Figure A1) provides a reference estimate of average ICEV labor intensity, since BEV production in the U.S. had not surpassed 7% as of 2022. In the past two decades, national labor intensity has ranged between approximately 17 and 28 WPV. Labor intensity reached its lowest point of 17 WPV in 2015 before steadily climbing toward a high of 28 WPV in 2021. + +<|ref|>sub_title<|/ref|><|det|>[[165, 530, 785, 568]]<|/det|> +## 4 Alameda: high labor intensity despite a decade of BEV production + +<|ref|>text<|/ref|><|det|>[[165, 576, 790, 707]]<|/det|> +Alameda County, California, is home to the vehicle assembly plant historically owned by North America Motor Manufacturing Incorporated (NUMMI) [26]. The plant was in operation from 1984 to 2010 as a joint venture between General Motors and Toyota, producing ca. 429,000 vehicles per year at its peak. The plant produced midsize economy passenger vehicles (Toyota Corolla and Pontiac Vibe) and light- duty trucks (Toyota Tacoma) (Figure 3a). The plant closed in 2010 after General Motors pulled out of the partnership in the aftermath of the global recession [27, 28]. The plant closure resulted in the direct loss of 4,700 manufacturing jobs [27] as well as the closure of 34 businesses in Alameda that supplied parts to the factory [29]. + +<|ref|>text<|/ref|><|det|>[[165, 707, 790, 736]]<|/det|> +Following the NUMMI plant closure, Tesla purchased the factory and began to retool the lines, first to produce the Model S sedan and Model X SUV starting in 2012 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[208, 87, 832, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[206, 465, 832, 548]]<|/det|> +
Fig. 3 (a) Vehicle production history in Alameda County, California. Before 2010, the New United Motors Manufacturing Incorporated (NUMMI) factory produced ICEVs including the Corolla and the Tacoma. The factory has since been taken over by Tesla which has now been producing BEVs for over a decade. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via government NAICS 3361 data from the U.S. Quarterly Workforce Indicators (QWI), as well as via news reports. (d) Labor intensity in Alameda compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.
+ +<|ref|>text<|/ref|><|det|>[[207, 563, 832, 666]]<|/det|> +and 2015, respectively, followed by the Model 3 and Model Y mass- market vehicles starting in 2017 and 2020, respectively. By 2019, the annual vehicle production volume surpassed that of the NUMMI plant at its peak, and by 2022, the plant was producing more than 450,000 units per year (Figure 3b). In the years between 2012 and 2022, Tesla has maintained a net hiring rate of ca. 2,500 manufacturing workers per year, totaling over 25,000 workers as of 2022 (Figure 3c). These employment numbers are corroborated by both government data and local news reports (see Methods section). + +<|ref|>text<|/ref|><|det|>[[207, 665, 832, 737]]<|/det|> +The NUMMI factory reached peak labor efficiency, i.e. its lowest labor intensity, in 2006 at 15 WPV (Figure 3d). Since then, labor intensity has risen year- on- year as vehicle production volume declined. After Tesla acquired the plant in 2012, labor intensity stayed above 50 WPV over the next five years, coinciding with the production of Model S and Model X. Labor intensity then began to decrease starting in 2017, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 86, 790, 129]]<|/det|> +when Tesla began production of the Model 3, its first mass- market BEV. During the period between 2019 and 2022, labor intensity averaged at 51 WPV. Overall, during the decade since Tesla began to build BEVs, the labor intensity stayed above 45 WPV. + +<|ref|>text<|/ref|><|det|>[[165, 130, 790, 315]]<|/det|> +To the best of our knowledge, the BEV labor force in Alameda includes labor for battery pack assembly for the Model S/X [30] but excludes battery pack assembly for the Model 3/Y which are reportedly manufactured off- site in Sparks, Nevada [31- 35]. This BEV labor force further excludes battery cell manufacturing since battery cells for the Model S and X are sourced from Panasonic in Japan [36], and cells for the Tesla Model 3 and Y are reportedly made in Sparks [31- 34]. Starting in 2017, Tesla also began to make Model 3 electric motors [35, 37] and battery packs in Sparks. The data after 2017 thus primarily reflects the labor intensity for assembling Model 3 and Y vehicles, excluding electric motor manufacturing, battery cell manufacturing, and battery pack manufacturing, with the exception of Model S/X battery packs which presumably continued to be made on- site. When battery manufacturing workers from Sparks, Nevada are included, labor intensity further increases to 67 WPV (Figure A2) in 2022, reflecting an additional 50% increase in labor intensity. + +<|ref|>text<|/ref|><|det|>[[165, 315, 790, 415]]<|/det|> +Overall, Alameda highlights one example of an ICEV to BEV transition in which each BEV took more than twice as many workers to assemble, even before considering the additional labor needed to manufacture battery cells and electric motors. Tesla, now with more than a decade of BEV production, has reached annual production volumes exceeding its former ICEV counterparts. Yet, labor intensity between 2019 and 2022 (51 WPV) remained more than double that of the NUMMI plant during its peak productivity year in 2006 (16 WPV). + +<|ref|>sub_title<|/ref|><|det|>[[165, 429, 789, 450]]<|/det|> +## 5 Oakland: same plant owner, similar labor intensity + +<|ref|>text<|/ref|><|det|>[[165, 458, 790, 644]]<|/det|> +Oakland County, Michigan, is the home to the Orion Assembly plant owned by General Motors (GM). Before the 2008 recession, the plant produced the Chevy Malibu and Pontiac G6 passenger sedans with a production rate peaking at ca. 456,000 per year in 2004 (Figure 4a). Production numbers declined in the proceeding years, eventually reaching zero in 2008 when the plant was idled as GM declared bankruptcy [38] (Figure 4b). As the economy recovered from the recession, the Orion plant re- opened to produce the Buick Verano and Chevy Sonic passenger sedans [39, 40]. In 2016, GM began to convert its assembly lines to produce the Chevy Bolt BEV [23]. By 2021, the plant had transitioned to exclusively assembling the Bolt, with the production rate peaking at ca. 42,000 units per year. GM ended production of the Bolt in December 2023 with plans to renovate the Orion plant to make electric trucks starting in 2025 [24]. Since 2016, vehicle assembly employment in Oakland County mirrored the vehicle production rate: as vehicle production declined, so did employment (Figure 4c). + +<|ref|>text<|/ref|><|det|>[[165, 644, 790, 715]]<|/det|> +Data before 2016 reflects the labor intensity of ICEV assembly, which varied widely (Figure 4d). Before the 2008 recession, labor intensity varied between 24 WPV and 35 WPV. Following the factory shutdown in 2010, labor intensity decreased to 17 WPV. As the plant began to make the Chevy Bolt BEV in 2016, labor intensity began to rise. However, over the same period, the baseline national labor intensity (i.e. baseline + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[205, 85, 832, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[205, 462, 832, 568]]<|/det|> +
Fig. 4 (a) Vehicle production history in Oakland County, Michigan, home to the Orion Township assembly plant owned by General Motors. Before the 2008 recession, plants in the cities of Pontiac and Wixom were also actively producing vehicles, but both plants shut down in 2010, leaving the Orion plant as the sole operating plant in the county. In 2016, Orion began producing the Chevy Bolt BEV alongside GM ICEVs, before exclusively producing the Bolt as of 2021. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via averaging two government NAICS 3361 datasets: the U.S. Quarterly Workforce Indicators (QWI) and the U.S. Quarterly Census of Employment and Wages (QCEW). (d) Labor intensity in Oakland compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.
+ +<|ref|>text<|/ref|><|det|>[[206, 584, 830, 613]]<|/det|> +ICEV labor intensity) rose by a similar amount, which we attribute to a general market shift towards larger vehicle types [41]. + +<|ref|>text<|/ref|><|det|>[[206, 613, 832, 699]]<|/det|> +Oakland thus represents a case in which the transition to BEV assembly did not appreciably change the labor intensity trajectory compared to the rest of the U.S., which continued to assemble mostly ICEVs during the same period. However, while the labor intensity remained similar, the total number of jobs declined due to the reduced vehicle production volumes (Figure 4b,c). Finally, we note that the labor intensity corresponding to Chevy Bolt BEV assembly excludes battery pack assembly labor to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 87, 790, 130]]<|/det|> +the best of our knowledge1. Battery cell manufacturing labor is also excluded since the battery cells are manufactured off- site2. The labor intensity for the Bolt is expected to increase if either battery pack assembly or cell manufacturing activity is included. + +<|ref|>sub_title<|/ref|><|det|>[[165, 144, 712, 184]]<|/det|> +## 6 McLean: ten-fold increase in labor intensity during BEV factory production ramp + +<|ref|>text<|/ref|><|det|>[[165, 192, 790, 306]]<|/det|> +In this final case study, we turn to McLean County, Illinois, home to the former Mitsubishi vehicle assembly plant [44] (Figure 5). During Mitsubishi's ownership, vehicles produced included the Eclipse sedan and Outlander sport utility vehicle, with vehicle production plateauing ca. 69,000 vehicles in 2014. In the same year, the plant employed 1,250 workers. In 2015, Mitsubishi shut down operations due to global competitive pressures [45]. The plant was subsequently purchased by Rivian to be re- tooled for assembling electric pickup trucks [46]. In 2022, Rivian produced ca. 18,000 electric vehicles while employing 6,000 workers. + +<|ref|>text<|/ref|><|det|>[[165, 307, 790, 450]]<|/det|> +We thus calculate labor intensity in McLean to be 18 WPV during ICEV production in 2014, compared with 316 WPV during BEV production in 2022. The high labor intensity seen in 2022 mirrors the similarly high levels seen during Alameda's first five years of BEV production (Figure 3d). Both cases represent periods during which fledgling BEV makers undergo rapid production ramp- up and in which production levels have not yet reached a steady state. Rivian also reportedly manufactures battery packs on- site [47], contributing to an additional use of labor which is included in the labor intensity calculation. Note, however, that battery cell manufacturing is not factored into the labor intensity calculation to the best of our knowledge since Rivian is reportedly using cells supplied by Samsung that are not manufactured on- site [47]. + +<|ref|>sub_title<|/ref|><|det|>[[166, 464, 317, 483]]<|/det|> +## 7 Discussion + +<|ref|>sub_title<|/ref|><|det|>[[166, 493, 633, 510]]<|/det|> +### 7.1 More workers for BEV assembly, not fewer + +<|ref|>text<|/ref|><|det|>[[165, 516, 790, 644]]<|/det|> +The three case studies in Alameda, Oakland, and McLean counties collectively suggest that each BEV requires just as many, if not more, workers to assemble than each ICEV. We summarize this finding in Table 1, which compares the labor intensity before and after the BEV transition at each transition plant. For each comparison, we report the numbers corresponding to the year with the highest peak labor productivity (the inverse of labor intensity). In Alameda, labor intensity rose three- fold from 16 WPV to 46 WPV after the BEV transition. In Oakland, labor intensity rose two- fold from 17 WPV to 31 WPV. Finally, in McLean, labor intensity rose from 18 WPV to 316 WPV. In all three cases, the labor intensity increased following the BEV transition. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[203, 85, 832, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[206, 464, 832, 536]]<|/det|> +
Fig. 5 (a) Vehicle production history in McLean County, Illinois. Before 2016, Mitsubishi owned an ICEV production plant in the town of Normal. The factory has since been purchased by Rivian, which began producing electric SUVs and pick-up trucks in 2021. (b) Annual vehicle production volumes. (c) Employment numbers, sourced via news reports because government NAICS 3361 data was suppressed for McLean County. (d) Labor intensity in McLean compared to the U.S. average, measured in workers needed to produce 1,000 vehicles per annum.
+ +<|ref|>text<|/ref|><|det|>[[207, 552, 832, 651]]<|/det|> +Alameda highlighted a labor scenario for a maturing BEV assembly process. After ten years of BEV production, labor intensity in Alameda remained more than twice as high compared to its ICEV counterparts. McLean showed how labor intensity may increase ten- fold during the early years of vehicle production ramp towards volume production, especially for a new automaker. Oakland highlighted a case in which the labor intensity for making BEVs was no different from the baseline labor intensity for making ICEVs around the U.S. during the same time period. + +<|ref|>sub_title<|/ref|><|det|>[[207, 666, 752, 683]]<|/det|> +### 7.2 Explaining higher labor intensity in BEV assembly + +<|ref|>text<|/ref|><|det|>[[207, 689, 832, 732]]<|/det|> +We postulate several factors that may influence the labor intensity of BEV assembly: the degree of investment in manufacturing technology development, the higher complexity of premium- class vehicles, and the extent of vertical integration. These factors + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[237, 90, 718, 420]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[237, 421, 714, 502]]<|/det|> +Table 1 More workers per vehicle needed for BEV assembly for all three counties studied. Production volume, employment numbers, and labor intensity correspond to the year of minimum labor intensity. The lists of vehicle models highlight certain high-volume production models and are not exhaustive. (\*) Battery pack assembly for Model S and X reportedly took place in Alameda [30]; Battery pack assembly for Model 3 and Y reportedly took place offsite in Gigafactory 1 located in Sparks, Nevada [31-35]. + +
ICEV AssemblyBEV Assembly
Alameda, CA
OwnerNUMMI
Vehicle modelsTacoma, Corolla, Vibe
Peak productivity year2006
Production volume429,000
Employment6,700
Labor intensity16 WPV
Includes pack assembly?—
Includes cell manuf.?—
Oakland, MI
OwnerGeneral Motors
Vehicle modelsSonic, Verano, Malibu
Peak productivity year2013
Production volume159,000
Employment2,600
Labor intensity17 WPV
Includes pack assembly?—
Includes cell manuf.?—
McLean, IL
OwnerMitsubishi
Vehicle modelsOutlander, Galant, Eclipse
Peak productivity year2014
Production volume69,000
Employment1,300
Labor intensity18 WPV
Includes pack assembly?—
Includes cell manuf.?—
+ +<|ref|>text<|/ref|><|det|>[[165, 525, 790, 568]]<|/det|> +may explain why the labor intensity for BEV assembly today exceeds the labor intensity of past ICEV assembly, and, furthermore, why labor intensity outcomes are not uniform but vary by region. We discuss each of these factors in turn. + +<|ref|>text<|/ref|><|det|>[[165, 568, 790, 740]]<|/det|> +Investment in manufacturing technology development. Increased levels of investment in technology development have the counter- intuitive effect of suppressing near- term labor productivity as companies hire a larger workforce to improve the technology. Such may be the case in Alameda, where Tesla's investment in BEV manufacturing technology [48] has resulted in a workforce of salaried engineers who are co- located within the assembly plant [49]. This claim is corroborated by government occupation data, which shows that engineering occupations accounted for \(7\%\) of total assembly employment in California in 2021 compared to \(5\%\) at the national level and \(3\%\) in Michigan (Table A1). Wages data tells a similar story: the average income of assembly workers in Alameda, CA, more than doubled from 2013 to 2021, indicating a growing presence of high- paid engineering occupations. Comparatively, in Oakland, MI, the average income increased by only \(20\%\) over the same period. We note that + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[207, 87, 831, 172]]<|/det|> +the presence of the engineering and other non- production workforce does not appear to be the major driver of the increase in labor intensity of BEV assembly compared to ICEV assembly in Alameda. Even under the extreme assumption that all workers in Alameda prior to the BEV transition were strictly in production roles, Alameda's BEV labor intensity in 2021 would remain over 30 WPV, twice as high as during peak ICEV production in 20063. + +<|ref|>text<|/ref|><|det|>[[207, 173, 831, 343]]<|/det|> +Vehicle complexity. First- time BEV makers tend to produce and sell premium- class vehicles before they produce and sell economy- class vehicles. For example, the first mass- produced BEVs from Tesla (Model S) and Rivian (R1T) are both premium- class vehicles that sell for more than \$80,000 USD4. By comparison, before the BEV transition, the median sales price of ICEVs produced at the same plants was only \$28,000 USD, since production was dominated by mass- market vehicles such as the Toyota Tacoma and the Mitsubishi Outlander (Figure A3). The present- day bias towards more premium- class BEVs may thus partly explain the higher labor intensity observed for BEV assembly currently. As BEV manufacturers move towards offering more economy- class BEVs, labor intensity may yet decrease. This trend may already be seen in Oakland, where the Chevy Bolt EV's median sales price nearly matched that of its ICEV predecessors, as did labor intensity. + +<|ref|>text<|/ref|><|det|>[[207, 344, 831, 556]]<|/det|> +Vertical integration. The rise in BEV labor productivity also parallels a growing industry trend towards consolidating workers who have historically worked off- site (i.e. at the site of parts suppliers) but who now work within the assembly plant (Figure A4). This consolidation is most clearly evident in Alameda, where Tesla has chosen to design and manufacture many vehicle components in- house, including electric motors [50], semiconductors [30], and seats [51]. In contrast, legacy automakers, over a period between the late 1990s and early 2000s, have opted for a strategy of outsourcing in which many vehicle components, such as the engine and transmission, are designed and manufactured off- site by parts suppliers [48, 52]. Consequently, parts manufacturing workers (e.g. engine and transmission manufacturing workers) that supported vehicle assembly tended not to be co- located at the site of vehicle assembly [53]. A transition to BEVs does not necessarily guarantee that an automaker will move towards greater vertical integration. For example, in the Oakland assembly plant, GM reportedly assembled battery packs off- site, so the workers for battery pack assembly are not included from our measure of labor intensity for the Chevy Bolt. + +<|ref|>sub_title<|/ref|><|det|>[[208, 569, 451, 585]]<|/det|> +### 7.3 Parts manufacturing + +<|ref|>text<|/ref|><|det|>[[207, 592, 831, 693]]<|/det|> +While this study focused on the trajectory of automotive assembly jobs, the fate of automotive parts manufacturing jobs warrants further study. Parts manufacturing jobs comprised 66% of all auto manufacturing jobs in the U.S. in 2022 (Figure 1). For some parts manufacturing activities such as electrical, steering, suspension, brakes, seats, and interior trim, worker demand will persist within the context of BEV production. However, disruption in transmission and engine- related parts manufacturing jobs is expected since these components are simplified or absent in BEV powertrains + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 86, 790, 115]]<|/det|> +[54]. Engine manufacturing jobs will especially be impacted, considering the lack of combustion engines in BEVs. + +<|ref|>text<|/ref|><|det|>[[165, 116, 790, 272]]<|/det|> +[54]. Engine manufacturing jobs will especially be impacted, considering the lack of combustion engines in BEVs.In the U.S., engine manufacturing jobs accounted for \(7\%\) of all U.S. auto manufacturing jobs in 2022, or 56,486 workers. With the BEV transition, these workers face job losses but also the opportunity for re- employment in other parts of the BEV value chain. The most immediate source of worker re- employment is in battery cell manufacturing which accounts for up to \(75\%\) of the total labor intensity for producing a BEV powertrain [19]. Employment data on existing BEV OEMs also suggests that battery cell manufacturing including pack assembly can increase labor needed per BEV by over \(50\%\) (Figure A2 and Table A2) \(^{5}\) . Ultimately, whether new jobs in battery cell manufacturing will replace lost jobs in automotive parts manufacturing depends on the labor intensity needed to make battery cells, the geographical co- location of jobs, and skills [55- 58], which should be further investigated (Figure B5). + +<|ref|>sub_title<|/ref|><|det|>[[165, 286, 286, 302]]<|/det|> +### 7.4 Outlook + +<|ref|>text<|/ref|><|det|>[[165, 309, 790, 452]]<|/det|> +Our study challenges the narrative that BEVs require \(30\%\) less labor to assemble than ICEVs. The observed data in three transition plants indicates that BEVs are more labor intensive to assemble than ICEVs, rather than less. These results suggest that the path towards greater BEV manufacturing will require a workforce size at the assembly plant that matches or exceeds the size of the ICEV workforce. The demand for workers at BEV assembly sites is spurred by a continued need to innovate and improve upon existing BEV manufacturing technology, a drive towards greater vertical integration, and the present- day tendency towards the production of higher- cost BEVs. Our analysis suggests that rapid, widespread job displacement during the BEV transition is a smaller risk than many fear. + +<|ref|>sub_title<|/ref|><|det|>[[165, 467, 297, 486]]<|/det|> +## 8 Methods + +<|ref|>sub_title<|/ref|><|det|>[[165, 497, 452, 514]]<|/det|> +### 8.1 Vehicle Production Data + +<|ref|>text<|/ref|><|det|>[[165, 520, 790, 662]]<|/det|> +Vehicle production data \(V\) was obtained from the Automotive News Research & Data Center [25], which collates North American light- duty (i.e., passenger vehicles and pick- up trucks) vehicle sales, production, and inventory data on a monthly basis and organizes the data by automaker, vehicle make and model, and manufacturing plant location. Only vehicle production in the U.S. was considered for this work. Electric vehicle models were manually identified by cross- referencing publicly available lists of BEV makes and models. The counties in which manufacturing plants reside were manually identified using the counties' Federal Information Processing System (FIPS) codes to enable linking vehicle production data with county- level employment data (see Section 8.2). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[207, 85, 429, 101]]<|/det|> +### 8.2 Employment Data + +<|ref|>text<|/ref|><|det|>[[207, 107, 831, 150]]<|/det|> +County- level automotive manufacturing employment data \(W\) was sought from two government data sources, the Quarterly Census of Employment and Wages (QCEW) and the Quarterly Workforce Indicators (QWI), as well as from local news reports. + +<|ref|>text<|/ref|><|det|>[[207, 151, 831, 265]]<|/det|> +The QCEW dataset, administered by the U.S. Bureau of Labor Statistics (BLS), comprises a quarterly count of employment and wages for workers covered by unemployment insurance programs, which totals more than \(95\%\) of U.S. workers [59]. The data is aggregated and classified by industry according to the North American Industry Classification System (NAICS), and provided for county, metropolitan statistical area (MSA), state, and national levels. This dataset provides employment data at the level of an "establishment", defined as a single physical worksite engaged predominantly in one type of economic activity, e.g., making automotive vehicles. + +<|ref|>text<|/ref|><|det|>[[207, 265, 832, 435]]<|/det|> +The QWI dataset is administered by the U.S. Census Bureau and consists of administrative data from the Longitudinal Employer- Household Dynamics (LEHD) program, including Unemployment Insurance Wage Records, data from the Census Bureau, and data from the Office of Personnel Management (OPM). This allows the QWI dataset to provide both firm- level and worker- level data. The QWI data covers more than just those eligible for unemployment insurance benefits and includes all employers and their employees for which the administrative records are available. It provides detailed breakdowns by industry (using NAICS) and worker demographics (gender, age, education, race, and ethnicity), as well as earnings and various measures of job and worker flows. Unlike QCEW, which focuses on the establishment level, QWI produces insights into both the employer side (job creation, destruction, etc.) and employee side (turnover rates, accessions, separations) of labor dynamics. + +<|ref|>text<|/ref|><|det|>[[207, 435, 832, 577]]<|/det|> +This work sought national, state- level, and county- level employment data from both QCEW and QWI under NAICS code 3361, which corresponds to Motor Vehicle Manufacturing and encompasses assembly workers. Parts manufacturing labor is excluded from these counts because it has its own distinct NAICS code. Occupation data from the Occupational Employment and Wage Statistics (OEWS) program of BLS was used to confirm that approximately \(60 - 80\%\) of NAICS 3361 workers are in production occupations (Standard Occupational Classification code 51- 0000, which includes assemblers and fabricators), depending on the region of the U.S. For reference, Table A1 summarizes occupation- industry data for NAICS 3361 in Michigan, California, and the U.S. + +<|ref|>text<|/ref|><|det|>[[207, 578, 832, 677]]<|/det|> +As described in Section 8.3, NAICS 3361 data was sometimes suppressed at the county level from QCEW, QWI, or both databases. For this reason, local news reports, found via internet searches, provided another independent estimate of employment data for specific automotive factory sites. This data was used to substitute employment data if QCEW and QWI data were both suppressed, or to corroborate available QCEW and QWI data. Table 2 summarizes which data sources were used for each of the three counties. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[165, 84, 792, 141]]<|/det|> + +
CountyQCEW (Gov)QWI (Gov)NewsNotes
Alameda✓✓QCEW data was suppressed
Oakland✓✓Average of QCEW and QWI data was used
McLean✓QCEW and QWI data were both suppressed
+ +<|ref|>table_footnote<|/ref|><|det|>[[165, 145, 757, 157]]<|/det|> +Table 2 Summary of data sources used to study employment in the three transition counties. + +<|ref|>sub_title<|/ref|><|det|>[[165, 182, 320, 197]]<|/det|> +## 8.3 Limitations + +<|ref|>text<|/ref|><|det|>[[165, 204, 790, 318]]<|/det|> +Regional (state- level and county- level) employment data for a particular industry is often suppressed by QCEW and QWI databases to maintain employer anonymity. This most often occurs when a single large employer comprises a majority of the data for a particular industry in a particular region. See Table 2 for a description of the suppression instances relevant to this work. Using two government databases as well as local news reports ensured a sufficient level of redundancy to circumvent these suppression instances. In most cases, the employment numbers from these data sources agree with each other, improving confidence in the reported numbers. + +<|ref|>text<|/ref|><|det|>[[165, 318, 790, 418]]<|/det|> +Another limitation is that employment data corresponding to NAICS code 3361 covers both light- and heavy- duty vehicle manufacturing employment. The vehicle production data from Automotive News only covers light- duty vehicles, so combining the two datasets requires assuming a negligible volume of heavy- duty vehicle manufacturing presence within the selected counties. NAICS sub- code 33611 covers employment specifically for light- duty manufacturing, but that data is suppressed for the states and counties examined for this work. + +<|ref|>text<|/ref|><|det|>[[165, 418, 790, 547]]<|/det|> +Additionally, there are discontinuities in the WPV calculations shown in Fig. 3- 5. These discontinuities correspond to periods of zero and extremely low vehicle production. For Alameda and McLean, periods of zero vehicle production occurred during ownership transition from their respective ICE- making to BEV- making companies. For Oakland, this period occurred during the Great Recession. We also observed that in each county, such a small number of vehicles were produced in the first year resuming production that the WPV metric disproportionately inflated for that year. Thus, for each county, we discard labor intensity calculation for the time period in which zero vehicles were produced, plus the first subsequent year that production resumed. + +<|ref|>text<|/ref|><|det|>[[165, 547, 790, 632]]<|/det|> +We also acknowledge that another common metric for labor intensity is hours worked per vehicle. While the data on hours worked is available at the national level through surveys such as Current Employment Statistics (CES), county- level data is not made public by government agencies. For this reason, we chose to report labor intensity in units of workers per vehicle. From workers per vehicle, hours worked per vehicle can be approximated using the formula: + +<|ref|>equation<|/ref|><|det|>[[421, 641, 787, 672]]<|/det|> +\[\mathrm{HPV} = \frac{W\times t}{V} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[165, 682, 790, 740]]<|/det|> +where HPV is the hours worked per vehicle, \(W\) is the number of workers, \(V\) is the total vehicles produced, and \(t\) is the annualized hours worked per worker. The time input \(t\) can be estimated to be 2,236 hours, assuming an average of 43 hours worked per week for vehicle manufacturing workers according to BLS [60]. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[207, 81, 460, 101]]<|/det|> +## Resource Availability + +<|ref|>text<|/ref|><|det|>[[207, 111, 831, 141]]<|/det|> +Resource AvailabilityFurther information and requests should be directed to and will be fulfilled by Anna Stefanopoulou (annastef@umich.edu). + +<|ref|>sub_title<|/ref|><|det|>[[208, 155, 433, 175]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[207, 184, 831, 242]]<|/det|> +AcknowledgementsThe authors thank Kristin Dziczek for her insights and guidance on the auto manufacturing landscape in Michigan. The authors also thank Katelyn Freese and Katerina Freudenberg for their assistance with processing vehicle production data. We also appreciate help from Rebecca Gao for editing the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[208, 256, 466, 275]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[207, 284, 832, 356]]<|/det|> +Author ContributionsA.W.: conceptualization; methodology; writing - original draft; writing - review and editing; visualization. O.Y.A: methodology; software; investigation; data curation; visualization; writing - original draft; writing - review and editing. G.E.: methodology; writing - review and editing. A.S: conceptualization; writing - review and editing; funding acquisition; project administration. + +<|ref|>sub_title<|/ref|><|det|>[[207, 370, 424, 390]]<|/det|> +## Glossary of Terms + +<|ref|>text<|/ref|><|det|>[[216, 399, 650, 515]]<|/det|> +Glossary of TermsBEV Battery Electric VehicleGM General MotorsICEV Internal Combustion Engine VehicleOEWS Occupational Employment and Wage StatisticsNAICS North American Industry Classification SystemNUMMI New United Motors Manufacturing Inc.QCEW Quarterly Census of Employment and WagesQWI Quarterly Workforce Indicators + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[165, 81, 295, 101]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[170, 110, 792, 152]]<|/det|> +[1] International Labor Organization: COVID- 19 and the automotive industry. Technical report, International Labour Organization, Sectoral Policies Department (April 2020) + +<|ref|>text<|/ref|><|det|>[[172, 164, 791, 209]]<|/det|> +[2] U.S. Bureau of Labor Statistics: Automotive Industry: Employment, Earnings, and Hours. https://www.bls.gov/iag/tgs/iagauto.htm. Accessed: 2023- 10- 16 (2023) + +<|ref|>text<|/ref|><|det|>[[172, 220, 791, 264]]<|/det|> +[3] Klier, T., Rubenstein, J.: Charging Ahead—The Electrification of the Auto Industry. https://www.chicagofed.org/publications/blogs/chicago- fed- insights/2021/charging- ahead- electrification- auto- industry. Accessed: 2024- 3- 13 (2021) + +<|ref|>text<|/ref|><|det|>[[172, 275, 791, 319]]<|/det|> +[4] International Energy Agency: Corporate strategy – Global EV Outlook 2023 – Analysis. https://www.iea.org/reports/global- ev- outlook- 2023/corporate- strategy. Accessed: 2023- 10- 24 (2023) + +<|ref|>text<|/ref|><|det|>[[172, 329, 791, 387]]<|/det|> +[5] Committee on Assessment of Technologies for Improving Fuel Economy of Light- Duty Vehicles- Phase: Assessment of technologies for improving Light- Duty vehicle fuel economy—2025- 2035. Technical report, National Academies of Sciences (2021) + +<|ref|>text<|/ref|><|det|>[[172, 398, 789, 428]]<|/det|> +[6] UAW Research Department: Taking the high road: Strategies for a fair EV future. Technical report, UAW (January 2020) + +<|ref|>text<|/ref|><|det|>[[172, 438, 789, 468]]<|/det|> +[7] Emden, J., Murphy, L.: COP26: A just transition? – workshop summary. Technical report, Institute for Public Policy Research (January 2022) + +<|ref|>text<|/ref|><|det|>[[172, 479, 791, 523]]<|/det|> +[8] Romero- Lankao, P., Rosner, N., Brandtner, C., Rea, C., Mejia- Montero, A., Pilo, F., Dokshin, F., Castan- Broto, V., Burch, S., Schnur, S.: A framework to centre justice in energy transition innovations. Nature Energy, 1- 7 (2023) + +<|ref|>text<|/ref|><|det|>[[172, 534, 790, 577]]<|/det|> +[9] Just Transition Initiative: A framework for just transitions. Technical report, Center for Strategic & International Studies, Climate Investment Funds (January 2021) + +<|ref|>text<|/ref|><|det|>[[165, 588, 790, 618]]<|/det|> +[10] Lim, J., Aklin, M., Frank, M.R.: Location is a major barrier for transferring US fossil fuel employment to green jobs. Nat. Commun. 14(1), 5711 (2023) + +<|ref|>text<|/ref|><|det|>[[165, 629, 789, 659]]<|/det|> +[11] Laska, A., Hughes- Cromwick, E.: Electric vehicles: Policies to help america lead. Technical report, Third Way (November 2022) + +<|ref|>text<|/ref|><|det|>[[165, 669, 790, 699]]<|/det|> +[12] Ford Motor Company: Ford motor company - CEO strategic update. Technical report, Ford Motor Company (October 2017) + +<|ref|>text<|/ref|><|det|>[[165, 710, 789, 739]]<|/det|> +[13] Vellequette, L.P.: VW accelerates electric push with more models, more production. Technical report, Automotive News (March 2019) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[205, 85, 833, 129]]<|/det|> +[14] Levin, T.: Ford is slashing thousands of jobs as it goes electric. experts say a tidal wave of layoffs will rock the industry as it undergoes a seismic shift. Business Insider (2022) + +<|ref|>text<|/ref|><|det|>[[206, 141, 832, 170]]<|/det|> +[15] Charette, R.N.: How EVs are reshaping labor markets. Technical report, IEEE Spectrum (January 2023) + +<|ref|>text<|/ref|><|det|>[[206, 181, 832, 210]]<|/det|> +[16] Fichera, A.: Trump autoworkers speech fact check: What of electric vehicles? The New York Times (2023) + +<|ref|>text<|/ref|><|det|>[[206, 222, 832, 265]]<|/det|> +[17] Barrett, J., Bivens, J.: The stakes for workers in how policymakers manage the coming shift to all- electric vehicles. Technical report, Economic Policy Institute (September 2021) + +<|ref|>text<|/ref|><|det|>[[206, 277, 832, 320]]<|/det|> +[18] Küpper, D., Kuhlmann, K., Tominaga, K., Arora, A., Schlageter, J.: Shifting gears in auto manufacturing. Technical report, Boston Consulting Group (September 2020) + +<|ref|>text<|/ref|><|det|>[[206, 331, 832, 374]]<|/det|> +[19] Cotterman, T., Fuchs, E.R.H., Whitefoot, K.: The transition to electrified vehicles: Evaluating the labor demand of manufacturing conventional versus battery electric vehicle powertrains (2022) + +<|ref|>text<|/ref|><|det|>[[206, 386, 832, 429]]<|/det|> +[20] Cotterman, T.L.: Technology transitions in the electricity and automotive sectors: Embracing political, social, and economic constraints. PhD thesis, Carnegie Mellon University, Ann Arbor, United States (2022) + +<|ref|>text<|/ref|><|det|>[[206, 440, 832, 498]]<|/det|> +[21] Sakti, A., Azevedo, I.M.L., Fuchs, E.R.H., Michalek, J.J., Gallagher, K.G., Whitacre, J.F.: Consistency and robustness of forecasting for emerging technologies: The case of li- ion batteries for electric vehicles. Energy Policy 106, 415- 426 (2017) + +<|ref|>text<|/ref|><|det|>[[206, 509, 832, 552]]<|/det|> +[22] Funk, P., Davis, A., Vaishnav, P., Dewitt, B., Fuchs, E.: Individual inconsistency and aggregate rationality: Overcoming inconsistencies in expert judgment at the technical frontier. Technol. Forecast. Soc. Change 155, 119984 (2020) + +<|ref|>text<|/ref|><|det|>[[206, 563, 832, 620]]<|/det|> +[23] Burden, M.: GM Orion readies for Chevy Bolt EV production. https://www.detroitnews.com/story/business/autos/general-motors/2016/09/13/gm- orion- readies- chevy- bolt- ev- production/90294594/. Accessed: 2024- 3- 4 (2016) + +<|ref|>text<|/ref|><|det|>[[206, 632, 832, 704]]<|/det|> +[24] Noble, B., Hall, K.: GM laying off hundreds of workers as Chevrolet Camaro, Bolt production end. https://www.detroitnews.com/story/business/autos/chrysler/2023/12/14/gm- layoffs- autoworkers- uaw- chevrolet- camaro- bolt- production- end/71923598007/. Accessed: 2024- 3- 4 (2023) + +<|ref|>text<|/ref|><|det|>[[206, 716, 832, 730]]<|/det|> +[25] Automotive News: Automotive News Research & Data Center. Title of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[198, 87, 686, 101]]<|/det|> +publication associated with this dataset: Automotive News (2023) + +<|ref|>text<|/ref|><|det|>[[165, 111, 761, 128]]<|/det|> +[26] Austenfeld, J.R.B.: NUMMI - the great experiment. Technical report (2007) + +<|ref|>text<|/ref|><|det|>[[165, 138, 790, 181]]<|/det|> +[27] Shaiken, H.: Commitment is a Two- Way street: Toyota, california and NUMMI. Technical Report 202- 10, Institute for Research on Labor and Employment, UC Berkeley (2010) + +<|ref|>text<|/ref|><|det|>[[165, 193, 790, 236]]<|/det|> +[28] Troncoso, J.N.: End of the line: Reassembling the legacy of NUMMI, the american middle class in the era of globalization and recession. PhD thesis, University of California, Berkeley (2012) + +<|ref|>text<|/ref|><|det|>[[165, 247, 790, 290]]<|/det|> +[29] Johnston, A.: NUMMI, five years later: Picking up the pieces. https://www.kalw.org/show/crosscurrents/2015- 06- 01/ nummi- five- years- later- picking- up- the- pieces. Accessed: 2023- 10- 26 (2015) + +<|ref|>text<|/ref|><|det|>[[165, 301, 790, 344]]<|/det|> +[30] Cohen, J.: An Automotive Insider's Tour Of The Tesla Fremont Factory. https://cleantechnica.com/2014/06/25/automotive- insiders- tour- tesla- fremont- factory/. Accessed: 2023- 12- 5 (2014) + +<|ref|>text<|/ref|><|det|>[[165, 356, 800, 413]]<|/det|> +[31] Field, K.: Tesla Model 3 Battery Pack Cell Teardown Highlights Performance Improvements. https://cleantechnica.com/2019/01/28/ tesla- model- 3- battery- pack- cell- teardown- highlights- performance- improvements/. Accessed: 2023- 12- 4 (2019) + +<|ref|>text<|/ref|><|det|>[[165, 424, 790, 467]]<|/det|> +[32] Hawley, G.: Understanding Tesla's lithium ion batteries. https://evannex.com/blogs/news/understanding- teslas- lithium- ion- batteries. Accessed: 2023- 12- 4 (2023) + +<|ref|>text<|/ref|><|det|>[[165, 479, 790, 522]]<|/det|> +[33] Kane, M.: What Batteries Are Tesla Using In Its Electric Cars? https://insideevs.com/news/587455/batteries- tesla- using- electric- cars/. Accessed: 2023- 12- 4 (2022) + +<|ref|>text<|/ref|><|det|>[[165, 534, 790, 577]]<|/det|> +[34] The Tesla Team: Battery Cell Production Begins at the Gigafactory. https://www.tesla.com/blog/battery- cell- production- begins- gigafactory. Accessed: 2023- 12- 4 (2017) + +<|ref|>text<|/ref|><|det|>[[165, 589, 790, 632]]<|/det|> +[35] Korosec, K.: Tesla Will Make Electric Motors for the Model 3 at Its Massive Gigafactory. https://fortune.com/2017/01/17/ tesla- model- 3- motor- gigafactory/. Accessed: 2023- 12- 4 (2017) + +<|ref|>text<|/ref|><|det|>[[165, 644, 816, 700]]<|/det|> +[36] The Tesla Team: Panasonic Enters into Supply Agreement with Tesla Motors to Supply Automotive- Grade Battery Cells. https://www.tesla.com/blog/panasonic- enters- supply- agreement- tesla- motors- supply- automotivegrade- battery- c. Accessed: 2023- 12- 4 (2011) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[204, 85, 833, 145]]<|/det|> +[37] Lambert, F.: Tesla is building new 'drive unit production lines' at the Gigafactory, will not only manufacture battery packs. https://electrek.co/2016/10/15/tesla- drive- unit- production- lines- gigafactory- model- 3/. Accessed: 2023- 12- 5 (2016) + +<|ref|>text<|/ref|><|det|>[[204, 154, 767, 170]]<|/det|> +[38] Goldstein, A.: Janesville: An American Story. Simon & Schuster (2017) + +<|ref|>text<|/ref|><|det|>[[204, 181, 832, 211]]<|/det|> +[39] Vlasic, B.: With sonic, G.M. stands atomaking on its head. The New York Times (2011) + +<|ref|>text<|/ref|><|det|>[[205, 221, 832, 280]]<|/det|> +[40] Nadrowski, K.: Orion Assembly Site. https://web.archive.org/web/20110523235653/http://media.gm.com/content/media/us/en/news/news_detail.detail.brand.GM.html/content/Pages/news/Plant_Facts/Assembly/Orion. Accessed: 2023- 12- 5 (2011) + +<|ref|>text<|/ref|><|det|>[[205, 290, 832, 335]]<|/det|> +[41] Hula, A., Maguire, A., Bunker, A., Rojcek, T., Harrison, S.: The 2023 EPA automotive trends report. Technical Report EPA- 420- R- 23- 033, United States Environmental Protection Agency (December 2023) + +<|ref|>text<|/ref|><|det|>[[205, 344, 831, 389]]<|/det|> +[42] Motors, G.: Chevrolet Bolt EV Battery Production Resumes. https://news.gm.com/newsroom.detail.html/Pages/news/us/en/2021/sep/0920- bolt.html. Accessed: 2023- 12- 5 (2021) + +<|ref|>text<|/ref|><|det|>[[205, 399, 831, 444]]<|/det|> +[43] Motors, G.: Chevrolet Bolt EV Battery Production Resumes. https://media.chevrolet.com/media/us/en/chevrolet/home.detail.html/content/Pages/news/us/en/2021/sep/0920- bolt.html. Accessed: 2023- 12- 6 (2021) + +<|ref|>text<|/ref|><|det|>[[205, 453, 831, 498]]<|/det|> +[44] Motors, M.: Mitsubishi Motors North America, Inc. - Manufacturing Division. https://web.archive.org/web/20060506083640/http://media.mitsubishicars.com/detail?mid=MIT2004101847405&mime=ASC. Accessed: 2023- 12- 5 (2004) + +<|ref|>text<|/ref|><|det|>[[205, 508, 831, 539]]<|/det|> +[45] Yerak, B., Cancino, A.: Mitsubishi closing normal plant in illinois, ending U.S. production. Chicago Tribune (2015) + +<|ref|>text<|/ref|><|det|>[[205, 549, 831, 579]]<|/det|> +[46] The Detroit News: Rivian builds electric pickup truck and SUV. The Detroit News (2022) + +<|ref|>text<|/ref|><|det|>[[205, 589, 848, 648]]<|/det|> +[47] Weintraub, S.: Rivian R1T first drive: Easily the best pickup I've ever driven, both off- road and on. https://electrek.co/2021/09/28/rivian- r1t- first- drive- easily- the- best- pickup- ive- ever- driven- both- off- road- and- on/. Accessed: 2023- 12- 6 (2021) + +<|ref|>text<|/ref|><|det|>[[205, 658, 832, 702]]<|/det|> +[48] Cutcher- Gershenfeld, J., Brooks, D., Mulloy, M.: The decline and resurgence of the U.S. auto industry. Technical Report 399, Economic Policy Institute (May 2015) + +<|ref|>text<|/ref|><|det|>[[205, 712, 831, 728]]<|/det|> +[49] Furr, N., Dyer, J.: Lessons from tesla's approach to innovation. Harvard Business + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[198, 87, 306, 100]]<|/det|> +Review (2020) + +<|ref|>text<|/ref|><|det|>[[164, 111, 790, 142]]<|/det|> +[50] Bellon, T., White, J.: Focus: Build or buy? automakers chasing tesla rethink dependence on suppliers. Reuters (2022) + +<|ref|>text<|/ref|><|det|>[[165, 152, 790, 197]]<|/det|> +[51] Field, K.: Behind The Scenes At Tesla's Seat Factory #CleanTechnica Field Trip. https://cleantechnica.com/2019/04/27/behind-the-scenes-at-teslas-seat- factory/. Accessed: 2023- 12- 4 (2019) + +<|ref|>text<|/ref|><|det|>[[165, 207, 790, 237]]<|/det|> +[52] Chen, Y., Chowdhury, S.D., Donada, C.: Mirroring hypothesis and integrality: Evidence from tesla motors. J. Eng. Tech. Manage. 54, 41- 55 (2019) + +<|ref|>text<|/ref|><|det|>[[165, 247, 790, 291]]<|/det|> +[53] Herrigel, G., Wittke, V.: Varieties of vertical disintegration: The global trend toward heterogeneous supply relations and the reproduction of difference in US and german manufacturing. Industry Studies Association 2004- 15 (2004) + +<|ref|>text<|/ref|><|det|>[[165, 301, 790, 346]]<|/det|> +[54] Ruyter, A., Weller, S., Henry, I., Raimnie, A., Bentley, G., Nielsen, B.: Enabling a just transition in automotive: Evidence from the west midlands and south australia. Technical report, The British Academy (June 2022) + +<|ref|>text<|/ref|><|det|>[[165, 356, 790, 415]]<|/det|> +[55] Krusemark, L., Ganguly, S., Harp, T., Kulicki, A., Smith, C., Prasad, K.V.: Examining workforce needs for north america: Battery industry education and training needs assessment (BIETNA). Technical report, Center for Automotive Research (2024) + +<|ref|>text<|/ref|><|det|>[[165, 425, 790, 470]]<|/det|> +[56] Combemale, C., Whitefoot, K.S., Ales, L., Fuchs, E.R.H.: Not all technological change is equal: how the separability of tasks mediates the effect of technology change on skill demand. Ind Corp Change 30(6), 1361- 1387 (2022) + +<|ref|>text<|/ref|><|det|>[[165, 480, 789, 510]]<|/det|> +[57] Weaver, A., Osterman, P.: Skill demands and mismatch in U.S. manufacturing. ILR Review 70(2), 275- 307 (2017) + +<|ref|>text<|/ref|><|det|>[[165, 520, 790, 565]]<|/det|> +[58] Cotterman, T., Fuchs, E.R.H., Small, M.J., Whitefoot, K.: The Transition to Electrified Vehicles: Implications for the Future of Automotive Manufacturing and Worker Skills and Occupations (2022) + +<|ref|>text<|/ref|><|det|>[[165, 575, 789, 605]]<|/det|> +[59] Sadeghi, A.: The births and deaths of business establishments in the united states. Mon. Labor Rev. December 2008(1), 1- 18 (2008) + +<|ref|>text<|/ref|><|det|>[[165, 615, 790, 660]]<|/det|> +[60] U.S. Bureau of Labor Statistics: Automotive Industry: Employment, Earnings, and Hours. https://www.bls.gov/iag/tgs/iagauto.htm. Accessed: 2023- 10- 26 (2022) + +<|ref|>text<|/ref|><|det|>[[165, 670, 790, 700]]<|/det|> +[61] Federal Reserve Bank of St. Louis: Consumer Price Index for All Urban Consumers: New Vehicles in U.S. City Average (2024) + +<|ref|>text<|/ref|><|det|>[[165, 710, 790, 741]]<|/det|> +[62] Campagnol, N., Pfeiffer, A., Tryggestad, C.: Capturing the battery value- chain opportunity. Technical Report 1, McKinsey & Company (January 2022) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[206, 86, 832, 130]]<|/det|> +[63] Lambert, F.: Tesla Gigafactory 1 now employs over 3,000 workers as it becomes biggest battery factory in the world. https://electrek.co/2018/08/21/tesla- gigafactory- 1- 3000- workers/. Accessed: 2023- 12- 11 (2018) + +<|ref|>text<|/ref|><|det|>[[206, 141, 832, 185]]<|/det|> +[64] Knehr, K.W., Kubal, J.J., Nelson, P.A., Ahmed, S.: Battery performance and cost modeling for electric vehicles - a manual for BatPaC v5.0. Technical Report ANL/CSE- 22/1, Argonne National Laboratory (July 2022) + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[283, 128, 666, 545]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[163, 549, 790, 585]]<|/det|> +
Fig. A1 U.S.-level vehicle production (a), assembly workers (b), and labor intensity (c). Vehicle production data was obtained from the Automotive News Research & Data Center, while employment data was obtained from QCEW.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[234, 102, 792, 416]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[205, 421, 833, 491]]<|/det|> +
Fig. A2 Battery cell and pack manufacturing increases the labor intensity of making BEVs. (a) Annual vehicle production volume in Alameda, CA. (b) Employment in Alameda, CA and Sparks, NV, based on news reports (see Table A2). Employment in Sparks, NV, reflects additional workers for battery cell and pack manufacturing at Tesla Gigafactory 1. Data is shown only when Alameda and Sparks data are available for the same production year. (c) Comparison of labor intensity (WPV) with and without including workers from Sparks, NV.
+ +<|ref|>image<|/ref|><|det|>[[205, 520, 833, 680]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[205, 695, 833, 731]]<|/det|> +
Fig. A3 Comparison of vehicle sales prices (MSRP) in Alameda (a), Oakland (b), and McLean (c). Dollar values are inflation-adjusted to 2023 dollars based on the Consumer Price Index (CPI) for U.S. new vehicles [61]. MSRP for all available vehicle trims are shown.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[171, 140, 788, 321]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[163, 340, 792, 364]]<|/det|> +
Fig. A4 Concept illustration: vertical integration creates more workforce co-location at the site of vehicle assembly.
+ +<|ref|>table<|/ref|><|det|>[[208, 475, 745, 595]]<|/det|> + +
Location20132021
% of NAICS 3361 workers in productionCalifornia (State)66%62%
Michigan (State)74%81%
U.S.74%76%
% of NAICS 3361 workers in engineeringCalifornia (State)4%7%
Michigan (State)5%3%
U.S.5%5%
NAICS 3361 average monthly payAlameda, CA$6,243$16,462
Oakland, MI$7,557$8,907
U.S.$6,660$6,864
+ +<|ref|>text<|/ref|><|det|>[[210, 597, 745, 677]]<|/det|> +Table A1 Proportion of NAICS 3361 workers in production (SOC code 51- 0000) and architecture/engineering occupations (SOC code 17- 0000), and average monthly pay of NAICS 3361 workers for California, Michigan, and the U.S. Occupation data was obtained from the Occupational Employment and Wage Statistics (OEWS). Income data for Alameda, CA was obtained from QWI. Income data for Oakland, MI was obtained from averaging QWI and QCEW data. Income data for the U.S. was obtained from QCEW. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[238, 228, 797, 560]]<|/det|> + +
LocationDateNews SourceReported Employment
Tesla (Alameda)Jun 2012SFGATE1,000
Jul 2013Wired3,000
Jun 2016TheCountryCaller6,000
Oct 2017The Mercury News10,000
Mar 2019Forbes15,000
Jun 2022Tesla22,000
Tesla/PENA (Sparks)2016Electrek850
2017Electrek3,249
2018The Associated Press7,059
2022Tesla12,000
NUMMI (Alameda)Jan 2002SFGATE5,739
Mar 2006East Bay Times5,500
Apr 2010Recordnet.com4,700
Rivian (Normal)Oct 2021WGLT3,000
Apr 2022CIPROUD5,000
Jun 2022Energy News Network5,600
Jul 2022CIPROUD6,000
Mar 2023WGLT7,400
Mitsubishi (Normal)2004Chicago Tribune3,150
2014Local Wiki1,250
2015Chicago Tribune1,280
2016WQAD81,200
GM (Orion)2013CarGroup.org2,561
2022GM1,238
2023Wards Auto1,270
+ +<|ref|>table_footnote<|/ref|><|det|>[[238, 562, 756, 585]]<|/det|> +Table A2 List of news reports used to corroborate factory employment numbers. PENA: Panasonic Energy of North America. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[166, 81, 558, 102]]<|/det|> +## Appendix B Workers per GWh + +<|ref|>text<|/ref|><|det|>[[166, 111, 790, 168]]<|/det|> +McKinsey reported that, on average, new battery factories add approximately 80 jobs for every GWh of capacity, i.e. 80 workers per GWh [62]. This number carries some uncertainty since differences in value- chain coverage, e.g. battery- cell production only versus local module and pack production or co- location of R&D facilities, are unclear. + +<|ref|>text<|/ref|><|det|>[[166, 168, 790, 280]]<|/det|> +Tesla's Gigafactory 1 reportedly employed 3,249 people when the factory was producing 20 GWh of annual output [63]. Among these workers, 1,201 were employed by Panasonic, the main battery cell manufacturer, 93 are employed by Heitkamp & Thumann Group (H&T), a battery cell can supplier, and 1,955 were employed by Tesla. Assuming those employed by Panasonic and H&T are responsible for battery cell manufacturing, we infer that 1,294 workers are involved with producing 20 GWh of annual output, or 65 workers per GWh. If the employees from Tesla are included, then the calculation yields 162 workers per GWh. + +<|ref|>text<|/ref|><|det|>[[166, 281, 790, 338]]<|/det|> +The BatPaC v5.0 baseline factory model reported an annual labor of 3,876,000 hours per year to produce 50 GWh of output [64]. Assuming each worker works 2,236 hours per year and (equivalent to a 43- hour work- week, the U.S. average for automotive manufacturing [60]), this amounts to 35 workers per GWh. + +<|ref|>text<|/ref|><|det|>[[166, 339, 790, 438]]<|/det|> +Cotterman et al. [19] reported labor intensity per BEV powertrain assuming a 60kWh battery pack which varied depending on the data source and whether the labor was broken down between cell and pack/module assembly. For data sources where this breakdown was available, labor intensity ranged between 11 to 16 hours per 60kWh for industry data sources and 6 to 15 hours per 60kWh for public data sources. Assuming again 2,236 hours per year worked per worker, the range of labor demand is equivalent to 44 to 119 workers per GWh. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[268, 263, 763, 533]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[204, 540, 832, 565]]<|/det|> +
Fig. B5 Comparison of engine manufacturing parts jobs in 2022 against projected battery cell manufacturing jobs assuming 100% BEV uptake and under various assumptions of jobs per GWh.
+ +<--- Page Split ---> diff --git a/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/images_list.json b/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..4a12cb6c176025f5d81dfe9f85c22f9481a8b77a --- /dev/null +++ b/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. Conceptual outline and proposed concept for the meta borylation of phenol. A. Meta functionalization of phenol, a, electrophilic approach. b, template approach. c, transient directing group approach. B. Conceptual Background. a, common structural skeleton for O-Si interaction. b-e, known literature reports of O-Si interactions. C. Hypothesis, D. Reaction design. a, ligand design by structural modification, b, proposed approach for meta borylation.", + "footnote": [], + "bbox": [ + [ + 105, + 85, + 884, + 636 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Ligand design and optimization of reaction conditions. A. Optimization of steering group, B. Deleterious results with other ligands. C. Origin of meta selectivity, D. Proof of concept. Reactions are on 0.2 mmol scales. \\(^{a}\\) Conversion was reported. In parenthesis, isolated yields are reported. See SI for details.", + "footnote": [], + "bbox": [ + [ + 104, + 115, + 888, + 570 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. Substrates scope for substituted arenes. Reactions are in 0.5 mmol scale. aConversion was reported. b1.5 equiv. B2pin was used. c2.0 equiv. B2pin was used. See SI for details.", + "footnote": [], + "bbox": [ + [ + 102, + 207, + 895, + 802 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4. Substrates scope for the 4-substituted arenes and C6 borylation of indoles. Reactions are in 0.5 mmol scale. aConversions were reported. b2.0 equiv. B2pin2. See SI for details.", + "footnote": [], + "bbox": [ + [ + 105, + 194, + 884, + 775 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5. Applications, Catalyst Preparation and Testing. A. Late-stage meta-C-H borylation, B. Removal of silane group, C. Catalyst synthesis, D. Test of reactivity of catalyst 10. Reactions are in 0.5 mmol scale. aConversions were reported. See SI for details.", + "footnote": [], + "bbox": [ + [ + 103, + 84, + 888, + 448 + ] + ], + "page_idx": 8 + } +] \ No newline at end of file diff --git a/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b.mmd b/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b.mmd new file mode 100644 index 0000000000000000000000000000000000000000..42c0db28ef76cad3b1ecf1bb7fd1378b224fa615 --- /dev/null +++ b/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b.mmd @@ -0,0 +1,164 @@ + +# Meta Selective C-H Borylation Directed by Secondary Silicon Oxygen Interaction + +Buddhadeb Chattopadhyay ( \(\boxed{ \begin{array}{r l} \end{array} }\) buddhadeb.c@cbmr.res.in) Center of Biomedical Research (CBMR) https://orcid.org/0000- 0001- 8473- 2695 + +Saikat Guria Center of Biomedical Research (CBMR) + +Mirja Md Hassan Centre of Biomedical Research (CBMR) + +Sayan Dey Center of Biomedical Research (CBMR) + +Article + +Keywords: + +Posted Date: August 17th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1837437/v1 + +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. We declare that the authors have no competing interests except that We have filled an Indian Patent (Patent Application No: 202211036590) based on this work (including the ligand and catalyst). + +<--- Page Split ---> + +# Meta Selective C-H Borylation Directed by Secondary Silicon Oxygen Interaction + +Saikat Guria†, Mirja Md Mahamudul Hassan†, Sayan Dey†, Buddhadeb Chattopadhyay†\* + +†Department of Biological & Synthetic Chemistry, Centre of Biomedical Research, SGPGIMS Campus, Raebareli Road, Lucknow 226014, Uttar Pradesh, India \*Correspondence to: buddhadeb.c@cbmr.res.in + +Summary Paragraph: Remote meta selective C- H functionalization1,2,3 of aromatic compounds remains a challenging problem in chemical synthesis. Here, we report an iridium catalyst bearing a bidentate pyridine- pyridone (PY- PYRI) ligand framework that efficiently catalyzes this meta selective borylation reaction. We demonstrate that the developed concept can be employed to introduce a boron functionality at the remote meta position of phenols, phenol containing bioactive and drug molecules, which was an extraordinary challenge. Moreover, we have demonstrated that the method can also be applied for the remote C6 borylation of indole derivatives including tryptophan that was the key synthetic precursor for the total synthesis of Verruculogen and Fumitremorgin A alkaloids. The origin of the remote meta selectivity was described as a secondary silicon oxygen interaction4 that was never used in C- H functionalization chemistry. + +<--- Page Split ---> + +Transition metal- catalyzed C- H bond activation and functionalization \(^{5,6,7,8,9,10,11}\) of aromatic compounds has been branded as one of the most significant chemical transformations. This has a profound impact in modern synthetic organic chemistry, ranging from laboratory methods to industrial deployment. \(^{12,13}\) However, the key underlying principles for the success of the metal catalysis lies on the two important factors, such as: (i) design and synthesis of new generation ligand framework that can produce highly reactive catalyst system \(^{14,15}\) and (ii) substrates' structure modifications \(^{16}\) by which site selectivity could be controlled by the steric crowding \(^{17,18,19,20,21}\) or various weak interactions \(^{22,23,24,25}\) of the aromatic compounds among several similar type of C- H bonds via the ligand- substrate pre- organization \(^{26,27}\) . In recent times, many elegant approaches \(^{28}\) have been developed for the functionalization of proximal \(^{15,29,30,31}\) and remote C- H bonds \(^{1,3,32,33,34,35,36,37,38}\) of arenes by the design of either new ligand frameworks with an extended architectures featuring a weak coordinating functional groups \(^{39}\) or templates \(^{40}\) as well as transient mediators \(^{41}\) or transient directing groups \(^{42}\) attached with the substrates. While ligand having an extended architecture or template approaches are extremely important to functionalize the remotely located C- H bonds of arenes, but requirement of multi- step preparation of the linkers of the ligands and templates of the aromatic substrates significantly limit the wide application of the methods. \(^{43}\) + +Among numerous aromatic substrates, phenols are the most widespread aromatic compounds that acquired household products including several bioactive to important drug molecules. \(^{44}\) Moreover, it is well- documented that \(10\%\) of the top 200 selling pharmaceuticals contain a phenol and several others employ phenols as synthetic intermediates. \(^{45}\) Furthermore, phenols are also key components of the biopolymers melanin, lignin, resins and polyphenylene oxides. \(^{44,45,46}\) In industry, phenol is routinely used as a raw material to make numerous important components by means of its diversification via the synthetic manipulation. \(^{44,45}\) Thus, direct functionalization of phenols would be a significant development for the rapid access of numerous important products. \(^{46}\) In this context, traditional electrophilic substitution is an alternative methods that affords variously substituted phenols (Fig. 1A, a). \(^{47}\) Employing this method, one can easily access ortho and para substituted phenol derivatives, although often remain a chance to have mixture of isomers. However, functionalization of the remote meta C- H bonds of phenols is extremely difficult because of the extreme inertness of the meta C- H bonds. Several pioneering approaches have been developed by Yu and others either using template method \(^{48,49,50}\) or transient directing group by Larrosa \(^{2}\) (Fig 1A, b, c). But, achieving the meta functionalized products using these methods, it is essential to have specialized substrates that limits the application of the methods. + +Having tremendous importance of catalytic C- H borylation \(^{51,52,53,54,55,56}\) in organic synthesis, we report here a concept for the meta selective C- H borylation of phenols via an unprecedented Si- O interaction that has never been utilized in C- H functionalization chemistry. Literature reports revealed \(^{4}\) that the most common structural motif for this O- Si interaction can be found in the amide skeleton, where a filled p- orbital of oxygen atom can interact with the vacant d- orbital of the tetracoordinated silicon atom consisting of at least one electronegative atom. The role of this electronegative atom is to make silicon atom more electropositive by developing a partial positive charge on the silicon atom (Fig. 1B, a). Notably, while various reports of O- Si weak interactions have been shown in a number of intermediates (Fig 1B, b- e), \(^{4,57,58}\) there was no report of utilization of these interactions in the catalysis research. Inspired from this background reports, \(^{4,57,58}\) we have proposed a hypothesis where phenol is protected with an easily removable electropositive silane group and silane protected phenol meet all the necessary criteria (having electropositive silane with attached electronegative oxygen atom) for the O- Si weak interaction with 2- pyridine moiety having amide skeleton (4). (Fig. 1C). The reaction design (ligand and catalyst design) is shown in Fig. 1D, a, b. The designed ligand (PY- PYRI) consists of two parts, one part is the simple pyridine unit (PY) and another part is a 2- pyridine (PYRI) unit \(^{59}\) which was redesigned by the skeletal modification of bipyridine core structure. The origin of the remote meta selectivity is presented in Fig. 1D, b. We hypothesized that the designed ligand (PY- PYRI) would control the remote meta selectivity owing to the following two considerations. Firstly, in presence of [Ir(cod)(OMe)] \(_{2}\) , the ligand (PY- PYRI) will generate a complex (Int- A) without tautomerization of the 2- pyridine unit. Secondly, the p- orbital of the oxygen atom of the 2- pyridine unit will interact with the vacant d- orbital of the tetracoordinated electropositive silicon atom of the substrate (Fig. 1D, b). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1. Conceptual outline and proposed concept for the meta borylation of phenol. A. Meta functionalization of phenol, a, electrophilic approach. b, template approach. c, transient directing group approach. B. Conceptual Background. a, common structural skeleton for O-Si interaction. b-e, known literature reports of O-Si interactions. C. Hypothesis, D. Reaction design. a, ligand design by structural modification, b, proposed approach for meta borylation.
+ +The designed ligand (PY- PYRI) was prepared by the known synthetic methods (SI, for details), which was employed for the reaction optimization of steering silane group attached with phenol (Fig. 2A). We started our initial studies with the substrate (1a- I) featuring SiMe3 as the steering group under iridium- catalyzed conditions using the designed ligand (L1: PY- PYRI) at \(40^{\circ}C\) temperature, which afforded good meta/para selectivity ( \(\mathrm{m} / \mathrm{p} = 87 / 13\) ) with \(68\%\) conversion. Evaluating other silane based steering groups under the same reaction conditions, we observed that \(\mathrm{Si}(\mathrm{Pr})_3\) produced best meta selectivity (entry 2a: \(\mathrm{m} / \mathrm{p} = 94 / 6\) ) with excellent conversion ( \(95\%\) ). With this optimized \(\mathrm{Si}(\mathrm{Pr})_3\) as steering group, different other ligands have also been tested to observe the effect on the selectivity (Fig. 2B). It was found that replacing the tert- butyl group with methyl group, ligand (L2) gave \(91\%\) meta selectivity with less conversions ( \(68\%\) ). Similar selectivity was obtained when the reaction was performed with the ligand (L3) without any substituents on the pyridine unit of the ligand. Notably, the meta selectivity and conversion was found to be less using the bipyridine ligand (L4) compared to the ligands (L1- L3), which indicated the important role of the + +<--- Page Split ---> + +2- pyridine unit of the designed (PY- PYRI) ligand. Employment of other ligands (L5- L8) resulted in no reaction except the ligands (L9 & L10), which gave moderate meta selectivity. + +![](images/Figure_2.jpg) + +
Fig. 2. Ligand design and optimization of reaction conditions. A. Optimization of steering group, B. Deleterious results with other ligands. C. Origin of meta selectivity, D. Proof of concept. Reactions are on 0.2 mmol scales. \(^{a}\) Conversion was reported. In parenthesis, isolated yields are reported. See SI for details.
+ +At the outset, we proposed the tentative hypothesis that an O- Si secondary interaction between the oxygen atom of the 2- pyridine unit of the ligand and the silicon atom of the substrate's steering group would interact each other via the filled p- orbital and empty d- orbital (Fig. 2C). \(^{4}\) Moreover, due to the high electronegativity difference between oxygen and silicon, the O- Si bond will be highly polarized, thereby may interact via a weak O- Si interaction. To prove this hypothesis for the origin of the meta selectivity, we performed a reaction with substrates featuring C- Si bond (- CH2SiPr3: 1a- VII and - CH2SiMe3: 1a- VIII, Fig. 2D), in which lacking of electronegative atom attached with silicon atom causes non- polarised bond, resulted in very low meta selective borylation due to loss of interaction between 2- pyridine of catalyst and substrates silicon atom. This experiment indicated an "O- Si" secondary interaction between the ligand and substrate that guides the selectivity of the borylation. + +Using L1 (PY- PYRI) as ligand and Si(Pr)3 as steering group, we next performed the iridium- catalyzed meta borylation of a variety of phenols that afforded excellent meta selectivity and yields of the isolated borylated products (Fig. 3). For example, we first tested 2- chlorophenol for the borylation reaction, while our designed ligand (L1) gave high meta selectivity (m/p = 92/8), traditional dtbpy ligand provided poor meta selectivity (m/p = 63/37), which clearly demonstrated the utility of the designed (L1: PY- PYRI) ligand. Other 2- substituted phenols, such as 2- bromo (1c) and 2- iodo (1d) afforded high meta selectivity that have great synthetic values owing to the two different types of handles on the phenols. Likewise, phenols bearing various alkyl chain ranging from methyl to pentyl (1e- 1g) at the + +<--- Page Split ---> + +2- positions along with trifluoromethyl (1h), isopropyl (1i) and trifluoromethoxy (1l) smoothly underwent meta borylation irrespective of the nature of the substituent. Amino phenol (1k), substrate of momentous importance for the chemical and pharmaceutical industries60 is borylated with high meta selectivity \((m / p = 97 / 3)\) without borylation next to the amino group, which is known to give ortho borylation under iridium- catalyzed borylation conditions via in situ generation of NHBpin group.61 Thioether (1l) that usually directs borylation at the ortho position62 also underwent borylation with good meta selectivity. We observed that phenols containing functional groups such as cyano (1m), Bpin (1n), cyclic amine (1o), cyclohexyl (1p), ketomethyl (1q) and homologous ester (1r) afforded high level of meta selectivity and tolerated well under the employed reaction conditions. + +![](images/Figure_3.jpg) + +
Fig. 3. Substrates scope for substituted arenes. Reactions are in 0.5 mmol scale. aConversion was reported. b1.5 equiv. B2pin was used. c2.0 equiv. B2pin was used. See SI for details.
+ +Amide functionalities (1s & 1t) that are known to undergo numerous synthetic transformations63 exhibited excellent meta selective borylation. Borylation of phenols having \(\mathrm{CF}_3\) (1h) and CN (1m) substituents at the ortho position afforded exclusively meta borylation, the same substituents at the meta position of phenols (1u & 1v) also gave meta selective borylation, which indicated the generality of the developed method. Moreover, fluoro- substituted + +<--- Page Split ---> + +arene, which typically gives borylation next to the fluorine atom under standard iridium- catalyzed conditions, in this case, 3- fluorophenol (1w) gave meta borylation as the major product. Several disubstituted phenols (1x- 1ag) were also examined under the developed conditions that reacted smoothly to afford variously substituted meta borylated products in high yields. 2,2'- Biphenol, compound of paramount importance in medicinal chemistry as well as in chemical industry64 can selectively be mono- and diborylation (2ah & 2ai) by tuning the amount of boron reagent. A bulky substituent at the ortho positions (1aj) did not hamper the reaction that gave 96% meta borylated product with 90% isolated yield. + +![](images/Figure_4.jpg) + +
Fig. 4. Substrates scope for the 4-substituted arenes and C6 borylation of indoles. Reactions are in 0.5 mmol scale. aConversions were reported. b2.0 equiv. B2pin2. See SI for details.
+ +Next, we focused on the meta borylation of those phenols bearing a substituent at the para position (Fig. 4). Because, borylation at the remote meta position in presence of a para substituent remains an extraordinary challenge due to the steric reason. Moreover, we selected those substituents at the para position that already provided exclusive meta borylation of phenols when they were located at either ortho or meta positions. The reason for this selection is mainly to observe the overall effects of the borylation by the same substituents. For the + +<--- Page Split ---> + +testification, we begun with the 4- methyl phenol (3a) that afforded \(91\%\) meta selective borylation. Increasing the chain length from small methyl group to the relatively bulkier alkyl groups such as, ethyl (3b), pentyl (3c), hexyl (3d) and isopropyl (3e), the borylation underwent smoothly with further enhancement of the meta selectivity from \(91\%\) to \(100\%\) . Para- substituted ethers and thioethers bearing electronically different substituents (3f- 3j) reacted with \(100\%\) meta selectivity, which revealed that the scope of the meta borylation is very general regardless of the nature of the substituents. While 2- CN, 3- CN as well as 2- CF3 and 3- CF3 bearing phenols resulted in excellent meta borylation, the same substituents at the para position reacted to yield \(100\%\) meta borylation. Likewise, we also observed that chloro (3m) and bromo (3n) containing phenols reacted to give the meta borylation products solely irrespective of their position in the phenol. Moreover, it has been found that the phenols featuring bulky substituents at the para position (3o- 3r) also gave exclusively meta borylation, although conversion was moderate in case of the cyclohexyl group. + +In 2015, Baran et al. reported \(^{65}\) the first total synthesis of Verruculogen and Fumitremorgin A enabled by ligand- controlled C- H borylation as the key step of TIPS protected tryptophan. We were curious if our designed ligand system could provide the remote C6 borylation of TIPS protected indoles and TIPS protected tryptophan. For that reason we performed borylation of TIPS- protected tryptophan (3s) (synthetic key precursor of bioactive alkaloids Verruculogen and Fumitremorgin A) which provided C6 borylation with \(91\%\) selectivity with excellent conversions. We also found that TIPS- protected other indole derivatives (3t & 3u) and TIPS- protected carbazole (3v) smoothly underwent remote borylation affording excellent selectivity and conversion. This developed method provided a simple way to borylate the 3- substituted indoles derivatives that might be beneficial for the total synthesis or the late- stage functionalization of several bioactive molecules. + +Late- stage functionalization \(^{66}\) of complex bioactive and medicinally important molecules by the site selective C- H activation is a powerful method for the development of new drug candidates. \(^{67}\) In this context, introducing a boron functionality into the bioactive and medicinally important molecules would further enhance the identification of new lead molecules not only for the enormous importance of the boron- bearing small molecules \(^{68}\) but also for the uniqueness of the boron group towards the diverse derivatization towards numerous other functional groups. Thus, we tested our developed method for several commercially available bioactive and drug molecules (Fig. 5A). For example, cannabinoid core (5a: used as a psychoactive drug), methyl salicylate derivatives (5b: an anti- inflammatory and analgesic agent), tyrosol derivatives (5c: an antioxidant), eugenol derivatives (5d: a flavouring agent), sesamol derivatives (5e: an antioxidant), naproxen derivatives (5f: a nonsteroidal anti- inflammatory drug, NSAID), deoxyarbutin derivatives (5g: used for treatment of hyperpigmentation disorders) and homosalate (5h: used as a sunscreen) were meta borylated with high yield and selectivity. The steering silane group from the borylated phenols has been removed under a very mild reaction conditions at room temperature (ethylene glycol, KF, 1h) that afforded the meta borylated phenols in high yields (Fig. 5B). Notably, the meta borylated phenols can further be transformed to a number of substituted phenols/resorcinols that are difficult to prepare by otherwise. + +Next, we aimed to prepare the active catalyst (10) that was proposed to form in situ between the reaction of the designed ligand (L1: PY- PYRI) and [Ir(cod)(OME)]2 during the meta selective borylation conditions. Accordingly, we performed the reaction and isolated the catalyst [10: Ir(cod)(PY- PYRI)] in \(90\%\) yield (Fig. 5C). The catalyst structure was confirmed by X- ray crystallography and other spectroscopic data. The catalytic efficiency of this catalyst [10: Ir(cod)(PY- PYRI)] was further tested in the meta borylation reactions, which exhibited same level of meta selectivity with better product conversion (compared to the in situ generation) (Fig. 5D). Moreover, we checked the stability of the catalyst [10: Ir(cod)(PY- PYRI)] and found highly stable that can even be stored in open air. Furthermore, to verify the broad utility of this air stable catalyst [10: Ir(cod)(PY- PYRI)], we performed several test experiments using this catalyst [10: Ir(cod)(PY- PYRI)] that was stored in open air and found no loss of catalytic activity even after 30 days (Fig. 5D, SI for details). + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5. Applications, Catalyst Preparation and Testing. A. Late-stage meta-C-H borylation, B. Removal of silane group, C. Catalyst synthesis, D. Test of reactivity of catalyst 10. Reactions are in 0.5 mmol scale. aConversions were reported. See SI for details.
+ +In conclusion, we report a new class of ligand and catalyst that has demonstrated remarkable efficiency for the remote meta selective borylation of phenols featuring all types of substitutions at the arene ring. In addition, we have seen that our developed ligand system is beneficial for the remote C6- borylation of indoles derivatives including tryptophan which is a synthetic precursor of bioactive alkaloids (Verruculogen and Fumitremorgin A). Several late- stage meta borylations have been showcased with bioactive and drug molecules that might be useful for repurposing medicines and identification of new lead drug candidates. For the first time, an "O- Si" secondary interaction has been employed to tune the remote selectivity. We anticipate that the designed ligand and catalyst will also find wide application in the context of other C- H functionalization reactions. + +## References and notes + +1. Zhang, Z., Tanaka, K. & Yu, J-Q. 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Para-Selective C-H Borylation of Common Arene Building Blocks Enabled by Ion-Pairing with a Bulky Counteraction. J. Am. Chem. Soc. 141, 15477-15482 (2019). +37. Bastidas, J. R. M., Oleskey, T. J., Miller, S. L., Smith, M. R. & Maleczka, R. E. Para-Selective, Iridium-Catalyzed C-H Borylations of Sulfated Phenols, Benzyl Alcohols, and Anilines Directed by Ion-Pair Electrostatic Interactions. J. Am. Chem. Soc. 141, 15483-15487 (2019). +38. Chang, W. et. al. Computationally designed ligands enable tunable borylation of remote C-H bonds in arenes. Chem, 8, 1775-1788 (2022). +39. Engle, K. M., Mei, T., Wasa, M. & Yu, J-Q. Weak Coordination as a Powerful Means for Developing Broadly Useful C-H Functionalization Reactions. Acc. Chem. Res. 45, 788-802 (2012). +40. Leow, D., Li, G., Mei, T.-S. & Yu, J-Q. Activation of remote meta-C-H bonds assisted by an end-on template. Nature 486, 518-522 (2012). +41. Shi, H., Herron, A. N., Shao, Y., Shao, Q. & Yu, J-Q. Enantioselective remote meta-C-H arylation and alkylation via a chiral transient mediator, Nature 558, 581-585 (2018). +42. Gandeepan, P. & Ackermann, L. Transient Directing Groups for Transformative C-H Activation by Synergistic Metal Catalysis. Chem 4, 199-222 (2018). +43. Meng, G. et al. Achieving Site-Selectivity for C-H Activation Processes Based on Distance and Geometry: A Carpenter's Approach. J. Am. Chem. Soc. 142, 10571-10591 (2020). +44. Scott, K. A., Cox, P. B. & Njardarson, J. T. Phenols in Pharmaceuticals: Analysis of a Recurring Motif. J. Med. Chem. 65, 7044-7072 (2022). +45. Bartolomei, B., Gentile, G., Rosso, C., Filippini, G. & Prato, M. Turning the Light on Phenols: New Opportunities in Organic Synthesis. Chem. Eur. J. 27, 16062-16070 (2021). +46. Quideau, S., Deffieux, D., Douat-Casassus, C. & Pouységou, L. Angew. Chem. Int. Ed. 50, 586-621 (2011). +47. Huang, Z. & Lumb, J-P. Phenol-Directed C-H Functionalization. ACS Catal. 9, 521-555 (2019). +48. Dai, H-X., Li, G., Zhang, X-G., Stepan, A. F. & Yu, J-Q. Pd(II)-Catalyzed ortho- or meta-C-H Olefination of Phenol Derivatives. J. Am. Chem. Soc. 135, 7567-7571 (2013). +49. Wan, L., Dastbaravardeh, N., Li, G. & Yu, J-Q. Cross-Coupling of Remote meta-C-H Bonds Directed by a U-Shaped Template. J. Am. Chem. Soc. 135, 18056-18059 (2013). +50. Xu, J. et al. Sequential Functionalization of meta-C-H and ipso-C-O Bonds of Phenols, J. Am. Chem. Soc. 141, 1903-1907 (2019). +51. Iverson, C. N. & Smith, M. R. III., Stoichiometric and Catalytic B-C Bond Formation from Unactivated Hydrocarbons and Boranes. J. Am. Chem. Soc. 121, 7696-7697 (1999). +52. Ishiyama, T. et al. Mild iridium-catalyzed borylation of arenes. High turnover numbers, room temperature reactions, and isolation of a potential intermediate. J. Am. Chem. Soc. 124, 390-391 (2002). +53. Mkhalid, I. A. I., Barnard, J. H., Marder, T. B., Murphy, J. M. & Hartwig, J. F. C-H Activation for the Construction of C-B Bonds. Chem. Rev. 110, 890-931 (2010). +54. Hartwig, J. F. Regioselectivity of the borylation of alkanes and arenes. Chem. Soc. Rev. 40, 1992-2002 (2011). +55. Boller, T. M., Murphy, J. M., Hapke, M., Ishiyama, T., Miyaura, N. & Hartwig, J. F. Mechanism of the Mild Functionalization of Arenes by Diboron Reagents Catalyzed by Iridium Complexes. Intermediacy and Chemistry of Bipyridine-Ligated Iridium Trisboryl Complexes. J. Am. Chem. Soc. 127, 14263-14278 (2005). +56. Haldar, C., Hoque, M. E., Chaturvedi, J., Hassan, M. M. M. & Chattopadhyay, B. Ir-catalyzed proximal and distal C-H borylation of arenes. Chem. Commun., 57, 13059-13074 (2021). +57. Muhammad, S., Bassindale, A. R., Taylor, P. G., Male, L., Coles, S. J. & Hursthouse, M. B. Study of Binuclear Silicon Complexes of Diketopiperazine at SN2 Reaction Profile. Organometallics 30, 564-571 (2011). +58. Sohail, M. et al. Synthesis and Hydrolysis-Condensation Study of Water-Soluble Self-Assembled Pentacoordinate Polysilylamides. Organometallics 32, 1721-1731 (2013). +59. Li, Z. et. al. A tautomeric ligand enables directed C-H hydroxylation with molecular oxygen. Science 372, 1452-1457 (2021). +60. Lajiness, J. P. et. al. Design, Synthesis, and Evaluation of Duocarmycin O-Amino Phenol Prodrugs Subject to Tunable Reductive Activation. J. Med. Chem. 53, 7731-7738 (2010) + +<--- Page Split ---> + +61. Preshlock S. M. et al. A Traceless Directing Group for C-H Borylation. Angew. Chem., Int. Ed. 52, 12915-12919 (2013). +62. Li, H. L., Kuninobu, Y. & Kanai, M. Lewis Acid-Base Interaction-Controlled ortho-Selective C-H Borylation of Aryl Sulfides. Angew. Chem., Int. Ed. 56, 1495-1499 (2017). +63. Sun, W. et al. Chemodivergent transformations of amides using gem-diborylalkanes as pro-nucleophiles. Nat Commun 11, 3113 (2020). +64. Hua, Z., Vassar, V. C., Choi, H. & Ojima, I. New biphenol-based, fine-tunable monodentate phosphoramidite ligands for catalytic asymmetric transformations. Proc Natl Acad Sci, 101, 5411-5416 (2004). +65. Feng, Y., Holte, D., Zoller, J., Umemiya, S., Simke, L. R., Baran, P. S. Total Synthesis of Verruculogen and Fumitremorgin A Enabled by Ligand-Controlled C-H Borylation. J. Am. Chem. Soc. 137, 10160-10163 (2015). +66. Zhang, L. & Ritter, T. A Perspective on Late-Stage Aromatic C-H Bond Functionalization. J. Am. Chem. Soc. 144, 2399-2414 (2022). +67. Guillemard, L., Kaplaneris, N., Ackermann, L. & Johansson, M. J. Late-stage C-H functionalization offers new opportunities in drug discovery. Nat. Rev. Chem. 5, 522-545 (2021). +68. Thareja, S., Zhu, M., Ji, X., Wang, B. Boron-based small molecules in disease detection and treatment (2013-2016). Heterocycl. Commun. 23, 137-153 (2017). + +## Acknowledgements + +We thank Centre of Biomedical Research (CBMR) for providing research facility. We also thank IIT Kanpur for the X- ray crystallography data collection. Funding: This work was supported by SERB- SUPRA grant (SPR/2019/000158). SD and SG thank CSIR for their JRF, MMMH thanks UGC for an SRF. Author contributions: BC conceived the concept. SG developed the ligand. SG, MMMH and SD performed the experiments. BC supervised the project. All authors contributed to writing and proofreading of manuscript and SI. Competing interests: We have filled an Indian Patent (Patent Application No: 202211036590) based on this work (including the ligand and catalyst). Data and materials availability: X- ray dataset for catalyst 10 is freely available at the Cambridge Crystallographic Data Centre under deposition number 2180880. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupportingInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b_det.mmd b/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9db0b29143892efad57ea172e5df7d1a4b6a9529 --- /dev/null +++ b/preprint/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b/preprint__09b079d80b3efdeef69e111bf52a9dde06da16423667f03702d57d11bbbeec9b_det.mmd @@ -0,0 +1,194 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 785, 177]]<|/det|> +# Meta Selective C-H Borylation Directed by Secondary Silicon Oxygen Interaction + +<|ref|>text<|/ref|><|det|>[[44, 195, 755, 238]]<|/det|> +Buddhadeb Chattopadhyay ( \(\boxed{ \begin{array}{r l} \end{array} }\) buddhadeb.c@cbmr.res.in) Center of Biomedical Research (CBMR) https://orcid.org/0000- 0001- 8473- 2695 + +<|ref|>text<|/ref|><|det|>[[44, 243, 395, 285]]<|/det|> +Saikat Guria Center of Biomedical Research (CBMR) + +<|ref|>text<|/ref|><|det|>[[44, 290, 395, 333]]<|/det|> +Mirja Md Hassan Centre of Biomedical Research (CBMR) + +<|ref|>text<|/ref|><|det|>[[44, 338, 395, 378]]<|/det|> +Sayan Dey Center of Biomedical Research (CBMR) + +<|ref|>text<|/ref|><|det|>[[44, 417, 102, 435]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 455, 137, 474]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 493, 320, 512]]<|/det|> +Posted Date: August 17th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 531, 474, 551]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1837437/v1 + +<|ref|>text<|/ref|><|det|>[[42, 568, 910, 611]]<|/det|> +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 629, 944, 695]]<|/det|> +Additional Declarations: There is NO Competing Interest. We declare that the authors have no competing interests except that We have filled an Indian Patent (Patent Application No: 202211036590) based on this work (including the ligand and catalyst). + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[152, 80, 842, 100]]<|/det|> +# Meta Selective C-H Borylation Directed by Secondary Silicon Oxygen Interaction + +<|ref|>text<|/ref|><|det|>[[181, 115, 812, 134]]<|/det|> +Saikat Guria†, Mirja Md Mahamudul Hassan†, Sayan Dey†, Buddhadeb Chattopadhyay†\* + +<|ref|>text<|/ref|><|det|>[[140, 149, 857, 200]]<|/det|> +†Department of Biological & Synthetic Chemistry, Centre of Biomedical Research, SGPGIMS Campus, Raebareli Road, Lucknow 226014, Uttar Pradesh, India \*Correspondence to: buddhadeb.c@cbmr.res.in + +<|ref|>text<|/ref|><|det|>[[102, 215, 896, 359]]<|/det|> +Summary Paragraph: Remote meta selective C- H functionalization1,2,3 of aromatic compounds remains a challenging problem in chemical synthesis. Here, we report an iridium catalyst bearing a bidentate pyridine- pyridone (PY- PYRI) ligand framework that efficiently catalyzes this meta selective borylation reaction. We demonstrate that the developed concept can be employed to introduce a boron functionality at the remote meta position of phenols, phenol containing bioactive and drug molecules, which was an extraordinary challenge. Moreover, we have demonstrated that the method can also be applied for the remote C6 borylation of indole derivatives including tryptophan that was the key synthetic precursor for the total synthesis of Verruculogen and Fumitremorgin A alkaloids. The origin of the remote meta selectivity was described as a secondary silicon oxygen interaction4 that was never used in C- H functionalization chemistry. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 78, 895, 304]]<|/det|> +Transition metal- catalyzed C- H bond activation and functionalization \(^{5,6,7,8,9,10,11}\) of aromatic compounds has been branded as one of the most significant chemical transformations. This has a profound impact in modern synthetic organic chemistry, ranging from laboratory methods to industrial deployment. \(^{12,13}\) However, the key underlying principles for the success of the metal catalysis lies on the two important factors, such as: (i) design and synthesis of new generation ligand framework that can produce highly reactive catalyst system \(^{14,15}\) and (ii) substrates' structure modifications \(^{16}\) by which site selectivity could be controlled by the steric crowding \(^{17,18,19,20,21}\) or various weak interactions \(^{22,23,24,25}\) of the aromatic compounds among several similar type of C- H bonds via the ligand- substrate pre- organization \(^{26,27}\) . In recent times, many elegant approaches \(^{28}\) have been developed for the functionalization of proximal \(^{15,29,30,31}\) and remote C- H bonds \(^{1,3,32,33,34,35,36,37,38}\) of arenes by the design of either new ligand frameworks with an extended architectures featuring a weak coordinating functional groups \(^{39}\) or templates \(^{40}\) as well as transient mediators \(^{41}\) or transient directing groups \(^{42}\) attached with the substrates. While ligand having an extended architecture or template approaches are extremely important to functionalize the remotely located C- H bonds of arenes, but requirement of multi- step preparation of the linkers of the ligands and templates of the aromatic substrates significantly limit the wide application of the methods. \(^{43}\) + +<|ref|>text<|/ref|><|det|>[[102, 320, 896, 544]]<|/det|> +Among numerous aromatic substrates, phenols are the most widespread aromatic compounds that acquired household products including several bioactive to important drug molecules. \(^{44}\) Moreover, it is well- documented that \(10\%\) of the top 200 selling pharmaceuticals contain a phenol and several others employ phenols as synthetic intermediates. \(^{45}\) Furthermore, phenols are also key components of the biopolymers melanin, lignin, resins and polyphenylene oxides. \(^{44,45,46}\) In industry, phenol is routinely used as a raw material to make numerous important components by means of its diversification via the synthetic manipulation. \(^{44,45}\) Thus, direct functionalization of phenols would be a significant development for the rapid access of numerous important products. \(^{46}\) In this context, traditional electrophilic substitution is an alternative methods that affords variously substituted phenols (Fig. 1A, a). \(^{47}\) Employing this method, one can easily access ortho and para substituted phenol derivatives, although often remain a chance to have mixture of isomers. However, functionalization of the remote meta C- H bonds of phenols is extremely difficult because of the extreme inertness of the meta C- H bonds. Several pioneering approaches have been developed by Yu and others either using template method \(^{48,49,50}\) or transient directing group by Larrosa \(^{2}\) (Fig 1A, b, c). But, achieving the meta functionalized products using these methods, it is essential to have specialized substrates that limits the application of the methods. + +<|ref|>text<|/ref|><|det|>[[102, 558, 896, 862]]<|/det|> +Having tremendous importance of catalytic C- H borylation \(^{51,52,53,54,55,56}\) in organic synthesis, we report here a concept for the meta selective C- H borylation of phenols via an unprecedented Si- O interaction that has never been utilized in C- H functionalization chemistry. Literature reports revealed \(^{4}\) that the most common structural motif for this O- Si interaction can be found in the amide skeleton, where a filled p- orbital of oxygen atom can interact with the vacant d- orbital of the tetracoordinated silicon atom consisting of at least one electronegative atom. The role of this electronegative atom is to make silicon atom more electropositive by developing a partial positive charge on the silicon atom (Fig. 1B, a). Notably, while various reports of O- Si weak interactions have been shown in a number of intermediates (Fig 1B, b- e), \(^{4,57,58}\) there was no report of utilization of these interactions in the catalysis research. Inspired from this background reports, \(^{4,57,58}\) we have proposed a hypothesis where phenol is protected with an easily removable electropositive silane group and silane protected phenol meet all the necessary criteria (having electropositive silane with attached electronegative oxygen atom) for the O- Si weak interaction with 2- pyridine moiety having amide skeleton (4). (Fig. 1C). The reaction design (ligand and catalyst design) is shown in Fig. 1D, a, b. The designed ligand (PY- PYRI) consists of two parts, one part is the simple pyridine unit (PY) and another part is a 2- pyridine (PYRI) unit \(^{59}\) which was redesigned by the skeletal modification of bipyridine core structure. The origin of the remote meta selectivity is presented in Fig. 1D, b. We hypothesized that the designed ligand (PY- PYRI) would control the remote meta selectivity owing to the following two considerations. Firstly, in presence of [Ir(cod)(OMe)] \(_{2}\) , the ligand (PY- PYRI) will generate a complex (Int- A) without tautomerization of the 2- pyridine unit. Secondly, the p- orbital of the oxygen atom of the 2- pyridine unit will interact with the vacant d- orbital of the tetracoordinated electropositive silicon atom of the substrate (Fig. 1D, b). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[105, 85, 884, 636]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[102, 639, 896, 698]]<|/det|> +
Fig. 1. Conceptual outline and proposed concept for the meta borylation of phenol. A. Meta functionalization of phenol, a, electrophilic approach. b, template approach. c, transient directing group approach. B. Conceptual Background. a, common structural skeleton for O-Si interaction. b-e, known literature reports of O-Si interactions. C. Hypothesis, D. Reaction design. a, ligand design by structural modification, b, proposed approach for meta borylation.
+ +<|ref|>text<|/ref|><|det|>[[102, 713, 896, 888]]<|/det|> +The designed ligand (PY- PYRI) was prepared by the known synthetic methods (SI, for details), which was employed for the reaction optimization of steering silane group attached with phenol (Fig. 2A). We started our initial studies with the substrate (1a- I) featuring SiMe3 as the steering group under iridium- catalyzed conditions using the designed ligand (L1: PY- PYRI) at \(40^{\circ}C\) temperature, which afforded good meta/para selectivity ( \(\mathrm{m} / \mathrm{p} = 87 / 13\) ) with \(68\%\) conversion. Evaluating other silane based steering groups under the same reaction conditions, we observed that \(\mathrm{Si}(\mathrm{Pr})_3\) produced best meta selectivity (entry 2a: \(\mathrm{m} / \mathrm{p} = 94 / 6\) ) with excellent conversion ( \(95\%\) ). With this optimized \(\mathrm{Si}(\mathrm{Pr})_3\) as steering group, different other ligands have also been tested to observe the effect on the selectivity (Fig. 2B). It was found that replacing the tert- butyl group with methyl group, ligand (L2) gave \(91\%\) meta selectivity with less conversions ( \(68\%\) ). Similar selectivity was obtained when the reaction was performed with the ligand (L3) without any substituents on the pyridine unit of the ligand. Notably, the meta selectivity and conversion was found to be less using the bipyridine ligand (L4) compared to the ligands (L1- L3), which indicated the important role of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 81, 895, 113]]<|/det|> +2- pyridine unit of the designed (PY- PYRI) ligand. Employment of other ligands (L5- L8) resulted in no reaction except the ligands (L9 & L10), which gave moderate meta selectivity. + +<|ref|>image<|/ref|><|det|>[[104, 115, 888, 570]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[102, 574, 895, 618]]<|/det|> +
Fig. 2. Ligand design and optimization of reaction conditions. A. Optimization of steering group, B. Deleterious results with other ligands. C. Origin of meta selectivity, D. Proof of concept. Reactions are on 0.2 mmol scales. \(^{a}\) Conversion was reported. In parenthesis, isolated yields are reported. See SI for details.
+ +<|ref|>text<|/ref|><|det|>[[102, 634, 895, 778]]<|/det|> +At the outset, we proposed the tentative hypothesis that an O- Si secondary interaction between the oxygen atom of the 2- pyridine unit of the ligand and the silicon atom of the substrate's steering group would interact each other via the filled p- orbital and empty d- orbital (Fig. 2C). \(^{4}\) Moreover, due to the high electronegativity difference between oxygen and silicon, the O- Si bond will be highly polarized, thereby may interact via a weak O- Si interaction. To prove this hypothesis for the origin of the meta selectivity, we performed a reaction with substrates featuring C- Si bond (- CH2SiPr3: 1a- VII and - CH2SiMe3: 1a- VIII, Fig. 2D), in which lacking of electronegative atom attached with silicon atom causes non- polarised bond, resulted in very low meta selective borylation due to loss of interaction between 2- pyridine of catalyst and substrates silicon atom. This experiment indicated an "O- Si" secondary interaction between the ligand and substrate that guides the selectivity of the borylation. + +<|ref|>text<|/ref|><|det|>[[102, 793, 895, 905]]<|/det|> +Using L1 (PY- PYRI) as ligand and Si(Pr)3 as steering group, we next performed the iridium- catalyzed meta borylation of a variety of phenols that afforded excellent meta selectivity and yields of the isolated borylated products (Fig. 3). For example, we first tested 2- chlorophenol for the borylation reaction, while our designed ligand (L1) gave high meta selectivity (m/p = 92/8), traditional dtbpy ligand provided poor meta selectivity (m/p = 63/37), which clearly demonstrated the utility of the designed (L1: PY- PYRI) ligand. Other 2- substituted phenols, such as 2- bromo (1c) and 2- iodo (1d) afforded high meta selectivity that have great synthetic values owing to the two different types of handles on the phenols. Likewise, phenols bearing various alkyl chain ranging from methyl to pentyl (1e- 1g) at the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 80, 896, 207]]<|/det|> +2- positions along with trifluoromethyl (1h), isopropyl (1i) and trifluoromethoxy (1l) smoothly underwent meta borylation irrespective of the nature of the substituent. Amino phenol (1k), substrate of momentous importance for the chemical and pharmaceutical industries60 is borylated with high meta selectivity \((m / p = 97 / 3)\) without borylation next to the amino group, which is known to give ortho borylation under iridium- catalyzed borylation conditions via in situ generation of NHBpin group.61 Thioether (1l) that usually directs borylation at the ortho position62 also underwent borylation with good meta selectivity. We observed that phenols containing functional groups such as cyano (1m), Bpin (1n), cyclic amine (1o), cyclohexyl (1p), ketomethyl (1q) and homologous ester (1r) afforded high level of meta selectivity and tolerated well under the employed reaction conditions. + +<|ref|>image<|/ref|><|det|>[[102, 207, 895, 802]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[101, 803, 896, 833]]<|/det|> +
Fig. 3. Substrates scope for substituted arenes. Reactions are in 0.5 mmol scale. aConversion was reported. b1.5 equiv. B2pin was used. c2.0 equiv. B2pin was used. See SI for details.
+ +<|ref|>text<|/ref|><|det|>[[102, 850, 895, 914]]<|/det|> +Amide functionalities (1s & 1t) that are known to undergo numerous synthetic transformations63 exhibited excellent meta selective borylation. Borylation of phenols having \(\mathrm{CF}_3\) (1h) and CN (1m) substituents at the ortho position afforded exclusively meta borylation, the same substituents at the meta position of phenols (1u & 1v) also gave meta selective borylation, which indicated the generality of the developed method. Moreover, fluoro- substituted + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 80, 896, 192]]<|/det|> +arene, which typically gives borylation next to the fluorine atom under standard iridium- catalyzed conditions, in this case, 3- fluorophenol (1w) gave meta borylation as the major product. Several disubstituted phenols (1x- 1ag) were also examined under the developed conditions that reacted smoothly to afford variously substituted meta borylated products in high yields. 2,2'- Biphenol, compound of paramount importance in medicinal chemistry as well as in chemical industry64 can selectively be mono- and diborylation (2ah & 2ai) by tuning the amount of boron reagent. A bulky substituent at the ortho positions (1aj) did not hamper the reaction that gave 96% meta borylated product with 90% isolated yield. + +<|ref|>image<|/ref|><|det|>[[105, 194, 884, 775]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[102, 777, 894, 808]]<|/det|> +
Fig. 4. Substrates scope for the 4-substituted arenes and C6 borylation of indoles. Reactions are in 0.5 mmol scale. aConversions were reported. b2.0 equiv. B2pin2. See SI for details.
+ +<|ref|>text<|/ref|><|det|>[[103, 823, 895, 902]]<|/det|> +Next, we focused on the meta borylation of those phenols bearing a substituent at the para position (Fig. 4). Because, borylation at the remote meta position in presence of a para substituent remains an extraordinary challenge due to the steric reason. Moreover, we selected those substituents at the para position that already provided exclusive meta borylation of phenols when they were located at either ortho or meta positions. The reason for this selection is mainly to observe the overall effects of the borylation by the same substituents. For the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 80, 895, 256]]<|/det|> +testification, we begun with the 4- methyl phenol (3a) that afforded \(91\%\) meta selective borylation. Increasing the chain length from small methyl group to the relatively bulkier alkyl groups such as, ethyl (3b), pentyl (3c), hexyl (3d) and isopropyl (3e), the borylation underwent smoothly with further enhancement of the meta selectivity from \(91\%\) to \(100\%\) . Para- substituted ethers and thioethers bearing electronically different substituents (3f- 3j) reacted with \(100\%\) meta selectivity, which revealed that the scope of the meta borylation is very general regardless of the nature of the substituents. While 2- CN, 3- CN as well as 2- CF3 and 3- CF3 bearing phenols resulted in excellent meta borylation, the same substituents at the para position reacted to yield \(100\%\) meta borylation. Likewise, we also observed that chloro (3m) and bromo (3n) containing phenols reacted to give the meta borylation products solely irrespective of their position in the phenol. Moreover, it has been found that the phenols featuring bulky substituents at the para position (3o- 3r) also gave exclusively meta borylation, although conversion was moderate in case of the cyclohexyl group. + +<|ref|>text<|/ref|><|det|>[[102, 271, 895, 415]]<|/det|> +In 2015, Baran et al. reported \(^{65}\) the first total synthesis of Verruculogen and Fumitremorgin A enabled by ligand- controlled C- H borylation as the key step of TIPS protected tryptophan. We were curious if our designed ligand system could provide the remote C6 borylation of TIPS protected indoles and TIPS protected tryptophan. For that reason we performed borylation of TIPS- protected tryptophan (3s) (synthetic key precursor of bioactive alkaloids Verruculogen and Fumitremorgin A) which provided C6 borylation with \(91\%\) selectivity with excellent conversions. We also found that TIPS- protected other indole derivatives (3t & 3u) and TIPS- protected carbazole (3v) smoothly underwent remote borylation affording excellent selectivity and conversion. This developed method provided a simple way to borylate the 3- substituted indoles derivatives that might be beneficial for the total synthesis or the late- stage functionalization of several bioactive molecules. + +<|ref|>text<|/ref|><|det|>[[102, 430, 895, 654]]<|/det|> +Late- stage functionalization \(^{66}\) of complex bioactive and medicinally important molecules by the site selective C- H activation is a powerful method for the development of new drug candidates. \(^{67}\) In this context, introducing a boron functionality into the bioactive and medicinally important molecules would further enhance the identification of new lead molecules not only for the enormous importance of the boron- bearing small molecules \(^{68}\) but also for the uniqueness of the boron group towards the diverse derivatization towards numerous other functional groups. Thus, we tested our developed method for several commercially available bioactive and drug molecules (Fig. 5A). For example, cannabinoid core (5a: used as a psychoactive drug), methyl salicylate derivatives (5b: an anti- inflammatory and analgesic agent), tyrosol derivatives (5c: an antioxidant), eugenol derivatives (5d: a flavouring agent), sesamol derivatives (5e: an antioxidant), naproxen derivatives (5f: a nonsteroidal anti- inflammatory drug, NSAID), deoxyarbutin derivatives (5g: used for treatment of hyperpigmentation disorders) and homosalate (5h: used as a sunscreen) were meta borylated with high yield and selectivity. The steering silane group from the borylated phenols has been removed under a very mild reaction conditions at room temperature (ethylene glycol, KF, 1h) that afforded the meta borylated phenols in high yields (Fig. 5B). Notably, the meta borylated phenols can further be transformed to a number of substituted phenols/resorcinols that are difficult to prepare by otherwise. + +<|ref|>text<|/ref|><|det|>[[102, 669, 895, 829]]<|/det|> +Next, we aimed to prepare the active catalyst (10) that was proposed to form in situ between the reaction of the designed ligand (L1: PY- PYRI) and [Ir(cod)(OME)]2 during the meta selective borylation conditions. Accordingly, we performed the reaction and isolated the catalyst [10: Ir(cod)(PY- PYRI)] in \(90\%\) yield (Fig. 5C). The catalyst structure was confirmed by X- ray crystallography and other spectroscopic data. The catalytic efficiency of this catalyst [10: Ir(cod)(PY- PYRI)] was further tested in the meta borylation reactions, which exhibited same level of meta selectivity with better product conversion (compared to the in situ generation) (Fig. 5D). Moreover, we checked the stability of the catalyst [10: Ir(cod)(PY- PYRI)] and found highly stable that can even be stored in open air. Furthermore, to verify the broad utility of this air stable catalyst [10: Ir(cod)(PY- PYRI)], we performed several test experiments using this catalyst [10: Ir(cod)(PY- PYRI)] that was stored in open air and found no loss of catalytic activity even after 30 days (Fig. 5D, SI for details). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[103, 84, 888, 448]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[102, 450, 896, 494]]<|/det|> +
Fig. 5. Applications, Catalyst Preparation and Testing. A. Late-stage meta-C-H borylation, B. Removal of silane group, C. Catalyst synthesis, D. Test of reactivity of catalyst 10. Reactions are in 0.5 mmol scale. aConversions were reported. See SI for details.
+ +<|ref|>text<|/ref|><|det|>[[103, 510, 896, 639]]<|/det|> +In conclusion, we report a new class of ligand and catalyst that has demonstrated remarkable efficiency for the remote meta selective borylation of phenols featuring all types of substitutions at the arene ring. In addition, we have seen that our developed ligand system is beneficial for the remote C6- borylation of indoles derivatives including tryptophan which is a synthetic precursor of bioactive alkaloids (Verruculogen and Fumitremorgin A). Several late- stage meta borylations have been showcased with bioactive and drug molecules that might be useful for repurposing medicines and identification of new lead drug candidates. For the first time, an "O- Si" secondary interaction has been employed to tune the remote selectivity. We anticipate that the designed ligand and catalyst will also find wide application in the context of other C- H functionalization reactions. + +<|ref|>sub_title<|/ref|><|det|>[[104, 656, 264, 671]]<|/det|> +## References and notes + +<|ref|>text<|/ref|><|det|>[[101, 687, 897, 890]]<|/det|> +1. Zhang, Z., Tanaka, K. & Yu, J-Q. Remote site-selective C-H activation directed by a catalytic bifunctional template. Nature 543, 538-542 (2017). +2. Luo, J., Preciado, S. & Larrosa, I. Overriding Ortho-Para Selectivity via a Traceless Directing Group Relay Strategy: The Meta-Selective Arylation of Phenols, J. Am. Chem. Soc. 136, 4109-4112 (2014). +3. Sinha, S. K., Guin, S., Maiti, S., Biswas, J. P., Porey, S. & Maiti, D. Toolbox for Distal C-H Bond Functionalizations in Organic Molecules. Chem. Rev. 122, 5682-5841 (2022). +4. Lazareva, N. F., Sterkhova, I. V. & Vashchenko, A. V. 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Late-stage C-H functionalization offers new opportunities in drug discovery. Nat. Rev. Chem. 5, 522-545 (2021). +68. Thareja, S., Zhu, M., Ji, X., Wang, B. Boron-based small molecules in disease detection and treatment (2013-2016). Heterocycl. Commun. 23, 137-153 (2017). + +<|ref|>sub_title<|/ref|><|det|>[[104, 343, 238, 357]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[100, 360, 896, 530]]<|/det|> +We thank Centre of Biomedical Research (CBMR) for providing research facility. We also thank IIT Kanpur for the X- ray crystallography data collection. Funding: This work was supported by SERB- SUPRA grant (SPR/2019/000158). SD and SG thank CSIR for their JRF, MMMH thanks UGC for an SRF. Author contributions: BC conceived the concept. SG developed the ligand. SG, MMMH and SD performed the experiments. BC supervised the project. All authors contributed to writing and proofreading of manuscript and SI. Competing interests: We have filled an Indian Patent (Patent Application No: 202211036590) based on this work (including the ligand and catalyst). Data and materials availability: X- ray dataset for catalyst 10 is freely available at the Cambridge Crystallographic Data Centre under deposition number 2180880. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 130, 318, 150]]<|/det|> +SupportingInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/images_list.json b/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..cd0b807d436a7335278c63c0905e46a85d8fc3d0 --- /dev/null +++ b/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1: Thermo-hydrodynamic manipulation of Au NPs in NaCl solution. a, The sample consists of two glass slides that confine a \\(3\\mu \\mathrm{m}\\) thin liquid film of gold nanoparticles (AuNPs) dispersed in aqueous NaCl solution. The lower glass slide carries a \\(50\\mathrm{nm}\\) Au film that is locally heated by optical absorption of a focused laser of \\(\\lambda = 532\\mathrm{nm}\\) wavelength. b, The experimental setup comprises an inverted optical microscope equipped with an acusto-optical deflector controlled, steerable focused laser with a wavelength of \\(\\lambda = 532\\mathrm{nm}\\) . The AuNPs are observed using darkfield illumination with an oil-immersion dark-field condenser (NA 1.2) and a \\(100\\times\\) oil-immersion objective set to NA 0.6. Images are recorded with an EMCCD camera.", + "footnote": [], + "bbox": [ + [ + 80, + 81, + 900, + 333 + ] + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2: DLVO potential, lateral diffusion analysis, temperature distribution and thermo-osmotic flow field. a, Plot of the DLVO potential, equation (1), between a \\(250~\\mathrm{nm}\\) Au NP and a \\(50~\\mathrm{nm}\\) Au film on a glass surface as function of surface-surface distance \\(d\\) for different NaCl concentrations \\(c_0\\) . The shaded curves display the calculated probability density for finding the particle at this distance at the different salt concentrations (see Supplementary Information for details). The vertical dashed lines correspond to the mean distance of the particle as calculated from the probability density for a \\(3\\mu \\mathrm{m}\\) liquid film height. b, The measured diffusion coefficient \\(D_1 / D_0\\) parallel to the Au film with respect to the bulk diffusion coefficient \\(D_0\\) as function of the NaCl concentration \\(c_0\\) . Symbols correspond to the experimental values. The lines reflect the theoretical prediction including a distance dependent diffusion coefficient for three different Hamaker constants (dotted: \\(A_H = 4\\cdot 10^{-20}\\mathrm{J}\\) , dash-dotted: \\(5\\cdot 10^{-20}\\mathrm{J}\\) and dashed: \\(6\\cdot 10^{-20}\\mathrm{J}\\) ) of gold according to a Boltzmann weighting (see text). c, Relation between the mean distance \\(\\langle d\\rangle\\) and the NaCl concentration \\(c_0\\) . The symbols and the horizontal lines denote the calculated distances for measured concentrations. d, Simulation of the relative temperature increment in the \\(xz\\) -plane of the sample. e, Experimentally obtained temperature increment \\(\\Delta T_{\\mathrm{max}}\\) as a function of the incident laser power \\(P_0\\) (green data points) compared to the simulated values (green curve). f, Measured thermo-osmotic flow field in the \\(xy\\) -plane in close proximity to the gold film ( \\(z< 500\\mathrm{nm}\\) ). g, Measured thermo-osmotic flow field in the \\(xz\\) -plane. h, Illustration of the measured flow field planes in f and g. i, j, The \\(x\\) - and \\(z\\) -component of the measured flow velocities compared to the simulation results in k and l.", + "footnote": [], + "bbox": [ + [ + 78, + 81, + 920, + 576 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3: Forces on trapped NPs in \\(10\\mathrm{mM}\\) NaCl. a, The lateral trap stiffness obtained from the experimental position histograms (blue data points) as function the laser power \\(P_0\\) compared to the simulation result (blue solid line). b, The \\(x\\) -component of the thermo-osmotic drag force \\(F_{\\mathrm{T}}^{\\mathrm{TF}}\\) (blue line), the optical force \\(F_{\\mathrm{Z}}^{\\mathrm{OF}}\\) (green line) and the total force \\(F_{\\mathrm{Z}}^{\\mathrm{OF}} + F_{\\mathrm{Z}}^{\\mathrm{TF}}\\) (black dashed line) as function the incident laser power \\(P_0\\) for a NP located at \\(x = 0\\) , \\(d = 30\\) ( \\(z = d + R\\) ). The attractive DLVO force \\(F_{\\mathrm{DLVO}}^{\\mathrm{DLVO}}\\) is independent of the incident laser power and depicted as horizontal, red line. c, Trajectory of a AuNP for a heating laser power of \\(1.25\\mathrm{mW}\\) (see Supplementary Video 2 for details). The inset shows the corresponding lateral distribution histogram. d, Time traces of the \\(z\\) -position for three different laser powers. e, Trajectory of a AuNP for a heating laser power of \\(2.5\\mathrm{mW}\\) , which is above the threshold power of \\(2.25\\mathrm{mW}\\) .", + "footnote": [], + "bbox": [ + [ + 80, + 81, + 916, + 253 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4: Manipulation of NPs over a Au film in NaCl and SDS solution. a, A AuNP with \\(50 \\mathrm{nm}\\) radius trapped at a NaCl of \\(30 \\mathrm{mM}\\) (Supplementary Video 5). b, Manipulation of two AuNPs by a multiplexed laser beam (Supplementary Video 6). c, Control of three AuNPs (Supplementary Video 7). d, Actuation of a single AuNP on a circular trajectory by a steerable laser beam (Supplementary Video 8). The green and white, dashed arrows denote the moving direction of the laser focus and particle, respectively. e, Generation of thermo-viscous flows by rotating the laser focus on a circle with a rotation frequency of \\(f = 500 \\mathrm{Hz}\\) at high laser powers (Supplementary Video 9). Note, that laser movement (green arrow) and the thermo-viscous flow (white, dashed arrow) and have opposite directions. f, Attraction of a AuNP and PS NPs in \\(5 \\mathrm{mM}\\) SDS due to depletion (Supplementary Video 10). g, An AuNP (125 nm radius) trapped in an ensemble of polystyrene (PS) NPs of the same size at \\(10 \\mathrm{mM}\\) NaCl (Supplementary Video 11), where the PS-particles are repelled due to thermophoresis. h, Attraction of PS ellipsoids (2.39 \\(\\mu \\mathrm{m}\\) major-axis length, \\(0.34 \\mu \\mathrm{m}\\) minor-axis length) in \\(5 \\mathrm{mM}\\) SDS (Supplementary Video 12).", + "footnote": [], + "bbox": [ + [ + 80, + 460, + 916, + 784 + ] + ], + "page_idx": 6 + } +] \ No newline at end of file diff --git a/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397.mmd b/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d312ce619fc8ca3a78e4432e2bed82994498bc84 --- /dev/null +++ b/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397.mmd @@ -0,0 +1,185 @@ + +# Hydrodynamic manipulation of nano-objects by optically induced thermo-osmotic flows + +Martin Fränzl Leipzig University https://orcid.org/0000- 0001- 6754- 8554 Frank Cichos (cichos@physik.uni- leipzig.de) Leipzig University https://orcid.org/0000- 0002- 9803- 4975 + +## Article + +Keywords: nano- objects, microfluidics, hydrodynamics, thermo- osti + +Posted Date: September 24th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 879955/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on February 3rd, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28212- z. + +<--- Page Split ---> + +# Hydrodynamic manipulation of nano-objects by optically induced thermo-osmotic flows + +Martin Fränzl \(^{1}\) and Frank Cichos \(^{1,*}\) + +\(^{1}\) Peter Debye Institute for Soft Matter Physics, Molecular Nanophotonics Group, Universität Leipzig, Linnéstr. 5, 04103 Leipzig, Germany. + +\* cichos@physik.uni- leipzig.de + +The manipulation of nano- objects at the microscale is of great technological significance to construct new functional materials, to manipulate tiny amounts of liquids, to reconfigure sensorial systems or to detect minute concentrations of analytes in medical screening. It is commonly approached by the generation of potential energy landscapes, for example, with optical fields or by using pressure driven microfluidics. Here we show that strong hydrodynamic boundary flows enable the trapping and manipulation of nano- objects near surfaces. These thermo- osmotic flows are induced by modulating the van der Waals interaction at a solid- liquid interface with optically generated temperature fields. We use a thin gold film on a glass substrate to provide localized but reconfigurable point- like optical heating. Convergent boundary flows with velocities of tens of micrometres per second are observed and substantiated by a quantitative physical model. The hydrodynamic forces acting on suspended nanoparticles and attractive van der Waals or depletion induced forces enable precise positioning and guiding of the nanoparticles. Fast multiplexing of flow fields further provides the means for parallel manipulation of many nano- objects and the generation of complex flow fields. Our findings have direct consequences for the field of plasmonic nano- tweezers as well as other thermo- plasmonic trapping schemes and pave the way for a general scheme of nanoscopic manipulation with boundary flows. + +The control and manipulation of nano- objects is a key element for future nanophotonics \(^{1 - 5}\) , material science \(^{4,6,7}\) , biotechnology \(^{2,8,9}\) or even quantum sensing \(^{10}\) . Analytes dissolved in liquids, for example, need to be delivered, concentrated, separated or locally confined for further studies to become eventually processed and removed. Photonic elements including plasmonic nano- structures require precise positioning or controlled rearrangements to serve as adaptive functional structures. Key elements of the control at the micro- and nanoscale are often either pressure driven fluidics transporting liquid volume and solutes or the generation of potential energy landscapes or force fields. The latter is achieved with optical \(^{3}\) and plasmonic tweezers \(^{11,12}\) , magnetic fields \(^{13}\) , or using electrokinetic \(^{14}\) or opto- electronic \(^{15}\) effects. Especially in the field of plasmonic tweezers and nanoantennas where light is used to excite collective electron motion in noble- metals, the Joule losses lead to the unavoidable generation of heat at boundaries as an unwanted side effect \(^{16,17}\) . Yet, such optically generated temperature fields seem also suitable for the manipulation of nano- objects in liquids, for example, for the trapping of nanoparticles \(^{18}\) and single molecules \(^{19}\) or protein aggregates \(^{20}\) as well as for manufacturing active particles \(^{21 - 24}\) . Those techniques rely on a drift of molecules and particles in optically generated temperature gradients termed thermophoresis or suggest thermoelectric effects \(^{25}\) relying on a thermally induced charge separation. In addition, thermo- electrohydrodynamic effects using time- varying electric fields have been proposed for rapid particle transport \(^{26,27}\) and convective effects that arise from temperature- induced density changes in the large liquid cells have been reported \(^{28 - 31}\) . + +Here we report on a fundamental physical process that + +is able to provide a versatile trapping and manipulation of nano- objects and fluids near surfaces in the simplest geometries. Contrary to most other techniques, our scheme is based on hydrodynamic flows generated by optically induced thermo- osmosis. Thermo- osmosis relies on a perturbation of the interfacial interactions at a solid- liquid boundary and is present in all experiments involving temperature gradients in plasmonic structures including plasmonic tweezers. We show that local temperature gradients on a thin gold film induce strong interfacial flows of several 10 to \(100 \mu \mathrm{m} \mathrm{s}^{- 1}\) in its direct vicinity (10 nm) that results in a flow pattern reminiscent of convection. Based on a fully quantitative analysis of our experimental results we reveal that these thermo- osmotic flows on gold- water interfaces are induced by a temperature- induced perturbation of the van der Waals (vdW) interactions. Nano- objects suspended in the liquid are therefore dragged by the hydrodynamic forces originating from these flows. Utilizing attractive vdW interactions of the nano- object with the gold surface or temperature- induced depletion, we trap and manipulate different types of nano- objects near the surface. The fast heating at small scales allows us to multiplex flow fields and to manipulate multiple objects with great precision. Our detailed analysis of the flow fields, the localization accuracy of nano- objects, and a comparison with numerical and theoretical predictions provide a quantitative understanding of these effects and paves the way for controlling boundary layer dynamics to manipulate objects at the smallest length scales in solutions. + +Experimental configuration and working principle Our experiments rely on a simple sample geometry with a gold film (50 nm) that is deposited on a microscopy glass cov + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1: Thermo-hydrodynamic manipulation of Au NPs in NaCl solution. a, The sample consists of two glass slides that confine a \(3\mu \mathrm{m}\) thin liquid film of gold nanoparticles (AuNPs) dispersed in aqueous NaCl solution. The lower glass slide carries a \(50\mathrm{nm}\) Au film that is locally heated by optical absorption of a focused laser of \(\lambda = 532\mathrm{nm}\) wavelength. b, The experimental setup comprises an inverted optical microscope equipped with an acusto-optical deflector controlled, steerable focused laser with a wavelength of \(\lambda = 532\mathrm{nm}\) . The AuNPs are observed using darkfield illumination with an oil-immersion dark-field condenser (NA 1.2) and a \(100\times\) oil-immersion objective set to NA 0.6. Images are recorded with an EMCCD camera.
+ +erslip (Fig. 1a). The sample chamber contains a suspension of gold nanoparticles (AuNPs) or other nano- objects (polystyrene NPs and ellipsoids) with a controlled amount of salt (NaCl), surfactants (SDS, ...) or polymers (PEG). The gold film is heated locally in an inverted microscopy setup by a highly focused laser (532 nm) using beam steering optics (Acusto- Optic- Deflector, AOD). The nano- objects are observed using darkfield illumination with an oil- immersion darkfield condenser (NA 1.2) and a \(100\times\) oil- immersion objective set to NA 0.6 (Fig. 1b). Additional details of the experimental setup and sample preparation are provided in the Methods section. + +The trapping of nano- objects as detailed in the following is comprising two effects. i) The vertical confinement of the suspended objects as achieved by an attractive interaction of the suspended nano- objects with the gold surface, which is found to be the vdW interaction for gold nanoparticles and can be replaced by depletion forces for other materials. ii) The generation of thermo- osmotic boundary flows that are induced by the local heating and the corresponding perturbation of the liquid- solid interactions. This boundary flow is directed radially inwards to the heated spot and provides a confining hydrodynamic force on suspended objects at the heating spot. + +Dynamics of AuNPs close to a Au film Consider a single AuNP with a radius of \(R = 125\mathrm{nm}\) that is suspended in an aqueous solution of NaCl at a \(10\mathrm{mM}\) concentration and diffusing in a thin liquid film of about \(3\mu \mathrm{m}\) thickness over a \(50\mathrm{nm}\) Au film (Fig. 1a). Exploring the diffusion of the particle we observe a restriction of the \(z\) - positions to a thin layer close to the gold film. The gold particle never defocuses under these conditions while it does in deionized (DI) water (Supplementary Video 1). This restricted out- of- plane motion is the result of interactions comprising an attractive vdW contribution, a repulsion of the electrostatic double layers of the particle and surface32 as described by the DLVO theory and the gravitational potential (see Supplementary Information for details). + +\[V(d,c_{0}) = V_{\mathrm{E}}(d,c_{0}) + V_{\mathrm{vdW}}(d) + V_{\mathrm{G}}(d). \quad (1)\] + +The total potential for the experimental situation is depicted in Fig. 2a for different salt concentrations (see SI for parameters) as a function of the surface- to- surface distance \(d = z - R\) . The stronger screening of the surface charges at the gold film and the AuNP at higher salt concentration increase the importance of attractive vdW interactions to create this secondary minimum in the DLVO part of the potential. This potential influences the observed dynamics as well as the particle couples with its hydrodynamic flow field to the solid boundary33. The in- plane \(D_{\parallel}\) (equation 2) and out- of- plane \(D_{\perp}\) diffusion coefficient (see Supplementary Information for details) are modulated with the distance \(z\) of the particle from the wall. + +\[\frac{D_{\parallel}(z)}{D_0}\approx 1 - \frac{9}{16}\frac{R}{z} +\frac{1}{8}\left(\frac{R}{z}\right)^3\pm \dots := \gamma_{\parallel}^{-1}(z) \quad (2)\] + +Over the course of a diffusion trajectory, the particle samples different regions with different diffusion coefficients according to its probability density \(p(d) \propto \exp (- V(d, c_0) / (k_B T))\) to be at a distance \(d\) from the surface (filled regions in Fig. 2a). The observed in- plane diffusion coefficient is thus a weighted average of the diffusion coefficient over the different vertical positions \(d\) . Using \(p(d)\) we can calculate the corresponding salt concentration dependence of the in- plane diffusion coefficient and compare that to the experimental results. Fig. 2b shows that the experimentally observed \(D_{\parallel}\) is decreasing with increasing salt concentration due to the hydrodynamic coupling in fair + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2: DLVO potential, lateral diffusion analysis, temperature distribution and thermo-osmotic flow field. a, Plot of the DLVO potential, equation (1), between a \(250~\mathrm{nm}\) Au NP and a \(50~\mathrm{nm}\) Au film on a glass surface as function of surface-surface distance \(d\) for different NaCl concentrations \(c_0\) . The shaded curves display the calculated probability density for finding the particle at this distance at the different salt concentrations (see Supplementary Information for details). The vertical dashed lines correspond to the mean distance of the particle as calculated from the probability density for a \(3\mu \mathrm{m}\) liquid film height. b, The measured diffusion coefficient \(D_1 / D_0\) parallel to the Au film with respect to the bulk diffusion coefficient \(D_0\) as function of the NaCl concentration \(c_0\) . Symbols correspond to the experimental values. The lines reflect the theoretical prediction including a distance dependent diffusion coefficient for three different Hamaker constants (dotted: \(A_H = 4\cdot 10^{-20}\mathrm{J}\) , dash-dotted: \(5\cdot 10^{-20}\mathrm{J}\) and dashed: \(6\cdot 10^{-20}\mathrm{J}\) ) of gold according to a Boltzmann weighting (see text). c, Relation between the mean distance \(\langle d\rangle\) and the NaCl concentration \(c_0\) . The symbols and the horizontal lines denote the calculated distances for measured concentrations. d, Simulation of the relative temperature increment in the \(xz\) -plane of the sample. e, Experimentally obtained temperature increment \(\Delta T_{\mathrm{max}}\) as a function of the incident laser power \(P_0\) (green data points) compared to the simulated values (green curve). f, Measured thermo-osmotic flow field in the \(xy\) -plane in close proximity to the gold film ( \(z< 500\mathrm{nm}\) ). g, Measured thermo-osmotic flow field in the \(xz\) -plane. h, Illustration of the measured flow field planes in f and g. i, j, The \(x\) - and \(z\) -component of the measured flow velocities compared to the simulation results in k and l.
+ +agreement with the theoretical predictions (for three different Hamaker constants for the AuNP gold surface interaction). Calculating in addition the mean distance \(\langle d\rangle\) of the particle from the surface reveals that only in the case of \(c_0 = 10\mathrm{mM}\) the particle is confined in the DLVO potential well, while at lower salt concentrations an enhanced probability (Fig. 2a) of finding the particle near the surface is the cause of the observed diffusion coefficient. These calculations help us to estimate the mean distance \(\langle d\rangle\) of the particle from the surface, which is about \(1.5\mu \mathrm{m}\) and \(0.9\mu \mathrm{m}\) for the lowest NaCl concentrations (Fig. 2c). At a concentration of \(c_0 = 10\mathrm{mM}\) the particle is hovering at a distance + +of \(\langle d\rangle = 20\mathrm{nm}\) surface. Note that this corresponds to values of \(z / (2R)\approx 0.58\) , which is far below the commonly explored region of the hydrodynamic coupling of colloids to walls33 allowing to experimentally explore new terrains also in the field of hydrodynamic wall coupling of colloids. + +Hydrodynamic Particle Confinement When tightly focusing the light of \(532\mathrm{nm}\) wavelength to the gold film, a part of the incident energy (about \(30\%\) ) is absorbed and converted into heat that perturbs the liquid- solid interactions. The temperature rise at the gold surface can be determined using a thin nematic liquid crystal (5CB) film and substan + +<--- Page Split ---> + +tiated by finite element simulations with the complete threedimensional temperature profile in the solution (see Fig. 2d, e and Supplementary Information for details). + +These local temperature perturbations of the solidliquid interactions at the interface induce a thermo- osmotic flow34,35. Taking a liquid volume element close to the solid from the cold side and exchanging that with one at the hot side would not only transport heat since the liquid volumes have different temperatures, but also additional free energy as the liquid has a different interaction with the solid in these regions. The flow is induced in an ultrathin boundary layer corresponding in thickness to the length scale of liquid- solid interactions. Since the characteristic interaction length of liquid- solid interactions is only a few nanometers, the boundary flow on the substrate can be collapsed into a quasi- slip hydrodynamic boundary condition: + +\[v_{\parallel} = -\frac{1}{\eta}\int_{0}^{\infty}z h(z)\mathrm{d}z\frac{\nabla_{\parallel}T}{T} = \chi \frac{\nabla_{\parallel}T}{T}, \quad (3)\] + +where \(h(z)\) is the excess enthalpy, \(T\) the temperature and \(\nabla_{\parallel}T\) is the temperature gradient parallel to the surface. The integral can be summarized to a thermo- osmotic coefficient \(\chi\) . The thermo- osmotic coefficient \(\chi\) , therefore, contains all information about the interfacial interaction between the liquid and the solid. If \(\chi < 0\) the liquid is driven to the cold, whereas for \(\chi > 0\) , the liquid is driven to the hot. These boundary flows are present at all liquid- solid interfaces with tangential temperature gradients, though, they are commonly overlooked. They become particularly important for plasmonic and thermo- plasmonic trapping16, as those techniques rely on the dynamics of molecules and particles in the direct vicinity of plasmonic nanostructures. The boundary flow drives the flow field inside the fluid film. The resulting volumetric flow field can be tracked experimentally by single AuNPs in DI water, where the particles are not confined to a surface layer as reported above. We analyze the in- plane \((xy)\) position of the particle and its \(z\) - position, where the latter is estimated from the radius \(r_0\) of the defocussed particle images (see Supplementary Video 2 and Supplementary Information for details). The measured velocity distributions in the \(xy\) - plane near the gold layer and in the \(xz\) - plane are shown in Fig. 2f and g, respectively. The \(x\) - and \(z\) - component of the measured flow velocities are depicted in Fig. 2i, j and compare well to simulation results in Fig. 2k, l. From these measurements, we extract a thermo- osmotic coefficient on the order of \(\chi \sim 10\cdot 10^{- 10}\mathrm{m}^2\mathrm{s}^{- 1}\) (see Supplementary Information for details). We can break down the contributions to this value with equations (4) and (5) to estimate the double layer and vdW contributions using the experimental parameters. Note that AuNP do not show thermophoresis due to their high thermal conductivity and thus isothermal surface. + +\[\chi_{\mathrm{E}} = \frac{\epsilon\zeta^2}{8\eta}\approx 0.8\cdot 10^{-10}\mathrm{m}^2 /\mathrm{s}^{-1}. \quad (4)\] + +For the electrostatic contribution we used \(\zeta = - 30\mathrm{mV}^{36}\) and \(\epsilon = 80\epsilon_0\) (see Methods section for details). An estimate of the vdW contribution can be given by + +\[\chi_{\mathrm{vdW}} = \frac{A_{\mathrm{H}}\beta T}{3\pi\eta d_0}\approx 9.3\cdot 10^{-10}\mathrm{m}^2\mathrm{s}^{-1}, \quad (5)\] + +with \(\beta = 0.2\cdot 10^{- 3}\mathrm{K}^{- 1}\) being the thermal expansion coefficient of water and \(d_0 = 0.2\mathrm{nm}\) for the cut- off parameter34 (see Supplementary Information for details). The sum of both contributions \(\chi = \chi_{\mathrm{E}} + \chi_{\mathrm{vdW}} = 10.1\cdot 10^{- 10}\mathrm{m}^2\mathrm{s}^{- 1}\) matches well the experimental result and suggests that thermo- osmosis at gold- water interfaces is governed by vdW interactions. The obtained quasi slip velocities are ranging up to \(80\mu \mathrm{m / s}\) and provide, due to their omnipresence, a unique tool for nanofluidics. These thermo- osmotic flows are induced without any external pressure difference. They can be controlled by the light intensity heating laser and are quickly switched due to the extremely fast heat conduction at these length scales. Moreover the finding of the vdW dominated thermo- osmotic flows suggest that such contributions must be present in any plasmonic trapping experiment with extended gold structures12,16,17,27. + +Using \(F_T^{\mathrm{eff}} = 6\pi \eta R\gamma_{\parallel}v_x\) and \(F_T^{\mathrm{eff}} = 6\pi \eta R\gamma_{\perp}v_z\) , where \(\gamma_{\parallel}\) and \(\gamma_{\perp}\) are the correction factors for the friction coefficient of a sphere close to a surface we are able to extract the hydrodynamic forces that are exerted on the AuNP tracers (see equation (2) and Supplementary Information for details). The lateral forces allow to confine objects at the heating spot, yet the hydrodynamic force normal to the surface ( \(z\) - direction) is repulsive without any additional interaction. Finally, such boundary flows with substantial vertical velocity gradients also exhibit a vorticity (see Supplementary Information for details) that generates a torque on suspended objects causing them to rotate37. + +Single particle trapping and flow field multiplexing The repulsive normal component caused by the hydrodynamic drag is now superimposed with the attractive force due to the DLVO potential when increasing the NaCl concentration. The surface- to- surface distance between AuNP and gold film and the depth of the appearing secondary DLVO potential minimum can be controlled by the NaCl concentration. At a NaCl concentration of about \(c_0 = 10\mathrm{mM}\) , the attractive potential has a depth of about \(10k_{\mathrm{B}}T\) (see Fig. 2a) and is strong enough to compete with the vertical drag force and additional optical forces on the AuNP to trap the particle above the heating spot. + +Supplementary Video 3 demonstrates this trapping of an AuNP above the hot spot on the Au surface. This is purely the result of the hydrodynamic drag forces generated by the thermo- osmotic flow and the attractive vdW interaction between the AuNP and the Au film. This observation is substantiated by a quantitative evaluation of the lateral trap stiffness and vertical forces, as depicted in Fig. 3a, b. The fluctuations of the particle in the hydrodynamic flow arise from a balance of the restoring hydrodynamic currents and the diffusive currents. Analysis of the lateral position histograms (inset in Fig. 3c for \(1.25\mathrm{mW}\) ) yields an effective stiffness of the trap (Fig. 3a) that well matches the predictions based on the thermo- osmotic flow (Fig. 2i, j). The hydrodynamic trapping stiffness increases linearly up to a heating power of about \(1.8\mathrm{mW}\) . At this power, the vertical forces become strong enough to let the particle escape the secondary DLVO minimum, which is visible from the \(z\) - position time traces displayed in Fig. 3d. The AuNPs are then observed to move vertically out of the DLVO potential to follow the flow inside the sample and to eventually return + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3: Forces on trapped NPs in \(10\mathrm{mM}\) NaCl. a, The lateral trap stiffness obtained from the experimental position histograms (blue data points) as function the laser power \(P_0\) compared to the simulation result (blue solid line). b, The \(x\) -component of the thermo-osmotic drag force \(F_{\mathrm{T}}^{\mathrm{TF}}\) (blue line), the optical force \(F_{\mathrm{Z}}^{\mathrm{OF}}\) (green line) and the total force \(F_{\mathrm{Z}}^{\mathrm{OF}} + F_{\mathrm{Z}}^{\mathrm{TF}}\) (black dashed line) as function the incident laser power \(P_0\) for a NP located at \(x = 0\) , \(d = 30\) ( \(z = d + R\) ). The attractive DLVO force \(F_{\mathrm{DLVO}}^{\mathrm{DLVO}}\) is independent of the incident laser power and depicted as horizontal, red line. c, Trajectory of a AuNP for a heating laser power of \(1.25\mathrm{mW}\) (see Supplementary Video 2 for details). The inset shows the corresponding lateral distribution histogram. d, Time traces of the \(z\) -position for three different laser powers. e, Trajectory of a AuNP for a heating laser power of \(2.5\mathrm{mW}\) , which is above the threshold power of \(2.25\mathrm{mW}\) .
+ +to the boundary flow via sedimentation (Fig. 3e, Supplementary Video 4). The forces which eject the particle from the potential comprise the hydrodynamic and the optical forces due to the radiation pressure from the heating laser leaked through the film. We have evaluated the individual contributions in simulations. They are shown together with the hydrodynamic force and the total vertical force as compared to the attractive force of the DLVO potential (Fig. 3b) and provide quantitative agreement (threshold heating power of \(2.25\mathrm{mW}\) ) with our experimental results. Note that while the stationary distribution of particles in the vertical direction is not influenced by the diffusive dynamics, the escape rate from the potential well is heavily altered by the fact that the vertical diffusion coefficient \(D_{\perp}\) of the particle is decreasing to zero when approaching the gold film. This is enhancing the trapping times considerably (see Supplementary Information for details) but also increases the time required for the particle to enter the DLVO minimum by diffusion. + +The observed trapping is, hence, a vdW assisted thermodynamic process. Vertical confinement is achieved by vdW attraction and double layer repulsion, while lateral confinement is the result of thermo- osmotic flows induced in an ultra- thin sheet of liquid at the interface. No additional contributions, for example, due to convective flows with similar flow patterns (see Supplementary Information for details) or thermo- electric effects are required for a quantitative description \(^{25,31,38,39}\) . Precise tuning of the DLVO potential enables the trapping of even smaller Au NPs (Fig. 4a, Supplementary Video 5). + +The speed of heat diffusion, which is about 4 orders of magnitude faster than the particle diffusion \(^{40}\) allows us to introduce a flow field multiplexing. We switch the heating location between different positions inducing thermo- osmotic flow fields for time periods of about \(100\mu \mathrm{s}\) . With the help of this multiplexing, we are able to hold multiple \(R = 125\mathrm{nm}\) AuNPs (Fig. 4b, c) at distances of less than \(1\mu \mathrm{m}\) , which would not be possible with continuous heating of close- by locations (Supplementary Videos 6 and 7). A trapped AuNP can also be guided along the predefined path over the Au film as fast as \(10\mu \mathrm{m}\mathrm{s}^{- 1}\) (Fig. 4d, + +Supplementary Video 8). At larger manipulation speeds ( \(f > 100\mathrm{Hz}\) ) and higher heating power ( \(P_0 > 10\mathrm{mW}\) ) the thermo- osmotic attraction to the heating spot is combined with thermo- viscous flows \(^{41,42}\) . These flows originate from the temperature dependent viscosity \(\eta (T)\) of the liquid and are directed opposite to the scanning direction of the laser \(^{42}\) . The result of this combination of thermo- osmosis and thermo- viscous flows is a rotating ring- like particle structure (Fig. 4e and Supplementary Video 9). + +These different effects that can be exploited in a simple planar geometry give rise to numerous applications including for example a freely configurable nanoparticle on mirror geometry for plasmonic sensing \(^{5,43}\) . The multiplexing of local flow field may be be helpful to construct more complex effective flow fields for an efficient transport of analytes without external pressure. + +Beyond thermo- osmotic van der Waals trapping So far, the presented manipulation is based on thermo- osmotic flows that drive the lateral motion of suspended colloids and a vertical confinement due to the secondary minimum of the DLVO potential between AuNP and Au film. While the thermo- osmotic flows are characteristic for all systems containing a heated gold/water interface including all previous studies on thermo- plasmonic trapping, the DLVO potential minimum is much weaker for other materials like polymer colloids or macromolecules due to their smaller vdW attraction. Often, those system even show a repulsion from the heat source due to thermophoresis, which is not present for AuNP. A more generalized strategy therefore needs additional attractive contributions, which confine suspended colloids or molecules to regions close to the gold surface to take advantage of the thermo- osmotic flow. + +Such attractive contributions can arise from depletion interactions \(^{21,44}\) . Thereby a temperature gradient repels dissolved molecules from the heated regions generating a concentration gradient that drives suspended nano- objects to the heating spot. To demonstrate this effect we use the surfactant sodium dodecyl sulfate (SDS) at a concentration of \(5\mathrm{mM}\) well below the critical micelle concentration + +<--- Page Split ---> + +(8.2 mM) to avoid complications of micelle formation. We suspend additional polystyrene particles(PS) and AuNPs of the same size ( \(R = 125 \mathrm{nm}\) ) in the solution and compare their dynamics to a solution with AuNPs and PS particles without SDS but \(10 \mathrm{mM} \mathrm{NaCl}\) . Remarkably, the heated spot is attractive for both AuNPs and for PS NPs (Fig. 4f, Supplementary Video 10) in the SDS solution showing even PS colloidal crystal growth, while only the AuNP is trapped in the NaCl solution and the PS particles are repelled by thermophoresis (Fig. 4g, Supplementary Video 11). + +The observations in NaCl are readily explained by the fact that the AuNP is confined in the DLVO minimum as demonstrated above but the PS particle is not due to a 10 times lower Hamaker constant (see Supplementary Information for details). The PS particle samples the whole liquid film thickness equally and not preferentially the region close to the Au film and experiences an additional thermophoretic drift velocity given by + +\[\pmb {u} = -\frac{2}{3}\chi \frac{\nabla T}{T} = -D_{\mathrm{T}}\nabla T, \quad (6)\] + +where \(D_{\mathrm{T}}\) is the thermophoretic mobility \(^{34}\) and \(\nabla T\) the temperature gradient (Fig. 4g, Supplementary Video 11). For \(\chi >0\) the particle is driven to the cold. From equation (4) we find \(\chi \approx \chi_{\mathrm{E}} = 1.28\cdot 10^{- 10} \mathrm{m}^{2} \mathrm{s}^{- 1}\) and \(D_{\mathrm{T}} \approx 0.3 \mu \mathrm{m}^{2} \mathrm{K}^{- 1} \mathrm{s}^{- 1}\) , where we have used a measured zeta potential of \(\zeta \approx - 38 \mathrm{mV}\) . The vdW contribution, \(\chi_{\mathrm{vdW}}\) to + +either the thermophoretic drift or the attraction to the gold surface can be neglected due to the smaller Hamaker constant of PS. From the stationary probability distribution of the PS NP we find a Soret coefficient of \(S_{\mathrm{T}} \approx 0.24 \mathrm{K}^{- 1}\) (see Supplementary Information for details) in agreement with our theoretical prediction \(S_{\mathrm{T}} = D_{\mathrm{T}} / D_{\parallel} \approx 0.21 \mathrm{K}^{- 1}\) . + +In the SDS solution, the additional surfactant molecules now undergo thermophoresis to yield a concentration gradient in which suspended colloidal particles drift. The lower concentration in the heated regions promotes an effective attractive interaction of suspended colloids with the gold surface due to depletion forces. The drift velocity is described by an additional term to the thermodiffusion coefficient \(D_{\mathrm{T}}\) , that is, the second term in brackets in equation (7) \(^{21,34,44}\) . + +\[\pmb {u} = -\left(D_{\mathrm{T}} - \frac{k_{\mathrm{B}}}{3\eta} R^{2}c_{0}N_{\mathrm{A}}\left(T S_{\mathrm{DS}}^{\mathrm{SDS}} - 1\right)\right)\nabla T \quad (7)\] + +Here \(R\) is the size of the SDS molecule, \(c\) the concentration in units of mol/l and \(S_{\mathrm{DS}}^{\mathrm{SDS}}\) the Soret coefficient of SDS. For \(R = 2 \mathrm{nm}^{45}\) , \(c_{0} = 5 \mathrm{mM}\) and \(S_{\mathrm{DS}}^{\mathrm{SDS}} = 0.03 \mathrm{K}^{- 146}\) we find \(- 0.43 \mu \mathrm{m}^{2} \mathrm{K}^{- 1} \mathrm{s}^{- 1}\) for the additional depletion contribution, which exceeds the thermophoretic mobility, \(D_{\mathrm{T}} \approx 0.3 \mu \mathrm{m}^{2} \mathrm{K}^{- 1} \mathrm{s}^{- 1}\) , rendering the overall mobility negative. The PS NPs and the AuNPs are thus driven to the the heated Au film surface (Fig. 4f, Supplementary Video 10) which allows for further transport in the thermo- osmotic + +![](images/Figure_4.jpg) + +
Fig. 4: Manipulation of NPs over a Au film in NaCl and SDS solution. a, A AuNP with \(50 \mathrm{nm}\) radius trapped at a NaCl of \(30 \mathrm{mM}\) (Supplementary Video 5). b, Manipulation of two AuNPs by a multiplexed laser beam (Supplementary Video 6). c, Control of three AuNPs (Supplementary Video 7). d, Actuation of a single AuNP on a circular trajectory by a steerable laser beam (Supplementary Video 8). The green and white, dashed arrows denote the moving direction of the laser focus and particle, respectively. e, Generation of thermo-viscous flows by rotating the laser focus on a circle with a rotation frequency of \(f = 500 \mathrm{Hz}\) at high laser powers (Supplementary Video 9). Note, that laser movement (green arrow) and the thermo-viscous flow (white, dashed arrow) and have opposite directions. f, Attraction of a AuNP and PS NPs in \(5 \mathrm{mM}\) SDS due to depletion (Supplementary Video 10). g, An AuNP (125 nm radius) trapped in an ensemble of polystyrene (PS) NPs of the same size at \(10 \mathrm{mM}\) NaCl (Supplementary Video 11), where the PS-particles are repelled due to thermophoresis. h, Attraction of PS ellipsoids (2.39 \(\mu \mathrm{m}\) major-axis length, \(0.34 \mu \mathrm{m}\) minor-axis length) in \(5 \mathrm{mM}\) SDS (Supplementary Video 12).
+ +<--- Page Split ---> + +boundary flow. Additional contributions, as for example thermo- electric fields may even enhance the attractive components. Overall, this concept is readily transferred to other objects as shown in Figure 4h and Supplementary Video 12, where we have trapped ellipsoidal PS particles in a \(5\mathrm{mM}\) solution of SDS. Note that as compared to other schemes, our approach always includes thermo- osmotic boundary flows. + +Conclusion In conclusion, we have demonstrated that thermo- hydrodynamic boundary flows can manipulate nano- objects with unprecedented flexibility in a very simple sample geometry. These flows are the key for future thermo- optofluidic implementations with an extensive range of applications in the fields of i) nanoparticle sorting and separation; ii) assembly of nanophotonic circuits and plasmonic quantum sensors; iii) biotechnology on- chip laboratories and iv) manufacturing of nanomaterials and functional nanosurfaces. We have substantiated our experimental findings of thermo- osmotic flow assisted trapping with a quantitative theoretical description. A flow field multiplexing scheme has been further developed to allow for the simultaneous manipulation of many individual nano- objects and the generation of complex effective flow patterns. Our concept can be combined with other thermally induced effects such as thermophoresis, depletion forces and thermoviscous flows to form a fully- featured nanofluidic system- on- a- chip. Besides direct consequences for the field of plasmonic nano- tweezers and other thermoplasmonic trapping schemes, the use of thermo- hydrodynamic flows as a tool for nanofluidic applications will extend the limits at the forefront of nanotechnology and help to develop AI and feedback controlled schemes for the material synthesis. + +## References + +[1] O. M. Maragò, P. H. Jones, P. G. Gucciardi, G. Volpe, and A. C. 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Vollmer, Quantum Nanophotonic and Nanoplasmonic Sensing: Towards Quantum Optical Bioscience Laboratories on Chip, Nanophotonics 10, 1387- 1435 (2021).[49] J. Peng, H.- H. Jeong, Q. Lin, S. Cormier, H.- L. Liang, M. F. L. De Volder, S. Vignolini, and J. J. Baumberg, Scalable Electrochromic Nanopixels Using Plasmonics, Sci. Adv. 5, 10.1126/sciadv.aaw2205 (2019).[50] E. Mohammadi, A. Tittl, K. L. Tsakmakidis, T. V. Raziman, and A. G. Curto, Dual Nanoresonators for Ultrasonic Optical Detection, ACS Photonics 8, 1754- 1762 (2021). + +## Methods + +Experimental setup The experimental setup (Figure S19) consists of an inverted microscope (Olympus, IX71) with a mounted piezo translation stage (Physik Instrumente, P- 733.3). The microparticles are heated by a focused, continuous- wave laser at a wavelength of \(532 \mathrm{nm}\) (CNI, MGL- III- 532). The beam diameter is increased by a beam expander and sent to an acousto- optic deflector (AA Opto- Electronic, DTSXY- 400- 532) and a lens system to steer the laser focus in the sample plane. The deflected beam is focused by an oil- immersion objective (Olympus, UPlanApo \(\times 100 / 1.35\) , Oil, Iris, NA 0.5 - 1.35) to the sample plane \((w_0 \approx 0.8 \mu \mathrm{m}\) beam waist in the sample plane). The sample is illuminated with an oil- immersion darkfield condenser (Olympus, U- DCW, NA 1.2 - 1.4) and a white- light LED (Thorlabs, SOLIS- 3C). The scattered light is imaged by the objective and a tube lens ( \(250 \mathrm{mm}\) ) to an EMCCD (electron- multiplying charge- coupled device) camera (Andor, iXon DV885LC). The variable numerical aperture of the objective was set to a value below the minimum aperture of the darkfield condenser. The dichroic beam splitter (D) was selected to reflect the laser wavelength (Omega Optical, 560DRLP) and a notch filter (F) is used to block any remaining back reflections from the laser (Thorlabs, NF533- 17). The acousto- optic deflector (AOD), as well as the piezo stage, are driven by an AD/DA (analogue- digital/digital- analogue) converter (Jäger Messtechnik, ADwin- Gold II). A LabVIEW program running on a desktop PC (Intel Core i7 2600 4 \(\times 3.40 \mathrm{GHz}\) CPU) is used to record and process the images as well as to control the AOD feedback via the AD/DA converter. + +Sample preparation The sample consists of two glass coverslips \((22 \mathrm{mm} \times 22 \mathrm{mm})\) confining a thin liquid film. First, the coverslips were thoroughly cleaned by rinsing successively with acetone, isopropyl and Milli- Q water and dried with a nitrogen gun. Subsequently, the edges of one coverslip were covered with a thin layer of PDMS (polydimethylsiloxane) for sealing. The particle solution used for the experiments was prepared by dispersing \(0.250 \mu \mathrm{m}\) diameter gold particles (Cytodiagnostics) in ... solution. Finally, \(0.5 \mu \mathrm{l}\) of the mixed particle suspension is pipetted in the middle of one of the coverslips and the other is placed on top. Depending on the area covered by the liquid, typically about \((10 \mathrm{mm} \times 10 \mathrm{mm})\) , the resulting liquid film height is about \(5 \mu \mathrm{m}\) . + +## Acknowledgement + +The authors acknowledge financial support by ... + +## Author contributions + +M.F. and F.C. designed the experiments. M.F. performed the experiments. M.F. and F.C. analyzed the experimental data. M.F. and F.C. implemented and evaluated the numerical calculations. M.F. and F.C. wrote the manuscript. All authors discussed the results and commented on the manuscript. + +<--- Page Split ---> + +## Competing interests + +The authors declare no competing interest. + +## Additional information + +Supplementary information is available for this paper at https://doi.org ... + +Correspondence and requests for materials should be addressed to F.C. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Video6.mp4- Sl.pdf- Video3.mp4- Video2.mp4- Video8.mp4- Video7.mp4- Video1.mp4- Video5.mp4- Video4.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397_det.mmd b/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..4e851af734018f5ec642e0adc9c414b49f07e21b --- /dev/null +++ b/preprint/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397/preprint__09b108cd9ad0034506ae629f2fb23d3bf7f4cd8c58a23feee09b7b89729c5397_det.mmd @@ -0,0 +1,252 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 872, 175]]<|/det|> +# Hydrodynamic manipulation of nano-objects by optically induced thermo-osmotic flows + +<|ref|>text<|/ref|><|det|>[[44, 195, 570, 283]]<|/det|> +Martin Fränzl Leipzig University https://orcid.org/0000- 0001- 6754- 8554 Frank Cichos (cichos@physik.uni- leipzig.de) Leipzig University https://orcid.org/0000- 0002- 9803- 4975 + +<|ref|>sub_title<|/ref|><|det|>[[44, 325, 102, 342]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 362, 673, 382]]<|/det|> +Keywords: nano- objects, microfluidics, hydrodynamics, thermo- osti + +<|ref|>text<|/ref|><|det|>[[44, 400, 350, 419]]<|/det|> +Posted Date: September 24th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 438, 463, 458]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 879955/v1 + +<|ref|>text<|/ref|><|det|>[[44, 476, 910, 519]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 554, 935, 597]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on February 3rd, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28212- z. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[81, 95, 828, 136]]<|/det|> +# Hydrodynamic manipulation of nano-objects by optically induced thermo-osmotic flows + +<|ref|>text<|/ref|><|det|>[[82, 154, 348, 169]]<|/det|> +Martin Fränzl \(^{1}\) and Frank Cichos \(^{1,*}\) + +<|ref|>text<|/ref|><|det|>[[81, 187, 770, 214]]<|/det|> +\(^{1}\) Peter Debye Institute for Soft Matter Physics, Molecular Nanophotonics Group, Universität Leipzig, Linnéstr. 5, 04103 Leipzig, Germany. + +<|ref|>text<|/ref|><|det|>[[82, 223, 280, 235]]<|/det|> +\* cichos@physik.uni- leipzig.de + +<|ref|>text<|/ref|><|det|>[[80, 244, 916, 413]]<|/det|> +The manipulation of nano- objects at the microscale is of great technological significance to construct new functional materials, to manipulate tiny amounts of liquids, to reconfigure sensorial systems or to detect minute concentrations of analytes in medical screening. It is commonly approached by the generation of potential energy landscapes, for example, with optical fields or by using pressure driven microfluidics. Here we show that strong hydrodynamic boundary flows enable the trapping and manipulation of nano- objects near surfaces. These thermo- osmotic flows are induced by modulating the van der Waals interaction at a solid- liquid interface with optically generated temperature fields. We use a thin gold film on a glass substrate to provide localized but reconfigurable point- like optical heating. Convergent boundary flows with velocities of tens of micrometres per second are observed and substantiated by a quantitative physical model. The hydrodynamic forces acting on suspended nanoparticles and attractive van der Waals or depletion induced forces enable precise positioning and guiding of the nanoparticles. Fast multiplexing of flow fields further provides the means for parallel manipulation of many nano- objects and the generation of complex flow fields. Our findings have direct consequences for the field of plasmonic nano- tweezers as well as other thermo- plasmonic trapping schemes and pave the way for a general scheme of nanoscopic manipulation with boundary flows. + +<|ref|>text<|/ref|><|det|>[[80, 446, 481, 860]]<|/det|> +The control and manipulation of nano- objects is a key element for future nanophotonics \(^{1 - 5}\) , material science \(^{4,6,7}\) , biotechnology \(^{2,8,9}\) or even quantum sensing \(^{10}\) . Analytes dissolved in liquids, for example, need to be delivered, concentrated, separated or locally confined for further studies to become eventually processed and removed. Photonic elements including plasmonic nano- structures require precise positioning or controlled rearrangements to serve as adaptive functional structures. Key elements of the control at the micro- and nanoscale are often either pressure driven fluidics transporting liquid volume and solutes or the generation of potential energy landscapes or force fields. The latter is achieved with optical \(^{3}\) and plasmonic tweezers \(^{11,12}\) , magnetic fields \(^{13}\) , or using electrokinetic \(^{14}\) or opto- electronic \(^{15}\) effects. Especially in the field of plasmonic tweezers and nanoantennas where light is used to excite collective electron motion in noble- metals, the Joule losses lead to the unavoidable generation of heat at boundaries as an unwanted side effect \(^{16,17}\) . Yet, such optically generated temperature fields seem also suitable for the manipulation of nano- objects in liquids, for example, for the trapping of nanoparticles \(^{18}\) and single molecules \(^{19}\) or protein aggregates \(^{20}\) as well as for manufacturing active particles \(^{21 - 24}\) . Those techniques rely on a drift of molecules and particles in optically generated temperature gradients termed thermophoresis or suggest thermoelectric effects \(^{25}\) relying on a thermally induced charge separation. In addition, thermo- electrohydrodynamic effects using time- varying electric fields have been proposed for rapid particle transport \(^{26,27}\) and convective effects that arise from temperature- induced density changes in the large liquid cells have been reported \(^{28 - 31}\) . + +<|ref|>text<|/ref|><|det|>[[97, 869, 480, 881]]<|/det|> +Here we report on a fundamental physical process that + +<|ref|>text<|/ref|><|det|>[[515, 446, 916, 821]]<|/det|> +is able to provide a versatile trapping and manipulation of nano- objects and fluids near surfaces in the simplest geometries. Contrary to most other techniques, our scheme is based on hydrodynamic flows generated by optically induced thermo- osmosis. Thermo- osmosis relies on a perturbation of the interfacial interactions at a solid- liquid boundary and is present in all experiments involving temperature gradients in plasmonic structures including plasmonic tweezers. We show that local temperature gradients on a thin gold film induce strong interfacial flows of several 10 to \(100 \mu \mathrm{m} \mathrm{s}^{- 1}\) in its direct vicinity (10 nm) that results in a flow pattern reminiscent of convection. Based on a fully quantitative analysis of our experimental results we reveal that these thermo- osmotic flows on gold- water interfaces are induced by a temperature- induced perturbation of the van der Waals (vdW) interactions. Nano- objects suspended in the liquid are therefore dragged by the hydrodynamic forces originating from these flows. Utilizing attractive vdW interactions of the nano- object with the gold surface or temperature- induced depletion, we trap and manipulate different types of nano- objects near the surface. The fast heating at small scales allows us to multiplex flow fields and to manipulate multiple objects with great precision. Our detailed analysis of the flow fields, the localization accuracy of nano- objects, and a comparison with numerical and theoretical predictions provide a quantitative understanding of these effects and paves the way for controlling boundary layer dynamics to manipulate objects at the smallest length scales in solutions. + +<|ref|>text<|/ref|><|det|>[[515, 842, 915, 881]]<|/det|> +Experimental configuration and working principle Our experiments rely on a simple sample geometry with a gold film (50 nm) that is deposited on a microscopy glass cov + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[80, 81, 900, 333]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[80, 345, 917, 395]]<|/det|> +
Fig. 1: Thermo-hydrodynamic manipulation of Au NPs in NaCl solution. a, The sample consists of two glass slides that confine a \(3\mu \mathrm{m}\) thin liquid film of gold nanoparticles (AuNPs) dispersed in aqueous NaCl solution. The lower glass slide carries a \(50\mathrm{nm}\) Au film that is locally heated by optical absorption of a focused laser of \(\lambda = 532\mathrm{nm}\) wavelength. b, The experimental setup comprises an inverted optical microscope equipped with an acusto-optical deflector controlled, steerable focused laser with a wavelength of \(\lambda = 532\mathrm{nm}\) . The AuNPs are observed using darkfield illumination with an oil-immersion dark-field condenser (NA 1.2) and a \(100\times\) oil-immersion objective set to NA 0.6. Images are recorded with an EMCCD camera.
+ +<|ref|>text<|/ref|><|det|>[[80, 415, 481, 570]]<|/det|> +erslip (Fig. 1a). The sample chamber contains a suspension of gold nanoparticles (AuNPs) or other nano- objects (polystyrene NPs and ellipsoids) with a controlled amount of salt (NaCl), surfactants (SDS, ...) or polymers (PEG). The gold film is heated locally in an inverted microscopy setup by a highly focused laser (532 nm) using beam steering optics (Acusto- Optic- Deflector, AOD). The nano- objects are observed using darkfield illumination with an oil- immersion darkfield condenser (NA 1.2) and a \(100\times\) oil- immersion objective set to NA 0.6 (Fig. 1b). Additional details of the experimental setup and sample preparation are provided in the Methods section. + +<|ref|>text<|/ref|><|det|>[[80, 572, 481, 728]]<|/det|> +The trapping of nano- objects as detailed in the following is comprising two effects. i) The vertical confinement of the suspended objects as achieved by an attractive interaction of the suspended nano- objects with the gold surface, which is found to be the vdW interaction for gold nanoparticles and can be replaced by depletion forces for other materials. ii) The generation of thermo- osmotic boundary flows that are induced by the local heating and the corresponding perturbation of the liquid- solid interactions. This boundary flow is directed radially inwards to the heated spot and provides a confining hydrodynamic force on suspended objects at the heating spot. + +<|ref|>text<|/ref|><|det|>[[80, 751, 481, 880], [516, 415, 916, 468]]<|/det|> +Dynamics of AuNPs close to a Au film Consider a single AuNP with a radius of \(R = 125\mathrm{nm}\) that is suspended in an aqueous solution of NaCl at a \(10\mathrm{mM}\) concentration and diffusing in a thin liquid film of about \(3\mu \mathrm{m}\) thickness over a \(50\mathrm{nm}\) Au film (Fig. 1a). Exploring the diffusion of the particle we observe a restriction of the \(z\) - positions to a thin layer close to the gold film. The gold particle never defocuses under these conditions while it does in deionized (DI) water (Supplementary Video 1). This restricted out- of- plane motion is the result of interactions comprising an attractive vdW contribution, a repulsion of the electrostatic double layers of the particle and surface32 as described by the DLVO theory and the gravitational potential (see Supplementary Information for details). + +<|ref|>equation<|/ref|><|det|>[[576, 480, 915, 496]]<|/det|> +\[V(d,c_{0}) = V_{\mathrm{E}}(d,c_{0}) + V_{\mathrm{vdW}}(d) + V_{\mathrm{G}}(d). \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[516, 508, 916, 675]]<|/det|> +The total potential for the experimental situation is depicted in Fig. 2a for different salt concentrations (see SI for parameters) as a function of the surface- to- surface distance \(d = z - R\) . The stronger screening of the surface charges at the gold film and the AuNP at higher salt concentration increase the importance of attractive vdW interactions to create this secondary minimum in the DLVO part of the potential. This potential influences the observed dynamics as well as the particle couples with its hydrodynamic flow field to the solid boundary33. The in- plane \(D_{\parallel}\) (equation 2) and out- of- plane \(D_{\perp}\) diffusion coefficient (see Supplementary Information for details) are modulated with the distance \(z\) of the particle from the wall. + +<|ref|>equation<|/ref|><|det|>[[555, 686, 915, 716]]<|/det|> +\[\frac{D_{\parallel}(z)}{D_0}\approx 1 - \frac{9}{16}\frac{R}{z} +\frac{1}{8}\left(\frac{R}{z}\right)^3\pm \dots \coloneqq \gamma_{\parallel}^{-1}(z) \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[516, 726, 916, 880]]<|/det|> +Over the course of a diffusion trajectory, the particle samples different regions with different diffusion coefficients according to its probability density \(p(d) \propto \exp (- V(d, c_0) / (k_B T))\) to be at a distance \(d\) from the surface (filled regions in Fig. 2a). The observed in- plane diffusion coefficient is thus a weighted average of the diffusion coefficient over the different vertical positions \(d\) . Using \(p(d)\) we can calculate the corresponding salt concentration dependence of the in- plane diffusion coefficient and compare that to the experimental results. Fig. 2b shows that the experimentally observed \(D_{\parallel}\) is decreasing with increasing salt concentration due to the hydrodynamic coupling in fair + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[78, 81, 920, 576]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[80, 584, 917, 700]]<|/det|> +
Fig. 2: DLVO potential, lateral diffusion analysis, temperature distribution and thermo-osmotic flow field. a, Plot of the DLVO potential, equation (1), between a \(250~\mathrm{nm}\) Au NP and a \(50~\mathrm{nm}\) Au film on a glass surface as function of surface-surface distance \(d\) for different NaCl concentrations \(c_0\) . The shaded curves display the calculated probability density for finding the particle at this distance at the different salt concentrations (see Supplementary Information for details). The vertical dashed lines correspond to the mean distance of the particle as calculated from the probability density for a \(3\mu \mathrm{m}\) liquid film height. b, The measured diffusion coefficient \(D_1 / D_0\) parallel to the Au film with respect to the bulk diffusion coefficient \(D_0\) as function of the NaCl concentration \(c_0\) . Symbols correspond to the experimental values. The lines reflect the theoretical prediction including a distance dependent diffusion coefficient for three different Hamaker constants (dotted: \(A_H = 4\cdot 10^{-20}\mathrm{J}\) , dash-dotted: \(5\cdot 10^{-20}\mathrm{J}\) and dashed: \(6\cdot 10^{-20}\mathrm{J}\) ) of gold according to a Boltzmann weighting (see text). c, Relation between the mean distance \(\langle d\rangle\) and the NaCl concentration \(c_0\) . The symbols and the horizontal lines denote the calculated distances for measured concentrations. d, Simulation of the relative temperature increment in the \(xz\) -plane of the sample. e, Experimentally obtained temperature increment \(\Delta T_{\mathrm{max}}\) as a function of the incident laser power \(P_0\) (green data points) compared to the simulated values (green curve). f, Measured thermo-osmotic flow field in the \(xy\) -plane in close proximity to the gold film ( \(z< 500\mathrm{nm}\) ). g, Measured thermo-osmotic flow field in the \(xz\) -plane. h, Illustration of the measured flow field planes in f and g. i, j, The \(x\) - and \(z\) -component of the measured flow velocities compared to the simulation results in k and l.
+ +<|ref|>text<|/ref|><|det|>[[80, 721, 481, 877]]<|/det|> +agreement with the theoretical predictions (for three different Hamaker constants for the AuNP gold surface interaction). Calculating in addition the mean distance \(\langle d\rangle\) of the particle from the surface reveals that only in the case of \(c_0 = 10\mathrm{mM}\) the particle is confined in the DLVO potential well, while at lower salt concentrations an enhanced probability (Fig. 2a) of finding the particle near the surface is the cause of the observed diffusion coefficient. These calculations help us to estimate the mean distance \(\langle d\rangle\) of the particle from the surface, which is about \(1.5\mu \mathrm{m}\) and \(0.9\mu \mathrm{m}\) for the lowest NaCl concentrations (Fig. 2c). At a concentration of \(c_0 = 10\mathrm{mM}\) the particle is hovering at a distance + +<|ref|>text<|/ref|><|det|>[[515, 721, 916, 787]]<|/det|> +of \(\langle d\rangle = 20\mathrm{nm}\) surface. Note that this corresponds to values of \(z / (2R)\approx 0.58\) , which is far below the commonly explored region of the hydrodynamic coupling of colloids to walls33 allowing to experimentally explore new terrains also in the field of hydrodynamic wall coupling of colloids. + +<|ref|>text<|/ref|><|det|>[[515, 804, 915, 881]]<|/det|> +Hydrodynamic Particle Confinement When tightly focusing the light of \(532\mathrm{nm}\) wavelength to the gold film, a part of the incident energy (about \(30\%\) ) is absorbed and converted into heat that perturbs the liquid- solid interactions. The temperature rise at the gold surface can be determined using a thin nematic liquid crystal (5CB) film and substan + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[80, 83, 480, 122]]<|/det|> +tiated by finite element simulations with the complete threedimensional temperature profile in the solution (see Fig. 2d, e and Supplementary Information for details). + +<|ref|>text<|/ref|><|det|>[[80, 123, 481, 292]]<|/det|> +These local temperature perturbations of the solidliquid interactions at the interface induce a thermo- osmotic flow34,35. Taking a liquid volume element close to the solid from the cold side and exchanging that with one at the hot side would not only transport heat since the liquid volumes have different temperatures, but also additional free energy as the liquid has a different interaction with the solid in these regions. The flow is induced in an ultrathin boundary layer corresponding in thickness to the length scale of liquid- solid interactions. Since the characteristic interaction length of liquid- solid interactions is only a few nanometers, the boundary flow on the substrate can be collapsed into a quasi- slip hydrodynamic boundary condition: + +<|ref|>equation<|/ref|><|det|>[[150, 298, 480, 338]]<|/det|> +\[v_{\parallel} = -\frac{1}{\eta}\int_{0}^{\infty}z h(z)\mathrm{d}z\frac{\nabla_{\parallel}T}{T} = \chi \frac{\nabla_{\parallel}T}{T}, \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[80, 345, 481, 775]]<|/det|> +where \(h(z)\) is the excess enthalpy, \(T\) the temperature and \(\nabla_{\parallel}T\) is the temperature gradient parallel to the surface. The integral can be summarized to a thermo- osmotic coefficient \(\chi\) . The thermo- osmotic coefficient \(\chi\) , therefore, contains all information about the interfacial interaction between the liquid and the solid. If \(\chi < 0\) the liquid is driven to the cold, whereas for \(\chi > 0\) , the liquid is driven to the hot. These boundary flows are present at all liquid- solid interfaces with tangential temperature gradients, though, they are commonly overlooked. They become particularly important for plasmonic and thermo- plasmonic trapping16, as those techniques rely on the dynamics of molecules and particles in the direct vicinity of plasmonic nanostructures. The boundary flow drives the flow field inside the fluid film. The resulting volumetric flow field can be tracked experimentally by single AuNPs in DI water, where the particles are not confined to a surface layer as reported above. We analyze the in- plane \((xy)\) position of the particle and its \(z\) - position, where the latter is estimated from the radius \(r_0\) of the defocussed particle images (see Supplementary Video 2 and Supplementary Information for details). The measured velocity distributions in the \(xy\) - plane near the gold layer and in the \(xz\) - plane are shown in Fig. 2f and g, respectively. The \(x\) - and \(z\) - component of the measured flow velocities are depicted in Fig. 2i, j and compare well to simulation results in Fig. 2k, l. From these measurements, we extract a thermo- osmotic coefficient on the order of \(\chi \sim 10\cdot 10^{- 10}\mathrm{m}^2\mathrm{s}^{- 1}\) (see Supplementary Information for details). We can break down the contributions to this value with equations (4) and (5) to estimate the double layer and vdW contributions using the experimental parameters. Note that AuNP do not show thermophoresis due to their high thermal conductivity and thus isothermal surface. + +<|ref|>equation<|/ref|><|det|>[[163, 778, 480, 806]]<|/det|> +\[\chi_{\mathrm{E}} = \frac{\epsilon\zeta^2}{8\eta}\approx 0.8\cdot 10^{-10}\mathrm{m}^2 /\mathrm{s}^{-1}. \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[80, 812, 481, 852]]<|/det|> +For the electrostatic contribution we used \(\zeta = - 30\mathrm{mV}^{36}\) and \(\epsilon = 80\epsilon_0\) (see Methods section for details). An estimate of the vdW contribution can be given by + +<|ref|>equation<|/ref|><|det|>[[152, 856, 480, 884]]<|/det|> +\[\chi_{\mathrm{vdW}} = \frac{A_{\mathrm{H}}\beta T}{3\pi\eta d_0}\approx 9.3\cdot 10^{-10}\mathrm{m}^2\mathrm{s}^{-1}, \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[515, 82, 916, 288]]<|/det|> +with \(\beta = 0.2\cdot 10^{- 3}\mathrm{K}^{- 1}\) being the thermal expansion coefficient of water and \(d_0 = 0.2\mathrm{nm}\) for the cut- off parameter34 (see Supplementary Information for details). The sum of both contributions \(\chi = \chi_{\mathrm{E}} + \chi_{\mathrm{vdW}} = 10.1\cdot 10^{- 10}\mathrm{m}^2\mathrm{s}^{- 1}\) matches well the experimental result and suggests that thermo- osmosis at gold- water interfaces is governed by vdW interactions. The obtained quasi slip velocities are ranging up to \(80\mu \mathrm{m / s}\) and provide, due to their omnipresence, a unique tool for nanofluidics. These thermo- osmotic flows are induced without any external pressure difference. They can be controlled by the light intensity heating laser and are quickly switched due to the extremely fast heat conduction at these length scales. Moreover the finding of the vdW dominated thermo- osmotic flows suggest that such contributions must be present in any plasmonic trapping experiment with extended gold structures12,16,17,27. + +<|ref|>text<|/ref|><|det|>[[515, 289, 916, 445]]<|/det|> +Using \(F_T^{\mathrm{eff}} = 6\pi \eta R\gamma_{\parallel}v_x\) and \(F_T^{\mathrm{eff}} = 6\pi \eta R\gamma_{\perp}v_z\) , where \(\gamma_{\parallel}\) and \(\gamma_{\perp}\) are the correction factors for the friction coefficient of a sphere close to a surface we are able to extract the hydrodynamic forces that are exerted on the AuNP tracers (see equation (2) and Supplementary Information for details). The lateral forces allow to confine objects at the heating spot, yet the hydrodynamic force normal to the surface ( \(z\) - direction) is repulsive without any additional interaction. Finally, such boundary flows with substantial vertical velocity gradients also exhibit a vorticity (see Supplementary Information for details) that generates a torque on suspended objects causing them to rotate37. + +<|ref|>text<|/ref|><|det|>[[515, 465, 916, 620]]<|/det|> +Single particle trapping and flow field multiplexing The repulsive normal component caused by the hydrodynamic drag is now superimposed with the attractive force due to the DLVO potential when increasing the NaCl concentration. The surface- to- surface distance between AuNP and gold film and the depth of the appearing secondary DLVO potential minimum can be controlled by the NaCl concentration. At a NaCl concentration of about \(c_0 = 10\mathrm{mM}\) , the attractive potential has a depth of about \(10k_{\mathrm{B}}T\) (see Fig. 2a) and is strong enough to compete with the vertical drag force and additional optical forces on the AuNP to trap the particle above the heating spot. + +<|ref|>text<|/ref|><|det|>[[515, 621, 916, 881]]<|/det|> +Supplementary Video 3 demonstrates this trapping of an AuNP above the hot spot on the Au surface. This is purely the result of the hydrodynamic drag forces generated by the thermo- osmotic flow and the attractive vdW interaction between the AuNP and the Au film. This observation is substantiated by a quantitative evaluation of the lateral trap stiffness and vertical forces, as depicted in Fig. 3a, b. The fluctuations of the particle in the hydrodynamic flow arise from a balance of the restoring hydrodynamic currents and the diffusive currents. Analysis of the lateral position histograms (inset in Fig. 3c for \(1.25\mathrm{mW}\) ) yields an effective stiffness of the trap (Fig. 3a) that well matches the predictions based on the thermo- osmotic flow (Fig. 2i, j). The hydrodynamic trapping stiffness increases linearly up to a heating power of about \(1.8\mathrm{mW}\) . At this power, the vertical forces become strong enough to let the particle escape the secondary DLVO minimum, which is visible from the \(z\) - position time traces displayed in Fig. 3d. The AuNPs are then observed to move vertically out of the DLVO potential to follow the flow inside the sample and to eventually return + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[80, 81, 916, 253]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[80, 264, 916, 328]]<|/det|> +
Fig. 3: Forces on trapped NPs in \(10\mathrm{mM}\) NaCl. a, The lateral trap stiffness obtained from the experimental position histograms (blue data points) as function the laser power \(P_0\) compared to the simulation result (blue solid line). b, The \(x\) -component of the thermo-osmotic drag force \(F_{\mathrm{T}}^{\mathrm{TF}}\) (blue line), the optical force \(F_{\mathrm{Z}}^{\mathrm{OF}}\) (green line) and the total force \(F_{\mathrm{Z}}^{\mathrm{OF}} + F_{\mathrm{Z}}^{\mathrm{TF}}\) (black dashed line) as function the incident laser power \(P_0\) for a NP located at \(x = 0\) , \(d = 30\) ( \(z = d + R\) ). The attractive DLVO force \(F_{\mathrm{DLVO}}^{\mathrm{DLVO}}\) is independent of the incident laser power and depicted as horizontal, red line. c, Trajectory of a AuNP for a heating laser power of \(1.25\mathrm{mW}\) (see Supplementary Video 2 for details). The inset shows the corresponding lateral distribution histogram. d, Time traces of the \(z\) -position for three different laser powers. e, Trajectory of a AuNP for a heating laser power of \(2.5\mathrm{mW}\) , which is above the threshold power of \(2.25\mathrm{mW}\) .
+ +<|ref|>text<|/ref|><|det|>[[81, 349, 481, 595]]<|/det|> +to the boundary flow via sedimentation (Fig. 3e, Supplementary Video 4). The forces which eject the particle from the potential comprise the hydrodynamic and the optical forces due to the radiation pressure from the heating laser leaked through the film. We have evaluated the individual contributions in simulations. They are shown together with the hydrodynamic force and the total vertical force as compared to the attractive force of the DLVO potential (Fig. 3b) and provide quantitative agreement (threshold heating power of \(2.25\mathrm{mW}\) ) with our experimental results. Note that while the stationary distribution of particles in the vertical direction is not influenced by the diffusive dynamics, the escape rate from the potential well is heavily altered by the fact that the vertical diffusion coefficient \(D_{\perp}\) of the particle is decreasing to zero when approaching the gold film. This is enhancing the trapping times considerably (see Supplementary Information for details) but also increases the time required for the particle to enter the DLVO minimum by diffusion. + +<|ref|>text<|/ref|><|det|>[[81, 596, 481, 739]]<|/det|> +The observed trapping is, hence, a vdW assisted thermodynamic process. Vertical confinement is achieved by vdW attraction and double layer repulsion, while lateral confinement is the result of thermo- osmotic flows induced in an ultra- thin sheet of liquid at the interface. No additional contributions, for example, due to convective flows with similar flow patterns (see Supplementary Information for details) or thermo- electric effects are required for a quantitative description \(^{25,31,38,39}\) . Precise tuning of the DLVO potential enables the trapping of even smaller Au NPs (Fig. 4a, Supplementary Video 5). + +<|ref|>text<|/ref|><|det|>[[81, 740, 481, 880]]<|/det|> +The speed of heat diffusion, which is about 4 orders of magnitude faster than the particle diffusion \(^{40}\) allows us to introduce a flow field multiplexing. We switch the heating location between different positions inducing thermo- osmotic flow fields for time periods of about \(100\mu \mathrm{s}\) . With the help of this multiplexing, we are able to hold multiple \(R = 125\mathrm{nm}\) AuNPs (Fig. 4b, c) at distances of less than \(1\mu \mathrm{m}\) , which would not be possible with continuous heating of close- by locations (Supplementary Videos 6 and 7). A trapped AuNP can also be guided along the predefined path over the Au film as fast as \(10\mu \mathrm{m}\mathrm{s}^{- 1}\) (Fig. 4d, + +<|ref|>text<|/ref|><|det|>[[516, 349, 916, 467]]<|/det|> +Supplementary Video 8). At larger manipulation speeds ( \(f > 100\mathrm{Hz}\) ) and higher heating power ( \(P_0 > 10\mathrm{mW}\) ) the thermo- osmotic attraction to the heating spot is combined with thermo- viscous flows \(^{41,42}\) . These flows originate from the temperature dependent viscosity \(\eta (T)\) of the liquid and are directed opposite to the scanning direction of the laser \(^{42}\) . The result of this combination of thermo- osmosis and thermo- viscous flows is a rotating ring- like particle structure (Fig. 4e and Supplementary Video 9). + +<|ref|>text<|/ref|><|det|>[[516, 468, 916, 556]]<|/det|> +These different effects that can be exploited in a simple planar geometry give rise to numerous applications including for example a freely configurable nanoparticle on mirror geometry for plasmonic sensing \(^{5,43}\) . The multiplexing of local flow field may be be helpful to construct more complex effective flow fields for an efficient transport of analytes without external pressure. + +<|ref|>text<|/ref|><|det|>[[516, 581, 916, 788]]<|/det|> +Beyond thermo- osmotic van der Waals trapping So far, the presented manipulation is based on thermo- osmotic flows that drive the lateral motion of suspended colloids and a vertical confinement due to the secondary minimum of the DLVO potential between AuNP and Au film. While the thermo- osmotic flows are characteristic for all systems containing a heated gold/water interface including all previous studies on thermo- plasmonic trapping, the DLVO potential minimum is much weaker for other materials like polymer colloids or macromolecules due to their smaller vdW attraction. Often, those system even show a repulsion from the heat source due to thermophoresis, which is not present for AuNP. A more generalized strategy therefore needs additional attractive contributions, which confine suspended colloids or molecules to regions close to the gold surface to take advantage of the thermo- osmotic flow. + +<|ref|>text<|/ref|><|det|>[[516, 791, 916, 880]]<|/det|> +Such attractive contributions can arise from depletion interactions \(^{21,44}\) . Thereby a temperature gradient repels dissolved molecules from the heated regions generating a concentration gradient that drives suspended nano- objects to the heating spot. To demonstrate this effect we use the surfactant sodium dodecyl sulfate (SDS) at a concentration of \(5\mathrm{mM}\) well below the critical micelle concentration + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[80, 83, 481, 213]]<|/det|> +(8.2 mM) to avoid complications of micelle formation. We suspend additional polystyrene particles(PS) and AuNPs of the same size ( \(R = 125 \mathrm{nm}\) ) in the solution and compare their dynamics to a solution with AuNPs and PS particles without SDS but \(10 \mathrm{mM} \mathrm{NaCl}\) . Remarkably, the heated spot is attractive for both AuNPs and for PS NPs (Fig. 4f, Supplementary Video 10) in the SDS solution showing even PS colloidal crystal growth, while only the AuNP is trapped in the NaCl solution and the PS particles are repelled by thermophoresis (Fig. 4g, Supplementary Video 11). + +<|ref|>text<|/ref|><|det|>[[80, 214, 481, 318]]<|/det|> +The observations in NaCl are readily explained by the fact that the AuNP is confined in the DLVO minimum as demonstrated above but the PS particle is not due to a 10 times lower Hamaker constant (see Supplementary Information for details). The PS particle samples the whole liquid film thickness equally and not preferentially the region close to the Au film and experiences an additional thermophoretic drift velocity given by + +<|ref|>equation<|/ref|><|det|>[[187, 328, 480, 355]]<|/det|> +\[\pmb {u} = -\frac{2}{3}\chi \frac{\nabla T}{T} = -D_{\mathrm{T}}\nabla T, \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[80, 362, 481, 442]]<|/det|> +where \(D_{\mathrm{T}}\) is the thermophoretic mobility \(^{34}\) and \(\nabla T\) the temperature gradient (Fig. 4g, Supplementary Video 11). For \(\chi >0\) the particle is driven to the cold. From equation (4) we find \(\chi \approx \chi_{\mathrm{E}} = 1.28\cdot 10^{- 10} \mathrm{m}^{2} \mathrm{s}^{- 1}\) and \(D_{\mathrm{T}} \approx 0.3 \mu \mathrm{m}^{2} \mathrm{K}^{- 1} \mathrm{s}^{- 1}\) , where we have used a measured zeta potential of \(\zeta \approx - 38 \mathrm{mV}\) . The vdW contribution, \(\chi_{\mathrm{vdW}}\) to + +<|ref|>text<|/ref|><|det|>[[515, 83, 916, 162]]<|/det|> +either the thermophoretic drift or the attraction to the gold surface can be neglected due to the smaller Hamaker constant of PS. From the stationary probability distribution of the PS NP we find a Soret coefficient of \(S_{\mathrm{T}} \approx 0.24 \mathrm{K}^{- 1}\) (see Supplementary Information for details) in agreement with our theoretical prediction \(S_{\mathrm{T}} = D_{\mathrm{T}} / D_{\parallel} \approx 0.21 \mathrm{K}^{- 1}\) . + +<|ref|>text<|/ref|><|det|>[[515, 163, 916, 277]]<|/det|> +In the SDS solution, the additional surfactant molecules now undergo thermophoresis to yield a concentration gradient in which suspended colloidal particles drift. The lower concentration in the heated regions promotes an effective attractive interaction of suspended colloids with the gold surface due to depletion forces. The drift velocity is described by an additional term to the thermodiffusion coefficient \(D_{\mathrm{T}}\) , that is, the second term in brackets in equation (7) \(^{21,34,44}\) . + +<|ref|>equation<|/ref|><|det|>[[560, 285, 915, 316]]<|/det|> +\[\pmb {u} = -\left(D_{\mathrm{T}} - \frac{k_{\mathrm{B}}}{3\eta} R^{2}c_{0}N_{\mathrm{A}}\left(T S_{\mathrm{DS}}^{\mathrm{SDS}} - 1\right)\right)\nabla T \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[515, 324, 916, 441]]<|/det|> +Here \(R\) is the size of the SDS molecule, \(c\) the concentration in units of mol/l and \(S_{\mathrm{DS}}^{\mathrm{SDS}}\) the Soret coefficient of SDS. For \(R = 2 \mathrm{nm}^{45}\) , \(c_{0} = 5 \mathrm{mM}\) and \(S_{\mathrm{DS}}^{\mathrm{SDS}} = 0.03 \mathrm{K}^{- 146}\) we find \(- 0.43 \mu \mathrm{m}^{2} \mathrm{K}^{- 1} \mathrm{s}^{- 1}\) for the additional depletion contribution, which exceeds the thermophoretic mobility, \(D_{\mathrm{T}} \approx 0.3 \mu \mathrm{m}^{2} \mathrm{K}^{- 1} \mathrm{s}^{- 1}\) , rendering the overall mobility negative. The PS NPs and the AuNPs are thus driven to the the heated Au film surface (Fig. 4f, Supplementary Video 10) which allows for further transport in the thermo- osmotic + +<|ref|>image<|/ref|><|det|>[[80, 460, 916, 784]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[80, 800, 916, 883]]<|/det|> +
Fig. 4: Manipulation of NPs over a Au film in NaCl and SDS solution. a, A AuNP with \(50 \mathrm{nm}\) radius trapped at a NaCl of \(30 \mathrm{mM}\) (Supplementary Video 5). b, Manipulation of two AuNPs by a multiplexed laser beam (Supplementary Video 6). c, Control of three AuNPs (Supplementary Video 7). d, Actuation of a single AuNP on a circular trajectory by a steerable laser beam (Supplementary Video 8). The green and white, dashed arrows denote the moving direction of the laser focus and particle, respectively. e, Generation of thermo-viscous flows by rotating the laser focus on a circle with a rotation frequency of \(f = 500 \mathrm{Hz}\) at high laser powers (Supplementary Video 9). Note, that laser movement (green arrow) and the thermo-viscous flow (white, dashed arrow) and have opposite directions. f, Attraction of a AuNP and PS NPs in \(5 \mathrm{mM}\) SDS due to depletion (Supplementary Video 10). g, An AuNP (125 nm radius) trapped in an ensemble of polystyrene (PS) NPs of the same size at \(10 \mathrm{mM}\) NaCl (Supplementary Video 11), where the PS-particles are repelled due to thermophoresis. h, Attraction of PS ellipsoids (2.39 \(\mu \mathrm{m}\) major-axis length, \(0.34 \mu \mathrm{m}\) minor-axis length) in \(5 \mathrm{mM}\) SDS (Supplementary Video 12).
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[80, 84, 481, 175]]<|/det|> +boundary flow. Additional contributions, as for example thermo- electric fields may even enhance the attractive components. Overall, this concept is readily transferred to other objects as shown in Figure 4h and Supplementary Video 12, where we have trapped ellipsoidal PS particles in a \(5\mathrm{mM}\) solution of SDS. Note that as compared to other schemes, our approach always includes thermo- osmotic boundary flows. + +<|ref|>text<|/ref|><|det|>[[80, 191, 481, 512]]<|/det|> +Conclusion In conclusion, we have demonstrated that thermo- hydrodynamic boundary flows can manipulate nano- objects with unprecedented flexibility in a very simple sample geometry. These flows are the key for future thermo- optofluidic implementations with an extensive range of applications in the fields of i) nanoparticle sorting and separation; ii) assembly of nanophotonic circuits and plasmonic quantum sensors; iii) biotechnology on- chip laboratories and iv) manufacturing of nanomaterials and functional nanosurfaces. We have substantiated our experimental findings of thermo- osmotic flow assisted trapping with a quantitative theoretical description. A flow field multiplexing scheme has been further developed to allow for the simultaneous manipulation of many individual nano- objects and the generation of complex effective flow patterns. Our concept can be combined with other thermally induced effects such as thermophoresis, depletion forces and thermoviscous flows to form a fully- featured nanofluidic system- on- a- chip. Besides direct consequences for the field of plasmonic nano- tweezers and other thermoplasmonic trapping schemes, the use of thermo- hydrodynamic flows as a tool for nanofluidic applications will extend the limits at the forefront of nanotechnology and help to develop AI and feedback controlled schemes for the material synthesis. + +<|ref|>sub_title<|/ref|><|det|>[[80, 529, 172, 543]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[90, 552, 483, 880]]<|/det|> +[1] O. M. Maragò, P. H. Jones, P. G. Gucciardi, G. Volpe, and A. C. Ferrari, Optical Trapping and Manipulation of Nanostructures, Nat. Nanotechnol. 8, 807- 819 (2013). [2] D. Gao, W. Ding, M. Nieto- Vesperinas, X. Ding, M. Rahman, T. Zhang, C. Lim, and C.- W. Qiu, Optical Manipulation from the Microscale to the Nanoscale: Fundamentals, Advances and Prospects, Light Sci. Appl. 6, 17039 (2017). [3] C. Bradac, Nanoscale Optical Trapping: A Review, Adv. Opt. Mat. 6, 1800005 (2018). [4] J. Xavier, S. Vincent, F. Meder, and F. 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Israelachvili, Intermolecular and Surface Forces, 3rd Edition (Elsevier, Academic Press, Amsterdam, 2011).[33] B. Lin, J. Yu, and S. A. Rice, Direct Measurements of Constrained Brownian Motion of an Isolated Sphere between Two Walls, Phys. Rev. E 62, 3909- 3919 (2000).[34] A. Wurger, Thermal Non- Equilibrium Transport in Colloids, Rep. Prog. Phys. 73, 126601 (2010).[35] A. P. Bregulla, A. Wurger, K. Gunther, M. Mertig, and F. Cichos, Thermo- Osmotic Flow in Thin Films, Phys. Rev. Lett. 116, 188303 (2016).[36] M. Giesbers, J. Kleijn, and M. A. Cohen Stuart, The Electrical Double Layer on Gold Probed by Electrokinetic and Surface Force Measurements, J. Colloid Interface Sci. 248, 88- 95 (2002).[37] J. J. Bluemink, D. Lohse, A. Prosperetti, and L. Van Wijngaarden, A Sphere in a Uniformly Rotating or Shearing Flow, J. Fluid Mech. 600, 201- 233 (2008).[38] L. Lin, X. Peng, M. Wang, L. Scarabelli, Z. Mao, L. M. Liz- Marzán, M. F. Becker, and Y. 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Baumberg, Single- Molecule Strong Coupling at Room Temperature in Plasmonic Nanocavities, Nature 535, 127- 130 (2016).[44] Y. T. Maeda, A. Buguin, and A. Libchaber, Thermal Separation: Interplay between the Soret Effect and Entropic Force Gradient, Phys. Rev. Lett. 107, 038301 (2011).[45] O. Syshchyk, D. Afanasenkau, Z. Wang, H. Kriegs, J. Buitenhuis, and S. Wiegand, Influence of Temperature and Charge Effects on Thermophoresis of Polystyrene Beads, Eur. Phys. J. E 39, 129 (2016).[46] D. Vigolo, S. Buzzaccaro, and R. Piazza, Thermophoresis and Thermoelectricity in Surfactant Solutions, Langmuir 26, 7792- 7801 (2010).[47] Q. Zhang, H. Yu, M. Barbiero, B. Wang, and M. Gu, Artificial Neural Networks Enabled by Nanophotonics, Light Sci Appl 8, 42 (2019). + +<|ref|>text<|/ref|><|det|>[[515, 85, 916, 201]]<|/det|> +[48] J. Xavier, D. Yu, C. Jones, E. Zossimova, and F. Vollmer, Quantum Nanophotonic and Nanoplasmonic Sensing: Towards Quantum Optical Bioscience Laboratories on Chip, Nanophotonics 10, 1387- 1435 (2021).[49] J. Peng, H.- H. Jeong, Q. Lin, S. Cormier, H.- L. Liang, M. F. L. De Volder, S. Vignolini, and J. J. Baumberg, Scalable Electrochromic Nanopixels Using Plasmonics, Sci. Adv. 5, 10.1126/sciadv.aaw2205 (2019).[50] E. Mohammadi, A. Tittl, K. L. Tsakmakidis, T. V. Raziman, and A. G. Curto, Dual Nanoresonators for Ultrasonic Optical Detection, ACS Photonics 8, 1754- 1762 (2021). + +<|ref|>sub_title<|/ref|><|det|>[[515, 250, 590, 263]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[515, 277, 916, 565]]<|/det|> +Experimental setup The experimental setup (Figure S19) consists of an inverted microscope (Olympus, IX71) with a mounted piezo translation stage (Physik Instrumente, P- 733.3). The microparticles are heated by a focused, continuous- wave laser at a wavelength of \(532 \mathrm{nm}\) (CNI, MGL- III- 532). The beam diameter is increased by a beam expander and sent to an acousto- optic deflector (AA Opto- Electronic, DTSXY- 400- 532) and a lens system to steer the laser focus in the sample plane. The deflected beam is focused by an oil- immersion objective (Olympus, UPlanApo \(\times 100 / 1.35\) , Oil, Iris, NA 0.5 - 1.35) to the sample plane \((w_0 \approx 0.8 \mu \mathrm{m}\) beam waist in the sample plane). The sample is illuminated with an oil- immersion darkfield condenser (Olympus, U- DCW, NA 1.2 - 1.4) and a white- light LED (Thorlabs, SOLIS- 3C). The scattered light is imaged by the objective and a tube lens ( \(250 \mathrm{mm}\) ) to an EMCCD (electron- multiplying charge- coupled device) camera (Andor, iXon DV885LC). The variable numerical aperture of the objective was set to a value below the minimum aperture of the darkfield condenser. The dichroic beam splitter (D) was selected to reflect the laser wavelength (Omega Optical, 560DRLP) and a notch filter (F) is used to block any remaining back reflections from the laser (Thorlabs, NF533- 17). The acousto- optic deflector (AOD), as well as the piezo stage, are driven by an AD/DA (analogue- digital/digital- analogue) converter (Jäger Messtechnik, ADwin- Gold II). A LabVIEW program running on a desktop PC (Intel Core i7 2600 4 \(\times 3.40 \mathrm{GHz}\) CPU) is used to record and process the images as well as to control the AOD feedback via the AD/DA converter. + +<|ref|>text<|/ref|><|det|>[[515, 583, 916, 725]]<|/det|> +Sample preparation The sample consists of two glass coverslips \((22 \mathrm{mm} \times 22 \mathrm{mm})\) confining a thin liquid film. First, the coverslips were thoroughly cleaned by rinsing successively with acetone, isopropyl and Milli- Q water and dried with a nitrogen gun. Subsequently, the edges of one coverslip were covered with a thin layer of PDMS (polydimethylsiloxane) for sealing. The particle solution used for the experiments was prepared by dispersing \(0.250 \mu \mathrm{m}\) diameter gold particles (Cytodiagnostics) in ... solution. Finally, \(0.5 \mu \mathrm{l}\) of the mixed particle suspension is pipetted in the middle of one of the coverslips and the other is placed on top. Depending on the area covered by the liquid, typically about \((10 \mathrm{mm} \times 10 \mathrm{mm})\) , the resulting liquid film height is about \(5 \mu \mathrm{m}\) . + +<|ref|>sub_title<|/ref|><|det|>[[515, 747, 666, 761]]<|/det|> +## Acknowledgement + +<|ref|>text<|/ref|><|det|>[[515, 770, 818, 781]]<|/det|> +The authors acknowledge financial support by ... + +<|ref|>sub_title<|/ref|><|det|>[[515, 803, 689, 817]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[515, 828, 916, 880]]<|/det|> +M.F. and F.C. designed the experiments. M.F. performed the experiments. M.F. and F.C. analyzed the experimental data. M.F. and F.C. implemented and evaluated the numerical calculations. M.F. and F.C. wrote the manuscript. All authors discussed the results and commented on the manuscript. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[82, 83, 252, 97]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[82, 104, 350, 116]]<|/det|> +The authors declare no competing interest. + +<|ref|>sub_title<|/ref|><|det|>[[82, 132, 270, 146]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[80, 155, 480, 178]]<|/det|> +Supplementary information is available for this paper at https://doi.org ... + +<|ref|>text<|/ref|><|det|>[[80, 183, 480, 205]]<|/det|> +Correspondence and requests for materials should be addressed to F.C. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 130, 192, 363]]<|/det|> +- Video6.mp4- Sl.pdf- Video3.mp4- Video2.mp4- Video8.mp4- Video7.mp4- Video1.mp4- Video5.mp4- Video4.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/images_list.json b/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..3319ce8836d344568eccbbeb0a960c0f7b86e2dd --- /dev/null +++ b/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 68, + 55, + 910, + 650 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 40, + 40, + 825, + 777 + ] + ], + "page_idx": 17 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 45, + 135, + 945, + 308 + ] + ], + "page_idx": 18 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 55, + 50, + 936, + 565 + ] + ], + "page_idx": 19 + } +] \ No newline at end of file diff --git a/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777.mmd b/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777.mmd new file mode 100644 index 0000000000000000000000000000000000000000..39e4c63355b83bd36c3753b01a1cd3c76218a7fd --- /dev/null +++ b/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777.mmd @@ -0,0 +1,340 @@ + +# Deep Learning Approach for Evaluating Lumbar Intervertebral Disc Degeneration: Achieving High Accurate Segmentation for Quantitative Analysis on MRI + +Hua- dong Zheng Shanghai University Yue- li Sun ( \(\boxed{\pi}\) yueli_sun@foxmail.com) + +Longhua Hospital, Shanghai University of Traditional Chinese Medicine De- wei Kong + +Depart ment of Radiology \(\boxed{\pi}\) Longhua Hospital of Shanghai University of TCM + +Meng- chen Yin Longhua Hospital, Shanghai University of TCM + +Jiang Chen Dongzhimen Hospital of BeijingUniversity of Chinese Medicine + +Yong- peng Lin Dongzhimen Hospital, Beijing University of Chinese Medicine + +Xue- feng Ma Shenzhen Pingle Orthopedics Hospital (Shenzhen Pingshan District Hospital of TCM) + +Hong- shen Wang Guangdong Provincial Hospital of Chinese Medicine + +Guangjie Yuan Shanghai University + +Min Yao Longhua Hospital, Shanghai University of TCM + +Xuejun Cui Longhua Hospital, Shanghai University of TCM + +Yingzhong Tian Shanghai University + +Yongjun Wang Shanghai University of Traditional Medicine https://orcid.org/0000- 0001- 9333- 2423 + +# Article + +<--- Page Split ---> + +Keywords: lumbar disc degeneration, intervertebral disc degeneration, MRI, deep learning and image processing technology + +Posted Date: September 2nd, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 864336/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on February 11th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28387- 5. + +<--- Page Split ---> + +## Abstract + +Purpose: Using deep learning and image processing technology, a standardized automatic segmentation and quantitation network of lumbar disc degeneration based on T2MRI was proposed to help residents accurately evaluate the intervertebral disc (IVD) degeneration. + +Materials and Methods: A semantic segmentation network (BianqueNet) consist of self- attention mechanism skip connection module and deep feature extraction module was proposed to achieve highprecision segmentation of IVD related areas. A quantitative method was used to calculate the signal intensity difference (△SI) in IVD, average disc height (DH), disc height index (DHI), and disc height- to- diameter ratio (DHR). Quantitative ranges for these IVD parameters in a larger population was established among the 1051 MRI images collected from four hospitals around China. + +Results: The average dice coefficients of BianqueNet for vertebral bodies and intervertebral discs segmentation are \(97.04\%\) and \(94.76\%\) , respectively. This procedure was suitable for different MRI centers and different resolution of lumbar spine T2MRI (ICC \(= .874 \sim .958\) ). These geographic parameters of IVD degeneration have a significant negative correlation with the modified Pfirrmann Grade, while signal intensity in IVD degeneration had excellent reliability according to the modified Pfirrmann Grade (macroF1 \(= 90.63\% \sim 92.02\%\) ). + +Conclusion: we developed a fully automated deep learning- based lumbar spine segmentation network, which demonstrated strong versatility and high reliability to assist residents on IVD degeneration evaluating by means of IVD degeneration quantitation. + +Implication for Patient Care: Deep learning- based approaches have the potential to maximize diagnostic performance for detecting disc degeneration and assessing risk of disc herniation while reducing subjectivity, variability, and errors due to distraction and fatigue associated with human interpretation. + +## Introduction + +The intervertebral disc (IVD) plays an important role in distributing loads and absorbing shock in the spine, which is comprised of a gel- like nucleus pulposus (NP), collagenous annulus fibrosis (AF) layers, and ring- like cartilaginous endplates (EP). Identifying IVD structural changes, including IVD deformation, NP dehydration and EP ossification, due to chronic degeneration or acute injury, in patients undergoing MRI of the lumbar spine has many important clinical implications \(^{1}\) . It has been determined that IVD degeneration is a consequence of aging. Accumulated compressive overload usually lead to functional fatigue fractures in endplates and subsequently IVD herniation \(^{2 - 4}\) , which may lead to increased inflammation \(^{5}\) , nerve compression \(^{6}\) and release of pain factors \(^{7}\) . Lifestyle modifications and surgical interventions are likely to be most effective for treating IVD degeneration or herniation, but it is more important to initiate screening and prevention during the earliest stages of the disease process. + +<--- Page Split ---> + +MRI with morphologic cartilage imaging sequences has been shown to have high specificity but only moderate sensitivity for detecting dehydration and deformation within the IVD degeneration \(^{2,4}\) . Diagnostic performance is highly dependent on the level of reader expertise, and only moderate interobserver agreement between readers has been reported in most studies \(^{2}\) . Quantitative analysis is efficient and comprehensive in evaluating IVD degeneration by measuring the signal intensity and geometric information. Early research on quantitative measurement of intervertebral discs used general image processing programs for manual measurement \(^{8 - 10}\) . However, there is still no universal automatic IVD degeneration analysis tool in this field. The lack of a universal and widely accepted standard definition of IVD degeneration is one of the main reasons. + +There has been much recent interest in using deep learning methods in medical imaging \(^{11}\) . With the wide- spread application of convolutional neural network classifiers in medical images, many studies use the rectangular box surrounding the lumbar IVD as input, and the corresponding degeneration level as the label to train the classifier for learning degenerative features by neural network. However, the input rectangular bounding box of the intervertebral disc needs to be segmented artificially or detected automatically using complex algorithms \(^{1,12 - 17}\) . There are also some studies on the quantitative measurement of intervertebral discs based on deep learning, which did not use quantitative data to evaluate intervertebral disc degeneration \(^{18,19}\) . + +In this study, a fully automated deep learning- based lumbar spine segmentation network (LSSN) has been developed at our institution by using a deep convolutional neural network (CNN) with the self- attention skip connection, deep feature extraction module and the corresponding loss function. According to IVD degeneration features (water content loss and height decrease) \(^{20}\) , signal intensity difference and geometric parameters of IVD are calculated and validated with the modified Pfirrmann grading system. Finally, baseline ranges of lumbar IVD parameters among different gender and age and lumbar level was established based on a large population around China for quantitative and structured report. The diagram of this study is illustrated in Fig. 1. + +## Materials And Methods + +## MRI Data Sets + +This study was approved by Institutional Review Board (IRB) in all the participating sites. All retrospective subject data were obtained with a waiver of consent under IRB approval. The data were anonymized before being shared. + +## Data sets for segmentation training (Data set A & B) + +Training and validation of the proposed lumbar spine semantic segmentation method was carried out by performing an institutional review board- approved retrospective analysis of lumbar spine images from + +<--- Page Split ---> + +286 subjects who underwent MR imaging in the Longhua Hospital, Shanghai University of TCM between January 1, 2019, to December 31, 2020. Among these, there're 223 subjects using a 1.5- T MRI unit (MAGNETOM Aera XJ, SIEMENS) and 63 subjects using another 1.5- T MRI unit (MAGNETOM Avanto, SIEMENS), which were trained two separate segmentation networks for different resolution of 512\*512 (Data set A) and 320\*320 (Data set B). Mid- sagittal T2 images of different resolution were exported from Data set A and Data set B respectively, being randomly allocated into each training set or test set (Fig.1). All images in the segmentation data set were labeled by LabelMe (version 3.3.6, CSAIL, Massachusetts Institute of Technology) 21. Based on the structural features mentioned in the modified Pfirrmann grading system, the segmentation area of 14 parts, included 5 vertebral bodies (L1- L5), 5 lumbar IVDs (L1/L2- L5/S1), sacrum (S1), pre- iliac fat area, cerebrospinal fluid area in the spinal canal, and background as Fig. 2a. + +## Data set for quantitative analysis (Data set C) + +The proposed LSSN was used to extracted 1051 lumbar spine images as Data set C in four hospitals around China, including Longhua Hospital, Shanghai University of TCM, Guangdong Provincial Hospital of Chinese Medicine, Shenzhen Pingle Orthopedics Hospital, and Dongzhimen Hospital, Beijing University of Chinese Medicine between January 1, 2019, and March 30, 2021. The imaging parameters of all sites are summarized in Table 1. + +Table 1 Imaging Parameters for the MRI Sequences in the 4 Sites + +<--- Page Split ---> + + +
SiteCityStrength of the MagnetCompanyModelCoil
Longhua Hospital, Shanghai University of TCMShanghai1.5-TeslaSIEMENSMAGNETOM Aera XJ18-channel Spine Tim 4G coil
Guangdong Provincial Hospital of Chinese MedicineGuangzhou3-TeslaSIEMENSTIM Systems32-channel Spine Tim coil
Shenzhen Pingle Orthopedics HospitalShenzhen1.5-TeslaSIEMENSMAGNETOM Essenza8-channel quadrature body coil
Dongzhimen Hospital, Beijing University of Chinese MedicineBeijing1.5-TeslaSIEMENSMAGNETOM Amira24-channel quadrature body coil
+ +A research team, composed of a 4-year radiology resident (DW Kong), two 8-year orthopedic resident (J Chen, XF Ma), two 4-year orthopedic resident (YL Sun, YP Lin) and a 2-year orthopedic resident (MC Yin),discussed together for the final segment and Pfirrmann grade for each MR image. + +# Lumbar Spine Segmentation from MR Images + +# Convolutional Neural Network (CNN) Training + +The critical component of LSSN is an improved deeplabv3+ segmentation network22 with backbone ResNet-10123, called BianqueNet. The BianqueNet was built on the basis of deep feature extraction to extract richer semantic information and denser features. An illustration of this semantic segmentation network is shown in Fig. 2. The entire network consists of a swin transform skip connection (ST-SC)module and a deep feature extraction (DFE) module. Swin Transform is a hierarchical transform calculated by shifting the window, which has the advantages of high efficiency and low complexity 24.The skip connections structure designed in this study uses two successive Swin-Transformer blocks, with 1*1 convolutional layers in parallel at the same time, and finally the two output features are spliced.Through the pyramid pooling module, feature information of different depths through pooling operations + +<--- Page Split ---> + +of different scales can be obtained. By repeating check with feature map of 4096 channels multi- scale information \(^{25}\) , the network can achieve efficient features extraction with a dense semantic feature map of 256 channels. + +32- depth BMP images were exported from raw MRI to train the LSSN as input. In the upsampling phase, a modified upsampling operation with a deconvolution decoder was used to recover more detailed features of the segmentation target. In the feature extraction phase, the feature maps of different resolutions were obtained by down- sampling and output to the ST- SC module, which splices images and extracts features from different resolutions. According to feature pyramid \(^{26}\) , feature maps with low- resolution and high- resolution were integrated to extract more semantic and spatial information, in which a \(3 \times 3\) double convolutional layer was used for the fused feature map to improve the feature. Finally, a double upsampling operation was performed to obtain a dense prediction image. + +## Weighted Dice Loss Function + +A weighted dice loss function as below was proposed to enhance segmentation performance by estimating difficulties in difference images with typical or atypical structure, which ensured consistent in segmentation: + +\[L w d i c e = \frac{1}{c}\sum_{j = 1}^{C}\xi_{j}\left(1 - \frac{2\sum_{i = 1}^{N}p_{1i}g_{1i}}{\sum_{i = 1}^{N}p_{1i}g_{1i} + \sum_{i = 1}^{N}p_{0i}g_{1i} + \sum_{i = 1}^{N}p_{1i}g_{0i}}\right) \quad (1)\] + +This formula was used in the output of the softmax layer, where the is the probability of voxel (target) and is the probability of voxel (non- target). So was for and . represents different segmentation areas, represents the total number of channels, which is taken as 14. represent the weight of different segmentation channels. According to the experimental analysis results, channels weight was set to 0.9, 0.8 and 1 for vertebral body, IVD and the other respectively, which may achieve the best segmentation performance. + +For avoiding that the subsequent feature extraction operations are affected, corrosion and expansion operations were used to remove the burrs (Fig. 2b). + +## Lumbar IVD Quantitative Analysis + +## Parameters Calculation based on Pfirrmann Grading System + +Based on previous studies \(^{18,25 - 27}\) , some extraction and calculation methods were modified with histogram features of IVD. signal intensity difference (ΔSI) was obtained to quantify the blurring degree of boundary between NP and AF, which indicating water content in IVD. Average disc height (DH), disc + +<--- Page Split ---> + +height index (DHI), and disc height- to- diameter ratio (DHR) were obtained to quantify structural collapse in IVD degeneration. Specific calculation methods for each parameter are described in the Supplement File 1. + +## Versatility Test for Images with Different Origins + +IVD parameters extracted by LSSN in mid- sagittal lumbar MR images with different resolutions were compared with each other. In the data set B, 46 images with resolution of \(320*320\) were randomly selected to be segmented and quantified by model B. Meanwhile, these images were adjusted to \(512*512\) for segmentation and quantitation by model A. IVD parameters extracted from these two models were used for versatility test. If IVD parameters from LSSN shows good consistency under different origins of imaging, LSSN will be used into a larger population (Data set C) with different machines and models to establish degenerative ranges of IVD parameters in different people. + +## Quantitation for IVD Degeneration + +Relationship between IVD parameters (including \(\Delta \mathrm{SI}\) , DH, DHI, and HDR) and demographic information (including gender, age, and segment) and correlation between IVD parameters and IVD degeneration (based on the modified Pfirrmann grading system) were analyzed respectively in Data set C. Based on these results, a baseline of IVD degeneration in larger population was established, which may indicate a qualitative IVD degeneration in different population with accordance to the modified Pfirrmann grading system. Details in quantitative protocols were shown in the Supplement File 2. + +## Statistical analysis + +The intraclass correlation coefficient (ICC) was used to analyze the consistency between the IVD parameters calculated using the original resolution ( \(320*320\) ) from the data set B and the adjusted resolution ( \(512*512\) ). The macroF1- score and the Kendall correlation coefficient were used to test sensitivity and specificity in IVD degeneration grading performance among deep learning methods and 3 residents in the two data sets with different resolution according to the modified Pfirrmann grading system. An absolute value of r of 0- 0.4 was considered as weak correlation, 0.4- 0.6 as moderate correlation, and greater than 0.6 as strong correlation. + +Spearman rank correlation coefficient between IVD signal intensity and grading score has been calculated via SPSS (version 26, IBM, USA). Multiple regression analysis was performed on IVD quantitative parameters (DH, DHI, HDR) and baseline information (including gender, age, segment) to describe some characters in larger population via Stata (version 15.1, USA). + +## Results + +<--- Page Split ---> + +## Segmentation Performance + +The BianqueNet provided good segmentation performance of IVD- related areas. The mean Dice coefficients (mDice) and mean intersection over Union (mIoU) were \(94.45\%\) and \(89.88\%\) for whole lumbar spine, \(96.71\%\) and \(93.66\%\) for vertebral body, \(94.38\%\) and \(89.43\%\) for IVD. Based on deeplabv3+, Adding the three modules of DFE, ST- SC and FPN improved segmentation significantly as shown in Table 2. The average training time for BianqueNet was 10 hours in each data set. Segmentation of vertebral bodies and IVDs on the mid- sagittal MRI image for a patient took approximately 1 seconds with the trained network. + +Table 2 BianqueNet shows superior segmentation effectiveness demonstrated by the pixel-level Dice and IoU coefficient + +
ModelDFEModuleVertebral bodyIVDLumbar spine
ST- SCFPNmDicemIoUmDicemIoUmDicemIoV
DeepLabv3+0.96710.93660.94380.89430.94450.8988
DeepLabv3++DFE√0.96810.93840.94440.89600.94550.9006
DeepLabv3++DFE+ST-SC√√0.96920.94050.94580.89820.94680.9028
DeepLabv3++DFE+ST-SC+FPN (BianqueNet)√√√0.97030.94250.94800.90190.94700.9035
+ +## Versatility test for different resolution + +A total of 230 IVDS and 276 vertebral bodies of 46 subjects were segmented after resolution of MRI- exported images had been adjusted from \(320*320\) to \(512*512\) . The results showed a good consistency in using different parameter calculation algorithms for different resolution of MRI- exported images. Among them, the measurement of intervertebral disc geometric parameters DHI and DWR have extremely high ICC values, which are 0.958 (p=0.000) and 0.956 (p=0.000), respectively, and the ICC value of the \(\Delta SI\) is 0.874 (p=0.000), as shown in Table 3. + +Table 3 Consistency analysis of intervertebral disc parameters calculated by MRI of different sizes + +
MeasureIntraclass Correlationb
ICCa 95%CI
ΔSI.874*** 0.8400.9020
DHI.958*** 0.9430.9680
HDR.956*** 0.8860.9780
+ +<--- Page Split ---> + +Two-way mixed effects model where people effects are random and measures effects are fixed. ICC, intraclass correlation coefficient; 95% CI, 95% confidence interval; + +a. The estimator is the same, whether the interaction effect is present or not. + +b. Type A intraclass correlation coefficients using an absolute agreement definition. + +## Characteristics of IVD Parameters in a Larger Population + +After screening 1508 MRI images in 4 sites around China, a total of 1051 individuals were collected, in which there're 144 excluded for imaging quality and 313 excluded for irregular structures (especially in vertebral bodies). The demographic information (including age and gender) distributed evenly as shown in Table 4, which were integrated to conduct correlation analysis with IVD parameters. + +Table 4 Included Patient Demographic Information from the Four Sites around China + +
SiteNumberAge(F/M)
20-2930-3940-4950-5960-6970-89
Longhua Hospital, Shanghai University of TCM43332/2152/5149/4534/3553/3912/10
Shenzhen Pingle Orthopedics Hospital22216/1820/2019/2018/2113/239/25
Guangdong Provincial Hospital of Chinese Medicine24619/2420/1523/1722/1718/1522/34
Dongzhimen Hospital, Beijing University of Chinese Medicine1507/813/1821/1713/812/118/14
Total105174/71105/104112/9987/8196/8851/83
+ +Supplement Figure (1- 4) and Table 5 shows comprehensive distribution of IVD parameters in a larger population and multiple regression analysis result among IVD parameters and each demographic information respectively. \(\Delta S / \mathrm{in}\) IVDs decreased with age, while DH, DHI and DHR of IVDs increased with age, reaching peak at the age of 50- 60 ( \(P< 0.01\) ). There're no significant different between male and + +<--- Page Split ---> + +female in in IVDs, while DH, DHI and DHR of IVDs were significantly higher in males than those in females \((P< 0.01)\) . In additions, DH, DHI and DHR were significantly higher in lower segmental IVDs (L3-L4, L4-L5 and L5-S1) than upper ones (L1-L2 and L2-L3), and disc height of L4-L5 IVDs was highest \((P< 0.01)\) . In the further analysis of all the IVD height parameters, the influence of segments on the parameters is greater than those of age. For the IVD height, the influence of gender is greater than age. For DHI and HDR, gender and age have similar effects. + +Table 5 The results of multiple regression analysis of signal intensity peak difference, DH, DHI, HDR and gender, different ages, and different disc positions + +
N16151ΔS/DHDHIHDR
female-.0279-.2541***-.1121***.1115***
male0.0000.0000.0000.000
20-300.0000.0000.0000.00
30-40-.1669***.0796***.0557*.1100***
40-50-.3802***.1110***.0927***.0980***
50-60-.4826***.1612***.1577***.0440
60-70-.6002***.1427***.1687***.0099
70-90-.5137***.0328.0806***-.0674***
L1-L2.2800***-.7181***-.6708***-.4932***
L2-L3.1719***-.3832***-.4155***-.2912***
L3-L4.0907***-.1593***-.1942***-.1122***
L4-L50.0000.0000.0000.000
L5-S1.1526***-.0520**-.0312.1105***
+ +\\*\\*\\* \(\mathsf{p}< 0.01\) \\*\\* \(\mathsf{p}< 0.05\) \\* \(\mathsf{p}< 0.1\) + +## Correlation with IVD Degeneration Grading + +Considering structural collapse with IVD degeneration according to the modified Pfirrmann grading system, a regression analysis was conducted to investigate correlation between its certain grading (For analyzing the correlation between degeneration segments and \(\Delta SI,\) the corresponding grading were 1, 2, 3, 4, and (5- 8). For analyzing the correlation between the degeneration segments and geometric parameters, the corresponding grading are (1- 5), 6, 7, 8)) and IVD parameters in different age, gender and segments. As shown in Table 6, IVD parameters showed a good accordance to the modified Pfirrmann grade. + +<--- Page Split ---> + +Table 6 Correlations between IVD Parameters and Modified Pfirrmann Grading + +
lumbar level\(\Delta S/\)DHDHIHDR
-ffemalemalefemalemalefemale
L1/L2-.421***-.296***-.304***-.235***-.473***-.397***
L2/L3-.481***-.417***-.354***-.398***-.575***-.455***
L3/L4-.639***-.470***-.530***-.443***-.626***-.539***
L4/L5-.656***-.696***-.560***-.665***-.709***-.758***
L5/S1-.701***-.687***-.641***-.664***-.744***-.778***
+ +*** p<0.01 ** p<0.05 * p<0.1 + +r, Spearman rank correlation coefficients + +Regarding water content loss with IVD degeneration, result from a further regression analysis showed a stronger correlation between the modified Pfirrmann grade (1,2,3,4, and (5-8)) and \(\Delta S/\) (R=-0.966, \(P=0.000\) ). Specific ranges of according to the modified Pfirrmann grade (1,2,3,4, and (5-8)) were calculated and listed in **Table 7.** + +Table 7 Quantitative ranges of $\Delta S/$ according to the modified Pfirrmann Grade (1-8) + +
modified Pfirrmann Grade12345-8
Number1541130162213151034
(mean±SD)121.97±9.9695.34±7.2072.34±7.8144.63±8.49 20.60±9.28
+ +According to the results of multiple regression analysis, gender and segements have significant correlations with \(\Delta S/\), while age, gender and segments have significant correlations with geometric parameters. **Fig.3** and **Supplement Table (1-4)** showed comprehensive distribution of IVD parameters in a larger population. + +# Discussion + +<--- Page Split ---> + +Our study described a fully automated deep learning–based lumbar IVD quantitative system utilizing a CNN with the self-attention skip connection, deep feature extraction module and the corresponding loss function for segmenting IVD-related areas to extract geometric and signal parameters. The proposed deep learning approach achieved high accuracy for segmentation and measurement. More specifically, our method showed high consistency with the modified Pfirrmann grading system. + +Compared with previously reported conventional image processing methods for lumbar spine MRI, our method is focus on quantitative measurement other than degeneration grade classification. Standard and accurate ranges of in IVD was established to quantify IVD degeneration, which has strong applicability and accuracy for grading IVD degeneration (macroF1: 92.02% and 90.63% in two data sets) as shown in Table 8. + +Table 8 Accuracy of IVD degeneration grading with ΔS/in IVD + +
Modified Pfirrmann Grade12345-8macro-average (%)macroF1(%)
Data set APrecision (%)60.7697.2899.4097.8989.0888.8992.02
Recall (%)10090.9697.8490.0598.1595.40
Data set BPrecision (%)/81.8293.5510085.7190.2790.63
Recall (%)/90.0090.6383.3310090.99
+ +By means of LSSN, all the IVD parameters will be extracted and quantified from MR images in about half of second, which may describe water content loss and structural collapse in IVD, indicating degeneration process. Fig.4 shows a potential application for structural MRI report output from the IVD degeneration quantitative analysis. + +Pfirrmann grading system, as the most used IVD imaging grading method, was designed based on symptomatic patients with an average age of about 40 years old 15. Therefore, its reliability for early IVD degeneration or IVD degeneration in the elderly people may be unsatisfied. This study proposed an automatic quantitative method for IVD degeneration assessment in asymptomatic patients of different ages. In addition, multiple quantitative sequences in imaging are generally used together to accurately evaluate IVD degeneration or lesions, which are too time-consuming to popularize MR imaging quantitative analysis in IVD. Our LSSN may meet both patients' affordability and clinical diagnosis needs. + +Our study has limitations. First, we only included MR images with relatively regular outline in IVD-related areas, most of which were accurately segmented by LSSN. Second, accuracy of LSSN is dependent on the depth of BMP exported from MR imaging system. Third, the subjects included in this study did not take symptoms (such as low back pain) into account, lacking clinical validation on IVD degeneration. + +<--- Page Split ---> + +Finally, as a retrospective study, IVD parameters extracted from Data set C did not exactly represent the real- world setting. Further research is needed to determine the applicability of this LSSN in a prospective multi- institutional study in patients with low back pain. + +In conclusion, we developed a fully automated deep learning- based lumbar spine segmentation network, which demonstrated strong versatility and high reliability to assist residents on IVD degeneration grading by means of IVD degeneration quantitation. + +## Declarations + +## Acknowledged + +This study was supported by the National Natural Science Foundation of China (81930116, 81804115, 81873317, and 81704096). + +## Author contributions + +Guarantor of integrity of entire study, YL Sun, YJ Wang; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, HD Zheng, MC Yin, M Yao, XJ Cui; clinical studies, YL Sun, DW Kong, MC Yin, J Chen, YP Lin, XF Ma; experimental studies, YZ Tian, HS Wang, GJ Yuan; statistical analysis, HD Zheng, M Yao, XJ Cui; and manuscript editing, HD Zheng, YL Sun, YJ Wang. + +## Disclosure of Conflict of Interest + +All author disclosed no relevant relationships. + +## References + +1. 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Spine J. 27, 1042-1048 (2018).28. Abdollah V, Parent EC, Battie MC. Reliability and validity of lumbar disc height quantification methods using magnetic resonance images. Biomed Tech (Berl). 64, 111-117 (2018).29. Dabbs VM, Dabbs LG. Correlation between disc height narrowing and low-back pain. Spine (Phila Pa 1976). 15, 1366-9 (1990). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +The flowchart of the study process + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +The fully automatic IVD quantitative analysis system based on semantic segmentation network. The proposed method consisted of segmentation CNNs with SW- SC module and DFE module, histogram- based signal intensity quantization, and area- based fully automated geometric measurement. (a) Segmentation label of lumbar spine related area, (b) Each image channel output by the model corresponds to a segmentation area, (c) The outline of the segmented area is displayed on the original + +<--- Page Split ---> + +image. Signal intensity histogram calculation (d) Cerebrospinal fluid area, (e) Presacral fat area, (f) L3L4 intervertebral disc area. (g) Intervertebral disc parameter calculation, (h) Vertebral body corner detection result (red point) and feature point calculation result (green point), (i) 80% area extraction result of the intervertebral disc center. + +![](images/Figure_3.jpg) + +
Figure 3
+ +Characteristics of IVD Parameters ((a) The mean and standard deviation (σ) of the \(\Delta \mathrm{SI}\) of each the Modified Pfirrmann Grading System (level 1, 2, 3, 4, 5), \(\Delta \mathrm{SI}\) (b), DH (c), DHI (d) and HDR (e)) in different age, gender, and segments + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4
+ +Quantitative analysis results of typical cases. (a) 23- year- old male(Longhua Hospital); (b) 49- year- old female(Dongzhimen Hospital, Beijing University of Chinese Medicine); (c) 63- year- old male(Guangdong Provincial Hospital of Chinese Medicine); (d) 81- year- old male(Shenzhen Pingle Orthopedics Hospital). + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SupplementFigures.pdf- SupplementFile1signalintensityandgeographicmeasurementIVD.docx- SupplementFile2IVDquantitativeanalysiswithPfirmannGrading.docx- SupplementTable1.docx- SupplementTable2.docx + +<--- Page Split ---> + +- SupplementTable3.docx- SupplementTable4.docx + +<--- Page Split ---> diff --git a/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777_det.mmd b/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..91b68afcf7e0da5ca4997fb6cba1816e4fe65b68 --- /dev/null +++ b/preprint/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777/preprint__09b35197b5ad4345c614483e5c88cb8e2e52505854eacc518df23d1042b94777_det.mmd @@ -0,0 +1,465 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 945, 241]]<|/det|> +# Deep Learning Approach for Evaluating Lumbar Intervertebral Disc Degeneration: Achieving High Accurate Segmentation for Quantitative Analysis on MRI + +<|ref|>text<|/ref|><|det|>[[44, 263, 715, 330]]<|/det|> +Hua- dong Zheng Shanghai University Yue- li Sun ( \(\boxed{\pi}\) yueli_sun@foxmail.com) + +<|ref|>text<|/ref|><|det|>[[44, 332, 715, 375]]<|/det|> +Longhua Hospital, Shanghai University of Traditional Chinese Medicine De- wei Kong + +<|ref|>text<|/ref|><|det|>[[50, 378, 714, 398]]<|/det|> +Depart ment of Radiology \(\boxed{\pi}\) Longhua Hospital of Shanghai University of TCM + +<|ref|>text<|/ref|><|det|>[[44, 403, 465, 444]]<|/det|> +Meng- chen Yin Longhua Hospital, Shanghai University of TCM + +<|ref|>text<|/ref|><|det|>[[44, 449, 600, 490]]<|/det|> +Jiang Chen Dongzhimen Hospital of BeijingUniversity of Chinese Medicine + +<|ref|>text<|/ref|><|det|>[[44, 495, 586, 537]]<|/det|> +Yong- peng Lin Dongzhimen Hospital, Beijing University of Chinese Medicine + +<|ref|>text<|/ref|><|det|>[[44, 542, 794, 584]]<|/det|> +Xue- feng Ma Shenzhen Pingle Orthopedics Hospital (Shenzhen Pingshan District Hospital of TCM) + +<|ref|>text<|/ref|><|det|>[[44, 589, 510, 630]]<|/det|> +Hong- shen Wang Guangdong Provincial Hospital of Chinese Medicine + +<|ref|>text<|/ref|><|det|>[[44, 635, 231, 676]]<|/det|> +Guangjie Yuan Shanghai University + +<|ref|>text<|/ref|><|det|>[[44, 681, 465, 723]]<|/det|> +Min Yao Longhua Hospital, Shanghai University of TCM + +<|ref|>text<|/ref|><|det|>[[44, 728, 465, 769]]<|/det|> +Xuejun Cui Longhua Hospital, Shanghai University of TCM + +<|ref|>text<|/ref|><|det|>[[44, 774, 231, 815]]<|/det|> +Yingzhong Tian Shanghai University + +<|ref|>text<|/ref|><|det|>[[44, 820, 794, 862]]<|/det|> +Yongjun Wang Shanghai University of Traditional Medicine https://orcid.org/0000- 0001- 9333- 2423 + +<|ref|>title<|/ref|><|det|>[[44, 902, 101, 920]]<|/det|> +# Article + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 905, 88]]<|/det|> +Keywords: lumbar disc degeneration, intervertebral disc degeneration, MRI, deep learning and image processing technology + +<|ref|>text<|/ref|><|det|>[[44, 105, 344, 125]]<|/det|> +Posted Date: September 2nd, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 144, 463, 164]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 864336/v1 + +<|ref|>text<|/ref|><|det|>[[42, 181, 910, 225]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 259, 945, 303]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on February 11th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28387- 5. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 159, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[42, 84, 949, 150]]<|/det|> +Purpose: Using deep learning and image processing technology, a standardized automatic segmentation and quantitation network of lumbar disc degeneration based on T2MRI was proposed to help residents accurately evaluate the intervertebral disc (IVD) degeneration. + +<|ref|>text<|/ref|><|det|>[[42, 166, 955, 302]]<|/det|> +Materials and Methods: A semantic segmentation network (BianqueNet) consist of self- attention mechanism skip connection module and deep feature extraction module was proposed to achieve highprecision segmentation of IVD related areas. A quantitative method was used to calculate the signal intensity difference (△SI) in IVD, average disc height (DH), disc height index (DHI), and disc height- to- diameter ratio (DHR). Quantitative ranges for these IVD parameters in a larger population was established among the 1051 MRI images collected from four hospitals around China. + +<|ref|>text<|/ref|><|det|>[[42, 318, 951, 454]]<|/det|> +Results: The average dice coefficients of BianqueNet for vertebral bodies and intervertebral discs segmentation are \(97.04\%\) and \(94.76\%\) , respectively. This procedure was suitable for different MRI centers and different resolution of lumbar spine T2MRI (ICC \(= .874 \sim .958\) ). These geographic parameters of IVD degeneration have a significant negative correlation with the modified Pfirrmann Grade, while signal intensity in IVD degeneration had excellent reliability according to the modified Pfirrmann Grade (macroF1 \(= 90.63\% \sim 92.02\%\) ). + +<|ref|>text<|/ref|><|det|>[[42, 470, 930, 538]]<|/det|> +Conclusion: we developed a fully automated deep learning- based lumbar spine segmentation network, which demonstrated strong versatility and high reliability to assist residents on IVD degeneration evaluating by means of IVD degeneration quantitation. + +<|ref|>text<|/ref|><|det|>[[42, 553, 951, 620]]<|/det|> +Implication for Patient Care: Deep learning- based approaches have the potential to maximize diagnostic performance for detecting disc degeneration and assessing risk of disc herniation while reducing subjectivity, variability, and errors due to distraction and fatigue associated with human interpretation. + +<|ref|>sub_title<|/ref|><|det|>[[44, 642, 208, 669]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[41, 681, 940, 914]]<|/det|> +The intervertebral disc (IVD) plays an important role in distributing loads and absorbing shock in the spine, which is comprised of a gel- like nucleus pulposus (NP), collagenous annulus fibrosis (AF) layers, and ring- like cartilaginous endplates (EP). Identifying IVD structural changes, including IVD deformation, NP dehydration and EP ossification, due to chronic degeneration or acute injury, in patients undergoing MRI of the lumbar spine has many important clinical implications \(^{1}\) . It has been determined that IVD degeneration is a consequence of aging. Accumulated compressive overload usually lead to functional fatigue fractures in endplates and subsequently IVD herniation \(^{2 - 4}\) , which may lead to increased inflammation \(^{5}\) , nerve compression \(^{6}\) and release of pain factors \(^{7}\) . Lifestyle modifications and surgical interventions are likely to be most effective for treating IVD degeneration or herniation, but it is more important to initiate screening and prevention during the earliest stages of the disease process. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 44, 944, 253]]<|/det|> +MRI with morphologic cartilage imaging sequences has been shown to have high specificity but only moderate sensitivity for detecting dehydration and deformation within the IVD degeneration \(^{2,4}\) . Diagnostic performance is highly dependent on the level of reader expertise, and only moderate interobserver agreement between readers has been reported in most studies \(^{2}\) . Quantitative analysis is efficient and comprehensive in evaluating IVD degeneration by measuring the signal intensity and geometric information. Early research on quantitative measurement of intervertebral discs used general image processing programs for manual measurement \(^{8 - 10}\) . However, there is still no universal automatic IVD degeneration analysis tool in this field. The lack of a universal and widely accepted standard definition of IVD degeneration is one of the main reasons. + +<|ref|>text<|/ref|><|det|>[[41, 272, 952, 455]]<|/det|> +There has been much recent interest in using deep learning methods in medical imaging \(^{11}\) . With the wide- spread application of convolutional neural network classifiers in medical images, many studies use the rectangular box surrounding the lumbar IVD as input, and the corresponding degeneration level as the label to train the classifier for learning degenerative features by neural network. However, the input rectangular bounding box of the intervertebral disc needs to be segmented artificially or detected automatically using complex algorithms \(^{1,12 - 17}\) . There are also some studies on the quantitative measurement of intervertebral discs based on deep learning, which did not use quantitative data to evaluate intervertebral disc degeneration \(^{18,19}\) . + +<|ref|>text<|/ref|><|det|>[[41, 472, 958, 655]]<|/det|> +In this study, a fully automated deep learning- based lumbar spine segmentation network (LSSN) has been developed at our institution by using a deep convolutional neural network (CNN) with the self- attention skip connection, deep feature extraction module and the corresponding loss function. According to IVD degeneration features (water content loss and height decrease) \(^{20}\) , signal intensity difference and geometric parameters of IVD are calculated and validated with the modified Pfirrmann grading system. Finally, baseline ranges of lumbar IVD parameters among different gender and age and lumbar level was established based on a large population around China for quantitative and structured report. The diagram of this study is illustrated in Fig. 1. + +<|ref|>sub_title<|/ref|><|det|>[[44, 676, 354, 704]]<|/det|> +## Materials And Methods + +<|ref|>sub_title<|/ref|><|det|>[[44, 718, 262, 747]]<|/det|> +## MRI Data Sets + +<|ref|>text<|/ref|><|det|>[[43, 763, 952, 829]]<|/det|> +This study was approved by Institutional Review Board (IRB) in all the participating sites. All retrospective subject data were obtained with a waiver of consent under IRB approval. The data were anonymized before being shared. + +<|ref|>sub_title<|/ref|><|det|>[[44, 858, 722, 887]]<|/det|> +## Data sets for segmentation training (Data set A & B) + +<|ref|>text<|/ref|><|det|>[[43, 902, 949, 945]]<|/det|> +Training and validation of the proposed lumbar spine semantic segmentation method was carried out by performing an institutional review board- approved retrospective analysis of lumbar spine images from + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 45, 951, 295]]<|/det|> +286 subjects who underwent MR imaging in the Longhua Hospital, Shanghai University of TCM between January 1, 2019, to December 31, 2020. Among these, there're 223 subjects using a 1.5- T MRI unit (MAGNETOM Aera XJ, SIEMENS) and 63 subjects using another 1.5- T MRI unit (MAGNETOM Avanto, SIEMENS), which were trained two separate segmentation networks for different resolution of 512\*512 (Data set A) and 320\*320 (Data set B). Mid- sagittal T2 images of different resolution were exported from Data set A and Data set B respectively, being randomly allocated into each training set or test set (Fig.1). All images in the segmentation data set were labeled by LabelMe (version 3.3.6, CSAIL, Massachusetts Institute of Technology) 21. Based on the structural features mentioned in the modified Pfirrmann grading system, the segmentation area of 14 parts, included 5 vertebral bodies (L1- L5), 5 lumbar IVDs (L1/L2- L5/S1), sacrum (S1), pre- iliac fat area, cerebrospinal fluid area in the spinal canal, and background as Fig. 2a. + +<|ref|>sub_title<|/ref|><|det|>[[44, 323, 640, 352]]<|/det|> +## Data set for quantitative analysis (Data set C) + +<|ref|>text<|/ref|><|det|>[[42, 366, 951, 480]]<|/det|> +The proposed LSSN was used to extracted 1051 lumbar spine images as Data set C in four hospitals around China, including Longhua Hospital, Shanghai University of TCM, Guangdong Provincial Hospital of Chinese Medicine, Shenzhen Pingle Orthopedics Hospital, and Dongzhimen Hospital, Beijing University of Chinese Medicine between January 1, 2019, and March 30, 2021. The imaging parameters of all sites are summarized in Table 1. + +<|ref|>text<|/ref|><|det|>[[44, 496, 608, 518]]<|/det|> +Table 1 Imaging Parameters for the MRI Sequences in the 4 Sites + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[42, 42, 955, 490]]<|/det|> + +
SiteCityStrength of the MagnetCompanyModelCoil
Longhua Hospital, Shanghai University of TCMShanghai1.5-TeslaSIEMENSMAGNETOM Aera XJ18-channel Spine Tim 4G coil
Guangdong Provincial Hospital of Chinese MedicineGuangzhou3-TeslaSIEMENSTIM Systems32-channel Spine Tim coil
Shenzhen Pingle Orthopedics HospitalShenzhen1.5-TeslaSIEMENSMAGNETOM Essenza8-channel quadrature body coil
Dongzhimen Hospital, Beijing University of Chinese MedicineBeijing1.5-TeslaSIEMENSMAGNETOM Amira24-channel quadrature body coil
+ +<|ref|>text<|/ref|><|det|>[[42, 545, 945, 607]]<|/det|> +A research team, composed of a 4-year radiology resident (DW Kong), two 8-year orthopedic resident (J Chen, XF Ma), two 4-year orthopedic resident (YL Sun, YP Lin) and a 2-year orthopedic resident (MC Yin),discussed together for the final segment and Pfirrmann grade for each MR image. + +<|ref|>title<|/ref|><|det|>[[44, 642, 743, 668]]<|/det|> +# Lumbar Spine Segmentation from MR Images + +<|ref|>title<|/ref|><|det|>[[42, 700, 641, 725]]<|/det|> +# Convolutional Neural Network (CNN) Training + +<|ref|>text<|/ref|><|det|>[[42, 746, 953, 950]]<|/det|> +The critical component of LSSN is an improved deeplabv3+ segmentation network22 with backbone ResNet-10123, called BianqueNet. The BianqueNet was built on the basis of deep feature extraction to extract richer semantic information and denser features. An illustration of this semantic segmentation network is shown in Fig. 2. The entire network consists of a swin transform skip connection (ST-SC)module and a deep feature extraction (DFE) module. Swin Transform is a hierarchical transform calculated by shifting the window, which has the advantages of high efficiency and low complexity 24.The skip connections structure designed in this study uses two successive Swin-Transformer blocks, with 1*1 convolutional layers in parallel at the same time, and finally the two output features are spliced.Through the pyramid pooling module, feature information of different depths through pooling operations + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 940, 112]]<|/det|> +of different scales can be obtained. By repeating check with feature map of 4096 channels multi- scale information \(^{25}\) , the network can achieve efficient features extraction with a dense semantic feature map of 256 channels. + +<|ref|>text<|/ref|><|det|>[[41, 130, 955, 312]]<|/det|> +32- depth BMP images were exported from raw MRI to train the LSSN as input. In the upsampling phase, a modified upsampling operation with a deconvolution decoder was used to recover more detailed features of the segmentation target. In the feature extraction phase, the feature maps of different resolutions were obtained by down- sampling and output to the ST- SC module, which splices images and extracts features from different resolutions. According to feature pyramid \(^{26}\) , feature maps with low- resolution and high- resolution were integrated to extract more semantic and spatial information, in which a \(3 \times 3\) double convolutional layer was used for the fused feature map to improve the feature. Finally, a double upsampling operation was performed to obtain a dense prediction image. + +<|ref|>sub_title<|/ref|><|det|>[[42, 340, 429, 370]]<|/det|> +## Weighted Dice Loss Function + +<|ref|>text<|/ref|><|det|>[[42, 384, 936, 451]]<|/det|> +A weighted dice loss function as below was proposed to enhance segmentation performance by estimating difficulties in difference images with typical or atypical structure, which ensured consistent in segmentation: + +<|ref|>equation<|/ref|><|det|>[[60, 476, 840, 530]]<|/det|> +\[L w d i c e = \frac{1}{c}\sum_{j = 1}^{C}\xi_{j}\left(1 - \frac{2\sum_{i = 1}^{N}p_{1i}g_{1i}}{\sum_{i = 1}^{N}p_{1i}g_{1i} + \sum_{i = 1}^{N}p_{0i}g_{1i} + \sum_{i = 1}^{N}p_{1i}g_{0i}}\right) \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[41, 553, 930, 690]]<|/det|> +This formula was used in the output of the softmax layer, where the is the probability of voxel (target) and is the probability of voxel (non- target). So was for and . represents different segmentation areas, represents the total number of channels, which is taken as 14. represent the weight of different segmentation channels. According to the experimental analysis results, channels weight was set to 0.9, 0.8 and 1 for vertebral body, IVD and the other respectively, which may achieve the best segmentation performance. + +<|ref|>text<|/ref|><|det|>[[42, 707, 919, 752]]<|/det|> +For avoiding that the subsequent feature extraction operations are affected, corrosion and expansion operations were used to remove the burrs (Fig. 2b). + +<|ref|>sub_title<|/ref|><|det|>[[42, 780, 551, 813]]<|/det|> +## Lumbar IVD Quantitative Analysis + +<|ref|>sub_title<|/ref|><|det|>[[45, 839, 830, 869]]<|/det|> +## Parameters Calculation based on Pfirrmann Grading System + +<|ref|>text<|/ref|><|det|>[[42, 883, 940, 952]]<|/det|> +Based on previous studies \(^{18,25 - 27}\) , some extraction and calculation methods were modified with histogram features of IVD. signal intensity difference (ΔSI) was obtained to quantify the blurring degree of boundary between NP and AF, which indicating water content in IVD. Average disc height (DH), disc + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 951, 110]]<|/det|> +height index (DHI), and disc height- to- diameter ratio (DHR) were obtained to quantify structural collapse in IVD degeneration. Specific calculation methods for each parameter are described in the Supplement File 1. + +<|ref|>sub_title<|/ref|><|det|>[[44, 140, 671, 169]]<|/det|> +## Versatility Test for Images with Different Origins + +<|ref|>text<|/ref|><|det|>[[42, 183, 953, 342]]<|/det|> +IVD parameters extracted by LSSN in mid- sagittal lumbar MR images with different resolutions were compared with each other. In the data set B, 46 images with resolution of \(320*320\) were randomly selected to be segmented and quantified by model B. Meanwhile, these images were adjusted to \(512*512\) for segmentation and quantitation by model A. IVD parameters extracted from these two models were used for versatility test. If IVD parameters from LSSN shows good consistency under different origins of imaging, LSSN will be used into a larger population (Data set C) with different machines and models to establish degenerative ranges of IVD parameters in different people. + +<|ref|>sub_title<|/ref|><|det|>[[44, 370, 490, 399]]<|/det|> +## Quantitation for IVD Degeneration + +<|ref|>text<|/ref|><|det|>[[42, 413, 940, 549]]<|/det|> +Relationship between IVD parameters (including \(\Delta \mathrm{SI}\) , DH, DHI, and HDR) and demographic information (including gender, age, and segment) and correlation between IVD parameters and IVD degeneration (based on the modified Pfirrmann grading system) were analyzed respectively in Data set C. Based on these results, a baseline of IVD degeneration in larger population was established, which may indicate a qualitative IVD degeneration in different population with accordance to the modified Pfirrmann grading system. Details in quantitative protocols were shown in the Supplement File 2. + +<|ref|>sub_title<|/ref|><|det|>[[45, 579, 330, 610]]<|/det|> +## Statistical analysis + +<|ref|>text<|/ref|><|det|>[[42, 624, 953, 783]]<|/det|> +The intraclass correlation coefficient (ICC) was used to analyze the consistency between the IVD parameters calculated using the original resolution ( \(320*320\) ) from the data set B and the adjusted resolution ( \(512*512\) ). The macroF1- score and the Kendall correlation coefficient were used to test sensitivity and specificity in IVD degeneration grading performance among deep learning methods and 3 residents in the two data sets with different resolution according to the modified Pfirrmann grading system. An absolute value of r of 0- 0.4 was considered as weak correlation, 0.4- 0.6 as moderate correlation, and greater than 0.6 as strong correlation. + +<|ref|>text<|/ref|><|det|>[[44, 799, 925, 888]]<|/det|> +Spearman rank correlation coefficient between IVD signal intensity and grading score has been calculated via SPSS (version 26, IBM, USA). Multiple regression analysis was performed on IVD quantitative parameters (DH, DHI, HDR) and baseline information (including gender, age, segment) to describe some characters in larger population via Stata (version 15.1, USA). + +<|ref|>sub_title<|/ref|><|det|>[[45, 911, 144, 936]]<|/det|> +## Results + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 44, 468, 75]]<|/det|> +## Segmentation Performance + +<|ref|>text<|/ref|><|det|>[[41, 89, 950, 245]]<|/det|> +The BianqueNet provided good segmentation performance of IVD- related areas. The mean Dice coefficients (mDice) and mean intersection over Union (mIoU) were \(94.45\%\) and \(89.88\%\) for whole lumbar spine, \(96.71\%\) and \(93.66\%\) for vertebral body, \(94.38\%\) and \(89.43\%\) for IVD. Based on deeplabv3+, Adding the three modules of DFE, ST- SC and FPN improved segmentation significantly as shown in Table 2. The average training time for BianqueNet was 10 hours in each data set. Segmentation of vertebral bodies and IVDs on the mid- sagittal MRI image for a patient took approximately 1 seconds with the trained network. + +<|ref|>table<|/ref|><|det|>[[44, 325, 886, 481]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[41, 263, 950, 305]]<|/det|> +Table 2 BianqueNet shows superior segmentation effectiveness demonstrated by the pixel-level Dice and IoU coefficient + +
ModelDFEModuleVertebral bodyIVDLumbar spine
ST- SCFPNmDicemIoUmDicemIoUmDicemIoV
DeepLabv3+0.96710.93660.94380.89430.94450.8988
DeepLabv3++DFE√0.96810.93840.94440.89600.94550.9006
DeepLabv3++DFE+ST-SC√√0.96920.94050.94580.89820.94680.9028
DeepLabv3++DFE+ST-SC+FPN (BianqueNet)√√√0.97030.94250.94800.90190.94700.9035
+ +<|ref|>sub_title<|/ref|><|det|>[[43, 518, 613, 550]]<|/det|> +## Versatility test for different resolution + +<|ref|>text<|/ref|><|det|>[[41, 563, 953, 699]]<|/det|> +A total of 230 IVDS and 276 vertebral bodies of 46 subjects were segmented after resolution of MRI- exported images had been adjusted from \(320*320\) to \(512*512\) . The results showed a good consistency in using different parameter calculation algorithms for different resolution of MRI- exported images. Among them, the measurement of intervertebral disc geometric parameters DHI and DWR have extremely high ICC values, which are 0.958 (p=0.000) and 0.956 (p=0.000), respectively, and the ICC value of the \(\Delta SI\) is 0.874 (p=0.000), as shown in Table 3. + +<|ref|>table<|/ref|><|det|>[[331, 751, 666, 937]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[43, 715, 894, 737]]<|/det|> +Table 3 Consistency analysis of intervertebral disc parameters calculated by MRI of different sizes + +
MeasureIntraclass Correlationb
ICCa 95%CI
ΔSI.874*** 0.8400.9020
DHI.958*** 0.9430.9680
HDR.956*** 0.8860.9780
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 46, 910, 88]]<|/det|> +Two-way mixed effects model where people effects are random and measures effects are fixed. ICC, intraclass correlation coefficient; 95% CI, 95% confidence interval; + +<|ref|>text<|/ref|><|det|>[[42, 106, 697, 127]]<|/det|> +a. The estimator is the same, whether the interaction effect is present or not. + +<|ref|>text<|/ref|><|det|>[[42, 144, 758, 165]]<|/det|> +b. Type A intraclass correlation coefficients using an absolute agreement definition. + +<|ref|>sub_title<|/ref|><|det|>[[42, 194, 904, 226]]<|/det|> +## Characteristics of IVD Parameters in a Larger Population + +<|ref|>text<|/ref|><|det|>[[42, 240, 941, 330]]<|/det|> +After screening 1508 MRI images in 4 sites around China, a total of 1051 individuals were collected, in which there're 144 excluded for imaging quality and 313 excluded for irregular structures (especially in vertebral bodies). The demographic information (including age and gender) distributed evenly as shown in Table 4, which were integrated to conduct correlation analysis with IVD parameters. + +<|ref|>table<|/ref|><|det|>[[40, 380, 956, 805]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[42, 347, 775, 368]]<|/det|> +Table 4 Included Patient Demographic Information from the Four Sites around China + +
SiteNumberAge(F/M)
20-2930-3940-4950-5960-6970-89
Longhua Hospital, Shanghai University of TCM43332/2152/5149/4534/3553/3912/10
Shenzhen Pingle Orthopedics Hospital22216/1820/2019/2018/2113/239/25
Guangdong Provincial Hospital of Chinese Medicine24619/2420/1523/1722/1718/1522/34
Dongzhimen Hospital, Beijing University of Chinese Medicine1507/813/1821/1713/812/118/14
Total105174/71105/104112/9987/8196/8851/83
+ +<|ref|>text<|/ref|><|det|>[[42, 854, 940, 944]]<|/det|> +Supplement Figure (1- 4) and Table 5 shows comprehensive distribution of IVD parameters in a larger population and multiple regression analysis result among IVD parameters and each demographic information respectively. \(\Delta S / \mathrm{in}\) IVDs decreased with age, while DH, DHI and DHR of IVDs increased with age, reaching peak at the age of 50- 60 ( \(P< 0.01\) ). There're no significant different between male and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 955, 179]]<|/det|> +female in in IVDs, while DH, DHI and DHR of IVDs were significantly higher in males than those in females \((P< 0.01)\) . In additions, DH, DHI and DHR were significantly higher in lower segmental IVDs (L3-L4, L4-L5 and L5-S1) than upper ones (L1-L2 and L2-L3), and disc height of L4-L5 IVDs was highest \((P< 0.01)\) . In the further analysis of all the IVD height parameters, the influence of segments on the parameters is greater than those of age. For the IVD height, the influence of gender is greater than age. For DHI and HDR, gender and age have similar effects. + +<|ref|>table<|/ref|><|det|>[[225, 253, 774, 696]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[41, 196, 941, 240]]<|/det|> +Table 5 The results of multiple regression analysis of signal intensity peak difference, DH, DHI, HDR and gender, different ages, and different disc positions + +
N16151ΔS/DHDHIHDR
female-.0279-.2541***-.1121***.1115***
male0.0000.0000.0000.000
20-300.0000.0000.0000.00
30-40-.1669***.0796***.0557*.1100***
40-50-.3802***.1110***.0927***.0980***
50-60-.4826***.1612***.1577***.0440
60-70-.6002***.1427***.1687***.0099
70-90-.5137***.0328.0806***-.0674***
L1-L2.2800***-.7181***-.6708***-.4932***
L2-L3.1719***-.3832***-.4155***-.2912***
L3-L4.0907***-.1593***-.1942***-.1122***
L4-L50.0000.0000.0000.000
L5-S1.1526***-.0520**-.0312.1105***
+ +<|ref|>table_footnote<|/ref|><|det|>[[44, 696, 287, 714]]<|/det|> +\\*\\*\\* \(\mathsf{p}< 0.01\) \\*\\* \(\mathsf{p}< 0.05\) \\* \(\mathsf{p}< 0.1\) + +<|ref|>sub_title<|/ref|><|det|>[[44, 745, 684, 777]]<|/det|> +## Correlation with IVD Degeneration Grading + +<|ref|>text<|/ref|><|det|>[[42, 792, 957, 927]]<|/det|> +Considering structural collapse with IVD degeneration according to the modified Pfirrmann grading system, a regression analysis was conducted to investigate correlation between its certain grading (For analyzing the correlation between degeneration segments and \(\Delta SI,\) the corresponding grading were 1, 2, 3, 4, and (5- 8). For analyzing the correlation between the degeneration segments and geometric parameters, the corresponding grading are (1- 5), 6, 7, 8)) and IVD parameters in different age, gender and segments. As shown in Table 6, IVD parameters showed a good accordance to the modified Pfirrmann grade. + +<--- Page Split ---> +<|ref|>table_caption<|/ref|><|det|>[[42, 45, 720, 63]]<|/det|> +Table 6 Correlations between IVD Parameters and Modified Pfirrmann Grading + +<|ref|>table<|/ref|><|det|>[[111, 80, 886, 327]]<|/det|> + +
lumbar level\(\Delta S/\)DHDHIHDR
-ffemalemalefemalemalefemale
L1/L2-.421***-.296***-.304***-.235***-.473***-.397***
L2/L3-.481***-.417***-.354***-.398***-.575***-.455***
L3/L4-.639***-.470***-.530***-.443***-.626***-.539***
L4/L5-.656***-.696***-.560***-.665***-.709***-.758***
L5/S1-.701***-.687***-.641***-.664***-.744***-.778***
+ +<|ref|>text<|/ref|><|det|>[[42, 343, 287, 359]]<|/det|> +*** p<0.01 ** p<0.05 * p<0.1 + +<|ref|>text<|/ref|><|det|>[[42, 382, 395, 397]]<|/det|> +r, Spearman rank correlation coefficients + +<|ref|>text<|/ref|><|det|>[[42, 458, 936, 543]]<|/det|> +Regarding water content loss with IVD degeneration, result from a further regression analysis showed a stronger correlation between the modified Pfirrmann grade (1,2,3,4, and (5-8)) and \(\Delta S/\) (R=-0.966, \(P=0.000\) ). Specific ranges of according to the modified Pfirrmann grade (1,2,3,4, and (5-8)) were calculated and listed in **Table 7.** + +<|ref|>table_caption<|/ref|><|det|>[[42, 563, 760, 580]]<|/det|> +Table 7 Quantitative ranges of $\Delta S/$ according to the modified Pfirrmann Grade (1-8) + +<|ref|>table<|/ref|><|det|>[[47, 597, 952, 754]]<|/det|> + +
modified Pfirrmann Grade12345-8
Number1541130162213151034
(mean±SD)121.97±9.9695.34±7.2072.34±7.8144.63±8.49 20.60±9.28
+ +<|ref|>text<|/ref|><|det|>[[42, 795, 953, 878]]<|/det|> +According to the results of multiple regression analysis, gender and segements have significant correlations with \(\Delta S/\), while age, gender and segments have significant correlations with geometric parameters. **Fig.3** and **Supplement Table (1-4)** showed comprehensive distribution of IVD parameters in a larger population. + +<|ref|>title<|/ref|><|det|>[[42, 905, 192, 926]]<|/det|> +# Discussion + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 936, 156]]<|/det|> +Our study described a fully automated deep learning–based lumbar IVD quantitative system utilizing a CNN with the self-attention skip connection, deep feature extraction module and the corresponding loss function for segmenting IVD-related areas to extract geometric and signal parameters. The proposed deep learning approach achieved high accuracy for segmentation and measurement. More specifically, our method showed high consistency with the modified Pfirrmann grading system. + +<|ref|>text<|/ref|><|det|>[[42, 177, 944, 284]]<|/det|> +Compared with previously reported conventional image processing methods for lumbar spine MRI, our method is focus on quantitative measurement other than degeneration grade classification. Standard and accurate ranges of in IVD was established to quantify IVD degeneration, which has strong applicability and accuracy for grading IVD degeneration (macroF1: 92.02% and 90.63% in two data sets) as shown in Table 8. + +<|ref|>table_caption<|/ref|><|det|>[[42, 304, 576, 323]]<|/det|> +Table 8 Accuracy of IVD degeneration grading with ΔS/in IVD + +<|ref|>table<|/ref|><|det|>[[42, 339, 952, 544]]<|/det|> +
Modified Pfirrmann Grade12345-8macro-average (%)macroF1(%)
Data set APrecision (%)60.7697.2899.4097.8989.0888.8992.02
Recall (%)10090.9697.8490.0598.1595.40
Data set BPrecision (%)/81.8293.5510085.7190.2790.63
Recall (%)/90.0090.6383.3310090.99
+ +<|ref|>text<|/ref|><|det|>[[42, 583, 950, 669]]<|/det|> +By means of LSSN, all the IVD parameters will be extracted and quantified from MR images in about half of second, which may describe water content loss and structural collapse in IVD, indicating degeneration process. Fig.4 shows a potential application for structural MRI report output from the IVD degeneration quantitative analysis. + +<|ref|>text<|/ref|><|det|>[[42, 689, 950, 847]]<|/det|> +Pfirrmann grading system, as the most used IVD imaging grading method, was designed based on symptomatic patients with an average age of about 40 years old 15. Therefore, its reliability for early IVD degeneration or IVD degeneration in the elderly people may be unsatisfied. This study proposed an automatic quantitative method for IVD degeneration assessment in asymptomatic patients of different ages. In addition, multiple quantitative sequences in imaging are generally used together to accurately evaluate IVD degeneration or lesions, which are too time-consuming to popularize MR imaging quantitative analysis in IVD. Our LSSN may meet both patients' affordability and clinical diagnosis needs. + +<|ref|>text<|/ref|><|det|>[[42, 867, 940, 954]]<|/det|> +Our study has limitations. First, we only included MR images with relatively regular outline in IVD-related areas, most of which were accurately segmented by LSSN. Second, accuracy of LSSN is dependent on the depth of BMP exported from MR imaging system. Third, the subjects included in this study did not take symptoms (such as low back pain) into account, lacking clinical validation on IVD degeneration. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 944, 111]]<|/det|> +Finally, as a retrospective study, IVD parameters extracted from Data set C did not exactly represent the real- world setting. Further research is needed to determine the applicability of this LSSN in a prospective multi- institutional study in patients with low back pain. + +<|ref|>text<|/ref|><|det|>[[42, 128, 949, 194]]<|/det|> +In conclusion, we developed a fully automated deep learning- based lumbar spine segmentation network, which demonstrated strong versatility and high reliability to assist residents on IVD degeneration grading by means of IVD degeneration quantitation. + +<|ref|>sub_title<|/ref|><|det|>[[44, 216, 213, 242]]<|/det|> +## Declarations + +<|ref|>sub_title<|/ref|><|det|>[[44, 257, 283, 291]]<|/det|> +## Acknowledged + +<|ref|>text<|/ref|><|det|>[[42, 307, 932, 350]]<|/det|> +This study was supported by the National Natural Science Foundation of China (81930116, 81804115, 81873317, and 81704096). + +<|ref|>sub_title<|/ref|><|det|>[[44, 382, 406, 415]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[41, 431, 940, 590]]<|/det|> +Guarantor of integrity of entire study, YL Sun, YJ Wang; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, HD Zheng, MC Yin, M Yao, XJ Cui; clinical studies, YL Sun, DW Kong, MC Yin, J Chen, YP Lin, XF Ma; experimental studies, YZ Tian, HS Wang, GJ Yuan; statistical analysis, HD Zheng, M Yao, XJ Cui; and manuscript editing, HD Zheng, YL Sun, YJ Wang. + +<|ref|>sub_title<|/ref|><|det|>[[44, 619, 613, 654]]<|/det|> +## Disclosure of Conflict of Interest + +<|ref|>text<|/ref|><|det|>[[44, 671, 438, 691]]<|/det|> +All author disclosed no relevant relationships. + +<|ref|>sub_title<|/ref|><|det|>[[44, 715, 195, 740]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[57, 757, 951, 922]]<|/det|> +1. Schnake, K. J., Putzier, M., Haas, N. P. & Kandziora, F. Mechanical concepts for disc regeneration. Eur. Spine J. 15, 354-360 (2006). +2. Myers, E. R. & Wilson, S. E. Biomechanics of osteoporosis and vertebral fracture. Spine vol. 22, 25S-31S (1997). +3. Chu, J. Y., Skrzypiec, D., Pollintine, P. & Adams, M. A. Can compressive stress be measured experimentally within the annulus fibrous of degenerated intervertebral discs? Proc. Inst. Mech. Eng. Part H J. Eng. Med. 222, 161-170 (2008). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 44, 930, 110]]<|/det|> +4. Zhao, F. D., Pollintine, P., Hole, B. D., Adams, M. A. & Dolan, P. Vertebral fractures usually affect the cranial endplate because it is thinner and supported by less-dense trabecular bone. Bone 44, 372–379 (2009). + +<|ref|>text<|/ref|><|det|>[[57, 116, 930, 160]]<|/det|> +5. Richardson, S. M. et al. Degenerate Human Nucleus Pulposus Cells Promote Neurite Outgrowth in Neural Cells. PLoS One 7, (2012). + +<|ref|>text<|/ref|><|det|>[[57, 166, 944, 211]]<|/det|> +6. Stefanakis, M. et al. Annulus fissures are mechanically and chemically conducive to the ingrowth of nerves and blood vessels. Spine (Phila. Pa. 1976). 37, 1883–1891 (2012). + +<|ref|>text<|/ref|><|det|>[[57, 216, 945, 260]]<|/det|> +7. Khan, A. N. et al. Inflammatory biomarkers of low back pain and disc degeneration: a review. Ann. N. Y. Acad. Sci. 1410, 68–84 (2017). + +<|ref|>text<|/ref|><|det|>[[57, 265, 945, 333]]<|/det|> +8. Christian W.A. Pfirrmann, Alexander Metzdorf, Achim Elfering, Juerg Hodler, N. B. Effect of Aging and Degeneration on Disc Volume and Shape: A Quantitative Study in Asymptomatic Volunteers. J. Orthop. Res. Sept. 25, 1121–1127 (2007). + +<|ref|>text<|/ref|><|det|>[[57, 337, 951, 382]]<|/det|> +9. Ma, J. et al. Is fractal dimension a reliable imaging biomarker for the quantitative classification of an intervertebral disk? Eur. Spine J. 29, 1175–1180 (2020). + +<|ref|>text<|/ref|><|det|>[[55, 386, 936, 431]]<|/det|> +10. Jarman, J. P. et al. Intervertebral disc height loss demonstrates the threshold of major pathological changes during degeneration. Eur. Spine J. 24, 1944–1950 (2015). + +<|ref|>text<|/ref|><|det|>[[55, 435, 745, 457]]<|/det|> +11. Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). + +<|ref|>text<|/ref|><|det|>[[55, 462, 855, 506]]<|/det|> +12. Jamaludin, A., Kadir, T. & Zisserman, A. SpineNet: Automated classification and evidence visualization in spinal MRIs. Med. Image Anal. 41, 63–73 (2017). + +<|ref|>text<|/ref|><|det|>[[55, 511, 933, 555]]<|/det|> +13. Luoma, K. et al. Low back pain in relation to lumbar disc degeneration. Spine (Phila. Pa. 1976). 25, 487–492 (2000). + +<|ref|>text<|/ref|><|det|>[[55, 560, 904, 604]]<|/det|> +14. Lootus, M., Kadir, T. & Zisserman, A. Automated radiological grading of spinal MRI. Lect. Notes Comput. Vis. Biomech. 20, 119–130 (2015). + +<|ref|>text<|/ref|><|det|>[[55, 609, 940, 654]]<|/det|> +15. Pfirrmann, C. W. A., Metzdorf, A., Zanetti, M., Hodler, J. & Boos, N. Magnetic resonance classification of lumbar intervertebral disc degeneration. Spine (Phila. Pa. 1976). 26, 1873–1878 (2001). + +<|ref|>text<|/ref|><|det|>[[55, 658, 945, 725]]<|/det|> +16. Castro-Mateos, I., Hua, R., Pozo, J. M., Lazary, A. & Frangi, A. F. Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images. Eur. Spine J. 25, 2721–2727 (2016). + +<|ref|>text<|/ref|><|det|>[[55, 729, 935, 775]]<|/det|> +17. Shinde, J. V., Joshi, Y. V. & Manthalkar, R. R. Multidomain Feature Level Fusion for Classification of Lumbar Intervertebral Disc Using Spine MR Images. IETE J. Res. 0, 1–14 (2020). + +<|ref|>text<|/ref|><|det|>[[55, 780, 940, 848]]<|/det|> +18. Huang, J. et al. Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images. Spine J. 20, 590–599 (2020). + +<|ref|>text<|/ref|><|det|>[[55, 852, 940, 897]]<|/det|> +19. Pang, S. et al. Direct automated quantitative measurement of spine by cascade amplifier regression network with manifold regularization. Med. Image Anal. 55, 103–115 (2019). + +<|ref|>text<|/ref|><|det|>[[55, 901, 920, 946]]<|/det|> +20. Griffith, J. F. et al. Modified Pfirrmann grading system for lumbar intervertebral disc degeneration. Spine (Phila. Pa. 1976). 32, 708–712 (2007). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[45, 44, 936, 560]]<|/det|> +21. Marois, B. & Syssau, P. Pratiques des banques françaises en termes d'analyse du risque-pays. Rev. française Gest. 32, 77-94 (2006).22. Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F. & Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European conference on computer vision (ECCV). 801-818 (2018).23. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 770-778 (2016).24. Liu, Z. et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. arXiv preprint arXiv:2103.14030 (2021).25. Zhao, H., Shi, J., Qi, X., Wang, X. & Jia, J. Pyramid scene parsing network. Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017. 2881-2890 (2017).26. Li, X. et al. Weighted feature pyramid networks for object detection. Proc. - 2019 IEEE Int'l Conf Parallel Distrib. Process. with Appl. Big Data Cloud Comput. Sustain. Comput. Commun. Soc. Comput. Networking, ISPA/BDCloud/SustainCom/SocialCom. 1500-1504 (2019)27. Waldenberg, C., Hebella, H., Brisby, H. & Lagerstrand, K. M. MRI histogram analysis enables objective and continuous classification of intervertebral disc degeneration. Eur. Spine J. 27, 1042-1048 (2018).28. Abdollah V, Parent EC, Battie MC. Reliability and validity of lumbar disc height quantification methods using magnetic resonance images. Biomed Tech (Berl). 64, 111-117 (2018).29. Dabbs VM, Dabbs LG. Correlation between disc height narrowing and low-back pain. Spine (Phila Pa 1976). 15, 1366-9 (1990). + +<|ref|>sub_title<|/ref|><|det|>[[44, 575, 143, 600]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[68, 55, 910, 650]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 694, 115, 712]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[44, 737, 346, 756]]<|/det|> +The flowchart of the study process + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[40, 40, 825, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 800, 118, 820]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[42, 841, 925, 955]]<|/det|> +The fully automatic IVD quantitative analysis system based on semantic segmentation network. The proposed method consisted of segmentation CNNs with SW- SC module and DFE module, histogram- based signal intensity quantization, and area- based fully automated geometric measurement. (a) Segmentation label of lumbar spine related area, (b) Each image channel output by the model corresponds to a segmentation area, (c) The outline of the segmented area is displayed on the original + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 944, 133]]<|/det|> +image. Signal intensity histogram calculation (d) Cerebrospinal fluid area, (e) Presacral fat area, (f) L3L4 intervertebral disc area. (g) Intervertebral disc parameter calculation, (h) Vertebral body corner detection result (red point) and feature point calculation result (green point), (i) 80% area extraction result of the intervertebral disc center. + +<|ref|>image<|/ref|><|det|>[[45, 135, 945, 308]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[43, 346, 117, 365]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[42, 387, 933, 455]]<|/det|> +Characteristics of IVD Parameters ((a) The mean and standard deviation (σ) of the \(\Delta \mathrm{SI}\) of each the Modified Pfirrmann Grading System (level 1, 2, 3, 4, 5), \(\Delta \mathrm{SI}\) (b), DH (c), DHI (d) and HDR (e)) in different age, gender, and segments + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 50, 936, 565]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 593, 118, 613]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 635, 933, 701]]<|/det|> +Quantitative analysis results of typical cases. (a) 23- year- old male(Longhua Hospital); (b) 49- year- old female(Dongzhimen Hospital, Beijing University of Chinese Medicine); (c) 63- year- old male(Guangdong Provincial Hospital of Chinese Medicine); (d) 81- year- old male(Shenzhen Pingle Orthopedics Hospital). + +<|ref|>sub_title<|/ref|><|det|>[[44, 723, 311, 751]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 774, 765, 795]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 812, 700, 939]]<|/det|> +- SupplementFigures.pdf- SupplementFile1signalintensityandgeographicmeasurementIVD.docx- SupplementFile2IVDquantitativeanalysiswithPfirmannGrading.docx- SupplementTable1.docx- SupplementTable2.docx + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 46, 297, 92]]<|/det|> +- SupplementTable3.docx- SupplementTable4.docx + +<--- Page Split ---> diff --git a/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/images_list.json b/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..7aa175f0c087780d23ef3f330773558d00bd7407 --- /dev/null +++ b/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. Synthesis ZIF films from ultradilute solutions. a, Schematic of the ZIF film synthesis approach. b, Composition diagram comparing the precursor solution composition used in this study with those reported in the literature (Supplementary Table 1). AFM (c) and the corresponding height profile (d) of a monolayer ZIF film on HOPG. e, Monolayer and multilayer ZIF films on HOPG with discrete thicknesses as a function of synthesis time. Error", + "footnote": [], + "bbox": [ + [ + 115, + 80, + 875, + 770 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Structure determination of 2DZIF films. a, Bright-field TEM image of the 2DZIF film supported on suspended graphene, and (b) its corresponding SAED pattern. The pattern from graphene is identified with green circles and those from 2DZIF with white circles. c, In-plane GIXRD data from an aZIF film (top) and a 2DZIF film (middle) prepared on \\(\\mathrm{Si / SiO_2}\\) and graphene/Si/SiO2, respectively, along with a radially integrated trace (bottom) of the SAED pattern shown in (B). d, N1s XPS spectra from ZIF-8, ZIF-L, aZIF and 2DZIF films. The N-Zn and N-H coordination environments are shown on the right. e, DFT-relaxed structure of the 2DZIF and a visualization of the 6-MR (right). f, HRTEM image of the 2DZIF film lying flat on the \\(hk0\\) plane, resting on suspended graphene, and (g) corresponding Fourier transform compared with the simulated diffraction pattern from the proposed structure oriented along the \\(c\\) -out-of-plane direction. h, Left: CTF-corrected image of the highlighted area in (f) based on a defocus value of -130 nm analyzed from the Thon rings in the Fourier transform pattern. Right: simulated projected potential map along the [001] direction of 2DZIF.", + "footnote": [], + "bbox": [ + [ + 115, + 80, + 875, + 586 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Supplementary_Figure_12.jpg", + "caption": "Fig. 3. 2DZIF structure and its relationship with ZIF-L. a, Schematic contrasting the arrangement of layers within a ZIF-L crystal with that of a monolayer 2DZIF. b, Registry between 2DZIF and supercell of graphene based on SAED data in Supplementary Fig. 12. c, Structures of a ZIF-L layer (left) and 2DZIF (right) viewed along the [001], [100], and [010] directions. d, Schematic illustrating the etching of 2DZIF in water. SEM (e) and AFM (f) images of the triangular grains of 2DZIF obtained by a short etching in water. g, AFM height profile corresponding to the line in (f).", + "footnote": [], + "bbox": [ + [ + 115, + 81, + 884, + 450 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4. Applications of 2DZIF and aZIF. a, Schematic of a 2DZIF film supported on nanoporous graphene reinforced with PTMSP. b, \\(\\mathrm{H}_2\\) , \\(\\mathrm{CO}_2\\) , \\(\\mathrm{N}_2\\) and \\(\\mathrm{CH}_4\\) permeances of the support film (PTMSP-reinforced NG) and the supported 2DZIF film. c, 2DZIF membrane separation performance for an equimolar \\(\\mathrm{H}_2 / \\mathrm{N}_2\\) mixed feed. d, Comparison of the \\(\\mathrm{H}_2 / \\mathrm{N}_2\\) separation performance of 2DZIF membranes with the state-of-the-art (Supplementary Table", + "footnote": [], + "bbox": [ + [ + 115, + 75, + 881, + 760 + ] + ], + "page_idx": 13 + } +] \ No newline at end of file diff --git a/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2.mmd b/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2.mmd new file mode 100644 index 0000000000000000000000000000000000000000..b99d5dbe8a60aac809b0f444921a6cbe63c09591 --- /dev/null +++ b/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2.mmd @@ -0,0 +1,383 @@ + +# Nanometer-thick crystalline and amorphous zeolitic imidazolate framework films for membrane and patterning applications + +Qi Liu École Polytechnique Fédérale de Lausanne Yurun Miao + +Johns Hopkins University https://orcid.org/0000- 0001- 6429- 8297 + +Luis Francisco Villalobos Yale University https://orcid.org/0000- 0002- 0745- 4246 + +Shaoxian Li École Polytechnique Fédérale de Lausanne + +Deepu J. Babu École Polytechnique Fédérale de Lausanne + +Cailing Chen King Abdullah University of Science and Technology https://orcid.org/0000- 0003- 2598- 1354 + +Heng- Yu Chi École Polytechnique Fédérale de Lausanne + +Mohammad Tohidi Vahdat École Polytechnique Fédérale de Lausanne + +Jian Hao École Polytechnique Fédérale de Lausanne + +Shuqing Song École Polytechnique Fédérale de Lausanne + +Yu Han King Abdullah University of Science and Technology https://orcid.org/0000- 0003- 1462- 1118 + +Michael Tsapatsis Johns Hopkins University https://orcid.org/0000- 0001- 5610- 3525 + +Kumar Varoon Agrawal ( kumar.agrawal@epfl.ch) École Polytechnique Fédérale de Lausanne https://orcid.org/0000- 0002- 5170- 6412 + +Article + +Keywords: + +<--- Page Split ---> + +**Posted Date:** March 14th, 2023 + +**DOI:** https://doi.org/10.21203/rs.3.rs-2666142/v1 + +**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +**Additional Declarations:** Yes there is potential Competing Interest. A patent application based on the finding reported in the manuscript is filed. + +**Version of Record:** A version of this preprint was published at Nature Materials on September 21st, 2023. See the published version at https://doi.org/10.1038/s41563-023-01669-z. + +<--- Page Split ---> + +# Nanometer-thick crystalline and amorphous zeolitic imidazolate framework films for membrane and patterning applications + +Qi Liu1†, Yurun Miao2, Luis Francisco Villalobos1, Shaoxian Li1, Deepu J. Babu1†, Cailing Chen4, Heng- Yu Chi1, Mohammad Tohidi Vahdat1,5, Jian Hao1, Shuqing Song1, Yu Han4, Michael Tsapatsis2,3, Kumar Varoon Agrawal1\* + +1. Laboratory of Advanced Separations, École Polytechnique Fédérale de Lausanne (EPFL), 1950 Sion, Switzerland. + +2. Department of Chemical and Biomolecular, Engineering & Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA + +3. Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA + +4. Advanced Membranes and Porous Materials Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. + +5. Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), EPFL, Lausanne, Switzerland. + +†Present address: College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, 215123, China. + +†Present address: Materials Science and Metallurgical Engineering, Indian Institute of Technology, Hyderabad, Telangana 502 284, India. + +\*Corresponding author: kumar.agrawal@epfl.ch + +<--- Page Split ---> + +## Abstract + +Zeolitic imidazolate frameworks (ZIFs) are a subset of metal- organic frameworks (MOFs) with more than 200 characterized crystalline and amorphous networks made of divalent transition metal centers (e.g., \(\mathrm{Zn^{2 + }}\) and \(\mathrm{Co^{2 + }}\) ) linked by imidazolate linkers. ZIF thin films have been pursued intensively motivated by the desire to prepare membranes for selective gas and liquid separations. To achieve membranes with high throughput, as in A- scale biological channels with nanometer- scale pathlengths, ZIF films with the minimum possible thickness, down to just one unit cell, are highly desired. Control of ZIF film thickness at the 10- nm- scale may also enable emerging, MOF- inspired, applications including patterned crystalline MOF films, and amorphous organic- inorganic resists for high- resolution electron- beam (e- beam) and extreme UV (EUV) lithography. However, the state- of- the- art methods yield ZIF films with thicknesses exceeding 40 nanometers. Here, we report a deposition method from ultra- dilute precursor mixtures that within minutes yields uniform ZIF deposits with nm- scale thickness control. On crystalline substrate such as graphene, two- dimensional crystalline ZIF (2DZIF) film with thickness of a unit- cell could be achieved, which composed of a six- membered zinc- imidazolate coordination ring enabling record- high \(\mathrm{H}_2\) permselective separation performance. Deposition under identical conditions on amorphous substrates yields macroscopically smooth amorphous ZIF (aZIF) films, which can be used as negative- and positive- tone resists yielding pattern features down to \(20 \mathrm{nm}\) . The method reported here will likely accelerate the development of 2D crystalline and ultrathin amorphous MOF films for applications ranging from separation membranes to sensors and patterning for microelectronic applications. + +## Main Text: + +ZIFs \(^{1,2}\) are a class of MOFs that hold promise for applications in molecular separations \(^{3 - 10}\) , patterning \(^{11 - 15}\) and sensing \(^{16}\) . Their chemical and physical properties have been widely explored as a function of framework flexibility \(^{17 - 20}\) as well as structural defects \(^{21,22}\) . The realization of two- dimensional (2D) ZIF films with thickness down to that afforded by a single structural building unit is highly desired to make ZIF analogues to graphene and related 2D materials with an added advantage; the intrinsic nanoporosity of ZIF can be used to separate molecules while maximizing the permselective flux \(^{23}\) . Another highly desirable feature is a nanometer- scale control over the film thickness, which can allow one to fabricate nanoscale patterns, which are desirable for incorporation in microelectronic devices \(^{24}\) , and for the development of + +<--- Page Split ---> + +next generation organic- inorganic high- resolution photo- and e- beam- resists17. However, the realization of 2D crystalline and ultrathin amorphous ZIF films has remained elusive. While layered ZIFs such as ZIF- L25, \(\mathrm{Zn_2(bim)_4^{26}}\) , and analogs27 have been reported, the individual ZIF layers in these materials have a small aspect ratio which prevents the realization of continuous 2D ZIF films with structural uniformity over a macroscopic (e.g., wafer) length scale. The state- of- the- art of ZIF deposition methods yields polycrystalline films with thickness larger than \(40 \mathrm{nm}\) nanometer28- 31. This is mainly due to difficulty in achieving in- plane film growth without film thickening. + +Considerable knowledge exists on ZIF/MOF crystal nucleation and growth in solution32- 37. Based upon data from synchrotron X- ray scattering, density- functional theory (DFT) and molecular dynamics simulations, and other techniques, it is generally accepted that ZIF formation involves a sequence of events starting from the formation of small ( \(\sim 1 \mathrm{nm}\) ) metastable prenucleation clusters, which evolve through aggregation followed by intra- aggregate ZIF nucleation and growth. Recent studies on surface- directed MOF growth38- 46 indicate that the diffusion of MOF precursors in the vicinity of the 2D material, and the MOF- 2D material interactions, are key to regulate the crystallinity of the MOF film and the ability to maintain in- plane/horizontal growth (desired for ultrathin films) versus out- of- plane/vertical (undesired) growth. + +Herein, we report macroscopically uniform 2D ZIF films with exquisite nanometer- scale control over the film thickness by suppressing the out- of- plane growth by using an ultradilute growth solution. The ultralow precursor concentration restricts homogeneous nucleation in the solution and facilitates the growth of nanometer- thick films over an immersed substrate with deposition timescales of a few minutes. The film crystallinity is determined by the interaction of molecular precursors with the substrate ranging from substrate- registry- determined ordered film to amorphous films in the absence of any crystallographic registry. The film thickness could be controlled with a resolution of a single layer by controlling the deposition time and number of coatings. + +The ZIF films were synthesized by immersing a substrate in an ultradilute precursor solution ( \(\leq 2 \mathrm{mM} \mathrm{Zn}^{2 + }\) and \(\leq 16 \mathrm{mM} 2\) - methylimidazole (2- mIm), respectively) for a few minutes (Fig. 1a). The use of such ultradilute solutions for the growth of ZIF films has not been reported before (Fig. 1b, Supplementary Table 1). They were used here in an effort to suppress the homogeneous nucleation in the bulk solution. With a diminished nuclei population in the bulk + +<--- Page Split ---> + +solution, the attachment of preformed nuclei to the substrate can be reduced or eliminated. This is expected to promote film growth by the assembly of molecular precursors on the substrate. Since this thin film growth mode is anticipated to be sensitive on the type of the substrate, we carried out synthesis using distinct substrates: (i) graphitic substrates with atomically smooth terrace such as highly oriented pyrolytic graphite (HOPG) or graphene, (ii) Si/SiO₂ wafer with a 300- nm- thick oxide layer, and (iii) single crystal sapphire (Al₂O₃). + +ZIF films prepared on HOPG using a growth solution of 1 mM \(\mathrm{Zn^{2 + }}\) and 8 mM 2- mM and reaction time of 5 min were examined by optical and scanning electron microscopy (SEM) (Supplementary Fig. 1). A sharp change in contrast was observed at the air/precursor- solution interface beyond which the film had a uniform contrast indicating that the film was smooth, continuous, and macroscopically uniform. Atomic force microscopy (AFM) imaging near the interface confirmed that the ZIF film is indeed continuous and has a thickness of approximately 2 nm (Fig. 1c and d). When the synthesis time was reduced to 2 min, we observed a submonolayer film with micrometer- sized domains (Supplementary Fig. 2). The domains were faceted and had thickness of 2 nm, consistent with the thickness of the continuous film indicating that the film is crystalline consisting of micron- sized grains. We could obtain 4 and 6 nm thick films by increasing the growth time from 5 min to 10 and 15 min, respectively (Fig. 1e and Supplementary Fig. 3a- f). Increasing growth time to 20 min further did not lead to thicker film, indicating precursor depletion (Supplementary Fig. 3g- i). Thicker (8 nm) films could be obtained by doubling the precursor concentration (Supplementary Fig. 3j- l). A discrete, 2 nm, increase in film thickness further suggests a crystalline order. A fitting of film thickness with the number of probable layers yielded a monolayer thickness of 2 nm (Fig. 1e). Macroscopically large ZIF films spanning several centimeters in width could be obtained on chemical vapor deposition (CVD) derived graphene film resting on a Cu foil (Fig. 1f and 1g). + +We also obtained macroscopically smooth, continuous, and uniform ZIF films on Si/SiO₂ wafers (Fig. 1h). AFM of one of these films, prepared using 2 mM \(\mathrm{Zn^{2 + }}\) and 16 mM 2- mM and deposition time of 10 s, indicated that the film is smooth with thickness near 8 nm (Fig. 1i and 1j). Ellipsometry of several ZIF films on Si/SiO₂ wafers, prepared by varying the synthesis time, indicated that the film thickness could be tuned in the range of 8- 18 nm consistent with the corresponding AFM data (Supplementary Fig. 4). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1. Synthesis ZIF films from ultradilute solutions. a, Schematic of the ZIF film synthesis approach. b, Composition diagram comparing the precursor solution composition used in this study with those reported in the literature (Supplementary Table 1). AFM (c) and the corresponding height profile (d) of a monolayer ZIF film on HOPG. e, Monolayer and multilayer ZIF films on HOPG with discrete thicknesses as a function of synthesis time. Error
+ +<--- Page Split ---> + +bars in this figure represent the standard deviation of measurement. f, Optical image of a 2DZIF film on CVD graphene resting on a Cu foil. g, Scanning electron microscopy (SEM) image of a 2DZIF film on CVD graphene. The image was compiled by combining \(1155 (35 \times 33)\) images by scanning the whole surface of the large sample. h, SEM and optical images of a ZIF film on \(\mathrm{Si / SiO_2}\) . AFM (i) and the corresponding height profile (j) of the ZIF film on \(\mathrm{Si / SiO_2}\) . + +Graphene supported ZIF film could be suspended on a holey transmission electron microscopy (TEM) grid (Fig. 2a). The film was devoid of large crystals and appeared uniform. Selected area electron diffraction (SAED) from a micrometer- sized area yielded three sets of diffraction patterns (Fig. 2b). The first two sets ((01), highlighted with green circles) had six- fold symmetry originating from two slightly misoriented (by \(3.0^{\circ}\) ) grains of graphene, while the last set had two- fold symmetry and belonged to a single grain of ZIF (highlighted with white circles), confirming that ZIF prepared on graphene was crystalline with grains at least a micron in size consistent with the AFM- based imaging of grains in the submonolayer film (Supplementary Fig. 2). Hereafter, the ZIF films on graphitic substrates are referred to as 2DZIF. The fact that a single 2DZIF grain could grow over two slightly misoriented graphene grains indicates that the growth could accommodate a small mismatch in its registry with the substrate. Diffraction pattern from 2DZIF, typically representing a single grain, was observed from every single spot over a large area. Based on the diffraction pattern, \(a\) and \(b\) lattice parameters of 2.4 and \(2.0 \mathrm{nm}\) , respectively, could be fitted. + +We carried out synchrotron grazing incidence X- ray diffraction (GIXRD) of a 10- nm- thick ZIF film on graphene resting on a \(\mathrm{Si / SiO_2}\) wafer (Supplementary Fig. 5- 7). The in- plane GIXRD pattern revealed sharp diffraction peaks, consistent with the peak positions obtained by the radial integration of the SAED pattern (Fig. 2c and Supplementary Table 2), confirming that the film formed on the graphitic substrate exhibits crystalline order. In contrast, we did not observe diffraction from the ZIF films prepared directly on \(\mathrm{Si / SiO_2}\) wafer without the graphene layer indicating that these films were amorphous (hereafter referred to as aZIF films, Fig. 2c). When a single crystal sapphire ( \(\mathrm{Al_2O_3}\) ) was used as the substrate, highly crystalline 2DZIF film was formed (Supplementary Fig. 8 and 9). The presence of the order in the ZIF film when prepared over a crystalline substrate and the lack of an order when prepared over the amorphous substrate indicates a strong role of substrate registry in the formation of the ordered 2DZIF film. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2. Structure determination of 2DZIF films. a, Bright-field TEM image of the 2DZIF film supported on suspended graphene, and (b) its corresponding SAED pattern. The pattern from graphene is identified with green circles and those from 2DZIF with white circles. c, In-plane GIXRD data from an aZIF film (top) and a 2DZIF film (middle) prepared on \(\mathrm{Si / SiO_2}\) and graphene/Si/SiO2, respectively, along with a radially integrated trace (bottom) of the SAED pattern shown in (B). d, N1s XPS spectra from ZIF-8, ZIF-L, aZIF and 2DZIF films. The N-Zn and N-H coordination environments are shown on the right. e, DFT-relaxed structure of the 2DZIF and a visualization of the 6-MR (right). f, HRTEM image of the 2DZIF film lying flat on the \(hk0\) plane, resting on suspended graphene, and (g) corresponding Fourier transform compared with the simulated diffraction pattern from the proposed structure oriented along the \(c\) -out-of-plane direction. h, Left: CTF-corrected image of the highlighted area in (f) based on a defocus value of -130 nm analyzed from the Thon rings in the Fourier transform pattern. Right: simulated projected potential map along the [001] direction of 2DZIF.
+ +<--- Page Split ---> + +X- ray photoelectron spectroscopy (XPS) of the 2DZIF and aZIF films was carried out to gain further insights into their coordination environments (Fig. 2d). The N1s XPS data of 2DZIF when compared to that of ZIF- L layers and a prototypical non- layered ZIF (ZIF- 8) revealed that both 2DZIF and ZIF- L yield two peaks (399.0 and \(400.2\mathrm{eV}\) corresponding to N- Zn and N- H bonds, respectively) in contrast to a single peak (399.0 eV) from the ZIF- 8 crystals. This is consistent with the presence of abundant surface terminations (N- H) in the 2DZIF layers. In comparison, the population of N- H species was significantly diminished for aZIF indicating a nonlayered amorphous structure. The Zn2p XPS spectra was similar for all samples (Supplementary Fig. 10) indicating a similar coordination environment for Zn. + +To gain insight into the structure of 2DZIF, structural relaxation based on DFT was carried out starting with the \(a\) and \(b\) lattice parameters obtained by SAED. The \(c\) - axis parameter was estimated by the AFM measurements (2 nm), and was subsequently relaxed by DFT calculations. The relaxed structure has an orthorhombic space group Cmce with the following structural parameters; \(a = 24.196\mathrm{\AA}\) , \(b = 19.719\mathrm{\AA}\) , \(c = 20.908\mathrm{\AA}\) , \(\alpha = 90^{\circ}\) , \(\beta = 90^{\circ}\) , and \(\gamma = 90^{\circ}\) (Supplementary Table 2). The layer in 2DZIF is composed of alternating 4- member ring (MR) and 6- MR chains while terminal 2- mIm linkers are present on both sides of the layer (Fig. 2e and 3c). The pore aperture of 2DZIF is constituted by the 6- MR (Fig. 2e) which is attractive for gas separation. + +Aberration- corrected high- resolution TEM (AC- HRTEM) imaging of the 2DZIF film suspended on a TEM grid was carried out along the [001] crystallographic direction (Fig. 2f). The imaging was carried out using a low- dose beam condition47 to minimize damages to the beam sensitive 2DZIF lattice. Indeed, the obtained HRTEM image revealed the high crystallinity of the 2DZIF film. The corresponding Fourier transform validated the \(c\) - out- of- plane orientation of the film and was consistent with the simulated electron diffraction pattern from a film lying flat along the same orientation (Fig. 2g). Projection along the \(c\) - out- of- plane axis from the contrast transfer function (CTF) corrected image revealed alternating chains of 4- MR and 6- MR (Fig. 2h, left), consistent with the simulated [001]- projected electrostatic potential map of 2DZIF structure obtained by DFT structural relaxation (Fig. 2h, right). + +The registry or the lack of registry of 2DZIF with the underlying substrate plays an important role in determining its structure and morphology especially when contrasted against the closely related material, ZIF- L. Fig. 3a highlights the morphological differences in ZIF- L and 2DZIF. While the layers in ZIF- L and 2DZIF are stacked along the \(c\) - axis, the former grows as a leaf + +<--- Page Split ---> + +shaped layered crystals (Supplementary Fig. 11) whereas the latter can form macroscopically uniform monolayer films. The unique leaf shape is formed because ZIF- L layers stack with first progressively increasing and then progressively decreasing lateral size along the \(b\) - axis. An analysis of orientation of 2DZIF grain over graphene by SAED showed that the 2DZIF films crystallized maintaining a fixed set of orientation with graphene (Supplementary Fig. 12), indicating that substrate registry indeed plays a role in promoting in- plane growth of 2DZIF (Fig. 3b and Supplementary Note 1). While both ZIF- L and 2DZIF have orthorhombic lattices, the unit- cell parameters of 2DZIF grown on graphene, obtained by DFT relaxation, are distinct from those of ZIF- L where the latter has a significantly shorter parameter along the \(b\) (17.060 Å) axis compared to the former (19.719 Å, Fig. 3c and Supplementary Table 2). + +The grains of 2DZIF could be visualized by partial etching of 2DZIF films based on the well documented dissolution of ZIFs in water (Fig. 3d)48. After partial dissolution, the grain shape was triangular with a lateral size of 1- 2 \(\mu \mathrm{m}\) (Fig. 3e) consistent with earlier observations of domains in the sub- monolayer film. The three sides of the triangular grains could be assigned to be (110), (110) and (100) lattice planes, respectively, reported to be the minimum surface energy planes for ZIF layers (Supplementary Fig. 13)49. AFM images (Fig. 3f) confirmed that the grains have uniform thickness of \(\sim \mathrm{ca. 2 nm}\) consistent with the structure of 2DZIF. SAED of this sample showed the same pattern with 2DZIF confirming that the triangular domains were indeed 2DZIF (Supplementary Fig. 14). + +<--- Page Split ---> +![](images/Supplementary_Figure_12.jpg) + +
Fig. 3. 2DZIF structure and its relationship with ZIF-L. a, Schematic contrasting the arrangement of layers within a ZIF-L crystal with that of a monolayer 2DZIF. b, Registry between 2DZIF and supercell of graphene based on SAED data in Supplementary Fig. 12. c, Structures of a ZIF-L layer (left) and 2DZIF (right) viewed along the [001], [100], and [010] directions. d, Schematic illustrating the etching of 2DZIF in water. SEM (e) and AFM (f) images of the triangular grains of 2DZIF obtained by a short etching in water. g, AFM height profile corresponding to the line in (f).
+ +The \(3.2\mathrm{\AA}\) gap in the 6- MR of 2DZIF is attractive for sieving \(\mathrm{H}_2\) (kinetic diameter of \(2.89\mathrm{\AA}\) from larger gas molecules such as \(\mathrm{CO_2}\) ( \(3.30\mathrm{\AA}\) ), \(\mathrm{N}_2\) ( \(3.64\mathrm{\AA}\) ), and \(\mathrm{CH_4}\) ( \(3.80\mathrm{\AA}\) ) \(^{50,51}\) . The 2DZIF film is mechanically robust with Young's modulus of \(8.1\pm 2.1\) GPa (Supplementary Fig. 15), comparable to that of the three- dimensional analogs \(^{52}\) . Therefore, we probed the \(\mathrm{H}_2\) - sieving performance on the 2DZIF film. For this, 2DZIF films were grown on nanoporous graphene (NG, Supplementary Fig. 16) mechanically reinforced with a dense 250- nm- thick poly[1- (trimethylsilyl)propyne] (PTMSP) film (Supplementary Fig. 17 and 18) where the NG/PTMSP film acts as a support film (Fig. 4a). The pores in NG were intentionally designed to be large \((1.8\pm 1.2\mathrm{nm})^{53}\) to rule out any molecular sieving from NG and to allow the determination of \(\mathrm{H}_2\) - sieving from the 2DZIF film. The 2DZIF films, resting on a macroporous metal foil support (pore size of \(5\mu \mathrm{m}\) , area of \(1\mathrm{mm}^2\) ), exhibited a molecular cut- off for molecules larger than \(\mathrm{H}_2\) , + +<--- Page Split ---> + +indicating that gas transport was controlled by the 6- MR of 2DZIF (Fig. 4b and Supplementary Fig. 19 and 20). The \(\mathrm{H}_2\) permeance was large ( \(>15000\) gas permeation units or GPU; 1 GPU = \(3.35 \times 10^{- 10} \mathrm{~mol} \mathrm{~m}^{- 2} \mathrm{~s}^{- 1} \mathrm{~Pa}^{- 1}\) ) similar to that from the support film (Supplementary Table 3 and Supplementary Fig. 19), indicating a negligible transport resistance from the 2DZIF layer. + +When an equimolar \(\mathrm{H}_2:\mathrm{N}_2\) mixture was probed with feed pressure of 2 bar, a \(\mathrm{H}_2\) permeance of 17300 GPU with a \(\mathrm{H}_2 / \mathrm{N}_2\) separation factor of 115 could be obtained (Fig. 4c). Another membrane when tested under a high- pressure feed (8 bar), exhibited a high \(\mathrm{H}_2\) flux of 2.8 mol \(\mathrm{m}^{- 2} \mathrm{~s}^{- 1}\) and \(\mathrm{H}_2 / \mathrm{N}_2\) separation factor of 52 (Supplementary Fig. 21). This performance constitutes one of the best combinations of \(\mathrm{H}_2\) flux and \(\mathrm{H}_2 / \mathrm{N}_2\) separation factor (Fig. 4d and Supplementary Fig. 22, 23; Supplementary Table 5). Larger area (centimeter- scale) 2DZIF membrane could be also prepared (Supplementary Fig. 24), thanks to the highly uniform deposition of 2DZIF films on graphene (Fig. 1f and g). They also show attractive \(\mathrm{H}_2\) permselective separation performance (Supplementary Table 4), in agreement with the smaller- area membranes (Supplementary Note 2). + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4. Applications of 2DZIF and aZIF. a, Schematic of a 2DZIF film supported on nanoporous graphene reinforced with PTMSP. b, \(\mathrm{H}_2\) , \(\mathrm{CO}_2\) , \(\mathrm{N}_2\) and \(\mathrm{CH}_4\) permeances of the support film (PTMSP-reinforced NG) and the supported 2DZIF film. c, 2DZIF membrane separation performance for an equimolar \(\mathrm{H}_2 / \mathrm{N}_2\) mixed feed. d, Comparison of the \(\mathrm{H}_2 / \mathrm{N}_2\) separation performance of 2DZIF membranes with the state-of-the-art (Supplementary Table
+ +<--- Page Split ---> + +5). GO, CMP, and HOF refer to graphene oxide, conjugated microporous polymers and hydrogen-bonded organic frameworks, respectively. The dash line indicates Knudsen selectivity for the \(\mathrm{H}_2 / \mathrm{N}_2\) pair. e, Schematic of the patterning process. TEM (f) and AFM (g) images of nanoscale patterns made on an aZIF film. h, AFM height profile corresponding to the line in (g). + +Amorphous MOFs exhibit unique physical and chemical properties due to the absence of anisotropy and crystalline grains1,54. On one hand, they may not have the well- defined pore structures of crystalline MOFs required for certain molecular sieving applications, but at the same time, they do not exhibit grain boundaries and structural anisotropies of crystalline MOFs, which can create film non- uniformities. A potential use of organic- inorganic films is in next generation resists for photolithography55 in place of currently used polymeric resists, and, for this application, MOF- inspired metal- organic clusters have been proposed for high resolution patterning13. As a demonstration of the potential of our deposition method in this emerging application, an aZIF film was deposited on a silicon nitride support and subsequently exposed to a direct- write electron beam using 1:1 line- and space- patterns ranging from 10 to 40 nm in line width (or half pitch) (Fig. 4e). The aZIF films behave similarly to ZIF- L crystals, for which e- beam treatment can induce contrast in water dissolution behavior based on framework densification and disintegration of the ligand molecular structure56- 58. After development in water, the irradiated area was preserved while the non- irradiated area was dissolved (Fig. 4f), confirming aZIF as a negative- tone resist. The thickness of the remaining aZIF structure was determined to be \(\sim 25 \mathrm{nm}\) by AFM (Fig. 4g and h). The resolution of the resulting pattern, as exemplified by the well- resolved lines at 20 nm half pitch, is comparable to the state- of- the- art metal- containing resists13,59, which are an emerging class of material that hold promise in extreme ultraviolet lithography and electron beam lithography57- 61. aZIF can also be patterned in positive- tone mode by a vapor phase ligand pretreatment. The as- deposited aZIF is exposed to the sublimated vapor of 4,5- dichloroimidazole (dcIm) at \(75^{\circ} \mathrm{C}\) for 1.5 h, during which the aZIF matrix is partially exchanged or infiltrated with dcIm ligand. The dcIm- treated film is then exposed to a direct- write electron beam. After development in organic solvents, the irradiated area is removed while non- irradiated area is preserved, which showed similar sensitivity compared to the reported data (Supplementary Fig. 25 and Supplementary Table S6). Furthermore, to improve compatibility with microfabrication processes, aZIF films are spin- coated on silicon wafers, and their thicknesses can be controlled by spin speed (Supplementary Fig. 26- 28). The simple fabrication for ultrathin ZIF films reported in this + +<--- Page Split ---> + +study could accelerate the development of new ZIF- based resist materials for lithographic applications62- 64. + +The method reported here can be extended to other promising MOF structures. 2D film of UiO- 66- NH2 can also be deposited by on graphene (Supplementary Fig. 29). It makes this approach reported here broad and interesting to develop a number of 2D MOF films in the future. In conclusion, we report the synthesis of ZIFs as macroscopically uniform amorphous and crystalline 2D films from an ultradilute solution. The 2DZIF film yields exceptional H2- sieving performance, thanks to the ordered 2D structure with a high density of 6- MR hosting H2- selective gap, making such a film the ultimate selective layer for membrane application. In the absence of substrate registry, ultrathin amorphous films are demonstrated, which are promising for advancing the limit of nanoscale patterning. + +## Online content + +Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available online. + +## References + +1. Bennett, T. D. & Cheetham, A. K. Amorphous Metal–Organic Frameworks, Acc. Chem. Res. 47, 1555-1562 (2014). +2. Banerjee, R. et al. 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Mater. 33, 5681-5689 (2021). + +## Methods + +## Chemicals + +Zinc nitrate hexahydrate \(\mathrm{(Zn(NO_3)_2\cdot 6H_2O)}\) was purchased from Sigma-Aldrich. 2- methylimidazole (2-mIm) was from Chemie Brunschwig AG. HCl (32 wt%) was purchased from Reactolab S.A.. poly[1-(trimethylsilyl)-1-propylene] (PTMSP) was from ABCR. \(\mathrm{FeCl_3}\) (97%) and \(\mathrm{Na_2S_2O_8}\) was bought from Sigma-Aldrich. Cu foil (50 mm, 99.9%) were purchased from STREM. Toluene (AR) and methanol (AR) were from Fischer. All chemicals were used without further purifications. \(\mathrm{Si / SiO_2}\) wafers were purchased from University Wafer Inc. \(\mathrm{Si / SiO_2}\) wafer with single layer graphene was bought from Ted Pella. Highly oriented pyrolytic graphite (HOPG) (ZYA quality, GRAS/1.0x7x7) was purchased from ScanSens. Silicon nitride TEM supports (50 nm silicon nitride film on a \(200\mu \mathrm{m}\) silicon frame with nine viewing + +<--- Page Split ---> + +windows, each \(0.1 \times 0.1 \mathrm{mm}\) were purchased from Ted Pella. Sapphire (Al₂O₃) wafer (Z- cut, \(10 \mathrm{cm} \times 0.5 \mathrm{mm}\) ) was purchased from MTI. + +## Characterizations and Measurements + +SEM measurements were performed on a Teneo Scanning Electron Microscope operating at 1 kV. PXRD data were collected at a Bruker D8 Discover diffractometer with a Lynxeye XE detector, operated at \(40 \mathrm{kV}\) , \(400 \mathrm{mA}\) for Cu Kα \((\lambda = 1.5406 \mathrm{\AA})\) at ambient temperature and pressure. Bright- field TEM images and selected- area electron diffraction (SAED) images were obtained with a Talos F200X microscope operated at \(200 \mathrm{kV}\) . TEM images for patterns on silicon nitride were obtained on a ThermoFisher TF30 TEM operating at \(300 \mathrm{kV}\) . + +Low- dose AC- HRTEM was performed on a Cs- corrected FEI cubed G2 Titan 60- 300 electron microscope at \(300 \mathrm{kV}\) , using a Gatan K2 direct- detection camera in electron counting mode. The AC- HRTEM images were acquired with the dose fractionation function, and each image stack is composed of 120 frames with \(0.05 \mathrm{s}\) exposure for each frame, with a total electron dose of \(\sim 60 \mathrm{e}^{- }\mathrm{\AA}^{- 2}\) . The raw image was denoised by using an average background subtraction filter (ABSF). The CTF correction was performed based on the defocus value determined from the amorphous thon rings in the Fourier transform, and the projected electrostatic potential was simulated by the QSTEM software (QSTEM V2, 31). Simulated ED pattern was carried out by using Singlecrystal module of CrystalMaker software. + +AFM images and modulus measurement were recorded on a Bruker MultiMode 8 AFM. For modulus measurement, a Bruker Tap525A rectangular probe was used and calibrated with standard sample sapphire, polystyrene and HOPG. XPS were carried out on an Axis Supra (Kratos Analytical) using the monochromated K x- ray line of an aluminum anode. Synchrotron GIXRD was carried out at beamline BM01, Swiss- Norwegian beamline (SNBL) at the European Synchrotron Radiation Facility (ESRF) with wavelength of \(0.683 \mathrm{\AA}\) and \(0.960 \mathrm{\AA}\) . + +The gas separation performance of all membranes was recorded on a homemade permeation set- up. The pressure on the feed side was maintained at 2- 8 bar and on the permeate side at 1 bar during the measurements. All measurements were done after reaching the steady state with argon as the sweep gas. The membranes were sealed with stainless- steel gasket. The composition of permeate was analysed using an online Hiden Analytical HPR- 20 mass spectrometer. + +The permeances, \(J_{i}\) , of gas \(i\) was calculated by Eq. S1 + +<--- Page Split ---> + +\[J_{i} = X_{i} / (A\cdot \Delta P_{i}) \quad (S1)\] + +where \(X_{i}\) is the molar flow rate of component \(i\) across the membrane area, \(A\) and \(\Delta P_{i}\) is the transmembrane pressure difference for the component \(i\) . The selectivity \(\alpha_{ij}\) of two gases ( \(i\) and \(j\) , where \(i\) is the faster permeating gas) was calculated by Eq. S2 + +\[\alpha_{ij} = J_{i} / J_{j} \quad (S2)\] + +## Synthesis of 2DZIF/aZIF film + +In a petri dish containing \(29~\mathrm{ml}\) of \(\mathrm{Zn(NO_3)_2}\) aqueous solution at room temperature, the substrate (HOPG, graphene/Si/SiO \(_2\) for 2DZIF and Si/SiO \(_2\) for aZIF) was partially immersed. Then \(1\mathrm{ml}\) of \(2\mathrm{- mlm}\) aqueous solution was added. After a certain time, the substrate was removed to stop the reaction. + +## Synthesis of 2DZIF membrane for gas separation + +First, single- layer graphene was synthesized by using low- pressure CVD of methane on copper foil following the literature. Before the synthesis, the copper foil was annealed at \(1077^{\circ}\mathrm{C}\) in a \(\mathrm{H}_2 / \mathrm{Ar}\) atmosphere for \(60~\mathrm{min}\) . Then, \(\mathrm{CO_2}\) ( \(100~\mathrm{mL / min}\) ) and \(\mathrm{H}_2\) ( \(8~\mathrm{mL / min}\) ) flow was introduced successively, each for \(30~\mathrm{min}\) , to remove the contaminations. At last, \(\mathrm{CH_4}\) ( \(24~\mathrm{mL / min}\) ) and \(\mathrm{H}_2\) ( \(8~\mathrm{mL / min}\) ) flow was used to grow single- layer graphene on copper film for \(30~\mathrm{min}\) at pressure of \(460~\mathrm{mTorr}\) . + +After the synthesis of single- layer graphene, an \(\mathrm{O_2}\) plasma (MTI plasma cleaner, EQ- PCE- 3, \(13.56\mathrm{MHz}\) , \(17\mathrm{W}\) ) was carried out to introduce nanopores for application of 2DZIF film as selective layer in the membrane. Briefly, the atmosphere in the plasma chamber was exchanged by \(\mathrm{O_2}\) flow to pressure around \(50\mathrm{mTorr}\) . Then a plasma was generated for \(4\mathrm{s}\) to etch SLG to obtain NG. After the plasma, a solution of PTMSP in toluene ( \(1.25\mathrm{wt}\%\) ) was spin- coated on NG at \(1000\mathrm{rpm}\) for \(30\mathrm{s}\) and \(2000\mathrm{rpm}\) for \(30\mathrm{s}\) , respectively. The sample was dried in ambient air at room temperature overnight. Then, copper foil was etched by a combination of \(\mathrm{FeCl_3}\) ( \(0.5\mathrm{M}\) in water), HCl ( \(0.1\mathrm{M}\) in water) and water. The floating graphene/PTMSP film was transferred on the surface of a \(29\mathrm{ml}\) of aqueous solution of \(\mathrm{Zn(NO_3)_2}\) ( \(2\mathrm{mmol / L}\) ) in a petri dish, and an aqueous solution of \(2\mathrm{- mlm}\) ( \(1\mathrm{mL}\) , \(0.5\mathrm{mol / L}\) ) was added in. After \(2\mathrm{min}\) of reaction the resulted 2DZIF/graphene/PTMSP film was transferred to substrate (e.g., macroporous W support hosting \(5\mu \mathrm{m}\) holes over a \(1\mathrm{mm}^2\) area for membranes) for further characterizations or applications. For the synthesis of centimeter- scale 2DZIF membrane, \(1\mathrm{wt}\%\) of Teflon AF in GOLDEN perfluorinated fluid was spin- coated on NG at \(300\mathrm{rpm}\) for \(60\mathrm{s}\) , and heated at \(60^{\circ}\mathrm{C}\) + +<--- Page Split ---> + +for \(3\mathrm{h}\) . After that, the sample was put into a homemade membrane module (Supplementary Fig. 19) while Cu foil facing up. This allowed etching of Cu foil in the membrane module by \(10\mathrm{wt}\% \mathrm{Na}_2\mathrm{S}_2\mathrm{O}_8\) aqueous solution exposing NG surface for 2DZIF synthesis. Finally, 2DZIF film was synthesized by exposing NG to growth solution for \(10\mathrm{min}\) at room temperature (2 mM \(\mathrm{Zn(NO_3)_2}\) and \(16\mathrm{mM}2\mathrm{- }\mathrm{mM}\) ). + +## Sample preparation of 2DZIF on graphene for AFM + +For the AFM sample preparation of 2DZIF film on graphene/PTMSP substrate, the 2DZIF/graphene/PTMSP film was transferred on \(\mathrm{Si / SiO_2}\) wafer with 2DZIF layer facing the wafer. Then the sample was annealed at \(70^{\circ}\mathrm{C}\) for \(4\mathrm{h}\) , to increase the adhesion between film and \(\mathrm{Si / SiO_2}\) wafer. After that, the sample was immersed in toluene for \(12\mathrm{h}\) , to remove PTMSP layer. + +The AFM image of 2DZIF film with triangular morphology after \(5\mathrm{min}\) of water etching was collected directly on the film with 2DZIF layer facing up. Specifically, the 2DZIF/graphene/PTMSP film with triangular morphology was first scooped by glass slide with 2DZIF layer facing the slide, and a \(\mathrm{Si / SiO_2}\) wafer attached with double- sided carbon tape was pressed onto PTMSP layer. As a result, the 2DZIF/graphene/PTMSP film was transferred onto \(\mathrm{Si / SiO_2}\) wafer, resulting in 2DZIF layer facing up. AFM measurement of 2DZIF film on HOPG, \(\mathrm{Si / SiO_2}\) and graphene/ \(\mathrm{Si / SiO_2}\) was carried out directly on the sample without any treatment. + +## Sample preparation for TEM + +Similar to sample preparation for AFM, 2DZIF/graphene/PTMSP film was transferred on TEM grid with 2DZIF layer facing the TEM grid. Then the sample was annealed at \(70^{\circ}\mathrm{C}\) for \(4\mathrm{h}\) , to increase the adhesion between film and TEM grid. After that, the sample was immersed in toluene for \(12\mathrm{h}\) , to remove the PTMSP layer. + +For the electron patterning sample, aZIF layer was grown on a silicon nitride supports. Prior to the ZIF deposition, the silicon nitride supports were treated with oxygen plasma for \(10\mathrm{min}\) (29.6 W, \(400\mathrm{mTorr}\) oxygen pressure) in a plasma cleaner (Harrick Plasma) to improve the surface reactivity. + +## Electron beam patterning of aZIF films + +For negative tone patterning, aZIF films were exposed to electron beam using a Thermo Fisher Helios G4 UC Dual Beam microscope operating at \(20\mathrm{kV}\) accelerating voltage and \(400\mathrm{pA}\) + +<--- Page Split ---> + +beam current. The areal doses were \(80\mathrm{mC / cm^2}\) for all patterns. After exposure, the aZIF films were developed in deionized water for \(24\mathrm{h}\) and blow dried in a stream of nitrogen gas. + +For positive tone patterning, the aZIF films were first placed in a \(60\mathrm{mL}\) PFA vessel with a bed of \(0.2\mathrm{g}4,5\) - dichloroimidazole (dcIm) and heated at \(75^{\circ}\mathrm{C}\) for \(1.5\mathrm{h}\) . The dcIm- treated aZIF was then exposed to electron beam using a Thermo Fisher Helios G4 UC Dual Beam microscope operating at \(20\mathrm{kV}\) accelerating voltage and \(100\mathrm{pA}\) beam current. The areal doses were \(2\mathrm{mC / cm^2}\) for all patterns. After exposure, the aZIF films were immersed in NMP and acetone each for \(10\mathrm{s}\) and blow dried in a stream of nitrogen gas. + +## Spin-coating of aZIF films on silicon wafers + +Aqueous solutions of \(\mathrm{Zn(NO_3)_2}\) (4 mmol/L) and 2- Im (32 mmol/L) were mixed in a T connector (0.5 mm I.D.) at \(1\mathrm{mL / min}\) injection rate, respectively, using a two- channel syringe pump. The mixed solution was spin- coated on silicon wafers while spinning at speeds between 500 and 2000 rpm for \(1\mathrm{min}\) , followed by \(10\mathrm{s}\) spinning at 2000 rpm to completely dry the film. The spin- coated films were exposed to electron beam using line patterns of \(100\mathrm{nm}\) width and \(400\mathrm{nm}\) spacing and developed in deionized water for \(24\mathrm{h}\) . The height of the line patterns were measured to determine the thickness of spin- coated films. + +## Structural simulation + +The simulation of 2DZIF structure was carried out by the DFT calculations. At first, the reported ZIF- L structure was imported into Forcite module in Material Studio software (Accelrys, San Diego, CA) to calculate the initial structural model, and unit- cell was set to be orthorhombic and \(a = 24.0\mathrm{\AA}\) , \(b = 20.0\mathrm{\AA}\) , \(c = 20.0\mathrm{\AA}\) , \(\alpha = 90^{\circ}\) , \(\beta = 90^{\circ}\) , \(\gamma = 90^{\circ}\) , respectively, while the connectivity was kept. The calculation task was geometry optimization. The quality was set to be fine, and 'smart' algorithm was selected. After that, van der Waals DFT calculations were performed using the Quantum ESPRESSO package66,67. The Brillouin zone was sampled at the gamma point. An energy cutoff of 60 Ry was used for the plane wave expansion of the wavefunctions. A kinetic energy cutoff of 480 Ry on the charge was used together with ultra- soft pseudopotentials68,69. The relaxation was performed with the Perdew- Burke- Ernzerhof (PBE) functional70. The system was relaxed to the lowest energy configuration of atoms. The surface geometry had been optimized with the convergence thresholds of \(1 \times 10^{-4}\mathrm{Ry}\) and \(3.28 \times 10^{-3}\mathrm{Ry / Bohr}\) for the total energy and forces, respectively. + +## Data availability + +<--- Page Split ---> + +Data supporting the findings in the present work are available in the manuscript or Supplementary Information. Additional data are available from the corresponding author upon reasonable request. + +65. Huang, S. et al. Millisecond lattice gasification for high-density \(\mathrm{CO_2}\) - and \(\mathrm{O_2}\) -sieving nanopores in single-layer graphene. Sci. Adv. 7: eabf0116 (2021). + +66. Giannozzi, P. et al. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter 21, 390052 (2009). + +67. Giannozzi, P. et al. Advanced capabilities for materials modelling with Quantum ESPRESSO. J. Phys. Condens. Matter 29, 465901 (2017). + +68. Lejaeghere, K. et al. Reproducibility in density functional theory calculations of solids. Science 351, 6280 (2016). + +69. Prandini, G., Marrazzo, A., Castelli, I. E., Mounet, N. & Marzari, N. Precision and efficiency in solid-state pseudopotential calculations. npj Comput. Mater. 4, 72 (2018). + +70. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865-3868 (1996). + +Acknowledgments The authors acknowledge Dr. Dmitry Chernyshov and Dr. Diadkin Vadim at beamline BM01, the-Swiss-Norwegian Beamlines (SNBL), European Synchrotron Radiation Facility (ESRF) for assistance with synchrotron GIXRD experiments (doi:10.15151/ESRF-ES-670011338. ), Dr. Pascal Alexander Schouwink from EPFL for the help of XRD data. Dr. Mounir Mensi and Mojtaba Rezaei from EPFL for the help of XPS and AFM. This project is primarily supported by European Research Council Starting Grant (805437-UltimateMembranes). Parts of the work was supported by Swiss National Science Foundation (SNSF) Assistant Professor Energy Grant (PYAPP2_173645) and SNSF project (514601); Y. M. and M. T. acknowledge funding by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-SC0021212. + +Author contributions K. A. and Q. L. conceived the project and wrote the manuscript. Q. L., Y. M., D. B., and J. H. prepared the samples. Q. L., Y. M. and S. L. performed AFM + +<--- Page Split ---> + +measurements. Q. L. and Y. M. collected SEM data. Q. L. performed XRD measurement. S. L. collected XPS data. L. V. and H. C. collected TEM images and ED data. Q. L. and M. V. carried out structural simulations. C. C. and Y. H. helped with AC- HRTEM images. Q. L. and S. S. carried out gas permeance experiments. M. T. conceived and Y. M. performed the aZIF film deposition and e- beam patterning experiments. All authors discussed the results and commented on the manuscript. + +Competing interests: A patent application based on the findings reported here has been filed. + +## Additional information + +Supplementary information The online version contains supplementary material available at Correspondence and requests for materials should be addressed to Kumar Varoon Agrawal. Peer review information Nature thanks anonymous reviewer(s) for their contribution to the peer review of this work. + +Reprints and permissions information is available at http://www.nature.com/reprints. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Sl.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2_det.mmd b/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..a37c69450ce08e26ddf3ed8d9028e45431516a73 --- /dev/null +++ b/preprint/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2/preprint__09bb925dea8007aaf7d14d8f79096de27fcb7fe802393ba1d044ff47a75f57c2_det.mmd @@ -0,0 +1,516 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 945, 210]]<|/det|> +# Nanometer-thick crystalline and amorphous zeolitic imidazolate framework films for membrane and patterning applications + +<|ref|>text<|/ref|><|det|>[[44, 229, 636, 293]]<|/det|> +Qi Liu École Polytechnique Fédérale de Lausanne Yurun Miao + +<|ref|>text<|/ref|><|det|>[[55, 297, 635, 316]]<|/det|> +Johns Hopkins University https://orcid.org/0000- 0001- 6429- 8297 + +<|ref|>text<|/ref|><|det|>[[44, 322, 544, 363]]<|/det|> +Luis Francisco Villalobos Yale University https://orcid.org/0000- 0002- 0745- 4246 + +<|ref|>text<|/ref|><|det|>[[44, 369, 430, 410]]<|/det|> +Shaoxian Li École Polytechnique Fédérale de Lausanne + +<|ref|>text<|/ref|><|det|>[[44, 416, 430, 457]]<|/det|> +Deepu J. Babu École Polytechnique Fédérale de Lausanne + +<|ref|>text<|/ref|><|det|>[[44, 463, 867, 504]]<|/det|> +Cailing Chen King Abdullah University of Science and Technology https://orcid.org/0000- 0003- 2598- 1354 + +<|ref|>text<|/ref|><|det|>[[44, 509, 430, 550]]<|/det|> +Heng- Yu Chi École Polytechnique Fédérale de Lausanne + +<|ref|>text<|/ref|><|det|>[[44, 555, 430, 596]]<|/det|> +Mohammad Tohidi Vahdat École Polytechnique Fédérale de Lausanne + +<|ref|>text<|/ref|><|det|>[[44, 602, 430, 643]]<|/det|> +Jian Hao École Polytechnique Fédérale de Lausanne + +<|ref|>text<|/ref|><|det|>[[44, 649, 430, 689]]<|/det|> +Shuqing Song École Polytechnique Fédérale de Lausanne + +<|ref|>text<|/ref|><|det|>[[44, 695, 867, 736]]<|/det|> +Yu Han King Abdullah University of Science and Technology https://orcid.org/0000- 0003- 1462- 1118 + +<|ref|>text<|/ref|><|det|>[[44, 741, 636, 782]]<|/det|> +Michael Tsapatsis Johns Hopkins University https://orcid.org/0000- 0001- 5610- 3525 + +<|ref|>text<|/ref|><|det|>[[44, 787, 787, 829]]<|/det|> +Kumar Varoon Agrawal ( kumar.agrawal@epfl.ch) École Polytechnique Fédérale de Lausanne https://orcid.org/0000- 0002- 5170- 6412 + +<|ref|>text<|/ref|><|det|>[[44, 869, 101, 886]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 906, 135, 924]]<|/det|> +Keywords: + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[44, 46, 314, 64]]<|/det|> +**Posted Date:** March 14th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 84, 474, 103]]<|/det|> +**DOI:** https://doi.org/10.21203/rs.3.rs-2666142/v1 + +<|ref|>text<|/ref|><|det|>[[44, 120, 910, 164]]<|/det|> +**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 182, 910, 225]]<|/det|> +**Additional Declarations:** Yes there is potential Competing Interest. A patent application based on the finding reported in the manuscript is filed. + +<|ref|>text<|/ref|><|det|>[[44, 259, 945, 303]]<|/det|> +**Version of Record:** A version of this preprint was published at Nature Materials on September 21st, 2023. See the published version at https://doi.org/10.1038/s41563-023-01669-z. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[176, 90, 823, 140]]<|/det|> +# Nanometer-thick crystalline and amorphous zeolitic imidazolate framework films for membrane and patterning applications + +<|ref|>text<|/ref|><|det|>[[130, 167, 870, 238]]<|/det|> +Qi Liu1†, Yurun Miao2, Luis Francisco Villalobos1, Shaoxian Li1, Deepu J. Babu1†, Cailing Chen4, Heng- Yu Chi1, Mohammad Tohidi Vahdat1,5, Jian Hao1, Shuqing Song1, Yu Han4, Michael Tsapatsis2,3, Kumar Varoon Agrawal1\* + +<|ref|>text<|/ref|><|det|>[[118, 293, 880, 336]]<|/det|> +1. Laboratory of Advanced Separations, École Polytechnique Fédérale de Lausanne (EPFL), 1950 Sion, Switzerland. + +<|ref|>text<|/ref|><|det|>[[118, 351, 880, 395]]<|/det|> +2. Department of Chemical and Biomolecular, Engineering & Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA + +<|ref|>text<|/ref|><|det|>[[118, 407, 744, 427]]<|/det|> +3. Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA + +<|ref|>text<|/ref|><|det|>[[118, 439, 881, 508]]<|/det|> +4. Advanced Membranes and Porous Materials Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. + +<|ref|>text<|/ref|><|det|>[[118, 520, 880, 565]]<|/det|> +5. Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), EPFL, Lausanne, Switzerland. + +<|ref|>text<|/ref|><|det|>[[118, 577, 880, 620]]<|/det|> +†Present address: College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, 215123, China. + +<|ref|>text<|/ref|><|det|>[[118, 633, 881, 677]]<|/det|> +†Present address: Materials Science and Metallurgical Engineering, Indian Institute of Technology, Hyderabad, Telangana 502 284, India. + +<|ref|>text<|/ref|><|det|>[[118, 690, 515, 708]]<|/det|> +\*Corresponding author: kumar.agrawal@epfl.ch + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 86, 198, 101]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[115, 105, 883, 600]]<|/det|> +Zeolitic imidazolate frameworks (ZIFs) are a subset of metal- organic frameworks (MOFs) with more than 200 characterized crystalline and amorphous networks made of divalent transition metal centers (e.g., \(\mathrm{Zn^{2 + }}\) and \(\mathrm{Co^{2 + }}\) ) linked by imidazolate linkers. ZIF thin films have been pursued intensively motivated by the desire to prepare membranes for selective gas and liquid separations. To achieve membranes with high throughput, as in A- scale biological channels with nanometer- scale pathlengths, ZIF films with the minimum possible thickness, down to just one unit cell, are highly desired. Control of ZIF film thickness at the 10- nm- scale may also enable emerging, MOF- inspired, applications including patterned crystalline MOF films, and amorphous organic- inorganic resists for high- resolution electron- beam (e- beam) and extreme UV (EUV) lithography. However, the state- of- the- art methods yield ZIF films with thicknesses exceeding 40 nanometers. Here, we report a deposition method from ultra- dilute precursor mixtures that within minutes yields uniform ZIF deposits with nm- scale thickness control. On crystalline substrate such as graphene, two- dimensional crystalline ZIF (2DZIF) film with thickness of a unit- cell could be achieved, which composed of a six- membered zinc- imidazolate coordination ring enabling record- high \(\mathrm{H}_2\) permselective separation performance. Deposition under identical conditions on amorphous substrates yields macroscopically smooth amorphous ZIF (aZIF) films, which can be used as negative- and positive- tone resists yielding pattern features down to \(20 \mathrm{nm}\) . The method reported here will likely accelerate the development of 2D crystalline and ultrathin amorphous MOF films for applications ranging from separation membranes to sensors and patterning for microelectronic applications. + +<|ref|>sub_title<|/ref|><|det|>[[118, 644, 216, 660]]<|/det|> +## Main Text: + +<|ref|>text<|/ref|><|det|>[[115, 682, 884, 899]]<|/det|> +ZIFs \(^{1,2}\) are a class of MOFs that hold promise for applications in molecular separations \(^{3 - 10}\) , patterning \(^{11 - 15}\) and sensing \(^{16}\) . Their chemical and physical properties have been widely explored as a function of framework flexibility \(^{17 - 20}\) as well as structural defects \(^{21,22}\) . The realization of two- dimensional (2D) ZIF films with thickness down to that afforded by a single structural building unit is highly desired to make ZIF analogues to graphene and related 2D materials with an added advantage; the intrinsic nanoporosity of ZIF can be used to separate molecules while maximizing the permselective flux \(^{23}\) . Another highly desirable feature is a nanometer- scale control over the film thickness, which can allow one to fabricate nanoscale patterns, which are desirable for incorporation in microelectronic devices \(^{24}\) , and for the development of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 883, 274]]<|/det|> +next generation organic- inorganic high- resolution photo- and e- beam- resists17. However, the realization of 2D crystalline and ultrathin amorphous ZIF films has remained elusive. While layered ZIFs such as ZIF- L25, \(\mathrm{Zn_2(bim)_4^{26}}\) , and analogs27 have been reported, the individual ZIF layers in these materials have a small aspect ratio which prevents the realization of continuous 2D ZIF films with structural uniformity over a macroscopic (e.g., wafer) length scale. The state- of- the- art of ZIF deposition methods yields polycrystalline films with thickness larger than \(40 \mathrm{nm}\) nanometer28- 31. This is mainly due to difficulty in achieving in- plane film growth without film thickening. + +<|ref|>text<|/ref|><|det|>[[117, 295, 883, 536]]<|/det|> +Considerable knowledge exists on ZIF/MOF crystal nucleation and growth in solution32- 37. Based upon data from synchrotron X- ray scattering, density- functional theory (DFT) and molecular dynamics simulations, and other techniques, it is generally accepted that ZIF formation involves a sequence of events starting from the formation of small ( \(\sim 1 \mathrm{nm}\) ) metastable prenucleation clusters, which evolve through aggregation followed by intra- aggregate ZIF nucleation and growth. Recent studies on surface- directed MOF growth38- 46 indicate that the diffusion of MOF precursors in the vicinity of the 2D material, and the MOF- 2D material interactions, are key to regulate the crystallinity of the MOF film and the ability to maintain in- plane/horizontal growth (desired for ultrathin films) versus out- of- plane/vertical (undesired) growth. + +<|ref|>text<|/ref|><|det|>[[117, 558, 883, 775]]<|/det|> +Herein, we report macroscopically uniform 2D ZIF films with exquisite nanometer- scale control over the film thickness by suppressing the out- of- plane growth by using an ultradilute growth solution. The ultralow precursor concentration restricts homogeneous nucleation in the solution and facilitates the growth of nanometer- thick films over an immersed substrate with deposition timescales of a few minutes. The film crystallinity is determined by the interaction of molecular precursors with the substrate ranging from substrate- registry- determined ordered film to amorphous films in the absence of any crystallographic registry. The film thickness could be controlled with a resolution of a single layer by controlling the deposition time and number of coatings. + +<|ref|>text<|/ref|><|det|>[[117, 796, 883, 915]]<|/det|> +The ZIF films were synthesized by immersing a substrate in an ultradilute precursor solution ( \(\leq 2 \mathrm{mM} \mathrm{Zn}^{2 + }\) and \(\leq 16 \mathrm{mM} 2\) - methylimidazole (2- mIm), respectively) for a few minutes (Fig. 1a). The use of such ultradilute solutions for the growth of ZIF films has not been reported before (Fig. 1b, Supplementary Table 1). They were used here in an effort to suppress the homogeneous nucleation in the bulk solution. With a diminished nuclei population in the bulk + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 883, 225]]<|/det|> +solution, the attachment of preformed nuclei to the substrate can be reduced or eliminated. This is expected to promote film growth by the assembly of molecular precursors on the substrate. Since this thin film growth mode is anticipated to be sensitive on the type of the substrate, we carried out synthesis using distinct substrates: (i) graphitic substrates with atomically smooth terrace such as highly oriented pyrolytic graphite (HOPG) or graphene, (ii) Si/SiO₂ wafer with a 300- nm- thick oxide layer, and (iii) single crystal sapphire (Al₂O₃). + +<|ref|>text<|/ref|><|det|>[[115, 247, 884, 686]]<|/det|> +ZIF films prepared on HOPG using a growth solution of 1 mM \(\mathrm{Zn^{2 + }}\) and 8 mM 2- mM and reaction time of 5 min were examined by optical and scanning electron microscopy (SEM) (Supplementary Fig. 1). A sharp change in contrast was observed at the air/precursor- solution interface beyond which the film had a uniform contrast indicating that the film was smooth, continuous, and macroscopically uniform. Atomic force microscopy (AFM) imaging near the interface confirmed that the ZIF film is indeed continuous and has a thickness of approximately 2 nm (Fig. 1c and d). When the synthesis time was reduced to 2 min, we observed a submonolayer film with micrometer- sized domains (Supplementary Fig. 2). The domains were faceted and had thickness of 2 nm, consistent with the thickness of the continuous film indicating that the film is crystalline consisting of micron- sized grains. We could obtain 4 and 6 nm thick films by increasing the growth time from 5 min to 10 and 15 min, respectively (Fig. 1e and Supplementary Fig. 3a- f). Increasing growth time to 20 min further did not lead to thicker film, indicating precursor depletion (Supplementary Fig. 3g- i). Thicker (8 nm) films could be obtained by doubling the precursor concentration (Supplementary Fig. 3j- l). A discrete, 2 nm, increase in film thickness further suggests a crystalline order. A fitting of film thickness with the number of probable layers yielded a monolayer thickness of 2 nm (Fig. 1e). Macroscopically large ZIF films spanning several centimeters in width could be obtained on chemical vapor deposition (CVD) derived graphene film resting on a Cu foil (Fig. 1f and 1g). + +<|ref|>text<|/ref|><|det|>[[117, 707, 884, 849]]<|/det|> +We also obtained macroscopically smooth, continuous, and uniform ZIF films on Si/SiO₂ wafers (Fig. 1h). AFM of one of these films, prepared using 2 mM \(\mathrm{Zn^{2 + }}\) and 16 mM 2- mM and deposition time of 10 s, indicated that the film is smooth with thickness near 8 nm (Fig. 1i and 1j). Ellipsometry of several ZIF films on Si/SiO₂ wafers, prepared by varying the synthesis time, indicated that the film thickness could be tuned in the range of 8- 18 nm consistent with the corresponding AFM data (Supplementary Fig. 4). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 80, 875, 770]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 777, 883, 893]]<|/det|> +
Fig. 1. Synthesis ZIF films from ultradilute solutions. a, Schematic of the ZIF film synthesis approach. b, Composition diagram comparing the precursor solution composition used in this study with those reported in the literature (Supplementary Table 1). AFM (c) and the corresponding height profile (d) of a monolayer ZIF film on HOPG. e, Monolayer and multilayer ZIF films on HOPG with discrete thicknesses as a function of synthesis time. Error
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 883, 201]]<|/det|> +bars in this figure represent the standard deviation of measurement. f, Optical image of a 2DZIF film on CVD graphene resting on a Cu foil. g, Scanning electron microscopy (SEM) image of a 2DZIF film on CVD graphene. The image was compiled by combining \(1155 (35 \times 33)\) images by scanning the whole surface of the large sample. h, SEM and optical images of a ZIF film on \(\mathrm{Si / SiO_2}\) . AFM (i) and the corresponding height profile (j) of the ZIF film on \(\mathrm{Si / SiO_2}\) . + +<|ref|>text<|/ref|><|det|>[[116, 222, 883, 563]]<|/det|> +Graphene supported ZIF film could be suspended on a holey transmission electron microscopy (TEM) grid (Fig. 2a). The film was devoid of large crystals and appeared uniform. Selected area electron diffraction (SAED) from a micrometer- sized area yielded three sets of diffraction patterns (Fig. 2b). The first two sets ((01), highlighted with green circles) had six- fold symmetry originating from two slightly misoriented (by \(3.0^{\circ}\) ) grains of graphene, while the last set had two- fold symmetry and belonged to a single grain of ZIF (highlighted with white circles), confirming that ZIF prepared on graphene was crystalline with grains at least a micron in size consistent with the AFM- based imaging of grains in the submonolayer film (Supplementary Fig. 2). Hereafter, the ZIF films on graphitic substrates are referred to as 2DZIF. The fact that a single 2DZIF grain could grow over two slightly misoriented graphene grains indicates that the growth could accommodate a small mismatch in its registry with the substrate. Diffraction pattern from 2DZIF, typically representing a single grain, was observed from every single spot over a large area. Based on the diffraction pattern, \(a\) and \(b\) lattice parameters of 2.4 and \(2.0 \mathrm{nm}\) , respectively, could be fitted. + +<|ref|>text<|/ref|><|det|>[[116, 584, 883, 872]]<|/det|> +We carried out synchrotron grazing incidence X- ray diffraction (GIXRD) of a 10- nm- thick ZIF film on graphene resting on a \(\mathrm{Si / SiO_2}\) wafer (Supplementary Fig. 5- 7). The in- plane GIXRD pattern revealed sharp diffraction peaks, consistent with the peak positions obtained by the radial integration of the SAED pattern (Fig. 2c and Supplementary Table 2), confirming that the film formed on the graphitic substrate exhibits crystalline order. In contrast, we did not observe diffraction from the ZIF films prepared directly on \(\mathrm{Si / SiO_2}\) wafer without the graphene layer indicating that these films were amorphous (hereafter referred to as aZIF films, Fig. 2c). When a single crystal sapphire ( \(\mathrm{Al_2O_3}\) ) was used as the substrate, highly crystalline 2DZIF film was formed (Supplementary Fig. 8 and 9). The presence of the order in the ZIF film when prepared over a crystalline substrate and the lack of an order when prepared over the amorphous substrate indicates a strong role of substrate registry in the formation of the ordered 2DZIF film. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 80, 875, 586]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 586, 881, 900]]<|/det|> +
Fig. 2. Structure determination of 2DZIF films. a, Bright-field TEM image of the 2DZIF film supported on suspended graphene, and (b) its corresponding SAED pattern. The pattern from graphene is identified with green circles and those from 2DZIF with white circles. c, In-plane GIXRD data from an aZIF film (top) and a 2DZIF film (middle) prepared on \(\mathrm{Si / SiO_2}\) and graphene/Si/SiO2, respectively, along with a radially integrated trace (bottom) of the SAED pattern shown in (B). d, N1s XPS spectra from ZIF-8, ZIF-L, aZIF and 2DZIF films. The N-Zn and N-H coordination environments are shown on the right. e, DFT-relaxed structure of the 2DZIF and a visualization of the 6-MR (right). f, HRTEM image of the 2DZIF film lying flat on the \(hk0\) plane, resting on suspended graphene, and (g) corresponding Fourier transform compared with the simulated diffraction pattern from the proposed structure oriented along the \(c\) -out-of-plane direction. h, Left: CTF-corrected image of the highlighted area in (f) based on a defocus value of -130 nm analyzed from the Thon rings in the Fourier transform pattern. Right: simulated projected potential map along the [001] direction of 2DZIF.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 82, 883, 300]]<|/det|> +X- ray photoelectron spectroscopy (XPS) of the 2DZIF and aZIF films was carried out to gain further insights into their coordination environments (Fig. 2d). The N1s XPS data of 2DZIF when compared to that of ZIF- L layers and a prototypical non- layered ZIF (ZIF- 8) revealed that both 2DZIF and ZIF- L yield two peaks (399.0 and \(400.2\mathrm{eV}\) corresponding to N- Zn and N- H bonds, respectively) in contrast to a single peak (399.0 eV) from the ZIF- 8 crystals. This is consistent with the presence of abundant surface terminations (N- H) in the 2DZIF layers. In comparison, the population of N- H species was significantly diminished for aZIF indicating a nonlayered amorphous structure. The Zn2p XPS spectra was similar for all samples (Supplementary Fig. 10) indicating a similar coordination environment for Zn. + +<|ref|>text<|/ref|><|det|>[[116, 320, 883, 538]]<|/det|> +To gain insight into the structure of 2DZIF, structural relaxation based on DFT was carried out starting with the \(a\) and \(b\) lattice parameters obtained by SAED. The \(c\) - axis parameter was estimated by the AFM measurements (2 nm), and was subsequently relaxed by DFT calculations. The relaxed structure has an orthorhombic space group Cmce with the following structural parameters; \(a = 24.196\mathrm{\AA}\) , \(b = 19.719\mathrm{\AA}\) , \(c = 20.908\mathrm{\AA}\) , \(\alpha = 90^{\circ}\) , \(\beta = 90^{\circ}\) , and \(\gamma = 90^{\circ}\) (Supplementary Table 2). The layer in 2DZIF is composed of alternating 4- member ring (MR) and 6- MR chains while terminal 2- mIm linkers are present on both sides of the layer (Fig. 2e and 3c). The pore aperture of 2DZIF is constituted by the 6- MR (Fig. 2e) which is attractive for gas separation. + +<|ref|>text<|/ref|><|det|>[[116, 558, 883, 800]]<|/det|> +Aberration- corrected high- resolution TEM (AC- HRTEM) imaging of the 2DZIF film suspended on a TEM grid was carried out along the [001] crystallographic direction (Fig. 2f). The imaging was carried out using a low- dose beam condition47 to minimize damages to the beam sensitive 2DZIF lattice. Indeed, the obtained HRTEM image revealed the high crystallinity of the 2DZIF film. The corresponding Fourier transform validated the \(c\) - out- of- plane orientation of the film and was consistent with the simulated electron diffraction pattern from a film lying flat along the same orientation (Fig. 2g). Projection along the \(c\) - out- of- plane axis from the contrast transfer function (CTF) corrected image revealed alternating chains of 4- MR and 6- MR (Fig. 2h, left), consistent with the simulated [001]- projected electrostatic potential map of 2DZIF structure obtained by DFT structural relaxation (Fig. 2h, right). + +<|ref|>text<|/ref|><|det|>[[117, 820, 881, 915]]<|/det|> +The registry or the lack of registry of 2DZIF with the underlying substrate plays an important role in determining its structure and morphology especially when contrasted against the closely related material, ZIF- L. Fig. 3a highlights the morphological differences in ZIF- L and 2DZIF. While the layers in ZIF- L and 2DZIF are stacked along the \(c\) - axis, the former grows as a leaf + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 82, 884, 325]]<|/det|> +shaped layered crystals (Supplementary Fig. 11) whereas the latter can form macroscopically uniform monolayer films. The unique leaf shape is formed because ZIF- L layers stack with first progressively increasing and then progressively decreasing lateral size along the \(b\) - axis. An analysis of orientation of 2DZIF grain over graphene by SAED showed that the 2DZIF films crystallized maintaining a fixed set of orientation with graphene (Supplementary Fig. 12), indicating that substrate registry indeed plays a role in promoting in- plane growth of 2DZIF (Fig. 3b and Supplementary Note 1). While both ZIF- L and 2DZIF have orthorhombic lattices, the unit- cell parameters of 2DZIF grown on graphene, obtained by DFT relaxation, are distinct from those of ZIF- L where the latter has a significantly shorter parameter along the \(b\) (17.060 Å) axis compared to the former (19.719 Å, Fig. 3c and Supplementary Table 2). + +<|ref|>text<|/ref|><|det|>[[116, 345, 884, 564]]<|/det|> +The grains of 2DZIF could be visualized by partial etching of 2DZIF films based on the well documented dissolution of ZIFs in water (Fig. 3d)48. After partial dissolution, the grain shape was triangular with a lateral size of 1- 2 \(\mu \mathrm{m}\) (Fig. 3e) consistent with earlier observations of domains in the sub- monolayer film. The three sides of the triangular grains could be assigned to be (110), (110) and (100) lattice planes, respectively, reported to be the minimum surface energy planes for ZIF layers (Supplementary Fig. 13)49. AFM images (Fig. 3f) confirmed that the grains have uniform thickness of \(\sim \mathrm{ca. 2 nm}\) consistent with the structure of 2DZIF. SAED of this sample showed the same pattern with 2DZIF confirming that the triangular domains were indeed 2DZIF (Supplementary Fig. 14). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 81, 884, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 460, 883, 628]]<|/det|> +
Fig. 3. 2DZIF structure and its relationship with ZIF-L. a, Schematic contrasting the arrangement of layers within a ZIF-L crystal with that of a monolayer 2DZIF. b, Registry between 2DZIF and supercell of graphene based on SAED data in Supplementary Fig. 12. c, Structures of a ZIF-L layer (left) and 2DZIF (right) viewed along the [001], [100], and [010] directions. d, Schematic illustrating the etching of 2DZIF in water. SEM (e) and AFM (f) images of the triangular grains of 2DZIF obtained by a short etching in water. g, AFM height profile corresponding to the line in (f).
+ +<|ref|>text<|/ref|><|det|>[[117, 640, 884, 909]]<|/det|> +The \(3.2\mathrm{\AA}\) gap in the 6- MR of 2DZIF is attractive for sieving \(\mathrm{H}_2\) (kinetic diameter of \(2.89\mathrm{\AA}\) from larger gas molecules such as \(\mathrm{CO_2}\) ( \(3.30\mathrm{\AA}\) ), \(\mathrm{N}_2\) ( \(3.64\mathrm{\AA}\) ), and \(\mathrm{CH_4}\) ( \(3.80\mathrm{\AA}\) ) \(^{50,51}\) . The 2DZIF film is mechanically robust with Young's modulus of \(8.1\pm 2.1\) GPa (Supplementary Fig. 15), comparable to that of the three- dimensional analogs \(^{52}\) . Therefore, we probed the \(\mathrm{H}_2\) - sieving performance on the 2DZIF film. For this, 2DZIF films were grown on nanoporous graphene (NG, Supplementary Fig. 16) mechanically reinforced with a dense 250- nm- thick poly[1- (trimethylsilyl)propyne] (PTMSP) film (Supplementary Fig. 17 and 18) where the NG/PTMSP film acts as a support film (Fig. 4a). The pores in NG were intentionally designed to be large \((1.8\pm 1.2\mathrm{nm})^{53}\) to rule out any molecular sieving from NG and to allow the determination of \(\mathrm{H}_2\) - sieving from the 2DZIF film. The 2DZIF films, resting on a macroporous metal foil support (pore size of \(5\mu \mathrm{m}\) , area of \(1\mathrm{mm}^2\) ), exhibited a molecular cut- off for molecules larger than \(\mathrm{H}_2\) , + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 82, 883, 177]]<|/det|> +indicating that gas transport was controlled by the 6- MR of 2DZIF (Fig. 4b and Supplementary Fig. 19 and 20). The \(\mathrm{H}_2\) permeance was large ( \(>15000\) gas permeation units or GPU; 1 GPU = \(3.35 \times 10^{- 10} \mathrm{~mol} \mathrm{~m}^{- 2} \mathrm{~s}^{- 1} \mathrm{~Pa}^{- 1}\) ) similar to that from the support film (Supplementary Table 3 and Supplementary Fig. 19), indicating a negligible transport resistance from the 2DZIF layer. + +<|ref|>text<|/ref|><|det|>[[116, 190, 884, 432]]<|/det|> +When an equimolar \(\mathrm{H}_2:\mathrm{N}_2\) mixture was probed with feed pressure of 2 bar, a \(\mathrm{H}_2\) permeance of 17300 GPU with a \(\mathrm{H}_2 / \mathrm{N}_2\) separation factor of 115 could be obtained (Fig. 4c). Another membrane when tested under a high- pressure feed (8 bar), exhibited a high \(\mathrm{H}_2\) flux of 2.8 mol \(\mathrm{m}^{- 2} \mathrm{~s}^{- 1}\) and \(\mathrm{H}_2 / \mathrm{N}_2\) separation factor of 52 (Supplementary Fig. 21). This performance constitutes one of the best combinations of \(\mathrm{H}_2\) flux and \(\mathrm{H}_2 / \mathrm{N}_2\) separation factor (Fig. 4d and Supplementary Fig. 22, 23; Supplementary Table 5). Larger area (centimeter- scale) 2DZIF membrane could be also prepared (Supplementary Fig. 24), thanks to the highly uniform deposition of 2DZIF films on graphene (Fig. 1f and g). They also show attractive \(\mathrm{H}_2\) permselective separation performance (Supplementary Table 4), in agreement with the smaller- area membranes (Supplementary Note 2). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 75, 881, 760]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 777, 883, 896]]<|/det|> +
Fig. 4. Applications of 2DZIF and aZIF. a, Schematic of a 2DZIF film supported on nanoporous graphene reinforced with PTMSP. b, \(\mathrm{H}_2\) , \(\mathrm{CO}_2\) , \(\mathrm{N}_2\) and \(\mathrm{CH}_4\) permeances of the support film (PTMSP-reinforced NG) and the supported 2DZIF film. c, 2DZIF membrane separation performance for an equimolar \(\mathrm{H}_2 / \mathrm{N}_2\) mixed feed. d, Comparison of the \(\mathrm{H}_2 / \mathrm{N}_2\) separation performance of 2DZIF membranes with the state-of-the-art (Supplementary Table
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 883, 201]]<|/det|> +5). GO, CMP, and HOF refer to graphene oxide, conjugated microporous polymers and hydrogen-bonded organic frameworks, respectively. The dash line indicates Knudsen selectivity for the \(\mathrm{H}_2 / \mathrm{N}_2\) pair. e, Schematic of the patterning process. TEM (f) and AFM (g) images of nanoscale patterns made on an aZIF film. h, AFM height profile corresponding to the line in (g). + +<|ref|>text<|/ref|><|det|>[[115, 210, 883, 902]]<|/det|> +Amorphous MOFs exhibit unique physical and chemical properties due to the absence of anisotropy and crystalline grains1,54. On one hand, they may not have the well- defined pore structures of crystalline MOFs required for certain molecular sieving applications, but at the same time, they do not exhibit grain boundaries and structural anisotropies of crystalline MOFs, which can create film non- uniformities. A potential use of organic- inorganic films is in next generation resists for photolithography55 in place of currently used polymeric resists, and, for this application, MOF- inspired metal- organic clusters have been proposed for high resolution patterning13. As a demonstration of the potential of our deposition method in this emerging application, an aZIF film was deposited on a silicon nitride support and subsequently exposed to a direct- write electron beam using 1:1 line- and space- patterns ranging from 10 to 40 nm in line width (or half pitch) (Fig. 4e). The aZIF films behave similarly to ZIF- L crystals, for which e- beam treatment can induce contrast in water dissolution behavior based on framework densification and disintegration of the ligand molecular structure56- 58. After development in water, the irradiated area was preserved while the non- irradiated area was dissolved (Fig. 4f), confirming aZIF as a negative- tone resist. The thickness of the remaining aZIF structure was determined to be \(\sim 25 \mathrm{nm}\) by AFM (Fig. 4g and h). The resolution of the resulting pattern, as exemplified by the well- resolved lines at 20 nm half pitch, is comparable to the state- of- the- art metal- containing resists13,59, which are an emerging class of material that hold promise in extreme ultraviolet lithography and electron beam lithography57- 61. aZIF can also be patterned in positive- tone mode by a vapor phase ligand pretreatment. The as- deposited aZIF is exposed to the sublimated vapor of 4,5- dichloroimidazole (dcIm) at \(75^{\circ} \mathrm{C}\) for 1.5 h, during which the aZIF matrix is partially exchanged or infiltrated with dcIm ligand. The dcIm- treated film is then exposed to a direct- write electron beam. After development in organic solvents, the irradiated area is removed while non- irradiated area is preserved, which showed similar sensitivity compared to the reported data (Supplementary Fig. 25 and Supplementary Table S6). Furthermore, to improve compatibility with microfabrication processes, aZIF films are spin- coated on silicon wafers, and their thicknesses can be controlled by spin speed (Supplementary Fig. 26- 28). The simple fabrication for ultrathin ZIF films reported in this + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 84, 880, 128]]<|/det|> +study could accelerate the development of new ZIF- based resist materials for lithographic applications62- 64. + +<|ref|>text<|/ref|><|det|>[[117, 142, 883, 358]]<|/det|> +The method reported here can be extended to other promising MOF structures. 2D film of UiO- 66- NH2 can also be deposited by on graphene (Supplementary Fig. 29). It makes this approach reported here broad and interesting to develop a number of 2D MOF films in the future. In conclusion, we report the synthesis of ZIFs as macroscopically uniform amorphous and crystalline 2D films from an ultradilute solution. The 2DZIF film yields exceptional H2- sieving performance, thanks to the ordered 2D structure with a high density of 6- MR hosting H2- selective gap, making such a film the ultimate selective layer for membrane application. In the absence of substrate registry, ultrathin amorphous films are demonstrated, which are promising for advancing the limit of nanoscale patterning. + +<|ref|>sub_title<|/ref|><|det|>[[118, 380, 248, 397]]<|/det|> +## Online content + +<|ref|>text<|/ref|><|det|>[[118, 421, 881, 514]]<|/det|> +Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available online. + +<|ref|>sub_title<|/ref|><|det|>[[118, 537, 214, 554]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[112, 576, 884, 856]]<|/det|> +1. Bennett, T. D. & Cheetham, A. K. Amorphous Metal–Organic Frameworks, Acc. Chem. Res. 47, 1555-1562 (2014). +2. Banerjee, R. et al. High-Throughput Synthesis of Zeolitic Imidazolate Frameworks and Application to CO2 Capture, Science 319, 939-943 (2008). +3. 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Mater. 31, 2011146 (2021). + +<|ref|>text<|/ref|><|det|>[[115, 361, 880, 413]]<|/det|> +45. Sikdar, A. et al. Diffusion driven nanostructuring of metal-organic frameworks (MOFs) for graphene hydrogel based tunable heterostructures: highly active electrocatalysts for efficient water oxidation. J. Mater. Chem. A 9, 7640-7649 (2021). + +<|ref|>text<|/ref|><|det|>[[115, 423, 880, 458]]<|/det|> +46. Tao, J. et al. Controlling Metal-Organic Framework/ZnO Heterostructure Kinetics through Selective Ligand Binding to ZnO Surface Steps. Chem. Mater. 32, 6666-6675, (2020). + +<|ref|>text<|/ref|><|det|>[[115, 468, 880, 503]]<|/det|> +47. Zhu, Y. et al. Unravelling surface and interfacial structures of a metal-organic framework by transmission electron microscopy, Nat. Mater. 16, 532-536 (2017). + +<|ref|>text<|/ref|><|det|>[[115, 513, 880, 548]]<|/det|> +48. Zhang, H., Zhao, M. & Lin, Y. S. Stability of ZIF-8 in water under ambient conditions, Microporous Mesoporous Mater. 279, 201-210 (2019). + +<|ref|>text<|/ref|><|det|>[[115, 558, 880, 610]]<|/det|> +49. Motevalli, B., Taherifar, N., Wang, H. & Liu, J. Z. Ab Initio Simulations to Understand the Leaf-Shape Crystal Morphology of ZIF-L with Two-Dimensional Layered Network, J. Phys. Chem. C 121, 2221-2227 (2017). + +<|ref|>text<|/ref|><|det|>[[115, 620, 880, 655]]<|/det|> +50. Zhu, W., Li, X., Sun, Y., Guo, R., & Ding, S. Introducing hydrophilic ultra-thin ZIF-L into mixed matrix membranes for \(\mathrm{CO_2 / CH_4}\) separation, RSC Adv. 9, 2339-23399 (2019). + +<|ref|>text<|/ref|><|det|>[[115, 666, 880, 700]]<|/det|> +51. Yang, K. et al. ZIF-L membrane with a membrane-interlocked-support composite architecture for \(\mathrm{H_2 / CO_2}\) separation, Sci. Bull. 66, 1869-1876 (2021). + +<|ref|>text<|/ref|><|det|>[[115, 711, 880, 762]]<|/det|> +52. Tan, J. C., Bennett, T. D., Cheetham, A. K. Chemical structure, network topology, and porosity effects on the mechanical properties of Zeolitic Imidazolate Frameworks, Proc. Natl. Acad. Sci. 107, 9938-9943 (2010). + +<|ref|>text<|/ref|><|det|>[[115, 774, 880, 825]]<|/det|> +53. He, G. et al. High-permeance polymer-functionalized single-layer graphene membranes that surpass the postcombustion carbon capture target, Energy Environ. Sci. 12, 3305-3312 (2019). + +<|ref|>text<|/ref|><|det|>[[115, 836, 880, 871]]<|/det|> +54. Bennett, T. D. & Horike, S. Liquid, glass and amorphous solid states of coordination polymers and metal–organic frameworks, Nat. Rev. Mater. 3, 431-440 (2018). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 885, 120]]<|/det|> +55. Kosma, V., De Simone, D., Vandenberghe, G. Metal Based Materials for EUV Lithography, J. Photopolymer. Sci. Technol. 32, 179-183 (2019). + +<|ref|>text<|/ref|><|det|>[[117, 128, 881, 183]]<|/det|> +56. Conrad, S. et al. Controlling Dissolution and Transformation of Zeolitic Imidazolate Frameworks by using Electron-Beam-Induced Amorphization, Angew. Chem. Int. Ed. 57, 13592-13597 (2018). + +<|ref|>text<|/ref|><|det|>[[117, 192, 880, 227]]<|/det|> +57. Stowers, J. & Keszler, D. A. High resolution, high sensitivity inorganic resists, Microelectron. Eng. 86, 730-733 (2009). + +<|ref|>text<|/ref|><|det|>[[117, 236, 880, 272]]<|/det|> +58. Miao, Y., Tsapatsis, M. Electron Beam Patterning of Metal-Organic Frameworks. Chem. Mater. 33, 754-760 (2021). + +<|ref|>text<|/ref|><|det|>[[117, 281, 880, 317]]<|/det|> +59. Oleksak, R. P. et al. Chemical and Structural Investigation of High-Resolution Patterning with HafSOx, ACS Appl. Mater. Interfaces 6, 2917-2921 (2014). + +<|ref|>text<|/ref|><|det|>[[117, 326, 880, 362]]<|/det|> +60. Ghosh, S. et al. Chem. Mater. Two distinct stages of structural modification of ZIF-L MOF under electron-beam irradiation. 33, 5681-5689 (2021). + +<|ref|>text<|/ref|><|det|>[[117, 371, 880, 407]]<|/det|> +61. Luo, C. et al. Review of recent advances in inorganic photoresists, RSC Adv. 10, 8385-8395 (2020). + +<|ref|>text<|/ref|><|det|>[[117, 415, 881, 452]]<|/det|> +62. Manouras, T. & Argitis, P. High Sensitivity Resists for EUV Lithography: A Review of Material Design Strategies and Performance Results, Nanomaterials 10, 1593 (2020). + +<|ref|>text<|/ref|><|det|>[[117, 460, 880, 496]]<|/det|> +63. Gangaik, A. S., Georgiev, Y. M. & Holmes, J. D. New Generation Electron Beam Resists: A Review, Chem. Mater. 29, 1898-1917 (2017). + +<|ref|>text<|/ref|><|det|>[[117, 505, 880, 541]]<|/det|> +64. Ghash, S. et al. Two Distinct Stages of Structural Modification of ZIF-L MOF under Electron-Beam Irradiation, Chem. Mater. 33, 5681-5689 (2021). + +<|ref|>sub_title<|/ref|><|det|>[[118, 590, 196, 606]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[118, 628, 211, 644]]<|/det|> +## Chemicals + +<|ref|>text<|/ref|><|det|>[[117, 655, 883, 870]]<|/det|> +Zinc nitrate hexahydrate \(\mathrm{(Zn(NO_3)_2\cdot 6H_2O)}\) was purchased from Sigma-Aldrich. 2- methylimidazole (2-mIm) was from Chemie Brunschwig AG. HCl (32 wt%) was purchased from Reactolab S.A.. poly[1-(trimethylsilyl)-1-propylene] (PTMSP) was from ABCR. \(\mathrm{FeCl_3}\) (97%) and \(\mathrm{Na_2S_2O_8}\) was bought from Sigma-Aldrich. Cu foil (50 mm, 99.9%) were purchased from STREM. Toluene (AR) and methanol (AR) were from Fischer. All chemicals were used without further purifications. \(\mathrm{Si / SiO_2}\) wafers were purchased from University Wafer Inc. \(\mathrm{Si / SiO_2}\) wafer with single layer graphene was bought from Ted Pella. Highly oriented pyrolytic graphite (HOPG) (ZYA quality, GRAS/1.0x7x7) was purchased from ScanSens. Silicon nitride TEM supports (50 nm silicon nitride film on a \(200\mu \mathrm{m}\) silicon frame with nine viewing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 83, 880, 127]]<|/det|> +windows, each \(0.1 \times 0.1 \mathrm{mm}\) were purchased from Ted Pella. Sapphire (Al₂O₃) wafer (Z- cut, \(10 \mathrm{cm} \times 0.5 \mathrm{mm}\) ) was purchased from MTI. + +<|ref|>sub_title<|/ref|><|det|>[[118, 143, 446, 161]]<|/det|> +## Characterizations and Measurements + +<|ref|>text<|/ref|><|det|>[[117, 177, 883, 319]]<|/det|> +SEM measurements were performed on a Teneo Scanning Electron Microscope operating at 1 kV. PXRD data were collected at a Bruker D8 Discover diffractometer with a Lynxeye XE detector, operated at \(40 \mathrm{kV}\) , \(400 \mathrm{mA}\) for Cu Kα \((\lambda = 1.5406 \mathrm{\AA})\) at ambient temperature and pressure. Bright- field TEM images and selected- area electron diffraction (SAED) images were obtained with a Talos F200X microscope operated at \(200 \mathrm{kV}\) . TEM images for patterns on silicon nitride were obtained on a ThermoFisher TF30 TEM operating at \(300 \mathrm{kV}\) . + +<|ref|>text<|/ref|><|det|>[[116, 333, 883, 550]]<|/det|> +Low- dose AC- HRTEM was performed on a Cs- corrected FEI cubed G2 Titan 60- 300 electron microscope at \(300 \mathrm{kV}\) , using a Gatan K2 direct- detection camera in electron counting mode. The AC- HRTEM images were acquired with the dose fractionation function, and each image stack is composed of 120 frames with \(0.05 \mathrm{s}\) exposure for each frame, with a total electron dose of \(\sim 60 \mathrm{e}^{- }\mathrm{\AA}^{- 2}\) . The raw image was denoised by using an average background subtraction filter (ABSF). The CTF correction was performed based on the defocus value determined from the amorphous thon rings in the Fourier transform, and the projected electrostatic potential was simulated by the QSTEM software (QSTEM V2, 31). Simulated ED pattern was carried out by using Singlecrystal module of CrystalMaker software. + +<|ref|>text<|/ref|><|det|>[[117, 564, 883, 707]]<|/det|> +AFM images and modulus measurement were recorded on a Bruker MultiMode 8 AFM. For modulus measurement, a Bruker Tap525A rectangular probe was used and calibrated with standard sample sapphire, polystyrene and HOPG. XPS were carried out on an Axis Supra (Kratos Analytical) using the monochromated K x- ray line of an aluminum anode. Synchrotron GIXRD was carried out at beamline BM01, Swiss- Norwegian beamline (SNBL) at the European Synchrotron Radiation Facility (ESRF) with wavelength of \(0.683 \mathrm{\AA}\) and \(0.960 \mathrm{\AA}\) . + +<|ref|>text<|/ref|><|det|>[[117, 721, 883, 863]]<|/det|> +The gas separation performance of all membranes was recorded on a homemade permeation set- up. The pressure on the feed side was maintained at 2- 8 bar and on the permeate side at 1 bar during the measurements. All measurements were done after reaching the steady state with argon as the sweep gas. The membranes were sealed with stainless- steel gasket. The composition of permeate was analysed using an online Hiden Analytical HPR- 20 mass spectrometer. + +<|ref|>text<|/ref|><|det|>[[118, 879, 551, 897]]<|/det|> +The permeances, \(J_{i}\) , of gas \(i\) was calculated by Eq. S1 + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[367, 83, 692, 102]]<|/det|> +\[J_{i} = X_{i} / (A\cdot \Delta P_{i}) \quad (S1)\] + +<|ref|>text<|/ref|><|det|>[[115, 117, 881, 186]]<|/det|> +where \(X_{i}\) is the molar flow rate of component \(i\) across the membrane area, \(A\) and \(\Delta P_{i}\) is the transmembrane pressure difference for the component \(i\) . The selectivity \(\alpha_{ij}\) of two gases ( \(i\) and \(j\) , where \(i\) is the faster permeating gas) was calculated by Eq. S2 + +<|ref|>equation<|/ref|><|det|>[[392, 200, 696, 220]]<|/det|> +\[\alpha_{ij} = J_{i} / J_{j} \quad (S2)\] + +<|ref|>sub_title<|/ref|><|det|>[[118, 235, 376, 253]]<|/det|> +## Synthesis of 2DZIF/aZIF film + +<|ref|>text<|/ref|><|det|>[[117, 268, 882, 362]]<|/det|> +In a petri dish containing \(29~\mathrm{ml}\) of \(\mathrm{Zn(NO_3)_2}\) aqueous solution at room temperature, the substrate (HOPG, graphene/Si/SiO \(_2\) for 2DZIF and Si/SiO \(_2\) for aZIF) was partially immersed. Then \(1\mathrm{ml}\) of \(2\mathrm{- mlm}\) aqueous solution was added. After a certain time, the substrate was removed to stop the reaction. + +<|ref|>sub_title<|/ref|><|det|>[[118, 377, 545, 396]]<|/det|> +## Synthesis of 2DZIF membrane for gas separation + +<|ref|>text<|/ref|><|det|>[[117, 410, 883, 553]]<|/det|> +First, single- layer graphene was synthesized by using low- pressure CVD of methane on copper foil following the literature. Before the synthesis, the copper foil was annealed at \(1077^{\circ}\mathrm{C}\) in a \(\mathrm{H}_2 / \mathrm{Ar}\) atmosphere for \(60~\mathrm{min}\) . Then, \(\mathrm{CO_2}\) ( \(100~\mathrm{mL / min}\) ) and \(\mathrm{H}_2\) ( \(8~\mathrm{mL / min}\) ) flow was introduced successively, each for \(30~\mathrm{min}\) , to remove the contaminations. At last, \(\mathrm{CH_4}\) ( \(24~\mathrm{mL / min}\) ) and \(\mathrm{H}_2\) ( \(8~\mathrm{mL / min}\) ) flow was used to grow single- layer graphene on copper film for \(30~\mathrm{min}\) at pressure of \(460~\mathrm{mTorr}\) . + +<|ref|>text<|/ref|><|det|>[[116, 567, 883, 907]]<|/det|> +After the synthesis of single- layer graphene, an \(\mathrm{O_2}\) plasma (MTI plasma cleaner, EQ- PCE- 3, \(13.56\mathrm{MHz}\) , \(17\mathrm{W}\) ) was carried out to introduce nanopores for application of 2DZIF film as selective layer in the membrane. Briefly, the atmosphere in the plasma chamber was exchanged by \(\mathrm{O_2}\) flow to pressure around \(50\mathrm{mTorr}\) . Then a plasma was generated for \(4\mathrm{s}\) to etch SLG to obtain NG. After the plasma, a solution of PTMSP in toluene ( \(1.25\mathrm{wt}\%\) ) was spin- coated on NG at \(1000\mathrm{rpm}\) for \(30\mathrm{s}\) and \(2000\mathrm{rpm}\) for \(30\mathrm{s}\) , respectively. The sample was dried in ambient air at room temperature overnight. Then, copper foil was etched by a combination of \(\mathrm{FeCl_3}\) ( \(0.5\mathrm{M}\) in water), HCl ( \(0.1\mathrm{M}\) in water) and water. The floating graphene/PTMSP film was transferred on the surface of a \(29\mathrm{ml}\) of aqueous solution of \(\mathrm{Zn(NO_3)_2}\) ( \(2\mathrm{mmol / L}\) ) in a petri dish, and an aqueous solution of \(2\mathrm{- mlm}\) ( \(1\mathrm{mL}\) , \(0.5\mathrm{mol / L}\) ) was added in. After \(2\mathrm{min}\) of reaction the resulted 2DZIF/graphene/PTMSP film was transferred to substrate (e.g., macroporous W support hosting \(5\mu \mathrm{m}\) holes over a \(1\mathrm{mm}^2\) area for membranes) for further characterizations or applications. For the synthesis of centimeter- scale 2DZIF membrane, \(1\mathrm{wt}\%\) of Teflon AF in GOLDEN perfluorinated fluid was spin- coated on NG at \(300\mathrm{rpm}\) for \(60\mathrm{s}\) , and heated at \(60^{\circ}\mathrm{C}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 882, 200]]<|/det|> +for \(3\mathrm{h}\) . After that, the sample was put into a homemade membrane module (Supplementary Fig. 19) while Cu foil facing up. This allowed etching of Cu foil in the membrane module by \(10\mathrm{wt}\% \mathrm{Na}_2\mathrm{S}_2\mathrm{O}_8\) aqueous solution exposing NG surface for 2DZIF synthesis. Finally, 2DZIF film was synthesized by exposing NG to growth solution for \(10\mathrm{min}\) at room temperature (2 mM \(\mathrm{Zn(NO_3)_2}\) and \(16\mathrm{mM}2\mathrm{- }\mathrm{mM}\) ). + +<|ref|>sub_title<|/ref|><|det|>[[118, 216, 572, 235]]<|/det|> +## Sample preparation of 2DZIF on graphene for AFM + +<|ref|>text<|/ref|><|det|>[[117, 250, 882, 367]]<|/det|> +For the AFM sample preparation of 2DZIF film on graphene/PTMSP substrate, the 2DZIF/graphene/PTMSP film was transferred on \(\mathrm{Si / SiO_2}\) wafer with 2DZIF layer facing the wafer. Then the sample was annealed at \(70^{\circ}\mathrm{C}\) for \(4\mathrm{h}\) , to increase the adhesion between film and \(\mathrm{Si / SiO_2}\) wafer. After that, the sample was immersed in toluene for \(12\mathrm{h}\) , to remove PTMSP layer. + +<|ref|>text<|/ref|><|det|>[[117, 382, 882, 549]]<|/det|> +The AFM image of 2DZIF film with triangular morphology after \(5\mathrm{min}\) of water etching was collected directly on the film with 2DZIF layer facing up. Specifically, the 2DZIF/graphene/PTMSP film with triangular morphology was first scooped by glass slide with 2DZIF layer facing the slide, and a \(\mathrm{Si / SiO_2}\) wafer attached with double- sided carbon tape was pressed onto PTMSP layer. As a result, the 2DZIF/graphene/PTMSP film was transferred onto \(\mathrm{Si / SiO_2}\) wafer, resulting in 2DZIF layer facing up. AFM measurement of 2DZIF film on HOPG, \(\mathrm{Si / SiO_2}\) and graphene/ \(\mathrm{Si / SiO_2}\) was carried out directly on the sample without any treatment. + +<|ref|>sub_title<|/ref|><|det|>[[119, 565, 375, 583]]<|/det|> +## Sample preparation for TEM + +<|ref|>text<|/ref|><|det|>[[118, 597, 882, 690]]<|/det|> +Similar to sample preparation for AFM, 2DZIF/graphene/PTMSP film was transferred on TEM grid with 2DZIF layer facing the TEM grid. Then the sample was annealed at \(70^{\circ}\mathrm{C}\) for \(4\mathrm{h}\) , to increase the adhesion between film and TEM grid. After that, the sample was immersed in toluene for \(12\mathrm{h}\) , to remove the PTMSP layer. + +<|ref|>text<|/ref|><|det|>[[118, 706, 882, 798]]<|/det|> +For the electron patterning sample, aZIF layer was grown on a silicon nitride supports. Prior to the ZIF deposition, the silicon nitride supports were treated with oxygen plasma for \(10\mathrm{min}\) (29.6 W, \(400\mathrm{mTorr}\) oxygen pressure) in a plasma cleaner (Harrick Plasma) to improve the surface reactivity. + +<|ref|>sub_title<|/ref|><|det|>[[119, 814, 461, 832]]<|/det|> +## Electron beam patterning of aZIF films + +<|ref|>text<|/ref|><|det|>[[118, 847, 881, 891]]<|/det|> +For negative tone patterning, aZIF films were exposed to electron beam using a Thermo Fisher Helios G4 UC Dual Beam microscope operating at \(20\mathrm{kV}\) accelerating voltage and \(400\mathrm{pA}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 83, 881, 127]]<|/det|> +beam current. The areal doses were \(80\mathrm{mC / cm^2}\) for all patterns. After exposure, the aZIF films were developed in deionized water for \(24\mathrm{h}\) and blow dried in a stream of nitrogen gas. + +<|ref|>text<|/ref|><|det|>[[117, 141, 883, 284]]<|/det|> +For positive tone patterning, the aZIF films were first placed in a \(60\mathrm{mL}\) PFA vessel with a bed of \(0.2\mathrm{g}4,5\) - dichloroimidazole (dcIm) and heated at \(75^{\circ}\mathrm{C}\) for \(1.5\mathrm{h}\) . The dcIm- treated aZIF was then exposed to electron beam using a Thermo Fisher Helios G4 UC Dual Beam microscope operating at \(20\mathrm{kV}\) accelerating voltage and \(100\mathrm{pA}\) beam current. The areal doses were \(2\mathrm{mC / cm^2}\) for all patterns. After exposure, the aZIF films were immersed in NMP and acetone each for \(10\mathrm{s}\) and blow dried in a stream of nitrogen gas. + +<|ref|>sub_title<|/ref|><|det|>[[118, 299, 496, 318]]<|/det|> +## Spin-coating of aZIF films on silicon wafers + +<|ref|>text<|/ref|><|det|>[[117, 332, 883, 500]]<|/det|> +Aqueous solutions of \(\mathrm{Zn(NO_3)_2}\) (4 mmol/L) and 2- Im (32 mmol/L) were mixed in a T connector (0.5 mm I.D.) at \(1\mathrm{mL / min}\) injection rate, respectively, using a two- channel syringe pump. The mixed solution was spin- coated on silicon wafers while spinning at speeds between 500 and 2000 rpm for \(1\mathrm{min}\) , followed by \(10\mathrm{s}\) spinning at 2000 rpm to completely dry the film. The spin- coated films were exposed to electron beam using line patterns of \(100\mathrm{nm}\) width and \(400\mathrm{nm}\) spacing and developed in deionized water for \(24\mathrm{h}\) . The height of the line patterns were measured to determine the thickness of spin- coated films. + +<|ref|>sub_title<|/ref|><|det|>[[118, 516, 306, 533]]<|/det|> +## Structural simulation + +<|ref|>text<|/ref|><|det|>[[117, 548, 883, 862]]<|/det|> +The simulation of 2DZIF structure was carried out by the DFT calculations. At first, the reported ZIF- L structure was imported into Forcite module in Material Studio software (Accelrys, San Diego, CA) to calculate the initial structural model, and unit- cell was set to be orthorhombic and \(a = 24.0\mathrm{\AA}\) , \(b = 20.0\mathrm{\AA}\) , \(c = 20.0\mathrm{\AA}\) , \(\alpha = 90^{\circ}\) , \(\beta = 90^{\circ}\) , \(\gamma = 90^{\circ}\) , respectively, while the connectivity was kept. The calculation task was geometry optimization. The quality was set to be fine, and 'smart' algorithm was selected. After that, van der Waals DFT calculations were performed using the Quantum ESPRESSO package66,67. The Brillouin zone was sampled at the gamma point. An energy cutoff of 60 Ry was used for the plane wave expansion of the wavefunctions. A kinetic energy cutoff of 480 Ry on the charge was used together with ultra- soft pseudopotentials68,69. The relaxation was performed with the Perdew- Burke- Ernzerhof (PBE) functional70. The system was relaxed to the lowest energy configuration of atoms. The surface geometry had been optimized with the convergence thresholds of \(1 \times 10^{-4}\mathrm{Ry}\) and \(3.28 \times 10^{-3}\mathrm{Ry / Bohr}\) for the total energy and forces, respectively. + +<|ref|>sub_title<|/ref|><|det|>[[118, 878, 262, 895]]<|/det|> +## Data availability + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 881, 151]]<|/det|> +Data supporting the findings in the present work are available in the manuscript or Supplementary Information. Additional data are available from the corresponding author upon reasonable request. + +<|ref|>text<|/ref|><|det|>[[117, 171, 881, 216]]<|/det|> +65. Huang, S. et al. Millisecond lattice gasification for high-density \(\mathrm{CO_2}\) - and \(\mathrm{O_2}\) -sieving nanopores in single-layer graphene. Sci. Adv. 7: eabf0116 (2021). + +<|ref|>text<|/ref|><|det|>[[117, 234, 881, 279]]<|/det|> +66. Giannozzi, P. et al. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter 21, 390052 (2009). + +<|ref|>text<|/ref|><|det|>[[117, 297, 881, 342]]<|/det|> +67. Giannozzi, P. et al. Advanced capabilities for materials modelling with Quantum ESPRESSO. J. Phys. Condens. Matter 29, 465901 (2017). + +<|ref|>text<|/ref|><|det|>[[117, 361, 880, 405]]<|/det|> +68. Lejaeghere, K. et al. Reproducibility in density functional theory calculations of solids. Science 351, 6280 (2016). + +<|ref|>text<|/ref|><|det|>[[117, 424, 881, 469]]<|/det|> +69. Prandini, G., Marrazzo, A., Castelli, I. E., Mounet, N. & Marzari, N. Precision and efficiency in solid-state pseudopotential calculations. npj Comput. Mater. 4, 72 (2018). + +<|ref|>text<|/ref|><|det|>[[117, 488, 880, 532]]<|/det|> +70. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865-3868 (1996). + +<|ref|>text<|/ref|><|det|>[[115, 551, 883, 816]]<|/det|> +Acknowledgments The authors acknowledge Dr. Dmitry Chernyshov and Dr. Diadkin Vadim at beamline BM01, the-Swiss-Norwegian Beamlines (SNBL), European Synchrotron Radiation Facility (ESRF) for assistance with synchrotron GIXRD experiments (doi:10.15151/ESRF-ES-670011338. ), Dr. Pascal Alexander Schouwink from EPFL for the help of XRD data. Dr. Mounir Mensi and Mojtaba Rezaei from EPFL for the help of XPS and AFM. This project is primarily supported by European Research Council Starting Grant (805437-UltimateMembranes). Parts of the work was supported by Swiss National Science Foundation (SNSF) Assistant Professor Energy Grant (PYAPP2_173645) and SNSF project (514601); Y. M. and M. T. acknowledge funding by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-SC0021212. + +<|ref|>text<|/ref|><|det|>[[117, 860, 881, 905]]<|/det|> +Author contributions K. A. and Q. L. conceived the project and wrote the manuscript. Q. L., Y. M., D. B., and J. H. prepared the samples. Q. L., Y. M. and S. L. performed AFM + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 882, 225]]<|/det|> +measurements. Q. L. and Y. M. collected SEM data. Q. L. performed XRD measurement. S. L. collected XPS data. L. V. and H. C. collected TEM images and ED data. Q. L. and M. V. carried out structural simulations. C. C. and Y. H. helped with AC- HRTEM images. Q. L. and S. S. carried out gas permeance experiments. M. T. conceived and Y. M. performed the aZIF film deposition and e- beam patterning experiments. All authors discussed the results and commented on the manuscript. + +<|ref|>text<|/ref|><|det|>[[118, 238, 880, 258]]<|/det|> +Competing interests: A patent application based on the findings reported here has been filed. + +<|ref|>sub_title<|/ref|><|det|>[[119, 302, 320, 320]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[117, 333, 882, 444]]<|/det|> +Supplementary information The online version contains supplementary material available at Correspondence and requests for materials should be addressed to Kumar Varoon Agrawal. Peer review information Nature thanks anonymous reviewer(s) for their contribution to the peer review of this work. + +<|ref|>text<|/ref|><|det|>[[117, 454, 830, 473]]<|/det|> +Reprints and permissions information is available at http://www.nature.com/reprints. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 92, 765, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 130, 137, 149]]<|/det|> +- Sl.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/images_list.json b/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..36fd67c1d80e19add36f0fcf434ed914e499f2ef --- /dev/null +++ b/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Overview of the scaffold hopping approach. SAR and crystal structures of selected benzyl and aniline analogs. A) Crystal structure of compound 127 (yellow sticks) with 14-3-3o (grey surface) and phospho-ERα peptide (orange sticks). Interacting aminoacids are shown as sticks and water molecules as red spheres (PDB 8ALW). B) Ligand overlay of compound 127 and docking pose of the new MCR scaffold. C) General MCR scaffold and main points of variation. D) Overview of general synthetic routes. Detailed experimental conditions are described in the SI. E) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. F) Crystal structure overlay for compounds 1 (cyan sticks) and 127 (yellow sticks) bound to 14-3-3o (grey surface) /ERα (orange sticks). G) Crystal structure of compound 1 (cyan sticks) with 14-3-3o/ERα. Interacting aminoacid residues are shown as sticks and interacting water molecules as red spheres. H-I) Structural overlays of compounds 2 (brown sticks), and 10 (dark red sticks) with compound 1 (cyan sticks).", + "footnote": [], + "bbox": [ + [ + 123, + 145, + 890, + 797 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. SAR and crystal structures of selected double-ortho substituted analogs. A) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-E) Crystal structures of 14-3-3o/ERα with compounds 17 (dark pink sticks), 19 (teal sticks) and overlays of crystal structures for compounds 17 (dark pink sticks), 20 (pale green sticks), 21 (pale purple sticks) and 25 (bright yellow sticks).", + "footnote": [], + "bbox": [ + [ + 116, + 135, + 886, + 441 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. SAR and crystal structures of analogs substituted in positions X and Y. A) MS bar graphs at 1 \\(\\mu \\mathrm{M}\\) . For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-E) Crystal structure of 14-3-3o/ERα with compound 28 (dark yellow sticks), and overlays of crystal structures for compounds 28 (dark yellow sticks), 32 (pink sticks) and 33 (emerald green sticks) with 21 (pale purple sticks).", + "footnote": [], + "bbox": [ + [ + 120, + 320, + 884, + 636 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. SAR and crystal structures of 2,6-di-Me analogs substituted in positions X and W. Biophysical data (MS, TR-FRET, SPR) and cell data (NanoBRET). A) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-C) Overlays of crystal structures of 14-3-3α/ERα ternary complexes with compounds 40 (orange sticks), 41 (red sticks), and 42 (blue sticks). D) TR-FRET schematic and protein titration data for representative compounds at 100 μM compound or DMSO. E) SPR data for the binary 14-3-3α/ERα interaction and ternary interactions with 181, 17 and 41 (mean +/- SD, n=2). F-G) 14-3-3α-HaloTag/Nuc-ERα NanoBRET assay in HEK293T cells with compound titrations (1:2 dilution, starting at 40 μM). Data points excluded where compound dosage was toxic to the cells. MCR compounds compared to the previously described stabilizer 181 and 85, an inactive compound as the negative control. Bar graphs quantifying pEC50 values.", + "footnote": [], + "bbox": [ + [ + 95, + 90, + 925, + 740 + ] + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2.mmd b/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1085aea904be54d3c91eb0f44c1ec803bd3d92b0 --- /dev/null +++ b/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2.mmd @@ -0,0 +1,292 @@ + +# Rapid scaffold-hopping for molecular glues: from fragments to cell-active probes targeting the 14-3-3/ERα complex + +Michelle Arkin michelle.arkin@ucsf.edu + +University of California at San Francisco https://orcid.org/0000- 0002- 9366- 6770 + +Markella Konstantinidou University of California San Francisco https://orcid.org/0000- 0001- 5972- 4140 + +Marios Zingiridis University of Crete https://orcid.org/0009- 0008- 1150- 2926 + +Marloes Pennings Eindhoven University of Technology https://orcid.org/0000- 0002- 3366- 0238 + +Michael Fragkiadakis University of Crete + +Johanna Virta University of California San Francisco + +Jezrael Revalde University of California, San Francisco + +Emira Visser TU Eindhoven + +Christian Ottmann Eindhoven University of Technology https://orcid.org/0000- 0001- 7315- 0315 + +Luc Brunsveld TU Eindhoven https://orcid.org/0000- 0001- 5675- 511X + +Constantinos Neochoritis University of Crete https://orcid.org/0000- 0001- 5098- 5504 + +## Article + +Keywords: covalent, estrogen receptor, MCR, molecular glue, 14- 3- 3 + +Posted Date: February 28th, 2025 + +DOI: https://doi.org/10.21203/rs.3.rs- 6051794/v1 + +<--- Page Split ---> + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: Yes there is potential Competing Interest. M.R.A, C.O, and L.B. are co- founders of Ambagon Therapeutics. C.O. is an employee of Ambagon Therapeutics. + +Version of Record: A version of this preprint was published at Nature Communications on July 14th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 61176- 4. + +<--- Page Split ---> + +# Rapid scaffold-hopping for molecular glues: from fragments to cell-active probes targeting the 14-3-3/ERα complex + +Markella Konstantinidou\*[a], Marios Zingiridis[b], Marloes A.M. Pennings[c], Michael Fragkiadakis[b], Johanna M. Virta[a], Jezrael L. Revalde[a], Emira J. Visser[c], Christian Ottmann[c], Luc Brunsveld[c], Constantinos G. Neochoritis\*[b], Michelle R. Arkin\*[a] + +[a] M. Konstantinidou, J.M. Virta, J.L. Revalde, M.R. Arkin Department of Pharmaceutical Chemistry and Small Molecule Discovery Centre (SMDC) University of California San Francisco (UCSF) CA 94143 (USA) E- mail: markella.constantinidou@ucsf.edu, michelle.arkin@ucsf.edu [b] M. Zingiridis, M. Fragkiadakis, C.G. Neochoritis Department of Chemistry University of Crete Voutes, Heraklion, 70013, Greece E- mail: kneochor@uoc.gr [c] M.A.M. Pennings, E.J. Visser, C. Ottmann, L. Brunsveld Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems (ICMS) Eindhoven University of Technology 5600 MB Eindhoven, The Netherlands + +Abstract: Molecular glues, small molecules that bind cooperatively at a protein- protein interface, have emerged as powerful modalities for the modulation of protein- protein interactions (PPIs) and "undruggable" targets. The systematic identification of new chemical matter with a molecular glue mechanism of action remains a significant challenge in drug discovery. Here, we present a scaffold hopping approach, using as a starting point our previously developed molecular glues for the native 14- 3- 3/estrogen receptor alpha (ERα) complex. The novel, computationally designed scaffold was based on the Groebke- Blackburn- Bienaymé multi- component reaction (MCR), leading to drug- like analogs with multiple points of variation, thus enabling the rapid derivatization and optimization of the scaffold. Structure- activity relationships (SAR) were developed using intact mass spectrometry and TR- FRET. Rational structure- guided optimization was facilitated by crystal structures of ternary complexes with the glues, 14- 3- 3 and phospho- peptides mimicking the highly disordered C- terminus of ERα. We measured the kinetics of 14- 3- 3/ERα peptide binding by SPR, using a format in which a 14- 3- 3/molecular glue complex was immobilized on the SPR chip. The most potent compounds stabilized the complex by 100- fold and increased the residence time by 14- fold. Cellular stabilization of 14- 3- 3/ERα for the most potent analogs was confirmed using a NanoBRET assay with full- length proteins in live cells (EC50 = 2.7 – 5 μM). Our approach highlights the potential of MCR chemistry, combined with scaffold hopping, to drive the development and optimization of unprecedented molecular glue scaffolds. + +## Introduction + +The stabilization of native protein- protein interactions (PPIs) with small molecules offers an attractive strategy for the activation or inhibition of signaling pathways in a therapeutic context. \(^{1,2}\) PPIs were traditionally considered difficult targets due to the lack of well- defined pockets and the presence of large, hydrophobic surfaces. \(^{3 - 5}\) The fundamental understanding of the mechanism of action of molecular glues – small molecules that bind cooperatively at PPI interfaces and strengthen weak, pre- existing interactions – has enabled the stabilization of PPIs by taking into account the elements of cooperativity, molecular recognition and shape complementarity. \(^{6,7}\) + +A particularly challenging class of PPIs includes proteins that are intrinsically disordered and only become partially structured when bound to a protein partner, such as a chaperone binding to a client protein. \(^{8,9}\) 14- 3- 3 is an abundant scaffolding protein that recognizes specific phospho- serine or phospho- threonine motifs on disordered domains of the client and upon binding creates a structured binding interface. \(^{10}\) Molecular glues targeting 14- 3- 3/client complexes bind + +<--- Page Split ---> + +to the composite surface formed at this interface; the inherent cooperativity of this approach yields molecular glues with selectivity and potency. Of note, 14- 3- 3 proteins lack function – the function is instead encoded on the client protein and in particular on the phospho- site that is being recognized, leading either to activation or inhibition of signaling pathways. \(^{11}\) + +Among the extensive interactome of 14- 3- 3, here we focus on its native interaction with the hormone regulated transcription factor estrogen receptor \(\alpha\) (ERα). 14- 3- 3 recognizes the protein sequence surrounding phospho- T594 on the disordered C- terminus on the F- domain of ERα and acts as a negative regulator by inhibiting ERα binding to chromatin and blocking ERα- mediated transcription. \(^{12,13}\) To date, ERα small molecule drugs, acting either as inhibitors or degraders, target the adjacent ligand binding domain (LBD). \(^{14 - 17}\) However, mutations in the LBD are often associated with acquired endocrine resistance. \(^{18}\) Thus, stabilization of the native 14- 3- 3/ERα PPI could be useful as an alternative strategy to block ERα transcriptional activity in ERα positive breast cancer, especially in cases of acquired endocrine resistance. The feasibility of this approach, targeting the F- domain, is corroborated by studies using the natural product fusicoccin- A (FC- A) and its semi- synthetic analogs that stabilize the interaction between 14- 3- 3 and the C- terminus of ERα. \(^{19}\) We now require drug- like chemical probes to define the biological impact of targeting the F- domain to inhibit ERα in hormone- positive breast cancer. + +We have applied different strategies for the identification of chemical matter to stabilize the 14- 3- 3/ERα complex. We used a site- directed fragment- based technology, termed "disulfide tethering" with intact mass spectrometry as the readout to identify reversible fragments bound at the native cysteine (C38) of 14- 3- 3α in the presence of a phosphorylated peptide that represented the disordered C- terminus of ERα. \(^{20}\) Rational, structure- guided optimization of the reversible disulfide fragments led to irreversibly covalent, selective molecular glues that bound at the composite surface of 14- 3- 3α/ERα. \(^{21}\) For the development of non- covalent molecular glues, we used a fragment- linking approach, derived from the crystal structures of two diverse fragments that were identified in crystallographic and disulfide tethering screens. \(^{22}\) Thus, the 14- 3- 3/ERα PPI has served as a valuable system to test diverse molecular- glue discovery strategies. + +Here, we present a scaffold- hopping approach based on multi- component reaction chemistry (MCR). Multi- component reactions are defined as synthetic approaches where at least three starting materials react in a single step to form a complex scaffold, where most of the atoms contribute to the newly formed product. This broad definition covers reactions with various synthetic mechanisms. \(^{23,24}\) MCR chemistry, due to its highly divergent character, is an enticing strategy for developing new scaffolds and rapid structure- activity relationships (SAR), as it allows the combination of short synthetic routes with high diversity and complexity. MCR has emerged as an attractive alternative to multistep linear convergent synthetic approaches and has been successfully applied to the synthesis of active pharmaceutical ingredients (API). \(^{25 - 30}\) Here, we describe our strategy for the development of a drug- like MCR scaffold stabilizing the 14- 3- 3α/ERα complex. The most potent analogs of the series showed efficacy in orthogonal biophysical assays and cell- based PPI stabilization in the low micromolar range. + +## Results + +## Structure activity relationships (SAR) + +Structurally, 14- 3- 3 binds ERα by recognizing phospho- T594, the penultimate residue on the C- terminus of ERα. This creates a large, open, solvent- exposed pocket that can accommodate a small molecule. Although steric factors are not an issue for targeting the composite surface of the 14- 3- 3/ERα complex, we found it was important to rigidify an initially flexible scaffold to maximize the stabilization effect. \(^{21}\) Our aim in this work was to design a scaffold that would be more rigid from the beginning, locking in a favorable three- dimensional shape complementary to the large pocket. + +To this end, we used the freely accessible software AnchorQuery™, which performs pharmacophore- based screening of approximately 31 million compounds that are readily synthesizable through one- step multi- component reaction (MCR) chemistry. \(^{31,32}\) Although AnchorQuery™ was originally developed for PPI inhibitors \(^{33}\) , in this case it was successful in proposing MCR scaffolds for PPI stabilizers. AnchorQuery™ requires a ligand- bound crystal structure or docked binding pose as a starting point. We used the crystal structure of the previously disclosed compound 127 (PDB 8ALW) that was bound at the composite surface of the 14- 3- 3α/ERα complex, with a favorable ligand conformation, based on our biophysical data. \(^{21}\) The compound formed multiple favorable interactions both with 14- 3- 3α and the phospho- ERα peptide (Fig 1A). In the co- crystal structure, the irreversible chloroacetamide warhead of compound 127 + +<--- Page Split ---> + +formed a covalent bond with C38 of 14- 3- 3o. The \(p\) - chloro-phenyl ring occupied a small hydrophobic pocket that formed a halogen bond with K122 of 14- 3- 3. The tetrahydropyrane ring adopted a favorable conformation that allowed the formation of hydrophobic interactions with 14- 3- 3 residues (L218, I219), the terminal Val595 of ERα, and a water- + +![](images/Figure_1.jpg) + +
Figure 1. Overview of the scaffold hopping approach. SAR and crystal structures of selected benzyl and aniline analogs. A) Crystal structure of compound 127 (yellow sticks) with 14-3-3o (grey surface) and phospho-ERα peptide (orange sticks). Interacting aminoacids are shown as sticks and water molecules as red spheres (PDB 8ALW). B) Ligand overlay of compound 127 and docking pose of the new MCR scaffold. C) General MCR scaffold and main points of variation. D) Overview of general synthetic routes. Detailed experimental conditions are described in the SI. E) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. F) Crystal structure overlay for compounds 1 (cyan sticks) and 127 (yellow sticks) bound to 14-3-3o (grey surface) /ERα (orange sticks). G) Crystal structure of compound 1 (cyan sticks) with 14-3-3o/ERα. Interacting aminoacid residues are shown as sticks and interacting water molecules as red spheres. H-I) Structural overlays of compounds 2 (brown sticks), and 10 (dark red sticks) with compound 1 (cyan sticks).
+ +<--- Page Split ---> + +mediated hydrogen bond from the oxygen atom in the ring, which was part of a large water network. The overall ligand conformation also led to a key water- mediated hydrogen bond between the aniline nitrogen and the terminal carboxylic acid of Val595 of ERα, significantly contributing to molecular recognition. In this compound series, the introduction of large non- aromatic rings, such as the tetrahydropyrane, combined with aniline rings were necessary to limit the multiple ligand conformations and improved the affinity to the complex. + +Understanding the binding mode of 127, we were able to use AnchorQueryTM for a scaffold- hopping approach. The software required an anchor motif on the ligand that was bioisosteric to an amino acid residue and was kept constant for all pharmacophore- based searches of the database. A suitable anchor in our case was the \(p\) - chloro- phenyl ring that was deeply buried at the PPI interface in the small pocket surrounding K122 of 14- 3- 3 (Fig S1). This motif served as the "phenylalanine anchor". Selection of three additional pharmacophore points on different parts of the ligand were necessary for running queries and could include all possible ligand- protein interactions, such as hydrogen bond donors or acceptors, aromatic rings, hydrophobic residues or charged ions. We applied an additional filter, limiting the molecular weight of the hits to 400 Da. We screened all 27 MCR reactions, but interestingly all our top hits were based on the Groebke- Blackburn- Bienaymé (GBB) three- component reaction (GBB- 3CR).34- 36 The GBB- 3CR utilized aldehydes, 2- aminopyridines and isocyanides, leading to imidazo[1,2- a]pyridines.37,38 This privileged scaffold has been present in several clinical candidates and marketed drugs, such as zolpidem, mipropfen, minodronic acid, and olprinone.39 + +Comparison of the docking pose of the proposed GBB compounds with the co- crystal structure of compound 127 revealed a considerable 3D shape complementarity of the two scaffolds (Fig 1B). Additionally, the GBB scaffold was drug- like and more rigid compared to our original ligand, potentially restricting the possible ligand conformations. Further docking and selection of suitable substituents to achieve favorable ligand – protein interactions were performed with the docking software SeeSAR [version 14.0.0; BioSolveIT GmbH, Sankt Augustin, Germany, 2024, www.biosolveit.de/SeeSAR]. + +Once compounds were selected for synthesis, synthetic routes were established using commercially available aldehydes and 2- aminopyridines, whereas isocyanides were synthesized from primary aromatic amines (Fig 1C- 1D). Two general, three- step synthetic routes were developed based on the type of aldehyde building blocks used. In the first synthetic route (route A), Boc- protected aldehydes reacted with 2- aminopyridines and isocyanides in methanol, using scandium triflate as a Lewis acid catalyst to form the GBB intermediate. The Boc- protecting group was removed under acidic conditions and the chloroacetamide warhead was introduced with an amidation reaction, either with chloroacetyl chloride or an amide coupling. In the second synthetic route (route B), to reduce the cost of certain Boc- protected aldehydes, nitro- substituted aromatic aldehydes were used instead. The GBB- 3CR was performed under the same experimental conditions, followed by a nitro- reduction. The nitro- group was reduced either using iron trichloride and zinc or using ammoniotrihydroborate and gold catalysts with Au/TiO2.40 The two reduction methods led to comparable, almost quantitative yields. The last step, as previously, was the introduction of the electrophilic warhead. Thus, the GBB- 3CR allowed multiple combinations of the three main building blocks, facilitating the synthesis of analogs. Synthetic details are provided in the Supplementary Information. + +To test whether this scaffold was suitable for the development of 14- 3- 3α/ERα stabilizers, we synthesized a small set of derivatives varying only the isocyanide position, which according to our design was expected to interact with K122 of 14- 3- 3. We included benzyl and phenyl isocyanides with a diverse substitution pattern on the aryl ring. We maintained the covalent chloroacetamide warhead, based on our extensive investigation of electrophiles in our previous work.21 + +The compounds were tested in an intact mass spectrometry (MS) assay, which monitored the formation of the covalent bond with C38 of 14- 3- 3α. Binding measurements were made in the presence or absence of the phospho- ERα peptide and as a function of time to distinguish between cooperative stabilizers and neutral binders and to select compounds with fast binding kinetics. The phospho- ERα peptide was used at a concentration 2- fold above the dissociation constant (Kd). The assay was performed as a compound titration (Fig S2- S3). We additionally quantified compound binding at 1 μM (10:1 compound:protein) over several time points (table S2). Throughout this work, we reference the time- course data as bar graphs, using grey bars to represent binding to 14- 3- 3α alone ("apo") and colored bars to represent binding to 14- 3- 3 α/ERα complex. + +<--- Page Split ---> + +Testing the first analogs in the MS assay revealed striking differences among benzyl and phenyl analogs (Fig 1E, Fig S4). The benzyl analog 1, which lacked aryl ring substitutions, was a neutral binder. Introduction of electron withdrawing groups, such as halogens or nitro- groups in the o-, m- or p- position (compounds 2- 9) reduced binding in the presence of ERα, even in the case of a small F- substitution. Interestingly, the non- substituted phenyl analog 10 showed increased binding to 14- 3- 3o, but still lacked cooperativity, since it showed increased binding in the presence or absence of ERα. Introduction of halogens in the p- position significantly reduced apo binding, improving compound cooperativity (compounds 11 and 12). However, in contrast to the SAR observed in our previous series21, less binding was observed for the p- Cl analog (11), compared to the non- substituted analog (10). Remarkably, a Me- group in the o- position (13) led to the first molecular glue of the series; compound 13 showed binding in the presence of ERα and very low apo binding. Bigger substitutions in the o- position significantly decreased the observed binding, indicating unfavorable steric effects (14- 16) (Fig S4). + +We solved co- crystal structures of the 14- 3- 3o/ERα complex with compounds 1, 2 and 10 to elucidate their binding modes (Fig 1F- I). The crystal structure of compound 1 revealed a comparable binding mode to the original ligand 127, supporting the design and docking hypothesis (Fig 1F- G). The chloroacetamide moiety of 1 bound covalently to C38 of 14- 3- 3o, and two water- mediated hydrogen bonds were formed between the carbonyl group and R41 and the backbone of E115, and one direct hydrogen bond with N42 of 14- 3- 3 at 3A. The imidazopyridine ring was positioned towards the hydrophobic residues (L218, I219, G171 and L222) at the "roof" of the 14- 3- 3 pocket. In addition, the nitrogen of the five- membered ring formed a hydrogen bond with D215 of 14- 3- 3, which adopted two conformations upon binding of compound 1. Importantly, a large water network was formed between the benzylamine of 1 to N42, S45 of 14- 3- 3, which reached the terminal carboxylic acid of V595 and the pT594 of ERα, and K122 of 14- 3- 3. The benzyl ring, adjacent to the electrophilic warhead was positioned between the hydrophobic 14- 3- 3 residues F119 and I168, (Fig 1G). The structural overlay of analogs 1 and 2 showed an identical binding mode with the additional o- F substituent forming a halogen bond with I219 of 14- 3- 3 (Fig 1H). Replacing the benzyl ring (1) with a phenyl ring (10) resulted in a slight turn of the compound that placed the aryl rings of both analogs in the same position in the pocket surrounding K122. The water network that connected the amine of compound 1 to residues of 14- 3- 3 and ERα was conserved upon modification of the benzyl ring (1) to a phenyl ring (10). The removal of the methylene group positioned the aniline nitrogen of (10) closer the terminal carboxylic acid of ERα, in the position previously occupied by the methylene group of (1). Additionally, it led to increased axial rotation of the bond between the nitrogen and the aryl ring, which might have resulted in the increased apo binding in the MS assay, since this axial rotation was not possible for the benzyl analogs (Fig 1I). + +Since the o- Me substitution correlated with improved binding in the presence of ERα in the MS assay and taking into account the rotational nature of the aniline - phenyl ring bond, we designed and synthesized a series of analogs with double o- substitutions (Fig 2A, Fig S5). The symmetrical 2,6- di- Me analog 17 showed significantly faster binding in the presence of ERα and almost no apo binding, indicating molecular- glue- like binding. Analog 18 with an o- Et group in addition to o- Me, was weaker, indicating unfavorable steric effects (Fig S5). Analogs 19, 20, and 21 with additional o- OMe, o- F and o- Cl groups respectively, showed similar, rapid binding to the symmetric analog 17 and low apo binding. In agreement with our previous observation, the introduction of additional substitutions in the p- position (- Cl, - Me, - OH) significantly reduced binding (compounds 22- 25), especially for the triple substituted analogs (double o- and p- positions). + +Co- crystal structures of the 14- 3- 3o/ERα complex with compounds 17, 19, 20, 21, and 25 revealed differences in the positions of the double- ortho substituents (Fig 2B- E). In the case of the symmetric analog 17 one o- Me group was oriented in the back of the pocket, which was primarily hydrophobic, forming hydrophobic interactions with I219 (3.2A) and facing Val595 of ERα. The second o- Me group was oriented in the front of the pocket and formed hydrophobic interactions with F119 (3.8 A). The formation of these interactions seemed to favorably restrict the axial rotation of the aniline- phenyl bond, locking its position in a highly complementary shape with the composite 14- 3- 3/ERα surface. In analog 19 the larger o- OMe group was positioned in the front of the pocket and formed, together with the aniline nitrogen, a water- mediated hydrogen bond with the terminal carboxylic acid of Val595 of ERα. The orientation of the warhead amide differed in two analogs, but in both cases a direct hydrogen bond with N42 of 14- 3- 3 was formed (3.0 A). For the halogen- containing analogs o- F (20) and o- Cl (21) the halogens interacted with I219 and the common o- Me group with F119, whereas the aniline nitrogen interacted with the terminal carboxylic acid of Val595 of ERα via a water mediated- hydrogen bond. The binding mode of the triple substituted analog 25, although overall comparable with the double substituted analog 17, showed an additional hydrogen bond between the p- OH group and the backbone + +<--- Page Split ---> + +carbonyl group of I168 (3.2 Å). The presence of this direct interaction seemed to negatively affect the axial rotation of the aniline-phenyl bond, resulting in significantly reduced binding in the MS assay and loss of cooperativity. + +![](images/Figure_2.jpg) + +
Figure 2. SAR and crystal structures of selected double-ortho substituted analogs. A) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-E) Crystal structures of 14-3-3o/ERα with compounds 17 (dark pink sticks), 19 (teal sticks) and overlays of crystal structures for compounds 17 (dark pink sticks), 20 (pale green sticks), 21 (pale purple sticks) and 25 (bright yellow sticks).
+ +We synthesized analogs of compound 21, maintaining the 2- Cl,6- Me substitutions and varied positions X and Y on the scaffold (Fig 3A, Fig S6). Position X referred to substitutions on the imidazopyridine ring and was expected to contribute to additional hydrophobic interactions with Val595 of ERα. Position Y included modifications that were expected to improve interactions with the rim of the 14- 3- 3 pocket, primarily with variations of the aldehyde building blocks and to a smaller extent with different electrophilic warheads. In both cases, even the introduction of small groups led to surprising effects in the binding modes, as revealed by crystal structures. + +For position X, aiming for hydrophobic interactions with Val595 of ERα, we introduced groups with a range of radii (- F (26), - Cl (27), - Me (28), - i- Pr (29), - CF3 (30)). Steric effects seemed to play a bigger role than electronic effects, since the best tolerated substitutions were - Cl (27) and - Me (28). For position Y, derived from the aldehyde building blocks, the o- F analog (31) was tolerated, but was weaker compared to the unsubstituted (21); a plausible explanation being the rotational nature of the aryl ring- imidazopyridine ring bond, which lost axial symmetry with the presence of the o- F group. Introduction of a methylene linker (32) or replacement of the aryl ring with a cyclohexyl ring (33) that had a similar size made the compounds almost unable to bind in the MS assay. Modifications in the position of the electrophilic warhead were also not tolerated. The less- reactive ester analog (34) was inactive, whereas halogenated chloroacetamides (35- 37) did not lead to the expected mass adducts, showing instability in the MS and unclear chemical reactivity with 14- 3- 3. A plausible explanation was their increased reactivity, which correlated with reduced stability. The sulfamate warhead (38) was tolerated in the MS assay, but was significantly weaker than the chloroacetamide analog, in addition to being less atom efficient. + +Crystal structures were solved for 28, 32, and 33 and were compared as overlays with analog 21 (Fig 3B- E). The - Me group on the imidazopyridine ring of 28 was expected to contribute to hydrophobic interactions with Val595 of ERα. However, it disrupted the previously observed binding mode and instead moved the imidazopyridine ring upwards, in the hydrophobic pocket of 14- 3- 3 formed by L218, I219 and L222. This upward movement affected the conformation of L218, which moved upwards but maintained contact with 28. The movement in the pocket also altered the interactions of the double- ortho- substituted aryl ring; while the o- Cl still interacted with I219 on the roof of the binding site, the o- Me + +<--- Page Split ---> + +group moved further away from F119 in the bottom of the pocket, and the interaction was lost. The direct hydrogen bond between the amide and N42 and the water- mediated bond between the aniline nitrogen and the carboxylic acid of Val595 were maintained. Overall, the changes in binding mode were associated with slower binding, but a similar apparent Kd in the MS assay (Fig. S3). Analog 32, with an additional methylene group on the electrophilic warhead linker showed a slightly disrupted binding mode. While the biggest difference was the position of the aryl ring next to the longer linker, the imidazopyridine ring also moved further away from ERα; the latter correlated negatively with the binding observed in the MS assay. In contrast to the previous analogs, the o- Cl group on the aryl ring of 32 pointed out of the 14- 3- 3 pocket and due to increased distance, was unable to interact with F119. Thus, the presence of an additional methylene group on the linker was sufficient to make the compound unable to stabilize the complex. Analog 33 bearing a cyclohexyl ring instead of an aromatic ring maintained the interaction between the o- Cl group of the aryl ring and Ile219, but not the interaction of the o- Me group with F119. The orientation of the amide bond next to the warhead also differed; however, the hydrogen bond with N42 was still formed. The presence of the cyclohexyl ring, which had the possibility of adopting more conformations compared to the more rigid aryl ring, correlated with reduced binding to the 14- 3- 3/ERα complex in the MS assay. + +![](images/Figure_3.jpg) + +
Figure 3. SAR and crystal structures of analogs substituted in positions X and Y. A) MS bar graphs at 1 \(\mu \mathrm{M}\) . For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-E) Crystal structure of 14-3-3o/ERα with compound 28 (dark yellow sticks), and overlays of crystal structures for compounds 28 (dark yellow sticks), 32 (pink sticks) and 33 (emerald green sticks) with 21 (pale purple sticks).
+ +Investigation of modifications in positions X and Y provided valuable input in the groups that were tolerated; however, these single- point substitutions did not significantly improve the stabilization effect in the MS assay. Based on the crystallographic input, we synthesized four analogs combining the favorable substitutions from positions X and Y with the symmetric double o- Me analog 17, hypothesizing that a symmetric rotatable bond would correlate with improved potency (Fig 4A). This hypothesis was readily confirmed by evaluating the symmetric analogs 39- 42 in the MS assay. Analogs 39 and 40 included the - Me group on the imidazopyridine ring (X position) and the - F group on aryl ring (referred to as W position), respectively. Analogs 41 and 42 both had the F group in W position and a - Me or - Cl group in X position, respectively. All analogs showed comparable, fast, cooperative binding to 14- 3- 3/ERα in the MS assay, indicating that the symmetry of the 2,6- di- Me- aniline ring was more favorable compared to the asymmetric 2- Cl,6- Me. All four analogs showed low apo binding with analog 41 showing the lowest. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4. SAR and crystal structures of 2,6-di-Me analogs substituted in positions X and W. Biophysical data (MS, TR-FRET, SPR) and cell data (NanoBRET). A) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-C) Overlays of crystal structures of 14-3-3α/ERα ternary complexes with compounds 40 (orange sticks), 41 (red sticks), and 42 (blue sticks). D) TR-FRET schematic and protein titration data for representative compounds at 100 μM compound or DMSO. E) SPR data for the binary 14-3-3α/ERα interaction and ternary interactions with 181, 17 and 41 (mean +/- SD, n=2). F-G) 14-3-3α-HaloTag/Nuc-ERα NanoBRET assay in HEK293T cells with compound titrations (1:2 dilution, starting at 40 μM). Data points excluded where compound dosage was toxic to the cells. MCR compounds compared to the previously described stabilizer 181 and 85, an inactive compound as the negative control. Bar graphs quantifying pEC50 values.
+ +<--- Page Split ---> + +Crystal structures were solved for the symmetric analogs 40, 41 and 42, highlighting the significance of two rotational bonds in the scaffold: the aniline- aryl ring bond, as previously discussed and the aryl ring- imidazopyridine ring bond (Fig 4B- C). The o- F substituent in W position, adjacent to the aryl ring- imidazopyridine rotational bond, adopted two different orientations; an inward orientation for analog 40 and an outward orientation for 41 and 42, which each had an additional substituent on the imidazopyridine ring. With the inward conformation of analog 40, the o- F substituent interacted with P167 of 14- 3- 3, whereas for 41 and 42 the o- F group was oriented to made multiple stabilizing interactions, being approximately 3 Å from N42 of 14- 3- 3, and the o- Me substituent and aniline nitrogen of the compound itself, thus contributing both to ligand- protein interactions and intramolecular interactions, restricting the axial bond rotation. In agreement with previous observations, the presence of a substituent in the X position (41 and 42) led to an upward movement of the imidazopyridine ring, orienting the substituent toward the hydrophobic residues I219 and L222. Overall, the presence of the additional substituent on the imidazopyridine ring in the last two analogs, even though in a distant position compared to the o- F- substituted ring, correlated favorably with the restriction of the rotational bond on the F- aryl ring. The different substituents of the imidazopyridine ring, Me (41) or Cl (42), did not affect the position of the stabilizer in the crystal structures; however, in the MS assay the Me group showed lower apo binding to 14- 3- 3, resulting in higher cooperativity of the ternary complex. + +## TR-FRET + +Up to this point, the SAR was developed using the intact MS assay and supported by crystallography. In our previous work \(^{20 - 22}\) , we relied on a fluorescence anisotropy assay (FA) to confirm cooperativity. The FA assay typically used (5- carboxylfluorescein) FAM- labeled phospho- peptides to quantify peptide binding to the compound/14- 3- 3 complex. For the MCR scaffold, however, this assay proved to be unsuitable, since the extensively conjugated ring system was intrinsically fluorescent in the same wavelengths as the FAM- labeled peptide (480 nm and 520 nm). To circumvent this issue, we turned our attention to far- red fluorescent dyes. The cy5- labeled ERα- peptide with excitation wavelength of 651 nm and emission wavelength of 670 nm, significantly affected the Kd of the 14- 3- 3/ERα complex. The reported Kd using the acetylated ERα peptide in an isothermal calorimetry (ITC) experiment or the FAM- labeled- ERα in an FA assay is in the range of 1- 2 μM. \(^{21}\) The cy5- ERα- peptide, however, resulted in a Kd of 6 nM, indicating significant dye binding (Fig S7). + +As a suitable alternative to the FA assay, we developed a TR- FRET assay \(^{41}\) , using HIS- tagged- full- length 14- 3- 3σ, biotin- labeled- ERα peptide, an anti- HIS- tag monoclonal antibody conjugated with a Tb(III) criptate as the donor and streptavidin conjugated with the D2 dye as the acceptor. The observed Kd for this system was 30 nM. To ensure that the observed difference in Kd from ITC or the FAM- labeled- ERα in an FA assay was not related to the biotin- tag on the peptide, we performed a competition assay with the FAM- labeled peptide. The Kd of the biotin- peptide was 2.5 μM, in good agreement with the FAM- peptide's Kd (Fig S7). This result suggested that the lower Kd in the TR- FRET assay was due to avidity \(^{42}\) ; since 14- 3- 3 and the antibody were both dimers and streptavidin was tetravalent, different multivalent complexes could form, resulting in differences in the observed binding affinity between the labeled and the unlabeled proteins. + +We performed assay optimization with 2D- titrations of 14- 3- 3, biotin- ERα and donor/acceptor ratios. We then tested the synthesized compounds using the optimized experimental conditions: 200 nM 14- 3- 3 (top concentration, 2- fold dilution), 50 nM biotin- ERα, 0.166 nM MAb anti- 6HIS Tb and 6.25 nM SA- D2. The compounds were tested at 100 μM and DMSO was used a negative control. 14- 3- 3 was titrated using an Echo acoustic dispenser, followed by the addition of the compounds and the peptide; after 1h incubation at room temperature, the donor and acceptor were added. In contrast to the MS assay, maximum signal was obtained after 2h rather than 16h - a plausible explanation again being avidity. The tight Kd in the TR- FRET assay resulted in a small assay window and a hook effect was observed at higher protein concentrations (ca. \(50 - 100\) nM 14- 3- 3). + +Nevertheless, the TR- FRET assay was sensitive to the addition of molecular glues. In addition to quantifying fold stabilization for the 14- 3- 3/ERα complex \((AppKd_{(compound)} / AppKd_{(DMSO)})\) , we also quantified the fold increase in the TR- FRET signal (the ratio of observed Emax and Emin). To validate the TR- FRET assay, we included two positive controls: the natural product FC- A and our previously described stabilizer compound \(181^{21}\) . The two compounds gave comparable results. FC- A had an \(AppKd_{(compound)}\) of 11 nM, fold- stabilization of 3.09, and fold- increase of 5.52. Compound 181 had an \(AppKd_{(compound)}\) of 8.6 nM, fold- stabilization of 3.95 and fold- increase of 5.52 (Fig S8, table S3). + +<--- Page Split ---> + +In good agreement with the MS data, neutral binders 1 and 10 showed only a small signal shift compared to the DMSO control (Fig 4D, Fig S8). The 2- Me analog 13 showed 4.19- fold- stabilization and 2.36- fold increase, whereas 2,6- di- Me analog 17 showed 4.78- fold- stabilization and 2.58- fold increase, the same rank order as the MS assay. The more sterically hindered analog 18 (o- Me, o- Et) showed weaker stabilization (3.06- fold- stabilization and 2.14- fold increase). Analogs 19, 20, and 21, all bearing the o- Me group and additional o- OMe, o- F and o- Cl groups respectively, showed comparable stabilization (4.41 – 5.23- fold stabilization and 2.1 – 2.48- fold increase). The highest stabilization effect was observed for the final symmetric analogs 39 – 42. Analog 39 with a - Me group in X position had an AppKd(compound) of 7.8 nM, fold- stabilization of 4.35 and fold- increase of 2.61, whereas 40 with the - F group in W position had an AppKd(compound) of 8.4 nM, fold- stabilization of 4.04 and fold- increase of 3.12. Analog 41, which had both the - Me group in X position and the - F group in the W position had the lowest AppKd(compound) (5.2 nM) and the highest fold- stabilization (6.53) and fold- increase (3.71) of the series. Analog 42, which differed only in the X position (- Cl instead of - Me group) appeared weaker (AppKd(compound) 8.8 nM, fold- stabilization of 3.87 and fold- increase of 2.97). In summary, while the fold- changes were dampened by avidity, 14- 3- 3/ERα molecular glues showed the same rank- order in the TR- FRET assay as in the mass spectrometry assay used for initial SAR. + +## SPR + +Surface Plasmon Resonance (SPR) was then used to analyze the kinetic parameters of the ERα peptide binding to 14- 3- 3α in the presence of compounds 181, 17 and 41, and to compare the AppKd(compound) and kinetics to the binary ERα/14- 3- 3α interaction (Fig 4E, Fig S9). Here, 14- 3- 3α tagged with a Twinstrept- tag was captured on a SPR chip coated with Strep- Tactin XT, after which a 2- fold dilution series of acetylated ERα phospho- peptide was injected. For the binary interaction, the fast dissociation rate (koff) reached the limit of detection of the SPR instrument, due to a relative weak interaction with a Kd of 1.1 μM, which was in line with the ITC and FA experiments. The covalent bond between the chloroacetamide warhead of the compounds and C38 of 14- 3- 3α was formed after overnight incubation in the presence of ERα. Immobilization of this complex on the chip, followed by extensive washing to remove the bound ERα peptide, allowed us to determine the kinetics of ERα binding to the 14- 3- 3α/stabilizer complex. The previously described stabilizer 181 decreased the off- rate to 0.016 s- 1 and simultaneously increased the association rate (kOn) by a factor of 16, resulting in a low nanomolar affinity constant for the ERαpeptide/14- 3- 3 complex (3.9 nM; stabilization = 282- fold). The analogs of the newly designed scaffold, 17 and 41, both led to an 8- fold increase in association rate compared to the binary interaction. The binding of compound 41 induced a stronger decrease in dissociation rate of ERα compared to 17, which resulted in Kd values of 10.1 nM and 15.1 nM for 41 and 17, respectively. The higher stabilizing potency of 41 compared to 17 (stabilization = 110- fold for 41 and 71- fold for 17) were in rank- order agreement with the TR- FRET data. The decreased dissociation rates in the presence of the stabilizers, especially for 41 and 181, increased the residence time of ERα binding to 14- 3- 3 from approximately 3.4 s to 47.6 s and 62.5s, respectively. + +## NanoBRET + +To test the effects of the compounds on the full- length PPI, we used a NanoBRET assay we developed previously43. Compounds were tested using a C- terminal fusion14- 3- 3α- HaloTag and full length, N- terminal fusion NanoLuc- ERα (Fig 4F- G; Table S4). Briefly, NanoLuc- ERα and 14- 3- 3α- HaloTag plasmids were transfected in 1:10 ratio in hormone deprived HEK293T cells. After 48 hours post transfection, cells were seeded in assay plates with the experimental wells treated with 100 nM HaloTag NanoBRET 618 ligand (Promega) and no- ligand control wells treated with DMSO (v/v). Cells were treated for 24 hours with compounds in 1:2 dilution series starting at 40 μM. The BRET signal was read and normalized against DMSO treated samples. All active compounds resulted in an increase in BRET signal compared to the negative control, 85. The previously described compound 18121 stabilized the 14- 3- 3α/ERα complex in cells with an EC50 value of 5.2 μM and a 1.7- fold increase in the BRET signal. Of the compounds tested, the neutral binder 10 was less effective than 181 (EC50 value > 100 μM, 1.6- fold increase). Compound 13 showed an improved EC50 and fold- increase compared to 10 (12 μM and 1.8- fold). Compound 17 had the lowest EC50 value of 2.7 μM and showed 1.8- fold increase in BRET signal, a slight improvement compared to 181. Compounds 40 and 42 had the same 2- fold- increases in BRET signal, though exhibited slightly different EC50 values (7.2 and 4.6 μM, respectively). Compound 41 resulted in the highest increase in BRET signal, a 2.6- fold increase; however, it had a similar EC50 value to 181 of 5 μM. In the NanoBRET assay, compounds 17 and 41 performed most effectively, based on the EC50 value (17) and fold- increase in BRET signal (41) of the panel of compounds tested. The majority of MCR stabilizers showed similar EC50 values to the previous scaffold represented by 181, but consistently showed improved fold- increase in BRET signal. + +<--- Page Split ---> + +Compounds 181, 17, and 41, along with the negative control, 85, were tested in a NanoBRET assay where the cysteine of interest in 14- 3- 3α C38, was mutated to an asparagine, 14- 3- 3σC38N- HaloTag (Fig S10). The BRET signal did not increase for 85, 181, or 17 with increasing compound concentration. There was a minimal increase in BRET signal for compound 41, from 2.5 to 3.5 mBU at 20 μM 41. In comparison to the assays done with 14- 3- 3σWT- HaloTag, the non- normalized BRET signal increased from 6.7 to 16.1 mBU at the same concentration of 41 (data shown as normalized). In summary, when C38 was not present in 14- 3- 3σ, the compounds were unable to bind and stabilize the full- length 14- 3- 3σ/ERα complex in cells (Fig S10). + +## Discussion + +Using MCR chemistry, we demonstrated the potential of a scaffold- hopping approach for molecular glues stabilizing a native 14- 3- 3/cient PPI. Our approach combined computational de novo design with multi- component reaction chemistry, which included short, efficient synthetic routes with multiple points of variation, accelerating the synthesis of analogs. Thus two distinct optimization approaches led to two highly diverse chemical series, converging on similar efficacies as 14- 3- 3σ/ERα molecular glues. + +In the MCR approach, structure- activity relationships were established with an intact mass spectrometry assay, which allowed the distinction between neutral binders and stabilizers - cooperative molecular glues. Overall, the SAR showed that the introduction of even small substituents in the case of molecular glues could profoundly affect their potency and cooperativity. The best compounds, eg, 41, was already \(50\%\) bound at \(1\mu M\) after \(1\mathrm{hr}\) of incubation. Crystal structures of ternary complexes were crucial in elucidating small changes in the binding modes of otherwise highly similar analogs. Starting from the weak neutral binder 1 and by removing one methylene group, we significantly increased binding to the complex for compound 10. Introduction of substituents in the o- position was sufficient to reduce apo binding and turn the compounds from neutral binders to cooperative molecular glues. Although the number of rotational bonds is generally kept to a minimum, in this case we took advantage of two rotational bonds in the scaffold and with appropriate substituents achieved favorable ligand conformations resulting in increased potency, as shown for analogs 17 and 41. + +Several biophysical assays provided complementary strategies for evaluating these novel molecular glues. A TR- FRET assay circumvented the issue of scaffold fluorescence, which could have been a limiting factor in establishing SAR and allowed the rank- ordering of analogs. A new SPR assay, in which the covalent ligand was pre- associated with 14- 3- 3σ before immobilization, provided an insightful analysis of binding/unbinding kinetics and started to hint at differences between the two series of molecular glues. Saturating concentrations of compound 41, for instance, stabilized the ERα/14- 3- 3 complex by 110- fold and slowing the koff by 14- fold. Taken together, biophysical assays allowed the quantification of cooperativity (MS, TR- FRET) and kinetics (SPR); importantly, the observed SAR was consistent among these three assays. The NanoBRET assay further showed good correlation with the biophysical protein/phosphopeptide assays while demonstrating stabilization of the full- length proteins with an EC50 value of \(2.7 - 5\mu M\) in intact cells. Overall, the optimized, cell- active MCR scaffold will facilitate chemical biology approaches to study the 14- 3- 3/ERα interaction, which has so far been unexploited for drug discovery. + +## Methods + +## PROTEIN EXPRESSION AND PURIFICATION + +The 14- 3- 3 α isoform (full- length for mass spectrometry and TR- FRET assays, \(\Delta C\) for crystallography) with an N- terminal His6 tag was expressed in Rosetta™ 2(DE3)PLysS competent E. coli (Novagen) from a pPROEX HTb expression vector. After transformation following manufacturer's instructions, single colonies were picked to inoculate \(30~\mathrm{mL}\) precultures (LB), which were added to \(1.5\mathrm{L}\) terrific broth (TB) medium after overnight growth at \(37^{\circ}C\) , 250 rpm. Expression was induced upon reaching \(\mathrm{OD}_{600}\) \(1.9 - 2.1\) by adding \(400~\mu \mathrm{M}\) IPTG. After overnight expression at \(30^{\circ}C\) , \(150~\mathrm{rpm}\) , cells were harvested by centrifugation at \(6,500~\mathrm{rpm}\) , resuspended in lysis buffer ( \(50~\mathrm{mM}\) HEPES pH 7.5, 500 mM NaCl, \(20~\mathrm{mM}\) imidazole, \(10\%\) glycerol, \(1\mathrm{mM}\) TCEP), and lysed by sonication. The His6- tagged protein was purified by Ni- affinity chromatography (Ni- NTA Agarose, Invitrogen) (Wash buffer \(50~\mathrm{mM}\) HEPES pH 7.5, \(500~\mathrm{mM}\) NaCl, \(20~\mathrm{mM}\) imidazole, \(1\mathrm{mM}\) TCEP; Elution buffer \(50~\mathrm{mM}\) HEPES pH 7.5, \(500~\mathrm{mM}\) NaCl, \(500~\mathrm{mM}\) imidazole, \(1\mathrm{mM}\) TCEP) and analyzed for purity by SDS- PAGE and Q- Tof LC/MS. The protein was buffer exchanged (Storage buffer \(25~\mathrm{mM}\) HEPES pH 7.5, \(150~\mathrm{mM}\) NaCl, \(1~\mathrm{mM}\) TCEP) and concentrated to \(\sim 16~\mathrm{mg / mL}\) and aliquots flash- frozen for storage at \(- 80^{\circ}C\) . The \(\Delta C\) variant was truncated at the C- terminus after T231 to enhance crystallization and after the first Ni- affinity chromatography column, the construct was treated with TEV protease to cleave off the His6 tag during dialysis ( \(25~\mathrm{mM}\) + +<--- Page Split ---> + +HEPES, pH 7.5, 200 mM NaCl, 5% glycerol, 10 mM MgCl2, 250 μM TCEP) overnight at \(4^{\circ}C\) . The flow- through of a second Ni- affinity column was subjected to a final purification step by size exclusion chromatography (Superdex 75 pg 16/60 size exclusion column (GE Life Science) (SEC buffer: 25 mM HEPES pH 7.5, 100 mM NaCl, 10 mM MgCl2, 250 μM TCEP). The protein was concentrated to \(\sim 60\) mg/mL, analyzed for purity by SDS- PAGE and Q- Tof LC/MS and aliquots flash- frozen for storage at \(- 80^{\circ}C\) . + +## PEPTIDES + +Peptides for mass spectrometry, fluorescence anisotropy and TR- FRET assays were purchased from Elim Biopharmaceuticals, Inc. (Hayward, CA). Peptides for SPR and X- ray crystallography was purchased from GenScript Biotech Corp. The following peptides were used: + +Ac- KYYITGEAEGFPA(pT)V- COOH (MS assay, 15- mer), 5- FAM- AEGFPA(pT)V- COOH (FA assay, 8mer ERα- pp), cy5- KYYITGEAEGFPA(pT)V- COOH (FA assay, 15- mer), biotin- KYYITGEAEGFPA(pT)V- COOH (TR- FRET assay, 15- mer), Ac- EGFPA(pT)V- COOH (crystallography and SPR, 7- mer) + +## INTACT MASS SPECTROMETRY ASSAY + +Mass spectrometry dose response assays were performed on a Waters Acquity UPLC/ Xevo G2- XS Q- Tof mass spectrometer. A Waters UPLC Protein BEH- C4 Column (300 A, \(1.7 \mu \mathrm{m}\) , \(2.1 \mathrm{mm} \times 50 \mathrm{mm}\) ) was used to desalt the samples prior to application on the mass spectrometer. For 19- point MS dose responses, \(50 \mathrm{mM}\) compound stocks in DMSO were serially diluted in 3- fold increment in a master plate, then \(1000 \mathrm{nl}\) of the compounds were transferred in the assay plates. Master mixes containing \(100 \mathrm{nM}\) full- length wild type 14- 3- 3α in the absence or presence of \(2 \mu \mathrm{M}\) ERα were then dispensed into 384 well plates (Greiner Bio- One, catalog number 784201). Assay buffer was TRIS (10 mM, pH 8.0) and final volume per well was \(50 \mu \mathrm{l}\) , with final top concentration of compounds dose response series at 1 mM. The reaction mixtures were incubated for 1h at rt before subjected to MS. Four measurements (1h, 8h, 16h, 24h) were performed for time- course experiments. The injection volume for each sample was \(6 \mu \mathrm{l}\) . \(24 \mu \mathrm{l}\) of sample were needed for the time- course experiments, so the total volume in the assay plate was adjusted to \(50 \mu \mathrm{l}\) , to account for the dead volume in the injections. Data collection and automated processing followed a custom workflow, as previously described. \(^{44}\) z Plots were created using GraphPad Prism with the log(agonist) vs. response (variable slope, four parameters) fitting model. + +## \(\mathbf{K}_{\mathrm{D}}\) DETERMINATION FOR FAM-, cy5- AND BIOTIN-LABELED ERα PEPTIDES + +For \(\mathrm{K}_{\mathrm{D}}\) determination, N- terminal fluorescein- labeled ERα peptide (5- FAM) or cy5- labeled ERα peptide and HIS- tag FL 14- 3- 3α were diluted in buffer (10mM HEPES pH 7.5, 150mM NaCl, \(0.05\%\) tween 20, \(0.05\%\) BGG (bovine gamma globulin)). Two- fold dilution series of 14- 3- 3 were made in black, round- bottom 384- microwell plates (Greiner Bio- one 784900) in a final sample volume of \(10 \mu \mathrm{L}\) in triplicates. FAM- or cy5- labeled ERα peptides (final assay concentration 10nM) were dissolved in assay buffer and mixed with the protein dilution series on the plates. Fluorescence anisotropy measurements were performed after 1h incubation at room temperature on an Envision HTS Dual Detector 2105 plate reader (for FAM- labeled ERα peptide: filter set lex: 480, lem: 535, and D505fp/D538 advanced dual mirror). For cy5- labeled ERα peptide filter set lex: 620, lem: 688 nm, and D658fp/D688 advanced dual mirror). Data were reported at endpoint. Prism 10 (GraphPad) was used to generate plots using the [agonist] vs. response (variable slope, four parameters) fitting model to determine \(\mathrm{K}_{\mathrm{D}}\) values. + +For \(\mathrm{K}_{\mathrm{D}}\) determination of the biotin- labeled ERα peptide a competition assay was performed. 5- FAM- and biotin- labeled ERα peptide were diluted in buffer (10mM HEPES pH 7.5, 150mM NaCl, \(0.05\%\) tween 20, \(0.05\%\) BGG (bovine gamma globulin)). Two- fold dilution series of biotin- labeled ERα peptide were made in black, round- bottom 384- microwell plates (Greiner Bio- one 784900) in a final sample volume of \(10 \mu \mathrm{L}\) in triplicates. A mastermix of 14- 3- 3α and 5- FAM- ERα was dispensed on the assay plate (final assay concentrations: \(6 \mu \mathrm{M}\) 14- 3- 3α (IC80) and \(10 \mathrm{nM}\) 5- FAM- ERα). Fluorescence anisotropy measurements were performed after 1h incubation at room temperature using a Molecular Devices ID5 plate reader (filter set lex: \(485 \pm 20 \mathrm{nm}\) , lem: \(535 \pm 25 \mathrm{nm}\) ; integration time: \(50 \mathrm{ms}\) ; settle time: \(0 \mathrm{ms}\) ; shake 5 sec, medium, read height \(3.00 \mathrm{mm}\) , G- factor \(= 1\) ). Data were reported at endpoint. Prism 10 (GraphPad) was used to generate plots using the [agonist] vs. response (variable slope, four parameters) fitting model to determine \(\mathrm{K}_{\mathrm{D}}\) values. The obtained \(\mathrm{K}_{\mathrm{D}}\) value was corrected using the Cheng- Prusoff equation. + +\(\mathrm{K}_{\mathrm{d}} = \mathrm{IC}_{50} / (1 + [\mathrm{S}] / \mathrm{Km})\) \(\mathrm{K}_{\mathrm{d}} = 6.9 / (1 + 50 \mathrm{nM} / 30 \mathrm{nM}) = 2.5 \mu \mathrm{M}\) + +<--- Page Split ---> + +## TR-FRET PROTEIN TITRATIONS + +TR- FRET PROTEIN TITRATIONSFor assay optimization, 2D titrations of biotin- labeled ERα peptide, HIS- tag FL 14- 3- 3α and streptavidin- D2 were performed in assay buffer (10mM HEPES pH 7.5, 150mM NaCl, 0.05% tween 20, 0.05% BGG (bovine gamma globulin)). The donor (MAb anti- 6HIS Tb cryoplate gold) concentration was kept constant at 0.166 nM. For TR- FRET protein titrations, biotin- labeled ERα peptide (50 nM), the compounds or DMSO (100 μM), MAb anti- 6HIS Tb cryoplate gold (0.166 nM) and streptavidin- D2 (6.25 nM) were mixed in assay buffer (10mM HEPES pH 7.5, 150mM NaCl, 0.05% tween 20, 0.05% BGG (bovine gamma globulin)). 2- fold serial dilutions of HIS- tag FL 14- 3- 3α were performed (200 nM top assay concentration, 12- point dilution series). The assay was performed in 384- well microplates (Corning 4513, low volume white) at a volume of 10 μl per well. The following procedure was used: The compounds (50 mM stocks in DMSO) were transferred in echo LDV masterplates. 20 nL were transferred from the masterplate to the assay plate to achieve 100 μM compound concentration in the assay using Echo acoustic dispensing. The biotin- labeled ERα peptide was dissolved in assay buffer (10mM HEPES pH 7.5, 150mM NaCl, 0.05% tween 20, 0.05% BGG (bovine gamma globulin)) and dispensed in the assay plate using Dragonfly. 14- 3- 3 dilution series were prepared using Echo acoustic dispensing. Assay plates were incubated for 1hr at room temperature before the addition of a mastermix containing the donor (MAb anti- 6HIS Tb cryoplate gold) and acceptor (streptavidin- D2) in assay buffer. The mastermix was dispensed with Dragonfly. Assay plates were incubated for 2hr at room temperature prior to TR- FRET measurements using the Envision HTS Dual Detector 2105 plate reader equipped with the TR- FRET filter set (320/615/665 nm) and a D407/D630 advanced dual mirror. A 50 μs delay was employed to reduce background fluorescence. The TR- FRET signal was obtained through calculating the ratio of 665 nm to 615 nm fluorescence (x 1000), and Prism 10 (GraphPad) was used to generate plots using the [agonist] vs. response (variable slope, four parameters) fitting model. At least two independent experiments were performed. + +## SPR + +SPRThe SPR experiments were performed at \(25^{\circ}C\) using a Biacore X100 and a 200 nm Strep- Tactic XT derivatized linear polycarboxylate hydrogel chip, medium charge density (XanTec Bioanalytics). All proteins and peptides were dissolved in fresh running buffer prepared with ultrapure water and filtered through a \(0.2 \mu m\) filter (10 mM HEPES pH 7.4, 200 mM NaCl, 50 μM EDTA, 0.005% P20). First the surface was conditioned with a 1 min injection of 3 M Guanidine HCl. Then, the recombinant 14- 3- 3α- Twinstrept protein (250 nM) was captured on flow cell 2 of the sensor chip at a flow rate of \(10 \mu L / min\) for 2 minutes, which resulted in a capture level of 1000 RU. For the ternary interaction, 14- 3- 3α- Twinstrept protein (250 nM) was first incubated overnight with \(1 \mu M\) Ac- ERα peptide and \(20 \mu M\) compound prior to immobilization to the chip. The bound ERα peptide was washed away using running buffer flowed over the chip for 15 min. Flow cell 1 was left blank as a reference surface. After immobilization of the protein, the Biacore X100 was primed with running buffer. Multi- cycle kinetic measurements were conducted at a flow rate of \(30 \mu L / min\) . A 2- fold dilution series of analyte (Ac- ERα peptide) in running buffer were injected over the sensor chip for 2 min, followed by dissociation of 3 min (binary interaction), 7 or 13 minutes (ternary interaction). For the binary interaction, the highest concentration of ERα was 50 μM, and for the ternary interactions this was 250 nM. Between cycles of one multi- cycle measurement, no regeneration step was performed due to complete dissociation of the analyte. After a measurement, the chip was regenerated by 2 times 30 sec injections of 3 M Guanidine HCl. The data was corrected by double subtracting to the reference surface (flow cell 1) and buffer injection and analyzed using 1:1 interaction fitting model with the BIA evaluation software (2020). + +## X-RAY CRYSTALLOGRAPHY DATA COLLECTION AND REFINEMENT + +X- RAY CRYSTALLOGRAPHY DATA COLLECTION AND REFINEMENTThe 14- 3- 3αΔC protein, acetylated ERα and compounds (50 mM stock in DMSO) were dissolved in complexation buffer (25 mM HEPES pH=7.5, 2 mM MgCl₂ and 100 μM TCEP) and mixed in a 1:2:3 or 1:2:5 molecular stoichiometry (protein : peptide : compound) with a final protein concentration of 12 mg/mL. The complex was set- up for sitting- drop crystallization after overnight incubation at 4 °C, in a custom crystallization liquor (0.05 M HEPES (pH 7.1, 7.3, 7.5, 7.7), 0.19 M CaCl₂, 24- 29% PEG400, and 5% (v/v) glycerol). Crystals grew within 10- 14 days at 4 °C. Crystals were fished and flash- cooled in liquid nitrogen. X- ray diffraction (XRD) data were collected at the European Synchrotron Radiation Facility (ESRF Grenoble, France, beamline ID23- 1, ID30A- 3/MASSIF- 3, or ID23- 2) or at the Deutsches Elektronen- Synchrotron (DESY Hamburg, Germany, beamline PETRA III). Data was processed using CCP4i2 suite (version 8.0.019). After indexing and integrating the data, scaling was done using AIMLESS. The data was phased with MolRep, using PDB 4JC3 as template. The presence of co- crystallized ligands was verified by visual inspection of the Fo- Fc and 2Fo- Fc electron density maps in COOT (version 0.9.8.93). If electron density corresponding to the co- crystallized ligand was present, its structure, restraints, and covalent bond were generated using AceDRG. After + +<--- Page Split ---> + +building in the ligand, model rebuilding and refinement was performed using REFMAC5. The PDB REDO server (pdb- redo.edu) was used to complete the model building and refinement. The images were created using the PyMOL Molecular Graphics System (Schrödinger LLC, version 4.6.0). See SI table S10 for data collection and refinement statistics. + +The structures were deposited in the protein data bank (PDB) with IDs: 916S (28), 916T (32), 916U (33), 916V (40), 916W (41), 916X (42), 916Y (1), 916Z (2), 9170 (17), 9171 (19), 9172 (10), 9173 (20), 9174 (21), and 9175 (25). + +## NanoBRET + +NanoBRET assays were performed as previously described.43 HEK293T cells were cultured DMEM, high glucose (Gibco) supplemented with \(10\%\) charcoal stripped Fetal Bovine Serum (FBS; Gibco) and \(1\%\) penicillin/streptomycin. Cells were transfected with a 1:10 ratio of Nanoluc- ERa:14- 3- 3o- HaloTag plasmid for 48 hours using jetOPTIMUS transfection reagent (Polyplus). Cells were then seeded at 8,000 cells per well in a 384- well plate (Corning #3570) in FluoroBrite DMEM (phenol red- free; Gibco) with \(4\%\) charcoal stripped FBS and treated with \(100~\mathrm{mM}\) HaloTag NanoBRET 618 Ligand (Promega) or equivalent volume of DMSO as a no ligand negative control. Following plating, cells were treated for 24 hours with compound in 1:2 dilution series starting at \(40~\mu \mathrm{M}\) (0.35% DMSO final concentration). After 24 hours, the BRET signal was read using an EnVision XCite 2105 plate reader at 618 nm (HaloTag) and 460 nm (NIuc). The final corrected NanoBRET ratio was calculated using the following equation: + +\[CorrectedBRETratio = \left(\frac{618nm}{460nm}\right)_{HaloTagLigand} - \left(\frac{618nm}{460nm}\right)_{NoLigandcontrol}\] + +The BRET ratios were normalized to samples treated with DMSO. + +## DOCKING + +Computational design for SAR optimization and docking was performed with SeeSAR version 14.0.0; BioSolveIT GmbH, Sankt Augustin, Germany, 2022, www.biosolveit.de/SeeSAR + +## SOFTWARE VERSIONS + +Prism (10.2.1), Illustrator (22.1 (64- bit)), Biorender (64- bit), Pymol (4.6.0), CCP4i2 (8.0.003), COOT (0.9.8.1), Phenix (1.19.2- 4158) + +Supporting Information. Supplementary figures and tables, synthetic procedures, compound characterization, NMR spectra, crystallography data (PDF). + +## References + +1. Andrei, S. A. et al. Stabilization of protein-protein interactions in drug discovery. Expert Opin Drug Discov 12, 925-940 (2017). +2. Bier, D., Thiel, P., Briels, J. & Ottmann, C. Stabilization of Protein-Protein Interactions in chemical biology and drug discovery. Progress in Biophysics and Molecular Biology 119, 10-19 (2015). +3. Arkin, M. R. & Wells, J. A. Small-molecule inhibitors of protein-protein interactions: progressing towards the dream. Nat Rev Drug Discov 3, 301-317 (2004). +4. Arkin, M. R., Tang, Y. & Wells, J. A. Small-Molecule Inhibitors of Protein-Protein Interactions: Progressing toward the Reality. Chemistry & Biology 21, 1102-1114 (2014). +5. Smith, M. C. & Gestwicki, J. E. Features of protein-protein interactions that translate into potent inhibitors: topology, surface area and affinity. Expert Rev. Mol. Med. 14, e16 (2012). +6. Schreiber, S. L. Molecular glues and bifunctional compounds: Therapeutic modalities based on induced proximity. Cell Chemical Biology 31, 1050-1063 (2024). +7. Konstantinidou, M. & Arkin, M. R. Molecular glues for protein-protein interactions: Progressing toward a new dream. Cell Chem Biol 31, 1064-1088 (2024). +8. Ruan, H., Sun, Q., Zhang, W., Liu, Y. & Lai, L. Targeting intrinsically disordered proteins at the edge of chaos. Drug Discovery Today 24, 217-227 (2019). +9. Santofimia-Castaño, P. et al. Targeting intrinsically disordered proteins involved in cancer. Cell. Mol. Life Sci. 77, 1695-1707 (2020). +10. Aitken, A. 14-3-3 proteins: a historic overview. Semin Cancer Biol 16, 162-172 (2006). +11. Pennington, K. L., Chan, T. Y., Torres, M. P. & Andersen, J. L. The dynamic and stress-adaptive signaling hub of 14-3-3: emerging mechanisms of regulation and context-dependent protein-protein interactions. Oncogene 37, 5587-5604 (2018). + +<--- Page Split ---> + +12. De Vries-van Leeuwen, I. J. et al. Interaction of 14-3-3 proteins with the estrogen receptor alpha F domain provides a drug target interface. Proc Natl Acad Sci U S A 110, 8894-8899 (2013).13. Somsen, B. A. et al. Molecular basis and dual ligand regulation of tetrameric estrogen receptor \(\alpha /14-3-3\zeta\) protein complex. Journal of Biological Chemistry 299, 104855 (2023).14. Liu, Y., Ma, H. & Yao, J. ERα, A Key Target for Cancer Therapy: A Review. OTT Volume 13, 2183-2191 (2020).15. Paterni, I., Bertini, S., Granchi, C., Macchia, M. & Minutolo, F. Estrogen receptor ligands: a patent review update. Expert Opinion on Therapeutic Patents 23, 1247-1271 (2013).16. Scott, J. S. & Barlaam, B. Selective estrogen receptor degraders (SERDs) and covalent antagonists (SERCAs): a patent review (2015-present). Expert Opinion on Therapeutic Patents 32, 131-151 (2022).17. Scott, J. S. & Klinowska, T. C. M. Selective estrogen receptor degraders (SERDs) and covalent antagonists (SERCAs): a patent review (July 2021-December 2023). Expert Opinion on Therapeutic Patents 34, 333-350 (2024).18. Jeselsohn, R., Buchwalter, G., De Angelis, C., Brown, M. & Schiff, R. ESR1 mutations—a mechanism for acquired endocrine resistance in breast cancer. Nat Rev Clin Oncol 12, 573-583 (2015).19. Visser, E. J. et al. Estrogen Receptor \(\alpha /14-3-3\) molecular glues as alternative treatment strategy for endocrine resistant breast cancer. Preprint at https://doi.org/10.1101/2024.04.25.591105 (2024).20. Kenanova, D. N. et al. A Systematic Approach to the Discovery of Protein-Protein Interaction Stabilizers. ACS Cent. Sci. 9, 937-946 (2023).21. Konstantinidou, M. et al. Structure-Based Optimization of Covalent, Small-Molecule Stabilizers of the 14-3-3/ERα Protein-Protein Interaction from Nonselective Fragments. J. Am. Chem. Soc. 145, 20328-20343 (2023).22. Visser, E. J. et al. From Tethered to Freestanding Stabilizers of 14-3-3 Protein-Protein Interactions through Fragment Linking. Angew Chem Int Ed 62, e202308004 (2023).23. Dömling, A., Wang, W. & Wang, K. Chemistry and Biology Of Multicomponent Reactions. Chem. Rev. 112, 3083-3135 (2012).24. Zarganes-Tzitzikas, T., Chandgude, A. L. & Dömling, A. Multicomponent Reactions, Union of MCRs and Beyond. The Chemical Record 15, 981-996 (2015).25. Fotopoulou, E., Anastasiou, P. K., Tomza, C. & Neochoritis, C. G. The Ugi reaction as the green alternative towards active pharmaceutical ingredients. Tetrahedron Green Chem 3, 100044 (2024).26. Li, X., Zarganes-Tzitzikas, T., Kurpiewska, K. & Dömling, A. Amenamevir by Ugi-4CR. Green Chem. 25, 1322-1325 (2023).27. Zarganes-Tzitzikas, T., Neochoritis, C. G. & Dömling, A. Atorvastatin (Lipitor) by MCR. ACS Med. Chem. Lett. 10, 389-392 (2019).28. Znabet, A. et al. A highly efficient synthesis of telaprevir by strategic use of biocatalysis and multicomponent reactions. Chem. Commun. 46, 7918 (2010).29. Wehlan, H., Oehme, J., Schäfer, A. & Rossen, K. Development of Scalable Conditions for the Ugi Reaction—Application to the Synthesis of (R)-Lacosamide. Org. Process Res. Dev. 19, 1980-1986 (2015).30. Váradi, A. et al. Synthesis of Carfentanil Amide Opioids Using the Ugi Multicomponent Reaction. ACS Chem. Neurosci. 6, 1570-1577 (2015).31. Koes, D. et al. Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists. PLoS ONE 7, e32839 (2012).32. Koes, D. R., Dömling, A. & Camacho, C. J. AnchorQuery: Rapid online virtual screening for small-molecule protein-protein interaction inhibitors. Protein Sci 27, 229-232 (2018).33. Neochoritis, C. G. et al. Hitting on the move: Targeting intrinsically disordered protein states of the MDM2-p53 interaction. European Journal of Medicinal Chemistry 182, 111588 (2019).34. Groebke, K., Weber, L. & Mehlin, F. Synthesis of Imidazo[1,2-a] annulated Pyridines, Pyrazines and Pyrimidines by a Novel Three-Component Condensation. Synlett 1998, 661-663 (1998).35. Blackburn, C., Guan, B., Fleming, P., Shiosaki, K. & Tsai, S. Parallel synthesis of 3-aminoimidazo[1,2-a]pyridines and pyrazines by a new three-component condensation. Tetrahedron Letters 39, 3635-3638 (1998).36. Bienaymé, H. & Bouzid, K. A New Heterocyclic Multicomponent Reaction For the Combinatorial Synthesis of Fused 3-Aminoimidazoles. Angewandte Chemie International Edition 37, 2234-2237 (1998).37. Boltjes, A. & Dömling, A. The Groebke-Blackburn-Bienaymé Reaction. Eur J Org Chem 2019, 7007-7049 (2019).38. Shukla, P., Azad, C. S., Deswal, D. & Narula, A. K. Revisiting the GBB reaction and redefining its relevance in medicinal chemistry: A review. Drug Discovery Today 29, 104237 (2024).39. Devi, N., Singh, D., K. Rawal, R., Barwal, J. & Singh, V. Medicinal Attributes of Imidazo[1,2-a]pyridine Derivatives: An Update. CTMC 16, 2963-2994 (2016).40. Vasilikogiannaki, E., Gryparis, C., Kotzabasaki, V., Lykakis, I. N. & Stratakis, M. Facile Reduction of Nitroarenes into Anilines and Nitroalkanes into Hydroxylamines via the Rapid Activation of Ammonia- Borane Complex by Supported Gold Nanoparticles. Adv Synth Catal 355, 907-911 (2013).41. Degorce, F. HTRF: A Technology Tailored for Drug Discovery - A Review of Theoretical Aspects and Recent Applications. TOCHGENJ 3, 22-32 (2009). + +<--- Page Split ---> + +42. Vauquelin, G. & Charlton, S. J. Exploring avidity: understanding the potential gains in functional affinity and target residence time of bivalent and heterobivalent ligands. British J Pharmacology 168, 1771-1785 (2013). +43. Vickery, H. R., Virta, J. M., Konstantinidou, M. & Arkin, M. R. Development of a NanoBRET assay for evaluation of 14-3-3α molecular glues. SLAS Discov 29, 100165 (2024). +44. Hallenbeck, K. K. et al. A Liquid Chromatography/Mass Spectrometry Method for Screening Disulfide Tethering Fragments. SLAS Discov 2472555217732072 (2017) doi:10.1177/2472555217732072. + +Acknowledgements. This research was funded by the Ono Pharma Foundation Breakthrough Science Initiative Award, NIH/NIGMS GM147696 and the Netherlands Organization for Scientific Research (NWO) through Gravity program 024.001.035 and ENW M-grant OCENW.M20.200. We acknowledge Foundation for Research and Innovation (H.F.R.I.) under the "2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers" (Project Number: 0911) and Emperikion Idryma (to C.G.N). We thank Amanda Paulson for the automated mass spec data processing infrastructure in the SMDC. We acknowledge the European Synchrotron Radiation Facility (ESRF) for provision of synchrotron radiation facilities, and we would like to thank David Flot and Max Nanao for assistance and support in using beamlines ID23-1, ID23-2, ID30A-3 (mx2407 and mx2526). We thank DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III. + +## Author contributions. + +M.K. conceived the work, designed the compounds, performed the MS and TR-FRET experiments, and analyzed the data with contributions from C.O, L.C., C.G.N. and M.R.A. M.Z and M.F synthesized and characterized compounds. M.A.M.P solved most of the crystal structures and performed the SPR experiments. J.M.V. performed the NanoBRET assay. J.L.R. was involved in the development and optimization of the TR-FRET assay. E.M.J. solved the initial crystal structures. M.K., C.G.N., M.R.A, C.O. and L.B. supervised the project. M.K. wrote the manuscript with contributions from all authors. + +Conflict of interest. Michelle R. Arkin, Christian Ottmann and Luc Brunsveld are co- founders of Ambagon Therapeutics. + +Keywords: covalent • estrogen receptor • MCR • molecular glue • 14- 3- 3 + +TOC + +![PLACEHOLDER_17_0] + + +We describe a scaffold- hopping approach suitable for the identification of molecular glues stabilizing the 14- 3- 3α/ERα complex. The multi- component reaction- based scaffold was rapidly optimized, and validated in biophysical assays, while several co- crystal structures elucidated the binding modes. The best compounds were tested in a cellular NanoBRET assay, showing low micromolar potency. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +20250210MCRSupplement.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2_det.mmd b/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..680c6f98e33046d5034d1b68fdb858f9bd7d86ff --- /dev/null +++ b/preprint/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2/preprint__09caa31641cd2fa62399cf94c4caec3caa098ba1b127a492b84d0583324ff5f2_det.mmd @@ -0,0 +1,385 @@ +<|ref|>title<|/ref|><|det|>[[42, 106, 912, 210]]<|/det|> +# Rapid scaffold-hopping for molecular glues: from fragments to cell-active probes targeting the 14-3-3/ERα complex + +<|ref|>text<|/ref|><|det|>[[42, 230, 315, 277]]<|/det|> +Michelle Arkin michelle.arkin@ucsf.edu + +<|ref|>text<|/ref|><|det|>[[42, 301, 760, 323]]<|/det|> +University of California at San Francisco https://orcid.org/0000- 0002- 9366- 6770 + +<|ref|>text<|/ref|><|det|>[[42, 327, 744, 370]]<|/det|> +Markella Konstantinidou University of California San Francisco https://orcid.org/0000- 0001- 5972- 4140 + +<|ref|>text<|/ref|><|det|>[[42, 374, 578, 417]]<|/det|> +Marios Zingiridis University of Crete https://orcid.org/0009- 0008- 1150- 2926 + +<|ref|>text<|/ref|><|det|>[[42, 421, 728, 464]]<|/det|> +Marloes Pennings Eindhoven University of Technology https://orcid.org/0000- 0002- 3366- 0238 + +<|ref|>text<|/ref|><|det|>[[42, 468, 228, 508]]<|/det|> +Michael Fragkiadakis University of Crete + +<|ref|>text<|/ref|><|det|>[[42, 514, 384, 556]]<|/det|> +Johanna Virta University of California San Francisco + +<|ref|>text<|/ref|><|det|>[[42, 561, 386, 602]]<|/det|> +Jezrael Revalde University of California, San Francisco + +<|ref|>text<|/ref|><|det|>[[42, 608, 179, 647]]<|/det|> +Emira Visser TU Eindhoven + +<|ref|>text<|/ref|><|det|>[[42, 653, 728, 696]]<|/det|> +Christian Ottmann Eindhoven University of Technology https://orcid.org/0000- 0001- 7315- 0315 + +<|ref|>text<|/ref|><|det|>[[42, 700, 536, 742]]<|/det|> +Luc Brunsveld TU Eindhoven https://orcid.org/0000- 0001- 5675- 511X + +<|ref|>text<|/ref|><|det|>[[42, 746, 578, 788]]<|/det|> +Constantinos Neochoritis University of Crete https://orcid.org/0000- 0001- 5098- 5504 + +<|ref|>sub_title<|/ref|><|det|>[[42, 827, 103, 845]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 865, 628, 886]]<|/det|> +Keywords: covalent, estrogen receptor, MCR, molecular glue, 14- 3- 3 + +<|ref|>text<|/ref|><|det|>[[42, 904, 336, 923]]<|/det|> +Posted Date: February 28th, 2025 + +<|ref|>text<|/ref|><|det|>[[42, 941, 475, 961]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 6051794/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 915, 87]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 105, 936, 149]]<|/det|> +Additional Declarations: Yes there is potential Competing Interest. M.R.A, C.O, and L.B. are co- founders of Ambagon Therapeutics. C.O. is an employee of Ambagon Therapeutics. + +<|ref|>text<|/ref|><|det|>[[42, 184, 912, 227]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 14th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 61176- 4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[115, 88, 881, 125]]<|/det|> +# Rapid scaffold-hopping for molecular glues: from fragments to cell-active probes targeting the 14-3-3/ERα complex + +<|ref|>text<|/ref|><|det|>[[115, 138, 881, 191]]<|/det|> +Markella Konstantinidou\*[a], Marios Zingiridis[b], Marloes A.M. Pennings[c], Michael Fragkiadakis[b], Johanna M. Virta[a], Jezrael L. Revalde[a], Emira J. Visser[c], Christian Ottmann[c], Luc Brunsveld[c], Constantinos G. Neochoritis\*[b], Michelle R. Arkin\*[a] + +<|ref|>text<|/ref|><|det|>[[115, 210, 784, 420]]<|/det|> +[a] M. Konstantinidou, J.M. Virta, J.L. Revalde, M.R. Arkin Department of Pharmaceutical Chemistry and Small Molecule Discovery Centre (SMDC) University of California San Francisco (UCSF) CA 94143 (USA) E- mail: markella.constantinidou@ucsf.edu, michelle.arkin@ucsf.edu [b] M. Zingiridis, M. Fragkiadakis, C.G. Neochoritis Department of Chemistry University of Crete Voutes, Heraklion, 70013, Greece E- mail: kneochor@uoc.gr [c] M.A.M. Pennings, E.J. Visser, C. Ottmann, L. Brunsveld Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems (ICMS) Eindhoven University of Technology 5600 MB Eindhoven, The Netherlands + +<|ref|>text<|/ref|><|det|>[[114, 465, 882, 695]]<|/det|> +Abstract: Molecular glues, small molecules that bind cooperatively at a protein- protein interface, have emerged as powerful modalities for the modulation of protein- protein interactions (PPIs) and "undruggable" targets. The systematic identification of new chemical matter with a molecular glue mechanism of action remains a significant challenge in drug discovery. Here, we present a scaffold hopping approach, using as a starting point our previously developed molecular glues for the native 14- 3- 3/estrogen receptor alpha (ERα) complex. The novel, computationally designed scaffold was based on the Groebke- Blackburn- Bienaymé multi- component reaction (MCR), leading to drug- like analogs with multiple points of variation, thus enabling the rapid derivatization and optimization of the scaffold. Structure- activity relationships (SAR) were developed using intact mass spectrometry and TR- FRET. Rational structure- guided optimization was facilitated by crystal structures of ternary complexes with the glues, 14- 3- 3 and phospho- peptides mimicking the highly disordered C- terminus of ERα. We measured the kinetics of 14- 3- 3/ERα peptide binding by SPR, using a format in which a 14- 3- 3/molecular glue complex was immobilized on the SPR chip. The most potent compounds stabilized the complex by 100- fold and increased the residence time by 14- fold. Cellular stabilization of 14- 3- 3/ERα for the most potent analogs was confirmed using a NanoBRET assay with full- length proteins in live cells (EC50 = 2.7 – 5 μM). Our approach highlights the potential of MCR chemistry, combined with scaffold hopping, to drive the development and optimization of unprecedented molecular glue scaffolds. + +<|ref|>sub_title<|/ref|><|det|>[[115, 717, 212, 731]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[115, 741, 882, 831]]<|/det|> +The stabilization of native protein- protein interactions (PPIs) with small molecules offers an attractive strategy for the activation or inhibition of signaling pathways in a therapeutic context. \(^{1,2}\) PPIs were traditionally considered difficult targets due to the lack of well- defined pockets and the presence of large, hydrophobic surfaces. \(^{3 - 5}\) The fundamental understanding of the mechanism of action of molecular glues – small molecules that bind cooperatively at PPI interfaces and strengthen weak, pre- existing interactions – has enabled the stabilization of PPIs by taking into account the elements of cooperativity, molecular recognition and shape complementarity. \(^{6,7}\) + +<|ref|>text<|/ref|><|det|>[[115, 844, 882, 904]]<|/det|> +A particularly challenging class of PPIs includes proteins that are intrinsically disordered and only become partially structured when bound to a protein partner, such as a chaperone binding to a client protein. \(^{8,9}\) 14- 3- 3 is an abundant scaffolding protein that recognizes specific phospho- serine or phospho- threonine motifs on disordered domains of the client and upon binding creates a structured binding interface. \(^{10}\) Molecular glues targeting 14- 3- 3/client complexes bind + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 882, 149]]<|/det|> +to the composite surface formed at this interface; the inherent cooperativity of this approach yields molecular glues with selectivity and potency. Of note, 14- 3- 3 proteins lack function – the function is instead encoded on the client protein and in particular on the phospho- site that is being recognized, leading either to activation or inhibition of signaling pathways. \(^{11}\) + +<|ref|>text<|/ref|><|det|>[[115, 161, 882, 326]]<|/det|> +Among the extensive interactome of 14- 3- 3, here we focus on its native interaction with the hormone regulated transcription factor estrogen receptor \(\alpha\) (ERα). 14- 3- 3 recognizes the protein sequence surrounding phospho- T594 on the disordered C- terminus on the F- domain of ERα and acts as a negative regulator by inhibiting ERα binding to chromatin and blocking ERα- mediated transcription. \(^{12,13}\) To date, ERα small molecule drugs, acting either as inhibitors or degraders, target the adjacent ligand binding domain (LBD). \(^{14 - 17}\) However, mutations in the LBD are often associated with acquired endocrine resistance. \(^{18}\) Thus, stabilization of the native 14- 3- 3/ERα PPI could be useful as an alternative strategy to block ERα transcriptional activity in ERα positive breast cancer, especially in cases of acquired endocrine resistance. The feasibility of this approach, targeting the F- domain, is corroborated by studies using the natural product fusicoccin- A (FC- A) and its semi- synthetic analogs that stabilize the interaction between 14- 3- 3 and the C- terminus of ERα. \(^{19}\) We now require drug- like chemical probes to define the biological impact of targeting the F- domain to inhibit ERα in hormone- positive breast cancer. + +<|ref|>text<|/ref|><|det|>[[115, 339, 882, 474]]<|/det|> +We have applied different strategies for the identification of chemical matter to stabilize the 14- 3- 3/ERα complex. We used a site- directed fragment- based technology, termed "disulfide tethering" with intact mass spectrometry as the readout to identify reversible fragments bound at the native cysteine (C38) of 14- 3- 3α in the presence of a phosphorylated peptide that represented the disordered C- terminus of ERα. \(^{20}\) Rational, structure- guided optimization of the reversible disulfide fragments led to irreversibly covalent, selective molecular glues that bound at the composite surface of 14- 3- 3α/ERα. \(^{21}\) For the development of non- covalent molecular glues, we used a fragment- linking approach, derived from the crystal structures of two diverse fragments that were identified in crystallographic and disulfide tethering screens. \(^{22}\) Thus, the 14- 3- 3/ERα PPI has served as a valuable system to test diverse molecular- glue discovery strategies. + +<|ref|>text<|/ref|><|det|>[[115, 486, 882, 636]]<|/det|> +Here, we present a scaffold- hopping approach based on multi- component reaction chemistry (MCR). Multi- component reactions are defined as synthetic approaches where at least three starting materials react in a single step to form a complex scaffold, where most of the atoms contribute to the newly formed product. This broad definition covers reactions with various synthetic mechanisms. \(^{23,24}\) MCR chemistry, due to its highly divergent character, is an enticing strategy for developing new scaffolds and rapid structure- activity relationships (SAR), as it allows the combination of short synthetic routes with high diversity and complexity. MCR has emerged as an attractive alternative to multistep linear convergent synthetic approaches and has been successfully applied to the synthesis of active pharmaceutical ingredients (API). \(^{25 - 30}\) Here, we describe our strategy for the development of a drug- like MCR scaffold stabilizing the 14- 3- 3α/ERα complex. The most potent analogs of the series showed efficacy in orthogonal biophysical assays and cell- based PPI stabilization in the low micromolar range. + +<|ref|>sub_title<|/ref|><|det|>[[115, 651, 175, 665]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[117, 680, 378, 694]]<|/det|> +## Structure activity relationships (SAR) + +<|ref|>text<|/ref|><|det|>[[115, 695, 882, 770]]<|/det|> +Structurally, 14- 3- 3 binds ERα by recognizing phospho- T594, the penultimate residue on the C- terminus of ERα. This creates a large, open, solvent- exposed pocket that can accommodate a small molecule. Although steric factors are not an issue for targeting the composite surface of the 14- 3- 3/ERα complex, we found it was important to rigidify an initially flexible scaffold to maximize the stabilization effect. \(^{21}\) Our aim in this work was to design a scaffold that would be more rigid from the beginning, locking in a favorable three- dimensional shape complementary to the large pocket. + +<|ref|>text<|/ref|><|det|>[[115, 784, 882, 904]]<|/det|> +To this end, we used the freely accessible software AnchorQuery™, which performs pharmacophore- based screening of approximately 31 million compounds that are readily synthesizable through one- step multi- component reaction (MCR) chemistry. \(^{31,32}\) Although AnchorQuery™ was originally developed for PPI inhibitors \(^{33}\) , in this case it was successful in proposing MCR scaffolds for PPI stabilizers. AnchorQuery™ requires a ligand- bound crystal structure or docked binding pose as a starting point. We used the crystal structure of the previously disclosed compound 127 (PDB 8ALW) that was bound at the composite surface of the 14- 3- 3α/ERα complex, with a favorable ligand conformation, based on our biophysical data. \(^{21}\) The compound formed multiple favorable interactions both with 14- 3- 3α and the phospho- ERα peptide (Fig 1A). In the co- crystal structure, the irreversible chloroacetamide warhead of compound 127 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 136]]<|/det|> +formed a covalent bond with C38 of 14- 3- 3o. The \(p\) - chloro-phenyl ring occupied a small hydrophobic pocket that formed a halogen bond with K122 of 14- 3- 3. The tetrahydropyrane ring adopted a favorable conformation that allowed the formation of hydrophobic interactions with 14- 3- 3 residues (L218, I219), the terminal Val595 of ERα, and a water- + +<|ref|>image<|/ref|><|det|>[[123, 145, 890, 797]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[140, 796, 894, 901]]<|/det|> +
Figure 1. Overview of the scaffold hopping approach. SAR and crystal structures of selected benzyl and aniline analogs. A) Crystal structure of compound 127 (yellow sticks) with 14-3-3o (grey surface) and phospho-ERα peptide (orange sticks). Interacting aminoacids are shown as sticks and water molecules as red spheres (PDB 8ALW). B) Ligand overlay of compound 127 and docking pose of the new MCR scaffold. C) General MCR scaffold and main points of variation. D) Overview of general synthetic routes. Detailed experimental conditions are described in the SI. E) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. F) Crystal structure overlay for compounds 1 (cyan sticks) and 127 (yellow sticks) bound to 14-3-3o (grey surface) /ERα (orange sticks). G) Crystal structure of compound 1 (cyan sticks) with 14-3-3o/ERα. Interacting aminoacid residues are shown as sticks and interacting water molecules as red spheres. H-I) Structural overlays of compounds 2 (brown sticks), and 10 (dark red sticks) with compound 1 (cyan sticks).
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 883, 164]]<|/det|> +mediated hydrogen bond from the oxygen atom in the ring, which was part of a large water network. The overall ligand conformation also led to a key water- mediated hydrogen bond between the aniline nitrogen and the terminal carboxylic acid of Val595 of ERα, significantly contributing to molecular recognition. In this compound series, the introduction of large non- aromatic rings, such as the tetrahydropyrane, combined with aniline rings were necessary to limit the multiple ligand conformations and improved the affinity to the complex. + +<|ref|>text<|/ref|><|det|>[[114, 175, 883, 358]]<|/det|> +Understanding the binding mode of 127, we were able to use AnchorQueryTM for a scaffold- hopping approach. The software required an anchor motif on the ligand that was bioisosteric to an amino acid residue and was kept constant for all pharmacophore- based searches of the database. A suitable anchor in our case was the \(p\) - chloro- phenyl ring that was deeply buried at the PPI interface in the small pocket surrounding K122 of 14- 3- 3 (Fig S1). This motif served as the "phenylalanine anchor". Selection of three additional pharmacophore points on different parts of the ligand were necessary for running queries and could include all possible ligand- protein interactions, such as hydrogen bond donors or acceptors, aromatic rings, hydrophobic residues or charged ions. We applied an additional filter, limiting the molecular weight of the hits to 400 Da. We screened all 27 MCR reactions, but interestingly all our top hits were based on the Groebke- Blackburn- Bienaymé (GBB) three- component reaction (GBB- 3CR).34- 36 The GBB- 3CR utilized aldehydes, 2- aminopyridines and isocyanides, leading to imidazo[1,2- a]pyridines.37,38 This privileged scaffold has been present in several clinical candidates and marketed drugs, such as zolpidem, mipropfen, minodronic acid, and olprinone.39 + +<|ref|>text<|/ref|><|det|>[[115, 369, 883, 460]]<|/det|> +Comparison of the docking pose of the proposed GBB compounds with the co- crystal structure of compound 127 revealed a considerable 3D shape complementarity of the two scaffolds (Fig 1B). Additionally, the GBB scaffold was drug- like and more rigid compared to our original ligand, potentially restricting the possible ligand conformations. Further docking and selection of suitable substituents to achieve favorable ligand – protein interactions were performed with the docking software SeeSAR [version 14.0.0; BioSolveIT GmbH, Sankt Augustin, Germany, 2024, www.biosolveit.de/SeeSAR]. + +<|ref|>text<|/ref|><|det|>[[114, 471, 883, 668]]<|/det|> +Once compounds were selected for synthesis, synthetic routes were established using commercially available aldehydes and 2- aminopyridines, whereas isocyanides were synthesized from primary aromatic amines (Fig 1C- 1D). Two general, three- step synthetic routes were developed based on the type of aldehyde building blocks used. In the first synthetic route (route A), Boc- protected aldehydes reacted with 2- aminopyridines and isocyanides in methanol, using scandium triflate as a Lewis acid catalyst to form the GBB intermediate. The Boc- protecting group was removed under acidic conditions and the chloroacetamide warhead was introduced with an amidation reaction, either with chloroacetyl chloride or an amide coupling. In the second synthetic route (route B), to reduce the cost of certain Boc- protected aldehydes, nitro- substituted aromatic aldehydes were used instead. The GBB- 3CR was performed under the same experimental conditions, followed by a nitro- reduction. The nitro- group was reduced either using iron trichloride and zinc or using ammoniotrihydroborate and gold catalysts with Au/TiO2.40 The two reduction methods led to comparable, almost quantitative yields. The last step, as previously, was the introduction of the electrophilic warhead. Thus, the GBB- 3CR allowed multiple combinations of the three main building blocks, facilitating the synthesis of analogs. Synthetic details are provided in the Supplementary Information. + +<|ref|>text<|/ref|><|det|>[[115, 679, 882, 739]]<|/det|> +To test whether this scaffold was suitable for the development of 14- 3- 3α/ERα stabilizers, we synthesized a small set of derivatives varying only the isocyanide position, which according to our design was expected to interact with K122 of 14- 3- 3. We included benzyl and phenyl isocyanides with a diverse substitution pattern on the aryl ring. We maintained the covalent chloroacetamide warhead, based on our extensive investigation of electrophiles in our previous work.21 + +<|ref|>text<|/ref|><|det|>[[114, 751, 883, 873]]<|/det|> +The compounds were tested in an intact mass spectrometry (MS) assay, which monitored the formation of the covalent bond with C38 of 14- 3- 3α. Binding measurements were made in the presence or absence of the phospho- ERα peptide and as a function of time to distinguish between cooperative stabilizers and neutral binders and to select compounds with fast binding kinetics. The phospho- ERα peptide was used at a concentration 2- fold above the dissociation constant (Kd). The assay was performed as a compound titration (Fig S2- S3). We additionally quantified compound binding at 1 μM (10:1 compound:protein) over several time points (table S2). Throughout this work, we reference the time- course data as bar graphs, using grey bars to represent binding to 14- 3- 3α alone ("apo") and colored bars to represent binding to 14- 3- 3 α/ERα complex. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 255]]<|/det|> +Testing the first analogs in the MS assay revealed striking differences among benzyl and phenyl analogs (Fig 1E, Fig S4). The benzyl analog 1, which lacked aryl ring substitutions, was a neutral binder. Introduction of electron withdrawing groups, such as halogens or nitro- groups in the o-, m- or p- position (compounds 2- 9) reduced binding in the presence of ERα, even in the case of a small F- substitution. Interestingly, the non- substituted phenyl analog 10 showed increased binding to 14- 3- 3o, but still lacked cooperativity, since it showed increased binding in the presence or absence of ERα. Introduction of halogens in the p- position significantly reduced apo binding, improving compound cooperativity (compounds 11 and 12). However, in contrast to the SAR observed in our previous series21, less binding was observed for the p- Cl analog (11), compared to the non- substituted analog (10). Remarkably, a Me- group in the o- position (13) led to the first molecular glue of the series; compound 13 showed binding in the presence of ERα and very low apo binding. Bigger substitutions in the o- position significantly decreased the observed binding, indicating unfavorable steric effects (14- 16) (Fig S4). + +<|ref|>text<|/ref|><|det|>[[114, 266, 883, 555]]<|/det|> +We solved co- crystal structures of the 14- 3- 3o/ERα complex with compounds 1, 2 and 10 to elucidate their binding modes (Fig 1F- I). The crystal structure of compound 1 revealed a comparable binding mode to the original ligand 127, supporting the design and docking hypothesis (Fig 1F- G). The chloroacetamide moiety of 1 bound covalently to C38 of 14- 3- 3o, and two water- mediated hydrogen bonds were formed between the carbonyl group and R41 and the backbone of E115, and one direct hydrogen bond with N42 of 14- 3- 3 at 3A. The imidazopyridine ring was positioned towards the hydrophobic residues (L218, I219, G171 and L222) at the "roof" of the 14- 3- 3 pocket. In addition, the nitrogen of the five- membered ring formed a hydrogen bond with D215 of 14- 3- 3, which adopted two conformations upon binding of compound 1. Importantly, a large water network was formed between the benzylamine of 1 to N42, S45 of 14- 3- 3, which reached the terminal carboxylic acid of V595 and the pT594 of ERα, and K122 of 14- 3- 3. The benzyl ring, adjacent to the electrophilic warhead was positioned between the hydrophobic 14- 3- 3 residues F119 and I168, (Fig 1G). The structural overlay of analogs 1 and 2 showed an identical binding mode with the additional o- F substituent forming a halogen bond with I219 of 14- 3- 3 (Fig 1H). Replacing the benzyl ring (1) with a phenyl ring (10) resulted in a slight turn of the compound that placed the aryl rings of both analogs in the same position in the pocket surrounding K122. The water network that connected the amine of compound 1 to residues of 14- 3- 3 and ERα was conserved upon modification of the benzyl ring (1) to a phenyl ring (10). The removal of the methylene group positioned the aniline nitrogen of (10) closer the terminal carboxylic acid of ERα, in the position previously occupied by the methylene group of (1). Additionally, it led to increased axial rotation of the bond between the nitrogen and the aryl ring, which might have resulted in the increased apo binding in the MS assay, since this axial rotation was not possible for the benzyl analogs (Fig 1I). + +<|ref|>text<|/ref|><|det|>[[115, 564, 882, 702]]<|/det|> +Since the o- Me substitution correlated with improved binding in the presence of ERα in the MS assay and taking into account the rotational nature of the aniline - phenyl ring bond, we designed and synthesized a series of analogs with double o- substitutions (Fig 2A, Fig S5). The symmetrical 2,6- di- Me analog 17 showed significantly faster binding in the presence of ERα and almost no apo binding, indicating molecular- glue- like binding. Analog 18 with an o- Et group in addition to o- Me, was weaker, indicating unfavorable steric effects (Fig S5). Analogs 19, 20, and 21 with additional o- OMe, o- F and o- Cl groups respectively, showed similar, rapid binding to the symmetric analog 17 and low apo binding. In agreement with our previous observation, the introduction of additional substitutions in the p- position (- Cl, - Me, - OH) significantly reduced binding (compounds 22- 25), especially for the triple substituted analogs (double o- and p- positions). + +<|ref|>text<|/ref|><|det|>[[115, 713, 883, 910]]<|/det|> +Co- crystal structures of the 14- 3- 3o/ERα complex with compounds 17, 19, 20, 21, and 25 revealed differences in the positions of the double- ortho substituents (Fig 2B- E). In the case of the symmetric analog 17 one o- Me group was oriented in the back of the pocket, which was primarily hydrophobic, forming hydrophobic interactions with I219 (3.2A) and facing Val595 of ERα. The second o- Me group was oriented in the front of the pocket and formed hydrophobic interactions with F119 (3.8 A). The formation of these interactions seemed to favorably restrict the axial rotation of the aniline- phenyl bond, locking its position in a highly complementary shape with the composite 14- 3- 3/ERα surface. In analog 19 the larger o- OMe group was positioned in the front of the pocket and formed, together with the aniline nitrogen, a water- mediated hydrogen bond with the terminal carboxylic acid of Val595 of ERα. The orientation of the warhead amide differed in two analogs, but in both cases a direct hydrogen bond with N42 of 14- 3- 3 was formed (3.0 A). For the halogen- containing analogs o- F (20) and o- Cl (21) the halogens interacted with I219 and the common o- Me group with F119, whereas the aniline nitrogen interacted with the terminal carboxylic acid of Val595 of ERα via a water mediated- hydrogen bond. The binding mode of the triple substituted analog 25, although overall comparable with the double substituted analog 17, showed an additional hydrogen bond between the p- OH group and the backbone + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 883, 120]]<|/det|> +carbonyl group of I168 (3.2 Å). The presence of this direct interaction seemed to negatively affect the axial rotation of the aniline-phenyl bond, resulting in significantly reduced binding in the MS assay and loss of cooperativity. + +<|ref|>image<|/ref|><|det|>[[116, 135, 886, 441]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[124, 444, 881, 495]]<|/det|> +
Figure 2. SAR and crystal structures of selected double-ortho substituted analogs. A) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-E) Crystal structures of 14-3-3o/ERα with compounds 17 (dark pink sticks), 19 (teal sticks) and overlays of crystal structures for compounds 17 (dark pink sticks), 20 (pale green sticks), 21 (pale purple sticks) and 25 (bright yellow sticks).
+ +<|ref|>text<|/ref|><|det|>[[115, 516, 882, 608]]<|/det|> +We synthesized analogs of compound 21, maintaining the 2- Cl,6- Me substitutions and varied positions X and Y on the scaffold (Fig 3A, Fig S6). Position X referred to substitutions on the imidazopyridine ring and was expected to contribute to additional hydrophobic interactions with Val595 of ERα. Position Y included modifications that were expected to improve interactions with the rim of the 14- 3- 3 pocket, primarily with variations of the aldehyde building blocks and to a smaller extent with different electrophilic warheads. In both cases, even the introduction of small groups led to surprising effects in the binding modes, as revealed by crystal structures. + +<|ref|>text<|/ref|><|det|>[[115, 620, 882, 802]]<|/det|> +For position X, aiming for hydrophobic interactions with Val595 of ERα, we introduced groups with a range of radii (- F (26), - Cl (27), - Me (28), - i- Pr (29), - CF3 (30)). Steric effects seemed to play a bigger role than electronic effects, since the best tolerated substitutions were - Cl (27) and - Me (28). For position Y, derived from the aldehyde building blocks, the o- F analog (31) was tolerated, but was weaker compared to the unsubstituted (21); a plausible explanation being the rotational nature of the aryl ring- imidazopyridine ring bond, which lost axial symmetry with the presence of the o- F group. Introduction of a methylene linker (32) or replacement of the aryl ring with a cyclohexyl ring (33) that had a similar size made the compounds almost unable to bind in the MS assay. Modifications in the position of the electrophilic warhead were also not tolerated. The less- reactive ester analog (34) was inactive, whereas halogenated chloroacetamides (35- 37) did not lead to the expected mass adducts, showing instability in the MS and unclear chemical reactivity with 14- 3- 3. A plausible explanation was their increased reactivity, which correlated with reduced stability. The sulfamate warhead (38) was tolerated in the MS assay, but was significantly weaker than the chloroacetamide analog, in addition to being less atom efficient. + +<|ref|>text<|/ref|><|det|>[[115, 814, 882, 905]]<|/det|> +Crystal structures were solved for 28, 32, and 33 and were compared as overlays with analog 21 (Fig 3B- E). The - Me group on the imidazopyridine ring of 28 was expected to contribute to hydrophobic interactions with Val595 of ERα. However, it disrupted the previously observed binding mode and instead moved the imidazopyridine ring upwards, in the hydrophobic pocket of 14- 3- 3 formed by L218, I219 and L222. This upward movement affected the conformation of L218, which moved upwards but maintained contact with 28. The movement in the pocket also altered the interactions of the double- ortho- substituted aryl ring; while the o- Cl still interacted with I219 on the roof of the binding site, the o- Me + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 883, 300]]<|/det|> +group moved further away from F119 in the bottom of the pocket, and the interaction was lost. The direct hydrogen bond between the amide and N42 and the water- mediated bond between the aniline nitrogen and the carboxylic acid of Val595 were maintained. Overall, the changes in binding mode were associated with slower binding, but a similar apparent Kd in the MS assay (Fig. S3). Analog 32, with an additional methylene group on the electrophilic warhead linker showed a slightly disrupted binding mode. While the biggest difference was the position of the aryl ring next to the longer linker, the imidazopyridine ring also moved further away from ERα; the latter correlated negatively with the binding observed in the MS assay. In contrast to the previous analogs, the o- Cl group on the aryl ring of 32 pointed out of the 14- 3- 3 pocket and due to increased distance, was unable to interact with F119. Thus, the presence of an additional methylene group on the linker was sufficient to make the compound unable to stabilize the complex. Analog 33 bearing a cyclohexyl ring instead of an aromatic ring maintained the interaction between the o- Cl group of the aryl ring and Ile219, but not the interaction of the o- Me group with F119. The orientation of the amide bond next to the warhead also differed; however, the hydrogen bond with N42 was still formed. The presence of the cyclohexyl ring, which had the possibility of adopting more conformations compared to the more rigid aryl ring, correlated with reduced binding to the 14- 3- 3/ERα complex in the MS assay. + +<|ref|>image<|/ref|><|det|>[[120, 320, 884, 636]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[120, 639, 884, 688]]<|/det|> +
Figure 3. SAR and crystal structures of analogs substituted in positions X and Y. A) MS bar graphs at 1 \(\mu \mathrm{M}\) . For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-E) Crystal structure of 14-3-3o/ERα with compound 28 (dark yellow sticks), and overlays of crystal structures for compounds 28 (dark yellow sticks), 32 (pink sticks) and 33 (emerald green sticks) with 21 (pale purple sticks).
+ +<|ref|>text<|/ref|><|det|>[[114, 725, 883, 877]]<|/det|> +Investigation of modifications in positions X and Y provided valuable input in the groups that were tolerated; however, these single- point substitutions did not significantly improve the stabilization effect in the MS assay. Based on the crystallographic input, we synthesized four analogs combining the favorable substitutions from positions X and Y with the symmetric double o- Me analog 17, hypothesizing that a symmetric rotatable bond would correlate with improved potency (Fig 4A). This hypothesis was readily confirmed by evaluating the symmetric analogs 39- 42 in the MS assay. Analogs 39 and 40 included the - Me group on the imidazopyridine ring (X position) and the - F group on aryl ring (referred to as W position), respectively. Analogs 41 and 42 both had the F group in W position and a - Me or - Cl group in X position, respectively. All analogs showed comparable, fast, cooperative binding to 14- 3- 3/ERα in the MS assay, indicating that the symmetry of the 2,6- di- Me- aniline ring was more favorable compared to the asymmetric 2- Cl,6- Me. All four analogs showed low apo binding with analog 41 showing the lowest. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[95, 90, 925, 740]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[97, 740, 912, 825]]<|/det|> +
Figure 4. SAR and crystal structures of 2,6-di-Me analogs substituted in positions X and W. Biophysical data (MS, TR-FRET, SPR) and cell data (NanoBRET). A) MS bar graphs at 1 μM. For each compound, time course experiments were performed with measurements at 1h, 8h, 16h and 24h. ERα data are shown with different colors for each compound, and apo data in grey. B-C) Overlays of crystal structures of 14-3-3α/ERα ternary complexes with compounds 40 (orange sticks), 41 (red sticks), and 42 (blue sticks). D) TR-FRET schematic and protein titration data for representative compounds at 100 μM compound or DMSO. E) SPR data for the binary 14-3-3α/ERα interaction and ternary interactions with 181, 17 and 41 (mean +/- SD, n=2). F-G) 14-3-3α-HaloTag/Nuc-ERα NanoBRET assay in HEK293T cells with compound titrations (1:2 dilution, starting at 40 μM). Data points excluded where compound dosage was toxic to the cells. MCR compounds compared to the previously described stabilizer 181 and 85, an inactive compound as the negative control. Bar graphs quantifying pEC50 values.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 114, 883, 343]]<|/det|> +Crystal structures were solved for the symmetric analogs 40, 41 and 42, highlighting the significance of two rotational bonds in the scaffold: the aniline- aryl ring bond, as previously discussed and the aryl ring- imidazopyridine ring bond (Fig 4B- C). The o- F substituent in W position, adjacent to the aryl ring- imidazopyridine rotational bond, adopted two different orientations; an inward orientation for analog 40 and an outward orientation for 41 and 42, which each had an additional substituent on the imidazopyridine ring. With the inward conformation of analog 40, the o- F substituent interacted with P167 of 14- 3- 3, whereas for 41 and 42 the o- F group was oriented to made multiple stabilizing interactions, being approximately 3 Å from N42 of 14- 3- 3, and the o- Me substituent and aniline nitrogen of the compound itself, thus contributing both to ligand- protein interactions and intramolecular interactions, restricting the axial bond rotation. In agreement with previous observations, the presence of a substituent in the X position (41 and 42) led to an upward movement of the imidazopyridine ring, orienting the substituent toward the hydrophobic residues I219 and L222. Overall, the presence of the additional substituent on the imidazopyridine ring in the last two analogs, even though in a distant position compared to the o- F- substituted ring, correlated favorably with the restriction of the rotational bond on the F- aryl ring. The different substituents of the imidazopyridine ring, Me (41) or Cl (42), did not affect the position of the stabilizer in the crystal structures; however, in the MS assay the Me group showed lower apo binding to 14- 3- 3, resulting in higher cooperativity of the ternary complex. + +<|ref|>sub_title<|/ref|><|det|>[[116, 355, 182, 368]]<|/det|> +## TR-FRET + +<|ref|>text<|/ref|><|det|>[[114, 369, 883, 519]]<|/det|> +Up to this point, the SAR was developed using the intact MS assay and supported by crystallography. In our previous work \(^{20 - 22}\) , we relied on a fluorescence anisotropy assay (FA) to confirm cooperativity. The FA assay typically used (5- carboxylfluorescein) FAM- labeled phospho- peptides to quantify peptide binding to the compound/14- 3- 3 complex. For the MCR scaffold, however, this assay proved to be unsuitable, since the extensively conjugated ring system was intrinsically fluorescent in the same wavelengths as the FAM- labeled peptide (480 nm and 520 nm). To circumvent this issue, we turned our attention to far- red fluorescent dyes. The cy5- labeled ERα- peptide with excitation wavelength of 651 nm and emission wavelength of 670 nm, significantly affected the Kd of the 14- 3- 3/ERα complex. The reported Kd using the acetylated ERα peptide in an isothermal calorimetry (ITC) experiment or the FAM- labeled- ERα in an FA assay is in the range of 1- 2 μM. \(^{21}\) The cy5- ERα- peptide, however, resulted in a Kd of 6 nM, indicating significant dye binding (Fig S7). + +<|ref|>text<|/ref|><|det|>[[114, 532, 883, 669]]<|/det|> +As a suitable alternative to the FA assay, we developed a TR- FRET assay \(^{41}\) , using HIS- tagged- full- length 14- 3- 3σ, biotin- labeled- ERα peptide, an anti- HIS- tag monoclonal antibody conjugated with a Tb(III) criptate as the donor and streptavidin conjugated with the D2 dye as the acceptor. The observed Kd for this system was 30 nM. To ensure that the observed difference in Kd from ITC or the FAM- labeled- ERα in an FA assay was not related to the biotin- tag on the peptide, we performed a competition assay with the FAM- labeled peptide. The Kd of the biotin- peptide was 2.5 μM, in good agreement with the FAM- peptide's Kd (Fig S7). This result suggested that the lower Kd in the TR- FRET assay was due to avidity \(^{42}\) ; since 14- 3- 3 and the antibody were both dimers and streptavidin was tetravalent, different multivalent complexes could form, resulting in differences in the observed binding affinity between the labeled and the unlabeled proteins. + +<|ref|>text<|/ref|><|det|>[[114, 683, 883, 805]]<|/det|> +We performed assay optimization with 2D- titrations of 14- 3- 3, biotin- ERα and donor/acceptor ratios. We then tested the synthesized compounds using the optimized experimental conditions: 200 nM 14- 3- 3 (top concentration, 2- fold dilution), 50 nM biotin- ERα, 0.166 nM MAb anti- 6HIS Tb and 6.25 nM SA- D2. The compounds were tested at 100 μM and DMSO was used a negative control. 14- 3- 3 was titrated using an Echo acoustic dispenser, followed by the addition of the compounds and the peptide; after 1h incubation at room temperature, the donor and acceptor were added. In contrast to the MS assay, maximum signal was obtained after 2h rather than 16h - a plausible explanation again being avidity. The tight Kd in the TR- FRET assay resulted in a small assay window and a hook effect was observed at higher protein concentrations (ca. \(50 - 100\) nM 14- 3- 3). + +<|ref|>text<|/ref|><|det|>[[114, 817, 883, 907]]<|/det|> +Nevertheless, the TR- FRET assay was sensitive to the addition of molecular glues. In addition to quantifying fold stabilization for the 14- 3- 3/ERα complex \((AppKd_{(compound)} / AppKd_{(DMSO)})\) , we also quantified the fold increase in the TR- FRET signal (the ratio of observed Emax and Emin). To validate the TR- FRET assay, we included two positive controls: the natural product FC- A and our previously described stabilizer compound \(181^{21}\) . The two compounds gave comparable results. FC- A had an \(AppKd_{(compound)}\) of 11 nM, fold- stabilization of 3.09, and fold- increase of 5.52. Compound 181 had an \(AppKd_{(compound)}\) of 8.6 nM, fold- stabilization of 3.95 and fold- increase of 5.52 (Fig S8, table S3). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 300]]<|/det|> +In good agreement with the MS data, neutral binders 1 and 10 showed only a small signal shift compared to the DMSO control (Fig 4D, Fig S8). The 2- Me analog 13 showed 4.19- fold- stabilization and 2.36- fold increase, whereas 2,6- di- Me analog 17 showed 4.78- fold- stabilization and 2.58- fold increase, the same rank order as the MS assay. The more sterically hindered analog 18 (o- Me, o- Et) showed weaker stabilization (3.06- fold- stabilization and 2.14- fold increase). Analogs 19, 20, and 21, all bearing the o- Me group and additional o- OMe, o- F and o- Cl groups respectively, showed comparable stabilization (4.41 – 5.23- fold stabilization and 2.1 – 2.48- fold increase). The highest stabilization effect was observed for the final symmetric analogs 39 – 42. Analog 39 with a - Me group in X position had an AppKd(compound) of 7.8 nM, fold- stabilization of 4.35 and fold- increase of 2.61, whereas 40 with the - F group in W position had an AppKd(compound) of 8.4 nM, fold- stabilization of 4.04 and fold- increase of 3.12. Analog 41, which had both the - Me group in X position and the - F group in the W position had the lowest AppKd(compound) (5.2 nM) and the highest fold- stabilization (6.53) and fold- increase (3.71) of the series. Analog 42, which differed only in the X position (- Cl instead of - Me group) appeared weaker (AppKd(compound) 8.8 nM, fold- stabilization of 3.87 and fold- increase of 2.97). In summary, while the fold- changes were dampened by avidity, 14- 3- 3/ERα molecular glues showed the same rank- order in the TR- FRET assay as in the mass spectrometry assay used for initial SAR. + +<|ref|>sub_title<|/ref|><|det|>[[116, 313, 150, 326]]<|/det|> +## SPR + +<|ref|>text<|/ref|><|det|>[[114, 328, 883, 585]]<|/det|> +Surface Plasmon Resonance (SPR) was then used to analyze the kinetic parameters of the ERα peptide binding to 14- 3- 3α in the presence of compounds 181, 17 and 41, and to compare the AppKd(compound) and kinetics to the binary ERα/14- 3- 3α interaction (Fig 4E, Fig S9). Here, 14- 3- 3α tagged with a Twinstrept- tag was captured on a SPR chip coated with Strep- Tactin XT, after which a 2- fold dilution series of acetylated ERα phospho- peptide was injected. For the binary interaction, the fast dissociation rate (koff) reached the limit of detection of the SPR instrument, due to a relative weak interaction with a Kd of 1.1 μM, which was in line with the ITC and FA experiments. The covalent bond between the chloroacetamide warhead of the compounds and C38 of 14- 3- 3α was formed after overnight incubation in the presence of ERα. Immobilization of this complex on the chip, followed by extensive washing to remove the bound ERα peptide, allowed us to determine the kinetics of ERα binding to the 14- 3- 3α/stabilizer complex. The previously described stabilizer 181 decreased the off- rate to 0.016 s- 1 and simultaneously increased the association rate (kOn) by a factor of 16, resulting in a low nanomolar affinity constant for the ERαpeptide/14- 3- 3 complex (3.9 nM; stabilization = 282- fold). The analogs of the newly designed scaffold, 17 and 41, both led to an 8- fold increase in association rate compared to the binary interaction. The binding of compound 41 induced a stronger decrease in dissociation rate of ERα compared to 17, which resulted in Kd values of 10.1 nM and 15.1 nM for 41 and 17, respectively. The higher stabilizing potency of 41 compared to 17 (stabilization = 110- fold for 41 and 71- fold for 17) were in rank- order agreement with the TR- FRET data. The decreased dissociation rates in the presence of the stabilizers, especially for 41 and 181, increased the residence time of ERα binding to 14- 3- 3 from approximately 3.4 s to 47.6 s and 62.5s, respectively. + +<|ref|>sub_title<|/ref|><|det|>[[116, 599, 196, 612]]<|/det|> +## NanoBRET + +<|ref|>text<|/ref|><|det|>[[114, 613, 883, 867]]<|/det|> +To test the effects of the compounds on the full- length PPI, we used a NanoBRET assay we developed previously43. Compounds were tested using a C- terminal fusion14- 3- 3α- HaloTag and full length, N- terminal fusion NanoLuc- ERα (Fig 4F- G; Table S4). Briefly, NanoLuc- ERα and 14- 3- 3α- HaloTag plasmids were transfected in 1:10 ratio in hormone deprived HEK293T cells. After 48 hours post transfection, cells were seeded in assay plates with the experimental wells treated with 100 nM HaloTag NanoBRET 618 ligand (Promega) and no- ligand control wells treated with DMSO (v/v). Cells were treated for 24 hours with compounds in 1:2 dilution series starting at 40 μM. The BRET signal was read and normalized against DMSO treated samples. All active compounds resulted in an increase in BRET signal compared to the negative control, 85. The previously described compound 18121 stabilized the 14- 3- 3α/ERα complex in cells with an EC50 value of 5.2 μM and a 1.7- fold increase in the BRET signal. Of the compounds tested, the neutral binder 10 was less effective than 181 (EC50 value > 100 μM, 1.6- fold increase). Compound 13 showed an improved EC50 and fold- increase compared to 10 (12 μM and 1.8- fold). Compound 17 had the lowest EC50 value of 2.7 μM and showed 1.8- fold increase in BRET signal, a slight improvement compared to 181. Compounds 40 and 42 had the same 2- fold- increases in BRET signal, though exhibited slightly different EC50 values (7.2 and 4.6 μM, respectively). Compound 41 resulted in the highest increase in BRET signal, a 2.6- fold increase; however, it had a similar EC50 value to 181 of 5 μM. In the NanoBRET assay, compounds 17 and 41 performed most effectively, based on the EC50 value (17) and fold- increase in BRET signal (41) of the panel of compounds tested. The majority of MCR stabilizers showed similar EC50 values to the previous scaffold represented by 181, but consistently showed improved fold- increase in BRET signal. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 883, 195]]<|/det|> +Compounds 181, 17, and 41, along with the negative control, 85, were tested in a NanoBRET assay where the cysteine of interest in 14- 3- 3α C38, was mutated to an asparagine, 14- 3- 3σC38N- HaloTag (Fig S10). The BRET signal did not increase for 85, 181, or 17 with increasing compound concentration. There was a minimal increase in BRET signal for compound 41, from 2.5 to 3.5 mBU at 20 μM 41. In comparison to the assays done with 14- 3- 3σWT- HaloTag, the non- normalized BRET signal increased from 6.7 to 16.1 mBU at the same concentration of 41 (data shown as normalized). In summary, when C38 was not present in 14- 3- 3σ, the compounds were unable to bind and stabilize the full- length 14- 3- 3σ/ERα complex in cells (Fig S10). + +<|ref|>sub_title<|/ref|><|det|>[[115, 208, 204, 223]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 232, 883, 308]]<|/det|> +Using MCR chemistry, we demonstrated the potential of a scaffold- hopping approach for molecular glues stabilizing a native 14- 3- 3/cient PPI. Our approach combined computational de novo design with multi- component reaction chemistry, which included short, efficient synthetic routes with multiple points of variation, accelerating the synthesis of analogs. Thus two distinct optimization approaches led to two highly diverse chemical series, converging on similar efficacies as 14- 3- 3σ/ERα molecular glues. + +<|ref|>text<|/ref|><|det|>[[115, 319, 883, 471]]<|/det|> +In the MCR approach, structure- activity relationships were established with an intact mass spectrometry assay, which allowed the distinction between neutral binders and stabilizers - cooperative molecular glues. Overall, the SAR showed that the introduction of even small substituents in the case of molecular glues could profoundly affect their potency and cooperativity. The best compounds, eg, 41, was already \(50\%\) bound at \(1\mu M\) after \(1\mathrm{hr}\) of incubation. Crystal structures of ternary complexes were crucial in elucidating small changes in the binding modes of otherwise highly similar analogs. Starting from the weak neutral binder 1 and by removing one methylene group, we significantly increased binding to the complex for compound 10. Introduction of substituents in the o- position was sufficient to reduce apo binding and turn the compounds from neutral binders to cooperative molecular glues. Although the number of rotational bonds is generally kept to a minimum, in this case we took advantage of two rotational bonds in the scaffold and with appropriate substituents achieved favorable ligand conformations resulting in increased potency, as shown for analogs 17 and 41. + +<|ref|>text<|/ref|><|det|>[[115, 483, 883, 652]]<|/det|> +Several biophysical assays provided complementary strategies for evaluating these novel molecular glues. A TR- FRET assay circumvented the issue of scaffold fluorescence, which could have been a limiting factor in establishing SAR and allowed the rank- ordering of analogs. A new SPR assay, in which the covalent ligand was pre- associated with 14- 3- 3σ before immobilization, provided an insightful analysis of binding/unbinding kinetics and started to hint at differences between the two series of molecular glues. Saturating concentrations of compound 41, for instance, stabilized the ERα/14- 3- 3 complex by 110- fold and slowing the koff by 14- fold. Taken together, biophysical assays allowed the quantification of cooperativity (MS, TR- FRET) and kinetics (SPR); importantly, the observed SAR was consistent among these three assays. The NanoBRET assay further showed good correlation with the biophysical protein/phosphopeptide assays while demonstrating stabilization of the full- length proteins with an EC50 value of \(2.7 - 5\mu M\) in intact cells. Overall, the optimized, cell- active MCR scaffold will facilitate chemical biology approaches to study the 14- 3- 3/ERα interaction, which has so far been unexploited for drug discovery. + +<|ref|>sub_title<|/ref|><|det|>[[115, 661, 184, 676]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 686, 437, 700]]<|/det|> +## PROTEIN EXPRESSION AND PURIFICATION + +<|ref|>text<|/ref|><|det|>[[115, 701, 883, 896]]<|/det|> +The 14- 3- 3 α isoform (full- length for mass spectrometry and TR- FRET assays, \(\Delta C\) for crystallography) with an N- terminal His6 tag was expressed in Rosetta™ 2(DE3)PLysS competent E. coli (Novagen) from a pPROEX HTb expression vector. After transformation following manufacturer's instructions, single colonies were picked to inoculate \(30~\mathrm{mL}\) precultures (LB), which were added to \(1.5\mathrm{L}\) terrific broth (TB) medium after overnight growth at \(37^{\circ}C\) , 250 rpm. Expression was induced upon reaching \(\mathrm{OD}_{600}\) \(1.9 - 2.1\) by adding \(400~\mu \mathrm{M}\) IPTG. After overnight expression at \(30^{\circ}C\) , \(150~\mathrm{rpm}\) , cells were harvested by centrifugation at \(6,500~\mathrm{rpm}\) , resuspended in lysis buffer ( \(50~\mathrm{mM}\) HEPES pH 7.5, 500 mM NaCl, \(20~\mathrm{mM}\) imidazole, \(10\%\) glycerol, \(1\mathrm{mM}\) TCEP), and lysed by sonication. The His6- tagged protein was purified by Ni- affinity chromatography (Ni- NTA Agarose, Invitrogen) (Wash buffer \(50~\mathrm{mM}\) HEPES pH 7.5, \(500~\mathrm{mM}\) NaCl, \(20~\mathrm{mM}\) imidazole, \(1\mathrm{mM}\) TCEP; Elution buffer \(50~\mathrm{mM}\) HEPES pH 7.5, \(500~\mathrm{mM}\) NaCl, \(500~\mathrm{mM}\) imidazole, \(1\mathrm{mM}\) TCEP) and analyzed for purity by SDS- PAGE and Q- Tof LC/MS. The protein was buffer exchanged (Storage buffer \(25~\mathrm{mM}\) HEPES pH 7.5, \(150~\mathrm{mM}\) NaCl, \(1~\mathrm{mM}\) TCEP) and concentrated to \(\sim 16~\mathrm{mg / mL}\) and aliquots flash- frozen for storage at \(- 80^{\circ}C\) . The \(\Delta C\) variant was truncated at the C- terminus after T231 to enhance crystallization and after the first Ni- affinity chromatography column, the construct was treated with TEV protease to cleave off the His6 tag during dialysis ( \(25~\mathrm{mM}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 883, 164]]<|/det|> +HEPES, pH 7.5, 200 mM NaCl, 5% glycerol, 10 mM MgCl2, 250 μM TCEP) overnight at \(4^{\circ}C\) . The flow- through of a second Ni- affinity column was subjected to a final purification step by size exclusion chromatography (Superdex 75 pg 16/60 size exclusion column (GE Life Science) (SEC buffer: 25 mM HEPES pH 7.5, 100 mM NaCl, 10 mM MgCl2, 250 μM TCEP). The protein was concentrated to \(\sim 60\) mg/mL, analyzed for purity by SDS- PAGE and Q- Tof LC/MS and aliquots flash- frozen for storage at \(- 80^{\circ}C\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 180, 191, 193]]<|/det|> +## PEPTIDES + +<|ref|>text<|/ref|><|det|>[[115, 195, 883, 241]]<|/det|> +Peptides for mass spectrometry, fluorescence anisotropy and TR- FRET assays were purchased from Elim Biopharmaceuticals, Inc. (Hayward, CA). Peptides for SPR and X- ray crystallography was purchased from GenScript Biotech Corp. The following peptides were used: + +<|ref|>text<|/ref|><|det|>[[115, 241, 540, 315]]<|/det|> +Ac- KYYITGEAEGFPA(pT)V- COOH (MS assay, 15- mer), 5- FAM- AEGFPA(pT)V- COOH (FA assay, 8mer ERα- pp), cy5- KYYITGEAEGFPA(pT)V- COOH (FA assay, 15- mer), biotin- KYYITGEAEGFPA(pT)V- COOH (TR- FRET assay, 15- mer), Ac- EGFPA(pT)V- COOH (crystallography and SPR, 7- mer) + +<|ref|>sub_title<|/ref|><|det|>[[115, 330, 395, 344]]<|/det|> +## INTACT MASS SPECTROMETRY ASSAY + +<|ref|>text<|/ref|><|det|>[[115, 345, 883, 540]]<|/det|> +Mass spectrometry dose response assays were performed on a Waters Acquity UPLC/ Xevo G2- XS Q- Tof mass spectrometer. A Waters UPLC Protein BEH- C4 Column (300 A, \(1.7 \mu \mathrm{m}\) , \(2.1 \mathrm{mm} \times 50 \mathrm{mm}\) ) was used to desalt the samples prior to application on the mass spectrometer. For 19- point MS dose responses, \(50 \mathrm{mM}\) compound stocks in DMSO were serially diluted in 3- fold increment in a master plate, then \(1000 \mathrm{nl}\) of the compounds were transferred in the assay plates. Master mixes containing \(100 \mathrm{nM}\) full- length wild type 14- 3- 3α in the absence or presence of \(2 \mu \mathrm{M}\) ERα were then dispensed into 384 well plates (Greiner Bio- One, catalog number 784201). Assay buffer was TRIS (10 mM, pH 8.0) and final volume per well was \(50 \mu \mathrm{l}\) , with final top concentration of compounds dose response series at 1 mM. The reaction mixtures were incubated for 1h at rt before subjected to MS. Four measurements (1h, 8h, 16h, 24h) were performed for time- course experiments. The injection volume for each sample was \(6 \mu \mathrm{l}\) . \(24 \mu \mathrm{l}\) of sample were needed for the time- course experiments, so the total volume in the assay plate was adjusted to \(50 \mu \mathrm{l}\) , to account for the dead volume in the injections. Data collection and automated processing followed a custom workflow, as previously described. \(^{44}\) z Plots were created using GraphPad Prism with the log(agonist) vs. response (variable slope, four parameters) fitting model. + +<|ref|>sub_title<|/ref|><|det|>[[115, 555, 648, 569]]<|/det|> +## \(\mathbf{K}_{\mathrm{D}}\) DETERMINATION FOR FAM-, cy5- AND BIOTIN-LABELED ERα PEPTIDES + +<|ref|>text<|/ref|><|det|>[[115, 570, 883, 720]]<|/det|> +For \(\mathrm{K}_{\mathrm{D}}\) determination, N- terminal fluorescein- labeled ERα peptide (5- FAM) or cy5- labeled ERα peptide and HIS- tag FL 14- 3- 3α were diluted in buffer (10mM HEPES pH 7.5, 150mM NaCl, \(0.05\%\) tween 20, \(0.05\%\) BGG (bovine gamma globulin)). Two- fold dilution series of 14- 3- 3 were made in black, round- bottom 384- microwell plates (Greiner Bio- one 784900) in a final sample volume of \(10 \mu \mathrm{L}\) in triplicates. FAM- or cy5- labeled ERα peptides (final assay concentration 10nM) were dissolved in assay buffer and mixed with the protein dilution series on the plates. Fluorescence anisotropy measurements were performed after 1h incubation at room temperature on an Envision HTS Dual Detector 2105 plate reader (for FAM- labeled ERα peptide: filter set lex: 480, lem: 535, and D505fp/D538 advanced dual mirror). For cy5- labeled ERα peptide filter set lex: 620, lem: 688 nm, and D658fp/D688 advanced dual mirror). Data were reported at endpoint. Prism 10 (GraphPad) was used to generate plots using the [agonist] vs. response (variable slope, four parameters) fitting model to determine \(\mathrm{K}_{\mathrm{D}}\) values. + +<|ref|>text<|/ref|><|det|>[[115, 720, 883, 870]]<|/det|> +For \(\mathrm{K}_{\mathrm{D}}\) determination of the biotin- labeled ERα peptide a competition assay was performed. 5- FAM- and biotin- labeled ERα peptide were diluted in buffer (10mM HEPES pH 7.5, 150mM NaCl, \(0.05\%\) tween 20, \(0.05\%\) BGG (bovine gamma globulin)). Two- fold dilution series of biotin- labeled ERα peptide were made in black, round- bottom 384- microwell plates (Greiner Bio- one 784900) in a final sample volume of \(10 \mu \mathrm{L}\) in triplicates. A mastermix of 14- 3- 3α and 5- FAM- ERα was dispensed on the assay plate (final assay concentrations: \(6 \mu \mathrm{M}\) 14- 3- 3α (IC80) and \(10 \mathrm{nM}\) 5- FAM- ERα). Fluorescence anisotropy measurements were performed after 1h incubation at room temperature using a Molecular Devices ID5 plate reader (filter set lex: \(485 \pm 20 \mathrm{nm}\) , lem: \(535 \pm 25 \mathrm{nm}\) ; integration time: \(50 \mathrm{ms}\) ; settle time: \(0 \mathrm{ms}\) ; shake 5 sec, medium, read height \(3.00 \mathrm{mm}\) , G- factor \(= 1\) ). Data were reported at endpoint. Prism 10 (GraphPad) was used to generate plots using the [agonist] vs. response (variable slope, four parameters) fitting model to determine \(\mathrm{K}_{\mathrm{D}}\) values. The obtained \(\mathrm{K}_{\mathrm{D}}\) value was corrected using the Cheng- Prusoff equation. + +<|ref|>text<|/ref|><|det|>[[115, 870, 375, 900]]<|/det|> +\(\mathrm{K}_{\mathrm{d}} = \mathrm{IC}_{50} / (1 + [\mathrm{S}] / \mathrm{Km})\) \(\mathrm{K}_{\mathrm{d}} = 6.9 / (1 + 50 \mathrm{nM} / 30 \mathrm{nM}) = 2.5 \mu \mathrm{M}\) + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[116, 103, 340, 118]]<|/det|> +## TR-FRET PROTEIN TITRATIONS + +<|ref|>text<|/ref|><|det|>[[114, 118, 883, 418]]<|/det|> +TR- FRET PROTEIN TITRATIONSFor assay optimization, 2D titrations of biotin- labeled ERα peptide, HIS- tag FL 14- 3- 3α and streptavidin- D2 were performed in assay buffer (10mM HEPES pH 7.5, 150mM NaCl, 0.05% tween 20, 0.05% BGG (bovine gamma globulin)). The donor (MAb anti- 6HIS Tb cryoplate gold) concentration was kept constant at 0.166 nM. For TR- FRET protein titrations, biotin- labeled ERα peptide (50 nM), the compounds or DMSO (100 μM), MAb anti- 6HIS Tb cryoplate gold (0.166 nM) and streptavidin- D2 (6.25 nM) were mixed in assay buffer (10mM HEPES pH 7.5, 150mM NaCl, 0.05% tween 20, 0.05% BGG (bovine gamma globulin)). 2- fold serial dilutions of HIS- tag FL 14- 3- 3α were performed (200 nM top assay concentration, 12- point dilution series). The assay was performed in 384- well microplates (Corning 4513, low volume white) at a volume of 10 μl per well. The following procedure was used: The compounds (50 mM stocks in DMSO) were transferred in echo LDV masterplates. 20 nL were transferred from the masterplate to the assay plate to achieve 100 μM compound concentration in the assay using Echo acoustic dispensing. The biotin- labeled ERα peptide was dissolved in assay buffer (10mM HEPES pH 7.5, 150mM NaCl, 0.05% tween 20, 0.05% BGG (bovine gamma globulin)) and dispensed in the assay plate using Dragonfly. 14- 3- 3 dilution series were prepared using Echo acoustic dispensing. Assay plates were incubated for 1hr at room temperature before the addition of a mastermix containing the donor (MAb anti- 6HIS Tb cryoplate gold) and acceptor (streptavidin- D2) in assay buffer. The mastermix was dispensed with Dragonfly. Assay plates were incubated for 2hr at room temperature prior to TR- FRET measurements using the Envision HTS Dual Detector 2105 plate reader equipped with the TR- FRET filter set (320/615/665 nm) and a D407/D630 advanced dual mirror. A 50 μs delay was employed to reduce background fluorescence. The TR- FRET signal was obtained through calculating the ratio of 665 nm to 615 nm fluorescence (x 1000), and Prism 10 (GraphPad) was used to generate plots using the [agonist] vs. response (variable slope, four parameters) fitting model. At least two independent experiments were performed. + +<|ref|>sub_title<|/ref|><|det|>[[115, 432, 148, 445]]<|/det|> +## SPR + +<|ref|>text<|/ref|><|det|>[[115, 446, 883, 688]]<|/det|> +SPRThe SPR experiments were performed at \(25^{\circ}C\) using a Biacore X100 and a 200 nm Strep- Tactic XT derivatized linear polycarboxylate hydrogel chip, medium charge density (XanTec Bioanalytics). All proteins and peptides were dissolved in fresh running buffer prepared with ultrapure water and filtered through a \(0.2 \mu m\) filter (10 mM HEPES pH 7.4, 200 mM NaCl, 50 μM EDTA, 0.005% P20). First the surface was conditioned with a 1 min injection of 3 M Guanidine HCl. Then, the recombinant 14- 3- 3α- Twinstrept protein (250 nM) was captured on flow cell 2 of the sensor chip at a flow rate of \(10 \mu L / min\) for 2 minutes, which resulted in a capture level of 1000 RU. For the ternary interaction, 14- 3- 3α- Twinstrept protein (250 nM) was first incubated overnight with \(1 \mu M\) Ac- ERα peptide and \(20 \mu M\) compound prior to immobilization to the chip. The bound ERα peptide was washed away using running buffer flowed over the chip for 15 min. Flow cell 1 was left blank as a reference surface. After immobilization of the protein, the Biacore X100 was primed with running buffer. Multi- cycle kinetic measurements were conducted at a flow rate of \(30 \mu L / min\) . A 2- fold dilution series of analyte (Ac- ERα peptide) in running buffer were injected over the sensor chip for 2 min, followed by dissociation of 3 min (binary interaction), 7 or 13 minutes (ternary interaction). For the binary interaction, the highest concentration of ERα was 50 μM, and for the ternary interactions this was 250 nM. Between cycles of one multi- cycle measurement, no regeneration step was performed due to complete dissociation of the analyte. After a measurement, the chip was regenerated by 2 times 30 sec injections of 3 M Guanidine HCl. The data was corrected by double subtracting to the reference surface (flow cell 1) and buffer injection and analyzed using 1:1 interaction fitting model with the BIA evaluation software (2020). + +<|ref|>sub_title<|/ref|><|det|>[[118, 701, 596, 716]]<|/det|> +## X-RAY CRYSTALLOGRAPHY DATA COLLECTION AND REFINEMENT + +<|ref|>text<|/ref|><|det|>[[115, 717, 883, 896]]<|/det|> +X- RAY CRYSTALLOGRAPHY DATA COLLECTION AND REFINEMENTThe 14- 3- 3αΔC protein, acetylated ERα and compounds (50 mM stock in DMSO) were dissolved in complexation buffer (25 mM HEPES pH=7.5, 2 mM MgCl₂ and 100 μM TCEP) and mixed in a 1:2:3 or 1:2:5 molecular stoichiometry (protein : peptide : compound) with a final protein concentration of 12 mg/mL. The complex was set- up for sitting- drop crystallization after overnight incubation at 4 °C, in a custom crystallization liquor (0.05 M HEPES (pH 7.1, 7.3, 7.5, 7.7), 0.19 M CaCl₂, 24- 29% PEG400, and 5% (v/v) glycerol). Crystals grew within 10- 14 days at 4 °C. Crystals were fished and flash- cooled in liquid nitrogen. X- ray diffraction (XRD) data were collected at the European Synchrotron Radiation Facility (ESRF Grenoble, France, beamline ID23- 1, ID30A- 3/MASSIF- 3, or ID23- 2) or at the Deutsches Elektronen- Synchrotron (DESY Hamburg, Germany, beamline PETRA III). Data was processed using CCP4i2 suite (version 8.0.019). After indexing and integrating the data, scaling was done using AIMLESS. The data was phased with MolRep, using PDB 4JC3 as template. The presence of co- crystallized ligands was verified by visual inspection of the Fo- Fc and 2Fo- Fc electron density maps in COOT (version 0.9.8.93). If electron density corresponding to the co- crystallized ligand was present, its structure, restraints, and covalent bond were generated using AceDRG. After + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 883, 149]]<|/det|> +building in the ligand, model rebuilding and refinement was performed using REFMAC5. The PDB REDO server (pdb- redo.edu) was used to complete the model building and refinement. The images were created using the PyMOL Molecular Graphics System (Schrödinger LLC, version 4.6.0). See SI table S10 for data collection and refinement statistics. + +<|ref|>text<|/ref|><|det|>[[115, 163, 881, 194]]<|/det|> +The structures were deposited in the protein data bank (PDB) with IDs: 916S (28), 916T (32), 916U (33), 916V (40), 916W (41), 916X (42), 916Y (1), 916Z (2), 9170 (17), 9171 (19), 9172 (10), 9173 (20), 9174 (21), and 9175 (25). + +<|ref|>sub_title<|/ref|><|det|>[[115, 210, 195, 222]]<|/det|> +## NanoBRET + +<|ref|>text<|/ref|><|det|>[[115, 223, 883, 359]]<|/det|> +NanoBRET assays were performed as previously described.43 HEK293T cells were cultured DMEM, high glucose (Gibco) supplemented with \(10\%\) charcoal stripped Fetal Bovine Serum (FBS; Gibco) and \(1\%\) penicillin/streptomycin. Cells were transfected with a 1:10 ratio of Nanoluc- ERa:14- 3- 3o- HaloTag plasmid for 48 hours using jetOPTIMUS transfection reagent (Polyplus). Cells were then seeded at 8,000 cells per well in a 384- well plate (Corning #3570) in FluoroBrite DMEM (phenol red- free; Gibco) with \(4\%\) charcoal stripped FBS and treated with \(100~\mathrm{mM}\) HaloTag NanoBRET 618 Ligand (Promega) or equivalent volume of DMSO as a no ligand negative control. Following plating, cells were treated for 24 hours with compound in 1:2 dilution series starting at \(40~\mu \mathrm{M}\) (0.35% DMSO final concentration). After 24 hours, the BRET signal was read using an EnVision XCite 2105 plate reader at 618 nm (HaloTag) and 460 nm (NIuc). The final corrected NanoBRET ratio was calculated using the following equation: + +<|ref|>equation<|/ref|><|det|>[[252, 371, 744, 404]]<|/det|> +\[CorrectedBRETratio = \left(\frac{618nm}{460nm}\right)_{HaloTagLigand} - \left(\frac{618nm}{460nm}\right)_{NoLigandcontrol}\] + +<|ref|>text<|/ref|><|det|>[[115, 420, 545, 435]]<|/det|> +The BRET ratios were normalized to samples treated with DMSO. + +<|ref|>sub_title<|/ref|><|det|>[[115, 451, 187, 464]]<|/det|> +## DOCKING + +<|ref|>text<|/ref|><|det|>[[115, 464, 881, 493]]<|/det|> +Computational design for SAR optimization and docking was performed with SeeSAR version 14.0.0; BioSolveIT GmbH, Sankt Augustin, Germany, 2022, www.biosolveit.de/SeeSAR + +<|ref|>sub_title<|/ref|><|det|>[[115, 504, 280, 517]]<|/det|> +## SOFTWARE VERSIONS + +<|ref|>text<|/ref|><|det|>[[115, 517, 881, 545]]<|/det|> +Prism (10.2.1), Illustrator (22.1 (64- bit)), Biorender (64- bit), Pymol (4.6.0), CCP4i2 (8.0.003), COOT (0.9.8.1), Phenix (1.19.2- 4158) + +<|ref|>text<|/ref|><|det|>[[115, 558, 881, 588]]<|/det|> +Supporting Information. Supplementary figures and tables, synthetic procedures, compound characterization, NMR spectra, crystallography data (PDF). + +<|ref|>sub_title<|/ref|><|det|>[[115, 603, 197, 616]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[112, 616, 880, 905]]<|/det|> +1. Andrei, S. A. et al. Stabilization of protein-protein interactions in drug discovery. Expert Opin Drug Discov 12, 925-940 (2017). +2. Bier, D., Thiel, P., Briels, J. & Ottmann, C. Stabilization of Protein-Protein Interactions in chemical biology and drug discovery. Progress in Biophysics and Molecular Biology 119, 10-19 (2015). +3. Arkin, M. R. & Wells, J. A. Small-molecule inhibitors of protein-protein interactions: progressing towards the dream. Nat Rev Drug Discov 3, 301-317 (2004). +4. Arkin, M. R., Tang, Y. & Wells, J. A. Small-Molecule Inhibitors of Protein-Protein Interactions: Progressing toward the Reality. Chemistry & Biology 21, 1102-1114 (2014). +5. Smith, M. C. & Gestwicki, J. E. 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Structure-Based Optimization of Covalent, Small-Molecule Stabilizers of the 14-3-3/ERα Protein-Protein Interaction from Nonselective Fragments. J. Am. Chem. Soc. 145, 20328-20343 (2023).22. Visser, E. J. et al. From Tethered to Freestanding Stabilizers of 14-3-3 Protein-Protein Interactions through Fragment Linking. Angew Chem Int Ed 62, e202308004 (2023).23. Dömling, A., Wang, W. & Wang, K. Chemistry and Biology Of Multicomponent Reactions. Chem. Rev. 112, 3083-3135 (2012).24. Zarganes-Tzitzikas, T., Chandgude, A. L. & Dömling, A. Multicomponent Reactions, Union of MCRs and Beyond. The Chemical Record 15, 981-996 (2015).25. Fotopoulou, E., Anastasiou, P. K., Tomza, C. & Neochoritis, C. G. The Ugi reaction as the green alternative towards active pharmaceutical ingredients. Tetrahedron Green Chem 3, 100044 (2024).26. Li, X., Zarganes-Tzitzikas, T., Kurpiewska, K. & Dömling, A. Amenamevir by Ugi-4CR. Green Chem. 25, 1322-1325 (2023).27. Zarganes-Tzitzikas, T., Neochoritis, C. G. & Dömling, A. Atorvastatin (Lipitor) by MCR. ACS Med. Chem. Lett. 10, 389-392 (2019).28. Znabet, A. et al. A highly efficient synthesis of telaprevir by strategic use of biocatalysis and multicomponent reactions. Chem. Commun. 46, 7918 (2010).29. Wehlan, H., Oehme, J., Schäfer, A. & Rossen, K. Development of Scalable Conditions for the Ugi Reaction—Application to the Synthesis of (R)-Lacosamide. Org. Process Res. Dev. 19, 1980-1986 (2015).30. Váradi, A. et al. Synthesis of Carfentanil Amide Opioids Using the Ugi Multicomponent Reaction. ACS Chem. Neurosci. 6, 1570-1577 (2015).31. Koes, D. et al. Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists. PLoS ONE 7, e32839 (2012).32. Koes, D. R., Dömling, A. & Camacho, C. J. AnchorQuery: Rapid online virtual screening for small-molecule protein-protein interaction inhibitors. Protein Sci 27, 229-232 (2018).33. Neochoritis, C. G. et al. Hitting on the move: Targeting intrinsically disordered protein states of the MDM2-p53 interaction. European Journal of Medicinal Chemistry 182, 111588 (2019).34. Groebke, K., Weber, L. & Mehlin, F. Synthesis of Imidazo[1,2-a] annulated Pyridines, Pyrazines and Pyrimidines by a Novel Three-Component Condensation. Synlett 1998, 661-663 (1998).35. Blackburn, C., Guan, B., Fleming, P., Shiosaki, K. & Tsai, S. Parallel synthesis of 3-aminoimidazo[1,2-a]pyridines and pyrazines by a new three-component condensation. Tetrahedron Letters 39, 3635-3638 (1998).36. Bienaymé, H. & Bouzid, K. A New Heterocyclic Multicomponent Reaction For the Combinatorial Synthesis of Fused 3-Aminoimidazoles. Angewandte Chemie International Edition 37, 2234-2237 (1998).37. Boltjes, A. & Dömling, A. The Groebke-Blackburn-Bienaymé Reaction. Eur J Org Chem 2019, 7007-7049 (2019).38. Shukla, P., Azad, C. S., Deswal, D. & Narula, A. K. Revisiting the GBB reaction and redefining its relevance in medicinal chemistry: A review. Drug Discovery Today 29, 104237 (2024).39. Devi, N., Singh, D., K. Rawal, R., Barwal, J. & Singh, V. Medicinal Attributes of Imidazo[1,2-a]pyridine Derivatives: An Update. CTMC 16, 2963-2994 (2016).40. Vasilikogiannaki, E., Gryparis, C., Kotzabasaki, V., Lykakis, I. N. & Stratakis, M. Facile Reduction of Nitroarenes into Anilines and Nitroalkanes into Hydroxylamines via the Rapid Activation of Ammonia- Borane Complex by Supported Gold Nanoparticles. Adv Synth Catal 355, 907-911 (2013).41. Degorce, F. HTRF: A Technology Tailored for Drug Discovery - A Review of Theoretical Aspects and Recent Applications. TOCHGENJ 3, 22-32 (2009). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 872, 170]]<|/det|> +42. Vauquelin, G. & Charlton, S. J. Exploring avidity: understanding the potential gains in functional affinity and target residence time of bivalent and heterobivalent ligands. British J Pharmacology 168, 1771-1785 (2013). +43. Vickery, H. R., Virta, J. M., Konstantinidou, M. & Arkin, M. R. Development of a NanoBRET assay for evaluation of 14-3-3α molecular glues. SLAS Discov 29, 100165 (2024). +44. Hallenbeck, K. K. et al. A Liquid Chromatography/Mass Spectrometry Method for Screening Disulfide Tethering Fragments. SLAS Discov 2472555217732072 (2017) doi:10.1177/2472555217732072. + +<|ref|>text<|/ref|><|det|>[[115, 194, 882, 325]]<|/det|> +Acknowledgements. This research was funded by the Ono Pharma Foundation Breakthrough Science Initiative Award, NIH/NIGMS GM147696 and the Netherlands Organization for Scientific Research (NWO) through Gravity program 024.001.035 and ENW M-grant OCENW.M20.200. We acknowledge Foundation for Research and Innovation (H.F.R.I.) under the "2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers" (Project Number: 0911) and Emperikion Idryma (to C.G.N). We thank Amanda Paulson for the automated mass spec data processing infrastructure in the SMDC. We acknowledge the European Synchrotron Radiation Facility (ESRF) for provision of synchrotron radiation facilities, and we would like to thank David Flot and Max Nanao for assistance and support in using beamlines ID23-1, ID23-2, ID30A-3 (mx2407 and mx2526). We thank DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III. + +<|ref|>sub_title<|/ref|><|det|>[[116, 336, 266, 349]]<|/det|> +## Author contributions. + +<|ref|>text<|/ref|><|det|>[[115, 349, 882, 427]]<|/det|> +M.K. conceived the work, designed the compounds, performed the MS and TR-FRET experiments, and analyzed the data with contributions from C.O, L.C., C.G.N. and M.R.A. M.Z and M.F synthesized and characterized compounds. M.A.M.P solved most of the crystal structures and performed the SPR experiments. J.M.V. performed the NanoBRET assay. J.L.R. was involved in the development and optimization of the TR-FRET assay. E.M.J. solved the initial crystal structures. M.K., C.G.N., M.R.A, C.O. and L.B. supervised the project. M.K. wrote the manuscript with contributions from all authors. + +<|ref|>text<|/ref|><|det|>[[115, 440, 881, 470]]<|/det|> +Conflict of interest. Michelle R. Arkin, Christian Ottmann and Luc Brunsveld are co- founders of Ambagon Therapeutics. + +<|ref|>text<|/ref|><|det|>[[116, 483, 592, 498]]<|/det|> +Keywords: covalent • estrogen receptor • MCR • molecular glue • 14- 3- 3 + +<|ref|>text<|/ref|><|det|>[[116, 525, 142, 537]]<|/det|> +TOC + +<|ref|>image<|/ref|><|det|>[[118, 556, 737, 766]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[128, 774, 728, 844]]<|/det|> +We describe a scaffold- hopping approach suitable for the identification of molecular glues stabilizing the 14- 3- 3α/ERα complex. The multi- component reaction- based scaffold was rapidly optimized, and validated in biophysical assays, while several co- crystal structures elucidated the binding modes. The best compounds were tested in a cellular NanoBRET assay, showing low micromolar potency. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 355, 150]]<|/det|> +20250210MCRSupplement.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/images_list.json b/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a837b44f1196ff812c085eaf9481374a088a65ec --- /dev/null +++ b/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/images_list.json @@ -0,0 +1,152 @@ +[ + { + "type": "image", + "img_path": "images/Supplementary_Figure_1.jpg", + "caption": "Figure 1. Embryonic stem cells and cancer cells share compositionally similar epipheroperomes. a Schematic illustrating the biochemical and functional distinctions between epipheroperomes, defined as long-lasting heterooligomeric assemblies composed of tightly associated chaperones and co-chaperones, and traditional chaperones. Unlike chaperones, which assist in protein folding or assembly, epipheroperomes sequester proteins, reshaping protein-protein interactions, and consequently altering cellular phenotypes. The schematic also outlines key principles for the use of PU-probes in epipheroperome analysis. b Detection of epipheroperome components (chaperones and co-chaperones) through SDS-PAGE (bottom, total protein levels) and native-PAGE (top), followed by immunoblotting. See also Supplementary Fig. 1. c Visualization of HSP90 in epipheroperomes using the PU-TCO click probe. See also Supplementary Fig. 2. Gel images are representative of three independent experiments. d Epipheroperome constituent chaperones and co-chaperones identified through mass spectrometry analyses of PU-beads cargo. Representative data of two independent experiments. See Supplementary Fig. 3 for the GA-cargo. e Illustration of an isobaric, discriminant peptide pair from ESC lysate samples and HSP90 captured by PU- and GA-beads. Representative data of two independent experiments. f Schematic summary. Both cancer cells and pluripotent stem cells harbor epipheroperomes. These epipheroperomes undergo disassembly during differentiation processes. Source data are provided in Supplementary Data 1 and in Source data file.", + "footnote": [], + "bbox": [ + [ + 75, + 72, + 936, + 666 + ] + ], + "page_idx": 34 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 95, + 35, + 880, + 768 + ] + ], + "page_idx": 35 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Phosphorylation of key residues located in the charged linker supports HSP90 incorporation into epicarpemores. a Experiment outline and expected outcomes. b Tandem MS spectra of HSP90 Ser226 (bottom) and Ser255 (top) phosphorylated peptides are presented, supporting the sequence and phosphorylation site identification. c Comparison of the extracted ion chromatogram of HSP90 Ser255 phosphopeptide in the PU-bead cargo (red trace, left panel) and ESC lysate (black trace, left panel) with a representative unmodified tryptic peptide in the PU-bead cargo (blue trace, right panel) and ESC lysate (black trace, right panel). d Ion intensity values of all phosphopeptides and the ratio of mean peptide intensity for each phosphosite in the samples described in panel a (n = 4 Ca and n = 2, NT). e Ratio of individual peptide intensity for each phosphosite in the samples described in the schematic (S255 n = 5; S226 n = 4; S263 n = 8; S231 n = 5). Source data are provided as Source Data file and as Supplementary Data 3,6.", + "footnote": [], + "bbox": [ + [ + 70, + 40, + 888, + 817 + ] + ], + "page_idx": 36 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. Phosphorylation of key residues located in the charged linker of HSP90 leads to a conformational shift in the linker, exposing the middle domain of the protein. A Model of the HSP90-HSP90-HSP70-HSP70-HOP assembly used for the molecular dynamics simulations. A and B, protomers A and B, respectively. b Protein secondary structure elements (SSE) like alpha-helices and beta-strands of the charged linker of protomer A of ATP-bound HSP90 monitored throughout the MD simulation. WT (HSP90 S226/S255), phosphomimetic (HSP90 S226E/S255E) and non-phosphorylatable (HSP90 S226A/S255A) mutants were analyzed. The plot on the left reports SSE distribution by residue index throughout the charged linker and the plot on the right monitors each residue and its SSE assignment over time. Schematic illustrating the primary structure of the full-length HSP90 with color-coded domains is also shown: NTD, N-terminal domain; MD, middle domain and CTD, C-terminal domain. The charged linker (CL) and the location of the two key serine residues are also shown (top inset). The gray bar indicates the CL segment encompassing residues 218 to 232. c Cartoon representation of ATP-bound HSP90 protomer A in assemblies containing the phosphomimetic (HSP90 S226E/S255E) or the non-phosphorylatable (HSP90 S226A/S255A) mutants is shown. Green, reference trajectory; gray, representative trajectories of \\(n = 1,000\\) . The inset illustrates the surfaces available for the interaction between HSP90 A and HSP70 A when the CL is in the 'up' conformation. A blue arrow indicates the location of the key beta-strand in the charged linker. See also Supplementary Figs. 5 and 6.", + "footnote": [], + "bbox": [ + [ + 50, + 60, + 911, + 666 + ] + ], + "page_idx": 37 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. Phosphorylation of key residues located in the charged linker of HSP90 facilitates assembly motions conducive to epichepaterome core formation. A Calculated dynamic cross-correlation matrix of Ca atoms around their mean positions for 100 ns molecular dynamics simulations. ATP-bound WT (HSP90 S226/ S255), phosphoimimetic (HSP90 S226E/S255E) and non-phosphorylatable (HSP90 S226A/S255A) mutantcontaining HSP90-HSP90-HSP70-HSP70-HOP assemblies were analyzed. The cartoon below captures the key motions among the different domains of the individual assembly components. Extents of correlated motions and anti-correlated motions are color-coded from blue to red, which represent positive and negative correlations, respectively. The assembly contains two full-length HSP90beta proteins (protomer A and protomer B). The two HSP70 proteins (HSP70 A and HSP70 B) and the HOP protein are of sizes reported, and as per the constructs used in 7KW7. b Cartoon showing assemblies that are preferentially formed when the HSP90 charged linker is either phosphorylated (as in the EE mutant) or not phosphorylated (as in the WT protein).", + "footnote": [], + "bbox": [ + [ + 52, + 54, + 911, + 737 + ] + ], + "page_idx": 38 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6", + "footnote": [], + "bbox": [ + [ + 110, + 264, + 911, + 560 + ] + ], + "page_idx": 39 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Figure 7. Phosphorylation of key residues located in the charged linker supports HSP90 incorporation into epicheporemes. a Overview of the experimental design and expected outcomes. b Analysis of transfection efficacy in cells transfected with HSP90β mutants, as indicated in panel a. c Detection of epicheporeme components (chaperones and co-chaperones) through SDS-PAGE (bottom, total protein levels) and native-PAGE (top), followed by immunoblotting. Blue brackets indicate the approximate position of epicheporeme-incorporated chaperones. Data are presented as mean ± s.e.m., \\(n = 3\\) , one-way ANOVA with Sidak's post-hoc, EE vs AA. d Visualization of HSP90 in epicheporemes using the PU-TCO probe clicked to Cy5 (left) and the mCherry tag (middle). Right, merged images. MWM, molecular weight marker. e Detection and quantification of epicheporeme components through PU-beads capture as indicated in panel a. Protein amount loaded for 'Input' represents 2% of the protein amount incubated with the beads. Data are presented as mean ± s.e.m., \\(n = 3\\) , unpaired two-tailed t-test. Gel images are representative of three independent experiments. Source data are provided as Source data file.", + "footnote": [], + "bbox": [ + [ + 52, + 75, + 888, + 666 + ] + ], + "page_idx": 40 + }, + { + "type": "image", + "img_path": "images/Figure_8.jpg", + "caption": "Figure 8. Phosphorylation of key residues located in the HSP90 charged linker favors ESC proliferation and self-renewal potential. a ESC proliferation at 60 h post-transfection in E14 cells transfected with either the phosphomimetic HSP90βS226E,S255E (EE) or the nonphosphorylatable HSP90S226A,S255A (AA) mutant. Medium (1x) or high (2x) plasmid concentrations were employed. Data are presented as mean ± s.e.m., \\(n = 6\\) , one-way ANOVA with Sidak's post-hoc, EE vs AA. b Representative spectra ( \\(n = 3\\) independent experiments) of phosphopeptides, S255P (left) and S226P (right), and a representative unmodified tryptic peptide (middle) in mCherry-tagged WT HSP90β affinity-purified from ESC (red) or differentiated trophoblast (black) cells. c Representative spectra ( \\(n = 3\\) independent experiments) of a tryptic peptide from Oct4 protein co-purified from ESCs labeled with heavy or light isotope lysine and arginine expressing either the phosphomimetic (EE) or the nonphosphorylatable (AA) HSP90 mutant. Quantitative analysis via mass spectrometry (MS) to determine protein abundance is shown. d Overview of the experimental design and expected outcomes (panels e,f). e,f Detection and quantification of Oct4 protein expressed in cells transfected with the indicated HSP90 mutants or vector control (panel e) and sequestered into the epicatherome platforms (identified through PU-beads capture, panel f). (e) Data are presented as mean ± s.e.m., \\(n = 5\\) AA, \\(n = 5\\) EE, \\(n = 3\\) WT, \\(n = 3\\) empty vector, one-way ANOVA with Dunnett's post-hoc, EE vs AA, WT vs AA, empty vector vs AA. (f) Data are presented as mean ± s.e.m., \\(n = 3\\) , unpaired two-tailed t-test. Source data are provided as Source Data file and Supplementary Data 5.", + "footnote": [], + "bbox": [ + [ + 91, + 102, + 875, + 666 + ] + ], + "page_idx": 41 + }, + { + "type": "image", + "img_path": "images/Figure_9.jpg", + "caption": "Figure 9", + "footnote": [], + "bbox": [ + [ + 90, + 100, + 800, + 549 + ] + ], + "page_idx": 42 + }, + { + "type": "image", + "img_path": "images/Figure_10.jpg", + "caption": "Figure 10. Human tissues positive for epicarpemores exhibit p-Ser226 HSP90β positivity, and conversely, those negative for epicarpemores show no or negligible p-Ser226 signal within HSP90's charged linker. a Cartoon illustrating the processing of human tissue for biochemical analyses. Both tumor (T) and tumor adjacent (TA) tissues, determined by gross pathological evaluation to be potentially non-cancerous, were harvested and analyzed. b MDA-MB-468 breast cancer cells (epicarpemore-high) and ASPC1 pancreatic cancer cells (epicarpemore-low) served as controls for assessing p-Ser226 HSP90 levels. c The graph presents the relationship between epicarpemore positivity and HSP90 Ser226 phosphorylation for tissues described in panel a. Data represent mean ± s.e.m., with \\(n = 9\\) tumor (T) and \\(n = 9\\) paired tumor-adjacent (TA) tissues classified based on epicarpemore positivity or negativity, as determined by Native PAGE (see panel d); unpaired two-tailed t-test. d Detection of epicarpemores through native-PAGE (top), and of p-Ser226 HSP90 (middle) and total HSP90 (bottom) by SDS-PAGE, followed by immunoblotting, in tissues from the indicated patient specimens, as in panel a. Blue brackets indicate the approximate position of epicarpemore-incorporated HSP90. Note: Obtaining genuinely \"normal\" tissue adjacent to tumors presents challenges, especially in the case of pancreatic tissue. The relatively small size of the organ and the nature of surgical procedures for pancreatic cancer often lead to the collection of normal samples in close proximity to the tumor. It's crucial to acknowledge that, due to these challenges, we designate potentially normal tissue as tumor-adjacent tissue, recognizing that it may not entirely reflect a truly normal tissue state. PDAC, Pancreatic Ductal Adenocarcinoma; IDC, Invasive Ductal Carcinoma; ILC, Invasive Lobular Carcinoma; ER, Estrogen Receptor; PR, Progesterone Receptor. Source data are provided as Source data file.", + "footnote": [], + "bbox": [ + [ + 137, + 55, + 840, + 700 + ] + ], + "page_idx": 43 + } +] \ No newline at end of file diff --git a/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf.mmd b/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d8bd0141cc5c7513f5b3605472b00660fdbf8f02 --- /dev/null +++ b/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf.mmd @@ -0,0 +1,617 @@ + +# Phosphorylation-Driven Epichaperome Assembly: A Critical Regulator of Cellular Adaptability and Proliferation + +Gabriela Chiosis chiosig@MSKCC.ORG + +Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0003- 0486- 6920 + +Seth McNutt University of New Hampshire + +Tanaya Roychowdhury MSKCC + +Chiranjeevi Pasala Memorial Sloan Kettering Cancer Center + +Hieu Nguyen University of New Hampshire + +Daniel Thorton University of New Hampshire + +Sahil Sharma Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0001- 7281- 9224 + +Luke Boticelli University of New Hampshire + +Chander Digwal MSKCC https://orcid.org/0000- 0001- 8784- 1096 + +Suhasini Joshi Memorial Sloan Kettering Cancer Center + +Nan Yang University of New Hampshire + +palak panchal msKCC + +Souparna Chakrabarty Memorial Sloan Kettering Cancer Center + +Sadik Bay Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0001- 8089- 1330 + +Vladimir Markov + +<--- Page Split ---> + +Memorial Sloan Kettering Cancer Center + +Charlene Kwong Memorial Sloan Kettering Cancer Center + +Jeanine Lisanti Memorial Sloan Kettering Cancer Center + +Sun Young Chung Harvard Medical School https://orcid.org/0000- 0002- 3381- 919X + +Stephen Ginsberg NYU https://orcid.org/0000- 0002- 1797- 4288 + +Pengrong Yan Memorial Sloan Kettering Cancer Center + +Elisa de Stanchina Memorial Sloan Kettering Cancer Center + +Adriana Corben MSKCC + +Shanu Modi Memorial Sloan Kettering Cancer Center + +Mary Alpaugh Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0002- 0008- 2718 + +Giorgio Colombo University of Pavia https://orcid.org/0000- 0002- 1318- 668X + +Hediye Erdjumet- Bromage New York University Grossman School of Medicine + +Thomas Neubert NYU Langone + +Robert Chalkley University of California, San Francisco https://orcid.org/0000- 0002- 9757- 7302 + +Peter Baker UCSF + +Alma Burlingame University of California https://orcid.org/0000- 0002- 8403- 7307 + +Anna Rodina Sloan Kettering Institute https://orcid.org/0000- 0002- 3894- 6438 + +Feixia Chu University of New Hampshire https://orcid.org/0000- 0002- 3209- 8095 + +Article + +Keywords: + +<--- Page Split ---> + +**Posted Date:** April 3rd, 2024 + +**DOI:** https://doi.org/10.21203/rs.3.rs- 4114038/v1 + +**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +**Additional Declarations:** Yes there is potential Competing Interest. Memorial Sloan Kettering Cancer Center holds the intellectual rights to the epichaperome portfolio. G.C., A.R. and S.S. are inventors on the licensed intellectual property. All other authors declare no competing interests. + +**Version of Record:** A version of this preprint was published at Nature Communications on October 16th, 2024. See the published version at https://doi.org/10.1038/s41467-024-53178-5. + +<--- Page Split ---> + +1 Phosphorylation- Driven Epichaperome Assembly: A Critical Regulator of Cellular Adaptability and Proliferation + +3 Seth W. McNutt1,12, Tanaya Roychowdhury2,12, Chiranjeevi Pasala2, Hieu T. Nguyen1, Daniel T. Thornton1, Sahil Sharma2, Luke Botticelli1, Chander S. Digwal2, Suhasini Joshi2, Nan Yang1, Palak Panchal2, Souparna Chakrabarty2, Sadik Bay2, Vladimir Markov3, Charlene Kwong3, Jeanine Lisanti3, Sun Young Chung2, Stephen D. Ginsberg4,5, Pengrong Yan2, Elisa DeStanchina3, Adriana Corben6, Shanu Modi7, Mary Alpaug2, Giorgio Colombo8, Hediye Erdument- Bromage9, Thomas A. Neubert9, Robert J. Chalkley10, Peter R. Baker10, Alma L. Burlingame10, Anna Rodina2, Gabriela Chiosis2,7,13, Feixia Chu1,11,13 + +10 1. Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 11 03824, USA. 12 2. Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 13 3. Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. +14 4. Departments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU 15 Grossman School of Medicine, New York, NY, 10016, USA. +16 5. Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, 10962, USA. +17 6. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. +18 7. Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. +19 8. Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy. +20 9. Department of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of 21 Medicine, New York, NY, 10016, USA. +22 10. Mass Spectrometry Facility, University of California, San Francisco, California 94143, USA. +23 11. Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH 03824, USA. + +12co- first author, equally contributed to the work. + +13These authors jointly supervised this work: Feixia Chu, Gabriela Chiosis. + +14Correspondence: feixia.chu@unh.edu (F.C.), chiosig@mskcc.org (G.C.). + +Abstract. The intricate protein- chaperone network is vital for cellular function. Recent discoveries have unveiled the existence of specialized chaperone complexes called epichepomeres, protein assemblies orchestrating the reconfiguration of protein- protein interaction networks, enhancing cellular adaptability and proliferation. This study delves into the structural and regulatory aspects of epichepomeres, with a particular emphasis on the significance of post- translational modifications in shaping their formation and function. A central finding of this investigation is the identification of specific PTMs on HSP90, particularly at residues Ser226 and Ser255 situated within an intrinsically disordered region, as critical determinants in epicheporeme assembly. Our data demonstrate that the phosphorylation of these serine residues enhances HSP90's interaction with other chaperones and co- chaperones, creating a microenvironment conducive to epicheporeme formation. Furthermore, this study establishes a direct link between epicheporeme function and cellular physiology, especially in contexts where robust proliferation and adaptive behavior are essential, such as cancer and stem cell maintenance. These findings not only provide mechanistic insights but also hold promise for the development of novel therapeutic strategies targeting chaperone complexes in diseases characterized by epicheporeme dysregulation, bridging the gap between fundamental research and precision medicine. + +<--- Page Split ---> + +## 1 Introduction + +Conventional wisdom, as crystallized in Beadle and Tatum's 1941 paradigm of "one gene- one enzyme- one function," has traditionally delineated targets as outcomes of protein expression changes or point mutations within proteins. However, it is increasingly apparent that protein dysfunctions in the context of many disorders, including cancer, neurodegenerative disorders, among others, are predominantly shaped by changes in interaction strengths and cellular mislocalization. These factors, in turn, can be modulated by variations in post- translational modifications (PTMs), stabilization of disease- associated protein conformations, and other protein- modifying mechanisms1,2. Within this complex context, Heat Shock Protein 90 (HSP90) emerges as a compelling exemplar, transcending the boundaries of conventional understanding3. + +Positioned as a versatile chaperone, often referred to as the guardian of the proteome, HSP90 assumes a pivotal task in the realm of maintaining cellular equilibrium by facilitating protein folding, stabilization, and degradation4. Under the canonical folding paradigm, HSP90 functions as a homodimer. Each promoter is composed of an N- terminal domain (NTD), a middle domain (MD), and a C- terminal dimerization domain (CTD)4,5. The NTD contains a nucleotide binding pocket, where ATP binding and hydrolysis takes place6. The chaperone cycle of HSP90 is coupled to a series of dynamic conformational changes accompanying its ATPase cycle. Beginning with NTD/MD and MD/CTD interdomain rotations and cross- monomer dimerization7, HSP90 transitions from open to closed conformational states, while folding client proteins8,9. HSP70 and HOP (HSP70- HSP90 organizing protein) bring client proteins to HSP90 and form the loading complex10. Other co- chaperones participate at different stages of the HSP90 chaperone cycle and regulate its conformational changes along the chaperone and ATPase cycle4. Co- chaperones may have different preferences for client proteins, fine- tuning subcellular pools of HSP90 to mitigate stressors and maintain proteostasis11. These assemblies are further shaped by PTMs in HSP90, co- chaperones and client proteins12. Overall, the highly orchestrated interactions among these proteins – both chaperones and clients – are transient in the chaperone cycle under physiological conditions. + +While this classical understanding portrays HSP90 as a dimeric entity that interacts dynamically with co- chaperones and client proteins, research has uncovered a spectrum of multimeric HSP90 forms, each sculpted by the cellular milieu and the presence of stress- inducing factors3. These multimers, whether homo- oligomeric or hetero- oligomeric, expand HSP90's functional repertoire, blurring the boundaries between traditional chaperone functions and newfound roles as holdases or scaffold proteins. In disease contexts, such as cancer and neurodegenerative disorders, HSP90's conformational adaptability gives rise to epichaperomes—distinctive hetero- oligomeric formations of tightly bound chaperone, co- chaperones and other factors13- 15. This phenomenon goes beyond mere biochemical curiosity; it represents a fundamental mechanism by which cells respond to stressors, whether of genetic, proteotoxic or environmental nature3,16- 18. Unlike chaperones which help proteins fold or assemble, epichaperomes exert a maladaptive influence, reshaping the assembly and connectivity of proteins pivotal for sustaining pathological traits. For example, in cancer, epichaperomes take on scaffolding functions not found in normal cells, altering the assembly and connectivity of proteins important for maintaining a malignant phenotype and enhancing their activity, which provides a survival advantage to cancer cells and tumor- supporting cells13,19. In Alzheimer's disease epichaperomes rewire the connectivity of, and thus negatively impact, proteins integral for synaptic plasticity, brain energetics and immune response15. + +The revelation of HSP90's maladaptive multimeric epichaperomes has also profound implications for therapeutic interventions, including in the treatment of diverse disease states including cancers and of neurodegenerative disorders. Rather than a blanket inhibition of all HSP90 pools, targeting + +<--- Page Split ---> + +1 specific pathologic conformations of HSP90 as found in epichaperomes while sparing normal 2 HSP90 functions holds the promise of enhancing the safety as well as the immunostimulatory and 3 anticancer effects of HSP90 inhibitors3. + +4 Despite these important mechanistic and therapeutic implications, key factors facilitating HSP90 5 incorporation in epichaperomes – namely the conformations that enable epichaperome formation 6 and structural elements that support the enrichment of such conformation - remain unknown. In 7 this study, we use a combination of chemical biology and unbiased mass spectrometry techniques 8 to elucidate the conformation of HSP90 populated in epichaperomes and to characterize 9 molecular factors that support and favor the enrichment of such conformation. Beyond structural 10 revelations, our findings demonstrate how these factors directly influences cellular behaviors, 11 particularly in contexts where robust proliferation and adaptation are crucial, such as cancer and 12 stem cell maintenance. This direct link between epichaperome function and fundamental cellular 13 processes has translational relevance for therapeutic development. + +## 14 RESULTS + +## 15 Pluripotent stem cells and cancer cells share epichaperomes + +16 Epichaperomes nucleated through enhanced interactions between HSP90 and HSP70, namely 17 the heat shock cognate 70 (HSC70) isoform, are a distinct feature of cancer cells13,19. 18 Epichaperomes containing HSP90 are detected in iPSCs (induced pluripotent stem cells)20, in 19 leukemia stem cells21,22 and in glioma cancer stem cells (CSCs)23. Hyperactivation of the 20 transcription factor c- MYC required in generating iPSCs24, maintaining embryonic stem cells 21 (ESCs)25 and CSCs26, is also a driving factor in epichaperome formation in tumors, irrespective 22 of the tumor type13,27. Notably, these epichaperomes are all sensitive to and can be disrupted by 23 small molecules such as PU- H71 (zelavespib) or PU- AD (icapamespib) that bind to HSP9013,23,28, 24 suggesting that a similar composition, facilitated by a specific conformation of HSP90, may 25 characterize epichaperomes in these distinct cellular contexts. + +26 To test this hypothesis, we initially explored the composition of epichaperomes in selected cellular 27 models, encompassing pluripotent stem cells and cancer cells. For pluripotent stem cells, we 28 examined two mouse embryonic stem cell lines (E14 and ZHBTC4) and a human induced 29 pluripotent cell line (hiPSC). Additionally, two cancer cell lines, well- characterized in terms of 30 epichaperome composition and function, were chosen as representative epichaperome- positive 31 (MDA- MB- 468) and - negative/low (ASPC1) cancer cells (Fig. 1a- f and Supplementary Figs. 32 1,2). + +33 In contrast to folding chaperone complexes, which are inherently dynamic and short- lived6, 34 epichaperomes represent long- lasting heterooligomeric assemblies composed of tightly 35 associated chaperones, co- chaperones, and various other factors. HSP90 is a major component 36 found within epichaperomes along with other chaperones, co- chaperones, and scaffolding 37 proteins like HSP70 (especially HSC70), CDC37, AHA1, and HOP13. Consequently, when we 38 analyzed cell homogenates containing epichaperomes using native PAGE followed by 39 immunoblotting with antibodies specific to epichaperome constituent chaperones and co- 40 chaperones, we observed a range of high- molecular- weight species, both distinct and indistinct, 41 in addition to the primary band(s) characteristic of chaperones. This observation held true for both 42 pluripotent stem cells and cancer cells (Fig. 1b, Supplementary Fig. 1a- d and refs. 13,19,20). 43 Notably, HSP90 immunoblotting revealed the presence of species comprising HSP90 in 44 epichaperome assemblies in cancer cells and pluripotent stem cells, in addition to the prominent 45 242 kDa band, which is a characteristic of non- transformed cells13,19,29. + +<--- Page Split ---> + +Epichaperomes undergo disassembly during iPSC differentiation20 or when cancer cells are treated with PU-H71 or PU-AD15,23,28,30. Therefore, next we induced the differentiation of the pluripotent stem cells under investigation. In the ZHBTc4 cell line, Oct4 expression is controlled by a Tet (tetracycline)- off oct4 regulatory system31. Down-regulation of Oct4 in ZHBTc4 cells has been reported to induce trophoblast differentiation, which is characterized by changes in cell morphology, specifically, cells flattening into epithelial-like cells, and is associated with slower growth32. Mouse embryonic E14 stem cells undergo spontaneous differentiation into embryoid bodies when cultured in suspension without antidifferentiation factors such as leukemia inhibitory factor33 and induced pluripotent stem cells differentiate into mature dopaminergic neurons using a floor-plate based differentiation protocol34. We confirmed that differentiation of these pluripotent stem cells was correlated with the disassembly of epichaperomes, as observed through native PAGE immunoblotting. This disassembly is evident by a reduction in high-molecular-weight chaperone species on native PAGE observed when immunoblotting for epichaperome constituent chaperones (see HSP90α/β, HOP, HSC70, CDC37, AHA1, HSP110 in Fig. 1b and Supplementary Fig. 1), with minimal changes observed in total chaperone levels on SDS PAGE. Notably, for HSP90, a decrease in bands other than those in the \(\sim 242\) kDa range was observed upon differentiation, supportive of epichaperome disassembly (see HSP90 immunoblotting). + +PU- H71 serves as an epichaperome probe that, in contrast to the tested antibodies which indiscriminately detect epichaperomes and other HSP90 pools, exhibits a preference for HSP90 when it is integrated into epichaperomes13. Labeled derivatives of PU- H71 can, therefore, be employed to detect HSP90 within epichaperomes, distinguishing it from other HSP90 pools (as illustrated in Fig. 1c and Supplementary Fig. 2a- c). To achieve this, we generated lysates from ZHBTc4, E14 cells, and MDA- MB- 468 cells under conditions that preserve native protein assemblies. Subsequently, we labeled these homogenates with a clickable PU- probe (PU- TCO, ref. 19). After running these labeled samples on native PAGE gels, we conjugated the PU- probe with a Cy5 dye and visualized epichaperomes, confirming the presence of epichaperomes in both the ESCs and the cancer cells. These epichaperomes were characterized by multimers observed at and above \(\sim 300\) kDa (Fig. 1c). Moreover, the labeling of epichaperomes by the PU- probe decreased upon ESC differentiation, supportive of epichaperome disassembly (Fig. 1c and Supplementary Fig. 2b). + +Additionally, we conducted labeling experiments using live E14 ESCs, instead of homogenates, employing a PU- CW800 probe (a derivative of PU- H71 conjugated with an 800 nm near- infrared dye) or a control derivative (an inactive PU- derivative that does not interact with epichaperomes) (see Supplementary Note 1). The most responsive target of the PU- probes, but not the control probe, was an HSP90 assembly of approximately 300 kDa, thus above the major 242 kDa band preferred by the anti- HSP90 antibody. This species was detected on Native- PAGE in PU- probe treated cell lysates but not in control treated cell lysates (Supplementary Fig. 2c). + +In summary, the predominant HSP90 band characteristic of epichaperomes is a 300 kDa assembly, distinctly differing from the typical \(\sim 242\) kDa band observed in non- transformed cells13, 19, 32 when analyzed on native PAGE gels. Mass spectrometric (MS) analysis of the \(\sim 300\) kDa assembly confirmed the presence of HSP90 and HSC70 as the primary protein components of this multimeric complex (Supplementary Data 1, 300 kDa LC- MS). This finding aligns with the composition of core epichaperome complexes previously reported in cancer cells13, 44. Consequently, these findings combined confirm that both cancer cells and pluripotent stem cells share HSP90 and HSC70 as integral constituents of their core epichaperomes. + +To gain further insights into epichaperome assemblies, we employed resin- based affinity purification experiments. Specifically, we utilized resins with immobilized PU- H71, referred to as PU- beads, and an inert control molecule on control beads, following established procedures13 + +<--- Page Split ---> + +1 (Fig. 1d). As an additional control, we employed a resin containing immobilized geldanamycin (GA), known for its ability to bind and isolate predominantly un-complexed HSP90 (GA-beads, 2 Supplementary Fig. 3 and ref.35). Subsequently, we subjected the protein cargo isolated by these 3 probes to unbiased MS analysis. To precisely determine the protein components of the cargo, we 4 conducted in-gel digestion of the entire gel lanes and employed liquid chromatography/mass 5 spectrometry (LC-MS/MS) in conjunction with the semi-quantitative spectra-counting method36,37 6 for the identification and quantification of cargo proteins (Supplementary Data 1). + +8 We observed that the cargo isolated by PU-beads from ESCs contained 26 of the 42 major 9 chaperone and co-chaperones identified prior in cancer cells13 as being epicatheprome 10 components (Fig. 1d). The interaction between PU-beads and epicathepromes was specific 11 towards PU-H71, because control resins did not purify noticeable protein complexes. Similarly, 12 GA-beads precipitated HSP90 but few co-purifying proteins and epicatheprome components 13 (Supplementary Fig. 3, Supplementary Data 1) consistent with previous results that GA isolates 14 largely an un-complexed HSP9038. + +15 In mammalian cells, HSP90 exists in two paralogs, HSP90α and HSP90β39, both of which have 16 been reported to play roles in epicatheprome formation in cancer cells13. To assess the isoform 17 composition of HSP90 within epicathepromes, we exploited the subtle difference between one pair 18 of isobaric peptides, namely 88Thr-Lys100 in HSP90α and 83Thr-Lys95 in HSP90β, where a 19 single amino acid distinguishes them (lle in HSP90α and Leu in HSP90β) (Supplementary Fig. 20 4a). The assignment of HSP90 isoforms relied on co-eluting peptides obtained from the isobaric 21 peptide present in purified HSP90β (Supplementary Fig. 4b,c). Extracted ion chromatograms of 22 the peptide mass revealed an approximate \(\sim 1.5 \beta /\alpha\) ratio in the ESC lysate and the cargo isolated 23 by PU-beads (Fig. 1e), while the GA-beads cargo exhibited a \(\sim 1.0 \beta /\alpha\) ratio. Similar findings were 24 obtained through spectra counting, with the HSP90β/HSP90α ratio determined using spectral 25 counting consistent with ratios obtained through MS intensity calculations (Supplementary Data 26 1: \(708 / 540 = 1.31\) for the PU-beads cargo; \(219 / 235 = 0.93\) for the GA-beads cargo). This validation 27 of spectra counting as an effective semi-quantitative method supports the conclusion that 28 epicathepromes isolated from ESCs exhibit a predominantly unbiased HSP90 paralog 29 composition, akin to what has been reported for cancer cells13. + +30 In summary, the wealth of complementary biochemical experiments presented here lends strong 31 support to the idea that both cancer cells and pluripotent stem cells harbor epicathepromes that 32 are compositionally similar. Notably, HSP90 and HSC70 emerge as major constituents of the core 33 epicatheprome structure, serving as a scaffold for recruiting various co-chaperones to create 34 specific epicatheprome assemblies. This shared architectural similarity between epicathepromes 35 in ESCs and cancer cells underscores the existence of a common epicatheprome-enabling HSP90 36 conformer that is enriched in both biological contexts. + +## 37 Epicatheprome-enabling conformation of HSP90 + +38 MS identification of cross-linked residues that are in spatial proximity but not necessarily close in 39 primary sequence, provides valuable distance restraints that can be employed for computational 40 modeling of proteins and protein complexes40- 42. Therefore, to determine the conformation of 41 HSP90 in epicathepromes, we used a chemical cross-linking and mass spectrometry (CX-MS) 42 approach to identify and quantify cross-linked peptides of PU-H71-favored HSP90 pools. + +43 To ensure the capture of the epicatheprome-enabling conformation, we first cross-linked cellular 44 lysates using the amine-reactive cross-linker DSS (disuccinimidyl suberate) prior to HSP90 45 capture on the PU-beads13,35 (Fig. 2a). Parallel experiments were conducted using GA-beads, 46 corresponding to solid-support immobilized GA, as a control13,35. The identity of cross-linked + +<--- Page Split ---> + +1 HSP90 peptides purified by PU- or GA- beads pull- down can be found in Supplementary Data 2. Notably, the alpha carbon distances between all cross- linked residues, as identified with high confidence, fell below the maximal span of DSS (30 Å). This suggests that proteins retained their native states without significant conformational perturbations during the cross- linking process. + +We calculated the cross- linking percentage for each pair of cross- linked PU- or GA- bound HSP90 residues. This calculation involved normalizing the MS ion intensity of cross- linked peptides by the sum of all cross- linked peptides and cross- linker- modified peptides containing the cross- linked residues. By doing so, we could mitigate the impact of variations in the reactivity of cross- linked residues, allowing us to primarily assess the influence of the distance between cross- linked residues and their local secondary structures43. + +Most cross- linked pairs from both PU- and GA- bound samples exhibited similar cross- linking percentages, with data points evenly distributed around a trend line with a slope of 1 (dotted line, Fig. 2b). This observation suggests a broad similarity in secondary and tertiary structures between these HSP90 populations. However, clear differences emerged, revealing conformational distinctions between the PU- and GA- favored HSP90 subpopulations (highlighted by orange circles, Fig. 2b). + +Notably, residues Lys58- Lys112 in HSP90α and Lys53- Lys107 in HSP90β, situated within the ligand- binding pocket, displayed a higher cross- linking percentage in PU- bound HSP90 populations compared to their GA- bound counterparts (Fig. 2b). This observation aligns with distinct pocket configurations preferred by each ligand, as previously observed through X- ray crystallography44- 48. Specifically, crystal structures show the bulkier GA binds more superficially, causing helices 4 and 5 (Fig. 2d) to move away from the nucleotide binding site, thereby preventing full closure of the ATP lid. Moreover, the side- chain amino functional group of Lys112 forms a hydrogen bond with a benzoquinone oxygen of GA. This pocket configuration aligns with the reduced cross- linking activity of the lysine pair mentioned above. Conversely, PU- H71 binds deeply within the pocket. In this configuration, helices 4 and 5 are packed against helix 2 with Lys112 and Lys58 in HSP90α (or Lys107 and Lys53 in HSP90β) positioned more favorably for cross- linking. This arrangement of lysine residues is more likely to be found in the closed conformation of HSP90 (Fig. 2c), as proposed by crystallographic studies (PDB: 2CG9)49. + +It is essential to reiterate that the cross- linking experiments were conducted to 'lock' HSP90 conformations with covalent bonds before resin- based affinity purification experiments using the PU- or GA- beads. Consequently, the X- ray structures of PU- or GA- bound HSP90 NTD closely reflect a preferred pocket configuration that each ligand may capture in the cell, and in this case, for PU- H71, it is indicative of the pocket configuration of HSP90 in the epipheropermes. + +Furthermore, differences in HSP90 conformation were corroborated by cross- linked pairs located at the interfaces between NTD/MT (HSP90α: Lys293- Lys363) and MD/CTD (HSP90α: Lys444- Lys616; HSP90β: Lys435- Lys607) (Fig. 2b). These interfaces undergo significant reorientation during the HSP90 conformational cycle, implying a distinct HSP90 conformation favored by PU- H71 compared to GA. Lys444 in HSP90α (Lys435 in HSP90β) and Lys616 in HSP90α (Lys607 in HSP90β) are positioned either within the middle of the MD or in proximity to the central axis of the HSP90 homodimer (Fig. 2c). The distance between these lysine residues can provide insights into the relative placement of the monomer arms in specific HSP90 conformations (e.g., 20 Å in closed- like conformations; 29 Å in open- like conformations). The lower cross- linking percentage observed for Lys444 and Lys616 in HSP90α (Lys435 and Lys607 in HSP90β) in GA- favored HSP90 suggests a longer distance (29 Å) between them, supporting GA's preference for binding to an open- like conformation. In contrast, the moderate cross- linking percentage detected for + +<--- Page Split ---> + +1 these residues in PU- H71- favored HSP90 implies a medium distance (20 Å) between them, 2 favoring a closed- like conformation enriched in epicarpemores (Fig. 2c). + +3 Additionally, a third pair of cross- linked residues (Lys293 and Lys363 in HSP90α) supports this 4 notion. Located near the interface between the NTD and the MD, their positions are sensitive to 5 the ligand binding state of the NTD, leading to changes in the relative positioning of secondary 6 structures near the NTD/MD interface and altering the distance between Lys293α and Lys363α. 7 Consistent with the cross- linked pair at MD/CTD interface, a closed- like conformation (16 Å) in 8 PU- H71 bound HSP90 will be more amenable than an open- like conformation (13 Å) in GA- bound 9 since the short distance might have limited the location of side- chains for cross- linking reactions. + +10 In summary, our CX- MS data, supported by several cross- linked residue pairs situated in 11 structurally distinct regions, the nucleotide- binding pocket, and the NTD/MD and MD/CTD 12 interfaces, shed light on the conformation adopted by HSP90 within epicarpemores. These 13 findings underscore the notion that an enrichment of the closed- like conformation of HSP90 in 14 specific cellular environments favors the formation of epicarpemores. + +## 15 Specific PTMs support HSP90 incorporation into epicarpemores + +16 To uncover the factors that facilitate the enrichment of the epicarpemore- favoring HSP90 17 conformation, we conducted a comprehensive examination of the HSP90 pools isolated by PU- 18 H71 and GA, searching for potential differences. Notably, we identified several peptides 19 phosphorylated on Ser231 and Ser263 in HSP90α (Ser226 and Ser255 in HSP90β) exclusively 20 in the PU- H71 cargo from ESCs (Fig. 3a,b and Supplementary Data 3). High- quality MS/MS 21 spectra (illustrated for Ser226 and Ser255 phosphopeptides in HSP90β, Fig. 3b) coupled with 22 precise mass accuracy allowed for the unequivocal identification of the peptide sequences and 23 the phosphorylation sites. In contrast, these phosphorylated peptides were notably absent in 24 substantial quantities in the GA cargo (Supplementary Data 3). + +25 Subsequently, we performed label- free quantitation of these phosphopeptides using ion intensity 26 measurements and observed a significant enrichment in the PU- beads cargo, particularly in the 27 case of Ser255 of HSP90β. For instance, the Ser255 phosphopeptide displayed a nearly three- 28 fold enrichment in the PU- H71 cargo compared to the lysate, after protein loading normalization 29 using a representative tryptic peptide (Fig. 3c). + +30 To gain further insights, we leveraged previously reported MS datasets of PU- H71- isolated cargo 31 from epicarpemore- positive cancer cells \(^{13,19}\) , including MDA- MB- 468 (triple negative breast 32 cancer), Daudi (Burkitt's lymphoma), IBL- 1 (AIDS- related immunoblastic lymphoma), and NCI- 33 H1975 (non- small cell lung carcinoma), as well as from non- transformed (NT) proliferating cells 34 in culture (e.g., MRC5, lung fibroblast and HMEC, mammary epithelial cells). This analysis 35 revealed that phosphorylation of these serine residues is also enriched in cancer cells when 36 compared to NT cells (Ca:NT S255 = 16; S226 = 8; S263 = 12, Fig. 3d) establishing it as a 37 hallmark of both ESC and cancer epicarpemores. This observation further supports the idea of a 38 shared structural and architectural foundation for epicarpemores among ESCs and cancer cells. + +39 As HSP90 is found alongside HSC70 in epicarpemores, we conducted an additional confirmatory 40 experiment. Here, we used YK5- B, a biotinylated probe that binds to HSC70 in epicarpemores, 41 and thus captures HSP90 in epicarpemores via HSC70 \(^{19}\) . PU- H71 and YK5- B were used to 42 isolate cargo from epicarpemore- positive cancer cells, including MDA- MB- 468 and OCI- Ly1 43 (breast cancer and diffuse large B- cell lymphoma, respectively), as well as from CCD- 18Co colon 44 cells in culture (i.e., non- transformed proliferating cells in culture). We found that the Ser255 and 45 S226 phosphopeptides of HSP90β were nearly four to five times more abundant in epicarpemore + +<--- Page Split ---> + +1 positive cancer cells compared to non- transformed proliferating cells in culture, for both the PU- 2 cargo and the YK5- B cargo. Similar enrichment was noted for Ser263 and Ser231 in HSP90α 3 (Fig. 3e). This analysis, thus, using both PU- H71 and YK5- B probes across diverse cell types, 4 underscores the robustness of our observations and reinforces the role of phosphorylation in the 5 acidic linker in shaping HSP90 within epicarpemores. + +6 In light of these findings, made with two distinct probes and observed in ESCs, five cancer cell 7 lines, each representative of a distinct cancer type, and of three non- transformed, but proliferating, 8 cells in culture, it is evident that the epicarpemore- specific agents target a subpopulation of 9 HSP90 characterized by high phosphorylation levels in the acidic linker between the NTD and the 10 MD, and this subpopulation predominantly assumes a closed- like conformation. In conjunction 11 with PU's preference for HSP90 within epicarpemores, and substantiated by YK5- B, a probe that 12 binds epicarpemores via HSC70, these results strongly indicate that phosphorylation at these 13 two serine residues is a key driver for HSP90 incorporation into epicarpemores and, 14 consequently, for epicarpemore formation. + +## 15 Specific PTMs drive epicarpemore formation and function + +16 To explore whether the phosphorylation of these serine residues plays a pivotal role in driving, 17 rather than merely resulting from, epicarpemore formation, we next studied the phosphomimetic 18 (HSP90βS226E,S255E) and the non- phosphorylatable (HSP90S226A,S255A) mutants. + +19 Notably, these serine residues are located within an intrinsically disordered region (IDR) of HSP90 20 (Supplementary Fig. 5). IDRs are pivotal elements in the intricate network of protein- protein 21 interactions (PPIs). These regions lack a fixed three- dimensional structure, granting them 22 exceptional flexibility. This structural adaptability enables proteins containing IDRs to assume 23 various conformations in response to specific cellular contexts or binding partners. Such 24 adaptability plays a crucial role in facilitating context- dependent involvement in distinct PPIs. In 25 the case of HSP90, these serine residues within the IDR may alter the dynamics and structure of 26 the charged linker, contributing to stabilizing the epicarpemore- enabling conformation of this 27 chaperone, and in turn facilitating epicarpemore formation. + +28 To explore this hypothesis, we conducted computational analyses to investigate the impact of 29 each mutation on the flexibility of the charged linker (Fig. 4a- c). We constructed a model of the 30 putative epicarpemore core - namely the \(\sim 300\) kDa assembly, see Fig.1 - based on the cryo- EM 31 structure of a multimeric HSP90 assembly (PDB: 7KW7). This structure represented 2xHSP90α, 32 protomer A and B, bound to 2xHSP70 and 1xHOP. To create the model, we substituted HSP90 33 with human HSP90β using the closed- state cryo- EM structure (PDB: 8EOB). Additionally, we 34 computationally inserted the charged linker, which was missing in the cryo- EM structures (Fig. 35 4a). + +36 We conducted all- atom molecular dynamics simulation of this pentameric protein assembly, with 37 each system containing all the components along with either the EE, AA, or WT HSP90 - in both 38 protomers. These simulations are intended to qualitatively explore the immediate response of the 39 assembly to the perturbation induced by mutations and not to provide an extensive 40 characterization of the assemblies' dynamics. By using a comparative MD- based approach we 41 explore how short- term changes in the structural dynamics of different components within a large 42 assembly may influence the emergence of states relevant for assembly stabilization. The 43 underlying premise is that nanosecond timescale residue fluctuations in regions specifically 44 responsive to certain states may facilitate large- scale rearrangements that underlie functional 45 changes. + +<--- Page Split ---> + +These simulations revealed that the structure and conformation of the charged linker were sensitive to the phosphorylation of the serine residues. In the pentameric assembly containing the phosphomimetic EE mutant (i.e., HSP90S226E/S255E), the linker of HSP90, protomer A, had a high probability of forming a \(\beta\) - strand bordering the Ser226Glu residue (2.1% of \(\beta\) - strand A). This strand remained stable over the duration of the simulation. This \(\beta\) - strand's formation significantly decreased in the pentameric assembly containing the wild-type (WT, i.e., HSP90S226/S255) protein (0.4% of \(\beta\) - strand A), with no secondary structure element found in the assembly containing the AA (i.e., HSP90S226A/S255A) mutant (Fig. 4b). Notably, ATP binding, but not ADP binding, favored a charged linker with a high content of \(\beta\) - strand A formation (2.1% vs. 0.3%, respectively, in the EE mutant) (Fig. 4b and Supplementary Fig. 6a). This finding emphasizes that the observed changes in the EE mutant were not merely due to the addition of charged residues; they were intricately tied to the phosphorylation status and the specific context, including the nucleotide environment permissive of the specific HSP90 conformation (i.e., closed-like). Intriguingly, the strategic formation of \(\beta\) - strand A not only stabilized the charged linker but also induced a conformational switch, flipping it into an 'up' conformation, thereby fully exposing the middle domain of HSP90, where HSP70 binds (Fig. 4c, see HSP90 protomer A – HSP70 interface). While other stabilized structural elements were observed in the analyzed assemblies containing either the WT or the mutant HSP90s, no other had a similar conformational effect on the charged linker as we observed for the \(\beta\) - strand A (see the effect of \(\alpha\) - helices 1 through 6 in Supplementary Fig. 6a,b). + +We conducted dynamical residue cross- correlation analyses to explore how different protein units or subdomains in the pentameric 2xHSP90- 2xHSP70- HOP assemblies, featuring either the WT (HSP90S226/S255) or mutant (HSP90S226E/S255E or HSP90S226A/S255A) HSP90s, correlate in their motions throughout the simulation (Fig. 5a,b). This analysis aimed to reveal how individual components move in relation to each other. Positive dynamical cross- correlations spanning different components of the assembly within the large epicapherome core may indicate enhanced cooperative motions, suggesting increased interactions that contribute to the stability of the assembled structure. Previous studies have employed similar analyses to investigate how ligand- induced modulations influence the overall flexibility of HSP90 assemblies, facilitating progress along the chaperone cycle, thereby supporting feasibility of this approach50. + +Indeed, we observed the highest correlation among the components in assemblies containing the HSP90 EE phosphomimetic, mimicking the case where the charged linker is phosphorylated, followed by the WT, and then the non- phosphorylatable HSP90 AA mutant (Fig. 5a). Notably, the coordinated movements observed in the assemblies containing the HSP90 phosphomimetic strongly support the idea that the HSP70- HSP90- HSP90- HSP70 or HSP70- HSP90- HSP90- HSP70- HOP assemblies can be preferentially stabilized when the HSP90 charged linker is phosphorylated (Fig. 5b). This observation aligns with the prominent \(\sim 300\) kDa band observed for the epicapherome core in native PAGE (see Fig. 1 showing HSP90 assemblies favored by PU- H71). + +In contrast, in the WT HSP90 assembly, coordinated movements were primarily observed between the two HSP90 protomers, within HSP90, and between HSP90 and HSP70 and HOP, specifically through HSP90 protomer B (Fig. 5a,b). These movements are more consistent and favorable in the context of HSP90- HSP90- HSP70 or HSP90- HSP90- HOP assemblies. This observation implies that the major, broad \(\sim 242\) kDa band detected by the HSP90 antibody – representing the primary HSP90- containing assembly observed in differentiated ESCs (Fig. 1) and in non- transformed cells13- 15,17,20 – may consist of such assemblies, along with HSP90 homo- oligomers. + +<--- Page Split ---> + +1 In summary, both MS evidence and computational models converge to support the conclusion that phosphorylation of the charged linker is a crucial contributor to epichaperome assembly, emphasizing its role in shaping not only HSP90, but also the stability and dynamics of the epichaperome structure. + +5 Next, we carried out an extensive biochemical and functional analysis to reinforce these findings. 6 Given the well- established tight association between HSP90 and other chaperones and co- chaperones in epichaperomes \(^{13,19,20,51}\) , our focus shifted to a comprehensive evaluation of chaperone and co- chaperone proteins co- purified with the phosphomimetic (HSP90β \(^{5226E,S255E}\) ) and non- phosphorylatable (HSP90 \(^{5226A,S255A}\) ) mutants. Our strategy involved the purification of protein complexes containing N- terminally mCherry- tagged HSP90β in ESCs while retaining the endogenous WT HSP90 proteins. Distinctly labeled ESCs (i.e., labeled with heavy or light isotope lysine and arginine) expressing either the phosphomimetic or non- phosphorylatable mutant were subjected to immunoprecipitation (IP), followed by SDS- PAGE separation and quantitative analysis via MS to determine protein abundance (Fig. 6a- c; Supplementary Data 4). It is worth noting that we performed IP separately for the phosphomimetic and non- phosphorylatable mutants to minimize subunit exchange during IP \(^{52}\) , thereby enhancing our ability to detect changes in co- chaperone binding more accurately than previous studies \(^{53}\) . + +We found co- chaperones were among the most abundant copurifying proteins, and most co- chaperones reported to participate in epichaperome formation \(^{13,19}\) displayed prominent changes in the phosphomimetic mutant (Fig. 6b,c). The increased presence of epichaperome- specific co- chaperones (such as AHA1 and FKBP4) \(^{13}\) in phosphomimetic complexes compared to non- phosphorylatable complexes highlights a stronger association with Ser226 \(^{P}\) /Ser255 \(^{P}\) HSP90 as opposed to the non- phosphorylatable protein. However, we observed a slight reduction in the levels of HSC70 and HOP within phosphomimetic complexes. This decrease is potentially associated with specific subpopulations of HSP90 complexes that become more prevalent when the non- phosphorylatable Ala mutant is overexpressed in cells. The introduction of two Ala residues in the unstructured linker region of HSP90 may prompt the recruitment of HSC70 and HOP, chaperones recognized for their ability to bind unstructured unfolded protein stretches \(^{54}\) . It is important to note that these assemblies are distinct from epichaperomes. Due to the anti- mCherry antibody capturing the entirety of the tagged HSP90, differentiation between specifically epichaperome- related HSP90 and a mixture of epichaperomes and other pools becomes challenging. + +To address these limitations, we adopted a multi- pronged approach. Firstly, we utilized immunoblotting with native cognate antibodies for chaperone assemblies retained on native PAGE, coupled with chemical blotting using PU- probes. Additionally, we employed affinity capture with PU- probes to quantify the amount of epichaperome components under each condition (Fig. 7a). For these experiments, we transfected cells with the phosphomimetic (HSP90β \(^{5226E,S255E}\) , EE mutant) and with the non- phosphorylatable (HSP90 \(^{5226A,S255A}\) , AA mutant) mutants, as well as with HSP90β WT or mCherry- tag only for control purposes. In this study, we chose human embryonic HEK293 cells as our cell model since they exhibit intermediate epichaperome expression levels (i.e., medium expressor, Supplementary Fig. 7), making them suitable for studying epichaperome dependence. We confirmed comparable transfection efficiency for each construct, with the tagged HSP90β protein expressed in addition to the endogenous HSP90β (Fig. 7b). + +Our findings revealed that cells expressing the EE mutant exhibited higher levels of epichaperomes compared to those expressing the AA mutant, as evidenced by immunoblotting of various epichaperome components (including HSP90α, HSC70, CDC37, AHA1, HOP, and HSP110) (Fig. 7c, native PAGE) and chemical blotting with the PU- Cy5 epichaperome probe + +<--- Page Split ---> + +1 (Fig. 7d). Notably, there was no significant change in the overall concentration of these proteins in association with their incorporation into epichaperomes (Fig. 7c, SDS PAGE). Epichaperome isolation using PU- beads as an affinity purification probe also revealed significantly greater incorporation of chaperones, including mCherry- HSP90β, and co- chaperones into epichaperomes in cells expressing the EE mutant compared to those containing the AA mutant HSP90 (Fig. 7e), with no substantial alterations observed in cells containing the control vectors (Supplementary Fig. 8a). In contrast, overexpression of wild- type HSP90 in HEK293 cells had a minimal impact on endogenous epichaperomes (Fig. 7c, native PAGE, and Supplementary Fig. 8a, PU- beads capture). This observation aligns with previous reports13 suggesting that factors beyond chaperone concentration play a pivotal role in driving HSP90 incorporation into epichaperomes. Notably, cargo isolated on the control probe (control beads, Supplementary Fig. 8b) showed no detection of HSP90. + +We further established the dependency of epichaperome function, beyond its formation, on the phosphorylation of HSP90 serine residues (Fig. 8,9). A key characteristic shared among high epichaperome- expressing cells in PSC, CSC, and cancer cells is the hyperactivity of the transcription factor c- MYC13,25- 27. In cancer, c- MYC is frequently overexpressed or mutated, resulting in sustained activation, which drives uncontrolled cell proliferation55. In ESCs, c- MYC plays a crucial role in maintaining pluripotency and self- renewal, crucial for preserving the undifferentiated state of ESCs56. We therefore investigated the impact of HSP90β Ser226P/Ser255P on cellular behaviors such as self- renewal and proliferation. + +To assess proliferation, ESCs were transfected with plasmids containing either the phosphomimetic (HSP90βS226E,S255E) or non- phosphorylatable (HSP90βS226A,S255A) mutant. Notably, ESCs transfected with the HSP90β phosphomimetic mutant displayed a significantly higher proliferative rate (P<0.0001, >25%) compared to those transfected with the non- phosphorylatable variant, regardless of whether medium (1x) or high (2x) plasmid concentrations were employed (Fig. 8a). This observation lends support to the notion that HSP90β Ser226P/Ser255P, and consequently, epichaperomes, play a crucial role in ESC proliferation. + +Differentiation of ESCs results in a decreased proliferative rate, as indicated by the doubling time of ZHBTC4 ES cells (~12 h) and trophoblast- differentiated cells (~25 h)32. Since differentiation is also closely associated with the disassembly of epichaperomes, we next examined the phosphorylation levels of HSP90β at Ser226 and Ser255 in cells with varying self- renewal capacities. We utilized the TET- repressible oct4 mouse ESC line ZHBTC4, where the Oct4 expression is suppressed in the presence of doxycycline for ESC differentiation into trophoblast- like cells (Troph)31. In this experiment, we expressed WT mCherry- HSP90β in ZHBTC4 cells and quantified phosphopeptides in both ESCs and trophoblast cells following ESC differentiation (Fig. 8b, Supplementary Data 5). After normalizing the data to mCherry- HSP90β protein loading (middle panel, ES/Troph = 0.44), we observed a 30% higher phosphorylation of HSP90β at Ser255 in stem cells compared to differentiated cells (left panel, ES/Troph = 0.57). Phosphorylation levels of HSP90β at Ser226 appeared to remain unchanged under these experimental conditions after normalizing to protein loading (right panel, ES/Troph = 0.45). + +Pluripotency hinges on crucial transcription factors like Oct4. Oct4 is widely recognized as one of the principal transcription factors governing the self- renewal of both pluripotent stem cells and cancer cells57. We find Oct4 interacts with epichaperomes in ESCs (Supplementary Data 1) and exhibits significant enrichment in the cargo captured with the Ser226/Ser255 phosphomimetic compared to the non- phosphorylatable HSP90 (Supplementary Data 4, Fig. 8c, 1.4- fold EE : AA). To validate the reliance of Oct4 on epichaperomes, we examined Oct4 levels in both MDA- MB- 468 cancer cells and HEK293 cells transfected with the various HSP90 plasmids. Additionally, + +<--- Page Split ---> + +1 we utilized affinity capture with PU- probes (Fig. 8d- f and Supplementary Fig.8a). Notably, we observed that cells expressing the phosphomimetic EE mutant showed significantly elevated levels of Oct4, both overall (Fig. 8e) and within epichaperomes (i.e., those sequestered within the epichaperomes, Fig. 8f), compared to cells expressing the HSP90 AA mutant. No detectable differences were observed under control conditions (WT HSP90 and empty vector only) (Supplementary Fig. 8a). Additionally, Oct4 was sequestered by epichaperomes in MDA- MB- 468 cells, supporting the idea that epichaperomes play a role in regulating pluripotency through both direct and indirect regulation of Oct4. + +Epichaperomes play a pivotal role in supporting enhanced proliferation by altering the regulation of various proteins involved in cell signaling3,13,19. Higher epichaperome levels translate to a greater number of proteins being affected, resulting in increased signaling output13,17,58. We therefore next assessed the signaling output of cells transfected with the various HSP90 mutants. We observed a significantly heightened epichaperome- dependent impact on key signaling effector proteins involved in cell growth and proliferation (i.e., MEK, AKT, and mTOR) in cells expressing the HSP90 EE mutant compared to those expressing the AA mutant. This was evident in both the increased phosphorylation status of these effector proteins (Fig. 9a,b) and their enhanced recruitment to epichaperome platforms (Supplementary Fig. 9a- c) in cells expressing the EE mutant, as compared to those expressing the AA mutant. Importantly, these effects occurred without notable changes in the expression levels of the proteins (Supplementary Fig. 9a, b). No measurable differences were observed under control conditions (WT HSP90 and empty vector only) (Fig. 9b and Supplementary Fig. 9a, b). + +Epichaperome formation fuels aggressive behaviors in cells51,59. Indeed, when observed under a microscope, we noted that, in comparison to cells expressing the non- phosphorylatable AA mutant (HSP90βS226A,S255A), those expressing the phosphomimetic EE mutant (HSP90βS226E,S255E) displayed a higher prevalence of cells with an elongated phenotype and several protrusions (Fig. 9a,b), supportive of a mesenchymal- like phenotype60. These morphological changes suggest a shift towards a more stem cell- like state, or a more aggressive phenotype in the context of cancer, in cells harboring the EE HSP90 mutant (i.e., with a high epichaperome load), a feature not observed in cells carrying the AA HSP90 mutant (i.e., not permissive of epichaperome formation). + +Previous studies have found that irrespective of the tumor type, 60- 70% of tumors contain HSP90- HSC70 epichaperomes13,19. Additionally, epichaperomes are known to specifically form in diseased tissue3. To assess whether our observations regarding the impact of the HSP90 charged linker, derived from cell models, extend to human patients and are not artifacts specific to cultured cells, we obtained surgical specimens from breast and pancreatic cancer surgeries (n = 18 tissues from 9 patients, Fig. 10a- d). Both tumor (n = 9) and tumor adjacent (n = 9) tissues, determined by gross pathological evaluation to be potentially non- cancerous, were analyzed for epichaperome levels using Native PAGE. Additionally, total HSP90β and phosphorylated HSP90β at Ser226 were assessed by SDS PAGE and immunoblotting with specific antibodies. To mitigate potential biases arising from varying HSP90 levels, each pair was normalized based on HSP90 concentration. Despite challenges in obtaining high- quality epichaperome profiles from surgical samples, a robust correlation emerged between epichaperome expression and Ser226 phosphorylation (Fig. 10c,d). Tissues positive for epichaperomes exhibited p- Ser226 HSP90β positivity, and conversely, those negative for epichaperomes showed no or negligible p- Ser226 signal. + +Collectively, these multifaceted biochemical and functional lines of evidence establish a compelling connection between structural features in HSP90 and the processes of epichaperome formation and function. These findings lend robust support to the hypothesis that the regulation of epichaperome processes in ESC and cancer cells—encompassing critical factors such as + +<--- Page Split ---> + +proliferative potential, self-renewal capacity, plasticity, and signaling output—crucially relies on the specific phosphorylation events taking place at key residues within HSP90's charged linker. + +## 3 DISCUSSION + +The intricate network of protein- chaperone interactions within cells plays a critical role in maintaining protein homeostasis and cellular function. In recent years, the discovery of epichaperomes as specialized chaperone complexes in both cancer cells and pluripotent stem cells has opened new avenues for understanding chaperone biology. This investigation offers valuable insights into the structural and regulatory intricacies of epichaperomes, with particular attention to the pivotal role played by PTMs of HSP90 in orchestrating their formation and function. + +A central discovery in this investigation is the recognition of specific PTMs on HSP90, especially at Ser226 and Ser255, as critical factors governing the assembly of epichaperomes. Our data reveal that phosphorylation of these serine residues enhances the association of HSP90 with other chaperones and co- chaperones, creating a microenvironment conducive to epichaperome formation. This finding underscores the significance of PTMs in regulating chaperone assemblies and highlights the potential of targeting these modifications for therapeutic intervention. + +Chaperones appear to be highly susceptible to structural and functional regulation by a spectrum of PTMs. For example, PTMs of HSP90 provide an important regulatory element, modulating co- chaperone and client protein binding61- 65, ATPase activity66, conformational cycle62,65- 67, turnover68 and small molecule affinity12,38. Similar to minor changes in primary sequence, these PTMs likely regulate the access to and occupancy of key conformational states of HSP90 for in vivo processing of some essential clients. Our investigation pinpoints crucial PTMs that remodel the functional profile of HSP90, metamorphosing it from a protein- folding entity into epichaperomes, a platform orchestrating the reorganization of PPI networks for heightened cellular adaptability and proliferation. + +Our study uncovered a fascinating aspect of PTMs in HSP90 within epichaperomes — phosphorylation events occur in an IDR of the protein. The strategic placement of these PTMs in the IDR holds profound significance, suggesting that they influence HSP90's conformation and function beyond the traditional structured regions. This adaptability is crucial for HSP90's participation in distinct PPIs, allowing it to stabilize the epichaperome- enabling conformation and restructure the interactions of numerous proteins in response to cellular stressors. Intriguingly, previous studies in yeast69, where the IDR was substituted with glycine- glycine- serine residues, align with our findings. These studies suggested that the charged linker (encompassing the IDR), influenced by the N- domain of HSP90, can adopt a structured form. This structured form, in turn, can stabilize interactions between specific HSP90 domains, influencing HSP90 dynamics, co- chaperone binding, and overall biological function, especially in conditions of cellular stress. + +Changes in PPI networks play a fundamental role in cellular responses to stressors and the coordination of various biological processes18. These alterations, often induced by external stressors, are vital for the cell's ability to adapt and function under different conditions. Notably, less than 10% of human PPIs remain unaffected by stress- induced perturbations, highlighting the widespread impact of cellular stress on the interactome. These changes, influenced by factors such as PTMs and protein conformation, are essential for species- specific adaptation and contribute to PPI network malfunctions observed in diseases. + +One intriguing question is which kinase could phosphorylate HSP90 at these serine residues? A likely candidate is casein kinase II (CK2)70,71. CK2 is sequestered to epichaperomes in ESCs and in cancer cells13. Notably, CK2 is overexpressed in highly proliferative cells72 and plays a role in + +<--- Page Split ---> + +1 phosphorylating numerous protein substrates involved in cell proliferation and survival73. 2 Moreover, the mutation of CK2 has been shown to abolish the viability of both PSCs74 and tumor cells75,76, indicating a potential direct link between epichaperome function and cellular physiology, possibly mediated by CK2 phosphorylation, which remains to be confirmed. + +The implications of our study go beyond providing structural and mechanistic insights. We present compelling evidence that phosphorylation of HSP90 at Ser226 and Ser255 not only promotes epichaperome formation but also influences cellular behaviors, including proliferation and self-renewal. This suggests a direct link between epichaperome function and cellular physiology, particularly crucial in contexts such as cancer and stem cell maintenance, where robust proliferation and adaptation are vital. + +Plasticity, a key characteristic associated with both ESCs and cancer cells77, is also implicated in our findings. The morphological changes observed in cells expressing the phosphomimetic HSP90 mutant—specifically, the higher prevalence of cells with an elongated phenotype and several protrusions—hint at a mesenchymal-like phenotype80. This phenotypic shift is often associated with increased plasticity and is indicative of a more stem cell-like state. Our findings suggest a potential role for epichaperomes in modulating this dynamic process of cellular transition between different phenotypic states. + +The link between pluripotency and cancer is particularly intriguing. Cellular stress is increasingly recognized as a pivotal factor that can shift the balance between cellular pluripotency and the development of malignancies. The process of dedifferentiation, observed in regeneration in plants and some vertebrates, involves the deactivation of genes responsible for cell- specific functions, re- entry into the cell cycle, proliferation, and activation of pluripotency- associated genes78. Tumors also undergo dedifferentiation, where cancer cells revert to a less differentiated state, re- express stem cell genes like Oct4, leading to the emergence of cancer stem- like cells with enhanced metastatic potential and treatment evasion79. Our study proposes epichaperomes as significant mediators of changes in cellular identity, partly through Oct4. + +The revelation of HSP90's dysfunctional multimeric states carries implications for therapeutic interventions3,16. Instead of universally inhibiting all HSP90 pools, a paradigm shift comes to the fore with precision medicine strategies. The prospect of targeting specific pathologic conformations while preserving normal HSP90 functions emerges as a promising direction. This shift beckons researchers to navigate the intricate interplay of HSP90 conformations as they forge ahead in the quest for innovative therapeutic approaches. Our study also confirms the notion that small molecule HSP90 binders have distinct preference for HSP90 conformers in cells, reinforcing the finding that not all HSP90 inhibitors act equally well or equally selectively on specific disease- promoting HSP90 conformations or disease- associated HSP90 assemblies in comparison with HSP90 conformers found in normal cells. The first feature determines drug efficacy, whereas the latter influences the safety profile during administration. + +In conclusion, our study unravels the intricate interplay between PTMs, conformational regulation, and biological functions of HSP90 within epichaperomes. These findings have implications for the development of novel therapeutic strategies targeting chaperone complexes in diseases characterized by epichaperome dysregulation, such as in cancers and neurodegenerative disorders. By deciphering the regulatory mechanisms underlying epichaperomes, we move one step closer to harnessing their potential for precision medicine and therapeutic intervention. + +## METHODS + +Human biospecimens research ethical regulation statement + +<--- Page Split ---> + +1 Surgical specimens were obtained in accordance with the guidelines and approval of the 2 Institutional Review Board at Memorial Sloan Kettering Cancer Center, Biospecimen Research 3 Protocol# 09- 121, project title: Ex- Vivo Testing of Breast Cancer Tumors for Sensitivity to 4 Inhibitors of Heat Shock Proteins and Signaling Pathway Inhibitors, S. Modi, PI, and Biospecimen 5 Research Protocol# Protocol# 09- 121, project title: Ex- Vivo Testing of Breast Cancer Tumors for 6 Sensitivity to Inhibitors of Heat Shock Proteins and Signaling Pathway Inhibitors, S. Modi, PI, and 7 Biospecimen Research Protocol# 14- 091, project title: Establishment and Characterization of 8 Unique Mouse Models Using Patient- Derived Xenografts . E. de Stanchina, PI. The source of 9 samples consists of unused portions of surgical specimens that are taken for reasons other than 10 research (i.e., for patients undergoing the procedures for medical reasons unrelated to need for 11 research samples or to the nature of the research). No individuals were excluded on the basis of 12 age, sex or ethnicity. Because breast cancer is a disease which overwhelmingly affects women, 13 and is a disease that is generally not seen in children, the vast majority of breast cancer patients 14 enrolled on protocol# 09- 121 were females \(>18\) years of age. Patient tissue samples were 15 obtained with consent provided in written form. Samples were de- identified before receipt for use 16 in the studies. + +## Reagents and Chemical Synthesis + +All commercial chemicals and solvents were purchased from Sigma Aldrich or Fisher Scientific and used without further purification. The identity and purity of each product was characterized by MS, HPLC, TLC, and NMR. Purity of target compounds has been determined to be \(>95\%\) by LC/MS on a Waters Autopurification system with PDA, MicroMass ZQ and ELSD detector and a reversed phase column (Waters X- Bridge C18, \(4.6 \times 150 \mathrm{mm}\) , \(5 \mu \mathrm{m}\) ) eluted with water/acetonitrile gradients, containing \(0.1\%\) TFA. Stock solutions of all inhibitors were prepared in molecular biology grade DMSO (Sigma Aldrich) at \(1,000 \times\) concentrations. The PU- TCO, PU- CW800 and YK5- B probes and relevant control probes, and the PU- beads and the control probes were generated using published protocols \(^{13,19,35,80 - 85}\) or as described in Supplementary Notes 1. The GA- biotin probe was purchased from Sigma (SML0985). Disuccinimidyl suberate (DSS) was acquired from ThermoFisher (21655). + +## Cell lines and culture conditions + +Cell line selection was not based on gender, sex or ethnicity. Cell lines were cultured according to the providers' recommended culture conditions. Cells were authenticated using short tandem repeat profiling and tested for mycoplasma. The breast cancer cell line MDA- MB- 468 (HTB- 132, RRID: CVCL_0419), pancreatic cancer cell line ASPC1 (CRL- 1682, RRID: CVCL_0152), nonsmall cell lung cancer cell line NCI- H1975 (CRL- 5908, RRID: CVCL_1511), B lymphoblast cell line Daudi (CCL- 213, RRID: CVCL_0008), lung fibroblast cells MRC5 (CCL- 171, RRID: CVCL_0440), the colon cell line CCD- 18Co (CRL- 1459, RRID: CVCL_2379) and the Human Embryonic Kidney 293 (HEK293) cell line (CRL- 1573, RRID: CVCL_0045) were purchased from ATCC. IBL- 1 (RRID: CVCL_9638) was derived from an AIDS- related immunoblastic lymphoma \(^{86}\) . Mammary epithelial primary cells HMEC (PCS- 600- 010) were purchased from Lonza. B- cell lymphoma cell line OCI- LY1 (RRID: CVCL_1879) was obtained from the Ontario Cancer Institute. E14 mouse ES cells \(^{87}\) were received as frozen ampules from TG Fazzio (U Mass Med School). Cells were feed- free and verified as of male mouse origin through sequencing. ZHBtC4 mouse ES cells \(^{31}\) were received from D. Levasseur (U of Iowa). Cells were cultured as ESCs without feeder cells in the absence of doxycycline. For hiPSC, healthy donor fibroblasts purchased from Coriell were reprogrammed using CytoTune Sendai viruses \(^{34}\) . + +## Mammalian cell culture and lysis + +Mouse feeder- free embryonic stem cells (E14 or ZHBtC4 line) were grown on tissue culture plates coated with \(0.2\%\) gelatin. ESCs were cultured in Dulbecco's Modified Eagle Medium (DMEM; + +<--- Page Split ---> + +1 Gibco 10829018) media supplemented with \(10\%\) fetal bovine serum (FBS, HyClone 2 SH30070.03HI), \(2 \text{mM L}\) - glutamine, \(0.1 \text{mM}\) nonessential amino acids (Gibco 11140050), \(100 \text{U}\) 3 \(\text{mL}^{- 1}\) penicillin/streptomycin (Gibco 15140122), \(0.1 \text{mM beta- mercaptoethanol (Sigma M6250),}\) 4 and \(103 \text{U mL}^{- 1}\) leukemia inhibitory factor (LIF). Cells are grown in \(37^{\circ}\text{C} /5\% \text{CO}_2\) incubator with 5 media change every 2 days, passaged or harvested when \(60 - 80\%\) confluent. After harvesting, 6 cell pellets are washed with phosphate- buffered saline (PBS, GenClone 25- 508) and flash frozen 7 before storing in \(- 80^{\circ}\text{C}\) . For pull- down and chemical cross- linking experiments, frozen cells are 8 thawed and lysed in Felts lysis buffer (20 mM HEPES pH 7.4, \(50 \text{mM KCl}, 5 \text{mM MgCl}_2, 0.01\%\) 9 NP- 40) in the presence of protease inhibitors, phosphatase and deacetylase inhibitors. + +## ESC and hiPSC differentiation + +11 ZHB T c4 cells were differentiated into trophoblasts through Oct4 repression. Cells were seeded 12 at a density of \(2\times 10^{5}\) cells \(\mathrm{mL}^{- 1}\) and grown in media with added doxycycline at a final 13 concentration of \(200 \text{ng mL}^{- 1}\) for \(96 \text{h}\) before harvest. E14 cells were spontaneously differentiated 14 using attached embryoid bodies (EB) culture. Briefly, cells were seeded at a density of \(5\times 10^{4}\) 15 cells/mL in sterile bacteriological petri dishes in differentiation media (ES media without LIF) and 16 cultured in \(37^{\circ}\text{C} /5\% \text{CO}_2\) incubator for 4 days to aggregate into EBs. When turned orange, media 17 were changed. On day 4, EBs were transferred into tissue culture dishes (without gelatin) at a 18 density of \(100 - 200 \text{EBs per 10 cm tissue culture dish. Attached EBs were cultured in differentiation 19 media in \(37^{\circ}\text{C} /5\% \text{CO}_2\) incubator for 14- 18 days before harvest. hiPSC differentiated in midbrain 20 dopaminergic neurons were a gift from Dr. Lorenz Studer. Cells were differentiated into midbrain 21 dopamine neurons by a modified dual- SMAD inhibition protocol as described \(^{20}\) . hESCs were 22 dissociated into single cells using Accutase and plated at high density on Matrigel (BD). The cells 23 were subjected to timed exposure to LDN193189 (100 nM, Stemgent), SB431542 (10 μM, Tocris), 24 SHH C25II (100 ng mL \(^{- 1}\) , R&D), Purmorphamine (2 μM, Stemgent), FGF8 (100 ng mL \(^{- 1}\) , R&D) 25 and CHIR99021 (CHIR; 3 μM, Stemgent) to induce midbrain floor plate precursors. For mDA 26 neuron induction, floor plate precursors were maintained in mDA differentiation media containing 27 Neurobasal/B27/L- Glut (NB/B27; Invitrogen) supplemented with CHIR (until day 13) and with 28 BDNF (brain- derived neurotrophic factor, 20n mL \(^{- 1}\) ; R&D), ascorbic acid (0.2 mM, Sigma), GDNF 29 (glial cell line- derived neurotrophic factor, 20 ng mL \(^{- 1}\) ; R&D), TGFβ3 (transforming growth factor 30 type β3, 1 ng mL \(^{- 1}\) ; R&D), dibutyryl cAMP (0.5 mM; Sigma), and DAPT (10 μM; Tocris). On day 31 20, cells were dissociated using Accutase and replated on dishes pre- coated with polyornithine 32 (PO; \(15 \text{μg mL}^{- 1}\) )/laminin (1 μg mL \(^{- 1}\) )/fibronectin (2 μg mL \(^{- 1}\) ) in differentiation medium 33 (NB/B27 + BDNF, ascorbic acid, GDNF, dbcAMP, TGFβ3 and DAPT). On day 30 of differentiation, 34 cells were dissociated using Accutase and replated on dishes pre- coated with polyornithine (PO; 35 15 μg mL \(^{- 1}\) )/ laminin (1 μg mL \(^{- 1}\) )/ fibronectin (2 μg mL \(^{- 1}\) ) in differentiation medium 36 (NB/B27 + BDNF, ascorbic acid, GDNF, dbcAMP, TGFβ3 and DAPT) supplemented with 10 μM 37 Y- 27632 (until day 32). Two days after plating, cells were treated with 1 μg mL \(^{- 1}\) mitomycin C 38 (Tocris) for 1 h to kill any remaining proliferative contaminants. The mDA neurons were fed every 39 2 to 3 days and maintained without passaging until they were assayed at day 65. To prevent 40 neurons from lifting off, laminin and fibronectin were supplemented into the media every 7- 10 41 days. + +## Cell culture and transfections + +43 Monolayer cultures of MDA- MB- 468 and HEK293 cells were grown in high glucose (4.5 g L \(^{- 1}\) ) 44 DMEM containing \(10\%\) FBS and \(1\times\) antibiotic and antimycotic ( \(100\times\) ABAM, GIBCO) in a \(37^{\circ}\text{C}\) 45 incubator supplied with \(5\%\) oxygen- air atmosphere. For native electrophoresis, and in- gel 46 fluorescence studies, \(1\times 10^{7}\) cells were seeded in \(100 \text{mm dishes (Corning)}\) at \(70\%\) confluency 47 in DMEM supplemented with \(10\%\) FBS and \(1\times\) ABAM. Next day, spent medium was changed with 48 fresh serum and antibiotic free DMEM for 1 h before performing transfections. Cells were 49 transfected using lipofectamine 3000 (Invitrogen) with 4 μg of mCherry empty vector, mCherry + +<--- Page Split ---> + +1 HSP90β- Wild type (mCherry- HSP90β- WT), mCherry- HSP90β- S226A, S255A mutant (mCherry- HSP90β- AA) or mCherry- HSP90β- S226E, S255E mutant (mCherry- HSP90β- EE) plasmids. See Supplementary Note 2 for plasmid sequences. Transfection mixtures were prepared in OptiMEM (Gibco). Post 6 h of transfection, medium was changed with 10% FBS and 1×ABAM supplemented DMEM. Cells were harvested in native lysis buffer for future analyses. + +## Primary specimen processing + +Frozen tumor and matched tumor adjacent tissues were cut into small pieces using surgical blades and weighed using a precision balance. \(74~\mathrm{mg}\) of tissue was homogenized in \(200~\mu \mathrm{L}\) of \(1\times\) native lysis buffer in \(1.5~\mathrm{mL}\) microtubule homogenizer for each sample. Homogenization was performed on dry ice. Post homogenization samples were incubated on ice for 30 min followed by centrifugation at \(12,000\times \mathrm{g}\) at \(4^{\circ}C\) for 15 min. Supernatant was collected, and protein quantification was done using BCA method. Samples were normalized using total HSP90β levels for each tissue pairs. An initial SDS- PAGE was run using \(5\mu \mathrm{g}\) of total protein for each sample. Total protein loads were adjusted to ensure equal levels of total HSP90β in tumor and corresponding matched adjacent tissue. Samples were then processed for native PAGE and SDS- PAGE to check for HSP90β and p- Ser226 HSP90β as described below. + +## Native gel electrophoresis and western blot + +Native gel electrophoresis was performed as reported88. Namely, \(1 \times 10^7\) cells were lysed in 20 mM Tris pH 7.4, 20 mM KCl, 5 mM \(\mathrm{MgCl}_2\) , 0.01% NP40, and 10% glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze- thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer's protocol (Pierce™ BCA Protein Assay Kit, ThermoFisher Scientific, Waltham, MA). One hundred micrograms (100 \(\mu \mathrm{g}\) ) of protein were loaded in 4 to 10% native gel and run using native \(1 \times\) Tris- Glycine buffer (25 mM Tris, 192 mM glycine) at \(4^{\circ}C\) in a cold room at 125V. Following electrophoresis, proteins were transferred to PVDF membrane, by wet transfer (25 mM Tris, 192 mM glycine, 20% (v/v) methanol, 0.02% SDS) at 100V in the cold room. Membranes were then blocked for 1 h in 5% BSA in TBS/0.1% Tween 20. The blots were then probed with the following antibodies: HSP90β (SMC- 107; RRID:AB_854214; 1:2,000) and HSP110 (SPC- 195; RRID:AB_2119373; 1:1,000) from Stressmarq; HSC70 (SPA- 815; RRID:AB_10617277; 1:1,000), and HOP (SRA- 1500; RRID:AB_10618972; 1:1,000) from Enzo; HSP90α (ab2928; RRID:AB_303423; 1:6,000), AHA1 (ab56721, RRID:AB_2273725, 1:1000) from Abcam; CDC37 (4793; RRID:AB_10695539; 1:1,000), HOP (5670; RRID:AB_10828378; 1:1,000), from Cell Signaling Technologies. The blots were washed with TBS/0.1% Tween 20 and incubated with appropriate HRP- conjugated secondary antibodies: goat anti- mouse (1030- 05, RRID: AB_2619742, 1:5,000), goat anti- rabbit (4010- 05, RRID: AB_2632593, 1:5,000) and goat anti- rat (3030- 05, RRID: AB_2716837, 1:5,000) (Southern Biotech, Birmingham, AL, USA). The chemiluminescent signal was detected with Enhanced Chemiluminescence (ECL) reagent according to manufacturer's instructions and visualized using Chemi Doc (Biorad) and analyzed using Image Studio Lite Version 5.2. (LI- COR Biosciences). NativeMark unstained protein standard (Invitrogen, LC0725) was used to estimate molecular weight of protein complexes in native gel electrophoresis and Western blotting. + +## SDS-PAGE and western blot + +Proteins were extracted in \(20~\mathrm{mM}\) Tris pH 7.4, \(20~\mathrm{mM}\) KCl, \(5\mathrm{mM}\mathrm{MgCl}_2\) , \(0.01\%\) NP40, and \(10\%\) glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze- thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer's protocol (Pierce™ BCA Protein Assay Kit, ThermoFisher Scientific, Waltham, MA). Ten to thirty micrograms (10 to \(30\mu \mathrm{g}\) ) of total protein were subjected to SDS- PAGE, transferred onto PVDF membrane, by wet transfer (Towbin buffer: \(25~\mathrm{mM}\) Tris, \(192~\mathrm{mM}\) glycine, \(20\%\) (v/v) + +<--- Page Split ---> + +1 methanol) at 100V in cold room. Membranes were then blocked for 1 h in \(5\%\) BSA in TBS/0.1% 2 Tween 20 and incubated overnight with the indicated antibodies. HSP90β (SMC- 107; 3 RRID:AB_854214; 1:2,000) and HSP110 (SPC- 195; RRID:AB_2119373; 1:1,000) from 4 Stressmarq; HSC70 (SPA- 815; RRID:AB_10617277; 1:1,000), and HOP (SRA- 1500; 5 RRID:AB_10618972; 1:1,000) from Enzo; HSP90α (ab2928; RRID:AB_303423; 1:6,000), AHA1 6 (ab56721, RRID:AB_2273725, 1:1,000) from Abcam; p-MEK1/2 (S217/221) (9154; 7 RRID:AB_2138017; 1:1,000), MEK1/2 (9122; RRID:AB_823567; 1:1,000), p-mTOR (S2448) 8 (5536; RRID:AB_10691552; 1:500), mTOR (2983; RRID:AB_2105622; 1:1,000), CDC37 (4793; 9 RRID:AB_10695539; 1:1,000), HOP (5670; RRID:AB_10828378; 1:1,000), p-S6 ribosomal 10 protein (Ser235/236) (4858; RRID:AB_916156; 1:2,000), S6 ribosomal protein (2217; 11 RRID:AB_331355; 1:3,000), Oct4 (2840, RRID:AB_2167691, 1:2,000), p-AKT (S473) (9271, 12 RRID:AB_329825, 1:2000), AKT (4691, RRID:AB_915783, 1:3000), HSP70 (ADI-SPA-810, 13 RRID:AB_10616513, 1:2000) from Cell Signaling Technologies, \(\beta\) - actin (A1978, RRID: 14 AB_476692, 1:3000) from Sigma-Aldrich, and mCherry (PA5-34974, RRID:AB_2552323, 15 1:2,000) and p-Ser226 HSP90β (PA5-105480, RRID:AB_2816908, 1:1,000) from Fisher 16 Scientific. The blots were washed with TBS/0.1% Tween 20 and incubated with appropriate HRP- 17 conjugated secondary antibodies: goat anti- mouse (1030- 05, RRID: AB_2619742, 1:5,000), goat 18 anti- rabbit (4010- 05, RRID: AB_2632593, 1:5,000) and goat anti- rat (3030- 05, RRID: 19 AB_2716837, 1:5,000) (Southern Biotech, Birmingham, AL, USA). The chemiluminescent signal 20 was detected with ECL reagent according to manufacturer's instructions and visualized using 21 ChemiDoc MP imaging system (Biorad) and analyzed using Image Studio Lite Version 5.2. (LI- 22 COR Biosciences). Thermo Scientific PageRuler Plus prestained protein ladder (Fisher Scientific, 23 26619) or Precision Plus protein standards (Bio- Rad, 161- 0375) were used as size standards in 24 protein electrophoresis and Western blotting. + +## Coomassie and Ponceau S staining + +Where indicated, gels after native PAGE or SDS- PAGE were washed with deionized water three times for 5 min and incubated with Coomassie G- 250 stain (Bio- Rad) for 1 h. The gels were washed with water after to remove the excess of the dye and imaged. Where indicated, membranes after protein transfer were incubated with Ponceau S solution (Sigma) for 10 min, then were washed with water to remove the excess of the dye and imaged. + +## Primary specimen analyses + +Specimens were harvested as previously reported89. Briefly, the surgical team delivered specimens in tightly sealed, sterile, leak- proof bags without fixatives. This maintained specimens in their fresh state, crucial for downstream analyses. Fresh specimens underwent sterile harvesting by the pathologist or assistant, using laminar flow hoods. Harvesting times were meticulously recorded, kept under 30 minutes post- surgery to mitigate cold ischemia effects. Primary breast tumor specimens were selectively obtained from the index lesion's periphery, avoiding central necrosis. Recognition criteria for necrotic tissue included color loss, softness, and demarcation from viable tissue. Normal breast tissue samples (e.g., normal dense/fibrous breast parenchyma) are taken from distant locations, at least 1 cm grossly away from the target lesion if feasible. In contrast, due to the relatively small size of the pancreas and the nature of surgical procedures, normal pancreas samples collected were typically in close proximity to the tumor. Whipple procedures typically involve the resection of the head of the pancreas, while distal procedures focus on the resection of the tail. Samples were initially stored in tubes with MEM and antibiotics and transported on wet ice to the laboratory immediately after procurement. Upon reaching the laboratory, samples were transferred to cryovials, 'snap' frozen, and stored at - 80 °C for future molecular analyses. + +## Chemical blotting + +<--- Page Split ---> + +1 For in- gel blotting using PUTCO, cells were harvested in \(20~\mathrm{mM}\) Tris pH 7.4, \(20~\mathrm{mM}\) KCl, \(5\mathrm{mM}\) \(\mathrm{MgCl}_2\) , \(0.01\%\) NP40, and \(10\%\) glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze- thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer's protocol (Pierce™ BCA Protein Assay Kit, Thermofisher Scientific, Waltham, MA). One hundred micrograms (100 \(\mu \mathrm{g}\) ) of protein were incubated with \(1\mu \mathrm{M}\) of PUTCO in a total volume of \(42~\mu \mathrm{L}\) . Post 3 h of incubation samples were loaded in 4 to \(10\%\) native gel and run using native \(1\times\) Tris- Glycine buffer at \(4^{\circ}\mathrm{C}\) in cold room at \(125\mathrm{V}\) . Following electrophoresis, the gel was incubated in \(30~\mathrm{mL}\) of \(700~\mathrm{nM}\) Cy5- Tetrazine containing ice cold \(1\times\) Tris- Glycine buffer at room temperature (RT) for 15 min for the click reaction to occur. After 15 min, the gel was washed thrice (5 min each) with ice cold \(1\times\) Tris- Glycine buffer. The gel was then imaged using ChemiDoc MP imaging system (Biorad). Alexa 546 channel (illumination: Epi- green, \(520 - 545\mathrm{~nm}\) excitation, Filter: 577- 613 nm filter for green- excitable fluorophores and stains) was used to visualize mCherry- tagged species, and native page ladder (NativeMark™ Unstained Protein Standard, Cat. No. LC0725, Invitrogen™). The Cy5 channel (illumination: Epi- far red, \(650 - 675\mathrm{~nm}\) excitation, Filter: 700- 730 nm filter for far red- excitable fluorophores and stains) was used for imaging PUTCO staining. Post capturing, the images from the two channels were merged to get the alignment of the bands with respect to the molecular weight ladder in Image Lab 6.1 (Bio- Rad). For in cell blotting using PU- CW800, E14 cells were plated at a seeding density of \(1\times 10^{6}\) per \(10~\mathrm{cm}\) plate and grown for 44 h before treatment with either PU- CW800 or control fluorophore (SS27) at a concentration of \(1\mu \mathrm{M}\) in culture media for 4 h while incubating at \(37^{\circ}\mathrm{C}\) , \(5\%\) CO2. Following the treatment, cells were harvested and lysed by dounce homogenization in Felts lysis buffer (20 mM HEPES at pH 7.4, \(50~\mathrm{mM}\) KCl, \(2\mathrm{mM}\) EDTA, and \(0.01\%\) NP40) supplemented with protease, phosphatase, and deacetylase inhibitors. Cell lysates were buffer exchanged with fresh Felts lysis buffer containing supplements to remove any unbound drug before loading into a native gel. For visualization of PU- CW800 fluorescence and total protein, \(200~\mu \mathrm{g}\) of cell lysate was loaded onto a \(4 - 10\%\) native gradient gel and resolved at \(4^{\circ}\mathrm{C}\) for 5 h. Fluorescence was visualized on LI- COR Odyssey CLx using Image StudioTM Software (LI- COR Biosciences) and then total protein was visualized on the same gel using Coomassie Brilliant Blue R250 stain. Band(s) with observable fluorescent signal were then processed by in- gel digestion and analyzed for LC- MS/MS to identify major proteins. + +## SILAC and ESC transfection + +For metabolic labeling with SILAC (stable- isotope labeling of amino acid in cell culture), ESCs were cultured and passaged five times at \(48\mathrm{~h}\) intervals in media containing SILAC DMEM (Thermo Fisher 88364) supplemented with 13C- and 15N- labeled heavy L- arginine ( \(84~\mathrm{mg~L^{- 1}}\) , Cambridge isotope CNLM- 539- H) and L- lysine ( \(146~\mathrm{mg~L^{- 1}}\) , Cambridge isotope CNLM- 291- H) or supplemented with 12C- and 14N- labeled light L- arginine (Fisher BP2505100) and L- lysine (Fisher J6222522) amino acids for five passages to ensure complete stable isotope incorporation. For heterologous expression of HSP90 AA or EE mutants, cells were then reverse transfected with plasmid DNA using LipofectamineTM 3000 Transfection Kit (Invitrogen #L3000015) and incubated at \(37^{\circ}\mathrm{C}\) , \(5\%\) CO2 for \(72\mathrm{~h}\) at which point they were harvested. + +## Measurement of cell proliferation + +E14 cells were transfected and incubated in \(37^{\circ}\mathrm{C} / 5\%\) CO2 incubator for \(24\mathrm{~h}\) . Cells were then replated to 6- well plate at the same dilution factor for each transfection treatment condition and then returned to incubator. At \(60\mathrm{~h}\) post- transfection, cell proliferation was determined via cell count for all conditions. + +## Confocal microscopy + +HEK293 cells transfected with mCherry- HSP90β- AA or mCherry- HSP90β- EE plasmids were seeded at a density of \(1.8\times 10^{6}\) cells \(\mathrm{mL^{- 1}}\) on coverslips in a monolayer in six well plates and then + +<--- Page Split ---> + +1 grown overnight for the cells to attach. Cover slips were mounted with ProLongTM Gold antifade 2 mountant with DAPI. Imaging was done using Leica SP8 Stellaris microscope. Images were 3 analyzed using Image J and Leica LAS X lite software. Cell morphology was manually inspected, 4 and the percentage of cells exhibiting an elongated phenotype and several protrusions was 5 calculated. Specifically, cells transfected with mCherry were assessed, and those displaying the 6 described features were counted. The percentage was then determined based on the total 7 number of mCherry- transfected cells observed. + +## Chemical precipitation and cross-linking + +The GA- affinity beads were prepared by incubating GA- biotin (Sigma SML0985) with Dynabeads M- 280 Streptavidin (ThermoFisher 11205D) at \(4^{\circ}C\) for \(2.5\mathrm{h}\) . The GA- bound beads were then incubated with cleared cell lysates or cross- linked cell lysates overnight at \(4^{\circ}C\) . For PU- beads affinity capture, cell lysates were incubated with PU- beads or control beads at \(4^{\circ}C\) for \(3.5\mathrm{h}\) . Following incubation, bead conjugates were washed three times in lysis buffer before elution with sample buffer. The chemical cross- linking and HSP90 purification experiments were carried out in \(>3\) replicates for both ligands. Samples were analyzed separately, and statistical significance was assessed. + +## Chemical precipitation and immunoblotting + +Cells were harvested in \(20\mathrm{mM}\) Tris pH 7.4, \(20\mathrm{mM}\) KCl, \(5\mathrm{mM}\) MgCl \(_2\) , \(0.01\%\) NP40, and \(10\%\) glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze- thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer's protocol (Pierce™ BCA Protein Assay Kit, Thermofisher Scientific, Waltham, MA). PU- beads and control beads were washed with the native gel buffer 3 times prior use. Post washing, \(40\mu \mathrm{L}\) aliquots of the beads were distributed into the sample tubes. Five hundred micrograms (500 \(\mu \mathrm{g}\) ) of total protein in \(300\mu \mathrm{L}\) final volume, adjusted with native lysis buffer were added. Samples were incubated for \(3\mathrm{h}\) at \(4^{\circ}C\) on a rotor, followed by washing with native lysis buffer four times. Post washing, \(30\mu \mathrm{L}\) of \(5\times\) Laemmli buffer was added to the beads and boiled at \(95^{\circ}C\) for \(5\mathrm{min}\) . Ten micrograms ( \(10\mu \mathrm{g}\) ) of the lysates \((2\%)\) was used as input for the pull- down experiment. Samples were then centrifuged at \(13,000\times \mathrm{g}\) for \(20\mathrm{min}\) and supernatant collected was loaded on to SDS- PAGE. The protein transfer and western blotting procedures were performed as described in SDS- PAGE and western blot section. + +## IUPred analysis for disorder prediction + +Sequence Preprocessing: The primary amino acid sequence of human HSP90β (P08238) and HSP90α (P07900) were extracted in FASTA format. These sequences served as the input for subsequent disorder prediction using the IUPred algorithm. Calculation of Disorder Scores: The IUPred algorithm utilizes energy potentials derived from pairwise amino acid interactions to assess the local structural propensities of each residue in the protein sequence. For each residue, IUPred computes a disorder score within the range of 0 to 1. A score of 0 suggests a higher likelihood of being ordered, while a score of 1 indicates a higher likelihood of being disordered. Threshold for Disorder Classification: To classify residues as either ordered or disordered, a threshold was applied to the calculated disorder scores. A common threshold of 0.5 was employed, designating residues with scores above 0.5 as disordered. The output of the IUPred analysis consisted of a disorder profile, providing disorder scores for each residue in the input protein sequence. Residues were categorized based on the applied threshold, facilitating the identification of regions with a high probability of disorder. All analyses were performed with the default parameters of the IUPred algorithm. The results presented here are based on the specific sequence input and the applied threshold for disorder classification. + +## Computational analyses + +<--- Page Split ---> + +Protein complex preparation and docking calculations: The structure comprising HSP90β- HSP70(2)- HOP proteins was developed using the molecular comparative modeling technique, employing Modeller v10.4, the Modeller Python script90, and experimental template structures (PDB codes: 7KW7, 8EOB)10,91. The cryo- EM structure of human HSP90β (8EOB) served as the basis for obtaining coordinates for HSP90β (protopers A and B) in the developing model. To construct the assembly involving HSP70 and HOP, we utilized the sequences and atomic cryo- EM structure from the HSP90- HSP70- HOP- GR (7KW7) template. As these structures lacked certain residues, including those in the charged linker (Glu222 - Lys273), we incorporated them as intrinsic loops during computational processing. The target sequence for each HSP90β protomer was extracted from Uniprot ID: P08238. After model generation, we selected the optimal model based on the Discrete Optimized Protein Energy (DOPE) score. The final model included full- length HSP90 (excluding a ten- residue N- terminal disordered segment). For HOP and HSP70, we maintained the sequences provided in PDB: 7KW7. The validated model, equipped with co- crystal ligands on each HSP90β protomer, was imported into Maestro v13.3 (Schrödinger LLC, 2022- 3). Mutagenesis was performed to substitute Ser226/Ser255 with phosphomimetic conditions (Glu226/Glu255) and de- phosphorylated conditions (Ala226/Ala255) in both protomers of HSP90β. The preparation of all complexes utilized the Protein Preparation Wizard, a module for creating reliable, all- atom protein models. This involved restraining the assignment of bonds and bond orders, adding hydrogens, correcting formal charges, and filling missing side chains. Pre- processing steps included generating hetero states, H- bond assignment, and energy minimization using the Optimized Potentials for Liquid Simulations (OPLS3) force field, with a maximum root- mean- square deviation (RMSD) of 0.30 Å, employing the molecular mechanics engine Impact v9.6. Essential water atoms within 5 Å of the binding pocket were retained, while remaining waters were deleted. Structural refinement at neutral pH was carried out through the Epik v6.1 module92. The final refined structure served as the receptor for docking simulations. Ligands, such as ATP and ADP, underwent preparation with the LigPrep node, where the optimized ligand minimization algorithm yielded more conformers with numerous rotatable bonds, enhanced efficiency, and robustness. Different possible protonation states based on machine learning were generated, and ligand structures were minimized at pH values within the range of 7.0 and +/- 2.0, to guide the selection of protonation states on acidic/basic groups on ligands consistent with their pKa values, using the OPLS_3 force field, Premin, Truncated Newton Conjugate Gradient (TNCG), and Epik v6.1 nodes. Subsequently, a receptor grid was generated around the co- crystal ligand with default parameters. Docking experiments were executed on the nucleotide binding pockets of both protomers using the XP (extra- precision) Glide program (Glide v9.6) and Prime- MMGBSA (molecular mechanics generalized born surface area) modules, respectively. The best poses in the resulting docked complexes served as the initial complex structure for MD simulations93. Molecular dynamics simulations: The pentameric assemblies were prepared in the following combinations: 2xHSP90(Ser226Ser255)- 2xHSP70- HOP, 2xHSP90(Glu226Glu255)- 2xHSP70- HOP-, 2xHSP90(Ala226Ala255)- 2xHSP70- HOP, each bound to either ATP or ADP. These complexes underwent individual 100 ns all- atomic molecular dynamics simulations using the Desmond v7.1 module of the MAESTRO Suite from Schrodinger (www.schrodinger.com). Before simulations, each assembly was built by embedding water molecules, adjusting temperature and pressure closer to the physiological environment through the OPLS3 force field and TIP4PEW water model. The system was neutralized with counter ions (Na+/Cl-) to balance the net charge in the simulation box. The particle mesh Ewald (PME) method94 was used for electrostatics with a 10 Å cut- off for Lennard- Jones interactions, and the SHAKE algorithm95 was applied to restrict the motion of all covalent bonds involving hydrogen atoms. The complex system underwent a six- step relaxation protocol before productive MD simulations. The solvated system was initially minimized with solute restraints and then without solute restraints, utilizing a hybrid method of steepest descent and the LBFGS (limited memory Broyden- Fletcher- Goldfarb- Shanno) algorithm96,97. The energy- minimized system underwent a + +<--- Page Split ---> + +brief 12 ps simulation within the NVT canonical ensemble at a temperature of \(10\mathrm{K}\) , followed by a similar simulation in the isothermal- isobaric (NPT) ensemble at \(10\mathrm{K}\) , with restraints on nonhydrogen solute atoms. Subsequently, the system was simulated for \(24\mathrm{ps}\) in the NPT ensemble at \(300\mathrm{K}\) with limited restraints on nonhydrogen solute atoms. In the final equilibration step, the system was simulated for \(24\mathrm{ps}\) in the NPT ensemble at \(300\mathrm{K}\) without constraints to reach an equilibrium state. The minimized and equilibrated system without restraints was then subjected to a \(100\mathrm{ns}\) NPT simulation for production. The temperatures and pressures of the system in the initial simulations were controlled by Berendsen thermostats and barostats, respectively \(^{96,97}\) . The relaxed system underwent productive simulations using the Nose'- Hoover thermostat at \(300\mathrm{K}\) and the Martyna- Tobias- Klein barostat at \(1.01325\mathrm{bar}\) pressure. Atomic- coordinate data for each receptor- ligand complex and system energies were recorded every 1000 ps. Residue- pair correlations were calculated along the MD trajectory using the script trj_essential_dynamics.py available in the Schrödinger suite. Additionally, the unexplored cryptic motions, distribution of secondary structural elements, and the array of protein folding in intrinsic disordered regions were thoroughly examined using the extracted meta- trajectory data from 1000 trajectories throughout the simulation period. The secondary structure elements (SSE) index was computed to illustrate the percentage occurrence of alpha- helices \((\alpha)\) and beta- strands \((\beta)\) during the simulation period, delineated by residue. + +## Immunoprecipitation of mCherry-HSP90 + +RFP Selector (NanoTag #N0410) resins were equilibrated with lysis buffer to prepare the resin. Cell lysates were then added and incubated with the resins at \(4^{\circ}\mathrm{C}\) with head over tail rotation for \(90\mathrm{min}\) . Following incubation, resins were washed twice with lysis buffer and once with PBS before elution with \(2\times\) sample buffer and incubation at \(95^{\circ}\mathrm{C}\) for \(5\mathrm{min}\) . Eluents were then run on a \(12.5\%\) SDS- PAGE. For SILAC samples, heavy and light replicates were immunoprecipitated separately, before combined and separated by SDS gel electrophoresis. + +## Chemical cross-linking + +Cell lysates, with a concentration of approximately \(3\mu \mathrm{g}\mu \mathrm{L}^{- 1}\) , underwent cross- linking using disuccinimidyl suberate (DSS; ThermoFisher# 21655) at a concentration of \(2.5\mathrm{mM}\) . This process occurred at room temperature for \(1\mathrm{h}\) . To terminate the reaction, \(0.8\mathrm{M}\mathrm{NH_4OH}\) (Sigma# 09859) was added, reaching a final concentration of \(25\mathrm{mM}\) , and incubated at room temperature for an additional \(15\mathrm{min}\) . The lysates were clarified through two rounds of centrifugation at \(16,200\times \mathrm{g}\) for \(15\mathrm{min}\) at \(4^{\circ}\mathrm{C}\) before proceeding to separate HSP90 using immobilized PU- H71 or GA. + +## SDS-PAGE and trypsin digestion + +After elution from PU- or GA- beads, samples were loaded into \(12.5\%\) SDS- PAGE gel for separation. The entire lanes were cut into 10- 15 bands and processed by in- gel digestion as described previously \(^{19}\) . Briefly, gel bands were cut into small cubes, washed with \(25\mathrm{mM}\) \(\mathrm{NH_4HCO_3 / 50\%}\) acetonitrile, reduced with \(10\mathrm{mM}\) DTT (in \(25\mathrm{mM}\mathrm{NH_4HCO_3}\) ) at \(56^{\circ}\mathrm{C}\) for \(1\mathrm{h}\) , alkylated with \(55\mathrm{mM}\) iodoacetamide (in \(25\mathrm{mM}\mathrm{NH_4HCO_3}\) ) in darkness for \(45\mathrm{min}\) . Gel pieces were washed again with \(25\mathrm{mM}\mathrm{NH_4HCO_3 / 50\%}\) acetonitrile and evaporated in a speed- vac to complete dryness. The dried gel samples were proteolyzed using varied volumes of trypsin (0.6- \(1.0\mu \mathrm{g}\) depending on the intensity of the gel bands) at \(37^{\circ}\mathrm{C}\) for \(4\mathrm{h}\) , before the extraction of tryptic peptides by \(50\%\) acetonitrile/ \(2\%\) acetic acid. Tryptic peptide mixture was concentrated down to \(\sim 7\mu \mathrm{L}\) before LC- MS/MS analysis. For validation experiments in Figure 3d,e, chemical precipitation and sample preparation for PTM analyses were performed as follows. For in- cell YK- B bait affinity purification, cells were plated in \(10\mathrm{cm}\) plates at \(6\times 10^{6}\) cells per plate and treated with \(50\mu \mathrm{M}\) YK5- B for \(4\mathrm{h}\) . Cells were next collected and lysed in \(20\mathrm{mM}\) Tris pH 7.4, \(150\mathrm{mM}\) NaCl and \(1\%\) NP40 buffer. Five hundred micrograms (500 \(\mu \mathrm{g}\) ) of total protein were incubated with streptavidin agarose beads (ThermoFisher Scientific) for \(1\mathrm{h}\) and beads were washed with \(20\mathrm{mM}\) + +<--- Page Split ---> + +1 Tris pH 7.4, 100 mM NaCl and \(0.1\%\) NP40 buffer (washing buffer). For in- lysate YK5- B bait affinity 2 purification, cells were lysed in the above- mentioned lysis buffer. Streptavidin agarose beads 3 were incubated with \(50\mu \mathrm{M}\) YK5- biotin for \(1\mathrm{h}\) , washed and added to \(500\mu \mathrm{g}\) of total protein and 4 incubated overnight. The beads were then washed with the washing buffer. For PU- H71 beads 5 pull- down, \(250\mu \mathrm{g}\) of the same protein lysates were incubated with \(40\mu \mathrm{l}\) PU- H71 beads for \(3\mathrm{h}\) 6 and washed. The samples were applied onto SDS- PAGE. Resulting gels were washed 3 times in 7 distilled deionized \(\mathsf{H}_2\mathsf{O}\) for \(15\mathrm{min}\) each and visualized by staining overnight with Simply Blue 8 Coomassie stain (Thermo Fisher Scientific). Stained protein gel regions were typically excised 9 into 6 gel sections per gel lane, and completely destained as described19. In- gel digestion was 10 performed overnight with MS- grade trypsin (Trypsin Gold, Mass spectrometry grade, Promega) 11 at \(5\mathrm{ng}\mathrm{mL}^{- 1}\) in \(50\mathrm{mM}\) \(\mathrm{NH_4HCO_3}\) digestion buffer and incubation at \(37^{\circ}C\) . After acidification with 12 \(10\%\) formic acid (final concentration of \(0.5 - 1\%\) formic acid), peptides were extracted with \(5\%\) 13 formic acid / \(50\%\) acetonitrile and resulting peptides were desalted using hand- packed, reversed 14 phase Empore C18 Extraction Disks (3M, Cat#3M2215), following an established method98. Each 15 of the 6 sections per sample, per gel lane, were excised and separately digested in- gel, at the 16 same time, using the same batch and amount of trypsin. The peptides from each of these gel 17 sections were purified and analyzed by nano- LC- MS/MS separately. + +## LC-MS data acquisition, protein and phosphopeptide identification + +LC- MS data acquisition, protein and phosphopeptide identification Briefly, the digestion mixtures were injected into an Dionex Ultimate 3000 RSLCname UHPLC system (Dionex Corporation, Sunnyvale, CA), and separated by a \(75\mu \mathrm{m}\times 25\mathrm{cm}\) PepMap RSLC column (100 A, \(2\mu \mathrm{m}\) ) at a flow rate of \(\sim 450\) nL min \(^{- 1}\) . The eluant was connected directly to a nanoelectrospray ionization source of an LTQ Orbitrap XL mass spectrometer (Thermo Scientific, Waltham, MA). LC- MS data were acquired in a data- dependent acquisition mode, cycling between a MS scan (m/z 315- 2,000) acquired in the Orbitrap, followed by low- energy CID analysis on three most intense multiply charged precursors acquired in the linear ion trap. The centroided peak lists of the CID spectra were generated using PAVA searched against a database that is consisted of the Swiss- Prot protein database using Batch- Tag, a program of the University of California San Francisco Protein Prospector software, version 5.9.2. For identification of proteins in pull- down experiments, a precursor mass tolerance of \(15\mathrm{ppm}\) and a fragment mass tolerance of \(0.5\mathrm{Da}\) were used for protein database searches (trypsin as enzyme; 1 miscleavage; carbamidomethyl (C) as constant modification; acetyl (protein N- term), acetyl+oxidation (protein N- term), Met- loss (protein N- term), Met- loss+acetyl (protein N- term, oxidation (M)). Protein hits were reported with a Protein Prospector protein score \(\geq 22\) , a protein discriminant score \(\geq 0.0\) and a peptide expectation value \(\leq 0.01^{99}\) . This set of thresholds of protein identification parameters does not return any substantial false positive protein hits from the randomized half of the concatenated database. After protein identification, PTM search was carried out with S/T/Y phosphorylation included in variable modifications among the identified proteins. A threshold of SLIP score \(>6\) was imposed for false phosphorylation site assignment \(< 5\%^{100}\) . Identified phosphopeptides were manually inspected by confirming the quality of MS/MS spectra and mass accuracy. Cross- linked peptides were identified using an integrated module in Protein Prospector, based on a bioinformation strategy developed in the UCSF Mass Spectrometry Facility \(^{41,42,101,102}\) . Key cross- linked peptides were identified and confirmed by manually examining the returned spectrum, peptide scores, mass accuracy and absence from uncross- linked samples. For validation experiments in Figure 3e, MS data acquisition and processing were performed as follows. Desalted peptides were concentrated to a very small droplet by vacuum centrifugation and reconstituted in \(10\mathrm{mL}0.1\%\) formic acid in \(\mathsf{H}_2\mathsf{O}\) . Approximately \(90\%\) of the peptides were analyzed by nano- LC- MS/MS). A Q Exactive HF mass spectrometer was coupled directly to an EASY- nLC 1000 (Thermo Fisher Scientific) equipped with a self- packed \(75\mathrm{mm}\times 18\mathrm{cm}\) reverse phase column (ReproSil- Pur C18, 3M, Dr. Maisch GmbH, Germany) for peptide separation. Analytical column temperature was maintained at \(50^{\circ}\mathrm{C}\) by a column oven (Sonation GmbH, + +<--- Page Split ---> + +1 Germany). Peptides were eluted with a \(3 - 40\%\) acetonitrile gradient over \(60\mathrm{min}\) at a flow rate of 2 \(250~\mathrm{nL}\min^{- 1}\) . The mass spectrometer was operated in DDA mode with survey scans acquired at 3 a resolution of 120,000 (at m/z 200) over a scan range of 300- 1750 m/z. Up to 15 of the most 4 abundant precursors from the survey scan were selected with an isolation window of 1.6 Th for 5 fragmentation by higher- energy collisional dissociation with normalized collision energy (NCE) of 6 27. The maximum injection time for the survey and MS/MS scans was \(20\mathrm{ms}\) and \(60\mathrm{ms}\) 7 respectively; the ion target value (Automatic Gain Control) for survey and MS/MS scan modes 8 was set to \(3\mathrm{e}^{6}\) and \(1\mathrm{e}^{6}\) , respectively. + +## Quantitation of phosphopeptides and crosslinked peptides + +Manually- confirmed, high- confidence phosphopeptides and cross- linked peptides were quantified by the peak height of the extracted ion chromatogram of each peptide monoisotope mass. For phosphopeptide quantitation, the protein loading of HSP90 peptides in lysates or from pull- down experiments was normalize to a representative, isoform specific tryptic peptide, ELISNSSDALDK for HSP90α and ELISNASDALDK for HSP90β. Phosphopeptides with different charge state or miscleavages were considered as different measurements for quantitation of each phosphosite. To assess the relative phosphorylation levels of different phosphosites in cancer cells and non- transformed cells, the ion intensity values of all phosphopeptides for each phosphosite were summed. The average ion intensities of each phosphosite between cancer and non- transformed cells were compared. Cross- linked peptides were identified using an integrated module in Protein Prospector, based on a bioinformation strategy developed in the UCSF Mass Spectrometry Facility41,42,101,102. Key cross- linked peptides were identified and confirmed by manually examining the returned spectrum, peptide scores, mass accuracy and absence from uncross- linked samples. Cross- linked peptides identified from various samples were pooled together, and the cross- linking propensity of each cross- linked peptide was assessed by its cross- linking percentage 43. Cross- linking percentage for each peptide pair was calculated using the following formula: + +\[\% \mathrm{XL} = \frac{\mathrm{Cross} - \mathrm{linked~peptide~Peak~Height~(PH)}}{\sum \mathrm{Cross} - \mathrm{linked~peptide~PH} + \mathrm{Dead} - \mathrm{end~XL}1\mathrm{PH} + \mathrm{Dead} - \mathrm{end~XL}2\mathrm{PH}}\] + +where the peak height is the apex peak height in LC- MS/MS runs. Dead- end XLs are cross- linker modified peptides where only one NHS- ester function of DSS is cross- linked to a Lys residue and the other NHS- ester function is hydrolyzed by water. + +## Homology modeling + +The mouse HSP90 sequences for both alpha and beta isoforms were aligned and the models were built using an open conformation template (PDB: 2IOQ), a closed conformation template (PDB: 2CG9), and an HSP70- bound model (derived from a cryo- EM structure of HSP90•HSP70•GR complex10 using UCSF Modeller. Structural visualization and analysis were carried out using UCSF Chimera. + +## Statistics and reproducibility + +Unless as specified above under Protein identification and Bioinformatics analyses, statistics were performed, and graphs were generated, using Prism 10 software (GraphPad). Statistical significance was determined using Student's t- Tests or ANOVA, as indicated. Means and standard errors were reported for all results unless otherwise specified. Effects achieving \(95\%\) confidence interval (i.e., \(p < 0.05\) ) were interpreted as statistically significant. No statistical methods were used to pre- determine sample sizes, but these are similar to those generally employed in the field. No samples were excluded from any analysis unless explicitly stated. + +<--- Page Split ---> + +1 Reporting summary. Further information on research design is available in the Nature Research 2 Reporting Summary linked to this article. + +## 3 DATA AVAILABILITY + +4 The source data underlying all main and supplementary figures are provided with this paper as a 5 Source Data file. Datasets and analytics associated with epicarperomics and proteomics 6 analyses are available in the Supplementary Information as Supplementary Data 1 through 6. LC- 7 MS data (i.e., proteomics and epicarperomics raw mass spectrometry data, peak lists, and 8 results) that support the findings of this study are deposited to the ProteomeXchange Consortium 9 via the PRIDE partner repository with the dataset identifier PXD050251 [Reviewer account 10 details: Username: reviewer_pxd050251@ebi.ac.uk; Password: TmZMDQ0W]. Protein 11 sequences (FASTA files) were obtained from UniProt (https://www.uniprot.org/). MD simulations 12 data were deposited in Zenodo [https://doi.org/ 10.5281/zenodo.10800912]103. 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Labusca, L. & Mashayekhi, K. Human adult pluripotency: Facts and questions. World J. Stem Cells 11, 1- 12 (2019).5 79. Lee, L. J., Papadopoli, D., Jewer, M., Del Rincon, S., Topisirovic, I., Lawrence, M. G. & Postovit, L. M. Cancer Plasticity: The Role of mRNA Translation. Trends Cancer 7, 134- 145 (2021).6 80. Rodina, A. et al. Identification of an allosteric pocket on human hsp70 reveals a mode of inhibition of this therapeutically important protein. Chem. Biol. 20, 1469- 1480 (2013).7 81. Kang, Y. et al. Heat shock protein 70 inhibitors. 1. 2,5'-thiodipyrimidine and 5- (phenylthio)pyrimidine acrylamides as irreversible binders to an allosteric site on heat shock protein 70. J. Med. Chem. 57, 1188- 1207 (2014).8 82. Rodina, A. et al. Affinity purification probes of potential use to investigate the endogenous Hsp70 interactome in cancer. ACS Chem. Biol. 9, 1698- 1705 (2014).9 83. Taldone, T. et al. Heat shock protein 70 inhibitors. 2. 2,5'-thiodipyrimidines, 5- (phenylthio)pyrimidines, 2- (pyridin- 3- ylthio)pyrimidines, and 3- (phenylthio)pyridines as reversible binders to an allosteric site on heat shock protein 70. J. Med. Chem. 57, 1208- 1224 (2014).10 84. Shrestha, L., Patel, H. J. & Chiosis, G. Chemical Tools to Investigate Mechanisms Associated with HSP90 and HSP70 in Disease. Cell Chem. Biol. 23, 158- 172 (2016).11 85. Taldone, T. et al. Design, synthesis, and evaluation of small molecule Hsp90 probes. Bioorg. Med. Chem. 19, 2603- 2614 (2011).12 86. Guasparri, I., Bubman, D. & Cesarman, E. EBV LMP2A affects LMP1- mediated NF- kappaB signaling and survival of lymphoma cells by regulating TRAF2 expression. Blood 111, 3813- 3820 (2008).13 87. Hooper, M., Hardy, K., Handyside, A., Hunter, S. & Monk, M. HPRT- deficient (Lesch- Nyhan) mouse embryos derived from germline colonization by cultured cells. Nature 326, 292- 295 (1987).14 88. Roychowdhury, T., Santhaseela, A. R., Sharma, S., Panchal, P., Rodina, A. & Chiosis, G. Use of Native- PAGE for the Identification of Epichaperomes in Cell Lines. Methods Mol. Biol. 2693, 175- 191 (2023).15 89. Corben, A. D. et al. Ex vivo treatment response of primary tumors and/or associated metastases for preclinical and clinical development of therapeutics. J. Vis. Exp., e52157 (2014).16 90. Sali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779- 815 (1993).17 91. Srivastava, D., Yadav, R. P., Singh, S., Boyd, K. & Artemeyv, N. O. Unique interface and dynamics of the complex of HSP90 with a specialized cochaperone AIPL1. Structure 31, 309- 317 e305 (2023).18 92. Johnston, R. C. et al. Epik: pK(a) and Protonation State Prediction through Machine Learning. J. Chem. Theory Comput. 19, 2380- 2388 (2023).19 93. Castelli, M. et al. How aberrant N-glycosylation can alter protein functionality and ligand binding: An atomistic view. Structure 31, 987- 1004. e1008 (2023).20 94. Darden, T., York, D. & Pedersen, L. Particle Mesh Ewald - an N.Log(N) Method for Ewald Sums in Large Systems. J. Chem. Phys. 98, 10089- 10092 (1993).21 95. Ryckaert, J., Ciccotti, G. & Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327- 341 (1977). + +<--- Page Split ---> + +1 96. Hayes, J. M. et al. Kinetics, in silico docking, molecular dynamics, and MM-GBSA binding studies on prototype indrubins, KT5720, and staurosporine as phosphorylase kinase ATP-binding site inhibitors: the role of water molecules examined. Proteins 79, 703-719 (2011). 5 97. Shan, Y., Kim, E. T., Eastwood, M. P., Dror, R. O., Seeliger, M. A. & Shaw, D. E. How does a drug molecule find its target binding site? J. Am. Chem. Soc. 133, 9181-9183 (2011). 8 98. Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre- fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896-1906 (2007). 11 99. Wu, T., Nance, J., Chu, F. & Fazzio, T. G. Characterization of R-Loop-Interacting Proteins in Embryonic Stem Cells Reveals Roles in rRNA Processing and Gene Expression. Mol. Cell. Proteomics 20, 100142 (2021). 14 100. Zhou, Y., Qiu, L., Sterpka, A., Wang, H., Chu, F. & Chen, X. Comparative Phosphoproteomic Profiling of Type III Adenylyl Cyclase Knockout and Control, Male, and Female Mice. Front. Cell. Neurosci. 13, 34 (2019). 17 101. Chu, F., Baker, P. R., Burlingame, A. L. & Chalkley, R. J. Finding chimeras: a bioinformatics strategy for identification of cross-linked peptides. Mol. Cell. Proteomics 9, 25-31 (2010). 20 102. Zeng-Elmore, X. et al. Molecular architecture of photoreceptor phosphodiesterase elucidated by chemical cross-linking and integrative modeling. J. Mol. Biol. 426, 3713-3728 (2014). 23 103. Pasala, C., Digwal, C. S., Sharma, S. & Chiosis, G. Molecular dynamics simulation data associated with manuscript: Phosphorylation-Driven Epichaperome Assembly: A Critical Regulator of Cellular Adaptability and Proliferation [Data set]. Zenodo, https://doi.org/10.5281/zenodo.10800912 (2024). 27 104. Kirschke, E., Goswami, D., Southworth, D., Griffin, P. R. & Agard, D. A. Glucocorticoid receptor function regulated by coordinated action of the Hsp90 and Hsp70 chaperone cycles. Cell 157, 1685-1697 (2014). + +## ACKNOWLEDGEMENTS + +This work was supported by the NIH (R01 CA172546, P01 CA186866, R56 AG061869, R01 HD09783, R01 AG067598, R01 AG074004, R01 AG072599, R56 AG072599, RF1 AG071805, P30 CA08748, P20 GM113131), NSF GRFP (LB), UNH Hamel Center (HTN), UNH Graduate School. G.Colombo acknowledges funding from Fondazione AIRC (Associazione Italiana Ricerca Sul Cancro) under IG 2022 - ID. 27139. We thank Dr. David A. Agard for providing the model of HSP90\\*HSP70\\*GR complex derived from a cryo- EM density map104, Thomas G. Fazzio (U Mass Med School) for the E14 cells, Dr. Lorenz Studer for the human iPSCs and iPSC- derived neurons, and Dana Levasseur (U Iowa) for the ZHBTC4 cells. We thank the Molecular Cytology Core, the Antitumor Assessment Core and our colleagues in the Departments of Surgery and Medicine at Memorial Sloan Kettering for providing the biospecimens for research. + +## AUTHOR CONTRIBUTIONS + +S.W.M performed the MS studies and biochemical and functional studies in mouse ESCs. T.R. performed the biochemical and functional validation studies in human cells. C.P. performed the MD simulations. H.T.N and D.T.T. performed MS studies of cargos and cross- linking experiments. S.S. performed chemical synthesis, compound identity and purity evaluations for the epicathepore probes. L.B. and N.Y. generated ESC culture samples and MS sample + +<--- Page Split ---> + +1 preparation. A.R., P.P., S.J., S.C., S.B. and H.E-B. performed experiments. C.S.D. provided 2 reagents. V.M., C.K., J.L., P.Y., E.deS., A.C., S.M., and M.A. were involved in various aspects of 3 biospecimen handling, including recruitment, procurement, or processing at different stages from 4 surgery to delivery to the laboratory. R.J.C. and P.R.B. provided Protein Prospector and 5 supported data analysis. F.C., T.A.N., G.Chiosis and A.L.B. participated in the design and 6 analysis of various experiments. H.E-B., A.R., S.D.G., G.Colombo and T.A.N. assisted with 7 manuscript writing and data analysis. F.C. and G.C. developed the concept and wrote the paper. + +## 8 COMPETING INTERESTS + +9 Memorial Sloan Kettering Cancer Center holds the intellectual rights to the epichepaterome 10 portfolio. G.C., A.R. and S.S. are inventors on the licensed intellectual property. All other authors 11 declare no competing interests. + +## 13 SUPPLEMENTARY INFORMATION + +14 Supplementary Figures 1 through 9 + +15 Supplementary Note 1. Synthesis and characterization of the chemical probes + +16 Supplementary Note 2. Full nucleotide sequence of the HSP90 plasmids in FASTA format + +17 + +18 Supplementary Data 1 contains LC-MS data and data analysis of PU-H71 and GA pull-down 19 samples as well as 300 kDa band sliced from native-PAGE, associated with Figure 1d,1e, and 20 Supplementary Figure 2c,3. + +21 Supplementary Data 2 contains LC-MS data and data analysis for the identification and 22 quantitation of HSP90 cross-linked peptides in PU-H71 or GA pull-down samples, associated 23 with Figure 2b. + +24 Supplementary Data 3 contains LC-MS data analysis of the HSP90 band from PU-H71 pull- 25 down or lysate samples, associated with Figure 3b,3c. + +26 Supplementary Data 4 contains LC-MS data for SILAC quantitation of mCherry-HSP90 EE or 27 AA mutant pull-down experiments in three replicates, associated with Figure 6b,6c, and 8c. 28 Normalized median intensity SILAC ratios were used for quantitation. + +29 Supplementary Data 5 contains LC-MS data analysis for SILAC quantitation of 30 phosphopeptides from WT mcherry-HSP90 in ES or differentiated trophoblast state, associated 31 with Figure 8b. + +32 Supplementary Data 6 contains LC-MS data analysis for label-free quantitation of 33 phosphopeptides from the HSP90 band of PU-H71 or YK55 pull-down samples from a variety of 34 cell lines, associated with Figure 3d,3e. + +<--- Page Split ---> +![](images/Supplementary_Figure_1.jpg) + +
Figure 1. Embryonic stem cells and cancer cells share compositionally similar epipheroperomes. a Schematic illustrating the biochemical and functional distinctions between epipheroperomes, defined as long-lasting heterooligomeric assemblies composed of tightly associated chaperones and co-chaperones, and traditional chaperones. Unlike chaperones, which assist in protein folding or assembly, epipheroperomes sequester proteins, reshaping protein-protein interactions, and consequently altering cellular phenotypes. The schematic also outlines key principles for the use of PU-probes in epipheroperome analysis. b Detection of epipheroperome components (chaperones and co-chaperones) through SDS-PAGE (bottom, total protein levels) and native-PAGE (top), followed by immunoblotting. See also Supplementary Fig. 1. c Visualization of HSP90 in epipheroperomes using the PU-TCO click probe. See also Supplementary Fig. 2. Gel images are representative of three independent experiments. d Epipheroperome constituent chaperones and co-chaperones identified through mass spectrometry analyses of PU-beads cargo. Representative data of two independent experiments. See Supplementary Fig. 3 for the GA-cargo. e Illustration of an isobaric, discriminant peptide pair from ESC lysate samples and HSP90 captured by PU- and GA-beads. Representative data of two independent experiments. f Schematic summary. Both cancer cells and pluripotent stem cells harbor epipheroperomes. These epipheroperomes undergo disassembly during differentiation processes. Source data are provided in Supplementary Data 1 and in Source data file.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Figure 2. An enrichment of the closed-like conformation of HSP90 favors epichaperomes formation. a Experiment outline. b Plot comparing cross-linking propensity of Lys residues in HSP90 bound to PU-H71 or GA. Average cross-linking percentage of PU-H71 (x-axis) and GA (y-axis) bound HSP90 cross-linked pairs are shown. Blue circles represent pairs with similar cross-linking propensity (dotted line with a slope of 1). Orange points indicate outlier cross-linked peptides, with cross-linked Lys residues 8 amino acids away and the cross-linking percentage difference \(\geq 1.5\) standard deviation of replicates. Solid orange circles represent \(\mathrm{p} \leq 0.05\) , \(\mathrm{n} = 3\) replicate measurements. c Homology model illustrating the HSP90 dimer in the open conformation (template PDB: 2IOQ), favored by geldanamycin (GA), and the closed conformation (template PDB: 2CG9), favored by PU-H71. One HSP90 protomer is colored to indicate the N-terminal domain (NTD, light blue), the middle domain (MD, dark blue), and the C-terminal domain (CTD, green). Cross-linked residues are labeled by pink dots and connected by red dashed lines. d NTD structures of PU-H71 (top, PDB: 2FWZ) and GA (bottom, PDB: 1YET)-bound HSP90. Source data are provided as Supplementary Data 2. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3. Phosphorylation of key residues located in the charged linker supports HSP90 incorporation into epicarpemores. a Experiment outline and expected outcomes. b Tandem MS spectra of HSP90 Ser226 (bottom) and Ser255 (top) phosphorylated peptides are presented, supporting the sequence and phosphorylation site identification. c Comparison of the extracted ion chromatogram of HSP90 Ser255 phosphopeptide in the PU-bead cargo (red trace, left panel) and ESC lysate (black trace, left panel) with a representative unmodified tryptic peptide in the PU-bead cargo (blue trace, right panel) and ESC lysate (black trace, right panel). d Ion intensity values of all phosphopeptides and the ratio of mean peptide intensity for each phosphosite in the samples described in panel a (n = 4 Ca and n = 2, NT). e Ratio of individual peptide intensity for each phosphosite in the samples described in the schematic (S255 n = 5; S226 n = 4; S263 n = 8; S231 n = 5). Source data are provided as Source Data file and as Supplementary Data 3,6.
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4. Phosphorylation of key residues located in the charged linker of HSP90 leads to a conformational shift in the linker, exposing the middle domain of the protein. A Model of the HSP90-HSP90-HSP70-HSP70-HOP assembly used for the molecular dynamics simulations. A and B, protomers A and B, respectively. b Protein secondary structure elements (SSE) like alpha-helices and beta-strands of the charged linker of protomer A of ATP-bound HSP90 monitored throughout the MD simulation. WT (HSP90 S226/S255), phosphomimetic (HSP90 S226E/S255E) and non-phosphorylatable (HSP90 S226A/S255A) mutants were analyzed. The plot on the left reports SSE distribution by residue index throughout the charged linker and the plot on the right monitors each residue and its SSE assignment over time. Schematic illustrating the primary structure of the full-length HSP90 with color-coded domains is also shown: NTD, N-terminal domain; MD, middle domain and CTD, C-terminal domain. The charged linker (CL) and the location of the two key serine residues are also shown (top inset). The gray bar indicates the CL segment encompassing residues 218 to 232. c Cartoon representation of ATP-bound HSP90 protomer A in assemblies containing the phosphomimetic (HSP90 S226E/S255E) or the non-phosphorylatable (HSP90 S226A/S255A) mutants is shown. Green, reference trajectory; gray, representative trajectories of \(n = 1,000\) . The inset illustrates the surfaces available for the interaction between HSP90 A and HSP70 A when the CL is in the 'up' conformation. A blue arrow indicates the location of the key beta-strand in the charged linker. See also Supplementary Figs. 5 and 6.
+ +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5. Phosphorylation of key residues located in the charged linker of HSP90 facilitates assembly motions conducive to epichepaterome core formation. A Calculated dynamic cross-correlation matrix of Ca atoms around their mean positions for 100 ns molecular dynamics simulations. ATP-bound WT (HSP90 S226/ S255), phosphoimimetic (HSP90 S226E/S255E) and non-phosphorylatable (HSP90 S226A/S255A) mutantcontaining HSP90-HSP90-HSP70-HSP70-HOP assemblies were analyzed. The cartoon below captures the key motions among the different domains of the individual assembly components. Extents of correlated motions and anti-correlated motions are color-coded from blue to red, which represent positive and negative correlations, respectively. The assembly contains two full-length HSP90beta proteins (protomer A and protomer B). The two HSP70 proteins (HSP70 A and HSP70 B) and the HOP protein are of sizes reported, and as per the constructs used in 7KW7. b Cartoon showing assemblies that are preferentially formed when the HSP90 charged linker is either phosphorylated (as in the EE mutant) or not phosphorylated (as in the WT protein).
+ +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 6
+ +Figure 6. Immunopurification reveals increased presence of epicarpemore- specific co- chaperones in phosphomimetic HSP90 complexes compared to non- phosphorylatable complexes. a Experiment outline and outcomes. b Representative spectra (n = 3 independent experiment) of proteins co- purified with the phosphomimetic HSP90S226E,S255E (EE, blue) and non- phosphorylatable HSP90S226A,S255A (AA, red) mutants. c Heatmap showing the identity of chaperone and co- chaperones identified as epicarpemore components in cancer cells (as per Rodina et al. Nature 2016) and enriched in the affinity purified HSP90S226E,S255E mutant. Scale bar, log2 average SILAC values EE/AA (n = 3). Source data are provided as a Source Data file and as Supplementary Data 4. + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Figure 7. Phosphorylation of key residues located in the charged linker supports HSP90 incorporation into epicheporemes. a Overview of the experimental design and expected outcomes. b Analysis of transfection efficacy in cells transfected with HSP90β mutants, as indicated in panel a. c Detection of epicheporeme components (chaperones and co-chaperones) through SDS-PAGE (bottom, total protein levels) and native-PAGE (top), followed by immunoblotting. Blue brackets indicate the approximate position of epicheporeme-incorporated chaperones. Data are presented as mean ± s.e.m., \(n = 3\) , one-way ANOVA with Sidak's post-hoc, EE vs AA. d Visualization of HSP90 in epicheporemes using the PU-TCO probe clicked to Cy5 (left) and the mCherry tag (middle). Right, merged images. MWM, molecular weight marker. e Detection and quantification of epicheporeme components through PU-beads capture as indicated in panel a. Protein amount loaded for 'Input' represents 2% of the protein amount incubated with the beads. Data are presented as mean ± s.e.m., \(n = 3\) , unpaired two-tailed t-test. Gel images are representative of three independent experiments. Source data are provided as Source data file.
+ +<--- Page Split ---> +![](images/Figure_8.jpg) + +
Figure 8. Phosphorylation of key residues located in the HSP90 charged linker favors ESC proliferation and self-renewal potential. a ESC proliferation at 60 h post-transfection in E14 cells transfected with either the phosphomimetic HSP90βS226E,S255E (EE) or the nonphosphorylatable HSP90S226A,S255A (AA) mutant. Medium (1x) or high (2x) plasmid concentrations were employed. Data are presented as mean ± s.e.m., \(n = 6\) , one-way ANOVA with Sidak's post-hoc, EE vs AA. b Representative spectra ( \(n = 3\) independent experiments) of phosphopeptides, S255P (left) and S226P (right), and a representative unmodified tryptic peptide (middle) in mCherry-tagged WT HSP90β affinity-purified from ESC (red) or differentiated trophoblast (black) cells. c Representative spectra ( \(n = 3\) independent experiments) of a tryptic peptide from Oct4 protein co-purified from ESCs labeled with heavy or light isotope lysine and arginine expressing either the phosphomimetic (EE) or the nonphosphorylatable (AA) HSP90 mutant. Quantitative analysis via mass spectrometry (MS) to determine protein abundance is shown. d Overview of the experimental design and expected outcomes (panels e,f). e,f Detection and quantification of Oct4 protein expressed in cells transfected with the indicated HSP90 mutants or vector control (panel e) and sequestered into the epicatherome platforms (identified through PU-beads capture, panel f). (e) Data are presented as mean ± s.e.m., \(n = 5\) AA, \(n = 5\) EE, \(n = 3\) WT, \(n = 3\) empty vector, one-way ANOVA with Dunnett's post-hoc, EE vs AA, WT vs AA, empty vector vs AA. (f) Data are presented as mean ± s.e.m., \(n = 3\) , unpaired two-tailed t-test. Source data are provided as Source Data file and Supplementary Data 5.
+ +<--- Page Split ---> +![](images/Figure_9.jpg) + +
Figure 9
+ +Figure 9. Regulation of epicarporene processes in ESC and cancer cells hinges on the specific phosphorylation events occurring at key residues within HSP90's charged linker. A overview of the experimental design and expected outcomes. b Detection and quantification of proteins involved in transducing signaling events that lead to cell proliferation, survival, and protein synthesis control. See Supplementary Fig. 9 for total protein levels and levels sequestered into epicarporenes. Data are presented as mean ± s.e.m., p- S6 n = 8; p- mTOR n = 3; p- MEK1/2 n = 6; p- AKT n = 5, unpaired two- tailed t- test. c Confocal microscopy shows morphological differences between the cells transfected with either the AA or the EE HSP90 mutant. Micrographs are representative of 96 cells for EE and 62 cells for AA. Scale bar, 10 μm. Data are presented as mean ± s.e.m., n = 8 wells for EE, n = 14 wells for AA, unpaired two- tailed t- test. Source data are provided as Source data file. + +<--- Page Split ---> +![](images/Figure_10.jpg) + +
Figure 10. Human tissues positive for epicarpemores exhibit p-Ser226 HSP90β positivity, and conversely, those negative for epicarpemores show no or negligible p-Ser226 signal within HSP90's charged linker. a Cartoon illustrating the processing of human tissue for biochemical analyses. Both tumor (T) and tumor adjacent (TA) tissues, determined by gross pathological evaluation to be potentially non-cancerous, were harvested and analyzed. b MDA-MB-468 breast cancer cells (epicarpemore-high) and ASPC1 pancreatic cancer cells (epicarpemore-low) served as controls for assessing p-Ser226 HSP90 levels. c The graph presents the relationship between epicarpemore positivity and HSP90 Ser226 phosphorylation for tissues described in panel a. Data represent mean ± s.e.m., with \(n = 9\) tumor (T) and \(n = 9\) paired tumor-adjacent (TA) tissues classified based on epicarpemore positivity or negativity, as determined by Native PAGE (see panel d); unpaired two-tailed t-test. d Detection of epicarpemores through native-PAGE (top), and of p-Ser226 HSP90 (middle) and total HSP90 (bottom) by SDS-PAGE, followed by immunoblotting, in tissues from the indicated patient specimens, as in panel a. Blue brackets indicate the approximate position of epicarpemore-incorporated HSP90. Note: Obtaining genuinely "normal" tissue adjacent to tumors presents challenges, especially in the case of pancreatic tissue. The relatively small size of the organ and the nature of surgical procedures for pancreatic cancer often lead to the collection of normal samples in close proximity to the tumor. It's crucial to acknowledge that, due to these challenges, we designate potentially normal tissue as tumor-adjacent tissue, recognizing that it may not entirely reflect a truly normal tissue state. PDAC, Pancreatic Ductal Adenocarcinoma; IDC, Invasive Ductal Carcinoma; ILC, Invasive Lobular Carcinoma; ER, Estrogen Receptor; PR, Progesterone Receptor. Source data are provided as Source data file.
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryData1. xlsx SupplementaryData2. xlsx SupplementaryData3. xlsx SupplementaryData4. xlsx SupplementaryData5. xlsx SupplementaryData6. xlsx SupplementaryInformation03.01.2024. pdf SourceData. xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf_det.mmd b/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6bf33c8c6cc719fc44678136995ec3e523ac1187 --- /dev/null +++ b/preprint/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf/preprint__09d33aed00231d67d90a8ec99efc3d40d229c84279128ce1b4364a1aa9264ebf_det.mmd @@ -0,0 +1,807 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 937, 208]]<|/det|> +# Phosphorylation-Driven Epichaperome Assembly: A Critical Regulator of Cellular Adaptability and Proliferation + +<|ref|>text<|/ref|><|det|>[[44, 230, 258, 276]]<|/det|> +Gabriela Chiosis chiosig@MSKCC.ORG + +<|ref|>text<|/ref|><|det|>[[44, 301, 767, 323]]<|/det|> +Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0003- 0486- 6920 + +<|ref|>text<|/ref|><|det|>[[44, 328, 311, 370]]<|/det|> +Seth McNutt University of New Hampshire + +<|ref|>text<|/ref|><|det|>[[44, 375, 243, 415]]<|/det|> +Tanaya Roychowdhury MSKCC + +<|ref|>text<|/ref|><|det|>[[44, 420, 408, 461]]<|/det|> +Chiranjeevi Pasala Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 466, 311, 508]]<|/det|> +Hieu Nguyen University of New Hampshire + +<|ref|>text<|/ref|><|det|>[[44, 513, 311, 554]]<|/det|> +Daniel Thorton University of New Hampshire + +<|ref|>text<|/ref|><|det|>[[44, 559, 767, 600]]<|/det|> +Sahil Sharma Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0001- 7281- 9224 + +<|ref|>text<|/ref|><|det|>[[44, 605, 311, 646]]<|/det|> +Luke Boticelli University of New Hampshire + +<|ref|>text<|/ref|><|det|>[[44, 651, 484, 692]]<|/det|> +Chander Digwal MSKCC https://orcid.org/0000- 0001- 8784- 1096 + +<|ref|>text<|/ref|><|det|>[[44, 697, 408, 738]]<|/det|> +Suhasini Joshi Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 744, 311, 785]]<|/det|> +Nan Yang University of New Hampshire + +<|ref|>text<|/ref|><|det|>[[44, 790, 168, 830]]<|/det|> +palak panchal msKCC + +<|ref|>text<|/ref|><|det|>[[44, 836, 311, 877]]<|/det|> +Souparna Chakrabarty Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 882, 767, 923]]<|/det|> +Sadik Bay Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0001- 8089- 1330 + +<|ref|>text<|/ref|><|det|>[[44, 928, 189, 946]]<|/det|> +Vladimir Markov + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 45, 408, 64]]<|/det|> +Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 70, 408, 110]]<|/det|> +Charlene Kwong Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 116, 408, 156]]<|/det|> +Jeanine Lisanti Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 162, 625, 202]]<|/det|> +Sun Young Chung Harvard Medical School https://orcid.org/0000- 0002- 3381- 919X + +<|ref|>text<|/ref|><|det|>[[44, 208, 454, 248]]<|/det|> +Stephen Ginsberg NYU https://orcid.org/0000- 0002- 1797- 4288 + +<|ref|>text<|/ref|><|det|>[[44, 254, 408, 294]]<|/det|> +Pengrong Yan Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 300, 408, 340]]<|/det|> +Elisa de Stanchina Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 346, 182, 386]]<|/det|> +Adriana Corben MSKCC + +<|ref|>text<|/ref|><|det|>[[44, 393, 408, 433]]<|/det|> +Shanu Modi Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 439, 767, 479]]<|/det|> +Mary Alpaugh Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0002- 0008- 2718 + +<|ref|>text<|/ref|><|det|>[[44, 485, 580, 526]]<|/det|> +Giorgio Colombo University of Pavia https://orcid.org/0000- 0002- 1318- 668X + +<|ref|>text<|/ref|><|det|>[[44, 531, 500, 572]]<|/det|> +Hediye Erdjumet- Bromage New York University Grossman School of Medicine + +<|ref|>text<|/ref|><|det|>[[44, 578, 195, 617]]<|/det|> +Thomas Neubert NYU Langone + +<|ref|>text<|/ref|><|det|>[[44, 623, 747, 664]]<|/det|> +Robert Chalkley University of California, San Francisco https://orcid.org/0000- 0002- 9757- 7302 + +<|ref|>text<|/ref|><|det|>[[44, 670, 147, 709]]<|/det|> +Peter Baker UCSF + +<|ref|>text<|/ref|><|det|>[[44, 716, 615, 757]]<|/det|> +Alma Burlingame University of California https://orcid.org/0000- 0002- 8403- 7307 + +<|ref|>text<|/ref|><|det|>[[44, 762, 625, 803]]<|/det|> +Anna Rodina Sloan Kettering Institute https://orcid.org/0000- 0002- 3894- 6438 + +<|ref|>text<|/ref|><|det|>[[44, 809, 670, 850]]<|/det|> +Feixia Chu University of New Hampshire https://orcid.org/0000- 0002- 3209- 8095 + +<|ref|>text<|/ref|><|det|>[[44, 892, 104, 910]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 931, 137, 950]]<|/det|> +Keywords: + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[44, 46, 288, 64]]<|/det|> +**Posted Date:** April 3rd, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 84, 475, 103]]<|/det|> +**DOI:** https://doi.org/10.21203/rs.3.rs- 4114038/v1 + +<|ref|>text<|/ref|><|det|>[[42, 120, 916, 164]]<|/det|> +**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 182, 950, 248]]<|/det|> +**Additional Declarations:** Yes there is potential Competing Interest. Memorial Sloan Kettering Cancer Center holds the intellectual rights to the epichaperome portfolio. G.C., A.R. and S.S. are inventors on the licensed intellectual property. All other authors declare no competing interests. + +<|ref|>text<|/ref|><|det|>[[42, 283, 944, 325]]<|/det|> +**Version of Record:** A version of this preprint was published at Nature Communications on October 16th, 2024. See the published version at https://doi.org/10.1038/s41467-024-53178-5. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 88, 860, 125]]<|/det|> +1 Phosphorylation- Driven Epichaperome Assembly: A Critical Regulator of Cellular Adaptability and Proliferation + +<|ref|>text<|/ref|><|det|>[[70, 141, 881, 234]]<|/det|> +3 Seth W. McNutt1,12, Tanaya Roychowdhury2,12, Chiranjeevi Pasala2, Hieu T. Nguyen1, Daniel T. Thornton1, Sahil Sharma2, Luke Botticelli1, Chander S. Digwal2, Suhasini Joshi2, Nan Yang1, Palak Panchal2, Souparna Chakrabarty2, Sadik Bay2, Vladimir Markov3, Charlene Kwong3, Jeanine Lisanti3, Sun Young Chung2, Stephen D. Ginsberg4,5, Pengrong Yan2, Elisa DeStanchina3, Adriana Corben6, Shanu Modi7, Mary Alpaug2, Giorgio Colombo8, Hediye Erdument- Bromage9, Thomas A. Neubert9, Robert J. Chalkley10, Peter R. Baker10, Alma L. Burlingame10, Anna Rodina2, Gabriela Chiosis2,7,13, Feixia Chu1,11,13 + +<|ref|>text<|/ref|><|det|>[[66, 275, 884, 550]]<|/det|> +10 1. Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 11 03824, USA. 12 2. Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 13 3. Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. +14 4. Departments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU 15 Grossman School of Medicine, New York, NY, 10016, USA. +16 5. Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, 10962, USA. +17 6. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. +18 7. Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. +19 8. Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy. +20 9. Department of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of 21 Medicine, New York, NY, 10016, USA. +22 10. Mass Spectrometry Facility, University of California, San Francisco, California 94143, USA. +23 11. Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH 03824, USA. + +<|ref|>text<|/ref|><|det|>[[115, 550, 656, 566]]<|/det|> +12co- first author, equally contributed to the work. + +<|ref|>text<|/ref|><|det|>[[115, 565, 655, 581]]<|/det|> +13These authors jointly supervised this work: Feixia Chu, Gabriela Chiosis. + +<|ref|>text<|/ref|><|det|>[[115, 580, 655, 596]]<|/det|> +14Correspondence: feixia.chu@unh.edu (F.C.), chiosig@mskcc.org (G.C.). + +<|ref|>text<|/ref|><|det|>[[113, 617, 883, 876]]<|/det|> +Abstract. The intricate protein- chaperone network is vital for cellular function. Recent discoveries have unveiled the existence of specialized chaperone complexes called epichepomeres, protein assemblies orchestrating the reconfiguration of protein- protein interaction networks, enhancing cellular adaptability and proliferation. This study delves into the structural and regulatory aspects of epichepomeres, with a particular emphasis on the significance of post- translational modifications in shaping their formation and function. A central finding of this investigation is the identification of specific PTMs on HSP90, particularly at residues Ser226 and Ser255 situated within an intrinsically disordered region, as critical determinants in epicheporeme assembly. Our data demonstrate that the phosphorylation of these serine residues enhances HSP90's interaction with other chaperones and co- chaperones, creating a microenvironment conducive to epicheporeme formation. Furthermore, this study establishes a direct link between epicheporeme function and cellular physiology, especially in contexts where robust proliferation and adaptive behavior are essential, such as cancer and stem cell maintenance. These findings not only provide mechanistic insights but also hold promise for the development of novel therapeutic strategies targeting chaperone complexes in diseases characterized by epicheporeme dysregulation, bridging the gap between fundamental research and precision medicine. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[78, 91, 222, 107]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[112, 118, 882, 263]]<|/det|> +Conventional wisdom, as crystallized in Beadle and Tatum's 1941 paradigm of "one gene- one enzyme- one function," has traditionally delineated targets as outcomes of protein expression changes or point mutations within proteins. However, it is increasingly apparent that protein dysfunctions in the context of many disorders, including cancer, neurodegenerative disorders, among others, are predominantly shaped by changes in interaction strengths and cellular mislocalization. These factors, in turn, can be modulated by variations in post- translational modifications (PTMs), stabilization of disease- associated protein conformations, and other protein- modifying mechanisms1,2. Within this complex context, Heat Shock Protein 90 (HSP90) emerges as a compelling exemplar, transcending the boundaries of conventional understanding3. + +<|ref|>text<|/ref|><|det|>[[111, 274, 882, 547]]<|/det|> +Positioned as a versatile chaperone, often referred to as the guardian of the proteome, HSP90 assumes a pivotal task in the realm of maintaining cellular equilibrium by facilitating protein folding, stabilization, and degradation4. Under the canonical folding paradigm, HSP90 functions as a homodimer. Each promoter is composed of an N- terminal domain (NTD), a middle domain (MD), and a C- terminal dimerization domain (CTD)4,5. The NTD contains a nucleotide binding pocket, where ATP binding and hydrolysis takes place6. The chaperone cycle of HSP90 is coupled to a series of dynamic conformational changes accompanying its ATPase cycle. Beginning with NTD/MD and MD/CTD interdomain rotations and cross- monomer dimerization7, HSP90 transitions from open to closed conformational states, while folding client proteins8,9. HSP70 and HOP (HSP70- HSP90 organizing protein) bring client proteins to HSP90 and form the loading complex10. Other co- chaperones participate at different stages of the HSP90 chaperone cycle and regulate its conformational changes along the chaperone and ATPase cycle4. Co- chaperones may have different preferences for client proteins, fine- tuning subcellular pools of HSP90 to mitigate stressors and maintain proteostasis11. These assemblies are further shaped by PTMs in HSP90, co- chaperones and client proteins12. Overall, the highly orchestrated interactions among these proteins – both chaperones and clients – are transient in the chaperone cycle under physiological conditions. + +<|ref|>text<|/ref|><|det|>[[111, 558, 882, 846]]<|/det|> +While this classical understanding portrays HSP90 as a dimeric entity that interacts dynamically with co- chaperones and client proteins, research has uncovered a spectrum of multimeric HSP90 forms, each sculpted by the cellular milieu and the presence of stress- inducing factors3. These multimers, whether homo- oligomeric or hetero- oligomeric, expand HSP90's functional repertoire, blurring the boundaries between traditional chaperone functions and newfound roles as holdases or scaffold proteins. In disease contexts, such as cancer and neurodegenerative disorders, HSP90's conformational adaptability gives rise to epichaperomes—distinctive hetero- oligomeric formations of tightly bound chaperone, co- chaperones and other factors13- 15. This phenomenon goes beyond mere biochemical curiosity; it represents a fundamental mechanism by which cells respond to stressors, whether of genetic, proteotoxic or environmental nature3,16- 18. Unlike chaperones which help proteins fold or assemble, epichaperomes exert a maladaptive influence, reshaping the assembly and connectivity of proteins pivotal for sustaining pathological traits. For example, in cancer, epichaperomes take on scaffolding functions not found in normal cells, altering the assembly and connectivity of proteins important for maintaining a malignant phenotype and enhancing their activity, which provides a survival advantage to cancer cells and tumor- supporting cells13,19. In Alzheimer's disease epichaperomes rewire the connectivity of, and thus negatively impact, proteins integral for synaptic plasticity, brain energetics and immune response15. + +<|ref|>text<|/ref|><|det|>[[112, 858, 882, 907]]<|/det|> +The revelation of HSP90's maladaptive multimeric epichaperomes has also profound implications for therapeutic interventions, including in the treatment of diverse disease states including cancers and of neurodegenerative disorders. Rather than a blanket inhibition of all HSP90 pools, targeting + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 89, 883, 138]]<|/det|> +1 specific pathologic conformations of HSP90 as found in epichaperomes while sparing normal 2 HSP90 functions holds the promise of enhancing the safety as well as the immunostimulatory and 3 anticancer effects of HSP90 inhibitors3. + +<|ref|>text<|/ref|><|det|>[[75, 150, 883, 310]]<|/det|> +4 Despite these important mechanistic and therapeutic implications, key factors facilitating HSP90 5 incorporation in epichaperomes – namely the conformations that enable epichaperome formation 6 and structural elements that support the enrichment of such conformation - remain unknown. In 7 this study, we use a combination of chemical biology and unbiased mass spectrometry techniques 8 to elucidate the conformation of HSP90 populated in epichaperomes and to characterize 9 molecular factors that support and favor the enrichment of such conformation. Beyond structural 10 revelations, our findings demonstrate how these factors directly influences cellular behaviors, 11 particularly in contexts where robust proliferation and adaptation are crucial, such as cancer and 12 stem cell maintenance. This direct link between epichaperome function and fundamental cellular 13 processes has translational relevance for therapeutic development. + +<|ref|>sub_title<|/ref|><|det|>[[70, 322, 202, 338]]<|/det|> +## 14 RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[70, 350, 637, 368]]<|/det|> +## 15 Pluripotent stem cells and cancer cells share epichaperomes + +<|ref|>text<|/ref|><|det|>[[70, 378, 883, 540]]<|/det|> +16 Epichaperomes nucleated through enhanced interactions between HSP90 and HSP70, namely 17 the heat shock cognate 70 (HSC70) isoform, are a distinct feature of cancer cells13,19. 18 Epichaperomes containing HSP90 are detected in iPSCs (induced pluripotent stem cells)20, in 19 leukemia stem cells21,22 and in glioma cancer stem cells (CSCs)23. Hyperactivation of the 20 transcription factor c- MYC required in generating iPSCs24, maintaining embryonic stem cells 21 (ESCs)25 and CSCs26, is also a driving factor in epichaperome formation in tumors, irrespective 22 of the tumor type13,27. Notably, these epichaperomes are all sensitive to and can be disrupted by 23 small molecules such as PU- H71 (zelavespib) or PU- AD (icapamespib) that bind to HSP9013,23,28, 24 suggesting that a similar composition, facilitated by a specific conformation of HSP90, may 25 characterize epichaperomes in these distinct cellular contexts. + +<|ref|>text<|/ref|><|det|>[[70, 551, 883, 666]]<|/det|> +26 To test this hypothesis, we initially explored the composition of epichaperomes in selected cellular 27 models, encompassing pluripotent stem cells and cancer cells. For pluripotent stem cells, we 28 examined two mouse embryonic stem cell lines (E14 and ZHBTC4) and a human induced 29 pluripotent cell line (hiPSC). Additionally, two cancer cell lines, well- characterized in terms of 30 epichaperome composition and function, were chosen as representative epichaperome- positive 31 (MDA- MB- 468) and - negative/low (ASPC1) cancer cells (Fig. 1a- f and Supplementary Figs. 32 1,2). + +<|ref|>text<|/ref|><|det|>[[70, 676, 883, 884]]<|/det|> +33 In contrast to folding chaperone complexes, which are inherently dynamic and short- lived6, 34 epichaperomes represent long- lasting heterooligomeric assemblies composed of tightly 35 associated chaperones, co- chaperones, and various other factors. HSP90 is a major component 36 found within epichaperomes along with other chaperones, co- chaperones, and scaffolding 37 proteins like HSP70 (especially HSC70), CDC37, AHA1, and HOP13. Consequently, when we 38 analyzed cell homogenates containing epichaperomes using native PAGE followed by 39 immunoblotting with antibodies specific to epichaperome constituent chaperones and co- 40 chaperones, we observed a range of high- molecular- weight species, both distinct and indistinct, 41 in addition to the primary band(s) characteristic of chaperones. This observation held true for both 42 pluripotent stem cells and cancer cells (Fig. 1b, Supplementary Fig. 1a- d and refs. 13,19,20). 43 Notably, HSP90 immunoblotting revealed the presence of species comprising HSP90 in 44 epichaperome assemblies in cancer cells and pluripotent stem cells, in addition to the prominent 45 242 kDa band, which is a characteristic of non- transformed cells13,19,29. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 90, 883, 363]]<|/det|> +Epichaperomes undergo disassembly during iPSC differentiation20 or when cancer cells are treated with PU-H71 or PU-AD15,23,28,30. Therefore, next we induced the differentiation of the pluripotent stem cells under investigation. In the ZHBTc4 cell line, Oct4 expression is controlled by a Tet (tetracycline)- off oct4 regulatory system31. Down-regulation of Oct4 in ZHBTc4 cells has been reported to induce trophoblast differentiation, which is characterized by changes in cell morphology, specifically, cells flattening into epithelial-like cells, and is associated with slower growth32. Mouse embryonic E14 stem cells undergo spontaneous differentiation into embryoid bodies when cultured in suspension without antidifferentiation factors such as leukemia inhibitory factor33 and induced pluripotent stem cells differentiate into mature dopaminergic neurons using a floor-plate based differentiation protocol34. We confirmed that differentiation of these pluripotent stem cells was correlated with the disassembly of epichaperomes, as observed through native PAGE immunoblotting. This disassembly is evident by a reduction in high-molecular-weight chaperone species on native PAGE observed when immunoblotting for epichaperome constituent chaperones (see HSP90α/β, HOP, HSC70, CDC37, AHA1, HSP110 in Fig. 1b and Supplementary Fig. 1), with minimal changes observed in total chaperone levels on SDS PAGE. Notably, for HSP90, a decrease in bands other than those in the \(\sim 242\) kDa range was observed upon differentiation, supportive of epichaperome disassembly (see HSP90 immunoblotting). + +<|ref|>text<|/ref|><|det|>[[113, 373, 882, 581]]<|/det|> +PU- H71 serves as an epichaperome probe that, in contrast to the tested antibodies which indiscriminately detect epichaperomes and other HSP90 pools, exhibits a preference for HSP90 when it is integrated into epichaperomes13. Labeled derivatives of PU- H71 can, therefore, be employed to detect HSP90 within epichaperomes, distinguishing it from other HSP90 pools (as illustrated in Fig. 1c and Supplementary Fig. 2a- c). To achieve this, we generated lysates from ZHBTc4, E14 cells, and MDA- MB- 468 cells under conditions that preserve native protein assemblies. Subsequently, we labeled these homogenates with a clickable PU- probe (PU- TCO, ref. 19). After running these labeled samples on native PAGE gels, we conjugated the PU- probe with a Cy5 dye and visualized epichaperomes, confirming the presence of epichaperomes in both the ESCs and the cancer cells. These epichaperomes were characterized by multimers observed at and above \(\sim 300\) kDa (Fig. 1c). Moreover, the labeling of epichaperomes by the PU- probe decreased upon ESC differentiation, supportive of epichaperome disassembly (Fig. 1c and Supplementary Fig. 2b). + +<|ref|>text<|/ref|><|det|>[[113, 592, 882, 706]]<|/det|> +Additionally, we conducted labeling experiments using live E14 ESCs, instead of homogenates, employing a PU- CW800 probe (a derivative of PU- H71 conjugated with an 800 nm near- infrared dye) or a control derivative (an inactive PU- derivative that does not interact with epichaperomes) (see Supplementary Note 1). The most responsive target of the PU- probes, but not the control probe, was an HSP90 assembly of approximately 300 kDa, thus above the major 242 kDa band preferred by the anti- HSP90 antibody. This species was detected on Native- PAGE in PU- probe treated cell lysates but not in control treated cell lysates (Supplementary Fig. 2c). + +<|ref|>text<|/ref|><|det|>[[113, 717, 882, 847]]<|/det|> +In summary, the predominant HSP90 band characteristic of epichaperomes is a 300 kDa assembly, distinctly differing from the typical \(\sim 242\) kDa band observed in non- transformed cells13, 19, 32 when analyzed on native PAGE gels. Mass spectrometric (MS) analysis of the \(\sim 300\) kDa assembly confirmed the presence of HSP90 and HSC70 as the primary protein components of this multimeric complex (Supplementary Data 1, 300 kDa LC- MS). This finding aligns with the composition of core epichaperome complexes previously reported in cancer cells13, 44. Consequently, these findings combined confirm that both cancer cells and pluripotent stem cells share HSP90 and HSC70 as integral constituents of their core epichaperomes. + +<|ref|>text<|/ref|><|det|>[[113, 858, 882, 907]]<|/det|> +To gain further insights into epichaperome assemblies, we employed resin- based affinity purification experiments. Specifically, we utilized resins with immobilized PU- H71, referred to as PU- beads, and an inert control molecule on control beads, following established procedures13 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 88, 883, 203]]<|/det|> +1 (Fig. 1d). As an additional control, we employed a resin containing immobilized geldanamycin (GA), known for its ability to bind and isolate predominantly un-complexed HSP90 (GA-beads, 2 Supplementary Fig. 3 and ref.35). Subsequently, we subjected the protein cargo isolated by these 3 probes to unbiased MS analysis. To precisely determine the protein components of the cargo, we 4 conducted in-gel digestion of the entire gel lanes and employed liquid chromatography/mass 5 spectrometry (LC-MS/MS) in conjunction with the semi-quantitative spectra-counting method36,37 6 for the identification and quantification of cargo proteins (Supplementary Data 1). + +<|ref|>text<|/ref|><|det|>[[70, 214, 883, 327]]<|/det|> +8 We observed that the cargo isolated by PU-beads from ESCs contained 26 of the 42 major 9 chaperone and co-chaperones identified prior in cancer cells13 as being epicatheprome 10 components (Fig. 1d). The interaction between PU-beads and epicathepromes was specific 11 towards PU-H71, because control resins did not purify noticeable protein complexes. Similarly, 12 GA-beads precipitated HSP90 but few co-purifying proteins and epicatheprome components 13 (Supplementary Fig. 3, Supplementary Data 1) consistent with previous results that GA isolates 14 largely an un-complexed HSP9038. + +<|ref|>text<|/ref|><|det|>[[70, 338, 883, 578]]<|/det|> +15 In mammalian cells, HSP90 exists in two paralogs, HSP90α and HSP90β39, both of which have 16 been reported to play roles in epicatheprome formation in cancer cells13. To assess the isoform 17 composition of HSP90 within epicathepromes, we exploited the subtle difference between one pair 18 of isobaric peptides, namely 88Thr-Lys100 in HSP90α and 83Thr-Lys95 in HSP90β, where a 19 single amino acid distinguishes them (lle in HSP90α and Leu in HSP90β) (Supplementary Fig. 20 4a). The assignment of HSP90 isoforms relied on co-eluting peptides obtained from the isobaric 21 peptide present in purified HSP90β (Supplementary Fig. 4b,c). Extracted ion chromatograms of 22 the peptide mass revealed an approximate \(\sim 1.5 \beta /\alpha\) ratio in the ESC lysate and the cargo isolated 23 by PU-beads (Fig. 1e), while the GA-beads cargo exhibited a \(\sim 1.0 \beta /\alpha\) ratio. Similar findings were 24 obtained through spectra counting, with the HSP90β/HSP90α ratio determined using spectral 25 counting consistent with ratios obtained through MS intensity calculations (Supplementary Data 26 1: \(708 / 540 = 1.31\) for the PU-beads cargo; \(219 / 235 = 0.93\) for the GA-beads cargo). This validation 27 of spectra counting as an effective semi-quantitative method supports the conclusion that 28 epicathepromes isolated from ESCs exhibit a predominantly unbiased HSP90 paralog 29 composition, akin to what has been reported for cancer cells13. + +<|ref|>text<|/ref|><|det|>[[70, 589, 883, 702]]<|/det|> +30 In summary, the wealth of complementary biochemical experiments presented here lends strong 31 support to the idea that both cancer cells and pluripotent stem cells harbor epicathepromes that 32 are compositionally similar. Notably, HSP90 and HSC70 emerge as major constituents of the core 33 epicatheprome structure, serving as a scaffold for recruiting various co-chaperones to create 34 specific epicatheprome assemblies. This shared architectural similarity between epicathepromes 35 in ESCs and cancer cells underscores the existence of a common epicatheprome-enabling HSP90 36 conformer that is enriched in both biological contexts. + +<|ref|>sub_title<|/ref|><|det|>[[70, 713, 525, 731]]<|/det|> +## 37 Epicatheprome-enabling conformation of HSP90 + +<|ref|>text<|/ref|><|det|>[[70, 742, 883, 824]]<|/det|> +38 MS identification of cross-linked residues that are in spatial proximity but not necessarily close in 39 primary sequence, provides valuable distance restraints that can be employed for computational 40 modeling of proteins and protein complexes40- 42. Therefore, to determine the conformation of 41 HSP90 in epicathepromes, we used a chemical cross-linking and mass spectrometry (CX-MS) 42 approach to identify and quantify cross-linked peptides of PU-H71-favored HSP90 pools. + +<|ref|>text<|/ref|><|det|>[[70, 835, 883, 900]]<|/det|> +43 To ensure the capture of the epicatheprome-enabling conformation, we first cross-linked cellular 44 lysates using the amine-reactive cross-linker DSS (disuccinimidyl suberate) prior to HSP90 45 capture on the PU-beads13,35 (Fig. 2a). Parallel experiments were conducted using GA-beads, 46 corresponding to solid-support immobilized GA, as a control13,35. The identity of cross-linked + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[74, 90, 882, 155]]<|/det|> +1 HSP90 peptides purified by PU- or GA- beads pull- down can be found in Supplementary Data 2. Notably, the alpha carbon distances between all cross- linked residues, as identified with high confidence, fell below the maximal span of DSS (30 Å). This suggests that proteins retained their native states without significant conformational perturbations during the cross- linking process. + +<|ref|>text<|/ref|><|det|>[[113, 166, 882, 264]]<|/det|> +We calculated the cross- linking percentage for each pair of cross- linked PU- or GA- bound HSP90 residues. This calculation involved normalizing the MS ion intensity of cross- linked peptides by the sum of all cross- linked peptides and cross- linker- modified peptides containing the cross- linked residues. By doing so, we could mitigate the impact of variations in the reactivity of cross- linked residues, allowing us to primarily assess the influence of the distance between cross- linked residues and their local secondary structures43. + +<|ref|>text<|/ref|><|det|>[[113, 275, 882, 372]]<|/det|> +Most cross- linked pairs from both PU- and GA- bound samples exhibited similar cross- linking percentages, with data points evenly distributed around a trend line with a slope of 1 (dotted line, Fig. 2b). This observation suggests a broad similarity in secondary and tertiary structures between these HSP90 populations. However, clear differences emerged, revealing conformational distinctions between the PU- and GA- favored HSP90 subpopulations (highlighted by orange circles, Fig. 2b). + +<|ref|>text<|/ref|><|det|>[[112, 382, 882, 590]]<|/det|> +Notably, residues Lys58- Lys112 in HSP90α and Lys53- Lys107 in HSP90β, situated within the ligand- binding pocket, displayed a higher cross- linking percentage in PU- bound HSP90 populations compared to their GA- bound counterparts (Fig. 2b). This observation aligns with distinct pocket configurations preferred by each ligand, as previously observed through X- ray crystallography44- 48. Specifically, crystal structures show the bulkier GA binds more superficially, causing helices 4 and 5 (Fig. 2d) to move away from the nucleotide binding site, thereby preventing full closure of the ATP lid. Moreover, the side- chain amino functional group of Lys112 forms a hydrogen bond with a benzoquinone oxygen of GA. This pocket configuration aligns with the reduced cross- linking activity of the lysine pair mentioned above. Conversely, PU- H71 binds deeply within the pocket. In this configuration, helices 4 and 5 are packed against helix 2 with Lys112 and Lys58 in HSP90α (or Lys107 and Lys53 in HSP90β) positioned more favorably for cross- linking. This arrangement of lysine residues is more likely to be found in the closed conformation of HSP90 (Fig. 2c), as proposed by crystallographic studies (PDB: 2CG9)49. + +<|ref|>text<|/ref|><|det|>[[112, 601, 882, 683]]<|/det|> +It is essential to reiterate that the cross- linking experiments were conducted to 'lock' HSP90 conformations with covalent bonds before resin- based affinity purification experiments using the PU- or GA- beads. Consequently, the X- ray structures of PU- or GA- bound HSP90 NTD closely reflect a preferred pocket configuration that each ligand may capture in the cell, and in this case, for PU- H71, it is indicative of the pocket configuration of HSP90 in the epipheropermes. + +<|ref|>text<|/ref|><|det|>[[112, 694, 882, 887]]<|/det|> +Furthermore, differences in HSP90 conformation were corroborated by cross- linked pairs located at the interfaces between NTD/MT (HSP90α: Lys293- Lys363) and MD/CTD (HSP90α: Lys444- Lys616; HSP90β: Lys435- Lys607) (Fig. 2b). These interfaces undergo significant reorientation during the HSP90 conformational cycle, implying a distinct HSP90 conformation favored by PU- H71 compared to GA. Lys444 in HSP90α (Lys435 in HSP90β) and Lys616 in HSP90α (Lys607 in HSP90β) are positioned either within the middle of the MD or in proximity to the central axis of the HSP90 homodimer (Fig. 2c). The distance between these lysine residues can provide insights into the relative placement of the monomer arms in specific HSP90 conformations (e.g., 20 Å in closed- like conformations; 29 Å in open- like conformations). The lower cross- linking percentage observed for Lys444 and Lys616 in HSP90α (Lys435 and Lys607 in HSP90β) in GA- favored HSP90 suggests a longer distance (29 Å) between them, supporting GA's preference for binding to an open- like conformation. In contrast, the moderate cross- linking percentage detected for + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 89, 882, 123]]<|/det|> +1 these residues in PU- H71- favored HSP90 implies a medium distance (20 Å) between them, 2 favoring a closed- like conformation enriched in epicarpemores (Fig. 2c). + +<|ref|>text<|/ref|><|det|>[[70, 133, 883, 247]]<|/det|> +3 Additionally, a third pair of cross- linked residues (Lys293 and Lys363 in HSP90α) supports this 4 notion. Located near the interface between the NTD and the MD, their positions are sensitive to 5 the ligand binding state of the NTD, leading to changes in the relative positioning of secondary 6 structures near the NTD/MD interface and altering the distance between Lys293α and Lys363α. 7 Consistent with the cross- linked pair at MD/CTD interface, a closed- like conformation (16 Å) in 8 PU- H71 bound HSP90 will be more amenable than an open- like conformation (13 Å) in GA- bound 9 since the short distance might have limited the location of side- chains for cross- linking reactions. + +<|ref|>text<|/ref|><|det|>[[70, 258, 882, 339]]<|/det|> +10 In summary, our CX- MS data, supported by several cross- linked residue pairs situated in 11 structurally distinct regions, the nucleotide- binding pocket, and the NTD/MD and MD/CTD 12 interfaces, shed light on the conformation adopted by HSP90 within epicarpemores. These 13 findings underscore the notion that an enrichment of the closed- like conformation of HSP90 in 14 specific cellular environments favors the formation of epicarpemores. + +<|ref|>sub_title<|/ref|><|det|>[[70, 350, 667, 368]]<|/det|> +## 15 Specific PTMs support HSP90 incorporation into epicarpemores + +<|ref|>text<|/ref|><|det|>[[70, 378, 882, 523]]<|/det|> +16 To uncover the factors that facilitate the enrichment of the epicarpemore- favoring HSP90 17 conformation, we conducted a comprehensive examination of the HSP90 pools isolated by PU- 18 H71 and GA, searching for potential differences. Notably, we identified several peptides 19 phosphorylated on Ser231 and Ser263 in HSP90α (Ser226 and Ser255 in HSP90β) exclusively 20 in the PU- H71 cargo from ESCs (Fig. 3a,b and Supplementary Data 3). High- quality MS/MS 21 spectra (illustrated for Ser226 and Ser255 phosphopeptides in HSP90β, Fig. 3b) coupled with 22 precise mass accuracy allowed for the unequivocal identification of the peptide sequences and 23 the phosphorylation sites. In contrast, these phosphorylated peptides were notably absent in 24 substantial quantities in the GA cargo (Supplementary Data 3). + +<|ref|>text<|/ref|><|det|>[[70, 534, 882, 615]]<|/det|> +25 Subsequently, we performed label- free quantitation of these phosphopeptides using ion intensity 26 measurements and observed a significant enrichment in the PU- beads cargo, particularly in the 27 case of Ser255 of HSP90β. For instance, the Ser255 phosphopeptide displayed a nearly three- 28 fold enrichment in the PU- H71 cargo compared to the lysate, after protein loading normalization 29 using a representative tryptic peptide (Fig. 3c). + +<|ref|>text<|/ref|><|det|>[[70, 626, 882, 774]]<|/det|> +30 To gain further insights, we leveraged previously reported MS datasets of PU- H71- isolated cargo 31 from epicarpemore- positive cancer cells \(^{13,19}\) , including MDA- MB- 468 (triple negative breast 32 cancer), Daudi (Burkitt's lymphoma), IBL- 1 (AIDS- related immunoblastic lymphoma), and NCI- 33 H1975 (non- small cell lung carcinoma), as well as from non- transformed (NT) proliferating cells 34 in culture (e.g., MRC5, lung fibroblast and HMEC, mammary epithelial cells). This analysis 35 revealed that phosphorylation of these serine residues is also enriched in cancer cells when 36 compared to NT cells (Ca:NT S255 = 16; S226 = 8; S263 = 12, Fig. 3d) establishing it as a 37 hallmark of both ESC and cancer epicarpemores. This observation further supports the idea of a 38 shared structural and architectural foundation for epicarpemores among ESCs and cancer cells. + +<|ref|>text<|/ref|><|det|>[[70, 784, 882, 898]]<|/det|> +39 As HSP90 is found alongside HSC70 in epicarpemores, we conducted an additional confirmatory 40 experiment. Here, we used YK5- B, a biotinylated probe that binds to HSC70 in epicarpemores, 41 and thus captures HSP90 in epicarpemores via HSC70 \(^{19}\) . PU- H71 and YK5- B were used to 42 isolate cargo from epicarpemore- positive cancer cells, including MDA- MB- 468 and OCI- Ly1 43 (breast cancer and diffuse large B- cell lymphoma, respectively), as well as from CCD- 18Co colon 44 cells in culture (i.e., non- transformed proliferating cells in culture). We found that the Ser255 and 45 S226 phosphopeptides of HSP90β were nearly four to five times more abundant in epicarpemore + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[72, 89, 883, 172]]<|/det|> +1 positive cancer cells compared to non- transformed proliferating cells in culture, for both the PU- 2 cargo and the YK5- B cargo. Similar enrichment was noted for Ser263 and Ser231 in HSP90α 3 (Fig. 3e). This analysis, thus, using both PU- H71 and YK5- B probes across diverse cell types, 4 underscores the robustness of our observations and reinforces the role of phosphorylation in the 5 acidic linker in shaping HSP90 within epicarpemores. + +<|ref|>text<|/ref|><|det|>[[70, 181, 883, 328]]<|/det|> +6 In light of these findings, made with two distinct probes and observed in ESCs, five cancer cell 7 lines, each representative of a distinct cancer type, and of three non- transformed, but proliferating, 8 cells in culture, it is evident that the epicarpemore- specific agents target a subpopulation of 9 HSP90 characterized by high phosphorylation levels in the acidic linker between the NTD and the 10 MD, and this subpopulation predominantly assumes a closed- like conformation. In conjunction 11 with PU's preference for HSP90 within epicarpemores, and substantiated by YK5- B, a probe that 12 binds epicarpemores via HSC70, these results strongly indicate that phosphorylation at these 13 two serine residues is a key driver for HSP90 incorporation into epicarpemores and, 14 consequently, for epicarpemore formation. + +<|ref|>sub_title<|/ref|><|det|>[[70, 338, 614, 356]]<|/det|> +## 15 Specific PTMs drive epicarpemore formation and function + +<|ref|>text<|/ref|><|det|>[[70, 366, 883, 418]]<|/det|> +16 To explore whether the phosphorylation of these serine residues plays a pivotal role in driving, 17 rather than merely resulting from, epicarpemore formation, we next studied the phosphomimetic 18 (HSP90βS226E,S255E) and the non- phosphorylatable (HSP90S226A,S255A) mutants. + +<|ref|>text<|/ref|><|det|>[[70, 428, 883, 573]]<|/det|> +19 Notably, these serine residues are located within an intrinsically disordered region (IDR) of HSP90 20 (Supplementary Fig. 5). IDRs are pivotal elements in the intricate network of protein- protein 21 interactions (PPIs). These regions lack a fixed three- dimensional structure, granting them 22 exceptional flexibility. This structural adaptability enables proteins containing IDRs to assume 23 various conformations in response to specific cellular contexts or binding partners. Such 24 adaptability plays a crucial role in facilitating context- dependent involvement in distinct PPIs. In 25 the case of HSP90, these serine residues within the IDR may alter the dynamics and structure of 26 the charged linker, contributing to stabilizing the epicarpemore- enabling conformation of this 27 chaperone, and in turn facilitating epicarpemore formation. + +<|ref|>text<|/ref|><|det|>[[70, 583, 883, 715]]<|/det|> +28 To explore this hypothesis, we conducted computational analyses to investigate the impact of 29 each mutation on the flexibility of the charged linker (Fig. 4a- c). We constructed a model of the 30 putative epicarpemore core - namely the \(\sim 300\) kDa assembly, see Fig.1 - based on the cryo- EM 31 structure of a multimeric HSP90 assembly (PDB: 7KW7). This structure represented 2xHSP90α, 32 protomer A and B, bound to 2xHSP70 and 1xHOP. To create the model, we substituted HSP90 33 with human HSP90β using the closed- state cryo- EM structure (PDB: 8EOB). Additionally, we 34 computationally inserted the charged linker, which was missing in the cryo- EM structures (Fig. 35 4a). + +<|ref|>text<|/ref|><|det|>[[70, 725, 883, 888]]<|/det|> +36 We conducted all- atom molecular dynamics simulation of this pentameric protein assembly, with 37 each system containing all the components along with either the EE, AA, or WT HSP90 - in both 38 protomers. These simulations are intended to qualitatively explore the immediate response of the 39 assembly to the perturbation induced by mutations and not to provide an extensive 40 characterization of the assemblies' dynamics. By using a comparative MD- based approach we 41 explore how short- term changes in the structural dynamics of different components within a large 42 assembly may influence the emergence of states relevant for assembly stabilization. The 43 underlying premise is that nanosecond timescale residue fluctuations in regions specifically 44 responsive to certain states may facilitate large- scale rearrangements that underlie functional 45 changes. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 90, 883, 411]]<|/det|> +These simulations revealed that the structure and conformation of the charged linker were sensitive to the phosphorylation of the serine residues. In the pentameric assembly containing the phosphomimetic EE mutant (i.e., HSP90S226E/S255E), the linker of HSP90, protomer A, had a high probability of forming a \(\beta\) - strand bordering the Ser226Glu residue (2.1% of \(\beta\) - strand A). This strand remained stable over the duration of the simulation. This \(\beta\) - strand's formation significantly decreased in the pentameric assembly containing the wild-type (WT, i.e., HSP90S226/S255) protein (0.4% of \(\beta\) - strand A), with no secondary structure element found in the assembly containing the AA (i.e., HSP90S226A/S255A) mutant (Fig. 4b). Notably, ATP binding, but not ADP binding, favored a charged linker with a high content of \(\beta\) - strand A formation (2.1% vs. 0.3%, respectively, in the EE mutant) (Fig. 4b and Supplementary Fig. 6a). This finding emphasizes that the observed changes in the EE mutant were not merely due to the addition of charged residues; they were intricately tied to the phosphorylation status and the specific context, including the nucleotide environment permissive of the specific HSP90 conformation (i.e., closed-like). Intriguingly, the strategic formation of \(\beta\) - strand A not only stabilized the charged linker but also induced a conformational switch, flipping it into an 'up' conformation, thereby fully exposing the middle domain of HSP90, where HSP70 binds (Fig. 4c, see HSP90 protomer A – HSP70 interface). While other stabilized structural elements were observed in the analyzed assemblies containing either the WT or the mutant HSP90s, no other had a similar conformational effect on the charged linker as we observed for the \(\beta\) - strand A (see the effect of \(\alpha\) - helices 1 through 6 in Supplementary Fig. 6a,b). + +<|ref|>text<|/ref|><|det|>[[112, 420, 882, 581]]<|/det|> +We conducted dynamical residue cross- correlation analyses to explore how different protein units or subdomains in the pentameric 2xHSP90- 2xHSP70- HOP assemblies, featuring either the WT (HSP90S226/S255) or mutant (HSP90S226E/S255E or HSP90S226A/S255A) HSP90s, correlate in their motions throughout the simulation (Fig. 5a,b). This analysis aimed to reveal how individual components move in relation to each other. Positive dynamical cross- correlations spanning different components of the assembly within the large epicapherome core may indicate enhanced cooperative motions, suggesting increased interactions that contribute to the stability of the assembled structure. Previous studies have employed similar analyses to investigate how ligand- induced modulations influence the overall flexibility of HSP90 assemblies, facilitating progress along the chaperone cycle, thereby supporting feasibility of this approach50. + +<|ref|>text<|/ref|><|det|>[[112, 592, 882, 739]]<|/det|> +Indeed, we observed the highest correlation among the components in assemblies containing the HSP90 EE phosphomimetic, mimicking the case where the charged linker is phosphorylated, followed by the WT, and then the non- phosphorylatable HSP90 AA mutant (Fig. 5a). Notably, the coordinated movements observed in the assemblies containing the HSP90 phosphomimetic strongly support the idea that the HSP70- HSP90- HSP90- HSP70 or HSP70- HSP90- HSP90- HSP70- HOP assemblies can be preferentially stabilized when the HSP90 charged linker is phosphorylated (Fig. 5b). This observation aligns with the prominent \(\sim 300\) kDa band observed for the epicapherome core in native PAGE (see Fig. 1 showing HSP90 assemblies favored by PU- H71). + +<|ref|>text<|/ref|><|det|>[[112, 750, 882, 876]]<|/det|> +In contrast, in the WT HSP90 assembly, coordinated movements were primarily observed between the two HSP90 protomers, within HSP90, and between HSP90 and HSP70 and HOP, specifically through HSP90 protomer B (Fig. 5a,b). These movements are more consistent and favorable in the context of HSP90- HSP90- HSP70 or HSP90- HSP90- HOP assemblies. This observation implies that the major, broad \(\sim 242\) kDa band detected by the HSP90 antibody – representing the primary HSP90- containing assembly observed in differentiated ESCs (Fig. 1) and in non- transformed cells13- 15,17,20 – may consist of such assemblies, along with HSP90 homo- oligomers. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 88, 883, 155]]<|/det|> +1 In summary, both MS evidence and computational models converge to support the conclusion that phosphorylation of the charged linker is a crucial contributor to epichaperome assembly, emphasizing its role in shaping not only HSP90, but also the stability and dynamics of the epichaperome structure. + +<|ref|>text<|/ref|><|det|>[[70, 165, 883, 375]]<|/det|> +5 Next, we carried out an extensive biochemical and functional analysis to reinforce these findings. 6 Given the well- established tight association between HSP90 and other chaperones and co- chaperones in epichaperomes \(^{13,19,20,51}\) , our focus shifted to a comprehensive evaluation of chaperone and co- chaperone proteins co- purified with the phosphomimetic (HSP90β \(^{5226E,S255E}\) ) and non- phosphorylatable (HSP90 \(^{5226A,S255A}\) ) mutants. Our strategy involved the purification of protein complexes containing N- terminally mCherry- tagged HSP90β in ESCs while retaining the endogenous WT HSP90 proteins. Distinctly labeled ESCs (i.e., labeled with heavy or light isotope lysine and arginine) expressing either the phosphomimetic or non- phosphorylatable mutant were subjected to immunoprecipitation (IP), followed by SDS- PAGE separation and quantitative analysis via MS to determine protein abundance (Fig. 6a- c; Supplementary Data 4). It is worth noting that we performed IP separately for the phosphomimetic and non- phosphorylatable mutants to minimize subunit exchange during IP \(^{52}\) , thereby enhancing our ability to detect changes in co- chaperone binding more accurately than previous studies \(^{53}\) . + +<|ref|>text<|/ref|><|det|>[[70, 385, 883, 627]]<|/det|> +We found co- chaperones were among the most abundant copurifying proteins, and most co- chaperones reported to participate in epichaperome formation \(^{13,19}\) displayed prominent changes in the phosphomimetic mutant (Fig. 6b,c). The increased presence of epichaperome- specific co- chaperones (such as AHA1 and FKBP4) \(^{13}\) in phosphomimetic complexes compared to non- phosphorylatable complexes highlights a stronger association with Ser226 \(^{P}\) /Ser255 \(^{P}\) HSP90 as opposed to the non- phosphorylatable protein. However, we observed a slight reduction in the levels of HSC70 and HOP within phosphomimetic complexes. This decrease is potentially associated with specific subpopulations of HSP90 complexes that become more prevalent when the non- phosphorylatable Ala mutant is overexpressed in cells. The introduction of two Ala residues in the unstructured linker region of HSP90 may prompt the recruitment of HSC70 and HOP, chaperones recognized for their ability to bind unstructured unfolded protein stretches \(^{54}\) . It is important to note that these assemblies are distinct from epichaperomes. Due to the anti- mCherry antibody capturing the entirety of the tagged HSP90, differentiation between specifically epichaperome- related HSP90 and a mixture of epichaperomes and other pools becomes challenging. + +<|ref|>text<|/ref|><|det|>[[70, 638, 883, 817]]<|/det|> +To address these limitations, we adopted a multi- pronged approach. Firstly, we utilized immunoblotting with native cognate antibodies for chaperone assemblies retained on native PAGE, coupled with chemical blotting using PU- probes. Additionally, we employed affinity capture with PU- probes to quantify the amount of epichaperome components under each condition (Fig. 7a). For these experiments, we transfected cells with the phosphomimetic (HSP90β \(^{5226E,S255E}\) , EE mutant) and with the non- phosphorylatable (HSP90 \(^{5226A,S255A}\) , AA mutant) mutants, as well as with HSP90β WT or mCherry- tag only for control purposes. In this study, we chose human embryonic HEK293 cells as our cell model since they exhibit intermediate epichaperome expression levels (i.e., medium expressor, Supplementary Fig. 7), making them suitable for studying epichaperome dependence. We confirmed comparable transfection efficiency for each construct, with the tagged HSP90β protein expressed in addition to the endogenous HSP90β (Fig. 7b). + +<|ref|>text<|/ref|><|det|>[[70, 828, 883, 893]]<|/det|> +Our findings revealed that cells expressing the EE mutant exhibited higher levels of epichaperomes compared to those expressing the AA mutant, as evidenced by immunoblotting of various epichaperome components (including HSP90α, HSC70, CDC37, AHA1, HOP, and HSP110) (Fig. 7c, native PAGE) and chemical blotting with the PU- Cy5 epichaperome probe + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 90, 883, 283]]<|/det|> +1 (Fig. 7d). Notably, there was no significant change in the overall concentration of these proteins in association with their incorporation into epichaperomes (Fig. 7c, SDS PAGE). Epichaperome isolation using PU- beads as an affinity purification probe also revealed significantly greater incorporation of chaperones, including mCherry- HSP90β, and co- chaperones into epichaperomes in cells expressing the EE mutant compared to those containing the AA mutant HSP90 (Fig. 7e), with no substantial alterations observed in cells containing the control vectors (Supplementary Fig. 8a). In contrast, overexpression of wild- type HSP90 in HEK293 cells had a minimal impact on endogenous epichaperomes (Fig. 7c, native PAGE, and Supplementary Fig. 8a, PU- beads capture). This observation aligns with previous reports13 suggesting that factors beyond chaperone concentration play a pivotal role in driving HSP90 incorporation into epichaperomes. Notably, cargo isolated on the control probe (control beads, Supplementary Fig. 8b) showed no detection of HSP90. + +<|ref|>text<|/ref|><|det|>[[113, 294, 882, 424]]<|/det|> +We further established the dependency of epichaperome function, beyond its formation, on the phosphorylation of HSP90 serine residues (Fig. 8,9). A key characteristic shared among high epichaperome- expressing cells in PSC, CSC, and cancer cells is the hyperactivity of the transcription factor c- MYC13,25- 27. In cancer, c- MYC is frequently overexpressed or mutated, resulting in sustained activation, which drives uncontrolled cell proliferation55. In ESCs, c- MYC plays a crucial role in maintaining pluripotency and self- renewal, crucial for preserving the undifferentiated state of ESCs56. We therefore investigated the impact of HSP90β Ser226P/Ser255P on cellular behaviors such as self- renewal and proliferation. + +<|ref|>text<|/ref|><|det|>[[113, 434, 882, 549]]<|/det|> +To assess proliferation, ESCs were transfected with plasmids containing either the phosphomimetic (HSP90βS226E,S255E) or non- phosphorylatable (HSP90βS226A,S255A) mutant. Notably, ESCs transfected with the HSP90β phosphomimetic mutant displayed a significantly higher proliferative rate (P<0.0001, >25%) compared to those transfected with the non- phosphorylatable variant, regardless of whether medium (1x) or high (2x) plasmid concentrations were employed (Fig. 8a). This observation lends support to the notion that HSP90β Ser226P/Ser255P, and consequently, epichaperomes, play a crucial role in ESC proliferation. + +<|ref|>text<|/ref|><|det|>[[113, 559, 882, 770]]<|/det|> +Differentiation of ESCs results in a decreased proliferative rate, as indicated by the doubling time of ZHBTC4 ES cells (~12 h) and trophoblast- differentiated cells (~25 h)32. Since differentiation is also closely associated with the disassembly of epichaperomes, we next examined the phosphorylation levels of HSP90β at Ser226 and Ser255 in cells with varying self- renewal capacities. We utilized the TET- repressible oct4 mouse ESC line ZHBTC4, where the Oct4 expression is suppressed in the presence of doxycycline for ESC differentiation into trophoblast- like cells (Troph)31. In this experiment, we expressed WT mCherry- HSP90β in ZHBTC4 cells and quantified phosphopeptides in both ESCs and trophoblast cells following ESC differentiation (Fig. 8b, Supplementary Data 5). After normalizing the data to mCherry- HSP90β protein loading (middle panel, ES/Troph = 0.44), we observed a 30% higher phosphorylation of HSP90β at Ser255 in stem cells compared to differentiated cells (left panel, ES/Troph = 0.57). Phosphorylation levels of HSP90β at Ser226 appeared to remain unchanged under these experimental conditions after normalizing to protein loading (right panel, ES/Troph = 0.45). + +<|ref|>text<|/ref|><|det|>[[113, 780, 882, 896]]<|/det|> +Pluripotency hinges on crucial transcription factors like Oct4. Oct4 is widely recognized as one of the principal transcription factors governing the self- renewal of both pluripotent stem cells and cancer cells57. We find Oct4 interacts with epichaperomes in ESCs (Supplementary Data 1) and exhibits significant enrichment in the cargo captured with the Ser226/Ser255 phosphomimetic compared to the non- phosphorylatable HSP90 (Supplementary Data 4, Fig. 8c, 1.4- fold EE : AA). To validate the reliance of Oct4 on epichaperomes, we examined Oct4 levels in both MDA- MB- 468 cancer cells and HEK293 cells transfected with the various HSP90 plasmids. Additionally, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[72, 90, 883, 219]]<|/det|> +1 we utilized affinity capture with PU- probes (Fig. 8d- f and Supplementary Fig.8a). Notably, we observed that cells expressing the phosphomimetic EE mutant showed significantly elevated levels of Oct4, both overall (Fig. 8e) and within epichaperomes (i.e., those sequestered within the epichaperomes, Fig. 8f), compared to cells expressing the HSP90 AA mutant. No detectable differences were observed under control conditions (WT HSP90 and empty vector only) (Supplementary Fig. 8a). Additionally, Oct4 was sequestered by epichaperomes in MDA- MB- 468 cells, supporting the idea that epichaperomes play a role in regulating pluripotency through both direct and indirect regulation of Oct4. + +<|ref|>text<|/ref|><|det|>[[70, 230, 883, 440]]<|/det|> +Epichaperomes play a pivotal role in supporting enhanced proliferation by altering the regulation of various proteins involved in cell signaling3,13,19. Higher epichaperome levels translate to a greater number of proteins being affected, resulting in increased signaling output13,17,58. We therefore next assessed the signaling output of cells transfected with the various HSP90 mutants. We observed a significantly heightened epichaperome- dependent impact on key signaling effector proteins involved in cell growth and proliferation (i.e., MEK, AKT, and mTOR) in cells expressing the HSP90 EE mutant compared to those expressing the AA mutant. This was evident in both the increased phosphorylation status of these effector proteins (Fig. 9a,b) and their enhanced recruitment to epichaperome platforms (Supplementary Fig. 9a- c) in cells expressing the EE mutant, as compared to those expressing the AA mutant. Importantly, these effects occurred without notable changes in the expression levels of the proteins (Supplementary Fig. 9a, b). No measurable differences were observed under control conditions (WT HSP90 and empty vector only) (Fig. 9b and Supplementary Fig. 9a, b). + +<|ref|>text<|/ref|><|det|>[[70, 450, 883, 578]]<|/det|> +Epichaperome formation fuels aggressive behaviors in cells51,59. Indeed, when observed under a microscope, we noted that, in comparison to cells expressing the non- phosphorylatable AA mutant (HSP90βS226A,S255A), those expressing the phosphomimetic EE mutant (HSP90βS226E,S255E) displayed a higher prevalence of cells with an elongated phenotype and several protrusions (Fig. 9a,b), supportive of a mesenchymal- like phenotype60. These morphological changes suggest a shift towards a more stem cell- like state, or a more aggressive phenotype in the context of cancer, in cells harboring the EE HSP90 mutant (i.e., with a high epichaperome load), a feature not observed in cells carrying the AA HSP90 mutant (i.e., not permissive of epichaperome formation). + +<|ref|>text<|/ref|><|det|>[[70, 590, 883, 831]]<|/det|> +Previous studies have found that irrespective of the tumor type, 60- 70% of tumors contain HSP90- HSC70 epichaperomes13,19. Additionally, epichaperomes are known to specifically form in diseased tissue3. To assess whether our observations regarding the impact of the HSP90 charged linker, derived from cell models, extend to human patients and are not artifacts specific to cultured cells, we obtained surgical specimens from breast and pancreatic cancer surgeries (n = 18 tissues from 9 patients, Fig. 10a- d). Both tumor (n = 9) and tumor adjacent (n = 9) tissues, determined by gross pathological evaluation to be potentially non- cancerous, were analyzed for epichaperome levels using Native PAGE. Additionally, total HSP90β and phosphorylated HSP90β at Ser226 were assessed by SDS PAGE and immunoblotting with specific antibodies. To mitigate potential biases arising from varying HSP90 levels, each pair was normalized based on HSP90 concentration. Despite challenges in obtaining high- quality epichaperome profiles from surgical samples, a robust correlation emerged between epichaperome expression and Ser226 phosphorylation (Fig. 10c,d). Tissues positive for epichaperomes exhibited p- Ser226 HSP90β positivity, and conversely, those negative for epichaperomes showed no or negligible p- Ser226 signal. + +<|ref|>text<|/ref|><|det|>[[70, 843, 882, 908]]<|/det|> +Collectively, these multifaceted biochemical and functional lines of evidence establish a compelling connection between structural features in HSP90 and the processes of epichaperome formation and function. These findings lend robust support to the hypothesis that the regulation of epichaperome processes in ESC and cancer cells—encompassing critical factors such as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 89, 883, 123]]<|/det|> +proliferative potential, self-renewal capacity, plasticity, and signaling output—crucially relies on the specific phosphorylation events taking place at key residues within HSP90's charged linker. + +<|ref|>sub_title<|/ref|><|det|>[[68, 133, 229, 150]]<|/det|> +## 3 DISCUSSION + +<|ref|>text<|/ref|><|det|>[[70, 162, 883, 259]]<|/det|> +The intricate network of protein- chaperone interactions within cells plays a critical role in maintaining protein homeostasis and cellular function. In recent years, the discovery of epichaperomes as specialized chaperone complexes in both cancer cells and pluripotent stem cells has opened new avenues for understanding chaperone biology. This investigation offers valuable insights into the structural and regulatory intricacies of epichaperomes, with particular attention to the pivotal role played by PTMs of HSP90 in orchestrating their formation and function. + +<|ref|>text<|/ref|><|det|>[[68, 270, 883, 368]]<|/det|> +A central discovery in this investigation is the recognition of specific PTMs on HSP90, especially at Ser226 and Ser255, as critical factors governing the assembly of epichaperomes. Our data reveal that phosphorylation of these serine residues enhances the association of HSP90 with other chaperones and co- chaperones, creating a microenvironment conducive to epichaperome formation. This finding underscores the significance of PTMs in regulating chaperone assemblies and highlights the potential of targeting these modifications for therapeutic intervention. + +<|ref|>text<|/ref|><|det|>[[68, 378, 883, 525]]<|/det|> +Chaperones appear to be highly susceptible to structural and functional regulation by a spectrum of PTMs. For example, PTMs of HSP90 provide an important regulatory element, modulating co- chaperone and client protein binding61- 65, ATPase activity66, conformational cycle62,65- 67, turnover68 and small molecule affinity12,38. Similar to minor changes in primary sequence, these PTMs likely regulate the access to and occupancy of key conformational states of HSP90 for in vivo processing of some essential clients. Our investigation pinpoints crucial PTMs that remodel the functional profile of HSP90, metamorphosing it from a protein- folding entity into epichaperomes, a platform orchestrating the reorganization of PPI networks for heightened cellular adaptability and proliferation. + +<|ref|>text<|/ref|><|det|>[[68, 536, 883, 711]]<|/det|> +Our study uncovered a fascinating aspect of PTMs in HSP90 within epichaperomes — phosphorylation events occur in an IDR of the protein. The strategic placement of these PTMs in the IDR holds profound significance, suggesting that they influence HSP90's conformation and function beyond the traditional structured regions. This adaptability is crucial for HSP90's participation in distinct PPIs, allowing it to stabilize the epichaperome- enabling conformation and restructure the interactions of numerous proteins in response to cellular stressors. Intriguingly, previous studies in yeast69, where the IDR was substituted with glycine- glycine- serine residues, align with our findings. These studies suggested that the charged linker (encompassing the IDR), influenced by the N- domain of HSP90, can adopt a structured form. This structured form, in turn, can stabilize interactions between specific HSP90 domains, influencing HSP90 dynamics, co- chaperone binding, and overall biological function, especially in conditions of cellular stress. + +<|ref|>text<|/ref|><|det|>[[68, 722, 883, 837]]<|/det|> +Changes in PPI networks play a fundamental role in cellular responses to stressors and the coordination of various biological processes18. These alterations, often induced by external stressors, are vital for the cell's ability to adapt and function under different conditions. Notably, less than 10% of human PPIs remain unaffected by stress- induced perturbations, highlighting the widespread impact of cellular stress on the interactome. These changes, influenced by factors such as PTMs and protein conformation, are essential for species- specific adaptation and contribute to PPI network malfunctions observed in diseases. + +<|ref|>text<|/ref|><|det|>[[68, 848, 883, 897]]<|/det|> +One intriguing question is which kinase could phosphorylate HSP90 at these serine residues? A likely candidate is casein kinase II (CK2)70,71. CK2 is sequestered to epichaperomes in ESCs and in cancer cells13. Notably, CK2 is overexpressed in highly proliferative cells72 and plays a role in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 88, 883, 155]]<|/det|> +1 phosphorylating numerous protein substrates involved in cell proliferation and survival73. 2 Moreover, the mutation of CK2 has been shown to abolish the viability of both PSCs74 and tumor cells75,76, indicating a potential direct link between epichaperome function and cellular physiology, possibly mediated by CK2 phosphorylation, which remains to be confirmed. + +<|ref|>text<|/ref|><|det|>[[66, 168, 883, 268]]<|/det|> +The implications of our study go beyond providing structural and mechanistic insights. We present compelling evidence that phosphorylation of HSP90 at Ser226 and Ser255 not only promotes epichaperome formation but also influences cellular behaviors, including proliferation and self-renewal. This suggests a direct link between epichaperome function and cellular physiology, particularly crucial in contexts such as cancer and stem cell maintenance, where robust proliferation and adaptation are vital. + +<|ref|>text<|/ref|><|det|>[[66, 277, 883, 391]]<|/det|> +Plasticity, a key characteristic associated with both ESCs and cancer cells77, is also implicated in our findings. The morphological changes observed in cells expressing the phosphomimetic HSP90 mutant—specifically, the higher prevalence of cells with an elongated phenotype and several protrusions—hint at a mesenchymal-like phenotype80. This phenotypic shift is often associated with increased plasticity and is indicative of a more stem cell-like state. Our findings suggest a potential role for epichaperomes in modulating this dynamic process of cellular transition between different phenotypic states. + +<|ref|>text<|/ref|><|det|>[[66, 402, 883, 547]]<|/det|> +The link between pluripotency and cancer is particularly intriguing. Cellular stress is increasingly recognized as a pivotal factor that can shift the balance between cellular pluripotency and the development of malignancies. The process of dedifferentiation, observed in regeneration in plants and some vertebrates, involves the deactivation of genes responsible for cell- specific functions, re- entry into the cell cycle, proliferation, and activation of pluripotency- associated genes78. Tumors also undergo dedifferentiation, where cancer cells revert to a less differentiated state, re- express stem cell genes like Oct4, leading to the emergence of cancer stem- like cells with enhanced metastatic potential and treatment evasion79. Our study proposes epichaperomes as significant mediators of changes in cellular identity, partly through Oct4. + +<|ref|>text<|/ref|><|det|>[[66, 558, 883, 735]]<|/det|> +The revelation of HSP90's dysfunctional multimeric states carries implications for therapeutic interventions3,16. Instead of universally inhibiting all HSP90 pools, a paradigm shift comes to the fore with precision medicine strategies. The prospect of targeting specific pathologic conformations while preserving normal HSP90 functions emerges as a promising direction. This shift beckons researchers to navigate the intricate interplay of HSP90 conformations as they forge ahead in the quest for innovative therapeutic approaches. Our study also confirms the notion that small molecule HSP90 binders have distinct preference for HSP90 conformers in cells, reinforcing the finding that not all HSP90 inhibitors act equally well or equally selectively on specific disease- promoting HSP90 conformations or disease- associated HSP90 assemblies in comparison with HSP90 conformers found in normal cells. The first feature determines drug efficacy, whereas the latter influences the safety profile during administration. + +<|ref|>text<|/ref|><|det|>[[66, 740, 883, 840]]<|/det|> +In conclusion, our study unravels the intricate interplay between PTMs, conformational regulation, and biological functions of HSP90 within epichaperomes. These findings have implications for the development of novel therapeutic strategies targeting chaperone complexes in diseases characterized by epichaperome dysregulation, such as in cancers and neurodegenerative disorders. By deciphering the regulatory mechanisms underlying epichaperomes, we move one step closer to harnessing their potential for precision medicine and therapeutic intervention. + +<|ref|>sub_title<|/ref|><|det|>[[66, 851, 214, 868]]<|/det|> +## METHODS + +<|ref|>text<|/ref|><|det|>[[66, 881, 627, 898]]<|/det|> +Human biospecimens research ethical regulation statement + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 90, 883, 346]]<|/det|> +1 Surgical specimens were obtained in accordance with the guidelines and approval of the 2 Institutional Review Board at Memorial Sloan Kettering Cancer Center, Biospecimen Research 3 Protocol# 09- 121, project title: Ex- Vivo Testing of Breast Cancer Tumors for Sensitivity to 4 Inhibitors of Heat Shock Proteins and Signaling Pathway Inhibitors, S. Modi, PI, and Biospecimen 5 Research Protocol# Protocol# 09- 121, project title: Ex- Vivo Testing of Breast Cancer Tumors for 6 Sensitivity to Inhibitors of Heat Shock Proteins and Signaling Pathway Inhibitors, S. Modi, PI, and 7 Biospecimen Research Protocol# 14- 091, project title: Establishment and Characterization of 8 Unique Mouse Models Using Patient- Derived Xenografts . E. de Stanchina, PI. The source of 9 samples consists of unused portions of surgical specimens that are taken for reasons other than 10 research (i.e., for patients undergoing the procedures for medical reasons unrelated to need for 11 research samples or to the nature of the research). No individuals were excluded on the basis of 12 age, sex or ethnicity. Because breast cancer is a disease which overwhelmingly affects women, 13 and is a disease that is generally not seen in children, the vast majority of breast cancer patients 14 enrolled on protocol# 09- 121 were females \(>18\) years of age. Patient tissue samples were 15 obtained with consent provided in written form. Samples were de- identified before receipt for use 16 in the studies. + +<|ref|>sub_title<|/ref|><|det|>[[115, 360, 410, 377]]<|/det|> +## Reagents and Chemical Synthesis + +<|ref|>text<|/ref|><|det|>[[112, 377, 883, 553]]<|/det|> +All commercial chemicals and solvents were purchased from Sigma Aldrich or Fisher Scientific and used without further purification. The identity and purity of each product was characterized by MS, HPLC, TLC, and NMR. Purity of target compounds has been determined to be \(>95\%\) by LC/MS on a Waters Autopurification system with PDA, MicroMass ZQ and ELSD detector and a reversed phase column (Waters X- Bridge C18, \(4.6 \times 150 \mathrm{mm}\) , \(5 \mu \mathrm{m}\) ) eluted with water/acetonitrile gradients, containing \(0.1\%\) TFA. Stock solutions of all inhibitors were prepared in molecular biology grade DMSO (Sigma Aldrich) at \(1,000 \times\) concentrations. The PU- TCO, PU- CW800 and YK5- B probes and relevant control probes, and the PU- beads and the control probes were generated using published protocols \(^{13,19,35,80 - 85}\) or as described in Supplementary Notes 1. The GA- biotin probe was purchased from Sigma (SML0985). Disuccinimidyl suberate (DSS) was acquired from ThermoFisher (21655). + +<|ref|>sub_title<|/ref|><|det|>[[115, 566, 393, 581]]<|/det|> +## Cell lines and culture conditions + +<|ref|>text<|/ref|><|det|>[[112, 581, 883, 837]]<|/det|> +Cell line selection was not based on gender, sex or ethnicity. Cell lines were cultured according to the providers' recommended culture conditions. Cells were authenticated using short tandem repeat profiling and tested for mycoplasma. The breast cancer cell line MDA- MB- 468 (HTB- 132, RRID: CVCL_0419), pancreatic cancer cell line ASPC1 (CRL- 1682, RRID: CVCL_0152), nonsmall cell lung cancer cell line NCI- H1975 (CRL- 5908, RRID: CVCL_1511), B lymphoblast cell line Daudi (CCL- 213, RRID: CVCL_0008), lung fibroblast cells MRC5 (CCL- 171, RRID: CVCL_0440), the colon cell line CCD- 18Co (CRL- 1459, RRID: CVCL_2379) and the Human Embryonic Kidney 293 (HEK293) cell line (CRL- 1573, RRID: CVCL_0045) were purchased from ATCC. IBL- 1 (RRID: CVCL_9638) was derived from an AIDS- related immunoblastic lymphoma \(^{86}\) . Mammary epithelial primary cells HMEC (PCS- 600- 010) were purchased from Lonza. B- cell lymphoma cell line OCI- LY1 (RRID: CVCL_1879) was obtained from the Ontario Cancer Institute. E14 mouse ES cells \(^{87}\) were received as frozen ampules from TG Fazzio (U Mass Med School). Cells were feed- free and verified as of male mouse origin through sequencing. ZHBtC4 mouse ES cells \(^{31}\) were received from D. Levasseur (U of Iowa). Cells were cultured as ESCs without feeder cells in the absence of doxycycline. For hiPSC, healthy donor fibroblasts purchased from Coriell were reprogrammed using CytoTune Sendai viruses \(^{34}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 848, 397, 864]]<|/det|> +## Mammalian cell culture and lysis + +<|ref|>text<|/ref|><|det|>[[112, 865, 883, 898]]<|/det|> +Mouse feeder- free embryonic stem cells (E14 or ZHBtC4 line) were grown on tissue culture plates coated with \(0.2\%\) gelatin. ESCs were cultured in Dulbecco's Modified Eagle Medium (DMEM; + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 88, 883, 237]]<|/det|> +1 Gibco 10829018) media supplemented with \(10\%\) fetal bovine serum (FBS, HyClone 2 SH30070.03HI), \(2 \text{mM L}\) - glutamine, \(0.1 \text{mM}\) nonessential amino acids (Gibco 11140050), \(100 \text{U}\) 3 \(\text{mL}^{- 1}\) penicillin/streptomycin (Gibco 15140122), \(0.1 \text{mM beta- mercaptoethanol (Sigma M6250),}\) 4 and \(103 \text{U mL}^{- 1}\) leukemia inhibitory factor (LIF). Cells are grown in \(37^{\circ}\text{C} /5\% \text{CO}_2\) incubator with 5 media change every 2 days, passaged or harvested when \(60 - 80\%\) confluent. After harvesting, 6 cell pellets are washed with phosphate- buffered saline (PBS, GenClone 25- 508) and flash frozen 7 before storing in \(- 80^{\circ}\text{C}\) . For pull- down and chemical cross- linking experiments, frozen cells are 8 thawed and lysed in Felts lysis buffer (20 mM HEPES pH 7.4, \(50 \text{mM KCl}, 5 \text{mM MgCl}_2, 0.01\%\) 9 NP- 40) in the presence of protease inhibitors, phosphatase and deacetylase inhibitors. + +<|ref|>sub_title<|/ref|><|det|>[[115, 247, 375, 263]]<|/det|> +## ESC and hiPSC differentiation + +<|ref|>text<|/ref|><|det|>[[70, 260, 883, 760]]<|/det|> +11 ZHB T c4 cells were differentiated into trophoblasts through Oct4 repression. Cells were seeded 12 at a density of \(2\times 10^{5}\) cells \(\mathrm{mL}^{- 1}\) and grown in media with added doxycycline at a final 13 concentration of \(200 \text{ng mL}^{- 1}\) for \(96 \text{h}\) before harvest. E14 cells were spontaneously differentiated 14 using attached embryoid bodies (EB) culture. Briefly, cells were seeded at a density of \(5\times 10^{4}\) 15 cells/mL in sterile bacteriological petri dishes in differentiation media (ES media without LIF) and 16 cultured in \(37^{\circ}\text{C} /5\% \text{CO}_2\) incubator for 4 days to aggregate into EBs. When turned orange, media 17 were changed. On day 4, EBs were transferred into tissue culture dishes (without gelatin) at a 18 density of \(100 - 200 \text{EBs per 10 cm tissue culture dish. Attached EBs were cultured in differentiation 19 media in \(37^{\circ}\text{C} /5\% \text{CO}_2\) incubator for 14- 18 days before harvest. hiPSC differentiated in midbrain 20 dopaminergic neurons were a gift from Dr. Lorenz Studer. Cells were differentiated into midbrain 21 dopamine neurons by a modified dual- SMAD inhibition protocol as described \(^{20}\) . hESCs were 22 dissociated into single cells using Accutase and plated at high density on Matrigel (BD). The cells 23 were subjected to timed exposure to LDN193189 (100 nM, Stemgent), SB431542 (10 μM, Tocris), 24 SHH C25II (100 ng mL \(^{- 1}\) , R&D), Purmorphamine (2 μM, Stemgent), FGF8 (100 ng mL \(^{- 1}\) , R&D) 25 and CHIR99021 (CHIR; 3 μM, Stemgent) to induce midbrain floor plate precursors. For mDA 26 neuron induction, floor plate precursors were maintained in mDA differentiation media containing 27 Neurobasal/B27/L- Glut (NB/B27; Invitrogen) supplemented with CHIR (until day 13) and with 28 BDNF (brain- derived neurotrophic factor, 20n mL \(^{- 1}\) ; R&D), ascorbic acid (0.2 mM, Sigma), GDNF 29 (glial cell line- derived neurotrophic factor, 20 ng mL \(^{- 1}\) ; R&D), TGFβ3 (transforming growth factor 30 type β3, 1 ng mL \(^{- 1}\) ; R&D), dibutyryl cAMP (0.5 mM; Sigma), and DAPT (10 μM; Tocris). On day 31 20, cells were dissociated using Accutase and replated on dishes pre- coated with polyornithine 32 (PO; \(15 \text{μg mL}^{- 1}\) )/laminin (1 μg mL \(^{- 1}\) )/fibronectin (2 μg mL \(^{- 1}\) ) in differentiation medium 33 (NB/B27 + BDNF, ascorbic acid, GDNF, dbcAMP, TGFβ3 and DAPT). On day 30 of differentiation, 34 cells were dissociated using Accutase and replated on dishes pre- coated with polyornithine (PO; 35 15 μg mL \(^{- 1}\) )/ laminin (1 μg mL \(^{- 1}\) )/ fibronectin (2 μg mL \(^{- 1}\) ) in differentiation medium 36 (NB/B27 + BDNF, ascorbic acid, GDNF, dbcAMP, TGFβ3 and DAPT) supplemented with 10 μM 37 Y- 27632 (until day 32). Two days after plating, cells were treated with 1 μg mL \(^{- 1}\) mitomycin C 38 (Tocris) for 1 h to kill any remaining proliferative contaminants. The mDA neurons were fed every 39 2 to 3 days and maintained without passaging until they were assayed at day 65. To prevent 40 neurons from lifting off, laminin and fibronectin were supplemented into the media every 7- 10 41 days. + +<|ref|>sub_title<|/ref|><|det|>[[115, 770, 370, 785]]<|/det|> +## Cell culture and transfections + +<|ref|>text<|/ref|><|det|>[[115, 785, 882, 900]]<|/det|> +43 Monolayer cultures of MDA- MB- 468 and HEK293 cells were grown in high glucose (4.5 g L \(^{- 1}\) ) 44 DMEM containing \(10\%\) FBS and \(1\times\) antibiotic and antimycotic ( \(100\times\) ABAM, GIBCO) in a \(37^{\circ}\text{C}\) 45 incubator supplied with \(5\%\) oxygen- air atmosphere. For native electrophoresis, and in- gel 46 fluorescence studies, \(1\times 10^{7}\) cells were seeded in \(100 \text{mm dishes (Corning)}\) at \(70\%\) confluency 47 in DMEM supplemented with \(10\%\) FBS and \(1\times\) ABAM. Next day, spent medium was changed with 48 fresh serum and antibiotic free DMEM for 1 h before performing transfections. Cells were 49 transfected using lipofectamine 3000 (Invitrogen) with 4 μg of mCherry empty vector, mCherry + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 89, 883, 170]]<|/det|> +1 HSP90β- Wild type (mCherry- HSP90β- WT), mCherry- HSP90β- S226A, S255A mutant (mCherry- HSP90β- AA) or mCherry- HSP90β- S226E, S255E mutant (mCherry- HSP90β- EE) plasmids. See Supplementary Note 2 for plasmid sequences. Transfection mixtures were prepared in OptiMEM (Gibco). Post 6 h of transfection, medium was changed with 10% FBS and 1×ABAM supplemented DMEM. Cells were harvested in native lysis buffer for future analyses. + +<|ref|>sub_title<|/ref|><|det|>[[115, 183, 373, 198]]<|/det|> +## Primary specimen processing + +<|ref|>text<|/ref|><|det|>[[113, 199, 883, 358]]<|/det|> +Frozen tumor and matched tumor adjacent tissues were cut into small pieces using surgical blades and weighed using a precision balance. \(74~\mathrm{mg}\) of tissue was homogenized in \(200~\mu \mathrm{L}\) of \(1\times\) native lysis buffer in \(1.5~\mathrm{mL}\) microtubule homogenizer for each sample. Homogenization was performed on dry ice. Post homogenization samples were incubated on ice for 30 min followed by centrifugation at \(12,000\times \mathrm{g}\) at \(4^{\circ}C\) for 15 min. Supernatant was collected, and protein quantification was done using BCA method. Samples were normalized using total HSP90β levels for each tissue pairs. An initial SDS- PAGE was run using \(5\mu \mathrm{g}\) of total protein for each sample. Total protein loads were adjusted to ensure equal levels of total HSP90β in tumor and corresponding matched adjacent tissue. Samples were then processed for native PAGE and SDS- PAGE to check for HSP90β and p- Ser226 HSP90β as described below. + +<|ref|>sub_title<|/ref|><|det|>[[115, 370, 495, 386]]<|/det|> +## Native gel electrophoresis and western blot + +<|ref|>text<|/ref|><|det|>[[112, 386, 883, 771]]<|/det|> +Native gel electrophoresis was performed as reported88. Namely, \(1 \times 10^7\) cells were lysed in 20 mM Tris pH 7.4, 20 mM KCl, 5 mM \(\mathrm{MgCl}_2\) , 0.01% NP40, and 10% glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze- thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer's protocol (Pierce™ BCA Protein Assay Kit, ThermoFisher Scientific, Waltham, MA). One hundred micrograms (100 \(\mu \mathrm{g}\) ) of protein were loaded in 4 to 10% native gel and run using native \(1 \times\) Tris- Glycine buffer (25 mM Tris, 192 mM glycine) at \(4^{\circ}C\) in a cold room at 125V. Following electrophoresis, proteins were transferred to PVDF membrane, by wet transfer (25 mM Tris, 192 mM glycine, 20% (v/v) methanol, 0.02% SDS) at 100V in the cold room. Membranes were then blocked for 1 h in 5% BSA in TBS/0.1% Tween 20. The blots were then probed with the following antibodies: HSP90β (SMC- 107; RRID:AB_854214; 1:2,000) and HSP110 (SPC- 195; RRID:AB_2119373; 1:1,000) from Stressmarq; HSC70 (SPA- 815; RRID:AB_10617277; 1:1,000), and HOP (SRA- 1500; RRID:AB_10618972; 1:1,000) from Enzo; HSP90α (ab2928; RRID:AB_303423; 1:6,000), AHA1 (ab56721, RRID:AB_2273725, 1:1000) from Abcam; CDC37 (4793; RRID:AB_10695539; 1:1,000), HOP (5670; RRID:AB_10828378; 1:1,000), from Cell Signaling Technologies. The blots were washed with TBS/0.1% Tween 20 and incubated with appropriate HRP- conjugated secondary antibodies: goat anti- mouse (1030- 05, RRID: AB_2619742, 1:5,000), goat anti- rabbit (4010- 05, RRID: AB_2632593, 1:5,000) and goat anti- rat (3030- 05, RRID: AB_2716837, 1:5,000) (Southern Biotech, Birmingham, AL, USA). The chemiluminescent signal was detected with Enhanced Chemiluminescence (ECL) reagent according to manufacturer's instructions and visualized using Chemi Doc (Biorad) and analyzed using Image Studio Lite Version 5.2. (LI- COR Biosciences). NativeMark unstained protein standard (Invitrogen, LC0725) was used to estimate molecular weight of protein complexes in native gel electrophoresis and Western blotting. + +<|ref|>sub_title<|/ref|><|det|>[[115, 783, 360, 798]]<|/det|> +## SDS-PAGE and western blot + +<|ref|>text<|/ref|><|det|>[[115, 799, 882, 895]]<|/det|> +Proteins were extracted in \(20~\mathrm{mM}\) Tris pH 7.4, \(20~\mathrm{mM}\) KCl, \(5\mathrm{mM}\mathrm{MgCl}_2\) , \(0.01\%\) NP40, and \(10\%\) glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze- thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer's protocol (Pierce™ BCA Protein Assay Kit, ThermoFisher Scientific, Waltham, MA). Ten to thirty micrograms (10 to \(30\mu \mathrm{g}\) ) of total protein were subjected to SDS- PAGE, transferred onto PVDF membrane, by wet transfer (Towbin buffer: \(25~\mathrm{mM}\) Tris, \(192~\mathrm{mM}\) glycine, \(20\%\) (v/v) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 90, 883, 475]]<|/det|> +1 methanol) at 100V in cold room. Membranes were then blocked for 1 h in \(5\%\) BSA in TBS/0.1% 2 Tween 20 and incubated overnight with the indicated antibodies. HSP90β (SMC- 107; 3 RRID:AB_854214; 1:2,000) and HSP110 (SPC- 195; RRID:AB_2119373; 1:1,000) from 4 Stressmarq; HSC70 (SPA- 815; RRID:AB_10617277; 1:1,000), and HOP (SRA- 1500; 5 RRID:AB_10618972; 1:1,000) from Enzo; HSP90α (ab2928; RRID:AB_303423; 1:6,000), AHA1 6 (ab56721, RRID:AB_2273725, 1:1,000) from Abcam; p-MEK1/2 (S217/221) (9154; 7 RRID:AB_2138017; 1:1,000), MEK1/2 (9122; RRID:AB_823567; 1:1,000), p-mTOR (S2448) 8 (5536; RRID:AB_10691552; 1:500), mTOR (2983; RRID:AB_2105622; 1:1,000), CDC37 (4793; 9 RRID:AB_10695539; 1:1,000), HOP (5670; RRID:AB_10828378; 1:1,000), p-S6 ribosomal 10 protein (Ser235/236) (4858; RRID:AB_916156; 1:2,000), S6 ribosomal protein (2217; 11 RRID:AB_331355; 1:3,000), Oct4 (2840, RRID:AB_2167691, 1:2,000), p-AKT (S473) (9271, 12 RRID:AB_329825, 1:2000), AKT (4691, RRID:AB_915783, 1:3000), HSP70 (ADI-SPA-810, 13 RRID:AB_10616513, 1:2000) from Cell Signaling Technologies, \(\beta\) - actin (A1978, RRID: 14 AB_476692, 1:3000) from Sigma-Aldrich, and mCherry (PA5-34974, RRID:AB_2552323, 15 1:2,000) and p-Ser226 HSP90β (PA5-105480, RRID:AB_2816908, 1:1,000) from Fisher 16 Scientific. The blots were washed with TBS/0.1% Tween 20 and incubated with appropriate HRP- 17 conjugated secondary antibodies: goat anti- mouse (1030- 05, RRID: AB_2619742, 1:5,000), goat 18 anti- rabbit (4010- 05, RRID: AB_2632593, 1:5,000) and goat anti- rat (3030- 05, RRID: 19 AB_2716837, 1:5,000) (Southern Biotech, Birmingham, AL, USA). The chemiluminescent signal 20 was detected with ECL reagent according to manufacturer's instructions and visualized using 21 ChemiDoc MP imaging system (Biorad) and analyzed using Image Studio Lite Version 5.2. (LI- 22 COR Biosciences). Thermo Scientific PageRuler Plus prestained protein ladder (Fisher Scientific, 23 26619) or Precision Plus protein standards (Bio- Rad, 161- 0375) were used as size standards in 24 protein electrophoresis and Western blotting. + +<|ref|>sub_title<|/ref|><|det|>[[115, 485, 420, 502]]<|/det|> +## Coomassie and Ponceau S staining + +<|ref|>text<|/ref|><|det|>[[115, 502, 882, 582]]<|/det|> +Where indicated, gels after native PAGE or SDS- PAGE were washed with deionized water three times for 5 min and incubated with Coomassie G- 250 stain (Bio- Rad) for 1 h. The gels were washed with water after to remove the excess of the dye and imaged. Where indicated, membranes after protein transfer were incubated with Ponceau S solution (Sigma) for 10 min, then were washed with water to remove the excess of the dye and imaged. + +<|ref|>sub_title<|/ref|><|det|>[[115, 595, 353, 610]]<|/det|> +## Primary specimen analyses + +<|ref|>text<|/ref|><|det|>[[113, 610, 882, 867]]<|/det|> +Specimens were harvested as previously reported89. Briefly, the surgical team delivered specimens in tightly sealed, sterile, leak- proof bags without fixatives. This maintained specimens in their fresh state, crucial for downstream analyses. Fresh specimens underwent sterile harvesting by the pathologist or assistant, using laminar flow hoods. Harvesting times were meticulously recorded, kept under 30 minutes post- surgery to mitigate cold ischemia effects. Primary breast tumor specimens were selectively obtained from the index lesion's periphery, avoiding central necrosis. Recognition criteria for necrotic tissue included color loss, softness, and demarcation from viable tissue. Normal breast tissue samples (e.g., normal dense/fibrous breast parenchyma) are taken from distant locations, at least 1 cm grossly away from the target lesion if feasible. In contrast, due to the relatively small size of the pancreas and the nature of surgical procedures, normal pancreas samples collected were typically in close proximity to the tumor. Whipple procedures typically involve the resection of the head of the pancreas, while distal procedures focus on the resection of the tail. Samples were initially stored in tubes with MEM and antibiotics and transported on wet ice to the laboratory immediately after procurement. Upon reaching the laboratory, samples were transferred to cryovials, 'snap' frozen, and stored at - 80 °C for future molecular analyses. + +<|ref|>sub_title<|/ref|><|det|>[[115, 878, 268, 895]]<|/det|> +## Chemical blotting + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 90, 883, 570]]<|/det|> +1 For in- gel blotting using PUTCO, cells were harvested in \(20~\mathrm{mM}\) Tris pH 7.4, \(20~\mathrm{mM}\) KCl, \(5\mathrm{mM}\) \(\mathrm{MgCl}_2\) , \(0.01\%\) NP40, and \(10\%\) glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze- thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer's protocol (Pierce™ BCA Protein Assay Kit, Thermofisher Scientific, Waltham, MA). One hundred micrograms (100 \(\mu \mathrm{g}\) ) of protein were incubated with \(1\mu \mathrm{M}\) of PUTCO in a total volume of \(42~\mu \mathrm{L}\) . Post 3 h of incubation samples were loaded in 4 to \(10\%\) native gel and run using native \(1\times\) Tris- Glycine buffer at \(4^{\circ}\mathrm{C}\) in cold room at \(125\mathrm{V}\) . Following electrophoresis, the gel was incubated in \(30~\mathrm{mL}\) of \(700~\mathrm{nM}\) Cy5- Tetrazine containing ice cold \(1\times\) Tris- Glycine buffer at room temperature (RT) for 15 min for the click reaction to occur. After 15 min, the gel was washed thrice (5 min each) with ice cold \(1\times\) Tris- Glycine buffer. The gel was then imaged using ChemiDoc MP imaging system (Biorad). Alexa 546 channel (illumination: Epi- green, \(520 - 545\mathrm{~nm}\) excitation, Filter: 577- 613 nm filter for green- excitable fluorophores and stains) was used to visualize mCherry- tagged species, and native page ladder (NativeMark™ Unstained Protein Standard, Cat. No. LC0725, Invitrogen™). The Cy5 channel (illumination: Epi- far red, \(650 - 675\mathrm{~nm}\) excitation, Filter: 700- 730 nm filter for far red- excitable fluorophores and stains) was used for imaging PUTCO staining. Post capturing, the images from the two channels were merged to get the alignment of the bands with respect to the molecular weight ladder in Image Lab 6.1 (Bio- Rad). For in cell blotting using PU- CW800, E14 cells were plated at a seeding density of \(1\times 10^{6}\) per \(10~\mathrm{cm}\) plate and grown for 44 h before treatment with either PU- CW800 or control fluorophore (SS27) at a concentration of \(1\mu \mathrm{M}\) in culture media for 4 h while incubating at \(37^{\circ}\mathrm{C}\) , \(5\%\) CO2. Following the treatment, cells were harvested and lysed by dounce homogenization in Felts lysis buffer (20 mM HEPES at pH 7.4, \(50~\mathrm{mM}\) KCl, \(2\mathrm{mM}\) EDTA, and \(0.01\%\) NP40) supplemented with protease, phosphatase, and deacetylase inhibitors. Cell lysates were buffer exchanged with fresh Felts lysis buffer containing supplements to remove any unbound drug before loading into a native gel. For visualization of PU- CW800 fluorescence and total protein, \(200~\mu \mathrm{g}\) of cell lysate was loaded onto a \(4 - 10\%\) native gradient gel and resolved at \(4^{\circ}\mathrm{C}\) for 5 h. Fluorescence was visualized on LI- COR Odyssey CLx using Image StudioTM Software (LI- COR Biosciences) and then total protein was visualized on the same gel using Coomassie Brilliant Blue R250 stain. Band(s) with observable fluorescent signal were then processed by in- gel digestion and analyzed for LC- MS/MS to identify major proteins. + +<|ref|>sub_title<|/ref|><|det|>[[115, 581, 357, 597]]<|/det|> +## SILAC and ESC transfection + +<|ref|>text<|/ref|><|det|>[[115, 597, 882, 741]]<|/det|> +For metabolic labeling with SILAC (stable- isotope labeling of amino acid in cell culture), ESCs were cultured and passaged five times at \(48\mathrm{~h}\) intervals in media containing SILAC DMEM (Thermo Fisher 88364) supplemented with 13C- and 15N- labeled heavy L- arginine ( \(84~\mathrm{mg~L^{- 1}}\) , Cambridge isotope CNLM- 539- H) and L- lysine ( \(146~\mathrm{mg~L^{- 1}}\) , Cambridge isotope CNLM- 291- H) or supplemented with 12C- and 14N- labeled light L- arginine (Fisher BP2505100) and L- lysine (Fisher J6222522) amino acids for five passages to ensure complete stable isotope incorporation. For heterologous expression of HSP90 AA or EE mutants, cells were then reverse transfected with plasmid DNA using LipofectamineTM 3000 Transfection Kit (Invitrogen #L3000015) and incubated at \(37^{\circ}\mathrm{C}\) , \(5\%\) CO2 for \(72\mathrm{~h}\) at which point they were harvested. + +<|ref|>sub_title<|/ref|><|det|>[[115, 754, 400, 769]]<|/det|> +## Measurement of cell proliferation + +<|ref|>text<|/ref|><|det|>[[115, 769, 882, 833]]<|/det|> +E14 cells were transfected and incubated in \(37^{\circ}\mathrm{C} / 5\%\) CO2 incubator for \(24\mathrm{~h}\) . Cells were then replated to 6- well plate at the same dilution factor for each transfection treatment condition and then returned to incubator. At \(60\mathrm{~h}\) post- transfection, cell proliferation was determined via cell count for all conditions. + +<|ref|>sub_title<|/ref|><|det|>[[115, 847, 300, 862]]<|/det|> +## Confocal microscopy + +<|ref|>text<|/ref|><|det|>[[115, 862, 882, 894]]<|/det|> +HEK293 cells transfected with mCherry- HSP90β- AA or mCherry- HSP90β- EE plasmids were seeded at a density of \(1.8\times 10^{6}\) cells \(\mathrm{mL^{- 1}}\) on coverslips in a monolayer in six well plates and then + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 88, 883, 203]]<|/det|> +1 grown overnight for the cells to attach. Cover slips were mounted with ProLongTM Gold antifade 2 mountant with DAPI. Imaging was done using Leica SP8 Stellaris microscope. Images were 3 analyzed using Image J and Leica LAS X lite software. Cell morphology was manually inspected, 4 and the percentage of cells exhibiting an elongated phenotype and several protrusions was 5 calculated. Specifically, cells transfected with mCherry were assessed, and those displaying the 6 described features were counted. The percentage was then determined based on the total 7 number of mCherry- transfected cells observed. + +<|ref|>sub_title<|/ref|><|det|>[[115, 215, 464, 231]]<|/det|> +## Chemical precipitation and cross-linking + +<|ref|>text<|/ref|><|det|>[[115, 231, 882, 359]]<|/det|> +The GA- affinity beads were prepared by incubating GA- biotin (Sigma SML0985) with Dynabeads M- 280 Streptavidin (ThermoFisher 11205D) at \(4^{\circ}C\) for \(2.5\mathrm{h}\) . The GA- bound beads were then incubated with cleared cell lysates or cross- linked cell lysates overnight at \(4^{\circ}C\) . For PU- beads affinity capture, cell lysates were incubated with PU- beads or control beads at \(4^{\circ}C\) for \(3.5\mathrm{h}\) . Following incubation, bead conjugates were washed three times in lysis buffer before elution with sample buffer. The chemical cross- linking and HSP90 purification experiments were carried out in \(>3\) replicates for both ligands. Samples were analyzed separately, and statistical significance was assessed. + +<|ref|>sub_title<|/ref|><|det|>[[115, 371, 490, 387]]<|/det|> +## Chemical precipitation and immunoblotting + +<|ref|>text<|/ref|><|det|>[[115, 387, 882, 593]]<|/det|> +Cells were harvested in \(20\mathrm{mM}\) Tris pH 7.4, \(20\mathrm{mM}\) KCl, \(5\mathrm{mM}\) MgCl \(_2\) , \(0.01\%\) NP40, and \(10\%\) glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze- thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer's protocol (Pierce™ BCA Protein Assay Kit, Thermofisher Scientific, Waltham, MA). PU- beads and control beads were washed with the native gel buffer 3 times prior use. Post washing, \(40\mu \mathrm{L}\) aliquots of the beads were distributed into the sample tubes. Five hundred micrograms (500 \(\mu \mathrm{g}\) ) of total protein in \(300\mu \mathrm{L}\) final volume, adjusted with native lysis buffer were added. Samples were incubated for \(3\mathrm{h}\) at \(4^{\circ}C\) on a rotor, followed by washing with native lysis buffer four times. Post washing, \(30\mu \mathrm{L}\) of \(5\times\) Laemmli buffer was added to the beads and boiled at \(95^{\circ}C\) for \(5\mathrm{min}\) . Ten micrograms ( \(10\mu \mathrm{g}\) ) of the lysates \((2\%)\) was used as input for the pull- down experiment. Samples were then centrifuged at \(13,000\times \mathrm{g}\) for \(20\mathrm{min}\) and supernatant collected was loaded on to SDS- PAGE. The protein transfer and western blotting procedures were performed as described in SDS- PAGE and western blot section. + +<|ref|>sub_title<|/ref|><|det|>[[115, 610, 449, 625]]<|/det|> +## IUPred analysis for disorder prediction + +<|ref|>text<|/ref|><|det|>[[115, 625, 882, 867]]<|/det|> +Sequence Preprocessing: The primary amino acid sequence of human HSP90β (P08238) and HSP90α (P07900) were extracted in FASTA format. These sequences served as the input for subsequent disorder prediction using the IUPred algorithm. Calculation of Disorder Scores: The IUPred algorithm utilizes energy potentials derived from pairwise amino acid interactions to assess the local structural propensities of each residue in the protein sequence. For each residue, IUPred computes a disorder score within the range of 0 to 1. A score of 0 suggests a higher likelihood of being ordered, while a score of 1 indicates a higher likelihood of being disordered. Threshold for Disorder Classification: To classify residues as either ordered or disordered, a threshold was applied to the calculated disorder scores. A common threshold of 0.5 was employed, designating residues with scores above 0.5 as disordered. The output of the IUPred analysis consisted of a disorder profile, providing disorder scores for each residue in the input protein sequence. Residues were categorized based on the applied threshold, facilitating the identification of regions with a high probability of disorder. All analyses were performed with the default parameters of the IUPred algorithm. The results presented here are based on the specific sequence input and the applied threshold for disorder classification. + +<|ref|>sub_title<|/ref|><|det|>[[115, 879, 323, 895]]<|/det|> +## Computational analyses + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 90, 884, 905]]<|/det|> +Protein complex preparation and docking calculations: The structure comprising HSP90β- HSP70(2)- HOP proteins was developed using the molecular comparative modeling technique, employing Modeller v10.4, the Modeller Python script90, and experimental template structures (PDB codes: 7KW7, 8EOB)10,91. The cryo- EM structure of human HSP90β (8EOB) served as the basis for obtaining coordinates for HSP90β (protopers A and B) in the developing model. To construct the assembly involving HSP70 and HOP, we utilized the sequences and atomic cryo- EM structure from the HSP90- HSP70- HOP- GR (7KW7) template. As these structures lacked certain residues, including those in the charged linker (Glu222 - Lys273), we incorporated them as intrinsic loops during computational processing. The target sequence for each HSP90β protomer was extracted from Uniprot ID: P08238. After model generation, we selected the optimal model based on the Discrete Optimized Protein Energy (DOPE) score. The final model included full- length HSP90 (excluding a ten- residue N- terminal disordered segment). For HOP and HSP70, we maintained the sequences provided in PDB: 7KW7. The validated model, equipped with co- crystal ligands on each HSP90β protomer, was imported into Maestro v13.3 (Schrödinger LLC, 2022- 3). Mutagenesis was performed to substitute Ser226/Ser255 with phosphomimetic conditions (Glu226/Glu255) and de- phosphorylated conditions (Ala226/Ala255) in both protomers of HSP90β. The preparation of all complexes utilized the Protein Preparation Wizard, a module for creating reliable, all- atom protein models. This involved restraining the assignment of bonds and bond orders, adding hydrogens, correcting formal charges, and filling missing side chains. Pre- processing steps included generating hetero states, H- bond assignment, and energy minimization using the Optimized Potentials for Liquid Simulations (OPLS3) force field, with a maximum root- mean- square deviation (RMSD) of 0.30 Å, employing the molecular mechanics engine Impact v9.6. Essential water atoms within 5 Å of the binding pocket were retained, while remaining waters were deleted. Structural refinement at neutral pH was carried out through the Epik v6.1 module92. The final refined structure served as the receptor for docking simulations. Ligands, such as ATP and ADP, underwent preparation with the LigPrep node, where the optimized ligand minimization algorithm yielded more conformers with numerous rotatable bonds, enhanced efficiency, and robustness. Different possible protonation states based on machine learning were generated, and ligand structures were minimized at pH values within the range of 7.0 and +/- 2.0, to guide the selection of protonation states on acidic/basic groups on ligands consistent with their pKa values, using the OPLS_3 force field, Premin, Truncated Newton Conjugate Gradient (TNCG), and Epik v6.1 nodes. Subsequently, a receptor grid was generated around the co- crystal ligand with default parameters. Docking experiments were executed on the nucleotide binding pockets of both protomers using the XP (extra- precision) Glide program (Glide v9.6) and Prime- MMGBSA (molecular mechanics generalized born surface area) modules, respectively. The best poses in the resulting docked complexes served as the initial complex structure for MD simulations93. Molecular dynamics simulations: The pentameric assemblies were prepared in the following combinations: 2xHSP90(Ser226Ser255)- 2xHSP70- HOP, 2xHSP90(Glu226Glu255)- 2xHSP70- HOP-, 2xHSP90(Ala226Ala255)- 2xHSP70- HOP, each bound to either ATP or ADP. These complexes underwent individual 100 ns all- atomic molecular dynamics simulations using the Desmond v7.1 module of the MAESTRO Suite from Schrodinger (www.schrodinger.com). Before simulations, each assembly was built by embedding water molecules, adjusting temperature and pressure closer to the physiological environment through the OPLS3 force field and TIP4PEW water model. The system was neutralized with counter ions (Na+/Cl-) to balance the net charge in the simulation box. The particle mesh Ewald (PME) method94 was used for electrostatics with a 10 Å cut- off for Lennard- Jones interactions, and the SHAKE algorithm95 was applied to restrict the motion of all covalent bonds involving hydrogen atoms. The complex system underwent a six- step relaxation protocol before productive MD simulations. The solvated system was initially minimized with solute restraints and then without solute restraints, utilizing a hybrid method of steepest descent and the LBFGS (limited memory Broyden- Fletcher- Goldfarb- Shanno) algorithm96,97. The energy- minimized system underwent a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 90, 883, 378]]<|/det|> +brief 12 ps simulation within the NVT canonical ensemble at a temperature of \(10\mathrm{K}\) , followed by a similar simulation in the isothermal- isobaric (NPT) ensemble at \(10\mathrm{K}\) , with restraints on nonhydrogen solute atoms. Subsequently, the system was simulated for \(24\mathrm{ps}\) in the NPT ensemble at \(300\mathrm{K}\) with limited restraints on nonhydrogen solute atoms. In the final equilibration step, the system was simulated for \(24\mathrm{ps}\) in the NPT ensemble at \(300\mathrm{K}\) without constraints to reach an equilibrium state. The minimized and equilibrated system without restraints was then subjected to a \(100\mathrm{ns}\) NPT simulation for production. The temperatures and pressures of the system in the initial simulations were controlled by Berendsen thermostats and barostats, respectively \(^{96,97}\) . The relaxed system underwent productive simulations using the Nose'- Hoover thermostat at \(300\mathrm{K}\) and the Martyna- Tobias- Klein barostat at \(1.01325\mathrm{bar}\) pressure. Atomic- coordinate data for each receptor- ligand complex and system energies were recorded every 1000 ps. Residue- pair correlations were calculated along the MD trajectory using the script trj_essential_dynamics.py available in the Schrödinger suite. Additionally, the unexplored cryptic motions, distribution of secondary structural elements, and the array of protein folding in intrinsic disordered regions were thoroughly examined using the extracted meta- trajectory data from 1000 trajectories throughout the simulation period. The secondary structure elements (SSE) index was computed to illustrate the percentage occurrence of alpha- helices \((\alpha)\) and beta- strands \((\beta)\) during the simulation period, delineated by residue. + +<|ref|>sub_title<|/ref|><|det|>[[115, 389, 460, 406]]<|/det|> +## Immunoprecipitation of mCherry-HSP90 + +<|ref|>text<|/ref|><|det|>[[115, 405, 882, 504]]<|/det|> +RFP Selector (NanoTag #N0410) resins were equilibrated with lysis buffer to prepare the resin. Cell lysates were then added and incubated with the resins at \(4^{\circ}\mathrm{C}\) with head over tail rotation for \(90\mathrm{min}\) . Following incubation, resins were washed twice with lysis buffer and once with PBS before elution with \(2\times\) sample buffer and incubation at \(95^{\circ}\mathrm{C}\) for \(5\mathrm{min}\) . Eluents were then run on a \(12.5\%\) SDS- PAGE. For SILAC samples, heavy and light replicates were immunoprecipitated separately, before combined and separated by SDS gel electrophoresis. + +<|ref|>sub_title<|/ref|><|det|>[[115, 515, 312, 530]]<|/det|> +## Chemical cross-linking + +<|ref|>text<|/ref|><|det|>[[115, 530, 882, 627]]<|/det|> +Cell lysates, with a concentration of approximately \(3\mu \mathrm{g}\mu \mathrm{L}^{- 1}\) , underwent cross- linking using disuccinimidyl suberate (DSS; ThermoFisher# 21655) at a concentration of \(2.5\mathrm{mM}\) . This process occurred at room temperature for \(1\mathrm{h}\) . To terminate the reaction, \(0.8\mathrm{M}\mathrm{NH_4OH}\) (Sigma# 09859) was added, reaching a final concentration of \(25\mathrm{mM}\) , and incubated at room temperature for an additional \(15\mathrm{min}\) . The lysates were clarified through two rounds of centrifugation at \(16,200\times \mathrm{g}\) for \(15\mathrm{min}\) at \(4^{\circ}\mathrm{C}\) before proceeding to separate HSP90 using immobilized PU- H71 or GA. + +<|ref|>sub_title<|/ref|><|det|>[[115, 640, 398, 656]]<|/det|> +## SDS-PAGE and trypsin digestion + +<|ref|>text<|/ref|><|det|>[[113, 657, 882, 899]]<|/det|> +After elution from PU- or GA- beads, samples were loaded into \(12.5\%\) SDS- PAGE gel for separation. The entire lanes were cut into 10- 15 bands and processed by in- gel digestion as described previously \(^{19}\) . Briefly, gel bands were cut into small cubes, washed with \(25\mathrm{mM}\) \(\mathrm{NH_4HCO_3 / 50\%}\) acetonitrile, reduced with \(10\mathrm{mM}\) DTT (in \(25\mathrm{mM}\mathrm{NH_4HCO_3}\) ) at \(56^{\circ}\mathrm{C}\) for \(1\mathrm{h}\) , alkylated with \(55\mathrm{mM}\) iodoacetamide (in \(25\mathrm{mM}\mathrm{NH_4HCO_3}\) ) in darkness for \(45\mathrm{min}\) . Gel pieces were washed again with \(25\mathrm{mM}\mathrm{NH_4HCO_3 / 50\%}\) acetonitrile and evaporated in a speed- vac to complete dryness. The dried gel samples were proteolyzed using varied volumes of trypsin (0.6- \(1.0\mu \mathrm{g}\) depending on the intensity of the gel bands) at \(37^{\circ}\mathrm{C}\) for \(4\mathrm{h}\) , before the extraction of tryptic peptides by \(50\%\) acetonitrile/ \(2\%\) acetic acid. Tryptic peptide mixture was concentrated down to \(\sim 7\mu \mathrm{L}\) before LC- MS/MS analysis. For validation experiments in Figure 3d,e, chemical precipitation and sample preparation for PTM analyses were performed as follows. For in- cell YK- B bait affinity purification, cells were plated in \(10\mathrm{cm}\) plates at \(6\times 10^{6}\) cells per plate and treated with \(50\mu \mathrm{M}\) YK5- B for \(4\mathrm{h}\) . Cells were next collected and lysed in \(20\mathrm{mM}\) Tris pH 7.4, \(150\mathrm{mM}\) NaCl and \(1\%\) NP40 buffer. Five hundred micrograms (500 \(\mu \mathrm{g}\) ) of total protein were incubated with streptavidin agarose beads (ThermoFisher Scientific) for \(1\mathrm{h}\) and beads were washed with \(20\mathrm{mM}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 90, 883, 362]]<|/det|> +1 Tris pH 7.4, 100 mM NaCl and \(0.1\%\) NP40 buffer (washing buffer). For in- lysate YK5- B bait affinity 2 purification, cells were lysed in the above- mentioned lysis buffer. Streptavidin agarose beads 3 were incubated with \(50\mu \mathrm{M}\) YK5- biotin for \(1\mathrm{h}\) , washed and added to \(500\mu \mathrm{g}\) of total protein and 4 incubated overnight. The beads were then washed with the washing buffer. For PU- H71 beads 5 pull- down, \(250\mu \mathrm{g}\) of the same protein lysates were incubated with \(40\mu \mathrm{l}\) PU- H71 beads for \(3\mathrm{h}\) 6 and washed. The samples were applied onto SDS- PAGE. Resulting gels were washed 3 times in 7 distilled deionized \(\mathsf{H}_2\mathsf{O}\) for \(15\mathrm{min}\) each and visualized by staining overnight with Simply Blue 8 Coomassie stain (Thermo Fisher Scientific). Stained protein gel regions were typically excised 9 into 6 gel sections per gel lane, and completely destained as described19. In- gel digestion was 10 performed overnight with MS- grade trypsin (Trypsin Gold, Mass spectrometry grade, Promega) 11 at \(5\mathrm{ng}\mathrm{mL}^{- 1}\) in \(50\mathrm{mM}\) \(\mathrm{NH_4HCO_3}\) digestion buffer and incubation at \(37^{\circ}C\) . After acidification with 12 \(10\%\) formic acid (final concentration of \(0.5 - 1\%\) formic acid), peptides were extracted with \(5\%\) 13 formic acid / \(50\%\) acetonitrile and resulting peptides were desalted using hand- packed, reversed 14 phase Empore C18 Extraction Disks (3M, Cat#3M2215), following an established method98. Each 15 of the 6 sections per sample, per gel lane, were excised and separately digested in- gel, at the 16 same time, using the same batch and amount of trypsin. The peptides from each of these gel 17 sections were purified and analyzed by nano- LC- MS/MS separately. + +<|ref|>sub_title<|/ref|><|det|>[[110, 374, 686, 390]]<|/det|> +## LC-MS data acquisition, protein and phosphopeptide identification + +<|ref|>text<|/ref|><|det|>[[110, 390, 883, 904]]<|/det|> +LC- MS data acquisition, protein and phosphopeptide identification Briefly, the digestion mixtures were injected into an Dionex Ultimate 3000 RSLCname UHPLC system (Dionex Corporation, Sunnyvale, CA), and separated by a \(75\mu \mathrm{m}\times 25\mathrm{cm}\) PepMap RSLC column (100 A, \(2\mu \mathrm{m}\) ) at a flow rate of \(\sim 450\) nL min \(^{- 1}\) . The eluant was connected directly to a nanoelectrospray ionization source of an LTQ Orbitrap XL mass spectrometer (Thermo Scientific, Waltham, MA). LC- MS data were acquired in a data- dependent acquisition mode, cycling between a MS scan (m/z 315- 2,000) acquired in the Orbitrap, followed by low- energy CID analysis on three most intense multiply charged precursors acquired in the linear ion trap. The centroided peak lists of the CID spectra were generated using PAVA searched against a database that is consisted of the Swiss- Prot protein database using Batch- Tag, a program of the University of California San Francisco Protein Prospector software, version 5.9.2. For identification of proteins in pull- down experiments, a precursor mass tolerance of \(15\mathrm{ppm}\) and a fragment mass tolerance of \(0.5\mathrm{Da}\) were used for protein database searches (trypsin as enzyme; 1 miscleavage; carbamidomethyl (C) as constant modification; acetyl (protein N- term), acetyl+oxidation (protein N- term), Met- loss (protein N- term), Met- loss+acetyl (protein N- term, oxidation (M)). Protein hits were reported with a Protein Prospector protein score \(\geq 22\) , a protein discriminant score \(\geq 0.0\) and a peptide expectation value \(\leq 0.01^{99}\) . This set of thresholds of protein identification parameters does not return any substantial false positive protein hits from the randomized half of the concatenated database. After protein identification, PTM search was carried out with S/T/Y phosphorylation included in variable modifications among the identified proteins. A threshold of SLIP score \(>6\) was imposed for false phosphorylation site assignment \(< 5\%^{100}\) . Identified phosphopeptides were manually inspected by confirming the quality of MS/MS spectra and mass accuracy. Cross- linked peptides were identified using an integrated module in Protein Prospector, based on a bioinformation strategy developed in the UCSF Mass Spectrometry Facility \(^{41,42,101,102}\) . Key cross- linked peptides were identified and confirmed by manually examining the returned spectrum, peptide scores, mass accuracy and absence from uncross- linked samples. For validation experiments in Figure 3e, MS data acquisition and processing were performed as follows. Desalted peptides were concentrated to a very small droplet by vacuum centrifugation and reconstituted in \(10\mathrm{mL}0.1\%\) formic acid in \(\mathsf{H}_2\mathsf{O}\) . Approximately \(90\%\) of the peptides were analyzed by nano- LC- MS/MS). A Q Exactive HF mass spectrometer was coupled directly to an EASY- nLC 1000 (Thermo Fisher Scientific) equipped with a self- packed \(75\mathrm{mm}\times 18\mathrm{cm}\) reverse phase column (ReproSil- Pur C18, 3M, Dr. Maisch GmbH, Germany) for peptide separation. Analytical column temperature was maintained at \(50^{\circ}\mathrm{C}\) by a column oven (Sonation GmbH, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 90, 884, 220]]<|/det|> +1 Germany). Peptides were eluted with a \(3 - 40\%\) acetonitrile gradient over \(60\mathrm{min}\) at a flow rate of 2 \(250~\mathrm{nL}\min^{- 1}\) . The mass spectrometer was operated in DDA mode with survey scans acquired at 3 a resolution of 120,000 (at m/z 200) over a scan range of 300- 1750 m/z. Up to 15 of the most 4 abundant precursors from the survey scan were selected with an isolation window of 1.6 Th for 5 fragmentation by higher- energy collisional dissociation with normalized collision energy (NCE) of 6 27. The maximum injection time for the survey and MS/MS scans was \(20\mathrm{ms}\) and \(60\mathrm{ms}\) 7 respectively; the ion target value (Automatic Gain Control) for survey and MS/MS scan modes 8 was set to \(3\mathrm{e}^{6}\) and \(1\mathrm{e}^{6}\) , respectively. + +<|ref|>sub_title<|/ref|><|det|>[[115, 233, 621, 250]]<|/det|> +## Quantitation of phosphopeptides and crosslinked peptides + +<|ref|>text<|/ref|><|det|>[[112, 250, 883, 525]]<|/det|> +Manually- confirmed, high- confidence phosphopeptides and cross- linked peptides were quantified by the peak height of the extracted ion chromatogram of each peptide monoisotope mass. For phosphopeptide quantitation, the protein loading of HSP90 peptides in lysates or from pull- down experiments was normalize to a representative, isoform specific tryptic peptide, ELISNSSDALDK for HSP90α and ELISNASDALDK for HSP90β. Phosphopeptides with different charge state or miscleavages were considered as different measurements for quantitation of each phosphosite. To assess the relative phosphorylation levels of different phosphosites in cancer cells and non- transformed cells, the ion intensity values of all phosphopeptides for each phosphosite were summed. The average ion intensities of each phosphosite between cancer and non- transformed cells were compared. Cross- linked peptides were identified using an integrated module in Protein Prospector, based on a bioinformation strategy developed in the UCSF Mass Spectrometry Facility41,42,101,102. Key cross- linked peptides were identified and confirmed by manually examining the returned spectrum, peptide scores, mass accuracy and absence from uncross- linked samples. Cross- linked peptides identified from various samples were pooled together, and the cross- linking propensity of each cross- linked peptide was assessed by its cross- linking percentage 43. Cross- linking percentage for each peptide pair was calculated using the following formula: + +<|ref|>equation<|/ref|><|det|>[[198, 523, 775, 560]]<|/det|> +\[\% \mathrm{XL} = \frac{\mathrm{Cross} - \mathrm{linked~peptide~Peak~Height~(PH)}}{\sum \mathrm{Cross} - \mathrm{linked~peptide~PH} + \mathrm{Dead} - \mathrm{end~XL}1\mathrm{PH} + \mathrm{Dead} - \mathrm{end~XL}2\mathrm{PH}}\] + +<|ref|>text<|/ref|><|det|>[[113, 577, 846, 627]]<|/det|> +where the peak height is the apex peak height in LC- MS/MS runs. Dead- end XLs are cross- linker modified peptides where only one NHS- ester function of DSS is cross- linked to a Lys residue and the other NHS- ester function is hydrolyzed by water. + +<|ref|>sub_title<|/ref|><|det|>[[115, 640, 291, 656]]<|/det|> +## Homology modeling + +<|ref|>text<|/ref|><|det|>[[113, 656, 882, 737]]<|/det|> +The mouse HSP90 sequences for both alpha and beta isoforms were aligned and the models were built using an open conformation template (PDB: 2IOQ), a closed conformation template (PDB: 2CG9), and an HSP70- bound model (derived from a cryo- EM structure of HSP90•HSP70•GR complex10 using UCSF Modeller. Structural visualization and analysis were carried out using UCSF Chimera. + +<|ref|>sub_title<|/ref|><|det|>[[115, 749, 364, 765]]<|/det|> +## Statistics and reproducibility + +<|ref|>text<|/ref|><|det|>[[113, 765, 882, 877]]<|/det|> +Unless as specified above under Protein identification and Bioinformatics analyses, statistics were performed, and graphs were generated, using Prism 10 software (GraphPad). Statistical significance was determined using Student's t- Tests or ANOVA, as indicated. Means and standard errors were reported for all results unless otherwise specified. Effects achieving \(95\%\) confidence interval (i.e., \(p < 0.05\) ) were interpreted as statistically significant. No statistical methods were used to pre- determine sample sizes, but these are similar to those generally employed in the field. No samples were excluded from any analysis unless explicitly stated. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 90, 883, 123]]<|/det|> +1 Reporting summary. Further information on research design is available in the Nature Research 2 Reporting Summary linked to this article. + +<|ref|>sub_title<|/ref|><|det|>[[70, 133, 296, 150]]<|/det|> +## 3 DATA AVAILABILITY + +<|ref|>text<|/ref|><|det|>[[70, 163, 883, 325]]<|/det|> +4 The source data underlying all main and supplementary figures are provided with this paper as a 5 Source Data file. Datasets and analytics associated with epicarperomics and proteomics 6 analyses are available in the Supplementary Information as Supplementary Data 1 through 6. LC- 7 MS data (i.e., proteomics and epicarperomics raw mass spectrometry data, peak lists, and 8 results) that support the findings of this study are deposited to the ProteomeXchange Consortium 9 via the PRIDE partner repository with the dataset identifier PXD050251 [Reviewer account 10 details: Username: reviewer_pxd050251@ebi.ac.uk; Password: TmZMDQ0W]. Protein 11 sequences (FASTA files) were obtained from UniProt (https://www.uniprot.org/). MD simulations 12 data were deposited in Zenodo [https://doi.org/ 10.5281/zenodo.10800912]103. Source data are 13 provided with this paper. + +<|ref|>sub_title<|/ref|><|det|>[[70, 350, 297, 367]]<|/det|> +## 15 CODE AVAILABILITY + +<|ref|>text<|/ref|><|det|>[[70, 379, 450, 396]]<|/det|> +16 No code was developed during this study. + +<|ref|>sub_title<|/ref|><|det|>[[70, 406, 240, 422]]<|/det|> +## 17 REFERENCES + +<|ref|>text<|/ref|><|det|>[[66, 435, 884, 902]]<|/det|> +18 1. Bludau, I. & Aebersold, R. Proteomic and interactomic insights into the molecular basis of 19 cell functional diversity. Nat. Rev. Mol. Cell Biol. 21, 327- 340 (2020). 20 2. Nussinov, R., Tsai, C. J. & Jang, H. Protein ensembles link genotype to phenotype. PLoS 21 Comput. Biol. 15, e1006648 (2019). 22 3. Chiosis, G., Digwal, C. S., Trepel, J. B. & Neckers, L. Structural and functional complexity 23 of HSP90 in cellular homeostasis and disease. Nat. Rev. Mol. Cell Biol. 24, 797- 815 24 (2023). 25 4. Schopf, F. 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Glucocorticoid receptor function regulated by coordinated action of the Hsp90 and Hsp70 chaperone cycles. Cell 157, 1685-1697 (2014). + +<|ref|>sub_title<|/ref|><|det|>[[115, 565, 344, 583]]<|/det|> +## ACKNOWLEDGEMENTS + +<|ref|>text<|/ref|><|det|>[[113, 594, 861, 771]]<|/det|> +This work was supported by the NIH (R01 CA172546, P01 CA186866, R56 AG061869, R01 HD09783, R01 AG067598, R01 AG074004, R01 AG072599, R56 AG072599, RF1 AG071805, P30 CA08748, P20 GM113131), NSF GRFP (LB), UNH Hamel Center (HTN), UNH Graduate School. G.Colombo acknowledges funding from Fondazione AIRC (Associazione Italiana Ricerca Sul Cancro) under IG 2022 - ID. 27139. We thank Dr. David A. Agard for providing the model of HSP90\\*HSP70\\*GR complex derived from a cryo- EM density map104, Thomas G. Fazzio (U Mass Med School) for the E14 cells, Dr. Lorenz Studer for the human iPSCs and iPSC- derived neurons, and Dana Levasseur (U Iowa) for the ZHBTC4 cells. We thank the Molecular Cytology Core, the Antitumor Assessment Core and our colleagues in the Departments of Surgery and Medicine at Memorial Sloan Kettering for providing the biospecimens for research. + +<|ref|>sub_title<|/ref|><|det|>[[115, 784, 348, 800]]<|/det|> +## AUTHOR CONTRIBUTIONS + +<|ref|>text<|/ref|><|det|>[[113, 812, 881, 892]]<|/det|> +S.W.M performed the MS studies and biochemical and functional studies in mouse ESCs. T.R. performed the biochemical and functional validation studies in human cells. C.P. performed the MD simulations. H.T.N and D.T.T. performed MS studies of cargos and cross- linking experiments. S.S. performed chemical synthesis, compound identity and purity evaluations for the epicathepore probes. L.B. and N.Y. generated ESC culture samples and MS sample + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[72, 88, 884, 203]]<|/det|> +1 preparation. A.R., P.P., S.J., S.C., S.B. and H.E-B. performed experiments. C.S.D. provided 2 reagents. V.M., C.K., J.L., P.Y., E.deS., A.C., S.M., and M.A. were involved in various aspects of 3 biospecimen handling, including recruitment, procurement, or processing at different stages from 4 surgery to delivery to the laboratory. R.J.C. and P.R.B. provided Protein Prospector and 5 supported data analysis. F.C., T.A.N., G.Chiosis and A.L.B. participated in the design and 6 analysis of various experiments. H.E-B., A.R., S.D.G., G.Colombo and T.A.N. assisted with 7 manuscript writing and data analysis. F.C. and G.C. developed the concept and wrote the paper. + +<|ref|>sub_title<|/ref|><|det|>[[72, 213, 333, 231]]<|/det|> +## 8 COMPETING INTERESTS + +<|ref|>text<|/ref|><|det|>[[70, 242, 883, 293]]<|/det|> +9 Memorial Sloan Kettering Cancer Center holds the intellectual rights to the epichepaterome 10 portfolio. G.C., A.R. and S.S. are inventors on the licensed intellectual property. All other authors 11 declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[70, 330, 410, 348]]<|/det|> +## 13 SUPPLEMENTARY INFORMATION + +<|ref|>text<|/ref|><|det|>[[70, 360, 420, 378]]<|/det|> +14 Supplementary Figures 1 through 9 + +<|ref|>text<|/ref|><|det|>[[70, 388, 748, 407]]<|/det|> +15 Supplementary Note 1. Synthesis and characterization of the chemical probes + +<|ref|>text<|/ref|><|det|>[[70, 416, 844, 436]]<|/det|> +16 Supplementary Note 2. Full nucleotide sequence of the HSP90 plasmids in FASTA format + +<|ref|>text<|/ref|><|det|>[[70, 448, 88, 462]]<|/det|> +17 + +<|ref|>text<|/ref|><|det|>[[70, 474, 866, 530]]<|/det|> +18 Supplementary Data 1 contains LC-MS data and data analysis of PU-H71 and GA pull-down 19 samples as well as 300 kDa band sliced from native-PAGE, associated with Figure 1d,1e, and 20 Supplementary Figure 2c,3. + +<|ref|>text<|/ref|><|det|>[[70, 541, 860, 596]]<|/det|> +21 Supplementary Data 2 contains LC-MS data and data analysis for the identification and 22 quantitation of HSP90 cross-linked peptides in PU-H71 or GA pull-down samples, associated 23 with Figure 2b. + +<|ref|>text<|/ref|><|det|>[[70, 608, 857, 645]]<|/det|> +24 Supplementary Data 3 contains LC-MS data analysis of the HSP90 band from PU-H71 pull- 25 down or lysate samples, associated with Figure 3b,3c. + +<|ref|>text<|/ref|><|det|>[[70, 657, 870, 712]]<|/det|> +26 Supplementary Data 4 contains LC-MS data for SILAC quantitation of mCherry-HSP90 EE or 27 AA mutant pull-down experiments in three replicates, associated with Figure 6b,6c, and 8c. 28 Normalized median intensity SILAC ratios were used for quantitation. + +<|ref|>text<|/ref|><|det|>[[70, 725, 874, 780]]<|/det|> +29 Supplementary Data 5 contains LC-MS data analysis for SILAC quantitation of 30 phosphopeptides from WT mcherry-HSP90 in ES or differentiated trophoblast state, associated 31 with Figure 8b. + +<|ref|>text<|/ref|><|det|>[[70, 793, 880, 848]]<|/det|> +32 Supplementary Data 6 contains LC-MS data analysis for label-free quantitation of 33 phosphopeptides from the HSP90 band of PU-H71 or YK55 pull-down samples from a variety of 34 cell lines, associated with Figure 3d,3e. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[75, 72, 936, 666]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[35, 700, 955, 911]]<|/det|> +
Figure 1. Embryonic stem cells and cancer cells share compositionally similar epipheroperomes. a Schematic illustrating the biochemical and functional distinctions between epipheroperomes, defined as long-lasting heterooligomeric assemblies composed of tightly associated chaperones and co-chaperones, and traditional chaperones. Unlike chaperones, which assist in protein folding or assembly, epipheroperomes sequester proteins, reshaping protein-protein interactions, and consequently altering cellular phenotypes. The schematic also outlines key principles for the use of PU-probes in epipheroperome analysis. b Detection of epipheroperome components (chaperones and co-chaperones) through SDS-PAGE (bottom, total protein levels) and native-PAGE (top), followed by immunoblotting. See also Supplementary Fig. 1. c Visualization of HSP90 in epipheroperomes using the PU-TCO click probe. See also Supplementary Fig. 2. Gel images are representative of three independent experiments. d Epipheroperome constituent chaperones and co-chaperones identified through mass spectrometry analyses of PU-beads cargo. Representative data of two independent experiments. See Supplementary Fig. 3 for the GA-cargo. e Illustration of an isobaric, discriminant peptide pair from ESC lysate samples and HSP90 captured by PU- and GA-beads. Representative data of two independent experiments. f Schematic summary. Both cancer cells and pluripotent stem cells harbor epipheroperomes. These epipheroperomes undergo disassembly during differentiation processes. Source data are provided in Supplementary Data 1 and in Source data file.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[95, 35, 880, 768]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[30, 10, 120, 30]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[28, 792, 969, 962]]<|/det|> +Figure 2. An enrichment of the closed-like conformation of HSP90 favors epichaperomes formation. a Experiment outline. b Plot comparing cross-linking propensity of Lys residues in HSP90 bound to PU-H71 or GA. Average cross-linking percentage of PU-H71 (x-axis) and GA (y-axis) bound HSP90 cross-linked pairs are shown. Blue circles represent pairs with similar cross-linking propensity (dotted line with a slope of 1). Orange points indicate outlier cross-linked peptides, with cross-linked Lys residues 8 amino acids away and the cross-linking percentage difference \(\geq 1.5\) standard deviation of replicates. Solid orange circles represent \(\mathrm{p} \leq 0.05\) , \(\mathrm{n} = 3\) replicate measurements. c Homology model illustrating the HSP90 dimer in the open conformation (template PDB: 2IOQ), favored by geldanamycin (GA), and the closed conformation (template PDB: 2CG9), favored by PU-H71. One HSP90 protomer is colored to indicate the N-terminal domain (NTD, light blue), the middle domain (MD, dark blue), and the C-terminal domain (CTD, green). Cross-linked residues are labeled by pink dots and connected by red dashed lines. d NTD structures of PU-H71 (top, PDB: 2FWZ) and GA (bottom, PDB: 1YET)-bound HSP90. Source data are provided as Supplementary Data 2. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[70, 40, 888, 817]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[33, 832, 951, 972]]<|/det|> +
Figure 3. Phosphorylation of key residues located in the charged linker supports HSP90 incorporation into epicarpemores. a Experiment outline and expected outcomes. b Tandem MS spectra of HSP90 Ser226 (bottom) and Ser255 (top) phosphorylated peptides are presented, supporting the sequence and phosphorylation site identification. c Comparison of the extracted ion chromatogram of HSP90 Ser255 phosphopeptide in the PU-bead cargo (red trace, left panel) and ESC lysate (black trace, left panel) with a representative unmodified tryptic peptide in the PU-bead cargo (blue trace, right panel) and ESC lysate (black trace, right panel). d Ion intensity values of all phosphopeptides and the ratio of mean peptide intensity for each phosphosite in the samples described in panel a (n = 4 Ca and n = 2, NT). e Ratio of individual peptide intensity for each phosphosite in the samples described in the schematic (S255 n = 5; S226 n = 4; S263 n = 8; S231 n = 5). Source data are provided as Source Data file and as Supplementary Data 3,6.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[50, 60, 911, 666]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[36, 720, 951, 950]]<|/det|> +
Figure 4. Phosphorylation of key residues located in the charged linker of HSP90 leads to a conformational shift in the linker, exposing the middle domain of the protein. A Model of the HSP90-HSP90-HSP70-HSP70-HOP assembly used for the molecular dynamics simulations. A and B, protomers A and B, respectively. b Protein secondary structure elements (SSE) like alpha-helices and beta-strands of the charged linker of protomer A of ATP-bound HSP90 monitored throughout the MD simulation. WT (HSP90 S226/S255), phosphomimetic (HSP90 S226E/S255E) and non-phosphorylatable (HSP90 S226A/S255A) mutants were analyzed. The plot on the left reports SSE distribution by residue index throughout the charged linker and the plot on the right monitors each residue and its SSE assignment over time. Schematic illustrating the primary structure of the full-length HSP90 with color-coded domains is also shown: NTD, N-terminal domain; MD, middle domain and CTD, C-terminal domain. The charged linker (CL) and the location of the two key serine residues are also shown (top inset). The gray bar indicates the CL segment encompassing residues 218 to 232. c Cartoon representation of ATP-bound HSP90 protomer A in assemblies containing the phosphomimetic (HSP90 S226E/S255E) or the non-phosphorylatable (HSP90 S226A/S255A) mutants is shown. Green, reference trajectory; gray, representative trajectories of \(n = 1,000\) . The inset illustrates the surfaces available for the interaction between HSP90 A and HSP70 A when the CL is in the 'up' conformation. A blue arrow indicates the location of the key beta-strand in the charged linker. See also Supplementary Figs. 5 and 6.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[52, 54, 911, 737]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[38, 750, 933, 908]]<|/det|> +
Figure 5. Phosphorylation of key residues located in the charged linker of HSP90 facilitates assembly motions conducive to epichepaterome core formation. A Calculated dynamic cross-correlation matrix of Ca atoms around their mean positions for 100 ns molecular dynamics simulations. ATP-bound WT (HSP90 S226/ S255), phosphoimimetic (HSP90 S226E/S255E) and non-phosphorylatable (HSP90 S226A/S255A) mutantcontaining HSP90-HSP90-HSP70-HSP70-HOP assemblies were analyzed. The cartoon below captures the key motions among the different domains of the individual assembly components. Extents of correlated motions and anti-correlated motions are color-coded from blue to red, which represent positive and negative correlations, respectively. The assembly contains two full-length HSP90beta proteins (protomer A and protomer B). The two HSP70 proteins (HSP70 A and HSP70 B) and the HOP protein are of sizes reported, and as per the constructs used in 7KW7. b Cartoon showing assemblies that are preferentially formed when the HSP90 charged linker is either phosphorylated (as in the EE mutant) or not phosphorylated (as in the WT protein).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[110, 264, 911, 560]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[30, 5, 118, 26]]<|/det|> +
Figure 6
+ +<|ref|>text<|/ref|><|det|>[[31, 760, 944, 875]]<|/det|> +Figure 6. Immunopurification reveals increased presence of epicarpemore- specific co- chaperones in phosphomimetic HSP90 complexes compared to non- phosphorylatable complexes. a Experiment outline and outcomes. b Representative spectra (n = 3 independent experiment) of proteins co- purified with the phosphomimetic HSP90S226E,S255E (EE, blue) and non- phosphorylatable HSP90S226A,S255A (AA, red) mutants. c Heatmap showing the identity of chaperone and co- chaperones identified as epicarpemore components in cancer cells (as per Rodina et al. Nature 2016) and enriched in the affinity purified HSP90S226E,S255E mutant. Scale bar, log2 average SILAC values EE/AA (n = 3). Source data are provided as a Source Data file and as Supplementary Data 4. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[52, 75, 888, 666]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[33, 733, 965, 890]]<|/det|> +
Figure 7. Phosphorylation of key residues located in the charged linker supports HSP90 incorporation into epicheporemes. a Overview of the experimental design and expected outcomes. b Analysis of transfection efficacy in cells transfected with HSP90β mutants, as indicated in panel a. c Detection of epicheporeme components (chaperones and co-chaperones) through SDS-PAGE (bottom, total protein levels) and native-PAGE (top), followed by immunoblotting. Blue brackets indicate the approximate position of epicheporeme-incorporated chaperones. Data are presented as mean ± s.e.m., \(n = 3\) , one-way ANOVA with Sidak's post-hoc, EE vs AA. d Visualization of HSP90 in epicheporemes using the PU-TCO probe clicked to Cy5 (left) and the mCherry tag (middle). Right, merged images. MWM, molecular weight marker. e Detection and quantification of epicheporeme components through PU-beads capture as indicated in panel a. Protein amount loaded for 'Input' represents 2% of the protein amount incubated with the beads. Data are presented as mean ± s.e.m., \(n = 3\) , unpaired two-tailed t-test. Gel images are representative of three independent experiments. Source data are provided as Source data file.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[91, 102, 875, 666]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 708, 962, 933]]<|/det|> +
Figure 8. Phosphorylation of key residues located in the HSP90 charged linker favors ESC proliferation and self-renewal potential. a ESC proliferation at 60 h post-transfection in E14 cells transfected with either the phosphomimetic HSP90βS226E,S255E (EE) or the nonphosphorylatable HSP90S226A,S255A (AA) mutant. Medium (1x) or high (2x) plasmid concentrations were employed. Data are presented as mean ± s.e.m., \(n = 6\) , one-way ANOVA with Sidak's post-hoc, EE vs AA. b Representative spectra ( \(n = 3\) independent experiments) of phosphopeptides, S255P (left) and S226P (right), and a representative unmodified tryptic peptide (middle) in mCherry-tagged WT HSP90β affinity-purified from ESC (red) or differentiated trophoblast (black) cells. c Representative spectra ( \(n = 3\) independent experiments) of a tryptic peptide from Oct4 protein co-purified from ESCs labeled with heavy or light isotope lysine and arginine expressing either the phosphomimetic (EE) or the nonphosphorylatable (AA) HSP90 mutant. Quantitative analysis via mass spectrometry (MS) to determine protein abundance is shown. d Overview of the experimental design and expected outcomes (panels e,f). e,f Detection and quantification of Oct4 protein expressed in cells transfected with the indicated HSP90 mutants or vector control (panel e) and sequestered into the epicatherome platforms (identified through PU-beads capture, panel f). (e) Data are presented as mean ± s.e.m., \(n = 5\) AA, \(n = 5\) EE, \(n = 3\) WT, \(n = 3\) empty vector, one-way ANOVA with Dunnett's post-hoc, EE vs AA, WT vs AA, empty vector vs AA. (f) Data are presented as mean ± s.e.m., \(n = 3\) , unpaired two-tailed t-test. Source data are provided as Source Data file and Supplementary Data 5.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[90, 100, 800, 549]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[30, 7, 118, 28]]<|/det|> +
Figure 9
+ +<|ref|>text<|/ref|><|det|>[[28, 732, 949, 860]]<|/det|> +Figure 9. Regulation of epicarporene processes in ESC and cancer cells hinges on the specific phosphorylation events occurring at key residues within HSP90's charged linker. A overview of the experimental design and expected outcomes. b Detection and quantification of proteins involved in transducing signaling events that lead to cell proliferation, survival, and protein synthesis control. See Supplementary Fig. 9 for total protein levels and levels sequestered into epicarporenes. Data are presented as mean ± s.e.m., p- S6 n = 8; p- mTOR n = 3; p- MEK1/2 n = 6; p- AKT n = 5, unpaired two- tailed t- test. c Confocal microscopy shows morphological differences between the cells transfected with either the AA or the EE HSP90 mutant. Micrographs are representative of 96 cells for EE and 62 cells for AA. Scale bar, 10 μm. Data are presented as mean ± s.e.m., n = 8 wells for EE, n = 14 wells for AA, unpaired two- tailed t- test. Source data are provided as Source data file. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[137, 55, 840, 700]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[50, 704, 950, 970]]<|/det|> +
Figure 10. Human tissues positive for epicarpemores exhibit p-Ser226 HSP90β positivity, and conversely, those negative for epicarpemores show no or negligible p-Ser226 signal within HSP90's charged linker. a Cartoon illustrating the processing of human tissue for biochemical analyses. Both tumor (T) and tumor adjacent (TA) tissues, determined by gross pathological evaluation to be potentially non-cancerous, were harvested and analyzed. b MDA-MB-468 breast cancer cells (epicarpemore-high) and ASPC1 pancreatic cancer cells (epicarpemore-low) served as controls for assessing p-Ser226 HSP90 levels. c The graph presents the relationship between epicarpemore positivity and HSP90 Ser226 phosphorylation for tissues described in panel a. Data represent mean ± s.e.m., with \(n = 9\) tumor (T) and \(n = 9\) paired tumor-adjacent (TA) tissues classified based on epicarpemore positivity or negativity, as determined by Native PAGE (see panel d); unpaired two-tailed t-test. d Detection of epicarpemores through native-PAGE (top), and of p-Ser226 HSP90 (middle) and total HSP90 (bottom) by SDS-PAGE, followed by immunoblotting, in tissues from the indicated patient specimens, as in panel a. Blue brackets indicate the approximate position of epicarpemore-incorporated HSP90. Note: Obtaining genuinely "normal" tissue adjacent to tumors presents challenges, especially in the case of pancreatic tissue. The relatively small size of the organ and the nature of surgical procedures for pancreatic cancer often lead to the collection of normal samples in close proximity to the tumor. It's crucial to acknowledge that, due to these challenges, we designate potentially normal tissue as tumor-adjacent tissue, recognizing that it may not entirely reflect a truly normal tissue state. PDAC, Pancreatic Ductal Adenocarcinoma; IDC, Invasive Ductal Carcinoma; ILC, Invasive Lobular Carcinoma; ER, Estrogen Receptor; PR, Progesterone Receptor. Source data are provided as Source data file.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 43, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[58, 130, 451, 336]]<|/det|> +SupplementaryData1. xlsx SupplementaryData2. xlsx SupplementaryData3. xlsx SupplementaryData4. xlsx SupplementaryData5. xlsx SupplementaryData6. xlsx SupplementaryInformation03.01.2024. pdf SourceData. xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/images_list.json b/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0d7207bdd12d1b6fb0b5fa58551528ccc78ee417 --- /dev/null +++ b/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/images_list.json @@ -0,0 +1,55 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "a", + "footnote": [], + "bbox": [], + "page_idx": 34 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "b", + "footnote": [], + "bbox": [ + [ + 66, + 60, + 999, + 245 + ] + ], + "page_idx": 36 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_2.jpg", + "caption": "d", + "footnote": [], + "bbox": [ + [ + 68, + 315, + 433, + 472 + ] + ], + "page_idx": 38 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_3.jpg", + "caption": "c", + "footnote": [], + "bbox": [ + [ + 540, + 293, + 790, + 455 + ] + ], + "page_idx": 40 + } +] \ No newline at end of file diff --git a/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28.mmd b/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28.mmd new file mode 100644 index 0000000000000000000000000000000000000000..65f828491dc99b4a0fc27c73a7c1a047ec8fbfe3 --- /dev/null +++ b/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28.mmd @@ -0,0 +1,597 @@ + +# STIFMap employs a convolutional neural network to reveal spatial mechanical heterogeneity and tension-dependent activation of an epithelial to mesenchymal transition within human breast cancers + +Valerie Weaver ( \(\boxed{\bullet}\) Valerie.weaver@ucsf.edu ) University of California, San Francisco https://orcid.org/0000- 0003- 4786- 6752 + +Connor Stashko Department of Surgery, University of California, San Francisco + +Mary- Kate Hayward Department of Surgery, University of California, San Francisco + +Jason Northey Department of Surgery, University of California, San Francisco + +Neil Pearson BioQ Pharma Incorporated + +Alastair Ironside Department of Pathology, Western General Hospital, NHS Lothian + +Johnathon Lakins 1 Department of Surgery, University of California, San Francisco + +Marie- Anne Goyette Department of Medical Oncology, Dana- Farber Cancer Institute + +Lakyn Mayo Department of Cell and Tissue Biology, School of Dentistry, University of California, San Francisco https://orcid.org/0000- 0002- 6642- 2332 + +Hege Russnes Oslo University Hospital https://orcid.org/0000- 0001- 8724- 1891 + +E Hwang Duke University + +Matthew Kutsy University of California San Francisco https://orcid.org/0000- 0002- 0752- 649X + +Kornelia Polyak Dana- Farber Cancer Institute https://orcid.org/0000- 0002- 5964- 0382 + +<--- Page Split ---> + +## Article + +# Keywords: + +Posted Date: October 24th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 2063113/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +STIFMap employs a convolutional neural network to reveal spatial mechanical heterogeneity and tension- dependent activation of an epithelial to mesenchymal transition within human breast cancers + +Connor Stashko1,2, Mary- Kate Hayward1,2,\*, Jason J. Northey1,2,\*, Neil Pearson, Alastair J. Ironside3, Johnathon N. Lakins1,2, Marie- Anne Goyette4, Lakyn Mayo5, Hege Russnes6,7, E. Shelley Hwang8, Matthew Kutys5,9, Kornelia Polyak4, Valerie M. Weaver1,2,9,10,\* + +1Department of Surgery, University of California, San Francisco, California, USA. 2Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA. + +3Department of Pathology, Western General Hospital, NHS Lothian, Edinburgh, UK + +4Department of Medical Oncology, Dana- Farber Cancer Institute, Boston, MA + +5Department of Cell and Tissue Biology, School of Dentistry, University of California, San + +6Francisco, San Francisco, CA. + +7Department of Pathology and Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway. + +8Department of Surgery, Duke University Medical Center, Durham, NC. + +9UCSF Helen Diller Comprehensive Cancer Center, University of California, San + +10Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration + +11Institute for Clinical Medicine, University of Oslo, Norway. + +12Department of Surgery, Duke University Medical Center, Durham, NC. + +13UCSF Helen Diller Comprehensive Cancer Center, University of California, San + +14Francisco, San Francisco, CA. + +15Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration + +16Medicine and Stem Cell Research, University of California, San Francisco, San + +17Francisco, CA. + +18These authors contributed equally to this work + +## Corresponding Author: + +19Valerie M. Weaver + +20Center for Bioengineering and Tissue Regeneration + +21Department of Surgery + +22513 Parnassus Avenue, 565 HSE + +23University of California, San Francisco + +24Telephone: (415) 476- 3826 + +25Email: valerie.weaver@ucsf.edu + +<--- Page Split ---> + +Intratumor heterogeneity in breast cancer associates with poor patient outcome. Tissue fibrosis and stromal stiffening accompany breast cancer development and associate with the aggressiveness of human breast cancer subtypes. Whether human breast cancers demonstrate stiffness heterogeneity, and if this is linked to breast tumor aggression remains unclear. To answer these questions, we developed a spatial method to measure the stiffness heterogeneity in human breast tumor tissues that also quantifies the local stromal stiffness each cell experiences and permits correlation with biomarkers of tumor aggression. Here, we present Spatially Transformed Inferential Force Maps (STIFMaps) to predict the elasticity across whole tissue sections with micron- resolution. The method exploits computer vision to precisely automate AFM indentation and then uses a trained convolutional neural network to predict matrix elasticity using collagen morphological features and ground truth AFM data. Because STIFMaps is compatible with biomarker staining we used the approach to register high- elasticity regions within sections of human breast tumors with markers of mechanical activation and an epithelial to mesenchymal transition (EMT) that associated with tumor aggression. The findings herein highlight the utility of STIFMaps for assessing the mechanical heterogeneity of human breast tissues across length scales from single cells to whole tissues. The method also reveals, for the first time, a direct association between stromal stiffness and EMT, thereby implicating stromal stiffness as a driver of human breast cancer aggression. + +<--- Page Split ---> + +Intratumor heterogeneity (ITH) is a feature of breast tumors ([1], [2], [3], [4]). Tumor heterogeneity predicts poor patient outcome as diversification of genetic, phenotypic and behavioral characteristics within a tumor supports progression, metastasis, and treatment resistance ([5], [6], [7]). Accordingly, much effort has been directed towards defining ITH and clarifying how it drives tumorigenesis ([8], [9]). Towards this goal, the ability to decipher the causal relationship between tumor heterogeneity and tumor behavior relies heavily on the availability of accurate and quantitative methods with which to measure and analyze individual features of the tumor. + +Tumor tissue variability is mediated, in part, by intrinsic stochastic gene expression as well as by genetic and epigenetic differences in the transformed cells. Sophisticated approaches including genetic tags and high throughput sequencing have permitted researchers to detect genomic abnormality at the single cell level to provide important insights into clonal evolution and have linked these findings to patient survival ([10], [11], [12]). State of the art spatial RNA sequencing (RNAseq) analysis has revealed underlying spatial associations between stress response gene expression profiles in cancer cells and inflammatory fibroblast gene signatures [13]. Importantly, tumors are organs comprised of transformed cells interacting with a diverse cellular and acellular stroma. Consistently, in situ multiplexing approaches have revealed wide diversity with respect to the frequency and phenotype of tumor- infiltrating immune cells and have used these findings to predict patient outcomes as well as checkpoint inhibitor responsiveness [14]. In situ immunofluorescence has also illustrated wide variability in oncogenic signaling, cellular metabolism and stress responsiveness between the epithelial and stromal cells within the hypoxic tumor core, and those cells that localize to the fibrotic tumor periphery, to predict treatment response in patients ([15], [16]). + +An important feature of all solid tumors is the remodeled and crosslinked extracellular matrix (ECM) that generates a stiffened, fibrotic stroma characterized by markedly reorganized interstitial collagens [17]. A stiff ECM modifies cell and nuclear shape, disrupts tissue organization, promotes cell growth, viability, and invasion, alters gene expression, and can even induce an epithelial to mesenchymal transition (EMT) in cells cultured in two- and three- dimensional substrates ([18]). Within experimental tumors in vivo, the stiffened tumor ECM promotes solid stress, disrupts vascular integrity to drive hypoxia and tumor aggression, and + +<--- Page Split ---> + +85 compromises drug delivery [19]. The stiff tumor ECM also increases cytokine and chemokine expression to promote myeloid cell infiltration and can even impede CD8 T cell infiltration, migration, and function [20]. Clinically, the level of tissue fibrosis correlates with worse patient outcome, and in situ analysis of human breast cancer tissues revealed that a stiff, fibrotic ECM associates with tumor progression as well as clinical subtype ([21], [22], [23]). Nevertheless, whether stromal stiffness tracks with human breast cancer aggression and, if so, how remains unclear. + +To clarify links between stromal stiffness heterogeneity and tumor aggression, approaches are needed that can be combined with state- of- the- art spatial genomics, proteomics, and multiplexing protocols ([24], [25], [26]). Although techniques do exist with which to monitor ECM heterogeneity and organization including H&E, second harmonic generation (SHG), trichrome, and picrosirius red (PS red) staining, none can be directly combined with immunostaining on the same slide ([21], [27], [28]). Moreover, these protocols do not provide quantitative insight into mechanically soft and stiff regions within the tumor. Methods to directly measure ECM stiffness include shear rheology, Atomic Force Microscopy (AFM), Magnetic Resonance Elastography, Sonoelastography, and unconfined compression analysis ([29], [30], [31], [32]). However, these approaches do not provide high resolution spatial and morphology information, particularly with respect to the state of the collagenous ECM. Although stromal stiffness can be measured directly with sub- micron resolution using Atomic Force Microscopy (AFM), current AFM methods are time- consuming, poorly resolved spatially, and require specialized equipment not readily available to most research and clinical laboratories ([33], [34]). An automated AFM developed by Plodinec and colleagues can rapidly quantify the material properties of tumor biopsies, but the method does not provide imaging of the probed tissue nor the positions from where measurements are taken. Thus, while the approach is useful for characterizing cell and tissue biomechanical properties, it is not suitable for studies whose goal is to link elasticity of the ECM to biological markers of tumor and stromal phenotype, genotype, and heterogeneity [35]. Importantly, all of the currently available approaches to quantify cell and stromal stiffness require manipulation of either fresh or cryopreserved tissue, precluding comprehensive spatial analysis of elasticity in archived formalin- fixed paraffin- embedded (FFPE) sections in tissue banks. + +<--- Page Split ---> + +Here, we present a novel approach termed Spatially Transformed Inferential Force Mapping (STIFMap) that is able to visualize the heterogeneous stiffness landscape of normal and tumor breast tissues with micron- resolution and to spatially register this tension phenotype with biological markers of tumor aggression. The method works on both cryopreserved and FFPE tissues and employs a single quick, inexpensive collagen stain that is visualized with standard fluorescence microscopy. The approach permits simultaneous quantification of the tension landscape of the stromal ECM together with co- staining for cell or ECM biomarkers of interest, and lends itself to quick assessment of the impact of biophysical ECM heterogeneity on tumor aggression. The method can be readily integrated with spatial proteomics and genomics as well as standard protein marker multiplexing protocols. To illustrate the potential of the approach we applied STIFMap to explore the relationship between stromal stiffness and markers of tumor aggression in human breast cancers. We were able to link tissue mechanics with indicators of mechanosignaling and biomarkers of EMT previously implicated in tumor aggression and treatment resistance ([36], [37]). The results highlight the potential utility of using stromal biophysical features to predict tumor behavior and possibly even patient outcome. + +## RESULTS + +## Design and development of an automated AFM system + +AFM has emerged as the method of choice to spatially analyze stromal stiffness in tumors ([38], [39]). However, executing AFM analysis is cumbersome, specialized, and not easily amenable to spatial registration with sequential in situ analysis and imaging. To improve upon these pitfalls, we developed AutoAFM to facilitate high- throughput, spatially- resolved acquisition of AFM data. We automated AFM movements by affixing servo motors onto the X and Y translation knobs of the AFM stage with custom- made, 3D- printed motor mounts (Fig. 1a, b, Extended Table 1, Extended Fig. 1a, b, Methods). Scripts were developed to enable the AFM to move along a user- specified path (Fig. 1c, d). The system was designed so that as the AFM moves from one point to the next, a feedback loop reports on the current position of the AFM and fine- tunes movements to poke the specimen within a user- designated tolerance of the desired positions. All movements and imaging were designed to be conducted using epifluorescence of propidium iodide- stained cells to guide the measurements. This strategy was chosen to remove artifacts from the cantilever shadow that could potentially be introduced into the images during + +<--- Page Split ---> + +stitching (Extended Fig. 1c). The system was engineered so that a completed AutoAFM scan will provide the location of each AFM force curve acquired over the tissue section being measured (Fig. 1e, f). The AutoAFM was designed such that scans can be acquired across as many points as the operator desires and are only spatially limited by the overall X- Y range of the AFM stage. + +Assessment of AutoAFM precision and validation of AFM measurements + +To validate movements of the AutoAFM, a series of elevated PDMS beams of varying widths were fabricated using photolithography followed by PDMS soft lithography (Extended Fig. 1d). Using this strategy, the height at which the AFM contacts the sample is known, so force curves collected on the beams registered as much higher than those collected on the surrounding PDMS surface. To determine the resolution limit of AutoAFM, we used the AFM to 'walk' along each beam and measured the accuracy of the AFM to contact the beam at each width. The measurements indicated that movements of the automated AFM are precise to within a few microns (Extended Fig. 1e). + +The Young's Modulus of an AFM cantilever is calibrated before measurements are performed (Methods). Nevertheless, the cantilever modulus can change over the course of data collection due to protein and cell debris deposition onto the cantilever. To ensure that the stiffness of the cantilever remained consistent throughout the measurements, we measured the elasticity of polyacrylamide (PA) gels of known Young's Moduli before and after probing each sample. The elasticities of the PA gels used were validated using shear rheology (Extended Fig. 2a). AFM measurements on each tissue were collected over 1- 2 hours. To verify that tissues did not degrade over the timespan of AFM measurements, we collected force curves at the same tissue positions over a defined length of time. AFM measurements collected in the same positions every 30 minutes for three hours revealed no noticeable differences in elasticity, indicating that tissue degradation does not occur over the timespan that AutoAFM measurements were acquired for this study (Extended Fig. 2b). Because tissues are viscoelastic substrates, the rate of force loading impacts the resulting force curve [40]. Accordingly, highly viscous substrates will appear stiffer when poked faster if they are assumed to be purely elastic. To address this potential anomaly an AFM velocity of 2 \(\mu \mathrm{m / s}\) was chosen since Young's Moduli measurements were constant at this rate (Extended Fig. 2c). + +<--- Page Split ---> + +To avoid any potential for tip fouling we chose AFM cantilevers that were triangular with \(5\mu \mathrm{m}\) spherical beads incorporated onto the cantilever tip (Extended Fig. 2d; [41]). To stitch together images taken during AutoAFM, the bead location was estimated for each image. To estimate the bead location within the image, the average image for each AutoAFM scan was taken, which revealed a faint but distinct outline of the cantilever (Extended Fig. 2e). This occurred because the stronger PI signals from the cells move during AutoAFM acquisition, but the faint cantilever image remains in the same position throughout. Five cantilevers with known bead locations were aligned with the average scan images to indicate the actual position of the bead during imaging (Extended Fig. 2f, g). + +## Overlaying AutoAFM Measurements onto Confocal Images + +In an effort to ensure visual spatial alignment between tissue morphological features and elasticity measurements, the AutoAFM measurements were overlaid with nuclear staining via alignment with AutoAFM PI positions. DAPI measurements were collected via confocal imaging at either \(40\mathrm{x}\) or \(63\mathrm{x}\) magnification, while AutoAFM PI images were collected at \(20\mathrm{x}\) magnification. A pipeline was then developed to translate low- resolution AutoAFM images onto high- resolution DAPI imaging (code available on GitHub). To do so, the two images were first manually pre- aligned so that the fields of view were similar, and the confocal DAPI image was downsampled to more closely resemble the resolution of the AutoAFM image (Extended Fig. 3a). Thereafter, we applied a Fourier- Mellin Transform to determine the scale and rotation of the AutoAFM image relative to the DAPI image [42]. Finally, translation between the two images was computed using phase contrast cross- correlation. Using this transformation matrix, AFM positions were mapped onto the high- resolution images (Extended Fig. 3b). The average mapping error was found to be \(2.57\mu \mathrm{m}\) , estimated by monitoring nuclei positions before and after transformation ( \(95\%\) confidence interval: \(2.09 - 3.06\mu \mathrm{m}\) ) (Extended Fig. 3c). + +## Deep learning model of tissue young's modulus from collagen morphology + +Interstitial fibrillar collagens are the major structural component of breast tissue ([43], [44], [45], [46]). As such, we reasoned that the elasticity of breast tissue could be inferred based on the morphology of interstitial collagen fibers, particularly given that stiff collagen fibers are thick and highly linear whereas more compliant collagen fibers are typically more dispersed, + +<--- Page Split ---> + +relaxed, and present as wavy fibers [47]. Although most investigators have used SHG imaging or PS red staining to visualize interstitial fibrillar collagens, collagen SHG imaging is susceptible to interference from additional fluorophores on co- stained tissues [48], and PS red coloring depends on the angle of the slide on the microscope relative to the polarizer [28]. Accordingly, we chose to stain the collagens using the collagen- binding adhesion protein 35- Orange Green 488 fusion protein (CNA35- OG488) ([49], [50]). CNA contains two subdomains, N1 and N2, which engage in a ‘collagen hug’ around triple helical collagen in the ECM [51]. CNA staining was selected because it is cheap to produce, plasmid sequences are freely available online, and the stain is not species specific [52]. The CNA stain is also rapid and easy to perform, and can be viewed on conventional fluorescent microscopes, lending itself to standard research laboratories as well as a clinical lab format. + +To register collagen morphological features with tissue stiffness, a convolutional neural network (CNN) was applied using CNA and DAPI imaging as the inputs and the corresponding AFM measurements as the output (Fig. 2a). A CNN was chosen due to its superiority compared to alternative models with image classification tasks [53]. We reasoned that a CNN would be able to learn how factors such as collagen fiber linearity, thickness, and proximity to cells impacts elasticity better than alternative models. Different CNN architectures were applied to predict tissue elasticity including ResNet, DenseNet, and models discovered using Neural Architecture Search, but the best performance came from an AlexNet modified for a regression output instead of classification (https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py). + +Neural networks are data hungry, such that their performance is greatly improved when more data is utilized. When given a small amount of training data, neural networks tend to overfit the training dataset and emphasize features that do not generalize well. To address this, we artificially enlarged our training dataset of a thousand data points by applying random rotations, mirroring, and adjustments to brightness, contrast, and sharpness (Fig. 2b). Based upon the fact that the Young’s Modulus of the sample is independent of these manipulations, we reasoned this would allow the model to learn which features were the most informative while preventing overfitting. Consistently, we found that the model generalized much better to validation data when transformations were applied to the training data. + +<--- Page Split ---> + +In the final model, we utilized both the DAPI and collagen channels. As dead cells are typically quite soft when probed using AFM, the additional information from the DAPI stain helped the model to learn and was included in our imaging studies. We also natural log- transformed elasticity measurements prior to training to alleviate the influence of outliers. At training completion, the correlation of predicted to actual Young's Moduli values was 0.689 (pearson R value; averaged across 25 trained models) when trained over 100 epochs (Fig. 2c, d), which performed significantly better than predicting elasticity based on the intensity of collagen and DAPI alone (multivariable (MV) regression line using all training and validation samples; r = 0.574; p(CNN over MV) = 5.23e- 10). The validation data is predicted more accurately than the training data as a result of the transformations applied to the training dataset. Saliency maps indicating image regions that contribute to tissue stiffness demonstrated that the trained models were able to incorporate morphological information from nuclei as well as collagen when predicting stiffness (Fig. 2e) ([54], [55], [56], [57]). + +## Generation of STIFMaps + +We next applied our trained CNNs to predict the elasticity of normal and tumor breast tissue sections across a region of interest using Spatially Transformed Inferential Force Maps (STIFMaps). We achieved this objective by segmenting the images into squares matching the input dimensions of the neural network and predicting the Young's Moduli for each square (Fig. 3a, Methods). We then colorized the original images to correspond to the predicted stiffness of each point (Fig. 3b). To validate the performance of these stiffness predictions, tissues were immunostained for two established markers of cellular mechanosignaling, activated \(\beta 1\) integrin and phospho- Myosin Light Chain 2 (pMLC2) that are typically increased in cells in response to a stiff ECM ([58], [59]). We used the predicted STIFMaps to evaluate the correlation between expression of these markers and tissue elasticity (Fig. 3c). Since a large proportion of the ECM is not directly in contact with cells, we looked at the \(99^{\text{th}}\) percentile of stain intensity for each percentile of ECM elasticity (Fig. 3d, Extended Fig. 4a, b). This allowed us to remove low- intensity pixels where there were no cells or stain present. The intensity of both mechanosignaling markers was found to positively correlate with the predicted Young's Modulus of the local tissue region (Fig. 3e), but not with the intensity of collagen or DAPI alone (Fig. 3f). We also applied a mask to better identify pixels located at the cell- ECM interface and + +<--- Page Split ---> + +observed the same trend (Extended Fig. 4c, d). The findings indicate that STIFMaps can accurately identify mechanical 'hotspots' within breast tumor tissue sections, thus providing an additional layer of information about the mechanical landscape of breast tissue that was not previously possible. + +## Utilizing STIFMaps with Formalin-Fixed Paraffin-Embedded Tissue + +FFPE tissues are frequently used for clinical analysis because this approach preserves cell and tissue morphology. Unlike cryopreserved tissues, which are needed for traditional AFM analysis, FFPE tissue are more readily available for research analysis and clinical translational studies. However, FFPE tissues are highly cross- linked due to formalin- fixation, and thus impossible to accurately measure stiffness by AFM. Accordingly, we asked if STIFMap could predict the elasticity of the original, unfixed tissue samples based solely on collagen morphology. We stained terminal duct lobular units (TDLUs) from cryo- preserved and FFPE breast tissues with CNA and DAPI. In consultation with a clinical breast cancer pathologist, we detected no discernable morphological differences between the collagen morphology detected using the CNA collagen stain in a patient- matched FFPE versus cryopreserved tissue (Extended Fig. 5). The results indicate that STIFMaps can be applied to predict the elasticity of FFPE tissues in which elasticity measurements are not currently possible. + +A stiff, fibrotic collagenous ECM drives an EMT and tumor metastasis in mice. + +STIFMaps revealed a spatial co- localization between tumor cells with activated \(\beta 1\) integrin, elevated actomyosin contractility (pMLC) and high- tension regions in the stroma of breast cancer tissues (Fig. 3e). The findings illustrate the feasibility of this method to more directly demonstrate clinical links between known regulators of breast tumor aggression and tissue tension. + +A stiff ECM can foster the growth, survival, and invasiveness of cultured premalignant and tumorigenic breast cancer cell lines by inducing an EMT ([60], [61], [62], [63]). A stiff, cross- linked collagenous stroma can also induce an EMT to promote tumor aggression and metastasis in vivo in experimental murine models of mammary cancer [64]. Nevertheless, there is currently no evidence to directly implicate a stiff, fibrotic tissue stroma in human breast cancer aggression and metastasis, nor to link this phenotype to induction of an EMT. Therefore, to + +<--- Page Split ---> + +directly test whether a stiff stroma could drive the aggressiveness and metastatic behavior of human breast cancers, and to determine if this is linked to an EMT, we manipulated HER2+ human breast cancer patient- derived xenografts (PDX) in vivo. We reasoned that PDXs are a model that more closely mirrors the heterogeneous phenotype of human breast tumors ([65], [66]). To assess this, we implanted three independent HER2+ human breast cancer PDXs (BCM- 3963; BCM- 3143B, HCI- 012) embedded within control (SOFT; 140 Pa) and non- metabolizable L- ribose cross- linked (STIFF; 1,200- 2,000 Pa) collagen gels into the fat pads (orthotopic) of NOD/SCID mice and monitored the impact on tumor phenotype and behavior (Fig. 4a). Immunofluorescence analysis revealed a significant increase in activated \(\beta 1\) integrin and phospho- Y397 focal adhesion kinase activity ( \(^{139}\mathrm{FAK}\) ) in the PDX tumors that developed within the stiffened collagen gels (Fig. 4b, c). We observed an increase in tumor outgrowth in all three independent HER2+ PDX tumors embedded within the stiffened collagen gels (Extended Fig. 6a- c). Markers of growth factor receptor signaling, as indicated by elevated phosphorylated MAP Kinase (pERK; Extended Fig. 6d), indicated that tissue tension and integrin signaling promoted tumor cell growth. There was also a greater number of larger metastatic lesions quantified in the lungs of the mice harboring the stiff tumors (Fig. 4 d- g). Consistent with a relationship between a stiff stroma, breast tumor aggression, and induction of an EMT, RNAseq analysis revealed a significant elevation of the 'Hallmark Epithelial Mesenchymal Transition' pathway in STIFF PDX tumors (Fig. 4h, i, MSIGDB pathway M5930). RT- PCR analysis validated the stiffness induction of the expression of several of the EMT genes including Vimentin (VIM), TWIST1, SLUG, and MMP2 (Fig. 4j- m, Extended Fig. 6f- k). These findings demonstrate that a stiff stroma induces integrin and growth factor receptor signaling to drive tumor aggression and metastasis of human breast tumor PDXs in vivo. The data also implicate stromal stiffness- dependent induction of an EMT in this phenotype. + +STIFMaps link stromal elasticity to EMT in patient tumors. + +Having established that high ECM tension can drive the aggressiveness and metastasis of HER2+ PDX breast tumors in association with induction of an EMT, we next applied STIFMaps to look for clinical evidence supporting this relationship. We previously showed using AFM and immunofluorescence analysis that the more aggressive triple- negative breast cancer (TNBC) and HER2+ human breast cancer subtypes have higher levels of activated \(\beta 1\) integrin and a stiffer + +<--- Page Split ---> + +invasive front ([21], [22]). We applied STIFMaps to explore if there was a significant association between stromal tension and EMT markers in clinical FFPE samples of TNBC and HER2+ breast tumors. We first looked within patient transcriptomic data and found that expression of collagen genes highly correlated with expression of EMT genes (Fig. 5a, b). Collagen genes were removed from all gene sets to not bias this analysis. We then stained TNBC FFPE tissue sections for ZEB1 and SLUG, two transcription factors induced by a stiff stroma previously implicated in EMT [67]. We detected expression of both ZEB1 and SLUG and found their levels to be significantly positively correlated with the predicted elasticity of the interstitial collagens in the stroma, but not individually with total collagen or DAPI intensity (Fig. 5c- e). A trend was also observed when a mounted TNBC tissue was subjected to a full imaging scan (Extended Fig. 7a, b). To determine the broader relevance of these clinical findings we next applied STIFMaps to a cohort of 21 HER2+ breast tumors with associated clinical follow- up data [68]. We co- stained these FFPE tissue sections with HER2 and ZEB1 as well as with CNA35 to stain tissue collagens and had a pathologist outline the tumor in each whole slide image. The predicted tissue elasticity from STIFMaps was significantly associated with ZEB1 stain intensity, but not with HER2, when compared to the correlation with collagen intensity alone (Fig. 5f, g, Extended Fig. 7c, d). We then looked at the spatial autocorrelation of each stain in each tissue by calculating Moran’s I, which revealed a trend showing that greater clustering of HER2, ZEB1, and elasticity associated with metastatic recurrence (Fig. 5h). This observation is consistent with worse overall survival among patients with high expression of EMT and collagen gene expression signatures (Fig. 5i, j). These findings demonstrate, for the first time, a spatial link between high stromal collagen elasticity and biomarkers of EMT in both TNBCs and HER2+ human breast tumors. Together with our PDX findings, these data link EMT to ECM stiffness and implicate tension- induced EMT in human breast tumor metastasis. The findings also suggest a stiff ECM could promote tumor aggression and compromise breast cancer patient outcome. + +## DISCUSSION + +Here we present a new method we term Spatially Transformed Inferential Force Mapping, STIFMap, which permits the spatial resolution and quantification with micron- resolution of the mechanical heterogeneity of the collagenous stroma within normal and tumor breast tissues. The method works on both cryopreserved and FFPE tissues and employs a quick, + +<--- Page Split ---> + +inexpensive staining protocol via CNA35 and DAPI. The approach permits simultaneous quantification of the tension landscape of the stromal ECM together with standard biomarker immunostaining approaches, and could be integrated with spatial proteomics and genomics as well as protein marker multiplexing protocols ([24], [25], [26]). Although methods do exist to broadly quantify tissue elasticity across a tissue section, they do not provide high- resolution spatial information [35]. AFM is a technique that directly probes tissue elasticity at the single cell scale [69]. However, standard AFM methods are not high throughput, require fresh or cryopreserved tissue, rely upon specialized equipment and operators, are time- consuming, and only collect sparely spaced data points over focused sections of a tissue ([33], [34]). In the absence of the AutoAFM algorithms presented herein, it is also challenging to overlay AFM data with biomarker staining, and the use of cryopreserved or fresh tissue compromises simultaneously conducting spatial genomic, transcriptomic, or proteomic analyses. STIFMaps overcomes current shortcomings of conventional AFM methods and can rapidly annotate the elasticity landscape of an entire tissue section with a simple collagen stain. The method is also amenable to FFPE tissue thereby expanding the scope and application of the method. Indeed, using STIFMap we were able to link, for the first time in clinical biopsies of human breast cancer, tissue mechanics with indicators of mechanosignaling and biomarkers of an EMT previously implicated in tumor aggression ([36], [37]). The results highlight the potential utility of using STIFMaps to quantify stromal biophysical features to predict tumor behavior and ultimately patient outcome. + +We and others showed both in vitro and in experimental models in vivo that a stiff ECM increases integrin mechanosignaling to foster tumor cell growth, survival, invasion and metastasis, and that this is accompanied by induction of an EMT ([60], [70], [61]). Here we demonstrate, for the first time using human breast tumor PDX biospecimens, that a stiff stroma can induce an EMT in vivo and that this is accompanied by metastasis. By correlating stromal tension with biomarkers of an EMT in human clinical specimens of TNBC and HER2+ breast cancer, we found clinical evidence that such a relationship also exists within human tumors, thereby providing validation of the experimental manipulations. Furthermore, we showed that expression of either collagen genes or an EMT signature associates with significantly worse patient outcome. The findings thereby directly link stromal tension to human breast cancer aggression and directly implicate induction of an EMT in this phenotype. Although STIFMaps + +<--- Page Split ---> + +provides researchers with a versatile tool to explore the role of stromal stiffness in clinical specimens, additional studies will be necessary to further clarify mechanisms through which a stiff ECM drives an EMT in tumors. Moreover, more work will be needed to assess the clinical relevance of stromal stiffness on patient outcome. + +There is growing interest in the application of artificial intelligence methods to classify clinical histological images ([71], [72], [73]). While early deep learning algorithms focused on routine tasks such as nuclei segmentation, current state- of- the- art algorithms are beginning to rival pathologists at tasks such as tumor grading and cancer detection [71]. Moving beyond what pathologists are able to detect, some new algorithms are even able to predict tumor recurrence and invasive potential in cohorts for which there is currently no means available for evaluating risk to progression [74]. Notwithstanding these advances, a number of caveats hinder development in this area such as suboptimal network architectures, the requirement for large numbers of samples, the immense computational processing power necessary to train highly sophisticated models, overfitting data that is generated by only one individual or group, and the difficulty in interpreting why deep learning models classify results in one group or another [75]. Nevertheless, improvements in deep learning such as neural architecture search to find more optimal networks and advancements in computational power continue to make computational pathology more mainstream and accessible in the clinic. While there are still issues to overcome, deep learning algorithms appear to be the future of histopathological analysis and tissue classification [76]. + +Tumors are highly heterogeneous at the genomic, transcriptomic, and proteomic levels ([1], [4]). Regions within human and murine tumors have been identified in which immune infiltration, cancer cell metabolism, and stress response pathways exhibit diverse phenotypes. Given that patient prognosis and outcomes have been linked to genomic heterogeneity, as well as variability in immune infiltration and hormone receptor expression, it is perhaps not surprising that there is a growing interest in understanding the relevance of and drivers of tumor heterogeneity [77]. In this regard, the level of tissue fibrosis also predicts patient outcome and recent data suggest the level and organization of tissue collagens and stromal stiffness varies widely within a patient's tumor ([78], [79], [21]). Yet, to date there are no tools with which to spatially resolve the mechanical stromal heterogeneity within a tumor and none that are amenable to scanning across a full tissue section of a tumor. With STIFMaps, it is now possible + +<--- Page Split ---> + +to evaluate the association between a biomarker or pathway of interest and the local and heterogeneous elasticity of the collagen- rich stroma within a given normal or malignant tissue. Moreover, the STIFMap method can be combined with spatial sequencing, in situ gene expression, metabolomics, and even proteomics to allow for unbiased screening of correlations between molecular heterogeneity and mechanically regulated pathways in clinical samples. Accordingly, STIFMaps opens the door for clinicians and translational researchers to explore the impact that tissue elasticity has on cancer cells in their native tissue microenvironments. + +## METHODS + +## Atomic Force Microscopy (AFM) + +AFM measurements were performed using an MFP- 3D BIO Inverted optical AFM (Asylum Research, Santa Barbara, CA) mounted on a Nikon TE2000- U inverted fluorescent microscope (Melville, NY) and placed on a vibration- isolation table (Herzan TS- 150). Silicon nitride cantilevers were used with a nominal spring constant of \(0.06 \mathrm{N m - 1}\) and a borosilicate glass spherical tip with \(5 \mu \mathrm{m}\) diameter (Novascan Tech). Cantilevers were calibrated using the thermal fluctuation method and verified by probing polyacrylamide gels of known elasticity. The specimens used were \(20 \mu \mathrm{m}\) thick OCT- embedded frozen human breast tissue sections thawed and equilibrated to room temperature by immersion in PBS for 5 minutes. Thawed sections were immersed in PBS containing phosphatase inhibitor (GenDEPOT Xpert #P3200- 001), protease inhibitor cocktail (GenDEPOT Xpert # P3100- 001), \(5 \mu \mathrm{g / mL}\) propidium iodide (ACROS, Cat# 440300250), and \(3 \mu \mathrm{g / mL}\) of CNA35- OG488. Specimens were indented at \(2 \mu \mathrm{m}\) per second loading rate. The Young's Moduli of the samples were determined by fitting force curves with the Hertz model using a Poisson ratio of 0.5. + +## AFM Forceplot Fitting Algorithm + +AFM force plots were post- processed to obtain Young's Moduli using a homemade algorithm (see GitHub repository). Briefly, force plots were smoothed using a moving average convolution across 100 datapoints to remove noise and then baseline- corrected using the first third of the AFM indentation curve. The contact point of each force curve was estimated as the point at which the derivative of the force curve increased above an empirically- determined threshold. Then, a more precise contact point was determined by applying a minimization function to fit a + +<--- Page Split ---> + +flat baseline plus a 1.5- power curve (Hertz Model) onto the AFM data using the estimated contact point as an initial guess. With the contact point determined, the Young's Modulus was calculated to minimize the squared error between the AFM data and the fit curve + +## AutoAFM Design + +The AutoAFM assembly's function is to ensure proper alignment of the motor relative to the microscope stage's adjustment knob in order to allow the motor to accurately control the knob's rotation and thus the movement of the microscope stage. It does this by supporting the weight of the motor and controlling its position, while allowing the motor to freely slide along its shaft axis. It also allows the operator to fine- tune the motor's position and orientation in space, ensuring good alignment with (and therefore accurate control of) the stage's adjustment knob. The assembly has three main components: the Stage Frame, the Motor Frame, and the Motor Bracket. The motors screw into the Motor Brackets via the four Motor Screws. The Motor Bracket sits in the Motor Frame and is pulled down against the Bracket Adjusters by the springs hooked around the Tensioning Pins. By turning the Bracket Adjusting Screws, the Bracket Adjusters can be individually moved forward and backwards, adjusting both the pitch and the roll of the Motor Bracket relative to the axis of the motor shaft. This allows easy manual adjustment of the motor to ensure good alignment between the motor shaft and the stage knob. The Stage Frame is hooked over the lip of the microscope stage, enclosing the stage's fine adjustment knob (not shown), and is able to slide freely along the edge of the stage. The Alignment Rods are press- fit into the Stage Frame and slip- fit into the Motor Frame, allowing the Motor Frame to slide freely towards and away from the Stage Frame along the motor shaft axis. The Motor Coupling joins the motor shaft to the Knob Adapter, which is screwed into the fine adjustment knob via the Adapter Screw. The Y Stage Frame has rollers attached to reduce friction with the AFM stage as it slides side- to- side. + +Motor mount components were 3D printed on either a Prusa MINI+ or LulzBot Mini 2 with PETG. A high infill was used for ease of sanding. Some dimensions were slightly oversized so that they could be gradually sanded to fit snugly. See https://github.com/cstashko/AutoAFM/STL for a full list of part STL files. Other components were ordered from McMaster- Carr. See supplementary table 1 for a full Bill of Materials. Motors were driven via an Arduino Mega 2560 + +<--- Page Split ---> + +Rev3 Classic microcontroller interfacing with RAMPS 1.4 + +(https://reprap.org/wiki/RAMPS_1.4). + +AutoAFM operates by moving the AFM cantilever to user- defined positions and acquiring an AFM force curve at each point. Within MicroManager, the user draws a path via the Freehand Line or Segmented Line tools and specifies the step size between points as well as the initial cantilever position [80]. For each point, the motors attempt to move to the desired location. An image is taken at the new location and stitched together with existing images using Phase Cross- Correlation to determine the actual AFM movement that occurred [81]. If the AFM cantilever is within tolerance of the desired position, then a force curve is acquired. Otherwise, the motors make additional movements until the cantilever position is within tolerance (Fig. 1c). At completion, AutoAFM returns force curves and positions for each of the user- specified points. Full code and a complete pipeline for AutoAFM acquisition is available via https://github.com/cstashko/AutoAFM/. + +## Polyacrylamide hydrogels + +Polyacrylamide (PA) hydrogels of varying rigidities were prepared as described ([82], [83]). Briefly, PA gels of specified rigidities were mixed according to previously reported ratios [83], omitting \(1\%\) potassium persulfate (PPS). Solutions were degassed for 20 minutes, then PPS was added and \(300~\mu \mathrm{L}\) was quickly deposited onto a Rain- \(\mathrm{X^{TM}}\) - coated \(60~\mathrm{mm}\) coverslip and sandwiched with a glutaraldehyde- activated coverslip. After one hour of polymerization, Rain- \(\mathrm{X^{TM}}\) - coated coverslips were removed. Gels were stored in PBS. For shear rheology studies, gels were cast directly onto the baseplate of an AR 2000 rheometer (TA Instruments) and immediately compressed to a barrel shape using a \(25~\mathrm{mm}\) diameter probe. Gels polymerized for two hours at room temperature with a \(1\%\) applied strain at a frequency of 1 rad\*s- 1 as previously described [84]. A Poisson's Ratio of 0.457 was used when calculating the Young's Modulus of PA gels [85]. + +## Micropatterned substrates for AFM control studies + +Photolithography and soft lithography were used to generate polydimethylsiloxane (PDMS, Dow Silicones Corporation) substrates with defined ridge topographies (15 \(\mu \mathrm{m}\) height, \(100~\mu \mathrm{m}\) length, and widths ranging from \(12~\mu \mathrm{m}\) to \(0.5~\mu \mathrm{m}\) ) for use in AFM control studies. Briefly, a silicon + +<--- Page Split ---> + +wafer was plasma treated (5 minutes, Harrick Plasma) and a \(2\mu \mathrm{m}\) tall adhesive layer of SU- 8 2002 (Kayaku Advanced Materials) was cast onto the wafer surface using a spin coater (Laurell Technologies). A \(4\mathrm{cm}\) square was UV patterned onto the adhesive layer using the PRIMO photopatterning system (Alvéole). A second \(15\mu \mathrm{m}\) layer of SU- 8 2010 (Kayaku Advanced Materials) was then cast onto the wafer, and ridge arrays ( \(100\mu \mathrm{m}\) length, \(12\mu \mathrm{m} - 0.5\mu \mathrm{m}\) width, \(30\mu \mathrm{m}\) spacing) were subsequently photopatterned. Patterned wafers were developed using propylene glycol monomethyl ether acetate (PGMEA, Sigma- Aldrich), cleaned with isopropyl alcohol (IPA, Sigma- Aldrich), and dried with n- pentane (Acros Organics) and N2 gas. PDMS was poured over wafer patterns and cured for 15 minutes at \(100^{\circ}\mathrm{C}\) to generate a negative mold. The negative mold was silanized overnight by vapor deposit of trichloro(1H,1H,2H,2H- perfluoroetyl)silane (TFPS, Sigma- Aldrich). A second layer of PDMS was poured over the silanized negative mold, and a glass coverslip was applied to sandwich the layer of PDMS. This PDMS was cured at \(100^{\circ}\mathrm{C}\) for 20 hours to generate a positive mold of ridges adhered to a glass coverslip, which was then used for AFM control studies. Fluorescent beads \(1.0\mu \mathrm{m}\) in diameter (ThermoFisher F8814, 1:500 dilution in water) were allowed to settle into the PDMS for visualization purposes during AutoAFM. + +## Image Registration + +Registration for images with the same scale and orientation was computed using the phase_cross_correlation function from skimage [81]. For images with different scales and orientations, transformations were found by applying a Fourier Mellin Transform [42]. Briefly, images were applied with a band- pass filter followed by a Hanning Window. Images were then transformed using a Fast Fourier Transform (FFT) and magnitudes were log- polar transformed. Translations between these transformed images were calculated using phase_cross_correlation, which can then be used to calculate the rotation and scaling differences in the original images. + +## Neural Network Design + +Networks were designed in Pytorch. Input images were used of size \(224 \times 224 \times 3\) pixels in which the three channels are DAPI, CNA35, and a layer of zeros, which was incorporated for ease of use with existing Python machine learning image loading functions. Within the training dataset, images were loaded with size \(448 \times 448 \times 3\) pixels, transformed via random rotation of 0- 180 + +<--- Page Split ---> + +degrees, randomly flipped with probability 0.5, received adjustments to brightness, contrast, and sharpness, and cropped to \(224 \times 224 \times 3\) to remove any zero pixels resulting from rotation. A mini batch size of 16 was used throughout. For each round of training, samples were randomly split by patient with a training:validation ratio of 0.8:0.2 so that the validation dataset only contained samples from patients that were not included in the training set. Mean squared error was used as a loss function. Learning rate and weight decay were set at 4e- 6 and 4e- 7, respectively. A dropout rate of 0.5 was used for fully- connected layers. Networks were trained for 100 epochs since this is when accuracy for the validation set converges. Code for network visualizations was modified from https://github.com/utkuozbulak/pytorch- cnn- visualizations#gradient- visualization. + +## Human breast tissues + +All human breast tissue specimens were collected prospectively from consenting patients (informed consent provided prior to surgery) undergoing surgery at the University of California, San Francisco, (UCSF) or Duke University Medical Center between 2010 and 2020. Samples were stored and analyzed with deidentified labels to protect patient data in accordance with the procedures outlined in the Institutional Review Board Protocol #10- 03832, approved by the UCSF Committee of Human Resources and the Duke University IRB (Pro00054515). Tissue specimens were flash frozen in OCT (Tissue- Tek) by slow immersion in liquid nitrogen or placement on dry ice and stored at \(- 80^{\circ}\mathrm{C}\) until ready for sectioning. H&Es were performed on an adjacent slide and were scanned using a ZEISS Axio Scan.Z1 digital slide scanner equipped with CMOS and color cameras, 10x, 20x and 40x objectives. H&E- stained tissues were assessed by a pathologist (A.J.I.) to identify regions of interest for AFM measurements. + +## CNA35 Transformation and Purification + +pET28a- EGFP- CNA35 was a gift from Jan Liphardt [52] (Addgene plasmid # 61603). CNA35 was expressed and purified as previously described [86]. Briefly, bacteria were incubated with 5 mL of 2YT media + 100 \(\mu \mathrm{g / mL}\) ampicillin + 25 \(\mu \mathrm{g / mL}\) kanamycin + 1% wt/v glucose overnight at \(30^{\circ}\mathrm{C}\) in a shaking incubator. The next day, the culture was diluted in 50 mL of 2YT media + kanamycin for three hours. The culture was then centrifuged and the supernatant discarded. Then, the sample was digested for cell wall removal for 30 min using 500 \(\mu \mathrm{L}\) of lysis buffer (50 mM Sodium Phosphate dibasic, 20 mM Imidazole, 300 mM NaCl, pH 8.0) supplemented with + +<--- Page Split ---> + +0.125 mg Lysozyme and 1 mM DTT. The sample was sonicated and centrifuged, then CNA35 was isolated from the solubilized supernatant via affinity chromatography (Qiagen Ni- NTA Agarose) according to manufacturer's instructions. The purified protein was supplemented with \(40\%\) glycerol and stored at \(- 20^{\circ}\mathrm{C}\) . Under typical isolation conditions we obtained a final concentration of approximately \(1.5\mathrm{mg / mL}\) CNA35. + +## Collagen/rBM hydrogels with orthotopic implantation of tumor cells + +Rat tail collagen- 1 (High concentration, Corning, Cat. #: 354249) was incubated with \(0.1\%\) acetic acid (non- crosslinked; SOFT) or \(0.1\%\) acetic acid with \(500\mathrm{mM}\) L- ribose (Chem Impex International, Cat. #: 28127) (cross- linked; STIFF) for at least 10 days before preparation of Col1/rBM hydrogels for orthotopic implantation of tumor cells or tumor fragments ([64], [87]). Col1 mixtures were then combined with basement membrane extract (R&D Systems, Cultrex BME, type 2, Pathclear, Cat. #: 3532- 005- 02) (20% final volume), PBS, and 1N NaOH to a slightly acidic pH (pH \(\sim 6.5\) ) as determined by pH strips. Col1/rBM with and without L- ribose was injected orthotopically into a cleared inguinal fat pad and allowed to set for 3- 5 minutes prior to implantation of a PDX tissue fragment approximately \(2\mathrm{x}2\mathrm{mm}\) in size. + +## Breast cancer Patient-Derived Xenografts (PDXs) + +PDX tissues were obtained from Dr. Alana Welm at the Huntsman Cancer Institute, University of Utah, Utah (HCI- 012) or Dr. Michael Lewis at Baylor College of Medicine, San Antonio, Texas (BCM- 3143B and BCM- 3963) ([65], [66]). For the PDX study, \(2\mathrm{x}2\mathrm{cm}\) breast tumor specimens were collected as fresh tissue with immersion in media (phenol red free- DMEM/F12) with \(10\%\) charcoal- stripped fetal bovine serum (FBS Benchmark, Cat. #: 100- 106) and GlutaMAX (Gibco, Cat. #: 35050- 061) supplementation for transportation to the Weaver laboratory at UCSF. PDX fragments were established from frozen and maintained in NOD- SCID immunodeficient mice. Once established tumors reached experimental endpoint, mice were sacrificed, and tumor tissue was divided into pieces for formalin fixation and paraffin embedding, embedding and freezing in OCT, and flash freezing in liquid nitrogen and cryopreservation in \(95\%\) FBS: \(5\%\) DMSO. Flash frozen tumor pieces were used for RNA and protein isolation for the downstream applications indicated. + +<--- Page Split ---> + +## Animals and Animal Care + +Animal husbandry and all procedures on mice were carried out in Laboratory Animal Resource Center (LARC) facilities at UCSF Parnassus in accordance with the guidelines stipulated by the Institutional Animal Care Use Committee (IACUC) protocols, #AN133001 and #AN179766, which adhere to the NIH Guide for the Care and Use of Laboratory Animals. NOD/SCID mice were purchased from Jackson Laboratories for orthotopic implantation assays. Mice were sacrificed twelve weeks after injection or at humane endpoint, and the tumors were excised and examined for tumor volume using calipers, histology by H&E of fixed tissue sections, proliferation and growth factor and integrin signaling via immunofluorescence in tissue sections, and gene expression using RNAseq and RT- PCR. + +## Monitoring of Tumor growth and metastasis + +Tumor growth was monitored by palpation and caliper measurement weekly or biweekly. Lung metastases were quantified by counting of surface lesions at time of animal sacrifice, and by examination of histological lung sections stained by H&E. Lungs were scanned using a ZEISS Axio Scan.Z1 digital slide scanner equipped with CMOS and color cameras, 10x, 20x and 40x objectives, and lesion area was determined by tracing metastatic lesions in QuPath [88]. + +## Quantitative Reverse Transcriptase-polymerase chain reaction (qRT-PCR) + +RNA was prepared from flash- frozen and pulverized mammary tumor tissues using TRIZol reagent (Invitrogen). Reverse transcription reactions were performed using M- MLV reverse transcriptase (Biochain, Cat. #: Z5040002) with random hexamer primers. cDNA was mixed with PerfeCTa SYBR Green FastMix (Quantibio, Cat. #: 95072- 05K) for qPCR analysis using an Eppendorf realplex2 epgradient S mastercycler. Thermal cycling conditions were 10 min at 95 \(^\circ \mathrm{C}\) , followed by 40 cycles of 15s at 95 \(^\circ \mathrm{C}\) and 45 s at 65 \(^\circ \mathrm{C}\) . Melting curve analysis was used to verify primer pair specificity. Relative mRNA expression was determined by the \(\Delta \Delta \mathrm{CT}\) method with normalization to GAPDH, 18S or KRT8. + +## Quantitative polymerase chain reaction (qPCR) Arrays + +Human EMT qPCR arrays were purchased from Qiagen (Cat. #: PAHS- 021Z), performed as described using RNA from PDX mammary tumors grown in SOFT and STIFF Col1/rBM + +<--- Page Split ---> + +hydrogels, and analyzed using available product resources from Qiagen. Selected genes were plotted for presentation in Figure 4 and Extended Figure 6. + +# Immunofluorescence + +Immunofluorescence was performed using the following specific antibodies: phospho- FAK (Y397) (Cell Signaling Technology, Cat. #: 8556, 1:200), phospho- p44/42 MAPK (ERK1/2) (T202/Y204) (Cell Signaling Technology, Cat. #: 9101, 1:200), Integrin \(\beta 1\) , activated (SigmaAldrich, clone HUTS- 4, Cat. #: MAB2079Z, 1:400), phospho- Myosin Light Chain 2 (Ser19) (Cell Signaling Technology, Cat. #: 3671, 1:200), SLUG (C19G7) (Cell Signaling Technology, Cat. #: 9585, 1:200), ZEB1 (E2G6Y) (Cell Signaling Technology, Cat. #: 70512), and Anti- ErbB2 / HER2 [3B5] (ab16901). For cryopreserved samples, frozen sections were fixed in 2- 4% paraformaldehyde, prior to permeabilization with 1- 3% triton- x- 100 and incubation with primary antibodies overnight at 4°C with 3 \(\mu \mathrm{g / mL}\) CNA35 where specified. Sections were then incubated with species- specific secondary antibodies conjugated to different fluorophores (AF- 555, - 647, Invitrogen). All washes were carried out using Phosphate- buffered saline (PBS) with 0.5% Tween- 20 and nuclei and/or actin filaments were counterstained using 4',6- diamidino- 2- phenylindole (DAPI, Cat. #: D1306) and Phalloidin- AF488 conjugate (Thermo Fisher Scientific, Cat. #: A12379), respectively. For FFPE samples, antigen retrieval was accomplished by boiling sections in 10 mM citrate buffer in a pressure cooker on high power for 3 minutes. Following blocking with 10% goat serum and 1% BSA in Tris- Buffered Saline (TBS), sections were incubated with primary antibodies overnight at 4°C with 3 \(\mu \mathrm{g / mL}\) CNA35. Sections were incubated for 1 hour with species- specific secondary antibodies conjugated to different fluorophores (AF- 555, - 647, Invitrogen). All washes were carried out using TBS with 0.025% Triton X- 100 and nuclei were counterstained using DAPI. Images of stained sections were acquired on either a Leica TCS SP5 Confocal microscope or an inverted Eclipse Ti- E Nikon microscope with CSU- X1 spinning disk confocal (Yokogawa Electric Corporation), 405 nm, 488 nm, 561, 635 nm lasers; a Plan Apo VC 60X/1.40 Oil or an Apo LWD 40X/1.15 Water- immersion \(\lambda \mathrm{S}\) objective; electronic shutters; a charge- coupled device (CCD) camera (Clara; Andor) and controlled by Metamorph. + +# Image Analysis + +<--- Page Split ---> + +For STIFMap generation, immunostaining images are first resized to the same resolution as the panels used to train the neural networks. Then, the image is decomposed into squares the same dimensions as the network training panels and separated by a user- defined step size that is smaller than the panel side length. The elasticity of each square is predicted using five independently trained models with different brightness, sharpness, and contrast transformations. Since elasticity predictions only apply to panel centers where the AFM cantilever would make contact, the elasticity of pixels between panel centers is inferred using cubic spine interpolation. STIFMaps are depicted as collagen pseudocolored to reflect the predicted elasticity of each position. + +Image analysis of percent positive area in PDX samples was performed using ImageJ and QuPath software ([88], [89]). For comparison, immunofluorescence images were subjected to same- level thresholding based on a determined range of positive fluorescence intensity in each channel and antibody staining panel and the threshold area was expressed as a percentage of whole cell or nuclear area using DAPI staining measured in the same manner. + +## RNA-seq library preparation, sequencing, and analysis + +RNA was isolated using TRIzol (Invitrogen, Cat. #: 15596018) followed by chloroform extraction. RNAseq library preparation was performed by the Functional Genomics Laboratory (FGL), a QB3- Berkeley Core Research Facility at UC Berkeley. Total RNA samples were checked on a Bioanalyzer (Agilent) for quality and only high- quality RNA samples (RIN \(>8\) ) were used. At the FGL, Oligo (dT)25 magnetic beads (Thermofisher) were used to enrich mRNA, and the treated RNAs were rechecked on the Bioanalyzer for their integrity. The library preparation for sequencing was done on Biomek FX (Beckman) with the KAPA hyper prep kit for RNA (now Roche). Truncated universal stub adapters were used for ligation, and indexed primers were used during PCR amplification to complete the adapters and to enrich the libraries for adapter- ligated fragments. Samples were checked for quality on an AATI (now Agilent) Fragment Analyzer. Samples were then transferred to the Vincent J. Coates Genomics Sequencing Laboratory (GSL), another QB3- Berkeley Core Research Facility at UC Berkeley, where Illumina sequencing libraries were prepared. qPCR was used to calculate sequence- able molarity with the KAPA Biosystems Illumina Quant qPCR Kits on a BioRad CFX Connect thermal cycler. Libraries were pooled evenly by molarity and sequenced on an Illumina + +<--- Page Split ---> + +NovaSeq6000 150PE S4. Raw sequencing data were converted into fastq format, sample- specific files using the Illumina bcl2fastq2 software on the sequencing centers local Linux server system. RNAseq fastq files were mapped to the primary assembly of the Gencode v33 human genome using Rsubread (version 2.0.1) and counted using featureCounts. Lowly expressed genes were filtered out if they did not have at least one count per million (CPM) in at least 4 samples. Data normalization was performed using calcNormFactors in edgeR (version 3.28.1). Gene ontology was performed using Gage (version 2.36.0) with gene lists from MSigDB version 7.2. + +## Nuvera Dataset Analysis + +Nuvera dataset AnalysisNuvera patient microarray data was obtained from GSE25066 using GEOquery (v2.60.0) [90]. Expression intensities were normalized between patients using the 'normBetweenArrays' function in the R package limma (v3.48.3) [91]. Gene set enrichment scores were computed using GSVA (v1.40.1) to estimate the abundance of each 'Hallmark' ('H' collection) gene set from MSIGDBR (v7.4.1) as well as a list of the 12 most highly expressed collagen genes [92]. All collagen genes were removed from Hallmark gene sets to prevent artifically high correlations due to the same gene being included in both sets. Correlations between GSVA scores were plotted in Python using Seaborn (v0.11.2) and Matplotlib (v3.5.1). Kaplan- Meier curves and statistical testing was conducted in Python using the 'lifelines' package (v.0.27.0). All analysis code is available via GitHub repository https://github.com/cstashko/STIFMaps. + +## Statistical Analysis + +Statistical AnalysisUnless otherwise stated, statistical analyses were performed using GraphPad Prism Version 9.1.2 or SciPy Version 1.7.3. Statistical tests used as well as significance is noted in the corresponding figure legends. Tests of normality were used to determine the appropriate statistical test. All independent variables are described in the text with measurements always from distinct samples (biological replicates) unless otherwise stated. All tests are two- tailed unless otherwise indicated. + +## Illustrations + +IllustrationsThe AutoAFM feedback system schematic (Fig. 1c) was created using BioRender (licensed to V.M.W.). The AlexNet visualization (Fig. 2a) was created using NN- SVG (http://alexlenail.me/NN- SVG/AlexNet.html). + +<--- Page Split ---> + +DATA AVAILABILITY + +DATA AVAILABILITYThe authors declare that all data supporting the findings of this study are available within this publication and its extended data. PDX RNAseq data has been deposited in NCBI's Gene Expression Omnibus [93] and are accessible through GEO Series accession number GSE179983 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179983). Neural networks, training data, stain imaging, and STIFMaps are available at https://github.com/cstashko/STIFMaps. AutoAFM part files and assembly instructions are available at https://github.com/cstashko/AutoAFM. + +CODE AVAILABILITY STATEMENTAll code necessary to implement STIFMaps is available via the Github repository https://github.com/cstashko/STIFMaps. AutoAFM code is available at https://github.com/cstashko/AutoAFM. All other code used in the preparation of this manuscript is publicly available from software and commercial sources. + +DECLARATION OF COMPETING INTERESTS + +The authors declare no competing interests. + +ACKNOWLEDGEMENTS + +ACKNOWLEDGEMENTSWe thank Nataliya Korets for care and handling of animals and for tissue histology. We also thank John Eichorst and Austin Edwards at the Biological Imaging Development Center for microscopy support, Dylan Romero at the UCSF Library Makers Lab for 3D printing components for AutoAFM, Joanna Ho for hardware engineering consultations, and Ilona Berestjuk for immunofluorescent protocol contributions. PDX tissues were obtained from Dr. Alana Welm (Huntsman Cancer Institute, University of Utah) and Dr. Michael Lewis (Baylor College of Medicine). RNAseq was conducted by QB3 Genomics (QB3 Genomics, UC Berkeley, Berkeley, CA, RRID:SCR_022170) supported by NIH S10 OD018174 Instrumentation Grant. pET28a- EGFP- CNA35 was a gift from Jan Liphardt. AlexNet Pytorch implementation code was obtained from https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py and network visualization code modified from https://github.com/utkuozbulak/pytorch- cnn- + +<--- Page Split ---> + +visualizations. We acknowledge support by the CIHR fellowship to M- A.G and R35 CA197623 to K.P. This work was supported by R35 CA242447- 01A1, R01 CA222508 and The Mark Foundation for Cancer Research to V.M.W. + +## AUTHOR CONTRIBUTIONS + +C.S. and V.M.W. conceived and designed the study. C.S. and V.M.W. directed the studies. C.S. performed all AFM analysis and subsequent imaging. C.S. implemented AutoAFM computer vision and STIFMap neural networks and developed image analysis pipelines. C.S. performed RNAseq and gene set enrichment analyses. M- K.H., J.J.N., and C.S. performed IHC staining and imaging for human tissue samples. J.J.N. and M- K.H. completed all animal studies. J.J.N. performed collagen/rBM hydrogel studies in vivo with PDX tissues. J.J.N. and M- K.H. performed H&E and IHC staining and qRT- PCR analysis of mouse tissues. A.J.I. assessed mouse and human tissue pathology. N.P. designed and assembled AutoAFM mounts. L.M. performed PRIMO PDMS fabrication under supervision from M.K. 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Paszek, M.J., et al., Tensional homeostasis and the malignant phenotype. Cancer Cell, 909 2005. 8(3): p. 241- 54. 910 59. Rubashkin, M.G., et al., Force engages vinculin and promotes tumor progression by 911 enhancing PI3K activation of phosphatidylinositol (3,4,5)- triphosphate. Cancer Res, 912 2014. 74(17): p. 4597- 611. 913 60. Mekhdjian, A.H., et al., Integrin- mediated traction force enhances pavillin molecular 914 associations and adhesion dynamics that increase the invasiveness of tumor cells into a 915 three- dimensional extracellular matrix. Mol Biol Cell, 2017. 28(11): p. 1467- 1488. 916 61. Wei, S.C., et al., Matrix stiffness drives epithelial- mesenchymal transition and tumour 917 metastasis through a TWIST1- G3BP2 mechanotransduction pathway. Nat Cell Biol, 918 2015. 17(5): p. 678- 88. 919 62. Nguyen- Ngoc, K.V., et al., ECM microenvironment regulates collective migration and 920 local dissemination in normal and malignant mammary epithelium. 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Wels, C., et al., Transcriptional activation of ZEB1 by Slug leads to cooperative 934 regulation of the epithelial- mesenchymal transition- like phenotype in melanoma. J Invest 935 Dermatol, 2011. 131(9): p. 1877- 85. + +<--- Page Split ---> + +936 68. Rye, I.H., et al., Intratumor heterogeneity defines treatment-resistant HER2+ breast tumors. Mol Oncol, 2018. 12(11): p. 1838- 1855. 937 69. Kiio, T.M. and S. Park, Nano- scientific Application of Atomic Force Microscopy in Pathology: from Molecules to Tissues. Int J Med Sci, 2020. 17(7): p. 844- 858. 940 70. Barnes, J.M., et al., A tension- mediated glycocalyx- integrin feedback loop promotes mesenchymal- like glioblastoma. Nat Cell Biol, 2018. 20(10): p. 1203- 1214. 941 71. Chang, H.Y., et al., Artificial Intelligence in Pathology. J Pathol Transl Med, 2019. 53(1): p. 1- 12. 942 72. Niazi, M.K.K., A.V. Parwani, and M.N. Gurcan, Digital pathology and artificial intelligence. Lancet Oncol, 2019. 20(5): p. e253- e261. 943 73. Cui, M. and D.Y. Zhang, Artificial intelligence and computational pathology. Lab Invest, 2021. 101(4): p. 412- 422. 944 74. Saito, A., et al., Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning. Mod Pathol, 2021. 34(2): p. 417- 425. 945 75. Komura, D. and S. Ishikawa, Machine Learning Methods for Histopathological Image Analysis. Comput Struct Biotechnol J, 2018. 16: p. 34- 42. 946 76. van der Laak, J., G. Litjens, and F. Ciompi, Deep learning in histopathology: the path to the clinic. Nat Med, 2021. 27(5): p. 775- 784. 947 77. Janiszewska, M., et al., The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2+ breast cancer. JCI Insight, 2021. 6(11). 948 78. Laklai, H., et al., Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression. Nat Med, 2016. 22(5): p. 497- 505. 949 79. Gruosso, T., et al., Spatially distinct tumor immune microenvironments stratify triple- negative breast cancers. J Clin Invest, 2019. 129(4): p. 1785- 1800. 950 80. Edelstein, A.D., et al., Advanced methods of microscope control using μManager software. J Biol Methods, 2014. 1(2). 951 81. van der Walt, S., et al., scikit- image: image processing in Python. PeerJ, 2014. 2: p. e453. 952 82. Przybyla, L., et al., Monitoring developmental force distributions in reconstituted embryonic epithelia. Methods, 2016. 94: p. 101- 13. 953 83. Lakins, J.N., A.R. Chin, and V.M. Weaver, Exploring the link between human embryonic stem cell organization and fate using tension- calibrated extracellular matrix functionalized polyacrylamide gels. Methods Mol Biol, 2012. 916: p. 317- 50. 954 84. Wisdom, K.M., et al., Matrix mechanical plasticity regulates cancer cell migration through confining microenvironments. Nat Commun, 2018. 9(1): p. 4144. 955 85. Takigawa, T., et al., Poisson's ratio of polyacrylamide (PAAm) gels. Polymer Gels and Networks, 1996. 4(1): p. 1- 5. 956 86. Shi, Q., et al., Rapid disorganization of mechanically interacting systems of mammary acini. Proc Natl Acad Sci U S A, 2014. 111(2): p. 658- 63. 957 87. Northey, J.J., et al., Stiff stroma increases breast cancer risk by inducing the oncogene ZNF217. J Clin Invest, 2020. 130(11): p. 5721- 5737. 958 88. Bankhead, P., et al., QuPath: Open source software for digital pathology image analysis. Sci Rep, 2017. 7(1): p. 16878. 959 89. Schindelin, J., et al., Fiji: an open- source platform for biological- image analysis. Nat Methods, 2012. 9(7): p. 676- 82. + +<--- Page Split ---> + +981 90. Davis, S. and P.S. Meltzer, GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 2007. 23(14): p. 1846-7. 983 91. Ritchie, M.E., et al., limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 2015. 43(7): p. e47. 985 92. Liberzon, A., et al., The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst, 2015. 1(6): p. 417-425. 987 93. Edgar, R., M. Domrachev, and A.E. Lash, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 2002. 30(1): p. 207-10. + +<--- Page Split ---> + +991 MAIN FIGURES + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + + +<--- Page Split ---> + +992 Figure 1. Overview of automated AFM acquisition system. a, Technical drawing of motor mount for interfacing servo motors with AFM translation knobs. b, Assembled motor mounts. 994 The Stage Frame slides along the edge of the stage (orange arrows) while the Motor Frame slides along the Alignment Rods, towards and away from the Stage Frame as the knobs turn (blue arrows). c, Schematic of AutoAFM feedback system. d, Example of AutoAFM feedback with desired AFM sampling positions (blue), actual AFM positions (red), and AFM path of movement and positions outside of the desired (orange). e, f, Representative images of AutoAFM collecting AFM measurements over a whole tissue (e) and a region of interest (f) in a breast tumor section. 1000 Scale bar, \(100\mu \mathrm{m}\) . + +<--- Page Split ---> +![](images/Figure_unknown_1.jpg) + + +<--- Page Split ---> + +1001 Figure 2. A convolutional neural network predicts the Young's Modulus of tissue. a, 1002 Example input- output relationships to the network with diagram depicting connectivity of 1003 different network layers. b, Example image transformations to increase the size of the network 1004 training dataset. c, Correlation between model predictions and actual Young's Modulus values 1005 for the training (blue line) and validation (orange line) datasets over the course of training. Error 1006 bars indicate \(95\%\) confidence intervals across 25 trained models. d, Dot plot of actual versus 1007 predicted Young's Modulus values for the validation datasets across 25 trained models. n = 1008 4768, Pearson \(\mathrm{r} = 0.687\) . e, Saliency maps reflecting image regions that influenced model 1009 predictions. Scale bar, \(20\mu \mathrm{m}\) . + +<--- Page Split ---> +![](images/Figure_unknown_2.jpg) + + +<--- Page Split ---> + +1010 Figure 3. STIFMaps predict high elasticity regions within tissues. a, Deconstruction of a 1011 CNA- and DAPI- stained image into squares of approximately \(50 \times 50 \mu \mathrm{m}\) . The Young's 1012 Modulus of each square is predicted. b, Elasticity predictions are aggregated and overlaid over 1013 collagen to produce the overall STIFMap for both a normal TDLU and triple negative breast 1014 cancer. c, Representative images of immunofluorescent staining for pMLC (top) and activated \(\beta 1\) 1015 integrin (bottom). d, Scatterplots of STIFMap intensity vs stain intensity for each pixel shown in 1016 (c) indicating the \(99^{\mathrm{th}}\) percentile of stain intensity for each STIFMap percentile. e, STIFMap 1017 percentiles versus the \(99^{\mathrm{th}}\) percentile of stain intensity for all acquired fields of view (FOVs). 1018 Error bars indicate a \(95\%\) confidence interval. \(\mathrm{n} = 60\) FOVs from 10 different patient tumor 1019 samples. Median Spearman r values, activated \(\beta 1\) integrin \(= 0.696\) , pMLC \(= 0.364\) . f, Violin 1020 plots of the Spearman correlation for each FOV comparing the \(99^{\mathrm{th}}\) percentile of staining 1021 intensity versus percentiles of DAPI, predicted elasticity, or collagen stain intensity. Internal 1022 gray bars indicate a Box-plot. \(\mathrm{n} = 60\) FOVs from 10 different patient tumor samples. Scale bar, 1023 \(50\mu \mathrm{m}\) . Statistical analyses were performed using Mann- Whitney U test, \(\mathrm{***P< 10^{- 5}}\) . + +<--- Page Split ---> +![](images/Figure_unknown_3.jpg) + + + +![PLACEHOLDER_40_1] + + + +![PLACEHOLDER_40_2] + + +<--- Page Split ---> + +Figure 4. Matrix elasticity associates with EMT in a PDX model of HER2+ breast cancer. a, Schematic showing the strategy for implantation of HER2- positive patient- derived xenograft (PDX) breast cancer tissues in SOFT (Col1/rBM, no L- ribose) and STIFF (Col1/rBM, crosslinked with L- ribose) hydrogels. b,c, Representative images of immunofluorescent staining of active \(\beta 1\) integrin (b) and phospho- FAK (c) in SOFT or STIFF HER2- positive PDX tumors (left). Scale bar, \(50 \mu \mathrm{m}\) . Quantification of average phospho- FAK (b) and active \(\beta 1\) integrin (c) positive cell area for all HER2- positive PDX tumors (right). SOFT; \(\mathrm{n} = 6\) , STIFF; \(\mathrm{n} = 6\) . d, Average number of lung metastases for mice bearing BCM- 3963 PDX tumors in SOFT and STIFF ECM stroma as determined by histological analysis. SOFT; \(\mathrm{n} = 10\) , STIFF; \(\mathrm{n} = 10\) . e, Average size of the metastatic lesions corresponding to the analysis in (d). f, Analysis as in (d) for mice bearing BCM- 3143B PDX tumors. SOFT; \(\mathrm{n} = 10\) , STIFF; \(\mathrm{n} = 10\) . g, Analysis as in (e) for metastatic lesions corresponding to the analysis in (f). h, Gene ontology terms from among the top 23 most significantly upregulated, using RNAseq data derived from all HER2- positive PDX tumors generated in SOFT (n=9) and STIFF (n=9) ECM stroma as above (n=3 for each PDX and condition). i, Volcano plot of p- value (- log10) vs. log fold change (logFC) for gene expression from the HALLMARK_epithelial- to- mesenchymal transition gene set for RNAseq data of HER2- positive PDX tumors developed in SOFT and STIFF ECM stroma. j- m, qRT- PCR arrays designed to examine Epithelial- to- mesenchymal transition related gene expression were used to analyze RNA isolated from PDX tumors developed in SOFT and STIFF ECM stroma. SOFT; \(\mathrm{n} = 7\) , STIFF; \(\mathrm{n} = 7\) . Bar plots for the average relative expression of the indicated mesenchymal genes are displayed. All graphs are presented as mean +/- S.E.M. Statistical tests used were Mann- Whitney U test (c, e, j- m) and unpaired \(t\) - test (b, d, f, g), \*P<0.03, \*\*P<0.002, \*\*\*P<0.0002, ns=non- significant. + +<--- Page Split ---> +![PLACEHOLDER_42_0] + + +<--- Page Split ---> + +Figure 5. EMT markers spatially overlap with high tension matrix and associate with poor survival in patient tumors. a, Pearson correlation between GSVA scores for collagen genes and hallmark pathway genes in the Nuvera dataset. b, Scatterplot of GSVA scores for collagen genes and hallmark EMT genes. Each point represents one patient. \(\mathrm{n} = 508\) patients. Pearson \(\mathrm{r} = 0.880\) . c, Representative FOVs for SLUG staining within FFPE tumors. Scale bar, \(50~\mu \mathrm{m}\) . d,e, Violin plots of the Spearman correlation for each FOV comparing the \(99^{\mathrm{th}}\) percentile of staining intensity versus percentiles of DAPI, predicted elasticity, or collagen stain intensity. Internal gray bars indicate a Box-plot. \(\mathrm{n} = 5\) ZEB1 FOVs and \(\mathrm{n} = 25\) SLUG FOVs. f, Representative whole slide image (WSI) and regions of interest (ROIs) of ZEB1 stain with STIFMap in HER2+ breast cancer cohort. Scale bar (WSI), \(1\mathrm{mm}\) . Scale bar (ROIs), \(100~\mu \mathrm{m}\) . g, Spearman correlation for each whole tissue section comparing the \(99^{\mathrm{th}}\) percentile of staining intensity versus percentiles of predicted elasticity and collagen stain intensity. \(\mathrm{n} = 21\) patient tumor samples. h, Box and whiskers plots to show the association between metastatic recurrence and spatial autocorrelation (Moran's I) for tissue markers and STIFMaps in the HER2+ breast cancer cohort. i,j, Kaplan- Meier curves comparing survival between the upper and lower quartiles of EMT (i) and collagen (j) GSVA scores within the Nuvera cohort. \(\mathrm{n} = 127\) patients in each group. Statistical analyses were performed using Mann- Whitney U, \(\mathrm{*P< 0.05}\) , \(\mathrm{**P< 0.01}\) , \(\mathrm{***P< 10^{-5}}\) . + +<--- Page Split ---> + +1064 EXTENDED DATA + +<--- Page Split ---> + +
ITEM NO.SW-File Name(File Name)DESCRIPTIONPROCESSSUPPLIERMATERIALPART NUMBERQTYPackage QtyUnit Price ($)Order QtyExtended Price ($)
1Microscope Stage_baseAFM Stage BaseINCLUDED1
2Microscope Stage_XAFM Stage X TranslationINCLUDED1
3Microscope Stage_YAFM Stage Y TranslationINCLUDED1
4Microscope Stage_X knobAFM X KnobINCLUDED1
5Microscope Stage_Y knobAFM Y KnobINCLUDED1
6clamp_screw_91290A19Screws frame_main into frame_clampPURCHASEDMCMASTER91290A1904107.1117.11
7clamp_nut_90576A103Screws frame_main into frame_clampPURCHASEDMCMASTER90576A10341004.2714.27
8clamp_washer_93475A2Screws frame_main into frame_clampPURCHASEDMCMASTER93475A23041001.8611.86
9X_frame_mainStage Frame_x3D PRINTED1
10NEMA 17MotorINCLUDEDNEMA 172
11motor_faceplateMotor Bracket3D PRINTED2
12sliderMotor Frame3D PRINTED2
13wedge_1Bracket Adjuster_front3D PRINTED2
14wedge_2Bracket Adjuster_back3D PRINTED2
15wedge_spring_5108N27Springs holding Motor Bracket with Motor FramePURCHASEDMCMASTER9044K113435.26210.52
16wedge_pin_mx36_9159Tensioning PinPURCHASEDMCMASTER91595A1404259.9119.91
17wedge_seal_9092000A0Bracket Adjusting ScrewsPURCHASEDMCMASTER92000A0774509.9419.94
18wedge_nut_90591A250Used with Bracket Adjusting ScrewsPURCHASEDMCMASTER90591A25041002.3312.33
19wedge_washer_97310A 111Used with Bracket Adjusting ScrewsPURCHASEDMCMASTER97310A11141002.8612.86
20slider_pin_91585A389Alignment RodPURCHASEDMCMASTER91585A389414.14416.56
21motor_screw_91290A111Screws Motor into Motor BracketPURCHASEDMCMASTER91290A11141008.7118.71
22X_frame_clampClamps Stage Frame_x onto AFM Stage3D PRINTED1
23knob_capKnob Adapter3D PRINTED2
24knob_screw_90044A247Adapter ScrewPURCHASEDMCMASTER90044A247257.4417.44
255mm_hub_988917106Motor CouplingPURCHASEDMCMASTER9889171062116.08232.16
268mm_hub_988917109Motor CouplingPURCHASEDMCMASTER9889171092116.08232.16
27Acetal_disk_59985K620Motor CouplingPURCHASEDMCMASTER59985K62213.2226.44
28rubber pad_XBetween X_frame_main and X_frame_clampINCLUDED1
29Y_frame_mainStage Frame_y3D PRINTED1
30Y_frame_clampClamps Stage Frame_y onto AFM Stage3D PRINTED1
31rubber pad_YBetween Y_frame_main and Y_frame_clampINCLUDED1
32rollers_1Screw into Y_frame_main from abovePURCHASEDMCMASTER3668K222118.63237.26
33rollers_2Screw into Y_frame_main from belowPURCHASEDMCMASTER3659K112136.88273.76
TOTAL263.29
+ +<--- Page Split ---> + +1065 Extended Table 1. AutoAFM Bill of Materials. List of components required to implement an 1066 AutoAFM system on an existing MFP 3D Bio AFM (Asylum Research). + +<--- Page Split ---> +![PLACEHOLDER_47_0] + + +<--- Page Split ---> + +Extended Figure 1. AutoAFM Principle and Validation. a, Photo of AutoAFM assembly. b, Technical drawing of all main components of the AutoAFM system. c, AutoAFM workflow. d, Overview of PDMS balance beam design (left) and actual fabrication (right) with points overlaid. e, Accuracy of AutoAFM movements along each PDMS beam. Scale bar, \(100\mu \mathrm{m}\) . + +<--- Page Split ---> +![PLACEHOLDER_49_0] + + + +![PLACEHOLDER_49_1] + + + +![PLACEHOLDER_49_2] + + + +![PLACEHOLDER_49_3] + + + +
PA Gel Nominal Stiffness (Pa)Actual Stiffness (Pa)
140146.71
400210.19
1040733.97
+ +![PLACEHOLDER_49_4] + + + +![PLACEHOLDER_49_5] + + + +![PLACEHOLDER_49_6] + + + +![PLACEHOLDER_49_7] + + +<--- Page Split ---> + +Extended Figure 2. AFM Control Experiments. a, Young's Moduli of polyacrylamide (PA) gels of different nominal elasticities measured using shear rheology. \(\mathrm{n} = 4\) gels of each elasticity. b, Time course of the same tissue region probed with AFM every thirty minutes for 3.5 hours. c, Elasticity of different tissue positions probed with AFM at different velocities. \(\mathrm{n} = 12\) positions. d, Representative images of AFM cantilevers used. e, AFM cantilever artifact in the average image for an AutoAFM scan. f, 5 \(\mu \mathrm{m}\) ball position on the end of an AFM cantilever. g, AFM cantilever ball positions fit onto the AFM artifact from an average image (e). Statistical analyses used were performed using Mann-Whitney U test, ns=non- significant. Scale bar, \(100\mu \mathrm{m}\) . + +<--- Page Split ---> +![PLACEHOLDER_51_0] + + +<--- Page Split ---> + +Extended Figure 3. Image Stitching and Overlaying. a, Pipeline for Fourier-Mellin Transformation. The confocal DAPI image was downsampled (bottom left) to better resemble the AFM image (top left). Then, both images were processed with a Bandpass Filter, Hanning Window, and Log- Polar Transformation. Translational differences between the final images were converted into scaling and rotation differences in the original images. b, Overall translation of AutoAFM data from a low- resolution AFM microscope image onto the high- resolution confocal image. c, Deviation in cell positions after transformation. \(\mathrm{n} = 50\) cell positions from five samples. Scale bar, \(50\mu \mathrm{m}\) . + +<--- Page Split ---> +![PLACEHOLDER_53_0] + + + +![PLACEHOLDER_53_1] + + + +![PLACEHOLDER_53_2] + + + +![PLACEHOLDER_53_3] + + +<--- Page Split ---> + +Extended Figure 4. Imaging Sensitivity Analysis. a, Scatterplots of stain intensity vs predicted stiffness (left), collagen intensity (middle), or DAPI intensity (right) shown for all pixels (blue) or aggregated to show the \(99^{\text{th}}\) percentile of stain intensity for each percentile of the indicated independent variable (red) for the representative pMLC stain shown in Fig. 3c. Aggregating data into percentiles is necessary to limit the influence of image regions where cells are not interacting with the ECM. b, Sensitivity analysis of the average Spearman correlation coefficient as shown in (a) for stain intensity compared to DAPI, collagen, and STIFMap depending on the stain threshold used. \(\mathrm{n} = 60\) FOVs from 10 patient tumor samples. c, Representative FOV indicating pixels that are at the interface between cells and collagen. d, Sensitivity analysis of the average Spearman correlation coefficient depending on the stain threshold used when only masked pixels are included. \(\mathrm{n} = 60\) FOVs from 10 patient tumor samples. Scale bar, \(50\mu \mathrm{m}\) . + +<--- Page Split ---> +![PLACEHOLDER_55_0] + + +<--- Page Split ---> + +1098 Extended Figure 5. Collagen Morphology Validation in FFPE Tissue. Five FOVs for an 1099 FFPE (top) or cryopreserved tissue (bottom) taken from the same patient stained with DAPI and 1100 CNA35. Scale bar, \(50\mu \mathrm{m}\) . + +<--- Page Split ---> +![PLACEHOLDER_57_0] + + +<--- Page Split ---> + +Extended Figure 6. A stiff stroma enhances mechanosignaling, tumor growth, metastasis, and mesenchymal gene expression in HER2- positive breast cancer patient- derived xenografts. a- c, Graphs showing average tumor growth in SOFT and STIFF matrices for the HER2- positive PDX models indicated as determined by caliper measurement. SOFT and STIFF, \(\mathrm{n = 10}\) each for BCM- 3963 and BCM3143B, \(\mathrm{n = 4}\) each for HCI- 012. d, Representative images of immunofluorescence staining of phospho- ERK in SOFT and STIFF HER2- positive PDX tumors (left). Scale bar, \(50\mu \mathrm{m}\) . Quantification of average phospho- ERK positive cell area for all HER2- positive PDX tumors (right). SOFT; \(\mathrm{n} = 6\) , STIFF; \(\mathrm{n} = 6\) . e, Representative images of lung metastases for mice bearing BCM- 3143B PDX tumors in SOFT and STIFF ECM stroma. Scale bar, \(100\mu \mathrm{m}\) . f- k, Graphs showing RT- PCR analysis of RNA extracted from HER2- positive PDX tumors with SOFT and STIFF matrices showing relative gene expression for the indicated mesenchymal and epithelial genes. SOFT; \(\mathrm{n} = 7\) , STIFF; \(\mathrm{n} = 7\) . l,m, Percentage of mice bearing HER2- positive PDX tumors with SOFT and STIFF matrices presenting detectable lung metastases. SOFT and STIFF, \(\mathrm{n = 10}\) each for BCM- 3963 and BCM3143B. All graphs are presented as mean +/- S.E.M. Statistical tests used were Mann- Whitney U test (f- k), unpaired \(t\) - test (d) and two- way ANOVA (with Bonferroni's multiple comparisons test) (a- c). \*P<0.03, \*\*P<0.002, \*\*\*P<0.0002, ns=non- significant. + +<--- Page Split ---> +![PLACEHOLDER_59_0] + +
a
+ +![PLACEHOLDER_59_1] + +
b
+ +![PLACEHOLDER_59_2] + +
d
+ +![PLACEHOLDER_59_3] + +
c
+ +![PLACEHOLDER_59_4] + + +<--- Page Split ---> + +1118 Extended Figure 7. Additional Patient EMT Staining Data. a, Representative WSI and ROIs for immunofluorescence staining of SLUG in a TNBC sample. Scale bar (WSI), 100 \(\mu \mathrm{m}\) . Scale bar (ROIs), 50 \(\mu \mathrm{m}\) . b, Quantification of the 99th percentile of SLUG staining intensity for each percentile of predicted matrix elasticity for the image shown in (a). c, Representative HER2 stain from the HER2+ breast cancer cohort. Scale bar, 1 mm. d, Correlation between HER2 intensity and either collagen intensity or predicted stiffness in the HER2+ cohort. \(\mathrm{n} = 21\) patient tumor samples. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +rs.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28_det.mmd b/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..b1d8bd59027419e1abc4271d6fa88153cbc6362a --- /dev/null +++ b/preprint/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28/preprint__09e9f783051f85aba41aac3aa9df36479c9f31b14fd396fee288daa064471a28_det.mmd @@ -0,0 +1,703 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 950, 275]]<|/det|> +# STIFMap employs a convolutional neural network to reveal spatial mechanical heterogeneity and tension-dependent activation of an epithelial to mesenchymal transition within human breast cancers + +<|ref|>text<|/ref|><|det|>[[44, 297, 746, 339]]<|/det|> +Valerie Weaver ( \(\boxed{\bullet}\) Valerie.weaver@ucsf.edu ) University of California, San Francisco https://orcid.org/0000- 0003- 4786- 6752 + +<|ref|>text<|/ref|><|det|>[[44, 344, 594, 386]]<|/det|> +Connor Stashko Department of Surgery, University of California, San Francisco + +<|ref|>text<|/ref|><|det|>[[44, 391, 594, 433]]<|/det|> +Mary- Kate Hayward Department of Surgery, University of California, San Francisco + +<|ref|>text<|/ref|><|det|>[[44, 438, 594, 480]]<|/det|> +Jason Northey Department of Surgery, University of California, San Francisco + +<|ref|>text<|/ref|><|det|>[[44, 485, 288, 525]]<|/det|> +Neil Pearson BioQ Pharma Incorporated + +<|ref|>text<|/ref|><|det|>[[44, 530, 621, 572]]<|/det|> +Alastair Ironside Department of Pathology, Western General Hospital, NHS Lothian + +<|ref|>text<|/ref|><|det|>[[44, 577, 604, 618]]<|/det|> +Johnathon Lakins 1 Department of Surgery, University of California, San Francisco + +<|ref|>text<|/ref|><|det|>[[44, 623, 604, 665]]<|/det|> +Marie- Anne Goyette Department of Medical Oncology, Dana- Farber Cancer Institute + +<|ref|>text<|/ref|><|det|>[[44, 670, 903, 732]]<|/det|> +Lakyn Mayo Department of Cell and Tissue Biology, School of Dentistry, University of California, San Francisco https://orcid.org/0000- 0002- 6642- 2332 + +<|ref|>text<|/ref|><|det|>[[44, 738, 620, 779]]<|/det|> +Hege Russnes Oslo University Hospital https://orcid.org/0000- 0001- 8724- 1891 + +<|ref|>text<|/ref|><|det|>[[44, 785, 192, 825]]<|/det|> +E Hwang Duke University + +<|ref|>text<|/ref|><|det|>[[44, 831, 744, 872]]<|/det|> +Matthew Kutsy University of California San Francisco https://orcid.org/0000- 0002- 0752- 649X + +<|ref|>text<|/ref|><|det|>[[44, 877, 664, 919]]<|/det|> +Kornelia Polyak Dana- Farber Cancer Institute https://orcid.org/0000- 0002- 5964- 0382 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 46, 102, 64]]<|/det|> +## Article + +<|ref|>title<|/ref|><|det|>[[44, 84, 135, 102]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 121, 328, 140]]<|/det|> +Posted Date: October 24th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 159, 474, 179]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2063113/v1 + +<|ref|>text<|/ref|><|det|>[[42, 197, 910, 240]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 88, 878, 160]]<|/det|> +STIFMap employs a convolutional neural network to reveal spatial mechanical heterogeneity and tension- dependent activation of an epithelial to mesenchymal transition within human breast cancers + +<|ref|>text<|/ref|><|det|>[[77, 193, 876, 290]]<|/det|> +Connor Stashko1,2, Mary- Kate Hayward1,2,\*, Jason J. Northey1,2,\*, Neil Pearson, Alastair J. Ironside3, Johnathon N. Lakins1,2, Marie- Anne Goyette4, Lakyn Mayo5, Hege Russnes6,7, E. Shelley Hwang8, Matthew Kutys5,9, Kornelia Polyak4, Valerie M. Weaver1,2,9,10,\* + +<|ref|>text<|/ref|><|det|>[[70, 325, 848, 365]]<|/det|> +1Department of Surgery, University of California, San Francisco, California, USA. 2Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA. + +<|ref|>text<|/ref|><|det|>[[70, 366, 840, 384]]<|/det|> +3Department of Pathology, Western General Hospital, NHS Lothian, Edinburgh, UK + +<|ref|>text<|/ref|><|det|>[[70, 386, 783, 404]]<|/det|> +4Department of Medical Oncology, Dana- Farber Cancer Institute, Boston, MA + +<|ref|>text<|/ref|><|det|>[[70, 405, 835, 424]]<|/det|> +5Department of Cell and Tissue Biology, School of Dentistry, University of California, San + +<|ref|>text<|/ref|><|det|>[[70, 425, 352, 442]]<|/det|> +6Francisco, San Francisco, CA. + +<|ref|>text<|/ref|><|det|>[[70, 444, 772, 463]]<|/det|> +7Department of Pathology and Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway. + +<|ref|>text<|/ref|><|det|>[[70, 465, 520, 483]]<|/det|> +8Department of Surgery, Duke University Medical Center, Durham, NC. + +<|ref|>text<|/ref|><|det|>[[70, 485, 770, 503]]<|/det|> +9UCSF Helen Diller Comprehensive Cancer Center, University of California, San + +<|ref|>text<|/ref|><|det|>[[70, 504, 520, 522]]<|/det|> +10Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration + +<|ref|>text<|/ref|><|det|>[[70, 524, 642, 542]]<|/det|> +11Institute for Clinical Medicine, University of Oslo, Norway. + +<|ref|>text<|/ref|><|det|>[[70, 543, 686, 562]]<|/det|> +12Department of Surgery, Duke University Medical Center, Durham, NC. + +<|ref|>text<|/ref|><|det|>[[70, 563, 760, 582]]<|/det|> +13UCSF Helen Diller Comprehensive Cancer Center, University of California, San + +<|ref|>text<|/ref|><|det|>[[70, 583, 352, 600]]<|/det|> +14Francisco, San Francisco, CA. + +<|ref|>text<|/ref|><|det|>[[70, 601, 780, 620]]<|/det|> +15Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration + +<|ref|>text<|/ref|><|det|>[[70, 621, 742, 639]]<|/det|> +16Medicine and Stem Cell Research, University of California, San Francisco, San + +<|ref|>text<|/ref|><|det|>[[70, 640, 234, 657]]<|/det|> +17Francisco, CA. + +<|ref|>text<|/ref|><|det|>[[70, 658, 497, 676]]<|/det|> +18These authors contributed equally to this work + +<|ref|>sub_title<|/ref|><|det|>[[70, 677, 338, 694]]<|/det|> +## Corresponding Author: + +<|ref|>text<|/ref|><|det|>[[70, 696, 525, 714]]<|/det|> +19Valerie M. Weaver + +<|ref|>text<|/ref|><|det|>[[70, 715, 523, 733]]<|/det|> +20Center for Bioengineering and Tissue Regeneration + +<|ref|>text<|/ref|><|det|>[[70, 734, 290, 750]]<|/det|> +21Department of Surgery + +<|ref|>text<|/ref|><|det|>[[70, 751, 377, 768]]<|/det|> +22513 Parnassus Avenue, 565 HSE + +<|ref|>text<|/ref|><|det|>[[70, 769, 427, 786]]<|/det|> +23University of California, San Francisco + +<|ref|>text<|/ref|><|det|>[[70, 787, 333, 803]]<|/det|> +24Telephone: (415) 476- 3826 + +<|ref|>text<|/ref|><|det|>[[70, 805, 374, 821]]<|/det|> +25Email: valerie.weaver@ucsf.edu + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 112, 886, 581]]<|/det|> +Intratumor heterogeneity in breast cancer associates with poor patient outcome. Tissue fibrosis and stromal stiffening accompany breast cancer development and associate with the aggressiveness of human breast cancer subtypes. Whether human breast cancers demonstrate stiffness heterogeneity, and if this is linked to breast tumor aggression remains unclear. To answer these questions, we developed a spatial method to measure the stiffness heterogeneity in human breast tumor tissues that also quantifies the local stromal stiffness each cell experiences and permits correlation with biomarkers of tumor aggression. Here, we present Spatially Transformed Inferential Force Maps (STIFMaps) to predict the elasticity across whole tissue sections with micron- resolution. The method exploits computer vision to precisely automate AFM indentation and then uses a trained convolutional neural network to predict matrix elasticity using collagen morphological features and ground truth AFM data. Because STIFMaps is compatible with biomarker staining we used the approach to register high- elasticity regions within sections of human breast tumors with markers of mechanical activation and an epithelial to mesenchymal transition (EMT) that associated with tumor aggression. The findings herein highlight the utility of STIFMaps for assessing the mechanical heterogeneity of human breast tissues across length scales from single cells to whole tissues. The method also reveals, for the first time, a direct association between stromal stiffness and EMT, thereby implicating stromal stiffness as a driver of human breast cancer aggression. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 114, 884, 318]]<|/det|> +Intratumor heterogeneity (ITH) is a feature of breast tumors ([1], [2], [3], [4]). Tumor heterogeneity predicts poor patient outcome as diversification of genetic, phenotypic and behavioral characteristics within a tumor supports progression, metastasis, and treatment resistance ([5], [6], [7]). Accordingly, much effort has been directed towards defining ITH and clarifying how it drives tumorigenesis ([8], [9]). Towards this goal, the ability to decipher the causal relationship between tumor heterogeneity and tumor behavior relies heavily on the availability of accurate and quantitative methods with which to measure and analyze individual features of the tumor. + +<|ref|>text<|/ref|><|det|>[[112, 324, 884, 710]]<|/det|> +Tumor tissue variability is mediated, in part, by intrinsic stochastic gene expression as well as by genetic and epigenetic differences in the transformed cells. Sophisticated approaches including genetic tags and high throughput sequencing have permitted researchers to detect genomic abnormality at the single cell level to provide important insights into clonal evolution and have linked these findings to patient survival ([10], [11], [12]). State of the art spatial RNA sequencing (RNAseq) analysis has revealed underlying spatial associations between stress response gene expression profiles in cancer cells and inflammatory fibroblast gene signatures [13]. Importantly, tumors are organs comprised of transformed cells interacting with a diverse cellular and acellular stroma. Consistently, in situ multiplexing approaches have revealed wide diversity with respect to the frequency and phenotype of tumor- infiltrating immune cells and have used these findings to predict patient outcomes as well as checkpoint inhibitor responsiveness [14]. In situ immunofluorescence has also illustrated wide variability in oncogenic signaling, cellular metabolism and stress responsiveness between the epithelial and stromal cells within the hypoxic tumor core, and those cells that localize to the fibrotic tumor periphery, to predict treatment response in patients ([15], [16]). + +<|ref|>text<|/ref|><|det|>[[112, 715, 884, 893]]<|/det|> +An important feature of all solid tumors is the remodeled and crosslinked extracellular matrix (ECM) that generates a stiffened, fibrotic stroma characterized by markedly reorganized interstitial collagens [17]. A stiff ECM modifies cell and nuclear shape, disrupts tissue organization, promotes cell growth, viability, and invasion, alters gene expression, and can even induce an epithelial to mesenchymal transition (EMT) in cells cultured in two- and three- dimensional substrates ([18]). Within experimental tumors in vivo, the stiffened tumor ECM promotes solid stress, disrupts vascular integrity to drive hypoxia and tumor aggression, and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 884, 265]]<|/det|> +85 compromises drug delivery [19]. The stiff tumor ECM also increases cytokine and chemokine expression to promote myeloid cell infiltration and can even impede CD8 T cell infiltration, migration, and function [20]. Clinically, the level of tissue fibrosis correlates with worse patient outcome, and in situ analysis of human breast cancer tissues revealed that a stiff, fibrotic ECM associates with tumor progression as well as clinical subtype ([21], [22], [23]). Nevertheless, whether stromal stiffness tracks with human breast cancer aggression and, if so, how remains unclear. + +<|ref|>text<|/ref|><|det|>[[110, 272, 884, 870]]<|/det|> +To clarify links between stromal stiffness heterogeneity and tumor aggression, approaches are needed that can be combined with state- of- the- art spatial genomics, proteomics, and multiplexing protocols ([24], [25], [26]). Although techniques do exist with which to monitor ECM heterogeneity and organization including H&E, second harmonic generation (SHG), trichrome, and picrosirius red (PS red) staining, none can be directly combined with immunostaining on the same slide ([21], [27], [28]). Moreover, these protocols do not provide quantitative insight into mechanically soft and stiff regions within the tumor. Methods to directly measure ECM stiffness include shear rheology, Atomic Force Microscopy (AFM), Magnetic Resonance Elastography, Sonoelastography, and unconfined compression analysis ([29], [30], [31], [32]). However, these approaches do not provide high resolution spatial and morphology information, particularly with respect to the state of the collagenous ECM. Although stromal stiffness can be measured directly with sub- micron resolution using Atomic Force Microscopy (AFM), current AFM methods are time- consuming, poorly resolved spatially, and require specialized equipment not readily available to most research and clinical laboratories ([33], [34]). An automated AFM developed by Plodinec and colleagues can rapidly quantify the material properties of tumor biopsies, but the method does not provide imaging of the probed tissue nor the positions from where measurements are taken. Thus, while the approach is useful for characterizing cell and tissue biomechanical properties, it is not suitable for studies whose goal is to link elasticity of the ECM to biological markers of tumor and stromal phenotype, genotype, and heterogeneity [35]. Importantly, all of the currently available approaches to quantify cell and stromal stiffness require manipulation of either fresh or cryopreserved tissue, precluding comprehensive spatial analysis of elasticity in archived formalin- fixed paraffin- embedded (FFPE) sections in tissue banks. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 885, 475]]<|/det|> +Here, we present a novel approach termed Spatially Transformed Inferential Force Mapping (STIFMap) that is able to visualize the heterogeneous stiffness landscape of normal and tumor breast tissues with micron- resolution and to spatially register this tension phenotype with biological markers of tumor aggression. The method works on both cryopreserved and FFPE tissues and employs a single quick, inexpensive collagen stain that is visualized with standard fluorescence microscopy. The approach permits simultaneous quantification of the tension landscape of the stromal ECM together with co- staining for cell or ECM biomarkers of interest, and lends itself to quick assessment of the impact of biophysical ECM heterogeneity on tumor aggression. The method can be readily integrated with spatial proteomics and genomics as well as standard protein marker multiplexing protocols. To illustrate the potential of the approach we applied STIFMap to explore the relationship between stromal stiffness and markers of tumor aggression in human breast cancers. We were able to link tissue mechanics with indicators of mechanosignaling and biomarkers of EMT previously implicated in tumor aggression and treatment resistance ([36], [37]). The results highlight the potential utility of using stromal biophysical features to predict tumor behavior and possibly even patient outcome. + +<|ref|>sub_title<|/ref|><|det|>[[115, 508, 203, 525]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[115, 533, 546, 552]]<|/det|> +## Design and development of an automated AFM system + +<|ref|>text<|/ref|><|det|>[[112, 559, 886, 893]]<|/det|> +AFM has emerged as the method of choice to spatially analyze stromal stiffness in tumors ([38], [39]). However, executing AFM analysis is cumbersome, specialized, and not easily amenable to spatial registration with sequential in situ analysis and imaging. To improve upon these pitfalls, we developed AutoAFM to facilitate high- throughput, spatially- resolved acquisition of AFM data. We automated AFM movements by affixing servo motors onto the X and Y translation knobs of the AFM stage with custom- made, 3D- printed motor mounts (Fig. 1a, b, Extended Table 1, Extended Fig. 1a, b, Methods). Scripts were developed to enable the AFM to move along a user- specified path (Fig. 1c, d). The system was designed so that as the AFM moves from one point to the next, a feedback loop reports on the current position of the AFM and fine- tunes movements to poke the specimen within a user- designated tolerance of the desired positions. All movements and imaging were designed to be conducted using epifluorescence of propidium iodide- stained cells to guide the measurements. This strategy was chosen to remove artifacts from the cantilever shadow that could potentially be introduced into the images during + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 884, 188]]<|/det|> +stitching (Extended Fig. 1c). The system was engineered so that a completed AutoAFM scan will provide the location of each AFM force curve acquired over the tissue section being measured (Fig. 1e, f). The AutoAFM was designed such that scans can be acquired across as many points as the operator desires and are only spatially limited by the overall X- Y range of the AFM stage. + +<|ref|>text<|/ref|><|det|>[[115, 220, 689, 239]]<|/det|> +Assessment of AutoAFM precision and validation of AFM measurements + +<|ref|>text<|/ref|><|det|>[[112, 245, 884, 450]]<|/det|> +To validate movements of the AutoAFM, a series of elevated PDMS beams of varying widths were fabricated using photolithography followed by PDMS soft lithography (Extended Fig. 1d). Using this strategy, the height at which the AFM contacts the sample is known, so force curves collected on the beams registered as much higher than those collected on the surrounding PDMS surface. To determine the resolution limit of AutoAFM, we used the AFM to 'walk' along each beam and measured the accuracy of the AFM to contact the beam at each width. The measurements indicated that movements of the automated AFM are precise to within a few microns (Extended Fig. 1e). + +<|ref|>text<|/ref|><|det|>[[111, 455, 884, 868]]<|/det|> +The Young's Modulus of an AFM cantilever is calibrated before measurements are performed (Methods). Nevertheless, the cantilever modulus can change over the course of data collection due to protein and cell debris deposition onto the cantilever. To ensure that the stiffness of the cantilever remained consistent throughout the measurements, we measured the elasticity of polyacrylamide (PA) gels of known Young's Moduli before and after probing each sample. The elasticities of the PA gels used were validated using shear rheology (Extended Fig. 2a). AFM measurements on each tissue were collected over 1- 2 hours. To verify that tissues did not degrade over the timespan of AFM measurements, we collected force curves at the same tissue positions over a defined length of time. AFM measurements collected in the same positions every 30 minutes for three hours revealed no noticeable differences in elasticity, indicating that tissue degradation does not occur over the timespan that AutoAFM measurements were acquired for this study (Extended Fig. 2b). Because tissues are viscoelastic substrates, the rate of force loading impacts the resulting force curve [40]. Accordingly, highly viscous substrates will appear stiffer when poked faster if they are assumed to be purely elastic. To address this potential anomaly an AFM velocity of 2 \(\mu \mathrm{m / s}\) was chosen since Young's Moduli measurements were constant at this rate (Extended Fig. 2c). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 321]]<|/det|> +To avoid any potential for tip fouling we chose AFM cantilevers that were triangular with \(5\mu \mathrm{m}\) spherical beads incorporated onto the cantilever tip (Extended Fig. 2d; [41]). To stitch together images taken during AutoAFM, the bead location was estimated for each image. To estimate the bead location within the image, the average image for each AutoAFM scan was taken, which revealed a faint but distinct outline of the cantilever (Extended Fig. 2e). This occurred because the stronger PI signals from the cells move during AutoAFM acquisition, but the faint cantilever image remains in the same position throughout. Five cantilevers with known bead locations were aligned with the average scan images to indicate the actual position of the bead during imaging (Extended Fig. 2f, g). + +<|ref|>sub_title<|/ref|><|det|>[[115, 352, 585, 371]]<|/det|> +## Overlaying AutoAFM Measurements onto Confocal Images + +<|ref|>text<|/ref|><|det|>[[112, 377, 884, 740]]<|/det|> +In an effort to ensure visual spatial alignment between tissue morphological features and elasticity measurements, the AutoAFM measurements were overlaid with nuclear staining via alignment with AutoAFM PI positions. DAPI measurements were collected via confocal imaging at either \(40\mathrm{x}\) or \(63\mathrm{x}\) magnification, while AutoAFM PI images were collected at \(20\mathrm{x}\) magnification. A pipeline was then developed to translate low- resolution AutoAFM images onto high- resolution DAPI imaging (code available on GitHub). To do so, the two images were first manually pre- aligned so that the fields of view were similar, and the confocal DAPI image was downsampled to more closely resemble the resolution of the AutoAFM image (Extended Fig. 3a). Thereafter, we applied a Fourier- Mellin Transform to determine the scale and rotation of the AutoAFM image relative to the DAPI image [42]. Finally, translation between the two images was computed using phase contrast cross- correlation. Using this transformation matrix, AFM positions were mapped onto the high- resolution images (Extended Fig. 3b). The average mapping error was found to be \(2.57\mu \mathrm{m}\) , estimated by monitoring nuclei positions before and after transformation ( \(95\%\) confidence interval: \(2.09 - 3.06\mu \mathrm{m}\) ) (Extended Fig. 3c). + +<|ref|>sub_title<|/ref|><|det|>[[115, 773, 705, 793]]<|/det|> +## Deep learning model of tissue young's modulus from collagen morphology + +<|ref|>text<|/ref|><|det|>[[112, 799, 884, 898]]<|/det|> +Interstitial fibrillar collagens are the major structural component of breast tissue ([43], [44], [45], [46]). As such, we reasoned that the elasticity of breast tissue could be inferred based on the morphology of interstitial collagen fibers, particularly given that stiff collagen fibers are thick and highly linear whereas more compliant collagen fibers are typically more dispersed, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 370]]<|/det|> +relaxed, and present as wavy fibers [47]. Although most investigators have used SHG imaging or PS red staining to visualize interstitial fibrillar collagens, collagen SHG imaging is susceptible to interference from additional fluorophores on co- stained tissues [48], and PS red coloring depends on the angle of the slide on the microscope relative to the polarizer [28]. Accordingly, we chose to stain the collagens using the collagen- binding adhesion protein 35- Orange Green 488 fusion protein (CNA35- OG488) ([49], [50]). CNA contains two subdomains, N1 and N2, which engage in a ‘collagen hug’ around triple helical collagen in the ECM [51]. CNA staining was selected because it is cheap to produce, plasmid sequences are freely available online, and the stain is not species specific [52]. The CNA stain is also rapid and easy to perform, and can be viewed on conventional fluorescent microscopes, lending itself to standard research laboratories as well as a clinical lab format. + +<|ref|>text<|/ref|><|det|>[[112, 376, 885, 630]]<|/det|> +To register collagen morphological features with tissue stiffness, a convolutional neural network (CNN) was applied using CNA and DAPI imaging as the inputs and the corresponding AFM measurements as the output (Fig. 2a). A CNN was chosen due to its superiority compared to alternative models with image classification tasks [53]. We reasoned that a CNN would be able to learn how factors such as collagen fiber linearity, thickness, and proximity to cells impacts elasticity better than alternative models. Different CNN architectures were applied to predict tissue elasticity including ResNet, DenseNet, and models discovered using Neural Architecture Search, but the best performance came from an AlexNet modified for a regression output instead of classification (https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py). + +<|ref|>text<|/ref|><|det|>[[112, 636, 885, 867]]<|/det|> +Neural networks are data hungry, such that their performance is greatly improved when more data is utilized. When given a small amount of training data, neural networks tend to overfit the training dataset and emphasize features that do not generalize well. To address this, we artificially enlarged our training dataset of a thousand data points by applying random rotations, mirroring, and adjustments to brightness, contrast, and sharpness (Fig. 2b). Based upon the fact that the Young’s Modulus of the sample is independent of these manipulations, we reasoned this would allow the model to learn which features were the most informative while preventing overfitting. Consistently, we found that the model generalized much better to validation data when transformations were applied to the training data. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 423]]<|/det|> +In the final model, we utilized both the DAPI and collagen channels. As dead cells are typically quite soft when probed using AFM, the additional information from the DAPI stain helped the model to learn and was included in our imaging studies. We also natural log- transformed elasticity measurements prior to training to alleviate the influence of outliers. At training completion, the correlation of predicted to actual Young's Moduli values was 0.689 (pearson R value; averaged across 25 trained models) when trained over 100 epochs (Fig. 2c, d), which performed significantly better than predicting elasticity based on the intensity of collagen and DAPI alone (multivariable (MV) regression line using all training and validation samples; r = 0.574; p(CNN over MV) = 5.23e- 10). The validation data is predicted more accurately than the training data as a result of the transformations applied to the training dataset. Saliency maps indicating image regions that contribute to tissue stiffness demonstrated that the trained models were able to incorporate morphological information from nuclei as well as collagen when predicting stiffness (Fig. 2e) ([54], [55], [56], [57]). + +<|ref|>sub_title<|/ref|><|det|>[[115, 456, 315, 474]]<|/det|> +## Generation of STIFMaps + +<|ref|>text<|/ref|><|det|>[[112, 480, 886, 896]]<|/det|> +We next applied our trained CNNs to predict the elasticity of normal and tumor breast tissue sections across a region of interest using Spatially Transformed Inferential Force Maps (STIFMaps). We achieved this objective by segmenting the images into squares matching the input dimensions of the neural network and predicting the Young's Moduli for each square (Fig. 3a, Methods). We then colorized the original images to correspond to the predicted stiffness of each point (Fig. 3b). To validate the performance of these stiffness predictions, tissues were immunostained for two established markers of cellular mechanosignaling, activated \(\beta 1\) integrin and phospho- Myosin Light Chain 2 (pMLC2) that are typically increased in cells in response to a stiff ECM ([58], [59]). We used the predicted STIFMaps to evaluate the correlation between expression of these markers and tissue elasticity (Fig. 3c). Since a large proportion of the ECM is not directly in contact with cells, we looked at the \(99^{\text{th}}\) percentile of stain intensity for each percentile of ECM elasticity (Fig. 3d, Extended Fig. 4a, b). This allowed us to remove low- intensity pixels where there were no cells or stain present. The intensity of both mechanosignaling markers was found to positively correlate with the predicted Young's Modulus of the local tissue region (Fig. 3e), but not with the intensity of collagen or DAPI alone (Fig. 3f). We also applied a mask to better identify pixels located at the cell- ECM interface and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 883, 186]]<|/det|> +observed the same trend (Extended Fig. 4c, d). The findings indicate that STIFMaps can accurately identify mechanical 'hotspots' within breast tumor tissue sections, thus providing an additional layer of information about the mechanical landscape of breast tissue that was not previously possible. + +<|ref|>sub_title<|/ref|><|det|>[[115, 219, 658, 239]]<|/det|> +## Utilizing STIFMaps with Formalin-Fixed Paraffin-Embedded Tissue + +<|ref|>text<|/ref|><|det|>[[112, 245, 884, 555]]<|/det|> +FFPE tissues are frequently used for clinical analysis because this approach preserves cell and tissue morphology. Unlike cryopreserved tissues, which are needed for traditional AFM analysis, FFPE tissue are more readily available for research analysis and clinical translational studies. However, FFPE tissues are highly cross- linked due to formalin- fixation, and thus impossible to accurately measure stiffness by AFM. Accordingly, we asked if STIFMap could predict the elasticity of the original, unfixed tissue samples based solely on collagen morphology. We stained terminal duct lobular units (TDLUs) from cryo- preserved and FFPE breast tissues with CNA and DAPI. In consultation with a clinical breast cancer pathologist, we detected no discernable morphological differences between the collagen morphology detected using the CNA collagen stain in a patient- matched FFPE versus cryopreserved tissue (Extended Fig. 5). The results indicate that STIFMaps can be applied to predict the elasticity of FFPE tissues in which elasticity measurements are not currently possible. + +<|ref|>text<|/ref|><|det|>[[115, 585, 737, 604]]<|/det|> +A stiff, fibrotic collagenous ECM drives an EMT and tumor metastasis in mice. + +<|ref|>text<|/ref|><|det|>[[113, 611, 884, 737]]<|/det|> +STIFMaps revealed a spatial co- localization between tumor cells with activated \(\beta 1\) integrin, elevated actomyosin contractility (pMLC) and high- tension regions in the stroma of breast cancer tissues (Fig. 3e). The findings illustrate the feasibility of this method to more directly demonstrate clinical links between known regulators of breast tumor aggression and tissue tension. + +<|ref|>text<|/ref|><|det|>[[113, 743, 884, 895]]<|/det|> +A stiff ECM can foster the growth, survival, and invasiveness of cultured premalignant and tumorigenic breast cancer cell lines by inducing an EMT ([60], [61], [62], [63]). A stiff, cross- linked collagenous stroma can also induce an EMT to promote tumor aggression and metastasis in vivo in experimental murine models of mammary cancer [64]. Nevertheless, there is currently no evidence to directly implicate a stiff, fibrotic tissue stroma in human breast cancer aggression and metastasis, nor to link this phenotype to induction of an EMT. Therefore, to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 85, 884, 716]]<|/det|> +directly test whether a stiff stroma could drive the aggressiveness and metastatic behavior of human breast cancers, and to determine if this is linked to an EMT, we manipulated HER2+ human breast cancer patient- derived xenografts (PDX) in vivo. We reasoned that PDXs are a model that more closely mirrors the heterogeneous phenotype of human breast tumors ([65], [66]). To assess this, we implanted three independent HER2+ human breast cancer PDXs (BCM- 3963; BCM- 3143B, HCI- 012) embedded within control (SOFT; 140 Pa) and non- metabolizable L- ribose cross- linked (STIFF; 1,200- 2,000 Pa) collagen gels into the fat pads (orthotopic) of NOD/SCID mice and monitored the impact on tumor phenotype and behavior (Fig. 4a). Immunofluorescence analysis revealed a significant increase in activated \(\beta 1\) integrin and phospho- Y397 focal adhesion kinase activity ( \(^{139}\mathrm{FAK}\) ) in the PDX tumors that developed within the stiffened collagen gels (Fig. 4b, c). We observed an increase in tumor outgrowth in all three independent HER2+ PDX tumors embedded within the stiffened collagen gels (Extended Fig. 6a- c). Markers of growth factor receptor signaling, as indicated by elevated phosphorylated MAP Kinase (pERK; Extended Fig. 6d), indicated that tissue tension and integrin signaling promoted tumor cell growth. There was also a greater number of larger metastatic lesions quantified in the lungs of the mice harboring the stiff tumors (Fig. 4 d- g). Consistent with a relationship between a stiff stroma, breast tumor aggression, and induction of an EMT, RNAseq analysis revealed a significant elevation of the 'Hallmark Epithelial Mesenchymal Transition' pathway in STIFF PDX tumors (Fig. 4h, i, MSIGDB pathway M5930). RT- PCR analysis validated the stiffness induction of the expression of several of the EMT genes including Vimentin (VIM), TWIST1, SLUG, and MMP2 (Fig. 4j- m, Extended Fig. 6f- k). These findings demonstrate that a stiff stroma induces integrin and growth factor receptor signaling to drive tumor aggression and metastasis of human breast tumor PDXs in vivo. The data also implicate stromal stiffness- dependent induction of an EMT in this phenotype. + +<|ref|>text<|/ref|><|det|>[[115, 744, 581, 763]]<|/det|> +STIFMaps link stromal elasticity to EMT in patient tumors. + +<|ref|>text<|/ref|><|det|>[[114, 769, 884, 896]]<|/det|> +Having established that high ECM tension can drive the aggressiveness and metastasis of HER2+ PDX breast tumors in association with induction of an EMT, we next applied STIFMaps to look for clinical evidence supporting this relationship. We previously showed using AFM and immunofluorescence analysis that the more aggressive triple- negative breast cancer (TNBC) and HER2+ human breast cancer subtypes have higher levels of activated \(\beta 1\) integrin and a stiffer + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 84, 886, 740]]<|/det|> +invasive front ([21], [22]). We applied STIFMaps to explore if there was a significant association between stromal tension and EMT markers in clinical FFPE samples of TNBC and HER2+ breast tumors. We first looked within patient transcriptomic data and found that expression of collagen genes highly correlated with expression of EMT genes (Fig. 5a, b). Collagen genes were removed from all gene sets to not bias this analysis. We then stained TNBC FFPE tissue sections for ZEB1 and SLUG, two transcription factors induced by a stiff stroma previously implicated in EMT [67]. We detected expression of both ZEB1 and SLUG and found their levels to be significantly positively correlated with the predicted elasticity of the interstitial collagens in the stroma, but not individually with total collagen or DAPI intensity (Fig. 5c- e). A trend was also observed when a mounted TNBC tissue was subjected to a full imaging scan (Extended Fig. 7a, b). To determine the broader relevance of these clinical findings we next applied STIFMaps to a cohort of 21 HER2+ breast tumors with associated clinical follow- up data [68]. We co- stained these FFPE tissue sections with HER2 and ZEB1 as well as with CNA35 to stain tissue collagens and had a pathologist outline the tumor in each whole slide image. The predicted tissue elasticity from STIFMaps was significantly associated with ZEB1 stain intensity, but not with HER2, when compared to the correlation with collagen intensity alone (Fig. 5f, g, Extended Fig. 7c, d). We then looked at the spatial autocorrelation of each stain in each tissue by calculating Moran’s I, which revealed a trend showing that greater clustering of HER2, ZEB1, and elasticity associated with metastatic recurrence (Fig. 5h). This observation is consistent with worse overall survival among patients with high expression of EMT and collagen gene expression signatures (Fig. 5i, j). These findings demonstrate, for the first time, a spatial link between high stromal collagen elasticity and biomarkers of EMT in both TNBCs and HER2+ human breast tumors. Together with our PDX findings, these data link EMT to ECM stiffness and implicate tension- induced EMT in human breast tumor metastasis. The findings also suggest a stiff ECM could promote tumor aggression and compromise breast cancer patient outcome. + +<|ref|>sub_title<|/ref|><|det|>[[115, 769, 234, 787]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[114, 794, 884, 893]]<|/det|> +Here we present a new method we term Spatially Transformed Inferential Force Mapping, STIFMap, which permits the spatial resolution and quantification with micron- resolution of the mechanical heterogeneity of the collagenous stroma within normal and tumor breast tissues. The method works on both cryopreserved and FFPE tissues and employs a quick, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 884, 606]]<|/det|> +inexpensive staining protocol via CNA35 and DAPI. The approach permits simultaneous quantification of the tension landscape of the stromal ECM together with standard biomarker immunostaining approaches, and could be integrated with spatial proteomics and genomics as well as protein marker multiplexing protocols ([24], [25], [26]). Although methods do exist to broadly quantify tissue elasticity across a tissue section, they do not provide high- resolution spatial information [35]. AFM is a technique that directly probes tissue elasticity at the single cell scale [69]. However, standard AFM methods are not high throughput, require fresh or cryopreserved tissue, rely upon specialized equipment and operators, are time- consuming, and only collect sparely spaced data points over focused sections of a tissue ([33], [34]). In the absence of the AutoAFM algorithms presented herein, it is also challenging to overlay AFM data with biomarker staining, and the use of cryopreserved or fresh tissue compromises simultaneously conducting spatial genomic, transcriptomic, or proteomic analyses. STIFMaps overcomes current shortcomings of conventional AFM methods and can rapidly annotate the elasticity landscape of an entire tissue section with a simple collagen stain. The method is also amenable to FFPE tissue thereby expanding the scope and application of the method. Indeed, using STIFMap we were able to link, for the first time in clinical biopsies of human breast cancer, tissue mechanics with indicators of mechanosignaling and biomarkers of an EMT previously implicated in tumor aggression ([36], [37]). The results highlight the potential utility of using STIFMaps to quantify stromal biophysical features to predict tumor behavior and ultimately patient outcome. + +<|ref|>text<|/ref|><|det|>[[112, 611, 884, 894]]<|/det|> +We and others showed both in vitro and in experimental models in vivo that a stiff ECM increases integrin mechanosignaling to foster tumor cell growth, survival, invasion and metastasis, and that this is accompanied by induction of an EMT ([60], [70], [61]). Here we demonstrate, for the first time using human breast tumor PDX biospecimens, that a stiff stroma can induce an EMT in vivo and that this is accompanied by metastasis. By correlating stromal tension with biomarkers of an EMT in human clinical specimens of TNBC and HER2+ breast cancer, we found clinical evidence that such a relationship also exists within human tumors, thereby providing validation of the experimental manipulations. Furthermore, we showed that expression of either collagen genes or an EMT signature associates with significantly worse patient outcome. The findings thereby directly link stromal tension to human breast cancer aggression and directly implicate induction of an EMT in this phenotype. Although STIFMaps + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 883, 187]]<|/det|> +provides researchers with a versatile tool to explore the role of stromal stiffness in clinical specimens, additional studies will be necessary to further clarify mechanisms through which a stiff ECM drives an EMT in tumors. Moreover, more work will be needed to assess the clinical relevance of stromal stiffness on patient outcome. + +<|ref|>text<|/ref|><|det|>[[112, 193, 884, 606]]<|/det|> +There is growing interest in the application of artificial intelligence methods to classify clinical histological images ([71], [72], [73]). While early deep learning algorithms focused on routine tasks such as nuclei segmentation, current state- of- the- art algorithms are beginning to rival pathologists at tasks such as tumor grading and cancer detection [71]. Moving beyond what pathologists are able to detect, some new algorithms are even able to predict tumor recurrence and invasive potential in cohorts for which there is currently no means available for evaluating risk to progression [74]. Notwithstanding these advances, a number of caveats hinder development in this area such as suboptimal network architectures, the requirement for large numbers of samples, the immense computational processing power necessary to train highly sophisticated models, overfitting data that is generated by only one individual or group, and the difficulty in interpreting why deep learning models classify results in one group or another [75]. Nevertheless, improvements in deep learning such as neural architecture search to find more optimal networks and advancements in computational power continue to make computational pathology more mainstream and accessible in the clinic. While there are still issues to overcome, deep learning algorithms appear to be the future of histopathological analysis and tissue classification [76]. + +<|ref|>text<|/ref|><|det|>[[112, 611, 884, 893]]<|/det|> +Tumors are highly heterogeneous at the genomic, transcriptomic, and proteomic levels ([1], [4]). Regions within human and murine tumors have been identified in which immune infiltration, cancer cell metabolism, and stress response pathways exhibit diverse phenotypes. Given that patient prognosis and outcomes have been linked to genomic heterogeneity, as well as variability in immune infiltration and hormone receptor expression, it is perhaps not surprising that there is a growing interest in understanding the relevance of and drivers of tumor heterogeneity [77]. In this regard, the level of tissue fibrosis also predicts patient outcome and recent data suggest the level and organization of tissue collagens and stromal stiffness varies widely within a patient's tumor ([78], [79], [21]). Yet, to date there are no tools with which to spatially resolve the mechanical stromal heterogeneity within a tumor and none that are amenable to scanning across a full tissue section of a tumor. With STIFMaps, it is now possible + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 266]]<|/det|> +to evaluate the association between a biomarker or pathway of interest and the local and heterogeneous elasticity of the collagen- rich stroma within a given normal or malignant tissue. Moreover, the STIFMap method can be combined with spatial sequencing, in situ gene expression, metabolomics, and even proteomics to allow for unbiased screening of correlations between molecular heterogeneity and mechanically regulated pathways in clinical samples. Accordingly, STIFMaps opens the door for clinicians and translational researchers to explore the impact that tissue elasticity has on cancer cells in their native tissue microenvironments. + +<|ref|>sub_title<|/ref|><|det|>[[114, 300, 213, 317]]<|/det|> +## METHODS + +<|ref|>sub_title<|/ref|><|det|>[[114, 325, 382, 344]]<|/det|> +## Atomic Force Microscopy (AFM) + +<|ref|>text<|/ref|><|det|>[[111, 350, 885, 690]]<|/det|> +AFM measurements were performed using an MFP- 3D BIO Inverted optical AFM (Asylum Research, Santa Barbara, CA) mounted on a Nikon TE2000- U inverted fluorescent microscope (Melville, NY) and placed on a vibration- isolation table (Herzan TS- 150). Silicon nitride cantilevers were used with a nominal spring constant of \(0.06 \mathrm{N m - 1}\) and a borosilicate glass spherical tip with \(5 \mu \mathrm{m}\) diameter (Novascan Tech). Cantilevers were calibrated using the thermal fluctuation method and verified by probing polyacrylamide gels of known elasticity. The specimens used were \(20 \mu \mathrm{m}\) thick OCT- embedded frozen human breast tissue sections thawed and equilibrated to room temperature by immersion in PBS for 5 minutes. Thawed sections were immersed in PBS containing phosphatase inhibitor (GenDEPOT Xpert #P3200- 001), protease inhibitor cocktail (GenDEPOT Xpert # P3100- 001), \(5 \mu \mathrm{g / mL}\) propidium iodide (ACROS, Cat# 440300250), and \(3 \mu \mathrm{g / mL}\) of CNA35- OG488. Specimens were indented at \(2 \mu \mathrm{m}\) per second loading rate. The Young's Moduli of the samples were determined by fitting force curves with the Hertz model using a Poisson ratio of 0.5. + +<|ref|>sub_title<|/ref|><|det|>[[114, 725, 383, 742]]<|/det|> +## AFM Forceplot Fitting Algorithm + +<|ref|>text<|/ref|><|det|>[[111, 749, 885, 900]]<|/det|> +AFM force plots were post- processed to obtain Young's Moduli using a homemade algorithm (see GitHub repository). Briefly, force plots were smoothed using a moving average convolution across 100 datapoints to remove noise and then baseline- corrected using the first third of the AFM indentation curve. The contact point of each force curve was estimated as the point at which the derivative of the force curve increased above an empirically- determined threshold. Then, a more precise contact point was determined by applying a minimization function to fit a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 162]]<|/det|> +flat baseline plus a 1.5- power curve (Hertz Model) onto the AFM data using the estimated contact point as an initial guess. With the contact point determined, the Young's Modulus was calculated to minimize the squared error between the AFM data and the fit curve + +<|ref|>sub_title<|/ref|><|det|>[[115, 196, 256, 213]]<|/det|> +## AutoAFM Design + +<|ref|>text<|/ref|><|det|>[[111, 216, 884, 737]]<|/det|> +The AutoAFM assembly's function is to ensure proper alignment of the motor relative to the microscope stage's adjustment knob in order to allow the motor to accurately control the knob's rotation and thus the movement of the microscope stage. It does this by supporting the weight of the motor and controlling its position, while allowing the motor to freely slide along its shaft axis. It also allows the operator to fine- tune the motor's position and orientation in space, ensuring good alignment with (and therefore accurate control of) the stage's adjustment knob. The assembly has three main components: the Stage Frame, the Motor Frame, and the Motor Bracket. The motors screw into the Motor Brackets via the four Motor Screws. The Motor Bracket sits in the Motor Frame and is pulled down against the Bracket Adjusters by the springs hooked around the Tensioning Pins. By turning the Bracket Adjusting Screws, the Bracket Adjusters can be individually moved forward and backwards, adjusting both the pitch and the roll of the Motor Bracket relative to the axis of the motor shaft. This allows easy manual adjustment of the motor to ensure good alignment between the motor shaft and the stage knob. The Stage Frame is hooked over the lip of the microscope stage, enclosing the stage's fine adjustment knob (not shown), and is able to slide freely along the edge of the stage. The Alignment Rods are press- fit into the Stage Frame and slip- fit into the Motor Frame, allowing the Motor Frame to slide freely towards and away from the Stage Frame along the motor shaft axis. The Motor Coupling joins the motor shaft to the Knob Adapter, which is screwed into the fine adjustment knob via the Adapter Screw. The Y Stage Frame has rollers attached to reduce friction with the AFM stage as it slides side- to- side. + +<|ref|>text<|/ref|><|det|>[[111, 742, 883, 867]]<|/det|> +Motor mount components were 3D printed on either a Prusa MINI+ or LulzBot Mini 2 with PETG. A high infill was used for ease of sanding. Some dimensions were slightly oversized so that they could be gradually sanded to fit snugly. See https://github.com/cstashko/AutoAFM/STL for a full list of part STL files. Other components were ordered from McMaster- Carr. See supplementary table 1 for a full Bill of Materials. Motors were driven via an Arduino Mega 2560 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 580, 108]]<|/det|> +Rev3 Classic microcontroller interfacing with RAMPS 1.4 + +<|ref|>text<|/ref|><|det|>[[112, 115, 418, 134]]<|/det|> +(https://reprap.org/wiki/RAMPS_1.4). + +<|ref|>text<|/ref|><|det|>[[111, 140, 875, 420]]<|/det|> +AutoAFM operates by moving the AFM cantilever to user- defined positions and acquiring an AFM force curve at each point. Within MicroManager, the user draws a path via the Freehand Line or Segmented Line tools and specifies the step size between points as well as the initial cantilever position [80]. For each point, the motors attempt to move to the desired location. An image is taken at the new location and stitched together with existing images using Phase Cross- Correlation to determine the actual AFM movement that occurred [81]. If the AFM cantilever is within tolerance of the desired position, then a force curve is acquired. Otherwise, the motors make additional movements until the cantilever position is within tolerance (Fig. 1c). At completion, AutoAFM returns force curves and positions for each of the user- specified points. Full code and a complete pipeline for AutoAFM acquisition is available via https://github.com/cstashko/AutoAFM/. + +<|ref|>sub_title<|/ref|><|det|>[[115, 456, 325, 474]]<|/det|> +## Polyacrylamide hydrogels + +<|ref|>text<|/ref|><|det|>[[111, 480, 886, 765]]<|/det|> +Polyacrylamide (PA) hydrogels of varying rigidities were prepared as described ([82], [83]). Briefly, PA gels of specified rigidities were mixed according to previously reported ratios [83], omitting \(1\%\) potassium persulfate (PPS). Solutions were degassed for 20 minutes, then PPS was added and \(300~\mu \mathrm{L}\) was quickly deposited onto a Rain- \(\mathrm{X^{TM}}\) - coated \(60~\mathrm{mm}\) coverslip and sandwiched with a glutaraldehyde- activated coverslip. After one hour of polymerization, Rain- \(\mathrm{X^{TM}}\) - coated coverslips were removed. Gels were stored in PBS. For shear rheology studies, gels were cast directly onto the baseplate of an AR 2000 rheometer (TA Instruments) and immediately compressed to a barrel shape using a \(25~\mathrm{mm}\) diameter probe. Gels polymerized for two hours at room temperature with a \(1\%\) applied strain at a frequency of 1 rad\*s- 1 as previously described [84]. A Poisson's Ratio of 0.457 was used when calculating the Young's Modulus of PA gels [85]. + +<|ref|>sub_title<|/ref|><|det|>[[115, 797, 520, 816]]<|/det|> +## Micropatterned substrates for AFM control studies + +<|ref|>text<|/ref|><|det|>[[112, 822, 884, 898]]<|/det|> +Photolithography and soft lithography were used to generate polydimethylsiloxane (PDMS, Dow Silicones Corporation) substrates with defined ridge topographies (15 \(\mu \mathrm{m}\) height, \(100~\mu \mathrm{m}\) length, and widths ranging from \(12~\mu \mathrm{m}\) to \(0.5~\mu \mathrm{m}\) ) for use in AFM control studies. Briefly, a silicon + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 510]]<|/det|> +wafer was plasma treated (5 minutes, Harrick Plasma) and a \(2\mu \mathrm{m}\) tall adhesive layer of SU- 8 2002 (Kayaku Advanced Materials) was cast onto the wafer surface using a spin coater (Laurell Technologies). A \(4\mathrm{cm}\) square was UV patterned onto the adhesive layer using the PRIMO photopatterning system (Alvéole). A second \(15\mu \mathrm{m}\) layer of SU- 8 2010 (Kayaku Advanced Materials) was then cast onto the wafer, and ridge arrays ( \(100\mu \mathrm{m}\) length, \(12\mu \mathrm{m} - 0.5\mu \mathrm{m}\) width, \(30\mu \mathrm{m}\) spacing) were subsequently photopatterned. Patterned wafers were developed using propylene glycol monomethyl ether acetate (PGMEA, Sigma- Aldrich), cleaned with isopropyl alcohol (IPA, Sigma- Aldrich), and dried with n- pentane (Acros Organics) and N2 gas. PDMS was poured over wafer patterns and cured for 15 minutes at \(100^{\circ}\mathrm{C}\) to generate a negative mold. The negative mold was silanized overnight by vapor deposit of trichloro(1H,1H,2H,2H- perfluoroetyl)silane (TFPS, Sigma- Aldrich). A second layer of PDMS was poured over the silanized negative mold, and a glass coverslip was applied to sandwich the layer of PDMS. This PDMS was cured at \(100^{\circ}\mathrm{C}\) for 20 hours to generate a positive mold of ridges adhered to a glass coverslip, which was then used for AFM control studies. Fluorescent beads \(1.0\mu \mathrm{m}\) in diameter (ThermoFisher F8814, 1:500 dilution in water) were allowed to settle into the PDMS for visualization purposes during AutoAFM. + +<|ref|>sub_title<|/ref|><|det|>[[115, 543, 269, 560]]<|/det|> +## Image Registration + +<|ref|>text<|/ref|><|det|>[[113, 567, 884, 744]]<|/det|> +Registration for images with the same scale and orientation was computed using the phase_cross_correlation function from skimage [81]. For images with different scales and orientations, transformations were found by applying a Fourier Mellin Transform [42]. Briefly, images were applied with a band- pass filter followed by a Hanning Window. Images were then transformed using a Fast Fourier Transform (FFT) and magnitudes were log- polar transformed. Translations between these transformed images were calculated using phase_cross_correlation, which can then be used to calculate the rotation and scaling differences in the original images. + +<|ref|>sub_title<|/ref|><|det|>[[115, 779, 304, 796]]<|/det|> +## Neural Network Design + +<|ref|>text<|/ref|><|det|>[[113, 803, 884, 901]]<|/det|> +Networks were designed in Pytorch. Input images were used of size \(224 \times 224 \times 3\) pixels in which the three channels are DAPI, CNA35, and a layer of zeros, which was incorporated for ease of use with existing Python machine learning image loading functions. Within the training dataset, images were loaded with size \(448 \times 448 \times 3\) pixels, transformed via random rotation of 0- 180 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 885, 320]]<|/det|> +degrees, randomly flipped with probability 0.5, received adjustments to brightness, contrast, and sharpness, and cropped to \(224 \times 224 \times 3\) to remove any zero pixels resulting from rotation. A mini batch size of 16 was used throughout. For each round of training, samples were randomly split by patient with a training:validation ratio of 0.8:0.2 so that the validation dataset only contained samples from patients that were not included in the training set. Mean squared error was used as a loss function. Learning rate and weight decay were set at 4e- 6 and 4e- 7, respectively. A dropout rate of 0.5 was used for fully- connected layers. Networks were trained for 100 epochs since this is when accuracy for the validation set converges. Code for network visualizations was modified from https://github.com/utkuozbulak/pytorch- cnn- visualizations#gradient- visualization. + +<|ref|>sub_title<|/ref|><|det|>[[115, 352, 287, 369]]<|/det|> +## Human breast tissues + +<|ref|>text<|/ref|><|det|>[[111, 376, 885, 658]]<|/det|> +All human breast tissue specimens were collected prospectively from consenting patients (informed consent provided prior to surgery) undergoing surgery at the University of California, San Francisco, (UCSF) or Duke University Medical Center between 2010 and 2020. Samples were stored and analyzed with deidentified labels to protect patient data in accordance with the procedures outlined in the Institutional Review Board Protocol #10- 03832, approved by the UCSF Committee of Human Resources and the Duke University IRB (Pro00054515). Tissue specimens were flash frozen in OCT (Tissue- Tek) by slow immersion in liquid nitrogen or placement on dry ice and stored at \(- 80^{\circ}\mathrm{C}\) until ready for sectioning. H&Es were performed on an adjacent slide and were scanned using a ZEISS Axio Scan.Z1 digital slide scanner equipped with CMOS and color cameras, 10x, 20x and 40x objectives. H&E- stained tissues were assessed by a pathologist (A.J.I.) to identify regions of interest for AFM measurements. + +<|ref|>sub_title<|/ref|><|det|>[[115, 688, 439, 706]]<|/det|> +## CNA35 Transformation and Purification + +<|ref|>text<|/ref|><|det|>[[111, 712, 880, 895]]<|/det|> +pET28a- EGFP- CNA35 was a gift from Jan Liphardt [52] (Addgene plasmid # 61603). CNA35 was expressed and purified as previously described [86]. Briefly, bacteria were incubated with 5 mL of 2YT media + 100 \(\mu \mathrm{g / mL}\) ampicillin + 25 \(\mu \mathrm{g / mL}\) kanamycin + 1% wt/v glucose overnight at \(30^{\circ}\mathrm{C}\) in a shaking incubator. The next day, the culture was diluted in 50 mL of 2YT media + kanamycin for three hours. The culture was then centrifuged and the supernatant discarded. Then, the sample was digested for cell wall removal for 30 min using 500 \(\mu \mathrm{L}\) of lysis buffer (50 mM Sodium Phosphate dibasic, 20 mM Imidazole, 300 mM NaCl, pH 8.0) supplemented with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 867, 214]]<|/det|> +0.125 mg Lysozyme and 1 mM DTT. The sample was sonicated and centrifuged, then CNA35 was isolated from the solubilized supernatant via affinity chromatography (Qiagen Ni- NTA Agarose) according to manufacturer's instructions. The purified protein was supplemented with \(40\%\) glycerol and stored at \(- 20^{\circ}\mathrm{C}\) . Under typical isolation conditions we obtained a final concentration of approximately \(1.5\mathrm{mg / mL}\) CNA35. + +<|ref|>sub_title<|/ref|><|det|>[[115, 247, 666, 266]]<|/det|> +## Collagen/rBM hydrogels with orthotopic implantation of tumor cells + +<|ref|>text<|/ref|><|det|>[[112, 271, 886, 501]]<|/det|> +Rat tail collagen- 1 (High concentration, Corning, Cat. #: 354249) was incubated with \(0.1\%\) acetic acid (non- crosslinked; SOFT) or \(0.1\%\) acetic acid with \(500\mathrm{mM}\) L- ribose (Chem Impex International, Cat. #: 28127) (cross- linked; STIFF) for at least 10 days before preparation of Col1/rBM hydrogels for orthotopic implantation of tumor cells or tumor fragments ([64], [87]). Col1 mixtures were then combined with basement membrane extract (R&D Systems, Cultrex BME, type 2, Pathclear, Cat. #: 3532- 005- 02) (20% final volume), PBS, and 1N NaOH to a slightly acidic pH (pH \(\sim 6.5\) ) as determined by pH strips. Col1/rBM with and without L- ribose was injected orthotopically into a cleared inguinal fat pad and allowed to set for 3- 5 minutes prior to implantation of a PDX tissue fragment approximately \(2\mathrm{x}2\mathrm{mm}\) in size. + +<|ref|>sub_title<|/ref|><|det|>[[115, 533, 514, 552]]<|/det|> +## Breast cancer Patient-Derived Xenografts (PDXs) + +<|ref|>text<|/ref|><|det|>[[112, 558, 886, 867]]<|/det|> +PDX tissues were obtained from Dr. Alana Welm at the Huntsman Cancer Institute, University of Utah, Utah (HCI- 012) or Dr. Michael Lewis at Baylor College of Medicine, San Antonio, Texas (BCM- 3143B and BCM- 3963) ([65], [66]). For the PDX study, \(2\mathrm{x}2\mathrm{cm}\) breast tumor specimens were collected as fresh tissue with immersion in media (phenol red free- DMEM/F12) with \(10\%\) charcoal- stripped fetal bovine serum (FBS Benchmark, Cat. #: 100- 106) and GlutaMAX (Gibco, Cat. #: 35050- 061) supplementation for transportation to the Weaver laboratory at UCSF. PDX fragments were established from frozen and maintained in NOD- SCID immunodeficient mice. Once established tumors reached experimental endpoint, mice were sacrificed, and tumor tissue was divided into pieces for formalin fixation and paraffin embedding, embedding and freezing in OCT, and flash freezing in liquid nitrogen and cryopreservation in \(95\%\) FBS: \(5\%\) DMSO. Flash frozen tumor pieces were used for RNA and protein isolation for the downstream applications indicated. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 90, 323, 108]]<|/det|> +## Animals and Animal Care + +<|ref|>text<|/ref|><|det|>[[111, 115, 884, 345]]<|/det|> +Animal husbandry and all procedures on mice were carried out in Laboratory Animal Resource Center (LARC) facilities at UCSF Parnassus in accordance with the guidelines stipulated by the Institutional Animal Care Use Committee (IACUC) protocols, #AN133001 and #AN179766, which adhere to the NIH Guide for the Care and Use of Laboratory Animals. NOD/SCID mice were purchased from Jackson Laboratories for orthotopic implantation assays. Mice were sacrificed twelve weeks after injection or at humane endpoint, and the tumors were excised and examined for tumor volume using calipers, histology by H&E of fixed tissue sections, proliferation and growth factor and integrin signaling via immunofluorescence in tissue sections, and gene expression using RNAseq and RT- PCR. + +<|ref|>sub_title<|/ref|><|det|>[[113, 378, 466, 396]]<|/det|> +## Monitoring of Tumor growth and metastasis + +<|ref|>text<|/ref|><|det|>[[112, 402, 884, 530]]<|/det|> +Tumor growth was monitored by palpation and caliper measurement weekly or biweekly. Lung metastases were quantified by counting of surface lesions at time of animal sacrifice, and by examination of histological lung sections stained by H&E. Lungs were scanned using a ZEISS Axio Scan.Z1 digital slide scanner equipped with CMOS and color cameras, 10x, 20x and 40x objectives, and lesion area was determined by tracing metastatic lesions in QuPath [88]. + +<|ref|>sub_title<|/ref|><|det|>[[113, 560, 707, 579]]<|/det|> +## Quantitative Reverse Transcriptase-polymerase chain reaction (qRT-PCR) + +<|ref|>text<|/ref|><|det|>[[111, 585, 884, 789]]<|/det|> +RNA was prepared from flash- frozen and pulverized mammary tumor tissues using TRIZol reagent (Invitrogen). Reverse transcription reactions were performed using M- MLV reverse transcriptase (Biochain, Cat. #: Z5040002) with random hexamer primers. cDNA was mixed with PerfeCTa SYBR Green FastMix (Quantibio, Cat. #: 95072- 05K) for qPCR analysis using an Eppendorf realplex2 epgradient S mastercycler. Thermal cycling conditions were 10 min at 95 \(^\circ \mathrm{C}\) , followed by 40 cycles of 15s at 95 \(^\circ \mathrm{C}\) and 45 s at 65 \(^\circ \mathrm{C}\) . Melting curve analysis was used to verify primer pair specificity. Relative mRNA expression was determined by the \(\Delta \Delta \mathrm{CT}\) method with normalization to GAPDH, 18S or KRT8. + +<|ref|>sub_title<|/ref|><|det|>[[113, 821, 553, 841]]<|/det|> +## Quantitative polymerase chain reaction (qPCR) Arrays + +<|ref|>text<|/ref|><|det|>[[112, 846, 884, 892]]<|/det|> +Human EMT qPCR arrays were purchased from Qiagen (Cat. #: PAHS- 021Z), performed as described using RNA from PDX mammary tumors grown in SOFT and STIFF Col1/rBM + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 883, 135]]<|/det|> +hydrogels, and analyzed using available product resources from Qiagen. Selected genes were plotted for presentation in Figure 4 and Extended Figure 6. + +<|ref|>title<|/ref|><|det|>[[113, 169, 282, 186]]<|/det|> +# Immunofluorescence + +<|ref|>text<|/ref|><|det|>[[110, 191, 886, 848]]<|/det|> +Immunofluorescence was performed using the following specific antibodies: phospho- FAK (Y397) (Cell Signaling Technology, Cat. #: 8556, 1:200), phospho- p44/42 MAPK (ERK1/2) (T202/Y204) (Cell Signaling Technology, Cat. #: 9101, 1:200), Integrin \(\beta 1\) , activated (SigmaAldrich, clone HUTS- 4, Cat. #: MAB2079Z, 1:400), phospho- Myosin Light Chain 2 (Ser19) (Cell Signaling Technology, Cat. #: 3671, 1:200), SLUG (C19G7) (Cell Signaling Technology, Cat. #: 9585, 1:200), ZEB1 (E2G6Y) (Cell Signaling Technology, Cat. #: 70512), and Anti- ErbB2 / HER2 [3B5] (ab16901). For cryopreserved samples, frozen sections were fixed in 2- 4% paraformaldehyde, prior to permeabilization with 1- 3% triton- x- 100 and incubation with primary antibodies overnight at 4°C with 3 \(\mu \mathrm{g / mL}\) CNA35 where specified. Sections were then incubated with species- specific secondary antibodies conjugated to different fluorophores (AF- 555, - 647, Invitrogen). All washes were carried out using Phosphate- buffered saline (PBS) with 0.5% Tween- 20 and nuclei and/or actin filaments were counterstained using 4',6- diamidino- 2- phenylindole (DAPI, Cat. #: D1306) and Phalloidin- AF488 conjugate (Thermo Fisher Scientific, Cat. #: A12379), respectively. For FFPE samples, antigen retrieval was accomplished by boiling sections in 10 mM citrate buffer in a pressure cooker on high power for 3 minutes. Following blocking with 10% goat serum and 1% BSA in Tris- Buffered Saline (TBS), sections were incubated with primary antibodies overnight at 4°C with 3 \(\mu \mathrm{g / mL}\) CNA35. Sections were incubated for 1 hour with species- specific secondary antibodies conjugated to different fluorophores (AF- 555, - 647, Invitrogen). All washes were carried out using TBS with 0.025% Triton X- 100 and nuclei were counterstained using DAPI. Images of stained sections were acquired on either a Leica TCS SP5 Confocal microscope or an inverted Eclipse Ti- E Nikon microscope with CSU- X1 spinning disk confocal (Yokogawa Electric Corporation), 405 nm, 488 nm, 561, 635 nm lasers; a Plan Apo VC 60X/1.40 Oil or an Apo LWD 40X/1.15 Water- immersion \(\lambda \mathrm{S}\) objective; electronic shutters; a charge- coupled device (CCD) camera (Clara; Andor) and controlled by Metamorph. + +<|ref|>title<|/ref|><|det|>[[113, 879, 238, 896]]<|/det|> +# Image Analysis + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 318]]<|/det|> +For STIFMap generation, immunostaining images are first resized to the same resolution as the panels used to train the neural networks. Then, the image is decomposed into squares the same dimensions as the network training panels and separated by a user- defined step size that is smaller than the panel side length. The elasticity of each square is predicted using five independently trained models with different brightness, sharpness, and contrast transformations. Since elasticity predictions only apply to panel centers where the AFM cantilever would make contact, the elasticity of pixels between panel centers is inferred using cubic spine interpolation. STIFMaps are depicted as collagen pseudocolored to reflect the predicted elasticity of each position. + +<|ref|>text<|/ref|><|det|>[[112, 325, 884, 450]]<|/det|> +Image analysis of percent positive area in PDX samples was performed using ImageJ and QuPath software ([88], [89]). For comparison, immunofluorescence images were subjected to same- level thresholding based on a determined range of positive fluorescence intensity in each channel and antibody staining panel and the threshold area was expressed as a percentage of whole cell or nuclear area using DAPI staining measured in the same manner. + +<|ref|>sub_title<|/ref|><|det|>[[113, 483, 552, 501]]<|/det|> +## RNA-seq library preparation, sequencing, and analysis + +<|ref|>text<|/ref|><|det|>[[110, 506, 885, 894]]<|/det|> +RNA was isolated using TRIzol (Invitrogen, Cat. #: 15596018) followed by chloroform extraction. RNAseq library preparation was performed by the Functional Genomics Laboratory (FGL), a QB3- Berkeley Core Research Facility at UC Berkeley. Total RNA samples were checked on a Bioanalyzer (Agilent) for quality and only high- quality RNA samples (RIN \(>8\) ) were used. At the FGL, Oligo (dT)25 magnetic beads (Thermofisher) were used to enrich mRNA, and the treated RNAs were rechecked on the Bioanalyzer for their integrity. The library preparation for sequencing was done on Biomek FX (Beckman) with the KAPA hyper prep kit for RNA (now Roche). Truncated universal stub adapters were used for ligation, and indexed primers were used during PCR amplification to complete the adapters and to enrich the libraries for adapter- ligated fragments. Samples were checked for quality on an AATI (now Agilent) Fragment Analyzer. Samples were then transferred to the Vincent J. Coates Genomics Sequencing Laboratory (GSL), another QB3- Berkeley Core Research Facility at UC Berkeley, where Illumina sequencing libraries were prepared. qPCR was used to calculate sequence- able molarity with the KAPA Biosystems Illumina Quant qPCR Kits on a BioRad CFX Connect thermal cycler. Libraries were pooled evenly by molarity and sequenced on an Illumina + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 267]]<|/det|> +NovaSeq6000 150PE S4. Raw sequencing data were converted into fastq format, sample- specific files using the Illumina bcl2fastq2 software on the sequencing centers local Linux server system. RNAseq fastq files were mapped to the primary assembly of the Gencode v33 human genome using Rsubread (version 2.0.1) and counted using featureCounts. Lowly expressed genes were filtered out if they did not have at least one count per million (CPM) in at least 4 samples. Data normalization was performed using calcNormFactors in edgeR (version 3.28.1). Gene ontology was performed using Gage (version 2.36.0) with gene lists from MSigDB version 7.2. + +<|ref|>sub_title<|/ref|><|det|>[[115, 300, 313, 317]]<|/det|> +## Nuvera Dataset Analysis + +<|ref|>text<|/ref|><|det|>[[112, 323, 884, 581]]<|/det|> +Nuvera dataset AnalysisNuvera patient microarray data was obtained from GSE25066 using GEOquery (v2.60.0) [90]. Expression intensities were normalized between patients using the 'normBetweenArrays' function in the R package limma (v3.48.3) [91]. Gene set enrichment scores were computed using GSVA (v1.40.1) to estimate the abundance of each 'Hallmark' ('H' collection) gene set from MSIGDBR (v7.4.1) as well as a list of the 12 most highly expressed collagen genes [92]. All collagen genes were removed from Hallmark gene sets to prevent artifically high correlations due to the same gene being included in both sets. Correlations between GSVA scores were plotted in Python using Seaborn (v0.11.2) and Matplotlib (v3.5.1). Kaplan- Meier curves and statistical testing was conducted in Python using the 'lifelines' package (v.0.27.0). All analysis code is available via GitHub repository https://github.com/cstashko/STIFMaps. + +<|ref|>sub_title<|/ref|><|det|>[[115, 611, 268, 629]]<|/det|> +## Statistical Analysis + +<|ref|>text<|/ref|><|det|>[[112, 635, 884, 760]]<|/det|> +Statistical AnalysisUnless otherwise stated, statistical analyses were performed using GraphPad Prism Version 9.1.2 or SciPy Version 1.7.3. Statistical tests used as well as significance is noted in the corresponding figure legends. Tests of normality were used to determine the appropriate statistical test. All independent variables are described in the text with measurements always from distinct samples (biological replicates) unless otherwise stated. All tests are two- tailed unless otherwise indicated. + +<|ref|>sub_title<|/ref|><|det|>[[115, 794, 214, 810]]<|/det|> +## Illustrations + +<|ref|>text<|/ref|><|det|>[[113, 818, 883, 890]]<|/det|> +IllustrationsThe AutoAFM feedback system schematic (Fig. 1c) was created using BioRender (licensed to V.M.W.). The AlexNet visualization (Fig. 2a) was created using NN- SVG (http://alexlenail.me/NN- SVG/AlexNet.html). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 115, 317, 135]]<|/det|> +DATA AVAILABILITY + +<|ref|>text<|/ref|><|det|>[[112, 140, 884, 320]]<|/det|> +DATA AVAILABILITYThe authors declare that all data supporting the findings of this study are available within this publication and its extended data. PDX RNAseq data has been deposited in NCBI's Gene Expression Omnibus [93] and are accessible through GEO Series accession number GSE179983 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179983). Neural networks, training data, stain imaging, and STIFMaps are available at https://github.com/cstashko/STIFMaps. AutoAFM part files and assembly instructions are available at https://github.com/cstashko/AutoAFM. + +<|ref|>text<|/ref|><|det|>[[112, 350, 884, 473]]<|/det|> +CODE AVAILABILITY STATEMENTAll code necessary to implement STIFMaps is available via the Github repository https://github.com/cstashko/STIFMaps. AutoAFM code is available at https://github.com/cstashko/AutoAFM. All other code used in the preparation of this manuscript is publicly available from software and commercial sources. + +<|ref|>text<|/ref|><|det|>[[112, 504, 516, 524]]<|/det|> +DECLARATION OF COMPETING INTERESTS + +<|ref|>text<|/ref|><|det|>[[112, 531, 460, 551]]<|/det|> +The authors declare no competing interests. + +<|ref|>text<|/ref|><|det|>[[112, 585, 336, 602]]<|/det|> +ACKNOWLEDGEMENTS + +<|ref|>text<|/ref|><|det|>[[111, 608, 884, 893]]<|/det|> +ACKNOWLEDGEMENTSWe thank Nataliya Korets for care and handling of animals and for tissue histology. We also thank John Eichorst and Austin Edwards at the Biological Imaging Development Center for microscopy support, Dylan Romero at the UCSF Library Makers Lab for 3D printing components for AutoAFM, Joanna Ho for hardware engineering consultations, and Ilona Berestjuk for immunofluorescent protocol contributions. PDX tissues were obtained from Dr. Alana Welm (Huntsman Cancer Institute, University of Utah) and Dr. Michael Lewis (Baylor College of Medicine). RNAseq was conducted by QB3 Genomics (QB3 Genomics, UC Berkeley, Berkeley, CA, RRID:SCR_022170) supported by NIH S10 OD018174 Instrumentation Grant. pET28a- EGFP- CNA35 was a gift from Jan Liphardt. AlexNet Pytorch implementation code was obtained from https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py and network visualization code modified from https://github.com/utkuozbulak/pytorch- cnn- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 161]]<|/det|> +visualizations. We acknowledge support by the CIHR fellowship to M- A.G and R35 CA197623 to K.P. This work was supported by R35 CA242447- 01A1, R01 CA222508 and The Mark Foundation for Cancer Research to V.M.W. + +<|ref|>sub_title<|/ref|><|det|>[[115, 194, 363, 211]]<|/det|> +## AUTHOR CONTRIBUTIONS + +<|ref|>text<|/ref|><|det|>[[111, 218, 884, 550]]<|/det|> +C.S. and V.M.W. conceived and designed the study. C.S. and V.M.W. directed the studies. C.S. performed all AFM analysis and subsequent imaging. C.S. implemented AutoAFM computer vision and STIFMap neural networks and developed image analysis pipelines. C.S. performed RNAseq and gene set enrichment analyses. M- K.H., J.J.N., and C.S. performed IHC staining and imaging for human tissue samples. J.J.N. and M- K.H. completed all animal studies. J.J.N. performed collagen/rBM hydrogel studies in vivo with PDX tissues. J.J.N. and M- K.H. performed H&E and IHC staining and qRT- PCR analysis of mouse tissues. A.J.I. assessed mouse and human tissue pathology. N.P. designed and assembled AutoAFM mounts. L.M. performed PRIMO PDMS fabrication under supervision from M.K. J.N.L. performed isolation of CNA35. E.S.H. and H.R. collected and provided human breast tissue specimens with patient data. M- A.G. performed staining for HER2 and ZEB1 within the clinical HER2+ breast cancer cohort. K.P. provided resources and supervision. V.M.W. and C.S. wrote the manuscript with editorial input from all authors. + +<|ref|>sub_title<|/ref|><|det|>[[115, 584, 242, 601]]<|/det|> +## REFERENCES + +<|ref|>text<|/ref|><|det|>[[110, 609, 884, 891]]<|/det|> +1. Li, Z., M. Seehawer, and K. Polyak, Untangling the web of intratumour heterogeneity. Nat Cell Biol, 2022. 24(8): p. 1192-1201. +2. Turashvili, G. and E. Brogi, Tumor Heterogeneity in Breast Cancer. Front Med (Lausanne), 2017. 4: p. 227. +3. Biffi, G. and D.A. Tuveson, Diversity and Biology of Cancer-Associated Fibroblasts. Physiol Rev, 2021. 101(1): p. 147-176. +4. 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Chapter 14: p. Unit14.23. 930 66. Zhang, X., et al., A renewable tissue resource of phenotypically stable, biologically and 931 ethnically diverse, patient- derived human breast cancer xenograft models. Cancer Res, 932 2013. 73(15): p. 4885- 97. 933 67. Wels, C., et al., Transcriptional activation of ZEB1 by Slug leads to cooperative 934 regulation of the epithelial- mesenchymal transition- like phenotype in melanoma. J Invest 935 Dermatol, 2011. 131(9): p. 1877- 85. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 90, 886, 875]]<|/det|> +936 68. Rye, I.H., et al., Intratumor heterogeneity defines treatment-resistant HER2+ breast tumors. Mol Oncol, 2018. 12(11): p. 1838- 1855. 937 69. Kiio, T.M. and S. Park, Nano- scientific Application of Atomic Force Microscopy in Pathology: from Molecules to Tissues. Int J Med Sci, 2020. 17(7): p. 844- 858. 940 70. Barnes, J.M., et al., A tension- mediated glycocalyx- integrin feedback loop promotes mesenchymal- like glioblastoma. Nat Cell Biol, 2018. 20(10): p. 1203- 1214. 941 71. Chang, H.Y., et al., Artificial Intelligence in Pathology. J Pathol Transl Med, 2019. 53(1): p. 1- 12. 942 72. Niazi, M.K.K., A.V. Parwani, and M.N. Gurcan, Digital pathology and artificial intelligence. Lancet Oncol, 2019. 20(5): p. e253- e261. 943 73. Cui, M. and D.Y. Zhang, Artificial intelligence and computational pathology. Lab Invest, 2021. 101(4): p. 412- 422. 944 74. Saito, A., et al., Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning. Mod Pathol, 2021. 34(2): p. 417- 425. 945 75. Komura, D. and S. Ishikawa, Machine Learning Methods for Histopathological Image Analysis. Comput Struct Biotechnol J, 2018. 16: p. 34- 42. 946 76. van der Laak, J., G. Litjens, and F. Ciompi, Deep learning in histopathology: the path to the clinic. Nat Med, 2021. 27(5): p. 775- 784. 947 77. Janiszewska, M., et al., The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2+ breast cancer. JCI Insight, 2021. 6(11). 948 78. Laklai, H., et al., Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression. Nat Med, 2016. 22(5): p. 497- 505. 949 79. Gruosso, T., et al., Spatially distinct tumor immune microenvironments stratify triple- negative breast cancers. J Clin Invest, 2019. 129(4): p. 1785- 1800. 950 80. Edelstein, A.D., et al., Advanced methods of microscope control using μManager software. J Biol Methods, 2014. 1(2). 951 81. van der Walt, S., et al., scikit- image: image processing in Python. PeerJ, 2014. 2: p. e453. 952 82. Przybyla, L., et al., Monitoring developmental force distributions in reconstituted embryonic epithelia. Methods, 2016. 94: p. 101- 13. 953 83. Lakins, J.N., A.R. Chin, and V.M. Weaver, Exploring the link between human embryonic stem cell organization and fate using tension- calibrated extracellular matrix functionalized polyacrylamide gels. Methods Mol Biol, 2012. 916: p. 317- 50. 954 84. Wisdom, K.M., et al., Matrix mechanical plasticity regulates cancer cell migration through confining microenvironments. Nat Commun, 2018. 9(1): p. 4144. 955 85. Takigawa, T., et al., Poisson's ratio of polyacrylamide (PAAm) gels. Polymer Gels and Networks, 1996. 4(1): p. 1- 5. 956 86. Shi, Q., et al., Rapid disorganization of mechanically interacting systems of mammary acini. Proc Natl Acad Sci U S A, 2014. 111(2): p. 658- 63. 957 87. Northey, J.J., et al., Stiff stroma increases breast cancer risk by inducing the oncogene ZNF217. J Clin Invest, 2020. 130(11): p. 5721- 5737. 958 88. Bankhead, P., et al., QuPath: Open source software for digital pathology image analysis. Sci Rep, 2017. 7(1): p. 16878. 959 89. Schindelin, J., et al., Fiji: an open- source platform for biological- image analysis. Nat Methods, 2012. 9(7): p. 676- 82. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 88, 884, 257]]<|/det|> +981 90. Davis, S. and P.S. Meltzer, GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 2007. 23(14): p. 1846-7. 983 91. Ritchie, M.E., et al., limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 2015. 43(7): p. e47. 985 92. Liberzon, A., et al., The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst, 2015. 1(6): p. 417-425. 987 93. Edgar, R., M. Domrachev, and A.E. Lash, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 2002. 30(1): p. 207-10. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 91, 256, 108]]<|/det|> +991 MAIN FIGURES + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[30, 0, 999, 750]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 88, 884, 320]]<|/det|> +992 Figure 1. Overview of automated AFM acquisition system. a, Technical drawing of motor mount for interfacing servo motors with AFM translation knobs. b, Assembled motor mounts. 994 The Stage Frame slides along the edge of the stage (orange arrows) while the Motor Frame slides along the Alignment Rods, towards and away from the Stage Frame as the knobs turn (blue arrows). c, Schematic of AutoAFM feedback system. d, Example of AutoAFM feedback with desired AFM sampling positions (blue), actual AFM positions (red), and AFM path of movement and positions outside of the desired (orange). e, f, Representative images of AutoAFM collecting AFM measurements over a whole tissue (e) and a region of interest (f) in a breast tumor section. 1000 Scale bar, \(100\mu \mathrm{m}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[30, 0, 999, 666]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[50, 88, 886, 320]]<|/det|> +1001 Figure 2. A convolutional neural network predicts the Young's Modulus of tissue. a, 1002 Example input- output relationships to the network with diagram depicting connectivity of 1003 different network layers. b, Example image transformations to increase the size of the network 1004 training dataset. c, Correlation between model predictions and actual Young's Modulus values 1005 for the training (blue line) and validation (orange line) datasets over the course of training. Error 1006 bars indicate \(95\%\) confidence intervals across 25 trained models. d, Dot plot of actual versus 1007 predicted Young's Modulus values for the validation datasets across 25 trained models. n = 1008 4768, Pearson \(\mathrm{r} = 0.687\) . e, Saliency maps reflecting image regions that influenced model 1009 predictions. Scale bar, \(20\mu \mathrm{m}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[10, 14, 999, 770]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[50, 88, 886, 456]]<|/det|> +1010 Figure 3. STIFMaps predict high elasticity regions within tissues. a, Deconstruction of a 1011 CNA- and DAPI- stained image into squares of approximately \(50 \times 50 \mu \mathrm{m}\) . The Young's 1012 Modulus of each square is predicted. b, Elasticity predictions are aggregated and overlaid over 1013 collagen to produce the overall STIFMap for both a normal TDLU and triple negative breast 1014 cancer. c, Representative images of immunofluorescent staining for pMLC (top) and activated \(\beta 1\) 1015 integrin (bottom). d, Scatterplots of STIFMap intensity vs stain intensity for each pixel shown in 1016 (c) indicating the \(99^{\mathrm{th}}\) percentile of stain intensity for each STIFMap percentile. e, STIFMap 1017 percentiles versus the \(99^{\mathrm{th}}\) percentile of stain intensity for all acquired fields of view (FOVs). 1018 Error bars indicate a \(95\%\) confidence interval. \(\mathrm{n} = 60\) FOVs from 10 different patient tumor 1019 samples. Median Spearman r values, activated \(\beta 1\) integrin \(= 0.696\) , pMLC \(= 0.364\) . f, Violin 1020 plots of the Spearman correlation for each FOV comparing the \(99^{\mathrm{th}}\) percentile of staining 1021 intensity versus percentiles of DAPI, predicted elasticity, or collagen stain intensity. Internal 1022 gray bars indicate a Box-plot. \(\mathrm{n} = 60\) FOVs from 10 different patient tumor samples. Scale bar, 1023 \(50\mu \mathrm{m}\) . Statistical analyses were performed using Mann- Whitney U test, \(\mathrm{***P< 10^{- 5}}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 0, 997, 99]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[0, 0, 997, 100]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[0, 0, 997, 1000]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 886, 690]]<|/det|> +Figure 4. Matrix elasticity associates with EMT in a PDX model of HER2+ breast cancer. a, Schematic showing the strategy for implantation of HER2- positive patient- derived xenograft (PDX) breast cancer tissues in SOFT (Col1/rBM, no L- ribose) and STIFF (Col1/rBM, crosslinked with L- ribose) hydrogels. b,c, Representative images of immunofluorescent staining of active \(\beta 1\) integrin (b) and phospho- FAK (c) in SOFT or STIFF HER2- positive PDX tumors (left). Scale bar, \(50 \mu \mathrm{m}\) . Quantification of average phospho- FAK (b) and active \(\beta 1\) integrin (c) positive cell area for all HER2- positive PDX tumors (right). SOFT; \(\mathrm{n} = 6\) , STIFF; \(\mathrm{n} = 6\) . d, Average number of lung metastases for mice bearing BCM- 3963 PDX tumors in SOFT and STIFF ECM stroma as determined by histological analysis. SOFT; \(\mathrm{n} = 10\) , STIFF; \(\mathrm{n} = 10\) . e, Average size of the metastatic lesions corresponding to the analysis in (d). f, Analysis as in (d) for mice bearing BCM- 3143B PDX tumors. SOFT; \(\mathrm{n} = 10\) , STIFF; \(\mathrm{n} = 10\) . g, Analysis as in (e) for metastatic lesions corresponding to the analysis in (f). h, Gene ontology terms from among the top 23 most significantly upregulated, using RNAseq data derived from all HER2- positive PDX tumors generated in SOFT (n=9) and STIFF (n=9) ECM stroma as above (n=3 for each PDX and condition). i, Volcano plot of p- value (- log10) vs. log fold change (logFC) for gene expression from the HALLMARK_epithelial- to- mesenchymal transition gene set for RNAseq data of HER2- positive PDX tumors developed in SOFT and STIFF ECM stroma. j- m, qRT- PCR arrays designed to examine Epithelial- to- mesenchymal transition related gene expression were used to analyze RNA isolated from PDX tumors developed in SOFT and STIFF ECM stroma. SOFT; \(\mathrm{n} = 7\) , STIFF; \(\mathrm{n} = 7\) . Bar plots for the average relative expression of the indicated mesenchymal genes are displayed. All graphs are presented as mean +/- S.E.M. Statistical tests used were Mann- Whitney U test (c, e, j- m) and unpaired \(t\) - test (b, d, f, g), \*P<0.03, \*\*P<0.002, \*\*\*P<0.0002, ns=non- significant. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 0, 999, 999]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 886, 530]]<|/det|> +Figure 5. EMT markers spatially overlap with high tension matrix and associate with poor survival in patient tumors. a, Pearson correlation between GSVA scores for collagen genes and hallmark pathway genes in the Nuvera dataset. b, Scatterplot of GSVA scores for collagen genes and hallmark EMT genes. Each point represents one patient. \(\mathrm{n} = 508\) patients. Pearson \(\mathrm{r} = 0.880\) . c, Representative FOVs for SLUG staining within FFPE tumors. Scale bar, \(50~\mu \mathrm{m}\) . d,e, Violin plots of the Spearman correlation for each FOV comparing the \(99^{\mathrm{th}}\) percentile of staining intensity versus percentiles of DAPI, predicted elasticity, or collagen stain intensity. Internal gray bars indicate a Box-plot. \(\mathrm{n} = 5\) ZEB1 FOVs and \(\mathrm{n} = 25\) SLUG FOVs. f, Representative whole slide image (WSI) and regions of interest (ROIs) of ZEB1 stain with STIFMap in HER2+ breast cancer cohort. Scale bar (WSI), \(1\mathrm{mm}\) . Scale bar (ROIs), \(100~\mu \mathrm{m}\) . g, Spearman correlation for each whole tissue section comparing the \(99^{\mathrm{th}}\) percentile of staining intensity versus percentiles of predicted elasticity and collagen stain intensity. \(\mathrm{n} = 21\) patient tumor samples. h, Box and whiskers plots to show the association between metastatic recurrence and spatial autocorrelation (Moran's I) for tissue markers and STIFMaps in the HER2+ breast cancer cohort. i,j, Kaplan- Meier curves comparing survival between the upper and lower quartiles of EMT (i) and collagen (j) GSVA scores within the Nuvera cohort. \(\mathrm{n} = 127\) patients in each group. Statistical analyses were performed using Mann- Whitney U, \(\mathrm{*P< 0.05}\) , \(\mathrm{**P< 0.01}\) , \(\mathrm{***P< 10^{-5}}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[46, 90, 280, 108]]<|/det|> +1064 EXTENDED DATA + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[78, 65, 910, 460]]<|/det|> +
ITEM NO.SW-File Name(File Name)DESCRIPTIONPROCESSSUPPLIERMATERIALPART NUMBERQTYPackage QtyUnit Price ($)Order QtyExtended Price ($)
1Microscope Stage_baseAFM Stage BaseINCLUDED1
2Microscope Stage_XAFM Stage X TranslationINCLUDED1
3Microscope Stage_YAFM Stage Y TranslationINCLUDED1
4Microscope Stage_X knobAFM X KnobINCLUDED1
5Microscope Stage_Y knobAFM Y KnobINCLUDED1
6clamp_screw_91290A19Screws frame_main into frame_clampPURCHASEDMCMASTER91290A1904107.1117.11
7clamp_nut_90576A103Screws frame_main into frame_clampPURCHASEDMCMASTER90576A10341004.2714.27
8clamp_washer_93475A2Screws frame_main into frame_clampPURCHASEDMCMASTER93475A23041001.8611.86
9X_frame_mainStage Frame_x3D PRINTED1
10NEMA 17MotorINCLUDEDNEMA 172
11motor_faceplateMotor Bracket3D PRINTED2
12sliderMotor Frame3D PRINTED2
13wedge_1Bracket Adjuster_front3D PRINTED2
14wedge_2Bracket Adjuster_back3D PRINTED2
15wedge_spring_5108N27Springs holding Motor Bracket with Motor FramePURCHASEDMCMASTER9044K113435.26210.52
16wedge_pin_mx36_9159Tensioning PinPURCHASEDMCMASTER91595A1404259.9119.91
17wedge_seal_9092000A0Bracket Adjusting ScrewsPURCHASEDMCMASTER92000A0774509.9419.94
18wedge_nut_90591A250Used with Bracket Adjusting ScrewsPURCHASEDMCMASTER90591A25041002.3312.33
19wedge_washer_97310A 111Used with Bracket Adjusting ScrewsPURCHASEDMCMASTER97310A11141002.8612.86
20slider_pin_91585A389Alignment RodPURCHASEDMCMASTER91585A389414.14416.56
21motor_screw_91290A111Screws Motor into Motor BracketPURCHASEDMCMASTER91290A11141008.7118.71
22X_frame_clampClamps Stage Frame_x onto AFM Stage3D PRINTED1
23knob_capKnob Adapter3D PRINTED2
24knob_screw_90044A247Adapter ScrewPURCHASEDMCMASTER90044A247257.4417.44
255mm_hub_988917106Motor CouplingPURCHASEDMCMASTER9889171062116.08232.16
268mm_hub_988917109Motor CouplingPURCHASEDMCMASTER9889171092116.08232.16
27Acetal_disk_59985K620Motor CouplingPURCHASEDMCMASTER59985K62213.2226.44
28rubber pad_XBetween X_frame_main and X_frame_clampINCLUDED1
29Y_frame_mainStage Frame_y3D PRINTED1
30Y_frame_clampClamps Stage Frame_y onto AFM Stage3D PRINTED1
31rubber pad_YBetween Y_frame_main and Y_frame_clampINCLUDED1
32rollers_1Screw into Y_frame_main from abovePURCHASEDMCMASTER3668K222118.63237.26
33rollers_2Screw into Y_frame_main from belowPURCHASEDMCMASTER3659K112136.88273.76
TOTAL263.29
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[45, 88, 884, 137]]<|/det|> +1065 Extended Table 1. AutoAFM Bill of Materials. List of components required to implement an 1066 AutoAFM system on an existing MFP 3D Bio AFM (Asylum Research). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[10, 0, 999, 700]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[44, 88, 885, 190]]<|/det|> +Extended Figure 1. AutoAFM Principle and Validation. a, Photo of AutoAFM assembly. b, Technical drawing of all main components of the AutoAFM system. c, AutoAFM workflow. d, Overview of PDMS balance beam design (left) and actual fabrication (right) with points overlaid. e, Accuracy of AutoAFM movements along each PDMS beam. Scale bar, \(100\mu \mathrm{m}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[108, 61, 455, 234]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[108, 238, 500, 410]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[128, 419, 507, 565]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[108, 586, 500, 744]]<|/det|> + + +<|ref|>table<|/ref|><|det|>[[530, 72, 805, 166]]<|/det|> +
PA Gel Nominal Stiffness (Pa)Actual Stiffness (Pa)
140146.71
400210.19
1040733.97
+ +<|ref|>image<|/ref|><|det|>[[480, 222, 884, 410]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[530, 419, 884, 565]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[148, 612, 333, 744]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[380, 612, 636, 744]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[50, 87, 886, 295]]<|/det|> +Extended Figure 2. AFM Control Experiments. a, Young's Moduli of polyacrylamide (PA) gels of different nominal elasticities measured using shear rheology. \(\mathrm{n} = 4\) gels of each elasticity. b, Time course of the same tissue region probed with AFM every thirty minutes for 3.5 hours. c, Elasticity of different tissue positions probed with AFM at different velocities. \(\mathrm{n} = 12\) positions. d, Representative images of AFM cantilevers used. e, AFM cantilever artifact in the average image for an AutoAFM scan. f, 5 \(\mu \mathrm{m}\) ball position on the end of an AFM cantilever. g, AFM cantilever ball positions fit onto the AFM artifact from an average image (e). Statistical analyses used were performed using Mann-Whitney U test, ns=non- significant. Scale bar, \(100\mu \mathrm{m}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[30, 0, 999, 700]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[48, 88, 884, 293]]<|/det|> +Extended Figure 3. Image Stitching and Overlaying. a, Pipeline for Fourier-Mellin Transformation. The confocal DAPI image was downsampled (bottom left) to better resemble the AFM image (top left). Then, both images were processed with a Bandpass Filter, Hanning Window, and Log- Polar Transformation. Translational differences between the final images were converted into scaling and rotation differences in the original images. b, Overall translation of AutoAFM data from a low- resolution AFM microscope image onto the high- resolution confocal image. c, Deviation in cell positions after transformation. \(\mathrm{n} = 50\) cell positions from five samples. Scale bar, \(50\mu \mathrm{m}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[20, 30, 981, 213]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[20, 244, 777, 455]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[20, 485, 777, 632]]<|/det|> + + +<|ref|>image<|/ref|><|det|>[[20, 660, 777, 844]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 886, 371]]<|/det|> +Extended Figure 4. Imaging Sensitivity Analysis. a, Scatterplots of stain intensity vs predicted stiffness (left), collagen intensity (middle), or DAPI intensity (right) shown for all pixels (blue) or aggregated to show the \(99^{\text{th}}\) percentile of stain intensity for each percentile of the indicated independent variable (red) for the representative pMLC stain shown in Fig. 3c. Aggregating data into percentiles is necessary to limit the influence of image regions where cells are not interacting with the ECM. b, Sensitivity analysis of the average Spearman correlation coefficient as shown in (a) for stain intensity compared to DAPI, collagen, and STIFMap depending on the stain threshold used. \(\mathrm{n} = 60\) FOVs from 10 patient tumor samples. c, Representative FOV indicating pixels that are at the interface between cells and collagen. d, Sensitivity analysis of the average Spearman correlation coefficient depending on the stain threshold used when only masked pixels are included. \(\mathrm{n} = 60\) FOVs from 10 patient tumor samples. Scale bar, \(50\mu \mathrm{m}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[10, 53, 980, 327]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[45, 88, 884, 163]]<|/det|> +1098 Extended Figure 5. Collagen Morphology Validation in FFPE Tissue. Five FOVs for an 1099 FFPE (top) or cryopreserved tissue (bottom) taken from the same patient stained with DAPI and 1100 CNA35. Scale bar, \(50\mu \mathrm{m}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[39, 25, 899, 633]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 886, 530]]<|/det|> +Extended Figure 6. A stiff stroma enhances mechanosignaling, tumor growth, metastasis, and mesenchymal gene expression in HER2- positive breast cancer patient- derived xenografts. a- c, Graphs showing average tumor growth in SOFT and STIFF matrices for the HER2- positive PDX models indicated as determined by caliper measurement. SOFT and STIFF, \(\mathrm{n = 10}\) each for BCM- 3963 and BCM3143B, \(\mathrm{n = 4}\) each for HCI- 012. d, Representative images of immunofluorescence staining of phospho- ERK in SOFT and STIFF HER2- positive PDX tumors (left). Scale bar, \(50\mu \mathrm{m}\) . Quantification of average phospho- ERK positive cell area for all HER2- positive PDX tumors (right). SOFT; \(\mathrm{n} = 6\) , STIFF; \(\mathrm{n} = 6\) . e, Representative images of lung metastases for mice bearing BCM- 3143B PDX tumors in SOFT and STIFF ECM stroma. Scale bar, \(100\mu \mathrm{m}\) . f- k, Graphs showing RT- PCR analysis of RNA extracted from HER2- positive PDX tumors with SOFT and STIFF matrices showing relative gene expression for the indicated mesenchymal and epithelial genes. SOFT; \(\mathrm{n} = 7\) , STIFF; \(\mathrm{n} = 7\) . l,m, Percentage of mice bearing HER2- positive PDX tumors with SOFT and STIFF matrices presenting detectable lung metastases. SOFT and STIFF, \(\mathrm{n = 10}\) each for BCM- 3963 and BCM3143B. All graphs are presented as mean +/- S.E.M. Statistical tests used were Mann- Whitney U test (f- k), unpaired \(t\) - test (d) and two- way ANOVA (with Bonferroni's multiple comparisons test) (a- c). \*P<0.03, \*\*P<0.002, \*\*\*P<0.0002, ns=non- significant. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[8, 42, 999, 150]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[8, 20, 28, 33]]<|/det|> +
a
+ +<|ref|>image<|/ref|><|det|>[[66, 60, 999, 245]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[68, 272, 87, 286]]<|/det|> +
b
+ +<|ref|>image<|/ref|><|det|>[[68, 315, 433, 472]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[68, 517, 88, 531]]<|/det|> +
d
+ +<|ref|>image<|/ref|><|det|>[[540, 293, 790, 455]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[510, 272, 528, 286]]<|/det|> +
c
+ +<|ref|>image<|/ref|><|det|>[[92, 520, 333, 752]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[45, 88, 886, 270]]<|/det|> +1118 Extended Figure 7. Additional Patient EMT Staining Data. a, Representative WSI and ROIs for immunofluorescence staining of SLUG in a TNBC sample. Scale bar (WSI), 100 \(\mu \mathrm{m}\) . Scale bar (ROIs), 50 \(\mu \mathrm{m}\) . b, Quantification of the 99th percentile of SLUG staining intensity for each percentile of predicted matrix elasticity for the image shown in (a). c, Representative HER2 stain from the HER2+ breast cancer cohort. Scale bar, 1 mm. d, Correlation between HER2 intensity and either collagen intensity or predicted stiffness in the HER2+ cohort. \(\mathrm{n} = 21\) patient tumor samples. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 131, 135, 149]]<|/det|> +rs.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/images_list.json b/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..bd07f2cf1c66c0029a96231f6337c43224336c84 --- /dev/null +++ b/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/images_list.json @@ -0,0 +1,115 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Overview of the Proposed TrafficSafe Framework. (a) The U.S. faces one of the highest crash risks among developed countries, with a rising trend. However, analyzing and addressing this issue is challenging due to the heterogeneous factors involved in crash events, including traffic conditions, human behavior, environmental impacts, and driver characteristics. To tackle this, we propose TrafficSafe, a framework designed for two key tasks: 1) Predicting crash outcomes and 2) Attributing crash factors with conditional risk analysis. By addressing questions such as why crashes occur and how to mitigate crash risks, TrafficSafe seeks to deliver optimal policy for safety improvement, aligning with the Vision Zero goal. (b) The TrafficSafe workflow incorporates multi-modal data, including driver behavior, vehicle details, infrastructure, and environmental conditions, represented through textual reports, satellite imagery, and other formats. Leveraging an AI-expert cooperative method, the crash data is transformed into textual prompts, resulting in the TrafficSafe Event dataset comprising 58,903 prompts. TrafficSafe LLM is created with accurate and trustworthy forecasting abilities for further analysis. Building on this pipeline, TrafficSafe Attribution operates across three dimensions: 1) Event-level risk analysis to identify feature contributions, 2) Conditional risk analysis to assess state-level risks under varying conditions, and 3) Data collection guidance to optimize the data acquisition process. The results of TrafficSafe Attribution provide actionable insights to enhance data analysis and collection, fostering a more comprehensive understanding of crash data and events.", + "footnote": [], + "bbox": [ + [ + 140, + 87, + 848, + 690 + ] + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: TrafficSafe Crash Outcomes Prediction Pipeline. Multi-modal crash data is collected and organized into textual prompts through an AI-expert cooperative process. The HSIS crash data, satellite images, and infrastructure data are used to extract general and infrastructure information, including the crash time, location, the road level, and so on. The vehicle data, and person data are converted into the event information and the unit information, including vehicle movements, driver characteristics (e.g., age, gender, alcohol use), vehicle attributes (e.g., manufacture year), and so on. TrafficSafe Event dataset is created with three prediction targets: Injury, Severity, and Type. The Injury task predicts the number of people injured in the crash event, the Severity task estimates the severity level of the crash, such as no apparent injury or fatal, and the Type task classifies type of crash, such as single vehicle with object or angle impacts right (The crash event consequences classification are provided in Supplementary Table 2 and Supplementary Table 3. The TrafficSafe LLM is fine-tuned using the TrafficSafe Event dataset. To reframe the crash outcomes prediction from a classification task to a language inference task, TrafficSafe LLM is fine-tuned by adding prediction targets as special tokens in its vocabulary and adjusting parameters using Low-Rank Adaptations (LoRA) \\(^{29}\\) .", + "footnote": [], + "bbox": [ + [ + 140, + 100, + 880, + 433 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: TrafficSafe LLM Provides Accurate and Trustworthy Predictions. TrafficSafe LLM produces robust confusion matrices for both the (a) Washington and (b) Illinois datasets (we select the best results for each task by F1-score). In contrast, baseline models tend to predict the most frequent category across both the (c) Washington and (d) Illinois datasets (we show baseline models with the best F1-score. The performances for other baseline models can be found in Extended Data Figure 3). Meanwhile, TrafficSafe LLM produces trustworthy predictions for both the (e) Washington and (f) Illinois datasets. Higher confidence levels in the model's predictions correspond to an increased likelihood of accuracy. Furthermore, (g) The TrafficSafe LLM achieves higher precision for fatal crash predictions. (h) Fatal crash predictions also exhibit higher confidence compared to average predictions in Illinois dataset. The Washington dataset is not shown due to limited fatal cases. (i) For fatal crashes, the TrafficSafe LLM achieves near-perfect precision (97.61%) when the confidence score exceeds 0.6, indicating that the TrafficSafe LLM can deliver highly accurate and trustworthy predictions for fatal crashes.", + "footnote": [], + "bbox": [ + [ + 105, + 92, + 899, + 720 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "(a) Sentence-based Feature Attribution Results for a Crash Resulting in Serious Injuries in Washington Dataset.", + "footnote": [], + "bbox": [], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5: Conditional Risk Analysis for the Serious Injury and Fatal Crashes. Higher confidence scores in TrafficSafe LLM's predictions correspond to greater accuracy, allowing the confidence score (calculated as the sum of feature contributions for all data components) to serve as an indicator of risk level for serious and fatal crashes. (a) The estimated risk levels for various feature combinations are presented, with each level corresponding to the average confidence score of TrafficSafe LLM's predictions under the same conditions. Each column represents a specific combination of conditions (marked by dark dots) alongside the corresponding feature contribution for selected factors. (b) Feature contributions of five key factors and their proportions relative to all factors are visualized. The inner circle represents the average feature contribution of each factor across different values, while the outer circle shows the percentage share of each factor in the total average feature contribution. The unit for BAC (Blood Alcohol Content) is \"mg/L\", which is omitted in this figure. (c) Average feature contribution for each factor under specific values. Bars are marked in pink if the value exceeds the corresponding factor's average shown in (a) and in blue if it does not. (d) The strong correlation between the number of risk factors and the risk level of crashes. The high-risk factors are defined as driving after drinking (both BAC <= 80 mg/L and BAC > 80 mg/L), driving in work zones, driving on freeways, pedestrian-involved crashes, and high-risk driver behaviors (aggressive or impairment-related). For each case, we tallied the number of these risk factors and calculated the average risk level for all cases sharing the same count. (e) Feature contributions of different data components during the training stage for Washington and Illinois datasets.", + "footnote": [], + "bbox": [ + [ + 112, + 100, + 866, + 586 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Extended Data Figure 3: The Confusion Matrix for TrafficSafe LLM and the Traditional Methods in (a) Washington Dataset and (b) Illinois Dataset.", + "footnote": [], + "bbox": [ + [ + 100, + 83, + 890, + 680 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "TrafficSafe Attribution - #EC36495", + "footnote": [], + "bbox": [ + [ + 485, + 120, + 888, + 420 + ] + ], + "page_idx": 31 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Extended Data Figure 5: One Example of Sentence-based Feature Attribution Results for A Crash Resulting in No Apparent Injury in Illinois Dataset.", + "footnote": [], + "bbox": [ + [ + 102, + 270, + 896, + 660 + ] + ], + "page_idx": 32 + } +] \ No newline at end of file diff --git a/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c.mmd b/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9bba9a0a4fa18b96666c3d4b0c5062f5f2c40189 --- /dev/null +++ b/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c.mmd @@ -0,0 +1,628 @@ + +# Customizing Large Language Models for Reliable and Interpretable Traffic Crash Prediction and Safety Interventions + +Hao Frank Yang haofrankyang@jhu.edu + +Johns Hopkins University https://orcid.org/0000- 0001- 6431- 8956 + +Yang Zhao Johns Hopkins University Pu Wang Johns Hopkins University Yibo Zhao Johns Hopkins University Hongru Du Johns Hopkins university + +Article + +Keywords: + +Posted Date: April 29th, 2025 + +DOI: https://doi.org/10.21203/rs.3.rs- 5947574/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on October 7th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 64574- w. + +<--- Page Split ---> + +# Customizing Large Language Models for Reliable and Interpretable Traffic Crash Prediction and Safety Interventions + +Yang Zhao \(^{1, 2 + }\) , Pu Wang \(^{1, 2 + }\) , Yibo Zhao \(^{1, 2}\) , Hongru Du \(^{1, 2}\) , and Hao (Frank) Yang \(^{1, 2*}\) + +\(^{1}\) Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, USA. \(^{2}\) Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA. \(^{+}\) The authors contributed equally. \(^{*}\) The corresponding authors information: haofrankyang@jhu.edu + +## ABSTRACT + +Predicting crash events is crucial for understanding crash distributions and their contributing factors, thereby enabling the design of proactive traffic safety policy interventions. However, existing methods struggle to interpret the complex interplay among various sources of traffic crash data, including numeric characteristics, textual reports, crash imagery, environmental conditions, and driver behavior records. As a result, they often fail to capture the rich semantic information and intricate interrelationships embedded in these diverse data sources, limiting their ability to identify critical crash risk factors. In this research, we propose TrafficSafe, a framework that adapts Large Language Models (LLMs) to reframe crash prediction and feature attribution as text- based reasoning. A multi- modal crash dataset including 58,903 real- world reports together with belonged infrastructure, environmental, driver, and vehicle information is collected and textualized into TrafficSafe Event dataset (totaling 12.74 million words). By customizing and fine- tuning state- of- the- art LLMs on this dataset, the proposed TrafficSafe LLM achieves a \(42\%\) average improvement in F1- score over baselines across multiple crash prediction tasks, particularly for severe crashes. To interpret these predictions and uncover contributing factors, we introduce TrafficSafe Attribution, a sentence- level feature attribution framework enabling conditional risk analysis. Findings show that alcohol- impaired driving is the leading factor in severe crashes, with aggressive and impairment- related behaviors having nearly twice the contribution for severe crashes compared to other driver behaviors. In addition, the co- occurrence of crash- contributing factors, such as alcohol- impaired driving, work zones, improper driving behaviors and other factors can significantly elevate risk levels. Furthermore, TrafficSafe Attribution highlights pivotal features during model training, guiding strategic crash data collection for iterative performance improvements. The proposed TrafficSafe offers a transformative leap in traffic safety research based on foundation models, providing a blueprint for translating advanced artificial intelligence technologies into responsible, actionable, and life- saving outcomes. It is now reshaping how traffic researchers and policymakers approach the road safety. + +## 1 Introduction + +Predicting traffic crash outcomes at the event level can greatly improve our understanding of crashes contributing factors and support the safety policy interventions. Currently, the United States has one of the highest traffic crash risks among developed countries (see Figure 1a), with 42,795 fatalities reported in \(2022^{2}\) . The number of fatalities still shows a persistent upward trend over recent decades, highlighting the urgent need for innovative + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Overview of the Proposed TrafficSafe Framework. (a) The U.S. faces one of the highest crash risks among developed countries, with a rising trend. However, analyzing and addressing this issue is challenging due to the heterogeneous factors involved in crash events, including traffic conditions, human behavior, environmental impacts, and driver characteristics. To tackle this, we propose TrafficSafe, a framework designed for two key tasks: 1) Predicting crash outcomes and 2) Attributing crash factors with conditional risk analysis. By addressing questions such as why crashes occur and how to mitigate crash risks, TrafficSafe seeks to deliver optimal policy for safety improvement, aligning with the Vision Zero goal. (b) The TrafficSafe workflow incorporates multi-modal data, including driver behavior, vehicle details, infrastructure, and environmental conditions, represented through textual reports, satellite imagery, and other formats. Leveraging an AI-expert cooperative method, the crash data is transformed into textual prompts, resulting in the TrafficSafe Event dataset comprising 58,903 prompts. TrafficSafe LLM is created with accurate and trustworthy forecasting abilities for further analysis. Building on this pipeline, TrafficSafe Attribution operates across three dimensions: 1) Event-level risk analysis to identify feature contributions, 2) Conditional risk analysis to assess state-level risks under varying conditions, and 3) Data collection guidance to optimize the data acquisition process. The results of TrafficSafe Attribution provide actionable insights to enhance data analysis and collection, fostering a more comprehensive understanding of crash data and events.
+ +<--- Page Split ---> + +approaches to uncover the major causes of crashes and provide actionable insights for policy interventions. An effective data- driven crash prediction model can learn from historical crash- related data, and offer potential guidance for reducing crash risk by identifying the leading factors of crashes3. Current research for crash prediction can be grouped into two groups: 1) macroscopic (statistic- level) prediction4–8 and 2) microscopic (event- level) prediction9–13. Macroscopic prediction typically relies on statistical methods to gain a general understanding of safety levels, compare the safety performance of different areas and time frames, identify high- risk zones, and track safety trends4,5. While these methods can partially predict when and where crashes are more likely to occur, they fail to forecast who is involved, what types will likely to be, why crashes happen, and how to mitigate risks at event granularity6,14. To address this limitation, microscopic crash prediction, which focuses on specific traffic conditions and circumstances, has been developed to predict the crashes consequences using machine learning (ML) approaches11,13. Despite their potential in answering who and what, these models face limitations in crash prediction precision and generalization5,6. Moreover, integrating multi- modal traffic crash data and interpreting model’s outputs (together with contributing factors) remain challenging. Consequently, existing crash prediction models struggle to accurately forecast crash outcomes and effectively incorporate their insights into the design of policy intervention. + +Crash data are inherently heterogeneous, making accurate prediction a significant challenge. After the crash happened, first responders compile textual and numerical on- site details, often supplemented by images, driver behavior data, and licensing records. Although these diverse sources hold immense potential for crash prediction and feature attribution, three key obstacles must be addressed: 1) Data Integration. Existing approaches often reduce multi- modal data to one- hot embeddings for classification tasks15–17. However, these approaches often neglect the valuable information contained within textual and behavioral data, potentially limiting the accuracy and reliability of crash prediction models11,13. 2) Method Generalization. Scaling crash- event prediction models to new data remains a complex endeavor due to the large variety of features, the complexity of representation extraction and encoding, and the diverse formats in which crash data appear18,19. Current machine learning solutions are often tailored to specific data types, limiting their adaptability when new cases or additional data modalities arise6,20–22. 3) Feature Learning and Attribution. Multi- modal crash data regularly include partially overlapping information, such as road attributes recorded in both on- site images and textual crash reports, complicating the accurate assessment of each feature’s unique contribution. + +Recent advancements in Large Language Models (LLMs), such as GPT- 423 and LLaMA 324, have demonstrated their potential for deriving complex crash patterns from multi- modal data25 and addressing persistent challenges. However, fully adapting LLMs to predict crash outcomes and inform effective safety interventions requires overcoming three primary technical hurdles in data, model, and interpretability. From a data perspective, diverse crash records, including images, textual notes, and driver behavior logs, must be reformatted into textual inputs suitable for LLM processing. In terms of modeling, the generative nature of LLMs, which have extensive output vocabularies (e.g., LLaMA 3’s 128,256 tokens), poses challenges for discriminative learning tasks and raises concerns about trustworthiness, particularly when crash outcomes (e.g., crash type or severity) are well- defined by public agencies into finite categories. Furthermore, interpreting LLM’s outputs for crash prediction becomes difficult, as it remains unclear how much we can trust the forecasting results and how individual factors contribute to crash outcomes. This lack of interpretability and robustness analysis hinders the development of data- driven, + +<--- Page Split ---> + +actionable plans for mitigating the crash risks. + +This study advances traditional traffic safety analysis by shifting from aggregate- level considerations to event- level crash prediction. We propose TrafficSafe (see Figure 1b), a novel LLM- driven framework designed for addressing these challenges to provide a comprehensive understanding of crash events. TrafficSafe comprises three main components: TrafficSafe Event dataset for multi- modal crash data integration, TrafficSafe LLM for crash outcomes prediction, and TrafficSafe Attribution for conditional risk analysis. Together, these components enable accurate and trustworthy crash consequences prediction and risk attribution, answering the when, where, who, what, why, and how to support targeted traffic safety interventions. By reframing crash outcome prediction as a text- based reasoning task, TrafficSafe exploits the inherent language reasoning capabilities of LLMs to offer actionable insights for crash prevention, ultimately paving the way for data- driven safety solutions. + +## 2 Novelties and Contributions + +This study advances traditional traffic safety analysis by shifting from aggregate- level considerations to event- level crash prediction. In particular, we customize LLMs to forecast expected crash consequences and attribute relevant features with enhanced accuracy and interpretability. Our proposed framework, TrafficSafe, supports reliable and accountable learning from multi- modal crash data, facilitating a deeper understanding of crash events. Key contributions and findings include: + +Unlocking multi- modal data integration and text reasoning for crash consequence prediction. We introduce the TrafficSafe framework to extend the LLMs for crash outcomes prediction. Rather than treating crash features as isolated numerical inputs, TrafficSafe integrates them into the broader semantic context of traffic data. To effectively utilize and integrate the multi- modal crash data, the TrafficSafe Event dataset is constructed with 58,903 textual prompts totaling over 12 million words. The TrafficSafe LLM is then fine- tuned by framing crash outcome prediction as a task specific token generation task. This approach yields a \(41.7\%\) increase in average F1- score across multiple crash consequence prediction tasks. + +Integrating traffic safety priors in LLMs for trustworthy crash predictions. Compared with existing LLMs, we incorporate crash- domain knowledge and priors into the model's vocabulary as special tokens. This addition allows us to tailor the output to specific crash categories, including crash type, severity, and number of injuries, thereby providing a direct way to measure the model's trustworthiness and link to targeted interventions. Experimental results of TrafficSafe LLM show a strong correlation between increasing confidence in the model's output and higher prediction accuracy, achieving over \(70\%\) accuracy when the confidence score exceeds \(60\%\) . Notably, the model reaches more than \(95\%\) precision for fatal crash predictions when the confidence score surpasses \(60\%\) . This feature offers quantitative evidence to support safety- oriented decision- making and helps close the gap regarding how to trust the model's predictive results. + +Advancing feature interpretation for conditional risk analysis and policy intervention, even in unseen scenarios. The TrafficSafe Attribution framework is proposed for conditional risk analysis, which is supported by a novel sentence- level feature contributions calculation method, enabling event- level feature attribution for textual inputs of TrafficSafe LLM. Then, the "what- if" conditional analysis can further identify and analyze + +<--- Page Split ---> + +the most critical risk conditions and their combinations. For instance, alcohol- impaired driving consistently emerges as a leading contributor to serious and fatal crashes. While driving in a work zone under sober conditions poses minimal risk, combining these conditions with alcohol consumption drastically increases danger, making it one of the most hazardous scenarios for severe crashes. Furthermore, aggressive and impairment- related behaviors demonstrate nearly double the impact on severe crashes compared to other driver behaviors. These insights lay the groundwork for implementing targeted traffic safety policies and interventions26. + +- Guiding optimal data collection for efficient model evolution and lifelong learning. A longstanding challenge in crash modeling is determining how to select valuable data from heterogeneous sources and prioritize which information first responders should capture during incident documentation. The proposed TrafficSafe Attribution addresses this by estimating the contributions of multi-modal data during training, then quantifying which data types have the greatest impact on model performance. Such insights guide more effective traffic safety data collection, improving crash prediction accuracy while also supporting efficient, continuous model evolution through a targeted, data-driven strategy. + +## 3 Results + +### 3.1 Multi-modal Crash Data + +Our cleaned dataset comprises crash data from Washington State in 2022, totaling 16,188 records, and from Illinois in 2022, totaling 42,715 records, after excluding cases with missing key attributes related to vehicle or crash object status. Primary sources include the Highway Safety Information System (HSIS) crash data27 and satellite images28. The HSIS crash data contains four major components: crash data, infrastructure data, vehicle data, and the person data. Crash data provides detailed descriptions of crashes, such as location, time, and injury severity. Infrastructure data includes information about road layouts and traffic characteristics, such as road level and speed limits. Vehicle data contains details such as manufacturing year and reported defects of the involved vehicles, while person data captures demographic and other relevant details about drivers and passengers, such as age and gender. Satellite images complement the HSIS data by providing additional visual context, including information about lanes, intersections, and other roadway attributes. Further information on raw data formats and types is available in Section 5.1.1. + +### 3.2 TrafficSafe Crash Outcomes Prediction Pipeline + +To leverage the multi- modal crash data described in Section 3.1 for crash prediction, we developed the TrafficSafe crash outcomes prediction pipeline, which transforms crash outcomes prediction into a text- based reasoning task. To achieve this, the raw crash data is organized into the textual TrafficSafe Event dataset, which is then used to fine- tune the TrafficSafe LLM. Figure 2 presents an overview of the TrafficSafe crash outcomes prediction pipeline, with subsequent sections detailing each stage of the pipeline. + +### 3.2.1 Constructing Prompts and Prediction Targets + +The TrafficSafe Event dataset is created through an AI- expert cooperative textualization process, organizing multimodal raw data for effective crash prediction. The detailed information about the raw data feature engineering and + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: TrafficSafe Crash Outcomes Prediction Pipeline. Multi-modal crash data is collected and organized into textual prompts through an AI-expert cooperative process. The HSIS crash data, satellite images, and infrastructure data are used to extract general and infrastructure information, including the crash time, location, the road level, and so on. The vehicle data, and person data are converted into the event information and the unit information, including vehicle movements, driver characteristics (e.g., age, gender, alcohol use), vehicle attributes (e.g., manufacture year), and so on. TrafficSafe Event dataset is created with three prediction targets: Injury, Severity, and Type. The Injury task predicts the number of people injured in the crash event, the Severity task estimates the severity level of the crash, such as no apparent injury or fatal, and the Type task classifies type of crash, such as single vehicle with object or angle impacts right (The crash event consequences classification are provided in Supplementary Table 2 and Supplementary Table 3. The TrafficSafe LLM is fine-tuned using the TrafficSafe Event dataset. To reframe the crash outcomes prediction from a classification task to a language inference task, TrafficSafe LLM is fine-tuned by adding prediction targets as special tokens in its vocabulary and adjusting parameters using Low-Rank Adaptations (LoRA) \(^{29}\) .
+ +117 the textualization process are available in Section 5.1.2. As shown in Figure 2, the constructed prompts are divided into five parts: one system prompt and four content parts, with each content part containing approximately 100 words. These parts include: + +- System Prompt: Provides an introduction and task-specific instructions. + +- General Information: Includes general information about the time and location of the prediction region and the roadway category. + +- Infrastructure Information: Describes road infrastructure, encompassing static features like the number of lanes and speed limits, as well as dynamic elements such as work zones, lighting, and road surface + +<--- Page Split ---> + +conditions. + +- Event Information: Contains detailed descriptions of crash events, such as the number of vehicles involved and their directions of movement. + +- Unit Information: Provides vehicle and individual details relevant for crash prediction, such as airbag status and the driver's age. + +The prediction targets consist of three variables: Injury, Severity, and Type (see Figure 2) \(^{30 - 32}\) . Specifically, Injury task predicts the number of people injured in the given crash event. Injury task is treated as a classification task with four categories: zero, one, two, and three or more than three, where crashes involving more than two injured people are grouped into a single category due to the limited number of such cases. The Severity task assesses the level of injury severity in a crash, classified into five levels from no apparent injury to fatal. Type task predicts the type of crash, such as the rear-end collision or collision with object, with 14 crash type categories in the Washington dataset and 16 in the Illinois dataset. Detailed information on the defined targets is available in Section 5.1.3. + +For each crash event, we perform the feature engineering and textualization process, organize the textualized data as input, and process labels corresponding to three tasks. The complete prompt examples are presented in Extended Data Figure 1 and Extended Data Figure 2. Ultimately, after filtering out data items with missing information, the TrafficSafe Event dataset merges the complementary information from multi- modal data sources and contains 58,903 crash records with approximately 12.74 million words. These records are split into training, validation, and test sets in a 7:1.5:1.5 ratio. + +#### 3.2.2 Adapting LLM for Crash Prediction + +Although vanilla LLMs like Llama 3 possess broad general knowledge and strong text reasoning capabilities, they demonstrate limited effectiveness on crash prediction tasks without the fine- tuning process (see Supplementary Section 1). To address this, we developed TrafficSafe LLM, a specialized model fine- tuned on the processed TrafficSafe Event dataset. This fine- tuning process enhances the LLM's comprehension of crash events and enables accurate outcome prediction. Specifically, special tokens are introduced into the LLM vocabulary as prediction targets (Number of Injury, Severity, and Crash Type), fine- tuning the model to generate these tokens during prediction. The details of the fine- tuning are provided in Section 5.2. + +### 3.3 Performance Evaluation of TrafficSafe LLM + +In this section, we evaluate the performance of TrafficSafe LLM and compare its performance with other baselines (see Section 5.2.6). The fine- tuning process is based on two vanilla LLMs with different sizes: Llama 3.1 8B and Llama 3.1 70B. Accuracy, precision, and F1- score are used as the evaluation metrics, the detail information is available in Section 5.2.5. + +TrafficSafe LLM provides accurate crash predictions, even in zero- shot scenarios. Table 1 compares the performances of TrafficSafe LLM and adopted baselines. The results show that the TrafficSafe LLM outperforms all the baselines in each task setting with an average F1- score improvement of 41.7% across multiple tasks. Specifically, in the crash Type prediction task in Washington dataset, the TrafficSafe LLM achieves F1- score of + +<--- Page Split ---> + + +Table 1: Performance Comparison of the three Crash Prediction Tasks on Washington Dataset and Illinois Dataset. We present quality metrics along with model rankings by averaging the column-wise rank. The zero-shot results for North Carolina and Maine were derived using the model trained on the Illinois dataset. In supervised finetuning experiments on the Washington and Illinois datasets, TrafficSafe LLM outperforms all other methods, with TrafficSafe 70B achieving the best performance. Additionally, TrafficSafe LLM demonstrates strong generalization capabilities in zero-shot experiments on the North Carolina and Maine datasets. + +
DatasetModelInjurySeverityTypeRank
AccuracyPrecisionF1-scoreAccuracyPrecisionF1-scoreAccuracyPrecision
WashingtonRandomForest330.5220.6490.5450.6280.5460.5490.7400.3980.2744 (4.11)
AdaBoost340.4950.2450.3280.4920.2450.3280.5630.2490.3028 (6.00)
CatBoost350.4950.2450.3280.4920.2450.3280.7150.4000.3296 (5.22)
DecisionTree360.4950.2450.3280.5280.4280.3720.6280.4060.3235 (4.67)
LogisticRegression370.4950.2450.3280.4920.2450.3280.5470.4010.3097 (5.67)
XGBoost380.5660.6650.4690.5340.4280.3670.7390.4130.2983 (4.00)
National Baseline390.3430.5550.4240.3530.5470.429////
TrafficSafe 8B0.6220.6300.6180.6400.6360.6340.7560.7630.7552 (2.22)
TrafficSafe 70B0.6300.6820.6490.6480.6440.6440.7600.7750.7591 (1.00)
IllinoisRandomForest330.4620.5540.3830.4300.4520.3380.6100.6700.6323 (4.11)
AdaBoost340.4030.1830.2510.3180.1470.2000.1090.0830.0838 (8.00)
CatBoost350.4570.5430.3880.4540.4460.4040.5350.6560.5794 (4.22)
DecisionTree360.4260.5140.4100.4170.3980.3610.5040.6240.5486 (5.33)
LogisticRegression370.4130.4390.4100.3600.3850.3550.3790.4770.4007 (6.33)
XGBoost380.4420.5750.3400.4050.4190.2780.6780.6940.6835 (4.56)
National Baseline390.3690.1360.1990.4420.1950.271////
TrafficSafe 8B0.5290.5290.5330.5780.5840.5710.7010.7680.7212 (1.89)
TrafficSafe 70B0.5340.5870.5430.5540.5610.5480.7270.7670.7371 (1.44)
North CarolinaTrafficSafe 8B (zero-shot)0.5110.7760.4680.5490.6380.4870.6910.7750.672/
MaineTrafficSafe 8B (zero-shot)0.5210.5730.4570.5420.5820.4930.7010.6220.613/
+ +0.759, which is more than \(130\%\) higher than all other comparative methods. TrafficSafe LLM performs well on both the Washington and Illinois datasets, demonstrating its stability across diverse geographical regions. Moreover, as shown in the confusion matrix in Figure 3a and Figure 3b, beyond improved metrics, TrafficSafe LLM demonstrates a more balanced prediction distribution. In contrast, as shown in Figure 3c and Figure 3d, traditional machine learning models tend to predict the dominant categories (e.g., zero under Injury prediction task, no apparent injury under Severity prediction task). The complete confusion matrix is shown in Extended Data Figure 3. Moreover, the ability of a model to generalize across unseen scenarios is vital to ensuring its robustness and applicability in real- world contexts. We tested TrafficSafe LLM generalization ability by using the TrafficSafe LLM trained on Illinois dataset and evaluating its performance on the unseen Maine and North Carolina datasets. Notably, without additional fine- tuning, TrafficSafe LLM achieved F1- scores averaging 0.542 in North Carolina and 0.521 in Maine, closely matching its performance in Illinois (see Table 1). This underscores TrafficSafe LLM's ability to generalize well to previously unseen datasets, further validating its potential for real- world applications. + +TrafficSafe LLM provides trustworthy crash predictions, where a higher confidence score links to higher accuracy. TrafficSafe LLM tailors LLMs for discriminative crash outcomes prediction tasks, generating predictions accompanied by confidence scores that represent the probabilities associated with specific special tokens. Figure 3e and Figure 3f illustrate the trend of accuracy in relation to the confidence scores of TrafficSafe LLM's predictions for the Washington and Illinois datasets. The results indicate that our model achieves greater accuracy at higher confidence levels. For instance, for the Injury prediction task in the Washington dataset, when + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: TrafficSafe LLM Provides Accurate and Trustworthy Predictions. TrafficSafe LLM produces robust confusion matrices for both the (a) Washington and (b) Illinois datasets (we select the best results for each task by F1-score). In contrast, baseline models tend to predict the most frequent category across both the (c) Washington and (d) Illinois datasets (we show baseline models with the best F1-score. The performances for other baseline models can be found in Extended Data Figure 3). Meanwhile, TrafficSafe LLM produces trustworthy predictions for both the (e) Washington and (f) Illinois datasets. Higher confidence levels in the model's predictions correspond to an increased likelihood of accuracy. Furthermore, (g) The TrafficSafe LLM achieves higher precision for fatal crash predictions. (h) Fatal crash predictions also exhibit higher confidence compared to average predictions in Illinois dataset. The Washington dataset is not shown due to limited fatal cases. (i) For fatal crashes, the TrafficSafe LLM achieves near-perfect precision (97.61%) when the confidence score exceeds 0.6, indicating that the TrafficSafe LLM can deliver highly accurate and trustworthy predictions for fatal crashes.
+ +<--- Page Split ---> + +the model's confidence score exceeds 0.40, the accuracy rises above 0.65, and with confidence scores over 0.60, the accuracy surpasses 0.80. The strong positive correlation between confidence scores and accuracy showcases the trustworthiness of the TrafficSafe framework. By providing reliable confidence scores alongside predictions, the framework empowers informed decision- making in real- world applications. + +### 3.4 TrafficSafe Attribution and Result Interpretation + +Understanding how TrafficSafe LLM generates accurate predictions and how various components of the input prompt influence the outcomes is fundamental to enabling evidence- based decision- making. As shown in figure 3e and 3f, the TrafficSafe LLM's confidence score strongly correlates with its predictive accuracy for fatal and serious injury crashes, therefore, we can use the confidence score to represent a case's real- world risk level. Notably, the TrafficSafe LLM's confidence scores tend to be lower than their corresponding precision values (see figure 3e and 3f, indicating that the confidence score is a conservative estimate of risk). + +Within the TrafficSafe Attribution framework, a sentence- based feature contributions calculation method was proposed to identify how each sentence contributes to the LLM's outputs based on Shapley theory which is recognized as a systematic and equitable method for attributing the contribution of each feature to a model's output40,41, thereby revealing crash- related factors at the event level (see Section 5.3 for details). In essence, each feature's contribution represents its share of responsibility for the model's confidence in a particular prediction. The sum of all feature contributions equals the confidence score itself. Figure 4 illustrates sentence- level feature contributions for the severity of individual crash events, using one crash from Washington and one from Illinois as examples. In the Washington crash example (Figure 4a), Driver Behavior (e.g., reckless driving or speeding) is the primary factor contributing to serious injury crashes with the feature contribution of 0.258. Person Info (e.g., no seatbelt use) also shows a substantial impact with the feature contribution of 0.149. By contrast, Dynamic Info (daylight and dry roads) lowers the probability of crash with serious injuries with a negative feature contribution of - 0.009. While, in the Illinois example (Figure 4b), an elevated BAC (Blood Alcohol Content, with feature contribution of 0.284) and the presence of a Work Zone (feature contribution of 0.462) notably increase the likelihood of fatal crash outcomes. Beyond the above examples, more additional sentence- level feature attribution analysis can be found in Extended Data Figure 4, 5, Supplementary Section 5 and 6. + +The following sections utilize TrafficSafe Attribution framework to examine feature importance from two perspectives: 1) at the inference stage, to identify key factors influencing crash predictions under various conditions and high- risk scenarios, and 2) at the training stage, to understand which data components are most important for model learning. + +### 3.4.1 Factor Attribution at Inference Stage for Conditional Risk Analysis + +Conditional analysis evaluates crash outcomes across various scenarios, such as driving with or without alcohol consumption, to quantify the risk factors associated with each scenario. Severe crashes (serious injuries and fatal crashes) were prioritized in the conditional analysis due to their critical importance for traffic safety. These crashes, particularly fatal ones, were predicted accurately and reliably by TrafficSafe LLM (see Figures 3g, 3h, and 3i). Five key contributing factors were identified for this conditional analysis: Driver BAC (BAC = 0 or not offered / BAC < 80 / BAC >= 80), Roadway Type (Highway / not highway), Work Zone (Work zone / not work zone), User Type (Pedalcyclist or pedestrian / not pedalcyclist or pedestrian), and Driver Behavior (Aggressive driving + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + + +![](images/Figure_5.jpg) + +
(a) Sentence-based Feature Attribution Results for a Crash Resulting in Serious Injuries in Washington Dataset.
+ +(b) Sentence-based Feature Attribution Results for a Crash Resulting in Fatalities in Illinois Dataset. + +Figure 4: Single Case Feature Attribution Results for Severity Task. The left part displays the full prompt from (a) Washington and (b) Illinois, with different colors representing various semantic text sequences. The right part illustrates the feature contribution assigned to each text sequence. Positive contributions signify a supportive role in the model's prediction, whereas negative contributions indicate a detracting influence. The absolute value of these contributions represents the importance of each sequence to the model's output. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 5: Conditional Risk Analysis for the Serious Injury and Fatal Crashes. Higher confidence scores in TrafficSafe LLM's predictions correspond to greater accuracy, allowing the confidence score (calculated as the sum of feature contributions for all data components) to serve as an indicator of risk level for serious and fatal crashes. (a) The estimated risk levels for various feature combinations are presented, with each level corresponding to the average confidence score of TrafficSafe LLM's predictions under the same conditions. Each column represents a specific combination of conditions (marked by dark dots) alongside the corresponding feature contribution for selected factors. (b) Feature contributions of five key factors and their proportions relative to all factors are visualized. The inner circle represents the average feature contribution of each factor across different values, while the outer circle shows the percentage share of each factor in the total average feature contribution. The unit for BAC (Blood Alcohol Content) is "mg/L", which is omitted in this figure. (c) Average feature contribution for each factor under specific values. Bars are marked in pink if the value exceeds the corresponding factor's average shown in (a) and in blue if it does not. (d) The strong correlation between the number of risk factors and the risk level of crashes. The high-risk factors are defined as driving after drinking (both BAC <= 80 mg/L and BAC > 80 mg/L), driving in work zones, driving on freeways, pedestrian-involved crashes, and high-risk driver behaviors (aggressive or impairment-related). For each case, we tallied the number of these risk factors and calculated the average risk level for all cases sharing the same count. (e) Feature contributions of different data components during the training stage for Washington and Illinois datasets.
+ +<--- Page Split ---> + +/ impairment- related behavior / traffic rules violations / improper driving / others). Collectively, these factors accounted for an average of \(79.33\%\) of the model's overall attribution in predicting serious and fatal crashes (see Figure 5b). A summary of key findings is provided: + +- The BAC record emerges as a critical determinant in predicting serious and fatal crashes. Among all contributing factors, BAC accounts for \(25.26\%\) of the total contribution to serious and fatal crash prediction (see Figure 5b). Notably, its contribution substantially increases when a driver consumes alcohol, irrespective of the amount. When drivers are under the influence of alcohol even if their BAC does not exceed the legal intoxication limit of \(80 \mathrm{mg / L}^{42,43}\) , this factor's feature contribution still reaches approximately 0.45, surpassing that of most other factors in many cases (see Figure 5a). Conversely, when a driver's BAC is recorded as "zero or not offered," its contribution approaches zero, indicating minimal impact on the model's predictions. + +- Driving in a work zone is already risky under sober conditions, but alcohol consumption significantly increases the danger, making it one of the most hazardous scenarios for severe-injury crashes. As shown in Figure 5a, driving in a work zone while sober ("Work Zone-Yes" and "BAC = 0 or not offered") contributes little to severe crash outcomes, with an average feature contribution of 0.03. However, after consuming alcohol (whether "BAC >= 80" or "BAC < 80"), the work zone feature contribution rises more than seven time to an average of 0.22. Furthermore, the overall crash risk increases substantially when driving in a work zone after drinking, as indicated by an average risk level of 0.78, compared to 0.44 under sober conditions. These findings indicate that work zones become especially hazardous when alcohol consumption is involved, creating one of the highest-risk scenarios for severe crash outcomes. Potential drunk driving warnings and risk mitigation strategies shall be closely linked with work-zone areas. + +- Aggressive driving and impairment-related behavior exhibit the highest contributions among driver behaviors. Furthermore, combined with other conditions, aggressive and impairment-related behaviors pose nearly twice the risk for severe crash outcomes compared to other driver behaviors. As illustrated in Figure 5c, aggressive driving emerges as the most significant contributor between driver behaviors, with feature contribution of 0.14. Impairment-related behavior, including driving under the influence of alcohol or drugs, also has a substantial influence, with average feature contribution of 0.11. In comparison, other improper driver behaviors, such as traffic rule violations (feature contribution of 0.07) and distractions like mobile phone use (categorized under improper driving, with feature contribution of 0.03), show below-average contributions to serious and fatal crashes. The "other" category, which includes normal driving and unknown behaviors, has the smallest impact, with feature contribution of 0.03. + +- The co-occurrence of risk factors significantly increases the expected crash risk level. As illustrated in Figure 5d, our analysis reveals a strong correlation between the number of risk factors present in a crash and the expected risk level for severe crash outcomes. When only one risk factor is involved, the risk level for severe crash outcomes is estimated at 0.59. This value increases to 0.62 with two risk factors, surges to 0.78 with three, and escalates to 0.94 when four risk factors co-occur. Notably, scenarios with three or more risk factors are markedly more dangerous than those with one or two. For example, while a combination of a BAC + +<--- Page Split ---> + +exceeding \(80\mathrm{mg / L}\) and driving in a non- freeway work zone yields an average risk level of 0.51, substituting the non- freeway work zone with a freeway work zone increases the average risk level dramatically to 0.97. Such elevated risks indicate that the synergy among multiple factors is far from merely additive; instead, they appear to compound one another, amplifying the potential for severe outcomes. These findings show that transportation agencies need to prioritize multi- faceted interventions tailored specifically to scenarios with overlapping high- risk conditions. + +### 3.4.2 Factor Attribution at Training Stage for Effective Data Collection and Model Development + +Event information and unit information are the most important components for the model training. While feature contributions at the inference stage reveal which features drive critical crash outcomes, understanding feature contributions during training provides deeper insights into which data components most effectively enhance model accuracy. As shown in Figure 5e, the feature contributions of each component in the Washington and Illinois datasets are shown, demonstrating their impact on the model's performance during training (see Supplementary Table 7 for detailed results and Section 5.3 for calculation details). The results indicate that in both the Washington and Illinois datasets, for the Severity task, the unit information describing attributes of the primary entities involved in the crash has the highest contribution to the model's performance (0.314 in Washington, 0.234 in Illinois). Event information, which provides information on the vehicle's movement prior to the crash, is followed by unit information and has the second highest contribution (0.110 in Washington, 0.158 in Illinois). For the Crash Type Prediction task, the event information has the highest contribution (0.388 in Washington, 0.283 in Illinois), followed by the unit information (0.257 in Washington, 0.279 in Illinois) and other components. These results can provide preliminary guidance on prioritizing the information collection for crash events, thereby improving crash prediction and feature attributions for better safety decision support. + +## 4 Discussion + +Deciphering traffic crash modeling as a linguistic learning task is a promising way for future safety research. Most of the existing ML models for crash prediction typically treat various factors as independent numerical input variables11,44. However, this approach fails to capture information richness from the textual crash reports, such as detailed descriptions of behaviors, vehicle movements prior to the crash, and the traffic conditions. To address these issues, we employ an AI- expert cooperative prompt design approach to process diverse data types, including crash reports (textual), satellite and crash images (visual), and infrastructure characteristics (categorical), into a textual TrafficSafe Event dataset and use LLM for prediction. This transformation reframes the task of crash prediction into a text reasoning problem, enabling the use of LLMs to analyze and predict outcomes while preserving the rich, detailed textual information in crash reports, rather than reducing it to simplistic numerical representations. As demonstrated by the results in Section 3.3, with our customization process, the TrafficSafe LLM outperforms all the baseline models, highlighting the advantages of reasoning through textual representations. Building on this, TrafficSafe Attribution extends the framework by enabling conditional analysis of textual prompts, quantifying the contribution of specific factors to crash outcomes under various scenarios. As shown in Section 3.4, this approach effectively identifies key contributors to crashes and high- risk scenarios, and offers data collection guidance for the iterative improvements in the future. + +<--- Page Split ---> + +Providing reliable and interpretable predictions with quantifiable trustworthiness. As illustrated in Figures 3e and 3f, TrafficSafe LLM demonstrates a deep understanding of input- output correlations, yielding predictions whose accuracy increases alongside higher confidence scores. In all tasks across Washington and Illinois dataset, when the TrafficSafe LLM's confidence score exceeds \(60\%\) , which captures over \(70\%\) of the crash events under consideration. Furthermore, the confidence scores for fatal crash predictions are notably higher than those of other crash categories ( \(60\%\) of model confident score leading to over \(95\%\) of the real- world occurrence risk, see Figures 3g, 3h, and 3i). This trackable confidence- precision correlation can provide decision- makers with a robust tool for forecasting crashes under quantifiable uncertainty. Beyond predictive trustworthiness, the TrafficSafe Attribution framework provides interpretable feature attribution by quantifying each feature's contribution to the confidence score (see Section 5.3). A higher feature contribution translates into a higher confidence score, which in turn yields greater prediction accuracy for the severe crash outcomes. Thus the factor with higher feature contribution value has higher impact to the model's prediction. For instance, alcohol- impaired driving (BAC \(>0\) ) increases the confidence score for severe crash predictions by more than 0.47, serving as a critical indicator for the likelihood of these severe crash outcomes. + +Identifying high- risk traffic crashes through conditional factors attribution even in unseen scenarios. The TrafficSafe framework enables a detailed, sentence- level analysis of crash factors through conditional attribution, yielding critical insights into high- risk scenarios. In data- rich situations where sufficient data is available for each condition, TrafficSafe can rank the risk levels associated with various conditions, offering a prioritized list of scenarios that pose the highest danger. This capability supports targeted policy interventions by identifying specific conditions that substantially increase crash risks. For instance, as shown in Figure 5a, driving in a work zone under sober conditions poses low level of risk; however, alcohol consumption in the same setting dramatically amplifies this risk, creating one of the most hazardous scenarios for severe crashes. This insight suggests potential policy interventions, such as mandatory BAC testing in work zones, to mitigate these risks. Likewise, Figure 5c highlights that aggressive driving and impairment- related behaviors markedly increase the likelihood of serious or fatal outcomes, emphasizing the importance of driver education to discourage aggressive behavior and driving under the influence. Moreover, TrafficSafe can be generalized to predict and understand data- sparse scenarios through "what- if" analysis, allowing hypothetical changes to specific conditions to be tested and their potential impact evaluated. For example, while this study lacked sufficient data to comprehensively analyze the effects of user type (e.g., pedestrians or not pedestrians) or roadway type (e.g., freeway or not freeway), TrafficSafe provides a reliable mechanism to simulate and analyze such conditions. + +Assessing the impact of data utility for improved future data collection and life- long learning. Traffic crash data are inherently complex and multi- modal, making it crucial to identify which components are most informative and how critical they are for future traffic safety data collection. During the training stage, data attribution analysis revealed that unit information (e.g., driver behavior and vehicle details) and event information (e.g., vehicle movement and environmental conditions) exert the greatest influence on crash prediction performance. Specifically, for the Severity task in the Illinois dataset, these features contributed 0.173 and 0.287, respectively, to the model's prediction confidence (see Figure 5e). These findings underscore the importance of prioritizing the collection of detailed, high- quality movement and behavior data in crash events, such as precise records of alcohol use, vehicle defects, vulnerable users' status, and road conditions. In contrast, although general information + +<--- Page Split ---> + +(feature contribution of 0.038) and infrastructure information (feature contribution of 0.019) remain valuable, their impact on some tasks is comparatively smaller. By directing data collection efforts toward gathering richer, more consistent information format in these critical safety domains, the accuracy of TrafficSafe can be further improved. Limitations of the proposed TrafficSafe. A primary limitation relates to the handling of multi- modal data. In the TrafficSafe framework, satellite images were processed into textual descriptions and incorporated into prompts. While this approach offers flexibility, advancements in multi- modal foundation models and increasing research on integrating multi- modal data with LLMs present promising alternatives. Leveraging specialized image encoders or utilizing multi- modal foundation models for processing image data are compelling directions. Another potential limitation lies in the efficiency of model training and attribution. Fine- tuning LLMs and computing feature contributions have always required substantial resources and time. Although we employed LoRA fine- tuning and a stratified sampling technique to enhance efficiency, implementing the complete framework still demands significant resources. This poses certain limitations when resources are scarce or in situations demanding rapid model deployment. + +## Reference + +1. Federal Highway Administration (FHWA). 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Data from the HSIS, sourced from multiple systems, encompasses a variety of formats, including categorical, numerical, and textual. In total, four main datasets were used: + +- Crash data. This dataset captures the essential spatio-temporal and contextual attributes of each crash. It includes crash date, time, day of the week, and month, along with location details such as route number, milepost, and the surrounding area's classification (e.g., rural or urban). Higher-level planning attributes (e.g., roadway and functional classifications, intersection-related indicators) are also recorded. In addition, it documents the dynamic circumstances leading up to the event, including the number of vehicles and pedestrians involved, vehicle travel directions (increasing or decreasing milepost), and any maneuvers performed (e.g., lane changes, straight-line movement). + +- Infrastructure data. This dataset details the physical and infrastructural features of the crash site. Key elements include the type of road surface (e.g., asphalt or concrete), average annual daily traffic (AADT), posted speed limits, and access control mechanisms. It also encompasses dimensions such as total road width, right and left shoulder widths, and median width (including median barriers if present), as well as road surface conditions (e.g., dry or wet) and ambient lighting at the time of the crash (e.g., daylight or dusk). + +- Vehicle data. This dataset consolidates information on the vehicles involved in each crash, including vehicle type (e.g., passenger car or truck), intended use (e.g., commercial or private), mechanical condition (e.g., defects), and relevant driver actions (e.g., lane changes or stopping). Additional information on airbag deployment and occupant ejection status provides further granularity. + +- Person data. This dataset compiles information about individuals involved in the crash, detailing demographic characteristics such as age, gender, and seating position. It also includes the use of safety equipment (e.g., seat belts or helmets) and any contributing factors, such as driver distraction or impairment. + +The satellite images obtained from Google Maps serve as a supplementary data source to complement the HSIS dataset. Details of the integration process are provided in Section 5.1.2. Overall, we collect 16,188 crash events data from Washington State and 42,715 events from Illinois State for further analysis. + +### 5.1.2 Feature Engineering and Textualization of Crash Data + +To adapt the multi- modal data to the input of LLMs, we followed the following process to generate textual prompt from raw data entry: + +<--- Page Split ---> + +- Data mapping and organization. For each crash, we associated the crash report with the involved vehicles and individuals using the crash ID, thus obtaining descriptions of the crash and the persons involved. The route ID and milepost were used to identify the specific road segment where the crash occurred, allowing us to gather related road and environment information from infrastructure data. The integrated data was then systematically organized into four categories: general information, infrastructure information, event information, and unit information, aligning with the components outlined in Section 3.2.1. + +- Satellite images textualization. The HSIS datasets provide GPS coordinates for crash locations in Washington and Illinois. To address missing information such as the number of road lanes, high-resolution satellite images (512 × 512 pixels at a zoom level of 19) were retrieved using these GPS coordinates via the Google Maps API. These images supplement the crash dataset with crucial infrastructure and environmental context. Descriptive textual annotations were generated from the satellite images using GPT-4, filling key gaps in the original dataset. These annotations include information such as the number of lanes at the crash site, whether the crash occurred at an intersection, and whether the surrounding area is residential. + +- Dimensionality reduction. Raw data include abundant attributes with rich and varied descriptions. However, some features suffer from insufficient distinction between attribute values due to the original classification's complexity. To address this, we performed dimensionality reduction on these attributes by combining domain experts' insights with GPT-4o clustering results. For example, similar classifications like "pedalyclist struck by vehicle" and "pedalyclist strikes vehicle" were clustered under a broader category such as "pedalyclist collisions". This process generalized the data and reduced redundancy. + +- Prompt generation using AI-expert textualization method. To generate logically coherent and continuous textual data suitable for LLM training, we transformed each category of data into text format using GPT-4. All data are organized as key-value pairs and we get four parts of the key-value pairs for each event case. Then GPT-4o is used to generate the text prompt for each section of the key-value pairs individually. For each part, we apply straightforward prompt to GPT-4o, such as "Please translate a python dictionary to paragraph, act as a crash data interpreter". The text content is extracted from GPT-4o's response for each part consisting of approximately 100 words. By linking four parts of text, we obtain a comprehensive textual description for each crash event case. The detailed process is shown in Extended Data Figure 7. + +#### 5.1.3 Define Prediction Targets + +We select three variables as the prediction targets: Injury, Severity, and crash Type. The three targets are defined as: + +- The Injury \(n_{l}^{\mathcal{P}}\in \{f(l)|l = 0,1,2,\dots \}\) , where \(i\) denotes the \(i\) -th data in the dataset, \(\mathcal{D}\in \{\mathcal{W},\mathcal{I}\}\) denotes the Washington dataset \(\mathcal{W}\) or the Illinois dataset \(\mathcal{I}\) , \(l\) represents the number of people injured, and \(f(l)\) denotes the label when the injured people is \(l\) . + +- The Severity \(s_{l}^{\mathcal{P}}\in \{S_{k}|k = 1,2,\dots \}\) (define on the KABCO scale \(^1\) ), where \(S_{k}\) is the \(k\) -th level of crash severity. + +<--- Page Split ---> + +- The Type \(t_{i}^{\mathcal{O}} \in \{T_{k}^{\mathcal{O}} | k = 1, 2, \dots \}\) , where \(T_{k}^{\mathcal{O}}\) is the \(k\) -th label of crash type in dataset \(\mathcal{D}\) . + +We utilize these three variables to describe the crash result \(\mathrm{CR}_{i}^{\mathcal{O}}\) . The crash outcome can be presented in the following format: \(\mathrm{CR}_{i}^{\mathcal{O}} = (n_{i}^{\mathcal{O}}, s_{i}^{\mathcal{O}}, t_{i}^{\mathcal{O}})\) . For numerical variables, the function \(f(l)\) describes the number of people injured in crash as follows: "zero" if \(l = 0\) , "one" if \(l = 1\) , "two" if \(l = 2\) , and "three and more than three" if \(l \geq 3\) , the values for \(S_{k}\) and \(T_{k}^{\mathcal{O}}\) are provided in the Supplementary Table 2 and Supplementary Table 3. + +### 5.2 TrafficSafe LLM + +We fine- tune TrafficSafe LLM by adapting LLaMa 3.124 to crash prediction tasks to enhance the LLMs' capabilities in interpreting crash data, identifying critical factors, and conducting feature attribution analysis to offer insights for crash prevention. In this section, we will introduce detailed information of the fine- tuning process. + +#### 5.2.1 Construct Training Data for LLMs + +In the training of LLMs, a single input consists of three components: the system prompt, the user prompt, and the target prompt. The system prompt introduces the task, for example: "You are a helpful assistant designed to predict the severity of a traffic crash ...". The user prompt comprises the four content parts detailed in Section 5.1.2 for each case. The target prompt represents the expected output. Examples of these prompts are shown in Extended Data Figure 1, Extended Data Figure 2, and Supplementary Section 3. We tokenize the text inputs using LLaMA 3.1's tokenizer. + +#### 5.2.2 Additional Special Tokens for Classification + +To adapt the LLM as a crash classifier, additional tokens have been incorporated into the tokenizer's vocabulary, and the detailed crash attributes categories are listed in Supplementary Table 2 and Supplementary Table 3. Specifically, for predicting the number of people Injuries of Washington dataset and Illinois dataset, we have introduced four special tokens: , , , and . Similarly, for predicting the Crash Severity of Washington dataset and Illinois dataset, we use five additional tokens: \(S_{k}\) , where \(1 \leq k \leq 5\) , corresponding to different levels of severity. The Type task differs slightly between the Washington and Illinois datasets. For Washington datasets, we utilize 14 special tokens: \(T_{k}^{\mathcal{W}}\) , where \(1 \leq k \leq 14\) , each representing a specific crash type. For Illinois datasets, we utilize 16 special tokens: \(T_{k}^{\mathcal{F}}\) , where \(1 \leq k \leq 16\) . The parameters of the input and output embedding layers are set as trainable, enabling the model to align the representations of these special tokens with the existing embedding space. + +#### 5.2.3 Supervised Fine-tuning + +During the fine- tuning phase, the traffic forecasting task is framed as a next- token generation task. This process can be described as: + +\[p_{\theta}(T_{i}) = \prod_{j = 1}^{|T_{i}|} p_{\theta}(t_{j}^{(i)} | t_{1}^{(i)}, \dots , t_{j - 1}^{(i)}), \quad (1)\] + +where \(T_{i}\) is the \(i\) - th item in the training data, \(p_{\theta}\) is the LLM, \(t_{j}^{(i)}\) denotes the \(j\) - th token in \(T_{i}\) . By maximizing the likelihood \(p_{\theta}(T) = \prod_{i = 1}^{N} p_{\theta}(T_{i})\) , the LLM's parameters are learned. Both the system prompt and the user prompt are masked for loss computation during training. We also used uniform data sampling strategy during the training process to facilitate the convergence of TrafficSafe LLM47. Through this process, the model learns to make prediction for a traffic crash. + +<--- Page Split ---> + +### 5.2.4 Data Split + +We split the Washington and Illinois dataset into training, validation, and test set in a 7:1.5:1.5 ratio. Since the Washington dataset contains relatively few crash events per year, we utilized as many reports as possible to ensure sufficient training data. However, the data distribution across different classes is highly imbalanced. For example, in the crash severity prediction task in Washington dataset, the ratio of \(\# S_{1} / \# S_{5}\) is nearly 100:1, where \(\# S_{k}\) is the number of data with label \(S_{k}\) . The imbalanced data distribution presents a significant challenge for the model's training and evaluation. In Section 5.2.3, we used uniform sampling strategy to train model on this unbalanced data. Similarly, to facilitate the model's evaluation, for the validation set and test set, we removed most of the data with crash severity category of \(S_{1}\) . Specifically, after processing, the dataset consisted of 16,188 records, with 11,332 used for training, 2,428 for validation, and 2,428 for testing. To balance the validation and test set for better evaluation, we removed 1428 \(S_{1}\) data and used 1000 remaining data for validation set and test set separately. Compared with the Washington state, more crash records can be used in Illinois state to generate dataset. As a result, we were able to balance all subsets, including the training, validation, and test sets. Ultimately, the Illinois dataset comprised 42,715 records, with 29,307 used for training, 6,704 for validation, and 6,704 for testing. + +#### 5.2.5 Evaluation Metrics + +In evaluating the model performance as a classification task, we employ weighted accuracy, precision, and F1- score as metrics. In the context of a classification task, we have four notations, True Positive \((TP)\) , True Negative \((TN)\) , False Positive \((FP)\) , False Negative \((FN)\) . Using these notations, we can represent the metrics as follows: + +- Accuracy is one of the most commonly used measures for the classification performance, and it is defined as a ratio between the correctly classified samples to the total number of samples as follows: + +\[\mathrm{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} \quad (2)\] + +- Precision represents the proportion of positive samples that were correctly classified to the total number of positive predicted samples, which reflect the performance of the prediction: + +\[\mathrm{Precision} = \frac{TP}{TP + FP} \quad (3)\] + +- F1-score combines results on precision and recall. It is the harmonic mean of precision and recall, which can be calculated using formula: + +\[\mathrm{F1 - score} = \frac{2}{\mathrm{Precision}^{-1} + \mathrm{Recall}^{-1}} = 2\cdot \left(\frac{\mathrm{Precision}\cdot\mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}\right) \quad (4)\] + +where \(\mathrm{Recall} = TP / (TP + FN)\) . + +#### 5.2.6 Adopted Baselines + +We follow the recent literature \(^{48}\) and also adopt XGBoost \(^{38}\) , Random forest (RF) \(^{33}\) , Decision Trees (DT) \(^{36}\) , Adaptive boosting (AdaBoost) \(^{49}\) , LogisticRegression (LR) \(^{37}\) , Categorical boosting (CatBoost) \(^{50}\) , and National Average \(^{39}\) as compared baselines. Building upon these foundational models, we particularly focus on enhancing + +<--- Page Split ---> + +their predictive capabilities through advanced techniques and parameter optimization. The detailed descriptions of these models are listed as follows: + +- XGBoost is a scalable and distributed gradient-boosting framework that constructs an ensemble of decision trees by minimizing a regularized loss function. It uses second-order gradients for optimization and includes features like shrinkage, column subsampling, and tree pruning to improve accuracy and prevent overfitting38. + +- AdaBoost34 is an iterative boosting method that sequentially trains weak classifiers (e.g., decision stumps) and assigns higher weights to misclassified instances in subsequent iterations. The final prediction is determined by a weighted majority vote of all classifiers. + +- Random Forest (RF)33 builds an ensemble of decision trees by randomly sampling both features and data points (via bootstrap aggregation). The aggregated (voted) output of these diverse trees reduces variance and provides robust performance across a variety of tasks. + +- Decision Trees (DT)36 recursively split the feature space based on selected thresholds, forming a hierarchical tree structure that is easy to interpret. Although they can capture complex interactions, DTs are prone to overfitting if not properly regularized. + +- Logistic Regression (LR)37 models the probability of a binary outcome through a linear combination of input features passed through the logistic function. Coefficients are typically estimated via maximum likelihood, providing a simple yet effective approach for classification. + +- CatBoost50 is a gradient-boosting algorithm that efficiently handles categorical features through techniques such as ordered boosting and gradient-based one-hot encoding. By systematically reducing target leakage in encoding, it achieves high predictive accuracy while mitigating overfitting in heterogeneous datasets. + +- National Average39 predicts crash severity distributions using calibrated Severity Distribution Functions (SDFs). It incorporates road design, traffic control, and crash data to estimate probabilities for different severity levels via a multinomial logit model. + +For these models, the Bayesian optimization method (BayesSearchCV) is used to facilitate the identification of optimal hyperparameters, such as max_depth and learning_rate. The details of the hyperparameters settings of these models are shown in Supplementary Section 4. + +### 5.3 TrafficSafe Attribution + +To identify the feature contribution of each factor to the prediction results, this paper introduces and adapts the concept of Shapley values40. In this section, we first explain the calculation process of Shapley values and subsequently propose a novel sentence-level feature contributions calculation method based on Shapley theory for attributing factors in LLMs. + +#### 5.3.1 Definition of Shapley Value + +Shapley value is a concept from cooperative game theory that has been widely adopted in machine learning to interpret model predictions51. It provides a way to fairly allocate the contribution of each feature to the outcome of + +<--- Page Split ---> + +a predictive model. In essence, the Shapley value quantifies how much each feature contributes to a prediction by considering all possible combinations of features. Formally, the Shapley value \(\phi\) of a feature (or player) \(i\) in a cooperative game is defined as: + +\[\phi_{i} = \sum_{S\subseteq N\backslash \{i\}}\frac{|S|!(n - |S| - 1)!}{n!}\Big[v(S\cup \{i\}) - v(S)\Big], \quad (5)\] + +where \(N = \{1,2,\ldots ,n\}\) is the index set of \(n\) features, \(S\) is a subset of \(N\) , and \(v(S)\) is the utility of the subset \(S\) , which represents a measurable value, such as accuracy or prediction score, achieved by the model using only the subset \(S\) of features. + +The Shapley value is utilized in both the training and inference stages in TrafficSafe. During the training stage, it quantifies the contributions of four primary categories of information: general information, infrastructure information, event information, and unit information. During the inference stage, the Shapley value is applied to assess the contributions of individual sentences to the prediction outcomes. The specific methodologies and implementation details are outlined in the subsequent sections. + +#### 5.3.2 Feature Contributions at the Training Stage + +The Shapley value is utilized to assess the influence of different components in the training set on the model during training. As outlined in Section 3.2.1, the \(j\) - th prompt \(p_{j}\) in the dataset \(P\) is divided into five parts: \(c_{0}\) : system prompt (i.e. "You are a helpful assistant designed to predict the severity of a traffic crash ..."), \(c_{1}\) : general information, \(c_{2}\) : infrastructure information, \(c_{3}\) : event information, and \(c_{4}\) : unit information. We denote \(p_{j}(k)\) as the \(c_{k}\) portion of \(p_{j}\) . Given an index set \(S\) , we can construct a variant \(p_{j}(S)\) by concatenating the parts in \(S\) . For example, if \(S = \{0,1,2\}\) , then \(p_{j}(S)\) contains \(c_{0}\) , \(c_{1}\) , and \(c_{2}\) . Formally, + +\[p_{j}(S) = \mathrm{concat}_{k\in S}p_{j}(k), \quad (6)\] + +where concat denotes concatenation. The resulting dataset based on \(S\) is \(P(S) = \{p_{j}(S) | j = 0,1,\ldots ,L\}\) , where \(L\) is the dataset size. + +Referring to Equation (5), the contribution of part \(c_{i}\) at training, \(\phi_{i}^{\mathrm{train}}\) , is + +\[\phi_{i}^{\mathrm{train}} = \sum_{S\subseteq N\backslash \{i\}}\frac{|S|!(n - |S| - 1)!}{n!}\cdot \Big[v\big(P(S\cup \{0,i\})\big) - v\big(P(S\cup \{0\})\big)\Big], \quad (7)\] + +where \(N = \{1,2,3,4\}\) indexes the four content parts, and \(v(P(S))\) is a performance metric (e.g., accuracy, F1- score) obtained after retraining the model only on prompts in \(P(S)\) . + +#### 5.3.3 Sentence-level Feature Contributions at the Inference Stage + +Unlike traditional machine learning models that primarily handle fixed- length feature vectors, LLMs process variable- length text sequences as input. This characteristic makes commonly used Shapley value approximation methods, such as KernelSHAP and DeepSHAP, less applicable to LLMs. Recent approaches like TokenSHAP and TransSHAP have been proposed to address this by decomposing input text into tokens and computing Shapley values at the token level. However, applying token- level Shapley value computation to TrafficSafe LLM + +<--- Page Split ---> + +introduces two primary challenges: 1) Computational limitations. The computational complexity of Shapley values is exponential in the number of players. In our TrafficSafe LLM, with an input size of approximately 500 tokens, large- scale computation of token- level Shapley values for crash data becomes impractical. 2) Limited interpretability. Decomposing the prompt at the token level disregards inter- token dependencies, and the arbitrary masking or replacement of tokens can lead to semantic ambiguity and contextual shifts. These issues hinder a precise understanding of how individual features contribute to predictions. Moreover, paragraph- level analysis is too coarse for detailed attribution, since it can merge distinct features into a single category (e.g., driver and vehicle details under "unit information"). + +To overcome these limitations, we propose a sentence- level feature contributions calculation method for inputs of LLMs, which proceeds as follows: + +- Sentence segmentation. The prompts are segmented using delimiters (e.g., commas ", or periods ".") to produce sentence-level units. + +- Feature groups annotation. GPT-4o is used to group and label these sentences (see Figure 4 for the groups' content). Each group is represented as \(c_{k}\) , where \(k \in N' = \{1,2,3,\ldots n\}\) . For the Washington dataset, \(n = 14\) , while for the Illinois dataset \(n = 12\) . Given index set \(S' \subseteq N' \setminus \{i\}\) , we can construct the the prompt \(p_j(S')\) similar to the process Equation (6). The dataset built upon \(S'\) can be written as \(P(S') = \{p_j(S') | j = 0,1,2,\ldots ,L\}\) , where \(L\) is the length of the dataset \(P\) . + +- Feature contributions calculation based on the feature groups. Based on the constructed dataset, the feature contribution for the \(i\) -th sentence-group \(\phi_i^{inf}\) can be calculated as: + +\[\phi_{i}^{inf} = \sum_{S'\subseteq N'\setminus \{i\}}\frac{|S'|!(n - |S'| - 1)!}{n!}\cdot \left[p_{\theta}(P(S'\cup \{0,i\})) - p_{\theta}(P(S'\cup \{0\}))\right] \quad (8)\] + +where \(p_{\theta}\) is the LLM that returns the predicted probability of the target. A higher \(\phi_{i}^{\mathrm{inf}}\) indicates a greater contribution of the \(i\) - th sentence group to the model's confidence. To reduce computational overhead, we adopt a stratified sampling- based Shapley estimation method using complementary contributions46. + +## Reference + +47. Du, H., Zhao, J., Zhao, Y., Xu, S., Lin, X., Chen, Y., Gardner, L. M. & Yang, H. F. Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study. arXiv preprint arXiv:2404.06962 (2024). + +48. Ahmed, S., Hossain, M. A., Ray, S. K., Bhuiyan, M. M. I. & Sabuj, S. R. A study on road accident prediction and contributing factors using explainable machine learning models: Analysis and performance. Transportation research interdisciplinary perspectives 19, 100814 (2023). + +49. Freund, Y., Schapire, R. & Abe, N. A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence 14, 1612 (1999). + +50. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems 31 (2018). + +<--- Page Split ---> + +71 Chen, H., Lundberg, S. M. & Lee, S.- I. Explaining a series of models by propagating Shapley values. Nature communications 13, 4512 (2022). + +72 Chen, H., Covert, I. C., Lundberg, S. M. & Lee, S.- I. Algorithms to estimate Shapley value feature attributions. Nature Machine Intelligence 5, 590- 601 (2023). + +73 Lundberg, S. M. & Lee, S.- I. A Unified Approach to Interpreting Model Predictions in Advances in Neural Information Processing Systems (eds Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. & Garnett, R.) 30 (Curran Associates, Inc., 2017). + +74 Goldsmith, R. & Horovicz, M. TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation. arXiv preprint arXiv:2407.10114 (2024). + +80 Kokalj, E., Škrlj, B., Lavrač, N., Pollak, S. & Robnik- Šikonja, M. BERT meets Shapley: Extending SHAP Explanations to Transformer- based Classifiers in Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation (eds Toivonen, H. & Boggia, M.) (Association for Computational Linguistics, Online, Apr. 2021), 16- 21. + +<--- Page Split ---> + +## 6 Data Availability + +Details of each raw data source and data processing are described in the Method Section. The processed data examples are available at https://github.com/Puw242/TrafficSafe. In compliance with HSIS data policy, requests for the complete raw dataset should be made via https://highways.dot.gov/research/ safety/hsis. + +## 7 Code Availability + +Code is publicly accessible at https://github.com/Puw242/TrafficSafe. + +## 8 Author Contributions + +Y.Z., P.W. and H.F.Y conceptualized and designed the study. P.W. and Yibo Z. collected data. P.W. and Yibo Z. processed the data and designed prompts. Y.Z. and P.W. performed experiments. Yibo Z. run the baseline models. Y.Z., P.W., Yibo Z. and H.F.Y prepared the figures. Y.Z., P.W., Yibo Z. and H.F.Y analyzed results. Y.Z., P.W., Yibo Z. and H.F.Y wrote the initial draft. H.D. and H.F.Y provided guidance and feedback for the study. H.D. and H.F.Y revised the manuscript. H.F.Y. acquired the funding. H.F.Y. provided computational resources. All authors prepared the final version of the manuscript. + +## 9 Competing Interests + +The authors declare no competing interests. + +<--- Page Split ---> + +## Example Prompt - #EC22961 + +You are a helpful assistant designed to predict the task target of a traffic crash. You need to make prediction based on the information below: + +## General Information + +This incident occurred on February 23, 2022, at 2:00 PM, in the city of Bremerton, Kitsap County, on the 303 route increasing milepost direction at milepost 1.87. The location is an Urban - Principal Arterial, not at an intersection and not related to a driveway. The type of roadway is classified as an Urban Multilane Undivided Non- Freeway. The level of access control is Non Limited Access Least Restrictive, the speed limit is 30, and the average annual daily traffic is 37000. + +## Infrastructure Information + +The road width is 52 feet, the road surface is made of Asphalt, the right and left shoulders width is unknown, and the surface type of the left shoulder is unknown. This road does not have a median- separated area, there is no barrier in the median and the median width is unknown. The condition of the road is unknown regarding work zone status, but it is known that the accident occurred during daylight and the road surface condition was dry. + +## Event Information + +There were no pedestrians involved, 3 vehicles involved. The accident has no influence of alcohol or drugs. There were no objects involved. Vehicle1 was moving North, in the direction of increasing milepost, Vehicle2 was also moving North, in the direction of increasing milepost. The first vehicle was moving straight when the second vehicle was stopped in traffic, legally standing. + +## Unit Information + +The unit 1 is a Vanette Under 10,000 lb, non- commercial vehicle. The airbag was not deployed. The vehicle had no defects. The driver was going straight ahead, was not ejected, and was distracted by an unknown factor. Person 1: Motor Vehicle Driver, Female, 47, Restraint use is unknown. The unit 2 is a Vanette Under 10,000 lb, non- commercial vehicle. The airbag was not deployed. The vehicle had no defects. The driver had stopped for traffic, was not ejected, and no violations or factors contributed to the incident. Person 1: Motor Vehicle Driver, Female, 26, Restraint use is unknown. The unit 3 is a Vanette Under 10,000 lb, non- commercial vehicle. The airbag was not deployed. The vehicle had no defects. The driver was going straight ahead, was not ejected, and was distracted by an unknown factor. A drug recognition expert was not requested. Person 1: Motor Vehicle Driver, Female, 50, Restraint use is unknown. + +## Targets + +Please predict the Injury number of the crash choosing from the following tokens (4 options available). + +Assistant: + +Please predict the Severity of the crash choosing from the following tokens (5 options available). + +Assistant: + +Please predict the crash Type of the crash choosing from the following tokens (14 options available). + +Assistant: + +Extended Data Figure 1: A Crash Event Prompt Example from Washington Dataset. + +<--- Page Split ---> + +## Example Prompt - #129094 + +You are a helpful assistant designed to predict the task target of a traffic crash. You need to make prediction based on the information below: + +## General Information + +This crash occurred in Cook County on 4/2/2022 at 0:00 o'clock. The crash happened in the city of Chicago, classified as Chicago area, on None at milepost 0.0. The roadway is classified as Unknown, and the location was identified as an Urban 2 Lane Roads. This crash was not related to an intersection. + +## Infrastructure Information + +The road surface was Dry with Darkness, Lighted Road lighting conditions and Clear weather at the time of the crash. The crash occurred on a Not Divided Two- way with Stop Sign in place, and it was confirmed that the crash did not occur in a work zone. + +## Event Information + +The crash involved 2 vehicles. The primary driver behavior in the crash was Unable to Determine, with secondary behavior was (Not Applicable). + +## Unit Information + +Vehicle 0, a 2014 model, was moving South and was traveling straight ahead before the crash. Vehicle 1, a 2016 model, was moving South and was traveling straight ahead before the crash. The driver was a 23- year- old male with no visible distractions, sitting in the Driver. The driver's blood alcohol content was not offered. The driver was a 59- year- old male with no visible distractions, sitting in the Driver. The driver's blood alcoho content was not offered. There was also a passenger, a 24- year- old male, seated in the Third Row Left. There was also a passenger, a 24- year- old male, seated in the third Row Right. + +## Targets + +Please predict the Injury number of the crash choosing from the following tokens (4 options available). + +Assistant: + +Please predict the Severity of the crash choosing from the following tokens (5 options available). + +Assistant: + +Please predict the crash Type of the crash choosing from the following tokens (16 options available). + +Assistant: + +Extended Data Figure 2: A Crash Event Prompt Prompt Example from Illinois Dataset. + +<--- Page Split ---> +![](images/Figure_unknown_1.jpg) + +
Extended Data Figure 3: The Confusion Matrix for TrafficSafe LLM and the Traditional Methods in (a) Washington Dataset and (b) Illinois Dataset.
+ +<--- Page Split ---> + +## One Crash Case in Washington - #EC36495 + +One Crash Case in Washington - #EC36495Assume the crash occurred on April 5, 2022, at 01:00 AM, in an unknown city, Mason county, on the 101 route increasing milepost direction at milepost 314.78. The location is a Rural- Principal Arterial, not at an intersection and not related to a driveway or other characteristic details are unknown. The roadway classification at the site of the accident is Rural 2 Lane Roads. The level of access control is Non Limited Access More Than Average Restriction, speed limit is 50, average annual daily traffic is 2300. The road width is 22, the road surface is made of Asphalt, the right shoulder width is 3 and there is no left shoulder, with the surface type of the left shoulder being unknown. This road is not median- separated, and there is no barrier or median width information available. The occurrence in the work zone is unknown, in conditions where the road surface was wet and the lighting was dark with no street lights. There were no pedestrians involved, 1 vehicle involved. The accident had no influence of alcohol or drugs. There was an object involved, specifically an Earth Bank or Ledge. Vehicle1 was moving north, in the direction of decreasing milepost. Vehicle2 direction and movement are unknown. The first vehicle was moving straight. The unit 1, is an unknown special vehicle type, not a commercial vehicle. The vehicle had tires punctured or blown. The driver was going straight ahead, totally ejected, and had a contributing factor of operating defective equipment. Person 1: Motor Vehicle Driver, Female, 53, Lap & Shoulder Used. + +![](images/Figure_5.jpg) + +
TrafficSafe Attribution - #EC36495
+ +Severity: (correct) Type: (correct) Injury: (correct) + +Crash Severity prediction feature attribution (This crash is a FATAL crash) + +Extended Data Figure 4: One Example of Sentence- based Feature Attribution Results for A Crash Resulting in Fatal in Washington Dataset. + +<--- Page Split ---> +![PLACEHOLDER_33_0] + +
Extended Data Figure 5: One Example of Sentence-based Feature Attribution Results for A Crash Resulting in No Apparent Injury in Illinois Dataset.
+ +<--- Page Split ---> +![PLACEHOLDER_34_0] + + +Extended Data Figure 6: Data Processing Process. Four raw datasets from HSIS (crash, infrastructure, vehicle, and person data) are used to construct a prompt through four steps. (1) Data mapping and organization: Link the datasets and organize them into four parts: general, infrastructure, event, and unit. (2) Satellite image textualization: Retrieve satellite images via GPS coordinates using the Google Maps API, then employ GPT- 4o to extract text- based information. (3) Dimensionality reduction: Combine targets with similar values using GPT- 4o. (4) Prompt generation: Use the processed data from the previous steps to generate a prompt for each part. + +<--- Page Split ---> +![PLACEHOLDER_35_0] + + +Extended Data Figure 7: AI- expert Textualization Process. An example for the infrastructure information part of an event case in Washington dataset is shown. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +Supplementary.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c_det.mmd b/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..7c81f4881e580ccea68372fe3b4c3bedcf20cef1 --- /dev/null +++ b/preprint/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c/preprint__09f1a55d00104ac396ababe3986f197810df2a7652eb1fe992b357090dd1b42c_det.mmd @@ -0,0 +1,850 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 952, 207]]<|/det|> +# Customizing Large Language Models for Reliable and Interpretable Traffic Crash Prediction and Safety Interventions + +<|ref|>text<|/ref|><|det|>[[44, 230, 279, 276]]<|/det|> +Hao Frank Yang haofrankyang@jhu.edu + +<|ref|>text<|/ref|><|det|>[[44, 303, 639, 321]]<|/det|> +Johns Hopkins University https://orcid.org/0000- 0001- 6431- 8956 + +<|ref|>text<|/ref|><|det|>[[44, 327, 279, 507]]<|/det|> +Yang Zhao Johns Hopkins University Pu Wang Johns Hopkins University Yibo Zhao Johns Hopkins University Hongru Du Johns Hopkins university + +<|ref|>text<|/ref|><|det|>[[44, 550, 104, 567]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 587, 137, 605]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 624, 300, 644]]<|/det|> +Posted Date: April 29th, 2025 + +<|ref|>text<|/ref|><|det|>[[44, 662, 475, 682]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5947574/v1 + +<|ref|>text<|/ref|><|det|>[[44, 700, 914, 742]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 760, 535, 780]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 816, 933, 858]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 7th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 64574- w. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[95, 83, 844, 155]]<|/det|> +# Customizing Large Language Models for Reliable and Interpretable Traffic Crash Prediction and Safety Interventions + +<|ref|>text<|/ref|><|det|>[[95, 164, 852, 186]]<|/det|> +Yang Zhao \(^{1, 2 + }\) , Pu Wang \(^{1, 2 + }\) , Yibo Zhao \(^{1, 2}\) , Hongru Du \(^{1, 2}\) , and Hao (Frank) Yang \(^{1, 2*}\) + +<|ref|>text<|/ref|><|det|>[[95, 214, 732, 288]]<|/det|> +\(^{1}\) Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, USA. \(^{2}\) Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA. \(^{+}\) The authors contributed equally. \(^{*}\) The corresponding authors information: haofrankyang@jhu.edu + +<|ref|>sub_title<|/ref|><|det|>[[96, 313, 201, 330]]<|/det|> +## ABSTRACT + +<|ref|>text<|/ref|><|det|>[[97, 367, 900, 770]]<|/det|> +Predicting crash events is crucial for understanding crash distributions and their contributing factors, thereby enabling the design of proactive traffic safety policy interventions. However, existing methods struggle to interpret the complex interplay among various sources of traffic crash data, including numeric characteristics, textual reports, crash imagery, environmental conditions, and driver behavior records. As a result, they often fail to capture the rich semantic information and intricate interrelationships embedded in these diverse data sources, limiting their ability to identify critical crash risk factors. In this research, we propose TrafficSafe, a framework that adapts Large Language Models (LLMs) to reframe crash prediction and feature attribution as text- based reasoning. A multi- modal crash dataset including 58,903 real- world reports together with belonged infrastructure, environmental, driver, and vehicle information is collected and textualized into TrafficSafe Event dataset (totaling 12.74 million words). By customizing and fine- tuning state- of- the- art LLMs on this dataset, the proposed TrafficSafe LLM achieves a \(42\%\) average improvement in F1- score over baselines across multiple crash prediction tasks, particularly for severe crashes. To interpret these predictions and uncover contributing factors, we introduce TrafficSafe Attribution, a sentence- level feature attribution framework enabling conditional risk analysis. Findings show that alcohol- impaired driving is the leading factor in severe crashes, with aggressive and impairment- related behaviors having nearly twice the contribution for severe crashes compared to other driver behaviors. In addition, the co- occurrence of crash- contributing factors, such as alcohol- impaired driving, work zones, improper driving behaviors and other factors can significantly elevate risk levels. Furthermore, TrafficSafe Attribution highlights pivotal features during model training, guiding strategic crash data collection for iterative performance improvements. The proposed TrafficSafe offers a transformative leap in traffic safety research based on foundation models, providing a blueprint for translating advanced artificial intelligence technologies into responsible, actionable, and life- saving outcomes. It is now reshaping how traffic researchers and policymakers approach the road safety. + +<|ref|>sub_title<|/ref|><|det|>[[75, 795, 222, 812]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[70, 822, 904, 902]]<|/det|> +Predicting traffic crash outcomes at the event level can greatly improve our understanding of crashes contributing factors and support the safety policy interventions. Currently, the United States has one of the highest traffic crash risks among developed countries (see Figure 1a), with 42,795 fatalities reported in \(2022^{2}\) . The number of fatalities still shows a persistent upward trend over recent decades, highlighting the urgent need for innovative + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 87, 848, 690]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[93, 696, 905, 909]]<|/det|> +
Figure 1: Overview of the Proposed TrafficSafe Framework. (a) The U.S. faces one of the highest crash risks among developed countries, with a rising trend. However, analyzing and addressing this issue is challenging due to the heterogeneous factors involved in crash events, including traffic conditions, human behavior, environmental impacts, and driver characteristics. To tackle this, we propose TrafficSafe, a framework designed for two key tasks: 1) Predicting crash outcomes and 2) Attributing crash factors with conditional risk analysis. By addressing questions such as why crashes occur and how to mitigate crash risks, TrafficSafe seeks to deliver optimal policy for safety improvement, aligning with the Vision Zero goal. (b) The TrafficSafe workflow incorporates multi-modal data, including driver behavior, vehicle details, infrastructure, and environmental conditions, represented through textual reports, satellite imagery, and other formats. Leveraging an AI-expert cooperative method, the crash data is transformed into textual prompts, resulting in the TrafficSafe Event dataset comprising 58,903 prompts. TrafficSafe LLM is created with accurate and trustworthy forecasting abilities for further analysis. Building on this pipeline, TrafficSafe Attribution operates across three dimensions: 1) Event-level risk analysis to identify feature contributions, 2) Conditional risk analysis to assess state-level risks under varying conditions, and 3) Data collection guidance to optimize the data acquisition process. The results of TrafficSafe Attribution provide actionable insights to enhance data analysis and collection, fostering a more comprehensive understanding of crash data and events.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 88, 906, 398]]<|/det|> +approaches to uncover the major causes of crashes and provide actionable insights for policy interventions. An effective data- driven crash prediction model can learn from historical crash- related data, and offer potential guidance for reducing crash risk by identifying the leading factors of crashes3. Current research for crash prediction can be grouped into two groups: 1) macroscopic (statistic- level) prediction4–8 and 2) microscopic (event- level) prediction9–13. Macroscopic prediction typically relies on statistical methods to gain a general understanding of safety levels, compare the safety performance of different areas and time frames, identify high- risk zones, and track safety trends4,5. While these methods can partially predict when and where crashes are more likely to occur, they fail to forecast who is involved, what types will likely to be, why crashes happen, and how to mitigate risks at event granularity6,14. To address this limitation, microscopic crash prediction, which focuses on specific traffic conditions and circumstances, has been developed to predict the crashes consequences using machine learning (ML) approaches11,13. Despite their potential in answering who and what, these models face limitations in crash prediction precision and generalization5,6. Moreover, integrating multi- modal traffic crash data and interpreting model’s outputs (together with contributing factors) remain challenging. Consequently, existing crash prediction models struggle to accurately forecast crash outcomes and effectively incorporate their insights into the design of policy intervention. + +<|ref|>text<|/ref|><|det|>[[67, 404, 905, 670]]<|/det|> +Crash data are inherently heterogeneous, making accurate prediction a significant challenge. After the crash happened, first responders compile textual and numerical on- site details, often supplemented by images, driver behavior data, and licensing records. Although these diverse sources hold immense potential for crash prediction and feature attribution, three key obstacles must be addressed: 1) Data Integration. Existing approaches often reduce multi- modal data to one- hot embeddings for classification tasks15–17. However, these approaches often neglect the valuable information contained within textual and behavioral data, potentially limiting the accuracy and reliability of crash prediction models11,13. 2) Method Generalization. Scaling crash- event prediction models to new data remains a complex endeavor due to the large variety of features, the complexity of representation extraction and encoding, and the diverse formats in which crash data appear18,19. Current machine learning solutions are often tailored to specific data types, limiting their adaptability when new cases or additional data modalities arise6,20–22. 3) Feature Learning and Attribution. Multi- modal crash data regularly include partially overlapping information, such as road attributes recorded in both on- site images and textual crash reports, complicating the accurate assessment of each feature’s unique contribution. + +<|ref|>text<|/ref|><|det|>[[67, 676, 905, 902]]<|/det|> +Recent advancements in Large Language Models (LLMs), such as GPT- 423 and LLaMA 324, have demonstrated their potential for deriving complex crash patterns from multi- modal data25 and addressing persistent challenges. However, fully adapting LLMs to predict crash outcomes and inform effective safety interventions requires overcoming three primary technical hurdles in data, model, and interpretability. From a data perspective, diverse crash records, including images, textual notes, and driver behavior logs, must be reformatted into textual inputs suitable for LLM processing. In terms of modeling, the generative nature of LLMs, which have extensive output vocabularies (e.g., LLaMA 3’s 128,256 tokens), poses challenges for discriminative learning tasks and raises concerns about trustworthiness, particularly when crash outcomes (e.g., crash type or severity) are well- defined by public agencies into finite categories. Furthermore, interpreting LLM’s outputs for crash prediction becomes difficult, as it remains unclear how much we can trust the forecasting results and how individual factors contribute to crash outcomes. This lack of interpretability and robustness analysis hinders the development of data- driven, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 90, 426, 107]]<|/det|> +actionable plans for mitigating the crash risks. + +<|ref|>text<|/ref|><|det|>[[68, 110, 906, 295]]<|/det|> +This study advances traditional traffic safety analysis by shifting from aggregate- level considerations to event- level crash prediction. We propose TrafficSafe (see Figure 1b), a novel LLM- driven framework designed for addressing these challenges to provide a comprehensive understanding of crash events. TrafficSafe comprises three main components: TrafficSafe Event dataset for multi- modal crash data integration, TrafficSafe LLM for crash outcomes prediction, and TrafficSafe Attribution for conditional risk analysis. Together, these components enable accurate and trustworthy crash consequences prediction and risk attribution, answering the when, where, who, what, why, and how to support targeted traffic safety interventions. By reframing crash outcome prediction as a text- based reasoning task, TrafficSafe exploits the inherent language reasoning capabilities of LLMs to offer actionable insights for crash prevention, ultimately paving the way for data- driven safety solutions. + +<|ref|>sub_title<|/ref|><|det|>[[70, 316, 350, 334]]<|/det|> +## 2 Novelties and Contributions + +<|ref|>text<|/ref|><|det|>[[70, 344, 905, 444]]<|/det|> +This study advances traditional traffic safety analysis by shifting from aggregate- level considerations to event- level crash prediction. In particular, we customize LLMs to forecast expected crash consequences and attribute relevant features with enhanced accuracy and interpretability. Our proposed framework, TrafficSafe, supports reliable and accountable learning from multi- modal crash data, facilitating a deeper understanding of crash events. Key contributions and findings include: + +<|ref|>text<|/ref|><|det|>[[123, 464, 905, 606]]<|/det|> +Unlocking multi- modal data integration and text reasoning for crash consequence prediction. We introduce the TrafficSafe framework to extend the LLMs for crash outcomes prediction. Rather than treating crash features as isolated numerical inputs, TrafficSafe integrates them into the broader semantic context of traffic data. To effectively utilize and integrate the multi- modal crash data, the TrafficSafe Event dataset is constructed with 58,903 textual prompts totaling over 12 million words. The TrafficSafe LLM is then fine- tuned by framing crash outcome prediction as a task specific token generation task. This approach yields a \(41.7\%\) increase in average F1- score across multiple crash consequence prediction tasks. + +<|ref|>text<|/ref|><|det|>[[123, 621, 905, 806]]<|/det|> +Integrating traffic safety priors in LLMs for trustworthy crash predictions. Compared with existing LLMs, we incorporate crash- domain knowledge and priors into the model's vocabulary as special tokens. This addition allows us to tailor the output to specific crash categories, including crash type, severity, and number of injuries, thereby providing a direct way to measure the model's trustworthiness and link to targeted interventions. Experimental results of TrafficSafe LLM show a strong correlation between increasing confidence in the model's output and higher prediction accuracy, achieving over \(70\%\) accuracy when the confidence score exceeds \(60\%\) . Notably, the model reaches more than \(95\%\) precision for fatal crash predictions when the confidence score surpasses \(60\%\) . This feature offers quantitative evidence to support safety- oriented decision- making and helps close the gap regarding how to trust the model's predictive results. + +<|ref|>text<|/ref|><|det|>[[123, 823, 905, 902]]<|/det|> +Advancing feature interpretation for conditional risk analysis and policy intervention, even in unseen scenarios. The TrafficSafe Attribution framework is proposed for conditional risk analysis, which is supported by a novel sentence- level feature contributions calculation method, enabling event- level feature attribution for textual inputs of TrafficSafe LLM. Then, the "what- if" conditional analysis can further identify and analyze + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[137, 88, 905, 230]]<|/det|> +the most critical risk conditions and their combinations. For instance, alcohol- impaired driving consistently emerges as a leading contributor to serious and fatal crashes. While driving in a work zone under sober conditions poses minimal risk, combining these conditions with alcohol consumption drastically increases danger, making it one of the most hazardous scenarios for severe crashes. Furthermore, aggressive and impairment- related behaviors demonstrate nearly double the impact on severe crashes compared to other driver behaviors. These insights lay the groundwork for implementing targeted traffic safety policies and interventions26. + +<|ref|>text<|/ref|><|det|>[[124, 246, 905, 390]]<|/det|> +- Guiding optimal data collection for efficient model evolution and lifelong learning. A longstanding challenge in crash modeling is determining how to select valuable data from heterogeneous sources and prioritize which information first responders should capture during incident documentation. The proposed TrafficSafe Attribution addresses this by estimating the contributions of multi-modal data during training, then quantifying which data types have the greatest impact on model performance. Such insights guide more effective traffic safety data collection, improving crash prediction accuracy while also supporting efficient, continuous model evolution through a targeted, data-driven strategy. + +<|ref|>sub_title<|/ref|><|det|>[[72, 411, 178, 428]]<|/det|> +## 3 Results + +<|ref|>sub_title<|/ref|><|det|>[[94, 441, 317, 458]]<|/det|> +### 3.1 Multi-modal Crash Data + +<|ref|>text<|/ref|><|det|>[[70, 462, 906, 686]]<|/det|> +Our cleaned dataset comprises crash data from Washington State in 2022, totaling 16,188 records, and from Illinois in 2022, totaling 42,715 records, after excluding cases with missing key attributes related to vehicle or crash object status. Primary sources include the Highway Safety Information System (HSIS) crash data27 and satellite images28. The HSIS crash data contains four major components: crash data, infrastructure data, vehicle data, and the person data. Crash data provides detailed descriptions of crashes, such as location, time, and injury severity. Infrastructure data includes information about road layouts and traffic characteristics, such as road level and speed limits. Vehicle data contains details such as manufacturing year and reported defects of the involved vehicles, while person data captures demographic and other relevant details about drivers and passengers, such as age and gender. Satellite images complement the HSIS data by providing additional visual context, including information about lanes, intersections, and other roadway attributes. Further information on raw data formats and types is available in Section 5.1.1. + +<|ref|>sub_title<|/ref|><|det|>[[72, 707, 494, 725]]<|/det|> +### 3.2 TrafficSafe Crash Outcomes Prediction Pipeline + +<|ref|>text<|/ref|><|det|>[[70, 728, 905, 829]]<|/det|> +To leverage the multi- modal crash data described in Section 3.1 for crash prediction, we developed the TrafficSafe crash outcomes prediction pipeline, which transforms crash outcomes prediction into a text- based reasoning task. To achieve this, the raw crash data is organized into the textual TrafficSafe Event dataset, which is then used to fine- tune the TrafficSafe LLM. Figure 2 presents an overview of the TrafficSafe crash outcomes prediction pipeline, with subsequent sections detailing each stage of the pipeline. + +<|ref|>sub_title<|/ref|><|det|>[[72, 844, 442, 861]]<|/det|> +### 3.2.1 Constructing Prompts and Prediction Targets + +<|ref|>text<|/ref|><|det|>[[70, 865, 904, 902]]<|/det|> +The TrafficSafe Event dataset is created through an AI- expert cooperative textualization process, organizing multimodal raw data for effective crash prediction. The detailed information about the raw data feature engineering and + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 100, 880, 433]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[92, 454, 905, 677]]<|/det|> +
Figure 2: TrafficSafe Crash Outcomes Prediction Pipeline. Multi-modal crash data is collected and organized into textual prompts through an AI-expert cooperative process. The HSIS crash data, satellite images, and infrastructure data are used to extract general and infrastructure information, including the crash time, location, the road level, and so on. The vehicle data, and person data are converted into the event information and the unit information, including vehicle movements, driver characteristics (e.g., age, gender, alcohol use), vehicle attributes (e.g., manufacture year), and so on. TrafficSafe Event dataset is created with three prediction targets: Injury, Severity, and Type. The Injury task predicts the number of people injured in the crash event, the Severity task estimates the severity level of the crash, such as no apparent injury or fatal, and the Type task classifies type of crash, such as single vehicle with object or angle impacts right (The crash event consequences classification are provided in Supplementary Table 2 and Supplementary Table 3. The TrafficSafe LLM is fine-tuned using the TrafficSafe Event dataset. To reframe the crash outcomes prediction from a classification task to a language inference task, TrafficSafe LLM is fine-tuned by adding prediction targets as special tokens in its vocabulary and adjusting parameters using Low-Rank Adaptations (LoRA) \(^{29}\) .
+ +<|ref|>text<|/ref|><|det|>[[60, 700, 904, 760]]<|/det|> +117 the textualization process are available in Section 5.1.2. As shown in Figure 2, the constructed prompts are divided into five parts: one system prompt and four content parts, with each content part containing approximately 100 words. These parts include: + +<|ref|>text<|/ref|><|det|>[[120, 777, 661, 796]]<|/det|> +- System Prompt: Provides an introduction and task-specific instructions. + +<|ref|>text<|/ref|><|det|>[[120, 810, 904, 849]]<|/det|> +- General Information: Includes general information about the time and location of the prediction region and the roadway category. + +<|ref|>text<|/ref|><|det|>[[120, 863, 904, 902]]<|/det|> +- Infrastructure Information: Describes road infrastructure, encompassing static features like the number of lanes and speed limits, as well as dynamic elements such as work zones, lighting, and road surface + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[137, 90, 219, 105]]<|/det|> +conditions. + +<|ref|>text<|/ref|><|det|>[[123, 122, 904, 160]]<|/det|> +- Event Information: Contains detailed descriptions of crash events, such as the number of vehicles involved and their directions of movement. + +<|ref|>text<|/ref|><|det|>[[123, 175, 904, 214]]<|/det|> +- Unit Information: Provides vehicle and individual details relevant for crash prediction, such as airbag status and the driver's age. + +<|ref|>text<|/ref|><|det|>[[90, 231, 905, 396]]<|/det|> +The prediction targets consist of three variables: Injury, Severity, and Type (see Figure 2) \(^{30 - 32}\) . Specifically, Injury task predicts the number of people injured in the given crash event. Injury task is treated as a classification task with four categories: zero, one, two, and three or more than three, where crashes involving more than two injured people are grouped into a single category due to the limited number of such cases. The Severity task assesses the level of injury severity in a crash, classified into five levels from no apparent injury to fatal. Type task predicts the type of crash, such as the rear-end collision or collision with object, with 14 crash type categories in the Washington dataset and 16 in the Illinois dataset. Detailed information on the defined targets is available in Section 5.1.3. + +<|ref|>text<|/ref|><|det|>[[90, 400, 905, 522]]<|/det|> +For each crash event, we perform the feature engineering and textualization process, organize the textualized data as input, and process labels corresponding to three tasks. The complete prompt examples are presented in Extended Data Figure 1 and Extended Data Figure 2. Ultimately, after filtering out data items with missing information, the TrafficSafe Event dataset merges the complementary information from multi- modal data sources and contains 58,903 crash records with approximately 12.74 million words. These records are split into training, validation, and test sets in a 7:1.5:1.5 ratio. + +<|ref|>title<|/ref|><|det|>[[90, 537, 380, 553]]<|/det|> +#### 3.2.2 Adapting LLM for Crash Prediction + +<|ref|>text<|/ref|><|det|>[[90, 556, 905, 699]]<|/det|> +Although vanilla LLMs like Llama 3 possess broad general knowledge and strong text reasoning capabilities, they demonstrate limited effectiveness on crash prediction tasks without the fine- tuning process (see Supplementary Section 1). To address this, we developed TrafficSafe LLM, a specialized model fine- tuned on the processed TrafficSafe Event dataset. This fine- tuning process enhances the LLM's comprehension of crash events and enables accurate outcome prediction. Specifically, special tokens are introduced into the LLM vocabulary as prediction targets (Number of Injury, Severity, and Crash Type), fine- tuning the model to generate these tokens during prediction. The details of the fine- tuning are provided in Section 5.2. + +<|ref|>sub_title<|/ref|><|det|>[[90, 717, 467, 735]]<|/det|> +### 3.3 Performance Evaluation of TrafficSafe LLM + +<|ref|>text<|/ref|><|det|>[[90, 738, 905, 818]]<|/det|> +In this section, we evaluate the performance of TrafficSafe LLM and compare its performance with other baselines (see Section 5.2.6). The fine- tuning process is based on two vanilla LLMs with different sizes: Llama 3.1 8B and Llama 3.1 70B. Accuracy, precision, and F1- score are used as the evaluation metrics, the detail information is available in Section 5.2.5. + +<|ref|>text<|/ref|><|det|>[[90, 823, 905, 902]]<|/det|> +TrafficSafe LLM provides accurate crash predictions, even in zero- shot scenarios. Table 1 compares the performances of TrafficSafe LLM and adopted baselines. The results show that the TrafficSafe LLM outperforms all the baselines in each task setting with an average F1- score improvement of 41.7% across multiple tasks. Specifically, in the crash Type prediction task in Washington dataset, the TrafficSafe LLM achieves F1- score of + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[95, 202, 901, 488]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[93, 88, 904, 191]]<|/det|> +Table 1: Performance Comparison of the three Crash Prediction Tasks on Washington Dataset and Illinois Dataset. We present quality metrics along with model rankings by averaging the column-wise rank. The zero-shot results for North Carolina and Maine were derived using the model trained on the Illinois dataset. In supervised finetuning experiments on the Washington and Illinois datasets, TrafficSafe LLM outperforms all other methods, with TrafficSafe 70B achieving the best performance. Additionally, TrafficSafe LLM demonstrates strong generalization capabilities in zero-shot experiments on the North Carolina and Maine datasets. + +
DatasetModelInjurySeverityTypeRank
AccuracyPrecisionF1-scoreAccuracyPrecisionF1-scoreAccuracyPrecision
WashingtonRandomForest330.5220.6490.5450.6280.5460.5490.7400.3980.2744 (4.11)
AdaBoost340.4950.2450.3280.4920.2450.3280.5630.2490.3028 (6.00)
CatBoost350.4950.2450.3280.4920.2450.3280.7150.4000.3296 (5.22)
DecisionTree360.4950.2450.3280.5280.4280.3720.6280.4060.3235 (4.67)
LogisticRegression370.4950.2450.3280.4920.2450.3280.5470.4010.3097 (5.67)
XGBoost380.5660.6650.4690.5340.4280.3670.7390.4130.2983 (4.00)
National Baseline390.3430.5550.4240.3530.5470.429////
TrafficSafe 8B0.6220.6300.6180.6400.6360.6340.7560.7630.7552 (2.22)
TrafficSafe 70B0.6300.6820.6490.6480.6440.6440.7600.7750.7591 (1.00)
IllinoisRandomForest330.4620.5540.3830.4300.4520.3380.6100.6700.6323 (4.11)
AdaBoost340.4030.1830.2510.3180.1470.2000.1090.0830.0838 (8.00)
CatBoost350.4570.5430.3880.4540.4460.4040.5350.6560.5794 (4.22)
DecisionTree360.4260.5140.4100.4170.3980.3610.5040.6240.5486 (5.33)
LogisticRegression370.4130.4390.4100.3600.3850.3550.3790.4770.4007 (6.33)
XGBoost380.4420.5750.3400.4050.4190.2780.6780.6940.6835 (4.56)
National Baseline390.3690.1360.1990.4420.1950.271////
TrafficSafe 8B0.5290.5290.5330.5780.5840.5710.7010.7680.7212 (1.89)
TrafficSafe 70B0.5340.5870.5430.5540.5610.5480.7270.7670.7371 (1.44)
North CarolinaTrafficSafe 8B (zero-shot)0.5110.7760.4680.5490.6380.4870.6910.7750.672/
MaineTrafficSafe 8B (zero-shot)0.5210.5730.4570.5420.5820.4930.7010.6220.613/
+ +<|ref|>text<|/ref|><|det|>[[75, 525, 905, 774]]<|/det|> +0.759, which is more than \(130\%\) higher than all other comparative methods. TrafficSafe LLM performs well on both the Washington and Illinois datasets, demonstrating its stability across diverse geographical regions. Moreover, as shown in the confusion matrix in Figure 3a and Figure 3b, beyond improved metrics, TrafficSafe LLM demonstrates a more balanced prediction distribution. In contrast, as shown in Figure 3c and Figure 3d, traditional machine learning models tend to predict the dominant categories (e.g., zero under Injury prediction task, no apparent injury under Severity prediction task). The complete confusion matrix is shown in Extended Data Figure 3. Moreover, the ability of a model to generalize across unseen scenarios is vital to ensuring its robustness and applicability in real- world contexts. We tested TrafficSafe LLM generalization ability by using the TrafficSafe LLM trained on Illinois dataset and evaluating its performance on the unseen Maine and North Carolina datasets. Notably, without additional fine- tuning, TrafficSafe LLM achieved F1- scores averaging 0.542 in North Carolina and 0.521 in Maine, closely matching its performance in Illinois (see Table 1). This underscores TrafficSafe LLM's ability to generalize well to previously unseen datasets, further validating its potential for real- world applications. + +<|ref|>text<|/ref|><|det|>[[75, 781, 904, 904]]<|/det|> +TrafficSafe LLM provides trustworthy crash predictions, where a higher confidence score links to higher accuracy. TrafficSafe LLM tailors LLMs for discriminative crash outcomes prediction tasks, generating predictions accompanied by confidence scores that represent the probabilities associated with specific special tokens. Figure 3e and Figure 3f illustrate the trend of accuracy in relation to the confidence scores of TrafficSafe LLM's predictions for the Washington and Illinois datasets. The results indicate that our model achieves greater accuracy at higher confidence levels. For instance, for the Injury prediction task in the Washington dataset, when + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[105, 92, 899, 720]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[93, 724, 904, 913]]<|/det|> +
Figure 3: TrafficSafe LLM Provides Accurate and Trustworthy Predictions. TrafficSafe LLM produces robust confusion matrices for both the (a) Washington and (b) Illinois datasets (we select the best results for each task by F1-score). In contrast, baseline models tend to predict the most frequent category across both the (c) Washington and (d) Illinois datasets (we show baseline models with the best F1-score. The performances for other baseline models can be found in Extended Data Figure 3). Meanwhile, TrafficSafe LLM produces trustworthy predictions for both the (e) Washington and (f) Illinois datasets. Higher confidence levels in the model's predictions correspond to an increased likelihood of accuracy. Furthermore, (g) The TrafficSafe LLM achieves higher precision for fatal crash predictions. (h) Fatal crash predictions also exhibit higher confidence compared to average predictions in Illinois dataset. The Washington dataset is not shown due to limited fatal cases. (i) For fatal crashes, the TrafficSafe LLM achieves near-perfect precision (97.61%) when the confidence score exceeds 0.6, indicating that the TrafficSafe LLM can deliver highly accurate and trustworthy predictions for fatal crashes.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[61, 88, 905, 169]]<|/det|> +the model's confidence score exceeds 0.40, the accuracy rises above 0.65, and with confidence scores over 0.60, the accuracy surpasses 0.80. The strong positive correlation between confidence scores and accuracy showcases the trustworthiness of the TrafficSafe framework. By providing reliable confidence scores alongside predictions, the framework empowers informed decision- making in real- world applications. + +<|ref|>sub_title<|/ref|><|det|>[[61, 188, 504, 205]]<|/det|> +### 3.4 TrafficSafe Attribution and Result Interpretation + +<|ref|>text<|/ref|><|det|>[[61, 208, 905, 330]]<|/det|> +Understanding how TrafficSafe LLM generates accurate predictions and how various components of the input prompt influence the outcomes is fundamental to enabling evidence- based decision- making. As shown in figure 3e and 3f, the TrafficSafe LLM's confidence score strongly correlates with its predictive accuracy for fatal and serious injury crashes, therefore, we can use the confidence score to represent a case's real- world risk level. Notably, the TrafficSafe LLM's confidence scores tend to be lower than their corresponding precision values (see figure 3e and 3f, indicating that the confidence score is a conservative estimate of risk). + +<|ref|>text<|/ref|><|det|>[[60, 333, 905, 644]]<|/det|> +Within the TrafficSafe Attribution framework, a sentence- based feature contributions calculation method was proposed to identify how each sentence contributes to the LLM's outputs based on Shapley theory which is recognized as a systematic and equitable method for attributing the contribution of each feature to a model's output40,41, thereby revealing crash- related factors at the event level (see Section 5.3 for details). In essence, each feature's contribution represents its share of responsibility for the model's confidence in a particular prediction. The sum of all feature contributions equals the confidence score itself. Figure 4 illustrates sentence- level feature contributions for the severity of individual crash events, using one crash from Washington and one from Illinois as examples. In the Washington crash example (Figure 4a), Driver Behavior (e.g., reckless driving or speeding) is the primary factor contributing to serious injury crashes with the feature contribution of 0.258. Person Info (e.g., no seatbelt use) also shows a substantial impact with the feature contribution of 0.149. By contrast, Dynamic Info (daylight and dry roads) lowers the probability of crash with serious injuries with a negative feature contribution of - 0.009. While, in the Illinois example (Figure 4b), an elevated BAC (Blood Alcohol Content, with feature contribution of 0.284) and the presence of a Work Zone (feature contribution of 0.462) notably increase the likelihood of fatal crash outcomes. Beyond the above examples, more additional sentence- level feature attribution analysis can be found in Extended Data Figure 4, 5, Supplementary Section 5 and 6. + +<|ref|>text<|/ref|><|det|>[[60, 646, 904, 725]]<|/det|> +The following sections utilize TrafficSafe Attribution framework to examine feature importance from two perspectives: 1) at the inference stage, to identify key factors influencing crash predictions under various conditions and high- risk scenarios, and 2) at the training stage, to understand which data components are most important for model learning. + +<|ref|>sub_title<|/ref|><|det|>[[60, 740, 589, 757]]<|/det|> +### 3.4.1 Factor Attribution at Inference Stage for Conditional Risk Analysis + +<|ref|>text<|/ref|><|det|>[[60, 760, 905, 902]]<|/det|> +Conditional analysis evaluates crash outcomes across various scenarios, such as driving with or without alcohol consumption, to quantify the risk factors associated with each scenario. Severe crashes (serious injuries and fatal crashes) were prioritized in the conditional analysis due to their critical importance for traffic safety. These crashes, particularly fatal ones, were predicted accurately and reliably by TrafficSafe LLM (see Figures 3g, 3h, and 3i). Five key contributing factors were identified for this conditional analysis: Driver BAC (BAC = 0 or not offered / BAC < 80 / BAC >= 80), Roadway Type (Highway / not highway), Work Zone (Work zone / not work zone), User Type (Pedalcyclist or pedestrian / not pedalcyclist or pedestrian), and Driver Behavior (Aggressive driving + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[149, 100, 850, 430]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[150, 470, 852, 802]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[100, 435, 888, 453]]<|/det|> +
(a) Sentence-based Feature Attribution Results for a Crash Resulting in Serious Injuries in Washington Dataset.
+ +<|ref|>text<|/ref|><|det|>[[144, 816, 848, 833]]<|/det|> +(b) Sentence-based Feature Attribution Results for a Crash Resulting in Fatalities in Illinois Dataset. + +<|ref|>text<|/ref|><|det|>[[93, 850, 904, 935]]<|/det|> +Figure 4: Single Case Feature Attribution Results for Severity Task. The left part displays the full prompt from (a) Washington and (b) Illinois, with different colors representing various semantic text sequences. The right part illustrates the feature contribution assigned to each text sequence. Positive contributions signify a supportive role in the model's prediction, whereas negative contributions indicate a detracting influence. The absolute value of these contributions represents the importance of each sequence to the model's output. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 100, 866, 586]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[93, 593, 904, 885]]<|/det|> +
Figure 5: Conditional Risk Analysis for the Serious Injury and Fatal Crashes. Higher confidence scores in TrafficSafe LLM's predictions correspond to greater accuracy, allowing the confidence score (calculated as the sum of feature contributions for all data components) to serve as an indicator of risk level for serious and fatal crashes. (a) The estimated risk levels for various feature combinations are presented, with each level corresponding to the average confidence score of TrafficSafe LLM's predictions under the same conditions. Each column represents a specific combination of conditions (marked by dark dots) alongside the corresponding feature contribution for selected factors. (b) Feature contributions of five key factors and their proportions relative to all factors are visualized. The inner circle represents the average feature contribution of each factor across different values, while the outer circle shows the percentage share of each factor in the total average feature contribution. The unit for BAC (Blood Alcohol Content) is "mg/L", which is omitted in this figure. (c) Average feature contribution for each factor under specific values. Bars are marked in pink if the value exceeds the corresponding factor's average shown in (a) and in blue if it does not. (d) The strong correlation between the number of risk factors and the risk level of crashes. The high-risk factors are defined as driving after drinking (both BAC <= 80 mg/L and BAC > 80 mg/L), driving in work zones, driving on freeways, pedestrian-involved crashes, and high-risk driver behaviors (aggressive or impairment-related). For each case, we tallied the number of these risk factors and calculated the average risk level for all cases sharing the same count. (e) Feature contributions of different data components during the training stage for Washington and Illinois datasets.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[61, 88, 904, 149]]<|/det|> +/ impairment- related behavior / traffic rules violations / improper driving / others). Collectively, these factors accounted for an average of \(79.33\%\) of the model's overall attribution in predicting serious and fatal crashes (see Figure 5b). A summary of key findings is provided: + +<|ref|>text<|/ref|><|det|>[[123, 165, 905, 330]]<|/det|> +- The BAC record emerges as a critical determinant in predicting serious and fatal crashes. Among all contributing factors, BAC accounts for \(25.26\%\) of the total contribution to serious and fatal crash prediction (see Figure 5b). Notably, its contribution substantially increases when a driver consumes alcohol, irrespective of the amount. When drivers are under the influence of alcohol even if their BAC does not exceed the legal intoxication limit of \(80 \mathrm{mg / L}^{42,43}\) , this factor's feature contribution still reaches approximately 0.45, surpassing that of most other factors in many cases (see Figure 5a). Conversely, when a driver's BAC is recorded as "zero or not offered," its contribution approaches zero, indicating minimal impact on the model's predictions. + +<|ref|>text<|/ref|><|det|>[[123, 343, 905, 550]]<|/det|> +- Driving in a work zone is already risky under sober conditions, but alcohol consumption significantly increases the danger, making it one of the most hazardous scenarios for severe-injury crashes. As shown in Figure 5a, driving in a work zone while sober ("Work Zone-Yes" and "BAC = 0 or not offered") contributes little to severe crash outcomes, with an average feature contribution of 0.03. However, after consuming alcohol (whether "BAC >= 80" or "BAC < 80"), the work zone feature contribution rises more than seven time to an average of 0.22. Furthermore, the overall crash risk increases substantially when driving in a work zone after drinking, as indicated by an average risk level of 0.78, compared to 0.44 under sober conditions. These findings indicate that work zones become especially hazardous when alcohol consumption is involved, creating one of the highest-risk scenarios for severe crash outcomes. Potential drunk driving warnings and risk mitigation strategies shall be closely linked with work-zone areas. + +<|ref|>text<|/ref|><|det|>[[123, 563, 905, 767]]<|/det|> +- Aggressive driving and impairment-related behavior exhibit the highest contributions among driver behaviors. Furthermore, combined with other conditions, aggressive and impairment-related behaviors pose nearly twice the risk for severe crash outcomes compared to other driver behaviors. As illustrated in Figure 5c, aggressive driving emerges as the most significant contributor between driver behaviors, with feature contribution of 0.14. Impairment-related behavior, including driving under the influence of alcohol or drugs, also has a substantial influence, with average feature contribution of 0.11. In comparison, other improper driver behaviors, such as traffic rule violations (feature contribution of 0.07) and distractions like mobile phone use (categorized under improper driving, with feature contribution of 0.03), show below-average contributions to serious and fatal crashes. The "other" category, which includes normal driving and unknown behaviors, has the smallest impact, with feature contribution of 0.03. + +<|ref|>text<|/ref|><|det|>[[123, 781, 905, 903]]<|/det|> +- The co-occurrence of risk factors significantly increases the expected crash risk level. As illustrated in Figure 5d, our analysis reveals a strong correlation between the number of risk factors present in a crash and the expected risk level for severe crash outcomes. When only one risk factor is involved, the risk level for severe crash outcomes is estimated at 0.59. This value increases to 0.62 with two risk factors, surges to 0.78 with three, and escalates to 0.94 when four risk factors co-occur. Notably, scenarios with three or more risk factors are markedly more dangerous than those with one or two. For example, while a combination of a BAC + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[137, 89, 905, 210]]<|/det|> +exceeding \(80\mathrm{mg / L}\) and driving in a non- freeway work zone yields an average risk level of 0.51, substituting the non- freeway work zone with a freeway work zone increases the average risk level dramatically to 0.97. Such elevated risks indicate that the synergy among multiple factors is far from merely additive; instead, they appear to compound one another, amplifying the potential for severe outcomes. These findings show that transportation agencies need to prioritize multi- faceted interventions tailored specifically to scenarios with overlapping high- risk conditions. + +<|ref|>sub_title<|/ref|><|det|>[[90, 232, 745, 248]]<|/det|> +### 3.4.2 Factor Attribution at Training Stage for Effective Data Collection and Model Development + +<|ref|>text<|/ref|><|det|>[[90, 253, 905, 541]]<|/det|> +Event information and unit information are the most important components for the model training. While feature contributions at the inference stage reveal which features drive critical crash outcomes, understanding feature contributions during training provides deeper insights into which data components most effectively enhance model accuracy. As shown in Figure 5e, the feature contributions of each component in the Washington and Illinois datasets are shown, demonstrating their impact on the model's performance during training (see Supplementary Table 7 for detailed results and Section 5.3 for calculation details). The results indicate that in both the Washington and Illinois datasets, for the Severity task, the unit information describing attributes of the primary entities involved in the crash has the highest contribution to the model's performance (0.314 in Washington, 0.234 in Illinois). Event information, which provides information on the vehicle's movement prior to the crash, is followed by unit information and has the second highest contribution (0.110 in Washington, 0.158 in Illinois). For the Crash Type Prediction task, the event information has the highest contribution (0.388 in Washington, 0.283 in Illinois), followed by the unit information (0.257 in Washington, 0.279 in Illinois) and other components. These results can provide preliminary guidance on prioritizing the information collection for crash events, thereby improving crash prediction and feature attributions for better safety decision support. + +<|ref|>sub_title<|/ref|><|det|>[[90, 565, 205, 581]]<|/det|> +## 4 Discussion + +<|ref|>text<|/ref|><|det|>[[90, 593, 905, 902]]<|/det|> +Deciphering traffic crash modeling as a linguistic learning task is a promising way for future safety research. Most of the existing ML models for crash prediction typically treat various factors as independent numerical input variables11,44. However, this approach fails to capture information richness from the textual crash reports, such as detailed descriptions of behaviors, vehicle movements prior to the crash, and the traffic conditions. To address these issues, we employ an AI- expert cooperative prompt design approach to process diverse data types, including crash reports (textual), satellite and crash images (visual), and infrastructure characteristics (categorical), into a textual TrafficSafe Event dataset and use LLM for prediction. This transformation reframes the task of crash prediction into a text reasoning problem, enabling the use of LLMs to analyze and predict outcomes while preserving the rich, detailed textual information in crash reports, rather than reducing it to simplistic numerical representations. As demonstrated by the results in Section 3.3, with our customization process, the TrafficSafe LLM outperforms all the baseline models, highlighting the advantages of reasoning through textual representations. Building on this, TrafficSafe Attribution extends the framework by enabling conditional analysis of textual prompts, quantifying the contribution of specific factors to crash outcomes under various scenarios. As shown in Section 3.4, this approach effectively identifies key contributors to crashes and high- risk scenarios, and offers data collection guidance for the iterative improvements in the future. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[91, 88, 905, 377]]<|/det|> +Providing reliable and interpretable predictions with quantifiable trustworthiness. As illustrated in Figures 3e and 3f, TrafficSafe LLM demonstrates a deep understanding of input- output correlations, yielding predictions whose accuracy increases alongside higher confidence scores. In all tasks across Washington and Illinois dataset, when the TrafficSafe LLM's confidence score exceeds \(60\%\) , which captures over \(70\%\) of the crash events under consideration. Furthermore, the confidence scores for fatal crash predictions are notably higher than those of other crash categories ( \(60\%\) of model confident score leading to over \(95\%\) of the real- world occurrence risk, see Figures 3g, 3h, and 3i). This trackable confidence- precision correlation can provide decision- makers with a robust tool for forecasting crashes under quantifiable uncertainty. Beyond predictive trustworthiness, the TrafficSafe Attribution framework provides interpretable feature attribution by quantifying each feature's contribution to the confidence score (see Section 5.3). A higher feature contribution translates into a higher confidence score, which in turn yields greater prediction accuracy for the severe crash outcomes. Thus the factor with higher feature contribution value has higher impact to the model's prediction. For instance, alcohol- impaired driving (BAC \(>0\) ) increases the confidence score for severe crash predictions by more than 0.47, serving as a critical indicator for the likelihood of these severe crash outcomes. + +<|ref|>text<|/ref|><|det|>[[91, 383, 905, 711]]<|/det|> +Identifying high- risk traffic crashes through conditional factors attribution even in unseen scenarios. The TrafficSafe framework enables a detailed, sentence- level analysis of crash factors through conditional attribution, yielding critical insights into high- risk scenarios. In data- rich situations where sufficient data is available for each condition, TrafficSafe can rank the risk levels associated with various conditions, offering a prioritized list of scenarios that pose the highest danger. This capability supports targeted policy interventions by identifying specific conditions that substantially increase crash risks. For instance, as shown in Figure 5a, driving in a work zone under sober conditions poses low level of risk; however, alcohol consumption in the same setting dramatically amplifies this risk, creating one of the most hazardous scenarios for severe crashes. This insight suggests potential policy interventions, such as mandatory BAC testing in work zones, to mitigate these risks. Likewise, Figure 5c highlights that aggressive driving and impairment- related behaviors markedly increase the likelihood of serious or fatal outcomes, emphasizing the importance of driver education to discourage aggressive behavior and driving under the influence. Moreover, TrafficSafe can be generalized to predict and understand data- sparse scenarios through "what- if" analysis, allowing hypothetical changes to specific conditions to be tested and their potential impact evaluated. For example, while this study lacked sufficient data to comprehensively analyze the effects of user type (e.g., pedestrians or not pedestrians) or roadway type (e.g., freeway or not freeway), TrafficSafe provides a reliable mechanism to simulate and analyze such conditions. + +<|ref|>text<|/ref|><|det|>[[91, 718, 905, 902]]<|/det|> +Assessing the impact of data utility for improved future data collection and life- long learning. Traffic crash data are inherently complex and multi- modal, making it crucial to identify which components are most informative and how critical they are for future traffic safety data collection. During the training stage, data attribution analysis revealed that unit information (e.g., driver behavior and vehicle details) and event information (e.g., vehicle movement and environmental conditions) exert the greatest influence on crash prediction performance. Specifically, for the Severity task in the Illinois dataset, these features contributed 0.173 and 0.287, respectively, to the model's prediction confidence (see Figure 5e). These findings underscore the importance of prioritizing the collection of detailed, high- quality movement and behavior data in crash events, such as precise records of alcohol use, vehicle defects, vulnerable users' status, and road conditions. In contrast, although general information + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[50, 88, 905, 355]]<|/det|> +(feature contribution of 0.038) and infrastructure information (feature contribution of 0.019) remain valuable, their impact on some tasks is comparatively smaller. By directing data collection efforts toward gathering richer, more consistent information format in these critical safety domains, the accuracy of TrafficSafe can be further improved. Limitations of the proposed TrafficSafe. A primary limitation relates to the handling of multi- modal data. In the TrafficSafe framework, satellite images were processed into textual descriptions and incorporated into prompts. While this approach offers flexibility, advancements in multi- modal foundation models and increasing research on integrating multi- modal data with LLMs present promising alternatives. Leveraging specialized image encoders or utilizing multi- modal foundation models for processing image data are compelling directions. Another potential limitation lies in the efficiency of model training and attribution. Fine- tuning LLMs and computing feature contributions have always required substantial resources and time. Although we employed LoRA fine- tuning and a stratified sampling technique to enhance efficiency, implementing the complete framework still demands significant resources. 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The llama 3 herd of models. \*arXiv preprint arXiv:2407.21783\* \*\*2\*\* (2024). + +<|ref|>text<|/ref|><|det|>[[56, 846, 905, 885]]<|/det|> +25. Gao, C., Lan, X., Lu, Z., Mao, J., Piao, J., Wang, H., Jin, D. & Li, Y. S3: Social-network Simulation System with Large Language Model-Empowered Agents. \*arXiv preprint arXiv:2307.14984\* (2023). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 90, 905, 128]]<|/det|> +26. Rezapour, M. & Ksabati, K. Identification of factors associated with various types of impaired driving. Humanities and social sciences communications 9, 1–11 (2022). + +<|ref|>text<|/ref|><|det|>[[60, 135, 905, 174]]<|/det|> +27. U.S. Department of Transportation, Federal Highway Administration. Highway Safety Information System (HSIS) https://highways.dot.gov. Accessed: January 13, 2025. 2025. + +<|ref|>text<|/ref|><|det|>[[60, 181, 905, 220]]<|/det|> +28. Developers, G. Google Maps Static API Documentation https://developers.google.com/maps/documentation/maps-static. Accessed: January 13, 2025. 2025. + +<|ref|>text<|/ref|><|det|>[[60, 228, 905, 267]]<|/det|> +29. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L. & Chen, W. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021). + +<|ref|>text<|/ref|><|det|>[[60, 275, 905, 314]]<|/det|> +30. Abdel-Aty, M., Keller, J. & Brady, P. A. Analysis of types of crashes at signalized intersections by using complete crash data and tree-based regression. Transportation Research Record 1908, 37–45 (2005). + +<|ref|>text<|/ref|><|det|>[[60, 321, 905, 360]]<|/det|> +31. Iranitalab, A. & Khattak, A. Comparison of four statistical and machine learning methods for crash severity prediction. Accident Analysis & Prevention 108, 27–36. ISSN: 0001-4575 (2017). + +<|ref|>text<|/ref|><|det|>[[60, 368, 905, 428]]<|/det|> +32. Savolainen, P. T., Mannering, F. L., Lord, D. & Quddus, M. A. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident Analysis & Prevention 43, 1666–1676. ISSN: 0001-4575 (2011). + +<|ref|>text<|/ref|><|det|>[[60, 436, 642, 454]]<|/det|> +33. Breiman, L. Random Forests. Machine Learning 45, 5–32 (Oct. 2001). + +<|ref|>text<|/ref|><|det|>[[60, 462, 905, 501]]<|/det|> +34. Freund, Y. & Schapire, R. E. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55, 119–139. ISSN: 0022-0000 (1997). + +<|ref|>text<|/ref|><|det|>[[60, 509, 905, 549]]<|/det|> +35. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: unbiased boosting with categorical features 2019. arXiv: 1706.09516 [cs.LG]. + +<|ref|>text<|/ref|><|det|>[[60, 556, 699, 575]]<|/det|> +36. Quinlan, J. R. Induction of decision trees. Machine learning 1, 81–106 (1986). + +<|ref|>text<|/ref|><|det|>[[60, 583, 905, 622]]<|/det|> +37. Cox, D. R. The regression analysis of binary sequences. Journal of the Royal Statistical Society Series B: Statistical Methodology 20, 215–232 (1958). + +<|ref|>text<|/ref|><|det|>[[60, 630, 905, 690]]<|/det|> +38. Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Association for Computing Machinery, San Francisco, California, USA, 2016), 785–794. ISBN: 9781450342322. + +<|ref|>text<|/ref|><|det|>[[60, 698, 905, 737]]<|/det|> +39. Lord, D., Geedipally, S., Pratt, M., Park, E. S., Khazraee, S. & Fitzpatrick, K. Safety Prediction Models for Six-Lane and One-Way Urban and Suburban Arterials ISBN: 978-0-309-29560-4 (Mar. 2022). + +<|ref|>text<|/ref|><|det|>[[60, 744, 905, 784]]<|/det|> +40. Bordt, S. & von Luxburg, U. From shapley values to generalized additive models and back in International Conference on Artificial Intelligence and Statistics (2023), 709–745. + +<|ref|>text<|/ref|><|det|>[[60, 792, 905, 831]]<|/det|> +41. Shapley, L. S. in Contributions to the Theory of Games II (eds Kuhn, H. W. & Tucker, A. W.) 307–317 (Princeton University Press, Princeton, 1953). + +<|ref|>text<|/ref|><|det|>[[60, 839, 905, 878]]<|/det|> +42. Washington State Legislature. Revised Code of Washington: Driving under the influence https://app.leg.wa.gov/rcw/default.aspx?cite=46.61.502. Accessed: 2025-01-19. 2025. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 88, 927, 291]]<|/det|> +43. Illinois Secretary of State. Driving Under the Influence (DUI) https://www.ilsos.gov/departments/drivers/traffic_safety/DUI/home.html. Accessed: 2025-01-19. 2025. +44. Bhuiyan, H., Ara, J., Hasib, K. M., Sourav, M. I. H., Karim, F. B., Sik-Lanyi, C., Governatori, G., Rakotoni-rainy, A. & Yasmin, S. Crash severity analysis and risk factors identification based on an alternate data source: a case study of developing country. Scientific reports 12, 21243 (2022). +45. Zhang, J., Huang, J., Jin, S. & Lu, S. Vision-language models for vision tasks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024). +46. Zhang, J., Sun, Q., Liu, J., Xiong, L., Pei, J. & Ren, K. Efficient Sampling Approaches to Shapley Value Approximation. Proc. ACM Manag. Data 1 (May 2023). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[58, 89, 190, 107]]<|/det|> +## 5 Methods + +<|ref|>sub_title<|/ref|><|det|>[[92, 119, 425, 136]]<|/det|> +### 5.1 TrafficSafe Event Dataset Construction + +<|ref|>text<|/ref|><|det|>[[60, 139, 904, 219]]<|/det|> +As introduced in Section 3.1, the raw crash data is multi- modal, and integrated from various sources. To adapt the raw data for LLMs' fine- tuning process, we employ the feature engineering and textualization process to generate textual inputs. In this section, we will discuss the formats of raw data and the textualization process (see Extended Data Figure 6). + +<|ref|>sub_title<|/ref|><|det|>[[92, 233, 205, 248]]<|/det|> +### 5.1.1 Raw Data + +<|ref|>text<|/ref|><|det|>[[60, 252, 904, 311]]<|/det|> +The raw crash data used in this study was obtained from the HSIS \(^{27}\) and Google Maps \(^{28}\) . Data from the HSIS, sourced from multiple systems, encompasses a variety of formats, including categorical, numerical, and textual. In total, four main datasets were used: + +<|ref|>text<|/ref|><|det|>[[121, 329, 905, 473]]<|/det|> +- Crash data. This dataset captures the essential spatio-temporal and contextual attributes of each crash. It includes crash date, time, day of the week, and month, along with location details such as route number, milepost, and the surrounding area's classification (e.g., rural or urban). Higher-level planning attributes (e.g., roadway and functional classifications, intersection-related indicators) are also recorded. In addition, it documents the dynamic circumstances leading up to the event, including the number of vehicles and pedestrians involved, vehicle travel directions (increasing or decreasing milepost), and any maneuvers performed (e.g., lane changes, straight-line movement). + +<|ref|>text<|/ref|><|det|>[[121, 485, 905, 586]]<|/det|> +- Infrastructure data. This dataset details the physical and infrastructural features of the crash site. Key elements include the type of road surface (e.g., asphalt or concrete), average annual daily traffic (AADT), posted speed limits, and access control mechanisms. It also encompasses dimensions such as total road width, right and left shoulder widths, and median width (including median barriers if present), as well as road surface conditions (e.g., dry or wet) and ambient lighting at the time of the crash (e.g., daylight or dusk). + +<|ref|>text<|/ref|><|det|>[[121, 599, 905, 680]]<|/det|> +- Vehicle data. This dataset consolidates information on the vehicles involved in each crash, including vehicle type (e.g., passenger car or truck), intended use (e.g., commercial or private), mechanical condition (e.g., defects), and relevant driver actions (e.g., lane changes or stopping). Additional information on airbag deployment and occupant ejection status provides further granularity. + +<|ref|>text<|/ref|><|det|>[[121, 694, 904, 754]]<|/det|> +- Person data. This dataset compiles information about individuals involved in the crash, detailing demographic characteristics such as age, gender, and seating position. It also includes the use of safety equipment (e.g., seat belts or helmets) and any contributing factors, such as driver distraction or impairment. + +<|ref|>text<|/ref|><|det|>[[60, 770, 904, 830]]<|/det|> +The satellite images obtained from Google Maps serve as a supplementary data source to complement the HSIS dataset. Details of the integration process are provided in Section 5.1.2. Overall, we collect 16,188 crash events data from Washington State and 42,715 events from Illinois State for further analysis. + +<|ref|>sub_title<|/ref|><|det|>[[60, 844, 499, 860]]<|/det|> +### 5.1.2 Feature Engineering and Textualization of Crash Data + +<|ref|>text<|/ref|><|det|>[[60, 863, 904, 901]]<|/det|> +To adapt the multi- modal data to the input of LLMs, we followed the following process to generate textual prompt from raw data entry: + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[123, 88, 905, 210]]<|/det|> +- Data mapping and organization. For each crash, we associated the crash report with the involved vehicles and individuals using the crash ID, thus obtaining descriptions of the crash and the persons involved. The route ID and milepost were used to identify the specific road segment where the crash occurred, allowing us to gather related road and environment information from infrastructure data. The integrated data was then systematically organized into four categories: general information, infrastructure information, event information, and unit information, aligning with the components outlined in Section 3.2.1. + +<|ref|>text<|/ref|><|det|>[[123, 225, 905, 369]]<|/det|> +- Satellite images textualization. The HSIS datasets provide GPS coordinates for crash locations in Washington and Illinois. To address missing information such as the number of road lanes, high-resolution satellite images (512 × 512 pixels at a zoom level of 19) were retrieved using these GPS coordinates via the Google Maps API. These images supplement the crash dataset with crucial infrastructure and environmental context. Descriptive textual annotations were generated from the satellite images using GPT-4, filling key gaps in the original dataset. These annotations include information such as the number of lanes at the crash site, whether the crash occurred at an intersection, and whether the surrounding area is residential. + +<|ref|>text<|/ref|><|det|>[[123, 384, 905, 505]]<|/det|> +- Dimensionality reduction. Raw data include abundant attributes with rich and varied descriptions. However, some features suffer from insufficient distinction between attribute values due to the original classification's complexity. To address this, we performed dimensionality reduction on these attributes by combining domain experts' insights with GPT-4o clustering results. For example, similar classifications like "pedalyclist struck by vehicle" and "pedalyclist strikes vehicle" were clustered under a broader category such as "pedalyclist collisions". This process generalized the data and reduced redundancy. + +<|ref|>text<|/ref|><|det|>[[123, 521, 905, 684]]<|/det|> +- Prompt generation using AI-expert textualization method. To generate logically coherent and continuous textual data suitable for LLM training, we transformed each category of data into text format using GPT-4. All data are organized as key-value pairs and we get four parts of the key-value pairs for each event case. Then GPT-4o is used to generate the text prompt for each section of the key-value pairs individually. For each part, we apply straightforward prompt to GPT-4o, such as "Please translate a python dictionary to paragraph, act as a crash data interpreter". The text content is extracted from GPT-4o's response for each part consisting of approximately 100 words. By linking four parts of text, we obtain a comprehensive textual description for each crash event case. The detailed process is shown in Extended Data Figure 7. + +<|ref|>title<|/ref|><|det|>[[77, 703, 310, 719]]<|/det|> +#### 5.1.3 Define Prediction Targets + +<|ref|>text<|/ref|><|det|>[[75, 724, 904, 742]]<|/det|> +We select three variables as the prediction targets: Injury, Severity, and crash Type. The three targets are defined as: + +<|ref|>text<|/ref|><|det|>[[123, 760, 904, 820]]<|/det|> +- The Injury \(n_{l}^{\mathcal{P}}\in \{f(l)|l = 0,1,2,\dots \}\) , where \(i\) denotes the \(i\) -th data in the dataset, \(\mathcal{D}\in \{\mathcal{W},\mathcal{I}\}\) denotes the Washington dataset \(\mathcal{W}\) or the Illinois dataset \(\mathcal{I}\) , \(l\) represents the number of people injured, and \(f(l)\) denotes the label when the injured people is \(l\) . + +<|ref|>text<|/ref|><|det|>[[123, 835, 904, 874]]<|/det|> +- The Severity \(s_{l}^{\mathcal{P}}\in \{S_{k}|k = 1,2,\dots \}\) (define on the KABCO scale \(^1\) ), where \(S_{k}\) is the \(k\) -th level of crash severity. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[123, 87, 776, 108]]<|/det|> +- The Type \(t_{i}^{\mathcal{O}} \in \{T_{k}^{\mathcal{O}} | k = 1, 2, \dots \}\) , where \(T_{k}^{\mathcal{O}}\) is the \(k\) -th label of crash type in dataset \(\mathcal{D}\) . + +<|ref|>text<|/ref|><|det|>[[92, 115, 905, 199]]<|/det|> +We utilize these three variables to describe the crash result \(\mathrm{CR}_{i}^{\mathcal{O}}\) . The crash outcome can be presented in the following format: \(\mathrm{CR}_{i}^{\mathcal{O}} = (n_{i}^{\mathcal{O}}, s_{i}^{\mathcal{O}}, t_{i}^{\mathcal{O}})\) . For numerical variables, the function \(f(l)\) describes the number of people injured in crash as follows: "zero" if \(l = 0\) , "one" if \(l = 1\) , "two" if \(l = 2\) , and "three and more than three" if \(l \geq 3\) , the values for \(S_{k}\) and \(T_{k}^{\mathcal{O}}\) are provided in the Supplementary Table 2 and Supplementary Table 3. + +<|ref|>sub_title<|/ref|><|det|>[[92, 214, 255, 231]]<|/det|> +### 5.2 TrafficSafe LLM + +<|ref|>text<|/ref|><|det|>[[92, 234, 904, 294]]<|/det|> +We fine- tune TrafficSafe LLM by adapting LLaMa 3.124 to crash prediction tasks to enhance the LLMs' capabilities in interpreting crash data, identifying critical factors, and conducting feature attribution analysis to offer insights for crash prevention. In this section, we will introduce detailed information of the fine- tuning process. + +<|ref|>title<|/ref|><|det|>[[92, 305, 373, 322]]<|/det|> +#### 5.2.1 Construct Training Data for LLMs + +<|ref|>text<|/ref|><|det|>[[92, 325, 905, 446]]<|/det|> +In the training of LLMs, a single input consists of three components: the system prompt, the user prompt, and the target prompt. The system prompt introduces the task, for example: "You are a helpful assistant designed to predict the severity of a traffic crash ...". The user prompt comprises the four content parts detailed in Section 5.1.2 for each case. The target prompt represents the expected output. Examples of these prompts are shown in Extended Data Figure 1, Extended Data Figure 2, and Supplementary Section 3. We tokenize the text inputs using LLaMA 3.1's tokenizer. + +<|ref|>title<|/ref|><|det|>[[92, 459, 432, 475]]<|/det|> +#### 5.2.2 Additional Special Tokens for Classification + +<|ref|>text<|/ref|><|det|>[[91, 479, 905, 682]]<|/det|> +To adapt the LLM as a crash classifier, additional tokens have been incorporated into the tokenizer's vocabulary, and the detailed crash attributes categories are listed in Supplementary Table 2 and Supplementary Table 3. Specifically, for predicting the number of people Injuries of Washington dataset and Illinois dataset, we have introduced four special tokens: , , , and . Similarly, for predicting the Crash Severity of Washington dataset and Illinois dataset, we use five additional tokens: \(S_{k}\) , where \(1 \leq k \leq 5\) , corresponding to different levels of severity. The Type task differs slightly between the Washington and Illinois datasets. For Washington datasets, we utilize 14 special tokens: \(T_{k}^{\mathcal{W}}\) , where \(1 \leq k \leq 14\) , each representing a specific crash type. For Illinois datasets, we utilize 16 special tokens: \(T_{k}^{\mathcal{F}}\) , where \(1 \leq k \leq 16\) . The parameters of the input and output embedding layers are set as trainable, enabling the model to align the representations of these special tokens with the existing embedding space. + +<|ref|>title<|/ref|><|det|>[[92, 695, 297, 711]]<|/det|> +#### 5.2.3 Supervised Fine-tuning + +<|ref|>text<|/ref|><|det|>[[92, 715, 904, 752]]<|/det|> +During the fine- tuning phase, the traffic forecasting task is framed as a next- token generation task. This process can be described as: + +<|ref|>equation<|/ref|><|det|>[[375, 750, 902, 794]]<|/det|> +\[p_{\theta}(T_{i}) = \prod_{j = 1}^{|T_{i}|} p_{\theta}(t_{j}^{(i)} | t_{1}^{(i)}, \dots , t_{j - 1}^{(i)}), \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[91, 803, 904, 903]]<|/det|> +where \(T_{i}\) is the \(i\) - th item in the training data, \(p_{\theta}\) is the LLM, \(t_{j}^{(i)}\) denotes the \(j\) - th token in \(T_{i}\) . By maximizing the likelihood \(p_{\theta}(T) = \prod_{i = 1}^{N} p_{\theta}(T_{i})\) , the LLM's parameters are learned. Both the system prompt and the user prompt are masked for loss computation during training. We also used uniform data sampling strategy during the training process to facilitate the convergence of TrafficSafe LLM47. Through this process, the model learns to make prediction for a traffic crash. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[92, 91, 207, 106]]<|/det|> +### 5.2.4 Data Split + +<|ref|>text<|/ref|><|det|>[[90, 110, 905, 377]]<|/det|> +We split the Washington and Illinois dataset into training, validation, and test set in a 7:1.5:1.5 ratio. Since the Washington dataset contains relatively few crash events per year, we utilized as many reports as possible to ensure sufficient training data. However, the data distribution across different classes is highly imbalanced. For example, in the crash severity prediction task in Washington dataset, the ratio of \(\# S_{1} / \# S_{5}\) is nearly 100:1, where \(\# S_{k}\) is the number of data with label \(S_{k}\) . The imbalanced data distribution presents a significant challenge for the model's training and evaluation. In Section 5.2.3, we used uniform sampling strategy to train model on this unbalanced data. Similarly, to facilitate the model's evaluation, for the validation set and test set, we removed most of the data with crash severity category of \(S_{1}\) . Specifically, after processing, the dataset consisted of 16,188 records, with 11,332 used for training, 2,428 for validation, and 2,428 for testing. To balance the validation and test set for better evaluation, we removed 1428 \(S_{1}\) data and used 1000 remaining data for validation set and test set separately. Compared with the Washington state, more crash records can be used in Illinois state to generate dataset. As a result, we were able to balance all subsets, including the training, validation, and test sets. Ultimately, the Illinois dataset comprised 42,715 records, with 29,307 used for training, 6,704 for validation, and 6,704 for testing. + +<|ref|>title<|/ref|><|det|>[[92, 389, 267, 405]]<|/det|> +#### 5.2.5 Evaluation Metrics + +<|ref|>text<|/ref|><|det|>[[90, 409, 905, 469]]<|/det|> +In evaluating the model performance as a classification task, we employ weighted accuracy, precision, and F1- score as metrics. In the context of a classification task, we have four notations, True Positive \((TP)\) , True Negative \((TN)\) , False Positive \((FP)\) , False Negative \((FN)\) . Using these notations, we can represent the metrics as follows: + +<|ref|>text<|/ref|><|det|>[[121, 478, 904, 518]]<|/det|> +- Accuracy is one of the most commonly used measures for the classification performance, and it is defined as a ratio between the correctly classified samples to the total number of samples as follows: + +<|ref|>equation<|/ref|><|det|>[[394, 531, 903, 568]]<|/det|> +\[\mathrm{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[121, 583, 904, 623]]<|/det|> +- Precision represents the proportion of positive samples that were correctly classified to the total number of positive predicted samples, which reflect the performance of the prediction: + +<|ref|>equation<|/ref|><|det|>[[439, 637, 903, 673]]<|/det|> +\[\mathrm{Precision} = \frac{TP}{TP + FP} \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[121, 689, 904, 728]]<|/det|> +- F1-score combines results on precision and recall. It is the harmonic mean of precision and recall, which can be calculated using formula: + +<|ref|>equation<|/ref|><|det|>[[295, 741, 903, 780]]<|/det|> +\[\mathrm{F1 - score} = \frac{2}{\mathrm{Precision}^{-1} + \mathrm{Recall}^{-1}} = 2\cdot \left(\frac{\mathrm{Precision}\cdot\mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}\right) \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[139, 792, 373, 811]]<|/det|> +where \(\mathrm{Recall} = TP / (TP + FN)\) . + +<|ref|>title<|/ref|><|det|>[[92, 823, 262, 840]]<|/det|> +#### 5.2.6 Adopted Baselines + +<|ref|>text<|/ref|><|det|>[[90, 843, 904, 903]]<|/det|> +We follow the recent literature \(^{48}\) and also adopt XGBoost \(^{38}\) , Random forest (RF) \(^{33}\) , Decision Trees (DT) \(^{36}\) , Adaptive boosting (AdaBoost) \(^{49}\) , LogisticRegression (LR) \(^{37}\) , Categorical boosting (CatBoost) \(^{50}\) , and National Average \(^{39}\) as compared baselines. Building upon these foundational models, we particularly focus on enhancing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 88, 904, 128]]<|/det|> +their predictive capabilities through advanced techniques and parameter optimization. The detailed descriptions of these models are listed as follows: + +<|ref|>text<|/ref|><|det|>[[120, 140, 905, 203]]<|/det|> +- XGBoost is a scalable and distributed gradient-boosting framework that constructs an ensemble of decision trees by minimizing a regularized loss function. It uses second-order gradients for optimization and includes features like shrinkage, column subsampling, and tree pruning to improve accuracy and prevent overfitting38. + +<|ref|>text<|/ref|><|det|>[[121, 214, 904, 273]]<|/det|> +- AdaBoost34 is an iterative boosting method that sequentially trains weak classifiers (e.g., decision stumps) and assigns higher weights to misclassified instances in subsequent iterations. The final prediction is determined by a weighted majority vote of all classifiers. + +<|ref|>text<|/ref|><|det|>[[121, 285, 904, 346]]<|/det|> +- Random Forest (RF)33 builds an ensemble of decision trees by randomly sampling both features and data points (via bootstrap aggregation). The aggregated (voted) output of these diverse trees reduces variance and provides robust performance across a variety of tasks. + +<|ref|>text<|/ref|><|det|>[[121, 359, 904, 419]]<|/det|> +- Decision Trees (DT)36 recursively split the feature space based on selected thresholds, forming a hierarchical tree structure that is easy to interpret. Although they can capture complex interactions, DTs are prone to overfitting if not properly regularized. + +<|ref|>text<|/ref|><|det|>[[121, 431, 904, 492]]<|/det|> +- Logistic Regression (LR)37 models the probability of a binary outcome through a linear combination of input features passed through the logistic function. Coefficients are typically estimated via maximum likelihood, providing a simple yet effective approach for classification. + +<|ref|>text<|/ref|><|det|>[[121, 504, 904, 565]]<|/det|> +- CatBoost50 is a gradient-boosting algorithm that efficiently handles categorical features through techniques such as ordered boosting and gradient-based one-hot encoding. By systematically reducing target leakage in encoding, it achieves high predictive accuracy while mitigating overfitting in heterogeneous datasets. + +<|ref|>text<|/ref|><|det|>[[121, 578, 904, 638]]<|/det|> +- National Average39 predicts crash severity distributions using calibrated Severity Distribution Functions (SDFs). It incorporates road design, traffic control, and crash data to estimate probabilities for different severity levels via a multinomial logit model. + +<|ref|>text<|/ref|><|det|>[[60, 650, 904, 710]]<|/det|> +For these models, the Bayesian optimization method (BayesSearchCV) is used to facilitate the identification of optimal hyperparameters, such as max_depth and learning_rate. The details of the hyperparameters settings of these models are shown in Supplementary Section 4. + +<|ref|>sub_title<|/ref|><|det|>[[92, 728, 295, 745]]<|/det|> +### 5.3 TrafficSafe Attribution + +<|ref|>text<|/ref|><|det|>[[60, 749, 904, 829]]<|/det|> +To identify the feature contribution of each factor to the prediction results, this paper introduces and adapts the concept of Shapley values40. In this section, we first explain the calculation process of Shapley values and subsequently propose a novel sentence-level feature contributions calculation method based on Shapley theory for attributing factors in LLMs. + +<|ref|>title<|/ref|><|det|>[[92, 843, 323, 860]]<|/det|> +#### 5.3.1 Definition of Shapley Value + +<|ref|>text<|/ref|><|det|>[[60, 863, 904, 902]]<|/det|> +Shapley value is a concept from cooperative game theory that has been widely adopted in machine learning to interpret model predictions51. It provides a way to fairly allocate the contribution of each feature to the outcome of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 88, 904, 147]]<|/det|> +a predictive model. In essence, the Shapley value quantifies how much each feature contributes to a prediction by considering all possible combinations of features. Formally, the Shapley value \(\phi\) of a feature (or player) \(i\) in a cooperative game is defined as: + +<|ref|>equation<|/ref|><|det|>[[320, 160, 901, 202]]<|/det|> +\[\phi_{i} = \sum_{S\subseteq N\backslash \{i\}}\frac{|S|!(n - |S| - 1)!}{n!}\Big[v(S\cup \{i\}) - v(S)\Big], \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[60, 216, 904, 277]]<|/det|> +where \(N = \{1,2,\ldots ,n\}\) is the index set of \(n\) features, \(S\) is a subset of \(N\) , and \(v(S)\) is the utility of the subset \(S\) , which represents a measurable value, such as accuracy or prediction score, achieved by the model using only the subset \(S\) of features. + +<|ref|>text<|/ref|><|det|>[[60, 280, 904, 383]]<|/det|> +The Shapley value is utilized in both the training and inference stages in TrafficSafe. During the training stage, it quantifies the contributions of four primary categories of information: general information, infrastructure information, event information, and unit information. During the inference stage, the Shapley value is applied to assess the contributions of individual sentences to the prediction outcomes. The specific methodologies and implementation details are outlined in the subsequent sections. + +<|ref|>title<|/ref|><|det|>[[60, 397, 434, 414]]<|/det|> +#### 5.3.2 Feature Contributions at the Training Stage + +<|ref|>text<|/ref|><|det|>[[60, 417, 904, 541]]<|/det|> +The Shapley value is utilized to assess the influence of different components in the training set on the model during training. As outlined in Section 3.2.1, the \(j\) - th prompt \(p_{j}\) in the dataset \(P\) is divided into five parts: \(c_{0}\) : system prompt (i.e. "You are a helpful assistant designed to predict the severity of a traffic crash ..."), \(c_{1}\) : general information, \(c_{2}\) : infrastructure information, \(c_{3}\) : event information, and \(c_{4}\) : unit information. We denote \(p_{j}(k)\) as the \(c_{k}\) portion of \(p_{j}\) . Given an index set \(S\) , we can construct a variant \(p_{j}(S)\) by concatenating the parts in \(S\) . For example, if \(S = \{0,1,2\}\) , then \(p_{j}(S)\) contains \(c_{0}\) , \(c_{1}\) , and \(c_{2}\) . Formally, + +<|ref|>equation<|/ref|><|det|>[[407, 558, 901, 577]]<|/det|> +\[p_{j}(S) = \mathrm{concat}_{k\in S}p_{j}(k), \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[60, 594, 904, 633]]<|/det|> +where concat denotes concatenation. The resulting dataset based on \(S\) is \(P(S) = \{p_{j}(S) | j = 0,1,\ldots ,L\}\) , where \(L\) is the dataset size. + +<|ref|>text<|/ref|><|det|>[[120, 637, 641, 656]]<|/det|> +Referring to Equation (5), the contribution of part \(c_{i}\) at training, \(\phi_{i}^{\mathrm{train}}\) , is + +<|ref|>equation<|/ref|><|det|>[[245, 668, 901, 711]]<|/det|> +\[\phi_{i}^{\mathrm{train}} = \sum_{S\subseteq N\backslash \{i\}}\frac{|S|!(n - |S| - 1)!}{n!}\cdot \Big[v\big(P(S\cup \{0,i\})\big) - v\big(P(S\cup \{0\})\big)\Big], \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[60, 725, 904, 765]]<|/det|> +where \(N = \{1,2,3,4\}\) indexes the four content parts, and \(v(P(S))\) is a performance metric (e.g., accuracy, F1- score) obtained after retraining the model only on prompts in \(P(S)\) . + +<|ref|>title<|/ref|><|det|>[[60, 780, 540, 797]]<|/det|> +#### 5.3.3 Sentence-level Feature Contributions at the Inference Stage + +<|ref|>text<|/ref|><|det|>[[60, 801, 904, 902]]<|/det|> +Unlike traditional machine learning models that primarily handle fixed- length feature vectors, LLMs process variable- length text sequences as input. This characteristic makes commonly used Shapley value approximation methods, such as KernelSHAP and DeepSHAP, less applicable to LLMs. Recent approaches like TokenSHAP and TransSHAP have been proposed to address this by decomposing input text into tokens and computing Shapley values at the token level. However, applying token- level Shapley value computation to TrafficSafe LLM + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 88, 904, 252]]<|/det|> +introduces two primary challenges: 1) Computational limitations. The computational complexity of Shapley values is exponential in the number of players. In our TrafficSafe LLM, with an input size of approximately 500 tokens, large- scale computation of token- level Shapley values for crash data becomes impractical. 2) Limited interpretability. Decomposing the prompt at the token level disregards inter- token dependencies, and the arbitrary masking or replacement of tokens can lead to semantic ambiguity and contextual shifts. These issues hinder a precise understanding of how individual features contribute to predictions. Moreover, paragraph- level analysis is too coarse for detailed attribution, since it can merge distinct features into a single category (e.g., driver and vehicle details under "unit information"). + +<|ref|>text<|/ref|><|det|>[[70, 255, 902, 293]]<|/det|> +To overcome these limitations, we propose a sentence- level feature contributions calculation method for inputs of LLMs, which proceeds as follows: + +<|ref|>text<|/ref|><|det|>[[121, 305, 902, 344]]<|/det|> +- Sentence segmentation. The prompts are segmented using delimiters (e.g., commas ", or periods ".") to produce sentence-level units. + +<|ref|>text<|/ref|><|det|>[[121, 355, 903, 458]]<|/det|> +- Feature groups annotation. GPT-4o is used to group and label these sentences (see Figure 4 for the groups' content). Each group is represented as \(c_{k}\) , where \(k \in N' = \{1,2,3,\ldots n\}\) . For the Washington dataset, \(n = 14\) , while for the Illinois dataset \(n = 12\) . Given index set \(S' \subseteq N' \setminus \{i\}\) , we can construct the the prompt \(p_j(S')\) similar to the process Equation (6). The dataset built upon \(S'\) can be written as \(P(S') = \{p_j(S') | j = 0,1,2,\ldots ,L\}\) , where \(L\) is the length of the dataset \(P\) . + +<|ref|>text<|/ref|><|det|>[[121, 469, 902, 508]]<|/det|> +- Feature contributions calculation based on the feature groups. Based on the constructed dataset, the feature contribution for the \(i\) -th sentence-group \(\phi_i^{inf}\) can be calculated as: + +<|ref|>equation<|/ref|><|det|>[[253, 526, 901, 570]]<|/det|> +\[\phi_{i}^{inf} = \sum_{S'\subseteq N'\setminus \{i\}}\frac{|S'|!(n - |S'| - 1)!}{n!}\cdot \left[p_{\theta}(P(S'\cup \{0,i\})) - p_{\theta}(P(S'\cup \{0\}))\right] \quad (8)\] + +<|ref|>text<|/ref|><|det|>[[131, 577, 903, 637]]<|/det|> +where \(p_{\theta}\) is the LLM that returns the predicted probability of the target. A higher \(\phi_{i}^{\mathrm{inf}}\) indicates a greater contribution of the \(i\) - th sentence group to the model's confidence. To reduce computational overhead, we adopt a stratified sampling- based Shapley estimation method using complementary contributions46. + +<|ref|>sub_title<|/ref|><|det|>[[70, 657, 181, 674]]<|/det|> +## Reference + +<|ref|>text<|/ref|><|det|>[[70, 684, 904, 744]]<|/det|> +47. Du, H., Zhao, J., Zhao, Y., Xu, S., Lin, X., Chen, Y., Gardner, L. M. & Yang, H. F. Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study. arXiv preprint arXiv:2404.06962 (2024). + +<|ref|>text<|/ref|><|det|>[[70, 750, 904, 810]]<|/det|> +48. Ahmed, S., Hossain, M. A., Ray, S. K., Bhuiyan, M. M. I. & Sabuj, S. R. A study on road accident prediction and contributing factors using explainable machine learning models: Analysis and performance. Transportation research interdisciplinary perspectives 19, 100814 (2023). + +<|ref|>text<|/ref|><|det|>[[70, 817, 904, 856]]<|/det|> +49. Freund, Y., Schapire, R. & Abe, N. A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence 14, 1612 (1999). + +<|ref|>text<|/ref|><|det|>[[70, 863, 904, 902]]<|/det|> +50. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems 31 (2018). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 88, 905, 125]]<|/det|> +71 Chen, H., Lundberg, S. M. & Lee, S.- I. Explaining a series of models by propagating Shapley values. Nature communications 13, 4512 (2022). + +<|ref|>text<|/ref|><|det|>[[60, 133, 905, 172]]<|/det|> +72 Chen, H., Covert, I. C., Lundberg, S. M. & Lee, S.- I. Algorithms to estimate Shapley value feature attributions. Nature Machine Intelligence 5, 590- 601 (2023). + +<|ref|>text<|/ref|><|det|>[[60, 181, 905, 241]]<|/det|> +73 Lundberg, S. M. & Lee, S.- I. A Unified Approach to Interpreting Model Predictions in Advances in Neural Information Processing Systems (eds Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. & Garnett, R.) 30 (Curran Associates, Inc., 2017). + +<|ref|>text<|/ref|><|det|>[[60, 250, 905, 289]]<|/det|> +74 Goldsmith, R. & Horovicz, M. TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation. arXiv preprint arXiv:2407.10114 (2024). + +<|ref|>text<|/ref|><|det|>[[60, 297, 905, 378]]<|/det|> +80 Kokalj, E., Škrlj, B., Lavrač, N., Pollak, S. & Robnik- Šikonja, M. BERT meets Shapley: Extending SHAP Explanations to Transformer- based Classifiers in Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation (eds Toivonen, H. & Boggia, M.) (Association for Computational Linguistics, Online, Apr. 2021), 16- 21. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[58, 90, 257, 108]]<|/det|> +## 6 Data Availability + +<|ref|>text<|/ref|><|det|>[[58, 117, 905, 198]]<|/det|> +Details of each raw data source and data processing are described in the Method Section. The processed data examples are available at https://github.com/Puw242/TrafficSafe. In compliance with HSIS data policy, requests for the complete raw dataset should be made via https://highways.dot.gov/research/ safety/hsis. + +<|ref|>sub_title<|/ref|><|det|>[[58, 219, 260, 237]]<|/det|> +## 7 Code Availability + +<|ref|>text<|/ref|><|det|>[[58, 247, 712, 265]]<|/det|> +Code is publicly accessible at https://github.com/Puw242/TrafficSafe. + +<|ref|>sub_title<|/ref|><|det|>[[58, 286, 299, 304]]<|/det|> +## 8 Author Contributions + +<|ref|>text<|/ref|><|det|>[[58, 313, 907, 435]]<|/det|> +Y.Z., P.W. and H.F.Y conceptualized and designed the study. P.W. and Yibo Z. collected data. P.W. and Yibo Z. processed the data and designed prompts. Y.Z. and P.W. performed experiments. Yibo Z. run the baseline models. Y.Z., P.W., Yibo Z. and H.F.Y prepared the figures. Y.Z., P.W., Yibo Z. and H.F.Y analyzed results. Y.Z., P.W., Yibo Z. and H.F.Y wrote the initial draft. H.D. and H.F.Y provided guidance and feedback for the study. H.D. and H.F.Y revised the manuscript. H.F.Y. acquired the funding. H.F.Y. provided computational resources. All authors prepared the final version of the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[58, 455, 286, 473]]<|/det|> +## 9 Competing Interests + +<|ref|>text<|/ref|><|det|>[[58, 484, 408, 501]]<|/det|> +The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[380, 144, 616, 161]]<|/det|> +## Example Prompt - #EC22961 + +<|ref|>text<|/ref|><|det|>[[147, 165, 860, 199]]<|/det|> +You are a helpful assistant designed to predict the task target of a traffic crash. You need to make prediction based on the information below: + +<|ref|>sub_title<|/ref|><|det|>[[148, 203, 297, 217]]<|/det|> +## General Information + +<|ref|>text<|/ref|><|det|>[[148, 218, 861, 299]]<|/det|> +This incident occurred on February 23, 2022, at 2:00 PM, in the city of Bremerton, Kitsap County, on the 303 route increasing milepost direction at milepost 1.87. The location is an Urban - Principal Arterial, not at an intersection and not related to a driveway. The type of roadway is classified as an Urban Multilane Undivided Non- Freeway. The level of access control is Non Limited Access Least Restrictive, the speed limit is 30, and the average annual daily traffic is 37000. + +<|ref|>sub_title<|/ref|><|det|>[[148, 303, 340, 317]]<|/det|> +## Infrastructure Information + +<|ref|>text<|/ref|><|det|>[[148, 318, 861, 399]]<|/det|> +The road width is 52 feet, the road surface is made of Asphalt, the right and left shoulders width is unknown, and the surface type of the left shoulder is unknown. This road does not have a median- separated area, there is no barrier in the median and the median width is unknown. The condition of the road is unknown regarding work zone status, but it is known that the accident occurred during daylight and the road surface condition was dry. + +<|ref|>sub_title<|/ref|><|det|>[[148, 403, 280, 417]]<|/det|> +## Event Information + +<|ref|>text<|/ref|><|det|>[[148, 418, 861, 483]]<|/det|> +There were no pedestrians involved, 3 vehicles involved. The accident has no influence of alcohol or drugs. There were no objects involved. Vehicle1 was moving North, in the direction of increasing milepost, Vehicle2 was also moving North, in the direction of increasing milepost. The first vehicle was moving straight when the second vehicle was stopped in traffic, legally standing. + +<|ref|>sub_title<|/ref|><|det|>[[148, 487, 272, 501]]<|/det|> +## Unit Information + +<|ref|>text<|/ref|><|det|>[[147, 502, 861, 662]]<|/det|> +The unit 1 is a Vanette Under 10,000 lb, non- commercial vehicle. The airbag was not deployed. The vehicle had no defects. The driver was going straight ahead, was not ejected, and was distracted by an unknown factor. Person 1: Motor Vehicle Driver, Female, 47, Restraint use is unknown. The unit 2 is a Vanette Under 10,000 lb, non- commercial vehicle. The airbag was not deployed. The vehicle had no defects. The driver had stopped for traffic, was not ejected, and no violations or factors contributed to the incident. Person 1: Motor Vehicle Driver, Female, 26, Restraint use is unknown. The unit 3 is a Vanette Under 10,000 lb, non- commercial vehicle. The airbag was not deployed. The vehicle had no defects. The driver was going straight ahead, was not ejected, and was distracted by an unknown factor. A drug recognition expert was not requested. Person 1: Motor Vehicle Driver, Female, 50, Restraint use is unknown. + +<|ref|>sub_title<|/ref|><|det|>[[476, 666, 531, 680]]<|/det|> +## Targets + +<|ref|>text<|/ref|><|det|>[[148, 686, 816, 702]]<|/det|> +Please predict the Injury number of the crash choosing from the following tokens (4 options available). + +<|ref|>text<|/ref|><|det|>[[148, 703, 275, 717]]<|/det|> +Assistant: + +<|ref|>text<|/ref|><|det|>[[148, 734, 775, 750]]<|/det|> +Please predict the Severity of the crash choosing from the following tokens (5 options available). + +<|ref|>text<|/ref|><|det|>[[148, 753, 385, 768]]<|/det|> +Assistant: + +<|ref|>text<|/ref|><|det|>[[148, 781, 800, 797]]<|/det|> +Please predict the crash Type of the crash choosing from the following tokens (14 options available). + +<|ref|>text<|/ref|><|det|>[[148, 799, 388, 813]]<|/det|> +Assistant: + +<|ref|>text<|/ref|><|det|>[[180, 836, 816, 854]]<|/det|> +Extended Data Figure 1: A Crash Event Prompt Example from Washington Dataset. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[385, 103, 614, 121]]<|/det|> +## Example Prompt - #129094 + +<|ref|>text<|/ref|><|det|>[[142, 124, 861, 158]]<|/det|> +You are a helpful assistant designed to predict the task target of a traffic crash. You need to make prediction based on the information below: + +<|ref|>sub_title<|/ref|><|det|>[[143, 161, 294, 176]]<|/det|> +## General Information + +<|ref|>text<|/ref|><|det|>[[143, 177, 863, 241]]<|/det|> +This crash occurred in Cook County on 4/2/2022 at 0:00 o'clock. The crash happened in the city of Chicago, classified as Chicago area, on None at milepost 0.0. The roadway is classified as Unknown, and the location was identified as an Urban 2 Lane Roads. This crash was not related to an intersection. + +<|ref|>sub_title<|/ref|><|det|>[[143, 246, 336, 260]]<|/det|> +## Infrastructure Information + +<|ref|>text<|/ref|><|det|>[[143, 261, 863, 310]]<|/det|> +The road surface was Dry with Darkness, Lighted Road lighting conditions and Clear weather at the time of the crash. The crash occurred on a Not Divided Two- way with Stop Sign in place, and it was confirmed that the crash did not occur in a work zone. + +<|ref|>sub_title<|/ref|><|det|>[[143, 315, 277, 329]]<|/det|> +## Event Information + +<|ref|>text<|/ref|><|det|>[[142, 330, 861, 363]]<|/det|> +The crash involved 2 vehicles. The primary driver behavior in the crash was Unable to Determine, with secondary behavior was (Not Applicable). + +<|ref|>sub_title<|/ref|><|det|>[[143, 367, 268, 381]]<|/det|> +## Unit Information + +<|ref|>text<|/ref|><|det|>[[142, 382, 863, 496]]<|/det|> +Vehicle 0, a 2014 model, was moving South and was traveling straight ahead before the crash. Vehicle 1, a 2016 model, was moving South and was traveling straight ahead before the crash. The driver was a 23- year- old male with no visible distractions, sitting in the Driver. The driver's blood alcohol content was not offered. The driver was a 59- year- old male with no visible distractions, sitting in the Driver. The driver's blood alcoho content was not offered. There was also a passenger, a 24- year- old male, seated in the Third Row Left. There was also a passenger, a 24- year- old male, seated in the third Row Right. + +<|ref|>sub_title<|/ref|><|det|>[[470, 497, 525, 512]]<|/det|> +## Targets + +<|ref|>text<|/ref|><|det|>[[142, 514, 812, 530]]<|/det|> +Please predict the Injury number of the crash choosing from the following tokens (4 options available). + +<|ref|>text<|/ref|><|det|>[[142, 532, 430, 546]]<|/det|> +Assistant: + +<|ref|>text<|/ref|><|det|>[[142, 565, 771, 581]]<|/det|> +Please predict the Severity of the crash choosing from the following tokens (5 options available). + +<|ref|>text<|/ref|><|det|>[[142, 585, 344, 600]]<|/det|> +Assistant: + +<|ref|>text<|/ref|><|det|>[[142, 612, 797, 628]]<|/det|> +Please predict the crash Type of the crash choosing from the following tokens (16 options available). + +<|ref|>text<|/ref|><|det|>[[142, 630, 293, 644]]<|/det|> +Assistant: + +<|ref|>text<|/ref|><|det|>[[168, 658, 828, 676]]<|/det|> +Extended Data Figure 2: A Crash Event Prompt Prompt Example from Illinois Dataset. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[100, 83, 890, 680]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[92, 688, 904, 723]]<|/det|> +
Extended Data Figure 3: The Confusion Matrix for TrafficSafe LLM and the Traditional Methods in (a) Washington Dataset and (b) Illinois Dataset.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[168, 105, 432, 118]]<|/det|> +## One Crash Case in Washington - #EC36495 + +<|ref|>text<|/ref|><|det|>[[120, 120, 473, 420]]<|/det|> +One Crash Case in Washington - #EC36495Assume the crash occurred on April 5, 2022, at 01:00 AM, in an unknown city, Mason county, on the 101 route increasing milepost direction at milepost 314.78. The location is a Rural- Principal Arterial, not at an intersection and not related to a driveway or other characteristic details are unknown. The roadway classification at the site of the accident is Rural 2 Lane Roads. The level of access control is Non Limited Access More Than Average Restriction, speed limit is 50, average annual daily traffic is 2300. The road width is 22, the road surface is made of Asphalt, the right shoulder width is 3 and there is no left shoulder, with the surface type of the left shoulder being unknown. This road is not median- separated, and there is no barrier or median width information available. The occurrence in the work zone is unknown, in conditions where the road surface was wet and the lighting was dark with no street lights. There were no pedestrians involved, 1 vehicle involved. The accident had no influence of alcohol or drugs. There was an object involved, specifically an Earth Bank or Ledge. Vehicle1 was moving north, in the direction of decreasing milepost. Vehicle2 direction and movement are unknown. The first vehicle was moving straight. The unit 1, is an unknown special vehicle type, not a commercial vehicle. The vehicle had tires punctured or blown. The driver was going straight ahead, totally ejected, and had a contributing factor of operating defective equipment. Person 1: Motor Vehicle Driver, Female, 53, Lap & Shoulder Used. + +<|ref|>image<|/ref|><|det|>[[485, 120, 888, 420]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[567, 110, 780, 122]]<|/det|> +
TrafficSafe Attribution - #EC36495
+ +<|ref|>text<|/ref|><|det|>[[120, 440, 473, 479]]<|/det|> +Severity: (correct) Type: (correct) Injury: (correct) + +<|ref|>text<|/ref|><|det|>[[556, 436, 808, 461]]<|/det|> +Crash Severity prediction feature attribution (This crash is a FATAL crash) + +<|ref|>text<|/ref|><|det|>[[93, 521, 904, 555]]<|/det|> +Extended Data Figure 4: One Example of Sentence- based Feature Attribution Results for A Crash Resulting in Fatal in Washington Dataset. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[102, 270, 896, 660]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[92, 692, 904, 727]]<|/det|> +
Extended Data Figure 5: One Example of Sentence-based Feature Attribution Results for A Crash Resulting in No Apparent Injury in Illinois Dataset.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[170, 262, 830, 599]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[93, 619, 905, 724]]<|/det|> +Extended Data Figure 6: Data Processing Process. Four raw datasets from HSIS (crash, infrastructure, vehicle, and person data) are used to construct a prompt through four steps. (1) Data mapping and organization: Link the datasets and organize them into four parts: general, infrastructure, event, and unit. (2) Satellite image textualization: Retrieve satellite images via GPS coordinates using the Google Maps API, then employ GPT- 4o to extract text- based information. (3) Dimensionality reduction: Combine targets with similar values using GPT- 4o. (4) Prompt generation: Use the processed data from the previous steps to generate a prompt for each part. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[168, 263, 833, 670]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[93, 697, 904, 732]]<|/det|> +Extended Data Figure 7: AI- expert Textualization Process. An example for the infrastructure information part of an event case in Washington dataset is shown. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 92, 768, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 250, 150]]<|/det|> +Supplementary.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/images_list.json b/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c12413a29e85868ccb8f0beda875023c5c9bf3b0 --- /dev/null +++ b/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. Aberration-corrected HAADF-STEM images of (a) Ru-1, (b) Ru-2, and (c) Ru-3. (d) Ru K-edge XANES spectra of Ru foil, Ru-1, Ru-2 and \\(\\mathrm{RuO_2}\\) . (e) Ru \\(\\mathrm{k}^{3}\\) -weighted Fourier transform of the EXAFS spectra of Ru foil, Ru-1, Ru-2 and \\(\\mathrm{RuO_2}\\) . (f) CO-probe DRIFTS results of CO-absorbed Ru-1, Ru-2 and Ru-3.", + "footnote": [], + "bbox": [ + [ + 152, + 181, + 840, + 424 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. (a) Schematic illustration of Ru-catalysed independent N-formylation and amide hydrogenation and a one-pot sequential \\(\\mathrm{CO_2}\\) hydrogenation reaction. (b) Catalytic yield in N-formylation of morpholine over different catalysts. (c) Catalytic yield of NFM hydrogenation over different catalysts. (d) Intrinsic TOF of Ru-1, Ru-2 and Ru-3 toward morpholine N-formylation and NFM hydrogenation, respectively. (e)", + "footnote": [], + "bbox": [ + [ + 149, + 437, + 844, + 703 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. (a) Schematic illustration of the influence of an excess \\(\\mathrm{CO_2}\\) atmosphere during the N-formylation of morpholine. (b) Catalytic conversion and selectivity of Ru-2 in the routes shown in (a). (c) \\(\\mathrm{H}_2\\) -TPR profiles of three Ru catalysts. (d) Catalytic yield in two reactions with hydrogen and deuterium and the corresponding KIE values.", + "footnote": [], + "bbox": [ + [ + 257, + 348, + 732, + 616 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4. (a) Energy profile of the reduction process. (b) Structures of the key intermediates in the reaction pathways. The asterisk denotes the adsorption site. Colour code: Ru: green; Al: pink; N: blue; C: grey; O: red; H: white.", + "footnote": [], + "bbox": [ + [ + 214, + 220, + 780, + 571 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5. Proposed catalytic reaction pathways for Ru-2 in the one-pot two-step catalytic process.", + "footnote": [], + "bbox": [ + [ + 230, + 290, + 760, + 537 + ] + ], + "page_idx": 17 + } +] \ No newline at end of file diff --git a/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a.mmd b/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..daee9942adbe71e3cb7f665ed93f8fb4bcce13be --- /dev/null +++ b/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a.mmd @@ -0,0 +1,353 @@ + +# Efficient amine-assisted hydrogenation of CO2 into methanol collectively catalysed by ruthenium single sites and ensembles in a unified catalyst + +Liang Chen chenliang@nimte.ac.cn + +Ningbo Institute of Materials Technology and Engineering, CAS https://orcid.org/0000- 0002- 0667- 540X + +Qihao Yang + +Ningbo Institute of materials technology & engineering, CAS https://orcid.org/0000- 0002- 0933- 4483 + +Yinming Wang + +Ningbo Institute of Materials Technology and Engineering, CAS + +Dianhui Pan + +Ningbo Institute of Materials Technology and Engineering, CAS + +Desheng Su + +Ningbo Institute of Materials Technology and Engineering, CAS + +Hao Liu + +Ningbo Institute of Materials Technology and Engineering, CAS + +Qiuju Zhang + +Ningbo Institute of Materials Technology and Engineering, CAS + +Sheng Dai + +East China University of Science and Technology https://orcid.org/0000- 0001- 5787- 0179 + +Ziqi Tian + +Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences https://orcid.org/0000- 0001- 5667- 597X + +Zhiyi Lu + +Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences https://orcid.org/0000- 0002- 2117- 4101 + +## Article + +Keywords: amine- assisted CO2 hydrogenation, supported metal catalyst, N- formylation, amide hydrogenation + +<--- Page Split ---> + +Posted Date: May 2nd, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4185890/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on January 11th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 55837- 7. + +<--- Page Split ---> + +Efficient amine-assisted hydrogenation of \(\mathrm{CO_2}\) into methanol collectively catalysed by ruthenium single sites and ensembles in a unified catalyst + +Qihao Yang \(^{1,2,\ddagger}\) , Yinning Wang \(^{1,2,\ddagger}\) , Dianhui Pan \(^{1,2,\ddagger}\) , Desheng Su \(^{1,3,\ddagger}\) , Hao Liu \(^{1,2}\) , Qiuju Zhang \(^{1,2}\) , Sheng Dai \(^{4}\) , Ziqi Tian \(^{1,2,\ast}\) , Zhiyi Lu \(^{1,2,\ast}\) , Liang Chen \(^{1,2,\ast}\) + +\(^{1}\) Key Laboratory of Advanced Fuel Cells and Electrolyzers Technology and Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, P. R. China \(^{2}\) University of Chinese Academy of Sciences, 100049 Beijing, P. R. China \(^{3}\) School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, P. R. China \(^{4}\) Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center, Institute of Fine Chemicals, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China + +\(^{4}\) Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center, Institute of Fine Chemicals, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China + +KEYWORDS: amine- assisted \(\mathrm{CO_2}\) hydrogenation, supported metal catalyst, N- formylation, amide hydrogenation. + +<--- Page Split ---> + +ABSTRACT: Amine-assisted sequential hydrogenation of carbon dioxide \(\mathrm{CO_2}\) , without the requirement of energy-intensive activation of inert \(\mathrm{CO_2}\) , is an efficient route to generate methanol (i.e., an essential secondary feedstock). However, this one-pot sequential hydrogenation process requires a multifunctional catalyst capable of efficiently catalysing both amine N- formylation and amide hydrogenation. Ruthenium- based catalysts possess substantial catalytic prowess in both steps of the sequential hydrogenation process, while the optimal Ru species required for the specific steps depends on distinct criteria. Herein, to optimize the two- step methanol production process, morpholine was used as a typical amine assistant, and \(\mathrm{Al_2O_3}\) - supported ruthenium catalysts (Ru- 1, Ru- 2, Ru- 3) with isolated Ru sites (Ru1) or/and Ru ensembles (Ru \(\mathrm{e}\) , including Ru clusters, \(\mathrm{Ru_c}\) , and Ru nanoparticles, \(\mathrm{Ru_p}\) ) configurations were rationally fabricated via the judicious annealing strategy. Compared to the energy- intensive \(\mathrm{CO_2}\) hydrogenation process, within the sequential catalytic system, morpholine, \(\mathrm{H_2}\) and \(\mathrm{CO_2}\) underwent an initial conversion to give N- formylmorpholine (NFM), which was subsequently hydrogenated, resulting in the low- temperature formation of methanol (120 °C). Additionally, the excellent regeneration (>99%) of morpholine guarantees a sustainable progression towards subsequent cycles. Experimental analysis and density functional theory (DFT) calculations suggest that the metallic \(\mathrm{Ru_e}\) are superior catalytic sites for the N- formylation step, whereas the isolated \(\mathrm{Ru_1}\) sites exhibit higher amide hydrogenation activity. Thus, the optimal Ru- 2 catalyst, which simultaneously features metallic \(\mathrm{Ru_c}\) and isolated \(\mathrm{Ru_1}\) sites, can efficiently synergize the conversion of \(\mathrm{CO_2}\) into methanol + +<--- Page Split ---> + +in a one- pot two- step process with exceptional selectivity (>95%), highlighting the crucial sensitivity of the process to the structure of active metal species. This work not only presents an advanced catalyst suitable for \(\mathrm{CO_2}\) - based methanol production but also elucidates the requirements for rational design of multiple optimized active sites within the single catalyst for multistep catalytic reactions in the future. + +## 1. Introduction + +The anthropogenic emissions of greenhouse gases, primarily \(\mathrm{CO_2}\) , are widely believed to be responsible for a range of adverse environmental issues1,2. The facile catalytic reduction of carbon dioxide \(\mathrm{CO_2}\) into value- added secondary carbon feedstocks (i.e., hydrocarbons, alcohols, and carboxylic acids) exhibits excellent potential for mitigating the excessive accumulation of \(\mathrm{CO_2}^{3 - 6}\) . As one of the most attractive chemical products generated via \(\mathrm{CO_2}\) reduction, methanol \(\mathrm{(CH_3OH)}\) has a variety of attractive potential applications, including serving as a basic industrial feedstock, functioning as a liquid organic hydrogen carrier (LOHC), and being utilized in direct methanol fuel cells (DMFCs)7- 11. Therefore, the commercial value of catalytic production of \(\mathrm{CH_3OH}\) from \(\mathrm{CO_2}\) is substantial. In the past decades, remarkable researches have been conducted on the selective production of methanol via \(\mathrm{CO_2}\) hydrogenation12- 18, mainly focusing on metal oxides (e.g., \(\mathrm{In_2O_3}\) , \(\mathrm{ZnO - ZrO_2}\) , \(\mathrm{In_2O_3 - ZrO_2}\) )19- 21 and metal/metal oxides (e.g., \(\mathrm{Cu/ZnO/Al_2O_3}\) , \(\mathrm{Cu/In_2O_3}\) , \(\mathrm{Cu/ZrO_2}\) , \(\mathrm{Pd/ZnO}\) )22- 28. However, the traditional \(\mathrm{CO_2}\) hydrogenation based on metal oxides is encumbered by the necessity of high catalytic temperature (>300 °C)19- 21, resulting in + +<--- Page Split ---> + +excessive energy consumption. Although the introduction of metal components into metal oxides promotes the activation of \(\mathrm{H}_2\) and thus achieves enhanced catalytic performance at relatively lower temperature ( \(< 250^{\circ}\mathrm{C})^{23 - 25}\) , this modification simultaneously leads to a trade- off with the decrease in \(\mathrm{CH}_3\mathrm{OH}\) selectivity, owing to the excessive hydrogenation of \(\mathrm{CO}_2\) and the exacerbation of the reverse water gas shift (RWGS) reaction29- 31. + +Compared to the traditional \(\mathrm{CO}_2\) hydrogenation process, the amine- assisted two- step hydrogenation of \(\mathrm{CO}_2\) to \(\mathrm{CH}_3\mathrm{OH}\) , involving N- formylation of amine with \(\mathrm{CO}_2\) (i.e., first step) and subsequent amide hydrogenation (i.e., second step) in the presence of homogeneous catalysts32- 35, especially ruthenium complexes32- 34, exhibits superior catalytic activity and selectivity at mild condition ( \(< 180^{\circ}\mathrm{C}\) ), thus making it to be a promising and energy- efficient alternative for methanol production. However, the inherent difficulties in separation and recycling of homogeneous catalysts, coupled with the inferior stability, hinder their widespread application and the scaling up of this amine- assisted \(\mathrm{CO}_2\) - to- MeOH route. Furthermore, for the sequential catalytic reaction systems, the theoretical requirement for achieving optimal overall catalytic performance involves the engagement of at least dual types of active sites36- 38. Therefore, the existent homogeneous catalysts with a single designated activity structure may not provide the optimum catalytic activity for both steps of the sequential \(\mathrm{CO}_2\) hydrogenation process. In this context, the immobilization of homogeneous catalysts onto stable heterogeneous matrix (e.g., \(\mathrm{Al}_2\mathrm{O}_3\) , \(\mathrm{In}_2\mathrm{O}_3\) , \(\mathrm{ZrO}_2\) ) can be the most straightforward strategy, as it offers the benefit of easy separation. + +<--- Page Split ---> + +More importantly, the atomic- scale heterogeneity of supported metal catalysts is almost unavoidable, resulting in diverse structural morphologies or/and local coordination environments for the active components39,40, which offers the potential to optimize each step of the sequential \(\mathrm{CO_2}\) hydrogenation reaction and thus fine- tune the overall catalytic performance. + +Based on the aforementioned considerations, we rationally synthesized a series of \(\mathrm{Al_2O_3}\) - based heterogeneous catalysts featuring isolated Ru sites (Ru1) or/and Ru ensembles (Rue, including Ru clusters, Ruc, and Ru nanoparticles, Rup) via a traditional impregnation method coupled with annealing treatment under different atmospheres. The catalysts (i.e., Ru- 1, Ru- 2, Ru- 3), with dominant morphological distributions of atomically dispersed Ru (Ru- 1), Ru clusters (Ru- 2) and Ru nanoparticles (Ru- 3), exhibited distinct activity and selectivity for the sequential \(\mathrm{CO_2}\) hydrogenation to \(\mathrm{CH_3OH}\) with morpholine as the amine assistant. Ru- 2, which simultaneously possesses atomically dispersed Ru1 sites and metallic Ruc species, presented optimal catalytic activity in the N- formylation of morpholine with \(\mathrm{CO_2}\) , as well as the subsequent hydrogenation of the generated amide intermediates (i.e., N- formylmorpholine, NFM) to give methanol with the regeneration of morpholine. Moreover, the superior catalytic performance (selectivity of \(\mathrm{CHO_3H} > 97\%\) , regeneration of morpholine \(>99\%\) ) of Ru- 2 towards one- pot two- step \(\mathrm{CO_2}\) hydrogenation was well maintained in three consecutive cycles. The mechanistic experiments and density functional theory (DFT) results reveal that the deoxygenative hydrogenation (C=O bond cleavage) and deaminative hydrogenation (C- N cleavage) + +<--- Page Split ---> + +are the rate- determining steps of morpholine N- formylation and NFM hydrogenation, which can be accelerated over \(\mathrm{Ru_e}\) and \(\mathrm{Ru_1}\) sites, respectively. This work presents a heterogeneous catalysis protocol for amine- assisted hydrogenation of \(\mathrm{CO_2}\) towards methanol production, highlighting the significance of active species heterogeneity in enhancing the catalytic performance for multistep sequential reactions. + +## 2. Results and Discussion + +2.1 Synthesis and characterizations of \(\mathrm{Al_2O_3}\) -based Ru catalysts + +A series of \(\mathrm{Al_2O_3}\) - supported Ru catalysts (Ru- 1, Ru- 2, Ru- 3) were rationally synthesized via an impregnation- annealing method, with Ru- Macho and \(\alpha\) - \(\mathrm{Al_2O_3}\) being employed as metal precursor and support, respectively (see Section 2 in Supplementary Information (SI) for details). The powder X- ray diffraction (PXRD) patterns exhibited no diffraction peaks related to Ru or \(\mathrm{RuO_2}\) phase in the three catalysts (Supplementary Fig. 1), which might be ascribed to the low contents (Supplementary Table 1) or/and small sizes of the Ru species. Transmission electron microscopy (TEM) images show that the morphologies of the synthesized catalysts with loaded Ru species remained consistent with the original \(\mathrm{Al_2O_3}\) matrix (Supplementary Fig. 2). To identify the differences in atomic- scale structures among the three Ru catalysts, the aberration- corrected high- angle annular dark- field scanning transmission electron microscopy (HAADF- STEM) technique was adopted. The Ru species in Ru- 1 catalysts were atomically dispersed (Fig. 1a and Supplementary Fig. + +<--- Page Split ---> + +3), while Ru clusters emerged in Ru- 2 and these clusters grew into larger nanoparticles in Ru- 3 (Fig. 1b- c and Supplementary Figs. 4- 5). + +![](images/Figure_1.jpg) + +
Fig. 1. Aberration-corrected HAADF-STEM images of (a) Ru-1, (b) Ru-2, and (c) Ru-3. (d) Ru K-edge XANES spectra of Ru foil, Ru-1, Ru-2 and \(\mathrm{RuO_2}\) . (e) Ru \(\mathrm{k}^{3}\) -weighted Fourier transform of the EXAFS spectra of Ru foil, Ru-1, Ru-2 and \(\mathrm{RuO_2}\) . (f) CO-probe DRIFTS results of CO-absorbed Ru-1, Ru-2 and Ru-3.
+ +Additional spectroscopic characterizations were conducted to elucidate the electronic structure and coordination environment of the catalysts. The high- resolution X- ray photoelectron spectroscopy (XPS) spectra of Ru 3p in Ru- 1 revealed two prominent peaks at the binding energies of 485.0 eV (Ru 3p \(_{1 / 2}\) ) and 462.2 eV (Ru 3p \(_{3 / 2}\) ), similar to those of Ru oxides \(^{41}\) , indicating the partially oxidized valence state of Ru species in Ru- 1 (Supplementary Fig. 6). In contrast, the binding energies of Ru species in Ru- 3 at 483.7 eV (Ru 3p \(_{1 / 2}\) ) and 461.8 eV (Ru 3p \(_{3 / 2}\) ) showed a striking resemblance to the characteristic signals of metallic Ru \(^{42}\) , + +<--- Page Split ---> + +consistently supporting the presence of nanoparticles as observed via HAADF- STEM. Similarly, Ru- 2 also demonstrated an average valence state that closely aligns with metallic Ru, but slightly elevated compared to Ru- 2, implying the potential existence of a subset of Ru species in the partially oxidized valence state. In addition, the XPS spectra signals of the P species, originating from the metal precursor (i.e., Ru- Macho), are notably prominent in Ru- 1 and Ru- 2, but significantly diminished in Ru- 3 (Supplementary Fig. 7), suggesting that the P species may contribute to the formation of the Ru species with oxidized valence state. + +To probe the local microstructure of Ru species with enhanced precision, X- ray absorption spectroscopy (XAS) was employed. As shown in Fig. 1d, X- ray adsorption near edge structure (XANES) analysis showed that the energy absorption thresholds of Ru- 1 and Ru- 2 were located between those of \(\mathrm{RuO_2}\) and Ru foil, but Ru- 1 aligned more closely with \(\mathrm{RuO_2}\) , illustrating an increase in the valence state from the Ru foil to Ru- 2, Ru- 1 and \(\mathrm{RuO_2}\) , which is consistent with the XPS observation results. The extended X- ray absorption fine structure (EXAFS) spectrum of Ru- 1 only exhibited one main peak at \(\sim 1.4 \AA\) , and no fingerprinting signal peak of Ru- Ru interactions \((\sim 2.3 \AA)\) cannot be observed, verifying the atomic dispersion of Ru in Ru- 1. The best fitting result of the obtained EXAFS data revealed that each Ru atom was coordinated by \(\sim 3\) O atoms and \(\sim 1\) P atom on average (Fig. 1e, Supplementary Fig. 8a and Supplementary Table 2). For the Ru- 2 catalyst, in addition to the prominent peak at \(\sim 1.4 \AA\) , a relatively weak peak at \(\sim 2.3 \AA\) that corresponds to Ru- Ru first coordination shell could be identified. The low Ru- Ru coordination number (C.N.) of \(3.0 \pm 1.0\) + +<--- Page Split ---> + +determined for Ru- 2 suggested the presence of tiny Ru clusters, which agrees well with the HADDF- STEM results (Fig. 1e, Supplementary Fig. 8b and Supplementary Table 2). + +To gain an in- depth understanding of the Ru species distribution in the catalysts, a CO- probe diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) experiment was conducted to acquire semiquantitative information about the proportions of the surface Ru species. The red characteristic peaks in the DRIFTS spectra (Fig. 1f, Supplementary Fig. 9), corresponding to two linearly adsorbed CO molecules on partially oxidized \(\mathrm{Ru_1}\) species, decreased in intensity with increasing size of \(\mathrm{Ru_e}\) from the Ru- 1 catalyst to the Ru- 3 catalyst, while the characteristic peaks for adsorbed CO on metallic \(\mathrm{Ru_e}\) species (blue) increased in intensity. The statistical percentages of \(\mathrm{Ru_1}\) and \(\mathrm{Ru_e}\) , determined by integrating the characteristic peaks, revealed that the \(\mathrm{Ru_1}\) contents in Ru- 1, Ru- 2 and Ru- 3 are 100%, 47.7%, and 37.2%, respectively (see Section 4 in the Supplementary Information and Supplementary Table 3). The metal dispersion (i.e., available Ru active sites) was further calculated on the basis of the CO adsorption determined from CO- pulse adsorption experiments (see Section 4 in the Supplementary Information and Supplementary Table 4). The metal dispersion of Ru- 1 (>99%), Ru- 2 (69.5%) and Ru- 3 (59.4%) also decreased with increasing proportion of Ru ensembles, which is consistent with the results of the CO- probe DRIFTS experiment. + +2.2 Catalytic performance of \(\mathrm{Al_2O_3}\) -based Ru catalysts towards the morpholine- assisted sequential \(\mathrm{CO_2}\) hydrogenation + +<--- Page Split ---> + +The Ru catalysts, featuring various surface metal species, exhibited exceptional catalytic performance in each reaction step (Fig. 2a). For the N- formylation of morpholine, both Ru- 2 and Ru- 3 demonstrated superior catalytic performance and intrinsic activity, with turnover frequencies (TOFs) twice that of Ru- 1 (Fig. 2b- c). In addition, the catalytic generation of amide reached a state of equilibrium after \(36\mathrm{h}\) for Ru- 2 and Ru- 3, while twice the time was required for Ru- 1 (Fig. 2b). This observation highlights the crucial role of \(\mathrm{Ru_e}\) species in N- formylation. Furthermore, Ru- 2 and Ru- 3 exhibited superior selectivity ( \(>95\%\) ) for the amide compared to Ru- 1 ( \(\sim 83\%\) ), with slight formic acid detected (Supplementary Fig. 10). + +![](images/Figure_2.jpg) + +
Fig. 2. (a) Schematic illustration of Ru-catalysed independent N-formylation and amide hydrogenation and a one-pot sequential \(\mathrm{CO_2}\) hydrogenation reaction. (b) Catalytic yield in N-formylation of morpholine over different catalysts. (c) Catalytic yield of NFM hydrogenation over different catalysts. (d) Intrinsic TOF of Ru-1, Ru-2 and Ru-3 toward morpholine N-formylation and NFM hydrogenation, respectively. (e)
+ +<--- Page Split ---> + +Yield- time profile of the one- pot two- step tandem process catalysed by Ru- 2. Reaction conditions: 393 K, 10 mmol substrate, 15 ml 1,4- dioxane as a solvent, 100 mg catalyst, 0.5 mmol \(\mathrm{CsCO_3}\) ; for the N- formylation process: \(\mathrm{P(CO_2):P(H_2) = 1:1}\) with a total pressure of 4 MPa; for amide hydrogenation, \(\mathrm{P(H_2) = 4MPa}\) . + +During the hydrogenation of NFM into amine and methanol, both Ru- 1 and Ru- 2 exhibited superior activity, with TOF values (Ru- 1: \(\sim 320 \mathrm{h}^{- 1}\) , Ru- 2: \(\sim 389 \mathrm{h}^{- 1}\) ) 4- 5 times higher than that of the Ru- 3 catalyst ( \(\sim 87 \mathrm{h}^{- 1}\) ). Notably, Ru- 2 showed a high yield and \(>99\%\) methanol selectivity under identical reaction conditions. Based on the exceptional performance of Ru- 2, we decided to investigate its potential utilization in a one- pot two- step tandem process. Over 144 h, Ru- 2 exhibited superb activity (methanol turnover number, \(\mathrm{TON}_{\mathrm{methanol}} = 3300\) ) and stability in three consecutive reaction cycles (Fig. 2e). The morphology of the Ru clusters was well maintained, as observed in the aberration- corrected HAADF- STEM image (Supplementary Fig. 11). We concluded that the \(\mathrm{Ru_e}\) and \(\mathrm{Ru_1}\) sites played dominant roles in the N- formylation and amide hydrogenation reactions, respectively. + +2.3 Catalytic mechanism of sequential \(\mathrm{CO_2}\) hydrogenation over \(\mathrm{Al_2O_3}\) -based Ru catalysts + +To confirm the catalytic reaction pathways of sequential \(\mathrm{CO_2}\) hydrogenation, a series of mechanistic experiments were performed using the optimal Ru- 2 catalyst. The influence of excess \(\mathrm{CO_2}\) during the N- formylation of morpholine was investigated, in which zwitterionic carbamates were spontaneously produced by + +<--- Page Split ---> + +morpholine in an aprotic solvent (Fig. 3a). In route 2, the reaction was initiated after \(\mathrm{CO_2}\) saturation (without additional \(\mathrm{CO_2}\) input during the catalytic reaction), in contrast to the original route 1, while in route 3, the excess \(\mathrm{CO_2}\) was evacuated after \(\mathrm{CO_2}\) saturation and replaced with 2 MPa \(\mathrm{N}_2\) . The absence of excess \(\mathrm{CO_2}\) led to a higher formate selectivity with similar conversion levels (Fig. 3b), indicating the significant role of \(\mathrm{CO_2}\) in inducing the critical intermediate (i.e., zwitterionic carbamate) during the N- formylation process. + +![](images/Figure_3.jpg) + +
Fig. 3. (a) Schematic illustration of the influence of an excess \(\mathrm{CO_2}\) atmosphere during the N-formylation of morpholine. (b) Catalytic conversion and selectivity of Ru-2 in the routes shown in (a). (c) \(\mathrm{H}_2\) -TPR profiles of three Ru catalysts. (d) Catalytic yield in two reactions with hydrogen and deuterium and the corresponding KIE values.
+ +The \(\mathrm{H}_2\) temperature- programmed reduction ( \(\mathrm{H}_2\) - TPR, Fig. 3c) profiles of the three catalysts revealed different \(\mathrm{H}_2\) affinities, where Ru- 2 and Ru- 3, possessing abundant \(\mathrm{Ru_e}\) sites, was prone to reduction at a lower temperature, thus manifesting the stronger + +<--- Page Split ---> + +\(\mathrm{H}_{2}\) activation ability compared with Ru- 1. To investigate the influence of \(\mathrm{H}_{2}\) activation in both steps of sequential \(\mathrm{CO}_{2}\) hydrogenation, hydrogen was replaced with deuterium (Fig. 3d). The yield was controlled at the intrinsic stage (conversion \(< 20\%\) ) to calculate the kinetic isotope effect (KIE) value. Similar to the TOF calculation with different catalysts, the reaction rate of N- formylation sharply decreased when hydrogen was replaced with deuterium, with a primary KIE value of \(2.45 (2 < \mathrm{KIE} < 7)\) , indicating that the rate determining step (RDS) in the N- formylation reaction is a step in which hydrogen is involved \(^{43,44}\) . However, this phenomenon was not observed in the amide reaction process, in which nearly identical reaction rates were obtained with hydrogen and deuterium (KIE=0.98). Thus, for the amide hydrogenation reaction, the RDS was speculated to be cleavage of the C- N bond. + +To further confirm this speculation, the catalytic hydrogenation of diverse carbonyl substrates was evaluated next (Supplementary Fig. 12). Due to substrate limitations, cyclohexylformamide was utilized as an analogue of NFM. The Ru- 2 catalyst exhibited almost no activity towards amides and carboxylic acids but achieved close to \(100\%\) yield for cyclohexylmethanol from aldehydes, with the absence of cyclohexylmethanamine or imine by- products, thereby highlighting the priority of C- N bond cleavage over carbonyl reduction during amide hydrogenation. + +Density functional theory (DFT) calculations were further conducted to gain insights into the reduction mechanism. Two Ru- containing models were constructed to consider the \(\mathrm{Ru}_{\mathrm{e}}\) and \(\mathrm{Ru}_{1}\) sites on the \(\mathrm{Al}_{2}\mathrm{O}_{3}\) substrate, labelled Ru- ensemble and + +<--- Page Split ---> + +Ru- SAC in Supplementary Fig. 13, respectively. The surface of \(\mathrm{Al}_2\mathrm{O}_3\) was passivated with a hydroxyl group, and the single Ru atom was coordinated with a \((\mathrm{CH}_3)_3\mathrm{P}\) ligand to reproduce the chemical environment determined by experiments. + +![](images/Figure_4.jpg) + +
Fig. 4. (a) Energy profile of the reduction process. (b) Structures of the key intermediates in the reaction pathways. The asterisk denotes the adsorption site. Colour code: Ru: green; Al: pink; N: blue; C: grey; O: red; H: white.
+ +The free energy profile in Fig. 4a and the relevant structures of intermediates in Fig. 4b indicate that the \(\mathrm{Ru_e}\) provides multiple sites that not only activate the unsaturated carbon of the carboxylic group in carbamates by binding with oxygen but also adsorb the active hydrogen to reduce carbamates. The activation energy barrier of the first reduction step was calculated to be 1.01 eV for the \(\mathrm{C} = \mathrm{O}\) cleavage in the zwitterionic + +<--- Page Split ---> + +carbamates (route 2 in Fig. 3a) under \(\mathrm{H}_2\) . Further protonation leads to elimination of the hydroxyl group, thus forming a formamide intermediate, which requires overcoming an energy barrier of \(1.18\mathrm{eV}\) . Despite the relatively low energy barriers of \(\mathrm{Ru_e}\) in the first few steps, the reduction step to form a hemiaminal (an intermediate of formamide hydrogenation) over \(\mathrm{Ru_e}\) requires overcoming a high energy barrier of \(1.45\mathrm{eV}\) , resulting in a sluggish rate to obtain the final product. However, the formamide hydrogenation may spill over onto the single Ru atom site. The migration of two hydrogen atoms from the Ru site to the intermediate would require overcoming two lower activation barriers of \(1.24\mathrm{eV}\) and \(0.95\mathrm{eV}\) . Thus, the presence of \(\mathrm{Ru_1}\) sites can accelerate the deep reduction of formamide to a hemiaminal. Finally, the hemiaminal desorbs and easily decomposes back into morpholine and formaldehyde. + +Based on the results of our experiments and DFT simulations, we proposed a potential catalytic reaction pathway for Ru- 2 involving several steps (Fig. 5). Initially, morpholine absorbs \(\mathrm{CO_2}\) to yield zwitterionic carbamates. Active hydrogen species generated via metallic \(\mathrm{Ru_e}\) then reduce these carbamates to form intermediate A (Fig. 5). Proton transfer from carbamates to intermediate A leads to the formation of intermediate B, which subsequently undergoes natural intramolecular dehydration to produce amide and \(\mathrm{H}_2\mathrm{O}\) . In addition, the electronegative oxygen in intermediate A can also coordinate to the atomically dispersed \(\mathrm{Ru_1}\) site to form intermediate C, which undergoes hydrogenation to form formate and morpholine. This process is probably the primary source of formic acid by- product formation. In the amide hydrogenation step, the amide is coordinated to the electropositive \(\mathrm{Ru_1}\) site and hydrogenated to form + +<--- Page Split ---> + +intermediate E, which undergoes C- N cleavage with the aid of the \(\mathrm{Ru}_1\) site to generate the adsorbed aldehyde (intermediate F) and regenerate the morpholine. The intermediate F is prone to hydrogenation (Supplementary Fig. 14), thus producing methanol. In contrast, the methylamine by- products via imine (intermediate G) pathway were not detected during amide hydrogenation (Supplementary Fig. 14). + +![](images/Figure_5.jpg) + +
Fig. 5. Proposed catalytic reaction pathways for Ru-2 in the one-pot two-step catalytic process.
+ +## 3. Conclusion + +In summary, we prepared a series of active heterogeneous Ru catalysts with multiple surface metal species, including atomically dispersed Ru species and Ru ensembles. Among these catalysts, Ru- 2, which contained both \(\mathrm{Ru}_1\) species and \(\mathrm{Ru}_\mathrm{e}\) sites, demonstrated excellent performance in the N- formylation and amide hydrogenation reactions, enabling efficient one- pot two- step methanol production under relatively mild conditions. The critical roles of the active metal species in the + +<--- Page Split ---> + +reaction process were revealed by combining experiments and theoretical calculations, and a possible reaction pathway was proposed. This study provides a potential candidate catalyst for selective reduction of \(\mathrm{CO_2}\) to methanol and reveals the synergistic effect of different metal species in complex multistep reactions at the atomic scale. The strategy of rationally designing multiple optimized active sites within a single catalyst paves the way for enhancing the catalytic performance in various multistep sequential reactions in the future. + +## ASSOCIATED CONTENT + +Supplementary Information. The Supplementary Information is available free of charge via the Internet at http://pubs.acs.org. + +Chemicals and characterization; Preparation of \(\mathrm{Al_2O_3}\) - based Ru catalysts; Catalyst evaluation; Characterization details; Supplementary Figs. 1- 14; Supplementary Tables 1- 4 + +## AUTHOR INFORMATION + +## Corresponding Author + +Liang Chen – Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P. R. China; University of Chinese Academy of Sciences, Beijing 100049, P. R. China; E- mail: chenliang@nimte.ac.cn + +Zhiyi Lu – Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P. R. China; University of Chinese Academy of + +<--- Page Split ---> + +Sciences, Beijing 100049, P. R. China; E- mail: luzhiyi@nimte.ac.cn + +Ziqi Tian – Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P. R. China; University of Chinese Academy of Sciences, Beijing 100049, P. R. China; E- mail: tianziqi@nimte.ac.cn + +## Author Contributions + +These authors contributed equally. + +## Notes + +The authors declare no competing financial interest. + +## ACKNOWLEDGMENT + +This work is supported by the National Natural Science Foundation of China (22101288), the Natural Science Foundation of Zhejiang Province (LQ22B010005 and LD21E020001), the Bellwethers Project of Zhejiang Research and Development Plan (2022C01158), the Ningbo Yongjiang Talent Introduction Programme (2021A- 036- B), the Science and Technology Innovation 2025 Program in Ningbo (2022Z205), Youth Innovation Promotion Association, CAS, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, Feringa Nobel Prize Scientist Joint Research Center, Transformational Technologies for Clean Energy and Demonstration, Strategic Priority Research Program of the Chinese Academy of Sciences (XDA21000000), DNL Cooperation Fund, CAS (Grant No. DNL202008), and “Transformational Technologies for Clean Energy and Demonstration”. + +<--- Page Split ---> + +## References + +1. 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Mechanism and microkinetics of methanol synthesis via \(\mathrm{CO}_{2}\) hydrogenation on indium oxide. J. Catal. 361, 313-321 (2018). + +22. Beck, A. et al. Following the structure of copper-zinc-alumina across the pressure gap in carbon dioxide hydrogenation. Nat. Catal. 4, 488-497 (2021). + +23. Bahruji, H. et al. Pd/ZnO catalysts for direct \(\mathrm{CO}_{2}\) hydrogenation to methanol. J. Catal. 343, 133-146 (2016). + +24. Behrens, M. et al. The active site of methanol synthesis over \(\mathrm{Cu/ZnO/Al}_{2}\mathrm{O}_{3}\) industrial catalysts. Science 336, 893-897 (2012). + +25. Wu, C. et al. Inverse \(\mathrm{ZrO}_{2} / \mathrm{Cu}\) as a highly efficient methanol synthesis catalyst from \(\mathrm{CO}_{2}\) hydrogenation. Nat. Commun. 11, 5767 (2020). + +26. Shi, Z. et al. \(\mathrm{CO}_{2}\) hydrogenation to methanol over Cu-In intermetallic catalysts: effect of reduction temperature. J. Catal. 379, 78-89 (2019). + +27. Li, K. & Chen, J. 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Suppressing dormant Ru states in the presence of conventional metal oxides promotes the Ru-MACHO-BH-catalyzed integration of \(\mathrm{CO_2}\) capture and hydrogenation to methanol. ACS Catal. 11, 12682-12691 (2021). + +33. Kar, S. et al. Mechanistic insights into ruthenium-pincer-catalyzed amine-assisted homogeneous hydrogenation of \(\mathrm{CO_2}\) to Methanol. J. Am. Chem. Soc. 141, 3160-3170 (2019). + +34. Zhang, L., Han, Z., Zhao, X., Wang, Z. & Ding, K. Highly efficient ruthenium-catalyzed N-formylation of amines with \(\mathrm{H_2}\) and \(\mathrm{CO_2}\) . Angew. Chem. Int. Ed. 54, 6186-6189 (2015). + +35. Jayarathne, U., Hazari, N. & Bernskoetter, W. H., Selective iron-catalyzed N-formylation of amines using dihydrogen and carbon dioxide. ACS Catal. 8, 1338-1345 (2018). + +<--- Page Split ---> + +36. Zecevic, J., Vanbutsele, G., De Jong, K. P. & Martens, J. A. Nanoscale intimacy in bifunctional catalysts for selective conversion of hydrocarbons. 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ACS Sustainable Chem. Eng. 7, 16501-16510 (2019). + +43. Li, Z. Covalent triazine framework supported non-noble metal nanoparticles with superior activity for catalytic hydrolysis of ammonia borane: from mechanistic study to catalyst design. Chem. Sci. 8, 781-788 (2017). + +44. Li, L. Accelerating chemo- and regioselective hydrogenation of alkynesover + +<--- Page Split ---> + +bimetallic nanoparticles in a metal- organic framework. ACS Catal. 10, 7753- 7762(2020). + +<--- Page Split ---> + +## Table of Contents + +A heterogeneous supported catalyst featuring atomically dispersed Ru sites and Ru- cluster sites exhibited superior catalytic performance for amine- assisted sequential hydrogenation of CO2 into hydrogen via the synergistic effect of the two types of surface- active Ru species. The rate- determining steps of the two reactions were elucidated and correlated with the intrinsic active species. + +![PLACEHOLDER_26_0] + + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a_det.mmd b/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..69703ccd79256dd9b86ead7c20946cc90e8649f2 --- /dev/null +++ b/preprint/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a/preprint__09fa1c94df81f8d0d856f1843bb02811e39dec61a630b4897174dcfd9f8b640a_det.mmd @@ -0,0 +1,461 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 940, 208]]<|/det|> +# Efficient amine-assisted hydrogenation of CO2 into methanol collectively catalysed by ruthenium single sites and ensembles in a unified catalyst + +<|ref|>text<|/ref|><|det|>[[44, 229, 291, 275]]<|/det|> +Liang Chen chenliang@nimte.ac.cn + +<|ref|>text<|/ref|><|det|>[[44, 301, 920, 344]]<|/det|> +Ningbo Institute of Materials Technology and Engineering, CAS https://orcid.org/0000- 0002- 0667- 540X + +<|ref|>text<|/ref|><|det|>[[44, 350, 147, 369]]<|/det|> +Qihao Yang + +<|ref|>text<|/ref|><|det|>[[44, 371, 930, 392]]<|/det|> +Ningbo Institute of materials technology & engineering, CAS https://orcid.org/0000- 0002- 0933- 4483 + +<|ref|>text<|/ref|><|det|>[[44, 396, 171, 415]]<|/det|> +Yinming Wang + +<|ref|>text<|/ref|><|det|>[[44, 417, 602, 438]]<|/det|> +Ningbo Institute of Materials Technology and Engineering, CAS + +<|ref|>text<|/ref|><|det|>[[44, 443, 151, 461]]<|/det|> +Dianhui Pan + +<|ref|>text<|/ref|><|det|>[[44, 464, 602, 485]]<|/det|> +Ningbo Institute of Materials Technology and Engineering, CAS + +<|ref|>text<|/ref|><|det|>[[44, 490, 150, 508]]<|/det|> +Desheng Su + +<|ref|>text<|/ref|><|det|>[[44, 511, 602, 531]]<|/det|> +Ningbo Institute of Materials Technology and Engineering, CAS + +<|ref|>text<|/ref|><|det|>[[44, 536, 115, 554]]<|/det|> +Hao Liu + +<|ref|>text<|/ref|><|det|>[[44, 557, 602, 577]]<|/det|> +Ningbo Institute of Materials Technology and Engineering, CAS + +<|ref|>text<|/ref|><|det|>[[44, 582, 150, 601]]<|/det|> +Qiuju Zhang + +<|ref|>text<|/ref|><|det|>[[44, 603, 602, 624]]<|/det|> +Ningbo Institute of Materials Technology and Engineering, CAS + +<|ref|>text<|/ref|><|det|>[[44, 629, 135, 647]]<|/det|> +Sheng Dai + +<|ref|>text<|/ref|><|det|>[[44, 650, 839, 671]]<|/det|> +East China University of Science and Technology https://orcid.org/0000- 0001- 5787- 0179 + +<|ref|>text<|/ref|><|det|>[[44, 676, 121, 694]]<|/det|> +Ziqi Tian + +<|ref|>text<|/ref|><|det|>[[44, 696, 830, 739]]<|/det|> +Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences https://orcid.org/0000- 0001- 5667- 597X + +<|ref|>text<|/ref|><|det|>[[44, 743, 115, 762]]<|/det|> +Zhiyi Lu + +<|ref|>text<|/ref|><|det|>[[44, 764, 830, 807]]<|/det|> +Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences https://orcid.org/0000- 0002- 2117- 4101 + +<|ref|>sub_title<|/ref|><|det|>[[44, 848, 103, 866]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 886, 861, 929]]<|/det|> +Keywords: amine- assisted CO2 hydrogenation, supported metal catalyst, N- formylation, amide hydrogenation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[44, 46, 290, 65]]<|/det|> +Posted Date: May 2nd, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 84, 475, 103]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4185890/v1 + +<|ref|>text<|/ref|><|det|>[[42, 120, 916, 164]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 181, 535, 201]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 236, 944, 280]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on January 11th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 55837- 7. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[147, 131, 850, 272]]<|/det|> +Efficient amine-assisted hydrogenation of \(\mathrm{CO_2}\) into methanol collectively catalysed by ruthenium single sites and ensembles in a unified catalyst + +<|ref|>text<|/ref|><|det|>[[147, 303, 850, 361]]<|/det|> +Qihao Yang \(^{1,2,\ddagger}\) , Yinning Wang \(^{1,2,\ddagger}\) , Dianhui Pan \(^{1,2,\ddagger}\) , Desheng Su \(^{1,3,\ddagger}\) , Hao Liu \(^{1,2}\) , Qiuju Zhang \(^{1,2}\) , Sheng Dai \(^{4}\) , Ziqi Tian \(^{1,2,\ast}\) , Zhiyi Lu \(^{1,2,\ast}\) , Liang Chen \(^{1,2,\ast}\) + +<|ref|>text<|/ref|><|det|>[[147, 393, 850, 600]]<|/det|> +\(^{1}\) Key Laboratory of Advanced Fuel Cells and Electrolyzers Technology and Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, P. R. China \(^{2}\) University of Chinese Academy of Sciences, 100049 Beijing, P. R. China \(^{3}\) School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, P. R. China \(^{4}\) Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center, Institute of Fine Chemicals, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China + +<|ref|>text<|/ref|><|det|>[[147, 616, 850, 748]]<|/det|> +\(^{4}\) Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center, Institute of Fine Chemicals, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China + +<|ref|>text<|/ref|><|det|>[[147, 780, 849, 836]]<|/det|> +KEYWORDS: amine- assisted \(\mathrm{CO_2}\) hydrogenation, supported metal catalyst, N- formylation, amide hydrogenation. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 90, 852, 895]]<|/det|> +ABSTRACT: Amine-assisted sequential hydrogenation of carbon dioxide \(\mathrm{CO_2}\) , without the requirement of energy-intensive activation of inert \(\mathrm{CO_2}\) , is an efficient route to generate methanol (i.e., an essential secondary feedstock). However, this one-pot sequential hydrogenation process requires a multifunctional catalyst capable of efficiently catalysing both amine N- formylation and amide hydrogenation. Ruthenium- based catalysts possess substantial catalytic prowess in both steps of the sequential hydrogenation process, while the optimal Ru species required for the specific steps depends on distinct criteria. Herein, to optimize the two- step methanol production process, morpholine was used as a typical amine assistant, and \(\mathrm{Al_2O_3}\) - supported ruthenium catalysts (Ru- 1, Ru- 2, Ru- 3) with isolated Ru sites (Ru1) or/and Ru ensembles (Ru \(\mathrm{e}\) , including Ru clusters, \(\mathrm{Ru_c}\) , and Ru nanoparticles, \(\mathrm{Ru_p}\) ) configurations were rationally fabricated via the judicious annealing strategy. Compared to the energy- intensive \(\mathrm{CO_2}\) hydrogenation process, within the sequential catalytic system, morpholine, \(\mathrm{H_2}\) and \(\mathrm{CO_2}\) underwent an initial conversion to give N- formylmorpholine (NFM), which was subsequently hydrogenated, resulting in the low- temperature formation of methanol (120 °C). Additionally, the excellent regeneration (>99%) of morpholine guarantees a sustainable progression towards subsequent cycles. Experimental analysis and density functional theory (DFT) calculations suggest that the metallic \(\mathrm{Ru_e}\) are superior catalytic sites for the N- formylation step, whereas the isolated \(\mathrm{Ru_1}\) sites exhibit higher amide hydrogenation activity. Thus, the optimal Ru- 2 catalyst, which simultaneously features metallic \(\mathrm{Ru_c}\) and isolated \(\mathrm{Ru_1}\) sites, can efficiently synergize the conversion of \(\mathrm{CO_2}\) into methanol + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 852, 262]]<|/det|> +in a one- pot two- step process with exceptional selectivity (>95%), highlighting the crucial sensitivity of the process to the structure of active metal species. This work not only presents an advanced catalyst suitable for \(\mathrm{CO_2}\) - based methanol production but also elucidates the requirements for rational design of multiple optimized active sites within the single catalyst for multistep catalytic reactions in the future. + +<|ref|>sub_title<|/ref|><|det|>[[149, 301, 280, 318]]<|/det|> +## 1. Introduction + +<|ref|>text<|/ref|><|det|>[[147, 350, 852, 892]]<|/det|> +The anthropogenic emissions of greenhouse gases, primarily \(\mathrm{CO_2}\) , are widely believed to be responsible for a range of adverse environmental issues1,2. The facile catalytic reduction of carbon dioxide \(\mathrm{CO_2}\) into value- added secondary carbon feedstocks (i.e., hydrocarbons, alcohols, and carboxylic acids) exhibits excellent potential for mitigating the excessive accumulation of \(\mathrm{CO_2}^{3 - 6}\) . As one of the most attractive chemical products generated via \(\mathrm{CO_2}\) reduction, methanol \(\mathrm{(CH_3OH)}\) has a variety of attractive potential applications, including serving as a basic industrial feedstock, functioning as a liquid organic hydrogen carrier (LOHC), and being utilized in direct methanol fuel cells (DMFCs)7- 11. Therefore, the commercial value of catalytic production of \(\mathrm{CH_3OH}\) from \(\mathrm{CO_2}\) is substantial. In the past decades, remarkable researches have been conducted on the selective production of methanol via \(\mathrm{CO_2}\) hydrogenation12- 18, mainly focusing on metal oxides (e.g., \(\mathrm{In_2O_3}\) , \(\mathrm{ZnO - ZrO_2}\) , \(\mathrm{In_2O_3 - ZrO_2}\) )19- 21 and metal/metal oxides (e.g., \(\mathrm{Cu/ZnO/Al_2O_3}\) , \(\mathrm{Cu/In_2O_3}\) , \(\mathrm{Cu/ZrO_2}\) , \(\mathrm{Pd/ZnO}\) )22- 28. However, the traditional \(\mathrm{CO_2}\) hydrogenation based on metal oxides is encumbered by the necessity of high catalytic temperature (>300 °C)19- 21, resulting in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 852, 298]]<|/det|> +excessive energy consumption. Although the introduction of metal components into metal oxides promotes the activation of \(\mathrm{H}_2\) and thus achieves enhanced catalytic performance at relatively lower temperature ( \(< 250^{\circ}\mathrm{C})^{23 - 25}\) , this modification simultaneously leads to a trade- off with the decrease in \(\mathrm{CH}_3\mathrm{OH}\) selectivity, owing to the excessive hydrogenation of \(\mathrm{CO}_2\) and the exacerbation of the reverse water gas shift (RWGS) reaction29- 31. + +<|ref|>text<|/ref|><|det|>[[147, 312, 852, 894]]<|/det|> +Compared to the traditional \(\mathrm{CO}_2\) hydrogenation process, the amine- assisted two- step hydrogenation of \(\mathrm{CO}_2\) to \(\mathrm{CH}_3\mathrm{OH}\) , involving N- formylation of amine with \(\mathrm{CO}_2\) (i.e., first step) and subsequent amide hydrogenation (i.e., second step) in the presence of homogeneous catalysts32- 35, especially ruthenium complexes32- 34, exhibits superior catalytic activity and selectivity at mild condition ( \(< 180^{\circ}\mathrm{C}\) ), thus making it to be a promising and energy- efficient alternative for methanol production. However, the inherent difficulties in separation and recycling of homogeneous catalysts, coupled with the inferior stability, hinder their widespread application and the scaling up of this amine- assisted \(\mathrm{CO}_2\) - to- MeOH route. Furthermore, for the sequential catalytic reaction systems, the theoretical requirement for achieving optimal overall catalytic performance involves the engagement of at least dual types of active sites36- 38. Therefore, the existent homogeneous catalysts with a single designated activity structure may not provide the optimum catalytic activity for both steps of the sequential \(\mathrm{CO}_2\) hydrogenation process. In this context, the immobilization of homogeneous catalysts onto stable heterogeneous matrix (e.g., \(\mathrm{Al}_2\mathrm{O}_3\) , \(\mathrm{In}_2\mathrm{O}_3\) , \(\mathrm{ZrO}_2\) ) can be the most straightforward strategy, as it offers the benefit of easy separation. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 94, 852, 260]]<|/det|> +More importantly, the atomic- scale heterogeneity of supported metal catalysts is almost unavoidable, resulting in diverse structural morphologies or/and local coordination environments for the active components39,40, which offers the potential to optimize each step of the sequential \(\mathrm{CO_2}\) hydrogenation reaction and thus fine- tune the overall catalytic performance. + +<|ref|>text<|/ref|><|det|>[[147, 275, 852, 895]]<|/det|> +Based on the aforementioned considerations, we rationally synthesized a series of \(\mathrm{Al_2O_3}\) - based heterogeneous catalysts featuring isolated Ru sites (Ru1) or/and Ru ensembles (Rue, including Ru clusters, Ruc, and Ru nanoparticles, Rup) via a traditional impregnation method coupled with annealing treatment under different atmospheres. The catalysts (i.e., Ru- 1, Ru- 2, Ru- 3), with dominant morphological distributions of atomically dispersed Ru (Ru- 1), Ru clusters (Ru- 2) and Ru nanoparticles (Ru- 3), exhibited distinct activity and selectivity for the sequential \(\mathrm{CO_2}\) hydrogenation to \(\mathrm{CH_3OH}\) with morpholine as the amine assistant. Ru- 2, which simultaneously possesses atomically dispersed Ru1 sites and metallic Ruc species, presented optimal catalytic activity in the N- formylation of morpholine with \(\mathrm{CO_2}\) , as well as the subsequent hydrogenation of the generated amide intermediates (i.e., N- formylmorpholine, NFM) to give methanol with the regeneration of morpholine. Moreover, the superior catalytic performance (selectivity of \(\mathrm{CHO_3H} > 97\%\) , regeneration of morpholine \(>99\%\) ) of Ru- 2 towards one- pot two- step \(\mathrm{CO_2}\) hydrogenation was well maintained in three consecutive cycles. The mechanistic experiments and density functional theory (DFT) results reveal that the deoxygenative hydrogenation (C=O bond cleavage) and deaminative hydrogenation (C- N cleavage) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 852, 262]]<|/det|> +are the rate- determining steps of morpholine N- formylation and NFM hydrogenation, which can be accelerated over \(\mathrm{Ru_e}\) and \(\mathrm{Ru_1}\) sites, respectively. This work presents a heterogeneous catalysis protocol for amine- assisted hydrogenation of \(\mathrm{CO_2}\) towards methanol production, highlighting the significance of active species heterogeneity in enhancing the catalytic performance for multistep sequential reactions. + +<|ref|>sub_title<|/ref|><|det|>[[149, 294, 367, 312]]<|/det|> +## 2. Results and Discussion + +<|ref|>text<|/ref|><|det|>[[148, 344, 664, 363]]<|/det|> +2.1 Synthesis and characterizations of \(\mathrm{Al_2O_3}\) -based Ru catalysts + +<|ref|>text<|/ref|><|det|>[[146, 395, 852, 862]]<|/det|> +A series of \(\mathrm{Al_2O_3}\) - supported Ru catalysts (Ru- 1, Ru- 2, Ru- 3) were rationally synthesized via an impregnation- annealing method, with Ru- Macho and \(\alpha\) - \(\mathrm{Al_2O_3}\) being employed as metal precursor and support, respectively (see Section 2 in Supplementary Information (SI) for details). The powder X- ray diffraction (PXRD) patterns exhibited no diffraction peaks related to Ru or \(\mathrm{RuO_2}\) phase in the three catalysts (Supplementary Fig. 1), which might be ascribed to the low contents (Supplementary Table 1) or/and small sizes of the Ru species. Transmission electron microscopy (TEM) images show that the morphologies of the synthesized catalysts with loaded Ru species remained consistent with the original \(\mathrm{Al_2O_3}\) matrix (Supplementary Fig. 2). To identify the differences in atomic- scale structures among the three Ru catalysts, the aberration- corrected high- angle annular dark- field scanning transmission electron microscopy (HAADF- STEM) technique was adopted. The Ru species in Ru- 1 catalysts were atomically dispersed (Fig. 1a and Supplementary Fig. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 94, 850, 150]]<|/det|> +3), while Ru clusters emerged in Ru- 2 and these clusters grew into larger nanoparticles in Ru- 3 (Fig. 1b- c and Supplementary Figs. 4- 5). + +<|ref|>image<|/ref|><|det|>[[152, 181, 840, 424]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 441, 852, 572]]<|/det|> +
Fig. 1. Aberration-corrected HAADF-STEM images of (a) Ru-1, (b) Ru-2, and (c) Ru-3. (d) Ru K-edge XANES spectra of Ru foil, Ru-1, Ru-2 and \(\mathrm{RuO_2}\) . (e) Ru \(\mathrm{k}^{3}\) -weighted Fourier transform of the EXAFS spectra of Ru foil, Ru-1, Ru-2 and \(\mathrm{RuO_2}\) . (f) CO-probe DRIFTS results of CO-absorbed Ru-1, Ru-2 and Ru-3.
+ +<|ref|>text<|/ref|><|det|>[[147, 603, 852, 883]]<|/det|> +Additional spectroscopic characterizations were conducted to elucidate the electronic structure and coordination environment of the catalysts. The high- resolution X- ray photoelectron spectroscopy (XPS) spectra of Ru 3p in Ru- 1 revealed two prominent peaks at the binding energies of 485.0 eV (Ru 3p \(_{1 / 2}\) ) and 462.2 eV (Ru 3p \(_{3 / 2}\) ), similar to those of Ru oxides \(^{41}\) , indicating the partially oxidized valence state of Ru species in Ru- 1 (Supplementary Fig. 6). In contrast, the binding energies of Ru species in Ru- 3 at 483.7 eV (Ru 3p \(_{1 / 2}\) ) and 461.8 eV (Ru 3p \(_{3 / 2}\) ) showed a striking resemblance to the characteristic signals of metallic Ru \(^{42}\) , + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 852, 373]]<|/det|> +consistently supporting the presence of nanoparticles as observed via HAADF- STEM. Similarly, Ru- 2 also demonstrated an average valence state that closely aligns with metallic Ru, but slightly elevated compared to Ru- 2, implying the potential existence of a subset of Ru species in the partially oxidized valence state. In addition, the XPS spectra signals of the P species, originating from the metal precursor (i.e., Ru- Macho), are notably prominent in Ru- 1 and Ru- 2, but significantly diminished in Ru- 3 (Supplementary Fig. 7), suggesting that the P species may contribute to the formation of the Ru species with oxidized valence state. + +<|ref|>text<|/ref|><|det|>[[147, 388, 852, 892]]<|/det|> +To probe the local microstructure of Ru species with enhanced precision, X- ray absorption spectroscopy (XAS) was employed. As shown in Fig. 1d, X- ray adsorption near edge structure (XANES) analysis showed that the energy absorption thresholds of Ru- 1 and Ru- 2 were located between those of \(\mathrm{RuO_2}\) and Ru foil, but Ru- 1 aligned more closely with \(\mathrm{RuO_2}\) , illustrating an increase in the valence state from the Ru foil to Ru- 2, Ru- 1 and \(\mathrm{RuO_2}\) , which is consistent with the XPS observation results. The extended X- ray absorption fine structure (EXAFS) spectrum of Ru- 1 only exhibited one main peak at \(\sim 1.4 \AA\) , and no fingerprinting signal peak of Ru- Ru interactions \((\sim 2.3 \AA)\) cannot be observed, verifying the atomic dispersion of Ru in Ru- 1. The best fitting result of the obtained EXAFS data revealed that each Ru atom was coordinated by \(\sim 3\) O atoms and \(\sim 1\) P atom on average (Fig. 1e, Supplementary Fig. 8a and Supplementary Table 2). For the Ru- 2 catalyst, in addition to the prominent peak at \(\sim 1.4 \AA\) , a relatively weak peak at \(\sim 2.3 \AA\) that corresponds to Ru- Ru first coordination shell could be identified. The low Ru- Ru coordination number (C.N.) of \(3.0 \pm 1.0\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 94, 850, 186]]<|/det|> +determined for Ru- 2 suggested the presence of tiny Ru clusters, which agrees well with the HADDF- STEM results (Fig. 1e, Supplementary Fig. 8b and Supplementary Table 2). + +<|ref|>text<|/ref|><|det|>[[147, 202, 852, 821]]<|/det|> +To gain an in- depth understanding of the Ru species distribution in the catalysts, a CO- probe diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) experiment was conducted to acquire semiquantitative information about the proportions of the surface Ru species. The red characteristic peaks in the DRIFTS spectra (Fig. 1f, Supplementary Fig. 9), corresponding to two linearly adsorbed CO molecules on partially oxidized \(\mathrm{Ru_1}\) species, decreased in intensity with increasing size of \(\mathrm{Ru_e}\) from the Ru- 1 catalyst to the Ru- 3 catalyst, while the characteristic peaks for adsorbed CO on metallic \(\mathrm{Ru_e}\) species (blue) increased in intensity. The statistical percentages of \(\mathrm{Ru_1}\) and \(\mathrm{Ru_e}\) , determined by integrating the characteristic peaks, revealed that the \(\mathrm{Ru_1}\) contents in Ru- 1, Ru- 2 and Ru- 3 are 100%, 47.7%, and 37.2%, respectively (see Section 4 in the Supplementary Information and Supplementary Table 3). The metal dispersion (i.e., available Ru active sites) was further calculated on the basis of the CO adsorption determined from CO- pulse adsorption experiments (see Section 4 in the Supplementary Information and Supplementary Table 4). The metal dispersion of Ru- 1 (>99%), Ru- 2 (69.5%) and Ru- 3 (59.4%) also decreased with increasing proportion of Ru ensembles, which is consistent with the results of the CO- probe DRIFTS experiment. + +<|ref|>text<|/ref|><|det|>[[148, 848, 849, 904]]<|/det|> +2.2 Catalytic performance of \(\mathrm{Al_2O_3}\) -based Ru catalysts towards the morpholine- assisted sequential \(\mathrm{CO_2}\) hydrogenation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 94, 853, 410]]<|/det|> +The Ru catalysts, featuring various surface metal species, exhibited exceptional catalytic performance in each reaction step (Fig. 2a). For the N- formylation of morpholine, both Ru- 2 and Ru- 3 demonstrated superior catalytic performance and intrinsic activity, with turnover frequencies (TOFs) twice that of Ru- 1 (Fig. 2b- c). In addition, the catalytic generation of amide reached a state of equilibrium after \(36\mathrm{h}\) for Ru- 2 and Ru- 3, while twice the time was required for Ru- 1 (Fig. 2b). This observation highlights the crucial role of \(\mathrm{Ru_e}\) species in N- formylation. Furthermore, Ru- 2 and Ru- 3 exhibited superior selectivity ( \(>95\%\) ) for the amide compared to Ru- 1 ( \(\sim 83\%\) ), with slight formic acid detected (Supplementary Fig. 10). + +<|ref|>image<|/ref|><|det|>[[149, 437, 844, 703]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 720, 853, 887]]<|/det|> +
Fig. 2. (a) Schematic illustration of Ru-catalysed independent N-formylation and amide hydrogenation and a one-pot sequential \(\mathrm{CO_2}\) hydrogenation reaction. (b) Catalytic yield in N-formylation of morpholine over different catalysts. (c) Catalytic yield of NFM hydrogenation over different catalysts. (d) Intrinsic TOF of Ru-1, Ru-2 and Ru-3 toward morpholine N-formylation and NFM hydrogenation, respectively. (e)
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 852, 225]]<|/det|> +Yield- time profile of the one- pot two- step tandem process catalysed by Ru- 2. Reaction conditions: 393 K, 10 mmol substrate, 15 ml 1,4- dioxane as a solvent, 100 mg catalyst, 0.5 mmol \(\mathrm{CsCO_3}\) ; for the N- formylation process: \(\mathrm{P(CO_2):P(H_2) = 1:1}\) with a total pressure of 4 MPa; for amide hydrogenation, \(\mathrm{P(H_2) = 4MPa}\) . + +<|ref|>text<|/ref|><|det|>[[146, 255, 853, 646]]<|/det|> +During the hydrogenation of NFM into amine and methanol, both Ru- 1 and Ru- 2 exhibited superior activity, with TOF values (Ru- 1: \(\sim 320 \mathrm{h}^{- 1}\) , Ru- 2: \(\sim 389 \mathrm{h}^{- 1}\) ) 4- 5 times higher than that of the Ru- 3 catalyst ( \(\sim 87 \mathrm{h}^{- 1}\) ). Notably, Ru- 2 showed a high yield and \(>99\%\) methanol selectivity under identical reaction conditions. Based on the exceptional performance of Ru- 2, we decided to investigate its potential utilization in a one- pot two- step tandem process. Over 144 h, Ru- 2 exhibited superb activity (methanol turnover number, \(\mathrm{TON}_{\mathrm{methanol}} = 3300\) ) and stability in three consecutive reaction cycles (Fig. 2e). The morphology of the Ru clusters was well maintained, as observed in the aberration- corrected HAADF- STEM image (Supplementary Fig. 11). We concluded that the \(\mathrm{Ru_e}\) and \(\mathrm{Ru_1}\) sites played dominant roles in the N- formylation and amide hydrogenation reactions, respectively. + +<|ref|>text<|/ref|><|det|>[[147, 678, 850, 734]]<|/det|> +2.3 Catalytic mechanism of sequential \(\mathrm{CO_2}\) hydrogenation over \(\mathrm{Al_2O_3}\) -based Ru catalysts + +<|ref|>text<|/ref|><|det|>[[147, 766, 851, 896]]<|/det|> +To confirm the catalytic reaction pathways of sequential \(\mathrm{CO_2}\) hydrogenation, a series of mechanistic experiments were performed using the optimal Ru- 2 catalyst. The influence of excess \(\mathrm{CO_2}\) during the N- formylation of morpholine was investigated, in which zwitterionic carbamates were spontaneously produced by + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[146, 92, 852, 336]]<|/det|> +morpholine in an aprotic solvent (Fig. 3a). In route 2, the reaction was initiated after \(\mathrm{CO_2}\) saturation (without additional \(\mathrm{CO_2}\) input during the catalytic reaction), in contrast to the original route 1, while in route 3, the excess \(\mathrm{CO_2}\) was evacuated after \(\mathrm{CO_2}\) saturation and replaced with 2 MPa \(\mathrm{N}_2\) . The absence of excess \(\mathrm{CO_2}\) led to a higher formate selectivity with similar conversion levels (Fig. 3b), indicating the significant role of \(\mathrm{CO_2}\) in inducing the critical intermediate (i.e., zwitterionic carbamate) during the N- formylation process. + +<|ref|>image<|/ref|><|det|>[[257, 348, 732, 616]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[146, 631, 852, 761]]<|/det|> +
Fig. 3. (a) Schematic illustration of the influence of an excess \(\mathrm{CO_2}\) atmosphere during the N-formylation of morpholine. (b) Catalytic conversion and selectivity of Ru-2 in the routes shown in (a). (c) \(\mathrm{H}_2\) -TPR profiles of three Ru catalysts. (d) Catalytic yield in two reactions with hydrogen and deuterium and the corresponding KIE values.
+ +<|ref|>text<|/ref|><|det|>[[147, 792, 852, 887]]<|/det|> +The \(\mathrm{H}_2\) temperature- programmed reduction ( \(\mathrm{H}_2\) - TPR, Fig. 3c) profiles of the three catalysts revealed different \(\mathrm{H}_2\) affinities, where Ru- 2 and Ru- 3, possessing abundant \(\mathrm{Ru_e}\) sites, was prone to reduction at a lower temperature, thus manifesting the stronger + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 855, 485]]<|/det|> +\(\mathrm{H}_{2}\) activation ability compared with Ru- 1. To investigate the influence of \(\mathrm{H}_{2}\) activation in both steps of sequential \(\mathrm{CO}_{2}\) hydrogenation, hydrogen was replaced with deuterium (Fig. 3d). The yield was controlled at the intrinsic stage (conversion \(< 20\%\) ) to calculate the kinetic isotope effect (KIE) value. Similar to the TOF calculation with different catalysts, the reaction rate of N- formylation sharply decreased when hydrogen was replaced with deuterium, with a primary KIE value of \(2.45 (2 < \mathrm{KIE} < 7)\) , indicating that the rate determining step (RDS) in the N- formylation reaction is a step in which hydrogen is involved \(^{43,44}\) . However, this phenomenon was not observed in the amide reaction process, in which nearly identical reaction rates were obtained with hydrogen and deuterium (KIE=0.98). Thus, for the amide hydrogenation reaction, the RDS was speculated to be cleavage of the C- N bond. + +<|ref|>text<|/ref|><|det|>[[147, 515, 852, 757]]<|/det|> +To further confirm this speculation, the catalytic hydrogenation of diverse carbonyl substrates was evaluated next (Supplementary Fig. 12). Due to substrate limitations, cyclohexylformamide was utilized as an analogue of NFM. The Ru- 2 catalyst exhibited almost no activity towards amides and carboxylic acids but achieved close to \(100\%\) yield for cyclohexylmethanol from aldehydes, with the absence of cyclohexylmethanamine or imine by- products, thereby highlighting the priority of C- N bond cleavage over carbonyl reduction during amide hydrogenation. + +<|ref|>text<|/ref|><|det|>[[147, 789, 851, 882]]<|/det|> +Density functional theory (DFT) calculations were further conducted to gain insights into the reduction mechanism. Two Ru- containing models were constructed to consider the \(\mathrm{Ru}_{\mathrm{e}}\) and \(\mathrm{Ru}_{1}\) sites on the \(\mathrm{Al}_{2}\mathrm{O}_{3}\) substrate, labelled Ru- ensemble and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 852, 187]]<|/det|> +Ru- SAC in Supplementary Fig. 13, respectively. The surface of \(\mathrm{Al}_2\mathrm{O}_3\) was passivated with a hydroxyl group, and the single Ru atom was coordinated with a \((\mathrm{CH}_3)_3\mathrm{P}\) ligand to reproduce the chemical environment determined by experiments. + +<|ref|>image<|/ref|><|det|>[[214, 220, 780, 571]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 589, 850, 682]]<|/det|> +
Fig. 4. (a) Energy profile of the reduction process. (b) Structures of the key intermediates in the reaction pathways. The asterisk denotes the adsorption site. Colour code: Ru: green; Al: pink; N: blue; C: grey; O: red; H: white.
+ +<|ref|>text<|/ref|><|det|>[[147, 714, 852, 882]]<|/det|> +The free energy profile in Fig. 4a and the relevant structures of intermediates in Fig. 4b indicate that the \(\mathrm{Ru_e}\) provides multiple sites that not only activate the unsaturated carbon of the carboxylic group in carbamates by binding with oxygen but also adsorb the active hydrogen to reduce carbamates. The activation energy barrier of the first reduction step was calculated to be 1.01 eV for the \(\mathrm{C} = \mathrm{O}\) cleavage in the zwitterionic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 92, 853, 486]]<|/det|> +carbamates (route 2 in Fig. 3a) under \(\mathrm{H}_2\) . Further protonation leads to elimination of the hydroxyl group, thus forming a formamide intermediate, which requires overcoming an energy barrier of \(1.18\mathrm{eV}\) . Despite the relatively low energy barriers of \(\mathrm{Ru_e}\) in the first few steps, the reduction step to form a hemiaminal (an intermediate of formamide hydrogenation) over \(\mathrm{Ru_e}\) requires overcoming a high energy barrier of \(1.45\mathrm{eV}\) , resulting in a sluggish rate to obtain the final product. However, the formamide hydrogenation may spill over onto the single Ru atom site. The migration of two hydrogen atoms from the Ru site to the intermediate would require overcoming two lower activation barriers of \(1.24\mathrm{eV}\) and \(0.95\mathrm{eV}\) . Thus, the presence of \(\mathrm{Ru_1}\) sites can accelerate the deep reduction of formamide to a hemiaminal. Finally, the hemiaminal desorbs and easily decomposes back into morpholine and formaldehyde. + +<|ref|>text<|/ref|><|det|>[[147, 515, 852, 907]]<|/det|> +Based on the results of our experiments and DFT simulations, we proposed a potential catalytic reaction pathway for Ru- 2 involving several steps (Fig. 5). Initially, morpholine absorbs \(\mathrm{CO_2}\) to yield zwitterionic carbamates. Active hydrogen species generated via metallic \(\mathrm{Ru_e}\) then reduce these carbamates to form intermediate A (Fig. 5). Proton transfer from carbamates to intermediate A leads to the formation of intermediate B, which subsequently undergoes natural intramolecular dehydration to produce amide and \(\mathrm{H}_2\mathrm{O}\) . In addition, the electronegative oxygen in intermediate A can also coordinate to the atomically dispersed \(\mathrm{Ru_1}\) site to form intermediate C, which undergoes hydrogenation to form formate and morpholine. This process is probably the primary source of formic acid by- product formation. In the amide hydrogenation step, the amide is coordinated to the electropositive \(\mathrm{Ru_1}\) site and hydrogenated to form + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 852, 262]]<|/det|> +intermediate E, which undergoes C- N cleavage with the aid of the \(\mathrm{Ru}_1\) site to generate the adsorbed aldehyde (intermediate F) and regenerate the morpholine. The intermediate F is prone to hydrogenation (Supplementary Fig. 14), thus producing methanol. In contrast, the methylamine by- products via imine (intermediate G) pathway were not detected during amide hydrogenation (Supplementary Fig. 14). + +<|ref|>image<|/ref|><|det|>[[230, 290, 760, 537]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 553, 850, 608]]<|/det|> +
Fig. 5. Proposed catalytic reaction pathways for Ru-2 in the one-pot two-step catalytic process.
+ +<|ref|>sub_title<|/ref|><|det|>[[148, 643, 267, 660]]<|/det|> +## 3. Conclusion + +<|ref|>text<|/ref|><|det|>[[147, 692, 852, 896]]<|/det|> +In summary, we prepared a series of active heterogeneous Ru catalysts with multiple surface metal species, including atomically dispersed Ru species and Ru ensembles. Among these catalysts, Ru- 2, which contained both \(\mathrm{Ru}_1\) species and \(\mathrm{Ru}_\mathrm{e}\) sites, demonstrated excellent performance in the N- formylation and amide hydrogenation reactions, enabling efficient one- pot two- step methanol production under relatively mild conditions. The critical roles of the active metal species in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 853, 336]]<|/det|> +reaction process were revealed by combining experiments and theoretical calculations, and a possible reaction pathway was proposed. This study provides a potential candidate catalyst for selective reduction of \(\mathrm{CO_2}\) to methanol and reveals the synergistic effect of different metal species in complex multistep reactions at the atomic scale. The strategy of rationally designing multiple optimized active sites within a single catalyst paves the way for enhancing the catalytic performance in various multistep sequential reactions in the future. + +<|ref|>sub_title<|/ref|><|det|>[[149, 367, 386, 386]]<|/det|> +## ASSOCIATED CONTENT + +<|ref|>text<|/ref|><|det|>[[148, 418, 850, 475]]<|/det|> +Supplementary Information. The Supplementary Information is available free of charge via the Internet at http://pubs.acs.org. + +<|ref|>text<|/ref|><|det|>[[147, 506, 851, 600]]<|/det|> +Chemicals and characterization; Preparation of \(\mathrm{Al_2O_3}\) - based Ru catalysts; Catalyst evaluation; Characterization details; Supplementary Figs. 1- 14; Supplementary Tables 1- 4 + +<|ref|>sub_title<|/ref|><|det|>[[149, 632, 395, 650]]<|/det|> +## AUTHOR INFORMATION + +<|ref|>sub_title<|/ref|><|det|>[[149, 678, 350, 695]]<|/det|> +## Corresponding Author + +<|ref|>text<|/ref|><|det|>[[147, 716, 853, 810]]<|/det|> +Liang Chen – Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P. R. China; University of Chinese Academy of Sciences, Beijing 100049, P. R. China; E- mail: chenliang@nimte.ac.cn + +<|ref|>text<|/ref|><|det|>[[147, 842, 850, 898]]<|/det|> +Zhiyi Lu – Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P. R. China; University of Chinese Academy of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 94, 704, 112]]<|/det|> +Sciences, Beijing 100049, P. R. China; E- mail: luzhiyi@nimte.ac.cn + +<|ref|>text<|/ref|><|det|>[[147, 145, 852, 240]]<|/det|> +Ziqi Tian – Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P. R. China; University of Chinese Academy of Sciences, Beijing 100049, P. R. China; E- mail: tianziqi@nimte.ac.cn + +<|ref|>sub_title<|/ref|><|det|>[[149, 265, 340, 282]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[149, 305, 439, 323]]<|/det|> +These authors contributed equally. + +<|ref|>sub_title<|/ref|><|det|>[[148, 357, 200, 373]]<|/det|> +## Notes + +<|ref|>text<|/ref|><|det|>[[148, 397, 570, 415]]<|/det|> +The authors declare no competing financial interest. + +<|ref|>sub_title<|/ref|><|det|>[[149, 448, 364, 465]]<|/det|> +## ACKNOWLEDGMENT + +<|ref|>text<|/ref|><|det|>[[147, 483, 852, 911]]<|/det|> +This work is supported by the National Natural Science Foundation of China (22101288), the Natural Science Foundation of Zhejiang Province (LQ22B010005 and LD21E020001), the Bellwethers Project of Zhejiang Research and Development Plan (2022C01158), the Ningbo Yongjiang Talent Introduction Programme (2021A- 036- B), the Science and Technology Innovation 2025 Program in Ningbo (2022Z205), Youth Innovation Promotion Association, CAS, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, Feringa Nobel Prize Scientist Joint Research Center, Transformational Technologies for Clean Energy and Demonstration, Strategic Priority Research Program of the Chinese Academy of Sciences (XDA21000000), DNL Cooperation Fund, CAS (Grant No. DNL202008), and “Transformational Technologies for Clean Energy and Demonstration”. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 95, 245, 111]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[145, 130, 852, 196]]<|/det|> +1. 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Adv. 3, e1701290 (2017). + +<|ref|>text<|/ref|><|det|>[[147, 354, 850, 409]]<|/det|> +21. Frei, M. S. et al. Mechanism and microkinetics of methanol synthesis via \(\mathrm{CO}_{2}\) hydrogenation on indium oxide. J. Catal. 361, 313-321 (2018). + +<|ref|>text<|/ref|><|det|>[[147, 427, 850, 483]]<|/det|> +22. Beck, A. et al. Following the structure of copper-zinc-alumina across the pressure gap in carbon dioxide hydrogenation. Nat. Catal. 4, 488-497 (2021). + +<|ref|>text<|/ref|><|det|>[[147, 501, 849, 556]]<|/det|> +23. Bahruji, H. et al. Pd/ZnO catalysts for direct \(\mathrm{CO}_{2}\) hydrogenation to methanol. J. Catal. 343, 133-146 (2016). + +<|ref|>text<|/ref|><|det|>[[147, 575, 850, 630]]<|/det|> +24. Behrens, M. et al. The active site of methanol synthesis over \(\mathrm{Cu/ZnO/Al}_{2}\mathrm{O}_{3}\) industrial catalysts. Science 336, 893-897 (2012). + +<|ref|>text<|/ref|><|det|>[[147, 649, 850, 704]]<|/det|> +25. Wu, C. et al. Inverse \(\mathrm{ZrO}_{2} / \mathrm{Cu}\) as a highly efficient methanol synthesis catalyst from \(\mathrm{CO}_{2}\) hydrogenation. Nat. Commun. 11, 5767 (2020). + +<|ref|>text<|/ref|><|det|>[[147, 723, 850, 778]]<|/det|> +26. Shi, Z. et al. \(\mathrm{CO}_{2}\) hydrogenation to methanol over Cu-In intermetallic catalysts: effect of reduction temperature. J. Catal. 379, 78-89 (2019). + +<|ref|>text<|/ref|><|det|>[[147, 797, 850, 852]]<|/det|> +27. Li, K. & Chen, J. G. \(\mathrm{CO}_{2}\) hydrogenation to methanol over \(\mathrm{ZrO}_{2}\) -containing catalysts: insights into \(\mathrm{ZrO}_{2}\) induced synergy. ACS Catal. 9, 7840-7861 (2019). + +<|ref|>text<|/ref|><|det|>[[147, 871, 850, 889]]<|/det|> +28. Samson, K. et al. Influence of \(\mathrm{ZrO}_{2}\) structure and copper electronic state on + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[181, 93, 849, 149]]<|/det|> +activity of \(\mathrm{Cu / ZrO_2}\) catalysts in methanol synthesis from \(\mathrm{CO_2}\) . ACS Catal. 4, 3730- 3741 (2014). + +<|ref|>text<|/ref|><|det|>[[147, 167, 850, 223]]<|/det|> +29. Kattel, S., Liu, P. & Chen, J. G. Tuning selectivity of \(\mathrm{CO_2}\) hydrogenation reactions at the metal/oxide interface. J. Am. Chem. Soc. 139, 9739-9754 (2017). + +<|ref|>text<|/ref|><|det|>[[147, 242, 850, 297]]<|/det|> +30. Studt, F. et al. Discovery of a Ni-Ga catalyst for carbon dioxide reduction to methanol. Nat. Chem. 6, 320-324 (2014). + +<|ref|>text<|/ref|><|det|>[[147, 315, 850, 409]]<|/det|> +31. Yin, Y. Z. et al. Pd@zeolitic imidazolate framework-8 derived PdZn alloy catalysts for efficient hydrogenation of \(\mathrm{CO_2}\) to methanol. Appl. Catal. B: Environ. 234, 143-152 (2018). + +<|ref|>text<|/ref|><|det|>[[147, 427, 851, 558]]<|/det|> +32. Bai, S.-T., Zhou, C., Wu, X., Sun, R. & Sels, B. Suppressing dormant Ru states in the presence of conventional metal oxides promotes the Ru-MACHO-BH-catalyzed integration of \(\mathrm{CO_2}\) capture and hydrogenation to methanol. ACS Catal. 11, 12682-12691 (2021). + +<|ref|>text<|/ref|><|det|>[[147, 575, 850, 668]]<|/det|> +33. Kar, S. et al. Mechanistic insights into ruthenium-pincer-catalyzed amine-assisted homogeneous hydrogenation of \(\mathrm{CO_2}\) to Methanol. J. Am. Chem. Soc. 141, 3160-3170 (2019). + +<|ref|>text<|/ref|><|det|>[[147, 686, 850, 780]]<|/det|> +34. Zhang, L., Han, Z., Zhao, X., Wang, Z. & Ding, K. Highly efficient ruthenium-catalyzed N-formylation of amines with \(\mathrm{H_2}\) and \(\mathrm{CO_2}\) . Angew. Chem. Int. Ed. 54, 6186-6189 (2015). + +<|ref|>text<|/ref|><|det|>[[147, 798, 850, 890]]<|/det|> +35. Jayarathne, U., Hazari, N. & Bernskoetter, W. H., Selective iron-catalyzed N-formylation of amines using dihydrogen and carbon dioxide. ACS Catal. 8, 1338-1345 (2018). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 93, 850, 186]]<|/det|> +36. Zecevic, J., Vanbutsele, G., De Jong, K. P. & Martens, J. A. Nanoscale intimacy in bifunctional catalysts for selective conversion of hydrocarbons. Nature 528, 245-248 (2015). + +<|ref|>text<|/ref|><|det|>[[147, 205, 849, 260]]<|/det|> +37. Jiao, F. et al. Selective conversion of syngas to light olefins. Science 351, 1065-1068 (2016). + +<|ref|>text<|/ref|><|det|>[[147, 279, 850, 373]]<|/det|> +38. Cheng, K. et al. Direct and highly selective conversion of synthesis gas into lower olefins: design of a bifunctional catalyst combining methanol synthesis and carbon-carbon coupling. Angew. Chem. Int. Ed. 55, 4725-4728 (2016). + +<|ref|>text<|/ref|><|det|>[[147, 391, 848, 446]]<|/det|> +39. Zhang, J. et al. Importance of species heterogeneity in supported metal catalysts. J. Am. Chem. Soc. 144, 5108-5115 (2022). + +<|ref|>text<|/ref|><|det|>[[147, 465, 850, 520]]<|/det|> +40. Liu, L. & Corma, A. Identification of the active sites in supported subnanometric metal catalysts. Nat. Catal. 4, 453-456 (2021). + +<|ref|>text<|/ref|><|det|>[[147, 539, 850, 630]]<|/det|> +41. Qadir, K. Intrinsic relation between catalytic activity of CO oxidation on Ru nanoparticles and Ru oxides uncovered with ambient pressure XPS. Nano Lett. 12, 5761-5768 (2012). + +<|ref|>text<|/ref|><|det|>[[147, 650, 850, 742]]<|/det|> +42. Meng, Z. Electron-rich ruthenium on nitrogen-doped carbons promoting levulinic acid hydrogenation to \(\gamma\) -valerolactone: effect of metal-support interaction. ACS Sustainable Chem. Eng. 7, 16501-16510 (2019). + +<|ref|>text<|/ref|><|det|>[[147, 761, 850, 854]]<|/det|> +43. Li, Z. Covalent triazine framework supported non-noble metal nanoparticles with superior activity for catalytic hydrolysis of ammonia borane: from mechanistic study to catalyst design. Chem. Sci. 8, 781-788 (2017). + +<|ref|>text<|/ref|><|det|>[[147, 873, 850, 891]]<|/det|> +44. Li, L. Accelerating chemo- and regioselective hydrogenation of alkynesover + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[181, 94, 850, 150]]<|/det|> +bimetallic nanoparticles in a metal- organic framework. ACS Catal. 10, 7753- 7762(2020). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[420, 94, 576, 111]]<|/det|> +## Table of Contents + +<|ref|>text<|/ref|><|det|>[[147, 144, 852, 312]]<|/det|> +A heterogeneous supported catalyst featuring atomically dispersed Ru sites and Ru- cluster sites exhibited superior catalytic performance for amine- assisted sequential hydrogenation of CO2 into hydrogen via the synergistic effect of the two types of surface- active Ru species. The rate- determining steps of the two reactions were elucidated and correlated with the intrinsic active species. + +<|ref|>image<|/ref|><|det|>[[330, 336, 666, 444]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 353, 150]]<|/det|> +SupplementaryInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/images_list.json b/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..28b7ec2d21c1b48fc8cb2c3065486905346580bf --- /dev/null +++ b/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "FIG. 1. a) Non-primitive hexagonal unit cell of the \\(\\mathrm{V_2O_3}\\) high-temperature rhombohedral metallic phase. b) Schematic of the rhombohedral-to-monoclinic distortion along each of the three equivalent hexagonal axes. c) PEEM experimental setup. X-ray radiation, with tunable energy resonant with the vanadium \\(\\mathrm{L_{2,3}}\\) edge, impinges on the sample surface and the emitted electrons are collected and imaged through electrostatic and magnetic lenses. The \\(\\mathrm{V_2O_3}\\) film is coated with gold metal electrodes, allowing to drive a current through the device (see sketch of a typical resistive switching current-voltage curve in the bottom panel) while simultaneously acquiring XLD-PEEM images.", + "footnote": [], + "bbox": [ + [ + 102, + 102, + 888, + 430 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "FIG. 2. XLD-PEEM images before (a) and during (b-j) the application of an electric current at \\(T = 120 \\mathrm{K}\\) . The homogeneous regions at the top and bottom of each image are the gold electrodes. The area in between is the exposed \\(\\mathrm{V}_2\\mathrm{O}_3\\) antiferromagnetic monoclinic phase, exhibiting a striped domain nanotexture. For currents larger than \\(1.5 \\mathrm{mA}\\) , the striped domains disappear in the region delimited by the white dashed lines, demonstrating the appearance of a rhombohedral metallic filament, which widens as the current is increased.", + "footnote": [], + "bbox": [ + [ + 520, + 105, + 900, + 627 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "FIG. 3. a) Detail of the XLD-PEEM image shown in Fig. 2 in the region where the metallic filament is formed upon the application of a current above the threshold \\(I_{th}\\) . b) Schematic of the monoclinic domains crossing at \\(60^{\\circ}\\) and forming a topological defect. Blue, red and yellow areas identify the three possible monoclinic domains corresponding to the three equivalent order parameter directions \\(\\hat{\\epsilon}_{n}\\) . The order parameter at the boundaries between different domains is oriented along \\(\\hat{\\epsilon}_{1} + \\hat{\\epsilon}_{2}\\) ( \\(2\\pi /3\\) ) for the red-blue interface and along \\(\\hat{\\epsilon}_{2} + \\hat{\\epsilon}_{3}\\) ( \\(4\\pi /3\\) ) for the blue-yellow interface. The mixed red-yellow triangular region indicates the local suppression of the strain at the topological defect. The energy functionals shown on the left and right, illustrate how a topological defect (green plot, solid line) decreases the insulator-metal energy difference, \\(\\Delta\\) .", + "footnote": [], + "bbox": [ + [ + 113, + 102, + 460, + 325 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "FIG. 4. a) I-V curve measured during the XLD-PEEM imaging. The sudden drop in the voltage measured at \\(I_{th} = 1.05 \\mathrm{mA}\\) indicates the first resistive switch. b) Line profiles of the XLD-PEEM images in Fig. 2. The grey shaded area indicates the progressive widening of the metallic rhombohedral filament. The direction of the line profiles is shown by the white dashed line in the XLD-PEEM image on top, where we report a detail of Fig. 2j. c) Width, \\(d\\) , of the metallic filament as a function of current. The blue/red markers represent the values of \\(d\\) obtained from the XLD-PEEM images below/above \\(I_{th}\\) . The green solid line shows an estimate of \\(d\\) , derived from a parallel resistors model predicting a sudden jump of \\(d\\) to \\(200 \\mathrm{mA}\\) in \\(I_{th}\\) (see Supplementary Information Section S5).", + "footnote": [], + "bbox": [ + [ + 220, + 100, + 797, + 573 + ] + ], + "page_idx": 6 + } +] \ No newline at end of file diff --git a/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb.mmd b/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb.mmd new file mode 100644 index 0000000000000000000000000000000000000000..c5c4a1cd301d0e1f90e113631e2e52e8f059d318 --- /dev/null +++ b/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb.mmd @@ -0,0 +1,181 @@ + +# Mott resistive switching initiated by topological defects + +Claudio Giannetti + +claudio.giannetti@unicatt.it + +Università Cattolica del Sacro Cuore https://orcid.org/0000- 0003- 2664- 9492 + +Alessandra Milloch Università Cattolica del Sacro Cuore + +Ignacio Figueruelo- Campanero IMDEA Nanociencia + +Wei- Fan Hsu KU Leuven + +Selene Mor Università Cattolica del Sacro Cuore + +Simon Mellaerts KU Leuven https://orcid.org/0000- 0002- 6715- 3066 + +Francesco Maccherozzi + +Diamond Light Source, Chilton, Didcot, Oxfordshire, OX11 0DE, UK. https://orcid.org/0000- 0003- 4074- 2319 + +Larissa Ishibe Veiga Diamond Light Source + +Sarnjeet Dhesi Diamond Light Source https://orcid.org/0000- 0003- 4966- 0002 + +Mauro Spera Università Cattolica del Sacro Cuore https://orcid.org/0000- 0001- 9041- 364X + +Jin Seo KU Leuven https://orcid.org/0000- 0003- 4937- 0769 + +Jean- Pierre Locquet https://orcid.org/0000- 0002- 4214- 7081 + +Michele Fabrizio International School for Advanced Studies https://orcid.org/0000- 0002- 2943- 3278 + +Mariela Menghini IMDEA Nanoscience https://orcid.org/0000- 0002- 1744- 798X + +<--- Page Split ---> + +## Article + +## Keywords: + +Posted Date: June 6th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4019377/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on October 31st, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 53726- z. + +<--- Page Split ---> + +# Mott resistive switching initiated by topological defects + +Alessandra Milloch, \(^{1,2,3,*}\) Ignacio Figueroelo- Campanero, \(^{4,5,\dagger}\) Wei- Fan Hsu, \(^{3}\) Selene Mor, \(^{1,2}\) Simon Mellaerts, \(^{3}\) Francesco Maccherozzi, \(^{6}\) Larissa Ishibe Veiga, \(^{6}\) Sarnjeet S. Dhesi, \(^{6}\) Mauro Spera, \(^{1}\) Jin Won Seo, \(^{7}\) Jean- Pierre Locquet, \(^{3}\) Michele Fabrizio, \(^{8}\) Mariela Menghini, \(^{4}\) and Claudio Giannetti \(^{1,2,9,\ddagger}\) + +\(^{1}\) Department of Mathematics and Physics, Università Cattolica del Sacro Cuore, Brescia I- 25133, Italy \(^{2}\) ILAMP (Interdisciplinary Laboratories for Advanced Materials Physics), Università Cattolica del Sacro Cuore, Brescia I- 25133, Italy \(^{3}\) Department of Physics and Astronomy, KU Leuven, B- 3001 Leuven, Belgium \(^{4}\) IMDEA Nanociencia, Cantoblanco, 28049 Madrid, Spain \(^{5}\) Facultad Ciencias Físicas, Universidad Complutense, 28040 Madrid, Spain \(^{6}\) Diamond Light Source, Didcot, Oxfordshire OX11 0DE, UK \(^{7}\) Department of Materials Engineering, KU Leuven, 3001 Leuven, Belgium \(^{8}\) Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy \(^{9}\) CNR- INO (National Institute of Optics), via Branze 45, 25123 Brescia, Italy + +Avalanche resistive switching is the fundamental process that triggers the sudden change of the electrical properties in solid- state devices under the action of intense electric fields [1]. Despite its relevance for information processing, ultrafast electronics, neuromorphic devices, resistive memories and brain- inspired computation [1- 14], the nature of the local stochastic fluctuations that drive the formation of metallic regions within the insulating state has remained hidden. + +Here, using operando X- ray nano- imaging, we have captured the origin of resistive switching in a \(\mathrm{V}_2\mathrm{O}_3\) - based device under working conditions. \(\mathrm{V}_2\mathrm{O}_3\) is a paradigmatic Mott material [3], which undergoes a first- order metal- to- insulator phase transition together with a lattice transformation that breaks the threefold rotational symmetry of the rhombohedral metallic phase [2, 5, 6, 8- 11, 15]. We reveal a new class of volatile electronic switching triggered by nanoscale topological defects appearing in the shear- strain based order parameter that describes the insulating phase. Our results pave the way to the use of strain engineering approaches to manipulate such topological defects and achieve the full dynamical control of the electronic Mott switching. Topology- driven, reversible electronic transitions are relevant across a broad range of quantum materials, comprising transition metal oxides, chalcogenides and kagome metals. + +The insulator- to- metal transition (IMT) in Mott materials is a key mechanism for the development of next generation Mottronic devices [3, 13]. The intrinsic correlated nature of the Mott insulating state makes these systems fragile to external stimuli [16, 17], such as the application of an electric field, which can drive the collapse of the electronic band structure and the sudden release of a large number of free carriers [18, 19]. At the macroscopic level, this phenomenon manifests in the resistive switching process, i.e., a sharp increase of the current flow when the applied voltage exceeds a threshold value [6, 20- 27]. This strong non- linearity triggered many efforts to develop neuromorphic building blocks for the hardware implementation of neural networks [14] or for ultrafast volatile and non- volatile memories or processors [12, 28, 29]. The state- of- the- art macroscopic models [30] are based on resistor networks that consider interconnected nodes transforming from the insulating to metallic state in the presence of an electric field. Above a certain threshold, a percolative, avalanche transition takes place, + +thus leading to the formation of conductive filaments and the consequent sudden drop in resistivity [22, 31]. + +The full control and exploitation of this process is currently prevented by a limited knowledge of the early- stage firing dynamics. Microscopically, little is known about the nature of the nanoscale regions that trigger the avalanche process. Also the relation between the electronic and structural properties of the switched regions and those of the pristine insulating template is a matter of debate. Pioneering optical microscopy experiments captured the real- time formation of macroscopic metallic channels [4, 32- 35], but lacked the resolution and sensitivity to address the microscopic origin of the switching process. + +Here, we adopt resonant X- ray microscopy to record nanoscale snapshots of the switching dynamics in a \(\mathrm{V}_2\mathrm{O}_3\) - based nanodevice during the application of an electric field. The results unveil the fundamental role played by the order parameter topology of the underlying lattice nanotexture. The breaking of the \(C_3\) symmetry upon transition to the insulating monoclinic phase leads to the formation of three twin shear- strain domains with boundaries oriented along the three hexagonal directions [36, 37]. The geometrical constraints then produce shear- strain topological defects at the corners of monoclinic domains crossing with an angle of \(60^{\circ}\) . These nanoscale + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
FIG. 1. a) Non-primitive hexagonal unit cell of the \(\mathrm{V_2O_3}\) high-temperature rhombohedral metallic phase. b) Schematic of the rhombohedral-to-monoclinic distortion along each of the three equivalent hexagonal axes. c) PEEM experimental setup. X-ray radiation, with tunable energy resonant with the vanadium \(\mathrm{L_{2,3}}\) edge, impinges on the sample surface and the emitted electrons are collected and imaged through electrostatic and magnetic lenses. The \(\mathrm{V_2O_3}\) film is coated with gold metal electrodes, allowing to drive a current through the device (see sketch of a typical resistive switching current-voltage curve in the bottom panel) while simultaneously acquiring XLD-PEEM images.
+ +topological defects act as seeds for the formation of the metallic phase, thus triggering the macroscopic volatile resistive switching. + +\(\mathrm{V_2O_3}\) is a prototypical Mott insulator that undergoes a thermally- driven transition from a high- temperature paramagnetic, rhombohedral metal to a low- temperature antiferromagnetic monoclinic insulator [38- 40]. The lattice transformation at the critical temperature \(T_{IMT}\) implies the breaking of the \(C_3\) symmetry of the non- primitive hexagonal unit cell of the high- temperature metallic phase (see Fig. 1a). The structural transition can thus be described [37] by a vector order parameter: + +\[\vec{\epsilon} = (\epsilon_{31},\epsilon_{23}) = \epsilon \left(\cos \phi_n,\sin \phi_n\right) \quad (1)\] + +associated to the shear strain components \(\epsilon_{31}\) and \(\epsilon_{23}\) that characterize the monoclinic distortion. Below \(T_{IMT}\) , the amplitude of the order parameter, \(\epsilon\) , becomes non- zero, while the phase can assume three different values: + +\[\phi_{n} = (2n - 1)\frac{\pi}{3} \quad (2)\] + +corresponding to the distortion along the three equivalent hexagonal axes of the rhombohedral phase, indicated in the following by the versors \(\hat{\epsilon}_n\) , \(n = 1,2,3\) (see Fig. 1b)). + +Resistive switching can be induced by applying an electric field across a patterned micro- gap at temperatures close to \(T_{IMT}\) [4, 33, 41]. The resistive switching device investigated here is formed by a \(20 \mathrm{nm} \mathrm{V}_2\mathrm{O}_3\) film coated with gold electrodes. \(\mathrm{V}_2\mathrm{O}_3\) is grown by oxygen- assisted Molecular Beam Epitaxy on a \((0001)\) - \(\mathrm{Al}_2\mathrm{O}_3\) substrate with a \(40 \mathrm{nm} \mathrm{Cr}_2\mathrm{O}_3\) buffer layer to reduce any interfacial residual strain [42]. The resulting \(\mathrm{V}_2\mathrm{O}_3\) film has the \(c\) axis oriented parallel to the surface normal, with \(T_{IMT} = 145 \mathrm{K}\) (see Supplementary Information Fig. S1). Two gold electrodes allow the application of an electric bias across the gap of width \(w = 2 \mu \mathrm{m}\) and length \(l = 30 \mu \mathrm{m}\) (figure 1c)). The gap region between the electrodes is imaged using PhotoEmission Electron Microscopy (PEEM), combined with X- ray Linear Dichroism (XLD) at the \(\mathrm{L_{2,3}}\) vanadium edge (513- 530 eV, see Figure S2) [36, 37, 43]. The XLD- PEEM images are obtained from the normalized difference between images recorded with the light electric field vector, \(\vec{E}\) , perpendicular and \(16^{\circ}\) to the surface normal at a photon energy \(520.6 \mathrm{eV}\) . Since the XLD signal depends on the angle between the in- plane component of \(\vec{E}\) and the position dependent order parameter, \(\vec{\epsilon} (\mathbf{r})\) [37] (see Fig. 1b) and c)), this technique provides a map - with \(\sim 30 \mathrm{nm}\) spatial resolution - of the + +<--- Page Split ---> + +three different monoclinic domains during the resistive switching process. + +Figure 2a) shows an XLD- PEEM image obtained in the monoclinic insulating phase at \(T = 120 \mathrm{K}\) . The \(\mathrm{V}_2\mathrm{O}_3\) nanotexture exhibits features typical of the monoclinic insulating phase [36, 37]. Monoclinic domains with different \(\phi_n\) give rise to different XLD contrast, which can be identified as different color intensities within the XLD- PEEM image. The minimization of the total strain leads to the formation of stripe- like domains, with symmetry- constrained directions [37]. Each monoclinic insulating domain extends over a few micrometers, thus connecting the two electrodes, and it is characterized by a width \(w_{dom} \sim 200 \mathrm{nm}\) [37]. + +The XLD- PEEM imaging is then repeated while driving a current, \(I\) , through the device and measuring the voltage drop, \(V\) , across the gold contacts. Figures 2b)- j) show the XLD- PEEM images acquired at increasing values of \(I\) , following the upward branch of the hysteresis cycle. The presence of an in- plane electric field across the electrodes introduces a weak image blurring that becomes significant for \(V \geq 6 - 8 \mathrm{V}\) . Despite this, the nanodomains are well resolved during the resistive switching process, which first manifests itself at the voltage drop observed between \(0.08 \mathrm{mA}\) and \(1.1 \mathrm{mA}\) (Fig. 2 c) and d) respectively). As the current is further increased, the melting of the monoclinic nanotexture in the region delimited by white dashed lines (Fig. 2 e)- j)) progresses with a widening channel with a homogenous intensity. The XLD contrast measured in the region between the white dashed lines corresponds to the signal of the high- temperature rhombohedral phase. This is also confirmed from the angle dependence of the XLD signal [37]. As shown in the Supplementary Information Fig. S3, images collected with two different X- rays polarization angles, with respect to the in- plane \(\mathrm{V}_2\mathrm{O}_3\) axes, show no intensity variation upon sample rotation in the metallic channel, as opposed to the lateral monoclinic domains, for which the XLD signal depends on the angle between the light polarization and \(\vec{\epsilon} (\mathbf{r})\) . The constant XLD contrast region in the middle of the gap therefore appears due to the formation of a metallic channel with rhombohedral lattice structure ( \(\epsilon = 0\) ). XLD- PEEM images obtained under the same conditions, but with a larger field of view, capture the whole gap of the device (see Supplementary Information Fig. S4). The metallic channel consistently forms in the same location within the gap with no additional metallic paths observed. Furthermore, when the applied current is removed, the metallic channel disappears and the monoclinic domains reappear with the same pre- switching configuration, indicating a volatile process. + +The formation of the metallic channel is pinned by a specific topology of the lattice nanotexture, characterized by V- shaped domains, i.e. at the crossing point of domains with the same \(\phi_n\) with directions that differ by \(\pi /3\) . Fig. 3a) shows a detail of the switching region, using a colorscale that highlights the three differ + +![](images/Figure_2.jpg) + +
FIG. 2. XLD-PEEM images before (a) and during (b-j) the application of an electric current at \(T = 120 \mathrm{K}\) . The homogeneous regions at the top and bottom of each image are the gold electrodes. The area in between is the exposed \(\mathrm{V}_2\mathrm{O}_3\) antiferromagnetic monoclinic phase, exhibiting a striped domain nanotexture. For currents larger than \(1.5 \mathrm{mA}\) , the striped domains disappear in the region delimited by the white dashed lines, demonstrating the appearance of a rhombohedral metallic filament, which widens as the current is increased.
+ +ent domains with a monoclinic distortion along \(\hat{\epsilon}_1\) (red, \(\phi_1 = \pi /3\) ), \(\hat{\epsilon}_2\) (blue, \(\phi_2 = \pi\) ) and \(\hat{\epsilon}_3\) (yellow, \(\phi_3 = 5\pi /3\) ). The stabilization of the monoclinic nanotexture is driven by the Saint- Venant compatibility condition [37], which ensures a continuity of the medium during a deformation + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
FIG. 3. a) Detail of the XLD-PEEM image shown in Fig. 2 in the region where the metallic filament is formed upon the application of a current above the threshold \(I_{th}\) . b) Schematic of the monoclinic domains crossing at \(60^{\circ}\) and forming a topological defect. Blue, red and yellow areas identify the three possible monoclinic domains corresponding to the three equivalent order parameter directions \(\hat{\epsilon}_{n}\) . The order parameter at the boundaries between different domains is oriented along \(\hat{\epsilon}_{1} + \hat{\epsilon}_{2}\) ( \(2\pi /3\) ) for the red-blue interface and along \(\hat{\epsilon}_{2} + \hat{\epsilon}_{3}\) ( \(4\pi /3\) ) for the blue-yellow interface. The mixed red-yellow triangular region indicates the local suppression of the strain at the topological defect. The energy functionals shown on the left and right, illustrate how a topological defect (green plot, solid line) decreases the insulator-metal energy difference, \(\Delta\) .
+ +via the curl- free condition: + +\[\vec{\nabla}\times \vec{\epsilon} (\mathbf{r}) = 0. \quad (3)\] + +The conservation of the parallel component of \(\vec{\epsilon} (\mathbf{r})\) across an interface between two different domains has two important implications: + +i) the interface between two different monoclinic domains is oriented along \(\hat{\epsilon}_{n}\) of the third domain; ii) the interface between a monoclinic and a rhombohedral metallic domain is oriented perpendicularly to \(\hat{\epsilon}_{n}\) of the monoclinic domain. + +If we consider, for example, a domain with order parameter along \(\hat{\epsilon}_{2}\) (blue in Figure 3b)), its interface is oriented along \(\hat{\epsilon}_{1}\) , i.e. at \(\pi /3\) angle, when it neighbours an \(\hat{\epsilon}_{3}\) domain (yellow), whereas it is oriented along \(\hat{\epsilon}_{3}\) , i.e. at \(2\pi /3\) angle, when it neighbours an \(\hat{\epsilon}_{1}\) domain (red), in agreement with the nanotexture reported in Fig. 3. The Saint- Venant condition corresponds to a fixed phase jump \(\delta \phi = 2\pi /3\) of \(\vec{\epsilon} (\mathbf{r})\) across any interface between two monoclinic domains. We note that this condition is satisfied throughout the nanotextured domain structure, except at the V- shaped vertex structure formed by two \(\hat{\epsilon}_{2}\) + +domains with boundaries oriented along \(\hat{\epsilon}_{1}\) and \(\hat{\epsilon}_{3}\) . If we consider a circuit \(\Gamma_{1}\) across the boundary between two striped domains, the total phase shift is given by \(\delta \phi = +2\pi /3 - 2\pi /3 = 0\) thus adhering to the curl- free condition in Eq. 3. In contrast, the topology of the V- shaped structure is such that, if we move around the internal apex \((\Gamma_{2})\) , the total phase- shift is constrained to \(\delta \phi = +2\pi /3 + 2\pi /3 = 4\pi /3\) , thus breaking the curl- free condition. The consequence is that the vertex of the V- shaped domains acts as a topological defect with a fractional Hopf index (see Supplementary Information Section S6). These topological defects are inherently characterized by the strong frustration of the local value of the order parameter \(\vec{\epsilon} (\mathbf{r})\) and fluctuations on spatial and temporal scales that cannot be captured by the present experiment. We further note that the formation of the topological defect is a direct and unavoidable consequence of the quasi- 1D confined geometry of the system. Whereas the component of the order parameter parallel to the electrodes \((\epsilon_{||})\) , see Fig. 3b) can be compensated outside the gap, the perpendicular component \((\epsilon_{\perp})\) has to be minimized to avoid the accumulation of excessive strain energy within the gap region. Thus, considering the directions of \(\vec{\epsilon} (\mathbf{r})\) at the boundaries between different monoclinic domains (see Fig. 3b), the formation of V- shaped domains is a unique configuration that fulfils the requirement \(\epsilon_{\perp} = 0\) . + +The suppression of the symmetry- breaking order parameter, \(\vec{\epsilon} (\mathbf{r})\) , at topological defects has far- reaching implications related to the nature of the resistive switching process. The electronic IMT can be described by a scalar order parameter \(\eta (\mathbf{r})\) [37], which depends on the position \(\mathbf{r}\) and is such that \(\eta = - 1\) in the metallic state and \(\eta = +1\) in the insulating state. The coupling between the electronic and structural transitions can be described by the energy functional [37]: + +\[F[\epsilon ,\eta ]\propto \int d\mathbf{r}\left\{\left(\eta^{2}(\mathbf{r}) - 1\right)^{2} - g(\epsilon^{2}(\mathbf{r}) - \epsilon_{t}^{2}(V))\eta (\mathbf{r})\right\} ,\] + +where \(g\) is the coupling between the electronic order parameter and the strain and \(\epsilon_{t}(V)\) is a threshold parameter that controls the first- order IMT and can depend on the applied voltage \(V\) . When \(\epsilon^{2}(\mathbf{r}) > \epsilon_{t}^{2}(V)\) , the insulating phase with \(\eta = +1\) is locally favoured, whereas for strain smaller than the threshold value, i.e. \(\epsilon^{2}(\mathbf{r}) < \epsilon_{t}^{2}(V)\) , the metallic solution is stabilized. \(\epsilon_{t}^{2}(V)\) thus represents the threshold above which the insulating monoclinic state \((\eta = +1\) , \(\epsilon \neq 0\) ) becomes stable. The description of the electric- field induced transition is based on the observation [18] that the electric field directly couples to the electronic bandstructure of a Mott insulator, making the metallic phase more stable. The transition can thus be described assuming that \(\epsilon_{t}^{2}(V)\) increases with increasing \(V\) . The energy difference between the insulating and metallic phase can be expressed as \(\Delta (\mathbf{r}, V) = F[- 1] - F[+1] \simeq g \left[ \epsilon^{2}(\mathbf{r}) - \epsilon_{t}^{2}(V) \right]\) . If we start from the insulating phase with \(\epsilon^{2}(\mathbf{r}) > \epsilon_{t}^{2}(V) = 0\) , the IMT takes place when \(V\) is increased up to the point + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
FIG. 4. a) I-V curve measured during the XLD-PEEM imaging. The sudden drop in the voltage measured at \(I_{th} = 1.05 \mathrm{mA}\) indicates the first resistive switch. b) Line profiles of the XLD-PEEM images in Fig. 2. The grey shaded area indicates the progressive widening of the metallic rhombohedral filament. The direction of the line profiles is shown by the white dashed line in the XLD-PEEM image on top, where we report a detail of Fig. 2j. c) Width, \(d\) , of the metallic filament as a function of current. The blue/red markers represent the values of \(d\) obtained from the XLD-PEEM images below/above \(I_{th}\) . The green solid line shows an estimate of \(d\) , derived from a parallel resistors model predicting a sudden jump of \(d\) to \(200 \mathrm{mA}\) in \(I_{th}\) (see Supplementary Information Section S5).
+ +that \(\Delta (\mathbf{r}, V) = 0\) . A topological defect, which locally suppresses \(\epsilon^2 (\mathbf{r})\) , thus acts as a seed with a lower threshold compared to the rest of the system. + +Intriguingly, we also note from Eq. 4 that the IMT can take place at a non- zero value of \(\epsilon^2 (\mathbf{r})\) , which allows the formation of a non- thermal metallic state \((\eta = - 1)\) with a finite monoclinic distortion \((\epsilon^2 (\mathbf{r}) \lesssim \epsilon_t^2 (V))\) , as already observed in non- equilibrium optical experiments [37, 44]. The nature of the early- stage switching process can be inferred by a direct comparison between the electrical state of the device and the melting of the monoclinic domains. The \(I - V\) curve of the device, as measured in- situ during the PEEM imaging, is plotted in Fig. 4a). + +XLD- PEEM images were recorded at specific values of \(I\) . The \(I - V\) plot shows that the first resistive switching event occurs at the threshold current \(I_{th} = 1.05 \mathrm{mA}\) . In Figure 4b) we report a linecut of the XLD- PEEM image acquired at specific values of \(I\) ; the image profile is taken along a line crossing the monoclinic domains in the middle of the device gap (see white solid line in Fig. 4, top panel). For large currents running through the device, the line profile in Fig. 4b) displays a flat region, which indicates the melting of the monoclinic nanodomains due to the formation of the rhombohedral metallic channel. As highlighted by the grey area in Fig. 4b), the width \(d\) of the metallic filament increases with the current, from + +<--- Page Split ---> + +\(d = 0.23\pm 0.05\mu \mathrm{m}\) at \(I = 1.5\mathrm{mA}\) to \(d = 3.7\pm 0.2\mu \mathrm{m}\) at \(I = 10\mathrm{mA}\) + +Modelling the device as a circuit with two parallel resistors (see Supplementary Information Section S5) allows an estimation of \(d\) of the rhombohedral filament corresponding to the observed voltage drop. For large currents running through the device, the experimentally determined values of \(d\) match well with those predicted for a metallic channel forming in the gap, which has the resistivity of the high- temperature rhombohedral phase, as shown in Fig. 4. However, in correspondence of the first resistive switching event at \(I_{th} = 1.05\mathrm{mA}\) , the model predicts the sudden formation of a \(\sim 200\mathrm{nm}\) wide metallic rhombohedral filament, which is not visible in the XLD- PEEM images (see Fig. 4c and Supplementary Information Fig. S5), despite being well above the experimental resolution of the microscope. To explain this discrepancy, one might suspect that a rhombohedral metallic filament forms below the surface of the \(\mathrm{V}_2\mathrm{O}_3\) film, where it is not detected by PEEM which has a surface sensitivity limited to the first few nanometers. In fact, two arguments act against this possibility: i) the presence of the \(\mathrm{Cr}_2\mathrm{O}_3\) buffer layer reduces the substrate- film lattice mismatch from \(4.2\%\) to \(0.1\%\) , thus almost entirely removing the residual epitaxial strain in the film [42], which is known to suppress the monoclinic phase and favour interfacial metallicity [42, 45]. In contrast to highly- strained films, in which the metal to insulator resistivity jump is strongly suppressed [45], the films in the present study display the 5- order of magnitude resistivity change typical of the unstrained metal- to- insulator transition (see Fig. S1); ii) the curl- free conditions force the interface between monoclinic and rhombohedral metallic regions to be oriented perpendicularly to the order parameter of the monoclinic domain. The formation of a sub- surface metallic layer would lead to a sharp \((\ll 20\mathrm{nm})\) monoclinic- rhombohedral interface parallel to \(\vec{\epsilon}\) , thus leading to a dramatic increase of the strain energy of the system. Our results are compatible with a complex scenario in which the topology- driven resistive switching likely occurs via the sudden transformation of a single \(200\mathrm{nm}\) wide insulating monoclinic domain into a metallic channel with a non- thermal monoclinic lattice structure. At a second stage, the Joule heating leads to the thermally driven monoclinic- to- rhombohedral structural transition and the formation of rhombohedral metallic channels perpendicular to both the metallic electrodes and the \(\hat{\epsilon}_2\) order parameter direction, as observed in Fig. 2. + +The X- ray- based nanoimaging of a Mott device under operating conditions allowed us to simultaneously capture the formation of nanoscale conductive paths and the topology of the underlying symmetry- broken nanotexture. The present results expand our knowledge of the resistive switching process in Mott materials by demonstrating the leading role of inherent topological defects in initiating the avalanche process. The + +methodologies used in this work imply that nanoscale strain engineering approaches could unlock a gate to manipulating topological defects and controlling the electronic switching dynamics in real devices, such as Mott- transition- based RRAM [46, 47], Mott memristor [48- 50] and artificial neurons [51, 52]. The concept of topology- driven resistive switching will be key to assessing the possible non- thermal nature of the early stage electronic phase [37] as well as the microscopic origin of memory and non- volatile effects recently observed in Mott devices [6]. We note that the relation between topological defects and electronic phase transitions established here is a general concept, potentially extendable to other systems that undergo first- order phase transitions accompanied by a symmetry breaking, as described by the energy functional (4). Relevant examples embrace transition- metal oxides [3, 53], such as vanadates, nickelates and manganites, and layered materials, such as \(1T\) - TaS2 [54- 57], in which the IMT is accompanied by charge-, lattice- and orbital- ordered states with reduced symmetry. Further platforms include cuprate superconductors [58] and kagome metals [59] in which light- or magnetic- induced discontinuous electronic transitions coexist with charge- order. Topological defects in the order parameter therefore provide a framework for understanding non- equilibrium electronic phase transitions, allowing all- optical control of hidden states of matter in a broad class of quantum materials [57, 60- 64]. + +We thank Diamond Lights Source for the provision of beamtime under proposal numbers MM- 27218, MM- 31711 and MM- 34455. We thank Manuel R. Osorio and Fernando J. Urbanos for the fabrication of sample electrodes at the Centre for Micro and Nanofabrication of IMDEA Nanociencia. A.M., S.M. and C.G. acknowledge financial support from MIUR through the PRIN 2015 (Prot. 2015C5SEJJ001) and PRIN 2017 (Prot. 20172H2SC4.005) programs and from the European Union - Next Generation EU through the MUR- PRIN2022 (Prot. 20228YCYY7) program. C.G. acknowledges support from Università Cattolica del Sacro Cuore through D.I, D.2.2 and D.3.1 grants. S.M. acknowledges partial financial support through the grant "Finanziamenti ponte per bandi esterni" from Università Cattolica del Sacro Cuore. I.F.C. and M.M. acknowledge support from the "Severo Ochoa" Programme for Centres of Excellence in R&D (CEX2020- 001039- S) and the Spanish AEI- MCIN PID2021- 122980OB- C52 (ECoSOC- ECLIPSE). I.F.C holds a FPI fellowship from the Spanish AEI- MCIN (PRE2020- 092625). 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Schmid, P. Hansmann, P. Pupuhal, K. Fursich, V. Zimmerman, et al., Nature of the current- induced insulator- to- metal transition in \(\mathrm{Ca}_2\mathrm{RuO}_4\) as revealed by transport- ARPES, arXiv:2308.05803 (2023).[20] D. J. Wouters, S. Menzel, J. A. J. Rupp, T. Hennen, and R. Waser, On the universality of the I- V switching characteristics in non- volatile and volatile resistive switching oxides, Faraday Discuss. 213, 183 (2019).[21] Y. Kalcheim, A. Camjayi, J. del Valle, P. Salev, M. Rozenberg, and I. K. Schuller, Non- thermal resistive switching in Mott insulator nanowires, Nature Communications 11, 2985 (2020).[22] P. Stoliar, L. Cario, E. Janod, B. Corraze, C. Guillot- Deudon, S. Salmon- Bourmand, V. Guiot, J. Tranchant, and M. Rozenberg, Universal Electric- Field- Driven Resistive Transition in Narrow- Gap Mott Insulators, Advanced Materials 25, 3222 (2013).[23] V. Guiot, L. Cario, E. Janod, B. Corraze, V. Ta Phuoc, M. Rozenberg, P. Stoliar, T. Cren, and D. 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Hsieh, Towards properties on demand in quantum materials, Nature materials 16, 1077 (2017).[18] G. Mazza, A. Amaricci, M. Capone, and M. Fabrizio, Field- driven mott gap collapse and resistive switch in correlated insulators, Physical review letters 117, 176401 (2016).[19] C. T. Suen, I. Markovic, M. Zonno, S. Zhdanovich, N.- H. Jo, M. Schmid, P. Hansmann, P. Pupuhal, K. Fursich, V. Zimmerman, et al., Nature of the current- induced insulator- to- metal transition in \(\mathrm{Ca}_2\mathrm{RuO}_4\) as revealed by transport- ARPES, arXiv:2308.05803 (2023).[20] D. J. Wouters, S. Menzel, J. A. J. Rupp, T. Hennen, and R. Waser, On the universality of the I- V switching characteristics in non- volatile and volatile resistive switching oxides, Faraday Discuss. 213, 183 (2019).[21] Y. Kalcheim, A. Camjayi, J. del Valle, P. Salev, M. Rozenberg, and I. K. Schuller, Non- thermal resistive switching in Mott insulator nanowires, Nature Communications 11, 2985 (2020).[22] P. Stoliar, L. Cario, E. Janod, B. Corraze, C. Guillot- Deudon, S. Salmon- Bourmand, V. Guiot, J. Tranchant, and M. Rozenberg, Universal Electric- Field- Driven Resistive Transition in Narrow- Gap Mott Insulators, Advanced Materials 25, 3222 (2013).[23] V. Guiot, L. Cario, E. Janod, B. Corraze, V. Ta Phuoc, M. Rozenberg, P. Stoliar, T. Cren, and D. Roditchev, Avalanche breakdown in \(\mathrm{CaTa}_4\mathrm{Se}_8\mathrm{- }x\mathrm{Te}_x\) narrow- gap Mott insulators, Nature communications 4, 1722 (2013).[24] F. Nakamura, M. Sakaki, Y. Yamanaka, S. Tamaru, T. Suzuki, and Y. Maeno, Electric- field- induced metal maintained by current of the Mott insulator \(\mathrm{Ca}_2\mathrm{RuO}_4\) , Scientific reports 3, 2536 (2013).[25] A. Fursina, R. Sofin, I. Shvets, and D. Natelson, Origin of hysteresis in resistive switching in magnetite is Joule heating, Physical Review B 79, 245131 (2009).[26] A. Fursina, R. Sofin, I. Shvets, and D. 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Kaaret, E. Lamb, G. Khalsa, H. P. Nair, Y. Sun, R. Bouck, N. Schreiber, J. P. Ruf, V. Ramaprasad, Y. Kubota, T. Togashi, V. A. Stoica, H. Padmanabhan, J. W. Freeland, N. A. Benedek, O. G. Shpyrko, J. W. Harter, R. D. Averitt, D. G. Schlom, K. M. Shen, A. J. Millis, and A. Singer, Picosecond volume expansion drives a later- time insulator- metal transition in a nano- textured Mott insulator, Nature Physics 10.1038/s41567- 024- 02396- 1 (2024). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SIV203ResistiveSwitching.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb_det.mmd b/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..568070492a4aa352d2a3fe29b7c108ab2fe4d0e4 --- /dev/null +++ b/preprint/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb/preprint__0a029bedc77ee23f09bec94b1dd73e6a81b91ac30d9e51c5375983d20db515bb_det.mmd @@ -0,0 +1,244 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 864, 175]]<|/det|> +# Mott resistive switching initiated by topological defects + +<|ref|>text<|/ref|><|det|>[[44, 196, 199, 214]]<|/det|> +Claudio Giannetti + +<|ref|>text<|/ref|><|det|>[[55, 223, 365, 240]]<|/det|> +claudio.giannetti@unicatt.it + +<|ref|>text<|/ref|><|det|>[[44, 269, 728, 289]]<|/det|> +Università Cattolica del Sacro Cuore https://orcid.org/0000- 0003- 2664- 9492 + +<|ref|>text<|/ref|><|det|>[[44, 294, 365, 333]]<|/det|> +Alessandra Milloch Università Cattolica del Sacro Cuore + +<|ref|>text<|/ref|><|det|>[[44, 339, 313, 378]]<|/det|> +Ignacio Figueruelo- Campanero IMDEA Nanociencia + +<|ref|>text<|/ref|><|det|>[[44, 385, 155, 424]]<|/det|> +Wei- Fan Hsu KU Leuven + +<|ref|>text<|/ref|><|det|>[[44, 431, 370, 472]]<|/det|> +Selene Mor Università Cattolica del Sacro Cuore + +<|ref|>text<|/ref|><|det|>[[44, 478, 510, 518]]<|/det|> +Simon Mellaerts KU Leuven https://orcid.org/0000- 0002- 6715- 3066 + +<|ref|>text<|/ref|><|det|>[[44, 524, 255, 542]]<|/det|> +Francesco Maccherozzi + +<|ref|>text<|/ref|><|det|>[[44, 546, 945, 587]]<|/det|> +Diamond Light Source, Chilton, Didcot, Oxfordshire, OX11 0DE, UK. https://orcid.org/0000- 0003- 4074- 2319 + +<|ref|>text<|/ref|><|det|>[[44, 593, 250, 632]]<|/det|> +Larissa Ishibe Veiga Diamond Light Source + +<|ref|>text<|/ref|><|det|>[[44, 639, 610, 679]]<|/det|> +Sarnjeet Dhesi Diamond Light Source https://orcid.org/0000- 0003- 4966- 0002 + +<|ref|>text<|/ref|><|det|>[[44, 685, 730, 725]]<|/det|> +Mauro Spera Università Cattolica del Sacro Cuore https://orcid.org/0000- 0001- 9041- 364X + +<|ref|>text<|/ref|><|det|>[[44, 732, 510, 772]]<|/det|> +Jin Seo KU Leuven https://orcid.org/0000- 0003- 4937- 0769 + +<|ref|>text<|/ref|><|det|>[[44, 778, 397, 818]]<|/det|> +Jean- Pierre Locquet https://orcid.org/0000- 0002- 4214- 7081 + +<|ref|>text<|/ref|><|det|>[[44, 824, 784, 865]]<|/det|> +Michele Fabrizio International School for Advanced Studies https://orcid.org/0000- 0002- 2943- 3278 + +<|ref|>text<|/ref|><|det|>[[44, 870, 598, 911]]<|/det|> +Mariela Menghini IMDEA Nanoscience https://orcid.org/0000- 0002- 1744- 798X + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 46, 103, 63]]<|/det|> +## Article + +<|ref|>sub_title<|/ref|><|det|>[[44, 84, 135, 102]]<|/det|> +## Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 121, 291, 140]]<|/det|> +Posted Date: June 6th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 159, 474, 179]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4019377/v1 + +<|ref|>text<|/ref|><|det|>[[42, 197, 914, 240]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 258, 534, 277]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 313, 940, 356]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 31st, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 53726- z. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[231, 100, 770, 117]]<|/det|> +# Mott resistive switching initiated by topological defects + +<|ref|>text<|/ref|><|det|>[[132, 128, 872, 170]]<|/det|> +Alessandra Milloch, \(^{1,2,3,*}\) Ignacio Figueroelo- Campanero, \(^{4,5,\dagger}\) Wei- Fan Hsu, \(^{3}\) Selene Mor, \(^{1,2}\) Simon Mellaerts, \(^{3}\) Francesco Maccherozzi, \(^{6}\) Larissa Ishibe Veiga, \(^{6}\) Sarnjeet S. Dhesi, \(^{6}\) Mauro Spera, \(^{1}\) Jin Won Seo, \(^{7}\) Jean- Pierre Locquet, \(^{3}\) Michele Fabrizio, \(^{8}\) Mariela Menghini, \(^{4}\) and Claudio Giannetti \(^{1,2,9,\ddagger}\) + +<|ref|>text<|/ref|><|det|>[[160, 173, 844, 300]]<|/det|> +\(^{1}\) Department of Mathematics and Physics, Università Cattolica del Sacro Cuore, Brescia I- 25133, Italy \(^{2}\) ILAMP (Interdisciplinary Laboratories for Advanced Materials Physics), Università Cattolica del Sacro Cuore, Brescia I- 25133, Italy \(^{3}\) Department of Physics and Astronomy, KU Leuven, B- 3001 Leuven, Belgium \(^{4}\) IMDEA Nanociencia, Cantoblanco, 28049 Madrid, Spain \(^{5}\) Facultad Ciencias Físicas, Universidad Complutense, 28040 Madrid, Spain \(^{6}\) Diamond Light Source, Didcot, Oxfordshire OX11 0DE, UK \(^{7}\) Department of Materials Engineering, KU Leuven, 3001 Leuven, Belgium \(^{8}\) Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy \(^{9}\) CNR- INO (National Institute of Optics), via Branze 45, 25123 Brescia, Italy + +<|ref|>text<|/ref|><|det|>[[165, 304, 830, 377]]<|/det|> +Avalanche resistive switching is the fundamental process that triggers the sudden change of the electrical properties in solid- state devices under the action of intense electric fields [1]. Despite its relevance for information processing, ultrafast electronics, neuromorphic devices, resistive memories and brain- inspired computation [1- 14], the nature of the local stochastic fluctuations that drive the formation of metallic regions within the insulating state has remained hidden. + +<|ref|>text<|/ref|><|det|>[[165, 376, 830, 510]]<|/det|> +Here, using operando X- ray nano- imaging, we have captured the origin of resistive switching in a \(\mathrm{V}_2\mathrm{O}_3\) - based device under working conditions. \(\mathrm{V}_2\mathrm{O}_3\) is a paradigmatic Mott material [3], which undergoes a first- order metal- to- insulator phase transition together with a lattice transformation that breaks the threefold rotational symmetry of the rhombohedral metallic phase [2, 5, 6, 8- 11, 15]. We reveal a new class of volatile electronic switching triggered by nanoscale topological defects appearing in the shear- strain based order parameter that describes the insulating phase. Our results pave the way to the use of strain engineering approaches to manipulate such topological defects and achieve the full dynamical control of the electronic Mott switching. Topology- driven, reversible electronic transitions are relevant across a broad range of quantum materials, comprising transition metal oxides, chalcogenides and kagome metals. + +<|ref|>text<|/ref|><|det|>[[87, 532, 486, 797]]<|/det|> +The insulator- to- metal transition (IMT) in Mott materials is a key mechanism for the development of next generation Mottronic devices [3, 13]. The intrinsic correlated nature of the Mott insulating state makes these systems fragile to external stimuli [16, 17], such as the application of an electric field, which can drive the collapse of the electronic band structure and the sudden release of a large number of free carriers [18, 19]. At the macroscopic level, this phenomenon manifests in the resistive switching process, i.e., a sharp increase of the current flow when the applied voltage exceeds a threshold value [6, 20- 27]. This strong non- linearity triggered many efforts to develop neuromorphic building blocks for the hardware implementation of neural networks [14] or for ultrafast volatile and non- volatile memories or processors [12, 28, 29]. The state- of- the- art macroscopic models [30] are based on resistor networks that consider interconnected nodes transforming from the insulating to metallic state in the presence of an electric field. Above a certain threshold, a percolative, avalanche transition takes place, + +<|ref|>text<|/ref|><|det|>[[515, 532, 916, 559]]<|/det|> +thus leading to the formation of conductive filaments and the consequent sudden drop in resistivity [22, 31]. + +<|ref|>text<|/ref|><|det|>[[515, 560, 916, 718]]<|/det|> +The full control and exploitation of this process is currently prevented by a limited knowledge of the early- stage firing dynamics. Microscopically, little is known about the nature of the nanoscale regions that trigger the avalanche process. Also the relation between the electronic and structural properties of the switched regions and those of the pristine insulating template is a matter of debate. Pioneering optical microscopy experiments captured the real- time formation of macroscopic metallic channels [4, 32- 35], but lacked the resolution and sensitivity to address the microscopic origin of the switching process. + +<|ref|>text<|/ref|><|det|>[[515, 720, 916, 878]]<|/det|> +Here, we adopt resonant X- ray microscopy to record nanoscale snapshots of the switching dynamics in a \(\mathrm{V}_2\mathrm{O}_3\) - based nanodevice during the application of an electric field. The results unveil the fundamental role played by the order parameter topology of the underlying lattice nanotexture. The breaking of the \(C_3\) symmetry upon transition to the insulating monoclinic phase leads to the formation of three twin shear- strain domains with boundaries oriented along the three hexagonal directions [36, 37]. The geometrical constraints then produce shear- strain topological defects at the corners of monoclinic domains crossing with an angle of \(60^{\circ}\) . These nanoscale + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[102, 102, 888, 430]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[84, 444, 919, 520]]<|/det|> +
FIG. 1. a) Non-primitive hexagonal unit cell of the \(\mathrm{V_2O_3}\) high-temperature rhombohedral metallic phase. b) Schematic of the rhombohedral-to-monoclinic distortion along each of the three equivalent hexagonal axes. c) PEEM experimental setup. X-ray radiation, with tunable energy resonant with the vanadium \(\mathrm{L_{2,3}}\) edge, impinges on the sample surface and the emitted electrons are collected and imaged through electrostatic and magnetic lenses. The \(\mathrm{V_2O_3}\) film is coated with gold metal electrodes, allowing to drive a current through the device (see sketch of a typical resistive switching current-voltage curve in the bottom panel) while simultaneously acquiring XLD-PEEM images.
+ +<|ref|>text<|/ref|><|det|>[[85, 544, 487, 584]]<|/det|> +topological defects act as seeds for the formation of the metallic phase, thus triggering the macroscopic volatile resistive switching. + +<|ref|>text<|/ref|><|det|>[[85, 585, 487, 704]]<|/det|> +\(\mathrm{V_2O_3}\) is a prototypical Mott insulator that undergoes a thermally- driven transition from a high- temperature paramagnetic, rhombohedral metal to a low- temperature antiferromagnetic monoclinic insulator [38- 40]. The lattice transformation at the critical temperature \(T_{IMT}\) implies the breaking of the \(C_3\) symmetry of the non- primitive hexagonal unit cell of the high- temperature metallic phase (see Fig. 1a). The structural transition can thus be described [37] by a vector order parameter: + +<|ref|>equation<|/ref|><|det|>[[170, 711, 485, 729]]<|/det|> +\[\vec{\epsilon} = (\epsilon_{31},\epsilon_{23}) = \epsilon \left(\cos \phi_n,\sin \phi_n\right) \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[85, 740, 487, 794]]<|/det|> +associated to the shear strain components \(\epsilon_{31}\) and \(\epsilon_{23}\) that characterize the monoclinic distortion. Below \(T_{IMT}\) , the amplitude of the order parameter, \(\epsilon\) , becomes non- zero, while the phase can assume three different values: + +<|ref|>equation<|/ref|><|det|>[[228, 802, 485, 828]]<|/det|> +\[\phi_{n} = (2n - 1)\frac{\pi}{3} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[85, 837, 487, 878]]<|/det|> +corresponding to the distortion along the three equivalent hexagonal axes of the rhombohedral phase, indicated in the following by the versors \(\hat{\epsilon}_n\) , \(n = 1,2,3\) (see Fig. 1b)). + +<|ref|>text<|/ref|><|det|>[[515, 544, 917, 867]]<|/det|> +Resistive switching can be induced by applying an electric field across a patterned micro- gap at temperatures close to \(T_{IMT}\) [4, 33, 41]. The resistive switching device investigated here is formed by a \(20 \mathrm{nm} \mathrm{V}_2\mathrm{O}_3\) film coated with gold electrodes. \(\mathrm{V}_2\mathrm{O}_3\) is grown by oxygen- assisted Molecular Beam Epitaxy on a \((0001)\) - \(\mathrm{Al}_2\mathrm{O}_3\) substrate with a \(40 \mathrm{nm} \mathrm{Cr}_2\mathrm{O}_3\) buffer layer to reduce any interfacial residual strain [42]. The resulting \(\mathrm{V}_2\mathrm{O}_3\) film has the \(c\) axis oriented parallel to the surface normal, with \(T_{IMT} = 145 \mathrm{K}\) (see Supplementary Information Fig. S1). Two gold electrodes allow the application of an electric bias across the gap of width \(w = 2 \mu \mathrm{m}\) and length \(l = 30 \mu \mathrm{m}\) (figure 1c)). The gap region between the electrodes is imaged using PhotoEmission Electron Microscopy (PEEM), combined with X- ray Linear Dichroism (XLD) at the \(\mathrm{L_{2,3}}\) vanadium edge (513- 530 eV, see Figure S2) [36, 37, 43]. The XLD- PEEM images are obtained from the normalized difference between images recorded with the light electric field vector, \(\vec{E}\) , perpendicular and \(16^{\circ}\) to the surface normal at a photon energy \(520.6 \mathrm{eV}\) . Since the XLD signal depends on the angle between the in- plane component of \(\vec{E}\) and the position dependent order parameter, \(\vec{\epsilon} (\mathbf{r})\) [37] (see Fig. 1b) and c)), this technique provides a map - with \(\sim 30 \mathrm{nm}\) spatial resolution - of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 102, 488, 130]]<|/det|> +three different monoclinic domains during the resistive switching process. + +<|ref|>text<|/ref|><|det|>[[85, 132, 488, 291]]<|/det|> +Figure 2a) shows an XLD- PEEM image obtained in the monoclinic insulating phase at \(T = 120 \mathrm{K}\) . The \(\mathrm{V}_2\mathrm{O}_3\) nanotexture exhibits features typical of the monoclinic insulating phase [36, 37]. Monoclinic domains with different \(\phi_n\) give rise to different XLD contrast, which can be identified as different color intensities within the XLD- PEEM image. The minimization of the total strain leads to the formation of stripe- like domains, with symmetry- constrained directions [37]. Each monoclinic insulating domain extends over a few micrometers, thus connecting the two electrodes, and it is characterized by a width \(w_{dom} \sim 200 \mathrm{nm}\) [37]. + +<|ref|>text<|/ref|><|det|>[[85, 293, 488, 793]]<|/det|> +The XLD- PEEM imaging is then repeated while driving a current, \(I\) , through the device and measuring the voltage drop, \(V\) , across the gold contacts. Figures 2b)- j) show the XLD- PEEM images acquired at increasing values of \(I\) , following the upward branch of the hysteresis cycle. The presence of an in- plane electric field across the electrodes introduces a weak image blurring that becomes significant for \(V \geq 6 - 8 \mathrm{V}\) . Despite this, the nanodomains are well resolved during the resistive switching process, which first manifests itself at the voltage drop observed between \(0.08 \mathrm{mA}\) and \(1.1 \mathrm{mA}\) (Fig. 2 c) and d) respectively). As the current is further increased, the melting of the monoclinic nanotexture in the region delimited by white dashed lines (Fig. 2 e)- j)) progresses with a widening channel with a homogenous intensity. The XLD contrast measured in the region between the white dashed lines corresponds to the signal of the high- temperature rhombohedral phase. This is also confirmed from the angle dependence of the XLD signal [37]. As shown in the Supplementary Information Fig. S3, images collected with two different X- rays polarization angles, with respect to the in- plane \(\mathrm{V}_2\mathrm{O}_3\) axes, show no intensity variation upon sample rotation in the metallic channel, as opposed to the lateral monoclinic domains, for which the XLD signal depends on the angle between the light polarization and \(\vec{\epsilon} (\mathbf{r})\) . The constant XLD contrast region in the middle of the gap therefore appears due to the formation of a metallic channel with rhombohedral lattice structure ( \(\epsilon = 0\) ). XLD- PEEM images obtained under the same conditions, but with a larger field of view, capture the whole gap of the device (see Supplementary Information Fig. S4). The metallic channel consistently forms in the same location within the gap with no additional metallic paths observed. Furthermore, when the applied current is removed, the metallic channel disappears and the monoclinic domains reappear with the same pre- switching configuration, indicating a volatile process. + +<|ref|>text<|/ref|><|det|>[[85, 796, 488, 876]]<|/det|> +The formation of the metallic channel is pinned by a specific topology of the lattice nanotexture, characterized by V- shaped domains, i.e. at the crossing point of domains with the same \(\phi_n\) with directions that differ by \(\pi /3\) . Fig. 3a) shows a detail of the switching region, using a colorscale that highlights the three differ + +<|ref|>image<|/ref|><|det|>[[520, 105, 900, 627]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[515, 643, 917, 752]]<|/det|> +
FIG. 2. XLD-PEEM images before (a) and during (b-j) the application of an electric current at \(T = 120 \mathrm{K}\) . The homogeneous regions at the top and bottom of each image are the gold electrodes. The area in between is the exposed \(\mathrm{V}_2\mathrm{O}_3\) antiferromagnetic monoclinic phase, exhibiting a striped domain nanotexture. For currents larger than \(1.5 \mathrm{mA}\) , the striped domains disappear in the region delimited by the white dashed lines, demonstrating the appearance of a rhombohedral metallic filament, which widens as the current is increased.
+ +<|ref|>text<|/ref|><|det|>[[515, 810, 916, 876]]<|/det|> +ent domains with a monoclinic distortion along \(\hat{\epsilon}_1\) (red, \(\phi_1 = \pi /3\) ), \(\hat{\epsilon}_2\) (blue, \(\phi_2 = \pi\) ) and \(\hat{\epsilon}_3\) (yellow, \(\phi_3 = 5\pi /3\) ). The stabilization of the monoclinic nanotexture is driven by the Saint- Venant compatibility condition [37], which ensures a continuity of the medium during a deformation + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 102, 460, 325]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 341, 488, 525]]<|/det|> +
FIG. 3. a) Detail of the XLD-PEEM image shown in Fig. 2 in the region where the metallic filament is formed upon the application of a current above the threshold \(I_{th}\) . b) Schematic of the monoclinic domains crossing at \(60^{\circ}\) and forming a topological defect. Blue, red and yellow areas identify the three possible monoclinic domains corresponding to the three equivalent order parameter directions \(\hat{\epsilon}_{n}\) . The order parameter at the boundaries between different domains is oriented along \(\hat{\epsilon}_{1} + \hat{\epsilon}_{2}\) ( \(2\pi /3\) ) for the red-blue interface and along \(\hat{\epsilon}_{2} + \hat{\epsilon}_{3}\) ( \(4\pi /3\) ) for the blue-yellow interface. The mixed red-yellow triangular region indicates the local suppression of the strain at the topological defect. The energy functionals shown on the left and right, illustrate how a topological defect (green plot, solid line) decreases the insulator-metal energy difference, \(\Delta\) .
+ +<|ref|>text<|/ref|><|det|>[[86, 550, 277, 563]]<|/det|> +via the curl- free condition: + +<|ref|>equation<|/ref|><|det|>[[230, 568, 485, 588]]<|/det|> +\[\vec{\nabla}\times \vec{\epsilon} (\mathbf{r}) = 0. \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[85, 596, 488, 638]]<|/det|> +The conservation of the parallel component of \(\vec{\epsilon} (\mathbf{r})\) across an interface between two different domains has two important implications: + +<|ref|>text<|/ref|><|det|>[[102, 646, 488, 725]]<|/det|> +i) the interface between two different monoclinic domains is oriented along \(\hat{\epsilon}_{n}\) of the third domain; ii) the interface between a monoclinic and a rhombohedral metallic domain is oriented perpendicularly to \(\hat{\epsilon}_{n}\) of the monoclinic domain. + +<|ref|>text<|/ref|><|det|>[[85, 732, 488, 877]]<|/det|> +If we consider, for example, a domain with order parameter along \(\hat{\epsilon}_{2}\) (blue in Figure 3b)), its interface is oriented along \(\hat{\epsilon}_{1}\) , i.e. at \(\pi /3\) angle, when it neighbours an \(\hat{\epsilon}_{3}\) domain (yellow), whereas it is oriented along \(\hat{\epsilon}_{3}\) , i.e. at \(2\pi /3\) angle, when it neighbours an \(\hat{\epsilon}_{1}\) domain (red), in agreement with the nanotexture reported in Fig. 3. The Saint- Venant condition corresponds to a fixed phase jump \(\delta \phi = 2\pi /3\) of \(\vec{\epsilon} (\mathbf{r})\) across any interface between two monoclinic domains. We note that this condition is satisfied throughout the nanotextured domain structure, except at the V- shaped vertex structure formed by two \(\hat{\epsilon}_{2}\) + +<|ref|>text<|/ref|><|det|>[[515, 103, 918, 460]]<|/det|> +domains with boundaries oriented along \(\hat{\epsilon}_{1}\) and \(\hat{\epsilon}_{3}\) . If we consider a circuit \(\Gamma_{1}\) across the boundary between two striped domains, the total phase shift is given by \(\delta \phi = +2\pi /3 - 2\pi /3 = 0\) thus adhering to the curl- free condition in Eq. 3. In contrast, the topology of the V- shaped structure is such that, if we move around the internal apex \((\Gamma_{2})\) , the total phase- shift is constrained to \(\delta \phi = +2\pi /3 + 2\pi /3 = 4\pi /3\) , thus breaking the curl- free condition. The consequence is that the vertex of the V- shaped domains acts as a topological defect with a fractional Hopf index (see Supplementary Information Section S6). These topological defects are inherently characterized by the strong frustration of the local value of the order parameter \(\vec{\epsilon} (\mathbf{r})\) and fluctuations on spatial and temporal scales that cannot be captured by the present experiment. We further note that the formation of the topological defect is a direct and unavoidable consequence of the quasi- 1D confined geometry of the system. Whereas the component of the order parameter parallel to the electrodes \((\epsilon_{||})\) , see Fig. 3b) can be compensated outside the gap, the perpendicular component \((\epsilon_{\perp})\) has to be minimized to avoid the accumulation of excessive strain energy within the gap region. Thus, considering the directions of \(\vec{\epsilon} (\mathbf{r})\) at the boundaries between different monoclinic domains (see Fig. 3b), the formation of V- shaped domains is a unique configuration that fulfils the requirement \(\epsilon_{\perp} = 0\) . + +<|ref|>text<|/ref|><|det|>[[515, 460, 917, 580]]<|/det|> +The suppression of the symmetry- breaking order parameter, \(\vec{\epsilon} (\mathbf{r})\) , at topological defects has far- reaching implications related to the nature of the resistive switching process. The electronic IMT can be described by a scalar order parameter \(\eta (\mathbf{r})\) [37], which depends on the position \(\mathbf{r}\) and is such that \(\eta = - 1\) in the metallic state and \(\eta = +1\) in the insulating state. The coupling between the electronic and structural transitions can be described by the energy functional [37]: + +<|ref|>equation<|/ref|><|det|>[[520, 584, 912, 615]]<|/det|> +\[F[\epsilon ,\eta ]\propto \int d\mathbf{r}\left\{\left(\eta^{2}(\mathbf{r}) - 1\right)^{2} - g(\epsilon^{2}(\mathbf{r}) - \epsilon_{t}^{2}(V))\eta (\mathbf{r})\right\} ,\] + +<|ref|>text<|/ref|><|det|>[[515, 625, 917, 877]]<|/det|> +where \(g\) is the coupling between the electronic order parameter and the strain and \(\epsilon_{t}(V)\) is a threshold parameter that controls the first- order IMT and can depend on the applied voltage \(V\) . When \(\epsilon^{2}(\mathbf{r}) > \epsilon_{t}^{2}(V)\) , the insulating phase with \(\eta = +1\) is locally favoured, whereas for strain smaller than the threshold value, i.e. \(\epsilon^{2}(\mathbf{r}) < \epsilon_{t}^{2}(V)\) , the metallic solution is stabilized. \(\epsilon_{t}^{2}(V)\) thus represents the threshold above which the insulating monoclinic state \((\eta = +1\) , \(\epsilon \neq 0\) ) becomes stable. The description of the electric- field induced transition is based on the observation [18] that the electric field directly couples to the electronic bandstructure of a Mott insulator, making the metallic phase more stable. The transition can thus be described assuming that \(\epsilon_{t}^{2}(V)\) increases with increasing \(V\) . The energy difference between the insulating and metallic phase can be expressed as \(\Delta (\mathbf{r}, V) = F[- 1] - F[+1] \simeq g \left[ \epsilon^{2}(\mathbf{r}) - \epsilon_{t}^{2}(V) \right]\) . If we start from the insulating phase with \(\epsilon^{2}(\mathbf{r}) > \epsilon_{t}^{2}(V) = 0\) , the IMT takes place when \(V\) is increased up to the point + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[220, 100, 797, 573]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[84, 585, 919, 671]]<|/det|> +
FIG. 4. a) I-V curve measured during the XLD-PEEM imaging. The sudden drop in the voltage measured at \(I_{th} = 1.05 \mathrm{mA}\) indicates the first resistive switch. b) Line profiles of the XLD-PEEM images in Fig. 2. The grey shaded area indicates the progressive widening of the metallic rhombohedral filament. The direction of the line profiles is shown by the white dashed line in the XLD-PEEM image on top, where we report a detail of Fig. 2j. c) Width, \(d\) , of the metallic filament as a function of current. The blue/red markers represent the values of \(d\) obtained from the XLD-PEEM images below/above \(I_{th}\) . The green solid line shows an estimate of \(d\) , derived from a parallel resistors model predicting a sudden jump of \(d\) to \(200 \mathrm{mA}\) in \(I_{th}\) (see Supplementary Information Section S5).
+ +<|ref|>text<|/ref|><|det|>[[85, 696, 487, 737]]<|/det|> +that \(\Delta (\mathbf{r}, V) = 0\) . A topological defect, which locally suppresses \(\epsilon^2 (\mathbf{r})\) , thus acts as a seed with a lower threshold compared to the rest of the system. + +<|ref|>text<|/ref|><|det|>[[85, 745, 487, 877]]<|/det|> +Intriguingly, we also note from Eq. 4 that the IMT can take place at a non- zero value of \(\epsilon^2 (\mathbf{r})\) , which allows the formation of a non- thermal metallic state \((\eta = - 1)\) with a finite monoclinic distortion \((\epsilon^2 (\mathbf{r}) \lesssim \epsilon_t^2 (V))\) , as already observed in non- equilibrium optical experiments [37, 44]. The nature of the early- stage switching process can be inferred by a direct comparison between the electrical state of the device and the melting of the monoclinic domains. The \(I - V\) curve of the device, as measured in- situ during the PEEM imaging, is plotted in Fig. 4a). + +<|ref|>text<|/ref|><|det|>[[515, 697, 917, 867]]<|/det|> +XLD- PEEM images were recorded at specific values of \(I\) . The \(I - V\) plot shows that the first resistive switching event occurs at the threshold current \(I_{th} = 1.05 \mathrm{mA}\) . In Figure 4b) we report a linecut of the XLD- PEEM image acquired at specific values of \(I\) ; the image profile is taken along a line crossing the monoclinic domains in the middle of the device gap (see white solid line in Fig. 4, top panel). For large currents running through the device, the line profile in Fig. 4b) displays a flat region, which indicates the melting of the monoclinic nanodomains due to the formation of the rhombohedral metallic channel. As highlighted by the grey area in Fig. 4b), the width \(d\) of the metallic filament increases with the current, from + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 102, 488, 128]]<|/det|> +\(d = 0.23\pm 0.05\mu \mathrm{m}\) at \(I = 1.5\mathrm{mA}\) to \(d = 3.7\pm 0.2\mu \mathrm{m}\) at \(I = 10\mathrm{mA}\) + +<|ref|>text<|/ref|><|det|>[[85, 135, 488, 757]]<|/det|> +Modelling the device as a circuit with two parallel resistors (see Supplementary Information Section S5) allows an estimation of \(d\) of the rhombohedral filament corresponding to the observed voltage drop. For large currents running through the device, the experimentally determined values of \(d\) match well with those predicted for a metallic channel forming in the gap, which has the resistivity of the high- temperature rhombohedral phase, as shown in Fig. 4. However, in correspondence of the first resistive switching event at \(I_{th} = 1.05\mathrm{mA}\) , the model predicts the sudden formation of a \(\sim 200\mathrm{nm}\) wide metallic rhombohedral filament, which is not visible in the XLD- PEEM images (see Fig. 4c and Supplementary Information Fig. S5), despite being well above the experimental resolution of the microscope. To explain this discrepancy, one might suspect that a rhombohedral metallic filament forms below the surface of the \(\mathrm{V}_2\mathrm{O}_3\) film, where it is not detected by PEEM which has a surface sensitivity limited to the first few nanometers. In fact, two arguments act against this possibility: i) the presence of the \(\mathrm{Cr}_2\mathrm{O}_3\) buffer layer reduces the substrate- film lattice mismatch from \(4.2\%\) to \(0.1\%\) , thus almost entirely removing the residual epitaxial strain in the film [42], which is known to suppress the monoclinic phase and favour interfacial metallicity [42, 45]. In contrast to highly- strained films, in which the metal to insulator resistivity jump is strongly suppressed [45], the films in the present study display the 5- order of magnitude resistivity change typical of the unstrained metal- to- insulator transition (see Fig. S1); ii) the curl- free conditions force the interface between monoclinic and rhombohedral metallic regions to be oriented perpendicularly to the order parameter of the monoclinic domain. The formation of a sub- surface metallic layer would lead to a sharp \((\ll 20\mathrm{nm})\) monoclinic- rhombohedral interface parallel to \(\vec{\epsilon}\) , thus leading to a dramatic increase of the strain energy of the system. Our results are compatible with a complex scenario in which the topology- driven resistive switching likely occurs via the sudden transformation of a single \(200\mathrm{nm}\) wide insulating monoclinic domain into a metallic channel with a non- thermal monoclinic lattice structure. At a second stage, the Joule heating leads to the thermally driven monoclinic- to- rhombohedral structural transition and the formation of rhombohedral metallic channels perpendicular to both the metallic electrodes and the \(\hat{\epsilon}_2\) order parameter direction, as observed in Fig. 2. + +<|ref|>text<|/ref|><|det|>[[86, 763, 488, 870]]<|/det|> +The X- ray- based nanoimaging of a Mott device under operating conditions allowed us to simultaneously capture the formation of nanoscale conductive paths and the topology of the underlying symmetry- broken nanotexture. The present results expand our knowledge of the resistive switching process in Mott materials by demonstrating the leading role of inherent topological defects in initiating the avalanche process. The + +<|ref|>text<|/ref|><|det|>[[515, 103, 917, 487]]<|/det|> +methodologies used in this work imply that nanoscale strain engineering approaches could unlock a gate to manipulating topological defects and controlling the electronic switching dynamics in real devices, such as Mott- transition- based RRAM [46, 47], Mott memristor [48- 50] and artificial neurons [51, 52]. The concept of topology- driven resistive switching will be key to assessing the possible non- thermal nature of the early stage electronic phase [37] as well as the microscopic origin of memory and non- volatile effects recently observed in Mott devices [6]. We note that the relation between topological defects and electronic phase transitions established here is a general concept, potentially extendable to other systems that undergo first- order phase transitions accompanied by a symmetry breaking, as described by the energy functional (4). Relevant examples embrace transition- metal oxides [3, 53], such as vanadates, nickelates and manganites, and layered materials, such as \(1T\) - TaS2 [54- 57], in which the IMT is accompanied by charge-, lattice- and orbital- ordered states with reduced symmetry. Further platforms include cuprate superconductors [58] and kagome metals [59] in which light- or magnetic- induced discontinuous electronic transitions coexist with charge- order. Topological defects in the order parameter therefore provide a framework for understanding non- equilibrium electronic phase transitions, allowing all- optical control of hidden states of matter in a broad class of quantum materials [57, 60- 64]. + +<|ref|>text<|/ref|><|det|>[[515, 500, 917, 870]]<|/det|> +We thank Diamond Lights Source for the provision of beamtime under proposal numbers MM- 27218, MM- 31711 and MM- 34455. We thank Manuel R. Osorio and Fernando J. Urbanos for the fabrication of sample electrodes at the Centre for Micro and Nanofabrication of IMDEA Nanociencia. A.M., S.M. and C.G. acknowledge financial support from MIUR through the PRIN 2015 (Prot. 2015C5SEJJ001) and PRIN 2017 (Prot. 20172H2SC4.005) programs and from the European Union - Next Generation EU through the MUR- PRIN2022 (Prot. 20228YCYY7) program. C.G. acknowledges support from Università Cattolica del Sacro Cuore through D.I, D.2.2 and D.3.1 grants. S.M. acknowledges partial financial support through the grant "Finanziamenti ponte per bandi esterni" from Università Cattolica del Sacro Cuore. I.F.C. and M.M. acknowledge support from the "Severo Ochoa" Programme for Centres of Excellence in R&D (CEX2020- 001039- S) and the Spanish AEI- MCIN PID2021- 122980OB- C52 (ECoSOC- ECLIPSE). I.F.C holds a FPI fellowship from the Spanish AEI- MCIN (PRE2020- 092625). W.- F.H., S.M., J.W.S. and J.- P.L. acknowledge financial support by the KU Leuven Research Funds Project No. C14/21/083, iBOF/21/084, KAC24/18/056 and C14/17/080, as well as the FWO AKUL/13/19 and AKUL/19/023, and the Research Funds of the INTERREG- E- TEST Project (EMR113) and INTERREG- VL- VL- PATHFINDER Project (0559). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 135, 490, 875]]<|/det|> +[1] Z. Wang, H. Wu, G. W. Burr, C. S. Hwang, K. L. Wang, Q. Xia, and J. J. Yang, Resistive switching materials for information processing, Nature Reviews Materials 5, 173 (2020).[2] Y. Zhou and S. Ramanathan, Mott Memory and Neuromorphic Devices, Proceedings of the IEEE 103, 1289 (2015).[3] Y. Tokura, M. Kawasaki, and N. Nagaosa, Emergent functions of quantum materials, Nature Physics 13, 1056 (2017).[4] J. del Valle, J. G. Ramirez, M. J. Rozenberg, and I. K. 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Singer, Picosecond volume expansion drives a later- time insulator- metal transition in a nano- textured Mott insulator, Nature Physics 10.1038/s41567- 024- 02396- 1 (2024). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 92, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 348, 150]]<|/det|> +SIV203ResistiveSwitching.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/images_list.json b/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..12b63c2d20e29b4f9d5a2ce308db96120a486f05 --- /dev/null +++ b/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/images_list.json @@ -0,0 +1,25 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Experimental approach. a, Saccade countermanding task. Monkeys initiated trials by fixating a central point. After a variable time, the center of the fixation point was extinguished, and a peripheral target was presented at one of two possible locations. On no stop-signal trials monkeys were required to shift gaze to the target, whereupon after \\(600\\pm 0\\) ms a high-pitch auditory feedback tone was delivered, and \\(600\\pm 0\\) ms later fluid reward was provided. On stop-signal trials ( \\(\\sim 40\\%\\) of trials) after the target appeared, the center of the fixation point was re-illuminated after a variable stop-signal delay, which instructed the monkey to cancel the saccade in which case the same high-pitch tone was presented \\(1,500\\pm 0\\) ms after target presentation followed \\(600\\pm 0\\) ms later by fluid reward. Stop-signal delay was adjusted such that monkeys successfully canceled the saccade in \\(\\sim 50\\%\\) of trials. In the remaining trials, monkeys made non-canceled errors, which were followed after \\(600\\pm 0\\) ms by a low-pitch tone, and no reward was delivered. Monkeys could not initiate trials earlier after errors. b, Grand average cumulative distributions of all RT for both monkeys on trials with no stop-signal (solid) and non-canceled errors (dashed). c, Grand average probability of non-canceled errors (P(error)) as a function of stop-signal delay. Inset shows the distribution of SSRT across all", + "footnote": [], + "bbox": [ + [ + 327, + 94, + 720, + 664 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Fig 6 | Extrinsic and intrinsic circuitry for executive control. The laminar distribution observed for Conflict (orange), Event Timing (dark blue), and Goal Maintenance (dark red) are summarized with selected anatomical connections based on published studies. Sampled neurons were likely broad spike pyramidal and narrow spike, possibly inhibitory neurons. The laminar densities of calretinin (CR), calbindin (CB), and parvalbumin (PV) neurons observed and of D1 and D2 receptors are indicated on the far right. Left, Conflict signal can arise in SEF through afferents from frontal eye field (FEF). SEF can receive coincident inputs from Fixation neurons (STOP) and Movement neurons (GO) in FEF, directly, or in SC, indirectly, via thalamus, terminating in L2/3. These inputs are integrated within the synapses of L2/3 and L5 Conflict neurons. Intracortical processing produces later activation of Conflict neurons in L6 which can relay this signal to the Thalamus. Right, Top: Schematic of the activity profile for Goal Maintenance and Event Timing neurons in distinct phases indicated by the number. We conjecture that Goal Maintenance neurons, mainly located in L2/3, suppress unwanted movement through push-pull basal ganglia circuitry with pyramidal neurons directly projecting to the indirect pathway (D2) and inhibitory neurons, inhibiting pyramidal neurons that can project to the direct (D1) pathway. The gray symbol indicates that these neurons are distinct from those reported in this study. Input from dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), terminating in L2/3 can inform SEF of the anticipated reward association based on the experienced stop-signal delay contingent on successful stopping. Dopamine (DA) neuron projections in L2/3 from the SNpc and VTA can also relay this information. These inputs can result in the phasic response in Goal Maintenance neurons (phase 1, red). Following the phasic response, activity can remain elevated via recurrent connections and balance of excitation and inhibition (phase 2, red). The auditory feedback tone, integrated with the task rule from DLPFC cues the termination of operant control on behavior, resulting in the inhibition of pyramidal and inter-neurons by CR and CB neurons. This results in the termination of the sustained activity (phase 3). Event Timing neurons can receive input from DLPFC and ACC terminating in L2/3 informing neurons in L2/3 and L5 about an upcoming event. Ramping results from recurrent connections (1, dark blue). SEF can receive information about stop-signal appearance and successful stopping from ventrolateral prefrontal cortex (VLPFC) and DLPFC and Conflict neurons within the microcircuitry. This information can suppress the ramping activity via inhibitory", + "footnote": [], + "bbox": [], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f.mmd b/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..df6f96eafb03cc7fa87b797bd91f7f3aa4cbed17 --- /dev/null +++ b/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f.mmd @@ -0,0 +1,608 @@ + +# Functional Architecture of Executive Control and Associated Event-Related Potentials + +Amirsaman Sajad Vanderbilt University Steven Errington Vanderbilt University https://orcid.org/0000- 0002- 0948- 6559 Jeffrey Schall ( \(\square\) jeffrey.d.schall@vanderbilt.edu ) York University https://orcid.org/0000- 0002- 5248- 943X + +## Article + +Keywords: frontal cortex, executive control, event- related potentials (ERP) + +Posted Date: May 17th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 468741/v1 + +License: © \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on October 21st, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 33942- 1. + +<--- Page Split ---> + +# Functional Architecture of Executive Control and + +# Associated Event-Related Potentials + +3 + +Amirsaman Sajad1*, Steven P. Errington1*, & Jeffrey D. Schall1,2 + +5 + +1 Department of Psychology, Vanderbilt Vision Research Center, Center for Integrative & + +Cognitive Neuroscience, Vanderbilt University, Nashville, TN + +2 Centre for Vision Research, Vision Science to Application, Department of Biology, York + +University, Toronto, ON + +* authors contributed equally to this work. + +11 + +Corresponding author: + +Jeffrey D. Schall, Ph.D. + +E- mail: schalljd@yorku.ca + +<--- Page Split ---> + +Medial frontal cortex enables executive control by monitoring relevant information and using it to adapt behavior. In macaques performing a saccade countermanding (stop- signal) task, we recorded EEG over and neural spiking across all layers of the supplementary eye field (SEF). We report the laminar organization of concurrently activated neurons monitoring the conflict between incompatible responses and the timing of events serving goal maintenance and executive control. We also show their relation to coincident event- related potentials (ERP). Neurons signaling response conflict were largely broad- spiking found across all layers. Neurons signaling the interval until specific task events were largely broad- spiking neurons concentrated in L3 and L5. Neurons predicting the duration of control and sustaining the task goal until the release of operant control were a mix of narrow- and broad- spiking neurons confined to L2/3. We complement these results with the first report of a monkey homologue of the N2/P3 ERP complex associated with response inhibition. N2 polarization varied with error likelihood and P3 polarization varied with the duration of expected control. The amplitude of the N2 and P3 were predicted by the spike rate of different classes of neurons located in L2/3 but not L5/6. These findings reveal important, new features of the cortical microcircuitry supporting executive control and producing associated ERP. + +Effective control of behavior is necessary to achieve goals, especially when faced with competing instructions inducing response conflict and requiring inhibition of prepotent responses and maintenance of task goals, and adaptation of performance. These features of executive control are investigated with the countermanding (stop- signal) task \(^{1}\) , during which macaque monkeys, like humans, exert response inhibition and adapt performance based on stimulus history, response outcomes, and the temporal structure of task events \(^{2}\) . + +Medial frontal cortex enables executive control, but circuit- level mechanisms remain uncertain \(^{3,4}\) . Hypotheses on executive control function have been tested in humans using noninvasive ERP measures derived from a negative- positive waveform known as the N2/P3 + +<--- Page Split ---> + +associated with stopping \(^{5}\) . However, their cortical source is unknown. Mechanistic hypotheses about the basis of these signals require information about neural spiking patterns across cortical layers \(^{6}\) . Moreover, understanding function at the resolution of layers can clarify circuit- level mechanisms because neurons in different layers have different extrinsic anatomical connections. We can obtain such information from the supplementary eye field, an agranular area on the dorsomedial convexity in macaques, immediately beneath where the frontal ERPs are sampled. SEF contributes to proactive but not reactive inhibition \(^{7}\) and its activation improves performance in the countermanding task by delaying response time \(^{8}\) through postponing the accumulation of pre- saccadic activity \(^{9}\) . SEF also supports working memory \(^{10, 11}\) , and signals surprise \(^{12}\) , event timing \(^{13, 14}\) , response conflict \(^{15}\) , plus errors and reinforcement \(^{16}\) . SEF in macaques is homologous to SEF in humans \(^{17}\) . + +The canonical cortical microcircuit derived from granular sensory areas \(^{18}\) does not explain agranular frontal areas like SEF \(^{19, 20, 21, 22, 23}\) . Recently we described the laminar microcircuitry of performance monitoring signals in the SEF, and relationship to the ERP indexing error monitoring known as the error- related negativity (ERN) \(^{16}\) . Here we describe the laminar microcircuitry of signals that monitor events occurring during successful stopping performance. We define three classes of neurons that concurrently signal response conflict, timing of events, and maintenance of task goals. We also establish that macaque monkeys produce the N2/P3 ERP associated with response inhibition, elucidating task factors indexed by this ERP complex and the neuron classes predicting their polarization. + +<--- Page Split ---> + +## RESULTS + +## Countermanding performance, neural sampling, and functional classification. + +Neurophysiological and electrophysiological data was recorded from two macaque monkeys performing the saccade countermanding task with explicit feedback tone cues (Fig. 1a) \(^{24}\) . Data collection and analysis was informed by the consensus guide for the stop- signal task \(^{25}\) . In 29 sessions we acquired 33,816 trials (Monkey Eu, male, 12 sessions 11,583 trials; X, female, 17 sessions 22,233 trials). Typical performance was produced by both monkeys. Response times (RT) on failed inhibition trials (noncancelled trials) (mean ± SD Eu: 294 ± 179 ms; X: 230 ± 83 ms) were systematically shorter than those on no stop- signal trials (Eu: 313 ± 119 ms, X: 263 ± 112 ms; mixed effects linear regression grouped by monkey, t(27507) = - 17.4, p < 10 \(^{- 5}\) ) (Fig. 1b). Characteristically, the probability of noncancelled errors increased with stop- signal delay (SSD) (Fig. 1b). These two observations validate the use of the independent race model \(^{26}\) to estimate the stop- signal reaction time (SSRT), the time needed to cancel a partially prepared saccade. Accordingly, neural modulation before SsRT can contribute to stopping but that after SsRT cannot \(^{7,26}\) . SsRT across sessions (Eu: 118 ± 23 ms, X: 103 ± 24 ms) did not differ between monkeys (t(27) = - 1.69, p = 0.1025). While there were other classes of errors made in the task, they were infrequent and therefore inconsequential to this study. Therefore, P(error) refers to the probability of noncancelled errors. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 | Experimental approach. a, Saccade countermanding task. Monkeys initiated trials by fixating a central point. After a variable time, the center of the fixation point was extinguished, and a peripheral target was presented at one of two possible locations. On no stop-signal trials monkeys were required to shift gaze to the target, whereupon after \(600\pm 0\) ms a high-pitch auditory feedback tone was delivered, and \(600\pm 0\) ms later fluid reward was provided. On stop-signal trials ( \(\sim 40\%\) of trials) after the target appeared, the center of the fixation point was re-illuminated after a variable stop-signal delay, which instructed the monkey to cancel the saccade in which case the same high-pitch tone was presented \(1,500\pm 0\) ms after target presentation followed \(600\pm 0\) ms later by fluid reward. Stop-signal delay was adjusted such that monkeys successfully canceled the saccade in \(\sim 50\%\) of trials. In the remaining trials, monkeys made non-canceled errors, which were followed after \(600\pm 0\) ms by a low-pitch tone, and no reward was delivered. Monkeys could not initiate trials earlier after errors. b, Grand average cumulative distributions of all RT for both monkeys on trials with no stop-signal (solid) and non-canceled errors (dashed). c, Grand average probability of non-canceled errors (P(error)) as a function of stop-signal delay. Inset shows the distribution of SSRT across all
+ +<--- Page Split ---> + +sessions for both monkeys. d, Neural spiking was recorded across all layers of agranular SEF (NeuN stain) using Plexon U- probe. Neurons with both broad (black) and narrow (red) spikes were sampled. Spiking modulation was measured relative to presentation of task events (thin solid, visual target; thick solid, stop- signal) and performance measures like SsRT (dashed vertical). Simultaneously, EEG was recorded from the cranial surface with an electrode positioned over the medial frontal cortex (10- 20 location Fz). Yellow rectangle portrays cortical area sampled in a T1 MR image. + +EEG was recorded with leads placed on the cranial surface beside the chamber over medial frontal cortex while a linear electrode array (Plexon, 24 channels, \(150 \mu m\) spacing) was inserted in SEF (Fig. 1c). SEF was localized by anatomical landmarks and intracortical electrical microstimulation \(^{20}\) . We recorded neural spiking in 29 sessions (Eu: 12, X: 17) sampling activity from 5 neighboring sites. Overall, 575 single units (Eu: 244, X: 331) were isolated, of which 213 (Eu: 105, X: 108) were modulated after SsRT. The description of the laminar distribution of signals is based on 16 of the 29 sessions during which electrode arrays were oriented perpendicular to cortical layers and we could assign neurons to different layers confidently \(^{20}\) (see Supplementary Fig. 1 of \(^{16}\) ). Additional information about laminar structure was assessed through the pattern of phase- amplitude coupling across SEF layers \(^{22}\) . Due to variability in the estimates and the indistinct nature of the L6 border with white matter, some units appeared beyond the average gray- matter estimate; these were assigned to the nearest cellular layer. In all, 119 isolated neurons (Eu: 54; X: 65) contributed to the results on laminar distribution of executive control signals subserving successful stopping (Supplementary Table 1a). + +To identify neural activity associated with saccade countermanding, we examined the activity across different SSDs on canceled trials in which the subject successfully inhibited the movement, and latency- matched no stop- signal trials in which no stopping was required \(^{27}\) . A consensus cluster algorithm \(^{28}\) with manual curation identified neurons with response facilitation (n = 129) and response suppression (n = 84) following the stop- signal (Supplementary Figure 1). Simultaneously, we observed distinct patterns in the cranial EEG related to successful + +<--- Page Split ---> + +stopping with characteristic N2 and P3 components (Fig 1c). Whilst we previously described neural signals after errors and associated with reward, here we focused on the interval in which response inhibition was accomplished. Specifically, we quantified spiking before and after SSRT and before the feedback tone (Tone), which terminated operant control on behavior. To elucidate contributions of the diverse neurons, we compared and contrasted how well neural spiking related to a variety of computational parameters inherent in the task. + +First, performance of the stop- signal task is explained as the outcome of a race between stochastic GO and STOP processes \(^{26}\) , instantiated by specific interactions enabling the interruption of the GO process by a STOP process \(^{29,30}\) (Supplementary Figure 2a). An influential theory of medial frontal function posits that it encodes the conflict between mutually incompatible processes \(^{31}\) . Such conflict arises naturally as the mathematical product of the activation of GO and STOP units, which is proportional to P(error). Hence, neural signals that scale with P(error) can encode conflict in this task. + +Second, inspired by reinforcement learning models, we considered the possibility that neural signals reflect the error- likelihood associated with an experienced SSD \(^{32}\) . Note, on some stop- signal error trials, the response was generated before the stop- signal appeared. The error- likelihood can only form based on trials in which SSD elapsed before RT such that monkeys could see the stop- signal (referred to as SSseen). Hence, neural signals that scale with P(error | SSseen) can encode error likelihood in this task. Conflict indexed by P(error) and error likelihood indexed by P(error | SSseen) diverge at longer SSDs (Supplementary Figure 2c). + +Third, monkeys can learn the timing of the various task events (Supplementary Figure 2b). For example, monkeys are sensitive to the adjustments of SSD that are made to maintain \(\sim 50\%\) success on stop- signal trials \(^{33}\) . Previous research has characterized time perception \(^{34,35}\) . Key features include sensitivity to log(interval) versus its absolute value with precision decreasing with duration and sensitivity to instantaneous expectation (i.e., hazard rate) of + +<--- Page Split ---> + +events (Supplementary Figure 2d- e). Therefore, neural activity around the time of SSD can scale with the timing or expectation of the stop- signal \(^{13, 14, 37, 38}\) . This expectation can be derived from experienced SSD and the estimated probability of stop- signal appearance + +(Supplementary Figure 2e). Moreover, to earn reward, monkeys were required to maintain fixation on the target on trials with no stop- signal or on the fixation spot on canceled trials for an extended period (Tone) until a tone secondary reinforcer (feedback) announced delivery of reward after another interval. Hence, neural activity associated with the tone can scale with the timing or instead the expectation of the tone, which was variable on canceled trials but predictable based on the experienced SSD (Supplementary Figure 2d). + +Alternatives were compared through mixed- effects model- comparison with Bayesian Information Criteria (BIC). As detailed below, many neurons signaled conflict and more signaled event timing with activity sustained until earning reward. + +Monitoring Conflict. We found 75 neurons in SEF with transient facilitation after SSRT on canceled trials, compared to latency- matched no stop- signal trials, that was proportional to P(error) (Fig. 2; Supplementary Figure 3; Supplementary Table 2). The transient modulation in these neurons was not just a visual response to the stop- signal because it did not happen on noncanceled trials (Supplementary Figure 1e). On average, this modulation started \(99 \pm 8\) ms (mean \(\pm\) SEM) after SSRT. Figure 2a shows the recruitment of these neurons through time. Nearly all (71/75) were recruited after SSRT, and the proportion of recruited neurons peaked at \(\sim 60\%\) \(\sim 110\) ms after SSRT and gradually reduced to \(8\%\) after 500 ms (Fig. 2a). As this facilitation occurs after SSRT, it cannot contribute to reactive response inhibition \(^{7}\). On canceled trials a minority of these neurons produced weak, persistent activity that lasted until the tone, and some also exhibited a brief transient response following the tone (Fig. 2; Supplementary Figure 1c). + +<--- Page Split ---> +![](images/Figure_6.jpg) + + +<--- Page Split ---> + +Fig. 2 | Time-depth organization of Conflict neuron spiking in SEF. a, Normalized population response of neurons with transient facilitation in discharge rate on successfully canceled (thick) relative to latency-matched no stop-signal (thin) trials for early SSD (top). Recruitment of this signal through time relative to SSRT (left) and auditory feedback tone (right), with dark and light shades representing the recruitment of broad-spiking (spike width \(\geq 250 \mu \mathrm{s}\) ) and narrow-spiking ( \(< 250 \mu \mathrm{s}\) ) neurons (bottom). Recruitment on SSRT-aligned activity (left panel) is defined as the difference between canceled and no stop-signal trials. Recruitment on tone-aligned activity (right panel) is defined as the activity on canceled trials relative to the baseline. Modulations starting 300ms after the tone are not included. b, Time-depth plot showing latency and proportion of recruited neurons through time at each depth from perpendicular penetrations. Symbols mark beginning of modulation for broad-spiking neurons (black triangles) and narrow-spiking neurons (white stars). Color map indicates the percentage of neurons relative to the overall sampling density (Supplementary Figure 1a) producing this signal through time at each depth. Dashed horizontal line marks L3-L5 boundary. The lower boundary of L6 is not discrete. c (left), Comparison of response of a representative neuron on successfully canceled (thick) relative to latency-matched no stop-signal (thin) trials for low (lighter) and higher (darker) P(error). Shaded area represents significant difference in discharge rate between the two conditions. c (right) Relationship between spike rate, sampled from the period with significant modulation for each neuron and the corresponding P(error). Along the ordinate scale is plotted the spiking rate, adjusted for neuron-specific variations. Along the abscissa scale is plotted the normalized P(error) (z-scale). In all, 225 points are plotted. Variation of spiking rate was best predicted by P(error) (highlighted by the best-fit line; Supplementary Table 2). + +We assessed how the magnitude of this transient modulation after SSRT varied with the various task and performance parameters described above. The magnitude of this modulation varied most closely with P(error) - a measure of conflict (Mixed- effects linear regression grouped by neuron, \(\mathrm{t}(104) = 3.57\) , \(\mathrm{p} = 5.4 \times 10^{- 4}\) ). This conflict model obtained lower BIC than models of SSD or any other quantity, with weak support against the \(\mathrm{P(error} | \mathrm{SS}_{\mathrm{seen}})\) ( \(\Delta \mathrm{BIC} = 1.29\) ) and strong support against other models ( \(\Delta \mathrm{BIC} > 2.7\) ) (Fig 2c; Supplementary Table 2). + +We noted that the vast majority (65/75) of Conflict neurons did not signal noncanceled errors, supporting previous findings (Supplementary Table 3) \(^{15,16}\) . However, many (41/75) also exhibited modulation that signaled outcome following the feedback tone and around the time of reward. Some exhibited higher discharge rates on unrewarded trials (previously identified as Loss signal \(^{16}\) ), and some, higher discharge rates on rewarded trials (previously identified as Gain signal \(^{16}\) ). The multiplexing of the conflict monitoring signal with Gain and + +<--- Page Split ---> + +Loss signals (in different task epochs) did not differ from that predicted based on their sampling prevalence \((X^{2}(3, N = 575) = 1.02, \text{p} = 0.79; \text{Supplementary Table 3})\) . + +Conflict neurons were found at all recording sites but more commonly at some \((X^{2}(4, N = 575) = 11.6, \text{p} = 0.020)\) . Using trough- to- peak duration of the action potential waveform, the majority (63/75) had broad spikes consistent with pyramidal neurons. This distribution did not differ from the overall sampling distribution in SEF \((X^{2}(1, N = 575) = 0.67, \text{p} = 0.41)\) . + +From sessions with perpendicular penetrations, we assigned 36 of the 75 Conflict neurons to a cortical layer. They were found in all layers at a relative prevalence across layers indistinguishable from that of the overall sampling distribution \((X^{2}(4, N = 293) = 4.28, \text{p} = 0.37; \text{Fig 2b; Supplementary Table 1b})\) . The timing of the modulation did not differ between L2/3 and L5/6 \((t(34) = 0.3367, \text{p} = 0.74, \text{two tailed})\) . The few neurons modulating with the tone were observed sparsely across all layers. + +In summary, as reported previously \(^{15}\) , neurons in SEF modulate in a manner consistent with signaling the co- activation of gaze- shifting (GO) and gaze- holding (STOP) processes. This co- activation has previously been interpreted as conflict \(^{31,39}\) . The new results show that these neurons are distributed across all SEF layers and are predominantly putative pyramidal neurons with broad spikes. + +Time keeping. Monkeys adapt performance by learning the temporal regularities of the task \(^{33,40}\) . We identified neurons across the layers of SEF with modulation representing event timing and interval duration through facilitation, suppression, and ramping activity \(^{13,14,37,41}\) + +(Supplementary Fig 2c, d). Following target presentation, the discharge rate of many neurons \((N = 84)\) ramped up until the saccade on trials in which they were generated (no stop- signal or noncanceled error trials). On canceled trials, however, the discharge rate was instead abruptly reduced after SsRT (Fig 3a; Supplementary Fig 1c- e). Because the first pronounced + +<--- Page Split ---> + +suppression began after SsRT, these neurons cannot contribute directly to response inhibition. + +Relative to SsRT, these neurons were suppressed before the facilitation in the conflict + +monitoring neurons (t- test, t(157) = - 3.60, p = 4.2×10- 4). The ramping activity from target to + +SSRT varied best with the time- based models of SSD (t(250) = 12.62, p = 0.0013) with strong + +support against other models ( \(\Delta\) BIC > 2.7) (Supplementary Table 2). The log- transformed + +model outperformed the linear model but evidence against the linear model was weak ( \(\Delta\) BIC = + +1.35). Because the discharge rate dropped sharply on canceled trials but not on noncanceled + +stop- signal trials (Supplementary Fig 1e), we conjecture that these neurons encode the + +temporal aspects of events leading to successful stopping and not the timing of the stop- signal + +appearance per se. Once successful stopping occurred, these neurons were suppressed. + +<--- Page Split ---> +![PLACEHOLDER_13_0] + + +<--- Page Split ---> + +Fig. 3 | Time-depth organization of Event Timing neuron spiking in SEF. a, Normalized population response of neurons with suppression of discharge rate on successfully canceled (thick) relative to latency- matched no stop- signal (thin) trials for early SSD (bottom). Recruitment of signal through time relative to SSRT (left) and auditory feedback tone (right), with dark and light shades representing the recruitment of broad- spiking (spike width \(\geq 250 \mu \mathrm{s}\) ) and narrow- spiking ( \(< 250 \mu \mathrm{s}\) ) neurons (bottom). Recruitment on SSRT- aligned activity (left panel) is defined as the difference between canceled and no stop- signal trials. Recruitment on tone- aligned activity (right panel) is defined as the activity on canceled trials relative to the baseline. Modulations starting 300ms after the tone are not shown. b, Time- depth plot showing latency and proportion of recruited neurons through time at each depth from perpendicular penetrations. Symbols mark beginning of modulation for broad- spiking neurons (black triangles) and narrow- spiking neurons (white stars). Color map indicates the percentage of neurons relative to the overall sampling density (Supplementary Figure 1a) producing this signal through time at each depth. Dashed horizontal line marks L3- L5 boundary. The lower boundary of L6 is not discrete. c, Left panel shows response of a representative neurons on successfully canceled (thick) and latency- matched no stop- signal (thin) trials for early (lighter) and later (darker) SSD. Pre- SSRT ramping activity occurs irrespective of trial class. Shaded area represents the time epoch used for sampling neuron activity (50 ms window pre- SSRT). Right panel plots relationship between discharge rate in the sampling interval and stop- signal delay. Along the ordinate scale is plotted the normalized spiking rate, adjusting for neuron- specific variations. Along the abscissa scale is plotted the normalized (z- transformed) stop- signal delay in logarithmic scale. In all, 252 points (84 neurons) are plotted. Each point plots the average spike- density and associated Log (SSD) in one of 3 bins corresponding to early-, mid-, or late- SSD, for each neuron. Variation of spiking rate was best predicted by the time of the stop- signal (highlighted by best- fit line). d, Left panel plots response of the same representative neuron as c indicating pre- tone ramping activity on successfully canceled (thick) relative to latency- matched no stop- signal (thin) trials for early (lighter) and later (darker) SSD. Shaded area represents the time epoch used for sampling neuron activity (50 ms window pre- Tone). Right panel plots relationship between discharge rate in the sampling interval and the time of feedback relative to stop- signal. Along the ordinate scale is plotted the spiking rate, adjusted for neuron- specific variations. Along the abscissa scale is plotted the normalized stop- signal delay in logarithmic scale (z- scale). In all, 144 points (38 neurons with pre- tone activity on canceled trials) are plotted. Each point plots the average spike- density and associated log (feedback time) in one of 3 bins corresponding to early-, mid-, or late- SSD, for each neuron. Variation of spiking rate was best predicted by the time of the feedback time (highlighted by best- fit line; Supplementary Table 2). + +A subset of these neurons (29/84) also exhibited monotonic ramping of discharge rate following the sharp suppression, persisting until after the feedback tone whereupon the spike rate again decreased (Fig 3d). In some neurons this decrease followed a brief transient response (Fig 3a). The variation in dynamics of the ramping before the tone was best accounted for by the time of the feedback tone after the stop- signal (t(112) = 3.41, \(9.1 \times 10^{- 4}\) ) with strong support against other models ( \(\Delta \text{BIC} > 5.0\) ). The linear and log- transformed models were indistinguishable ( \(\Delta \text{BIC} < 0.1\) ) (Supplementary Table 2). The termination of this modulation + +<--- Page Split ---> + +was best described by the time of the feedback tone and not the time at which fixation from stop- signal was broken (Supplementary Figure 4c). + +Because the ramping activity in this population of neurons scaled with the time of the stop- signal and the tone, followed by immediate suppression after their occurrence, we conjecture that these neurons represent event timing to accomplish the task. We will refer to these neurons as Event Timing neurons. While all of these neurons encoded the timing of events related to successful stopping, only \(\sim 30\%\) also encoded the timing of the feedback tone. + +Event Timing neurons were found in all penetrations, but more commonly in certain sites \((X^{2}(4, N = 575) > 39.3, p < 10^{- 5})\) (Fig 3b, Supplementary Table 1a). The majority (73 / 84) had broad spikes, corresponding to the overall sampling distribution in SEF \((X^{2}(1, N = 575) = 2.56, p = 0.11)\) . From sessions with perpendicular penetrations, we assigned the layer of 49 of the 84 neurons. The laminar organization of these neurons did not differ from the overall laminar sampling distribution \((X^{2}(4, N = 293) = 7.33, p = 0.12)\) . However, those with ramping activity before the tone (which resulted in a prolonged differential activity level between no- stop and canceled trials) were more confined to lower L3 and upper L5. The time of modulation after SSRT or around the tone did not vary across layers. + +In summary, neurons in SEF exhibit ramping activity that can signal the time preceding critical events for successful task performance. The new results show that these neurons are distributed across all SEF layers and are predominantly pyramidal neurons. Often these neurons also exhibited post- feedback ramping activity leading to the time of reward delivery. Accordingly, a higher proportion of these neurons were identified as Gain neurons compared to that predicted by the prevalence of Gain and Loss neurons \(^{16}\) \((X^{2}(3, N = 575) = 44.86, p = < 10^{- 5}\) ; Supplementary Table 3). + +Goal Maintenance. By design, to earn reward on canceled trials, monkeys were required to maintain fixation on the stop- signal until an auditory feedback tone occurred. As such, the state + +<--- Page Split ---> + +of response inhibition needed to be maintained for an arbitrary interval. Many other neurons (N = 54) in SEF produced spike rate modulation sufficient to contribute to this maintenance (Fig 4). These neurons produced significantly greater discharge rates on canceled trials after SSRT, compared to latency- matched no- stop trials. Modulation was weak or absent on noncancelled error trials, so this activity was not a response to the stop- signal. This modulation began too late to contribute to response inhibition but persisted while fixation maintenance was required (Supplementary Figure 1d, e). + +These neurons were distinguished from Conflict neurons by the more prolonged facilitation following SSRT (Supplementary Figure 1b, c). The peak recruitment of these neurons ( \(\sim 300\) ms) followed that of the neurons monitoring conflict ( \(\sim 110\) ms) and the suppression of the Event Timing neurons ( \(\sim 170\) ms). Compared to Conflict neurons, the phasic facilitation was followed by sustained activity until \(\sim 300\) ms after the feedback tone in a significantly higher proportion of these neurons ( \(X^{2}(1, N = 129) = 27.3\) , \(p < 10^{- 5}\) ) (Fig 4a). This modulation at tone presentation was also observed on no stop- signal trials. The variation in the magnitude of the phasic modulation was best described by the log- transformed duration until the feedback tone on canceled trials (Fig 3d) (t(152) = 3.53, \(p = 5.6 \times 10^{- 4}\) ), with strong evidence against non- time- based models ( \(\Delta \text{BIC} > 3.0\) ) and weak evidence against other time- based models ( \(\Delta \text{BIC} < 1\) ) (Supplementary Table 2). + +<--- Page Split ---> +![PLACEHOLDER_17_0] + + +<--- Page Split ---> + +Fig. 4 | Time-depth organization of Goal Maintenance neuron spiking in SEF. a, Normalized population response of neurons with prolonged facilitation in discharge rate on successful canceled (thick) relative to latency- matched no stop- signal (thin) trials for early SSD. b, Recruitment of this signal through time relative to SSRT (left) and auditory feedback tone (right), with dark and light shades representing the recruitment of broad- spiking (spike width \(\geq 250 \mu \mathrm{s}\) ) and narrow- spiking (< 250 \(\mu \mathrm{s}\) ) neurons. Recruitment on SSRT- aligned activity (left panel) is defined as the difference between canceled and no stop- signal trials. Recruitment on tone- aligned activity (right panel) is defined as the activity on canceled trials relative to the baseline. Modulations starting 300ms after the tone are not shown. c, Time- depth plot showing latency and proportion of recruited neurons through time at each depth from perpendicular penetrations. Symbols mark beginning of modulation for broad- spiking neurons (black triangles) and narrow- spiking neurons (white stars). Color map indicates the percentage of neurons relative to the overall sampling density (Supplementary Figure 1a) producing this signal through time at each depth. Dashed horizontal line marks L3- L5 boundary. The lower boundary of L6 is not discrete. d, Left panel compares response of a representative neuron on successfully canceled (thick) relative to latency- matched no stop- signal (thin) trials for early (lighter) and later (darker) SSD. Shaded area represents significant difference in discharge rate between the two conditions. Right panel plots relationship between discharge rate in the sampling interval and feedback tone time. Along the ordinate scale is plotted the spiking rate, adjusted for neuron- specific variations. Along the abscissa scale is plotted the normalized feedback time in logarithmic scale (z- scale). In all, 162 points (54 neurons) are plotted. Each point plots the average spike- density and associated Log (feedback time) in one of 3 bins corresponding to early-, mid-, or late- SSD, for each neuron. Variation of spiking rate was best predicted by the time of the feedback time (highlighted by best- fit line; Supplementary Table 2). + +In a large proportion of these neurons, the phasic response on canceled trials after SSRT was followed by a sustained elevated discharge rate that was interrupted after the tone. This sustained activity was also observed on no- stop trials. Consistent with the indirect contribution of SEF to saccade initiation, the termination of this modulation was unrelated to + +when monkeys stopped fixating on the stop- signal (on canceled trials) or the target (on no- stop trials), ruling out this signal as one directly involved in maintaining fixation (Supplementary + +Figure 5c). Furthermore, when the feedback tone cued upcoming reward, the activity was suppressed; when the tone cued failure, activity increased (Supplementary Figure 5d). + +Accordingly, by representing both time and valence of the feedback tone, a significant + +proportion of these neurons also signaled Loss as described previously \(^{16}\) ( \(X^{2}\) (3, \(N = 575\) ) = + +19.43, \(\mathrm{p} = 2.2 \times 10^{- 4}\) ; Supplementary Table 3). Based on the observation that this activity was + +sustained until the tone, which signaled when gaze could be shifted, and previous findings + +identifying SEF signals with working memory \(^{10,11}\) , we conjecture that these neurons sustain + +<--- Page Split ---> + +saccade inhibition to earn reward. Hence, we refer to these neurons as Goal Maintenance neurons. + +Goal Maintenance neurons were found in all penetrations but more commonly at certain sites \((X^{2}(4, N = 575) > 39.3, \mathsf{p} < 10^{- 5})\) . One third (18/54) were narrow- spiking, a proportion exceeding chance sampling \((X^{2}(1, N = 575) = 7.29, \mathsf{p} = 0.0069)\) . The laminar distribution of Goal Maintenance neurons (Fig. 4c) was significantly different from the laminar sampling distribution \((X^{2}(4, N = 293) = 11.24, \mathsf{p} = 0.024)\) (Supplementary Table 1b). These neurons were found significantly more often in L2/3 relative to L5/6 \((X^{2}(1, N = 293) = 10.37, \mathsf{p} = 1.3 \times 10^{- 4})\) . Their laminar distribution was also significantly different from that of Conflict neurons \((X^{2}(1, N = 70) = 11.54, \mathsf{p} = 6.8 \times 10^{- 4})\) and of Event Timing neurons \((X^{2}(1, N = 83) = 5.49, \mathsf{p} = 0.019)\) . Those in L2/3 modulated significantly earlier than those in L5/6 \((L2 / 3 \sim 85 \pm 64 \mathsf{ms}\) (mean \(\pm \mathsf{SD}\) ), \(L5 / 6 \sim 193 \pm 101\) ; \(t\) - test, \(t(32) = - 3.63, \mathsf{p} = 9.9 \times 10^{- 4})\) . + +In summary, consistent with previous studies \(^{10,11}\) , neurons in SEF produce activity sufficient to enable a working memory representation of the goal of saccade inhibition through time. The new results show that these neurons are most common in L2/3 and a relatively higher proportion have narrow spikes. Thus, at least some of these neurons can be inhibitory interneurons. + +Countermanding N2. To determine whether macaque monkeys produce ERP components associated with response inhibition homologous to humans \(^{5}\) , we simultaneously sampled EEG from an electrode located over the medial frontal cortex (Fz in 10- 20 system) while recording neural spikes in SEF (Fig. 5a). To eliminate components associated with visual responses and motor preparation and to isolate signals associated with response inhibition, we measured the difference in polarization on canceled trials and latency- matched no stop- signal trials for each SSD (Fig. 5b). Homologous to humans, we observed an enhanced N2/P3 sequence with successful stopping. + +<--- Page Split ---> +![PLACEHOLDER_20_0] + + +<--- Page Split ---> + +Fig. 5 | Event-related potentials for successful response inhibition. a, Grand average normalized EEG (z-transformed) on successful canceled (thick) relative to latency-matched no stop- signal (thin) trials for early SSD. b, the difference function highlights the N2 and P3 components, eliminating the effect of response stimulus-evoked ERP common to both canceled and no stop- signal trials. The shaded regions correspond to a ±50 ms sampling window around peak of N2 (orange) and P3 (gray) used for ERP amplitude calculation for c. c, Relationship between N2 amplitude and P(error | SSseen). Along the ordinate scale, the normalized ERP amplitude is plotted, adjusting for session-specific variations. Along the abscissa scale the normalized P(error | SSseen) is plotted (Supplementary Fig 2c). In all, 87 points (29 sessions) are plotted. Each point plots the average N2 and the associated P(error | SSseen) in one of 3 bins corresponding to early-, mid-, or late-SSD, for each session. P(error | SSseen) is the best parameter that described variations in N2 (highlighted by best-fit line). d, Relationship between P3 amplitude and the time of feedback relative to stop-signal. Along the ordinate scale is plotted the normalized ERP amplitude (z-scale), adjusted for session-specific variations in amplitude. Along the abscissa scale is plotted the normalized feedback time in logarithmic scale (z-scale). In all, 87 points (29 sessions) are plotted with each point plotting the average spike-density and associated Log (feedback time) in one of 3 bins corresponding to early-, mid-, or late-SSD, for each neuron. Variation of P3 amplitude was best predicted by the time of the feedback time (highlighted by best-fit line; Supplementary Table 2). e, Relationship between laminar neuronal discharge rate and N2. From sessions with perpendicular penetrations, relationship between ERP amplitude and spike rate for Conflict neurons (Aconflict), Event Timing neurons (AEvent Timing), recorded in L2/3 (top) and L5/6 (bottom). Partial regression plots are obtained by plotting on the ordinate scale, according to EEG convention, the residual from fixed-effects-adjusted ERP amplitude controlling for activity in the opposite layer. Along the abscissa scale is plotted the residual fixed-effects-adjusted neuronal discharge rate in the identified layer controlling for the activity in the opposite layer and stop-signal delay. Each point plots the average EEG voltage and associated spiking rate in one of 20 bins with equal numbers of trials per session. Only sessions with neurons in both L2/3 and L5/6 are included. A total of 120 points (from 6 session) are plotted for Conflict Neurons (left), and 100 points (5 sessions) are plotted for Event Timing neurons (right). The relationship between N2 and other neurons not reported in this study and Goal Maintenance neurons are shown in Supplementary Fig 7a. Variations in N2 amplitude was predicted by variation of spiking rate of Conflict and Event Timing neurons in L2/3 (highlighted by best-fit line) but not in L5/6. f, Relationship between laminar neuronal discharge rate and P3. From sessions with perpendicular penetrations, relationship between ERP amplitude and spike rate for Goal Maintenance neurons (AGoal Maintenance), recorded in L2/3 (top) and L5/6 (bottom). Partial regression plots are obtained by plotting on the ordinate scale, according to EEG convention, the residual from fixed-effects-adjusted ERP amplitude controlling for activity in the opposite layer and stop-signal delay. Similar conventions to panel e. Only sessions with neurons in both L2/3 and L5/6 are included. A total of 60 points (from 3 sessions) are plotted for Goal Maintenance neurons. The relationship between P3 and other neuron classes are shown in Supplementary Fig 7c. Variations in P3 amplitude was predicted by variation of spiking rate of Goal Maintenance neurons in L2/3 (highlighted by best-fit line) but not in L5/6. + +The N2 began \(\sim 150\) ms and peaked \(222\pm 17\) ms after the stop- signal, well after the visual ERP polarization (Supplementary Fig 6a). The N2 was observed after SsRT, too late to be a direct index of reactive response inhibition. Furthermore, the variability in the N2 peak time across sessions was significantly less when aligned on stop- signal appearance than on SsRT, further dissociating the N2 from reactive inhibition (F- test for variances, \(\mathrm{F}(28,28) = 0.29\) , \(\mathrm{p} =\) + +<--- Page Split ---> + +0.0018) (Supplementary Fig 6c). N2 amplitude varied most with P(error | SSseen) ( \(\Delta\) BIC \(>3.0\) against all competing models), with the largest negativity during the earliest SSD associated with the lowest error likelihood (t(85) = 2.42, p = 0.0178) (Fig 5c, Supplementary Table 2). In fact, no other competing model explained the variation in N2 amplitude. This outcome adds to the inconsistent and inconclusive evidence for the N2 association with conflict monitoring and response inhibition \(^5\) . + +We now describe relationships between neural spiking and the N2. Figure 5b illustrates the temporal relationship between the ERP and the recruitment of the three classes of neurons described above. The N2 coincided with the peak recruitment of Conflict and of Event Timing neurons. The relationship between neural events in SEF and the voltages measured on the cranium above SEF is both biophysical and statistical. The cranial voltage produced by synaptic currents associated with a given spike must follow Maxwell's equations as applied to the brain and head, regardless of the timing of the different events. Hence, we counted the spikes of the three classes of neurons separately in L2/3 and in L5/6 during a 100 ms window centered on the peak of the ERP. We devised multiple linear regression models with activity in upper layers (L2/3) and lower layers (L5/6) of each neuron class as predictors. Only successfully canceled trials were included in this analysis. We found that variation in the polarization of the N2 is not associated with the phasic spiking of Goal Maintenance neurons (L2/3: t(57) = - 1.28, p = 0.21; L5/6: t(57) = 0.60, p = 0.52) (Supplementary Figure 7a) but was predicted by the spiking activity in L2/3 but not in L5/6 of Conflict (L2/3: t(117) = - 3.6, p = 4.7×10⁻⁴; L5/6: t(117) = 0.046, p = 0.96) and of Event Timing neurons (L2/3: t(97) = - 4.60, p = 1.3×10⁻⁵; L5/6: t(97) = 1.67, p = 0.097) (Fig 5d). When the discharge rate of these L2/3 neurons was higher, the N2 exhibited a stronger negativity. Interestingly, N2 polarization was also predicted by the spiking activity in L2/3 but not in L5/6 of other neurons that were not modulated on canceled trials and so were not described in this manuscript (L2/3: t(317) = - 2.51, p = 0.012; L5/6: t(317) = - 1.60, p = 0.11). + +<--- Page Split ---> + +Similar results were obtained when controlling for the variation of ERP polarization and spike rate across different SSDs (not shown) and when measuring the difference in spiking and ERP between canceled and matched no stop- signal trials (Supplementary Figure 7b). + +Countermanding P3. The N2 was followed by a robust P3 (Fig 5a, b) beginning \(\sim 300\) ms and peaking \(358 \pm 17\) ms after the stop- signal, homologous to the human P3 \(^5\) . The peak polarization time was better synchronized on the stop- signal than on SsRT \((F(28,28) = 0.44, \mathsf{p} = 0.0345)\) (Supplementary Fig 6c). P3 polarization varied most with the log- transformed time of the feedback tone on canceled trials \((\Delta \mathsf{BIC} > 4.0\) against competing models) with weak support against other time- based models \((\Delta \mathsf{BIC} < 1.30)\) (Fig 5e, Supplementary Table 2). P3 polarization increased with time until feedback \((t(85) = 3.72, \mathsf{p} = 3.5 \times 10^{- 4})\) . The conclusions of these results do not differ if the analyses are performed on the raw EEG polarization in these intervals. + +Peak P3 polarization coincided with the peak recruitment of Goal Maintenance neurons, while the recruitment of Conflict and Event Timing neurons was decaying (Fig 5b). Accordingly, variation in P3 polarization was predicted by the spiking activity of Goal Maintenance neurons in L2/3 but not L5/6 (L2/3: \(t(57) = 5.46, \mathsf{p} = 1.1 \times 10^{- 6}\) ; L5/6: \(t(57) = 1.47, \mathsf{p} = 0.15\) ) (Fig. 5f). Higher spike rates are associated with greater P3 positivity. P3 amplitude was not associated with the spiking of Conflict (L2/3: \(t(97) = 0.44, \mathsf{p} = 0.66\) ; L5/6: \(t(97) = - 0.49, \mathsf{p} = 0.62\) ), Event Timing (L2/3: \(t(117) = - 1.19, \mathsf{p} = 0.24\) ; L5/6: \(t(117) = - 0.78, \mathsf{p} = 0.44\) ), or unmodulated neurons (L2/3: \(t(317) = - 1.11, \mathsf{p} = 0.27\) ; L5/6: \(t(317) = 0.054, \mathsf{p} = 0.96\) ) (Supplementary Figure 7c). Similar results were obtained when SSD was controlled for (not shown) and when measuring the difference in spiking and ERP between canceled and matched no stop- signal trials (Supplementary Figure 7d). + +(Supplementary Figure 7d). + +<--- Page Split ---> + +## DISCUSSION + +These results offer important, new insights into the cortical microcircuitry supporting executive control in primates. Model- based analysis of the latency, temporal dynamics, and variation in strength of neural spiking across the neuron sample revealed functionally distinct and theoretically important classes of neurons with particular biophysical and laminar properties. Moreover, a bridge between these neurophysiological findings and human electrophysiology was established through the specific associations observed between the N2 and P3 ERP observed in response inhibition tasks and classes of neurons in particular cortical layers. The novelty and importance of these findings is amplified by their complementarity with our previous description of the laminar organization of error and reward processing in SEF16. Based on the new results, we will discuss how SEF can contribute to conflict monitoring, time estimation, and goal maintenance. Coupled with extensive knowledge about connectivity of SEF42, 43, 44, this new information about the laminar distribution of neurons signaling response conflict, event timing, and maintaining goals suggest several specific hypotheses and research questions about how SEF and associated structures accomplish response inhibition and executive control (Fig. 6). Also, complementing our earlier description of the source of the ERN16, we now report a macaque homolog of the N2/P3 ERP components associated with response inhibition. The new results demonstrate one cortical source of these ERP components. + +<--- Page Split ---> +![PLACEHOLDER_25_0] + +
Fig 6 | Extrinsic and intrinsic circuitry for executive control. The laminar distribution observed for Conflict (orange), Event Timing (dark blue), and Goal Maintenance (dark red) are summarized with selected anatomical connections based on published studies. Sampled neurons were likely broad spike pyramidal and narrow spike, possibly inhibitory neurons. The laminar densities of calretinin (CR), calbindin (CB), and parvalbumin (PV) neurons observed and of D1 and D2 receptors are indicated on the far right. Left, Conflict signal can arise in SEF through afferents from frontal eye field (FEF). SEF can receive coincident inputs from Fixation neurons (STOP) and Movement neurons (GO) in FEF, directly, or in SC, indirectly, via thalamus, terminating in L2/3. These inputs are integrated within the synapses of L2/3 and L5 Conflict neurons. Intracortical processing produces later activation of Conflict neurons in L6 which can relay this signal to the Thalamus. Right, Top: Schematic of the activity profile for Goal Maintenance and Event Timing neurons in distinct phases indicated by the number. We conjecture that Goal Maintenance neurons, mainly located in L2/3, suppress unwanted movement through push-pull basal ganglia circuitry with pyramidal neurons directly projecting to the indirect pathway (D2) and inhibitory neurons, inhibiting pyramidal neurons that can project to the direct (D1) pathway. The gray symbol indicates that these neurons are distinct from those reported in this study. Input from dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), terminating in L2/3 can inform SEF of the anticipated reward association based on the experienced stop-signal delay contingent on successful stopping. Dopamine (DA) neuron projections in L2/3 from the SNpc and VTA can also relay this information. These inputs can result in the phasic response in Goal Maintenance neurons (phase 1, red). Following the phasic response, activity can remain elevated via recurrent connections and balance of excitation and inhibition (phase 2, red). The auditory feedback tone, integrated with the task rule from DLPFC cues the termination of operant control on behavior, resulting in the inhibition of pyramidal and inter-neurons by CR and CB neurons. This results in the termination of the sustained activity (phase 3). Event Timing neurons can receive input from DLPFC and ACC terminating in L2/3 informing neurons in L2/3 and L5 about an upcoming event. Ramping results from recurrent connections (1, dark blue). SEF can receive information about stop-signal appearance and successful stopping from ventrolateral prefrontal cortex (VLPFC) and DLPFC and Conflict neurons within the microcircuitry. This information can suppress the ramping activity via inhibitory
+ +<--- Page Split ---> + +connections by direct inhibitory connections onto Event Timing neurons (phase 2, dark blue). This resets these neurons for the next phase of ramping (phase 3, dark blue) which is terminated by the appearance of the feedback tone (4). The activity of Event Timing neurons can project to the caudate nucleus to inform the fronto- striatal reinforcement learning loop about the experienced timing of the event. Further details in text. + +Conflict. One class of SEF neuron was characterized by a pronounced facilitation after the stop- signal when saccades were inhibited. The modulation followed SSRT and scaled with P(error). These neurons were predominantly broad spiking and found in all layers. We hypothesize that these neurons signal response conflict \(^{15,39}\) defined as co- activation of mutually incompatible response processes \(^{31}\) . Previous research has characterized the neural mechanism of saccade countermanding \(^{27,45}\) . On canceled trials, gaze- shifting and gaze- holding neurons in the frontal eye field (FEF) and superior colliculus (SC) are co- active in a dynamically unstable manner that varies with P(error) precisely because these are the neurons producing the performance. In the interactive race model \(^{29,30}\) , the multiplicative conflict between GO and STOP accumulator units scales with P(error) (Supplementary Figure 2a) and can be used to adjust interactive race parameters to accomplish post- stopping slowing \(^{39}\) . Thus, these neurons signal a quantity central to theories of executive control. Furthermore, different neurons in SEF signal conflict, error, and reward, highlighting the possible independence of these executive control signals. + +Further evidence dissociating conflict, reward, and error signals is offered by comparing our results with those of a recent investigation of the nigrostriatal dopamine system of monkeys performing saccade countermanding \(^{46}\) . Dopamine (DA) neurons concentrated in the dorsolateral substantia nigra exhibited a pattern of activity that paralleled the conflict neurons in SEF. The DA neurons produced a brisk response to the stop- signal that was stronger when saccades were canceled in either direction. This observation is consistent with reports that besides responding to rewarding events, dopamine neurons respond to salient signals, such as a stop- signal. Unlike movement neurons in FEF \(^{27}\) and SC \(^{45}\) but like SEF, nearly all DA neurons + +<--- Page Split ---> + +modulate after SsRT. Moreover, the modulation of DA neurons scaled with P(error) just like the SEF neurons. + +The striking parallels between SEF and SNpc modulation patterns invites consideration of cause and effect. SEF is innervated by DA neurons in Substantia Nigra pas compacta (SNpc) \(^{47}\) , and SNpc DA neurons modulated significantly earlier than did the SEF conflict neurons (Supplementary Figure 8). However, because of the very slow conduction of DA axons \(^{48,49,50,51}\) , we estimate that the spike conduction time from SNpc to SEF is \(\sim 100\) ms (Supplementary Figure 8). Consequently, the estimated arrival times of DA spikes in SEF were not significantly different from the modulation times of the conflict neurons (Supplementary Figure 8). The influence of DA in SEF is slowed further by the well- known second- messenger delay of influence. Therefore, we infer that the SEF conflict modulation cannot be caused by DA inputs. However, because axon terminals from SEF are rare in SNpc \(^{42,44}\) , SEF neurons are unlikely to cause directly the modulation of the SNpc DA neurons. Instead, other investigators have shown that the phasic DA activation is delivered by the SC \(^{52}\) . Through the conflict neurons in L5, SEF can influence SC directly \(^{42}\) . Curiously, though, the modulation specifically after SsRT scaling with P(error) has not been observed in SC \(^{45}\) . + +Theories of DA function can facilitate understanding the putative conflict signal in SEF. From the reinforcement perspective, the phasic DA signal may act as an immediate eligibility trace broadcast to SEF and other regions to associate reinforcement with successful cancelation to the infrequent stop- signal. Such eligibility traces must be salient to be useful. The reinforcement perspective suggests an alternative to the conflict interpretation. The imbalance between gaze- holding and gaze- shifting arising on canceled trials increases with the progressive commitment from gaze- holding to gaze- shifting through time. Consequently, as the likelihood of unsuccessful response inhibition increases, the surprise of successful response cancelation increases. We observed a divergence in the values of P(error)—which is necessarily proportional to the product of the activation of GO and STOP units—and P(error | + +<--- Page Split ---> + +SSseen)—which is a proxy of error likelihood learned through experience with the task—at longer SSDs. Others have described SEF neural signals in terms of surprise \(^{12}\) . Thus, the modulation after SsRT scaling with P(error) may just be another element of the reinforcement learning needed to perform this task. Further research is needed to resolve the conflict and surprise hypotheses. + +Conflict neurons were found in all layers. To signal conflict, SEF can be informed about the dynamic state of gaze- shifting and gaze- holding through inputs from FEF and oculomotor thalamic nuclei. To signal surprise, SEF can be informed about saccade production from the thalamus \(^{53}\) and task rules from DLPFC and ventrolateral prefrontal cortex (VLPFC) \(^{54}\) . Based on previous conjectures \(^{6}\) and recent biophysical modelling \(^{55}\) we hypothesize that the integration of information producing the modulation of these neurons is derived through synaptic processes in L2/3. However, if this is so, and if the apical dendrites of L6 pyramidal neurons in SEF do not extend into L2/3, then this conflict signal can arise in L6 through translaminar interactions. The observation that conflict arises later in L6 is consistent with this supposition. Another implication of the hypothesis that conflict in L6 is derived from that in L2/3 is that the L6 feedback to thalamus will be delayed relative to the gaze- holding and gaze- shifting signals conveyed from the SC. + +Time estimation and goal maintenance. The interpretation of the other two classes of neurons that we found is framed by motivation more than reinforcement. To earn reward, monkeys must hold gaze for an extended period, which requires preventing blinks that would interrupt the camera- based eye tracker. This entails learning and possibly exploiting any regularities in the timing of task events \(^{33, 40}\) . A contribution of SEF and nearby areas in action timing and explicit time production tasks has been demonstrated \(^{13, 14}\) . We extend that description to this stop- signal task. + +<--- Page Split ---> + +A distinct group of SEF neurons produced ramping activity before saccades, which decayed after the gaze shift. But, when the saccade was countermanded, the ramping was interrupted by pronounced suppression. A previous description of these neurons recognized that the modulation on canceled trials arose too late to contribute to reactive inhibition but offered no explanation for these neurons 7. The new task design used here exposed a second period of ramping before the feedback tone on \(\sim 30\%\) of these neurons. This monotonically rising activity reached different levels for different interval durations ranging from \(\sim 1000\) to 1400 ms after SSRT on canceled trials. Our discovery of an association between spiking rate and the log- transformed duration of the preceding interval motivates a more integrated interpretation framed by a body of research on time keeping 37, 41 56 57. + +We interpret the ramping activity as representing the timing of task events. Spiking rate increases as the learned time of an event like the stop- signal approaches. Strong suppression after the event resets a proportion of these neurons to ramp until the next event, i.e., the feedback tone. The stop- signal and feedback tone events differ in two ways. First, they differ in predictability, for the stop- signal only occurs on a proportion of trials while feedback tone is not. Second, they differ in the action required following the event, for the stop- signal announces a prolonged period of fixation in which blinks must also be withheld while the tone announces the release of control over behavior. + +Recent work has shown that different neurons in the basal forebrain signal timing of events depending on surprise, salience, and uncertainty 37. We found similar differences in SEF. We conjecture that those neurons with ramping activity before both SSRT and the feedback tone encode the timing of expected salient events regardless of certainty or expected action. In contrast, the neurons with only ramping activity before successful stopping encode events that are less certain in occurrence or consequence. These differences were reinforced by the distribution of the neurons across the cortical layers. While Event Timing neurons were found in + +<--- Page Split ---> + +all layers, those that encoded timing regardless of predictability or action were most common in L3 and L5 with broad spikes consistent with pyramidal projection neurons. + +This laminar differentiation demonstrates that the timing of different types of events can engage different circuits mediated by different layer- specific extrinsic connections. The timing signal can be sent via cortico- cortical connections to other cortical areas to influence motor, cognitive, and limbic processes. Further research is needed to clarify this projection. Also, these neurons can contribute to fronto- striatal pathways to learn and update the temporal structure of the task \(^{57,58,59}\) . Axon terminals from SEF are dense in the caudate nucleus \(^{43}\) , arising from pyramidal neurons in L3 and L5 \(^{60,61,62}\) . In fact, neurons with this pattern of modulation have been described in a recent investigation of the caudate nucleus of monkeys performing saccade countermanding \(^{46}\) . Our finding that the suppression in the caudate nucleus occurred significantly later after SsRT than that of Event Timing neurons in SEF (Supplementary Figure 8) suggests a primary role of the cortex in this signaling. + +The rapid suppression of the ramping activity after SsRT merits consideration. One source can be intracortical inhibition from the narrow- spike, putative PV neurons that we observed. Another source can be the very small CB and CR neurons concentrated in L2/3 that are innervated by DLPFC and selectively inhibit pyramidal neurons \(^{63}\) , although our methods are unlikely to sample spikes from them. We note that although SEF is an agranular structure with weak interlaminar inhibitory connections \(^{21}\) , CR neurons in L2/3 can potently inhibit L5 neurons through specialized projections on the apical dendrites \(^{64}\) . This inhibition must be informed about the presence of the stop- signal and the cancelation of the saccade. We observe that such a signal is available in the conflict neurons. However, the suppression of Event Timing neurons occurred significantly earlier than the facilitation of the conflict neurons. Further research can resolve these cortical interactions. + +The Event Timing neurons that represent the duration of a preceding interval can support the patterns of modulation observed in the final class of neuron we found. The third + +<--- Page Split ---> + +class of neuron produced a phasic response after SsRT on canceled trials that scaled with the duration of the upcoming interval until the feedback tone. Recall that on canceled trials the interval from target presentation until tone presentation was of fixed duration, making it progressively shorter after progressively longer SSD. Such phasic responses have previously been observed when the timing of events followed discrete predictable durations \(^{65}\) similar to the time of feedback tone in our task following successful stopping. This phasic representation of the time was followed by sustained spiking until the tone. Note that by design, when the tone sounded, monkeys could shift gaze before receiving the fluid reward. We propose that these neurons can be identified with the operation of goal maintenance, which is necessary in canceled trials to prevent blinking or gaze shifts before the tone. This inference is consistent with an interpretation of the original theory of response inhibition \(^{26}\) and supported by previous evidence linking SEF to working memory \(^{10,11}\) and working memory to time representation \(^{66,67}\) . We have obtained further evidence for this interpretation in ongoing experiments with two other monkeys performing the same saccade countermanding task but with the requirement to maintain fixation on the stop- signal until the fluid reward is delivered. Goal maintenance neurons have been observed, but they continue spiking after the tone until the fluid reward when operant control over behavior is released (data not shown). + +Goal maintenance neurons were mainly found in L2/3. Inputs to these neurons from DLPFC, VLPFC, and ACC can signal task rules and the expected time of the secondary reinforcer when executive control can be released. Dopaminergic release in SEF from VTA where similar time- predicting signals are observed \(^{65}\) can enhance these influences through higher density of D1 relative to D2 receptors in L2/3 \(^{68,69}\) . The sustained discharge can be maintained through recurrent activation within SEF and between other structures \(^{11,70}\) . Also, many goal maintenance neurons had narrow spikes, consistent with PV inhibitory neurons, which can balance excitation and inhibition necessary for the maintenance of persistent activity in recurrent networks \(^{71,72,73,74}\) . + +<--- Page Split ---> + +We hypothesize that pyramidal Goal Maintenance neurons can encourage the suppression of movements through projections to the indirect pathway D2 neurons in the striatum \(^{60,61,62}\) . Inhibitory Goal Maintenance neurons, on the other hand, can inhibit the D1 direct (action- promoting) pathway and the frontal eye field to suppress movement. As PV neurons in primates do not have extrinsic connections, we propose that this can be mediated by the inhibition of other excitatory neurons (unidentified neurons and possibly Gain neurons identified in \(^{16}\) ) that send projections to these motor structures (gray neurons). Therefore, Goal Maintenance neurons can achieve their role by altering the balance in the push- pull mechanism mediated by the direct (D1) and indirect (D2) pathways. This function is consistent with the observation that many of these neurons also exhibit higher activity on unrewarded trials that, as previously described, influences post- error adjustments in RT in the next trial \(^{16}\) . Also consistent with this hypothesis, neurons with facilitated activity after SSRT were described in an investigation of the caudate nucleus of monkeys performing saccade countermanding \(^{46}\) . The facilitation in the caudate nucleus coincided with that measured in SEF (Supplementary Figure 8). The parallel between SEF and the striatum in patterns of modulation associated with proactive but not reactive inhibition are surprisingly, but satisfyingly, clear. + +Origin of Countermanding N2/P3. We showed that macaque monkeys exhibit a N2/P3 ERP complex homologous to that observed in humans \(^{5}\) . We discovered that variation in N2 and P3 polarization was predicted by spiking of specific, different neuron classes in L2/3 and not L5/6. These findings extend and parallel our previous demonstration that SEF contributes to the error- related negativity (ERN) \(^{16}\) . We found that variations in error- related spiking in L2/3 but not in L5/6 predicted variation of EEG polarization across both error and correct trials. Because action potentials are not large or sustained enough to produce event- related potentials, we surmise that this neural spiking coincides with coherent current flow strong enough to produce in the + +<--- Page Split ---> + +ERN \(^{55}\) . These new results show synaptic activity in L2/3 of SEF contributes to the N2/P3 complex. + +Disagreement persists about what the frontal N2 indexes \(^{75,76}\) . We found that the amplitude of the macaque homologue of the N2 during the stop- signal task varied most with the likelihood of error associated with experienced SSDs and not conflict and SSD as previously suggested \(^{5,77}\) . Further, we demonstrate that the spiking of different classes of neurons in L2/3 (but not L5/6) predicted the magnitude of the N2. Specifically, N2 magnitude was unrelated to spiking of Goal Maintenance neurons but co- varied with spiking of Conflict and Event Timing neurons in addition to the spiking of other neurons that did not modulate around the time of successful stopping. Recognizing that the N2 manifests the influence of different processes occurring in functionally distinct neurons can explain the disagreement about the nature of this ERP component. + +Likewise, the macaque homologue of the P3 component in this task resembled that reported in humans \(^{5}\) . Consistent with previous reports of P3 indexing expectation and temporal aspects of behavior \(^{75}\) , we found that P3 amplitude co- varied most with the expected time of the feedback tone. Reinforcing this interpretation, we found that P3 amplitude was predicted by the spiking of Goal Maintenance but not Conflict or Event Timing neurons. Therefore, we surmise that the P3 expressed in our experimental design indexes temporal prediction underlying goal maintenance. Overall, these results demonstrate that N2 and P3 index distinct processes mediated by the activity of different populations of neurons. + +## Conclusion + +Pioneering insights into the microcircuitry and mechanisms of primary visual cortex began by describing the properties of neurons in different layers \(^{78}\) . The present results complete the first catalogue for an agranular frontal lobe area. Contrasts with primary sensory areas will reveal the degree of computational uniformity across cortical areas. Being a source contributing to + +<--- Page Split ---> + +ERPs indexing performance monitoring and executive control, details about laminar processing in SEF will offer unprecedented insights into the microcircuitry of executive control. These results validate the tractability of formulating neural mechanism models of performance monitoring and executive control, especially when constrained by formal \(^{26}\) , algorithmic \(^{29,30}\) , and spiking network \(^{79}\) models of performance of a task with clear clinical relevance \(^{80}\) . + +<--- Page Split ---> + +## METHODS + +Animal care and surgical procedures. Data was collected from one male bonnet macaque (Eu, Macaca Radiata, 8.8kg) and one female rhesus macaque (X, Macaca Mulatta, 6.0kg) performing a countermanding task \(^{20,24}\) . All procedures were approved by the Vanderbilt Institutional Animal Care and Use Committee in accordance with the United States Department of Agriculture and Public Health Service Policy on Humane Care and Use of Laboratory Animals. Surgical details have been described previously \(^{81}\) . Briefly, magnetic resonance images (MRIs) were acquired with a Philips Intera Achieva 3T scanner using SENSE Flex- S surface coils placed above or below the animal's head. T1- weighted gradient- echo structural images were obtained with a 3D turbo field echo anatomical sequence (TR = 8.729 ms; 130 slices, 0.70 mm thickness). These images were used to ensure Cilux recording chambers were placed in the correct area. Chambers were implanted normal to the cortex (Monkey Eu: \(17^{\circ}\) ; Monkey X: \(9^{\circ}\) ; relative to stereotaxic vertical) centered on midline, 30mm (Monkey Eu) and 28mm (Monkey X) anterior to the interaural line. + +Acquiring EEG. EEG was recorded from the cranial surface with electrodes located over medial frontal cortex. Electrodes were referenced to linked ears using ear- clip electrodes (Electro- Cap International). The EEG from each electrode was amplified with a high- input impedance head stage (Plexon) and bandpass filtered between 0.7 and 170 Hz. Trials with blinks within 200ms before or after the analysis interval were removed. + +Cortical mapping and electrode placement. Chambers implanted over the medial frontal cortex were mapped using tungsten microelectrodes (2- 4 MΩ, FHC, Bowdoin, ME) to apply 200ms trains of biphasic micro- stimulation (333 Hz, 200 μs pulse width). The SEF was identified as the area from which saccades could be elicited using \(< 50 \mu \mathrm{A}\) of current \(^{82,83}\) . In both monkeys, the SEF chamber was placed over the left hemisphere. The dorsomedial location of + +<--- Page Split ---> + +the SEF makes it readily accessible for linear electrode array recordings across all cortical layers. A total of five penetrations were made into the cortex—two in monkey Eu, three in monkey X. Three of these penetration locations were perpendicular to the cortex. In monkey Eu, the perpendicular penetrations sampled activity at site P1, located 5 mm lateral to the midline and 31 mm anterior to the interaural line. In monkey X, the perpendicular penetrations sampled activity at site P2 and P3, located 5 mm lateral to the midline and 29 and 30 mm anterior to the interaural line, respectively. However, during the mapping of the bank of the cortical medial wall, we noted both monkeys had chambers place \(\sim 1\) mm to the right respective to the midline of the brain. This was confirmed through co- registered CT/MRI data. Subsequently, the stereotaxic estimate placed the electrodes at 4 mm lateral to the cortical midline opposed to the skull- based stereotaxic midline. + +Acquiring neural spiking. Spiking activity and local field potentials were recorded using a 24- channel Plexon U- probe with 150 μm between contacts, allowing sampling from all layers. The U- probes were 100 mm in length with 30 mm reinforced tubing, 210 μm probe diameter, 30° tip angle, with 500 μm between the tip and first contact. Contacts were referenced to the probe shaft and grounded to the headpost. We used custom built guide tubes consisting of 26- gauge polyether ether ketone (PEEK) tubing (Plastics One, Roanoke, VA) cut to length and glued into 19- gauge stainless steel hypodermic tubing (Small Parts Inc., Logansport, IN). This tubing had been cut to length, deburred, and polished so that they effectively support the U- probes as they penetrated dura and entered cortex. The stainless- steel guide tube provided mechanical support, while the PEEK tubing electrically insulated the shaft of the U- probe, and provided an inert, low- friction interface that aided in loading and penetration. + +Microdrive adapters were fit to recording chambers with \(< 400 \mu m\) of tolerance and locked in place at a single radial orientation (Crist Instruments, Hagerstown, MD). After setting up hydraulic microdrives (FHC, Bowdoin, ME) on these adapters, pivot points were locked in + +<--- Page Split ---> + +place by means of a custom mechanical clamp. Neither guide tubes nor U- probes were removed from the microdrives once recording commenced within a single monkey. These methods ensured that we were able to sample neural activity from precisely the same location relative to the chamber on repeated sessions. + +Electrophysiology data were processed with unity- gain high- input impedance head stages (HST/32o25- 36P- TR, Plexon). Spiking data were bandpass filtered between 100 Hz and 8 kHz and amplified 1000 times with a Plexon preamplifier, filtered in software with a 250 Hz high- pass filter and amplified an additional 32,000 times. Waveforms were digitized at 40 kHz from - 200 to 1200 μs relative to voltage threshold crossings. Thresholds were typically set at 3.5 standard deviations from the mean. All data were streamed to a single data acquisition system (MAP, Plexon, Dallas, TX). Time stamps of trial events were recorded at 500 Hz. Single units were sorted online using a software window discriminator and refined offline using principal components analysis implemented in Plexon offline sorter. + +Cortical depth and layer assignment. The retrospective depth of the electrode array relative to grey matter was assessed through the alignment of several physiological measures. Firstly, the pulse artifact was observed on a superficial channel which indicated where the electrode was in contact with either the dura mater or epidural saline in the recording chamber; these pulsated visibly in synchronization with the heartbeat. Secondly, a marked increase of power in the gamma frequency range (40- 80Hz) was observed at several electrode contacts, across all sessions. Previous literature has demonstrated elevated gamma power in superficial and middle layers relative to deeper layers \(^{84,85}\) . Thirdly, an automated depth alignment procedure was employed which maximized the similarity of CSD profiles evoked by passive visual stimulation between sessions \(^{20}\) . + +Further support for the laminar assignments was provided by an analysis of the depths of SEF layers measured in histological sections visualized with Nissl, neuronal nuclear antigen + +<--- Page Split ---> + +(NeuN), Gallyas myelin, acetylcholinesterase (AChE), non-phosphorylated neurofilament H (SMI- 32), and the calcium- binding proteins parvalbumin (PV), calbindin (CB), and calretinin (CR) \(^{16,20}\) . Additional information about laminar structure was assessed through the pattern of cross- frequency phase- amplitude coupling across SEF layers \(^{22}\) . Owing to variability in the depth estimates and the indistinct nature of the L6 border with white matter, some units appeared beyond the average gray- matter estimate; these were assigned to the nearest cellular layer. + +Acquiring eye position. Eye position data was collected at 1 kHz using an EyeLink 1000 infrared eye- tracking system (SR Research, Kanata, Ontario, Canada). This was streamed to a single data acquisition system (MAP, Plexon, Dallas, TX) and combined with other behavioral and neurophysiological data streams. + +Data collection protocol. The same protocol was used across monkeys and sessions. In each session, the monkey sat in an enclosed primate chair with their head restrained 45 cm from a CRT monitor (Dell P1130, background luminance of \(0.10 \text{cd} /\text{m}^2\) ). The monitor had a refresh rate of 70 Hz, and the screen subtended \(46^\circ \times 36^\circ\) of the visual angle. After advancing the electrode array to the desired depth, they were left for 3 to 4 hours until recordings stabilized across contacts. This led to consistently stable recordings with single units typically held indefinitely. Once these recordings stabilized, an hour of resting- state activity in near- total darkness was recorded. This was followed by the passive presentation of visual flashes followed by periods of total darkness in alternating blocks. Finally, the monkey performed approximately 2000 trials of the saccade countermanding (stop- signal) task. + +Countermanding task. The countermanding (stop- signal) task utilized in this study has been widely used previously \(^{25}\) . Briefly, trials were initiated when monkeys fixated at a central point. + +<--- Page Split ---> + +Following a variable time period, drawn from an aging function to avoid anticipation of the visual stimulus \(^{40}\) , the center of the fixation point was removed leaving an outline. Simultaneously, a peripheral target was presented to the left or right of the screen. + +On no stop- signal trials the monkey was required to shift gaze to the target. Fixation on the target was required for 600 ms, until an auditory tone sounded, whereupon monkeys could shift gaze anywhere. Fluid reward was delivered 600 ms later. + +On stop- signal trials, comprising less than half of all trials, the center of the fixation point was re- illuminated after a variable stop- signal delay (SSD). An initial set of SSDs, separated by 40- 60 ms for Monkey Eu and by 100 ms for monkey X, were selected for each recording session. To ensure that monkeys failed to countermand on \(\sim 50\%\) of stop- signal trials, SSD was adjusted through an adaptive staircasing procedure. When a monkey failed to inhibit a response, the SSD was decreased by 1, 2, or 3 steps (randomly drawn) to increase the likelihood of success on the next stop trial. When a monkey canceled the saccade, SSD was increased by 1, 2, or 3 steps (randomly drawn) to decrease the likelihood of success on the next stop trial. On stop- signal trials, the monkey was required to maintain fixation on the central point until the tone sounded, whereupon monkeys could shift gaze anywhere. Fluid reward was delivered 600 ms later. By design, the duration from target presentation until the tone was a fixed interval of 1500 ms. Thus, as SSD increased, the duration of fixation decreased + +## (Supplementary Figure 2b). + +Performance on this task is characterized by the probability of not canceling a saccade as a function of the SSD (the inhibition function) and the distribution of latencies of correct saccades in no- stop- signal trials and of noncanceled error saccades in stop- trials (Fig 1b). Performance of the stop- signal task is explained as the outcome of a race between a GO and a STOP process \(^{26}\) . The race model provides an estimate of the duration of the covert STOP process, the time taken to accomplish response inhibition, known as stop- signal reaction time (SSRT) \(^{29, 30, 79}\) . SSRT was calculated using two approaches—the conventional weighted- + +<--- Page Split ---> + +integration method and the more recent Bayesian Ex- Gaussian Estimation of Stop- Signal RT distributions (BEEST) \(^{86}\) (Supplementary Figure 3a, 4a, 5a). Compared to weighted integration method, the Bayesian approach provides estimates of the variability in SSRT and the fraction of trigger failures for a given session \(^{86}\). Individual parameters were estimated for each session. The priors were bounded uniform distributions \((\mu_{G0}, \mu_{Stop}; U(0.001, 1000); \sigma_{G0}, \sigma_{Stop}; U(1, 500); \tau_{G0}, \tau_{Stop}; U(1, 500); \text{pTF: } U(0,1))\). The posterior distributions were estimated using Metropolis- within- Gibbs sampling ran multiple through three chains. We ran the model for 5000 samples with a thinning of 5. None of our conclusions depend on the choice of SSRT calculation method. + +Analysis of EEG. Methods paralleling those used in human studies were used. The N2 and P3 were obtained from average EEG synchronized on stop- signal presentation. Peak N2 was the time when the mean ERP reached maximal negativity in a 150- 250 ms window after the stop- signal. Peak P3 was the time when the mean ERP in a 250- 400 ms window after the stop- signal. The amplitude of the N2 and P3 was quantified as the mean Z- transformed voltage for each SSD in a \(\pm 50\) ms window around the maximal ERP deflection determined for each session. Indistinguishable results were obtained with wider \((\pm 75 \text{ms})\) , and narrower \((\pm 25 \text{ms})\) windows or just the instantaneous maximal polarization. To characterize the polarizations associated with response inhibition, a difference ERP \((\Delta \text{ERP})\) was obtained by subtracting from the ERP recorded on canceled trials the ERP recorded on RT- matched no stop- signal trials. + +Analysis of neural spiking. Spike density functions (SDF) for individual trials were constructed by convolving the spike times with a kernel matching the time course of an excitatory post- synaptic potential with an area equal to 1 + +\[R(t) = \left\{1 - e^{\left(-\frac{t}{\tau_g}\right)}\right\} \cdot e^{\left(-\frac{t}{\tau_d}\right)}\] + +<--- Page Split ---> + +The influence of each spike (R(t)) increases with a short time constant ( \(\mathsf{T}_{\mathsf{g}} = 1\) ms) and decays slower ( \(\mathsf{T}_{\mathsf{d}} = 20\) ms). To analyze spiking activity associated with successful stopping, we compared the activity on canceled trials and on no stop- signal trials with RT greater than SSD + SSRT. This latency- matching compares trials in which countermanding was successful with trials in which countermanding would have been successful had the stop- signal been presented. Neurons were distinguished by patterns of modulation consisting of periods of facilitation or suppression using a consensus clustering algorithm 28 (Supplementary Fig 1B). The input to this analysis pipeline was the spike- density function on canceled trials and on latency- matched no stop- signal trials during the 100 ms preceding SSRT and 200 ms following SSRT. Results did not change much if interval durations were changed. + +To prevent outlying values from exerting excessive influence, population spike density plots were obtained by scaling the SDF of each neuron by the \(95\%\) confidence interval between the \(2.5\%\) lowest rate and the \(97.5\%\) highest rate in one of two intervals. The first interval was a 600 ms window centered on SSRT on canceled and on no stop- signal trials. The second interval was - 100 to +300 ms relative to the feedback tone. + +To identify spiking modulation, we applied methods previously employed. First, we calculated a difference function (ΔSDF), the difference between the SDF on canceled and latency- matched no stop- signal trials. Periods of statistically significant modulation were identified based on multiple criteria—(a) the difference function must exceed by at least 2 standard deviations a baseline difference measured in the 100 ms interval before the target appeared, (b) the difference must occur from 50 ms before to 900 ms after the stop- signal, and (c) the difference must persist for at least 100 ms (or for 50 ms if the difference exceeded baseline by 3 standard deviations). As commonly found in medial frontal cortex, some neurons exhibited low spiking rates. To obtain reliable estimates of modulation times, we also convolved the SDF with a square 8 ms window. The modulation intervals were validated by manual inspection. + +<--- Page Split ---> + +To determine modulation associated with the systematically variable timing of the feedback tone on canceled trials, the SDF was compared against the minimum value found between 500 ms before and 900 ms after the tone. Focusing on modulation occurring only during the period of operant control on behavior, modulations beginning less than 300 ms after the tone were not included. For comparisons across neurons and sessions, Z- transformed SDF or \(\Delta \mathrm{SDF}\) were used. + +Spike widths of this sample of neurons exhibited a bimodal distribution \(^{16}\) . Consequently, neurons were distinguished as narrow- or broad- spikes. Narrow spike neurons had peak- to- trough duration less than 250 \(\mu \mathrm{s}\) and broad spike, greater than or equal to 250 \(\mu \mathrm{s}\) . + +Mixed effects models. We fit the variation in modulation of spiking or polarization of ERP to models of each of the behavioral and task measures as detailed in Supplementary Figure 2. We related neural modulation to the following models: (a) response conflict conceived computationally as the mathematical product of the activation of the race model GO and STOP processes and quantified as the probability of noncanceled error (P(error)) as a function of SSD, (b) P(error) contingent on viewing the stop- signal, denoted P(error | SSseen) and referred to as error likelihood, (c) absolute and log- transformed SSD, (d) hazard rate of stop- signal, (e) absolute and log- transformed delay until feedback tone, and (f) hazard rate of feedback tone. Although these behavioral and task measures can be correlated, random variations allowed for their differentiation. + +To determine which performance measure accounted best for the variation of neural modulation, the performance and neural quantities were averaged within groups of early- , mid- , and late- SSD trials. SSD values greater than \(\sim 350\mathrm{ms}\) were not included because too few canceled trials were obtained. The analysis of the facilitation after SSRT as based on \(\Delta \mathrm{SDF}\) (Fig 2, Fig 4), but the major conclusions held if the analysis used SDF. The analysis of the modulation before SSRT or the feedback tone was based on the SDF of canceled trials. Before + +<--- Page Split ---> + +SSRT the SDF of canceled and no stop- signal trials was not different. Before the feedback tone, the interval was longer and more variable on canceled relative to no stop- signal trials. + +Mixed- effects models of \(\Delta \mathrm{SDF}\) , SDF, or \(\Delta \mathrm{ERP}\) values in relation to the various performance measures were compared using Bayesian Information Criteria (BIC). We report the results of the most basic version of each model with a main effect term corresponding to the performance parameter and random intercepts grouped by neuron (for spiking activity) or session (for ERP analysis). The values for each performance parameter were z- transform normalized for fair comparison between models related to different quantities. All constructed models had the same degrees of freedom, so BIC values between models could be compared directly. The model with the smallest BIC was endorsed as the best model. The fit of the other models relative to the best are reported using \(\Delta \mathrm{BIC}\) . As recommended \(^{87,88}\) , \(\Delta \mathrm{BIC}\) ( \(\mathrm{BIC}_{\mathrm{best}} - \mathrm{BIC}_{\mathrm{competing}})\) \(< 2\) offers weak support against the competing model, \(2 < \Delta \mathrm{BIC} < 6\) offers strong support against the competing model, and \(\Delta \mathrm{BIC} > 6\) conclusively rules out the competing model. More complex versions of these models resulted in similar conclusions. Mixed- effects models were performed using MATLAB's Statistical Toolbox. + +Relating N2/P3 and neural spiking. We used the method described previously to establish the relationship between spiking activity and the ERN \(^{16}\) . Single trial spiking was the mean convolved spike data for that trial recorded from neurons in L2/3 and in L5/6 of perpendicular penetrations within \(\pm 50\) ms of the N2 and P3 peaks. To account for variations in ERP voltage and spike counts across sessions, a fixed- effects adjustment was performed by centering each distribution on its mean and dividing by its most extreme value. To measure the N2/P3 amplitudes robustly, we grouped rank- ordered single- trial ERP values into 20 successive bins. From trials in each bin, we calculated the mean N2 and mean P3 magnitude (dependent variable), the mean spike count in the upper and lower layers (independent variables), and the average SSD, on Canceled trials. Data from all sessions were combined for a pooled partial + +<--- Page Split ---> + +correlation. Each point in Fig. 5 plots the paired values of the mean normalized ERP voltage and normalized activity for each of the 20 bins from every session. The statistical relationship between ERP magnitude and spiking activity was quantified through multiple linear regression on normalized data pooled across sessions. Three factors were considered: (1) spiking activity in L2/3, (2) spiking activity in L5/6, plus (3) SSD to prevent its variation from confounding the relationship between ERP and neural spiking. However, as presented in the main text, the inclusion of this factor did not change the results. + +## Code availability + +The analysis codes that were used for this study are available from the corresponding author upon request. + +## Data availability + +The data that support the findings of this study are available from the corresponding author upon request. + +## Acknowledgments + +The authors thank G. Luppino, M. Matsumoto, N. Palomero- Gallagher, and, L. Rapan for sharing data; J. Elsey, M. Feurtado, M. Maddox, S. Motorny, J. Parker, D. Richardson, M. Schall, C.R. Subravei, L. Toy, B. Williams, and R. Williams for animal care and other technical assistance; and Z. Fu, M. Matsumoto, P. Redgrave, U. Rutishauser, E. Sigworth, A. Tomarken, and G. Woodman for helpful discussions. Imaging data was collected in the Vanderbilt Institute of Imaging Science. This work was supported by R01- MH55806, R01- EY019882, P30- EY08126, Canadian Institutes of Health Research Post- Doctoral Fellowship, and by Robin and Richard Patton through the E. Bronson Ingram Chair in Neuroscience. + +## Author contributions + +Experimental design, J.D.S. Data collection, J.D.S. Data analysis, A.S. and S.E. Interpretation and preparation of manuscript, A.S., S.E., and J.D.S. + +<--- Page Split ---> + +## 1024 Competing interests + +1025 The authors declare no competing interests. + +<--- Page Split ---> + +1027 REFERENCES1028 1. Verbruggen F, Logan GD. Models of response inhibition in the stop-signal and stop-1029 change paradigms. Neurosci Biobehav Rev 33, 647-661 (2009).10301031 2. Emeric EE, et al. Influence of history on saccade countermanding performance in1032 humans and macaque monkeys. Vision Res 47, 35-49 (2007).10331033 3. 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J Neurophysiol 66, 559- 579 (1991). + +1301 84. Maier A, Adams GK, Aura C, Leopold DA. Distinct superficial and deep laminar domains of activity in the visual cortex during rest and stimulation. Front Syst Neurosci 4, (2010). + +1305 85. Xing D, Yeh CI, Burns S, Shapley RM. Laminar analysis of visually evoked activity in the primary visual cortex. Proc Natl Acad Sci U S A 109, 13871- 13876 (2012). + +1308 86. Matzke D, Love J, Heathcote A. A bayesian approach for estimating the probability of trigger failures in the stop- signal paradigm. Behav Res Methods 49, 267- 281 (2017). + +1311 87. Raftery AE. Bayesian model selection in social research. Sociological methodology, 111- 163 (1995). + +1313 88. Kass RE, Raftery AE. Bayes factors. Journal of the american statistical association 90, 773- 795 (1995). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SajadErringtonSchallSupplementaryInformationR0. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f_det.mmd b/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..fa4b6855f62efe2806df336ac26cb95500b23b11 --- /dev/null +++ b/preprint/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f/preprint__0a0dcde223c0f68e72f0ff32ff701bc4d28087fec6db01b2248e07b20064711f_det.mmd @@ -0,0 +1,799 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 890, 175]]<|/det|> +# Functional Architecture of Executive Control and Associated Event-Related Potentials + +<|ref|>text<|/ref|><|det|>[[44, 196, 592, 333]]<|/det|> +Amirsaman Sajad Vanderbilt University Steven Errington Vanderbilt University https://orcid.org/0000- 0002- 0948- 6559 Jeffrey Schall ( \(\square\) jeffrey.d.schall@vanderbilt.edu ) York University https://orcid.org/0000- 0002- 5248- 943X + +<|ref|>sub_title<|/ref|><|det|>[[44, 371, 102, 389]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 408, 673, 428]]<|/det|> +Keywords: frontal cortex, executive control, event- related potentials (ERP) + +<|ref|>text<|/ref|><|det|>[[44, 446, 295, 466]]<|/det|> +Posted Date: May 17th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 485, 463, 504]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 468741/v1 + +<|ref|>text<|/ref|><|det|>[[44, 522, 909, 565]]<|/det|> +License: © \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 600, 936, 644]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 21st, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 33942- 1. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[75, 90, 748, 116]]<|/det|> +# Functional Architecture of Executive Control and + +<|ref|>title<|/ref|><|det|>[[75, 142, 595, 168]]<|/det|> +# Associated Event-Related Potentials + +<|ref|>text<|/ref|><|det|>[[75, 195, 88, 208]]<|/det|> +3 + +<|ref|>text<|/ref|><|det|>[[75, 225, 653, 245]]<|/det|> +Amirsaman Sajad1*, Steven P. Errington1*, & Jeffrey D. Schall1,2 + +<|ref|>text<|/ref|><|det|>[[75, 260, 88, 273]]<|/det|> +5 + +<|ref|>text<|/ref|><|det|>[[75, 289, 821, 308]]<|/det|> +1 Department of Psychology, Vanderbilt Vision Research Center, Center for Integrative & + +<|ref|>text<|/ref|><|det|>[[75, 322, 600, 340]]<|/det|> +Cognitive Neuroscience, Vanderbilt University, Nashville, TN + +<|ref|>text<|/ref|><|det|>[[75, 354, 821, 374]]<|/det|> +2 Centre for Vision Research, Vision Science to Application, Department of Biology, York + +<|ref|>text<|/ref|><|det|>[[75, 387, 308, 404]]<|/det|> +University, Toronto, ON + +<|ref|>text<|/ref|><|det|>[[75, 419, 444, 437]]<|/det|> +* authors contributed equally to this work. + +<|ref|>text<|/ref|><|det|>[[75, 454, 88, 467]]<|/det|> +11 + +<|ref|>text<|/ref|><|det|>[[75, 484, 297, 500]]<|/det|> +Corresponding author: + +<|ref|>text<|/ref|><|det|>[[75, 515, 362, 532]]<|/det|> +Jeffrey D. Schall, Ph.D. + +<|ref|>text<|/ref|><|det|>[[75, 547, 384, 564]]<|/det|> +E- mail: schalljd@yorku.ca + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 884, 622]]<|/det|> +Medial frontal cortex enables executive control by monitoring relevant information and using it to adapt behavior. In macaques performing a saccade countermanding (stop- signal) task, we recorded EEG over and neural spiking across all layers of the supplementary eye field (SEF). We report the laminar organization of concurrently activated neurons monitoring the conflict between incompatible responses and the timing of events serving goal maintenance and executive control. We also show their relation to coincident event- related potentials (ERP). Neurons signaling response conflict were largely broad- spiking found across all layers. Neurons signaling the interval until specific task events were largely broad- spiking neurons concentrated in L3 and L5. Neurons predicting the duration of control and sustaining the task goal until the release of operant control were a mix of narrow- and broad- spiking neurons confined to L2/3. We complement these results with the first report of a monkey homologue of the N2/P3 ERP complex associated with response inhibition. N2 polarization varied with error likelihood and P3 polarization varied with the duration of expected control. The amplitude of the N2 and P3 were predicted by the spike rate of different classes of neurons located in L2/3 but not L5/6. These findings reveal important, new features of the cortical microcircuitry supporting executive control and producing associated ERP. + +<|ref|>text<|/ref|><|det|>[[111, 631, 860, 812]]<|/det|> +Effective control of behavior is necessary to achieve goals, especially when faced with competing instructions inducing response conflict and requiring inhibition of prepotent responses and maintenance of task goals, and adaptation of performance. These features of executive control are investigated with the countermanding (stop- signal) task \(^{1}\) , during which macaque monkeys, like humans, exert response inhibition and adapt performance based on stimulus history, response outcomes, and the temporal structure of task events \(^{2}\) . + +<|ref|>text<|/ref|><|det|>[[111, 823, 848, 905]]<|/det|> +Medial frontal cortex enables executive control, but circuit- level mechanisms remain uncertain \(^{3,4}\) . Hypotheses on executive control function have been tested in humans using noninvasive ERP measures derived from a negative- positive waveform known as the N2/P3 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 881, 428]]<|/det|> +associated with stopping \(^{5}\) . However, their cortical source is unknown. Mechanistic hypotheses about the basis of these signals require information about neural spiking patterns across cortical layers \(^{6}\) . Moreover, understanding function at the resolution of layers can clarify circuit- level mechanisms because neurons in different layers have different extrinsic anatomical connections. We can obtain such information from the supplementary eye field, an agranular area on the dorsomedial convexity in macaques, immediately beneath where the frontal ERPs are sampled. SEF contributes to proactive but not reactive inhibition \(^{7}\) and its activation improves performance in the countermanding task by delaying response time \(^{8}\) through postponing the accumulation of pre- saccadic activity \(^{9}\) . SEF also supports working memory \(^{10, 11}\) , and signals surprise \(^{12}\) , event timing \(^{13, 14}\) , response conflict \(^{15}\) , plus errors and reinforcement \(^{16}\) . SEF in macaques is homologous to SEF in humans \(^{17}\) . + +<|ref|>text<|/ref|><|det|>[[111, 438, 866, 715]]<|/det|> +The canonical cortical microcircuit derived from granular sensory areas \(^{18}\) does not explain agranular frontal areas like SEF \(^{19, 20, 21, 22, 23}\) . Recently we described the laminar microcircuitry of performance monitoring signals in the SEF, and relationship to the ERP indexing error monitoring known as the error- related negativity (ERN) \(^{16}\) . Here we describe the laminar microcircuitry of signals that monitor events occurring during successful stopping performance. We define three classes of neurons that concurrently signal response conflict, timing of events, and maintenance of task goals. We also establish that macaque monkeys produce the N2/P3 ERP associated with response inhibition, elucidating task factors indexed by this ERP complex and the neuron classes predicting their polarization. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 203, 106]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[115, 121, 780, 141]]<|/det|> +## Countermanding performance, neural sampling, and functional classification. + +<|ref|>text<|/ref|><|det|>[[111, 150, 876, 655]]<|/det|> +Neurophysiological and electrophysiological data was recorded from two macaque monkeys performing the saccade countermanding task with explicit feedback tone cues (Fig. 1a) \(^{24}\) . Data collection and analysis was informed by the consensus guide for the stop- signal task \(^{25}\) . In 29 sessions we acquired 33,816 trials (Monkey Eu, male, 12 sessions 11,583 trials; X, female, 17 sessions 22,233 trials). Typical performance was produced by both monkeys. Response times (RT) on failed inhibition trials (noncancelled trials) (mean ± SD Eu: 294 ± 179 ms; X: 230 ± 83 ms) were systematically shorter than those on no stop- signal trials (Eu: 313 ± 119 ms, X: 263 ± 112 ms; mixed effects linear regression grouped by monkey, t(27507) = - 17.4, p < 10 \(^{- 5}\) ) (Fig. 1b). Characteristically, the probability of noncancelled errors increased with stop- signal delay (SSD) (Fig. 1b). These two observations validate the use of the independent race model \(^{26}\) to estimate the stop- signal reaction time (SSRT), the time needed to cancel a partially prepared saccade. Accordingly, neural modulation before SsRT can contribute to stopping but that after SsRT cannot \(^{7,26}\) . SsRT across sessions (Eu: 118 ± 23 ms, X: 103 ± 24 ms) did not differ between monkeys (t(27) = - 1.69, p = 0.1025). While there were other classes of errors made in the task, they were infrequent and therefore inconsequential to this study. Therefore, P(error) refers to the probability of noncancelled errors. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[327, 94, 720, 664]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 682, 880, 896]]<|/det|> +
Fig. 1 | Experimental approach. a, Saccade countermanding task. Monkeys initiated trials by fixating a central point. After a variable time, the center of the fixation point was extinguished, and a peripheral target was presented at one of two possible locations. On no stop-signal trials monkeys were required to shift gaze to the target, whereupon after \(600\pm 0\) ms a high-pitch auditory feedback tone was delivered, and \(600\pm 0\) ms later fluid reward was provided. On stop-signal trials ( \(\sim 40\%\) of trials) after the target appeared, the center of the fixation point was re-illuminated after a variable stop-signal delay, which instructed the monkey to cancel the saccade in which case the same high-pitch tone was presented \(1,500\pm 0\) ms after target presentation followed \(600\pm 0\) ms later by fluid reward. Stop-signal delay was adjusted such that monkeys successfully canceled the saccade in \(\sim 50\%\) of trials. In the remaining trials, monkeys made non-canceled errors, which were followed after \(600\pm 0\) ms by a low-pitch tone, and no reward was delivered. Monkeys could not initiate trials earlier after errors. b, Grand average cumulative distributions of all RT for both monkeys on trials with no stop-signal (solid) and non-canceled errors (dashed). c, Grand average probability of non-canceled errors (P(error)) as a function of stop-signal delay. Inset shows the distribution of SSRT across all
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 880, 200]]<|/det|> +sessions for both monkeys. d, Neural spiking was recorded across all layers of agranular SEF (NeuN stain) using Plexon U- probe. Neurons with both broad (black) and narrow (red) spikes were sampled. Spiking modulation was measured relative to presentation of task events (thin solid, visual target; thick solid, stop- signal) and performance measures like SsRT (dashed vertical). Simultaneously, EEG was recorded from the cranial surface with an electrode positioned over the medial frontal cortex (10- 20 location Fz). Yellow rectangle portrays cortical area sampled in a T1 MR image. + +<|ref|>text<|/ref|><|det|>[[111, 232, 870, 702]]<|/det|> +EEG was recorded with leads placed on the cranial surface beside the chamber over medial frontal cortex while a linear electrode array (Plexon, 24 channels, \(150 \mu m\) spacing) was inserted in SEF (Fig. 1c). SEF was localized by anatomical landmarks and intracortical electrical microstimulation \(^{20}\) . We recorded neural spiking in 29 sessions (Eu: 12, X: 17) sampling activity from 5 neighboring sites. Overall, 575 single units (Eu: 244, X: 331) were isolated, of which 213 (Eu: 105, X: 108) were modulated after SsRT. The description of the laminar distribution of signals is based on 16 of the 29 sessions during which electrode arrays were oriented perpendicular to cortical layers and we could assign neurons to different layers confidently \(^{20}\) (see Supplementary Fig. 1 of \(^{16}\) ). Additional information about laminar structure was assessed through the pattern of phase- amplitude coupling across SEF layers \(^{22}\) . Due to variability in the estimates and the indistinct nature of the L6 border with white matter, some units appeared beyond the average gray- matter estimate; these were assigned to the nearest cellular layer. In all, 119 isolated neurons (Eu: 54; X: 65) contributed to the results on laminar distribution of executive control signals subserving successful stopping (Supplementary Table 1a). + +<|ref|>text<|/ref|><|det|>[[111, 710, 877, 891]]<|/det|> +To identify neural activity associated with saccade countermanding, we examined the activity across different SSDs on canceled trials in which the subject successfully inhibited the movement, and latency- matched no stop- signal trials in which no stopping was required \(^{27}\) . A consensus cluster algorithm \(^{28}\) with manual curation identified neurons with response facilitation (n = 129) and response suppression (n = 84) following the stop- signal (Supplementary Figure 1). Simultaneously, we observed distinct patterns in the cranial EEG related to successful + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 884, 269]]<|/det|> +stopping with characteristic N2 and P3 components (Fig 1c). Whilst we previously described neural signals after errors and associated with reward, here we focused on the interval in which response inhibition was accomplished. Specifically, we quantified spiking before and after SSRT and before the feedback tone (Tone), which terminated operant control on behavior. To elucidate contributions of the diverse neurons, we compared and contrasted how well neural spiking related to a variety of computational parameters inherent in the task. + +<|ref|>text<|/ref|><|det|>[[111, 311, 875, 524]]<|/det|> +First, performance of the stop- signal task is explained as the outcome of a race between stochastic GO and STOP processes \(^{26}\) , instantiated by specific interactions enabling the interruption of the GO process by a STOP process \(^{29,30}\) (Supplementary Figure 2a). An influential theory of medial frontal function posits that it encodes the conflict between mutually incompatible processes \(^{31}\) . Such conflict arises naturally as the mathematical product of the activation of GO and STOP units, which is proportional to P(error). Hence, neural signals that scale with P(error) can encode conflict in this task. + +<|ref|>text<|/ref|><|det|>[[111, 535, 880, 747]]<|/det|> +Second, inspired by reinforcement learning models, we considered the possibility that neural signals reflect the error- likelihood associated with an experienced SSD \(^{32}\) . Note, on some stop- signal error trials, the response was generated before the stop- signal appeared. The error- likelihood can only form based on trials in which SSD elapsed before RT such that monkeys could see the stop- signal (referred to as SSseen). Hence, neural signals that scale with P(error | SSseen) can encode error likelihood in this task. Conflict indexed by P(error) and error likelihood indexed by P(error | SSseen) diverge at longer SSDs (Supplementary Figure 2c). + +<|ref|>text<|/ref|><|det|>[[111, 758, 883, 905]]<|/det|> +Third, monkeys can learn the timing of the various task events (Supplementary Figure 2b). For example, monkeys are sensitive to the adjustments of SSD that are made to maintain \(\sim 50\%\) success on stop- signal trials \(^{33}\) . Previous research has characterized time perception \(^{34,35}\) . Key features include sensitivity to log(interval) versus its absolute value with precision decreasing with duration and sensitivity to instantaneous expectation (i.e., hazard rate) of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 880, 170]]<|/det|> +events (Supplementary Figure 2d- e). Therefore, neural activity around the time of SSD can scale with the timing or expectation of the stop- signal \(^{13, 14, 37, 38}\) . This expectation can be derived from experienced SSD and the estimated probability of stop- signal appearance + +<|ref|>text<|/ref|><|det|>[[111, 183, 880, 363]]<|/det|> +(Supplementary Figure 2e). Moreover, to earn reward, monkeys were required to maintain fixation on the target on trials with no stop- signal or on the fixation spot on canceled trials for an extended period (Tone) until a tone secondary reinforcer (feedback) announced delivery of reward after another interval. Hence, neural activity associated with the tone can scale with the timing or instead the expectation of the tone, which was variable on canceled trials but predictable based on the experienced SSD (Supplementary Figure 2d). + +<|ref|>text<|/ref|><|det|>[[113, 376, 880, 459]]<|/det|> +Alternatives were compared through mixed- effects model- comparison with Bayesian Information Criteria (BIC). As detailed below, many neurons signaled conflict and more signaled event timing with activity sustained until earning reward. + +<|ref|>text<|/ref|><|det|>[[111, 503, 880, 876]]<|/det|> +Monitoring Conflict. We found 75 neurons in SEF with transient facilitation after SSRT on canceled trials, compared to latency- matched no stop- signal trials, that was proportional to P(error) (Fig. 2; Supplementary Figure 3; Supplementary Table 2). The transient modulation in these neurons was not just a visual response to the stop- signal because it did not happen on noncanceled trials (Supplementary Figure 1e). On average, this modulation started \(99 \pm 8\) ms (mean \(\pm\) SEM) after SSRT. Figure 2a shows the recruitment of these neurons through time. Nearly all (71/75) were recruited after SSRT, and the proportion of recruited neurons peaked at \(\sim 60\%\) \(\sim 110\) ms after SSRT and gradually reduced to \(8\%\) after 500 ms (Fig. 2a). As this facilitation occurs after SSRT, it cannot contribute to reactive response inhibition \(^{7}\). On canceled trials a minority of these neurons produced weak, persistent activity that lasted until the tone, and some also exhibited a brief transient response following the tone (Fig. 2; Supplementary Figure 1c). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[144, 90, 845, 930]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 90, 879, 411]]<|/det|> +Fig. 2 | Time-depth organization of Conflict neuron spiking in SEF. a, Normalized population response of neurons with transient facilitation in discharge rate on successfully canceled (thick) relative to latency-matched no stop-signal (thin) trials for early SSD (top). Recruitment of this signal through time relative to SSRT (left) and auditory feedback tone (right), with dark and light shades representing the recruitment of broad-spiking (spike width \(\geq 250 \mu \mathrm{s}\) ) and narrow-spiking ( \(< 250 \mu \mathrm{s}\) ) neurons (bottom). Recruitment on SSRT-aligned activity (left panel) is defined as the difference between canceled and no stop-signal trials. Recruitment on tone-aligned activity (right panel) is defined as the activity on canceled trials relative to the baseline. Modulations starting 300ms after the tone are not included. b, Time-depth plot showing latency and proportion of recruited neurons through time at each depth from perpendicular penetrations. Symbols mark beginning of modulation for broad-spiking neurons (black triangles) and narrow-spiking neurons (white stars). Color map indicates the percentage of neurons relative to the overall sampling density (Supplementary Figure 1a) producing this signal through time at each depth. Dashed horizontal line marks L3-L5 boundary. The lower boundary of L6 is not discrete. c (left), Comparison of response of a representative neuron on successfully canceled (thick) relative to latency-matched no stop-signal (thin) trials for low (lighter) and higher (darker) P(error). Shaded area represents significant difference in discharge rate between the two conditions. c (right) Relationship between spike rate, sampled from the period with significant modulation for each neuron and the corresponding P(error). Along the ordinate scale is plotted the spiking rate, adjusted for neuron-specific variations. Along the abscissa scale is plotted the normalized P(error) (z-scale). In all, 225 points are plotted. Variation of spiking rate was best predicted by P(error) (highlighted by the best-fit line; Supplementary Table 2). + +<|ref|>text<|/ref|><|det|>[[112, 451, 875, 680]]<|/det|> +We assessed how the magnitude of this transient modulation after SSRT varied with the various task and performance parameters described above. The magnitude of this modulation varied most closely with P(error) - a measure of conflict (Mixed- effects linear regression grouped by neuron, \(\mathrm{t}(104) = 3.57\) , \(\mathrm{p} = 5.4 \times 10^{- 4}\) ). This conflict model obtained lower BIC than models of SSD or any other quantity, with weak support against the \(\mathrm{P(error} | \mathrm{SS}_{\mathrm{seen}})\) ( \(\Delta \mathrm{BIC} = 1.29\) ) and strong support against other models ( \(\Delta \mathrm{BIC} > 2.7\) ) (Fig 2c; Supplementary Table 2). + +<|ref|>text<|/ref|><|det|>[[112, 710, 861, 888]]<|/det|> +We noted that the vast majority (65/75) of Conflict neurons did not signal noncanceled errors, supporting previous findings (Supplementary Table 3) \(^{15,16}\) . However, many (41/75) also exhibited modulation that signaled outcome following the feedback tone and around the time of reward. Some exhibited higher discharge rates on unrewarded trials (previously identified as Loss signal \(^{16}\) ), and some, higher discharge rates on rewarded trials (previously identified as Gain signal \(^{16}\) ). The multiplexing of the conflict monitoring signal with Gain and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 875, 140]]<|/det|> +Loss signals (in different task epochs) did not differ from that predicted based on their sampling prevalence \((X^{2}(3, N = 575) = 1.02, \text{p} = 0.79; \text{Supplementary Table 3})\) . + +<|ref|>text<|/ref|><|det|>[[111, 151, 874, 300]]<|/det|> +Conflict neurons were found at all recording sites but more commonly at some \((X^{2}(4, N = 575) = 11.6, \text{p} = 0.020)\) . Using trough- to- peak duration of the action potential waveform, the majority (63/75) had broad spikes consistent with pyramidal neurons. This distribution did not differ from the overall sampling distribution in SEF \((X^{2}(1, N = 575) = 0.67, \text{p} = 0.41)\) . + +<|ref|>text<|/ref|><|det|>[[111, 311, 872, 490]]<|/det|> +From sessions with perpendicular penetrations, we assigned 36 of the 75 Conflict neurons to a cortical layer. They were found in all layers at a relative prevalence across layers indistinguishable from that of the overall sampling distribution \((X^{2}(4, N = 293) = 4.28, \text{p} = 0.37; \text{Fig 2b; Supplementary Table 1b})\) . The timing of the modulation did not differ between L2/3 and L5/6 \((t(34) = 0.3367, \text{p} = 0.74, \text{two tailed})\) . The few neurons modulating with the tone were observed sparsely across all layers. + +<|ref|>text<|/ref|><|det|>[[111, 502, 881, 651]]<|/det|> +In summary, as reported previously \(^{15}\) , neurons in SEF modulate in a manner consistent with signaling the co- activation of gaze- shifting (GO) and gaze- holding (STOP) processes. This co- activation has previously been interpreted as conflict \(^{31,39}\) . The new results show that these neurons are distributed across all SEF layers and are predominantly putative pyramidal neurons with broad spikes. + +<|ref|>text<|/ref|><|det|>[[111, 695, 872, 808]]<|/det|> +Time keeping. Monkeys adapt performance by learning the temporal regularities of the task \(^{33,40}\) . We identified neurons across the layers of SEF with modulation representing event timing and interval duration through facilitation, suppression, and ramping activity \(^{13,14,37,41}\) + +<|ref|>text<|/ref|><|det|>[[111, 789, 872, 905]]<|/det|> +(Supplementary Fig 2c, d). Following target presentation, the discharge rate of many neurons \((N = 84)\) ramped up until the saccade on trials in which they were generated (no stop- signal or noncanceled error trials). On canceled trials, however, the discharge rate was instead abruptly reduced after SsRT (Fig 3a; Supplementary Fig 1c- e). Because the first pronounced + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 872, 108]]<|/det|> +suppression began after SsRT, these neurons cannot contribute directly to response inhibition. + +<|ref|>text<|/ref|><|det|>[[110, 119, 870, 140]]<|/det|> +Relative to SsRT, these neurons were suppressed before the facilitation in the conflict + +<|ref|>text<|/ref|><|det|>[[110, 150, 865, 171]]<|/det|> +monitoring neurons (t- test, t(157) = - 3.60, p = 4.2×10- 4). The ramping activity from target to + +<|ref|>text<|/ref|><|det|>[[110, 183, 866, 203]]<|/det|> +SSRT varied best with the time- based models of SSD (t(250) = 12.62, p = 0.0013) with strong + +<|ref|>text<|/ref|><|det|>[[110, 214, 870, 235]]<|/det|> +support against other models ( \(\Delta\) BIC > 2.7) (Supplementary Table 2). The log- transformed + +<|ref|>text<|/ref|><|det|>[[110, 245, 870, 266]]<|/det|> +model outperformed the linear model but evidence against the linear model was weak ( \(\Delta\) BIC = + +<|ref|>text<|/ref|><|det|>[[110, 277, 866, 297]]<|/det|> +1.35). Because the discharge rate dropped sharply on canceled trials but not on noncanceled + +<|ref|>text<|/ref|><|det|>[[110, 308, 825, 329]]<|/det|> +stop- signal trials (Supplementary Fig 1e), we conjecture that these neurons encode the + +<|ref|>text<|/ref|><|det|>[[110, 340, 870, 360]]<|/det|> +temporal aspects of events leading to successful stopping and not the timing of the stop- signal + +<|ref|>text<|/ref|><|det|>[[110, 372, 830, 392]]<|/det|> +appearance per se. Once successful stopping occurred, these neurons were suppressed. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[142, 90, 844, 920]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 90, 879, 610]]<|/det|> +Fig. 3 | Time-depth organization of Event Timing neuron spiking in SEF. a, Normalized population response of neurons with suppression of discharge rate on successfully canceled (thick) relative to latency- matched no stop- signal (thin) trials for early SSD (bottom). Recruitment of signal through time relative to SSRT (left) and auditory feedback tone (right), with dark and light shades representing the recruitment of broad- spiking (spike width \(\geq 250 \mu \mathrm{s}\) ) and narrow- spiking ( \(< 250 \mu \mathrm{s}\) ) neurons (bottom). Recruitment on SSRT- aligned activity (left panel) is defined as the difference between canceled and no stop- signal trials. Recruitment on tone- aligned activity (right panel) is defined as the activity on canceled trials relative to the baseline. Modulations starting 300ms after the tone are not shown. b, Time- depth plot showing latency and proportion of recruited neurons through time at each depth from perpendicular penetrations. Symbols mark beginning of modulation for broad- spiking neurons (black triangles) and narrow- spiking neurons (white stars). Color map indicates the percentage of neurons relative to the overall sampling density (Supplementary Figure 1a) producing this signal through time at each depth. Dashed horizontal line marks L3- L5 boundary. The lower boundary of L6 is not discrete. c, Left panel shows response of a representative neurons on successfully canceled (thick) and latency- matched no stop- signal (thin) trials for early (lighter) and later (darker) SSD. Pre- SSRT ramping activity occurs irrespective of trial class. Shaded area represents the time epoch used for sampling neuron activity (50 ms window pre- SSRT). Right panel plots relationship between discharge rate in the sampling interval and stop- signal delay. Along the ordinate scale is plotted the normalized spiking rate, adjusting for neuron- specific variations. Along the abscissa scale is plotted the normalized (z- transformed) stop- signal delay in logarithmic scale. In all, 252 points (84 neurons) are plotted. Each point plots the average spike- density and associated Log (SSD) in one of 3 bins corresponding to early-, mid-, or late- SSD, for each neuron. Variation of spiking rate was best predicted by the time of the stop- signal (highlighted by best- fit line). d, Left panel plots response of the same representative neuron as c indicating pre- tone ramping activity on successfully canceled (thick) relative to latency- matched no stop- signal (thin) trials for early (lighter) and later (darker) SSD. Shaded area represents the time epoch used for sampling neuron activity (50 ms window pre- Tone). Right panel plots relationship between discharge rate in the sampling interval and the time of feedback relative to stop- signal. Along the ordinate scale is plotted the spiking rate, adjusted for neuron- specific variations. Along the abscissa scale is plotted the normalized stop- signal delay in logarithmic scale (z- scale). In all, 144 points (38 neurons with pre- tone activity on canceled trials) are plotted. Each point plots the average spike- density and associated log (feedback time) in one of 3 bins corresponding to early-, mid-, or late- SSD, for each neuron. Variation of spiking rate was best predicted by the time of the feedback time (highlighted by best- fit line; Supplementary Table 2). + +<|ref|>text<|/ref|><|det|>[[112, 650, 880, 860]]<|/det|> +A subset of these neurons (29/84) also exhibited monotonic ramping of discharge rate following the sharp suppression, persisting until after the feedback tone whereupon the spike rate again decreased (Fig 3d). In some neurons this decrease followed a brief transient response (Fig 3a). The variation in dynamics of the ramping before the tone was best accounted for by the time of the feedback tone after the stop- signal (t(112) = 3.41, \(9.1 \times 10^{- 4}\) ) with strong support against other models ( \(\Delta \text{BIC} > 5.0\) ). The linear and log- transformed models were indistinguishable ( \(\Delta \text{BIC} < 0.1\) ) (Supplementary Table 2). The termination of this modulation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 844, 141]]<|/det|> +was best described by the time of the feedback tone and not the time at which fixation from stop- signal was broken (Supplementary Figure 4c). + +<|ref|>text<|/ref|><|det|>[[112, 152, 879, 291]]<|/det|> +Because the ramping activity in this population of neurons scaled with the time of the stop- signal and the tone, followed by immediate suppression after their occurrence, we conjecture that these neurons represent event timing to accomplish the task. We will refer to these neurons as Event Timing neurons. While all of these neurons encoded the timing of events related to successful stopping, only \(\sim 30\%\) also encoded the timing of the feedback tone. + +<|ref|>text<|/ref|><|det|>[[112, 312, 880, 586]]<|/det|> +Event Timing neurons were found in all penetrations, but more commonly in certain sites \((X^{2}(4, N = 575) > 39.3, p < 10^{- 5})\) (Fig 3b, Supplementary Table 1a). The majority (73 / 84) had broad spikes, corresponding to the overall sampling distribution in SEF \((X^{2}(1, N = 575) = 2.56, p = 0.11)\) . From sessions with perpendicular penetrations, we assigned the layer of 49 of the 84 neurons. The laminar organization of these neurons did not differ from the overall laminar sampling distribution \((X^{2}(4, N = 293) = 7.33, p = 0.12)\) . However, those with ramping activity before the tone (which resulted in a prolonged differential activity level between no- stop and canceled trials) were more confined to lower L3 and upper L5. The time of modulation after SSRT or around the tone did not vary across layers. + +<|ref|>text<|/ref|><|det|>[[112, 599, 874, 810]]<|/det|> +In summary, neurons in SEF exhibit ramping activity that can signal the time preceding critical events for successful task performance. The new results show that these neurons are distributed across all SEF layers and are predominantly pyramidal neurons. Often these neurons also exhibited post- feedback ramping activity leading to the time of reward delivery. Accordingly, a higher proportion of these neurons were identified as Gain neurons compared to that predicted by the prevalence of Gain and Loss neurons \(^{16}\) \((X^{2}(3, N = 575) = 44.86, p = < 10^{- 5}\) ; Supplementary Table 3). + +<|ref|>text<|/ref|><|det|>[[113, 855, 872, 906]]<|/det|> +Goal Maintenance. By design, to earn reward on canceled trials, monkeys were required to maintain fixation on the stop- signal until an auditory feedback tone occurred. As such, the state + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 880, 300]]<|/det|> +of response inhibition needed to be maintained for an arbitrary interval. Many other neurons (N = 54) in SEF produced spike rate modulation sufficient to contribute to this maintenance (Fig 4). These neurons produced significantly greater discharge rates on canceled trials after SSRT, compared to latency- matched no- stop trials. Modulation was weak or absent on noncancelled error trials, so this activity was not a response to the stop- signal. This modulation began too late to contribute to response inhibition but persisted while fixation maintenance was required (Supplementary Figure 1d, e). + +<|ref|>text<|/ref|><|det|>[[112, 312, 876, 653]]<|/det|> +These neurons were distinguished from Conflict neurons by the more prolonged facilitation following SSRT (Supplementary Figure 1b, c). The peak recruitment of these neurons ( \(\sim 300\) ms) followed that of the neurons monitoring conflict ( \(\sim 110\) ms) and the suppression of the Event Timing neurons ( \(\sim 170\) ms). Compared to Conflict neurons, the phasic facilitation was followed by sustained activity until \(\sim 300\) ms after the feedback tone in a significantly higher proportion of these neurons ( \(X^{2}(1, N = 129) = 27.3\) , \(p < 10^{- 5}\) ) (Fig 4a). This modulation at tone presentation was also observed on no stop- signal trials. The variation in the magnitude of the phasic modulation was best described by the log- transformed duration until the feedback tone on canceled trials (Fig 3d) (t(152) = 3.53, \(p = 5.6 \times 10^{- 4}\) ), with strong evidence against non- time- based models ( \(\Delta \text{BIC} > 3.0\) ) and weak evidence against other time- based models ( \(\Delta \text{BIC} < 1\) ) (Supplementary Table 2). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[150, 90, 845, 911]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 90, 880, 444]]<|/det|> +Fig. 4 | Time-depth organization of Goal Maintenance neuron spiking in SEF. a, Normalized population response of neurons with prolonged facilitation in discharge rate on successful canceled (thick) relative to latency- matched no stop- signal (thin) trials for early SSD. b, Recruitment of this signal through time relative to SSRT (left) and auditory feedback tone (right), with dark and light shades representing the recruitment of broad- spiking (spike width \(\geq 250 \mu \mathrm{s}\) ) and narrow- spiking (< 250 \(\mu \mathrm{s}\) ) neurons. Recruitment on SSRT- aligned activity (left panel) is defined as the difference between canceled and no stop- signal trials. Recruitment on tone- aligned activity (right panel) is defined as the activity on canceled trials relative to the baseline. Modulations starting 300ms after the tone are not shown. c, Time- depth plot showing latency and proportion of recruited neurons through time at each depth from perpendicular penetrations. Symbols mark beginning of modulation for broad- spiking neurons (black triangles) and narrow- spiking neurons (white stars). Color map indicates the percentage of neurons relative to the overall sampling density (Supplementary Figure 1a) producing this signal through time at each depth. Dashed horizontal line marks L3- L5 boundary. The lower boundary of L6 is not discrete. d, Left panel compares response of a representative neuron on successfully canceled (thick) relative to latency- matched no stop- signal (thin) trials for early (lighter) and later (darker) SSD. Shaded area represents significant difference in discharge rate between the two conditions. Right panel plots relationship between discharge rate in the sampling interval and feedback tone time. Along the ordinate scale is plotted the spiking rate, adjusted for neuron- specific variations. Along the abscissa scale is plotted the normalized feedback time in logarithmic scale (z- scale). In all, 162 points (54 neurons) are plotted. Each point plots the average spike- density and associated Log (feedback time) in one of 3 bins corresponding to early-, mid-, or late- SSD, for each neuron. Variation of spiking rate was best predicted by the time of the feedback time (highlighted by best- fit line; Supplementary Table 2). + +<|ref|>text<|/ref|><|det|>[[112, 483, 867, 603]]<|/det|> +In a large proportion of these neurons, the phasic response on canceled trials after SSRT was followed by a sustained elevated discharge rate that was interrupted after the tone. This sustained activity was also observed on no- stop trials. Consistent with the indirect contribution of SEF to saccade initiation, the termination of this modulation was unrelated to + +<|ref|>text<|/ref|><|det|>[[112, 612, 872, 663]]<|/det|> +when monkeys stopped fixating on the stop- signal (on canceled trials) or the target (on no- stop trials), ruling out this signal as one directly involved in maintaining fixation (Supplementary + +<|ref|>text<|/ref|><|det|>[[112, 674, 840, 725]]<|/det|> +Figure 5c). Furthermore, when the feedback tone cued upcoming reward, the activity was suppressed; when the tone cued failure, activity increased (Supplementary Figure 5d). + +<|ref|>text<|/ref|><|det|>[[112, 736, 843, 757]]<|/det|> +Accordingly, by representing both time and valence of the feedback tone, a significant + +<|ref|>text<|/ref|><|det|>[[112, 767, 857, 789]]<|/det|> +proportion of these neurons also signaled Loss as described previously \(^{16}\) ( \(X^{2}\) (3, \(N = 575\) ) = + +<|ref|>text<|/ref|><|det|>[[112, 799, 861, 820]]<|/det|> +19.43, \(\mathrm{p} = 2.2 \times 10^{- 4}\) ; Supplementary Table 3). Based on the observation that this activity was + +<|ref|>text<|/ref|><|det|>[[112, 832, 839, 852]]<|/det|> +sustained until the tone, which signaled when gaze could be shifted, and previous findings + +<|ref|>text<|/ref|><|det|>[[112, 863, 848, 884]]<|/det|> +identifying SEF signals with working memory \(^{10,11}\) , we conjecture that these neurons sustain + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 835, 140]]<|/det|> +saccade inhibition to earn reward. Hence, we refer to these neurons as Goal Maintenance neurons. + +<|ref|>text<|/ref|><|det|>[[111, 150, 876, 460]]<|/det|> +Goal Maintenance neurons were found in all penetrations but more commonly at certain sites \((X^{2}(4, N = 575) > 39.3, \mathsf{p} < 10^{- 5})\) . One third (18/54) were narrow- spiking, a proportion exceeding chance sampling \((X^{2}(1, N = 575) = 7.29, \mathsf{p} = 0.0069)\) . The laminar distribution of Goal Maintenance neurons (Fig. 4c) was significantly different from the laminar sampling distribution \((X^{2}(4, N = 293) = 11.24, \mathsf{p} = 0.024)\) (Supplementary Table 1b). These neurons were found significantly more often in L2/3 relative to L5/6 \((X^{2}(1, N = 293) = 10.37, \mathsf{p} = 1.3 \times 10^{- 4})\) . Their laminar distribution was also significantly different from that of Conflict neurons \((X^{2}(1, N = 70) = 11.54, \mathsf{p} = 6.8 \times 10^{- 4})\) and of Event Timing neurons \((X^{2}(1, N = 83) = 5.49, \mathsf{p} = 0.019)\) . Those in L2/3 modulated significantly earlier than those in L5/6 \((L2 / 3 \sim 85 \pm 64 \mathsf{ms}\) (mean \(\pm \mathsf{SD}\) ), \(L5 / 6 \sim 193 \pm 101\) ; \(t\) - test, \(t(32) = - 3.63, \mathsf{p} = 9.9 \times 10^{- 4})\) . + +<|ref|>text<|/ref|><|det|>[[112, 470, 880, 619]]<|/det|> +In summary, consistent with previous studies \(^{10,11}\) , neurons in SEF produce activity sufficient to enable a working memory representation of the goal of saccade inhibition through time. The new results show that these neurons are most common in L2/3 and a relatively higher proportion have narrow spikes. Thus, at least some of these neurons can be inhibitory interneurons. + +<|ref|>text<|/ref|><|det|>[[112, 662, 875, 907]]<|/det|> +Countermanding N2. To determine whether macaque monkeys produce ERP components associated with response inhibition homologous to humans \(^{5}\) , we simultaneously sampled EEG from an electrode located over the medial frontal cortex (Fz in 10- 20 system) while recording neural spikes in SEF (Fig. 5a). To eliminate components associated with visual responses and motor preparation and to isolate signals associated with response inhibition, we measured the difference in polarization on canceled trials and latency- matched no stop- signal trials for each SSD (Fig. 5b). Homologous to humans, we observed an enhanced N2/P3 sequence with successful stopping. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[300, 88, 696, 910]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 90, 880, 719]]<|/det|> +Fig. 5 | Event-related potentials for successful response inhibition. a, Grand average normalized EEG (z-transformed) on successful canceled (thick) relative to latency-matched no stop- signal (thin) trials for early SSD. b, the difference function highlights the N2 and P3 components, eliminating the effect of response stimulus-evoked ERP common to both canceled and no stop- signal trials. The shaded regions correspond to a ±50 ms sampling window around peak of N2 (orange) and P3 (gray) used for ERP amplitude calculation for c. c, Relationship between N2 amplitude and P(error | SSseen). Along the ordinate scale, the normalized ERP amplitude is plotted, adjusting for session-specific variations. Along the abscissa scale the normalized P(error | SSseen) is plotted (Supplementary Fig 2c). In all, 87 points (29 sessions) are plotted. Each point plots the average N2 and the associated P(error | SSseen) in one of 3 bins corresponding to early-, mid-, or late-SSD, for each session. P(error | SSseen) is the best parameter that described variations in N2 (highlighted by best-fit line). d, Relationship between P3 amplitude and the time of feedback relative to stop-signal. Along the ordinate scale is plotted the normalized ERP amplitude (z-scale), adjusted for session-specific variations in amplitude. Along the abscissa scale is plotted the normalized feedback time in logarithmic scale (z-scale). In all, 87 points (29 sessions) are plotted with each point plotting the average spike-density and associated Log (feedback time) in one of 3 bins corresponding to early-, mid-, or late-SSD, for each neuron. Variation of P3 amplitude was best predicted by the time of the feedback time (highlighted by best-fit line; Supplementary Table 2). e, Relationship between laminar neuronal discharge rate and N2. From sessions with perpendicular penetrations, relationship between ERP amplitude and spike rate for Conflict neurons (Aconflict), Event Timing neurons (AEvent Timing), recorded in L2/3 (top) and L5/6 (bottom). Partial regression plots are obtained by plotting on the ordinate scale, according to EEG convention, the residual from fixed-effects-adjusted ERP amplitude controlling for activity in the opposite layer. Along the abscissa scale is plotted the residual fixed-effects-adjusted neuronal discharge rate in the identified layer controlling for the activity in the opposite layer and stop-signal delay. Each point plots the average EEG voltage and associated spiking rate in one of 20 bins with equal numbers of trials per session. Only sessions with neurons in both L2/3 and L5/6 are included. A total of 120 points (from 6 session) are plotted for Conflict Neurons (left), and 100 points (5 sessions) are plotted for Event Timing neurons (right). The relationship between N2 and other neurons not reported in this study and Goal Maintenance neurons are shown in Supplementary Fig 7a. Variations in N2 amplitude was predicted by variation of spiking rate of Conflict and Event Timing neurons in L2/3 (highlighted by best-fit line) but not in L5/6. f, Relationship between laminar neuronal discharge rate and P3. From sessions with perpendicular penetrations, relationship between ERP amplitude and spike rate for Goal Maintenance neurons (AGoal Maintenance), recorded in L2/3 (top) and L5/6 (bottom). Partial regression plots are obtained by plotting on the ordinate scale, according to EEG convention, the residual from fixed-effects-adjusted ERP amplitude controlling for activity in the opposite layer and stop-signal delay. Similar conventions to panel e. Only sessions with neurons in both L2/3 and L5/6 are included. A total of 60 points (from 3 sessions) are plotted for Goal Maintenance neurons. The relationship between P3 and other neuron classes are shown in Supplementary Fig 7c. Variations in P3 amplitude was predicted by variation of spiking rate of Goal Maintenance neurons in L2/3 (highlighted by best-fit line) but not in L5/6. + +<|ref|>text<|/ref|><|det|>[[112, 752, 876, 900]]<|/det|> +The N2 began \(\sim 150\) ms and peaked \(222\pm 17\) ms after the stop- signal, well after the visual ERP polarization (Supplementary Fig 6a). The N2 was observed after SsRT, too late to be a direct index of reactive response inhibition. Furthermore, the variability in the N2 peak time across sessions was significantly less when aligned on stop- signal appearance than on SsRT, further dissociating the N2 from reactive inhibition (F- test for variances, \(\mathrm{F}(28,28) = 0.29\) , \(\mathrm{p} =\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 870, 268]]<|/det|> +0.0018) (Supplementary Fig 6c). N2 amplitude varied most with P(error | SSseen) ( \(\Delta\) BIC \(>3.0\) against all competing models), with the largest negativity during the earliest SSD associated with the lowest error likelihood (t(85) = 2.42, p = 0.0178) (Fig 5c, Supplementary Table 2). In fact, no other competing model explained the variation in N2 amplitude. This outcome adds to the inconsistent and inconclusive evidence for the N2 association with conflict monitoring and response inhibition \(^5\) . + +<|ref|>text<|/ref|><|det|>[[110, 279, 879, 880]]<|/det|> +We now describe relationships between neural spiking and the N2. Figure 5b illustrates the temporal relationship between the ERP and the recruitment of the three classes of neurons described above. The N2 coincided with the peak recruitment of Conflict and of Event Timing neurons. The relationship between neural events in SEF and the voltages measured on the cranium above SEF is both biophysical and statistical. The cranial voltage produced by synaptic currents associated with a given spike must follow Maxwell's equations as applied to the brain and head, regardless of the timing of the different events. Hence, we counted the spikes of the three classes of neurons separately in L2/3 and in L5/6 during a 100 ms window centered on the peak of the ERP. We devised multiple linear regression models with activity in upper layers (L2/3) and lower layers (L5/6) of each neuron class as predictors. Only successfully canceled trials were included in this analysis. We found that variation in the polarization of the N2 is not associated with the phasic spiking of Goal Maintenance neurons (L2/3: t(57) = - 1.28, p = 0.21; L5/6: t(57) = 0.60, p = 0.52) (Supplementary Figure 7a) but was predicted by the spiking activity in L2/3 but not in L5/6 of Conflict (L2/3: t(117) = - 3.6, p = 4.7×10⁻⁴; L5/6: t(117) = 0.046, p = 0.96) and of Event Timing neurons (L2/3: t(97) = - 4.60, p = 1.3×10⁻⁵; L5/6: t(97) = 1.67, p = 0.097) (Fig 5d). When the discharge rate of these L2/3 neurons was higher, the N2 exhibited a stronger negativity. Interestingly, N2 polarization was also predicted by the spiking activity in L2/3 but not in L5/6 of other neurons that were not modulated on canceled trials and so were not described in this manuscript (L2/3: t(317) = - 2.51, p = 0.012; L5/6: t(317) = - 1.60, p = 0.11). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 877, 172]]<|/det|> +Similar results were obtained when controlling for the variation of ERP polarization and spike rate across different SSDs (not shown) and when measuring the difference in spiking and ERP between canceled and matched no stop- signal trials (Supplementary Figure 7b). + +<|ref|>text<|/ref|><|det|>[[112, 216, 883, 490]]<|/det|> +Countermanding P3. The N2 was followed by a robust P3 (Fig 5a, b) beginning \(\sim 300\) ms and peaking \(358 \pm 17\) ms after the stop- signal, homologous to the human P3 \(^5\) . The peak polarization time was better synchronized on the stop- signal than on SsRT \((F(28,28) = 0.44, \mathsf{p} = 0.0345)\) (Supplementary Fig 6c). P3 polarization varied most with the log- transformed time of the feedback tone on canceled trials \((\Delta \mathsf{BIC} > 4.0\) against competing models) with weak support against other time- based models \((\Delta \mathsf{BIC} < 1.30)\) (Fig 5e, Supplementary Table 2). P3 polarization increased with time until feedback \((t(85) = 3.72, \mathsf{p} = 3.5 \times 10^{- 4})\) . The conclusions of these results do not differ if the analyses are performed on the raw EEG polarization in these intervals. + +<|ref|>text<|/ref|><|det|>[[112, 502, 880, 833]]<|/det|> +Peak P3 polarization coincided with the peak recruitment of Goal Maintenance neurons, while the recruitment of Conflict and Event Timing neurons was decaying (Fig 5b). Accordingly, variation in P3 polarization was predicted by the spiking activity of Goal Maintenance neurons in L2/3 but not L5/6 (L2/3: \(t(57) = 5.46, \mathsf{p} = 1.1 \times 10^{- 6}\) ; L5/6: \(t(57) = 1.47, \mathsf{p} = 0.15\) ) (Fig. 5f). Higher spike rates are associated with greater P3 positivity. P3 amplitude was not associated with the spiking of Conflict (L2/3: \(t(97) = 0.44, \mathsf{p} = 0.66\) ; L5/6: \(t(97) = - 0.49, \mathsf{p} = 0.62\) ), Event Timing (L2/3: \(t(117) = - 1.19, \mathsf{p} = 0.24\) ; L5/6: \(t(117) = - 0.78, \mathsf{p} = 0.44\) ), or unmodulated neurons (L2/3: \(t(317) = - 1.11, \mathsf{p} = 0.27\) ; L5/6: \(t(317) = 0.054, \mathsf{p} = 0.96\) ) (Supplementary Figure 7c). Similar results were obtained when SSD was controlled for (not shown) and when measuring the difference in spiking and ERP between canceled and matched no stop- signal trials (Supplementary Figure 7d). + +<|ref|>text<|/ref|><|det|>[[115, 825, 350, 844]]<|/det|> +(Supplementary Figure 7d). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 231, 107]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[111, 118, 880, 655]]<|/det|> +These results offer important, new insights into the cortical microcircuitry supporting executive control in primates. Model- based analysis of the latency, temporal dynamics, and variation in strength of neural spiking across the neuron sample revealed functionally distinct and theoretically important classes of neurons with particular biophysical and laminar properties. Moreover, a bridge between these neurophysiological findings and human electrophysiology was established through the specific associations observed between the N2 and P3 ERP observed in response inhibition tasks and classes of neurons in particular cortical layers. The novelty and importance of these findings is amplified by their complementarity with our previous description of the laminar organization of error and reward processing in SEF16. Based on the new results, we will discuss how SEF can contribute to conflict monitoring, time estimation, and goal maintenance. Coupled with extensive knowledge about connectivity of SEF42, 43, 44, this new information about the laminar distribution of neurons signaling response conflict, event timing, and maintaining goals suggest several specific hypotheses and research questions about how SEF and associated structures accomplish response inhibition and executive control (Fig. 6). Also, complementing our earlier description of the source of the ERN16, we now report a macaque homolog of the N2/P3 ERP components associated with response inhibition. The new results demonstrate one cortical source of these ERP components. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 90, 883, 442]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 460, 880, 905]]<|/det|> +
Fig 6 | Extrinsic and intrinsic circuitry for executive control. The laminar distribution observed for Conflict (orange), Event Timing (dark blue), and Goal Maintenance (dark red) are summarized with selected anatomical connections based on published studies. Sampled neurons were likely broad spike pyramidal and narrow spike, possibly inhibitory neurons. The laminar densities of calretinin (CR), calbindin (CB), and parvalbumin (PV) neurons observed and of D1 and D2 receptors are indicated on the far right. Left, Conflict signal can arise in SEF through afferents from frontal eye field (FEF). SEF can receive coincident inputs from Fixation neurons (STOP) and Movement neurons (GO) in FEF, directly, or in SC, indirectly, via thalamus, terminating in L2/3. These inputs are integrated within the synapses of L2/3 and L5 Conflict neurons. Intracortical processing produces later activation of Conflict neurons in L6 which can relay this signal to the Thalamus. Right, Top: Schematic of the activity profile for Goal Maintenance and Event Timing neurons in distinct phases indicated by the number. We conjecture that Goal Maintenance neurons, mainly located in L2/3, suppress unwanted movement through push-pull basal ganglia circuitry with pyramidal neurons directly projecting to the indirect pathway (D2) and inhibitory neurons, inhibiting pyramidal neurons that can project to the direct (D1) pathway. The gray symbol indicates that these neurons are distinct from those reported in this study. Input from dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), terminating in L2/3 can inform SEF of the anticipated reward association based on the experienced stop-signal delay contingent on successful stopping. Dopamine (DA) neuron projections in L2/3 from the SNpc and VTA can also relay this information. These inputs can result in the phasic response in Goal Maintenance neurons (phase 1, red). Following the phasic response, activity can remain elevated via recurrent connections and balance of excitation and inhibition (phase 2, red). The auditory feedback tone, integrated with the task rule from DLPFC cues the termination of operant control on behavior, resulting in the inhibition of pyramidal and inter-neurons by CR and CB neurons. This results in the termination of the sustained activity (phase 3). Event Timing neurons can receive input from DLPFC and ACC terminating in L2/3 informing neurons in L2/3 and L5 about an upcoming event. Ramping results from recurrent connections (1, dark blue). SEF can receive information about stop-signal appearance and successful stopping from ventrolateral prefrontal cortex (VLPFC) and DLPFC and Conflict neurons within the microcircuitry. This information can suppress the ramping activity via inhibitory
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 868, 168]]<|/det|> +connections by direct inhibitory connections onto Event Timing neurons (phase 2, dark blue). This resets these neurons for the next phase of ramping (phase 3, dark blue) which is terminated by the appearance of the feedback tone (4). The activity of Event Timing neurons can project to the caudate nucleus to inform the fronto- striatal reinforcement learning loop about the experienced timing of the event. Further details in text. + +<|ref|>text<|/ref|><|det|>[[111, 208, 884, 644]]<|/det|> +Conflict. One class of SEF neuron was characterized by a pronounced facilitation after the stop- signal when saccades were inhibited. The modulation followed SSRT and scaled with P(error). These neurons were predominantly broad spiking and found in all layers. We hypothesize that these neurons signal response conflict \(^{15,39}\) defined as co- activation of mutually incompatible response processes \(^{31}\) . Previous research has characterized the neural mechanism of saccade countermanding \(^{27,45}\) . On canceled trials, gaze- shifting and gaze- holding neurons in the frontal eye field (FEF) and superior colliculus (SC) are co- active in a dynamically unstable manner that varies with P(error) precisely because these are the neurons producing the performance. In the interactive race model \(^{29,30}\) , the multiplicative conflict between GO and STOP accumulator units scales with P(error) (Supplementary Figure 2a) and can be used to adjust interactive race parameters to accomplish post- stopping slowing \(^{39}\) . Thus, these neurons signal a quantity central to theories of executive control. Furthermore, different neurons in SEF signal conflict, error, and reward, highlighting the possible independence of these executive control signals. + +<|ref|>text<|/ref|><|det|>[[111, 655, 880, 900]]<|/det|> +Further evidence dissociating conflict, reward, and error signals is offered by comparing our results with those of a recent investigation of the nigrostriatal dopamine system of monkeys performing saccade countermanding \(^{46}\) . Dopamine (DA) neurons concentrated in the dorsolateral substantia nigra exhibited a pattern of activity that paralleled the conflict neurons in SEF. The DA neurons produced a brisk response to the stop- signal that was stronger when saccades were canceled in either direction. This observation is consistent with reports that besides responding to rewarding events, dopamine neurons respond to salient signals, such as a stop- signal. Unlike movement neurons in FEF \(^{27}\) and SC \(^{45}\) but like SEF, nearly all DA neurons + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 877, 140]]<|/det|> +modulate after SsRT. Moreover, the modulation of DA neurons scaled with P(error) just like the SEF neurons. + +<|ref|>text<|/ref|><|det|>[[111, 152, 880, 588]]<|/det|> +The striking parallels between SEF and SNpc modulation patterns invites consideration of cause and effect. SEF is innervated by DA neurons in Substantia Nigra pas compacta (SNpc) \(^{47}\) , and SNpc DA neurons modulated significantly earlier than did the SEF conflict neurons (Supplementary Figure 8). However, because of the very slow conduction of DA axons \(^{48,49,50,51}\) , we estimate that the spike conduction time from SNpc to SEF is \(\sim 100\) ms (Supplementary Figure 8). Consequently, the estimated arrival times of DA spikes in SEF were not significantly different from the modulation times of the conflict neurons (Supplementary Figure 8). The influence of DA in SEF is slowed further by the well- known second- messenger delay of influence. Therefore, we infer that the SEF conflict modulation cannot be caused by DA inputs. However, because axon terminals from SEF are rare in SNpc \(^{42,44}\) , SEF neurons are unlikely to cause directly the modulation of the SNpc DA neurons. Instead, other investigators have shown that the phasic DA activation is delivered by the SC \(^{52}\) . Through the conflict neurons in L5, SEF can influence SC directly \(^{42}\) . Curiously, though, the modulation specifically after SsRT scaling with P(error) has not been observed in SC \(^{45}\) . + +<|ref|>text<|/ref|><|det|>[[111, 599, 880, 907]]<|/det|> +Theories of DA function can facilitate understanding the putative conflict signal in SEF. From the reinforcement perspective, the phasic DA signal may act as an immediate eligibility trace broadcast to SEF and other regions to associate reinforcement with successful cancelation to the infrequent stop- signal. Such eligibility traces must be salient to be useful. The reinforcement perspective suggests an alternative to the conflict interpretation. The imbalance between gaze- holding and gaze- shifting arising on canceled trials increases with the progressive commitment from gaze- holding to gaze- shifting through time. Consequently, as the likelihood of unsuccessful response inhibition increases, the surprise of successful response cancelation increases. We observed a divergence in the values of P(error)—which is necessarily proportional to the product of the activation of GO and STOP units—and P(error | + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 864, 235]]<|/det|> +SSseen)—which is a proxy of error likelihood learned through experience with the task—at longer SSDs. Others have described SEF neural signals in terms of surprise \(^{12}\) . Thus, the modulation after SsRT scaling with P(error) may just be another element of the reinforcement learning needed to perform this task. Further research is needed to resolve the conflict and surprise hypotheses. + +<|ref|>text<|/ref|><|det|>[[111, 247, 881, 619]]<|/det|> +Conflict neurons were found in all layers. To signal conflict, SEF can be informed about the dynamic state of gaze- shifting and gaze- holding through inputs from FEF and oculomotor thalamic nuclei. To signal surprise, SEF can be informed about saccade production from the thalamus \(^{53}\) and task rules from DLPFC and ventrolateral prefrontal cortex (VLPFC) \(^{54}\) . Based on previous conjectures \(^{6}\) and recent biophysical modelling \(^{55}\) we hypothesize that the integration of information producing the modulation of these neurons is derived through synaptic processes in L2/3. However, if this is so, and if the apical dendrites of L6 pyramidal neurons in SEF do not extend into L2/3, then this conflict signal can arise in L6 through translaminar interactions. The observation that conflict arises later in L6 is consistent with this supposition. Another implication of the hypothesis that conflict in L6 is derived from that in L2/3 is that the L6 feedback to thalamus will be delayed relative to the gaze- holding and gaze- shifting signals conveyed from the SC. + +<|ref|>text<|/ref|><|det|>[[111, 664, 860, 875]]<|/det|> +Time estimation and goal maintenance. The interpretation of the other two classes of neurons that we found is framed by motivation more than reinforcement. To earn reward, monkeys must hold gaze for an extended period, which requires preventing blinks that would interrupt the camera- based eye tracker. This entails learning and possibly exploiting any regularities in the timing of task events \(^{33, 40}\) . A contribution of SEF and nearby areas in action timing and explicit time production tasks has been demonstrated \(^{13, 14}\) . We extend that description to this stop- signal task. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 876, 397]]<|/det|> +A distinct group of SEF neurons produced ramping activity before saccades, which decayed after the gaze shift. But, when the saccade was countermanded, the ramping was interrupted by pronounced suppression. A previous description of these neurons recognized that the modulation on canceled trials arose too late to contribute to reactive inhibition but offered no explanation for these neurons 7. The new task design used here exposed a second period of ramping before the feedback tone on \(\sim 30\%\) of these neurons. This monotonically rising activity reached different levels for different interval durations ranging from \(\sim 1000\) to 1400 ms after SSRT on canceled trials. Our discovery of an association between spiking rate and the log- transformed duration of the preceding interval motivates a more integrated interpretation framed by a body of research on time keeping 37, 41 56 57. + +<|ref|>text<|/ref|><|det|>[[111, 407, 877, 650]]<|/det|> +We interpret the ramping activity as representing the timing of task events. Spiking rate increases as the learned time of an event like the stop- signal approaches. Strong suppression after the event resets a proportion of these neurons to ramp until the next event, i.e., the feedback tone. The stop- signal and feedback tone events differ in two ways. First, they differ in predictability, for the stop- signal only occurs on a proportion of trials while feedback tone is not. Second, they differ in the action required following the event, for the stop- signal announces a prolonged period of fixation in which blinks must also be withheld while the tone announces the release of control over behavior. + +<|ref|>text<|/ref|><|det|>[[111, 662, 881, 876]]<|/det|> +Recent work has shown that different neurons in the basal forebrain signal timing of events depending on surprise, salience, and uncertainty 37. We found similar differences in SEF. We conjecture that those neurons with ramping activity before both SSRT and the feedback tone encode the timing of expected salient events regardless of certainty or expected action. In contrast, the neurons with only ramping activity before successful stopping encode events that are less certain in occurrence or consequence. These differences were reinforced by the distribution of the neurons across the cortical layers. While Event Timing neurons were found in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 875, 140]]<|/det|> +all layers, those that encoded timing regardless of predictability or action were most common in L3 and L5 with broad spikes consistent with pyramidal projection neurons. + +<|ref|>text<|/ref|><|det|>[[111, 152, 883, 492]]<|/det|> +This laminar differentiation demonstrates that the timing of different types of events can engage different circuits mediated by different layer- specific extrinsic connections. The timing signal can be sent via cortico- cortical connections to other cortical areas to influence motor, cognitive, and limbic processes. Further research is needed to clarify this projection. Also, these neurons can contribute to fronto- striatal pathways to learn and update the temporal structure of the task \(^{57,58,59}\) . Axon terminals from SEF are dense in the caudate nucleus \(^{43}\) , arising from pyramidal neurons in L3 and L5 \(^{60,61,62}\) . In fact, neurons with this pattern of modulation have been described in a recent investigation of the caudate nucleus of monkeys performing saccade countermanding \(^{46}\) . Our finding that the suppression in the caudate nucleus occurred significantly later after SsRT than that of Event Timing neurons in SEF (Supplementary Figure 8) suggests a primary role of the cortex in this signaling. + +<|ref|>text<|/ref|><|det|>[[111, 504, 885, 842]]<|/det|> +The rapid suppression of the ramping activity after SsRT merits consideration. One source can be intracortical inhibition from the narrow- spike, putative PV neurons that we observed. Another source can be the very small CB and CR neurons concentrated in L2/3 that are innervated by DLPFC and selectively inhibit pyramidal neurons \(^{63}\) , although our methods are unlikely to sample spikes from them. We note that although SEF is an agranular structure with weak interlaminar inhibitory connections \(^{21}\) , CR neurons in L2/3 can potently inhibit L5 neurons through specialized projections on the apical dendrites \(^{64}\) . This inhibition must be informed about the presence of the stop- signal and the cancelation of the saccade. We observe that such a signal is available in the conflict neurons. However, the suppression of Event Timing neurons occurred significantly earlier than the facilitation of the conflict neurons. Further research can resolve these cortical interactions. + +<|ref|>text<|/ref|><|det|>[[112, 855, 846, 905]]<|/det|> +The Event Timing neurons that represent the duration of a preceding interval can support the patterns of modulation observed in the final class of neuron we found. The third + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 883, 622]]<|/det|> +class of neuron produced a phasic response after SsRT on canceled trials that scaled with the duration of the upcoming interval until the feedback tone. Recall that on canceled trials the interval from target presentation until tone presentation was of fixed duration, making it progressively shorter after progressively longer SSD. Such phasic responses have previously been observed when the timing of events followed discrete predictable durations \(^{65}\) similar to the time of feedback tone in our task following successful stopping. This phasic representation of the time was followed by sustained spiking until the tone. Note that by design, when the tone sounded, monkeys could shift gaze before receiving the fluid reward. We propose that these neurons can be identified with the operation of goal maintenance, which is necessary in canceled trials to prevent blinking or gaze shifts before the tone. This inference is consistent with an interpretation of the original theory of response inhibition \(^{26}\) and supported by previous evidence linking SEF to working memory \(^{10,11}\) and working memory to time representation \(^{66,67}\) . We have obtained further evidence for this interpretation in ongoing experiments with two other monkeys performing the same saccade countermanding task but with the requirement to maintain fixation on the stop- signal until the fluid reward is delivered. Goal maintenance neurons have been observed, but they continue spiking after the tone until the fluid reward when operant control over behavior is released (data not shown). + +<|ref|>text<|/ref|><|det|>[[112, 631, 849, 907]]<|/det|> +Goal maintenance neurons were mainly found in L2/3. Inputs to these neurons from DLPFC, VLPFC, and ACC can signal task rules and the expected time of the secondary reinforcer when executive control can be released. Dopaminergic release in SEF from VTA where similar time- predicting signals are observed \(^{65}\) can enhance these influences through higher density of D1 relative to D2 receptors in L2/3 \(^{68,69}\) . The sustained discharge can be maintained through recurrent activation within SEF and between other structures \(^{11,70}\) . Also, many goal maintenance neurons had narrow spikes, consistent with PV inhibitory neurons, which can balance excitation and inhibition necessary for the maintenance of persistent activity in recurrent networks \(^{71,72,73,74}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 884, 590]]<|/det|> +We hypothesize that pyramidal Goal Maintenance neurons can encourage the suppression of movements through projections to the indirect pathway D2 neurons in the striatum \(^{60,61,62}\) . Inhibitory Goal Maintenance neurons, on the other hand, can inhibit the D1 direct (action- promoting) pathway and the frontal eye field to suppress movement. As PV neurons in primates do not have extrinsic connections, we propose that this can be mediated by the inhibition of other excitatory neurons (unidentified neurons and possibly Gain neurons identified in \(^{16}\) ) that send projections to these motor structures (gray neurons). Therefore, Goal Maintenance neurons can achieve their role by altering the balance in the push- pull mechanism mediated by the direct (D1) and indirect (D2) pathways. This function is consistent with the observation that many of these neurons also exhibit higher activity on unrewarded trials that, as previously described, influences post- error adjustments in RT in the next trial \(^{16}\) . Also consistent with this hypothesis, neurons with facilitated activity after SSRT were described in an investigation of the caudate nucleus of monkeys performing saccade countermanding \(^{46}\) . The facilitation in the caudate nucleus coincided with that measured in SEF (Supplementary Figure 8). The parallel between SEF and the striatum in patterns of modulation associated with proactive but not reactive inhibition are surprisingly, but satisfyingly, clear. + +<|ref|>text<|/ref|><|det|>[[111, 630, 883, 876]]<|/det|> +Origin of Countermanding N2/P3. We showed that macaque monkeys exhibit a N2/P3 ERP complex homologous to that observed in humans \(^{5}\) . We discovered that variation in N2 and P3 polarization was predicted by spiking of specific, different neuron classes in L2/3 and not L5/6. These findings extend and parallel our previous demonstration that SEF contributes to the error- related negativity (ERN) \(^{16}\) . We found that variations in error- related spiking in L2/3 but not in L5/6 predicted variation of EEG polarization across both error and correct trials. Because action potentials are not large or sustained enough to produce event- related potentials, we surmise that this neural spiking coincides with coherent current flow strong enough to produce in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 827, 139]]<|/det|> +ERN \(^{55}\) . These new results show synaptic activity in L2/3 of SEF contributes to the N2/P3 complex. + +<|ref|>text<|/ref|><|det|>[[112, 151, 876, 460]]<|/det|> +Disagreement persists about what the frontal N2 indexes \(^{75,76}\) . We found that the amplitude of the macaque homologue of the N2 during the stop- signal task varied most with the likelihood of error associated with experienced SSDs and not conflict and SSD as previously suggested \(^{5,77}\) . Further, we demonstrate that the spiking of different classes of neurons in L2/3 (but not L5/6) predicted the magnitude of the N2. Specifically, N2 magnitude was unrelated to spiking of Goal Maintenance neurons but co- varied with spiking of Conflict and Event Timing neurons in addition to the spiking of other neurons that did not modulate around the time of successful stopping. Recognizing that the N2 manifests the influence of different processes occurring in functionally distinct neurons can explain the disagreement about the nature of this ERP component. + +<|ref|>text<|/ref|><|det|>[[112, 472, 880, 715]]<|/det|> +Likewise, the macaque homologue of the P3 component in this task resembled that reported in humans \(^{5}\) . Consistent with previous reports of P3 indexing expectation and temporal aspects of behavior \(^{75}\) , we found that P3 amplitude co- varied most with the expected time of the feedback tone. Reinforcing this interpretation, we found that P3 amplitude was predicted by the spiking of Goal Maintenance but not Conflict or Event Timing neurons. Therefore, we surmise that the P3 expressed in our experimental design indexes temporal prediction underlying goal maintenance. Overall, these results demonstrate that N2 and P3 index distinct processes mediated by the activity of different populations of neurons. + +<|ref|>sub_title<|/ref|><|det|>[[115, 760, 216, 778]]<|/det|> +## Conclusion + +<|ref|>text<|/ref|><|det|>[[112, 790, 872, 905]]<|/det|> +Pioneering insights into the microcircuitry and mechanisms of primary visual cortex began by describing the properties of neurons in different layers \(^{78}\) . The present results complete the first catalogue for an agranular frontal lobe area. Contrasts with primary sensory areas will reveal the degree of computational uniformity across cortical areas. Being a source contributing to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 884, 235]]<|/det|> +ERPs indexing performance monitoring and executive control, details about laminar processing in SEF will offer unprecedented insights into the microcircuitry of executive control. These results validate the tractability of formulating neural mechanism models of performance monitoring and executive control, especially when constrained by formal \(^{26}\) , algorithmic \(^{29,30}\) , and spiking network \(^{79}\) models of performance of a task with clear clinical relevance \(^{80}\) . + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 209, 107]]<|/det|> +## METHODS + +<|ref|>text<|/ref|><|det|>[[111, 120, 876, 526]]<|/det|> +Animal care and surgical procedures. Data was collected from one male bonnet macaque (Eu, Macaca Radiata, 8.8kg) and one female rhesus macaque (X, Macaca Mulatta, 6.0kg) performing a countermanding task \(^{20,24}\) . All procedures were approved by the Vanderbilt Institutional Animal Care and Use Committee in accordance with the United States Department of Agriculture and Public Health Service Policy on Humane Care and Use of Laboratory Animals. Surgical details have been described previously \(^{81}\) . Briefly, magnetic resonance images (MRIs) were acquired with a Philips Intera Achieva 3T scanner using SENSE Flex- S surface coils placed above or below the animal's head. T1- weighted gradient- echo structural images were obtained with a 3D turbo field echo anatomical sequence (TR = 8.729 ms; 130 slices, 0.70 mm thickness). These images were used to ensure Cilux recording chambers were placed in the correct area. Chambers were implanted normal to the cortex (Monkey Eu: \(17^{\circ}\) ; Monkey X: \(9^{\circ}\) ; relative to stereotaxic vertical) centered on midline, 30mm (Monkey Eu) and 28mm (Monkey X) anterior to the interaural line. + +<|ref|>text<|/ref|><|det|>[[112, 567, 840, 715]]<|/det|> +Acquiring EEG. EEG was recorded from the cranial surface with electrodes located over medial frontal cortex. Electrodes were referenced to linked ears using ear- clip electrodes (Electro- Cap International). The EEG from each electrode was amplified with a high- input impedance head stage (Plexon) and bandpass filtered between 0.7 and 170 Hz. Trials with blinks within 200ms before or after the analysis interval were removed. + +<|ref|>text<|/ref|><|det|>[[112, 760, 882, 907]]<|/det|> +Cortical mapping and electrode placement. Chambers implanted over the medial frontal cortex were mapped using tungsten microelectrodes (2- 4 MΩ, FHC, Bowdoin, ME) to apply 200ms trains of biphasic micro- stimulation (333 Hz, 200 μs pulse width). The SEF was identified as the area from which saccades could be elicited using \(< 50 \mu \mathrm{A}\) of current \(^{82,83}\) . In both monkeys, the SEF chamber was placed over the left hemisphere. The dorsomedial location of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 883, 426]]<|/det|> +the SEF makes it readily accessible for linear electrode array recordings across all cortical layers. A total of five penetrations were made into the cortex—two in monkey Eu, three in monkey X. Three of these penetration locations were perpendicular to the cortex. In monkey Eu, the perpendicular penetrations sampled activity at site P1, located 5 mm lateral to the midline and 31 mm anterior to the interaural line. In monkey X, the perpendicular penetrations sampled activity at site P2 and P3, located 5 mm lateral to the midline and 29 and 30 mm anterior to the interaural line, respectively. However, during the mapping of the bank of the cortical medial wall, we noted both monkeys had chambers place \(\sim 1\) mm to the right respective to the midline of the brain. This was confirmed through co- registered CT/MRI data. Subsequently, the stereotaxic estimate placed the electrodes at 4 mm lateral to the cortical midline opposed to the skull- based stereotaxic midline. + +<|ref|>text<|/ref|><|det|>[[112, 471, 876, 811]]<|/det|> +Acquiring neural spiking. Spiking activity and local field potentials were recorded using a 24- channel Plexon U- probe with 150 μm between contacts, allowing sampling from all layers. The U- probes were 100 mm in length with 30 mm reinforced tubing, 210 μm probe diameter, 30° tip angle, with 500 μm between the tip and first contact. Contacts were referenced to the probe shaft and grounded to the headpost. We used custom built guide tubes consisting of 26- gauge polyether ether ketone (PEEK) tubing (Plastics One, Roanoke, VA) cut to length and glued into 19- gauge stainless steel hypodermic tubing (Small Parts Inc., Logansport, IN). This tubing had been cut to length, deburred, and polished so that they effectively support the U- probes as they penetrated dura and entered cortex. The stainless- steel guide tube provided mechanical support, while the PEEK tubing electrically insulated the shaft of the U- probe, and provided an inert, low- friction interface that aided in loading and penetration. + +<|ref|>text<|/ref|><|det|>[[113, 823, 867, 905]]<|/det|> +Microdrive adapters were fit to recording chambers with \(< 400 \mu m\) of tolerance and locked in place at a single radial orientation (Crist Instruments, Hagerstown, MD). After setting up hydraulic microdrives (FHC, Bowdoin, ME) on these adapters, pivot points were locked in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 866, 202]]<|/det|> +place by means of a custom mechanical clamp. Neither guide tubes nor U- probes were removed from the microdrives once recording commenced within a single monkey. These methods ensured that we were able to sample neural activity from precisely the same location relative to the chamber on repeated sessions. + +<|ref|>text<|/ref|><|det|>[[112, 216, 882, 491]]<|/det|> +Electrophysiology data were processed with unity- gain high- input impedance head stages (HST/32o25- 36P- TR, Plexon). Spiking data were bandpass filtered between 100 Hz and 8 kHz and amplified 1000 times with a Plexon preamplifier, filtered in software with a 250 Hz high- pass filter and amplified an additional 32,000 times. Waveforms were digitized at 40 kHz from - 200 to 1200 μs relative to voltage threshold crossings. Thresholds were typically set at 3.5 standard deviations from the mean. All data were streamed to a single data acquisition system (MAP, Plexon, Dallas, TX). Time stamps of trial events were recorded at 500 Hz. Single units were sorted online using a software window discriminator and refined offline using principal components analysis implemented in Plexon offline sorter. + +<|ref|>text<|/ref|><|det|>[[112, 536, 879, 842]]<|/det|> +Cortical depth and layer assignment. The retrospective depth of the electrode array relative to grey matter was assessed through the alignment of several physiological measures. Firstly, the pulse artifact was observed on a superficial channel which indicated where the electrode was in contact with either the dura mater or epidural saline in the recording chamber; these pulsated visibly in synchronization with the heartbeat. Secondly, a marked increase of power in the gamma frequency range (40- 80Hz) was observed at several electrode contacts, across all sessions. Previous literature has demonstrated elevated gamma power in superficial and middle layers relative to deeper layers \(^{84,85}\) . Thirdly, an automated depth alignment procedure was employed which maximized the similarity of CSD profiles evoked by passive visual stimulation between sessions \(^{20}\) . + +<|ref|>text<|/ref|><|det|>[[112, 856, 868, 905]]<|/det|> +Further support for the laminar assignments was provided by an analysis of the depths of SEF layers measured in histological sections visualized with Nissl, neuronal nuclear antigen + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 872, 300]]<|/det|> +(NeuN), Gallyas myelin, acetylcholinesterase (AChE), non-phosphorylated neurofilament H (SMI- 32), and the calcium- binding proteins parvalbumin (PV), calbindin (CB), and calretinin (CR) \(^{16,20}\) . Additional information about laminar structure was assessed through the pattern of cross- frequency phase- amplitude coupling across SEF layers \(^{22}\) . Owing to variability in the depth estimates and the indistinct nature of the L6 border with white matter, some units appeared beyond the average gray- matter estimate; these were assigned to the nearest cellular layer. + +<|ref|>text<|/ref|><|det|>[[112, 344, 875, 459]]<|/det|> +Acquiring eye position. Eye position data was collected at 1 kHz using an EyeLink 1000 infrared eye- tracking system (SR Research, Kanata, Ontario, Canada). This was streamed to a single data acquisition system (MAP, Plexon, Dallas, TX) and combined with other behavioral and neurophysiological data streams. + +<|ref|>text<|/ref|><|det|>[[112, 504, 880, 812]]<|/det|> +Data collection protocol. The same protocol was used across monkeys and sessions. In each session, the monkey sat in an enclosed primate chair with their head restrained 45 cm from a CRT monitor (Dell P1130, background luminance of \(0.10 \text{cd} /\text{m}^2\) ). The monitor had a refresh rate of 70 Hz, and the screen subtended \(46^\circ \times 36^\circ\) of the visual angle. After advancing the electrode array to the desired depth, they were left for 3 to 4 hours until recordings stabilized across contacts. This led to consistently stable recordings with single units typically held indefinitely. Once these recordings stabilized, an hour of resting- state activity in near- total darkness was recorded. This was followed by the passive presentation of visual flashes followed by periods of total darkness in alternating blocks. Finally, the monkey performed approximately 2000 trials of the saccade countermanding (stop- signal) task. + +<|ref|>text<|/ref|><|det|>[[113, 856, 861, 907]]<|/det|> +Countermanding task. The countermanding (stop- signal) task utilized in this study has been widely used previously \(^{25}\) . Briefly, trials were initiated when monkeys fixated at a central point. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 877, 172]]<|/det|> +Following a variable time period, drawn from an aging function to avoid anticipation of the visual stimulus \(^{40}\) , the center of the fixation point was removed leaving an outline. Simultaneously, a peripheral target was presented to the left or right of the screen. + +<|ref|>text<|/ref|><|det|>[[111, 184, 872, 268]]<|/det|> +On no stop- signal trials the monkey was required to shift gaze to the target. Fixation on the target was required for 600 ms, until an auditory tone sounded, whereupon monkeys could shift gaze anywhere. Fluid reward was delivered 600 ms later. + +<|ref|>text<|/ref|><|det|>[[110, 279, 881, 658]]<|/det|> +On stop- signal trials, comprising less than half of all trials, the center of the fixation point was re- illuminated after a variable stop- signal delay (SSD). An initial set of SSDs, separated by 40- 60 ms for Monkey Eu and by 100 ms for monkey X, were selected for each recording session. To ensure that monkeys failed to countermand on \(\sim 50\%\) of stop- signal trials, SSD was adjusted through an adaptive staircasing procedure. When a monkey failed to inhibit a response, the SSD was decreased by 1, 2, or 3 steps (randomly drawn) to increase the likelihood of success on the next stop trial. When a monkey canceled the saccade, SSD was increased by 1, 2, or 3 steps (randomly drawn) to decrease the likelihood of success on the next stop trial. On stop- signal trials, the monkey was required to maintain fixation on the central point until the tone sounded, whereupon monkeys could shift gaze anywhere. Fluid reward was delivered 600 ms later. By design, the duration from target presentation until the tone was a fixed interval of 1500 ms. Thus, as SSD increased, the duration of fixation decreased + +<|ref|>sub_title<|/ref|><|det|>[[115, 666, 350, 684]]<|/det|> +## (Supplementary Figure 2b). + +<|ref|>text<|/ref|><|det|>[[110, 696, 876, 907]]<|/det|> +Performance on this task is characterized by the probability of not canceling a saccade as a function of the SSD (the inhibition function) and the distribution of latencies of correct saccades in no- stop- signal trials and of noncanceled error saccades in stop- trials (Fig 1b). Performance of the stop- signal task is explained as the outcome of a race between a GO and a STOP process \(^{26}\) . The race model provides an estimate of the duration of the covert STOP process, the time taken to accomplish response inhibition, known as stop- signal reaction time (SSRT) \(^{29, 30, 79}\) . SSRT was calculated using two approaches—the conventional weighted- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 884, 364]]<|/det|> +integration method and the more recent Bayesian Ex- Gaussian Estimation of Stop- Signal RT distributions (BEEST) \(^{86}\) (Supplementary Figure 3a, 4a, 5a). Compared to weighted integration method, the Bayesian approach provides estimates of the variability in SSRT and the fraction of trigger failures for a given session \(^{86}\). Individual parameters were estimated for each session. The priors were bounded uniform distributions \((\mu_{G0}, \mu_{Stop}; U(0.001, 1000); \sigma_{G0}, \sigma_{Stop}; U(1, 500); \tau_{G0}, \tau_{Stop}; U(1, 500); \text{pTF: } U(0,1))\). The posterior distributions were estimated using Metropolis- within- Gibbs sampling ran multiple through three chains. We ran the model for 5000 samples with a thinning of 5. None of our conclusions depend on the choice of SSRT calculation method. + +<|ref|>text<|/ref|><|det|>[[110, 407, 876, 717]]<|/det|> +Analysis of EEG. Methods paralleling those used in human studies were used. The N2 and P3 were obtained from average EEG synchronized on stop- signal presentation. Peak N2 was the time when the mean ERP reached maximal negativity in a 150- 250 ms window after the stop- signal. Peak P3 was the time when the mean ERP in a 250- 400 ms window after the stop- signal. The amplitude of the N2 and P3 was quantified as the mean Z- transformed voltage for each SSD in a \(\pm 50\) ms window around the maximal ERP deflection determined for each session. Indistinguishable results were obtained with wider \((\pm 75 \text{ms})\) , and narrower \((\pm 25 \text{ms})\) windows or just the instantaneous maximal polarization. To characterize the polarizations associated with response inhibition, a difference ERP \((\Delta \text{ERP})\) was obtained by subtracting from the ERP recorded on canceled trials the ERP recorded on RT- matched no stop- signal trials. + +<|ref|>text<|/ref|><|det|>[[111, 760, 880, 843]]<|/det|> +Analysis of neural spiking. Spike density functions (SDF) for individual trials were constructed by convolving the spike times with a kernel matching the time course of an excitatory post- synaptic potential with an area equal to 1 + +<|ref|>equation<|/ref|><|det|>[[388, 855, 608, 893]]<|/det|> +\[R(t) = \left\{1 - e^{\left(-\frac{t}{\tau_g}\right)}\right\} \cdot e^{\left(-\frac{t}{\tau_d}\right)}\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 880, 397]]<|/det|> +The influence of each spike (R(t)) increases with a short time constant ( \(\mathsf{T}_{\mathsf{g}} = 1\) ms) and decays slower ( \(\mathsf{T}_{\mathsf{d}} = 20\) ms). To analyze spiking activity associated with successful stopping, we compared the activity on canceled trials and on no stop- signal trials with RT greater than SSD + SSRT. This latency- matching compares trials in which countermanding was successful with trials in which countermanding would have been successful had the stop- signal been presented. Neurons were distinguished by patterns of modulation consisting of periods of facilitation or suppression using a consensus clustering algorithm 28 (Supplementary Fig 1B). The input to this analysis pipeline was the spike- density function on canceled trials and on latency- matched no stop- signal trials during the 100 ms preceding SSRT and 200 ms following SSRT. Results did not change much if interval durations were changed. + +<|ref|>text<|/ref|><|det|>[[112, 408, 880, 556]]<|/det|> +To prevent outlying values from exerting excessive influence, population spike density plots were obtained by scaling the SDF of each neuron by the \(95\%\) confidence interval between the \(2.5\%\) lowest rate and the \(97.5\%\) highest rate in one of two intervals. The first interval was a 600 ms window centered on SSRT on canceled and on no stop- signal trials. The second interval was - 100 to +300 ms relative to the feedback tone. + +<|ref|>text<|/ref|><|det|>[[112, 567, 876, 907]]<|/det|> +To identify spiking modulation, we applied methods previously employed. First, we calculated a difference function (ΔSDF), the difference between the SDF on canceled and latency- matched no stop- signal trials. Periods of statistically significant modulation were identified based on multiple criteria—(a) the difference function must exceed by at least 2 standard deviations a baseline difference measured in the 100 ms interval before the target appeared, (b) the difference must occur from 50 ms before to 900 ms after the stop- signal, and (c) the difference must persist for at least 100 ms (or for 50 ms if the difference exceeded baseline by 3 standard deviations). As commonly found in medial frontal cortex, some neurons exhibited low spiking rates. To obtain reliable estimates of modulation times, we also convolved the SDF with a square 8 ms window. The modulation intervals were validated by manual inspection. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 875, 267]]<|/det|> +To determine modulation associated with the systematically variable timing of the feedback tone on canceled trials, the SDF was compared against the minimum value found between 500 ms before and 900 ms after the tone. Focusing on modulation occurring only during the period of operant control on behavior, modulations beginning less than 300 ms after the tone were not included. For comparisons across neurons and sessions, Z- transformed SDF or \(\Delta \mathrm{SDF}\) were used. + +<|ref|>text<|/ref|><|det|>[[113, 279, 879, 364]]<|/det|> +Spike widths of this sample of neurons exhibited a bimodal distribution \(^{16}\) . Consequently, neurons were distinguished as narrow- or broad- spikes. Narrow spike neurons had peak- to- trough duration less than 250 \(\mu \mathrm{s}\) and broad spike, greater than or equal to 250 \(\mu \mathrm{s}\) . + +<|ref|>text<|/ref|><|det|>[[112, 408, 881, 714]]<|/det|> +Mixed effects models. We fit the variation in modulation of spiking or polarization of ERP to models of each of the behavioral and task measures as detailed in Supplementary Figure 2. We related neural modulation to the following models: (a) response conflict conceived computationally as the mathematical product of the activation of the race model GO and STOP processes and quantified as the probability of noncanceled error (P(error)) as a function of SSD, (b) P(error) contingent on viewing the stop- signal, denoted P(error | SSseen) and referred to as error likelihood, (c) absolute and log- transformed SSD, (d) hazard rate of stop- signal, (e) absolute and log- transformed delay until feedback tone, and (f) hazard rate of feedback tone. Although these behavioral and task measures can be correlated, random variations allowed for their differentiation. + +<|ref|>text<|/ref|><|det|>[[112, 727, 877, 906]]<|/det|> +To determine which performance measure accounted best for the variation of neural modulation, the performance and neural quantities were averaged within groups of early- , mid- , and late- SSD trials. SSD values greater than \(\sim 350\mathrm{ms}\) were not included because too few canceled trials were obtained. The analysis of the facilitation after SSRT as based on \(\Delta \mathrm{SDF}\) (Fig 2, Fig 4), but the major conclusions held if the analysis used SDF. The analysis of the modulation before SSRT or the feedback tone was based on the SDF of canceled trials. Before + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 142]]<|/det|> +SSRT the SDF of canceled and no stop- signal trials was not different. Before the feedback tone, the interval was longer and more variable on canceled relative to no stop- signal trials. + +<|ref|>text<|/ref|><|det|>[[111, 153, 884, 558]]<|/det|> +Mixed- effects models of \(\Delta \mathrm{SDF}\) , SDF, or \(\Delta \mathrm{ERP}\) values in relation to the various performance measures were compared using Bayesian Information Criteria (BIC). We report the results of the most basic version of each model with a main effect term corresponding to the performance parameter and random intercepts grouped by neuron (for spiking activity) or session (for ERP analysis). The values for each performance parameter were z- transform normalized for fair comparison between models related to different quantities. All constructed models had the same degrees of freedom, so BIC values between models could be compared directly. The model with the smallest BIC was endorsed as the best model. The fit of the other models relative to the best are reported using \(\Delta \mathrm{BIC}\) . As recommended \(^{87,88}\) , \(\Delta \mathrm{BIC}\) ( \(\mathrm{BIC}_{\mathrm{best}} - \mathrm{BIC}_{\mathrm{competing}})\) \(< 2\) offers weak support against the competing model, \(2 < \Delta \mathrm{BIC} < 6\) offers strong support against the competing model, and \(\Delta \mathrm{BIC} > 6\) conclusively rules out the competing model. More complex versions of these models resulted in similar conclusions. Mixed- effects models were performed using MATLAB's Statistical Toolbox. + +<|ref|>text<|/ref|><|det|>[[111, 600, 879, 907]]<|/det|> +Relating N2/P3 and neural spiking. We used the method described previously to establish the relationship between spiking activity and the ERN \(^{16}\) . Single trial spiking was the mean convolved spike data for that trial recorded from neurons in L2/3 and in L5/6 of perpendicular penetrations within \(\pm 50\) ms of the N2 and P3 peaks. To account for variations in ERP voltage and spike counts across sessions, a fixed- effects adjustment was performed by centering each distribution on its mean and dividing by its most extreme value. To measure the N2/P3 amplitudes robustly, we grouped rank- ordered single- trial ERP values into 20 successive bins. From trials in each bin, we calculated the mean N2 and mean P3 magnitude (dependent variable), the mean spike count in the upper and lower layers (independent variables), and the average SSD, on Canceled trials. Data from all sessions were combined for a pooled partial + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 870, 300]]<|/det|> +correlation. Each point in Fig. 5 plots the paired values of the mean normalized ERP voltage and normalized activity for each of the 20 bins from every session. The statistical relationship between ERP magnitude and spiking activity was quantified through multiple linear regression on normalized data pooled across sessions. Three factors were considered: (1) spiking activity in L2/3, (2) spiking activity in L5/6, plus (3) SSD to prevent its variation from confounding the relationship between ERP and neural spiking. However, as presented in the main text, the inclusion of this factor did not change the results. + +<|ref|>sub_title<|/ref|><|det|>[[115, 345, 260, 363]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[115, 377, 857, 427]]<|/det|> +The analysis codes that were used for this study are available from the corresponding author upon request. + +<|ref|>sub_title<|/ref|><|det|>[[115, 440, 254, 458]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[115, 472, 842, 522]]<|/det|> +The data that support the findings of this study are available from the corresponding author upon request. + +<|ref|>sub_title<|/ref|><|det|>[[115, 536, 277, 554]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[111, 567, 874, 810]]<|/det|> +The authors thank G. Luppino, M. Matsumoto, N. Palomero- Gallagher, and, L. Rapan for sharing data; J. Elsey, M. Feurtado, M. Maddox, S. Motorny, J. Parker, D. Richardson, M. Schall, C.R. Subravei, L. Toy, B. Williams, and R. Williams for animal care and other technical assistance; and Z. Fu, M. Matsumoto, P. Redgrave, U. Rutishauser, E. Sigworth, A. Tomarken, and G. Woodman for helpful discussions. Imaging data was collected in the Vanderbilt Institute of Imaging Science. This work was supported by R01- MH55806, R01- EY019882, P30- EY08126, Canadian Institutes of Health Research Post- Doctoral Fellowship, and by Robin and Richard Patton through the E. Bronson Ingram Chair in Neuroscience. + +<|ref|>sub_title<|/ref|><|det|>[[115, 824, 297, 842]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[55, 856, 866, 906]]<|/det|> +Experimental design, J.D.S. Data collection, J.D.S. Data analysis, A.S. and S.E. Interpretation and preparation of manuscript, A.S., S.E., and J.D.S. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[57, 90, 291, 107]]<|/det|> +## 1024 Competing interests + +<|ref|>text<|/ref|><|det|>[[57, 122, 468, 140]]<|/det|> +1025 The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 88, 891, 888]]<|/det|> +1027 REFERENCES1028 1. Verbruggen F, Logan GD. Models of response inhibition in the stop-signal and stop-1029 change paradigms. Neurosci Biobehav Rev 33, 647-661 (2009).10301031 2. Emeric EE, et al. Influence of history on saccade countermanding performance in1032 humans and macaque monkeys. Vision Res 47, 35-49 (2007).10331033 3. Kolling N, Wittmann MK, Behrens TE, Boorman ED, Mars RB, Rushworth MF. Value,1035 search, persistence and model updating in anterior cingulate cortex. Nat Neurosci 19,1036 1280-1285 (2016).10371037 4. Shenhav A, Cohen JD, Botvinick MM. 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Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory. Proc Natl Acad Sci U S A 101, 1368- 1373 (2004). + +<|ref|>text<|/ref|><|det|>[[57, 682, 870, 718]]<|/det|> +1270 75. Huster RJ, Messel MS, Thunberg C, Raud L. The P300 as marker of inhibitory control - fact or fiction? Cortex 132, 334- 348 (2020). + +<|ref|>text<|/ref|><|det|>[[57, 738, 870, 792]]<|/det|> +1273 76. Huster RJ, Enriquez- Geppert S, Lavallee CF, Falkenstein M, Herrmann CS. Electroencephalography of response inhibition tasks: functional networks and cognitive contributions. Int J Psychophysiol 87, 217- 233 (2013). + +<|ref|>text<|/ref|><|det|>[[57, 812, 846, 847]]<|/det|> +1277 77. Smith JL, Smith EA, Provost AL, Heathcote A. Sequence effects support the conflict theory of N2 and P3 in the Go/NoGo task. Int J Psychophysiol 75, 217- 226 (2010). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 90, 870, 127]]<|/det|> +1281 78. Hubel DH, Wiesel TN. 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Behav Res Methods 49, 267- 281 (2017). + +<|ref|>text<|/ref|><|det|>[[57, 655, 880, 690]]<|/det|> +1311 87. Raftery AE. Bayesian model selection in social research. Sociological methodology, 111- 163 (1995). + +<|ref|>text<|/ref|><|det|>[[57, 710, 866, 745]]<|/det|> +1313 88. Kass RE, Raftery AE. Bayes factors. Journal of the american statistical association 90, 773- 795 (1995). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 559, 150]]<|/det|> +SajadErringtonSchallSupplementaryInformationR0. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/images_list.json b/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..470313c0c6683d03b041c7e2e92a86dab71eb93a --- /dev/null +++ b/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/images_list.json @@ -0,0 +1,100 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Schematic volcanic stratigraphy and paleomagnetic sampling profiles of volcanic units emplaced during Oligocene bimodal volcanism in Northern Yemen (after ref. 5,10). Section abbreviations, from west to east, are: ESC: Escarpment, BM: Bayt Mawjan, A: Section A, BB: Bayt Baws, JS: Jabal Shahirah, SK: Shibam Kawkabam, WD: Wadi Dhar, and JK: Jabal Kura'a. Sites are annotated with magnetic polarity data5 where white and black are reverse and normal polarity, respectively. Sites outlined in boxes denote those dated by \\(^{40}\\mathrm{Ar} / ^{39}\\mathrm{Ar}\\) (ref. 5,7,11) or \\(^{206}\\mathrm{Pb} / ^{238}\\mathrm{U}\\) geochronology (data presented here) and ages are shown in detail Fig. 2. Ages and sites denoted with an asterisk (*) are from correlative units in Ethiopia7.", + "footnote": [], + "bbox": [ + [ + 115, + 92, + 625, + 580 + ] + ], + "page_idx": 21 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. \\(^{40}\\mathrm{Ar} / ^{39}\\mathrm{Ar}\\) and \\(^{206}\\mathrm{Pb} / ^{238}\\mathrm{U}\\) ages for the main silici units from the Northern Yemen section of the Afro-Arabian volcanic province (subset A). The grey field highlights the ages and associated uncertainties \\((2\\sigma)\\) of the Escarpment Ignmibrite, Green Tuff, SAM and Sana'a Ignmibrites, and Iftar Alkalb. Ranked single-zircon and \\(^{206}\\mathrm{Pb} / ^{238}\\mathrm{U}\\) dates are shown for the Escarpment, SAM, and Sana'a Ignmibrites. Horizontal grey bars outlined in black indicate the weighted mean \\(^{206}\\mathrm{Pb} / ^{238}\\mathrm{U}\\) ages with 95% confidence interval. B. Minimum total eruptive volume DRE (km \\(^3\\) ) values are from on-land and correlated deep-sea tephra layers found in Ocean Drilling Program cores from the Indian Ocean, Leg \\(115^{1,10,28}\\) .", + "footnote": [], + "bbox": [], + "page_idx": 22 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. Bivariate plots showing Th/Y versus Eu/Eu\\* for zircon crystals are denoted by age, dating method, and inclusion in final age calculations. Zircons \\(>33\\) Ma (from preliminary LA-ICP-MS dating, average \\(2\\sigma\\) uncertainty \\(\\pm 3\\) Ma) are denoted by diamond symbols. Non-luminescent (CL-dark) zircon crystals from the Escarpment Ignimbrite and Iftar Alkalb are denoted by black symbols. Subsets B-E show Th/Y versus Eu/Eu\\* in detail for the Escarpment Ignimbrite (subset B), SAM Ignimbrite (subset C), Sana'a Ignimbrite (subset D), and Iftar Alkalb (subset E).", + "footnote": [], + "bbox": [ + [ + 123, + 95, + 680, + 630 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 70, + 100, + 643, + 660 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 65, + 65, + 650, + 760 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 42, + 207, + 572, + 550 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 55, + 70, + 608, + 600 + ] + ], + "page_idx": 28 + } +] \ No newline at end of file diff --git a/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24.mmd b/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24.mmd new file mode 100644 index 0000000000000000000000000000000000000000..0409882423a1f42632d50011fd6ccc4e153df5c7 --- /dev/null +++ b/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24.mmd @@ -0,0 +1,331 @@ + +# Magma flux of silicic supereruptions from the Afro-Arabian large igneous province + +Jennifer Thines ( \(\boxed{ \begin{array}{r l} \end{array} }\) jennifer- thines@uiowa.edu ) University of Iowa https://orcid.org/0000- 0001- 9589- 960X + +Ingrid Ukstins University of Auckland + +Corey Wall Boise State University + +Mark Schmitz Boise State University + +## Article + +Keywords: volcanos, earth science, silicic supereruptions, magma + +Posted Date: May 13th, 2021 + +DOI: https://doi.org/10.21203/rs.3. rs- 469569/v1 + +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on November 2nd, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 26468- 5. + +<--- Page Split ---> + +## 1 Magma flux of silicic supereruptions from the Afro-Arabian large igneous + +## 2 province + +2 provinceJennifer E. Thines\*1, Ingrid A. Ukstins2, Corey Wall3 & Mark Schmitz3 1Department of Earth and Environmental Sciences, University of Iowa, 115 Trowbridge Hall, Iowa City, IA 52242 USA 2School of Environment, The University of Auckland, Private Bag 92 019, Auckland, New Zealand 3Department of Geosciences, 1295 University Drive, Boise State University, Boise, ID 83706, USA + +## 10 Abstract + +10 AbstractThe Main Silicics phase of the Afro- Arabian large igneous province preserves some of the largest volcanic eruptions on Earth, with six units totaling \(>8,600 \text{km}^3\) dense rock equivalent (DRE). The large volumes of rapidly emplaced individual eruptions present a case study for examining the tempo of generation and emplacement of voluminous silicic magmas. We use high- precision \(^{206}\text{Pb}/^{238}\text{U}\) zircon dating to differentiate individual eruption ages and show that the largest sequentially dated eruptions occurred within a timeframe of \(48 \pm 34 \text{kyr}\) (29.755 \(\pm 0.023 \text{Ma}\) to \(29.707 \pm 0.025 \text{Ma}\) ), yielding a maximum magma flux of \(3.09 \times 10^{- 1} \text{km}^3/\text{yr}\) for \(4,339 \text{km}^3\) DRE and making this sequence the highest known flux of silicic volcanism on Earth. The Main Silicics phase of volcanism occurred within a timeframe of \(130 \pm 150 \text{kyr}\) (29.80 \(\pm 0.80 \text{Ma}\) to \(29.67 \pm 0.13 \text{Ma}\) ), yielding a maximum magma flux of \(3.05 \times 10^{- 2} \text{km}^3/\text{yr}\) . We also provide a robust tie- point for calibration of the geomagnetic polarity timescale by integrating + +<--- Page Split ---> + +recalculated \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) data with our high- precision \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) ages to yield new constraints on the duration of the C11n.1r Subchron. + +## Background + +Many of the largest silicic eruptions on Earth occur in large igneous provinces (LIPs), with total eruptive volumes often exceeding \(1,000 \mathrm{km}^3\) dense rock equivalent (DRE) for individual events, which are likely to be emplaced in rapid succession \(^{1 - 3}\) . Although LIPs are generally considered to represent the most productive magmatic systems on Earth \(^{4}\) , uncertainty about volume estimates and imprecise or inaccurate age data for individual events often preclude robust estimates of magma flux and volcanic output rates. The Northern Yemen section of the Afro- Arabian LIP is an ideal testbed for using high- 15 \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) zircon dating to quantify the magma flux of a series of flood volcanic eruptions, with three silicic supereruptions occurring within a 70 to 310 kyr timeframe at ca. 29.7 Ma \(^{5 - 7}\) . Previous paleomagnetism and \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) studies \(^{5 - 7}\) indicate these are a set of normal to reversed polarity units that encompass the duration of the C11n.1r Subchron, although overlapping ages for individual eruptions, due to analytical uncertainties, are currently unable to distinguish between the geomagnetic polarity timescale (GPTS) of Cande and Kent \(^{8}\) and Huestis and Acton \(^{9}\) . In contrast to existing \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) ages, the \(0.1\%\) precision of state- of- art chemical abrasion thermal ionization mass spectroscopy (CA- TIMS) U- Pb ages of zircons can distinguish between the ages of these units outside analytical uncertainty. + +Oligocene volcanism in Northern Yemen (Fig. 1) has been divided into three phases based on field observations, whole rock geochemical correlations, and \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) dating \(^{5 - 7,10}\) : Main Basalts (31 to 29.7 Ma), Main Silicics (29.7 to 29.5 Ma), and Upper Bimodal + +<--- Page Split ---> + +(29.6 to 27.7 Ma). The Main Basalts phase is characterized by effusive basaltic volcanism and volumetrically represents 60 to 70% of the total erupted volume of Afro- Arabian lavas10,11. The Main Silicics phase saw the rapid emplacement of seven silicic pyroclastic units and the Upper Bimodal phase includes small- volume basaltic and rhyolitic eruptions10. + +We focus on the Main Silicics phase, which contains some of the largest known silicic eruptions on Earth, with an estimated minimum eruptive total volume of 8,633 km3 DRE emplaced in present- day Yemen and Ethiopia over a period from 29.7 to 29.5 Ma1,10. Volcano- stratigraphic correlations in Yemen10 suggest the emplacement of the Jabal Kura'a Ignimbrite (1,627 km3 DRE; \(\sim 29.6\) Ma) and Escarpment Ignimbrite (358 km3 DRE; \(\sim 29.6\) Ma), was followed by a brief period of subsidence and erosion, and then the rapid emplacement of the Green Tuff (58 km3 DRE; \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) age = 29.59 \(\pm\) 0.12 Ma7; Fig. 2), SAM Ignimbrite (2,330 km3 DRE; \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) age = 29.47 \(\pm\) 0.14 Ma7, Sana'a Ignimbrite (1,593 km3 DRE; \(\sim 29.5\) Ma; Fig. 2), and Iftar Alkalb caldera collapse mega- breccia (2,667 km3 DRE; \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) age = 29.48 \(\pm\) 0.13 Ma5; Fig. 2). The Green Tuff has been interpreted as representing the initial airfall deposit preceding the emplacement of the SAM Ignimbrite based on the sharp upper contact between the units with no evidence of a time gap during emplacement10. These bracketed \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) ages indicate all four units, with a cumulative estimated minimum total eruptive volume of \(\sim 6,650\) km3 DRE, were emplaced in rapid succession within a timeframe of 70 to 310 kyr5,7,10 but there are no robust estimates of magma generation rates or magma flux over this time interval. + +<--- Page Split ---> + +## 68 Results + +The Escarpment Ignmibrite contains elongate prismatic crystals (typically 50 to 120 \(\mu \mathrm{m}\) in length and, rarely, up to \(150\mu \mathrm{m}\) ) and smaller equant crystals (50 to \(75\mu \mathrm{m}\) in length). Some prismatic crystals have oscillatory zoning with U- rich non- luminescent cores (CL dark). The SAM Ignmibrite contains elongate prismatic crystals that are both smaller (30 to \(75\mu \mathrm{m}\) , rarely up to \(125\mu \mathrm{m}\) ) and less numerous than those found in the Escarpment Ignmibrite. Few crystals have subtle oscillatory zoning and one larger crystal \(\sim 120\mu \mathrm{m}\) in length has a non- luminescent, oscillatory zoned core with a lighter overgrowth rim. Crystals in the SAM Ignmibrite have a weakly paramagnetic behavior, likely due to abundant Fe- Ti oxide and apatite inclusions. The Sana’a Ignmibrite contains small elongate prismatic crystals (30 to \(75\mu \mathrm{m}\) ) with subtle to no oscillatory zoning. Zircon is abundant in Iftar Alkalb as anhedral to euhedral elongate prismatic and equant crystals that range in length from 30 to \(120\mu \mathrm{m}\) . Internal morphologies are variable with populations of non- luminescent and luminescent crystals with no oscillatory zoning, crystals with non- luminescent cores and lighter rims, and a few crystals with strong oscillatory zoning (see Supplementary Information 1 for CL images). In total, 273 laser ablation spot analyses were conducted on 79 crystals from the Escarpment Ignmibrite, 46 crystals from the SAM Ignmibrite, 31 crystals from the Sana’a Ignmibrite, and 95 crystals from Iftar Alkalb. The median uncertainty of a single LA- ICP- MS \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) spot analysis is 3 Ma, too imprecise to distinguish ancestry populations (crystals that grew in an earlier pulse and were later incorporated in a different pulse \(^{12,13}\) ) for this magmatic system but adequate to determine older xenocrystic zircon crystals. Every unit except the Escarpment Ignmibrite contains \(>10\%\) + +<--- Page Split ---> + +zircon crystals with LA- ICP- MS \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) ages \(>33\) Ma. The Sana'a Ignimbrite and Iftar Alkalb contain significant proportions of older zircons (30% and 29% respectively), although in the Sana'a Ignimbrite this may be due to the low sample number (n = 31). There is no correlation between age and trace element (U, Th, Y, HREE) concentrations. CL dark zircon crystals in the Escarpment Ignimbrite and Iftar Alkalb have among the highest HREE concentrations and europium anomalies (Eu/Eu\*) in each respective unit (Supplementary Information 2). + +32 grains that showed no sign of inclusions and yielded consistent U- Pb laser ablation dates were plucked from their respective grain mounts for high- precision CA- ID- TIMS geochronology (Supplementary Information 2). Preference was given to zircon crystals that captured the full range of compositions found in each unit. Six zircon crystals from the Escarpment Ignimbrite yielded a weighted mean \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) date of \(29.755 \pm 0.023\) Ma (MSWD = 0.62; Fig. 3). Excluding the oldest zircon crystal from the SAM Ignimbrite (which was older than 29.778 Ma, and inferred to be an ancestry), the remaining eight zircon crystals yielded a weighted mean date of \(29.728 \pm 0.017\) Ma (MSWD = 0.34). Six zircon crystals from the Sana'a Ignimbrite yielded a weighted mean date of \(29.707 \pm 0.025\) Ma (MSWD = 0.65; Fig. 3), excluding three zircon crystals older than 29.745 Ma, also inferred to be ancestry. The weighted mean \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) dates have been interpreted as the eruption age of each respective unit. Although Iftar Alkalb is the stratigraphically youngest unit dated, nine zircon crystals were consistently older ( \(29.731 \pm 0.089\) Ma to \(30.320 \pm 0.094\) Ma; Fig. 3) than the weighted mean ages of the other units and so no date was assigned; we attribute this to the emplacement mechanism of the caldera collapse breccia with abundant mega- clasts of underlying + +<--- Page Split ---> + +stratigraphy contributing xenolithic material or antecrystals that are recording an earlier stage of zircon crystallization. + +Sanidine from the Green Tuff, SAM Ignmibrite, and Iftar Alkalb were previously dated via the \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) method \(^{5,7}\) . Those dates have been recalculated using a 28.201 Ma monitor age for the Fish Canyon sanidine \(^{14}\) . Recalculations (Supplementary Information 3) yield a \(29.78 \pm 0.12\) Ma age for the Green Tuff, \(29.66 \pm 0.14\) Ma age for the SAM Ignmibrite, and \(29.67 \pm 0.08\) Ma age for Iftar Alkalb (Fig. 2). Previous \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) ages \(^{5,7,11}\) from the Shibam Kawkabam Ignmibrite ( \(30.35 \pm 0.13\) Ma), Kura'a Basalt ( \(30.22 \pm 0.26\) Ma), Akraban Andesite ( \(29.80 \pm 0.08\) Ma), an overlying small- volume rhyolitic tuff ( \(28.58 \pm 0.14\) Ma) and ignmibrite ( \(28.18 \pm 0.10\) Ma), and the Bayt Mawjan Ignmibrite ( \(27.85 \pm 0.12\) Ma) have also been recalculated and are compiled and presented here as a revised chronostratigraphy of the Northern Yemen flood volcanics (Fig. 2). + +## Discussion + +## Short-Term Accumulation Rates + +Elements that are normally incompatible during magma differentiation (e.g., U, Nb, Th, Y and Hf) and the europium anomaly (Eu/Eu\*) in rare earth element patterns resulting from feldspar fractionation are useful indicators of magma differentiation. Assuming both elements remain incompatible, more differentiated rhyolites will evolve towards higher Th/Y ratios while Eu/Eu\* will decrease with continued feldspar crystallization \(^{15}\) . With a few exceptions, zircons dated via CA- TIMS for these units show the same trend: the least evolved zircon with the highest Eu/Eu\* and lowest Th/Y values + +<--- Page Split ---> + +are older than the most evolved zircon by \(0.01 \pm 0.16\) Ma in the Escarpment Ignimbrite, \(0.02 \pm 0.09\) Ma in the SAM Ignimbrite, and \(0.07 \pm 0.17\) Ma in the Sana'a Ignimbrite. Thus ages for zircon crystals spanning the full geochemical ranges are statistically indistinguishable, suggesting that these large volume magmas were rapidly differentiated within \(10^{3}\) to \(10^{4}\) years. Eu/Eu\* and Th/Y are not correlated for zircons in the Iftar Alkalb mega- breccia and there is no age relationship between the most and least evolved zircons (Fig. 4), further supporting that the zircons in Iftar Alkalb are of a mixed xenolithic or antecrystic origin. + +Short- term accumulation rates were calculated for 50, 100, and 400 kyr of residence for the Escarpment, SAM, and Sana'a Ignimbrites based on the trace element concentrations and CA- TIMS U- Pb zircon dates. For 50 kyr residence, short- term accumulation rates are \(7.2 \times 10^{- 3} \text{km}^3 / \text{yr}\) , \(4.8 \times 10^{- 2} \text{km}^3 / \text{yr}\) , and \(3.2 \times 10^{- 2} \text{km}^3 / \text{yr} \text{for the Escarpment, SAM, and Sana'a Ignimbrites, respectively. For 100 kyr residence, short- term accumulation rates are} 3.6 \times 10^{- 3} \text{km}^3 / \text{yr}\) , \(2.4 \times 10^{- 2} \text{km}^3 / \text{yr}\) , and \(1.6 \times 10^{- 2} \text{km}^3 / \text{yr} \text{for the Escarpment, SAM, and Sana'a Ignimbrites, respectively. For 400 kyr residence, short- term accumulation rates are} 9.0 \times 10^{- 4} \text{km}^3 / \text{yr}\) , \(6.0 \times 10^{- 3} \text{km}^3 / \text{yr}\) , and \(4.0 \times 10^{- 3} \text{km}^3 / \text{y} \text{for the Escarpment, SAM, and Sana'a Ignimbrites, respectively. Upper estimates of} 7.2 \times 10^{- 3} \text{to} 3.2 \times 10^{- 2} \text{km}^3 / \text{yr} \text{for 50 kyr residence and} 3.6 \times 10^{- 3} \text{to} 2.4 \times 10^{- 2} \text{km}^3 / \text{yr} \text{for 100 kyr residence are similar to those calculated for other rapidly assembled large- volume silicic systems (e.g., Yellowstone supereruptions}^{16,17} \text{and Oruani eruption within the Taupo volcanic zone}^{18}). \text{The most conservative estimates using 400 kyr residence} (9.0 \times 10^{- 4} \text{to} 6.0 \times 10^{- 4} \text{km}^3 / \text{yr} \text{are similar to but lower than the minimum calculated magma flux from Yellowstone} (2.8 \times 10^{- 3} \text{km}^3 / \text{yr} \text{for the 280 km}^3 \text{Mesa Falls Tuff}^{17}). + +<--- Page Split ---> + +## Long-Term Magma Flux + +U- Pb zircon dating shows that three sequential eruptions of Afro- Arabian silicic volcanics - the Escarpment Ignimbrite, the Green Tuff and SAM Ignimbrite, and Sana'a Ignimbrites - were collectively emplaced within a timespan of \(48 \pm 34\) kyr (calculated using the square root of the sum of the uncertainties), yielding a magma flux of \(5.29 \times 10^{- 2}\) to \(3.09 \times 10^{- 1}\) km\(^3\) /yr for \(4,339 \text{km}^3\) DRE. The estimated minimum total eruptive volume for the entirety of the Main Silicics phase is \(8,633 \text{km}^3\) DRE over a duration of \(130 \pm 150\) kyr, constrained by the ages of the Akraban Andesite and Iftar Alkalb, which yield a lower magma flux of \(3.05 \times 10^{- 2}\) to \(6.64 \times 10^{- 2}\) km\(^3\) /yr. Magma flux for other regions of the Afro- Arabian province, such as the Ethiopian stratigraphy, are difficult to constrain. While there was wide- scale silicic volcanism following the termination of the main pulse of flood basalt emplacement\(^{19,20}\), unit volume estimates outside of Northern Yemen remain sparse due to poor exposure and post- emplacement tectonic disruption. Notably, a series of silicic supereruptions in the Tana Basin, Ethiopia\(^{21}\) have recently been dated at \(31.108 \pm 0.020\) to \(30.844 \pm 0.027\) Ma with an estimated minimum eruptive volume of \(2,000\) to \(3,000 \text{km}^3\), corresponding to a magma flux of \(0.8\) to \(1.1 \times 10^{- 2} \text{km}^3\) /yr. + +Magma fluxes of basaltic and andesitic systems are thought to be higher than those of silicic systems by up to two orders of magnitude\(^4\). Average fluxes in silicic systems are calculated to be highest for continental arcs ( \(4.90 \pm 0.15 \times 10^{- 3} \text{km}^3\) /yr) followed by oceanic arcs ( \(4.50 \pm 0.79 \times 10^{- 3} \text{km}^3\) /yr), continental rifts ( \(4.48 \pm 0.86 \times 10^{- 3} \text{km}^3\) /yr), continental hotspots ( \(1.29 \pm 0.25 \times 10^{- 3} \text{km}^3\) /yr), and continental volcanic fields ( \(6.47 \pm 1.96 \times 10^{- 4} \text{km}^3\) /yr). The magma flux of the Main Silicics phase of the Northern Yemen + +<--- Page Split ---> + +section of the Afro- Arabian province is most similar to - but notably higher than - the magma fluxes of Taupo ( \(1.15 \times 10^{- 2} \mathrm{km}^{3} / \mathrm{yr}\) ; ref. \(^{22}\) ), the silicic portion of Kamchatka ( \(1.05 \times 10^{- 2} \mathrm{km}^{3} / \mathrm{yr}\) , ref. \(^{23}\) ) and Quaternary phonolites from the Kenya rift valley ( \(1.20 \times 10^{- 2} \mathrm{km}^{3} / \mathrm{yr}\) ; ref. \(^{22}\) ). Our findings are consistent with observations at other large- volume silicic systems that record rapid periods of differentiation and magma reservoir assembly superimposed on lower background fluxes. While some silicic systems have produced more voluminous individual eruptions (e.g., Fish Canyon Tuff with 4,500 \(\mathrm{km}^{3}\) DRE \(^{24}\) ), and larger cumulative eruptive volumes over longer time intervals (e.g., Parana- Etendeka LIP with 20,000 to 35,000 \(\mathrm{km}^{3}\) over 6 \(\mathrm{Myr}^{25,26}\) ), the eruptions of the Main Silicics phase in Northern Yemen represent the largest long- term flux of silicic volcanism on Earth. + +## Afro-Arabian volcanism and Oligocene Environmental Change + +Some volcanic provinces appear to coincide with major global environmental change and mass extinctions (e.g., Siberian Traps, Karoo- Ferrar, Emieshan and Central Atlantic LIPs), yet others, even those with silicic supereruptions (e.g., Parana- Etendeka LIP), do not \(^{27}\) . Afro- Arabian silicic volcanism represents the greatest flux of large- volume silicic magma eruption on Earth and is correlated to 10 to 15 cm thick tephra layers located \(>2,700 \mathrm{km}\) away in the Indian Ocean \(^{28}\) (Fig. 2), suggesting volcanic fallout on a near- global scale. The timing of these supereruptions in relation to several Rupelian- aged cooling events that have been identified in Chrons C12 (Oi1a, Oi1b, and Oi \(^{29,30}\) ) and C10 (Oi2\* and Oi2a \(^{29,30}\) ) indicate that the perturbations in \(\delta^{18} \mathrm{O}\) and \(\delta^{13} \mathrm{C}\) pre- date the eruptions \(^{31,32}\) (Fig. 2). Other silicic supereruptions, such as the \(\sim 31 \mathrm{Ma}\) caldera- forming eruptions in the Tana Basin \(^{21}\) and \(\sim 28 \mathrm{Ma}\) eruption of the Fish Canyon Tuff \(^{24}\) , likewise + +<--- Page Split ---> + +do not coincide with global cooling events. Challenges remain in discerning the various roles of the tempo, volatile budget, eruption mechanism, and volume of magma extruded from large igneous provinces and their effect on global environmental change. However, robust temporal constraints continue to provide critical insight into this relationship. + +## Implications for Geomagnetic Polarity Time Scale + +Previous efforts have been made to correlate Oligocene Afro- Arabian volcanic deposits with the geomagnetic polarity time scale5,33 but those were unable to unambiguously distinguish between the GPTS of Cande and Kent8 and Huestis and Acton9. Recent studies on the Oligocene magnetic polarity sequence have utilized astronomical age models29, radio-isotope age models30, recalculations of the Cande and Kent8 GPTS using updated40Ar/39Ar flux monitor ages34, and a combination of all three30. One of the lingering issues with distinguishing between an appropriate method for determining the Rupelian age (33.9 to 28.1 Ma) is the lack of tie points from radio-isotopic dates. The Rupelian/Chattian boundary Global Boundary Stratotype Section and Point (GSSP) records a nearly continuous record of astronomically- tuned magnetostratigraphy for the Oligocene but only provides one tie- point for the Rupelian for the uppermost Chron C12r with a gap between \(31.8 \pm 0.2\) Ma and \(27.0 \pm 0.1\) Ma30,35. The 2012 Geologic Time Scale for the Paleogene30 favored an integrated radio-isotope, GPTS, and cyclostratigraphy model with 6th- order polynomial fit to produce a complete C- sequence. The C11n.1r Subchron is estimated to have a duration of 0.050 Ma with a -0.654 Ma discrepancy between radio-isotopic and astronomic age models30. The only discrepancy between the combined age model of the 2012 Geologic Time Scale and + +<--- Page Split ---> + +new 2020 Geologic Time Scale for the time range of interest is a shift of the base of Chron C12n to 30.977 Ma from 31.034 Ma \(^{30,36}\) . + +We propose that the \(29.728 \pm 0.017\) Ma \(^{206}\) Pb/ \(^{238}\) U zircon age of the SAM Ignimbrite and \(29.67 \pm 0.13\) Ma \(^{40}\) Ar/ \(^{39}\) Ar sanidine age of Iftar Alkalb - further constrained to 29.67 \(\pm 0.13\) Ma by the \(29.707 \pm 0.025\) Ma \(^{206}\) Pb/ \(^{238}\) U age of the Sana'a Ignimbrite - can be used as tie- points for the GPTS. Our chrono- and magnetostratigraphy are definitively in agreement with the Cande and Kent \(^{8}\) GPTS (Fig. 2). Discrepancies between our results and the 2020 Geologic Time Scale arise from the sparsity of radio- isotope dates for the Rupelian coupled with the short duration of the C11n.1r Subchron. Our findings are within the 0.654 Ma discrepancy between the radio- isotopic and astronomic age models and could thus serve as robust tie points for future time scale calibrations. + +## Methods + +Samples from the Sana'a area of Northern Yemen were previously collected and described \(^{10}\) (Fig. 1). Paleomagnetic data was measured on 587 oriented drill cores collected at 71 sites \(^{5}\) (Fig. 1). Zircon U- Pb petrochronology was undertaken at the Boise State University Isotope Laboratory. Zircon crystals from the Escarpment, SAM, and Sana'a Ignimbrites, and Iftar Alkalb were separated using standard magnetic and heavy liquid techniques and annealed at \(900^{\circ}\) C for 60 hours. Zircons were imaged using a JEOL T- 300 scanning electron microscope (SEM) fitted with a Gatan Mini cathodoluminescence (CL) detector and JEOL back- scattered electron (BSE) detector under 15 kV probe current and 2 mA accelerating voltage operating conditions (Supplementary Information 1). Trace element analyses and preliminary U- Pb dating for 31 to 95 crystals per unit (Supplementary Information 2) were performed using a + +<--- Page Split ---> + +ThermoElectron X- Series II quadrupole inductively coupled plasma mass spectrometer (ICP- MS) and New Wave Research UP- 213 Nd:YAG UV (213 \(\mu \mathrm{m}\) ) laser ablation system with a 10 Hz at 5 J/cm \(^2\) pulsed laser and 15 \(\mu \mathrm{m}\) spot size. NIST SRM- 610 and SRM- 612 glasses were used as standards for trace element concentrations and Plesovice zircon standard \(^{37}\) was used for U- Pb calibration. Zircon standards were measured every 10 unknowns; glass standards were analyzed at the beginning of two 109- spot cycles. + +A total of 32 crystals from the four units were selected for CA- TIMS analysis on the basis of morphology, zoning, chemistry, and preliminary \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) dates. After chemical abrasion \(^{38}\) in a single aggressive high- temperature step, residual crystals were rinsed and spiked with ET535 tracer solution \(^{39,40}\) , dissolved in concentrated HF, converted to a chloride matrix, and U and Pb purified by ion chromatography following the detailed procedures described in Macdonald et al. \(^{41}\) . High- precision isotope dilution U and Pb isotope ratio measurements were made using a single Re filament silica gel technique on an Isotopx Isoprobe- T multi- collector thermal ionization mass spectrometer (TIMS) equipped with an ion- counting Daly detector (Supplementary Information 2). Dates are calculated using the decay constants of Jaffey et al. \(^{42}\) . Analytical uncertainties on dates are reported to 2σ and propagated using the algorithms of Schmitz and Schoene \(^{43}\) . + +## Data Availability + +Supplementary Information 1 contains cathodoluminescence (CL) images of zircon crystals analyzed by LA- ICP- MS and CA- TIMS. Supplementary Information 2 contains details on the calculation of CA- TIMS \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) dates and zircon LA- ICP- MS + +<--- Page Split ---> + +geochronologic and trace element concentrations. Supplementary Information 3 contains details on the recalculation of \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) ages. + +## References + +1. Bryan, S. E. et al. The largest volcanic eruptions on Earth. Earth Sci. Rev. 102, 207–229 (2010). + +2. Bryan, S. E. & Ernst, R. E. Revised definition of Large Igneous Provinces (LIPs). Earth Sci. Rev. 86, 175–202 (2008). + +3. Coffin, M. F. & Eldholm, O. Volcanism and continental break-up: a global compilation of large igneous provinces. Geol. Soc. London Spec. Pub. 68, 17–30 (1992). + +4. White, S. M., Crisp, J. A. & Spera, F. J. Long-term volumetric eruption rates and magma budgets. Geochem. Geophys. 7, (2006). + +5. Riisager, P. et al. 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Magma generation at a large, hyperactive silicic volcano (Taupo, New Zealand) revealed by U-Th and U-Pb systematics in zircons. J. Petrol. 46, 3–32 (2005). + +18. Rooney, T. O. The Cenozoic magmatism of East-Africa: Part I — Flood basalts and pulsed magmatism. Lithos 286–287, 264–301 (2017). + +19. Krans, S. R., Rooney, T. O., Kappelman, J., Yirgu, G. & Ayalew, D. From initiation to termination: a petrostratigraphic tour of the Ethiopian Low-Ti Flood Basalt Province. Contrib. Mineral. Petrol. 173, 37 (2018). + +20. Prave, A. R. et al. Geology and geochronology of the Tana Basin, Ethiopia: LIP volcanism, Super eruptions and Eocene-Oligocene environmental change. Earth Planet. Sci. Lett. 443, 1–8 (2016). + +21. Crisp, J. A. Rates of magma emplacement and volcanic output. J. Volcanol. Geotherm. Res. 20, 177–211 (1984). + +<--- Page Split ---> + +22. Erlich, E. N. & Volynets, O. N. Chapter 3 acid volcanism in Kamchatka. Bull. Volcanol. 42, 175-254 (1979). + +23. Lipman, P., Dungan, M. & Bachmann, O. Comagmatic granophyric granite in the Fish Canyon Tuff, Colorado: Implications for magma-chamber processes during a large ash-flow eruption. Geology 25, 915-918 (1997). + +24. Gibson, S. A., Thompson, R. N. & Day, J. A. Timescales and mechanisms of plume–lithosphere interactions: \(^{40}\mathrm{Ar}/^{39}\mathrm{Ar}\) geochronology and geochemistry of alkaline igneous rocks from the Parana–Etendeka large igneous province. Earth Planet. Sci. Lett. 251, 1–17 (2006). + +25. Simões, M. S., Lima, E. F., Rossetti, L. M. M. & Sommer, C. A. The low-Ti high-temperature dacitic volcanism of the southern Parana–Etendeka LIP: Geochemistry, implications for trans-Atlantic correlations and comparison with other Phanerozoic LIPs. Lithos 342–343, 187–205 (2019). + +26. Black, B. A., Weiss, B. P., Elkins-Tanton, L. T., Veselovskiy, R. V. & Latyshev, A. Siberian Traps volcanicistic rocks and the role of magma-water interactions. Geol. Soc. Am. Bull. 127, 1437–1452 (2015). + +27. Ukstins Peate, I., Kent, A. J. R., Baker, J. A. & Menzies, M. A. Extreme geochemical heterogeneity in Afro-Arabian Oligocene tephras: Preserving fractional crystallization and mafic recharge processes in silicic magma chambers. Lithos 102, 260–278 (2008). + +<--- Page Split ---> + +28. Pálike, H. et al. The Heartbeat of the Oligocene Climate System. Science 314, 1894–1898 (2006). + +29. Vandenberghe, N. et al. Chapter 28 - The Paleogene Period. in The Geologic Time Scale (eds. Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M.) 855–921 (Elsevier, 2012). + +30. Cramer, B. S., Toggweiler, J. R., Wright, J. D., Katz, M. E. & Miller, K. G. Ocean overturning since the Late Cretaceous: Inferences from a new benthic foraminiferal isotope compilation. Paleoceanography 24, PA4216 (2009). + +31. Zachos, J., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, Rhythms, and Aberrations in Global Climate 65 Ma to Present. Science 292, 686–693 (2001). + +32. Rochette, P. et al. Magnetostratigraphy and timing of the Oligocene Ethiopian traps. Earth Planet. Sci. Lett. 164, 497–510 (1998). + +33. Guidry, E. P. et al. Oligocene–Miocene magnetostratigraphy of deep-sea sediments from the equatorial Pacific (IODP Site U1333). Geol. Soc. London Spec. Pub. 373, 13–27 (2013). + +34. Coccioni, R. et al. Integrated stratigraphy of the Oligocene pelagic sequence in the Umbria-Marche basin (northeastern Apennines, Italy): A potential Global Stratotype Section and Point (GSSP) for the Rupelian/Chattian boundary. Geol. Soc. Am. Bull. 120, 487–511 (2008). + +<--- Page Split ---> + +35. Speijer, R. P., Pálike, H., Hollis, C. J., Hooker, J. J. & Ogg, J. G. Chapter 28 – The Paleogene Period. in The Geologic Time Scale (eds. Gradstein, F. M., Ogg, J. G., Schmitz, M., D. & Ogg, G. M.) 1087–1140 (Elsevier, 2020). + +36. Sláma, J. et al. Plešovice zircon – a new natural reference material for U-Pb and Hf isotopic microanalysis. Chem. Geol. 249, 1–35 (2008). + +37. Mattinson, J. M. Zircon U–Pb chemical abrasion (“CA-TIMS”) method: Combined annealing and multi-step partial dissolution analysis for improved precision and accuracy of zircon ages. Chem. Geol. 220, 47–66 (2005). + +38. Condon, D.J., Schoene, B., McLean, N.M., Bowring, S.A. & Parrish, R.R. Metrology and traceability of U-Pb isotope dilution geochronology (EARTHTIME Tracer Calibration Part I). Geochim. Cosmochim. Acta 164, 464–480 (2015). + +39. McLean, N.M., Condon, D.J., Schoene, B. & Bowring, S.A. Evaluating uncertainties in the calibration of isotopic reference materials and multi-element isotopic tracers (EARTHTIME Tracer Calibration Part II). Geochim. Cosmochim. Acta 164, 481–501 (2015). + +40. Macdonald, F.A., Schmitz, M.D., Strauss, J.V., Halverson, G.P., Gibson, T.M., Eyster, A., Cox, G., Mamrol, P., Crowley, J.L. Cryogenian of Yukon. Precambrian Res. 319, 114-143 (2018). + +41. Jaffey, A. H., Flynn, K. F., Glendenin, L. E., Bentley, W. C. & Essling, A. M. Precision Measurement of Half-Lives and Specific Activities of U 235 and U 238. Phys. Rev. C 4, 1889–1906 (1971). + +<--- Page Split ---> + +42. Schmitz, M. D. & Schoene, B. Derivation of isotope ratios, errors, and error correlations for U-Pb geochronology using \(^{205}\mathrm{Pb}\) - \(^{235}\mathrm{U}\) - \(^{233}\mathrm{U}\) )-spiked isotope dilution thermal ionization mass spectrometric data. Geochem. Geophys. 8, 1-20 (2007). + +43. Steiger, R. H. & Jäger, E. Subcommission on geochronology: convention on the use of decay constants in go- and cosmochronology. Earth Plant. Sci. Lett. 36, 359-362 (1977). + +44. Min, K., Mundil, R., Renne, P. R. & Ludwig, K. R. A test for systematic errors in \(^{40}\mathrm{Ar}\) / \(^{39}\mathrm{Ar}\) geochronology through comparison with U/Pb analysis of 1.1-Ga rhyolite. Geochim. Cosmochim. Acta 64, 73-98 (2000). + +45. Audi, G., Bersillon, O., Blachot, J. & Wapstra, A. H. The NUBASE evaluation of nuclear and decay properties. Nuclear Physics A 729, 3-128 (2003). + +46. Mercer, C. M. & Hiddes, K. V. Ar/Ar - a software tool to promote the robust comparison of K-Ar and \(^{40}\mathrm{Ar}\) / \(^{39}\mathrm{Ar}\) dates published using different decay, isotopic, and monitor-age parameters. Chem. Geol. 440, 148-163 (2016). + +## Acknowledgments + +This material is based upon work supported by the National Science Foundation under Grant Nos. EAR- 1759200 and EAR- 1759353. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We thank the AGes program, members of the Boise State University Isotope Geology Laboratory for support with sample preparation, and B.D. Cramer for insightful discussions. + +<--- Page Split ---> + +## Author Contributions + +J.E.T., I.A.U, and M.S. designed the research project as part of the AGeS2 + +Geochronology Program. Sample material was provided by I.A.U. C.W. and J.E.T. + +prepared samples and analyzed the data with help from M.S. J.E.T. wrote the + +manuscript with support from I.A.U. Progress was overseen by I.A.U, the PhD thesis + +advisor of J.E.T. + +## Competing Interests + +The authors declare no competing interests. + +## Additional Information + +Correspondence and requests for materials should be addressed to J.E.T. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1. Schematic volcanic stratigraphy and paleomagnetic sampling profiles of volcanic units emplaced during Oligocene bimodal volcanism in Northern Yemen (after ref. 5,10). Section abbreviations, from west to east, are: ESC: Escarpment, BM: Bayt Mawjan, A: Section A, BB: Bayt Baws, JS: Jabal Shahirah, SK: Shibam Kawkabam, WD: Wadi Dhar, and JK: Jabal Kura'a. Sites are annotated with magnetic polarity data5 where white and black are reverse and normal polarity, respectively. Sites outlined in boxes denote those dated by \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) (ref. 5,7,11) or \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) geochronology (data presented here) and ages are shown in detail Fig. 2. Ages and sites denoted with an asterisk (*) are from correlative units in Ethiopia7.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + + +<--- Page Split ---> + +Figure 2. Composite stratigraphy of Northern Yemen bimodal flood volcanic units is shown using the average thickness of each unit \(^{10}\) . Four units have been correlated to Indian Ocean tephra layers \(^{28}\) and are annotated by the colored symbols. Paleomagnetic data \(^{5}\) are indicated where white = reverse polarity and black = normal polarity. Dashed lines show the approximate locations of the paleomagnetic reversals in the stratigraphy. Minor Unit #4 and AMPH 2 are from different sample localities and both underlie the Bayt Mawjan Ignmibrite, but their stratigraphic order relative to each other is unknown. Symbols for \(^{40}\) Ar/ \(^{39}\) Ar ages \(^{5,7,11}\) are colored based on polarity. The grey field highlights the \(^{40}\) Ar/ \(^{39}\) Ar and \(^{206}\) Pb/ \(^{238}\) U ages with associated uncertainties of two pulses of Afro- Arabian silicic volcanism. The Escarpment Ignmibrite, Green Tuff, SAM and Sana’a Ignmibrites, and Iftar Alkalb are a set of normal to reversed polarity that encompass the duration of the C11n.1r Subchron and are compared to the GPTS of Cande and Kent \(^{8}\) as reported the 2020 Geologic Time Scale \(^{36}\) . Benthic foraminiferal \(δ^{18}\) O and \(δ^{13}\) C curves are from the 2020 Geologic Time Scale \(^{36}\) . + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 3. \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) and \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) ages for the main silici units from the Northern Yemen section of the Afro-Arabian volcanic province (subset A). The grey field highlights the ages and associated uncertainties \((2\sigma)\) of the Escarpment Ignmibrite, Green Tuff, SAM and Sana'a Ignmibrites, and Iftar Alkalb. Ranked single-zircon and \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) dates are shown for the Escarpment, SAM, and Sana'a Ignmibrites. Horizontal grey bars outlined in black indicate the weighted mean \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) ages with 95% confidence interval. B. Minimum total eruptive volume DRE (km \(^3\) ) values are from on-land and correlated deep-sea tephra layers found in Ocean Drilling Program cores from the Indian Ocean, Leg \(115^{1,10,28}\) .
+ +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 4. Bivariate plots showing Th/Y versus Eu/Eu\* for zircon crystals are denoted by age, dating method, and inclusion in final age calculations. Zircons \(>33\) Ma (from preliminary LA-ICP-MS dating, average \(2\sigma\) uncertainty \(\pm 3\) Ma) are denoted by diamond symbols. Non-luminescent (CL-dark) zircon crystals from the Escarpment Ignimbrite and Iftar Alkalb are denoted by black symbols. Subsets B-E show Th/Y versus Eu/Eu\* in detail for the Escarpment Ignimbrite (subset B), SAM Ignimbrite (subset C), Sana'a Ignimbrite (subset D), and Iftar Alkalb (subset E).
+ +<--- Page Split ---> + +## Figures + +![](images/Figure_2.jpg) + +
Figure 1
+ +Schematic volcanic stratigraphy and paleomagnetic sampling profiles of volcanic units emplaced during Oligocene bimodal volcanism in Northern Yemen (after ref. 5,10). Section abbreviations, from west to east, are: ESC: Escarpment, BM: Bayt 429 Mawjan, A: Section A, BB: Bayt Baws, JS: Jabal Shahirah, SK: Shibam Kawkabam, WD: Wadi Dhar, and JK: Jabal Kura'a. Sites are annotated with magnetic polarity data5 where white and black are reverse and normal polarity, respectively. Sites outlined in boxes denote those dated by 40Ar/39Ar (ref. 5,7,11) or 206Pb/238U geochronology (data presented here) and ages are shown in detail Fig. 2. Ages and sites denoted with an asterisk (\*) are from correlative units in Ethiopia7. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 2
+ +Composite stratigraphy of Northern Yemen bimodal flood volcanic units is shown using the average thickness of each unit10. Four units have been correlated to Indian Ocean tephra layers28 and are annotated by the colored symbols. Paleomagnetic data5 are indicated where white = reverse polarity and black = normal polarity. Dashed lines show the approximate locations of the paleomagnetic reversals in the stratigraphy. Minor Unit #4 and AMPH 2 are from different sample localities and both underlie the + +<--- Page Split ---> + +Bayt Mawjan Ignimbrite, but their stratigraphic order relative to each other is unknown. Symbols for 40Ar/39Ar ages5,7,11 are colored based on polarity. The grey field highlights the 40Ar/39Ar and 206Pb/238U ages with associated uncertainties of two pulses of Afro- Arabian silicic volcanism. The Escarpment Ignimbrite, Green Tuff, SAM and Sana'a Ignimbrites, and Iftar Alkalb are a set of normal to reversed polarity that encompass the duration of the C11n.1r Subchron and are compared to the GPTS of Cande and Kent8 as reported the 2020 Geologic Time Scale36. Benthic foraminiferal o180 and o13C curves are from the 2020 Geologic Time Scale36 + +![](images/Figure_4.jpg) + +
Figure 3
+ +40Ar/39Ar and 206Pb/238U ages for the main silicic units from the Northern Yemen section of the Afro- Arabian volcanic province (subset A). The grey field highlights the ages and associated uncertainties (2) of the Escarpment Ignimbrite, Green Tuff, SAM and Sana'a Ignimbrites, and Iftar Alkalb. Ranked single- zircon and 206Pb/238U dates are shown for the Escarpment, SAM, and Sana'a Ignimbrites. Horizontal grey bars outlined in black indicate the weighted mean 206Pb/238U ages with 95% confidence interval. B. Minimum total eruptive volume DRE (km3) values are from on- land and correlated deep- sea tephra layers found in Ocean Drilling Program cores from the Indian Ocean, Leg 1151,10,28. + +<--- Page Split ---> +![PLACEHOLDER_29_0] + +
Figure 4
+ +Bivariate plots showing Th/Y versus Eu/Eu\* for zircon crystals are denoted by age, dating method, and inclusion in final age calculations. Zircons \(>33\) Ma (from 4preliminary LA- ICP- MS dating, average 2 uncertainty \(\pm 3\) Ma) are denoted by diamond symbols. Non- luminescent (CL- dark) zircon crystals from the Escarpment Ignimbrite and Iftar Alkalb are denoted by black symbols. Subsets B- E show Th/Y versus Eu/Eu\* in detail for the Escarpment Ignimbrite (subset B), SAM Ignimbrite (subset C), Sana'a 468 Ignimbrite (subset D), and Iftar Alkalb (subset E). + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SupplementaryInformation1.docx + +<--- Page Split ---> + +SupplementaryInformation2.xlsxSupplementaryInformation3.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24_det.mmd b/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..2450f2c1bd2997ab505ab4929ca78a5d693170c2 --- /dev/null +++ b/preprint/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24/preprint__0a12712672277ba47bb32c7ead3c46d22335c62c595bf9b51a8e924c159b8d24_det.mmd @@ -0,0 +1,427 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 940, 176]]<|/det|> +# Magma flux of silicic supereruptions from the Afro-Arabian large igneous province + +<|ref|>text<|/ref|><|det|>[[44, 194, 572, 238]]<|/det|> +Jennifer Thines ( \(\boxed{ \begin{array}{r l} \end{array} }\) jennifer- thines@uiowa.edu ) University of Iowa https://orcid.org/0000- 0001- 9589- 960X + +<|ref|>text<|/ref|><|det|>[[44, 243, 253, 283]]<|/det|> +Ingrid Ukstins University of Auckland + +<|ref|>text<|/ref|><|det|>[[44, 290, 247, 330]]<|/det|> +Corey Wall Boise State University + +<|ref|>text<|/ref|><|det|>[[44, 336, 247, 376]]<|/det|> +Mark Schmitz Boise State University + +<|ref|>sub_title<|/ref|><|det|>[[44, 417, 102, 435]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 455, 608, 475]]<|/det|> +Keywords: volcanos, earth science, silicic supereruptions, magma + +<|ref|>text<|/ref|><|det|>[[44, 494, 295, 512]]<|/det|> +Posted Date: May 13th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 531, 463, 550]]<|/det|> +DOI: https://doi.org/10.21203/rs.3. rs- 469569/v1 + +<|ref|>text<|/ref|><|det|>[[44, 568, 910, 611]]<|/det|> +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 647, 950, 690]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on November 2nd, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 26468- 5. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[66, 88, 800, 110]]<|/det|> +## 1 Magma flux of silicic supereruptions from the Afro-Arabian large igneous + +<|ref|>sub_title<|/ref|><|det|>[[66, 125, 201, 143]]<|/det|> +## 2 province + +<|ref|>text<|/ref|><|det|>[[66, 156, 872, 386]]<|/det|> +2 provinceJennifer E. Thines\*1, Ingrid A. Ukstins2, Corey Wall3 & Mark Schmitz3 1Department of Earth and Environmental Sciences, University of Iowa, 115 Trowbridge Hall, Iowa City, IA 52242 USA 2School of Environment, The University of Auckland, Private Bag 92 019, Auckland, New Zealand 3Department of Geosciences, 1295 University Drive, Boise State University, Boise, ID 83706, USA + +<|ref|>sub_title<|/ref|><|det|>[[66, 418, 200, 437]]<|/det|> +## 10 Abstract + +<|ref|>text<|/ref|><|det|>[[66, 465, 880, 872]]<|/det|> +10 AbstractThe Main Silicics phase of the Afro- Arabian large igneous province preserves some of the largest volcanic eruptions on Earth, with six units totaling \(>8,600 \text{km}^3\) dense rock equivalent (DRE). The large volumes of rapidly emplaced individual eruptions present a case study for examining the tempo of generation and emplacement of voluminous silicic magmas. We use high- precision \(^{206}\text{Pb}/^{238}\text{U}\) zircon dating to differentiate individual eruption ages and show that the largest sequentially dated eruptions occurred within a timeframe of \(48 \pm 34 \text{kyr}\) (29.755 \(\pm 0.023 \text{Ma}\) to \(29.707 \pm 0.025 \text{Ma}\) ), yielding a maximum magma flux of \(3.09 \times 10^{- 1} \text{km}^3/\text{yr}\) for \(4,339 \text{km}^3\) DRE and making this sequence the highest known flux of silicic volcanism on Earth. The Main Silicics phase of volcanism occurred within a timeframe of \(130 \pm 150 \text{kyr}\) (29.80 \(\pm 0.80 \text{Ma}\) to \(29.67 \pm 0.13 \text{Ma}\) ), yielding a maximum magma flux of \(3.05 \times 10^{- 2} \text{km}^3/\text{yr}\) . We also provide a robust tie- point for calibration of the geomagnetic polarity timescale by integrating + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[63, 87, 800, 144]]<|/det|> +recalculated \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) data with our high- precision \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) ages to yield new constraints on the duration of the C11n.1r Subchron. + +<|ref|>sub_title<|/ref|><|det|>[[63, 174, 232, 194]]<|/det|> +## Background + +<|ref|>text<|/ref|><|det|>[[110, 220, 881, 808]]<|/det|> +Many of the largest silicic eruptions on Earth occur in large igneous provinces (LIPs), with total eruptive volumes often exceeding \(1,000 \mathrm{km}^3\) dense rock equivalent (DRE) for individual events, which are likely to be emplaced in rapid succession \(^{1 - 3}\) . Although LIPs are generally considered to represent the most productive magmatic systems on Earth \(^{4}\) , uncertainty about volume estimates and imprecise or inaccurate age data for individual events often preclude robust estimates of magma flux and volcanic output rates. The Northern Yemen section of the Afro- Arabian LIP is an ideal testbed for using high- 15 \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) zircon dating to quantify the magma flux of a series of flood volcanic eruptions, with three silicic supereruptions occurring within a 70 to 310 kyr timeframe at ca. 29.7 Ma \(^{5 - 7}\) . Previous paleomagnetism and \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) studies \(^{5 - 7}\) indicate these are a set of normal to reversed polarity units that encompass the duration of the C11n.1r Subchron, although overlapping ages for individual eruptions, due to analytical uncertainties, are currently unable to distinguish between the geomagnetic polarity timescale (GPTS) of Cande and Kent \(^{8}\) and Huestis and Acton \(^{9}\) . In contrast to existing \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) ages, the \(0.1\%\) precision of state- of- art chemical abrasion thermal ionization mass spectroscopy (CA- TIMS) U- Pb ages of zircons can distinguish between the ages of these units outside analytical uncertainty. + +<|ref|>text<|/ref|><|det|>[[111, 815, 877, 905]]<|/det|> +Oligocene volcanism in Northern Yemen (Fig. 1) has been divided into three phases based on field observations, whole rock geochemical correlations, and \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) dating \(^{5 - 7,10}\) : Main Basalts (31 to 29.7 Ma), Main Silicics (29.7 to 29.5 Ma), and Upper Bimodal + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 870, 249]]<|/det|> +(29.6 to 27.7 Ma). The Main Basalts phase is characterized by effusive basaltic volcanism and volumetrically represents 60 to 70% of the total erupted volume of Afro- Arabian lavas10,11. The Main Silicics phase saw the rapid emplacement of seven silicic pyroclastic units and the Upper Bimodal phase includes small- volume basaltic and rhyolitic eruptions10. + +<|ref|>text<|/ref|><|det|>[[111, 262, 880, 841]]<|/det|> +We focus on the Main Silicics phase, which contains some of the largest known silicic eruptions on Earth, with an estimated minimum eruptive total volume of 8,633 km3 DRE emplaced in present- day Yemen and Ethiopia over a period from 29.7 to 29.5 Ma1,10. Volcano- stratigraphic correlations in Yemen10 suggest the emplacement of the Jabal Kura'a Ignimbrite (1,627 km3 DRE; \(\sim 29.6\) Ma) and Escarpment Ignimbrite (358 km3 DRE; \(\sim 29.6\) Ma), was followed by a brief period of subsidence and erosion, and then the rapid emplacement of the Green Tuff (58 km3 DRE; \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) age = 29.59 \(\pm\) 0.12 Ma7; Fig. 2), SAM Ignimbrite (2,330 km3 DRE; \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) age = 29.47 \(\pm\) 0.14 Ma7, Sana'a Ignimbrite (1,593 km3 DRE; \(\sim 29.5\) Ma; Fig. 2), and Iftar Alkalb caldera collapse mega- breccia (2,667 km3 DRE; \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) age = 29.48 \(\pm\) 0.13 Ma5; Fig. 2). The Green Tuff has been interpreted as representing the initial airfall deposit preceding the emplacement of the SAM Ignimbrite based on the sharp upper contact between the units with no evidence of a time gap during emplacement10. These bracketed \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) ages indicate all four units, with a cumulative estimated minimum total eruptive volume of \(\sim 6,650\) km3 DRE, were emplaced in rapid succession within a timeframe of 70 to 310 kyr5,7,10 but there are no robust estimates of magma generation rates or magma flux over this time interval. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[65, 90, 189, 109]]<|/det|> +## 68 Results + +<|ref|>text<|/ref|><|det|>[[110, 131, 884, 900]]<|/det|> +The Escarpment Ignmibrite contains elongate prismatic crystals (typically 50 to 120 \(\mu \mathrm{m}\) in length and, rarely, up to \(150\mu \mathrm{m}\) ) and smaller equant crystals (50 to \(75\mu \mathrm{m}\) in length). Some prismatic crystals have oscillatory zoning with U- rich non- luminescent cores (CL dark). The SAM Ignmibrite contains elongate prismatic crystals that are both smaller (30 to \(75\mu \mathrm{m}\) , rarely up to \(125\mu \mathrm{m}\) ) and less numerous than those found in the Escarpment Ignmibrite. Few crystals have subtle oscillatory zoning and one larger crystal \(\sim 120\mu \mathrm{m}\) in length has a non- luminescent, oscillatory zoned core with a lighter overgrowth rim. Crystals in the SAM Ignmibrite have a weakly paramagnetic behavior, likely due to abundant Fe- Ti oxide and apatite inclusions. The Sana’a Ignmibrite contains small elongate prismatic crystals (30 to \(75\mu \mathrm{m}\) ) with subtle to no oscillatory zoning. Zircon is abundant in Iftar Alkalb as anhedral to euhedral elongate prismatic and equant crystals that range in length from 30 to \(120\mu \mathrm{m}\) . Internal morphologies are variable with populations of non- luminescent and luminescent crystals with no oscillatory zoning, crystals with non- luminescent cores and lighter rims, and a few crystals with strong oscillatory zoning (see Supplementary Information 1 for CL images). In total, 273 laser ablation spot analyses were conducted on 79 crystals from the Escarpment Ignmibrite, 46 crystals from the SAM Ignmibrite, 31 crystals from the Sana’a Ignmibrite, and 95 crystals from Iftar Alkalb. The median uncertainty of a single LA- ICP- MS \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) spot analysis is 3 Ma, too imprecise to distinguish ancestry populations (crystals that grew in an earlier pulse and were later incorporated in a different pulse \(^{12,13}\) ) for this magmatic system but adequate to determine older xenocrystic zircon crystals. Every unit except the Escarpment Ignmibrite contains \(>10\%\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 880, 314]]<|/det|> +zircon crystals with LA- ICP- MS \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) ages \(>33\) Ma. The Sana'a Ignimbrite and Iftar Alkalb contain significant proportions of older zircons (30% and 29% respectively), although in the Sana'a Ignimbrite this may be due to the low sample number (n = 31). There is no correlation between age and trace element (U, Th, Y, HREE) concentrations. CL dark zircon crystals in the Escarpment Ignimbrite and Iftar Alkalb have among the highest HREE concentrations and europium anomalies (Eu/Eu\*) in each respective unit (Supplementary Information 2). + +<|ref|>text<|/ref|><|det|>[[111, 330, 880, 880]]<|/det|> +32 grains that showed no sign of inclusions and yielded consistent U- Pb laser ablation dates were plucked from their respective grain mounts for high- precision CA- ID- TIMS geochronology (Supplementary Information 2). Preference was given to zircon crystals that captured the full range of compositions found in each unit. Six zircon crystals from the Escarpment Ignimbrite yielded a weighted mean \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) date of \(29.755 \pm 0.023\) Ma (MSWD = 0.62; Fig. 3). Excluding the oldest zircon crystal from the SAM Ignimbrite (which was older than 29.778 Ma, and inferred to be an ancestry), the remaining eight zircon crystals yielded a weighted mean date of \(29.728 \pm 0.017\) Ma (MSWD = 0.34). Six zircon crystals from the Sana'a Ignimbrite yielded a weighted mean date of \(29.707 \pm 0.025\) Ma (MSWD = 0.65; Fig. 3), excluding three zircon crystals older than 29.745 Ma, also inferred to be ancestry. The weighted mean \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) dates have been interpreted as the eruption age of each respective unit. Although Iftar Alkalb is the stratigraphically youngest unit dated, nine zircon crystals were consistently older ( \(29.731 \pm 0.089\) Ma to \(30.320 \pm 0.094\) Ma; Fig. 3) than the weighted mean ages of the other units and so no date was assigned; we attribute this to the emplacement mechanism of the caldera collapse breccia with abundant mega- clasts of underlying + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 857, 144]]<|/det|> +stratigraphy contributing xenolithic material or antecrystals that are recording an earlier stage of zircon crystallization. + +<|ref|>text<|/ref|><|det|>[[111, 156, 872, 530]]<|/det|> +Sanidine from the Green Tuff, SAM Ignmibrite, and Iftar Alkalb were previously dated via the \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) method \(^{5,7}\) . Those dates have been recalculated using a 28.201 Ma monitor age for the Fish Canyon sanidine \(^{14}\) . Recalculations (Supplementary Information 3) yield a \(29.78 \pm 0.12\) Ma age for the Green Tuff, \(29.66 \pm 0.14\) Ma age for the SAM Ignmibrite, and \(29.67 \pm 0.08\) Ma age for Iftar Alkalb (Fig. 2). Previous \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) ages \(^{5,7,11}\) from the Shibam Kawkabam Ignmibrite ( \(30.35 \pm 0.13\) Ma), Kura'a Basalt ( \(30.22 \pm 0.26\) Ma), Akraban Andesite ( \(29.80 \pm 0.08\) Ma), an overlying small- volume rhyolitic tuff ( \(28.58 \pm 0.14\) Ma) and ignmibrite ( \(28.18 \pm 0.10\) Ma), and the Bayt Mawjan Ignmibrite ( \(27.85 \pm 0.12\) Ma) have also been recalculated and are compiled and presented here as a revised chronostratigraphy of the Northern Yemen flood volcanics (Fig. 2). + +<|ref|>sub_title<|/ref|><|det|>[[115, 556, 222, 576]]<|/det|> +## Discussion + +<|ref|>sub_title<|/ref|><|det|>[[115, 605, 417, 625]]<|/det|> +## Short-Term Accumulation Rates + +<|ref|>text<|/ref|><|det|>[[111, 655, 875, 888]]<|/det|> +Elements that are normally incompatible during magma differentiation (e.g., U, Nb, Th, Y and Hf) and the europium anomaly (Eu/Eu\*) in rare earth element patterns resulting from feldspar fractionation are useful indicators of magma differentiation. Assuming both elements remain incompatible, more differentiated rhyolites will evolve towards higher Th/Y ratios while Eu/Eu\* will decrease with continued feldspar crystallization \(^{15}\) . With a few exceptions, zircons dated via CA- TIMS for these units show the same trend: the least evolved zircon with the highest Eu/Eu\* and lowest Th/Y values + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 872, 354]]<|/det|> +are older than the most evolved zircon by \(0.01 \pm 0.16\) Ma in the Escarpment Ignimbrite, \(0.02 \pm 0.09\) Ma in the SAM Ignimbrite, and \(0.07 \pm 0.17\) Ma in the Sana'a Ignimbrite. Thus ages for zircon crystals spanning the full geochemical ranges are statistically indistinguishable, suggesting that these large volume magmas were rapidly differentiated within \(10^{3}\) to \(10^{4}\) years. Eu/Eu\* and Th/Y are not correlated for zircons in the Iftar Alkalb mega- breccia and there is no age relationship between the most and least evolved zircons (Fig. 4), further supporting that the zircons in Iftar Alkalb are of a mixed xenolithic or antecrystic origin. + +<|ref|>text<|/ref|><|det|>[[110, 365, 880, 879]]<|/det|> +Short- term accumulation rates were calculated for 50, 100, and 400 kyr of residence for the Escarpment, SAM, and Sana'a Ignimbrites based on the trace element concentrations and CA- TIMS U- Pb zircon dates. For 50 kyr residence, short- term accumulation rates are \(7.2 \times 10^{- 3} \text{km}^3 / \text{yr}\) , \(4.8 \times 10^{- 2} \text{km}^3 / \text{yr}\) , and \(3.2 \times 10^{- 2} \text{km}^3 / \text{yr} \text{for the Escarpment, SAM, and Sana'a Ignimbrites, respectively. For 100 kyr residence, short- term accumulation rates are} 3.6 \times 10^{- 3} \text{km}^3 / \text{yr}\) , \(2.4 \times 10^{- 2} \text{km}^3 / \text{yr}\) , and \(1.6 \times 10^{- 2} \text{km}^3 / \text{yr} \text{for the Escarpment, SAM, and Sana'a Ignimbrites, respectively. For 400 kyr residence, short- term accumulation rates are} 9.0 \times 10^{- 4} \text{km}^3 / \text{yr}\) , \(6.0 \times 10^{- 3} \text{km}^3 / \text{yr}\) , and \(4.0 \times 10^{- 3} \text{km}^3 / \text{y} \text{for the Escarpment, SAM, and Sana'a Ignimbrites, respectively. Upper estimates of} 7.2 \times 10^{- 3} \text{to} 3.2 \times 10^{- 2} \text{km}^3 / \text{yr} \text{for 50 kyr residence and} 3.6 \times 10^{- 3} \text{to} 2.4 \times 10^{- 2} \text{km}^3 / \text{yr} \text{for 100 kyr residence are similar to those calculated for other rapidly assembled large- volume silicic systems (e.g., Yellowstone supereruptions}^{16,17} \text{and Oruani eruption within the Taupo volcanic zone}^{18}). \text{The most conservative estimates using 400 kyr residence} (9.0 \times 10^{- 4} \text{to} 6.0 \times 10^{- 4} \text{km}^3 / \text{yr} \text{are similar to but lower than the minimum calculated magma flux from Yellowstone} (2.8 \times 10^{- 3} \text{km}^3 / \text{yr} \text{for the 280 km}^3 \text{Mesa Falls Tuff}^{17}). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 340, 110]]<|/det|> +## Long-Term Magma Flux + +<|ref|>text<|/ref|><|det|>[[111, 120, 875, 670]]<|/det|> +U- Pb zircon dating shows that three sequential eruptions of Afro- Arabian silicic volcanics - the Escarpment Ignimbrite, the Green Tuff and SAM Ignimbrite, and Sana'a Ignimbrites - were collectively emplaced within a timespan of \(48 \pm 34\) kyr (calculated using the square root of the sum of the uncertainties), yielding a magma flux of \(5.29 \times 10^{- 2}\) to \(3.09 \times 10^{- 1}\) km\(^3\) /yr for \(4,339 \text{km}^3\) DRE. The estimated minimum total eruptive volume for the entirety of the Main Silicics phase is \(8,633 \text{km}^3\) DRE over a duration of \(130 \pm 150\) kyr, constrained by the ages of the Akraban Andesite and Iftar Alkalb, which yield a lower magma flux of \(3.05 \times 10^{- 2}\) to \(6.64 \times 10^{- 2}\) km\(^3\) /yr. Magma flux for other regions of the Afro- Arabian province, such as the Ethiopian stratigraphy, are difficult to constrain. While there was wide- scale silicic volcanism following the termination of the main pulse of flood basalt emplacement\(^{19,20}\), unit volume estimates outside of Northern Yemen remain sparse due to poor exposure and post- emplacement tectonic disruption. Notably, a series of silicic supereruptions in the Tana Basin, Ethiopia\(^{21}\) have recently been dated at \(31.108 \pm 0.020\) to \(30.844 \pm 0.027\) Ma with an estimated minimum eruptive volume of \(2,000\) to \(3,000 \text{km}^3\), corresponding to a magma flux of \(0.8\) to \(1.1 \times 10^{- 2} \text{km}^3\) /yr. + +<|ref|>text<|/ref|><|det|>[[111, 680, 870, 876]]<|/det|> +Magma fluxes of basaltic and andesitic systems are thought to be higher than those of silicic systems by up to two orders of magnitude\(^4\). Average fluxes in silicic systems are calculated to be highest for continental arcs ( \(4.90 \pm 0.15 \times 10^{- 3} \text{km}^3\) /yr) followed by oceanic arcs ( \(4.50 \pm 0.79 \times 10^{- 3} \text{km}^3\) /yr), continental rifts ( \(4.48 \pm 0.86 \times 10^{- 3} \text{km}^3\) /yr), continental hotspots ( \(1.29 \pm 0.25 \times 10^{- 3} \text{km}^3\) /yr), and continental volcanic fields ( \(6.47 \pm 1.96 \times 10^{- 4} \text{km}^3\) /yr). The magma flux of the Main Silicics phase of the Northern Yemen + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 87, 881, 460]]<|/det|> +section of the Afro- Arabian province is most similar to - but notably higher than - the magma fluxes of Taupo ( \(1.15 \times 10^{- 2} \mathrm{km}^{3} / \mathrm{yr}\) ; ref. \(^{22}\) ), the silicic portion of Kamchatka ( \(1.05 \times 10^{- 2} \mathrm{km}^{3} / \mathrm{yr}\) , ref. \(^{23}\) ) and Quaternary phonolites from the Kenya rift valley ( \(1.20 \times 10^{- 2} \mathrm{km}^{3} / \mathrm{yr}\) ; ref. \(^{22}\) ). Our findings are consistent with observations at other large- volume silicic systems that record rapid periods of differentiation and magma reservoir assembly superimposed on lower background fluxes. While some silicic systems have produced more voluminous individual eruptions (e.g., Fish Canyon Tuff with 4,500 \(\mathrm{km}^{3}\) DRE \(^{24}\) ), and larger cumulative eruptive volumes over longer time intervals (e.g., Parana- Etendeka LIP with 20,000 to 35,000 \(\mathrm{km}^{3}\) over 6 \(\mathrm{Myr}^{25,26}\) ), the eruptions of the Main Silicics phase in Northern Yemen represent the largest long- term flux of silicic volcanism on Earth. + +<|ref|>sub_title<|/ref|><|det|>[[115, 486, 699, 508]]<|/det|> +## Afro-Arabian volcanism and Oligocene Environmental Change + +<|ref|>text<|/ref|><|det|>[[110, 536, 886, 910]]<|/det|> +Some volcanic provinces appear to coincide with major global environmental change and mass extinctions (e.g., Siberian Traps, Karoo- Ferrar, Emieshan and Central Atlantic LIPs), yet others, even those with silicic supereruptions (e.g., Parana- Etendeka LIP), do not \(^{27}\) . Afro- Arabian silicic volcanism represents the greatest flux of large- volume silicic magma eruption on Earth and is correlated to 10 to 15 cm thick tephra layers located \(>2,700 \mathrm{km}\) away in the Indian Ocean \(^{28}\) (Fig. 2), suggesting volcanic fallout on a near- global scale. The timing of these supereruptions in relation to several Rupelian- aged cooling events that have been identified in Chrons C12 (Oi1a, Oi1b, and Oi \(^{29,30}\) ) and C10 (Oi2\* and Oi2a \(^{29,30}\) ) indicate that the perturbations in \(\delta^{18} \mathrm{O}\) and \(\delta^{13} \mathrm{C}\) pre- date the eruptions \(^{31,32}\) (Fig. 2). Other silicic supereruptions, such as the \(\sim 31 \mathrm{Ma}\) caldera- forming eruptions in the Tana Basin \(^{21}\) and \(\sim 28 \mathrm{Ma}\) eruption of the Fish Canyon Tuff \(^{24}\) , likewise + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 876, 250]]<|/det|> +do not coincide with global cooling events. Challenges remain in discerning the various roles of the tempo, volatile budget, eruption mechanism, and volume of magma extruded from large igneous provinces and their effect on global environmental change. However, robust temporal constraints continue to provide critical insight into this relationship. + +<|ref|>sub_title<|/ref|><|det|>[[113, 278, 579, 300]]<|/det|> +## Implications for Geomagnetic Polarity Time Scale + +<|ref|>text<|/ref|><|det|>[[110, 325, 875, 910]]<|/det|> +Previous efforts have been made to correlate Oligocene Afro- Arabian volcanic deposits with the geomagnetic polarity time scale5,33 but those were unable to unambiguously distinguish between the GPTS of Cande and Kent8 and Huestis and Acton9. Recent studies on the Oligocene magnetic polarity sequence have utilized astronomical age models29, radio-isotope age models30, recalculations of the Cande and Kent8 GPTS using updated40Ar/39Ar flux monitor ages34, and a combination of all three30. One of the lingering issues with distinguishing between an appropriate method for determining the Rupelian age (33.9 to 28.1 Ma) is the lack of tie points from radio-isotopic dates. The Rupelian/Chattian boundary Global Boundary Stratotype Section and Point (GSSP) records a nearly continuous record of astronomically- tuned magnetostratigraphy for the Oligocene but only provides one tie- point for the Rupelian for the uppermost Chron C12r with a gap between \(31.8 \pm 0.2\) Ma and \(27.0 \pm 0.1\) Ma30,35. The 2012 Geologic Time Scale for the Paleogene30 favored an integrated radio-isotope, GPTS, and cyclostratigraphy model with 6th- order polynomial fit to produce a complete C- sequence. The C11n.1r Subchron is estimated to have a duration of 0.050 Ma with a -0.654 Ma discrepancy between radio-isotopic and astronomic age models30. The only discrepancy between the combined age model of the 2012 Geologic Time Scale and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 845, 145]]<|/det|> +new 2020 Geologic Time Scale for the time range of interest is a shift of the base of Chron C12n to 30.977 Ma from 31.034 Ma \(^{30,36}\) . + +<|ref|>text<|/ref|><|det|>[[111, 157, 883, 459]]<|/det|> +We propose that the \(29.728 \pm 0.017\) Ma \(^{206}\) Pb/ \(^{238}\) U zircon age of the SAM Ignimbrite and \(29.67 \pm 0.13\) Ma \(^{40}\) Ar/ \(^{39}\) Ar sanidine age of Iftar Alkalb - further constrained to 29.67 \(\pm 0.13\) Ma by the \(29.707 \pm 0.025\) Ma \(^{206}\) Pb/ \(^{238}\) U age of the Sana'a Ignimbrite - can be used as tie- points for the GPTS. Our chrono- and magnetostratigraphy are definitively in agreement with the Cande and Kent \(^{8}\) GPTS (Fig. 2). Discrepancies between our results and the 2020 Geologic Time Scale arise from the sparsity of radio- isotope dates for the Rupelian coupled with the short duration of the C11n.1r Subchron. Our findings are within the 0.654 Ma discrepancy between the radio- isotopic and astronomic age models and could thus serve as robust tie points for future time scale calibrations. + +<|ref|>sub_title<|/ref|><|det|>[[114, 488, 199, 507]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[111, 536, 883, 907]]<|/det|> +Samples from the Sana'a area of Northern Yemen were previously collected and described \(^{10}\) (Fig. 1). Paleomagnetic data was measured on 587 oriented drill cores collected at 71 sites \(^{5}\) (Fig. 1). Zircon U- Pb petrochronology was undertaken at the Boise State University Isotope Laboratory. Zircon crystals from the Escarpment, SAM, and Sana'a Ignimbrites, and Iftar Alkalb were separated using standard magnetic and heavy liquid techniques and annealed at \(900^{\circ}\) C for 60 hours. Zircons were imaged using a JEOL T- 300 scanning electron microscope (SEM) fitted with a Gatan Mini cathodoluminescence (CL) detector and JEOL back- scattered electron (BSE) detector under 15 kV probe current and 2 mA accelerating voltage operating conditions (Supplementary Information 1). Trace element analyses and preliminary U- Pb dating for 31 to 95 crystals per unit (Supplementary Information 2) were performed using a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 870, 323]]<|/det|> +ThermoElectron X- Series II quadrupole inductively coupled plasma mass spectrometer (ICP- MS) and New Wave Research UP- 213 Nd:YAG UV (213 \(\mu \mathrm{m}\) ) laser ablation system with a 10 Hz at 5 J/cm \(^2\) pulsed laser and 15 \(\mu \mathrm{m}\) spot size. NIST SRM- 610 and SRM- 612 glasses were used as standards for trace element concentrations and Plesovice zircon standard \(^{37}\) was used for U- Pb calibration. Zircon standards were measured every 10 unknowns; glass standards were analyzed at the beginning of two 109- spot cycles. + +<|ref|>text<|/ref|><|det|>[[110, 336, 883, 744]]<|/det|> +A total of 32 crystals from the four units were selected for CA- TIMS analysis on the basis of morphology, zoning, chemistry, and preliminary \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) dates. After chemical abrasion \(^{38}\) in a single aggressive high- temperature step, residual crystals were rinsed and spiked with ET535 tracer solution \(^{39,40}\) , dissolved in concentrated HF, converted to a chloride matrix, and U and Pb purified by ion chromatography following the detailed procedures described in Macdonald et al. \(^{41}\) . High- precision isotope dilution U and Pb isotope ratio measurements were made using a single Re filament silica gel technique on an Isotopx Isoprobe- T multi- collector thermal ionization mass spectrometer (TIMS) equipped with an ion- counting Daly detector (Supplementary Information 2). Dates are calculated using the decay constants of Jaffey et al. \(^{42}\) . Analytical uncertainties on dates are reported to 2σ and propagated using the algorithms of Schmitz and Schoene \(^{43}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 757, 270, 777]]<|/det|> +## Data Availability + +<|ref|>text<|/ref|><|det|>[[111, 790, 867, 882]]<|/det|> +Supplementary Information 1 contains cathodoluminescence (CL) images of zircon crystals analyzed by LA- ICP- MS and CA- TIMS. Supplementary Information 2 contains details on the calculation of CA- TIMS \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) dates and zircon LA- ICP- MS + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 812, 144]]<|/det|> +geochronologic and trace element concentrations. Supplementary Information 3 contains details on the recalculation of \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) ages. + +<|ref|>sub_title<|/ref|><|det|>[[113, 159, 225, 178]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[110, 193, 883, 250]]<|/det|> +1. Bryan, S. E. et al. The largest volcanic eruptions on Earth. Earth Sci. Rev. 102, 207–229 (2010). + +<|ref|>text<|/ref|><|det|>[[110, 277, 844, 333]]<|/det|> +2. Bryan, S. E. & Ernst, R. E. Revised definition of Large Igneous Provinces (LIPs). Earth Sci. Rev. 86, 175–202 (2008). + +<|ref|>text<|/ref|><|det|>[[110, 347, 884, 404]]<|/det|> +3. Coffin, M. F. & Eldholm, O. Volcanism and continental break-up: a global compilation of large igneous provinces. Geol. Soc. London Spec. Pub. 68, 17–30 (1992). + +<|ref|>text<|/ref|><|det|>[[110, 418, 844, 475]]<|/det|> +4. White, S. M., Crisp, J. A. & Spera, F. J. Long-term volumetric eruption rates and magma budgets. Geochem. Geophys. 7, (2006). + +<|ref|>text<|/ref|><|det|>[[110, 500, 864, 627]]<|/det|> +5. Riisager, P. et al. Paleomagnetism and \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) Geochronology of Yemeni Oligocene volcanics: Implications for timing and duration of Afro-Arabian traps and geometry of the Oligocene paleomagnetic field. Earth Planet. Sci. Lett. 237, 647–672 (2005). + +<|ref|>text<|/ref|><|det|>[[110, 656, 857, 747]]<|/det|> +6. Riisager, P., Baker, J. & Utskins, I. Paleomagnetic and Anisotropy of Magnetic Susceptibility Studies of Oligocene Flood Volcanism in Yemen. AGU Fall Meeting Abstracts 82, (2001). + +<|ref|>text<|/ref|><|det|>[[110, 775, 840, 866]]<|/det|> +7. Ukstins, I. A. et al. Matching conjugate volcanic rifted margins: \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) chronostratigraphy of pre- and syn-rift bimodal flood volcanism in Ethiopia and Yemen. Earth Planet. Sci. Lett. 198, 289–306 (2002). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 880, 145]]<|/det|> +8. Cande, S. C. & Kent, D. V. Revised calibration of the geomagnetic polarity timescale for the Late Cretaceous and Cenozoic. J. Geophys. Res. 100, 6093–6095 (1995). + +<|ref|>text<|/ref|><|det|>[[110, 172, 866, 265]]<|/det|> +9. Huestis, S. P. & Acton, G. D. On the construction of geomagnetic timescales from non-prejudicial treatment of magnetic anomaly data from multiple ridges. Geophys. J. Int. 129, 176–182 (1997). + +<|ref|>text<|/ref|><|det|>[[110, 291, 864, 383]]<|/det|> +10. Ukstins Peate, I. et al. Volcanic stratigraphy of large-volume silicic pyroclastic eruptions during Oligocene Afro-Arabian flood volcanism in Yemen. Bull. Volcanol. 68, 135–156 (2005). + +<|ref|>text<|/ref|><|det|>[[110, 410, 858, 504]]<|/det|> +11. Baker, J., Snee, L. & Menzies, M. A brief Oligocene period of flood volcanism in Yemen: implications for the duration and rate of continental flood volcanism at the Afro-Arabian triple junction. Earth Planet. Sci. Lett. 138, 39–55 (1996). + +<|ref|>text<|/ref|><|det|>[[110, 530, 857, 586]]<|/det|> +12. Hildreth, W. & Wilson, C. J. N. Compositional Zoning of the Bishop Tuff. J. Petrol. 48, 951–999 (2007). + +<|ref|>text<|/ref|><|det|>[[110, 600, 830, 666]]<|/det|> +13. Miller, J. S., Matzel, J. E. P., Miller, C. F., Burgess, S. D. & Miller, R. B. Zircon growth and recycling during the assembly of large, composite arc plutons. J. + +<|ref|>text<|/ref|><|det|>[[110, 672, 856, 728]]<|/det|> +Volcanol. Geotherm. Res. 167, 282–299 (2007). Kuiper, K. F. et al. Synchronizing Rock Clocks of Earth History. Science 320, 500–504 (2008). + +<|ref|>text<|/ref|><|det|>[[110, 740, 870, 832]]<|/det|> +14. Rivera, T. A., Storey, M., Schmitz, M. D. & Crowley, J. L. Age intercalibration of \(^{40}\mathrm{Ar}/^{39}\mathrm{Ar}\) sanidine and chemically distinct U/Pb zircon populations from the Alder Creek Rhyolite Quaternary geochronology standar. Chem. Geol. 345 87–98 (2013). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 861, 180]]<|/det|> +15. Wotzlaw, J.-F., Bindeman, I. N., Stern, R. A., D'Abzac, F.-X. & Schaltegger, U. Rapid heterogeneous assembly of multiple magma reservoirs prior to Yellowstone supereruptions. Nature Sci. Rep. 5, 14026 (2015). + +<|ref|>text<|/ref|><|det|>[[111, 207, 864, 335]]<|/det|> +16. Rivera, T. A., Schmitz, M. D., Jicha, B. R. & Crowley, J. L. Zircon petrochronology and \(^{40}\mathrm{Ar}/^{39}\mathrm{Ar}\) sanidine dates for the Mesa Falls Tuff: crystal-scale records of magmatic evolution and the short lifespan of a large Yellowstone magma chamber. J. Petrol 57 1677–1704 (2016). + +<|ref|>text<|/ref|><|det|>[[111, 362, 872, 454]]<|/det|> +17. Charlier, B. L. A. et al. Magma generation at a large, hyperactive silicic volcano (Taupo, New Zealand) revealed by U-Th and U-Pb systematics in zircons. J. Petrol. 46, 3–32 (2005). + +<|ref|>text<|/ref|><|det|>[[111, 482, 872, 538]]<|/det|> +18. Rooney, T. O. The Cenozoic magmatism of East-Africa: Part I — Flood basalts and pulsed magmatism. Lithos 286–287, 264–301 (2017). + +<|ref|>text<|/ref|><|det|>[[111, 566, 880, 658]]<|/det|> +19. Krans, S. R., Rooney, T. O., Kappelman, J., Yirgu, G. & Ayalew, D. From initiation to termination: a petrostratigraphic tour of the Ethiopian Low-Ti Flood Basalt Province. Contrib. Mineral. Petrol. 173, 37 (2018). + +<|ref|>text<|/ref|><|det|>[[111, 686, 849, 777]]<|/det|> +20. Prave, A. R. et al. Geology and geochronology of the Tana Basin, Ethiopia: LIP volcanism, Super eruptions and Eocene-Oligocene environmental change. Earth Planet. Sci. Lett. 443, 1–8 (2016). + +<|ref|>text<|/ref|><|det|>[[111, 806, 808, 861]]<|/det|> +21. Crisp, J. A. Rates of magma emplacement and volcanic output. J. Volcanol. Geotherm. Res. 20, 177–211 (1984). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 808, 144]]<|/det|> +22. Erlich, E. N. & Volynets, O. N. Chapter 3 acid volcanism in Kamchatka. Bull. Volcanol. 42, 175-254 (1979). + +<|ref|>text<|/ref|><|det|>[[110, 172, 860, 266]]<|/det|> +23. Lipman, P., Dungan, M. & Bachmann, O. Comagmatic granophyric granite in the Fish Canyon Tuff, Colorado: Implications for magma-chamber processes during a large ash-flow eruption. Geology 25, 915-918 (1997). + +<|ref|>text<|/ref|><|det|>[[110, 291, 880, 418]]<|/det|> +24. Gibson, S. A., Thompson, R. N. & Day, J. A. Timescales and mechanisms of plume–lithosphere interactions: \(^{40}\mathrm{Ar}/^{39}\mathrm{Ar}\) geochronology and geochemistry of alkaline igneous rocks from the Parana–Etendeka large igneous province. Earth Planet. Sci. Lett. 251, 1–17 (2006). + +<|ref|>text<|/ref|><|det|>[[110, 446, 875, 572]]<|/det|> +25. Simões, M. S., Lima, E. F., Rossetti, L. M. M. & Sommer, C. A. The low-Ti high-temperature dacitic volcanism of the southern Parana–Etendeka LIP: Geochemistry, implications for trans-Atlantic correlations and comparison with other Phanerozoic LIPs. Lithos 342–343, 187–205 (2019). + +<|ref|>text<|/ref|><|det|>[[110, 600, 870, 692]]<|/det|> +26. Black, B. A., Weiss, B. P., Elkins-Tanton, L. T., Veselovskiy, R. V. & Latyshev, A. Siberian Traps volcanicistic rocks and the role of magma-water interactions. Geol. Soc. Am. Bull. 127, 1437–1452 (2015). + +<|ref|>text<|/ref|><|det|>[[110, 720, 881, 847]]<|/det|> +27. Ukstins Peate, I., Kent, A. J. R., Baker, J. A. & Menzies, M. A. Extreme geochemical heterogeneity in Afro-Arabian Oligocene tephras: Preserving fractional crystallization and mafic recharge processes in silicic magma chambers. Lithos 102, 260–278 (2008). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 87, 830, 144]]<|/det|> +28. Pálike, H. et al. The Heartbeat of the Oligocene Climate System. Science 314, 1894–1898 (2006). + +<|ref|>text<|/ref|><|det|>[[60, 172, 870, 265]]<|/det|> +29. Vandenberghe, N. et al. Chapter 28 - The Paleogene Period. in The Geologic Time Scale (eds. Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M.) 855–921 (Elsevier, 2012). + +<|ref|>text<|/ref|><|det|>[[60, 292, 870, 385]]<|/det|> +30. Cramer, B. S., Toggweiler, J. R., Wright, J. D., Katz, M. E. & Miller, K. G. Ocean overturning since the Late Cretaceous: Inferences from a new benthic foraminiferal isotope compilation. Paleoceanography 24, PA4216 (2009). + +<|ref|>text<|/ref|><|det|>[[60, 411, 857, 469]]<|/det|> +31. Zachos, J., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, Rhythms, and Aberrations in Global Climate 65 Ma to Present. Science 292, 686–693 (2001). + +<|ref|>text<|/ref|><|det|>[[60, 496, 872, 553]]<|/det|> +32. Rochette, P. et al. Magnetostratigraphy and timing of the Oligocene Ethiopian traps. Earth Planet. Sci. Lett. 164, 497–510 (1998). + +<|ref|>text<|/ref|><|det|>[[60, 580, 875, 673]]<|/det|> +33. Guidry, E. P. et al. Oligocene–Miocene magnetostratigraphy of deep-sea sediments from the equatorial Pacific (IODP Site U1333). Geol. Soc. London Spec. Pub. 373, 13–27 (2013). + +<|ref|>text<|/ref|><|det|>[[60, 700, 872, 827]]<|/det|> +34. Coccioni, R. et al. Integrated stratigraphy of the Oligocene pelagic sequence in the Umbria-Marche basin (northeastern Apennines, Italy): A potential Global Stratotype Section and Point (GSSP) for the Rupelian/Chattian boundary. Geol. Soc. Am. Bull. 120, 487–511 (2008). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 864, 177]]<|/det|> +35. Speijer, R. P., Pálike, H., Hollis, C. J., Hooker, J. J. & Ogg, J. G. Chapter 28 – The Paleogene Period. in The Geologic Time Scale (eds. Gradstein, F. M., Ogg, J. G., Schmitz, M., D. & Ogg, G. M.) 1087–1140 (Elsevier, 2020). + +<|ref|>text<|/ref|><|det|>[[110, 192, 872, 248]]<|/det|> +36. Sláma, J. et al. Plešovice zircon – a new natural reference material for U-Pb and Hf isotopic microanalysis. Chem. Geol. 249, 1–35 (2008). + +<|ref|>text<|/ref|><|det|>[[110, 268, 850, 360]]<|/det|> +37. Mattinson, J. M. Zircon U–Pb chemical abrasion (“CA-TIMS”) method: Combined annealing and multi-step partial dissolution analysis for improved precision and accuracy of zircon ages. Chem. Geol. 220, 47–66 (2005). + +<|ref|>text<|/ref|><|det|>[[110, 388, 871, 480]]<|/det|> +38. Condon, D.J., Schoene, B., McLean, N.M., Bowring, S.A. & Parrish, R.R. Metrology and traceability of U-Pb isotope dilution geochronology (EARTHTIME Tracer Calibration Part I). Geochim. Cosmochim. Acta 164, 464–480 (2015). + +<|ref|>text<|/ref|><|det|>[[110, 508, 872, 632]]<|/det|> +39. McLean, N.M., Condon, D.J., Schoene, B. & Bowring, S.A. Evaluating uncertainties in the calibration of isotopic reference materials and multi-element isotopic tracers (EARTHTIME Tracer Calibration Part II). Geochim. Cosmochim. Acta 164, 481–501 (2015). + +<|ref|>text<|/ref|><|det|>[[110, 647, 852, 740]]<|/det|> +40. Macdonald, F.A., Schmitz, M.D., Strauss, J.V., Halverson, G.P., Gibson, T.M., Eyster, A., Cox, G., Mamrol, P., Crowley, J.L. Cryogenian of Yukon. Precambrian Res. 319, 114-143 (2018). + +<|ref|>text<|/ref|><|det|>[[110, 754, 845, 844]]<|/det|> +41. Jaffey, A. H., Flynn, K. F., Glendenin, L. E., Bentley, W. C. & Essling, A. M. Precision Measurement of Half-Lives and Specific Activities of U 235 and U 238. Phys. Rev. C 4, 1889–1906 (1971). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 866, 180]]<|/det|> +42. Schmitz, M. D. & Schoene, B. Derivation of isotope ratios, errors, and error correlations for U-Pb geochronology using \(^{205}\mathrm{Pb}\) - \(^{235}\mathrm{U}\) - \(^{233}\mathrm{U}\) )-spiked isotope dilution thermal ionization mass spectrometric data. Geochem. Geophys. 8, 1-20 (2007). + +<|ref|>text<|/ref|><|det|>[[110, 207, 877, 300]]<|/det|> +43. Steiger, R. H. & Jäger, E. Subcommission on geochronology: convention on the use of decay constants in go- and cosmochronology. Earth Plant. Sci. Lett. 36, 359-362 (1977). + +<|ref|>text<|/ref|><|det|>[[110, 327, 864, 418]]<|/det|> +44. Min, K., Mundil, R., Renne, P. R. & Ludwig, K. R. A test for systematic errors in \(^{40}\mathrm{Ar}\) / \(^{39}\mathrm{Ar}\) geochronology through comparison with U/Pb analysis of 1.1-Ga rhyolite. Geochim. Cosmochim. Acta 64, 73-98 (2000). + +<|ref|>text<|/ref|><|det|>[[110, 446, 836, 504]]<|/det|> +45. Audi, G., Bersillon, O., Blachot, J. & Wapstra, A. H. The NUBASE evaluation of nuclear and decay properties. Nuclear Physics A 729, 3-128 (2003). + +<|ref|>text<|/ref|><|det|>[[110, 531, 847, 621]]<|/det|> +46. Mercer, C. M. & Hiddes, K. V. Ar/Ar - a software tool to promote the robust comparison of K-Ar and \(^{40}\mathrm{Ar}\) / \(^{39}\mathrm{Ar}\) dates published using different decay, isotopic, and monitor-age parameters. Chem. Geol. 440, 148-163 (2016). + +<|ref|>sub_title<|/ref|><|det|>[[115, 652, 292, 671]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[110, 685, 883, 882]]<|/det|> +This material is based upon work supported by the National Science Foundation under Grant Nos. EAR- 1759200 and EAR- 1759353. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We thank the AGes program, members of the Boise State University Isotope Geology Laboratory for support with sample preparation, and B.D. Cramer for insightful discussions. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 316, 109]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[111, 123, 850, 145]]<|/det|> +J.E.T., I.A.U, and M.S. designed the research project as part of the AGeS2 + +<|ref|>text<|/ref|><|det|>[[111, 157, 830, 179]]<|/det|> +Geochronology Program. Sample material was provided by I.A.U. C.W. and J.E.T. + +<|ref|>text<|/ref|><|det|>[[111, 192, 790, 214]]<|/det|> +prepared samples and analyzed the data with help from M.S. J.E.T. wrote the + +<|ref|>text<|/ref|><|det|>[[111, 226, 849, 248]]<|/det|> +manuscript with support from I.A.U. Progress was overseen by I.A.U, the PhD thesis + +<|ref|>text<|/ref|><|det|>[[111, 261, 259, 281]]<|/det|> +advisor of J.E.T. + +<|ref|>sub_title<|/ref|><|det|>[[115, 297, 308, 317]]<|/det|> +## Competing Interests + +<|ref|>text<|/ref|><|det|>[[115, 332, 500, 353]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[115, 367, 327, 387]]<|/det|> +## Additional Information + +<|ref|>text<|/ref|><|det|>[[115, 402, 759, 423]]<|/det|> +Correspondence and requests for materials should be addressed to J.E.T. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 92, 625, 580]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 601, 870, 904]]<|/det|> +
Figure 1. Schematic volcanic stratigraphy and paleomagnetic sampling profiles of volcanic units emplaced during Oligocene bimodal volcanism in Northern Yemen (after ref. 5,10). Section abbreviations, from west to east, are: ESC: Escarpment, BM: Bayt Mawjan, A: Section A, BB: Bayt Baws, JS: Jabal Shahirah, SK: Shibam Kawkabam, WD: Wadi Dhar, and JK: Jabal Kura'a. Sites are annotated with magnetic polarity data5 where white and black are reverse and normal polarity, respectively. Sites outlined in boxes denote those dated by \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) (ref. 5,7,11) or \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) geochronology (data presented here) and ages are shown in detail Fig. 2. Ages and sites denoted with an asterisk (*) are from correlative units in Ethiopia7.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 90, 789, 899]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 880, 567]]<|/det|> +Figure 2. Composite stratigraphy of Northern Yemen bimodal flood volcanic units is shown using the average thickness of each unit \(^{10}\) . Four units have been correlated to Indian Ocean tephra layers \(^{28}\) and are annotated by the colored symbols. Paleomagnetic data \(^{5}\) are indicated where white = reverse polarity and black = normal polarity. Dashed lines show the approximate locations of the paleomagnetic reversals in the stratigraphy. Minor Unit #4 and AMPH 2 are from different sample localities and both underlie the Bayt Mawjan Ignmibrite, but their stratigraphic order relative to each other is unknown. Symbols for \(^{40}\) Ar/ \(^{39}\) Ar ages \(^{5,7,11}\) are colored based on polarity. The grey field highlights the \(^{40}\) Ar/ \(^{39}\) Ar and \(^{206}\) Pb/ \(^{238}\) U ages with associated uncertainties of two pulses of Afro- Arabian silicic volcanism. The Escarpment Ignmibrite, Green Tuff, SAM and Sana’a Ignmibrites, and Iftar Alkalb are a set of normal to reversed polarity that encompass the duration of the C11n.1r Subchron and are compared to the GPTS of Cande and Kent \(^{8}\) as reported the 2020 Geologic Time Scale \(^{36}\) . Benthic foraminiferal \(δ^{18}\) O and \(δ^{13}\) C curves are from the 2020 Geologic Time Scale \(^{36}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[118, 92, 816, 550]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 572, 850, 875]]<|/det|> +
Figure 3. \(^{40}\mathrm{Ar} / ^{39}\mathrm{Ar}\) and \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) ages for the main silici units from the Northern Yemen section of the Afro-Arabian volcanic province (subset A). The grey field highlights the ages and associated uncertainties \((2\sigma)\) of the Escarpment Ignmibrite, Green Tuff, SAM and Sana'a Ignmibrites, and Iftar Alkalb. Ranked single-zircon and \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) dates are shown for the Escarpment, SAM, and Sana'a Ignmibrites. Horizontal grey bars outlined in black indicate the weighted mean \(^{206}\mathrm{Pb} / ^{238}\mathrm{U}\) ages with 95% confidence interval. B. Minimum total eruptive volume DRE (km \(^3\) ) values are from on-land and correlated deep-sea tephra layers found in Ocean Drilling Program cores from the Indian Ocean, Leg \(115^{1,10,28}\) .
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 95, 680, 630]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 658, 879, 894]]<|/det|> +
Figure 4. Bivariate plots showing Th/Y versus Eu/Eu\* for zircon crystals are denoted by age, dating method, and inclusion in final age calculations. Zircons \(>33\) Ma (from preliminary LA-ICP-MS dating, average \(2\sigma\) uncertainty \(\pm 3\) Ma) are denoted by diamond symbols. Non-luminescent (CL-dark) zircon crystals from the Escarpment Ignimbrite and Iftar Alkalb are denoted by black symbols. Subsets B-E show Th/Y versus Eu/Eu\* in detail for the Escarpment Ignimbrite (subset B), SAM Ignimbrite (subset C), Sana'a Ignimbrite (subset D), and Iftar Alkalb (subset E).
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 43, 142, 68]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[70, 100, 643, 660]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 684, 114, 703]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[40, 725, 950, 884]]<|/det|> +Schematic volcanic stratigraphy and paleomagnetic sampling profiles of volcanic units emplaced during Oligocene bimodal volcanism in Northern Yemen (after ref. 5,10). Section abbreviations, from west to east, are: ESC: Escarpment, BM: Bayt 429 Mawjan, A: Section A, BB: Bayt Baws, JS: Jabal Shahirah, SK: Shibam Kawkabam, WD: Wadi Dhar, and JK: Jabal Kura'a. Sites are annotated with magnetic polarity data5 where white and black are reverse and normal polarity, respectively. Sites outlined in boxes denote those dated by 40Ar/39Ar (ref. 5,7,11) or 206Pb/238U geochronology (data presented here) and ages are shown in detail Fig. 2. Ages and sites denoted with an asterisk (\*) are from correlative units in Ethiopia7. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[65, 65, 650, 760]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 792, 118, 811]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[42, 833, 950, 945]]<|/det|> +Composite stratigraphy of Northern Yemen bimodal flood volcanic units is shown using the average thickness of each unit10. Four units have been correlated to Indian Ocean tephra layers28 and are annotated by the colored symbols. Paleomagnetic data5 are indicated where white = reverse polarity and black = normal polarity. Dashed lines show the approximate locations of the paleomagnetic reversals in the stratigraphy. Minor Unit #4 and AMPH 2 are from different sample localities and both underlie the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 45, 951, 202]]<|/det|> +Bayt Mawjan Ignimbrite, but their stratigraphic order relative to each other is unknown. Symbols for 40Ar/39Ar ages5,7,11 are colored based on polarity. The grey field highlights the 40Ar/39Ar and 206Pb/238U ages with associated uncertainties of two pulses of Afro- Arabian silicic volcanism. The Escarpment Ignimbrite, Green Tuff, SAM and Sana'a Ignimbrites, and Iftar Alkalb are a set of normal to reversed polarity that encompass the duration of the C11n.1r Subchron and are compared to the GPTS of Cande and Kent8 as reported the 2020 Geologic Time Scale36. Benthic foraminiferal o180 and o13C curves are from the 2020 Geologic Time Scale36 + +<|ref|>image<|/ref|><|det|>[[42, 207, 572, 550]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 570, 116, 589]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[40, 611, 950, 769]]<|/det|> +40Ar/39Ar and 206Pb/238U ages for the main silicic units from the Northern Yemen section of the Afro- Arabian volcanic province (subset A). The grey field highlights the ages and associated uncertainties (2) of the Escarpment Ignimbrite, Green Tuff, SAM and Sana'a Ignimbrites, and Iftar Alkalb. Ranked single- zircon and 206Pb/238U dates are shown for the Escarpment, SAM, and Sana'a Ignimbrites. Horizontal grey bars outlined in black indicate the weighted mean 206Pb/238U ages with 95% confidence interval. B. Minimum total eruptive volume DRE (km3) values are from on- land and correlated deep- sea tephra layers found in Ocean Drilling Program cores from the Indian Ocean, Leg 1151,10,28. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 70, 608, 600]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 633, 118, 652]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[41, 674, 949, 811]]<|/det|> +Bivariate plots showing Th/Y versus Eu/Eu\* for zircon crystals are denoted by age, dating method, and inclusion in final age calculations. Zircons \(>33\) Ma (from 4preliminary LA- ICP- MS dating, average 2 uncertainty \(\pm 3\) Ma) are denoted by diamond symbols. Non- luminescent (CL- dark) zircon crystals from the Escarpment Ignimbrite and Iftar Alkalb are denoted by black symbols. Subsets B- E show Th/Y versus Eu/Eu\* in detail for the Escarpment Ignimbrite (subset B), SAM Ignimbrite (subset C), Sana'a 468 Ignimbrite (subset D), and Iftar Alkalb (subset E). + +<|ref|>sub_title<|/ref|><|det|>[[44, 832, 311, 860]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 883, 764, 903]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 921, 377, 940]]<|/det|> +- SupplementaryInformation1.docx + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 46, 377, 92]]<|/det|> +SupplementaryInformation2.xlsxSupplementaryInformation3.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/images_list.json b/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..848fd31db9d315ee7478b305d66c03c14b531eba --- /dev/null +++ b/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/images_list.json @@ -0,0 +1,138 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1: The T. brucei ATP synthase structure with lipids and ligands.", + "footnote": [], + "bbox": [ + [ + 115, + 128, + 884, + 540 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2: Identification of conserved \\(\\mathbf{F}_0\\) subunits.", + "footnote": [], + "bbox": [ + [ + 117, + 81, + 880, + 258 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3: A divergent peripheral stalk allows high flexibility during rotary catalysis. a, N-terminal OSCP extension provides a permanent central stalk attachment, while the C-terminal extension provides a phylum-specific attachment to the divergent peripheral stalk. b, The C-terminal helices of subunits - \\(\\beta\\) and - \\(d\\) provide a permanent \\(\\mathrm{F_1}\\) attachment. c, Substeps of the \\(c\\) - ring during transition from rotational state 1 to 2. d, \\(\\mathrm{F_1}\\) motion accommodating steps shown in (c). After advancing along with the rotor to state 1e, the \\(\\mathrm{F_1}\\) rotates in the opposite direction when transitioning to state 2a. e, Tilting motion of \\(\\mathrm{F_1}\\) and accommodating bending of the peripheral stalk.", + "footnote": [], + "bbox": [ + [ + 115, + 295, + 880, + 700 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4: The lumenal half-channel contains ordered water molecules and is confined by an \\(\\mathbf{F}_0\\) -bound lipid. a, Subunit- \\(a\\) (green) with the matrix (orange) and lumenal (light blue) channels, and an ordered phosphatidylcholine (PC1; blue). E102 of the \\(c_{10}\\) -ring shown in grey. b, Close-up view of the highly conserved R146a and N209a, which coordinate two water molecules between helices H5-6a. c, Sideview of the lumenal channel with proton pathway (light blue) and confining phosphatidylcholine (blue). d, Chain of ordered water molecules in the lumenal channel. Distances between the W1-W5 (red) are 5.2, 3.9, 7.3 and 4.8 Å, respectively. e, The ordered waters extend to H155a, which likely mediates the transfer of protons to D202a.", + "footnote": [], + "bbox": [ + [ + 115, + 82, + 885, + 470 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5: The homotypic dimerization motif of subunit-g generates a conserved oligomerization module. a, Side view with dimerising subunits colored. b,c, The dimer interface is constituted by (b) subunit- \\(e^{\\prime}\\) contacting subunit- \\(a\\) in the membrane and subunit- \\(f\\) in the lumen, (c) subunits \\(e\\) and \\(g\\) from both monomers forming a subcomplex with bound lipids. d, Subunit- \\(g\\) and - \\(e\\) form a dimerization motif in the trypanosomal (type-IV) ATP synthase dimer (this study), the same structural element forms the oligomerization motif in the porcine ATP synthase tetramer. The structural similarity of the pseudo-dimer (i.e., two diagonal monomers from adjacent dimers) in the porcine structure with the trypanosomal dimer suggests that type I and IV ATP synthase dimers have evolved through divergence from a common ancestor. e, The dimeric subunit- \\(e / g\\) structures are conserved in pig (PDB 6ZNA) and T. brucei (this work) and contain a conserved GXXXG motif (orange) mediating interaction of transmembrane helices. f, Models of the ATP synthase dimers fitted into subtomogram averages of short oligomers \\(^{15}\\) : matrix view, left; cut-through, middle, lumenal view, right (EMD-3560).", + "footnote": [], + "bbox": [ + [ + 149, + 80, + 848, + 638 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Fig. 6: RNAi knockdown of subunit-g results in monomerization of ATP synthase. a, Growth curves of non-induced (solid lines) and tetracycline-induced (dashed lines) RNAi cell lines grown in the presence (black) or absence (brown) of glucose. The insets show relative levels of the respective target mRNA at indicated days post-induction (DPI) normalized to the levels of 18S rRNA (black bars) or \\(\\beta\\) -tubulin (white bars). b, Immunoblots of mitochondrial lysates from indicated RNAi cell lines resolved by BN-PAGE probed with antibodies against indicated ATP synthase subunits. c, Representative immunoblots of whole cell lysates from indicated RNAi cell lines probed with indicated antibodies. d, Quantification of three replicates of immunoblots in (c). Values were normalized to the signal of the loading control Hsp70 and to non-induced cells. Plots show means with standard deviations (SD).", + "footnote": [], + "bbox": [ + [ + 120, + 85, + 880, + 510 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Fig. 7: Monomerization of ATP synthase by subunit- \\(g\\) knockdown results in aberrant mitochondrial ultrastructure but does not abolish catalytic activity. a, Transmission electron micrographs of sections of non-induced or 4 days induced RNAi cell lines. Mitochondrial membranes and cristae are marked with blue and red arrowheads, respectively. Top panel shows examples of irregular, elongated and round cross-sections of mitochondria quantified in (b). b, Cristae numbers per vesicle from indicated induced (IND) or non-induced (NON) cell lines counted separately in irregular, elongated and round mitochondrial cross-section. Boxes and whiskers show \\(25^{\\mathrm{th}}\\) to \\(75^{\\mathrm{th}}\\) and \\(5^{\\mathrm{th}}\\) to \\(95^{\\mathrm{th}}\\) percentiles, respectively. The numbers of analysed cross-sections are indicated for each data point. Unpaired t-test, p-values are shown. c, Mitochondrial membrane polarization capacity of non-induced or RNAi-induced cell lines two and four DPI measured by Safranine O. Black and gray arrow indicate addition of ATP and oligomycin, respectively. d, ATP production in permeabilized non-induced (0) or RNAi-induced cells 2 and 4 DPI in the presence of indicated substrates and inhibitors. Error bars represent SD of three replicates. Gly3P, DL-glycerol phosphate; KCN, potassium cyanide; CATR, carboxyatractyloside", + "footnote": [], + "bbox": [ + [ + 175, + 239, + 822, + 626 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_8.jpg", + "caption": "Fig. 8: The subunit-e/g module is an ancestral oligomerization motif of ATP synthase. Schematic model of the evolution of type-I and IV ATP synthases. Mitochondrial ATP synthases are derived from a monomeric complex of proteobacterial origin. In a mitochondrial ancestor, acquisition of mitochondria-specific subunits, including the subunit-e/g module resulted in the assembly of ATP synthase double rows, the structural basis for cristae biogenesis. Through divergence, different ATP synthase architectures evolved, with the subunit-e/g module functioning as an oligomerization (type I) or dimerization (type IV) motif, resulting in distinct row assemblies between mitochondrial lineages.", + "footnote": [], + "bbox": [ + [ + 115, + 81, + 880, + 255 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Extended_Data_Figure_5.jpg", + "caption": "Extended Data Fig. 5 Bound detergents of the \\(\\mathbf{F}_0\\) region.", + "footnote": [], + "bbox": [], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Extended_Data_Figure_8.jpg", + "caption": "Extended Data Fig. 8 Phylogenetic distribution and sequence conservancy of subunit-e and -g.", + "footnote": [], + "bbox": [], + "page_idx": 26 + } +] \ No newline at end of file diff --git a/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54.mmd b/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54.mmd new file mode 100644 index 0000000000000000000000000000000000000000..01f182621f807971893211ea85c6cf25ae5524f7 --- /dev/null +++ b/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54.mmd @@ -0,0 +1,384 @@ + +# An ancestral interaction module promotes oligomerization in divergent mitochondrial ATP synthases + +Alexey Amunts ( amunts@scilifelab.se ) Stockholm University https://orcid.org/0000- 0002- 5302- 1740 + +Ondrej Gahura Institute of Parasitology, Biology Centre CAS https://orcid.org/0000- 0002- 2925- 4763 + +Alexander Muhleip Stockholm University https://orcid.org/0000- 0002- 1877- 2282 + +Carolina Hierro- Yap Institute of Parasitology, Biology Centre CAS + +Brian Panicucci Biology Centre + +Minal Jain Institute of Parasitology, Biology Centre CAS + +David Hollaus Institute of Parasitology, Biology Centre CAS https://orcid.org/0000- 0001- 7403- 6434 + +Martina Slapnickova Institute of Parasitology, Biology Centre CAS + +Alena Zikova Biology Centre https://orcid.org/0000- 0002- 8686- 0225 + +Article + +Keywords: + +Posted Date: December 30th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 1196040/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on October 11th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 33588- z. + +<--- Page Split ---> + +# 1.1.1.1.1.1.1.1.1.1.1 + +<--- Page Split ---> + +# An ancestral interaction module promotes oligomerization in divergent mitochondrial ATP synthases + +Ondřej Gahura1,†, Alexander Mühleip2,†, Carolina Hierro- Yap1,3, Brian Panicucci1, Minal Jain1,3, David Hollaus3, Martina Slapničková1, Alena Zíková1,3,*, Alexey Amunts2,4 + +1Institute of Parasitology, Biology Centre CAS, Ceske Budejovice, Czech Republic 2Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17165 Solna, Sweden + +3Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic + +4Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden + +5Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden + +6Correspondence to: azikova@paru.cas.cz; amunts@scilifelab.se + +7These authors contributed equally to this work. + +## Abstract + +Mitochondrial ATP synthase forms stable dimers arranged into oligomeric assemblies that generate the inner- membrane curvature essential for efficient energy conversion. Here, we report cryo- EM structures of the intact ATP synthase dimer from Trypanosoma brucei in ten different rotational states. The model consists of 25 subunits, including nine lineage- specific, as well as 36 lipids. The rotary mechanism is influenced by the divergent peripheral stalk, conferring a greater conformational flexibility. Proton transfer in the lumenal half- channel occurs via a chain of five ordered water molecules. The dimerization interface is formed by subunit- g that is critical for interactions but not for the catalytic activity. Although overall dimer architecture varies among eukaryotes, we find that subunit- g together with subunit- e form an ancestral oligomerization motif, which is shared between the trypanosomal and mammalian lineages. Therefore, our data defines the subunit- g/e module as a structural component determining ATP synthase oligomeric assemblies. + +<--- Page Split ---> + +Mitochondrial ATP synthase consists of the soluble \(\mathrm{F_1}\) and membrane- bound \(\mathrm{F_0}\) subcomplexes, and occurs in dimers that assemble into oligomers to induce the formation of inner- membrane folds, called cristae. The cristae are the sites for oxidative phosphorylation and energy conversion in eukaryotic cells. Dissociation of ATP synthase dimers into monomers results in the loss of native cristae architecture and impairs mitochondrial function \(^{1,2}\) . While cristae morphology varies substantially between organisms from different lineages, ranging from flat lamellar in opisthokonts to coiled tubular in ciliates and discoidal in euglenozoans \(^{3}\) , the mitochondrial ATP synthase dimers represent a universal occurrence to maintain the membrane shape \(^{4}\) . + +ATP synthase dimers of variable size and architecture, classified into types I to IV have recently been resolved by high- resolution cryo- EM studies. In the structure of the type- I ATP synthase dimer from mammals, the monomers are only weakly associated \(^{5,6}\) , and in yeast insertions in the membrane subunits form tighter contacts \(^{7}\) . The structure of the type- II ATP synthase dimer from the alga \*Polytomella\* sp. showed that the dimer interface is formed by phylum- specific components \(^{8}\) . The type- III ATP synthase dimer from the ciliate \*Tetrahymena\* thermophila is characterized by parallel rotary axes, and a substoichiometric subunit, as well as multiple lipids were identified at the dimer interface, while additional protein components that tie the monomers together are distributed between the matrix, transmembrane, and lumenal regions \(^{9}\) . The structure of the type- IV ATP synthase with native lipids from \*Euglena gracilis\* also showed that specific protein- lipid interactions contribute to the dimerization, and that the central and peripheral stalks interact with each other directly \(^{10}\) . Finally, a unique apicomplexan ATP synthase dimerizes via 11 parasite- specific components that contribute \(\sim 7000 \mathrm{\AA}^2\) buried surface area \(^{11}\) , and unlike all other ATP synthases, that assemble into rows, it associates in higher oligomeric states of pentagonal pyramids in the curved apical membrane regions. Together, the available structural data suggest a diversity of oligomerization, and it remains unknown whether common elements mediating these interactions exist or whether dimerization of ATP synthase occurred independently and multiple times in evolution \(^{4}\) . + +The ATP synthase of \*Trypanosoma brucei\*, a representative of kinetoplastids and an established medically important model organism causing the sleeping sickness, is highly divergent, exemplified by the pyramid- shaped \(\mathrm{F_1}\) head containing a phylum specific subunit \(^{12,13}\) . The dimers are sensitive to the lack of cardiolipin \(^{14}\) and form short left- handed helical segments that extend across the membrane ridge of the discoidal cristae \(^{15}\) . Uniquely among aerobic eukaryotes, the mammalian life cycle stage of \*T. brucei\* utilizes the reverse mode of ATP synthase, using the enzyme as a proton pump to maintain mitochondrial membrane potential at the expense of ATP \(^{16,17}\) . In contrast, the insect stages of the parasite employ the ATP- producing forward mode of the enzyme \(^{18,19}\) . + +Given the conservation of the core subunits, the different nature of oligomerization and the ability to test structural hypotheses biochemically, we reasoned that investigation of the \*T. brucei\* ATP synthase structure and function would provide the missing evolutionary link to understand how the monomers interact to form physiological dimers. Here, we address this question by combining structural, functional and evolutionary analysis of the \*T. brucei\* ATP synthase dimer. + +<--- Page Split ---> + +## Results + +## Cryo-EM structure of the T. brucei ATP synthase + +We purified ATP synthase dimers from cultured T. brucei procyclic trypomastigotes by affinity chromatography with a recombinant natural protein inhibitor TbIF \(^{20}\) , and subjected the sample to cryo- EM analysis (Extended Data Fig. 1 and 2). Using masked refinements, maps were obtained for the membrane region, the rotor, and the peripheral stalk. To describe the conformational space of the T. brucei ATP synthase, we resolved ten distinct rotary substates, which were refined to 3.5- 4.3 Å resolution. Finally, particles with both monomers in rotational state 1 were selected, and the consensus structure of the dimer was refined to 3.2 Å resolution (Extended Data Table 1, Extended Data Fig. 2). + +Unlike the wide- angle architecture of dimers found in animals and fungi, the T. brucei ATP synthase displays an angle of \(60^{\circ}\) between the two \(\mathrm{F}_{1} / \mathrm{c}\) - ring subcomplexes. The model of the T. brucei ATP synthase includes all 25 different subunits, nine of which are lineage- specific (Fig. 1a, Supplementary Video 1, Extended Data Fig. 3). We named the subunits according to the previously proposed nomenclature \(^{21 - 23}\) (Extended Data Table 2). In addition, we identified and modeled 36 bound phospholipids, including 24 cardiolipins (Extended Data Fig. 4). Both detergents used during purification, n- dodecyl \(\beta\) - D- maltoside ( \(\beta\) - DDM) and glyco- diosgenin (GDN) are also resolved in the periphery of the membrane region (Extended Data Fig. 5). + +In the catalytic region, \(\mathrm{F}_{1}\) is augmented by three copies of subunit p18, each bound to subunit- \(\alpha^{12,13}\) . Our structure shows that p18 is involved in the unusual attachment of \(\mathrm{F}_{1}\) to the peripheral stalk. The membrane region includes eight conserved \(\mathrm{F}_{0}\) subunits ( \(b\) , \(d\) , \(f\) , \(8\) , \(i / j\) , \(k\) , \(e\) , and \(g\) ) arranged around the central proton translocator subunit- \(a\) . We identified those subunits based on the structural similarity and matching topology to their yeast counterparts (Fig 2). For subunit- \(b\) , a single transmembrane helix superimposes well with \(b\mathrm{H}1\) from yeast and anchors the newly identified subunit- \(e\) and - \(g\) to the \(\mathrm{F}_{0}\) (Fig 2a); a long helix \(b\mathrm{H}2\) , which constitutes the central part of the peripheral stalk in other organisms is absent in \(T\) . brucei. The sequence of this highly reduced subunit- \(b\) shows \(18\%\) identity and \(40\%\) similarity to \(E\) . gracilis subunit- \(b^{10}\) , representing a diverged homolog (Extended Data Fig. 6). No alternative subunit- \(b^{24}\) is found in our structure. + +The membrane region contains a peripheral subcomplex, formed primarily by the phylum- specific ATPTB1,6,12 and ATPEG3 (Fig. 1b). It is separated from the conserved core by a membrane- intrinsic cavity, in which nine bound cardiolipins are resolved (Fig. 1c), and the C- terminus of ATPTB12 interacts with the lumenal \(\beta\) - barrel of the \(c_{10}\) - ring. In the cavity of the decameric \(c\) - ring near the matrix side, 10 Arg66c residues coordinate a ligand density, which is consistent with a pyrimidine ribonucleoside triphosphate (Fig. 1d). We assign this density as uridine- triphosphate (UTP), due to its large requirement in the mitochondrial RNA metabolism of African trypanosomes being a substrate for post- transcriptional RNA editing \(^{25}\) , and addition of poly- uridine tails to gRNAs and rRNAs \(^{26,27}\) , as well as due to low abundance of cytidine triphosphate (CTP) \(^{28}\) . The nucleotide base is inserted between two Arg82c residues, whereas the triphosphate region is coordinated by another five Arg82c residues, with Tyr79s and Asn76s providing asymmetric coordination contacts. The presence of a nucleotide inside the \(c\) - ring is + +<--- Page Split ---> + +surprising, given the recent reports of phospholipids inside the \(c\) - rings in mammals \(^{5,6}\) and ciliates \(^{9}\) , indicating that a range of different ligands can provide structural scaffolding. + +![](images/Figure_1.jpg) + +
Fig. 1: The T. brucei ATP synthase structure with lipids and ligands.
+ +a, Front and side views of the composite model with both monomers in rotational state 1. The two \(\mathrm{F}_1 / c_{10}\) - ring complexes, each augmented by three copies of the phylum- specific p18 subunit, are tied together at a \(60^{\circ}\) - angle. The membrane- bound \(\mathrm{F}_0\) region displays a unique architecture and is composed of both conserved and phylum- specific subunits. b, Side view of the \(\mathrm{F}_0\) region showing the lumenal interaction of the ten- stranded \(\beta\) - barrel of the \(c\) - ring (grey) with ATPTB12 (pale blue). The lipid- filled peripheral \(\mathrm{F}_0\) cavity is indicated. c, Close- up view of the bound lipids within the peripheral \(\mathrm{F}_0\) cavity with cryo- EM density shown. d, Top view into the decameric \(c\) - ring with a bound pyrimidine ribonucleoside triphosphate, assigned as UTP. Map density shown in transparent blue, interacting residues shown. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2: Identification of conserved \(\mathbf{F}_0\) subunits.
+ +a, Top view of the membrane region with \(T\) . brucei subunits (colored) overlaid with \(S\) . cerevisiae structure (gray transparent). Close structural superposition and matching topology allowed the assignment of conserved subunits based on matching topology and location. b, Superposition of subunits- \(e\) and - \(g\) with their \(S\) . cerevisiae counterparts (PDB 6B2Z) confirms their identity. + +## Peripheral stalk flexibility and distinct rotational states + +The trypanosomal peripheral stalk displays a markedly different architecture compared to its yeast and mammalian counterparts. In the opisthokont complexes, the peripheral stalk is organized around the long \(b\mathrm{H}2\) , which extends from the membrane \(\sim 15 \mathrm{nm}\) into the matrix and attaches to OSCP at the top of \(\mathrm{F}_1^{5,7}\) . By contrast, \(T\) . brucei lacks the canonical \(b\mathrm{H}2\) and instead, helices 5- 7 of divergent subunit- \(d\) and the C- terminal helix of extended subunit- 8 bind to a C- terminal extension of OSCP at the apical part of the peripheral stalk (Fig. 3a). The interaction between OSCP and subunit- \(d\) and - 8 is stabilized by soluble ATPTB3 and ATPTB4. The peripheral stalk is rooted to the membrane subcomplex by a transmembrane helix of subunit- 8, wrapped on the matrix side by helices 8- 11 of subunit- \(d\) . Apart from the canonical contacts at the top of \(\mathrm{F}_1\) , the peripheral stalk is attached to the \(\mathrm{F}_1\) via a euglenozoa- specific C- terminal extension of OSCP, which contains a disordered linker and a terminal helix hairpin extending between the \(\mathrm{F}_1\) - bound p18 and subunits - \(d\) and - 8 of the peripheral stalk (Fig. 3a, Supplementary Videos 2,3). Another interaction of \(\mathrm{F}_1\) with the peripheral stalk occurs between the stacked C- terminal helices of subunit- \(\beta\) and - \(d\) (Fig. 3b), the latter of which structurally belongs to \(\mathrm{F}_1\) and is connected to the peripheral stalk via a flexible linker. + +To assess whether the unusual peripheral stalk architecture influences the rotary mechanism, we analysed 10 classes representing different rotational states. The three main states (1- 3) result from a \(\sim 120^{\circ}\) rotation of the central stalk subunit- \(\gamma\) , and we identified five (1a- 1e), four (2a- 2d) and one (3) classes of the respective main states. The rotor positions of the rotational states 1a, 2a and 3 are related by steps of \(117^{\circ}\) , \(136^{\circ}\) and \(107^{\circ}\) , respectively. Throughout all the identified substeps of the rotational state 1 (classes 1a to 1e) the rotor turns by \(\sim 33^{\circ}\) , which corresponds approximately to the advancement by one subunit- \(c\) of the \(c_{10}\) - ring. While rotating along with the rotor, the \(\mathrm{F}_1\) headpiece lags behind, advancing by only \(\sim 13^{\circ}\) . During the following transition from 1e to 2a, the rotor advances by \(\sim 84^{\circ}\) , whereas the \(\mathrm{F}_1\) headpiece rotates \(\sim 22^{\circ}\) in the opposite direction (Fig. 3c,d). This generates a counter- directional torque between the two motors, + +<--- Page Split ---> + +which is consistent with a power- stroke mechanism. Albeit with small differences in step size, this mechanism is consistent with a previous observation in the Polytomella ATP synthase8. However, due to its large, rigid peripheral stalk, the Polytomella ATP synthase mainly displays rotational substeps, whereas the Trypanosoma \(\mathrm{F_1}\) also displays a tilting motion of \(\sim 8^{\circ}\) revealed by rotary states 1 and 2 (Fig. 3e, Supplementary Video 2). The previously reported hinge motion between the N- and C- terminal domains of \(\mathrm{OSCP^8}\) is not found in our structures, instead, the conformational changes of the \(\mathrm{F_1 / c_{10}}\) - ring subcomplex are accommodated by a \(5^{\circ}\) bending of the apical part of the peripheral stalk. (Fig. 3e, Supplementary Videos 2,3). Together, the structural data indicate that the divergent peripheral stalk attachment confers greater conformational flexibility to the \(T\) . brucei ATP synthase. + +![](images/Figure_3.jpg) + +
Fig. 3: A divergent peripheral stalk allows high flexibility during rotary catalysis. a, N-terminal OSCP extension provides a permanent central stalk attachment, while the C-terminal extension provides a phylum-specific attachment to the divergent peripheral stalk. b, The C-terminal helices of subunits - \(\beta\) and - \(d\) provide a permanent \(\mathrm{F_1}\) attachment. c, Substeps of the \(c\) - ring during transition from rotational state 1 to 2. d, \(\mathrm{F_1}\) motion accommodating steps shown in (c). After advancing along with the rotor to state 1e, the \(\mathrm{F_1}\) rotates in the opposite direction when transitioning to state 2a. e, Tilting motion of \(\mathrm{F_1}\) and accommodating bending of the peripheral stalk.
+ +<--- Page Split ---> + +Lumenal proton half- channel is insulated by a lipid and contains ordered water moleculesThe mechanism of proton translocation involves sequential protonation of E102 of subunits- \(c\) , rotation of the \(c_{10}\) - ring with neutralized E102c exposed to the phospholipid bilayer, and release of protons on the other side of the membrane. The sites of proton binding and release are separated by the conserved R146 contributed by the horizontal helix H5 of subunit- \(a\) and are accessible from the cristae lumen and mitochondrial matrix by aqueous half- channels (Fig. 4a). Together, R146 and the adjacent N209 coordinate a pair of water molecules in between helices H5 and H6 (Fig. 4b). A similar coordination has been observed in the Polytomella ATP synthase8. The coordination of water likely restricts the R146 to rotamers that extend towards the \(c\) - ring, with which it is thought to interact.In our structure, the lumenal half- channel is filled with a network of resolved water densities, ending in a chain of five ordered water molecules (W1- W5; Fig. 4c,d,e). The presence of ordered water molecules in the aqueous channel is consistent with a Grotthuss- type mechanism for proton transfer, which would not require long- distance diffusion of water molecules5. However, because some distances between the observed water molecules are too large for direct hydrogen bonding, proton transfer may involve both coordinated and disordered water molecules. The distance of 7 Å between the last resolved water (W1) and D202a, the conserved residue that is thought to transfer protons to the \(c\) - ring, is too long for direct proton transfer. Instead, it may occur via the adjacent H155a. Therefore, our structure resolves individual elements participating in proton transport (Fig. 4d,e).The lumenal proton half- channel in the mammalian5,6 and apicomplexan11 ATP synthase is lined by the transmembrane part of \(b\) H2, which is absent in T. brucei. Instead, the position of \(b\) H2 is occupied by a fully ordered phosphatidylcholine in our structure (PC1; Fig. 4a,c). Therefore, a bound lipid replaces a proteinaceous element in the proton path. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4: The lumenal half-channel contains ordered water molecules and is confined by an \(\mathbf{F}_0\) -bound lipid. a, Subunit- \(a\) (green) with the matrix (orange) and lumenal (light blue) channels, and an ordered phosphatidylcholine (PC1; blue). E102 of the \(c_{10}\) -ring shown in grey. b, Close-up view of the highly conserved R146a and N209a, which coordinate two water molecules between helices H5-6a. c, Sideview of the lumenal channel with proton pathway (light blue) and confining phosphatidylcholine (blue). d, Chain of ordered water molecules in the lumenal channel. Distances between the W1-W5 (red) are 5.2, 3.9, 7.3 and 4.8 Å, respectively. e, The ordered waters extend to H155a, which likely mediates the transfer of protons to D202a.
+ +## Subunit- \(g\) facilitates assembly of different ATP synthase oligomers + +Despite sharing a set of conserved \(\mathrm{F}_0\) subunits, the T. brucei ATP synthase dimer displays a markedly different dimer architecture compared to previously determined structures. First, its dimerization interface of \(3,600\mathrm{\AA}^2\) is smaller than that of the E. gracilis type- IV (10,000 \(\mathrm{\AA}^2\) ) and the T. thermophila type- III ATP synthases (16,000 \(\mathrm{\AA}^2\) ). Second, unlike mammalian and fungal ATP synthase, in which the peripheral stalks extend in the plane defined by the two rotary axes, in our structure the monomers are rotated such that the peripheral stalks are offset laterally on the opposite sides of the plane. Due to the rotated monomers, this architecture is associated with a specific dimerization interface, where two subunit- \(g\) copies interact homotypically on the \(\mathrm{C}_2\) symmetry axis (Fig. 5a, Supplementary Video 1). Both copies of H1- \(2_{\mathrm{g}}\) extend horizontally along the matrix side of the membrane, clamping against each other (Fig. 5c,e). This facilitates formation of contacts between an associated transmembrane helix + +<--- Page Split ---> + +of subunit- \(e\) with the neighbouring monomer via subunit- \(a\) ' in the membrane, and - \(f\) ' in the lumen, thereby further contributing to the interface (Fig. 5b). Thus, the ATP synthase dimer is assembled via the subunit- \(e / g\) module. The C- terminal part of the subunit- \(e\) helix extends into the lumen, towards the ten- stranded \(\beta\) - barrel of the \(c\) - ring (Extended Data Fig. 7a). The terminal 23 residues are disordered with poorly resolved density connecting to the detergent plug of the \(c\) - ring \(\beta\) - barrel (Extended Data Fig. 7b). This resembles the lumenal C- terminus of subunit- \(e\) in the bovine structure \(^5\) , indicating a conserved interaction with the \(c\) - ring. + +The \(e / g\) module is held together by four bound cardiolipins in the matrix leaflet, anchoring it to the remaining \(\mathrm{F}_0\) region (Fig. 5c). The head groups of the lipids are coordinated by polar and charged residues with their acyl chains filling a central cavity in the membrane region at the dimer interface (Fig 5c, Extended Data Fig. 4f). Cardiolipin binding has previously been reported to be obligatory for dimerization in secondary transporters \(^{29}\) and the depletion of cardiolipin synthase resulted in reduced levels of ATP synthase in bloodstream trypanosomes \(^{14}\) . + +Interestingly, for yeasts, early blue native gel electrophoresis \(^{30}\) and subtomogram averaging studies \(^{2}\) suggested subunit- \(g\) as potentially dimer- mediating, however the \(e / g\) modules are located laterally opposed on either side of the dimer long axis, in the periphery of the complex, \(\sim 8.5 \mathrm{nm}\) apart from each other. Because the \(e / g\) modules do not interact directly within the yeast ATP synthase dimer, they have been proposed to serve as membrane- bending elements, whereas the major dimer contacts are formed by subunit- \(a\) and - \(i / j^{7}\) . In mammals, the \(e / g\) module occupies the same position as in yeasts, forming the interaction between two diagonal monomers in a tetramer \(^{5,6,31}\) , as well as between parallel dimers \(^{32}\) . The comparison with our structure shows that the overall organization of the intra- dimeric trypanosomal and inter- dimeric mammalian \(e / g\) module is structurally similar (Fig. 5d). Furthermore, kinetoplastid parasites and mammals share conserved GXXXG motifs in subunit- \(e^{33}\) and - \(g\) (Extended Data Fig. 8), which allow close interaction of their transmembrane helices (Fig. 5e), providing further evidence for subunit homology. However, while the mammalian ATP synthase dimers are arranged perpendicularly to the long axis of their rows along the edge of cristae \(^{34}\) , the \(T\) . brucei dimers on the rims of discoidal cristae are inclined \(\sim 45^{\circ}\) to the row axis \(^{15}\) . Therefore, the \(e / g\) module occupies equivalent positions in the rows of both evolutionary distant groups (Fig. 5f and reference 32). + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5: The homotypic dimerization motif of subunit-g generates a conserved oligomerization module. a, Side view with dimerising subunits colored. b,c, The dimer interface is constituted by (b) subunit- \(e^{\prime}\) contacting subunit- \(a\) in the membrane and subunit- \(f\) in the lumen, (c) subunits \(e\) and \(g\) from both monomers forming a subcomplex with bound lipids. d, Subunit- \(g\) and - \(e\) form a dimerization motif in the trypanosomal (type-IV) ATP synthase dimer (this study), the same structural element forms the oligomerization motif in the porcine ATP synthase tetramer. The structural similarity of the pseudo-dimer (i.e., two diagonal monomers from adjacent dimers) in the porcine structure with the trypanosomal dimer suggests that type I and IV ATP synthase dimers have evolved through divergence from a common ancestor. e, The dimeric subunit- \(e / g\) structures are conserved in pig (PDB 6ZNA) and T. brucei (this work) and contain a conserved GXXXG motif (orange) mediating interaction of transmembrane helices. f, Models of the ATP synthase dimers fitted into subtomogram averages of short oligomers \(^{15}\) : matrix view, left; cut-through, middle, lumenal view, right (EMD-3560).
+ +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Fig. 6: RNAi knockdown of subunit-g results in monomerization of ATP synthase. a, Growth curves of non-induced (solid lines) and tetracycline-induced (dashed lines) RNAi cell lines grown in the presence (black) or absence (brown) of glucose. The insets show relative levels of the respective target mRNA at indicated days post-induction (DPI) normalized to the levels of 18S rRNA (black bars) or \(\beta\) -tubulin (white bars). b, Immunoblots of mitochondrial lysates from indicated RNAi cell lines resolved by BN-PAGE probed with antibodies against indicated ATP synthase subunits. c, Representative immunoblots of whole cell lysates from indicated RNAi cell lines probed with indicated antibodies. d, Quantification of three replicates of immunoblots in (c). Values were normalized to the signal of the loading control Hsp70 and to non-induced cells. Plots show means with standard deviations (SD).
+ +<--- Page Split ---> + +## Subunit- \(g\) retains the dimer but is not essential for the catalytic monomer + +To validate structural insights, we knocked down each individual \(\mathrm{F_0}\) subunit by inducible RNA interference (RNAi). All target mRNAs dropped to \(5 - 20\%\) of their original levels after two and four days of induction (Fig. 6a and Extended Data Fig. 9a). Western blot analysis of wholecell lysates resolved by denaturing electrophoresis revealed decreased levels of \(\mathrm{F_0}\) subunits ATPB1 and - \(d\) suggesting that the integrity of the \(\mathrm{F_0}\) moiety depends on the presence of other \(\mathrm{F_0}\) subunits (Fig. 6c,d). Immunoblotting of mitochondrial complexes resolved by blue native polyacrylamide gel electrophoresis (BN- PAGE) with antibodies against \(\mathrm{F_1}\) and \(\mathrm{F_0}\) subunits revealed a strong decrease or nearly complete loss of dimeric and monomeric forms of ATP synthases four days after induction of RNAi of most subunits \((b, e, f, i / j, k, 8\) , ATPTB3, ATPTB4, ATPTB6, ATPTB11, ATPTB12, ATPEG3 and ATPEG4), documenting an increased instability of the enzyme or defects in its assembly. Simultaneous accumulation in \(\mathrm{F_1}\) - ATPase, as observed by BN- PAGE, demonstrated that the catalytic moiety remains intact after the disruption of the peripheral stalk or the membrane subcomplex (Fig. 6b,c,d and Extended Data Fig. 9b). + +In contrast to the other targeted \(\mathrm{F_0}\) subunits, the downregulation of subunit- \(g\) with RNAi resulted in a specific loss of dimeric complexes with concomitant accumulation of monomers (Fig. 6b), indicating that it is required for dimerization, but not for the assembly and stability of the monomeric \(\mathrm{F_1F_0}\) ATP synthase units. Transmission electron microscopy of thin cell sections revealed that the ATP synthase monomerization in the subunit- \(g^{\mathrm{RNAi}}\) cell line had the same effect on mitochondrial ultrastructure as nearly complete loss of monomers and dimers upon knockdown of subunit- 8. Both cell lines exhibited decreased cristae counts and aberrant cristae morphology (Fig. 7a,b), including the appearance of round shapes reminiscent of structures detected upon deletion of subunit- \(g\) or - e in Saccharomyces cerevisiae1. These results indicate that monomerization prevents the trypanosomal ATP synthase from assembling into short helical rows on the rims of the discoidal cristae15, as has been reported for impaired oligomerization in counterparts from other eukaryotes2,35. + +Despite the altered mitochondrial ultrastructure, the subunit- \(g^{\mathrm{RNAi}}\) cells showed only a very mild growth phenotype, in contrast to all other RNAi cell lines that exhibited steadily slowed growth from day three to four after the RNAi induction (Fig. 7a, Extended Data Fig. 9a). This is consistent with the growth defects observed after the ablation of \(\mathrm{F_0}\) subunit ATPTB19 and \(\mathrm{F_1}\) subunits- \(\alpha\) and \(\mathrm{p18^{12}}\) . Thus, the monomerization of ATP synthase upon subunit- \(g\) ablation had only a negligible effect on the fitness of trypanosomes cultured in glucose- rich medium, in which ATP production by substrate level phosphorylation partially compensates for compromised oxidative phosphorylation36. + +Measurement of oligomycin- sensitive ATP- dependent mitochondrial membrane polarization by safranin O assay in permeabilized cells showed that the proton pumping activity of the ATP synthase in the induced subunit- \(g^{\mathrm{RNAi}}\) cells is negligibly affected, demonstrating that the monomerized enzyme is catalytically functional. By contrast, RNAi downregulation of subunit- 8, ATPTB4 and ATPTB11, and ATPTB1 resulted in a strong decline of the mitochondrial membrane polarization capacity, consistent with the loss of both monomeric and dimeric ATP synthase forms (Fig. 7c). Accordingly, knockdown of the same subunits resulted in inability to produce ATP by oxidative phosphorylation (Fig. 7d). However, upon subunit- \(g\) + +<--- Page Split ---> + +ablation the ATP production was affected only partially, confirming that the monomerized ATP synthase remains catalytically active. The \(\sim 50\%\) drop in ATP production of subunit- \(g^{\mathrm{RNAi}}\) cells can be attributed to the decreased oxidative phosphorylation efficiency due to the impaired cristae morphology. Indeed, when cells were cultured in the absence of glucose, enforcing the need for oxidative phosphorylation, knockdown of subunit- \(g\) results in a growth arrest, albeit one to two days later than knockdown of all other tested subunits (Fig. 6a). The data show that dimerization is critical when oxidative phosphorylation is the predominant source of ATP. + +![](images/Figure_7.jpg) + +
Fig. 7: Monomerization of ATP synthase by subunit- \(g\) knockdown results in aberrant mitochondrial ultrastructure but does not abolish catalytic activity. a, Transmission electron micrographs of sections of non-induced or 4 days induced RNAi cell lines. Mitochondrial membranes and cristae are marked with blue and red arrowheads, respectively. Top panel shows examples of irregular, elongated and round cross-sections of mitochondria quantified in (b). b, Cristae numbers per vesicle from indicated induced (IND) or non-induced (NON) cell lines counted separately in irregular, elongated and round mitochondrial cross-section. Boxes and whiskers show \(25^{\mathrm{th}}\) to \(75^{\mathrm{th}}\) and \(5^{\mathrm{th}}\) to \(95^{\mathrm{th}}\) percentiles, respectively. The numbers of analysed cross-sections are indicated for each data point. Unpaired t-test, p-values are shown. c, Mitochondrial membrane polarization capacity of non-induced or RNAi-induced cell lines two and four DPI measured by Safranine O. Black and gray arrow indicate addition of ATP and oligomycin, respectively. d, ATP production in permeabilized non-induced (0) or RNAi-induced cells 2 and 4 DPI in the presence of indicated substrates and inhibitors. Error bars represent SD of three replicates. Gly3P, DL-glycerol phosphate; KCN, potassium cyanide; CATR, carboxyatractyloside
+ +<--- Page Split ---> + +## Discussion + +Our structure of the mitochondrial ATP synthase dimer from the mammalian parasite T. brucei offers new insight into the mechanism of membrane shaping, rotary catalysis, and proton transfer. Considering that trypanosomes belong to an evolutionarily divergent group of Kinetoplastida, the ATP synthase dimer has several interesting features that differ from other dimer structures. The subunit- \(b\) found in bacterial and other mitochondrial F- type ATP synthases appears to be highly reduced to a single transmembrane helix \(b\mathrm{H}1\) . The long \(b\mathrm{H}2\) , which constitutes the central part of the peripheral stalk in other organisms, and is also involved in the composition of the lumenal proton half- channel, is completely absent in \(T\) . brucei. Interestingly, the position of \(b\mathrm{H}2\) in the proton half channel is occupied by a fully ordered phosphatidylcholine molecule that replaces a well- conserved proteinaceous element in the proton path. Lack of the canonical \(b\mathrm{H}2\) also affects composition of the peripheral stalk in which the divergent subunit- \(d\) and subunit- \(8\) binds directly to a C- terminal extension of OSCP, indicating a remodeled peripheral stalk architecture. The peripheral stalk contacts the \(\mathrm{F}_1\) headpiece at several positions conferring greater conformational flexibility to the ATP synthase. + +Using the structural and functional data, we also identified a conserved structural element of the ATP synthase that is responsible for its multimerization. Particularly, subunit- \(g\) is required for the dimerization, but dispensable for the assembly of the \(\mathrm{F}_1\mathrm{F}_0\) monomers. Although the monomerized enzyme is catalytically competent, the inability to form dimers results in defective cristae structure, and consequently leads to compromised oxidative phosphorylation and cease of proliferation. The cristae- shaping properties of mitochondrial ATP synthase dimers are critical for sufficient ATP production by oxidative phosphorylation, but not for other mitochondrial functions, as demonstrated by the lack of growth phenotype of subunit- \(g^{\mathrm{RNAi}}\) cells in the presence of glucose. Thus, trypanosomal subunit- \(g\) depletion strain represents an experimental tool to assess the roles of the enzyme's primary catalytic function and mitochondria- specific membrane- shaping activity, highlighting the importance of the latter for oxidative phosphorylation. + +Based on our data and previously published structures, we propose an ancestral state with double rows of ATP synthase monomers connected by \(e / g\) modules longitudinally and by other \(\mathrm{F}_0\) subunits transversally. During the course of evolution, different pairs of adjacent ATP synthase monomer units formed stable dimers in individual lineages (Fig. 8). This gave rise to the highly divergent type- I and type- IV ATP synthase dimers with subunit- \(e / g\) modules serving either as oligomerization or dimerization motives, respectively. Because trypanosomes belong to the deep- branching eukaryotic supergroup Discoba, the proposed arrangement might have been present in the last eukaryotic common ancestor. Although sequence similarity of subunit- \(g\) is low and restricted to the single transmembrane helix, we found homologs of subunit- \(g\) in addition to Opisthokonta and Discoba also in Archaeplastida and Amoebozoa, which represent other eukaryotic supergroups, thus supporting the ancestral role in oligomerization (Extended Data Fig. 8). Taken together, our analysis reveals that mitochondrial ATP synthases that display markedly diverged architecture share the ancestral structural module that promotes oligomerization. + +<--- Page Split ---> +![](images/Figure_8.jpg) + +
Fig. 8: The subunit-e/g module is an ancestral oligomerization motif of ATP synthase. Schematic model of the evolution of type-I and IV ATP synthases. Mitochondrial ATP synthases are derived from a monomeric complex of proteobacterial origin. In a mitochondrial ancestor, acquisition of mitochondria-specific subunits, including the subunit-e/g module resulted in the assembly of ATP synthase double rows, the structural basis for cristae biogenesis. Through divergence, different ATP synthase architectures evolved, with the subunit-e/g module functioning as an oligomerization (type I) or dimerization (type IV) motif, resulting in distinct row assemblies between mitochondrial lineages.
+ +## Materials and Methods + +## Cell culture and isolation of mitochondria + +T. brucei procyclic strains were cultured in SDM-79 medium supplemented with \(10\%\) (v/v) fetal bovine serum. For growth curves in glucose-free conditions, cells were grown in SDM-80 medium with \(10\%\) dialysed FBS. RNAi cell lines were grown in presence of \(2.5 \mu \mathrm{g / ml}\) phleomycin and \(1 \mu \mathrm{g / ml}\) puromycin. For ATP synthase purification, mitochondria were isolated from the Lister strain 427. Typically, \(1.5 \times 10^{11}\) cells were harvested, washed in \(20 \mathrm{mM}\) sodium phosphate buffer pH 7.9 with \(150 \mathrm{mM}\) NaCl and \(20 \mathrm{mM}\) glucose, resuspended in hypotonic buffer \(1 \mathrm{mM}\) Tris-HCl pH 8.0, \(1 \mathrm{mM}\) EDTA, and disrupted by 10 strokes in a 40-ml Dounce homogenizer. The lysis was stopped by immediate addition of sucrose to \(0.25 \mathrm{M}\) . Crude mitochondria were pelleted (15 min at \(16,000 \mathrm{xg}\) , \(4^{\circ}\mathrm{C}\) ), resuspended in \(20 \mathrm{mM}\) Tris-HCl pH 8.0, \(250 \mathrm{mM}\) sucrose, \(5 \mathrm{mM}\) MgCl₂, \(0.3 \mathrm{mM}\) CaCl₂ and treated with \(5 \mu \mathrm{g / ml}\) DNase I. After 60 min on ice, one volume of the STE buffer (20 mM Tris-HCl pH 8.0, \(250 \mathrm{mM}\) sucrose, \(2 \mathrm{mM}\) EDTA) was added and mitochondria were pelleted (15 min at \(16000 \mathrm{xg}\) , \(4^{\circ}\mathrm{C}\) ). The pellet was resuspended in \(60\%\) (v/v) Percoll in STE and loaded on six linear 10-35% Percoll gradients in STE in polycarbonate tubes for SW28 rotor (Beckman). Gradients were centrifuged for 1 h at \(24,000 \mathrm{rpm}\) , \(4^{\circ}\mathrm{C}\) . The middle phase containing mitochondrial vesicles (15-20 ml per tube) was collected, washed four times in the STE buffer, and pellets were snap-frozen in liquid nitrogen and stored at \(- 80^{\circ}\mathrm{C}\) . + +## Plasmid construction and generation of RNAi cell lines + +To downregulate ATP synthase subunits by RNAi, DNA fragments corresponding to individual target sequences were amplified by PCR from Lister 427 strain genomic DNA using + +<--- Page Split ---> + +forward and reverse primers extended with restriction sites XhoI&KpnI and XbaI&BamHI, respectively (Extended Data Table 3). Each fragment was inserted into the multiple cloning sites 1 and 2 of pAZ0055 vector, derived from pRPHYG- iSL (courtesy of Sam Alsford) by replacement of hygromycin resistance gene with phleomycin resistance gene, with restriction enzymes KpnI/BamHI and XhoI/XbaI, respectively. Resulting constructs with tetracycline inducible T7 polymerase driven RNAi cassettes were linearized with NotI and transfected into a cell line derived from the Lister strain 427 by integration of the SmOx construct for expression of T7 polymerase and the tetracycline repressor \(^{37}\) into the \(\beta\) - tubulin locus. RNAi was induced in selected semi-clonal populations by addition of \(1 \mu \mathrm{g / ml}\) tetracycline and the downregulation of target mRNAs was verified by quantitative RT- PCR 2 and 4 days post induction. The total RNA isolated by an RNeasy Mini Kit (Qiagen) was treated with \(2 \mu \mathrm{g}\) of DNase I, and then reverse transcribed to cDNA with TaqMan Reverse Transcription kit (Applied Biosciences). qPCR reactions were set with Light Cycler 480 SYBR Green I Master mix (Roche), \(2 \mu \mathrm{l}\) of cDNA and \(0.3 \mu \mathrm{M}\) primers (Extended Data Table 3), and run on LightCycler 480 (Roche). Relative expression of target genes was calculated using - \(\Delta \Delta \mathrm{Ct}\) method with 18S rRNA or \(\beta\) - tubulin as endogenous reference genes and normalized to noninduced cells. + +## Denaturing and blue native polyacrylamide electrophoresis and immunoblotting + +Whole cell lysates for denaturing sodium dodecyl sulphate polyacrylamide electrophoresis (SDS- PAGE) were prepared from cells resuspended in PBS buffer ( \(10 \mathrm{mM}\) phosphate buffer, \(130 \mathrm{mM}\) NaCl, pH 7.3) by addition of \(3 \mathrm{x}\) Laemmli buffer ( \(150 \mathrm{mM}\) Tris pH 6.8, \(300 \mathrm{mM}\) 1,4- dithiothreitol, \(6\%\) (w/v) SDS, \(30\%\) (w/v) glycerol, \(0.02\%\) (w/v) bromophenol blue) to final concentration of \(1 \times 10^{7}\) cells in \(30 \mu \mathrm{l}\) . The lysates were boiled at \(97^{\circ} \mathrm{C}\) for \(10 \mathrm{min}\) and stored at \(- 20^{\circ} \mathrm{C}\) . For immunoblotting, lysates from \(3 \times 10^{6}\) cells were separated on \(4 - 20\%\) gradient Tris- glycine polyacrylamide gels (BioRad 4568094), electroblotted onto a PVDF membrane (Pierce 88518), and probed with respective antibodies (Extended Data Table 4). Membranes were incubated with the Clarity Western ECL substrate (BioRad 1705060EM) and chemiluminescence was detected on a ChemiDoc instrument (BioRad). Band intensities were quantified densitometrically using the ImageLab software. The levels of individual subunits were normalized to the signal of mHsp70. + +Blue native PAGE (BN- PAGE) was performed as described earlier \(^{12}\) with following modifications. Crude mitochondrial vesicles from \(2.5 \times 10^{8}\) cells were resuspended in \(40 \mu \mathrm{l}\) of Solubilization buffer A ( \(2 \mathrm{mM}\) \(\epsilon\) - aminocaproic acid (ACA), \(1 \mathrm{mM}\) EDTA, \(50 \mathrm{mM}\) NaCl, \(50 \mathrm{mM}\) Bis- Tris/HCl, pH 7.0) and solubilized with \(2\%\) (w/v) dodecylamtolamide ( \(\beta\) - DDM) for \(1 \mathrm{h}\) on ice. Lysates were cleared at \(16,000 \mathrm{g}\) for \(30 \mathrm{min}\) at \(4^{\circ} \mathrm{C}\) and their protein concentration was estimated using bicinchoninic acid assay. For each time point, a volume of mitochondrial lysate corresponding to \(4 \mu \mathrm{g}\) of total protein was mixed with \(1.5 \mu \mathrm{l}\) of loading dye ( \(500 \mathrm{mM}\) ACA, \(5\%\) (w/v) Coomassie Brilliant Blue G- 250) and \(5\%\) (w/v) glycerol and with \(1 \mathrm{M}\) ACA until a final volume of \(20 \mu \mathrm{l}\) /well, and resolved on a native PAGE 3- 12% Bis- Tris gel (Invitrogen). After the electrophoresis ( \(3 \mathrm{h}\) , \(140 \mathrm{V}\) , \(4^{\circ} \mathrm{C}\) ), proteins were transferred by electroblotting onto a + +<--- Page Split ---> + +PVDF membrane (2 h, 100 V, \(4^{\circ}\mathrm{C}\) , stirring), followed by immunodetection with an appropriate antibody (Extended Data Table 4). + +## Mitochondrial membrane polarization measurement + +The capacity to polarize mitochondrial membrane was determined fluorometrically employing safranin O dye (Sigma S2255) in permeabilized cells. For each sample, \(2 \times 10^{7}\) cells were harvested and washed with ANT buffer (8 mM KCl, 110 mM K- gluconate, 10 mM NaCl, 10 mM free- acid Hepes, 10 mM \(\mathrm{K_2HPO_4}\) , 0.015 mM EGTA potassium salt, 10 mM mannitol, 0.5 mg/ml fatty acid- free BSA, 1.5 mM \(\mathrm{MgCl_2}\) , pH 7.25). The cells were permeabilized by \(8 \mu \mathrm{M}\) digitonin in 2 ml of ANT buffer containing \(5 \mu \mathrm{M}\) safranin O. Fluorescence was recorded for 700 s in a Hitachi F- 7100 spectrofluorimeter (Hitachi High Technologies) at a 5- Hz acquisition rate, using 495 nm and 585 nm excitation and emission wavelengths, respectively. \(1 \mathrm{mM}\) ATP (PanReac AppliChem A1348,0025) and \(10 \mu \mathrm{g / ml}\) oligomycin (Sigma O4876) were added after 230 s and 500 s, respectively. Final addition of the uncoupler SF 6847 (250 nM; Enzo Life Sciences BML- EI215- 0050) served as a control for maximal depolarization. All experiments were performed at room temperature and constant stirring. + +## ATP production assay + +ATP production assayATP production in digitonin- isolated mitochondria was performed as described previously38. Briefly, \(1 \times 10^{8}\) cells per time point were lysed in SoTE buffer (600 mM sorbitol, 2 mM EDTA, 20 mM Tris- HCl, pH 7.75) containing 0.015% (w/v) digitonin for 5 min on ice. After centrifugation (3 min, 4,000 g, \(4^{\circ}\mathrm{C}\) ), the soluble cytosolic fraction was discarded and the organellar pellet was resuspended in 75 \(\mu \mathrm{l}\) of ATP production assay buffer (600 mM sorbitol, 10 mM \(\mathrm{MgSO_4}\) , 15 mM potassium phosphate buffer pH 7.4, 20 mM Tris- HCl pH 7.4, 2.5 mg/ml fatty acid- free BSA). ATP production was induced by addition of 20 mM DL- glycerol phosphate (sodium salt) and 67 \(\mu \mathrm{M}\) ADP. Control samples were preincubated with the inhibitors potassium cyanide (1 mM) and carboxyatractyloside (6.5 \(\mu \mathrm{M}\) ) for 10 min at room temperature. After 30 min at room temperature, the reaction was stopped by addition of 1.5 \(\mu \mathrm{l}\) of 70% perchloric acid. The concentration of ATP was estimated using the Roche ATP Bioluminescence Assay Kit HS II in a Tecan Spark plate reader. The luminescence values of the RNAi induced samples were normalized to that of the corresponding noninduced sample. + +## Thin sectioning and transmission electron microscopy + +The samples were centrifuged and pellet was transferred to the specimen carriers which were completed with 20% BSA and immediately frozen using high pressure freezer Leica EM ICE (Leica Microsystems). Freeze substitution was performed in the presence of 2% osmium tetroxide diluted in 100% acetone at \(- 90^{\circ}\mathrm{C}\) . After 96 h, specimens were warmed to \(- 20^{\circ}\mathrm{C}\) at a slope 5 \({}^{\circ}\mathrm{C / h}\) . After the next 24 h, the temperature was increased to 3\({}^{\circ}\mathrm{C}\) (3\({}^{\circ}\mathrm{C / h}\) ). At room temperature, samples were washed in acetone and infiltrated with 25%, 50%, 75% acetone/resin EMbed 812 (EMS) mixture 1 h at each step. Finally, samples were infiltrated in 100% resin and polymerized at 60\({}^{\circ}\mathrm{C}\) for 48h. Ultrathin sections (70 nm) were cut using a + +<--- Page Split ---> + +diamond knife, placed on copper grids and stained with uranyl acetate and lead citrate. TEM micrographs were taken with Mega View III camera (SIS) using a JEOL 1010 TEM operating at an accelerating voltage of \(80\mathrm{kV}\) . + +## Purification of T. brucei ATP synthase dimers + +Mitochondria from \(3\mathrm{x}10^{11}\) cells were lysed by \(1\%\) (w/v) \(\beta\) - DDM in \(60\mathrm{ml}\) of \(20\mathrm{mM}\) Bis- tris propane pH 8.0 with \(10\%\) glycerol and EDTA- free Complete protease inhibitors (Roche) for \(20\mathrm{min}\) at \(4^{\circ}\mathrm{C}\) . The lysate was cleared by centrifugation at \(30,000\mathrm{xg}\) for \(20\mathrm{min}\) at \(4^{\circ}\mathrm{C}\) and adjusted to pH 6.8 by drop- wise addition of \(1\mathrm{M}3\) - (N- morpholino) propanesulfonic acid pH 5.9. Recombinant TbIF1 without dimerization region, whose affinity to \(\mathrm{F_1}\) - ATPase was increased by N- terminal truncation and substitution of tyrosine 36 with tryptophan20, with a C- terminal glutathione S- transferase (GST) tag (TbIF1(9- 64)- Y36W- GST) was added in approximately 10- fold molar excess over the estimated content of ATP synthase. Binding of TbIF1 was facilitated by addition of neutralized \(2\mathrm{mM}\) ATP with \(4\mathrm{mM}\) magnesium sulphate. After \(5\mathrm{min}\) , sodium chloride was added to \(100\mathrm{mM}\) , the lysate was filtered through a \(0.2\mu \mathrm{m}\) syringe filter and immediately loaded on \(5\mathrm{ml}\) GSTrap HP column (Cytiva) equilibrated in 20 mM Bis- Tris- Propane pH 6.8 binding buffer containing \(0.1\%\) (w/v) glyco- diosgenin (GDN; Avanti Polar Lipids), \(10\%\) (v/v) glycerol, \(100\mathrm{mM}\) sodium chloride, \(1\mathrm{mM}\) tris(2- carboxyethyl)phosphine (TCEP), \(1\mathrm{mM}\) ATP, \(2\mathrm{mM}\) magnesium sulphate, \(15\mu \mathrm{g / ml}\) cardiolipin, \(50\mu \mathrm{g / ml}\) 1- palmitoyl- 2- oleoyl- sn- glycero- 3- phosphocholine (POPC), \(25\mu \mathrm{g / ml}\) 1- palmitoyl- 2- oleoyl- sn- glycero- 3- phosphoethanolamine (POPE) and \(10\mu \mathrm{g / ml}\) 1- palmitoyl- 2- oleoyl- sn- glycero- 3- [phospho- rac- (1- glycerol)] (POPG). All phospholipids were purchased from Avanti Polar Lipids (catalog numbers 840012C, 850457C, 850757C and 840757, respectively). ATP synthase was eluted with a gradient of \(20\mathrm{mM}\) reduced glutathione in Tris pH 8.0 buffer containing the same components as the binding buffer. Fractions containing ATP synthase were pooled and concentrated to \(150\mu \mathrm{l}\) on Vivaspin centrifugal concentrator with 30 kDa molecular weight cut- off. The sample was fractionated by size exclusion chromatography on a Superose 6 Increase 3.2/300 GL column (Cytiva) equilibrated in a buffer containing 20 mM Tris pH 8.0, \(100\mathrm{mM}\) sodium chloride, \(2\mathrm{mM}\) magnesium chloride, \(0.1\%\) (w/v) GDN, \(3.75\mu \mathrm{g / ml}\) cardiolipin, \(12.5\mu \mathrm{g / ml}\) POPC, \(6.25\mu \mathrm{g / ml}\) POPE and \(2.5\mu \mathrm{g / ml}\) POPG at 0.03 ml/min. Fractions corresponding to ATP synthase were pooled, supplemented with \(0.05\%\) (w/v) \(\beta\) - DDM that we and others experimentally found to better preserve dimer assemblies in cryo- EM39, and concentrated to \(50\mu \mathrm{l}\) . + +## Preparation of cryo-EM grids and data collection + +Samples were vitrified on glow- discharged Quantifoil R1.2/1.3 Au 300- mesh grids after blotting for 3 sec, followed by plunging into liquid ethane using a Vitrobot Mark IV. 5,199 movies were collected using EPU 1.9 on a Titan Krios (ThermoFisher Scientific) operated at \(300\mathrm{kV}\) at a nominal magnification of \(165\mathrm{kx}\) (0.83 A/pixel) with a Quantum K2 camera (Gatan) using a slit width of \(20\mathrm{eV}\) . Data was collected with an exposure rate of 3.6 electrons/px/s, a total exposure of 33 electrons/ \(\mathrm{\AA}^2\) and 20 frames per movie. + +<--- Page Split ---> + +Image processing was performed within the Scipion 2 framework40, using RELION- 3.0 unless specified otherwise. Movies were motion- corrected using the RELION implementation of the MotionCor2. 294,054 particles were initially picked using reference- based picking in Gautomatch (http://www.mrc- lmb.cam.ac.uk/kzhang/Gautomatch) and Contrast- transfer function parameters were using GCTF41. Subsequent image processing was performed in RELION- 3.0 and 2D and 3D classification was used to select 100,605 particles, which were then extracted in an unbinned 560- pixel box (Fig. S1). An initial model of the ATP synthase dimer was obtained using de novo 3D model generation. Using masked refinement with applied \(\mathrm{C}_2\) symmetry, a 2.7- Å structure of the membrane region was obtained following per- particle CTF refinement and Bayesian polishing. Following \(\mathrm{C}_2\) - symmetry expansion and signal subtraction of one monomer, a 3.7 Å map of the peripheral stalk was obtained. Using 3D classification \((\mathrm{T} = 100)\) of aligned particles, with a mask on the \(\mathrm{F}_{1 / c}\) - ring region, 10 different rotational substates were then separated and maps at 3.5- 4.3 Å resolution were obtained using 3D refinement. The authors note that the number of classes identified in this study likely reflects the limited number of particles, rather than the complete conformational space of the complex. By combining particles from all states belonging to main rotational state 1, a 3.7- Å map of the rotor and a 3.2- Å consensus map of the complete ATP synthase dimer with both rotors in main rotational state 1 were obtained. + +## Model building, refinement and data visualization + +An initial atomic model of the static \(\mathrm{F}_0\) membrane region was built automatically using Bucaneer42. Subunits were subsequently assigned directly from the cryo- EM map, 15 of them corresponding to previously identified T. brucei ATP synthase subunits21, while three subunits (ATPTB14, ATPEG3, ATPEG4) were newly identified using BLAST searches. Manual model building was performed in Coot using the T. brucei \(\mathrm{F}_1\) (PDB 6F5D)13 and homology models43 of the E. gracilis OSCP and c- ring (PDB 6TDU)10 as starting models. Ligands were manually fitted to the map and restraints were generated by the GRADE server (http://grade.globalphasing.org). Real- space refinement was performed in PHENIX using autosharpened, local- resolution- filtered maps of the membrane region, peripheral stalk tip, c- ring/central stalk and \(\mathrm{F}_1\mathrm{F}_0\) monomers in different rotational states, respectively, using secondary structure restraints. Model statistics were generated using MolProbity44 and EMRinger45 Finally, the respective refined models were combined into a composite ATP synthase dimer model and real- space refined against the local- resolution- filtered consensus ATP synthase dimer map with both monomers in rotational state 1, applying reference restraints. Figures of the structures were prepared using ChimeraX46, the proton half- channels were traced using HOLLOW47. + +## Data availability + +The atomic coordinates have been deposited in the Protein Data Bank (PDB) and are available under the accession codes: XXXX (membrane- region), XXXX (peripheral stalk), XXXX (rotor), XXXX (F1Fo dimer), XXXX (rotational state 1a), XXXX (rotational state 1b), XXXX + +<--- Page Split ---> + +(rotational state 1c), XXXX (rotational state 1d), XXXX (rotational state 1e), XXXX (rotational state 2a), XXXX (rotational state 2b), XXXX (rotational state 2c), XXXX (rotational state 2d), XXXX (rotational state 3). The local resolution filtered cryo- EM maps, half maps, masks and FSC- curves have been deposited in the Electron Microscopy Data Bank with the accession codes: EMD- XXXX (membrane- region), EMD- XXXX (peripheral stalk), EMD- XXXX (rotor), EMD- XXXX (F₁F₀ dimer), EMD- XXXX (rotational state 1a), EMD- XXXX (rotational state 1b), EMD- XXXX (rotational state 1c), EMD- XXXX (rotational state 1d), EMD- XXXX (rotational state 1e), EMD- XXXX (rotational state 2a), EMD- XXXX (rotational state 2b), EMD- XXXX (rotational state 2c), EMD- XXXX (rotational state 2d), EMD- XXXX (rotational state 3). + +## Acknowledgements + +We are grateful to Sir John E. Walker and Martin Montgomery for invaluable assistance with ATP synthase purification in the initial stage of the project. We acknowledge cryo- electron microscopy and tomography core facility of CIISB, Instruct- CZ Centre, supported by MEYS CR (LM2018127). This work was supported by the Czech Science Foundation grants number 18- 17529S to A.Z. and 20- 04150Y to O.G. and by European Regional Development Fund (ERDF) and Ministry of Education, Youth and Sport (MEYS) project CZ.02.1.01/0.0/0.0/16_019/0000759 to A.Z., Swedish Foundation for Strategic Research (FFL15:0325), Ragnar Söderberg Foundation (M44/16), European Research Council (ERC- 2018- StG- 805230), Knut and Alice Wallenberg Foundation (2018.0080), and EMBO Young Investigator Programme to A.A. + +## Author contributions + +A.Z. and A.A. conceived and designed the work. O.G. prepared the sample for cryo- EM. O.G. and A.M. performed initial screening. A.M. processed the cryo- EM data and built the model. O.G., A.M. and A.A. analyzed the structure. B.P., C.H.Y., M.J., M.S., O.G. and A.Z. performed biochemical analysis. O.G., A.M., A.A. and A.Z. interpreted the data. O.G., A.M., A.A. and A.Z. wrote and revised the manuscript. All authors contributed to the analysis and approved the final version of the manuscript. + +## Competing interests + +The authors declare no competing interests. + +## References + +1. Paumard, P. et al. The ATP synthase is involved in generating mitochondrial cristae morphology. EMBO J 21, 221-30 (2002). +2. Davies, K.M., Anselmi, C., Wittig, I., Faraldo-Gomez, J.D. & Kuhlbrandt, W. Structure of the yeast F₁F₀-ATP synthase dimer and its role in shaping the mitochondrial cristae. Proc Natl Acad Sci U S A 109, 13602-7 (2012). + +<--- Page Split ---> + +641 3. Panek, T., Elias, M., Vancova, M., Lukes, J. & Hashimi, H. Returning to the Fold for 642 Lessons in Mitochondrial Crista Diversity and Evolution. Curr Biol 30, R575-R588 643 (2020). 644 4. Kuhlbrandt, W. Structure and Mechanisms of F-Type ATP Synthases. Annu Rev 645 Biochem 88, 515-549 (2019). 646 5. Spikes, T.E., Montgomery, M.G. & Walker, J.E. Structure of the dimeric ATP synthase 647 from bovine mitochondria. Proc Natl Acad Sci U S A 117, 23519-23526 (2020). 648 6. Pinke, G., Zhou, L. & Sazanov, L.A. Cryo-EM structure of the entire mammalian F- 649 type ATP synthase. Nat Struct Mol Biol 27, 1077-1085 (2020). 650 7. Guo, H., Bueler, S.A. & Rubinstein, J.L. Atomic model for the dimeric \(\mathrm{F}_0\) region of 651 mitochondrial ATP synthase. Science 358, 936-940 (2017). 652 8. Murphy, B.J. et al. Rotary substates of mitochondrial ATP synthase reveal the basis of 653 flexible \(\mathrm{F}_1\mathrm{-}\mathrm{F}_0\) coupling. Science 364, eaaw9128 (2019). 654 9. Flygaard, R.K., Mühleip, A., Tobiasson, V. & Amunts, A. Type III ATP synthase is a 655 symmetry-deviated dimer that induces membrane curvature through tetramerization. 656 Nature Communications 11, 5342 (2020). 657 10. Muhleip, A., McComas, S.E. & Amunts, A. Structure of a mitochondrial ATP synthase 658 with bound native cardiolipin. Elife 8, e51179 (2019). 659 11. Mühleip, A. et al. ATP synthase hexamer assemblies shape cristae of Toxoplasma 660 mitochondria. Nature Communications 12, 120 (2021). 661 12. Gahura, O. et al. The \(\mathrm{F}_1\) -ATPase from Trypanosoma brucei is elaborated by three 662 copies of an additional p18-subunit. FEBS J 285, 614-628 (2018). 663 13. Montgomery, M.G., Gahura, O., Leslie, A.G.W., Zikova, A. & Walker, J.E. ATP 664 synthase from Trypanosoma brucei has an elaborated canonical \(\mathrm{F}_1\) -domain and 665 conventional catalytic sites. Proc Natl Acad Sci U S A 115, 2102-2107 (2018). 666 14. Serricchio, M. et al. Depletion of cardiolipin induces major changes in energy 667 metabolism in Trypanosoma brucei bloodstream forms. FASEB J 35, 21176 (2020). 668 15. Muhleip, A.W., Dewar, C.E., Schnaufer, A., Kuhlbrandt, W. & Davies, K.M. In situ 669 structure of trypanosomal ATP synthase dimer reveals a unique arrangement of 670 catalytic subunits. Proc Natl Acad Sci U S A 114, 992-997 (2017). 671 16. Schnaufer, A., Clark-Walker, G.D., Steinberg, A.G. & Stuart, K. The \(\mathrm{F}_1\) -ATP synthase 672 complex in bloodstream stage trypanosomes has an unusual and essential function. 673 EMBO J 24, 4029-40 (2005). 674 17. Brown, S.V., Hosking, P., Li, J. & Williams, N. ATP synthase is responsible for 675 maintaining mitochondrial membrane potential in bloodstream form Trypanosoma 676 brucei. Eukaryot Cell 5, 45-53 (2006). 677 18. Gahura, O., Hierro-Yap, C. & Zikova, A. Redesigned and reversed: Architectural and 678 functional oddities of the trypanosomal ATP synthase. Parasitology 148, 1151-1160 679 (2021). 680 19. Hierro-Yap, C. et al. Bioenergetic consequences of \(\mathrm{F}_0\mathrm{F}_1\) -ATP synthase/ATPase 681 deficiency in two life cycle stages of Trypanosoma brucei. J Biol Chem 296, 100357 682 (2021). + +<--- Page Split ---> + +683 20. Gahura, O., Panicucci, B., Vachova, H., Walker, J.E. & Zikova, A. Inhibition of F1- ATPase from Trypanosoma brucei by its regulatory protein inhibitor TbIF1. FEBS J 285, 4413- 4423 (2018). 686 21. Zikova, A., Schnaufer, A., Dalley, R.A., Panigrahi, A.K. & Stuart, K.D. The F(0)F(1)- ATP synthase complex contains novel subunits and is essential for procyclic Trypanosoma brucei. PLoS Pathog 5, e1000436 (2009). 689 22. Perez, E. et al. The mitochondrial respiratory chain of the secondary green alga Euglena gracilis shares many additional subunits with parasitic Trypanosomatidae. Mitochondrion 19 Pt B, 338- 49 (2014). 692 23. Sathish Yadav, K.N. et al. Atypical composition and structure of the mitochondrial dimeric ATP synthase from Euglena gracilis. Biochim Biophys Acta 1858, 267- 275 (2017). 695 24. Dewar, C.E., Oeljeklaus, S., Warscheid, B. and Schneider, A. Characterisation of a highly diverged mitochondrial ATP synthase peripheral stalk subunit b in Trypanosoma brucei (2021). https://www.biorxiv.org/content/10.1101/2021.10.13.464200v1 699 25. Aphasizheva, I. et al. Lexis and Grammar of Mitochondrial RNA Processing in Trypanosomes. Trends Parasitol 36, 337- 355 (2020). 700 26. Blum, B., Bakalara, N. & Simpson, L. A model for RNA editing in kinetoplastid mitochondria: "guide" RNA molecules transcribed from maxicircle DNA provide the edited information. Cell 60, 189- 98 (1990). 703 27. Adler, B.K., Harris, M.E., Bertrand, K.I. & Hajduk, S.L. Modification of Trypanosoma brucei mitochondrial rRNA by posttranscriptional 3' polyuridine tail formation. Mol Cell Biol 11, 5878- 84 (1991). 708 28. Hofer, A., Steverding, D., Chabes, A., Brun, R. & Thelander, L. Trypanosoma brucei CTP synthetase: a target for the treatment of African sleeping sickness. Proc Natl Acad Sci U S A 98, 6412- 6 (2001). 709 29. Gupta, K. et al. The role of interfacial lipids in stabilizing membrane protein oligomers. Nature 541, 421- 424 (2017). 712 30. Arnold, I., Pfeiffer, K., Neupert, W., Stuart, R.A. & Schagger, H. Yeast mitochondrial F1Fo- ATP synthase exists as a dimer: identification of three dimer- specific subunits. EMBO J 17, 7170- 8 (1998). 713 31. Gu, J. et al. Cryo- EM structure of the mammalian ATP synthase tetramer bound with inhibitory protein IF1. Science 364, 1068- 1075 (2019). 716 32. Spikes, T.E., Montgomery, M.G. & Walker, J.E. Interface mobility between monomers in dimeric bovine ATP synthase participates in the ultrastructure of inner mitochondrial membranes. Proc Natl Acad Sci U S A 118, e2021012118 (2021). 719 33. Cadena, L.R. et al. Mitochondrial contact site and cristae organization system and F1Fo- ATP synthase crosstalk is a fundamental property of mitochondrial cristae. mSphere 6, e0032721 (2021). 723 34. Davies, K.M. et al. Macromolecular organization of ATP synthase and complex I in whole mitochondria. Proc Natl Acad Sci U S A 108, 14121- 6 (2011). + +<--- Page Split ---> + +725 35. Blum, T.B., Hahn, A., Meier, T., Davies, K.M. & Kühlbrandt, W. Dimers of mitochondrial ATP synthase induce membrane curvature and self-assemble into rows. Proc Natl Acad Sci U S A 116, 4250- 4255 (2019). 726 36. Bochud- Allemann, N. & Schneider, A. Mitochondrial substrate level phosphorylation is essential for growth of procyclic Trypanosoma brucei. J Biol Chem 277, 32849- 54 (2002). 737. Poon, S.K., Peacock, L., Gibson, W., Gull, K. & Kelly, S. A modular and optimized single marker system for generating Trypanosoma brucei cell lines expressing T7 RNA polymerase and the tetracycline repressor. Open Biol 2, 110037 (2012). 738. Allemann, N. & Schneider, A. ATP production in isolated mitochondria of procyclic Trypanosoma brucei. Mol Biochem Parasitol 111, 87- 94 (2000). 739. Aibara, S., Dienemann, C., & Cramer, P.. Structure of an inactive RNA polymerase II dimer. Nucleic Acids Research, gkab783 (2021). 740. de la Rosa- Trevin, J.M. et al. Scipion: A software framework toward integration, reproducibility and validation in 3D electron microscopy. J Struct Biol 195, 93- 9 (2016). 741. Zhang, K. Gctf: Real- time CTF determination and correction. J Struct Biol 193, 1- 12 (2016). 742. Cowtan, K. The Buccaneer software for automated model building. 1. Tracing protein chains. Acta Crystallogr D Biol Crystallogr 62, 1002- 11 (2006). 743. Waterhouse, A. et al. SWISS- MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46, W296- W303 (2018). 744. Williams, C.J., Headd, J.J., Moriarty, N.W., Prisant, M.G., Videau, L.L., Deis, L.N., Verma, V., Keedy, D.A., Hintze, B.J., Chen, V.B. and Jain, S. MolProbity: More and better reference data for improved all- atom structure validation. Protein Science, 27, 293- 315 (2018). 745. Barad, B.A., Echols, N., Wang, R.Y.R., Cheng, Y., DiMaio, F., Adams, P.D. and Fraser, J.S. EMRinger: side chain- directed model and map validation for 3D cryo- electron microscopy. Nature methods, 12, 943- 946 (2015). 746. Goddard, T.D. et al. UCSF ChimeraX: Meeting modern challenges in visualization and analysis. Protein Sci 27, 14- 25 (2018). 747. Ho, B.K. & Gruswitz, F. HOLLOW: generating accurate representations of channel and interior surfaces in molecular structures. BMC Struct Biol 8, 49 (2008). + +<--- Page Split ---> +![](images/Extended_Data_Figure_5.jpg) + + +Extended Data Fig. 1 Purification of the T. brucei ATP synthase dimer. + +a, Size exclusion chromatography trace with peaks enriched with ATP synthase dimers (D), monomers (M) and \(\mathrm{F_1}\) - ATPase \((\mathrm{F_1})\) labelled. b, Fractions from size exclusion chromatography marked with green bar in (a) resolved by native BN- PAGE. c, Dimer- enriched fraction resolved by SDS- PAGE stained by Coomassie blue dye. Bands are annotated based on mass spectrometry identification from excised gel pieces. + +<--- Page Split ---> +![](images/Extended_Data_Figure_8.jpg) + + +Extended Data Fig. 2 Cryo- EM data processing of the T. brucei ATP synthase dimer. a, Representative micrograph. b, 2D class averages. c, Fourier Shell Correlation (FSC) curves showing the estimated resolutions of ATP synthase maps according to the gold standard 0.143 criterion. d, Data processing scheme resulting in maps covering all regions of the complex, as well as 10 rotational states. + +<--- Page Split ---> +![PLACEHOLDER_27_0] + + +## Extended Data Fig. 3 Conserved and phylum specific elements generate the T. brucei ATP synthase architecture. + +The canonical OSCP/F \(_1 / c\) - ring monomers (dark grey) are tied together by both conserved \(\mathrm{F}_0\) subunits and extensions of lineage- specific subunits (red). The \(\mathrm{F}_0\) periphery and peripheral stalk attachment are composed of lineage specific subunits (blue). + +<--- Page Split ---> +![PLACEHOLDER_28_0] + + +Extended Data Fig. 4 The \(\mathbf{F}_0\) region coordinates numerous bound lipids. a, \(\mathrm{F}_0\) top view, cardiolipin (CDL), phosphatidylcholine (PC) and phosphatidylethanolamine (PE) are bound at the dimer interface, the lumenal proton half- channel and the peripheral \(\mathrm{F}_0\) cavity. b, The \(60^{\circ}\) - dimer angle generates a curved \(\mathrm{F}_0\) region with phospholipids bound in an arc- shaped bilayer. c- f, Bound lipids with cryo- EM density and coordinating residues. + +<--- Page Split ---> +![PLACEHOLDER_29_0] + +
Extended Data Fig. 5 Bound detergents of the \(\mathbf{F}_0\) region.
+ +GDN (a,b) and \(\beta\) - DDM (c,d) molecules bound in the periphery of the membrane region with cryo- EM map densities shown (transparent), indicating that both glycosides are retained in the detergent micelle. + +<--- Page Split ---> +![PLACEHOLDER_30_0] + + +Extended Data Fig. 7 The C- terminal tail of subunit- e interacts with the \(c_{10}\) - ring. a, The cryo- EM map reveals disordered detergent density of the detergent belt surrounding the membrane region as well as a detergent plug on the luminal side of the \(c\) - ring. b, The helical C- terminus of subunit- \(e\) extends into the lumen towards the \(c\) - ring. The terminal 23 residues are disordered and likely interact with the \(\beta\) - barrel. + +<--- Page Split ---> +![PLACEHOLDER_31_0] + +
Extended Data Fig. 8 Phylogenetic distribution and sequence conservancy of subunit-e and -g.
+ +a, Distribution of subunits \(e\) and \(g\) mapped on the phylogenetic tree of eukaryotes3. Homologs of subunits \(e\) and \(g\) were searched in non-redundant GenBank and UniprotKB protein databases by PSI- BLAST, and phmmer and hmmsearch4, respectively, using individual sequences of representatives from \(H\) . sapiens and \(T\) . brucei, and in the case of hmmsearch a multiple sequence alignment (MSA) of representatives from Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and \(T\) . brucei, as queries. Groups, in which at least one structure of ATP synthase is available, are marked. Abbreviations of species used in MSA in panels (c) and (d) are shown. b, Sequence logo of GXXXG motifs and flanking regions of subunits \(e\) and \(g\) . Hits from hmmsearch were clustered by CD- HIT Suite5 to 50% sequence identity and MSA of representative sequences of each cluster was generated by Clustal Omega4. The sequence logos were created from MSA in Geneious Prime (Biomatters Ltd.). c,d, MSA of sequences of subunits \(g\) (c) and \(e\) (d) from species representing major groups shown in (a) generated by MUSCLE7 and visualized in Geneious Prime. The experimentally determined or predicted transmembrane regions are highlighted in yellow. Species abbreviations: Tb – T. brucei, Hs – H. sapiens, Sc – S. cerevisiae, Sr – Salpingoeca rosetta, Tt – Thecamonas trahens, Dd – Dictyostelium discoideum, Cm – Cyanidioschyzon merolae, Cv – Chlorella vulgaris, At – Arabidopsis thaliana, Os – Oryza sativa. + +<--- Page Split ---> +![PLACEHOLDER_32_0] + + +<--- Page Split ---> +![PLACEHOLDER_33_0] + + +Extended Data Fig. 9 Effects of RNAi knock- down of ATP synthase subunits on viability and stability and dimerization of ATP synthase. + +a, Growth curves of indicated non- induced (solid lines) and tetracycline induced (dashed lines) RNAi cells lines in the presence (black) or absence (brown) of glucose. The insets show relative levels of the respective target mRNA at indicated days post induction (DPI) normalized to the levels of 18S rRNA (black bars) or \(\beta\) - tubulin (white bars). b, Immunoblots of mitochondrial lysates from indicated RNAi cell lines resolved by BN- PAGE probed by antibodies against indicated ATP synthase subunits. c, Immunoblots of whole cell lysates from indicated RNAi cell lines probed with indicated antibodies. + +<--- Page Split ---> + +
Membrane regionRotorPeripher-
ral stalk		F1F6
dimer		Rot.
1a		Rot.
1b		Rot.
1c		Rot.
1d		Rot.
1e		Rot.
2a		Rot.
2b		Rot.
2c		Rot.
2d		Rot.
3
Data collection
Microscope
Titan Krios
Voltage (kV)
300
Camera
K2 Summit
Magnification
165 kx
Exposure (e/Ų)
33
Defocus range (μm)
-1.6 to -3.2
Pixel size (Å)
0.83
Movies collected
5,199
Frames per movie
20
Data processing
Initial particles
100,605 (C2 symmetry-expanded: 201,210)
Final no. particles
100,605118,683201,21036,92519,76426,42723,01916,99134,48212,17324,09611,03517,83317,312
SymmetryC2C1C1C2C1C1C1C1C1C2C1C2C1
Map resolution (Å)2.73.73.73.23.73.53.73.83.74.33.53.83.83.7
Sharpening B factor-46.2-74.4-92.5-49.8-61.8-61.1-57.6-45.6-58.0-73.8-54.5-65.2-54.9-61.7
EMD ID
Model refinement statistics
CC (map/model)0.860.830.820.710.790.790.820.790.690.710.810.770.770.79
Resolution (map/model)2.653.43.683.133.483.563.363.553.573.943.393.733.643.58
No. of atoms76,69019,66912,083251,552129,568129,568129,568129,568129,569129,563129,563129,563129,566129,566
No. of residues4074128576715,3567872787278727872787278787872787278727872
No. of lipids36003621212121212121222121
No. of ATP/ADP000105555555655
No. of Mg ions000105555555655
B-factor (Ų)
- protein54.0556.1377.8884.4855.6570.3780.2283.2770.70112,7279.9365.5266.49101.5
- ligands50.5758.25-69.9440.9972.2963.1878.4363.7675,2574.4761.7946.5583.68
Rotamer outliers (%)0.440.400.310.220.420.090.180.260.580.180.270.480.420.39
Ramachandran (%)
- outliers0.000.000.000.010.0010.0030.0040.010.0030.010.000.040.040.04
- allowed1.571.911.591.561.521.651.441.491.491.671.581.471.651.79
- favored98.4398.0898.4198.4298.4798.3498.5698.4098.4898.3198.4298.4998.3198.17
Clash score1.662.442.322.262.602.652.532.672.992.382.302.522.383.57
MolProbity score0.921.031.011.001.051.031.041.051.091.021.011.041.021.15
RMSD
- bonds (Å)0.0040.0040.020.0030.0030.0030.0040.0030.0030.0020.0030.0030.0030.003
- angles (°)0.4550.4160.3860.4070.4140.4240.4170.4070.4120.4100.4160.4190.4280.421
EMRinger score5.113.961.612.563.242.953.322.853.321.352.892.322.492.8
PDB ID
+ +Extended Data Table 1. Data collection, processing, model refinement and validation statistics. + +<--- Page Split ---> + + +
Subunit nameTriTrypDB Lister strain 427 IDTriTrypDB TREU927 strain IDUniprot TREU927 strain IDResiduesResidues built
F1 subcomplex
αTb427_070081800
Tb427_070081900		Tb927.7.7420
Tb927.7.7430		Q57TX958445-151,
161-584
βTb427_030013500Tb927.3.1380Q57XX151926-514
γTb427_100005200Tb927.10.180B0Z0F63052-301
δTb427_060054900Tb927.6.4990Q586H118222-182
εTb427_100054600Tb427.10.5050N/A7511-75
p18Tb427_050022900Tb927.5.1710Q57ZP018823-188
F0 subcomplex
OSCPTb427_100087100Tb927.10.8030Q38AG125518-202,
208-255
amt encodedmt encodedN/A2311-231
bTb427_040009100Tb927.4.720Q580A010526-105
cTb427_100018700
Tb427_110057900
Tb427_070019000		Tb927.10.1570
Tb927.11.5280
Tb927.7.1470		Q38C84
Q385P0
Q57WQ3		118
118
370		41-118
41-118
17-325,
332-354
dTb427_050035800Tb927.5.2930Q57ZW937017-325,
332-354
eTb427_110010200Tb927.11.600N/A921-383
fTb427_030016600Tb927.3.1690Q57ZE21452-136
gTb427_020016900Tb927.2.3610Q586X814416-144
ijTb427_030029400Tb927.3.2880Q57ZM41042-104
kTb427_070011800Tb927.7.840Q57VT012420-124
8Tb427_040037300Tb927.4.3450Q585K511429-114
ATBTB1Tb427_100008400Tb927.10.520Q38C183961-383
ATPTB3Tb427_110067400Tb927.11.6250Q385E42692-269
ATPTB4Tb427_100105100Tb927.10.9830Q389Z315721-157
ATPTB6Tb427_110017200Tb927.11.1270Q387C51692-169
ATPTB11Tb427_030021500Tb927.3.2180Q582T115618-156
ATPTB12Tb427_050037400Tb927.5.3090Q57Z841015-100
ATPEG3Tb427_060009300Tb927.6.590Q583U49814-98
ATPEG4N/ATb927.11.2245N/A621-62
+ +Extended Data Table 2. Composition of T. brucei ATP synthase dimer. + +<--- Page Split ---> + +
SubunitPrimer pair sequences
Primers for amplification of RNAi cassettes
bTAATCTCGAGGGTACCGTTGAGTGAGGAGGAACGGG GCAGTCTAGAGGATCTCATCCCTTCCACCCACCACT
eTAATCTCGAGGGTACCGGGAGTACAGAAGGGCTACATAGATCTAGAGGATCCCGTCGACACCATCAGCTG
fATACTCGAGGGTACCGTTGAGTACCGCTTTACGC GCGTCTAGAGGATCCAGCACTGATCACCAAACTGC
gACTGCTCGAGGGTACCACGCGGGAATTCAAAAAGACCGGGTCTAGAGGATCCCGTTGCGGTGCTTGTCATTA
ijTAATCTCGAGGGTACCGGAATATCCGATGCTAGTCGCCGCCGTCTAGAGGATCCACTTCGCTCTACTGCATGCA
kATTACTCGAGCCCGGCGTCAGTGCAAGGGGATTTT GCCGTCTAGAGGATCCCTTTCTCTGAAAAACGCACACA
8ATGACTCGAGGGTACCCGGGCTATGGTGTGGTATTATATGC GACGTCTAGAGGATCCGCAGAAAACTCCCAACGACA
ATPTB3ACTGCTCGAGGGTACCAAAGAGGAGGTGAGGTCTGC GCAGTCTAGAGGATCCCCCTAGGGTTCTTCGAAGCA
ATPTB4CTGACTCGAGGGTACCTTCCTTTCTGCTGCATCGGCAGGTCTAGAGGATCCCTCCCTGGGCTTCAAATTTG
ATPTB6ACTGCTCGAGGGTACCCAACATGGCAGTATCCGGTGCAGTCTAGAGGATCTTTATTAGTGGCGGTGGTGGT
ATPTB11ACTGCTCGAGGGTACCCGCGCTCGTTCTCTCCATTTCCAGAAGCTTGGATCCAGGTTGGGGTGTTTAGGGAG
ATPTB12TAATCTCGAGGGTACCGACGCATCAAAGGAATGCCGCCGTCTAGAGGATCCAGCAGCCAACAAACAGACAA
ATPEG3TACACTCGAGGGTACCAAACCTGAAGGCCCTCACACGCAGTCTAGAGGATCCCTTTTCGTGCCGCCTGATA
Primers for quantification of mRNA levels by qPCR
bCCAAGAGTGATGATGGCCCCCGTTTAGGGTGCGGAAAAC
eCAAGCCTTGCACACACTTTATGCCGAAAGAAGTACGCCAC
fTTTTCTACATACGCAGCAGTTACCATTCCATGCGCGTG
gGCAATTGTGTGAGCTGAACGATCTGGCCGCATTGCATAAC
ijAGAGTAAAAGCGCGCCTACG
+ +<--- Page Split ---> + +
CAGTTGGAAAACCGGTAGCC
kACACAAAACACTTCCAGCAGA
CGCTATGACGGACAGGTGT
8GCTACGGCGACTTGGTGC
CGTCCGCGGTATTTGTTCA
ATPTB3AACGTTTATATCAGCGGGCG
CTGTTTGGTCTGCACACGA
ATPTB4CCAAACTTTGAAGCAGCGG
ATTCCTTGGATCCGCACCTT
ATPTB6TCGGCATAGGAGAAGTAACGA
GATTCGGTTTGGACTTGCG
ATPTB11CAACGGCCCCACATTCTC
ACACCGCGGTCATTCATTG
ATPTB12GCACTTCATTCTCCCGACTG
ACATGATGTAACACCTCCGC
ATPEG3TGGCCCCACATGACTGAAAA
GGAAGTGATCCGCCGGATTT
+ +Extended Data Table 3. List of primers used in the study. + +<--- Page Split ---> + + +
TargetTypeReferenceDilution SDS-PAGEDilution BN-PAGE
Primary antibodies
subunit-βrabbit polyclonal11:20001:2000
p18rabbit polyclonal11:1000-
ATPTB1rabbit polyclonal11:10001:1000
subunit-drabbit polyclonal11:10001.500
mtHsp70mouse monoclonal21:5000-
Secondary antibodies
goat anti-rabbit IgG HRP conjugateBioRad 17210191:20001:2000
goat anti-mouse IgG HRP conjugateBioRad 17210111:20001:2000
+ +**Extended Data Table 4. List of antibodies used in the study.** + +<--- Page Split ---> + +## Extended Data references: + +1. Muhleip, A., McComas, S.E. & Amunts, A. Structure of a mitochondrial ATP synthase with bound native cardiolipin. \*Elife\* 8, e51179 (2019). +2. Larkin, M.A. et al. (2007). Clustal W and Clustal X version 2.0. \*Bioinformatics\*, 23, 2947-2948 (2007). +3. Burki, F., Roger, A.J., Brown, M.W. & Simpson, A.G.B. The New Tree of Eukaryotes. \*Trends Ecol Evol\* 35, 43-55 (2020). +4. Protein Sequence Similarity Search. \*Curr Protoc Bioinformatics\* 60, 3151-31523 (2017). +5. Huang, Y., Niu, B., Gao, Y., Fu, L. & Li, W. CD-HIT Suite: a web server for clustering and comparing biological sequences. \*Bioinformatics\* 26, 680-2 (2010). +6. Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. \*Mol Syst Biol\* 7, 539 (2011). +7. Edgar, R.C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. \*Nucleic Acids Res\* 32, 1792-7 (2004). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Video1.mp4- Video2.mp4- Video3.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54_det.mmd b/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..682e2ae228ddd1600d25d3a41f28f7498382e66e --- /dev/null +++ b/preprint/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54/preprint__0a57595338b1390798459ee5d63f40d3108931b635b32aecd97babb2ece11f54_det.mmd @@ -0,0 +1,473 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 866, 208]]<|/det|> +# An ancestral interaction module promotes oligomerization in divergent mitochondrial ATP synthases + +<|ref|>text<|/ref|><|det|>[[44, 229, 600, 271]]<|/det|> +Alexey Amunts ( amunts@scilifelab.se ) Stockholm University https://orcid.org/0000- 0002- 5302- 1740 + +<|ref|>text<|/ref|><|det|>[[44, 277, 800, 319]]<|/det|> +Ondrej Gahura Institute of Parasitology, Biology Centre CAS https://orcid.org/0000- 0002- 2925- 4763 + +<|ref|>text<|/ref|><|det|>[[44, 323, 600, 365]]<|/det|> +Alexander Muhleip Stockholm University https://orcid.org/0000- 0002- 1877- 2282 + +<|ref|>text<|/ref|><|det|>[[44, 370, 440, 411]]<|/det|> +Carolina Hierro- Yap Institute of Parasitology, Biology Centre CAS + +<|ref|>text<|/ref|><|det|>[[44, 416, 186, 456]]<|/det|> +Brian Panicucci Biology Centre + +<|ref|>text<|/ref|><|det|>[[44, 462, 440, 503]]<|/det|> +Minal Jain Institute of Parasitology, Biology Centre CAS + +<|ref|>text<|/ref|><|det|>[[44, 509, 800, 550]]<|/det|> +David Hollaus Institute of Parasitology, Biology Centre CAS https://orcid.org/0000- 0001- 7403- 6434 + +<|ref|>text<|/ref|><|det|>[[44, 555, 440, 596]]<|/det|> +Martina Slapnickova Institute of Parasitology, Biology Centre CAS + +<|ref|>text<|/ref|><|det|>[[44, 602, 540, 643]]<|/det|> +Alena Zikova Biology Centre https://orcid.org/0000- 0002- 8686- 0225 + +<|ref|>text<|/ref|><|det|>[[44, 684, 102, 701]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 722, 135, 740]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 759, 344, 778]]<|/det|> +Posted Date: December 30th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 797, 475, 816]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1196040/v1 + +<|ref|>text<|/ref|><|det|>[[44, 835, 910, 878]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 913, 936, 956]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 11th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 33588- z. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[0, 0, 997, 997]]<|/det|> +# 1.1.1.1.1.1.1.1.1.1.1 + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[140, 84, 858, 133]]<|/det|> +# An ancestral interaction module promotes oligomerization in divergent mitochondrial ATP synthases + +<|ref|>text<|/ref|><|det|>[[115, 168, 880, 214]]<|/det|> +Ondřej Gahura1,†, Alexander Mühleip2,†, Carolina Hierro- Yap1,3, Brian Panicucci1, Minal Jain1,3, David Hollaus3, Martina Slapničková1, Alena Zíková1,3,*, Alexey Amunts2,4 + +<|ref|>text<|/ref|><|det|>[[115, 243, 874, 286]]<|/det|> +1Institute of Parasitology, Biology Centre CAS, Ceske Budejovice, Czech Republic 2Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17165 Solna, Sweden + +<|ref|>text<|/ref|><|det|>[[115, 293, 870, 312]]<|/det|> +3Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic + +<|ref|>text<|/ref|><|det|>[[115, 319, 844, 338]]<|/det|> +4Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden + +<|ref|>text<|/ref|><|det|>[[115, 345, 844, 364]]<|/det|> +5Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden + +<|ref|>text<|/ref|><|det|>[[115, 440, 650, 459]]<|/det|> +6Correspondence to: azikova@paru.cas.cz; amunts@scilifelab.se + +<|ref|>text<|/ref|><|det|>[[115, 465, 512, 483]]<|/det|> +7These authors contributed equally to this work. + +<|ref|>sub_title<|/ref|><|det|>[[118, 540, 198, 556]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[113, 563, 883, 852]]<|/det|> +Mitochondrial ATP synthase forms stable dimers arranged into oligomeric assemblies that generate the inner- membrane curvature essential for efficient energy conversion. Here, we report cryo- EM structures of the intact ATP synthase dimer from Trypanosoma brucei in ten different rotational states. The model consists of 25 subunits, including nine lineage- specific, as well as 36 lipids. The rotary mechanism is influenced by the divergent peripheral stalk, conferring a greater conformational flexibility. Proton transfer in the lumenal half- channel occurs via a chain of five ordered water molecules. The dimerization interface is formed by subunit- g that is critical for interactions but not for the catalytic activity. Although overall dimer architecture varies among eukaryotes, we find that subunit- g together with subunit- e form an ancestral oligomerization motif, which is shared between the trypanosomal and mammalian lineages. Therefore, our data defines the subunit- g/e module as a structural component determining ATP synthase oligomeric assemblies. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 84, 880, 253]]<|/det|> +Mitochondrial ATP synthase consists of the soluble \(\mathrm{F_1}\) and membrane- bound \(\mathrm{F_0}\) subcomplexes, and occurs in dimers that assemble into oligomers to induce the formation of inner- membrane folds, called cristae. The cristae are the sites for oxidative phosphorylation and energy conversion in eukaryotic cells. Dissociation of ATP synthase dimers into monomers results in the loss of native cristae architecture and impairs mitochondrial function \(^{1,2}\) . While cristae morphology varies substantially between organisms from different lineages, ranging from flat lamellar in opisthokonts to coiled tubular in ciliates and discoidal in euglenozoans \(^{3}\) , the mitochondrial ATP synthase dimers represent a universal occurrence to maintain the membrane shape \(^{4}\) . + +<|ref|>text<|/ref|><|det|>[[67, 258, 880, 595]]<|/det|> +ATP synthase dimers of variable size and architecture, classified into types I to IV have recently been resolved by high- resolution cryo- EM studies. In the structure of the type- I ATP synthase dimer from mammals, the monomers are only weakly associated \(^{5,6}\) , and in yeast insertions in the membrane subunits form tighter contacts \(^{7}\) . The structure of the type- II ATP synthase dimer from the alga \*Polytomella\* sp. showed that the dimer interface is formed by phylum- specific components \(^{8}\) . The type- III ATP synthase dimer from the ciliate \*Tetrahymena\* thermophila is characterized by parallel rotary axes, and a substoichiometric subunit, as well as multiple lipids were identified at the dimer interface, while additional protein components that tie the monomers together are distributed between the matrix, transmembrane, and lumenal regions \(^{9}\) . The structure of the type- IV ATP synthase with native lipids from \*Euglena gracilis\* also showed that specific protein- lipid interactions contribute to the dimerization, and that the central and peripheral stalks interact with each other directly \(^{10}\) . Finally, a unique apicomplexan ATP synthase dimerizes via 11 parasite- specific components that contribute \(\sim 7000 \mathrm{\AA}^2\) buried surface area \(^{11}\) , and unlike all other ATP synthases, that assemble into rows, it associates in higher oligomeric states of pentagonal pyramids in the curved apical membrane regions. Together, the available structural data suggest a diversity of oligomerization, and it remains unknown whether common elements mediating these interactions exist or whether dimerization of ATP synthase occurred independently and multiple times in evolution \(^{4}\) . + +<|ref|>text<|/ref|><|det|>[[67, 601, 880, 771]]<|/det|> +The ATP synthase of \*Trypanosoma brucei\*, a representative of kinetoplastids and an established medically important model organism causing the sleeping sickness, is highly divergent, exemplified by the pyramid- shaped \(\mathrm{F_1}\) head containing a phylum specific subunit \(^{12,13}\) . The dimers are sensitive to the lack of cardiolipin \(^{14}\) and form short left- handed helical segments that extend across the membrane ridge of the discoidal cristae \(^{15}\) . Uniquely among aerobic eukaryotes, the mammalian life cycle stage of \*T. brucei\* utilizes the reverse mode of ATP synthase, using the enzyme as a proton pump to maintain mitochondrial membrane potential at the expense of ATP \(^{16,17}\) . In contrast, the insect stages of the parasite employ the ATP- producing forward mode of the enzyme \(^{18,19}\) . + +<|ref|>text<|/ref|><|det|>[[68, 776, 880, 887]]<|/det|> +Given the conservation of the core subunits, the different nature of oligomerization and the ability to test structural hypotheses biochemically, we reasoned that investigation of the \*T. brucei\* ATP synthase structure and function would provide the missing evolutionary link to understand how the monomers interact to form physiological dimers. Here, we address this question by combining structural, functional and evolutionary analysis of the \*T. brucei\* ATP synthase dimer. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 185, 101]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[118, 108, 547, 126]]<|/det|> +## Cryo-EM structure of the T. brucei ATP synthase + +<|ref|>text<|/ref|><|det|>[[117, 131, 881, 281]]<|/det|> +We purified ATP synthase dimers from cultured T. brucei procyclic trypomastigotes by affinity chromatography with a recombinant natural protein inhibitor TbIF \(^{20}\) , and subjected the sample to cryo- EM analysis (Extended Data Fig. 1 and 2). Using masked refinements, maps were obtained for the membrane region, the rotor, and the peripheral stalk. To describe the conformational space of the T. brucei ATP synthase, we resolved ten distinct rotary substates, which were refined to 3.5- 4.3 Å resolution. Finally, particles with both monomers in rotational state 1 were selected, and the consensus structure of the dimer was refined to 3.2 Å resolution (Extended Data Table 1, Extended Data Fig. 2). + +<|ref|>text<|/ref|><|det|>[[117, 286, 881, 437]]<|/det|> +Unlike the wide- angle architecture of dimers found in animals and fungi, the T. brucei ATP synthase displays an angle of \(60^{\circ}\) between the two \(\mathrm{F}_{1} / \mathrm{c}\) - ring subcomplexes. The model of the T. brucei ATP synthase includes all 25 different subunits, nine of which are lineage- specific (Fig. 1a, Supplementary Video 1, Extended Data Fig. 3). We named the subunits according to the previously proposed nomenclature \(^{21 - 23}\) (Extended Data Table 2). In addition, we identified and modeled 36 bound phospholipids, including 24 cardiolipins (Extended Data Fig. 4). Both detergents used during purification, n- dodecyl \(\beta\) - D- maltoside ( \(\beta\) - DDM) and glyco- diosgenin (GDN) are also resolved in the periphery of the membrane region (Extended Data Fig. 5). + +<|ref|>text<|/ref|><|det|>[[115, 441, 881, 648]]<|/det|> +In the catalytic region, \(\mathrm{F}_{1}\) is augmented by three copies of subunit p18, each bound to subunit- \(\alpha^{12,13}\) . Our structure shows that p18 is involved in the unusual attachment of \(\mathrm{F}_{1}\) to the peripheral stalk. The membrane region includes eight conserved \(\mathrm{F}_{0}\) subunits ( \(b\) , \(d\) , \(f\) , \(8\) , \(i / j\) , \(k\) , \(e\) , and \(g\) ) arranged around the central proton translocator subunit- \(a\) . We identified those subunits based on the structural similarity and matching topology to their yeast counterparts (Fig 2). For subunit- \(b\) , a single transmembrane helix superimposes well with \(b\mathrm{H}1\) from yeast and anchors the newly identified subunit- \(e\) and - \(g\) to the \(\mathrm{F}_{0}\) (Fig 2a); a long helix \(b\mathrm{H}2\) , which constitutes the central part of the peripheral stalk in other organisms is absent in \(T\) . brucei. The sequence of this highly reduced subunit- \(b\) shows \(18\%\) identity and \(40\%\) similarity to \(E\) . gracilis subunit- \(b^{10}\) , representing a diverged homolog (Extended Data Fig. 6). No alternative subunit- \(b^{24}\) is found in our structure. + +<|ref|>text<|/ref|><|det|>[[113, 653, 880, 880]]<|/det|> +The membrane region contains a peripheral subcomplex, formed primarily by the phylum- specific ATPTB1,6,12 and ATPEG3 (Fig. 1b). It is separated from the conserved core by a membrane- intrinsic cavity, in which nine bound cardiolipins are resolved (Fig. 1c), and the C- terminus of ATPTB12 interacts with the lumenal \(\beta\) - barrel of the \(c_{10}\) - ring. In the cavity of the decameric \(c\) - ring near the matrix side, 10 Arg66c residues coordinate a ligand density, which is consistent with a pyrimidine ribonucleoside triphosphate (Fig. 1d). We assign this density as uridine- triphosphate (UTP), due to its large requirement in the mitochondrial RNA metabolism of African trypanosomes being a substrate for post- transcriptional RNA editing \(^{25}\) , and addition of poly- uridine tails to gRNAs and rRNAs \(^{26,27}\) , as well as due to low abundance of cytidine triphosphate (CTP) \(^{28}\) . The nucleotide base is inserted between two Arg82c residues, whereas the triphosphate region is coordinated by another five Arg82c residues, with Tyr79s and Asn76s providing asymmetric coordination contacts. The presence of a nucleotide inside the \(c\) - ring is + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 84, 880, 121]]<|/det|> +surprising, given the recent reports of phospholipids inside the \(c\) - rings in mammals \(^{5,6}\) and ciliates \(^{9}\) , indicating that a range of different ligands can provide structural scaffolding. + +<|ref|>image<|/ref|><|det|>[[115, 128, 884, 540]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 544, 712, 562]]<|/det|> +
Fig. 1: The T. brucei ATP synthase structure with lipids and ligands.
+ +<|ref|>text<|/ref|><|det|>[[115, 568, 881, 737]]<|/det|> +a, Front and side views of the composite model with both monomers in rotational state 1. The two \(\mathrm{F}_1 / c_{10}\) - ring complexes, each augmented by three copies of the phylum- specific p18 subunit, are tied together at a \(60^{\circ}\) - angle. The membrane- bound \(\mathrm{F}_0\) region displays a unique architecture and is composed of both conserved and phylum- specific subunits. b, Side view of the \(\mathrm{F}_0\) region showing the lumenal interaction of the ten- stranded \(\beta\) - barrel of the \(c\) - ring (grey) with ATPTB12 (pale blue). The lipid- filled peripheral \(\mathrm{F}_0\) cavity is indicated. c, Close- up view of the bound lipids within the peripheral \(\mathrm{F}_0\) cavity with cryo- EM density shown. d, Top view into the decameric \(c\) - ring with a bound pyrimidine ribonucleoside triphosphate, assigned as UTP. Map density shown in transparent blue, interacting residues shown. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 81, 880, 258]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[118, 261, 518, 279]]<|/det|> +
Fig. 2: Identification of conserved \(\mathbf{F}_0\) subunits.
+ +<|ref|>text<|/ref|><|det|>[[117, 285, 881, 378]]<|/det|> +a, Top view of the membrane region with \(T\) . brucei subunits (colored) overlaid with \(S\) . cerevisiae structure (gray transparent). Close structural superposition and matching topology allowed the assignment of conserved subunits based on matching topology and location. b, Superposition of subunits- \(e\) and - \(g\) with their \(S\) . cerevisiae counterparts (PDB 6B2Z) confirms their identity. + +<|ref|>sub_title<|/ref|><|det|>[[118, 408, 596, 425]]<|/det|> +## Peripheral stalk flexibility and distinct rotational states + +<|ref|>text<|/ref|><|det|>[[115, 432, 881, 714]]<|/det|> +The trypanosomal peripheral stalk displays a markedly different architecture compared to its yeast and mammalian counterparts. In the opisthokont complexes, the peripheral stalk is organized around the long \(b\mathrm{H}2\) , which extends from the membrane \(\sim 15 \mathrm{nm}\) into the matrix and attaches to OSCP at the top of \(\mathrm{F}_1^{5,7}\) . By contrast, \(T\) . brucei lacks the canonical \(b\mathrm{H}2\) and instead, helices 5- 7 of divergent subunit- \(d\) and the C- terminal helix of extended subunit- 8 bind to a C- terminal extension of OSCP at the apical part of the peripheral stalk (Fig. 3a). The interaction between OSCP and subunit- \(d\) and - 8 is stabilized by soluble ATPTB3 and ATPTB4. The peripheral stalk is rooted to the membrane subcomplex by a transmembrane helix of subunit- 8, wrapped on the matrix side by helices 8- 11 of subunit- \(d\) . Apart from the canonical contacts at the top of \(\mathrm{F}_1\) , the peripheral stalk is attached to the \(\mathrm{F}_1\) via a euglenozoa- specific C- terminal extension of OSCP, which contains a disordered linker and a terminal helix hairpin extending between the \(\mathrm{F}_1\) - bound p18 and subunits - \(d\) and - 8 of the peripheral stalk (Fig. 3a, Supplementary Videos 2,3). Another interaction of \(\mathrm{F}_1\) with the peripheral stalk occurs between the stacked C- terminal helices of subunit- \(\beta\) and - \(d\) (Fig. 3b), the latter of which structurally belongs to \(\mathrm{F}_1\) and is connected to the peripheral stalk via a flexible linker. + +<|ref|>text<|/ref|><|det|>[[115, 719, 881, 908]]<|/det|> +To assess whether the unusual peripheral stalk architecture influences the rotary mechanism, we analysed 10 classes representing different rotational states. The three main states (1- 3) result from a \(\sim 120^{\circ}\) rotation of the central stalk subunit- \(\gamma\) , and we identified five (1a- 1e), four (2a- 2d) and one (3) classes of the respective main states. The rotor positions of the rotational states 1a, 2a and 3 are related by steps of \(117^{\circ}\) , \(136^{\circ}\) and \(107^{\circ}\) , respectively. Throughout all the identified substeps of the rotational state 1 (classes 1a to 1e) the rotor turns by \(\sim 33^{\circ}\) , which corresponds approximately to the advancement by one subunit- \(c\) of the \(c_{10}\) - ring. While rotating along with the rotor, the \(\mathrm{F}_1\) headpiece lags behind, advancing by only \(\sim 13^{\circ}\) . During the following transition from 1e to 2a, the rotor advances by \(\sim 84^{\circ}\) , whereas the \(\mathrm{F}_1\) headpiece rotates \(\sim 22^{\circ}\) in the opposite direction (Fig. 3c,d). This generates a counter- directional torque between the two motors, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 272]]<|/det|> +which is consistent with a power- stroke mechanism. Albeit with small differences in step size, this mechanism is consistent with a previous observation in the Polytomella ATP synthase8. However, due to its large, rigid peripheral stalk, the Polytomella ATP synthase mainly displays rotational substeps, whereas the Trypanosoma \(\mathrm{F_1}\) also displays a tilting motion of \(\sim 8^{\circ}\) revealed by rotary states 1 and 2 (Fig. 3e, Supplementary Video 2). The previously reported hinge motion between the N- and C- terminal domains of \(\mathrm{OSCP^8}\) is not found in our structures, instead, the conformational changes of the \(\mathrm{F_1 / c_{10}}\) - ring subcomplex are accommodated by a \(5^{\circ}\) bending of the apical part of the peripheral stalk. (Fig. 3e, Supplementary Videos 2,3). Together, the structural data indicate that the divergent peripheral stalk attachment confers greater conformational flexibility to the \(T\) . brucei ATP synthase. + +<|ref|>image<|/ref|><|det|>[[115, 295, 880, 700]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 707, 881, 856]]<|/det|> +
Fig. 3: A divergent peripheral stalk allows high flexibility during rotary catalysis. a, N-terminal OSCP extension provides a permanent central stalk attachment, while the C-terminal extension provides a phylum-specific attachment to the divergent peripheral stalk. b, The C-terminal helices of subunits - \(\beta\) and - \(d\) provide a permanent \(\mathrm{F_1}\) attachment. c, Substeps of the \(c\) - ring during transition from rotational state 1 to 2. d, \(\mathrm{F_1}\) motion accommodating steps shown in (c). After advancing along with the rotor to state 1e, the \(\mathrm{F_1}\) rotates in the opposite direction when transitioning to state 2a. e, Tilting motion of \(\mathrm{F_1}\) and accommodating bending of the peripheral stalk.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 82, 882, 530]]<|/det|> +Lumenal proton half- channel is insulated by a lipid and contains ordered water moleculesThe mechanism of proton translocation involves sequential protonation of E102 of subunits- \(c\) , rotation of the \(c_{10}\) - ring with neutralized E102c exposed to the phospholipid bilayer, and release of protons on the other side of the membrane. The sites of proton binding and release are separated by the conserved R146 contributed by the horizontal helix H5 of subunit- \(a\) and are accessible from the cristae lumen and mitochondrial matrix by aqueous half- channels (Fig. 4a). Together, R146 and the adjacent N209 coordinate a pair of water molecules in between helices H5 and H6 (Fig. 4b). A similar coordination has been observed in the Polytomella ATP synthase8. The coordination of water likely restricts the R146 to rotamers that extend towards the \(c\) - ring, with which it is thought to interact.In our structure, the lumenal half- channel is filled with a network of resolved water densities, ending in a chain of five ordered water molecules (W1- W5; Fig. 4c,d,e). The presence of ordered water molecules in the aqueous channel is consistent with a Grotthuss- type mechanism for proton transfer, which would not require long- distance diffusion of water molecules5. However, because some distances between the observed water molecules are too large for direct hydrogen bonding, proton transfer may involve both coordinated and disordered water molecules. The distance of 7 Å between the last resolved water (W1) and D202a, the conserved residue that is thought to transfer protons to the \(c\) - ring, is too long for direct proton transfer. Instead, it may occur via the adjacent H155a. Therefore, our structure resolves individual elements participating in proton transport (Fig. 4d,e).The lumenal proton half- channel in the mammalian5,6 and apicomplexan11 ATP synthase is lined by the transmembrane part of \(b\) H2, which is absent in T. brucei. Instead, the position of \(b\) H2 is occupied by a fully ordered phosphatidylcholine in our structure (PC1; Fig. 4a,c). Therefore, a bound lipid replaces a proteinaceous element in the proton path. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 82, 885, 470]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 475, 881, 641]]<|/det|> +
Fig. 4: The lumenal half-channel contains ordered water molecules and is confined by an \(\mathbf{F}_0\) -bound lipid. a, Subunit- \(a\) (green) with the matrix (orange) and lumenal (light blue) channels, and an ordered phosphatidylcholine (PC1; blue). E102 of the \(c_{10}\) -ring shown in grey. b, Close-up view of the highly conserved R146a and N209a, which coordinate two water molecules between helices H5-6a. c, Sideview of the lumenal channel with proton pathway (light blue) and confining phosphatidylcholine (blue). d, Chain of ordered water molecules in the lumenal channel. Distances between the W1-W5 (red) are 5.2, 3.9, 7.3 and 4.8 Å, respectively. e, The ordered waters extend to H155a, which likely mediates the transfer of protons to D202a.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 673, 692, 690]]<|/det|> +## Subunit- \(g\) facilitates assembly of different ATP synthase oligomers + +<|ref|>text<|/ref|><|det|>[[115, 696, 881, 902]]<|/det|> +Despite sharing a set of conserved \(\mathrm{F}_0\) subunits, the T. brucei ATP synthase dimer displays a markedly different dimer architecture compared to previously determined structures. First, its dimerization interface of \(3,600\mathrm{\AA}^2\) is smaller than that of the E. gracilis type- IV (10,000 \(\mathrm{\AA}^2\) ) and the T. thermophila type- III ATP synthases (16,000 \(\mathrm{\AA}^2\) ). Second, unlike mammalian and fungal ATP synthase, in which the peripheral stalks extend in the plane defined by the two rotary axes, in our structure the monomers are rotated such that the peripheral stalks are offset laterally on the opposite sides of the plane. Due to the rotated monomers, this architecture is associated with a specific dimerization interface, where two subunit- \(g\) copies interact homotypically on the \(\mathrm{C}_2\) symmetry axis (Fig. 5a, Supplementary Video 1). Both copies of H1- \(2_{\mathrm{g}}\) extend horizontally along the matrix side of the membrane, clamping against each other (Fig. 5c,e). This facilitates formation of contacts between an associated transmembrane helix + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 880, 215]]<|/det|> +of subunit- \(e\) with the neighbouring monomer via subunit- \(a\) ' in the membrane, and - \(f\) ' in the lumen, thereby further contributing to the interface (Fig. 5b). Thus, the ATP synthase dimer is assembled via the subunit- \(e / g\) module. The C- terminal part of the subunit- \(e\) helix extends into the lumen, towards the ten- stranded \(\beta\) - barrel of the \(c\) - ring (Extended Data Fig. 7a). The terminal 23 residues are disordered with poorly resolved density connecting to the detergent plug of the \(c\) - ring \(\beta\) - barrel (Extended Data Fig. 7b). This resembles the lumenal C- terminus of subunit- \(e\) in the bovine structure \(^5\) , indicating a conserved interaction with the \(c\) - ring. + +<|ref|>text<|/ref|><|det|>[[115, 220, 880, 352]]<|/det|> +The \(e / g\) module is held together by four bound cardiolipins in the matrix leaflet, anchoring it to the remaining \(\mathrm{F}_0\) region (Fig. 5c). The head groups of the lipids are coordinated by polar and charged residues with their acyl chains filling a central cavity in the membrane region at the dimer interface (Fig 5c, Extended Data Fig. 4f). Cardiolipin binding has previously been reported to be obligatory for dimerization in secondary transporters \(^{29}\) and the depletion of cardiolipin synthase resulted in reduced levels of ATP synthase in bloodstream trypanosomes \(^{14}\) . + +<|ref|>text<|/ref|><|det|>[[115, 357, 880, 677]]<|/det|> +Interestingly, for yeasts, early blue native gel electrophoresis \(^{30}\) and subtomogram averaging studies \(^{2}\) suggested subunit- \(g\) as potentially dimer- mediating, however the \(e / g\) modules are located laterally opposed on either side of the dimer long axis, in the periphery of the complex, \(\sim 8.5 \mathrm{nm}\) apart from each other. Because the \(e / g\) modules do not interact directly within the yeast ATP synthase dimer, they have been proposed to serve as membrane- bending elements, whereas the major dimer contacts are formed by subunit- \(a\) and - \(i / j^{7}\) . In mammals, the \(e / g\) module occupies the same position as in yeasts, forming the interaction between two diagonal monomers in a tetramer \(^{5,6,31}\) , as well as between parallel dimers \(^{32}\) . The comparison with our structure shows that the overall organization of the intra- dimeric trypanosomal and inter- dimeric mammalian \(e / g\) module is structurally similar (Fig. 5d). Furthermore, kinetoplastid parasites and mammals share conserved GXXXG motifs in subunit- \(e^{33}\) and - \(g\) (Extended Data Fig. 8), which allow close interaction of their transmembrane helices (Fig. 5e), providing further evidence for subunit homology. However, while the mammalian ATP synthase dimers are arranged perpendicularly to the long axis of their rows along the edge of cristae \(^{34}\) , the \(T\) . brucei dimers on the rims of discoidal cristae are inclined \(\sim 45^{\circ}\) to the row axis \(^{15}\) . Therefore, the \(e / g\) module occupies equivalent positions in the rows of both evolutionary distant groups (Fig. 5f and reference 32). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[149, 80, 848, 638]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 644, 883, 909]]<|/det|> +
Fig. 5: The homotypic dimerization motif of subunit-g generates a conserved oligomerization module. a, Side view with dimerising subunits colored. b,c, The dimer interface is constituted by (b) subunit- \(e^{\prime}\) contacting subunit- \(a\) in the membrane and subunit- \(f\) in the lumen, (c) subunits \(e\) and \(g\) from both monomers forming a subcomplex with bound lipids. d, Subunit- \(g\) and - \(e\) form a dimerization motif in the trypanosomal (type-IV) ATP synthase dimer (this study), the same structural element forms the oligomerization motif in the porcine ATP synthase tetramer. The structural similarity of the pseudo-dimer (i.e., two diagonal monomers from adjacent dimers) in the porcine structure with the trypanosomal dimer suggests that type I and IV ATP synthase dimers have evolved through divergence from a common ancestor. e, The dimeric subunit- \(e / g\) structures are conserved in pig (PDB 6ZNA) and T. brucei (this work) and contain a conserved GXXXG motif (orange) mediating interaction of transmembrane helices. f, Models of the ATP synthase dimers fitted into subtomogram averages of short oligomers \(^{15}\) : matrix view, left; cut-through, middle, lumenal view, right (EMD-3560).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 85, 880, 510]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[120, 515, 880, 700]]<|/det|> +
Fig. 6: RNAi knockdown of subunit-g results in monomerization of ATP synthase. a, Growth curves of non-induced (solid lines) and tetracycline-induced (dashed lines) RNAi cell lines grown in the presence (black) or absence (brown) of glucose. The insets show relative levels of the respective target mRNA at indicated days post-induction (DPI) normalized to the levels of 18S rRNA (black bars) or \(\beta\) -tubulin (white bars). b, Immunoblots of mitochondrial lysates from indicated RNAi cell lines resolved by BN-PAGE probed with antibodies against indicated ATP synthase subunits. c, Representative immunoblots of whole cell lysates from indicated RNAi cell lines probed with indicated antibodies. d, Quantification of three replicates of immunoblots in (c). Values were normalized to the signal of the loading control Hsp70 and to non-induced cells. Plots show means with standard deviations (SD).
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 85, 750, 102]]<|/det|> +## Subunit- \(g\) retains the dimer but is not essential for the catalytic monomer + +<|ref|>text<|/ref|><|det|>[[115, 107, 880, 371]]<|/det|> +To validate structural insights, we knocked down each individual \(\mathrm{F_0}\) subunit by inducible RNA interference (RNAi). All target mRNAs dropped to \(5 - 20\%\) of their original levels after two and four days of induction (Fig. 6a and Extended Data Fig. 9a). Western blot analysis of wholecell lysates resolved by denaturing electrophoresis revealed decreased levels of \(\mathrm{F_0}\) subunits ATPB1 and - \(d\) suggesting that the integrity of the \(\mathrm{F_0}\) moiety depends on the presence of other \(\mathrm{F_0}\) subunits (Fig. 6c,d). Immunoblotting of mitochondrial complexes resolved by blue native polyacrylamide gel electrophoresis (BN- PAGE) with antibodies against \(\mathrm{F_1}\) and \(\mathrm{F_0}\) subunits revealed a strong decrease or nearly complete loss of dimeric and monomeric forms of ATP synthases four days after induction of RNAi of most subunits \((b, e, f, i / j, k, 8\) , ATPTB3, ATPTB4, ATPTB6, ATPTB11, ATPTB12, ATPEG3 and ATPEG4), documenting an increased instability of the enzyme or defects in its assembly. Simultaneous accumulation in \(\mathrm{F_1}\) - ATPase, as observed by BN- PAGE, demonstrated that the catalytic moiety remains intact after the disruption of the peripheral stalk or the membrane subcomplex (Fig. 6b,c,d and Extended Data Fig. 9b). + +<|ref|>text<|/ref|><|det|>[[115, 376, 881, 601]]<|/det|> +In contrast to the other targeted \(\mathrm{F_0}\) subunits, the downregulation of subunit- \(g\) with RNAi resulted in a specific loss of dimeric complexes with concomitant accumulation of monomers (Fig. 6b), indicating that it is required for dimerization, but not for the assembly and stability of the monomeric \(\mathrm{F_1F_0}\) ATP synthase units. Transmission electron microscopy of thin cell sections revealed that the ATP synthase monomerization in the subunit- \(g^{\mathrm{RNAi}}\) cell line had the same effect on mitochondrial ultrastructure as nearly complete loss of monomers and dimers upon knockdown of subunit- 8. Both cell lines exhibited decreased cristae counts and aberrant cristae morphology (Fig. 7a,b), including the appearance of round shapes reminiscent of structures detected upon deletion of subunit- \(g\) or - e in Saccharomyces cerevisiae1. These results indicate that monomerization prevents the trypanosomal ATP synthase from assembling into short helical rows on the rims of the discoidal cristae15, as has been reported for impaired oligomerization in counterparts from other eukaryotes2,35. + +<|ref|>text<|/ref|><|det|>[[115, 605, 880, 758]]<|/det|> +Despite the altered mitochondrial ultrastructure, the subunit- \(g^{\mathrm{RNAi}}\) cells showed only a very mild growth phenotype, in contrast to all other RNAi cell lines that exhibited steadily slowed growth from day three to four after the RNAi induction (Fig. 7a, Extended Data Fig. 9a). This is consistent with the growth defects observed after the ablation of \(\mathrm{F_0}\) subunit ATPTB19 and \(\mathrm{F_1}\) subunits- \(\alpha\) and \(\mathrm{p18^{12}}\) . Thus, the monomerization of ATP synthase upon subunit- \(g\) ablation had only a negligible effect on the fitness of trypanosomes cultured in glucose- rich medium, in which ATP production by substrate level phosphorylation partially compensates for compromised oxidative phosphorylation36. + +<|ref|>text<|/ref|><|det|>[[115, 763, 881, 913]]<|/det|> +Measurement of oligomycin- sensitive ATP- dependent mitochondrial membrane polarization by safranin O assay in permeabilized cells showed that the proton pumping activity of the ATP synthase in the induced subunit- \(g^{\mathrm{RNAi}}\) cells is negligibly affected, demonstrating that the monomerized enzyme is catalytically functional. By contrast, RNAi downregulation of subunit- 8, ATPTB4 and ATPTB11, and ATPTB1 resulted in a strong decline of the mitochondrial membrane polarization capacity, consistent with the loss of both monomeric and dimeric ATP synthase forms (Fig. 7c). Accordingly, knockdown of the same subunits resulted in inability to produce ATP by oxidative phosphorylation (Fig. 7d). However, upon subunit- \(g\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 234]]<|/det|> +ablation the ATP production was affected only partially, confirming that the monomerized ATP synthase remains catalytically active. The \(\sim 50\%\) drop in ATP production of subunit- \(g^{\mathrm{RNAi}}\) cells can be attributed to the decreased oxidative phosphorylation efficiency due to the impaired cristae morphology. Indeed, when cells were cultured in the absence of glucose, enforcing the need for oxidative phosphorylation, knockdown of subunit- \(g\) results in a growth arrest, albeit one to two days later than knockdown of all other tested subunits (Fig. 6a). The data show that dimerization is critical when oxidative phosphorylation is the predominant source of ATP. + +<|ref|>image<|/ref|><|det|>[[175, 239, 822, 626]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 633, 881, 914]]<|/det|> +
Fig. 7: Monomerization of ATP synthase by subunit- \(g\) knockdown results in aberrant mitochondrial ultrastructure but does not abolish catalytic activity. a, Transmission electron micrographs of sections of non-induced or 4 days induced RNAi cell lines. Mitochondrial membranes and cristae are marked with blue and red arrowheads, respectively. Top panel shows examples of irregular, elongated and round cross-sections of mitochondria quantified in (b). b, Cristae numbers per vesicle from indicated induced (IND) or non-induced (NON) cell lines counted separately in irregular, elongated and round mitochondrial cross-section. Boxes and whiskers show \(25^{\mathrm{th}}\) to \(75^{\mathrm{th}}\) and \(5^{\mathrm{th}}\) to \(95^{\mathrm{th}}\) percentiles, respectively. The numbers of analysed cross-sections are indicated for each data point. Unpaired t-test, p-values are shown. c, Mitochondrial membrane polarization capacity of non-induced or RNAi-induced cell lines two and four DPI measured by Safranine O. Black and gray arrow indicate addition of ATP and oligomycin, respectively. d, ATP production in permeabilized non-induced (0) or RNAi-induced cells 2 and 4 DPI in the presence of indicated substrates and inhibitors. Error bars represent SD of three replicates. Gly3P, DL-glycerol phosphate; KCN, potassium cyanide; CATR, carboxyatractyloside
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 211, 100]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[118, 108, 880, 390]]<|/det|> +Our structure of the mitochondrial ATP synthase dimer from the mammalian parasite T. brucei offers new insight into the mechanism of membrane shaping, rotary catalysis, and proton transfer. Considering that trypanosomes belong to an evolutionarily divergent group of Kinetoplastida, the ATP synthase dimer has several interesting features that differ from other dimer structures. The subunit- \(b\) found in bacterial and other mitochondrial F- type ATP synthases appears to be highly reduced to a single transmembrane helix \(b\mathrm{H}1\) . The long \(b\mathrm{H}2\) , which constitutes the central part of the peripheral stalk in other organisms, and is also involved in the composition of the lumenal proton half- channel, is completely absent in \(T\) . brucei. Interestingly, the position of \(b\mathrm{H}2\) in the proton half channel is occupied by a fully ordered phosphatidylcholine molecule that replaces a well- conserved proteinaceous element in the proton path. Lack of the canonical \(b\mathrm{H}2\) also affects composition of the peripheral stalk in which the divergent subunit- \(d\) and subunit- \(8\) binds directly to a C- terminal extension of OSCP, indicating a remodeled peripheral stalk architecture. The peripheral stalk contacts the \(\mathrm{F}_1\) headpiece at several positions conferring greater conformational flexibility to the ATP synthase. + +<|ref|>text<|/ref|><|det|>[[118, 395, 880, 620]]<|/det|> +Using the structural and functional data, we also identified a conserved structural element of the ATP synthase that is responsible for its multimerization. Particularly, subunit- \(g\) is required for the dimerization, but dispensable for the assembly of the \(\mathrm{F}_1\mathrm{F}_0\) monomers. Although the monomerized enzyme is catalytically competent, the inability to form dimers results in defective cristae structure, and consequently leads to compromised oxidative phosphorylation and cease of proliferation. The cristae- shaping properties of mitochondrial ATP synthase dimers are critical for sufficient ATP production by oxidative phosphorylation, but not for other mitochondrial functions, as demonstrated by the lack of growth phenotype of subunit- \(g^{\mathrm{RNAi}}\) cells in the presence of glucose. Thus, trypanosomal subunit- \(g\) depletion strain represents an experimental tool to assess the roles of the enzyme's primary catalytic function and mitochondria- specific membrane- shaping activity, highlighting the importance of the latter for oxidative phosphorylation. + +<|ref|>text<|/ref|><|det|>[[118, 626, 880, 888]]<|/det|> +Based on our data and previously published structures, we propose an ancestral state with double rows of ATP synthase monomers connected by \(e / g\) modules longitudinally and by other \(\mathrm{F}_0\) subunits transversally. During the course of evolution, different pairs of adjacent ATP synthase monomer units formed stable dimers in individual lineages (Fig. 8). This gave rise to the highly divergent type- I and type- IV ATP synthase dimers with subunit- \(e / g\) modules serving either as oligomerization or dimerization motives, respectively. Because trypanosomes belong to the deep- branching eukaryotic supergroup Discoba, the proposed arrangement might have been present in the last eukaryotic common ancestor. Although sequence similarity of subunit- \(g\) is low and restricted to the single transmembrane helix, we found homologs of subunit- \(g\) in addition to Opisthokonta and Discoba also in Archaeplastida and Amoebozoa, which represent other eukaryotic supergroups, thus supporting the ancestral role in oligomerization (Extended Data Fig. 8). Taken together, our analysis reveals that mitochondrial ATP synthases that display markedly diverged architecture share the ancestral structural module that promotes oligomerization. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 81, 880, 255]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 264, 880, 414]]<|/det|> +
Fig. 8: The subunit-e/g module is an ancestral oligomerization motif of ATP synthase. Schematic model of the evolution of type-I and IV ATP synthases. Mitochondrial ATP synthases are derived from a monomeric complex of proteobacterial origin. In a mitochondrial ancestor, acquisition of mitochondria-specific subunits, including the subunit-e/g module resulted in the assembly of ATP synthase double rows, the structural basis for cristae biogenesis. Through divergence, different ATP synthase architectures evolved, with the subunit-e/g module functioning as an oligomerization (type I) or dimerization (type IV) motif, resulting in distinct row assemblies between mitochondrial lineages.
+ +<|ref|>sub_title<|/ref|><|det|>[[118, 444, 321, 460]]<|/det|> +## Materials and Methods + +<|ref|>sub_title<|/ref|><|det|>[[118, 468, 459, 484]]<|/det|> +## Cell culture and isolation of mitochondria + +<|ref|>text<|/ref|><|det|>[[115, 491, 881, 812]]<|/det|> +T. brucei procyclic strains were cultured in SDM-79 medium supplemented with \(10\%\) (v/v) fetal bovine serum. For growth curves in glucose-free conditions, cells were grown in SDM-80 medium with \(10\%\) dialysed FBS. RNAi cell lines were grown in presence of \(2.5 \mu \mathrm{g / ml}\) phleomycin and \(1 \mu \mathrm{g / ml}\) puromycin. For ATP synthase purification, mitochondria were isolated from the Lister strain 427. Typically, \(1.5 \times 10^{11}\) cells were harvested, washed in \(20 \mathrm{mM}\) sodium phosphate buffer pH 7.9 with \(150 \mathrm{mM}\) NaCl and \(20 \mathrm{mM}\) glucose, resuspended in hypotonic buffer \(1 \mathrm{mM}\) Tris-HCl pH 8.0, \(1 \mathrm{mM}\) EDTA, and disrupted by 10 strokes in a 40-ml Dounce homogenizer. The lysis was stopped by immediate addition of sucrose to \(0.25 \mathrm{M}\) . Crude mitochondria were pelleted (15 min at \(16,000 \mathrm{xg}\) , \(4^{\circ}\mathrm{C}\) ), resuspended in \(20 \mathrm{mM}\) Tris-HCl pH 8.0, \(250 \mathrm{mM}\) sucrose, \(5 \mathrm{mM}\) MgCl₂, \(0.3 \mathrm{mM}\) CaCl₂ and treated with \(5 \mu \mathrm{g / ml}\) DNase I. After 60 min on ice, one volume of the STE buffer (20 mM Tris-HCl pH 8.0, \(250 \mathrm{mM}\) sucrose, \(2 \mathrm{mM}\) EDTA) was added and mitochondria were pelleted (15 min at \(16000 \mathrm{xg}\) , \(4^{\circ}\mathrm{C}\) ). The pellet was resuspended in \(60\%\) (v/v) Percoll in STE and loaded on six linear 10-35% Percoll gradients in STE in polycarbonate tubes for SW28 rotor (Beckman). Gradients were centrifuged for 1 h at \(24,000 \mathrm{rpm}\) , \(4^{\circ}\mathrm{C}\) . The middle phase containing mitochondrial vesicles (15-20 ml per tube) was collected, washed four times in the STE buffer, and pellets were snap-frozen in liquid nitrogen and stored at \(- 80^{\circ}\mathrm{C}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 841, 567, 858]]<|/det|> +## Plasmid construction and generation of RNAi cell lines + +<|ref|>text<|/ref|><|det|>[[115, 866, 880, 902]]<|/det|> +To downregulate ATP synthase subunits by RNAi, DNA fragments corresponding to individual target sequences were amplified by PCR from Lister 427 strain genomic DNA using + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 881, 411]]<|/det|> +forward and reverse primers extended with restriction sites XhoI&KpnI and XbaI&BamHI, respectively (Extended Data Table 3). Each fragment was inserted into the multiple cloning sites 1 and 2 of pAZ0055 vector, derived from pRPHYG- iSL (courtesy of Sam Alsford) by replacement of hygromycin resistance gene with phleomycin resistance gene, with restriction enzymes KpnI/BamHI and XhoI/XbaI, respectively. Resulting constructs with tetracycline inducible T7 polymerase driven RNAi cassettes were linearized with NotI and transfected into a cell line derived from the Lister strain 427 by integration of the SmOx construct for expression of T7 polymerase and the tetracycline repressor \(^{37}\) into the \(\beta\) - tubulin locus. RNAi was induced in selected semi-clonal populations by addition of \(1 \mu \mathrm{g / ml}\) tetracycline and the downregulation of target mRNAs was verified by quantitative RT- PCR 2 and 4 days post induction. The total RNA isolated by an RNeasy Mini Kit (Qiagen) was treated with \(2 \mu \mathrm{g}\) of DNase I, and then reverse transcribed to cDNA with TaqMan Reverse Transcription kit (Applied Biosciences). qPCR reactions were set with Light Cycler 480 SYBR Green I Master mix (Roche), \(2 \mu \mathrm{l}\) of cDNA and \(0.3 \mu \mathrm{M}\) primers (Extended Data Table 3), and run on LightCycler 480 (Roche). Relative expression of target genes was calculated using - \(\Delta \Delta \mathrm{Ct}\) method with 18S rRNA or \(\beta\) - tubulin as endogenous reference genes and normalized to noninduced cells. + +<|ref|>sub_title<|/ref|><|det|>[[115, 440, 767, 459]]<|/det|> +## Denaturing and blue native polyacrylamide electrophoresis and immunoblotting + +<|ref|>text<|/ref|><|det|>[[115, 463, 881, 688]]<|/det|> +Whole cell lysates for denaturing sodium dodecyl sulphate polyacrylamide electrophoresis (SDS- PAGE) were prepared from cells resuspended in PBS buffer ( \(10 \mathrm{mM}\) phosphate buffer, \(130 \mathrm{mM}\) NaCl, pH 7.3) by addition of \(3 \mathrm{x}\) Laemmli buffer ( \(150 \mathrm{mM}\) Tris pH 6.8, \(300 \mathrm{mM}\) 1,4- dithiothreitol, \(6\%\) (w/v) SDS, \(30\%\) (w/v) glycerol, \(0.02\%\) (w/v) bromophenol blue) to final concentration of \(1 \times 10^{7}\) cells in \(30 \mu \mathrm{l}\) . The lysates were boiled at \(97^{\circ} \mathrm{C}\) for \(10 \mathrm{min}\) and stored at \(- 20^{\circ} \mathrm{C}\) . For immunoblotting, lysates from \(3 \times 10^{6}\) cells were separated on \(4 - 20\%\) gradient Tris- glycine polyacrylamide gels (BioRad 4568094), electroblotted onto a PVDF membrane (Pierce 88518), and probed with respective antibodies (Extended Data Table 4). Membranes were incubated with the Clarity Western ECL substrate (BioRad 1705060EM) and chemiluminescence was detected on a ChemiDoc instrument (BioRad). Band intensities were quantified densitometrically using the ImageLab software. The levels of individual subunits were normalized to the signal of mHsp70. + +<|ref|>text<|/ref|><|det|>[[115, 694, 881, 881]]<|/det|> +Blue native PAGE (BN- PAGE) was performed as described earlier \(^{12}\) with following modifications. Crude mitochondrial vesicles from \(2.5 \times 10^{8}\) cells were resuspended in \(40 \mu \mathrm{l}\) of Solubilization buffer A ( \(2 \mathrm{mM}\) \(\epsilon\) - aminocaproic acid (ACA), \(1 \mathrm{mM}\) EDTA, \(50 \mathrm{mM}\) NaCl, \(50 \mathrm{mM}\) Bis- Tris/HCl, pH 7.0) and solubilized with \(2\%\) (w/v) dodecylamtolamide ( \(\beta\) - DDM) for \(1 \mathrm{h}\) on ice. Lysates were cleared at \(16,000 \mathrm{g}\) for \(30 \mathrm{min}\) at \(4^{\circ} \mathrm{C}\) and their protein concentration was estimated using bicinchoninic acid assay. For each time point, a volume of mitochondrial lysate corresponding to \(4 \mu \mathrm{g}\) of total protein was mixed with \(1.5 \mu \mathrm{l}\) of loading dye ( \(500 \mathrm{mM}\) ACA, \(5\%\) (w/v) Coomassie Brilliant Blue G- 250) and \(5\%\) (w/v) glycerol and with \(1 \mathrm{M}\) ACA until a final volume of \(20 \mu \mathrm{l}\) /well, and resolved on a native PAGE 3- 12% Bis- Tris gel (Invitrogen). After the electrophoresis ( \(3 \mathrm{h}\) , \(140 \mathrm{V}\) , \(4^{\circ} \mathrm{C}\) ), proteins were transferred by electroblotting onto a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 85, 879, 120]]<|/det|> +PVDF membrane (2 h, 100 V, \(4^{\circ}\mathrm{C}\) , stirring), followed by immunodetection with an appropriate antibody (Extended Data Table 4). + +<|ref|>sub_title<|/ref|><|det|>[[118, 151, 541, 168]]<|/det|> +## Mitochondrial membrane polarization measurement + +<|ref|>text<|/ref|><|det|>[[115, 174, 880, 400]]<|/det|> +The capacity to polarize mitochondrial membrane was determined fluorometrically employing safranin O dye (Sigma S2255) in permeabilized cells. For each sample, \(2 \times 10^{7}\) cells were harvested and washed with ANT buffer (8 mM KCl, 110 mM K- gluconate, 10 mM NaCl, 10 mM free- acid Hepes, 10 mM \(\mathrm{K_2HPO_4}\) , 0.015 mM EGTA potassium salt, 10 mM mannitol, 0.5 mg/ml fatty acid- free BSA, 1.5 mM \(\mathrm{MgCl_2}\) , pH 7.25). The cells were permeabilized by \(8 \mu \mathrm{M}\) digitonin in 2 ml of ANT buffer containing \(5 \mu \mathrm{M}\) safranin O. Fluorescence was recorded for 700 s in a Hitachi F- 7100 spectrofluorimeter (Hitachi High Technologies) at a 5- Hz acquisition rate, using 495 nm and 585 nm excitation and emission wavelengths, respectively. \(1 \mathrm{mM}\) ATP (PanReac AppliChem A1348,0025) and \(10 \mu \mathrm{g / ml}\) oligomycin (Sigma O4876) were added after 230 s and 500 s, respectively. Final addition of the uncoupler SF 6847 (250 nM; Enzo Life Sciences BML- EI215- 0050) served as a control for maximal depolarization. All experiments were performed at room temperature and constant stirring. + +<|ref|>sub_title<|/ref|><|det|>[[118, 429, 300, 445]]<|/det|> +## ATP production assay + +<|ref|>text<|/ref|><|det|>[[115, 451, 880, 696]]<|/det|> +ATP production assayATP production in digitonin- isolated mitochondria was performed as described previously38. Briefly, \(1 \times 10^{8}\) cells per time point were lysed in SoTE buffer (600 mM sorbitol, 2 mM EDTA, 20 mM Tris- HCl, pH 7.75) containing 0.015% (w/v) digitonin for 5 min on ice. After centrifugation (3 min, 4,000 g, \(4^{\circ}\mathrm{C}\) ), the soluble cytosolic fraction was discarded and the organellar pellet was resuspended in 75 \(\mu \mathrm{l}\) of ATP production assay buffer (600 mM sorbitol, 10 mM \(\mathrm{MgSO_4}\) , 15 mM potassium phosphate buffer pH 7.4, 20 mM Tris- HCl pH 7.4, 2.5 mg/ml fatty acid- free BSA). ATP production was induced by addition of 20 mM DL- glycerol phosphate (sodium salt) and 67 \(\mu \mathrm{M}\) ADP. Control samples were preincubated with the inhibitors potassium cyanide (1 mM) and carboxyatractyloside (6.5 \(\mu \mathrm{M}\) ) for 10 min at room temperature. After 30 min at room temperature, the reaction was stopped by addition of 1.5 \(\mu \mathrm{l}\) of 70% perchloric acid. The concentration of ATP was estimated using the Roche ATP Bioluminescence Assay Kit HS II in a Tecan Spark plate reader. The luminescence values of the RNAi induced samples were normalized to that of the corresponding noninduced sample. + +<|ref|>sub_title<|/ref|><|det|>[[118, 726, 558, 742]]<|/det|> +## Thin sectioning and transmission electron microscopy + +<|ref|>text<|/ref|><|det|>[[115, 748, 880, 899]]<|/det|> +The samples were centrifuged and pellet was transferred to the specimen carriers which were completed with 20% BSA and immediately frozen using high pressure freezer Leica EM ICE (Leica Microsystems). Freeze substitution was performed in the presence of 2% osmium tetroxide diluted in 100% acetone at \(- 90^{\circ}\mathrm{C}\) . After 96 h, specimens were warmed to \(- 20^{\circ}\mathrm{C}\) at a slope 5 \({}^{\circ}\mathrm{C / h}\) . After the next 24 h, the temperature was increased to 3\({}^{\circ}\mathrm{C}\) (3\({}^{\circ}\mathrm{C / h}\) ). At room temperature, samples were washed in acetone and infiltrated with 25%, 50%, 75% acetone/resin EMbed 812 (EMS) mixture 1 h at each step. Finally, samples were infiltrated in 100% resin and polymerized at 60\({}^{\circ}\mathrm{C}\) for 48h. Ultrathin sections (70 nm) were cut using a + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 880, 140]]<|/det|> +diamond knife, placed on copper grids and stained with uranyl acetate and lead citrate. TEM micrographs were taken with Mega View III camera (SIS) using a JEOL 1010 TEM operating at an accelerating voltage of \(80\mathrm{kV}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 170, 494, 187]]<|/det|> +## Purification of T. brucei ATP synthase dimers + +<|ref|>text<|/ref|><|det|>[[115, 191, 881, 722]]<|/det|> +Mitochondria from \(3\mathrm{x}10^{11}\) cells were lysed by \(1\%\) (w/v) \(\beta\) - DDM in \(60\mathrm{ml}\) of \(20\mathrm{mM}\) Bis- tris propane pH 8.0 with \(10\%\) glycerol and EDTA- free Complete protease inhibitors (Roche) for \(20\mathrm{min}\) at \(4^{\circ}\mathrm{C}\) . The lysate was cleared by centrifugation at \(30,000\mathrm{xg}\) for \(20\mathrm{min}\) at \(4^{\circ}\mathrm{C}\) and adjusted to pH 6.8 by drop- wise addition of \(1\mathrm{M}3\) - (N- morpholino) propanesulfonic acid pH 5.9. Recombinant TbIF1 without dimerization region, whose affinity to \(\mathrm{F_1}\) - ATPase was increased by N- terminal truncation and substitution of tyrosine 36 with tryptophan20, with a C- terminal glutathione S- transferase (GST) tag (TbIF1(9- 64)- Y36W- GST) was added in approximately 10- fold molar excess over the estimated content of ATP synthase. Binding of TbIF1 was facilitated by addition of neutralized \(2\mathrm{mM}\) ATP with \(4\mathrm{mM}\) magnesium sulphate. After \(5\mathrm{min}\) , sodium chloride was added to \(100\mathrm{mM}\) , the lysate was filtered through a \(0.2\mu \mathrm{m}\) syringe filter and immediately loaded on \(5\mathrm{ml}\) GSTrap HP column (Cytiva) equilibrated in 20 mM Bis- Tris- Propane pH 6.8 binding buffer containing \(0.1\%\) (w/v) glyco- diosgenin (GDN; Avanti Polar Lipids), \(10\%\) (v/v) glycerol, \(100\mathrm{mM}\) sodium chloride, \(1\mathrm{mM}\) tris(2- carboxyethyl)phosphine (TCEP), \(1\mathrm{mM}\) ATP, \(2\mathrm{mM}\) magnesium sulphate, \(15\mu \mathrm{g / ml}\) cardiolipin, \(50\mu \mathrm{g / ml}\) 1- palmitoyl- 2- oleoyl- sn- glycero- 3- phosphocholine (POPC), \(25\mu \mathrm{g / ml}\) 1- palmitoyl- 2- oleoyl- sn- glycero- 3- phosphoethanolamine (POPE) and \(10\mu \mathrm{g / ml}\) 1- palmitoyl- 2- oleoyl- sn- glycero- 3- [phospho- rac- (1- glycerol)] (POPG). All phospholipids were purchased from Avanti Polar Lipids (catalog numbers 840012C, 850457C, 850757C and 840757, respectively). ATP synthase was eluted with a gradient of \(20\mathrm{mM}\) reduced glutathione in Tris pH 8.0 buffer containing the same components as the binding buffer. Fractions containing ATP synthase were pooled and concentrated to \(150\mu \mathrm{l}\) on Vivaspin centrifugal concentrator with 30 kDa molecular weight cut- off. The sample was fractionated by size exclusion chromatography on a Superose 6 Increase 3.2/300 GL column (Cytiva) equilibrated in a buffer containing 20 mM Tris pH 8.0, \(100\mathrm{mM}\) sodium chloride, \(2\mathrm{mM}\) magnesium chloride, \(0.1\%\) (w/v) GDN, \(3.75\mu \mathrm{g / ml}\) cardiolipin, \(12.5\mu \mathrm{g / ml}\) POPC, \(6.25\mu \mathrm{g / ml}\) POPE and \(2.5\mu \mathrm{g / ml}\) POPG at 0.03 ml/min. Fractions corresponding to ATP synthase were pooled, supplemented with \(0.05\%\) (w/v) \(\beta\) - DDM that we and others experimentally found to better preserve dimer assemblies in cryo- EM39, and concentrated to \(50\mu \mathrm{l}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 752, 518, 769]]<|/det|> +## Preparation of cryo-EM grids and data collection + +<|ref|>text<|/ref|><|det|>[[118, 775, 881, 887]]<|/det|> +Samples were vitrified on glow- discharged Quantifoil R1.2/1.3 Au 300- mesh grids after blotting for 3 sec, followed by plunging into liquid ethane using a Vitrobot Mark IV. 5,199 movies were collected using EPU 1.9 on a Titan Krios (ThermoFisher Scientific) operated at \(300\mathrm{kV}\) at a nominal magnification of \(165\mathrm{kx}\) (0.83 A/pixel) with a Quantum K2 camera (Gatan) using a slit width of \(20\mathrm{eV}\) . Data was collected with an exposure rate of 3.6 electrons/px/s, a total exposure of 33 electrons/ \(\mathrm{\AA}^2\) and 20 frames per movie. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 108, 880, 446]]<|/det|> +Image processing was performed within the Scipion 2 framework40, using RELION- 3.0 unless specified otherwise. Movies were motion- corrected using the RELION implementation of the MotionCor2. 294,054 particles were initially picked using reference- based picking in Gautomatch (http://www.mrc- lmb.cam.ac.uk/kzhang/Gautomatch) and Contrast- transfer function parameters were using GCTF41. Subsequent image processing was performed in RELION- 3.0 and 2D and 3D classification was used to select 100,605 particles, which were then extracted in an unbinned 560- pixel box (Fig. S1). An initial model of the ATP synthase dimer was obtained using de novo 3D model generation. Using masked refinement with applied \(\mathrm{C}_2\) symmetry, a 2.7- Å structure of the membrane region was obtained following per- particle CTF refinement and Bayesian polishing. Following \(\mathrm{C}_2\) - symmetry expansion and signal subtraction of one monomer, a 3.7 Å map of the peripheral stalk was obtained. Using 3D classification \((\mathrm{T} = 100)\) of aligned particles, with a mask on the \(\mathrm{F}_{1 / c}\) - ring region, 10 different rotational substates were then separated and maps at 3.5- 4.3 Å resolution were obtained using 3D refinement. The authors note that the number of classes identified in this study likely reflects the limited number of particles, rather than the complete conformational space of the complex. By combining particles from all states belonging to main rotational state 1, a 3.7- Å map of the rotor and a 3.2- Å consensus map of the complete ATP synthase dimer with both rotors in main rotational state 1 were obtained. + +<|ref|>sub_title<|/ref|><|det|>[[118, 467, 525, 484]]<|/det|> +## Model building, refinement and data visualization + +<|ref|>text<|/ref|><|det|>[[115, 489, 880, 789]]<|/det|> +An initial atomic model of the static \(\mathrm{F}_0\) membrane region was built automatically using Bucaneer42. Subunits were subsequently assigned directly from the cryo- EM map, 15 of them corresponding to previously identified T. brucei ATP synthase subunits21, while three subunits (ATPTB14, ATPEG3, ATPEG4) were newly identified using BLAST searches. Manual model building was performed in Coot using the T. brucei \(\mathrm{F}_1\) (PDB 6F5D)13 and homology models43 of the E. gracilis OSCP and c- ring (PDB 6TDU)10 as starting models. Ligands were manually fitted to the map and restraints were generated by the GRADE server (http://grade.globalphasing.org). Real- space refinement was performed in PHENIX using autosharpened, local- resolution- filtered maps of the membrane region, peripheral stalk tip, c- ring/central stalk and \(\mathrm{F}_1\mathrm{F}_0\) monomers in different rotational states, respectively, using secondary structure restraints. Model statistics were generated using MolProbity44 and EMRinger45 Finally, the respective refined models were combined into a composite ATP synthase dimer model and real- space refined against the local- resolution- filtered consensus ATP synthase dimer map with both monomers in rotational state 1, applying reference restraints. Figures of the structures were prepared using ChimeraX46, the proton half- channels were traced using HOLLOW47. + +<|ref|>sub_title<|/ref|><|det|>[[118, 821, 262, 837]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[118, 843, 880, 899]]<|/det|> +The atomic coordinates have been deposited in the Protein Data Bank (PDB) and are available under the accession codes: XXXX (membrane- region), XXXX (peripheral stalk), XXXX (rotor), XXXX (F1Fo dimer), XXXX (rotational state 1a), XXXX (rotational state 1b), XXXX + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 880, 272]]<|/det|> +(rotational state 1c), XXXX (rotational state 1d), XXXX (rotational state 1e), XXXX (rotational state 2a), XXXX (rotational state 2b), XXXX (rotational state 2c), XXXX (rotational state 2d), XXXX (rotational state 3). The local resolution filtered cryo- EM maps, half maps, masks and FSC- curves have been deposited in the Electron Microscopy Data Bank with the accession codes: EMD- XXXX (membrane- region), EMD- XXXX (peripheral stalk), EMD- XXXX (rotor), EMD- XXXX (F₁F₀ dimer), EMD- XXXX (rotational state 1a), EMD- XXXX (rotational state 1b), EMD- XXXX (rotational state 1c), EMD- XXXX (rotational state 1d), EMD- XXXX (rotational state 1e), EMD- XXXX (rotational state 2a), EMD- XXXX (rotational state 2b), EMD- XXXX (rotational state 2c), EMD- XXXX (rotational state 2d), EMD- XXXX (rotational state 3). + +<|ref|>sub_title<|/ref|><|det|>[[119, 302, 288, 318]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[115, 325, 880, 512]]<|/det|> +We are grateful to Sir John E. Walker and Martin Montgomery for invaluable assistance with ATP synthase purification in the initial stage of the project. We acknowledge cryo- electron microscopy and tomography core facility of CIISB, Instruct- CZ Centre, supported by MEYS CR (LM2018127). This work was supported by the Czech Science Foundation grants number 18- 17529S to A.Z. and 20- 04150Y to O.G. and by European Regional Development Fund (ERDF) and Ministry of Education, Youth and Sport (MEYS) project CZ.02.1.01/0.0/0.0/16_019/0000759 to A.Z., Swedish Foundation for Strategic Research (FFL15:0325), Ragnar Söderberg Foundation (M44/16), European Research Council (ERC- 2018- StG- 805230), Knut and Alice Wallenberg Foundation (2018.0080), and EMBO Young Investigator Programme to A.A. + +<|ref|>sub_title<|/ref|><|det|>[[119, 542, 303, 558]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[115, 565, 880, 677]]<|/det|> +A.Z. and A.A. conceived and designed the work. O.G. prepared the sample for cryo- EM. O.G. and A.M. performed initial screening. A.M. processed the cryo- EM data and built the model. O.G., A.M. and A.A. analyzed the structure. B.P., C.H.Y., M.J., M.S., O.G. and A.Z. performed biochemical analysis. O.G., A.M., A.A. and A.Z. interpreted the data. O.G., A.M., A.A. and A.Z. wrote and revised the manuscript. All authors contributed to the analysis and approved the final version of the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[119, 707, 293, 723]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[119, 731, 471, 747]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[119, 779, 214, 794]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[115, 797, 880, 889]]<|/det|> +1. Paumard, P. et al. The ATP synthase is involved in generating mitochondrial cristae morphology. EMBO J 21, 221-30 (2002). +2. Davies, K.M., Anselmi, C., Wittig, I., Faraldo-Gomez, J.D. & Kuhlbrandt, W. Structure of the yeast F₁F₀-ATP synthase dimer and its role in shaping the mitochondrial cristae. 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Adler, B.K., Harris, M.E., Bertrand, K.I. & Hajduk, S.L. Modification of Trypanosoma brucei mitochondrial rRNA by posttranscriptional 3' polyuridine tail formation. Mol Cell Biol 11, 5878- 84 (1991). 708 28. Hofer, A., Steverding, D., Chabes, A., Brun, R. & Thelander, L. Trypanosoma brucei CTP synthetase: a target for the treatment of African sleeping sickness. Proc Natl Acad Sci U S A 98, 6412- 6 (2001). 709 29. Gupta, K. et al. The role of interfacial lipids in stabilizing membrane protein oligomers. Nature 541, 421- 424 (2017). 712 30. Arnold, I., Pfeiffer, K., Neupert, W., Stuart, R.A. & Schagger, H. Yeast mitochondrial F1Fo- ATP synthase exists as a dimer: identification of three dimer- specific subunits. EMBO J 17, 7170- 8 (1998). 713 31. Gu, J. et al. Cryo- EM structure of the mammalian ATP synthase tetramer bound with inhibitory protein IF1. Science 364, 1068- 1075 (2019). 716 32. Spikes, T.E., Montgomery, M.G. & Walker, J.E. Interface mobility between monomers in dimeric bovine ATP synthase participates in the ultrastructure of inner mitochondrial membranes. Proc Natl Acad Sci U S A 118, e2021012118 (2021). 719 33. Cadena, L.R. et al. Mitochondrial contact site and cristae organization system and F1Fo- ATP synthase crosstalk is a fundamental property of mitochondrial cristae. mSphere 6, e0032721 (2021). 723 34. Davies, K.M. et al. Macromolecular organization of ATP synthase and complex I in whole mitochondria. Proc Natl Acad Sci U S A 108, 14121- 6 (2011). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 80, 884, 710]]<|/det|> +725 35. Blum, T.B., Hahn, A., Meier, T., Davies, K.M. & Kühlbrandt, W. Dimers of mitochondrial ATP synthase induce membrane curvature and self-assemble into rows. Proc Natl Acad Sci U S A 116, 4250- 4255 (2019). 726 36. Bochud- Allemann, N. & Schneider, A. Mitochondrial substrate level phosphorylation is essential for growth of procyclic Trypanosoma brucei. J Biol Chem 277, 32849- 54 (2002). 737. Poon, S.K., Peacock, L., Gibson, W., Gull, K. & Kelly, S. A modular and optimized single marker system for generating Trypanosoma brucei cell lines expressing T7 RNA polymerase and the tetracycline repressor. Open Biol 2, 110037 (2012). 738. Allemann, N. & Schneider, A. ATP production in isolated mitochondria of procyclic Trypanosoma brucei. Mol Biochem Parasitol 111, 87- 94 (2000). 739. Aibara, S., Dienemann, C., & Cramer, P.. Structure of an inactive RNA polymerase II dimer. Nucleic Acids Research, gkab783 (2021). 740. de la Rosa- Trevin, J.M. et al. Scipion: A software framework toward integration, reproducibility and validation in 3D electron microscopy. J Struct Biol 195, 93- 9 (2016). 741. Zhang, K. Gctf: Real- time CTF determination and correction. J Struct Biol 193, 1- 12 (2016). 742. Cowtan, K. The Buccaneer software for automated model building. 1. Tracing protein chains. Acta Crystallogr D Biol Crystallogr 62, 1002- 11 (2006). 743. Waterhouse, A. et al. SWISS- MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46, W296- W303 (2018). 744. Williams, C.J., Headd, J.J., Moriarty, N.W., Prisant, M.G., Videau, L.L., Deis, L.N., Verma, V., Keedy, D.A., Hintze, B.J., Chen, V.B. and Jain, S. MolProbity: More and better reference data for improved all- atom structure validation. Protein Science, 27, 293- 315 (2018). 745. Barad, B.A., Echols, N., Wang, R.Y.R., Cheng, Y., DiMaio, F., Adams, P.D. and Fraser, J.S. EMRinger: side chain- directed model and map validation for 3D cryo- electron microscopy. Nature methods, 12, 943- 946 (2015). 746. Goddard, T.D. et al. UCSF ChimeraX: Meeting modern challenges in visualization and analysis. Protein Sci 27, 14- 25 (2018). 747. Ho, B.K. & Gruswitz, F. HOLLOW: generating accurate representations of channel and interior surfaces in molecular structures. BMC Struct Biol 8, 49 (2008). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[125, 135, 884, 560]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[118, 592, 730, 610]]<|/det|> +Extended Data Fig. 1 Purification of the T. brucei ATP synthase dimer. + +<|ref|>text<|/ref|><|det|>[[118, 611, 880, 704]]<|/det|> +a, Size exclusion chromatography trace with peaks enriched with ATP synthase dimers (D), monomers (M) and \(\mathrm{F_1}\) - ATPase \((\mathrm{F_1})\) labelled. b, Fractions from size exclusion chromatography marked with green bar in (a) resolved by native BN- PAGE. c, Dimer- enriched fraction resolved by SDS- PAGE stained by Coomassie blue dye. Bands are annotated based on mass spectrometry identification from excised gel pieces. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 80, 884, 761]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[116, 785, 881, 876]]<|/det|> +Extended Data Fig. 2 Cryo- EM data processing of the T. brucei ATP synthase dimer. a, Representative micrograph. b, 2D class averages. c, Fourier Shell Correlation (FSC) curves showing the estimated resolutions of ATP synthase maps according to the gold standard 0.143 criterion. d, Data processing scheme resulting in maps covering all regions of the complex, as well as 10 rotational states. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 80, 884, 250]]<|/det|> + +<|ref|>sub_title<|/ref|><|det|>[[118, 277, 880, 313]]<|/det|> +## Extended Data Fig. 3 Conserved and phylum specific elements generate the T. brucei ATP synthase architecture. + +<|ref|>text<|/ref|><|det|>[[118, 315, 880, 370]]<|/det|> +The canonical OSCP/F \(_1 / c\) - ring monomers (dark grey) are tied together by both conserved \(\mathrm{F}_0\) subunits and extensions of lineage- specific subunits (red). The \(\mathrm{F}_0\) periphery and peripheral stalk attachment are composed of lineage specific subunits (blue). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 78, 884, 799]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[115, 815, 884, 909]]<|/det|> +Extended Data Fig. 4 The \(\mathbf{F}_0\) region coordinates numerous bound lipids. a, \(\mathrm{F}_0\) top view, cardiolipin (CDL), phosphatidylcholine (PC) and phosphatidylethanolamine (PE) are bound at the dimer interface, the lumenal proton half- channel and the peripheral \(\mathrm{F}_0\) cavity. b, The \(60^{\circ}\) - dimer angle generates a curved \(\mathrm{F}_0\) region with phospholipids bound in an arc- shaped bilayer. c- f, Bound lipids with cryo- EM density and coordinating residues. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 103, 884, 632]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 645, 601, 663]]<|/det|> +
Extended Data Fig. 5 Bound detergents of the \(\mathbf{F}_0\) region.
+ +<|ref|>text<|/ref|><|det|>[[117, 664, 881, 720]]<|/det|> +GDN (a,b) and \(\beta\) - DDM (c,d) molecules bound in the periphery of the membrane region with cryo- EM map densities shown (transparent), indicating that both glycosides are retained in the detergent micelle. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 81, 885, 344]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[117, 370, 880, 465]]<|/det|> +Extended Data Fig. 7 The C- terminal tail of subunit- e interacts with the \(c_{10}\) - ring. a, The cryo- EM map reveals disordered detergent density of the detergent belt surrounding the membrane region as well as a detergent plug on the luminal side of the \(c\) - ring. b, The helical C- terminus of subunit- \(e\) extends into the lumen towards the \(c\) - ring. The terminal 23 residues are disordered and likely interact with the \(\beta\) - barrel. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 85, 888, 490]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 498, 880, 535]]<|/det|> +
Extended Data Fig. 8 Phylogenetic distribution and sequence conservancy of subunit-e and -g.
+ +<|ref|>text<|/ref|><|det|>[[115, 536, 881, 857]]<|/det|> +a, Distribution of subunits \(e\) and \(g\) mapped on the phylogenetic tree of eukaryotes3. Homologs of subunits \(e\) and \(g\) were searched in non-redundant GenBank and UniprotKB protein databases by PSI- BLAST, and phmmer and hmmsearch4, respectively, using individual sequences of representatives from \(H\) . sapiens and \(T\) . brucei, and in the case of hmmsearch a multiple sequence alignment (MSA) of representatives from Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and \(T\) . brucei, as queries. Groups, in which at least one structure of ATP synthase is available, are marked. Abbreviations of species used in MSA in panels (c) and (d) are shown. b, Sequence logo of GXXXG motifs and flanking regions of subunits \(e\) and \(g\) . Hits from hmmsearch were clustered by CD- HIT Suite5 to 50% sequence identity and MSA of representative sequences of each cluster was generated by Clustal Omega4. The sequence logos were created from MSA in Geneious Prime (Biomatters Ltd.). c,d, MSA of sequences of subunits \(g\) (c) and \(e\) (d) from species representing major groups shown in (a) generated by MUSCLE7 and visualized in Geneious Prime. The experimentally determined or predicted transmembrane regions are highlighted in yellow. Species abbreviations: Tb – T. brucei, Hs – H. sapiens, Sc – S. cerevisiae, Sr – Salpingoeca rosetta, Tt – Thecamonas trahens, Dd – Dictyostelium discoideum, Cm – Cyanidioschyzon merolae, Cv – Chlorella vulgaris, At – Arabidopsis thaliana, Os – Oryza sativa. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[125, 88, 884, 770]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[125, 84, 844, 730]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[117, 733, 880, 770]]<|/det|> +Extended Data Fig. 9 Effects of RNAi knock- down of ATP synthase subunits on viability and stability and dimerization of ATP synthase. + +<|ref|>text<|/ref|><|det|>[[117, 771, 881, 904]]<|/det|> +a, Growth curves of indicated non- induced (solid lines) and tetracycline induced (dashed lines) RNAi cells lines in the presence (black) or absence (brown) of glucose. The insets show relative levels of the respective target mRNA at indicated days post induction (DPI) normalized to the levels of 18S rRNA (black bars) or \(\beta\) - tubulin (white bars). b, Immunoblots of mitochondrial lysates from indicated RNAi cell lines resolved by BN- PAGE probed by antibodies against indicated ATP synthase subunits. c, Immunoblots of whole cell lysates from indicated RNAi cell lines probed with indicated antibodies. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[30, 80, 951, 768]]<|/det|> +
Membrane regionRotorPeripher-
ral stalk		F1F6
dimer		Rot.
1a		Rot.
1b		Rot.
1c		Rot.
1d		Rot.
1e		Rot.
2a		Rot.
2b		Rot.
2c		Rot.
2d		Rot.
3
Data collection
Microscope
Titan Krios
Voltage (kV)
300
Camera
K2 Summit
Magnification
165 kx
Exposure (e/Ų)
33
Defocus range (μm)
-1.6 to -3.2
Pixel size (Å)
0.83
Movies collected
5,199
Frames per movie
20
Data processing
Initial particles
100,605 (C2 symmetry-expanded: 201,210)
Final no. particles
100,605118,683201,21036,92519,76426,42723,01916,99134,48212,17324,09611,03517,83317,312
SymmetryC2C1C1C2C1C1C1C1C1C2C1C2C1
Map resolution (Å)2.73.73.73.23.73.53.73.83.74.33.53.83.83.7
Sharpening B factor-46.2-74.4-92.5-49.8-61.8-61.1-57.6-45.6-58.0-73.8-54.5-65.2-54.9-61.7
EMD ID
Model refinement statistics
CC (map/model)0.860.830.820.710.790.790.820.790.690.710.810.770.770.79
Resolution (map/model)2.653.43.683.133.483.563.363.553.573.943.393.733.643.58
No. of atoms76,69019,66912,083251,552129,568129,568129,568129,568129,569129,563129,563129,563129,566129,566
No. of residues4074128576715,3567872787278727872787278787872787278727872
No. of lipids36003621212121212121222121
No. of ATP/ADP000105555555655
No. of Mg ions000105555555655
B-factor (Ų)
- protein54.0556.1377.8884.4855.6570.3780.2283.2770.70112,7279.9365.5266.49101.5
- ligands50.5758.25-69.9440.9972.2963.1878.4363.7675,2574.4761.7946.5583.68
Rotamer outliers (%)0.440.400.310.220.420.090.180.260.580.180.270.480.420.39
Ramachandran (%)
- outliers0.000.000.000.010.0010.0030.0040.010.0030.010.000.040.040.04
- allowed1.571.911.591.561.521.651.441.491.491.671.581.471.651.79
- favored98.4398.0898.4198.4298.4798.3498.5698.4098.4898.3198.4298.4998.3198.17
Clash score1.662.442.322.262.602.652.532.672.992.382.302.522.383.57
MolProbity score0.921.031.011.001.051.031.041.051.091.021.011.041.021.15
RMSD
- bonds (Å)0.0040.0040.020.0030.0030.0030.0040.0030.0030.0020.0030.0030.0030.003
- angles (°)0.4550.4160.3860.4070.4140.4240.4170.4070.4120.4100.4160.4190.4280.421
EMRinger score5.113.961.612.563.242.953.322.853.321.352.892.322.492.8
PDB ID
+ +<|ref|>text<|/ref|><|det|>[[118, 777, 880, 817]]<|/det|> +Extended Data Table 1. Data collection, processing, model refinement and validation statistics. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[117, 79, 880, 828]]<|/det|> + +
Subunit nameTriTrypDB Lister strain 427 IDTriTrypDB TREU927 strain IDUniprot TREU927 strain IDResiduesResidues built
F1 subcomplex
αTb427_070081800
Tb427_070081900		Tb927.7.7420
Tb927.7.7430		Q57TX958445-151,
161-584
βTb427_030013500Tb927.3.1380Q57XX151926-514
γTb427_100005200Tb927.10.180B0Z0F63052-301
δTb427_060054900Tb927.6.4990Q586H118222-182
εTb427_100054600Tb427.10.5050N/A7511-75
p18Tb427_050022900Tb927.5.1710Q57ZP018823-188
F0 subcomplex
OSCPTb427_100087100Tb927.10.8030Q38AG125518-202,
208-255
amt encodedmt encodedN/A2311-231
bTb427_040009100Tb927.4.720Q580A010526-105
cTb427_100018700
Tb427_110057900
Tb427_070019000		Tb927.10.1570
Tb927.11.5280
Tb927.7.1470		Q38C84
Q385P0
Q57WQ3		118
118
370		41-118
41-118
17-325,
332-354
dTb427_050035800Tb927.5.2930Q57ZW937017-325,
332-354
eTb427_110010200Tb927.11.600N/A921-383
fTb427_030016600Tb927.3.1690Q57ZE21452-136
gTb427_020016900Tb927.2.3610Q586X814416-144
ijTb427_030029400Tb927.3.2880Q57ZM41042-104
kTb427_070011800Tb927.7.840Q57VT012420-124
8Tb427_040037300Tb927.4.3450Q585K511429-114
ATBTB1Tb427_100008400Tb927.10.520Q38C183961-383
ATPTB3Tb427_110067400Tb927.11.6250Q385E42692-269
ATPTB4Tb427_100105100Tb927.10.9830Q389Z315721-157
ATPTB6Tb427_110017200Tb927.11.1270Q387C51692-169
ATPTB11Tb427_030021500Tb927.3.2180Q582T115618-156
ATPTB12Tb427_050037400Tb927.5.3090Q57Z841015-100
ATPEG3Tb427_060009300Tb927.6.590Q583U49814-98
ATPEG4N/ATb927.11.2245N/A621-62
+ +<|ref|>table_caption<|/ref|><|det|>[[117, 852, 727, 867]]<|/det|> +Extended Data Table 2. Composition of T. brucei ATP synthase dimer. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[208, 102, 787, 905]]<|/det|> +
SubunitPrimer pair sequences
Primers for amplification of RNAi cassettes
bTAATCTCGAGGGTACCGTTGAGTGAGGAGGAACGGG GCAGTCTAGAGGATCTCATCCCTTCCACCCACCACT
eTAATCTCGAGGGTACCGGGAGTACAGAAGGGCTACATAGATCTAGAGGATCCCGTCGACACCATCAGCTG
fATACTCGAGGGTACCGTTGAGTACCGCTTTACGC GCGTCTAGAGGATCCAGCACTGATCACCAAACTGC
gACTGCTCGAGGGTACCACGCGGGAATTCAAAAAGACCGGGTCTAGAGGATCCCGTTGCGGTGCTTGTCATTA
ijTAATCTCGAGGGTACCGGAATATCCGATGCTAGTCGCCGCCGTCTAGAGGATCCACTTCGCTCTACTGCATGCA
kATTACTCGAGCCCGGCGTCAGTGCAAGGGGATTTT GCCGTCTAGAGGATCCCTTTCTCTGAAAAACGCACACA
8ATGACTCGAGGGTACCCGGGCTATGGTGTGGTATTATATGC GACGTCTAGAGGATCCGCAGAAAACTCCCAACGACA
ATPTB3ACTGCTCGAGGGTACCAAAGAGGAGGTGAGGTCTGC GCAGTCTAGAGGATCCCCCTAGGGTTCTTCGAAGCA
ATPTB4CTGACTCGAGGGTACCTTCCTTTCTGCTGCATCGGCAGGTCTAGAGGATCCCTCCCTGGGCTTCAAATTTG
ATPTB6ACTGCTCGAGGGTACCCAACATGGCAGTATCCGGTGCAGTCTAGAGGATCTTTATTAGTGGCGGTGGTGGT
ATPTB11ACTGCTCGAGGGTACCCGCGCTCGTTCTCTCCATTTCCAGAAGCTTGGATCCAGGTTGGGGTGTTTAGGGAG
ATPTB12TAATCTCGAGGGTACCGACGCATCAAAGGAATGCCGCCGTCTAGAGGATCCAGCAGCCAACAAACAGACAA
ATPEG3TACACTCGAGGGTACCAAACCTGAAGGCCCTCACACGCAGTCTAGAGGATCCCTTTTCGTGCCGCCTGATA
Primers for quantification of mRNA levels by qPCR
bCCAAGAGTGATGATGGCCCCCGTTTAGGGTGCGGAAAAC
eCAAGCCTTGCACACACTTTATGCCGAAAGAAGTACGCCAC
fTTTTCTACATACGCAGCAGTTACCATTCCATGCGCGTG
gGCAATTGTGTGAGCTGAACGATCTGGCCGCATTGCATAAC
ijAGAGTAAAAGCGCGCCTACG
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[210, 81, 785, 442]]<|/det|> +
CAGTTGGAAAACCGGTAGCC
kACACAAAACACTTCCAGCAGA
CGCTATGACGGACAGGTGT
8GCTACGGCGACTTGGTGC
CGTCCGCGGTATTTGTTCA
ATPTB3AACGTTTATATCAGCGGGCG
CTGTTTGGTCTGCACACGA
ATPTB4CCAAACTTTGAAGCAGCGG
ATTCCTTGGATCCGCACCTT
ATPTB6TCGGCATAGGAGAAGTAACGA
GATTCGGTTTGGACTTGCG
ATPTB11CAACGGCCCCACATTCTC
ACACCGCGGTCATTCATTG
ATPTB12GCACTTCATTCTCCCGACTG
ACATGATGTAACACCTCCGC
ATPEG3TGGCCCCACATGACTGAAAA
GGAAGTGATCCGCCGGATTT
+ +<|ref|>text<|/ref|><|det|>[[117, 464, 615, 481]]<|/det|> +Extended Data Table 3. List of primers used in the study. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[68, 94, 929, 310]]<|/det|> + +
TargetTypeReferenceDilution SDS-PAGEDilution BN-PAGE
Primary antibodies
subunit-βrabbit polyclonal11:20001:2000
p18rabbit polyclonal11:1000-
ATPTB1rabbit polyclonal11:10001:1000
subunit-drabbit polyclonal11:10001.500
mtHsp70mouse monoclonal21:5000-
Secondary antibodies
goat anti-rabbit IgG HRP conjugateBioRad 17210191:20001:2000
goat anti-mouse IgG HRP conjugateBioRad 17210111:20001:2000
+ +<|ref|>text<|/ref|><|det|>[[118, 327, 634, 340]]<|/det|> +**Extended Data Table 4. List of antibodies used in the study.** + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 123, 350, 139]]<|/det|> +## Extended Data references: + +<|ref|>text<|/ref|><|det|>[[115, 140, 881, 386]]<|/det|> +1. Muhleip, A., McComas, S.E. & Amunts, A. Structure of a mitochondrial ATP synthase with bound native cardiolipin. \*Elife\* 8, e51179 (2019). +2. Larkin, M.A. et al. (2007). Clustal W and Clustal X version 2.0. \*Bioinformatics\*, 23, 2947-2948 (2007). +3. Burki, F., Roger, A.J., Brown, M.W. & Simpson, A.G.B. The New Tree of Eukaryotes. \*Trends Ecol Evol\* 35, 43-55 (2020). +4. Protein Sequence Similarity Search. \*Curr Protoc Bioinformatics\* 60, 3151-31523 (2017). +5. Huang, Y., Niu, B., Gao, Y., Fu, L. & Li, W. CD-HIT Suite: a web server for clustering and comparing biological sequences. \*Bioinformatics\* 26, 680-2 (2010). +6. Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. \*Mol Syst Biol\* 7, 539 (2011). +7. Edgar, R.C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. \*Nucleic Acids Res\* 32, 1792-7 (2004). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 114]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 131, 191, 203]]<|/det|> +- Video1.mp4- Video2.mp4- Video3.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/images_list.json b/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..da6fda3b208b62d99d9c8f9e806c4b216990bb79 --- /dev/null +++ b/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/images_list.json @@ -0,0 +1,48 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Development and validation of the SenMayo gene set. (A) Human samples from Cohort A (bone and bone marrow biopsies) and cohort B (highly enriched osteocyte fractions) were used for transcriptome-wide RNA-seq analyses; (B) Making use of TRRUST analyses, \\(^{30}\\) we found several inflammation- and stress-associated genes, including SIRT1 and NFKB1, to be upregulated in the elderly women; (C) In both gene sets, CDKN1A/P21 \\(^{Cip1}\\) and several SASP markers such as CCL2 and IL6 showed consistent upregulation with aging, while CDKN2A/p16 \\(^{lnk4a}\\) (due to comparatively low expression) did not change significantly; (D) The commonly used senescence/SASP gene set (R-HSA-2559582) failed to predict the aging process in either human cohort; (E) The SenMayo gene set includes growth factors, transmembrane receptors, and cytokines/chemokines that are highly influenced by other members of the gene set. The circle size depicts groupwise interactions; (F) SenMayo encodes a dense network of nine different protein classes within a strong interaction network. The size of each circle represents", + "footnote": [], + "bbox": [ + [ + 115, + 85, + 860, + 687 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. The SenMayo gene set tracks genetic and pharmacologic clearance of senescent cells. (A) The SenMayo panel successfully indicated aging in bone in mice (p-value=0.0023), n=25 (12 young, 13 old (all \\(\\mathbb{Q}\\) ); (B) The elimination of \\(p16^{\\text{ink4a}}\\) -expressing senescent cells by AP20187 administration was shown previously to reverse the aging bone phenotype.37 The SenMayo gene set successfully demonstrated the significant reversal of the aging phenotype at the gene expression level upon the elimination of \\(p16^{\\text{ink4a}}\\) -expressing senescent cells (p=0.0054), n=29 (13 Veh, 16 AP (all \\(\\mathbb{Q}\\) ); (C) By specifically using the expression patterns of the SenMayo gene set,", + "footnote": [], + "bbox": [], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. SASP-associated hematopoietic cells in human bone marrow are mainly of monocytic origin and communicate via the MIF pathway. (A) Using a previously published scRNA-seq dataset from human bone marrow (GSE120446, \\(^{46}\\) n=68,478 cells), we performed GSEA at the", + "footnote": [], + "bbox": [ + [ + 112, + 87, + 884, + 848 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6. The in silico predicted importance of the Mif pathway is reflected in the aged INK-ATTAC mouse model. (A) We compared young (n=12) and old vehicle-treated mice (n=13), and old mice treated with AP (n=16). (A) Upregulation of Mif was confirmed by RT-qPCR (n=24 young (12 Veh, 12 old (all \\(\\odot\\) )); (B) The clearance of senescent cells in the aged cohort by AP treatment reduced this Mif expression (n=26 old (12 Veh, 14 AP (all \\(\\odot\\) )).\\*p<0.05", + "footnote": [], + "bbox": [], + "page_idx": 15 + } +] \ No newline at end of file diff --git a/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f.mmd b/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..8798b100f830fbffb8adff333745bb50e015b404 --- /dev/null +++ b/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f.mmd @@ -0,0 +1,440 @@ + +# A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues + +Sundeep Khosla ( khosla.sundeep@mayo.edu ) Mayo Clinic https://orcid.org/0000- 0002- 2936- 4372 + +Dominik Saul Mayo Clinic https://orcid.org/0000- 0002- 0673- 3710 + +Robyn Laura Kosinsky Mayo Clinic https://orcid.org/0000- 0003- 2869- 7762 + +Elizabeth Atkinson Mayo Clinic + +Madison Doolittle Mayo Clinic + +Xu Zhang Mayo Clinic https://orcid.org/0000- 0002- 9784- 0481 + +Nathan LeBrasseur Mayo Clinic https://orcid.org/0000- 0002- 2002- 0418 + +Robert Pignolo Mayo Clinic + +Paul Robbins University of Minnesota https://orcid.org/0000- 0003- 1068- 7099 + +Laura Niedernhofer University of Minnesota https://orcid.org/0000- 0002- 1074- 1385 + +Yuji Ikeno Barshop Institute + +Diana Jurk PhD https://orcid.org/0000- 0003- 4486- 0857 + +Joao Passos Mayo Clinic https://orcid.org/0000- 0001- 8765- 1890 + +LaTonya Hickson Mayo Clinic + +Ailing Xue Mayo Clinic + +David Monroe + +<--- Page Split ---> + +Mayo Clinic https://orcid.org/0000- 0002- 4818- 0114 + +Tamara Tchkonia Mayo Clinic https://orcid.org/0000- 0003- 4623- 7145 + +James Kirkland + +Mayo Clinic https://orcid.org/0000- 0003- 1676- 4905 + +Joshua Farr + +Mayo Clinic https://orcid.org/0000- 0002- 3179- 6414 + +## Article + +Keywords: bone, senescence, SASP, gene set, aging + +Posted Date: December 13th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 1034608/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on August 16th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 32552- 1. + +<--- Page Split ---> + +# A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues + +Abbreviated Title: A new gene set identifies senescent cells + +Authors: Dominik Saul, M.D. \(^{1,2,3}\) , Robyn Laura Kosinsky, Ph.D. \(^{4}\) , Elizabeth J Atkinson, M.S. \(^{5}\) , Madison L. Doolittle, Ph.D. \(^{1,2}\) , Xu Zhang, Ph.D. \(^{2,6}\) Nathan K. LeBrasseur, Ph.D. \(^{2,6}\) , Robert J. Pignolo, M.D., Ph.D. \(^{1,2,6}\) , Paul D. Robbins, Ph.D. \(^{7}\) , Laura J. Niedernhofer, M.D., Ph.D. \(^{7}\) , Yuji Ikeno, M.D., Ph.D. \(^{8}\) , Diana Jurk, Ph.D. \(^{2,6}\) , João F. Passos, Ph.D. \(^{2,6}\) , LaTonya J. Hickson, M.D. \(^{9}\) , Ailing Xue, M.D. \(^{2}\) , David G. Monroe, Ph.D. \(^{1,2}\) , Tamara Tchkonia, Ph.D., M.S. \(^{2,6}\) , James L. Kirkland, M.D., Ph.D. \(^{2,6}\) , Joshua N. Farr, Ph.D. \(^{1,2,6}\) and Sundeep Khosla, M.D. \(^{1,2,6}\) + +## Affiliations: + +\(^{1}\) Division of Endocrinology, Mayo Clinic, Rochester, MN, 55905, USA. \(^{2}\) Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN 55905, USA. \(^{3}\) Department of Trauma, Orthopedics and Reconstructive Surgery, Georg- August- University of Goettingen, Germany. \(^{4}\) Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, 55905, USA. \(^{5}\) Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA. \(^{6}\) Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA. \(^{7}\) Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA. \(^{8}\) Department of Pathology, University of Texas Health, San Antonio, TX, USA. \(^{9}\) Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL, USA. + +Funding: This work was supported by the German Research Foundation (D.F.G., 413501650) (D.S.), National Institutes of Health (NIH) grants P01 AG062413 (S.K., J.N.F., N.K.L., R.P., P.D.R., L.J.N., Y.I., J.P., D.G.M., T.T., J.L.K.), R21 AG065868 (S.K., J.N.F.), K01 AR070241 (J.N.F.), R01 AG063707 (D.G.M.), R37 AG 013925 (J.L.K., T.T.), R33AG 61456 (J.L.K., T.T., R.P., P.D.R., L.J.N., S.K.), 1R01AG068048- 01 (JFP), R56 AG 60907 and R01 AG55529 (N.K.L.), the Connor Fund (J.L.K., T.T.), Robert P. and Arlene R. Kogod (J.L.K.), Robert J. and Theresa W. Ryan (J.L.K., T.T.), the Noaber Foundation (J.L.K., T.T.), and Mildred Scheel postdoc fellowship by the German Cancer Aid (R.L.K.). + +## Emails: + +- Dominik Saul: saul.dominik@mayo.edu; ORCiD: 0000-0002-0673-3710- Robyn Laura Kosinsky: kosinsky.robynlaura@mayo.edu; ORCiD: 0000-0003-2869-7762- Elizabeth J Atkinson: Atkinson@mayo.edu; 0000-0002-1191-3775- Madison L. Doolittle: doolittle.madison@mayo.edu; ORCiD: 0000-0003-0912-0095- Xu Zhang: zhang.xu@mayo.edu; ORCiD: 0000-0002-9784-0481- Nathan LeBrasseur: LeBrasseur.Nathan@mayo.edu; ORCiD: 0000-0002-2002-0418- Robert J. Pignolo: Pignolo.Robert@mayo.edu- Paul Robbins: probbins@umn.edu; ORCiD: 0000-0003-1068-7099- Laura Niedernhofer, Iniedern@umn.edu; ORCiD: 0000-0002-1074-1385- Yuji Ikeno: Ikeno@uthscsa.edu- Diana Jurk: jurk.diana@mayo.edu- João Passos: Passos.Joao@mayo.edu; ORCiD: 0000-0001-8765-1890- LaTonya J. Hickson: Hickson.Latonya@mayo.edu- Ailing Xue: Xue.Ailing@mayo.edu- David G. Monroe: Monroe.David@mayo.edu; ORCiD: 0000-0002-4818-0114 + +<--- Page Split ---> + +- Tamara Tchkonia: Tchkonia.Tamar@mayo.edu; ORCiD: 0000-0003-4623-7145- James Kirkland: Kirkland.James@mayo.edu; ORCiD: 0000-0003-1676-4905- Joshua N. Farr: farr.joshua@mayo.edu; ORCiD: 0000-0002-3179-6414- Sundeep Khosla: khosla.sundeep@mayo.edu; ORCiD: 0000-0002-2936-4372 + +## \*Corresponding authors: + +\*Joshua N. Farr, Ph.D., Guggenheim 7- 11D, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905; Tel. +1- 507- 538- 0085; Fax. +1- 507- 284- 9111; Email: farr.joshua@mayo.edu + +\*Sundeep Khosla, M.D., Guggenheim 7- 11, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905; Tel: +1- 507- 255- 6663; Email: khosla.sundeep@mayo.edu + +Key Words: bone, senescence, SASP, gene set, aging + +Supplementary Material: This manuscript contains Supplementary Methods and Figures + +Disclosures: Patents on senolytic drugs and their uses and on SASP biomarkers are held by Mayo Clinic and the University of Minnesota. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and was conducted in compliance with Mayo Clinic Conflict of Interest policies. + +<--- Page Split ---> + +## Abstract + +Although cellular senescence is increasingly recognized as driving multiple age- related co- morbidities through the senescence- associated secretory phenotype (SASP), in vivo senescent cell identification, particularly in bulk or single cell RNA- sequencing (scRNA- seq) data remains challenging. Here, we generated a novel gene set (SenMayo) and first validated its enrichment in bone biopsies from two aged human cohorts. SenMayo also identified senescent cells in aged murine brain tissue, demonstrating applicability across tissues and species. For direct validation, we demonstrated significant reductions in SenMayo in bone following genetic clearance of senescent cells in mice, with similar findings in adipose tissue from humans in a pilot study of pharmacological senescent cell clearance. In direct comparisons, SenMayo outperformed all six existing senescence/SASP gene sets in identifying senescent cells across tissues and in demonstrating responses to senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from publicly available human and murine bone marrow/bone scRNA- seq data and identified monocytic and osteolineage cells, respectively, as showing the highest levels of senescence/SASP genes. Using pseudotime and cellular communication patterns, we found senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Moreover, using this senescence panel, we were able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways associated with these cells, which may be particularly useful for evolving efforts to map senescent cells (e.g., SenNet). In addition, SenMayo represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs. + +<--- Page Split ---> + +## Introduction + +Cellular senescence is now recognized as a fundamental mechanism of aging in animals and humans. Accumulation of DNA damage and/or other cellular stressors \(^{1 - 4}\) causes proliferating \(^{5,6}\) as well as terminally differentiated, non- dividing cells \(^{7 - 10}\) to undergo senescence. Characteristics of senescent cells include profound chromatin and secretome changes, along with increased expression of a number of senescence markers, including Cdkna/p16 \(^{nk4a}\) and Cdkna/p21 \(^{Cip1}\) , immune evasion, and resistance to apoptosis. \(^{1,11}\) Senescent cells can develop a senescence- associated secretory phenotype (SASP), consisting of pro- inflammatory cytokines, chemokines, extracellular matrix- degrading proteins, and other factors that have deleterious paracrine and systemic effects. \(^{12 - 15}\) Further, because senescent cells accumulate in multiple tissues in temporal and spatial synchrony with age- associated functional decline in both animals and humans, \(^{5,6,16}\) they have been hypothesized to drive the deterioration linked to numerous chronic diseases. \(^{1}\) + +Importantly, the SASP as a feature of cellular senescence represents not just a locally or systemically detrimental set of factors that, in the aging organism, cause physical, metabolic, and cognitive decline, \(^{17 - 21}\) but is also a therapeutic target of interest. \(^{22 - 24}\) Thus, given the broad availability of next- generation sequencing, there is considerable interest in monitoring responses to senolytic treatments. However, this has been challenging, especially at the single cell level. \(^{25}\) In part, this is due to an imprecise definition of the heterogeneous population of senescent cells and their associated SASP which complicates appropriate monitoring of senescent cell clearance. + +Due to variations in the composition of a "senescence gene set" in the current literature, in the present study we sought to identify commonly regulated genes in various age- related data sets in a transcriptome- wide approach that included whole- transcriptome as well as single cell RNA- sequencing (scRNA- seq). \(^{26}\) Based on an extensive review of the literature, we defined a panel of 125 genes as our senescence gene set ("SenMayo"), which we then validated in our own as well as publicly available datasets of tissues from aged humans and mice, including changes + +<--- Page Split ---> + +in this gene set following the clearance of senescent cells. Recognizing the difficulty of identifying senescent cells within scRNA- seq analyses, we next applied SenMayo to available scRNA- seq data from human and murine bone marrow/bone hematopoietic and mesenchymal cells, ascertained the identity of the senescent cells in these analyses, and characterized the communication patterns of senescent hematopoietic or mesenchymal cells with other cells in their microenvironment. Finally, we experimentally validated key predictions from our in silico analyses in a mouse model of aging and following genetic clearance of senescent cells. + +<--- Page Split ---> + +## Results + +Development and validation of SenMayo in human datasets. We first analyzed previously published \(^{27,28}\) as well as unpublished (see Methods) transcriptome- wide mRNA- seq analyses of human whole bone biopsies. These included bone and bone marrow (Cohort A) \(^{27}\) as well as bone biopsies that were processed to remove bone marrow and bone surface cells and were thus highly enriched for osteocytes (Cohort B) \(^{28}\) from young vs. elderly women (Fig. 1A). We used transcriptional regulatory relationships \(^{29}\) to evaluate whether senescence- and SASP- associated pathways were enriched with aging in humans and noted enrichment of genes regulating inflammatory mediators, including NFKB1, RELA, and STAT3 (Fig. 1B). As expected, both aged cohorts displayed an upregulation of senescence- and SASP markers such as CDKN1A/p21 \(^{Cip1}\) , CCL2, and IL6 (Fig. 1C). It should be noted that some canonical markers of senescence, including CDKN2A/p16 \(^{nk4a}\) , did not show the predicted increase with aging due to comparatively low expression levels. Given the limitations of single gene analyses to predict the complex mechanisms of cellular aging, we next tested whether a previously published combination of senescence/SASP genes (R- HSA- 2559582) is enriched in our aging cohorts. However, this Gene Set Enrichment (GSEA)- based approach failed to predict an age- related senescence/SASP increase in either cohort (Fig. 1D). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1. Development and validation of the SenMayo gene set. (A) Human samples from Cohort A (bone and bone marrow biopsies) and cohort B (highly enriched osteocyte fractions) were used for transcriptome-wide RNA-seq analyses; (B) Making use of TRRUST analyses, \(^{30}\) we found several inflammation- and stress-associated genes, including SIRT1 and NFKB1, to be upregulated in the elderly women; (C) In both gene sets, CDKN1A/P21 \(^{Cip1}\) and several SASP markers such as CCL2 and IL6 showed consistent upregulation with aging, while CDKN2A/p16 \(^{lnk4a}\) (due to comparatively low expression) did not change significantly; (D) The commonly used senescence/SASP gene set (R-HSA-2559582) failed to predict the aging process in either human cohort; (E) The SenMayo gene set includes growth factors, transmembrane receptors, and cytokines/chemokines that are highly influenced by other members of the gene set. The circle size depicts groupwise interactions; (F) SenMayo encodes a dense network of nine different protein classes within a strong interaction network. The size of each circle represents
+ +<--- Page Split ---> + +the connectivity with other members of the gene set; \(^{30}\) (G) Genes included in the SenMayo gene set were significantly enriched with aging in both human cohorts. Cohort A: \(n = 38\) (19 young, 19 old, all \(\div\) ), Cohort B: \(n = 30\) (15 young, 15 old, all \(\div\) ). \(^{**}p< 0.01\) , \(^{***}p< 0.0001\) . Figure 1A was designed using Biorender.com. + +In order to develop a more robust gene panel associated with cellular senescence, we next generated a novel gene set to predict the expression of aging- related senescence genes by performing an in- depth, rigorous literature search (see Methods for details of how these genes were selected). The result was a novel senescence gene set of 125 genes (SenMayo) that consisted predominantly of SASP factors ( \(n = 83\) ) but also included transmembrane ( \(n = 20\) ) and intracellular ( \(n = 22\) ) proteins (Table 1). Within this SenMayo gene set, which comprised 9 distinct clusters, cytokines/chemokines were the most densely connected regulators according to the number of descendent proteins in STRING analysis (Fig. 1E, F). Predominant connectivity (whole network density: 0.277, PPI <0.0001) was shown by IL1A, CXCL8, CCL2 (cytokines/chemokines, blue), IGF1 (growth factor, green), C3 and IGFBP4 (protease inhibitor, turquoise), TNFRSF1A, EGF and EGFR (transmembrane signal receptors, red), and MMP2, PLAT, and HGF ([metalloproteinases, grey) (Fig. 1F). Notably, when testing the enrichment of SenMayo within our two human mRNA- seq cohorts, senescence/SASP genes were significantly enriched in the bone samples obtained from elderly women ( \(p = 0.002\) [Cohort A] and \(p = 0.003\) [Cohort B]; Fig. 1G). Using Cohort A as an example, within the R- HSA- 2559582 gene set, 2 out of 50 available genes were significantly enriched in the biopsies from elderly women (Suppl. Fig. 1A), while 13 out of 120 available genes of the SenMayo gene set were significantly enriched in the elderly women (Suppl. Fig. 1B). Note that the GSEA analysis includes not only genes that differ significantly between groups, but also evaluates overall trends for differences in gene expression between groups and hence provides considerably greater power than examining individual genes. \(^{31}\) The canonical SASP markers CCL24, SEMA3F, FGF2, and IGFBP7 were consistently enriched in Cohort A (Suppl. Fig. 1C) and Cohort B (Suppl. Fig. 1D). In addition, SEMA3F was significantly correlated with the senescence marker, CDKN1A/p21 \(^{Cip1}\) , in both cohorts (Suppl. Fig. 1E, F). + +<--- Page Split ---> + +SenMayo is applicable across tissues and species. To evaluate the applicability of SenMayo across tissues and species, we next analyzed publicly available mRNA- seq data from brain tissue isolated from young vs. aged mice (GSE14526532, GSE12877033, GSE9483234, Fig. 2A- C). As is evident, aged mouse brain cells (microglia) and regions (prefrontal cortex, dorsal hippocampus) displayed a highly significant enrichment of senescence/SASP genes using the SenMayo gene list (p=0.005, p=0.001, p<0.001, respectively), while the previously published gene set (R- HSA- 2559582) did not reach statistical significance (p=0.157, p=0.117, p=0.192, respectively). In addition, using murine bone marrow from the tabula muris senis (a murine single cell transcriptome atlas of young vs. aged tissues35), the applicability of SenMayo in predicting the aging process was confirmed by GSEA (Fig. 2D) Thus, SenMayo identifies senescent cells associated with aging across tissues (bone/bone marrow and brain) and species (humans and mice). + +![](images/Figure_3.jpg) + + +<--- Page Split ---> + +Figure 2. The SenMayo gene set predicts aging across tissues and species. (A) Compared to the conventional gene set, the SenMayo list is significantly enriched during the aging process in murine brain microglia \((p = 0.1565\) vs. \(p = 0.0052\) ; GSE145265), \(n = 4\) (2 young, 2 aged, all \(③\) ), (B) murine prefrontal cortex \((p = 0.1169\) vs. \(p = 0.0013\) ; GSE128770), \(n = 48\) (24 young, 24 aged, all \(③\) ), and (C) murine dorsal hippocampus \((p = 0.1916\) vs. \(p< 0.001\) ; GSE94832), \(n = 12\) (6 young (3 \(②\) ), 6 aged (2 \(②\) ). Likewise, the murine bone marrow (D) within the tabula muris senis (GSE149590 \(^{36}\) ) has a higher enrichment of the SenMayo genes within the old cohort \((p = 0.6043\) vs. \(p = 0.0362\) ), \(n = 11\) (4 young (2 \(②\) , 2 \(②\) ), 7 old (7 \(②\) , 0 \(②\) ). + +SenMayo not only predicts aging, but also demonstrates clearance of senescent cells. In order to independently validate our in silico analyses, we next made use of our previously described \(p16\) - INK- ATTAC mouse model that allows for inducible clearance of \(p16^{\text{lnk4a}}\) - expressing senescent cells after administration of the drug AP20187 (AP). \(^{37}\) In previous studies, we have demonstrated increases in \(Cdkna2a/p16^{\text{lnk4a}}\) and \(Cdkna1a/p21^{\text{Clp1}}\) mRNA levels with aging in bones from these mice \(^{7}\) as well as reductions in these mRNAs following clearance of senescent cells in \(p16\) - INK- ATTAC mice treated with AP and concordant changes in other markers of cellular senescence (e.g., telomeric DNA damage markers in osteocytes). \(^{37}\) Importantly, in young vs. old mice, SenMayo was expressed at a significantly higher level in bones from the old mice (Fig. 3A) and was significantly reduced following AP treatment of old \(p16\) - INK- ATTAC mice (Fig. 3B). Moreover, by using the SenMayo genes, a higher overlap of young vs. old + AP- treated mice as compared to young vs. old + vehicle- treated mice was observed through principal component analysis (PCA) (Fig. 3C). + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 3. The SenMayo gene set tracks genetic and pharmacologic clearance of senescent cells. (A) The SenMayo panel successfully indicated aging in bone in mice (p-value=0.0023), n=25 (12 young, 13 old (all \(\mathbb{Q}\) ); (B) The elimination of \(p16^{\text{ink4a}}\) -expressing senescent cells by AP20187 administration was shown previously to reverse the aging bone phenotype.37 The SenMayo gene set successfully demonstrated the significant reversal of the aging phenotype at the gene expression level upon the elimination of \(p16^{\text{ink4a}}\) -expressing senescent cells (p=0.0054), n=29 (13 Veh, 16 AP (all \(\mathbb{Q}\) ); (C) By specifically using the expression patterns of the SenMayo gene set,
+ +<--- Page Split ---> + +our bone RNA- seq revealed no similarities in gene expression patterns between young and old + veh treated mice, and a substantial overlap of expression profiles of old + AP mice with young mice; (D) We used a previously published mRNA- seq dataset from human adipose tissue of our group, \(^{24,38}\) to evaluate changes in SenMayo following D+Q treatment. Adipose tissue was collected before and 11 days after three days of oral D+Q treatment. Figure was designed using Biorender.com; (E) Using SenMayo, there was a was a reduction of SenMayo ( \(p = 0.0184\) ) in the subcutaneous fat samples in the subjects treated with D+Q, consistent with a reduction in senescent cell burden following D+Q treatment ( \(n = 10\) (8 \(\mathcal{O}\) , 2 \(\mathcal{P}\) )). + +We further validated the ability of SenMayo to predict senescent cell clearance by examining a human cohort. In a phase I pilot study, the senolytic combination of Dasatinib plus Quercetin (D+Q) \(^{39}\) was administered to subjects with diabetic kidney disease for 3 consecutive days. \(^{24,38}\) We performed RNA- seq from adipose tissue samples obtained from these subjects before and 11 days after D+Q treatment (male: female=9:3, age: 68.8[±9.3] years:65.3[±6.6] years, Fig. 3D). \(^{24,38}\) As shown in Fig. 3E, there was a significant reduction in SenMayo ( \(p = 0.0184\) ) in the subcutaneous adipose tissue samples in the subjects following D+Q treatment, consistent with a reduction in senescent cell burden, which was independently validated by demonstrating reductions in p16 \(^{\text{Ink4a + }}\) , p21 \(^{\text{Cip1 + }}\) , and SA- βgal+ cells in the adipose tissue biopsy samples following D+Q treatment. \(^{24,38}\) Thus, these direct interventional studies in mice and humans demonstrate that not only is SenMayo associated with aging, but it is also reduced following clearance of senescent cells. + +SenMayo outperforms existing senescence/SASP gene sets. In addition to directly comparing SenMayo to the R- HSA- 2559582 senescence/SASP gene set, we also compared it to five additional senescence/SASP gene sets \(^{40 - 44}\) in all of the mouse and human models described above. As shown in Table 2, SenMayo consistently outperformed these gene sets (based on normalized enrichment scores [NES] and p- values) both in the ability to identify senescent cells with aging across tissues and species and in demonstrating responses to senescent cell clearance. + +The SenMayo gene set identifies senescent hematopoietic and mesenchymal cells within scRNA- seq bone marrow/bone datasets. Although scRNA- Seq provides extremely important information + +<--- Page Split ---> + +regarding changes in gene expression at the individual cell level, it has been problematic for evaluating cellular senescence in a given cell. In part, this is because the Cdkna2a/p16ink4a mRNA is expressed at relatively low levels, even in senescent cells,45 and may not be reliably detected in scRNA- seq data. Although Cdkna1a/p21Cip1 is generally expressed at higher levels in RNA- seq data, presence or absence of Cdkna1a/p21Cip1 also may not consistently identify a senescent cell.42 As such, having validated SenMayo as being associated not only with aging but also specifically with cellular senescence, we next tested whether it could identify senescent cells at the single cell level. To evaluate this first for hematopoietic cells, we analyzed publicly available single cell bone marrow datasets from 20 healthy donors across a broad age range (24- 84 years)46 and evaluated 68,478 hematopoietic cells for expression of the SenMayo gene set (GSE120446),46 Fig. 4A). + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 4. SASP-associated hematopoietic cells in human bone marrow are mainly of monocytic origin and communicate via the MIF pathway. (A) Using a previously published scRNA-seq dataset from human bone marrow (GSE120446, \(^{46}\) n=68,478 cells), we performed GSEA at the
+ +<--- Page Split ---> + +single cell level to uncover cells responsible for senescence/SASP- associated gene expression. The highest enrichment score (ES) for the SenMayo gene set occurred within the \(\mathrm{CD14^{+}}\) and \(\mathrm{CD16^{+}}\) monocytic cell cluster, represented in a Uniform Manifold Approximation and Projection (UMAP). We selected the top \(10\%\) of senescence/SASP- expressing cells to form the "SASP cells" \((n = 6,850\) cells) cluster displaying a (B) independent enrichment of canonical senescence genes including \(\mathrm{CDKN1A / p21^{CIP1}}\) and TGFB1 and which was likewise enriched for two aging signatures (GenAge: genes associated with aging in model organisms;47 and CellAge: positively regulated genes associated with aging in human cells; (C) The SASP cells showed the highest interaction strength with T cells in the bone marrow; (D) Among the interaction targets of SASP cells, T cells were predominantly targeted via the MHC- I, MIF, and PECAM1 pathways; (E) Members of the MIF and PECAM1 signaling pathways showed high expression patterns within the SASP population; (F) SASP cells were characterized by distinct co- expression patterns predicting (functional) clusters (e.g., JUN and CDKN2A), potentially overcoming difficulties of low expression of specific senescence- associated genes such as \(\mathrm{CDKN2A / P16^{ink4A}}\) . These strong indicators of co- expression were mathematically isolated by z- scores (G) and spatially summarized (H) in sub- cell populations within the SASP cluster, as indicated by kernel gene- weighted density estimation in a t- distributed Stochastic Neighbor Embedding (tSNE) representation. \*\*\*p<0.0001, \(n = 22\) (10 \(\hat{\mathcal{O}}\) , 12 \(\hat{\mathcal{O}}\) ). + +This analysis detected multiple cellular clusters that were more highly enriched than others for senescence/SASP genes, i.e., had higher enrichment scores (ES). These high ES clusters included \(\mathrm{CD14^{+}}\) and \(\mathrm{CD16^{+}}\) monocytes as well as macrophages (Fig. 4A, Suppl. Fig. 2A). By selecting the top \(10\%\) of cells with the highest expression of senescence/SASP- associated genes, we generated a new cluster of cells, consisting of 6,850 cells, predominantly of monocytic origin (referred to as "SASP cells" in Fig. 4B). These SASP cells showed an increase in canonical markers of senescence such as \(\mathrm{CDKN1A / p21^{CIP1}}\) and TGFB1, which are independent and not included in the SenMayo gene set, as well as enrichment of previously published gene sets indicative of human47 and cellular aging48 (Table 3). Visually, the SASP cells had a high correlation with genes in two established aging gene sets (GenAge and positively regulated in CellAge, Fig. 4B). To further elucidate the replicative state of these cells, we compared their cell cycle state with the other clusters. A shift towards the G1 phase occurred within the SASP cells (Suppl. Fig. S2B), consistent with replicative arrest. This finding was supported by cell cycle arrest gene enrichment within the SASP cells (Suppl. Fig. S2C). In addition, pseudotime analysis (Suppl. Fig. S2D, left panel), which permits elucidation of the temporal gene expression pattern of a specific + +<--- Page Split ---> + +cell type, revealed an increase in SASP cells over time (representing differentiation), particularly in \(\mathrm{CD14^{+}}\) monocytes, \(\mathrm{CD16^{+}}\) monocytes, and macrophages (Suppl. Fig. S2D, middle panel). + +In addition to intracellular signaling pathways differentially regulated in SASP- secreting cells, these cells have been demonstrated to affect surrounding cells. \(^{13,49}\) To explore these intercellular interactions, we evaluated potential ligand- receptor interactions and secretion patterns based on underlying gene expression levels in different hematopoietic cell types in human bone marrow. \(^{50}\) The strongest interaction of SASP cells was found with T cells, followed by monocytic cells and B cells (Fig. 4C). Among the affected pathways, the major histocompatibility complex class I (MHC- I), Macrophage Migration Inhibitory Factor (MIF), and Platelet And Endothelial Cell Adhesion Molecule 1 (PECAM1, CD31) pathways were most highly enriched (Fig. 4D, E). Of note, in the pseudotime analysis described above, MIF expression also increased markedly in terminally differentiated \(\mathrm{CD14^{+}}\) and \(\mathrm{CD16^{+}}\) monocytes and macrophages and SASP cells (Suppl. Fig. S2D, right panel). Moreover, MIF pathway members including \(\mathrm{CD74}\) , CXCR4, and \(\mathrm{CD44}\) had overall high expression in SASP cells (Fig. 4E). Compared to other cell types, the overall outgoing interaction strength of SASP cells was remarkably high (Suppl. Fig. S3A). Besides their importance as senders, mediators, and influencers (defined by signalling network analysis using centrality measures; for details see, \(^{50,51}\) Suppl. Fig. S3B, C), SASP cells displayed a substantial incoming signaling pattern dominated by the MIF, ANNEXIN, CD45, IGBB2, MHC- I, MHC- II, and PECAM1 pathways (Suppl. Fig. S3D). Within these SASP cells, the strongest direct receptor- ligand MIF interaction between the ligand \(\mathrm{CD74^{+}}\) and the receptor CD44 was mainly detected in other monocytic cells, while the MIF interaction via the ligand \(\mathrm{CD74^{+}}\) - receptor CXCR4 pair was significant for SASP to \(\mathrm{CD10^{+}}\) B and \(\mathrm{CD20^{+}}\) B cells as well as plasmacytoid dendritic cells. The PECAM1 pathway targeted plasma cells and \(\mathrm{CD16^{+}}\) monocytes (Suppl. Fig. S3E). + +Further analysis revealed that the SASP cells were characterized by distinct patterns of co- expression out of which several markers were found to be strongly associated with each other + +<--- Page Split ---> + +(Fig. 4F- G) – e.g., EREG/IL1B, ICAM1/CDKN1A, and JUN/CDKN2A. Out of the 125 genes within the SenMayo panel, some were consistently upregulated (red in Fig. 4F), while others were simultaneously downregulated (blue in Fig. 4F). After we found that some of the “canonical” SASP markers such as EREG/IL1B and SASP/senescence markers such as ICAM1/CDKN1A showed high concordance in their cell- wise expression patterns, we aimed to find surrogate genes for certain low- expressed genes – e.g. CDKN2A/p16ink4a. Within the SASP cluster, we found a strong correlation between JUN and CDKN2A/p16ink4a expression, which represents a potential approach to overcome the challenge of low CDKN2A/p16ink4a expression in sequencing datasets. To independently confirm these correlations, we depicted these genes in a pairwise fashion with kernel density estimation within the SASP cell clusters (Fig. 4H), where the overall SASP cells are in blue and the red/yellow colors indicate higher levels of expression within the SASP cells of each gene.45 These analyses thus demonstrate the validity of the SenMayo gene set in a human bone marrow scRNA- seq dataset and identify monocytic cells as the hematopoietic cell population with the highest proportion of SASP- associated cells. + +To further test SenMayo in single cell datasets and potentially contrast bone marrow hematopoietic cells to bone/bone marrow mesenchymal cells, we next evaluated a published murine dataset that contained scRNA- seq data from bone and bone marrow mesenchymal cells (GSE128423,52 Fig. 5A, n=35,368 cells). We detected a heterogenous distribution of highly enriched cells for SenMayo (“SASP cells”, n=3,537), which likewise were enriched in both GenAge and CellAge (Fig. 5B), canonical markers of senescence (Cdkn1a/p21Cip1 and Tgfβ1, Fig. 5B) and was primarily comprised of cells from the osteolineage (OLC1 and 2) as well as leptin receptor- positive (Lepr+) MSC cluster (Suppl. Fig. S4A shows the fraction of the original clusters that were subsequently assigned to the newly created SASP cluster and Suppl. Fig. S4B indicates the percentage of cells within each cluster that were in the top 10% of cells enriched for SenMayo genes). Interestingly, 21% of osteolineage cells (24% in OLC 1 and 18% in OLC2) had the highest enrichment for SASP factors (Suppl. Fig. S4B, Table 2). Similar to the human hematopoietic bone + +<--- Page Split ---> + +marrow dataset, murine bone/bone marrow mesenchymal SASP cells displayed a shift in cell cycle phase to the G1 phase (Suppl. Fig. S4C). This was confirmed by gene ontology analysis revealing enrichment of senescence- and cell cycle arrest- associated genes in these cell clusters (Suppl. Fig. 4D). The murine mesenchymal SASP cells were characterized by a high interaction with osteolineage and chondrocytic cells (Fig. 5C), with the MIF and PECAM1 pathways again among those significantly enriched, where these cells mostly acted as senders and influencers (Fig. 5D, Suppl. Fig. S4E). Notably, SASP cells had one of the highest outgoing interaction strengths (Suppl. Fig. S4F). A direct communication of these mesenchymal SASP cells mostly appeared in the MIF pathway (via L/R Mif/Ackr3) with chondrocytic cells and mineralizing osteocytes (Suppl. Fig. S4G). + +<--- Page Split ---> +![PLACEHOLDER_20_0] + + +<--- Page Split ---> + +Figure 5. In murine bone and bone marrow mesenchymal cells, osteolineage cells constitute the largest proportion of SASP cells and communicate with osteolineage and chondrocytic cells via the MIF and PECAM1 pathways and show characteristics of terminal differentiation. (A) We analyzed a publicly available murine bone and bone marrow gene set (GSE12842352), and enriched 35,368 cells for the newly created SenMayo gene set; (B) The top 10% senescence/SASP gene- expressing cells \((n = 3,537)\) were assigned to the newly created "SASP cells" cluster. They displayed an increase in canonical markers of senescence including Cdkna/p21Cip1 and Tgfβ1, and were enriched in the GenAge and CellAge gene sets (GenAge, CellAge 47); (C) The strongest interaction of the SASP cells was narrowed down to chondrocytic cells, while the osteolineage cells were another important crosstalk neighbor; (D) Outgoing interaction patterns of SASP cells (pink, left bottom quarter) indicated the importance of several signaling pathways that resulted in a significant enrichment of Mk, Angptl, Mif and Pecam1; (E) In pseudotime, the SASP cluster was most abundant in the terminal branches, and overexpressed Cdkna/p21Cip1 in terminal states (top- left inlay, bottom red color on the left, terminal branch); (F) In their terminal differentiation, the SASP cluster was enriched in several factors, out of which distinct co- expressional patterns were extracted; (G) While the terminal differentiation was marked by a simultaneous loss of Pappa and Fgf7 (cluster 1, green in F), a significant correlation of Dkk1 with Cdkna2/p16nkl, likewise Bmp2 and Cdkna/p21Cip1, was mathematically predicted (cluster 2, pink in F). \*\*\*p<0.0001, \(n = 8\) (4 bone, 4 bone marrow, all \(\odot\) ). + +The three main origins for the SASP cluster (namely Lepr+ MSCs, OLC 1, and OLC 2), as depicted in pseudotime, demonstrated that the SASP cells accumulated in a terminal developmental branch, coinciding with increased Cdkna/p21Cip1 and Trp53 expression (Fig. 5E). Further analysis of these pseudotime expression patterns showed that certain genes followed defined modules (green, blue, and red in Fig. 5F), which then formed co- expressional patterns (Suppl. Fig. S5A). Within the SASP cluster, these co- expressional patterns could be imaged at an individual cell level, predicting genes of similar abundance within some cells (Fig. 5G). For example, while Pappa and Fgf7 were simultaneously downregulated in terminally differentiated stages (Fig. 5F, blue color in the green cluster, Fig. 5G top), they were part of a modular cluster (Suppl. Fig. S5A, black boxes on the left, fifth square from above). We also performed kernel- weighed density estimation (Suppl. Fig. S5B), confirming our results that Fgf7 and Pappa were co- expressed in the SASP cells. Likewise, Dkk1 and Cdkna2/p16nkl4a displayed the mathematically predicted comparable expression patterns in kernel- weighed density, displayed in tSNE, as did Bmp2 and Cdkna/p21Cip1 (Fig. 5G, Suppl. Fig. S5B). + +<--- Page Split ---> + +Further experimental validation of in silico analyses. The above analyses of both hematopoietic and mesenchymal scRNA- seq data pointed to Mif as a key SASP gene that should increase with senescent cell burden and be reduced following clearance of senescent cells. Thus, as a final validation of our in silico analyses, we examined Mif mRNA levels by RT- qPCR in our mouse models and found that as predicted, Mif mRNA levels were increased in the bones from old compared to young mice (Fig. 6A) and were significantly reduced following the genetic clearance of senescent cells with AP in old INK- ATTAC mice (Fig. 6B). + +![PLACEHOLDER_22_0] + +
Figure 6. The in silico predicted importance of the Mif pathway is reflected in the aged INK-ATTAC mouse model. (A) We compared young (n=12) and old vehicle-treated mice (n=13), and old mice treated with AP (n=16). (A) Upregulation of Mif was confirmed by RT-qPCR (n=24 young (12 Veh, 12 old (all \(\odot\) )); (B) The clearance of senescent cells in the aged cohort by AP treatment reduced this Mif expression (n=26 old (12 Veh, 14 AP (all \(\odot\) )).\*p<0.05
+ +<--- Page Split ---> + +## Discussion + +The identification and characterization of senescent cells, particularly in bulk or scRNA- seq data, has been problematic for a number of reasons, including variable detection of low levels of the Cdkna/p16ink4a transcript even in senescent cells45 and the lack of a consistent gene panel that can reliably identify these cells. Thus, we generated a gene set (SenMayo) consisting of 125 previously identified senescence/SASP- associated factors and first validated it in bone biopsy samples from two human cohorts consisting of young vs elderly women.27,28 Importantly, to establish this as a senescence, rather than just "aging" gene set, we demonstrated that clearance of senescent cells in mice and in humans resulted in significant reductions of SenMayo. Using publicly available RNA- seq data, we demonstrated applicability across tissues and species and also found that SenMayo performed better than six existing senescence/SASP gene panels.14,40- 44 We next applied SenMayo to publicly available bone marrow/bone scRNA- seq data and successfully characterized hematopoietic and mesenchymal cells expressing high levels of senescence/SASP markers at the single cell level, demonstrated co- expression (where feasible) with the key senescence genes, Cdkna/p16ink4a and Cdkna/p21Cip1, and analyzed intercellular communication patterns of senescent cells with other cells in their microenvironment. Based on these analyses, we found that senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells.53 Finally, as a key validation of our in silico analyses, we then examined Mif mRNA levels by RT- qPCR in our mouse models and found that as predicted, Mif mRNA levels were increased in bones from old compared to young mice and were significantly reduced following the genetic clearance of senescent cells in the old mice. + +The heterogeneous composition of the SASP, which consists of a multitude of growth factors, chemokines, cytokines, and matrix- degrading proteins, has been experimentally verified using various in vitro systems to induce cell stress, in vivo using multiple pre- clinical animal + +<--- Page Split ---> + +models of aging and disease, and has been linked to several pathophysiological conditions in humans as well as clinical outcomes. \(^{54,55}\) In the current study, we were able to group these factors into 9 distinct clusters to form tightly connected networks with distinct key molecules. The importance of these and other SASP factors has been verified in multiple biological contexts. \(^{56 - 63}\) Interestingly, the control of the SASP itself by RELA/p65, which we detected in two sequencing datasets of aging women, has recently been experimentally verified in U2OS osteosarcoma cells. \(^{64}\) + +Transcriptome- wide state- of- the- art technologies such as scRNA- seq will help shape our understanding of not just aging, but also therapeutics that potentially target fundamental mechanisms of aging, such as senolytics. As noted earlier, a confounder in these analyses is the generally low expression of the canonical marker of senescence, \(Cdkna2a/p16^{ink4a}\) , which is clearly detectable by RT- qPCR in the setting of aging, but poses challenges when using transcriptome- wide approaches. \(^{45}\) Hence, we propose a species- specific co- expression analysis with JUN (Homo sapiens) or Dkk1 (Mus musculus), based on modules of comparable expression to address this challenge. To our knowledge, we for the first time leveraged publicly available single cell datasets to enrich for a senescence/SASP gene set. Since we did not include commonly used senescence- markers ( \(Cdkna2a/p16^{ink4a}\) , \(Cdkna1a/p21^{Cip1}\) ) in the SenMayo panel, we were still able to rely on them to confirm a senescent cell state. Additional verification included a shift in the cell cycle phase to G1, as senescence prevents cells from proceeding to the S or M phases. \(^{59,65,66}\) With \(Cdkna1a/p21^{Cip1}\) being expressed at relatively higher levels, we were able to verify a senescent status of SASP cells, confirming our approach to identify single cells expressing high levels of SenMayo genes as being senescent. + +The use of pseudotime in scRNA- seq datasets to predict age- associated changes and fate commitment has been demonstrated previously in muscle stem cells (MuSCs) and fibro- adipose progenitors (FAPs). \(^{67}\) These analyses pointed to the importance of TGF- \(\beta\) signaling, but without specifically focusing on age- related expression changes. By contrast, we used + +<--- Page Split ---> + +pseudotime analyses to establish a novel approach to identify age- dependent transcriptional changes in senescence/SASP genes distinct from Cdkna2/p16nk4a and Cdkna1a/p21Cip1. + +Using a z- score based probabilistic model with pairwise correlations (bigSCale68) to construct transcriptional networks, several groups have successfully established the use of within- cell networks in single cell datasets25,69 and we made use of this approach to define senescence modules of similar expression. With overall agreement between pseudotime, network analyses, and direct pairwise z- score prediction, we overcame the downside of normalized expression, and a z- score predicted space allowed us to assign clusters and spatially depict them within cellular aggregates. These modules may serve as sources for novel senescent markers and pathways.70 + +As noted earlier, the MIF pathway emerged as a key intercellular communication pathway used by both hematopoietic and mesenchymal cells in bone marrow expressing high levels of senescence/SASP genes. This is perhaps not surprising given the importance of MIF as a proinflammatory cytokine, inhibitor of p53, and positive regulator of NF- \(\kappa\) B.71 MIF appears to be pivotal for cellular senescence, aging, and joint inflammation; however, its presence has been associated with a beneficial effect on the healthy lung and in MSCs.72–77 Of note, recent evidence indicates an important role for MIF signaling in immune evasion by tumors78 and parasites,79 raising the possibility that increased MIF expression by multiple senescent cell types may play a role in the ability of senescent cells to resist immune clearance, particularly with aging,53 and this possibility warrants further study. Importantly, we also used Mif expression to validate our in silico predictions based on the scRNA- seq analyses, and confirmed both an increase in Mif expression with aging in murine bone as well as a reduction in Mif mRNA levels following genetic clearance of senescent cells. + +The development and validation of SenMayo, as demonstrated here, may be particularly timely in the context of the recent establishment of a major NIH Common Fund consortium to map senescent cells (SenNET, https://sennetconsortium.org/). The goal of this program is to "comprehensively identify and characterize the differences in senescent cells across the body, + +<--- Page Split ---> + +across various states of human health, and across the lifespan." The application of SenMayo to bulk or scRNA- seq data from SenNET should greatly facilitate this goal and could provide a standardized gene set that is used across the multiple sites involved in this consortium. + +In summary, our studies contribute a novel gene set (SenMayo) that increases with aging across tissues and species, is responsive to senescent cell clearance, and can be used both in bulk and scRNA- seq analyses to identify cells expressing high levels of senescence/SASP genes. This gene set also has potential utility in the clinical evaluation of senescent cell burden and for studies of senolytic therapies. In addition, SenMayo circumvents current limitations in the transcriptional identification of senescent cells at the single cell level, thereby allowing for detailed analyses (e.g. pseudotime, intercellular signaling) that will facilitate better characterization of these cells in future studies. + +<--- Page Split ---> + +## Methods + +## MATERIALS AND METHODS + +Generation of SenMayo. Our own GSEA gene set for senescence- associated genes was generated by combining genes that had been reported in previous studies to be enriched in senescent and/or SASP- secreting cells and experimentally verified in at least human or mouse cells. We screened 1,656 studies, but following removal of studies reporting duplicates, case reports, and non- human or non- murine genes, formulated a list of 15 studies from which we identified 125 genes that constituted SenMayo (Table 118,26,48,55,61,80–89). Note that we intentionally did not include CDKN2A/p16ink or CDKN1A/p21Cip1 in SenMayo as we used these genes, in part, to validate our senescence/SASP gene set. + +RNA- seq. Transcriptome- wide gene expression data from young \((n = 15, 30.9 \pm 4.0\) years and \(n = 19, 30.3 \pm 5.4\) years) and postmenopausal females \((n = 15, 68.7 \pm 4.8\) years and \(n = 19, 73.1 \pm 6.6\) years) as well as 10 diabetic kidney disease patients (8 male, 2 female, \(71.25 \pm 7.85\) years and \(65.0 \pm 8.0\) years, respectively) were analyzed from three previous studies performed by our group (GSE141595: NCT02554695, GSE72815: NCT02349113,24,38: NCT02848131)27,28. After the original interventional study in diabetic kidney disease patients was completed, one additional female patient was added. All human studies were approved by the Mayo Clinic Institutional Review Board and written informed consent was obtained from all participants. RNA was isolated from whole bone biopsies (which included bone and bone marrow cells, Cohort A)27 as well as bone biopsies that were processed to remove bone marrow and bone surface cells and were thus highly enriched for osteocytes (Cohort B),28 and adipose tissue, 2- 5 cm inferior to the navel (for details, see38). Subcutaneous adipose tissue was obtained by an elliptical incisional biopsy at a point to the right or left, and 2- 5 cm inferior to the navel.24,38 Sequencing was performed on a HiSeq2000 (Illumina®), fastq files were mapped to the human reference genome hg19, and analysis was performed as previously described.27,28 Significantly differentially regulated genes + +<--- Page Split ---> + +were selected by a Benjamini- Hochberg adjusted p- value \(< 0.05\) and \(\log_{2}\) - fold changes above 0.5 or below - 0.5. Gene Set Enrichment Analysis (GSEA31,90) was performed with default settings (1000 permutations for gene sets, Signal2Noise metric for ranking genes). The network analysis was conducted with Cytoscape 3.8.2.30 For mRNA- seq of murine material, tibiae were centrifuged as noted above to remove bone marrow elements and then were immediately homogenized in QIAzol Lysis Reagent (QIAGEN, Valencia, CA) and stored at - 80°C, until the time of RNA extraction. RNA- sequencing was performed on a HiSeq2000 (Illumina®), fastq files were mapped to the murine reference genome mm10, and analysis was performed as previously described.27,28 An example of the code used for RNA- seq can be found in the provided R notebook (Methods: GSE72815_YOE_Notebook.Rmd). + +Mouse strains and drug treatments. All animal protocols were approved by the Institutional Animal Care and Use Committee (IACUC), and all experiments were performed in accordance with IACUC guidelines. Mice were housed in ventilated cages in a pathogen- free facility (12- hour light/dark cycle, 23°C) and had access to food (standard mouse diet, Lab Diet 5053, St. Louis, MO) and water ad libitum. Mouse experiments for a genetic targeting approach of senescent cells have been described by our group earlier.37 Briefly, old (20 months) female mice were injected intraperitoneally with vehicle (4% of 100% EtOH, 10%PEG400, 86% of 2% Tween 20 in deionized Water) or AP20187 (B/B homodimerizer, Clontech; 10 mg of AP20187 per kg body mass) twice weekly at the age of 20 months for a total of 4 months (old mice were sacrificed at 24 months of age). In addition, young (6- month) INK- ATTAC mice were used as a control comparison cohort. + +Quantitative real- time polymerase chain reaction (RT- qPCR) analysis. For bone analyses, tibiae were centrifuged to remove marrow elements and then immediately homogenized in QIAzol Lysis Reagent (QIAGEN, Valencia, CA) and stored at - 80°C. Subsequent RNA extraction, cDNA synthesis, and targeted gene expression measurements of mRNA levels by RT- qPCR were + +<--- Page Split ---> + +performed as described previously. \(^{91}\) Total RNA was extracted according to the manufacturer's instructions using QIAzol Lysis Reagent. Purification with RNeasy Mini Columns (QIAGEN, Valencia, CA) was subsequently performed. On- column RNase- free DNase solution (QIAGEN, Valencia, CA) was applied to degrade contaminating genomic DNA. RNA quantity was assessed with Nanodrop spectrophotometry (Thermo Fisher Scientific, Wilmington, DE). Standard reverse transcriptase was performed using High- Capacity cDNA Reverse Transcription Kit (Applied Biosystems by Life Technologies, Foster City, CA). Transcript mRNA levels were determined by RT- qPCR on the ABI Prism 7900HT Real Time System (Applied Biosystems, Carlsbad, CA) using SYBR green (Qiagen, Valencia, CA). The mouse forward primer sequence (5'- 3') for Mif was: 5'- GCCACCATGCCTATGTTTCATC- 3' and Reverse Primer Sequence 5'- GGGTGAGCTCCGACAGAAAC- 3'. RNA was normalized using two reference genes (Actb [forward: 5'- AATCGTGCGTGACATCAAAGAG- 3', reverse: 5'- GCCATCTCCTGCTCGAAGTC- 3'], Gapdh [forward: 5'- GACCTGACCTGCCGTCTAGAAA- 3', reverse: 5'- CCTGCTTCACCACCTTCTTGA- 3']) from which the most stable housekeeping gene was determined by the geNorm algorithm. For each sample, the median cycle threshold (Ct) of each gene (run in triplicate) was normalized to the geometric mean of the median Ct of the most stable reference gene. The delta Ct for each gene was used to calculate the relative mRNA expression changes for each sample. Genes with Ct values \(>35\) were considered not expressed (NE), as done previously. \(^{92}\) + +Single- cell RNA- seq (scRNA- seq) analysis. Transcriptome- wide analysis of human bone marrow mononuclear cells at a single cell level was based on a previously published dataset. \(^{46}\) Here, bone marrow was isolated from healthy female (n=10) and male (n=10) donors (50.6±14.9 years) and droplet- based scRNA- seq was performed. A minimum sequencing depth of 50,000 reads/cell with a mean of 880 genes/cell was reported. In addition, we analyzed droplet- based scRNA- seq data from bone marrow cells isolated from C57BL/6 mice (n=14) \(^{52}\) and from C57BL/6JN mice (n=30) \(^{35}\) + +<--- Page Split ---> + +and from the tabula muris senis. \(^{35}\) Sequencing data were aligned to the human reference genome Grch38 and the mouse genome mm10, respectively. Data with at least 500 unique molecular identifiers (UMIs), log10 genes per UMI \(>0.8\) , \(>250\) genes per cell and a mitochondrial ratio of less than \(20\%\) were extracted, normalized, and integrated using the Seurat package v3.0 in R4.0.2. Subsequent R- packages were Nebulosa (3.13 \(^{93}\) ), Monocle (2.18.0 \(^{94}\) ), dittoSeq (1.2.6 \(^{95}\) ), Escape (1.0.1, "Borcherding N, Andrews J (2021). escape: Easy single cell analysis platform for enrichment. R package version 1.2.0."), Cellchat \(^{50}\) (within the Cellchat package, and for Fig. 2C, "CD10+ B cells", "CD20+ B cells", "Plasma cells", "Plasmacytoid dendritic cells", "Conventional dendritic cells" were summarized as "B cells", "CD4+ naïve T cells", "CD4+ memory T cells", "CD8+ naïve T cells", "CD8+ effector T cells" were summarized as "T cells", "Early erythroid progenitors", "Early erythrocytes", "Late erythrocytes" as "Ery", "HSPCs" as "HSPCs", "Monocyte progenitors", "CD14+ monocytes", "CD16+ monocytes", "Macrophages", "Natural killer cells" as "Mono" and "SASP cells" as "SASP". For Figure 3C, "Chondro- hyper", "Chondro- prehyper", "Chondro- progen", "Chondro- prol/rest", "Chondrocyte" were summarized as "Chondro", "EC", "Pericytes" as "Endo", "Fibroblast" as "Fibro", "Lymphocyte", "Mast cell" as "Immune", "Lepr MSC", "MSC" as "MSC", "Mineralizing Osteocyte", "OLC 1", "OLC 2", "Osteoblast", "Osteocyte" as "Osteo" and "SASP cells" as "SASP"), bigSCale (2.1 \(^{70}\) ), gprofiler2 (0.2.0 \(^{96}\) ), igraph (1.2.6, Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695), PCAtools (2.4.0, Blighe K, Lun A (2021). PCAtools: PCAtools: Everything Principal Components Analysis. R package version 2.4.0), and corrplot (0.89). + +Pseudotime is a progression of cells along a virtually estimated path, mimicking temporal development. By using Monocle, an independent component analysis (ICA) dimensional reduction is performed, followed by a projection of a minimal spanning tree (MST) of the cell's location in this reduced space. Each cell is assigned a pseudotemporal space. \(^{97,98}\) Monocle 2 was used to preprocess, perform UMAP reduction, and reduce the dimensionality using the DDRTree algorithm with a maximum of four dimensions. Subsequently, the cells were ordered and genes + +<--- Page Split ---> + +plotted along the reduced dimension. Differential gene testing has been performed with the formula “\~sm.ns(Pseudotime)”, and the results were restricted by a qvalue<0.1.97 An example of the code used for scRNA- seq can be found in the provided R notebook (Methods: R_notebook_Fig4_5_sup2to5.Rmd). + +Author contributions. D.S., J.N.F., and S.K. conceived and directed the project. D.S. and J.N.F. designed the experiments and interpreted the data with input from S.K. Experiments were performed by D.S. and R.L.K. D.S. and S.K. wrote the manuscript. All authors reviewed the manuscript. J.N.F. and S.K. oversaw all experimental design, data analyses, and manuscript preparation. J.N.F., S.K., and D.S. accept responsibility for the integrity of the data analysis. + +Acknowledgements. This work was supported by the German Research Foundation (D.F.G., 413501650) (D.S.), National Institutes of Health (NIH) grants P01 AG062413 (S.K., J.N.F., N.K.L., R.P., P.D.R., L.J.N., Y.I., J.P., D.G.M., T.T., J.L.K.), R21 AG065868 (S.K., J.N.F), K01 AR070241 (J.N.F.), R01 AG063707 (D.G.M.), R37 AG 013925 (J.L.K., T.T.), R33AG 61456 (J.L.K., T.T., R.P., P.D.R., L.J.N., S.K.), 1R01AG068048- 01 (JFP), R56 AG 60907 and R01 AG55529 (N.K.L.), the Connor Fund (J.L.K., T.T.), Robert P. and Arlene R. Kogod (J.L.K.), Robert J. and Theresa W. Ryan (J.L.K., T.T.), the Noaber Foundation (J.L.K., T.T.), and Mildred Scheel postdoc fellowship by the German Cancer Aid (R.L.K.). X.Z. is supported by the Robert and Arlene Kogod Center on Aging Career Development Award. + +The authors thank SA Johnsen and FH Hamdan for inspiring discussions. + +Competing interests. Patents on senolytic drugs and their uses and SASP biomarkers are held by Mayo Clinic and the University of Minnesota. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and was conducted in compliance with Mayo Clinic Conflict of Interest policies. + +<--- Page Split ---> + +Table 1. Genes included in the SenMayo panel. The relationship of each gene to senescence/aging is described in the reference indicated. + +
Gene(human)ClassificationStateReference
ACVR1BTransmembrane signal receptorsTransmembrane26
ANGMiscellaneousSecreted80.87
ANGPT1Intercellular signal moleculeSecreted55
ANGPTL4Intercellular signal moleculeSecreted18.55
AREGGrowth factorIntracellular80.87.88
AXLTransmembrane signal receptorsTransmembrane18.88
BEX3MiscellaneousIntracellular18
BMP2Growth factorSecreted55.88
BMP6Growth factorSecreted88
C3Protease inhibitorsSecreted55
CCL1Cytokine/ChemokineSecreted88
CCL13Cytokine/ChemokineSecreted80.88
CCL16Cytokine/ChemokineSecreted26.80.88
CCL20Cytokine/ChemokineSecreted26.55.82,83.85,87.88
CCL24Cytokine/ChemokineSecreted26.80.82
CCL26Cytokine/ChemokineSecreted87
CCL3Cytokine/ChemokineSecreted80.88
CCL3L1Cytokine/ChemokineSecreted55.84.87.88
CCL4Cytokine/ChemokineSecreted55
CCL5Cytokine/ChemokineSecreted87
CCL7Cytokine/ChemokineSecreted26.81.87
CCL8Cytokine/ChemokineSecreted88
CD55MiscellaneousSecreted80.88
CD9Transmembrane signal receptorsTransmembrane18.88
CSF1Cytokine/ChemokineSecreted83
CSF2Cytokine/ChemokineSecreted26.55.61,80.87.88
CSF2RBTransmembrane signal receptorsTransmembrane88
CST4Protease inhibitorsSecreted84
CTNNB1Transcription factors and regulatorsTransmembrane87
CTSB(Metallo-)proteasesSecreted80
CXCL1Cytokine/ChemokineSecreted26.55.61,80.87-89
CXCL10Cytokine/ChemokineSecreted55.81.87
CXCL12Cytokine/ChemokineSecreted80.87
CXCL16Cytokine/ChemokineSecreted87
CXCL2Cytokine/ChemokineSecreted26.61.80.87
CXCL3Cytokine/ChemokineSecreted55.63.80
CXCL8Cytokine/ChemokineSecreted26.55.61,80.82.89
CXCR2Cytokine/ChemokineTransmembrane26
DKK1Intercellular signal moleculeSecreted55
EDN1Intercellular signal moleculeSecreted55
EGFTransmembrane signal receptorsTransmembrane80
EGFRTransmembrane signal receptorsTransmembrane80.88
EREGGrowth factorSecreted80.87.88
ESM1Intercellular signal moleculeSecreted55
ETS2Transcription factors and regulatorsIntracellular88
FASTransmembrane signal receptorsTransmembrane80.88
FGF1Growth factorSecreted26.55
FGF2Growth factorSecreted55.80
FGF7Growth factorSecreted55.80.88
GDF15Growth factorSecreted26.55.88
GEMMiscellaneousIntracellular88
GMFGIntercellular signal moleculeIntracellular88
HGF(Metallo-)proteasesSecreted26.80.87.88
HMGB1Transcription factors and regulatorsIntracellular55.87
ICAM1MiscellaneousIntracellular61.80.87.88
ICAM3MiscellaneousIntracellular87.88
IGF1Growth factorSecreted18.81.86.88
IGFBP1Protease inhibitorsSecreted88
IGFBP2Protease inhibitorsSecreted26.61.80.87.88
IGFBP3Protease inhibitorsSecreted26.48.55.61,80.87
IGFBP4Protease inhibitorsSecreted26.61.80.87
IGFBP5Protease inhibitorsSecreted26.55.61.87
IGFBP6Protease inhibitorsSecreted26.61.80.87.88
IGFBP7MiscellaneousSecreted26.55.61.80.82.87
IL10Cytokine/ChemokineSecreted85
IL13Cytokine/ChemokineSecreted80.88
IL15Cytokine/ChemokineSecreted40.83.87.88
+ +<--- Page Split ---> + +
IL18Cytokine/ChemokineSecreted55.87
IL1ACytokine/ChemokineSecreted26.40,55,61,80,81,87-89
IL1BCytokine/ChemokineSecreted18,40,85,87-89
IL2Cytokine/ChemokineSecreted87
IL32Cytokine/ChemokineSecreted55
IL6Cytokine/ChemokineSecreted26,40,55,61,81-83,85,87,88
IL6STTransmembrane signal receptorsTransmembrane40
IL7Cytokine/ChemokineSecreted40,88
INHAGrowth factorSecreted88
IQGAP2MiscellaneousIntracellular88
ITGA2Transmembrane signal receptorsTransmembrane88
ITPKAProtein modifying enzymesIntracellular88
JUNTranscription factors and regulatorsIntracellular88
KITLGGrowth factorIntracellular40,87
LCP1MiscellaneousIntracellular55
MIFProtein modifying enzymesSecreted18,40,87,88
MMP1(Metallo-)proteasesSecreted40,61,88
MMP10(Metallo-)proteasesSecreted40,61,89
MMP12(Metallo-)proteasesSecreted40,87
MMP13(Metallo-)proteasesSecreted40,87
MMP14(Metallo-)proteasesIntracellular40,87
MMP2(Metallo-)proteasesSecreted40,61,84,88
MMP3(Metallo-)proteasesSecreted55,84
MMP9(Metallo-)proteasesSecreted88
NAP1L4MiscellaneousIntracellular40,87,88
NRG1Growth factorSecreted55
PAPPA(Metallo-)proteasesSecreted88
PECAM1MiscellaneousIntracellular88
PGFGrowth factorSecreted87
PIGFProtein modifying enzymesTransmembrane40,88
PLAT(Metallo-)proteasesSecreted40,87
PLAU(Metallo-)proteasesSecreted40
PLAURTransmembrane signal receptorsTransmembrane40,88
PTBP1MiscellaneousIntracellular55
PTGER2Transmembrane signal receptorsTransmembrane55
PTGESProtein modifying enzymesIntracellular88
RPS6KA5Protein modifying enzymesIntracellular88
SCAMP4MiscellaneousIntracellular55
SELPLGTransmembrane signal receptorsTransmembrane55
SEMA3FIntercellular signal moleculeSecreted55
SERPINB4Protease inhibitorsIntracellular55
SERPINE1Protease inhibitorsSecreted26,40,55,61,82,84,87,88
SERPINE2Protease inhibitorsSecreted40,87
SPP1Cytokine/ChemokineSecreted55
SPXIntercellular signal moleculeSecreted55
TIMP2Protease inhibitorsSecreted18,40,87,88
TNFCytokine/ChemokineSecreted81,85
TNFRSF10CTransmembrane signal receptorsTransmembrane40
TNFRSF11BTransmembrane signal receptorsTransmembrane40,88
TNFRSF1ATransmembrane signal receptorsTransmembrane40,87
TNFRSF1BTransmembrane signal receptorsTransmembrane40
TUBGCP2MiscellaneousIntracellular88
VEGFAGrowth factorSecreted26,40,82,88
VEGFCGrowth factorSecreted88
VGFIntercellular signal moleculeSecreted55
WNT16Intercellular signal moleculeSecreted55
WNT2Intercellular signal moleculeTransmembrane88
+ +<--- Page Split ---> + + +Table 2. Comparison of SenMayo with 6 existing senescence/SASP gene sets. Note that in GSEA analyses, p-values \(< 0.25\) are considered potentially significant31,99, although we also identified p-values \(< 0.05\) and \(< 0.01\) (NES, normalized Enrichment Score). + +
Human AgingMouse AgingMouse Genetic Clearance of Senescent CellsHuman Pharmacological Clearance of Senescent Cells
Cohort ACohort BMicrogliaPrefrontal cortexDorsal hippocampusBone marrowMouse INK-ATTAC (old vs young)Mouse INK-ATTAC (old, vehicle vs AP)Adipose (Control vs D+Q)
NESp-valueNESp-valueNESp-valueNESP-valueNESp-valueNESp-valueNESNp-valueNESp-valueNESp-value
R-HSA-25595821.10750.28260.67800.92781.17940.1565-1.24670.11691.21170.19160.92000.60431.42350.03261.00060.44421.39960.0344
Casella_up1.00890.47480.69700.88850.97370.47900.95370.52091.39490.05930.88740.6339-1.08610.31680.68650.96270.67510.9983
Purcell0.93290.59440.95850.50931.51200.01781.17310.23041.81170.00001.52240.04191.38940.0778-0.73000.8874-1.06960.3278
Hernandez0.78490.77710.78460.78020.71460.94610.86620.6710-0.81000.7650-0.57780.97891.25130.17181.47960.02661.40190.0501
Fridman_up1.42490.01691.54070.01741.43970.02060.96390.54491.74820.00001.61000.01631.07620.31451.34130.03471.41130.0220
Sencan-0.84600.93620.80380.82350.86740.81441.03280.40061.53020.00110.72470.86670.88380.7312-1.06740.31821.53750.0000
SenMayo1.51220.00231.49820.00311.46240.00521.60980.00131.85010.00001.51000.03621.50420.00231.45860.00541.32390.0184
+ +
p-value<0.25
<0.05
<0.01
+ +<--- Page Split ---> + +Table 3. Top 20 significantly upregulated genes in the human and murine SASP clusters. + +
Geneavg_log2FCAdj. p-value
Human
S100A91.9743176780
CXCL81.8177752530
CST31.8138352950
TYROBP1.7427739520
LST11.7044565150
FCN11.7041481190
FCER1G1.6980711860
LYZ1.6958790210
CCL31.681097610
S100A81.6391675240
CTSS1.6051075330
AIF11.5375602820
S100A121.5010133810
SAT11.4757403240
G0S21.4717682590
S100A111.4265831670
PSAP1.4121560190
NEAT11.4020088890
CSTA1.3461710610
SERPINA11.3430127630
Murine
Ccl21.3853854564.4042E-274
Cxcl141.3487655310
Cxcl121.3480992210
Hp1.329671382.6772E-298
Trf1.324835066.8972E-280
Sering11.3048710380
Mt11.2946178370
Tmem176b1.2383300750
Mt21.2241586940
Igfbp41.2109057730
Grem11.2070567240
Cd3021.1952207470
Apoe1.1630629930
Msmp1.162402443.2104E-194
Adipoq1.1408883657.4114E-283
Cyr611.1364266250
Gas61.1104743290
Mmp131.0954882960
Tmem176a1.0877655810
Col3a11.0827201541.1707E-254
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Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J. & Peterson, H. gpofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Research 9; 10.12688/f1000research.24956.2 (2020). +97. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature biotechnology 32, 381–386; 10.1038/nbt.2859 (2014). +98. Reid, J. E. & Wernisch, L. Pseudotime estimation: deconfounding single cell time series. Bioinformatics (Oxford, England) 32, 2973–2980; 10.1093/bioinformatics/btw372 (2016). + +<--- Page Split ---> + +99. Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nature protocols 14, 482–517; 10.1038/s41596-018-0103-9 (2019). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SenMayomanuscriptallsupplementaryfigures.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f_det.mmd b/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..8794d35c89f24e31df72b3e8ad96b74b117eb7d4 --- /dev/null +++ b/preprint/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f/preprint__0a6508a983f37e141aa8dd5412d3637dcad95223e127d307f2bb548398049c6f_det.mmd @@ -0,0 +1,535 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 933, 207]]<|/det|> +# A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues + +<|ref|>text<|/ref|><|det|>[[44, 228, 520, 270]]<|/det|> +Sundeep Khosla ( khosla.sundeep@mayo.edu ) Mayo Clinic https://orcid.org/0000- 0002- 2936- 4372 + +<|ref|>text<|/ref|><|det|>[[44, 275, 520, 316]]<|/det|> +Dominik Saul Mayo Clinic https://orcid.org/0000- 0002- 0673- 3710 + +<|ref|>text<|/ref|><|det|>[[44, 322, 520, 363]]<|/det|> +Robyn Laura Kosinsky Mayo Clinic https://orcid.org/0000- 0003- 2869- 7762 + +<|ref|>text<|/ref|><|det|>[[44, 369, 210, 408]]<|/det|> +Elizabeth Atkinson Mayo Clinic + +<|ref|>text<|/ref|><|det|>[[44, 415, 202, 454]]<|/det|> +Madison Doolittle Mayo Clinic + +<|ref|>text<|/ref|><|det|>[[44, 460, 518, 501]]<|/det|> +Xu Zhang Mayo Clinic https://orcid.org/0000- 0002- 9784- 0481 + +<|ref|>text<|/ref|><|det|>[[44, 507, 520, 548]]<|/det|> +Nathan LeBrasseur Mayo Clinic https://orcid.org/0000- 0002- 2002- 0418 + +<|ref|>text<|/ref|><|det|>[[44, 554, 175, 592]]<|/det|> +Robert Pignolo Mayo Clinic + +<|ref|>text<|/ref|><|det|>[[44, 599, 620, 640]]<|/det|> +Paul Robbins University of Minnesota https://orcid.org/0000- 0003- 1068- 7099 + +<|ref|>text<|/ref|><|det|>[[44, 646, 620, 687]]<|/det|> +Laura Niedernhofer University of Minnesota https://orcid.org/0000- 0002- 1074- 1385 + +<|ref|>text<|/ref|><|det|>[[44, 693, 208, 732]]<|/det|> +Yuji Ikeno Barshop Institute + +<|ref|>text<|/ref|><|det|>[[44, 739, 452, 779]]<|/det|> +Diana Jurk PhD https://orcid.org/0000- 0003- 4486- 0857 + +<|ref|>text<|/ref|><|det|>[[44, 786, 520, 826]]<|/det|> +Joao Passos Mayo Clinic https://orcid.org/0000- 0001- 8765- 1890 + +<|ref|>text<|/ref|><|det|>[[44, 832, 200, 871]]<|/det|> +LaTonya Hickson Mayo Clinic + +<|ref|>text<|/ref|><|det|>[[44, 878, 150, 916]]<|/det|> +Ailing Xue Mayo Clinic + +<|ref|>text<|/ref|><|det|>[[44, 923, 169, 942]]<|/det|> +David Monroe + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 45, 519, 65]]<|/det|> +Mayo Clinic https://orcid.org/0000- 0002- 4818- 0114 + +<|ref|>text<|/ref|><|det|>[[44, 70, 519, 110]]<|/det|> +Tamara Tchkonia Mayo Clinic https://orcid.org/0000- 0003- 4623- 7145 + +<|ref|>text<|/ref|><|det|>[[44, 115, 184, 135]]<|/det|> +James Kirkland + +<|ref|>text<|/ref|><|det|>[[52, 139, 519, 158]]<|/det|> +Mayo Clinic https://orcid.org/0000- 0003- 1676- 4905 + +<|ref|>text<|/ref|><|det|>[[44, 163, 152, 182]]<|/det|> +Joshua Farr + +<|ref|>text<|/ref|><|det|>[[52, 185, 519, 205]]<|/det|> +Mayo Clinic https://orcid.org/0000- 0002- 3179- 6414 + +<|ref|>sub_title<|/ref|><|det|>[[44, 245, 102, 263]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 282, 486, 303]]<|/det|> +Keywords: bone, senescence, SASP, gene set, aging + +<|ref|>text<|/ref|><|det|>[[44, 321, 344, 341]]<|/det|> +Posted Date: December 13th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 358, 475, 379]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1034608/v1 + +<|ref|>text<|/ref|><|det|>[[42, 395, 910, 440]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 473, 930, 518]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on August 16th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 32552- 1. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[152, 88, 844, 123]]<|/det|> +# A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues + +<|ref|>text<|/ref|><|det|>[[115, 137, 601, 154]]<|/det|> +Abbreviated Title: A new gene set identifies senescent cells + +<|ref|>text<|/ref|><|det|>[[115, 167, 883, 267]]<|/det|> +Authors: Dominik Saul, M.D. \(^{1,2,3}\) , Robyn Laura Kosinsky, Ph.D. \(^{4}\) , Elizabeth J Atkinson, M.S. \(^{5}\) , Madison L. Doolittle, Ph.D. \(^{1,2}\) , Xu Zhang, Ph.D. \(^{2,6}\) Nathan K. LeBrasseur, Ph.D. \(^{2,6}\) , Robert J. Pignolo, M.D., Ph.D. \(^{1,2,6}\) , Paul D. Robbins, Ph.D. \(^{7}\) , Laura J. Niedernhofer, M.D., Ph.D. \(^{7}\) , Yuji Ikeno, M.D., Ph.D. \(^{8}\) , Diana Jurk, Ph.D. \(^{2,6}\) , João F. Passos, Ph.D. \(^{2,6}\) , LaTonya J. Hickson, M.D. \(^{9}\) , Ailing Xue, M.D. \(^{2}\) , David G. Monroe, Ph.D. \(^{1,2}\) , Tamara Tchkonia, Ph.D., M.S. \(^{2,6}\) , James L. Kirkland, M.D., Ph.D. \(^{2,6}\) , Joshua N. Farr, Ph.D. \(^{1,2,6}\) and Sundeep Khosla, M.D. \(^{1,2,6}\) + +<|ref|>sub_title<|/ref|><|det|>[[115, 282, 216, 297]]<|/det|> +## Affiliations: + +<|ref|>text<|/ref|><|det|>[[115, 297, 884, 474]]<|/det|> +\(^{1}\) Division of Endocrinology, Mayo Clinic, Rochester, MN, 55905, USA. \(^{2}\) Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN 55905, USA. \(^{3}\) Department of Trauma, Orthopedics and Reconstructive Surgery, Georg- August- University of Goettingen, Germany. \(^{4}\) Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, 55905, USA. \(^{5}\) Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA. \(^{6}\) Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA. \(^{7}\) Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA. \(^{8}\) Department of Pathology, University of Texas Health, San Antonio, TX, USA. \(^{9}\) Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL, USA. + +<|ref|>text<|/ref|><|det|>[[115, 487, 883, 618]]<|/det|> +Funding: This work was supported by the German Research Foundation (D.F.G., 413501650) (D.S.), National Institutes of Health (NIH) grants P01 AG062413 (S.K., J.N.F., N.K.L., R.P., P.D.R., L.J.N., Y.I., J.P., D.G.M., T.T., J.L.K.), R21 AG065868 (S.K., J.N.F.), K01 AR070241 (J.N.F.), R01 AG063707 (D.G.M.), R37 AG 013925 (J.L.K., T.T.), R33AG 61456 (J.L.K., T.T., R.P., P.D.R., L.J.N., S.K.), 1R01AG068048- 01 (JFP), R56 AG 60907 and R01 AG55529 (N.K.L.), the Connor Fund (J.L.K., T.T.), Robert P. and Arlene R. Kogod (J.L.K.), Robert J. and Theresa W. Ryan (J.L.K., T.T.), the Noaber Foundation (J.L.K., T.T.), and Mildred Scheel postdoc fellowship by the German Cancer Aid (R.L.K.). + +<|ref|>sub_title<|/ref|><|det|>[[115, 633, 181, 648]]<|/det|> +## Emails: + +<|ref|>text<|/ref|><|det|>[[143, 649, 880, 904]]<|/det|> +- Dominik Saul: saul.dominik@mayo.edu; ORCiD: 0000-0002-0673-3710- Robyn Laura Kosinsky: kosinsky.robynlaura@mayo.edu; ORCiD: 0000-0003-2869-7762- Elizabeth J Atkinson: Atkinson@mayo.edu; 0000-0002-1191-3775- Madison L. Doolittle: doolittle.madison@mayo.edu; ORCiD: 0000-0003-0912-0095- Xu Zhang: zhang.xu@mayo.edu; ORCiD: 0000-0002-9784-0481- Nathan LeBrasseur: LeBrasseur.Nathan@mayo.edu; ORCiD: 0000-0002-2002-0418- Robert J. Pignolo: Pignolo.Robert@mayo.edu- Paul Robbins: probbins@umn.edu; ORCiD: 0000-0003-1068-7099- Laura Niedernhofer, Iniedern@umn.edu; ORCiD: 0000-0002-1074-1385- Yuji Ikeno: Ikeno@uthscsa.edu- Diana Jurk: jurk.diana@mayo.edu- João Passos: Passos.Joao@mayo.edu; ORCiD: 0000-0001-8765-1890- LaTonya J. Hickson: Hickson.Latonya@mayo.edu- Ailing Xue: Xue.Ailing@mayo.edu- David G. Monroe: Monroe.David@mayo.edu; ORCiD: 0000-0002-4818-0114 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[143, 90, 808, 159]]<|/det|> +- Tamara Tchkonia: Tchkonia.Tamar@mayo.edu; ORCiD: 0000-0003-4623-7145- James Kirkland: Kirkland.James@mayo.edu; ORCiD: 0000-0003-1676-4905- Joshua N. Farr: farr.joshua@mayo.edu; ORCiD: 0000-0002-3179-6414- Sundeep Khosla: khosla.sundeep@mayo.edu; ORCiD: 0000-0002-2936-4372 + +<|ref|>sub_title<|/ref|><|det|>[[116, 174, 330, 190]]<|/det|> +## \*Corresponding authors: + +<|ref|>text<|/ref|><|det|>[[115, 189, 883, 238]]<|/det|> +\*Joshua N. Farr, Ph.D., Guggenheim 7- 11D, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905; Tel. +1- 507- 538- 0085; Fax. +1- 507- 284- 9111; Email: farr.joshua@mayo.edu + +<|ref|>text<|/ref|><|det|>[[115, 252, 883, 286]]<|/det|> +\*Sundeep Khosla, M.D., Guggenheim 7- 11, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905; Tel: +1- 507- 255- 6663; Email: khosla.sundeep@mayo.edu + +<|ref|>text<|/ref|><|det|>[[115, 300, 560, 317]]<|/det|> +Key Words: bone, senescence, SASP, gene set, aging + +<|ref|>text<|/ref|><|det|>[[115, 332, 830, 350]]<|/det|> +Supplementary Material: This manuscript contains Supplementary Methods and Figures + +<|ref|>text<|/ref|><|det|>[[115, 364, 883, 429]]<|/det|> +Disclosures: Patents on senolytic drugs and their uses and on SASP biomarkers are held by Mayo Clinic and the University of Minnesota. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and was conducted in compliance with Mayo Clinic Conflict of Interest policies. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 191, 107]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[112, 120, 886, 876]]<|/det|> +Although cellular senescence is increasingly recognized as driving multiple age- related co- morbidities through the senescence- associated secretory phenotype (SASP), in vivo senescent cell identification, particularly in bulk or single cell RNA- sequencing (scRNA- seq) data remains challenging. Here, we generated a novel gene set (SenMayo) and first validated its enrichment in bone biopsies from two aged human cohorts. SenMayo also identified senescent cells in aged murine brain tissue, demonstrating applicability across tissues and species. For direct validation, we demonstrated significant reductions in SenMayo in bone following genetic clearance of senescent cells in mice, with similar findings in adipose tissue from humans in a pilot study of pharmacological senescent cell clearance. In direct comparisons, SenMayo outperformed all six existing senescence/SASP gene sets in identifying senescent cells across tissues and in demonstrating responses to senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from publicly available human and murine bone marrow/bone scRNA- seq data and identified monocytic and osteolineage cells, respectively, as showing the highest levels of senescence/SASP genes. Using pseudotime and cellular communication patterns, we found senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Moreover, using this senescence panel, we were able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways associated with these cells, which may be particularly useful for evolving efforts to map senescent cells (e.g., SenNet). In addition, SenMayo represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 223, 107]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[113, 120, 886, 492]]<|/det|> +Cellular senescence is now recognized as a fundamental mechanism of aging in animals and humans. Accumulation of DNA damage and/or other cellular stressors \(^{1 - 4}\) causes proliferating \(^{5,6}\) as well as terminally differentiated, non- dividing cells \(^{7 - 10}\) to undergo senescence. Characteristics of senescent cells include profound chromatin and secretome changes, along with increased expression of a number of senescence markers, including Cdkna/p16 \(^{nk4a}\) and Cdkna/p21 \(^{Cip1}\) , immune evasion, and resistance to apoptosis. \(^{1,11}\) Senescent cells can develop a senescence- associated secretory phenotype (SASP), consisting of pro- inflammatory cytokines, chemokines, extracellular matrix- degrading proteins, and other factors that have deleterious paracrine and systemic effects. \(^{12 - 15}\) Further, because senescent cells accumulate in multiple tissues in temporal and spatial synchrony with age- associated functional decline in both animals and humans, \(^{5,6,16}\) they have been hypothesized to drive the deterioration linked to numerous chronic diseases. \(^{1}\) + +<|ref|>text<|/ref|><|det|>[[113, 503, 886, 714]]<|/det|> +Importantly, the SASP as a feature of cellular senescence represents not just a locally or systemically detrimental set of factors that, in the aging organism, cause physical, metabolic, and cognitive decline, \(^{17 - 21}\) but is also a therapeutic target of interest. \(^{22 - 24}\) Thus, given the broad availability of next- generation sequencing, there is considerable interest in monitoring responses to senolytic treatments. However, this has been challenging, especially at the single cell level. \(^{25}\) In part, this is due to an imprecise definition of the heterogeneous population of senescent cells and their associated SASP which complicates appropriate monitoring of senescent cell clearance. + +<|ref|>text<|/ref|><|det|>[[113, 726, 886, 906]]<|/det|> +Due to variations in the composition of a "senescence gene set" in the current literature, in the present study we sought to identify commonly regulated genes in various age- related data sets in a transcriptome- wide approach that included whole- transcriptome as well as single cell RNA- sequencing (scRNA- seq). \(^{26}\) Based on an extensive review of the literature, we defined a panel of 125 genes as our senescence gene set ("SenMayo"), which we then validated in our own as well as publicly available datasets of tissues from aged humans and mice, including changes + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 886, 300]]<|/det|> +in this gene set following the clearance of senescent cells. Recognizing the difficulty of identifying senescent cells within scRNA- seq analyses, we next applied SenMayo to available scRNA- seq data from human and murine bone marrow/bone hematopoietic and mesenchymal cells, ascertained the identity of the senescent cells in these analyses, and characterized the communication patterns of senescent hematopoietic or mesenchymal cells with other cells in their microenvironment. Finally, we experimentally validated key predictions from our in silico analyses in a mouse model of aging and following genetic clearance of senescent cells. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 183, 107]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[112, 116, 886, 622]]<|/det|> +Development and validation of SenMayo in human datasets. We first analyzed previously published \(^{27,28}\) as well as unpublished (see Methods) transcriptome- wide mRNA- seq analyses of human whole bone biopsies. These included bone and bone marrow (Cohort A) \(^{27}\) as well as bone biopsies that were processed to remove bone marrow and bone surface cells and were thus highly enriched for osteocytes (Cohort B) \(^{28}\) from young vs. elderly women (Fig. 1A). We used transcriptional regulatory relationships \(^{29}\) to evaluate whether senescence- and SASP- associated pathways were enriched with aging in humans and noted enrichment of genes regulating inflammatory mediators, including NFKB1, RELA, and STAT3 (Fig. 1B). As expected, both aged cohorts displayed an upregulation of senescence- and SASP markers such as CDKN1A/p21 \(^{Cip1}\) , CCL2, and IL6 (Fig. 1C). It should be noted that some canonical markers of senescence, including CDKN2A/p16 \(^{nk4a}\) , did not show the predicted increase with aging due to comparatively low expression levels. Given the limitations of single gene analyses to predict the complex mechanisms of cellular aging, we next tested whether a previously published combination of senescence/SASP genes (R- HSA- 2559582) is enriched in our aging cohorts. However, this Gene Set Enrichment (GSEA)- based approach failed to predict an age- related senescence/SASP increase in either cohort (Fig. 1D). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 85, 860, 687]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 700, 883, 894]]<|/det|> +
Figure 1. Development and validation of the SenMayo gene set. (A) Human samples from Cohort A (bone and bone marrow biopsies) and cohort B (highly enriched osteocyte fractions) were used for transcriptome-wide RNA-seq analyses; (B) Making use of TRRUST analyses, \(^{30}\) we found several inflammation- and stress-associated genes, including SIRT1 and NFKB1, to be upregulated in the elderly women; (C) In both gene sets, CDKN1A/P21 \(^{Cip1}\) and several SASP markers such as CCL2 and IL6 showed consistent upregulation with aging, while CDKN2A/p16 \(^{lnk4a}\) (due to comparatively low expression) did not change significantly; (D) The commonly used senescence/SASP gene set (R-HSA-2559582) failed to predict the aging process in either human cohort; (E) The SenMayo gene set includes growth factors, transmembrane receptors, and cytokines/chemokines that are highly influenced by other members of the gene set. The circle size depicts groupwise interactions; (F) SenMayo encodes a dense network of nine different protein classes within a strong interaction network. The size of each circle represents
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 156]]<|/det|> +the connectivity with other members of the gene set; \(^{30}\) (G) Genes included in the SenMayo gene set were significantly enriched with aging in both human cohorts. Cohort A: \(n = 38\) (19 young, 19 old, all \(\div\) ), Cohort B: \(n = 30\) (15 young, 15 old, all \(\div\) ). \(^{**}p< 0.01\) , \(^{***}p< 0.0001\) . Figure 1A was designed using Biorender.com. + +<|ref|>text<|/ref|><|det|>[[112, 180, 886, 911]]<|/det|> +In order to develop a more robust gene panel associated with cellular senescence, we next generated a novel gene set to predict the expression of aging- related senescence genes by performing an in- depth, rigorous literature search (see Methods for details of how these genes were selected). The result was a novel senescence gene set of 125 genes (SenMayo) that consisted predominantly of SASP factors ( \(n = 83\) ) but also included transmembrane ( \(n = 20\) ) and intracellular ( \(n = 22\) ) proteins (Table 1). Within this SenMayo gene set, which comprised 9 distinct clusters, cytokines/chemokines were the most densely connected regulators according to the number of descendent proteins in STRING analysis (Fig. 1E, F). Predominant connectivity (whole network density: 0.277, PPI <0.0001) was shown by IL1A, CXCL8, CCL2 (cytokines/chemokines, blue), IGF1 (growth factor, green), C3 and IGFBP4 (protease inhibitor, turquoise), TNFRSF1A, EGF and EGFR (transmembrane signal receptors, red), and MMP2, PLAT, and HGF ([metalloproteinases, grey) (Fig. 1F). Notably, when testing the enrichment of SenMayo within our two human mRNA- seq cohorts, senescence/SASP genes were significantly enriched in the bone samples obtained from elderly women ( \(p = 0.002\) [Cohort A] and \(p = 0.003\) [Cohort B]; Fig. 1G). Using Cohort A as an example, within the R- HSA- 2559582 gene set, 2 out of 50 available genes were significantly enriched in the biopsies from elderly women (Suppl. Fig. 1A), while 13 out of 120 available genes of the SenMayo gene set were significantly enriched in the elderly women (Suppl. Fig. 1B). Note that the GSEA analysis includes not only genes that differ significantly between groups, but also evaluates overall trends for differences in gene expression between groups and hence provides considerably greater power than examining individual genes. \(^{31}\) The canonical SASP markers CCL24, SEMA3F, FGF2, and IGFBP7 were consistently enriched in Cohort A (Suppl. Fig. 1C) and Cohort B (Suppl. Fig. 1D). In addition, SEMA3F was significantly correlated with the senescence marker, CDKN1A/p21 \(^{Cip1}\) , in both cohorts (Suppl. Fig. 1E, F). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 459]]<|/det|> +SenMayo is applicable across tissues and species. To evaluate the applicability of SenMayo across tissues and species, we next analyzed publicly available mRNA- seq data from brain tissue isolated from young vs. aged mice (GSE14526532, GSE12877033, GSE9483234, Fig. 2A- C). As is evident, aged mouse brain cells (microglia) and regions (prefrontal cortex, dorsal hippocampus) displayed a highly significant enrichment of senescence/SASP genes using the SenMayo gene list (p=0.005, p=0.001, p<0.001, respectively), while the previously published gene set (R- HSA- 2559582) did not reach statistical significance (p=0.157, p=0.117, p=0.192, respectively). In addition, using murine bone marrow from the tabula muris senis (a murine single cell transcriptome atlas of young vs. aged tissues35), the applicability of SenMayo in predicting the aging process was confirmed by GSEA (Fig. 2D) Thus, SenMayo identifies senescent cells associated with aging across tissues (bone/bone marrow and brain) and species (humans and mice). + +<|ref|>image<|/ref|><|det|>[[112, 470, 883, 899]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 220]]<|/det|> +Figure 2. The SenMayo gene set predicts aging across tissues and species. (A) Compared to the conventional gene set, the SenMayo list is significantly enriched during the aging process in murine brain microglia \((p = 0.1565\) vs. \(p = 0.0052\) ; GSE145265), \(n = 4\) (2 young, 2 aged, all \(③\) ), (B) murine prefrontal cortex \((p = 0.1169\) vs. \(p = 0.0013\) ; GSE128770), \(n = 48\) (24 young, 24 aged, all \(③\) ), and (C) murine dorsal hippocampus \((p = 0.1916\) vs. \(p< 0.001\) ; GSE94832), \(n = 12\) (6 young (3 \(②\) ), 6 aged (2 \(②\) ). Likewise, the murine bone marrow (D) within the tabula muris senis (GSE149590 \(^{36}\) ) has a higher enrichment of the SenMayo genes within the old cohort \((p = 0.6043\) vs. \(p = 0.0362\) ), \(n = 11\) (4 young (2 \(②\) , 2 \(②\) ), 7 old (7 \(②\) , 0 \(②\) ). + +<|ref|>text<|/ref|><|det|>[[112, 245, 886, 655]]<|/det|> +SenMayo not only predicts aging, but also demonstrates clearance of senescent cells. In order to independently validate our in silico analyses, we next made use of our previously described \(p16\) - INK- ATTAC mouse model that allows for inducible clearance of \(p16^{\text{lnk4a}}\) - expressing senescent cells after administration of the drug AP20187 (AP). \(^{37}\) In previous studies, we have demonstrated increases in \(Cdkna2a/p16^{\text{lnk4a}}\) and \(Cdkna1a/p21^{\text{Clp1}}\) mRNA levels with aging in bones from these mice \(^{7}\) as well as reductions in these mRNAs following clearance of senescent cells in \(p16\) - INK- ATTAC mice treated with AP and concordant changes in other markers of cellular senescence (e.g., telomeric DNA damage markers in osteocytes). \(^{37}\) Importantly, in young vs. old mice, SenMayo was expressed at a significantly higher level in bones from the old mice (Fig. 3A) and was significantly reduced following AP treatment of old \(p16\) - INK- ATTAC mice (Fig. 3B). Moreover, by using the SenMayo genes, a higher overlap of young vs. old + AP- treated mice as compared to young vs. old + vehicle- treated mice was observed through principal component analysis (PCA) (Fig. 3C). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 80, 822, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 787, 883, 903]]<|/det|> +
Figure 3. The SenMayo gene set tracks genetic and pharmacologic clearance of senescent cells. (A) The SenMayo panel successfully indicated aging in bone in mice (p-value=0.0023), n=25 (12 young, 13 old (all \(\mathbb{Q}\) ); (B) The elimination of \(p16^{\text{ink4a}}\) -expressing senescent cells by AP20187 administration was shown previously to reverse the aging bone phenotype.37 The SenMayo gene set successfully demonstrated the significant reversal of the aging phenotype at the gene expression level upon the elimination of \(p16^{\text{ink4a}}\) -expressing senescent cells (p=0.0054), n=29 (13 Veh, 16 AP (all \(\mathbb{Q}\) ); (C) By specifically using the expression patterns of the SenMayo gene set,
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 219]]<|/det|> +our bone RNA- seq revealed no similarities in gene expression patterns between young and old + veh treated mice, and a substantial overlap of expression profiles of old + AP mice with young mice; (D) We used a previously published mRNA- seq dataset from human adipose tissue of our group, \(^{24,38}\) to evaluate changes in SenMayo following D+Q treatment. Adipose tissue was collected before and 11 days after three days of oral D+Q treatment. Figure was designed using Biorender.com; (E) Using SenMayo, there was a was a reduction of SenMayo ( \(p = 0.0184\) ) in the subcutaneous fat samples in the subjects treated with D+Q, consistent with a reduction in senescent cell burden following D+Q treatment ( \(n = 10\) (8 \(\mathcal{O}\) , 2 \(\mathcal{P}\) )). + +<|ref|>text<|/ref|><|det|>[[113, 245, 884, 619]]<|/det|> +We further validated the ability of SenMayo to predict senescent cell clearance by examining a human cohort. In a phase I pilot study, the senolytic combination of Dasatinib plus Quercetin (D+Q) \(^{39}\) was administered to subjects with diabetic kidney disease for 3 consecutive days. \(^{24,38}\) We performed RNA- seq from adipose tissue samples obtained from these subjects before and 11 days after D+Q treatment (male: female=9:3, age: 68.8[±9.3] years:65.3[±6.6] years, Fig. 3D). \(^{24,38}\) As shown in Fig. 3E, there was a significant reduction in SenMayo ( \(p = 0.0184\) ) in the subcutaneous adipose tissue samples in the subjects following D+Q treatment, consistent with a reduction in senescent cell burden, which was independently validated by demonstrating reductions in p16 \(^{\text{Ink4a + }}\) , p21 \(^{\text{Cip1 + }}\) , and SA- βgal+ cells in the adipose tissue biopsy samples following D+Q treatment. \(^{24,38}\) Thus, these direct interventional studies in mice and humans demonstrate that not only is SenMayo associated with aging, but it is also reduced following clearance of senescent cells. + +<|ref|>text<|/ref|><|det|>[[114, 630, 884, 841]]<|/det|> +SenMayo outperforms existing senescence/SASP gene sets. In addition to directly comparing SenMayo to the R- HSA- 2559582 senescence/SASP gene set, we also compared it to five additional senescence/SASP gene sets \(^{40 - 44}\) in all of the mouse and human models described above. As shown in Table 2, SenMayo consistently outperformed these gene sets (based on normalized enrichment scores [NES] and p- values) both in the ability to identify senescent cells with aging across tissues and species and in demonstrating responses to senescent cell clearance. + +<|ref|>text<|/ref|><|det|>[[115, 854, 883, 906]]<|/det|> +The SenMayo gene set identifies senescent hematopoietic and mesenchymal cells within scRNA- seq bone marrow/bone datasets. Although scRNA- Seq provides extremely important information + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 397]]<|/det|> +regarding changes in gene expression at the individual cell level, it has been problematic for evaluating cellular senescence in a given cell. In part, this is because the Cdkna2a/p16ink4a mRNA is expressed at relatively low levels, even in senescent cells,45 and may not be reliably detected in scRNA- seq data. Although Cdkna1a/p21Cip1 is generally expressed at higher levels in RNA- seq data, presence or absence of Cdkna1a/p21Cip1 also may not consistently identify a senescent cell.42 As such, having validated SenMayo as being associated not only with aging but also specifically with cellular senescence, we next tested whether it could identify senescent cells at the single cell level. To evaluate this first for hematopoietic cells, we analyzed publicly available single cell bone marrow datasets from 20 healthy donors across a broad age range (24- 84 years)46 and evaluated 68,478 hematopoietic cells for expression of the SenMayo gene set (GSE120446),46 Fig. 4A). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 87, 884, 848]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 856, 884, 907]]<|/det|> +
Figure 4. SASP-associated hematopoietic cells in human bone marrow are mainly of monocytic origin and communicate via the MIF pathway. (A) Using a previously published scRNA-seq dataset from human bone marrow (GSE120446, \(^{46}\) n=68,478 cells), we performed GSEA at the
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 380]]<|/det|> +single cell level to uncover cells responsible for senescence/SASP- associated gene expression. The highest enrichment score (ES) for the SenMayo gene set occurred within the \(\mathrm{CD14^{+}}\) and \(\mathrm{CD16^{+}}\) monocytic cell cluster, represented in a Uniform Manifold Approximation and Projection (UMAP). We selected the top \(10\%\) of senescence/SASP- expressing cells to form the "SASP cells" \((n = 6,850\) cells) cluster displaying a (B) independent enrichment of canonical senescence genes including \(\mathrm{CDKN1A / p21^{CIP1}}\) and TGFB1 and which was likewise enriched for two aging signatures (GenAge: genes associated with aging in model organisms;47 and CellAge: positively regulated genes associated with aging in human cells; (C) The SASP cells showed the highest interaction strength with T cells in the bone marrow; (D) Among the interaction targets of SASP cells, T cells were predominantly targeted via the MHC- I, MIF, and PECAM1 pathways; (E) Members of the MIF and PECAM1 signaling pathways showed high expression patterns within the SASP population; (F) SASP cells were characterized by distinct co- expression patterns predicting (functional) clusters (e.g., JUN and CDKN2A), potentially overcoming difficulties of low expression of specific senescence- associated genes such as \(\mathrm{CDKN2A / P16^{ink4A}}\) . These strong indicators of co- expression were mathematically isolated by z- scores (G) and spatially summarized (H) in sub- cell populations within the SASP cluster, as indicated by kernel gene- weighted density estimation in a t- distributed Stochastic Neighbor Embedding (tSNE) representation. \*\*\*p<0.0001, \(n = 22\) (10 \(\hat{\mathcal{O}}\) , 12 \(\hat{\mathcal{O}}\) ). + +<|ref|>text<|/ref|><|det|>[[113, 404, 884, 876]]<|/det|> +This analysis detected multiple cellular clusters that were more highly enriched than others for senescence/SASP genes, i.e., had higher enrichment scores (ES). These high ES clusters included \(\mathrm{CD14^{+}}\) and \(\mathrm{CD16^{+}}\) monocytes as well as macrophages (Fig. 4A, Suppl. Fig. 2A). By selecting the top \(10\%\) of cells with the highest expression of senescence/SASP- associated genes, we generated a new cluster of cells, consisting of 6,850 cells, predominantly of monocytic origin (referred to as "SASP cells" in Fig. 4B). These SASP cells showed an increase in canonical markers of senescence such as \(\mathrm{CDKN1A / p21^{CIP1}}\) and TGFB1, which are independent and not included in the SenMayo gene set, as well as enrichment of previously published gene sets indicative of human47 and cellular aging48 (Table 3). Visually, the SASP cells had a high correlation with genes in two established aging gene sets (GenAge and positively regulated in CellAge, Fig. 4B). To further elucidate the replicative state of these cells, we compared their cell cycle state with the other clusters. A shift towards the G1 phase occurred within the SASP cells (Suppl. Fig. S2B), consistent with replicative arrest. This finding was supported by cell cycle arrest gene enrichment within the SASP cells (Suppl. Fig. S2C). In addition, pseudotime analysis (Suppl. Fig. S2D, left panel), which permits elucidation of the temporal gene expression pattern of a specific + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 140]]<|/det|> +cell type, revealed an increase in SASP cells over time (representing differentiation), particularly in \(\mathrm{CD14^{+}}\) monocytes, \(\mathrm{CD16^{+}}\) monocytes, and macrophages (Suppl. Fig. S2D, middle panel). + +<|ref|>text<|/ref|><|det|>[[112, 149, 886, 845]]<|/det|> +In addition to intracellular signaling pathways differentially regulated in SASP- secreting cells, these cells have been demonstrated to affect surrounding cells. \(^{13,49}\) To explore these intercellular interactions, we evaluated potential ligand- receptor interactions and secretion patterns based on underlying gene expression levels in different hematopoietic cell types in human bone marrow. \(^{50}\) The strongest interaction of SASP cells was found with T cells, followed by monocytic cells and B cells (Fig. 4C). Among the affected pathways, the major histocompatibility complex class I (MHC- I), Macrophage Migration Inhibitory Factor (MIF), and Platelet And Endothelial Cell Adhesion Molecule 1 (PECAM1, CD31) pathways were most highly enriched (Fig. 4D, E). Of note, in the pseudotime analysis described above, MIF expression also increased markedly in terminally differentiated \(\mathrm{CD14^{+}}\) and \(\mathrm{CD16^{+}}\) monocytes and macrophages and SASP cells (Suppl. Fig. S2D, right panel). Moreover, MIF pathway members including \(\mathrm{CD74}\) , CXCR4, and \(\mathrm{CD44}\) had overall high expression in SASP cells (Fig. 4E). Compared to other cell types, the overall outgoing interaction strength of SASP cells was remarkably high (Suppl. Fig. S3A). Besides their importance as senders, mediators, and influencers (defined by signalling network analysis using centrality measures; for details see, \(^{50,51}\) Suppl. Fig. S3B, C), SASP cells displayed a substantial incoming signaling pattern dominated by the MIF, ANNEXIN, CD45, IGBB2, MHC- I, MHC- II, and PECAM1 pathways (Suppl. Fig. S3D). Within these SASP cells, the strongest direct receptor- ligand MIF interaction between the ligand \(\mathrm{CD74^{+}}\) and the receptor CD44 was mainly detected in other monocytic cells, while the MIF interaction via the ligand \(\mathrm{CD74^{+}}\) - receptor CXCR4 pair was significant for SASP to \(\mathrm{CD10^{+}}\) B and \(\mathrm{CD20^{+}}\) B cells as well as plasmacytoid dendritic cells. The PECAM1 pathway targeted plasma cells and \(\mathrm{CD16^{+}}\) monocytes (Suppl. Fig. S3E). + +<|ref|>text<|/ref|><|det|>[[113, 855, 884, 906]]<|/det|> +Further analysis revealed that the SASP cells were characterized by distinct patterns of co- expression out of which several markers were found to be strongly associated with each other + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 524]]<|/det|> +(Fig. 4F- G) – e.g., EREG/IL1B, ICAM1/CDKN1A, and JUN/CDKN2A. Out of the 125 genes within the SenMayo panel, some were consistently upregulated (red in Fig. 4F), while others were simultaneously downregulated (blue in Fig. 4F). After we found that some of the “canonical” SASP markers such as EREG/IL1B and SASP/senescence markers such as ICAM1/CDKN1A showed high concordance in their cell- wise expression patterns, we aimed to find surrogate genes for certain low- expressed genes – e.g. CDKN2A/p16ink4a. Within the SASP cluster, we found a strong correlation between JUN and CDKN2A/p16ink4a expression, which represents a potential approach to overcome the challenge of low CDKN2A/p16ink4a expression in sequencing datasets. To independently confirm these correlations, we depicted these genes in a pairwise fashion with kernel density estimation within the SASP cell clusters (Fig. 4H), where the overall SASP cells are in blue and the red/yellow colors indicate higher levels of expression within the SASP cells of each gene.45 These analyses thus demonstrate the validity of the SenMayo gene set in a human bone marrow scRNA- seq dataset and identify monocytic cells as the hematopoietic cell population with the highest proportion of SASP- associated cells. + +<|ref|>text<|/ref|><|det|>[[112, 535, 886, 907]]<|/det|> +To further test SenMayo in single cell datasets and potentially contrast bone marrow hematopoietic cells to bone/bone marrow mesenchymal cells, we next evaluated a published murine dataset that contained scRNA- seq data from bone and bone marrow mesenchymal cells (GSE128423,52 Fig. 5A, n=35,368 cells). We detected a heterogenous distribution of highly enriched cells for SenMayo (“SASP cells”, n=3,537), which likewise were enriched in both GenAge and CellAge (Fig. 5B), canonical markers of senescence (Cdkn1a/p21Cip1 and Tgfβ1, Fig. 5B) and was primarily comprised of cells from the osteolineage (OLC1 and 2) as well as leptin receptor- positive (Lepr+) MSC cluster (Suppl. Fig. S4A shows the fraction of the original clusters that were subsequently assigned to the newly created SASP cluster and Suppl. Fig. S4B indicates the percentage of cells within each cluster that were in the top 10% of cells enriched for SenMayo genes). Interestingly, 21% of osteolineage cells (24% in OLC 1 and 18% in OLC2) had the highest enrichment for SASP factors (Suppl. Fig. S4B, Table 2). Similar to the human hematopoietic bone + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 397]]<|/det|> +marrow dataset, murine bone/bone marrow mesenchymal SASP cells displayed a shift in cell cycle phase to the G1 phase (Suppl. Fig. S4C). This was confirmed by gene ontology analysis revealing enrichment of senescence- and cell cycle arrest- associated genes in these cell clusters (Suppl. Fig. 4D). The murine mesenchymal SASP cells were characterized by a high interaction with osteolineage and chondrocytic cells (Fig. 5C), with the MIF and PECAM1 pathways again among those significantly enriched, where these cells mostly acted as senders and influencers (Fig. 5D, Suppl. Fig. S4E). Notably, SASP cells had one of the highest outgoing interaction strengths (Suppl. Fig. S4F). A direct communication of these mesenchymal SASP cells mostly appeared in the MIF pathway (via L/R Mif/Ackr3) with chondrocytic cells and mineralizing osteocytes (Suppl. Fig. S4G). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[110, 87, 884, 911]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 883, 395]]<|/det|> +Figure 5. In murine bone and bone marrow mesenchymal cells, osteolineage cells constitute the largest proportion of SASP cells and communicate with osteolineage and chondrocytic cells via the MIF and PECAM1 pathways and show characteristics of terminal differentiation. (A) We analyzed a publicly available murine bone and bone marrow gene set (GSE12842352), and enriched 35,368 cells for the newly created SenMayo gene set; (B) The top 10% senescence/SASP gene- expressing cells \((n = 3,537)\) were assigned to the newly created "SASP cells" cluster. They displayed an increase in canonical markers of senescence including Cdkna/p21Cip1 and Tgfβ1, and were enriched in the GenAge and CellAge gene sets (GenAge, CellAge 47); (C) The strongest interaction of the SASP cells was narrowed down to chondrocytic cells, while the osteolineage cells were another important crosstalk neighbor; (D) Outgoing interaction patterns of SASP cells (pink, left bottom quarter) indicated the importance of several signaling pathways that resulted in a significant enrichment of Mk, Angptl, Mif and Pecam1; (E) In pseudotime, the SASP cluster was most abundant in the terminal branches, and overexpressed Cdkna/p21Cip1 in terminal states (top- left inlay, bottom red color on the left, terminal branch); (F) In their terminal differentiation, the SASP cluster was enriched in several factors, out of which distinct co- expressional patterns were extracted; (G) While the terminal differentiation was marked by a simultaneous loss of Pappa and Fgf7 (cluster 1, green in F), a significant correlation of Dkk1 with Cdkna2/p16nkl, likewise Bmp2 and Cdkna/p21Cip1, was mathematically predicted (cluster 2, pink in F). \*\*\*p<0.0001, \(n = 8\) (4 bone, 4 bone marrow, all \(\odot\) ). + +<|ref|>text<|/ref|><|det|>[[113, 421, 884, 860]]<|/det|> +The three main origins for the SASP cluster (namely Lepr+ MSCs, OLC 1, and OLC 2), as depicted in pseudotime, demonstrated that the SASP cells accumulated in a terminal developmental branch, coinciding with increased Cdkna/p21Cip1 and Trp53 expression (Fig. 5E). Further analysis of these pseudotime expression patterns showed that certain genes followed defined modules (green, blue, and red in Fig. 5F), which then formed co- expressional patterns (Suppl. Fig. S5A). Within the SASP cluster, these co- expressional patterns could be imaged at an individual cell level, predicting genes of similar abundance within some cells (Fig. 5G). For example, while Pappa and Fgf7 were simultaneously downregulated in terminally differentiated stages (Fig. 5F, blue color in the green cluster, Fig. 5G top), they were part of a modular cluster (Suppl. Fig. S5A, black boxes on the left, fifth square from above). We also performed kernel- weighed density estimation (Suppl. Fig. S5B), confirming our results that Fgf7 and Pappa were co- expressed in the SASP cells. Likewise, Dkk1 and Cdkna2/p16nkl4a displayed the mathematically predicted comparable expression patterns in kernel- weighed density, displayed in tSNE, as did Bmp2 and Cdkna/p21Cip1 (Fig. 5G, Suppl. Fig. S5B). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 886, 300]]<|/det|> +Further experimental validation of in silico analyses. The above analyses of both hematopoietic and mesenchymal scRNA- seq data pointed to Mif as a key SASP gene that should increase with senescent cell burden and be reduced following clearance of senescent cells. Thus, as a final validation of our in silico analyses, we examined Mif mRNA levels by RT- qPCR in our mouse models and found that as predicted, Mif mRNA levels were increased in the bones from old compared to young mice (Fig. 6A) and were significantly reduced following the genetic clearance of senescent cells with AP in old INK- ATTAC mice (Fig. 6B). + +<|ref|>image<|/ref|><|det|>[[113, 310, 357, 483]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 498, 884, 582]]<|/det|> +
Figure 6. The in silico predicted importance of the Mif pathway is reflected in the aged INK-ATTAC mouse model. (A) We compared young (n=12) and old vehicle-treated mice (n=13), and old mice treated with AP (n=16). (A) Upregulation of Mif was confirmed by RT-qPCR (n=24 young (12 Veh, 12 old (all \(\odot\) )); (B) The clearance of senescent cells in the aged cohort by AP treatment reduced this Mif expression (n=26 old (12 Veh, 14 AP (all \(\odot\) )).\*p<0.05
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 214, 107]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[112, 120, 886, 816]]<|/det|> +The identification and characterization of senescent cells, particularly in bulk or scRNA- seq data, has been problematic for a number of reasons, including variable detection of low levels of the Cdkna/p16ink4a transcript even in senescent cells45 and the lack of a consistent gene panel that can reliably identify these cells. Thus, we generated a gene set (SenMayo) consisting of 125 previously identified senescence/SASP- associated factors and first validated it in bone biopsy samples from two human cohorts consisting of young vs elderly women.27,28 Importantly, to establish this as a senescence, rather than just "aging" gene set, we demonstrated that clearance of senescent cells in mice and in humans resulted in significant reductions of SenMayo. Using publicly available RNA- seq data, we demonstrated applicability across tissues and species and also found that SenMayo performed better than six existing senescence/SASP gene panels.14,40- 44 We next applied SenMayo to publicly available bone marrow/bone scRNA- seq data and successfully characterized hematopoietic and mesenchymal cells expressing high levels of senescence/SASP markers at the single cell level, demonstrated co- expression (where feasible) with the key senescence genes, Cdkna/p16ink4a and Cdkna/p21Cip1, and analyzed intercellular communication patterns of senescent cells with other cells in their microenvironment. Based on these analyses, we found that senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells.53 Finally, as a key validation of our in silico analyses, we then examined Mif mRNA levels by RT- qPCR in our mouse models and found that as predicted, Mif mRNA levels were increased in bones from old compared to young mice and were significantly reduced following the genetic clearance of senescent cells in the old mice. + +<|ref|>text<|/ref|><|det|>[[114, 822, 885, 905]]<|/det|> +The heterogeneous composition of the SASP, which consists of a multitude of growth factors, chemokines, cytokines, and matrix- degrading proteins, has been experimentally verified using various in vitro systems to induce cell stress, in vivo using multiple pre- clinical animal + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 885, 299]]<|/det|> +models of aging and disease, and has been linked to several pathophysiological conditions in humans as well as clinical outcomes. \(^{54,55}\) In the current study, we were able to group these factors into 9 distinct clusters to form tightly connected networks with distinct key molecules. The importance of these and other SASP factors has been verified in multiple biological contexts. \(^{56 - 63}\) Interestingly, the control of the SASP itself by RELA/p65, which we detected in two sequencing datasets of aging women, has recently been experimentally verified in U2OS osteosarcoma cells. \(^{64}\) + +<|ref|>text<|/ref|><|det|>[[112, 310, 886, 781]]<|/det|> +Transcriptome- wide state- of- the- art technologies such as scRNA- seq will help shape our understanding of not just aging, but also therapeutics that potentially target fundamental mechanisms of aging, such as senolytics. As noted earlier, a confounder in these analyses is the generally low expression of the canonical marker of senescence, \(Cdkna2a/p16^{ink4a}\) , which is clearly detectable by RT- qPCR in the setting of aging, but poses challenges when using transcriptome- wide approaches. \(^{45}\) Hence, we propose a species- specific co- expression analysis with JUN (Homo sapiens) or Dkk1 (Mus musculus), based on modules of comparable expression to address this challenge. To our knowledge, we for the first time leveraged publicly available single cell datasets to enrich for a senescence/SASP gene set. Since we did not include commonly used senescence- markers ( \(Cdkna2a/p16^{ink4a}\) , \(Cdkna1a/p21^{Cip1}\) ) in the SenMayo panel, we were still able to rely on them to confirm a senescent cell state. Additional verification included a shift in the cell cycle phase to G1, as senescence prevents cells from proceeding to the S or M phases. \(^{59,65,66}\) With \(Cdkna1a/p21^{Cip1}\) being expressed at relatively higher levels, we were able to verify a senescent status of SASP cells, confirming our approach to identify single cells expressing high levels of SenMayo genes as being senescent. + +<|ref|>text<|/ref|><|det|>[[113, 790, 885, 907]]<|/det|> +The use of pseudotime in scRNA- seq datasets to predict age- associated changes and fate commitment has been demonstrated previously in muscle stem cells (MuSCs) and fibro- adipose progenitors (FAPs). \(^{67}\) These analyses pointed to the importance of TGF- \(\beta\) signaling, but without specifically focusing on age- related expression changes. By contrast, we used + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 884, 140]]<|/det|> +pseudotime analyses to establish a novel approach to identify age- dependent transcriptional changes in senescence/SASP genes distinct from Cdkna2/p16nk4a and Cdkna1a/p21Cip1. + +<|ref|>text<|/ref|><|det|>[[113, 152, 886, 365]]<|/det|> +Using a z- score based probabilistic model with pairwise correlations (bigSCale68) to construct transcriptional networks, several groups have successfully established the use of within- cell networks in single cell datasets25,69 and we made use of this approach to define senescence modules of similar expression. With overall agreement between pseudotime, network analyses, and direct pairwise z- score prediction, we overcame the downside of normalized expression, and a z- score predicted space allowed us to assign clusters and spatially depict them within cellular aggregates. These modules may serve as sources for novel senescent markers and pathways.70 + +<|ref|>text<|/ref|><|det|>[[112, 375, 886, 780]]<|/det|> +As noted earlier, the MIF pathway emerged as a key intercellular communication pathway used by both hematopoietic and mesenchymal cells in bone marrow expressing high levels of senescence/SASP genes. This is perhaps not surprising given the importance of MIF as a proinflammatory cytokine, inhibitor of p53, and positive regulator of NF- \(\kappa\) B.71 MIF appears to be pivotal for cellular senescence, aging, and joint inflammation; however, its presence has been associated with a beneficial effect on the healthy lung and in MSCs.72–77 Of note, recent evidence indicates an important role for MIF signaling in immune evasion by tumors78 and parasites,79 raising the possibility that increased MIF expression by multiple senescent cell types may play a role in the ability of senescent cells to resist immune clearance, particularly with aging,53 and this possibility warrants further study. Importantly, we also used Mif expression to validate our in silico predictions based on the scRNA- seq analyses, and confirmed both an increase in Mif expression with aging in murine bone as well as a reduction in Mif mRNA levels following genetic clearance of senescent cells. + +<|ref|>text<|/ref|><|det|>[[113, 790, 884, 907]]<|/det|> +The development and validation of SenMayo, as demonstrated here, may be particularly timely in the context of the recent establishment of a major NIH Common Fund consortium to map senescent cells (SenNET, https://sennetconsortium.org/). The goal of this program is to "comprehensively identify and characterize the differences in senescent cells across the body, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 171]]<|/det|> +across various states of human health, and across the lifespan." The application of SenMayo to bulk or scRNA- seq data from SenNET should greatly facilitate this goal and could provide a standardized gene set that is used across the multiple sites involved in this consortium. + +<|ref|>text<|/ref|><|det|>[[112, 183, 886, 428]]<|/det|> +In summary, our studies contribute a novel gene set (SenMayo) that increases with aging across tissues and species, is responsive to senescent cell clearance, and can be used both in bulk and scRNA- seq analyses to identify cells expressing high levels of senescence/SASP genes. This gene set also has potential utility in the clinical evaluation of senescent cell burden and for studies of senolytic therapies. In addition, SenMayo circumvents current limitations in the transcriptional identification of senescent cells at the single cell level, thereby allowing for detailed analyses (e.g. pseudotime, intercellular signaling) that will facilitate better characterization of these cells in future studies. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 192, 107]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 121, 358, 139]]<|/det|> +## MATERIALS AND METHODS + +<|ref|>text<|/ref|><|det|>[[113, 152, 886, 396]]<|/det|> +Generation of SenMayo. Our own GSEA gene set for senescence- associated genes was generated by combining genes that had been reported in previous studies to be enriched in senescent and/or SASP- secreting cells and experimentally verified in at least human or mouse cells. We screened 1,656 studies, but following removal of studies reporting duplicates, case reports, and non- human or non- murine genes, formulated a list of 15 studies from which we identified 125 genes that constituted SenMayo (Table 118,26,48,55,61,80–89). Note that we intentionally did not include CDKN2A/p16ink or CDKN1A/p21Cip1 in SenMayo as we used these genes, in part, to validate our senescence/SASP gene set. + +<|ref|>text<|/ref|><|det|>[[112, 435, 886, 908]]<|/det|> +RNA- seq. Transcriptome- wide gene expression data from young \((n = 15, 30.9 \pm 4.0\) years and \(n = 19, 30.3 \pm 5.4\) years) and postmenopausal females \((n = 15, 68.7 \pm 4.8\) years and \(n = 19, 73.1 \pm 6.6\) years) as well as 10 diabetic kidney disease patients (8 male, 2 female, \(71.25 \pm 7.85\) years and \(65.0 \pm 8.0\) years, respectively) were analyzed from three previous studies performed by our group (GSE141595: NCT02554695, GSE72815: NCT02349113,24,38: NCT02848131)27,28. After the original interventional study in diabetic kidney disease patients was completed, one additional female patient was added. All human studies were approved by the Mayo Clinic Institutional Review Board and written informed consent was obtained from all participants. RNA was isolated from whole bone biopsies (which included bone and bone marrow cells, Cohort A)27 as well as bone biopsies that were processed to remove bone marrow and bone surface cells and were thus highly enriched for osteocytes (Cohort B),28 and adipose tissue, 2- 5 cm inferior to the navel (for details, see38). Subcutaneous adipose tissue was obtained by an elliptical incisional biopsy at a point to the right or left, and 2- 5 cm inferior to the navel.24,38 Sequencing was performed on a HiSeq2000 (Illumina®), fastq files were mapped to the human reference genome hg19, and analysis was performed as previously described.27,28 Significantly differentially regulated genes + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 397]]<|/det|> +were selected by a Benjamini- Hochberg adjusted p- value \(< 0.05\) and \(\log_{2}\) - fold changes above 0.5 or below - 0.5. Gene Set Enrichment Analysis (GSEA31,90) was performed with default settings (1000 permutations for gene sets, Signal2Noise metric for ranking genes). The network analysis was conducted with Cytoscape 3.8.2.30 For mRNA- seq of murine material, tibiae were centrifuged as noted above to remove bone marrow elements and then were immediately homogenized in QIAzol Lysis Reagent (QIAGEN, Valencia, CA) and stored at - 80°C, until the time of RNA extraction. RNA- sequencing was performed on a HiSeq2000 (Illumina®), fastq files were mapped to the murine reference genome mm10, and analysis was performed as previously described.27,28 An example of the code used for RNA- seq can be found in the provided R notebook (Methods: GSE72815_YOE_Notebook.Rmd). + +<|ref|>text<|/ref|><|det|>[[112, 438, 886, 748]]<|/det|> +Mouse strains and drug treatments. All animal protocols were approved by the Institutional Animal Care and Use Committee (IACUC), and all experiments were performed in accordance with IACUC guidelines. Mice were housed in ventilated cages in a pathogen- free facility (12- hour light/dark cycle, 23°C) and had access to food (standard mouse diet, Lab Diet 5053, St. Louis, MO) and water ad libitum. Mouse experiments for a genetic targeting approach of senescent cells have been described by our group earlier.37 Briefly, old (20 months) female mice were injected intraperitoneally with vehicle (4% of 100% EtOH, 10%PEG400, 86% of 2% Tween 20 in deionized Water) or AP20187 (B/B homodimerizer, Clontech; 10 mg of AP20187 per kg body mass) twice weekly at the age of 20 months for a total of 4 months (old mice were sacrificed at 24 months of age). In addition, young (6- month) INK- ATTAC mice were used as a control comparison cohort. + +<|ref|>text<|/ref|><|det|>[[113, 790, 885, 905]]<|/det|> +Quantitative real- time polymerase chain reaction (RT- qPCR) analysis. For bone analyses, tibiae were centrifuged to remove marrow elements and then immediately homogenized in QIAzol Lysis Reagent (QIAGEN, Valencia, CA) and stored at - 80°C. Subsequent RNA extraction, cDNA synthesis, and targeted gene expression measurements of mRNA levels by RT- qPCR were + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 80, 886, 690]]<|/det|> +performed as described previously. \(^{91}\) Total RNA was extracted according to the manufacturer's instructions using QIAzol Lysis Reagent. Purification with RNeasy Mini Columns (QIAGEN, Valencia, CA) was subsequently performed. On- column RNase- free DNase solution (QIAGEN, Valencia, CA) was applied to degrade contaminating genomic DNA. RNA quantity was assessed with Nanodrop spectrophotometry (Thermo Fisher Scientific, Wilmington, DE). Standard reverse transcriptase was performed using High- Capacity cDNA Reverse Transcription Kit (Applied Biosystems by Life Technologies, Foster City, CA). Transcript mRNA levels were determined by RT- qPCR on the ABI Prism 7900HT Real Time System (Applied Biosystems, Carlsbad, CA) using SYBR green (Qiagen, Valencia, CA). The mouse forward primer sequence (5'- 3') for Mif was: 5'- GCCACCATGCCTATGTTTCATC- 3' and Reverse Primer Sequence 5'- GGGTGAGCTCCGACAGAAAC- 3'. RNA was normalized using two reference genes (Actb [forward: 5'- AATCGTGCGTGACATCAAAGAG- 3', reverse: 5'- GCCATCTCCTGCTCGAAGTC- 3'], Gapdh [forward: 5'- GACCTGACCTGCCGTCTAGAAA- 3', reverse: 5'- CCTGCTTCACCACCTTCTTGA- 3']) from which the most stable housekeeping gene was determined by the geNorm algorithm. For each sample, the median cycle threshold (Ct) of each gene (run in triplicate) was normalized to the geometric mean of the median Ct of the most stable reference gene. The delta Ct for each gene was used to calculate the relative mRNA expression changes for each sample. Genes with Ct values \(>35\) were considered not expressed (NE), as done previously. \(^{92}\) + +<|ref|>text<|/ref|><|det|>[[113, 725, 886, 907]]<|/det|> +Single- cell RNA- seq (scRNA- seq) analysis. Transcriptome- wide analysis of human bone marrow mononuclear cells at a single cell level was based on a previously published dataset. \(^{46}\) Here, bone marrow was isolated from healthy female (n=10) and male (n=10) donors (50.6±14.9 years) and droplet- based scRNA- seq was performed. A minimum sequencing depth of 50,000 reads/cell with a mean of 880 genes/cell was reported. In addition, we analyzed droplet- based scRNA- seq data from bone marrow cells isolated from C57BL/6 mice (n=14) \(^{52}\) and from C57BL/6JN mice (n=30) \(^{35}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 80, 886, 720]]<|/det|> +and from the tabula muris senis. \(^{35}\) Sequencing data were aligned to the human reference genome Grch38 and the mouse genome mm10, respectively. Data with at least 500 unique molecular identifiers (UMIs), log10 genes per UMI \(>0.8\) , \(>250\) genes per cell and a mitochondrial ratio of less than \(20\%\) were extracted, normalized, and integrated using the Seurat package v3.0 in R4.0.2. Subsequent R- packages were Nebulosa (3.13 \(^{93}\) ), Monocle (2.18.0 \(^{94}\) ), dittoSeq (1.2.6 \(^{95}\) ), Escape (1.0.1, "Borcherding N, Andrews J (2021). escape: Easy single cell analysis platform for enrichment. R package version 1.2.0."), Cellchat \(^{50}\) (within the Cellchat package, and for Fig. 2C, "CD10+ B cells", "CD20+ B cells", "Plasma cells", "Plasmacytoid dendritic cells", "Conventional dendritic cells" were summarized as "B cells", "CD4+ naïve T cells", "CD4+ memory T cells", "CD8+ naïve T cells", "CD8+ effector T cells" were summarized as "T cells", "Early erythroid progenitors", "Early erythrocytes", "Late erythrocytes" as "Ery", "HSPCs" as "HSPCs", "Monocyte progenitors", "CD14+ monocytes", "CD16+ monocytes", "Macrophages", "Natural killer cells" as "Mono" and "SASP cells" as "SASP". For Figure 3C, "Chondro- hyper", "Chondro- prehyper", "Chondro- progen", "Chondro- prol/rest", "Chondrocyte" were summarized as "Chondro", "EC", "Pericytes" as "Endo", "Fibroblast" as "Fibro", "Lymphocyte", "Mast cell" as "Immune", "Lepr MSC", "MSC" as "MSC", "Mineralizing Osteocyte", "OLC 1", "OLC 2", "Osteoblast", "Osteocyte" as "Osteo" and "SASP cells" as "SASP"), bigSCale (2.1 \(^{70}\) ), gprofiler2 (0.2.0 \(^{96}\) ), igraph (1.2.6, Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695), PCAtools (2.4.0, Blighe K, Lun A (2021). PCAtools: PCAtools: Everything Principal Components Analysis. R package version 2.4.0), and corrplot (0.89). + +<|ref|>text<|/ref|><|det|>[[113, 727, 886, 907]]<|/det|> +Pseudotime is a progression of cells along a virtually estimated path, mimicking temporal development. By using Monocle, an independent component analysis (ICA) dimensional reduction is performed, followed by a projection of a minimal spanning tree (MST) of the cell's location in this reduced space. Each cell is assigned a pseudotemporal space. \(^{97,98}\) Monocle 2 was used to preprocess, perform UMAP reduction, and reduce the dimensionality using the DDRTree algorithm with a maximum of four dimensions. Subsequently, the cells were ordered and genes + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 204]]<|/det|> +plotted along the reduced dimension. Differential gene testing has been performed with the formula “\~sm.ns(Pseudotime)”, and the results were restricted by a qvalue<0.1.97 An example of the code used for scRNA- seq can be found in the provided R notebook (Methods: R_notebook_Fig4_5_sup2to5.Rmd). + +<|ref|>text<|/ref|><|det|>[[113, 246, 886, 397]]<|/det|> +Author contributions. D.S., J.N.F., and S.K. conceived and directed the project. D.S. and J.N.F. designed the experiments and interpreted the data with input from S.K. Experiments were performed by D.S. and R.L.K. D.S. and S.K. wrote the manuscript. All authors reviewed the manuscript. J.N.F. and S.K. oversaw all experimental design, data analyses, and manuscript preparation. J.N.F., S.K., and D.S. accept responsibility for the integrity of the data analysis. + +<|ref|>text<|/ref|><|det|>[[112, 438, 886, 722]]<|/det|> +Acknowledgements. This work was supported by the German Research Foundation (D.F.G., 413501650) (D.S.), National Institutes of Health (NIH) grants P01 AG062413 (S.K., J.N.F., N.K.L., R.P., P.D.R., L.J.N., Y.I., J.P., D.G.M., T.T., J.L.K.), R21 AG065868 (S.K., J.N.F), K01 AR070241 (J.N.F.), R01 AG063707 (D.G.M.), R37 AG 013925 (J.L.K., T.T.), R33AG 61456 (J.L.K., T.T., R.P., P.D.R., L.J.N., S.K.), 1R01AG068048- 01 (JFP), R56 AG 60907 and R01 AG55529 (N.K.L.), the Connor Fund (J.L.K., T.T.), Robert P. and Arlene R. Kogod (J.L.K.), Robert J. and Theresa W. Ryan (J.L.K., T.T.), the Noaber Foundation (J.L.K., T.T.), and Mildred Scheel postdoc fellowship by the German Cancer Aid (R.L.K.). X.Z. is supported by the Robert and Arlene Kogod Center on Aging Career Development Award. + +<|ref|>text<|/ref|><|det|>[[115, 728, 701, 748]]<|/det|> +The authors thank SA Johnsen and FH Hamdan for inspiring discussions. + +<|ref|>text<|/ref|><|det|>[[113, 790, 886, 906]]<|/det|> +Competing interests. Patents on senolytic drugs and their uses and SASP biomarkers are held by Mayo Clinic and the University of Minnesota. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and was conducted in compliance with Mayo Clinic Conflict of Interest policies. + +<--- Page Split ---> +<|ref|>table_caption<|/ref|><|det|>[[114, 91, 884, 122]]<|/det|> +Table 1. Genes included in the SenMayo panel. The relationship of each gene to senescence/aging is described in the reference indicated. + +<|ref|>table<|/ref|><|det|>[[114, 135, 984, 899]]<|/det|> +
Gene(human)ClassificationStateReference
ACVR1BTransmembrane signal receptorsTransmembrane26
ANGMiscellaneousSecreted80.87
ANGPT1Intercellular signal moleculeSecreted55
ANGPTL4Intercellular signal moleculeSecreted18.55
AREGGrowth factorIntracellular80.87.88
AXLTransmembrane signal receptorsTransmembrane18.88
BEX3MiscellaneousIntracellular18
BMP2Growth factorSecreted55.88
BMP6Growth factorSecreted88
C3Protease inhibitorsSecreted55
CCL1Cytokine/ChemokineSecreted88
CCL13Cytokine/ChemokineSecreted80.88
CCL16Cytokine/ChemokineSecreted26.80.88
CCL20Cytokine/ChemokineSecreted26.55.82,83.85,87.88
CCL24Cytokine/ChemokineSecreted26.80.82
CCL26Cytokine/ChemokineSecreted87
CCL3Cytokine/ChemokineSecreted80.88
CCL3L1Cytokine/ChemokineSecreted55.84.87.88
CCL4Cytokine/ChemokineSecreted55
CCL5Cytokine/ChemokineSecreted87
CCL7Cytokine/ChemokineSecreted26.81.87
CCL8Cytokine/ChemokineSecreted88
CD55MiscellaneousSecreted80.88
CD9Transmembrane signal receptorsTransmembrane18.88
CSF1Cytokine/ChemokineSecreted83
CSF2Cytokine/ChemokineSecreted26.55.61,80.87.88
CSF2RBTransmembrane signal receptorsTransmembrane88
CST4Protease inhibitorsSecreted84
CTNNB1Transcription factors and regulatorsTransmembrane87
CTSB(Metallo-)proteasesSecreted80
CXCL1Cytokine/ChemokineSecreted26.55.61,80.87-89
CXCL10Cytokine/ChemokineSecreted55.81.87
CXCL12Cytokine/ChemokineSecreted80.87
CXCL16Cytokine/ChemokineSecreted87
CXCL2Cytokine/ChemokineSecreted26.61.80.87
CXCL3Cytokine/ChemokineSecreted55.63.80
CXCL8Cytokine/ChemokineSecreted26.55.61,80.82.89
CXCR2Cytokine/ChemokineTransmembrane26
DKK1Intercellular signal moleculeSecreted55
EDN1Intercellular signal moleculeSecreted55
EGFTransmembrane signal receptorsTransmembrane80
EGFRTransmembrane signal receptorsTransmembrane80.88
EREGGrowth factorSecreted80.87.88
ESM1Intercellular signal moleculeSecreted55
ETS2Transcription factors and regulatorsIntracellular88
FASTransmembrane signal receptorsTransmembrane80.88
FGF1Growth factorSecreted26.55
FGF2Growth factorSecreted55.80
FGF7Growth factorSecreted55.80.88
GDF15Growth factorSecreted26.55.88
GEMMiscellaneousIntracellular88
GMFGIntercellular signal moleculeIntracellular88
HGF(Metallo-)proteasesSecreted26.80.87.88
HMGB1Transcription factors and regulatorsIntracellular55.87
ICAM1MiscellaneousIntracellular61.80.87.88
ICAM3MiscellaneousIntracellular87.88
IGF1Growth factorSecreted18.81.86.88
IGFBP1Protease inhibitorsSecreted88
IGFBP2Protease inhibitorsSecreted26.61.80.87.88
IGFBP3Protease inhibitorsSecreted26.48.55.61,80.87
IGFBP4Protease inhibitorsSecreted26.61.80.87
IGFBP5Protease inhibitorsSecreted26.55.61.87
IGFBP6Protease inhibitorsSecreted26.61.80.87.88
IGFBP7MiscellaneousSecreted26.55.61.80.82.87
IL10Cytokine/ChemokineSecreted85
IL13Cytokine/ChemokineSecreted80.88
IL15Cytokine/ChemokineSecreted40.83.87.88
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[113, 85, 981, 718]]<|/det|> +
IL18Cytokine/ChemokineSecreted55.87
IL1ACytokine/ChemokineSecreted26.40,55,61,80,81,87-89
IL1BCytokine/ChemokineSecreted18,40,85,87-89
IL2Cytokine/ChemokineSecreted87
IL32Cytokine/ChemokineSecreted55
IL6Cytokine/ChemokineSecreted26,40,55,61,81-83,85,87,88
IL6STTransmembrane signal receptorsTransmembrane40
IL7Cytokine/ChemokineSecreted40,88
INHAGrowth factorSecreted88
IQGAP2MiscellaneousIntracellular88
ITGA2Transmembrane signal receptorsTransmembrane88
ITPKAProtein modifying enzymesIntracellular88
JUNTranscription factors and regulatorsIntracellular88
KITLGGrowth factorIntracellular40,87
LCP1MiscellaneousIntracellular55
MIFProtein modifying enzymesSecreted18,40,87,88
MMP1(Metallo-)proteasesSecreted40,61,88
MMP10(Metallo-)proteasesSecreted40,61,89
MMP12(Metallo-)proteasesSecreted40,87
MMP13(Metallo-)proteasesSecreted40,87
MMP14(Metallo-)proteasesIntracellular40,87
MMP2(Metallo-)proteasesSecreted40,61,84,88
MMP3(Metallo-)proteasesSecreted55,84
MMP9(Metallo-)proteasesSecreted88
NAP1L4MiscellaneousIntracellular40,87,88
NRG1Growth factorSecreted55
PAPPA(Metallo-)proteasesSecreted88
PECAM1MiscellaneousIntracellular88
PGFGrowth factorSecreted87
PIGFProtein modifying enzymesTransmembrane40,88
PLAT(Metallo-)proteasesSecreted40,87
PLAU(Metallo-)proteasesSecreted40
PLAURTransmembrane signal receptorsTransmembrane40,88
PTBP1MiscellaneousIntracellular55
PTGER2Transmembrane signal receptorsTransmembrane55
PTGESProtein modifying enzymesIntracellular88
RPS6KA5Protein modifying enzymesIntracellular88
SCAMP4MiscellaneousIntracellular55
SELPLGTransmembrane signal receptorsTransmembrane55
SEMA3FIntercellular signal moleculeSecreted55
SERPINB4Protease inhibitorsIntracellular55
SERPINE1Protease inhibitorsSecreted26,40,55,61,82,84,87,88
SERPINE2Protease inhibitorsSecreted40,87
SPP1Cytokine/ChemokineSecreted55
SPXIntercellular signal moleculeSecreted55
TIMP2Protease inhibitorsSecreted18,40,87,88
TNFCytokine/ChemokineSecreted81,85
TNFRSF10CTransmembrane signal receptorsTransmembrane40
TNFRSF11BTransmembrane signal receptorsTransmembrane40,88
TNFRSF1ATransmembrane signal receptorsTransmembrane40,87
TNFRSF1BTransmembrane signal receptorsTransmembrane40
TUBGCP2MiscellaneousIntracellular88
VEGFAGrowth factorSecreted26,40,82,88
VEGFCGrowth factorSecreted88
VGFIntercellular signal moleculeSecreted55
WNT16Intercellular signal moleculeSecreted55
WNT2Intercellular signal moleculeTransmembrane88
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[22, 192, 980, 504]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[45, 116, 928, 161]]<|/det|> +Table 2. Comparison of SenMayo with 6 existing senescence/SASP gene sets. Note that in GSEA analyses, p-values \(< 0.25\) are considered potentially significant31,99, although we also identified p-values \(< 0.05\) and \(< 0.01\) (NES, normalized Enrichment Score). + +
Human AgingMouse AgingMouse Genetic Clearance of Senescent CellsHuman Pharmacological Clearance of Senescent Cells
Cohort ACohort BMicrogliaPrefrontal cortexDorsal hippocampusBone marrowMouse INK-ATTAC (old vs young)Mouse INK-ATTAC (old, vehicle vs AP)Adipose (Control vs D+Q)
NESp-valueNESp-valueNESp-valueNESP-valueNESp-valueNESp-valueNESNp-valueNESp-valueNESp-value
R-HSA-25595821.10750.28260.67800.92781.17940.1565-1.24670.11691.21170.19160.92000.60431.42350.03261.00060.44421.39960.0344
Casella_up1.00890.47480.69700.88850.97370.47900.95370.52091.39490.05930.88740.6339-1.08610.31680.68650.96270.67510.9983
Purcell0.93290.59440.95850.50931.51200.01781.17310.23041.81170.00001.52240.04191.38940.0778-0.73000.8874-1.06960.3278
Hernandez0.78490.77710.78460.78020.71460.94610.86620.6710-0.81000.7650-0.57780.97891.25130.17181.47960.02661.40190.0501
Fridman_up1.42490.01691.54070.01741.43970.02060.96390.54491.74820.00001.61000.01631.07620.31451.34130.03471.41130.0220
Sencan-0.84600.93620.80380.82350.86740.81441.03280.40061.53020.00110.72470.86670.88380.7312-1.06740.31821.53750.0000
SenMayo1.51220.00231.49820.00311.46240.00521.60980.00131.85010.00001.51000.03621.50420.00231.45860.00541.32390.0184
+ +<|ref|>table<|/ref|><|det|>[[30, 530, 115, 610]]<|/det|> + +
p-value<0.25
<0.05
<0.01
+ +<--- Page Split ---> +<|ref|>table_caption<|/ref|><|det|>[[115, 92, 823, 107]]<|/det|> +Table 3. Top 20 significantly upregulated genes in the human and murine SASP clusters. + +<|ref|>table<|/ref|><|det|>[[115, 120, 882, 619]]<|/det|> + +
Geneavg_log2FCAdj. p-value
Human
S100A91.9743176780
CXCL81.8177752530
CST31.8138352950
TYROBP1.7427739520
LST11.7044565150
FCN11.7041481190
FCER1G1.6980711860
LYZ1.6958790210
CCL31.681097610
S100A81.6391675240
CTSS1.6051075330
AIF11.5375602820
S100A121.5010133810
SAT11.4757403240
G0S21.4717682590
S100A111.4265831670
PSAP1.4121560190
NEAT11.4020088890
CSTA1.3461710610
SERPINA11.3430127630
Murine
Ccl21.3853854564.4042E-274
Cxcl141.3487655310
Cxcl121.3480992210
Hp1.329671382.6772E-298
Trf1.324835066.8972E-280
Sering11.3048710380
Mt11.2946178370
Tmem176b1.2383300750
Mt21.2241586940
Igfbp41.2109057730
Grem11.2070567240
Cd3021.1952207470
Apoe1.1630629930
Msmp1.162402443.2104E-194
Adipoq1.1408883657.4114E-283
Cyr611.1364266250
Gas61.1104743290
Mmp131.0954882960
Tmem176a1.0877655810
Col3a11.0827201541.1707E-254
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Bioinformatics (Oxford, England) 32, 2973–2980; 10.1093/bioinformatics/btw372 (2016). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 822, 138]]<|/det|> +99. Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nature protocols 14, 482–517; 10.1038/s41596-018-0103-9 (2019). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 510, 150]]<|/det|> +- SenMayomanuscriptallsupplementaryfigures.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/images_list.json b/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8af940580e6eade7959f5e8c19a63383feb734cf --- /dev/null +++ b/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. Cumulative mortality rate of irradiated Aedes mosquitoes exposed to three sex ratio (SR) (Males/Females) treatments (1:3 = control, 49:1 and 99:1 for Ae. aegypti; and 50:1 and", + "footnote": [], + "bbox": [ + [ + 123, + 110, + 856, + 816 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Impact of mating harassment on feeding success in semi-field cages. a. Impact", + "footnote": [], + "bbox": [ + [ + 122, + 85, + 873, + 468 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. Study site and climatic conditions. a, Satellite maps of field site in Guangzhou city (map data: Google, DigitalGlobal). Release area outlined with green while control and buffer areas are outlined with blue and orange in the satellite image respectively. b, Spatial distribution of the monitoring tools/methods. Grey points represent ovitraps, blue points represent BG traps, and the purple points represent the positions to perform Human Landing Catch. c, d, Daily average temperature (c) and precipitation (d) in the study area from March to November 2021.", + "footnote": [], + "bbox": [ + [ + 139, + 390, + 877, + 692 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4. Suppression efficiency of mosquito populations after sterile male releases. a, Dynamics of larval suppression. Larval reduction is observed in the release area as compared to the control area ( \\(n = 14\\) , \\(P = 0.0023\\) , Two-tailed Wilcoxon matched-pairs signed rank test). b, Dynamics of adult female suppression. A total of 4 BG traps in the release area and 6 in the control area. Female reduction is observed in the release area ( \\(n = 16\\) , \\(P = 0.0107\\) , Two-tailed Wilcoxon matched-pairs signed rank test). The red dotted lines indicate the suppression efficiency in both (a) and (b). c, Number of female adults captured via Human Landing Catch (HLC) in the release and control areas after 11 weeks of release. Two positions were selected to perform HLC in", + "footnote": [], + "bbox": [ + [ + 120, + 284, + 870, + 616 + ] + ], + "page_idx": 15 + } +] \ No newline at end of file diff --git a/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1.mmd b/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1.mmd new file mode 100644 index 0000000000000000000000000000000000000000..5aab1b3d020722712e093ac2f3327118c5f588ad --- /dev/null +++ b/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1.mmd @@ -0,0 +1,626 @@ + +# Mating harassment may boost the effectiveness of the sterile insect technique for Aedes mosquitoes + +Jeremy Bouyer bouyer@cirad.fr + +Insect Pest Control Sub- Programme, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency (IAEA) https://orcid.org/0000- 0002- 1913- 416X + +Dongjing Zhang Sun Yat- sen University - Michigan State University Joint Center of Vector Control for Tropical Diseases, Sun Yat- Sen University + +Hamidou Maiga IAEA + +Yongjun Li Guangzhou University Mame Thiemo Bakhoum IAEA https://orcid.org/0000- 0001- 6794- 4426 + +Gang Wang Sun Yat- sen University + +Yan Sun Sun Yat- sen University + +David Damiens Institut de Recherche pour le Développement + +Wadaka Mamai IAEA + +Nanwintoum Somda IAEA + +Thomas Wallner IAEA + +Odet Bueno Masso IAEA + +Claudia Martina IAEA + +Simran Kotla IAEA + +Hanano Yamada + +<--- Page Split ---> + +IAEA + +Lu Deng Environmental Health Institute, National Environment Agency + +Cheong Huat Tan Environmental Health Institute https://orcid.org/0000- 0001- 6263- 9721 + +Jiatian Guo Sun Yat- sen University + +Qingdeng Feng Sun Yat- sen University + +Junyan Zhang Sun Yat- sen University + +Xufei Zhao Sun Yat- sen University + +Dilinuer Paerhande Sun Yat- sen University + +Wenjie Pan SYSU Nuclear and Insect Biotechnology Co., + +Yu Wu Sun Yat- sen University + +Xiaoying Zheng Sun Yat- sen University + +Zhongdao Wu Zhongshan School of Medicine, Sun Yat- sen University, Guangzhou, 510080, China + +Zhiyong Xi Michigan State University https://orcid.org/0000- 0001- 7786- 012X + +Marc Vreysen IAEA + +Biological Sciences - Article + +Keywords: + +Posted Date: August 11th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3128571/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on March 4th, 2024. See the published version at https://doi.org/10.1038/s41467-024-46268-x. + +<--- Page Split ---> + +1 Mating harassment may boost the effectiveness of the sterile insect technique for Aedes mosquitoes + +3 Dongjing Zhang \(^{1*}\) , Hamidou Maiga \(^{2*}\) , Yongjun Li \(^{3,4*}\) , Mame Thierno Bakhoum \(^{2,5*}\) , 4 Gang Wang \(^{1*}\) , Yan Sun \(^{1}\) , David Damiens \(^{6}\) , Wadaka Mamai \(^{2}\) , Nanwintoum Séverin 5 Bimbilé Somda \(^{2,7}\) , Thomas Wallner \(^{2}\) , Odet Bueno Masso \(^{2}\) , Claudia Martina \(^{2}\) , Simran 6 Singh Kotla \(^{2}\) , Hanano Yamada \(^{2}\) , Deng Lu \(^{8}\) , Cheong Huat Tan \(^{8}\) , Jiatian Guo \(^{1}\) , Qingdeng 7 Feng \(^{1}\) , Junyan Zhang \(^{1}\) , Xufei Zhao \(^{1}\) , Dilinuer Paerhande \(^{1}\) , Wenjie Pan \(^{9}\) , Yu Wu \(^{1}\) , 8 Xiaoying Zheng \(^{1}\) , Zhongdao Wu \(^{1}\) , Zhiyong Xi \(^{4,10}\) , Marc J.B. Vreysen \(^{2}\) , Jérémy 9 Bouyer \(^{2,11*}\) # + +10 1 Chinese Atomic Energy Agency Center of Excellence on Nuclear Technology 11 Applications for Insect Control, Key Laboratory of Tropical Disease Control of the 12 Ministry of Education, Sun Yat-sen University, Guangzhou, China 13 2 Insect Pest Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in 14 Food and Agriculture, IAEA, Vienna, Austria 15 3 Department of Pathogen Biology, School of Medicine, Jinan University, Guangzhou, 16 China 17 4 Guangzhou Wolbaki Biotech Co., Ltd, Guangzhou, China 18 5 Institut Sénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de 19 Recherches Vétérinaires, BP 2057 Dakar, Sénégal 20 6 Institut de Recherche pour le Développement (IRD), UMR MIVEGEC 21 (CNRS/IRD/Université de Montpellier), IRD Réunion/GIP CYROI (Recherche Santé 22 Bio-innovation), Sainte Clotilde, Reunion Island- France 23 7 Unité de Formation et de Recherche en Science et Technologie (UFR/ST), Université + +<--- Page Split ---> + +24 Norbert ZONGO (UNZ), BP 376 Koudougou, Burkina Faso 25 8 National Environment Agency, Singapore 26 9SYSU Nuclear and Insect Biotechnology Co., Ltd., Dongguan, China 27 10Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA 28 11UMR ASTRE, CIRAD, F- 34398 Montpellier, France 29 12These authors contributed equally to this work 30 13corresponding author: j.bouyer@iaea.org + +The sterile insect technique (SIT) is based on the overflooding of a target population with released sterile males inducing sterility in the wild female population. The SIT has proven to be effective against several insect pest species of agricultural and veterinary importance and is under development for Aedes mosquitoes. Here, we show that the release of sterile males in high sterile male to wild female ratios may also impact the target female population through mating harassment. Under laboratory conditions, male to female ratios above 50 to 1 reduced the longevity of female Aedes mosquitoes by reducing their feeding success. Under semi-field conditions, blood uptake of females from an artificial host and biting rates on humans were also strongly reduced. Finally, in a field SIT trial conducted in a 1.17 ha area in China, the female biting rate was reduced by 80%, concurrent to a reduction of female mosquito density of 40% due to the swarming of males around humans attempting to mate with the female mosquitoes. This suggests that the SIT does not only suppress mosquito vector populations through the induction + +<--- Page Split ---> + +of sterility, but might also reduce disease transmission due to increased female mortality and lower host contact. + +The SIT is based on the sequential release of sterile male insects over the target area where they will mate with the virgin native female insects1, resulting in the induction of sterility in the wild female population proportionally to the ratio of sterile to wild insects. This impairs the reproduction rate of the female population and as a result, fewer insects will be available in subsequent generations, reducing the density of the target population over time. The SIT has been successfully used to manage populations of various insect pests of agricultural, animal, or human health importance2, and more recently, there has been a renewed interest to develop and implement the SIT against mosquitoes3. Aedes mosquitoes are major vectors of viruses such as dengue, chikungunya, Zika and yellow fever that are severely impacting human health. Traditional vector control strategies such as the use of broad-spectrum insecticides have serious environmental drawbacks and sanitation through reduction or removal of mosquito breeding sites requires the collaboration of the resident human population and has limited impact4,5. In 2023, 42 SIT pilot projects were being implemented worldwide against mosquitoes6. Released males are attracted by hosts, including humans7, and can swarm around them in the search of mates, a behaviour that is exploited to monitor their density through the Human Landing Catch method8. Alternatively, they can be trapped using CO2- baited adult traps9. Continuous, inundative releases of sterile males, like those required for SIT, can lead to high sterile to wild male and male to female ratios, sometimes over 100 to 1, particularly when the target population is suppressed. Could such high sex ratios have some influence on the fitness of females? + +Mating is an essential component of adult life for all species with sexual reproduction. In most insects, a single or a moderate number of matings are sufficient for females to + +<--- Page Split ---> + +maximize their reproductive success \(^{10 - 12}\) . Therefore, females generally prefer a lower mating rate than males \(^{13}\) and are often resistant or reluctant to re- mate \(^{14}\) . This apparent divergence leads males from a wide range of animal species to compel females to mate by coercion or harassment \(^{15}\) . As a consequence, a ratio of 10 sterile male Aedes aegypti to 1 female resulted in increased mortality of the females but did not impact the fitness of the surviving ones \(^{12}\) . Mating harassment is a form of sexual conflict where repeated attempts to copulate by the male can be costly for the female \(^{15}\) . These costs can be direct (effects on harassed females) or indirect (effects on descendants of harassed females) \(^{12}\) . Harassment behaviours are even more frequent when individuals are confined to closed environments, like a rearing cage in the laboratory. Under mass- rearing conditions for example, a reduced 1:3 male to female ratio is recommended to reduce mating harassment and maximize production in both Ae. albopictus \(^{16,17}\) and Ae. aegypti \(^{18}\) . The same applies to other insects like tsetse flies where a 1:4 male to female ratio increases female fecundity in Glossina fuscipes fuscipes and G. pallidipes \(^{19}\) . However, the effects of large sex ratios such as those observed during an SIT programme are largely unknown. + +Here we explored the impact of mating harassment by sterile male mosquitoes on the survival and feeding success of Ae. albopictus and Ae. aegypti females under laboratory, semi- field and field conditions. We show that both parameters are strongly reduced by mating harassment. + +## Survival of mosquitoes caged at different sex ratios + +We first observed the effect of high fertile male to female ratios in Ae. aegypti and Ae. albopictus in confined laboratory cages. In both species, increased male to female ratios were associated with higher mortality of the females and also of male Ae. albopictus (Extended Data Figs. 1, 2, 3). Even with a male to female ratio of 3:7, which is only slightly higher than the + +<--- Page Split ---> + +control at 1:3, mortality of female Ae. aegypti significantly increased (Extended Data Fig. 2, Extended Data Table S1, \(P = 0.021\) ). Female mortality reached \(14.5\% \pm 3.9\%\) after 8 days under a male to female ratio of 99:1 as compared with \(2.8\% \pm 1.2\%\) in the control group (male to female ratio of 1:3). The impact of harassment on the survival of female Ae. albopictus was even more pronounced than in Ae. aegypti. A male to female ratio of 50:1 was enough to increase mortality of females significantly after 8 days (Extended Data Fig. 1, Extended Data Table 1, \(P = 1.47\mathrm{e}^{-08}\) ), i.e., \(38.9\% \pm 1.9\%\) , similarly to under a male to female ratio of 100:1, whereas in the control group mortality remained at \(1.5\%\) . + +Fertile male Ae. aegypti did not experience increased mortality with increased sex ratios (Extended Data Figs. 1, 2, Extended Data Table 1, \(P > 0.05\) ). On the contrary, the mortality of male Ae. albopictus also increased with a male to female ratio of 50:1 after 8 days (Extended Data Fig. 1, Extended Data Table 1, \(P = 8.42\mathrm{e} - 08\) ), i.e. \(19.0\% \pm 4.2\%\) , similarly to the batch with a male to female ratio of 100:1, whereas in the control group mortality remained at \(2.9\%\) . This may be related to more male Ae. albopictus being more aggressive, but this will warrant further research. + +A practical application is that the increase in female mortality could be used as an additional process to separate the sterile males from the females by keeping them for some days in the insectary following mechanical separation that results in \(1\%\) or more female contamination of the sterile male batches \(^{20}\) (see Supplementary Information). We thus repeated the same experiments with irradiated mosquitoes to assess whether similar results would be obtained. In general, irradiation exacerbated the negative impact of mating harassment (Fig. 1). Caging of sterile males and females under laboratory conditions at a sex ratio of 100:1 decreased the female contamination of the sterile male batches to \(\sim 0.6\%\) and \(0.7\%\) due mortality for female Ae. aegypti and Ae. albopictus, respectively, within the first eight days. When a predetermined threshold is agreed with the public health authorities, e.g., \(1\%^{20}\) , this + +<--- Page Split ---> + +121 might be an effective way of eliminating females instead of removing residual females 122 manually or discarding the full batch of sterile males. Nevertheless, this would probably be 123 cost- prohibitive in an operational programme (see Supplementary Information). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1. Cumulative mortality rate of irradiated Aedes mosquitoes exposed to three sex ratio (SR) (Males/Females) treatments (1:3 = control, 49:1 and 99:1 for Ae. aegypti; and 50:1 and
+ +<--- Page Split ---> + +100:1 for Ae. albopictus) over 8 days. Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (3 repeats). a) Cumulative mortality rate of Ae. aegypti females increased with sex ratio and was \(26.7\% \pm 14.0\%\) at 8 days for a ratio of 99:1 as compared to \(3.9\% \pm 2.4\%\) in the control group. b) In Ae. albopictus, the tendency was even stronger and the cumulative mortality reached \(40.0\% \pm \mathrm{SD} = 8.8\%\) at 8 days for a ratio of 100:1 as compared to \(3.8\%\) in the control group. In Fig (a) and (b), ns represents not significant; \\*\\* represents \(P< 0.01\) ; \\*\\*\\* represents \(P< 0.001\) . + +## What causes mortality in high male to female ratios? + +To better understand the mechanisms leading to increased mortality, we filmed sexual interactions of the mosquitoes at a high resolution (1080P). Females were strongly harassed when sex ratios were biased towards males (see Suppl. Movie S1). At the highest male to female ratio of 99:1, females were completely prevented from feeding and were lying immobile at the bottom of the cage to escape further mating attempts from males who were aggregated around the females by groups of three to five individuals. Any attempt of females to escape attracted more males, probably induced by their wing beat. To verify this hypothesis, some females were glued on their back to a pin (see Suppl. Movie S2), and those females accepted two or three mates, but refused to re- mate thereafter. However, each time they were trying to escape and fly off, new males were attracted and were aggregating around them. In nature, such aggregates may drop to the ground, where they attract immediately predators, and again increase female mortality. + +From these mosquito recordings, it was clear that feeding inhibition was the main factor increasing mortality in females. Although described here for the first time intra- specifically, this finding is consistent with the previous study21 showing feeding inhibition of female Ae. + +<--- Page Split ---> + +aegypti by male Ae. albopictus. Interspecific mating of male Ae. albopictus with female Ae. aegypti actually occurs and is named satyrization22,23. + +## Mating harassment and feeding success in semi-field cages + +We first explored the impact of a high irradiated males to non- irradiated female ratio on the feeding success of females on an artificial host (Hemotek). A male to female ratio of 99:1, reduced blood feeding success to \(1\% \pm 1\%\) as compared with \(16\% \pm 4\%\) at a male to female ratio of 1:1 (odds ratio 16.50, \(\mathrm{SE} = 9.98\) , \(P < 10^{- 4}\) ) (Fig. 2a). Male mosquitoes were observed forming swarms around the artificial hosts waiting to mate with a female attempting to take a blood meal thus reducing their feeding success (see Suppl. Movie S3). + +A similar experiment was set up but now using a human host. When a collector exposed one of his legs from foot to knee (human bait) in a semi- field cage, and killed the female mosquitoes after landing on the exposed leg but before feeding began, the rate of caught females was reduced to \(38\%\) ( \(\mathrm{SE} = 6\%\) ) at a male to female ratio of 99 to 1 as compared to \(77\%\) ( \(\mathrm{SE} = 6\%\) ) with a male to female ratio of 1:1 (odds ratio 5.30, \(\mathrm{SE} = 2.15\) , \(P < 10^{- 4}\) ) (Fig. 2b). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2. Impact of mating harassment on feeding success in semi-field cages. a. Impact
+ +of the male to female ratio on the engorgement rate of females on an artificial host (Hemotek). Fewer females were engorged in the male: female treatment ratio 99:1 as compared to the control ratio 1:1 \((n = 4\) , odds ratio 16.50, \(\mathrm{SE} = 9.98\) , \(P < 10^{- 4}\) ). b. Impact of the male to female ratio on the engorgement on the catch rate of females by a volunteer collector. Fewer females were collected when attempting to bite a human collector in the male: female treatment ratio of 99:1 as compared to the control ratio 1:1 \((n = 3\) , odds ratio 5.30, \(\mathrm{SE} = 2.15\) , \(P < 10^{- 4}\) ). + +In both semi- field trials, mating harassment thus resulted in feeding inhibition. Aggregation of sterile males around human hosts during mosquito SIT programmes is well- known7,24. + +Mating harassment and human landing catches under field conditions + +<--- Page Split ---> + +The data from an Ae. albopictus field trial conducted in the centre of Guangzhou, China, were used to investigate the existence of feeding inhibition in real settings (Fig. 3). Before the release of sterile males, ovitraps were deployed bi- weekly in both the release and the untreated site to collect baseline data from March to August 2021 (Extended Data Fig. 6a). In addition, the density of the adult female populations was estimated with Human Landing Catch (HLC) (Extended Data Fig. 6b). Before the beginning of the release, no significant difference was observed in the number of hatched eggs per ovitrap and number of females caught with HLC in the untreated and release areas (Extended Data Fig. 6a, 6b). + +![](images/Figure_3.jpg) + +
Fig. 3. Study site and climatic conditions. a, Satellite maps of field site in Guangzhou city (map data: Google, DigitalGlobal). Release area outlined with green while control and buffer areas are outlined with blue and orange in the satellite image respectively. b, Spatial distribution of the monitoring tools/methods. Grey points represent ovitraps, blue points represent BG traps, and the purple points represent the positions to perform Human Landing Catch. c, d, Daily average temperature (c) and precipitation (d) in the study area from March to November 2021.
+ +<--- Page Split ---> + +On \(13^{\text{th}}\) August 2021, the release of sterile male mosquitoes was initiated at a frequency of twice per week. During a period of 15 weeks, a total of 3 million male mosquitoes were released (Extended Data Fig. 6c). Aedes albopictus populations were monitored weekly with ovitraps, adult-collecting BG traps and irregular HLC. During the release period, the mosquito population was reduced in the release area by \(47.56\%\) and \(35.96\%\) as measured in the ovitraps and the BG traps, respectively, in comparison with the untreated area (Figs. 4a, 4b). From \(6^{\text{th}}\) September to \(8^{\text{th}}\) November, the efficiency of suppression was maintained at an average rate of \(60.53\%\) (min to max: \(39.03\% - 86.07\%\) ) in the ovitrap catches. However, the suppression efficiency showed large variations after 8 November, and this might be attributed to the low ambient temperatures (12- 22 °C) (Fig. 3b) or to possible immigration of fertile females in the release area in view of its small size (1.17 ha). The temporal fluctuations of adult females were similar to the larval samples, i.e., an average suppression of \(47.2\%\) (min to max: \(34.62\% - 92.5\%\) ) for the period 15 September to 2 December (excluding the data collected on 29 to 30 September) (Fig. 4b). + +We compared the sex ratio obtained by BG traps and HLC from \(3^{\text{rd}}\) to \(6^{\text{th}}\) November (11 weeks after the first release of sterile males), and a higher sex ratio was found in HLC than in the BG traps (70.5:1 vs 16.6:1, Fig. 4d). Quantitative polymerase chain reaction (qPCR) targeting Wolbachia wsp gene indicated that over \(95\%\) of caught males with BG traps or HLC were the released sterile males (Fig. 4e). In HLC, the sex ratio was close to the experimental set- up in our lab and semi- field studies presented above. An average of 0.5 adult females were collected in the release area versus 2.8 females in the untreated area using HLC. This indicated a suppression of \(>82.0\%\) of adult females, a much higher suppression rate than what was observed with BG traps during the same period (42.3% during 3rd- 4th November, Fig. 4d). The higher suppression rate obtained with the HLC might possibly be due to the high + +<--- Page Split ---> + +overflooding rate of males surrounding the catchers, which could have prevented the approach of female mosquitoes by the sterile males, as was observed in the semi- field trial. In Aedes species, males are known to swarm around the hosts using pheromonal and acoustic cues, presumably to intercept females attempting to feed25- 27. Male Ae. albopictus are particularly attracted to humans7 and our results show that they aggregated in higher numbers around humans than BG traps. + +![](images/Figure_4.jpg) + +
Fig. 4. Suppression efficiency of mosquito populations after sterile male releases. a, Dynamics of larval suppression. Larval reduction is observed in the release area as compared to the control area ( \(n = 14\) , \(P = 0.0023\) , Two-tailed Wilcoxon matched-pairs signed rank test). b, Dynamics of adult female suppression. A total of 4 BG traps in the release area and 6 in the control area. Female reduction is observed in the release area ( \(n = 16\) , \(P = 0.0107\) , Two-tailed Wilcoxon matched-pairs signed rank test). The red dotted lines indicate the suppression efficiency in both (a) and (b). c, Number of female adults captured via Human Landing Catch (HLC) in the release and control areas after 11 weeks of release. Two positions were selected to perform HLC in
+ +<--- Page Split ---> + +the release area and 6 positions in the control area. Three replicates were performed. An average of 0.5 females were collected in the release area while 2.8 females were collected in the control area. d, Relation between the suppression efficiency and ratio of males to females. An average of \(82.86\%\) suppression of adult female was achieved via HLC on \(3^{\text{rd}}\) and \(4^{\text{th}}\) November with a 70.5 ratio of males to females, while \(42.31\%\) reduction of adult females was observed via BG trapping on 3- 4 November (indicated by black arrow in (b)) with a 16.6 ratio of males to females. e, Proportion of sterile males in the collected males via HLC and BG trapping. In both collecting methods, over \(95\%\) of collected males (HLC: 39/40; BG: 88/92) are sterile males, which were identified through qPCR based on the wsp gene of Wolbachia. The Wolbachia- negative samples were considered as the released sterile males. + +In various insect species, mating harassment is associated with costs that negatively affect the physical condition and hence, longevity of females, either through physical damage \(^{28,29}\) or toxic effects from the accessory gland secretions \(^{30,31}\) . In this study, however, females that were exposed to males at a 1:3 or 99:1 ratio and that were separated from the males immediately after the mating (Extended Data Fig. 5), did not show any increase in mortality. This would indicate that depletion of energy reserves and reduced feeding success were the main factors that reduced their longevity, as observed in other studies where reduced fertility was also documented \(^{11,32}\) . Similar results were observed in other species when sex ratios were strongly biased toward males, although to a lesser extent, like in the tsetse fly G. morsitans morsitans \(^{33}\) , in the dung fly Sepsis cynipsea \(^{34}\) , and the field cricket Gryllus bimaculatus \(^{35}\) . Prevention of copulation by blocking or damaging the external genitalia of male tsetse flies resulted in reduced longevity of females caged with them, suggesting that the reduced female survival resulted from the physical aspects of male harassment rather than by components of the ejaculate \(^{33}\) . In addition, male tsetse flies have a shorter lifespan due to being engaged in + +<--- Page Split ---> + +mating harassment of the females, as was likewise observed in our study in Ae. aegypti. Like in tsetse, Ae. aegypti female mortality was increased equally by caging them with males that had modified claspers to prevent mating or unmodified males12. These authors even suggest potential benefits (higher fitness) obtained from ejaculate components, a common phenomenon in insects that is considered as part of nuptial feeding36. + +The SIT is generally combined with other methods in an integrated pest management approach to first suppress the target population to a level low enough that sufficient sterile to wild male ratios can be obtained to induce enough sterility in the female wild population, e.g. in Aedes mosquitoes6 or tsetse37. Hence, high sex ratios are not uncommon in SIT field trials. In operational tsetse fly SIT programmes, sterile to wild male ratios up to 100 were observed in some cases37,38. The sterile to wild male ratio peaked at 50 to 1 in another successful suppression program against Ae. albopictus in China39. One of the main benefits of the SIT is its inverse density- dependent properties40 or in other words, the sterile to wild male ratio increases with each generation and with the rate of suppression and this can drive an insect population to extinction38. Our data show that feeding inhibition of the females might act synergistically to the induction of sterility in the female population. + +## Conclusion + +Overall, our results allow us to propose two new additional mechanisms contributing to the efficiency of the SIT against mosquito- borne diseases. First, we hypothesize that high male to female ratio increases female mortality through feeding inhibition thus directly reducing female lifespan. Second, at high male to female ratios, males reduce female feeding success and biting rate (and hence transmission rate). The SIT may thus directly reduce disease transmission at high male to female ratios through an impact on two critical components of vectorial capacity, namely female longevity and host contact41. This may as well occur in all + +<--- Page Split ---> + +286 genetic control methods based on inundative release of males, like the incompatible insect technique39,42 or RIDL43 or even those driving maleness into wild populations44. These hypotheses warrant more field research to assess the impact of these mechanisms on disease transmission. + +## Authors contributions + +291 J. Bouyer, D.Z., and Z.X. developed the concept and methodology; H. Maiga, M.T.B., D.D., W. M., N.S.B.S., T.W., O.B.M., C.M. and S.S.K performed the lab experiments; Y. Li, H.M., W.M., N.S.B.S. and H.Y. performed the semi-field experiments; D. Zhang, G.W., Y.S., J.G., Q.F., J.Z., X. Zhao, D.P., W.P., Y.W., X. Zheng, and Z.W. performed the field trial, D. Lu, C.H.T. and J.B. performed the movies; J. Bouyer, D.Z., C.H.T., Y.W., Z.X. and M.J.B.V. performed coordination for the project; D. Zhang obtained regulatory approvals for mosquito releases; Z. Xi obtained the ethical permit for the semi-field trial involving human bait; J. Bouyer provided oversight of the project and contributed to all experimental designs, data analysis and data interpretation; J. Bouyer, D.Z., Y.L., D.D., C.M., D.L., Z.X. and M.J.B.V. wrote the manuscript. All authors participated in manuscript editing and final approval. + +Supplementary Information is available for this paper. + +Correspondence and requests for materials should be addressed to JB. Reprints and + +permissions information is available at www.nature.com/reprints. + +<--- Page Split ---> + +## References + +Dyck, V. A., Hendrichs, J. & Robinson, A. S. Sterile insect technique: principles and practice in area- wide integrated pest management. (CRC press, 2021). + +Vreysen, M. J. B. & Klassen, W. in Sterile Insect Technique. Principles and Practice in Area- Wide Integrated Pest Management (eds A. Dyck, J. Hendrichs, & A.S. Robinson) 75- 112 (CRC Press, 2021). + +Lees, R. S., Carvalho, D. O. & Bouyer, J. in Sterile Insect Technique. Principles and Practice in Area- Wide Integrated Pest Management. (eds A.V. Dyck, J. Hendrichs, & A. S. Robinson) (vol. in press, Second edition ed., 2020). + +Benelli, G. & Mehlhorn, H. Declining malaria, rising of dengue and Zika virus: insights for mosquito vector control. Parasitol. Res. 115, 1747- 1754, doi:10.1007/s00436- 016- 4971- z (2016). + +WHO & UNICEF. Global vector control response 2017- 2030. (2017). + +Bouyer, J., Yamada, H., Pereira, R., Bourtzis, K. & Vreysen, M. J. B. Phased Conditional Approach for Mosquito Management using the Sterile Insect Technique. Trends Parasitol. + +36, 325- 336 (2020). + +Paris, V., Hardy, C., Hoffmann, A. A. & Ross, P. A. How often are male mosquitoes attracted to humans? bioRxiv, 2023.2003. 2008.531798 (2023). + +Velo, E. et al. A Mark- Release- Recapture study to estimate field performance of imported radio- sterilized male Aedes albopictus in Albania. Frontiers in Bioengineering and Biotechnology 10, 833698 (2022). + +Claudel, I. et al. Optimization of adult mosquito trap settings to monitor populations of Aedes and Culex mosquitoes, vectors of arboviruses in La Reunion. Scientific Reports 12, 19544 (2022). + +<--- Page Split ---> + +Walker, W. F. Sperm utilization strategies in nonsocial insects. American Naturalist 115, 780–799 (1980). + +Arnqvist, G. & Nilsson, T. The evolution of polyandry: multiple mating and female fitness in insects. Anim. Behav. 60, 145–164, doi:10.1006/anbe.2000.1446 (2000). + +Helinski, M. E. H. & Harrington, L. C. The role of male harassment on female fitness for the dengue vector mosquito Aedes aegypti. Behav. Ecol. Sociobiol. 66, 1131–1140 (2012). + +Parker, G. A. in Sexual Selection and Reproductive Competition in Insects (eds M. S. Blum & N. A. 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Guidance Framework for Testing the Sterile Insect Technique as a Vector Control Tool against Aedes-Borne Diseases, Geneva & Vienna. (2020). + +Soghigian, J., Gibbs, K., Stanton, A., Kaiser, R. & Livdahl, T. Sexual harassment and feeding inhibition between two invasive dengue vectors. Environmental health insights 8, S16007 (2014). + +<--- Page Split ---> + +Maiga, H., Gilles, J. R. L., Lees, R. S., Yamada, H. & Bouyer, J. Demonstration of resistance to satyrization behavior in Aedes aegypti from La Réunion island. Parasite 27, 22 (2020). + +Tripet, F. et al. Competitive reduction by satyrization? Evidence for interspecific mating in nature and asymmetric reproductive competition between invasive mosquito vectors. The American journal of tropical medicine and hygiene 85, 265 (2011). + +Oliva, C. F., Damiens, D. & Benedict, M. Q. Male reproductive biology of Aedes mosquitoes. Acta Trop. 132: S12- S19 (2014). + +Jaenson, T. G. 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Nature 373, 241- 244 (1995). + +Wolfner, M. F. Tokens of love: functions and regulation of Drosophila male accessory gland products. Insect Biochem Mol. Biol. 27, 179- 192 (1997). + +Watson, P. J., Arnqvist, G. & Stallman, R. R. Sexual conflict and the energetic costs of mating and mate choice in water striders. American Naturalist 151, 46- 58 (1998). + +<--- Page Split ---> + +Clutton- Brock, T. & Langley, P. Persistent courtship reduces male and female longevity in captive tsetse flies Glossina morsitans morsitans Westwood (Diptera: Glossinidae). Behav. Ecol. 8, 392- 395 (1997). + +Muhlhauser, C. & Blanckenhorn, W. U. The cost of avoiding matings in the dung fly Sepsis cynipsea. Behav. Ecol. 13, 359- 365 (2002). + +Bateman, P. W., Ferguson, J. W. H. & Yetman, C. A. Courtship and copulation, but not ejaculate, reduce the longevity of female field crickets (Gryllus bimaculatus). J. Zool. 268, 341- 346 (2006). + +Vahed, K. The function of nuptial feeding in insects: a review of empirical studies. Biological reviews 73, 43- 78 (1998). + +Dicko, A. H. et al. Using species distribution models to optimize vector control: the tsetse eradication campaign in Senegal. Proc. Natl. Acad. Sci. U.S.A. 111, 10149- 10154 (2014). + +Vreysen, M. J. B., Saleh, K. M., Lancelot, R. & Bouyer, J. Factory tsetse flies must behave like wild flies: a prerequisite for the sterile insect technique. PLoS Negl. Trop. Dis. 5(2): e907. (2011). + +Zheng, X. et al. Incompatible and sterile insect techniques combined eliminate mosquitoes. Nature 572, 56- 61, doi:https://doi.org/10.1038/s41586- 019- 1407- 9 (2019). + +Feldmann, U. & Hendrichs, J. Integrating the sterile insect technique as a key component of area- wide tsetse and trypanosomiasis intervention. Vol. 3, Food and Agriculture Organization (2001). + +Kramer, L. D. & Ciota., A. T. Dissecting vectorial capacity for mosquito- borne viruses. Current opinion in virology 15, 112- 118 (2015). + +Crawford, J. E. et al. Efficient production of male Wolbachia- infected Aedes aegypti mosquitoes enables large- scale suppression of wild populations. Nat. Biotechnol. 38, 482- 492 (2020). + +<--- Page Split ---> + +405 43 Carvalho, D. O. et al. Suppression of a field population of Aedes aegypti in Brazil by sustained release of transgenic male mosquitoes. PloS Negl. Trop. Dis. 9, e0003864 (2015). 407 44 Adelman, Z. N. & Tu, Z. Control of Mosquito-Borne Infectious Diseases: Sex and Gene Drive. 408 Trends Parasitol. 32, 219-229 (2016). + +<--- Page Split ---> + +## 1 Methods + +## 1. Laboratory trials + +All experiments on Ae. aegypti were carried out at the Insect Pest Control Laboratory (ICPL), IAEA, Vienna, Austria whereas experiments on Ae. albopictus were conducted independently at IRD, Saint- Denis, La Reunion Island, except one preliminary experiment on Ae. albopictus also conducted at IPCL (Extended Data Fig. 4). + +### 1.1 Mosquito colonies and mass-rearing + +Three established mosquito colonies of Ae. aegypti and Ae. albopictus were used to perform these experiments. + +At the IPCL, the strain of Ae. aegypti and Ae. albopictus originated respectively from Juazeiro, Brazil in 2012 (provided by Biofabrica Moscamed, IAEA Collaborative Center) and Rimini, Italy in 2018 (provided by Centro Agricoltura Ambiente, IAEA Collaborative Center). These two strains were maintained at the IPCL in a \(264~\mathrm{m}^2\) container- based laboratory under controlled environmental conditions: the larval rearing room was maintained at \(28\pm 2^{\circ}\mathrm{C}\) , \(80\pm 10\%\) RH and the adult rearing room at \(26\pm 2^{\circ}\mathrm{C}\) , \(60\pm 10\%\) RH, with a 14:10 hour light: dark (L:D) photoperiod with 1- hour periods of simulated dawn and dusk in both rooms. Aedes mosquito eggs older than 2 weeks were obtained from mass- rearing procedures developed at the IPCL \(^{42,43}\) . Based on the egg hatch rate calculated from sub- samples of 100 eggs, batches of eggs corresponding to approximately 18,000 first instar larval (L1) were estimated following the method described by Zheng, et al. \(^{44}\) , weighed and then hatched separately in glass jam jars filled with \(700~\mathrm{mL}\) of boiled and cooled reverse osmosis water with the addition of \(10~\mathrm{mL}\) of larval FAO/IAEA diet \(^{42,45}\) . The larvae were reared into mass- rearing trays following the mass- rearing procedures developed by the IPCL \(^{42}\) . Larvae were reared with larval diet (4% w/v) + +<--- Page Split ---> + +composed of a combination of powdered tuna meal (50%), black soldier fly (35%) and brewer's yeast (15%). + +At IRD, Saint- Denis, Reunion Island, the strain of Aedes albopictus used in the experiments originated from Saint- Benoit, Reunion Island and was reared as adults in a climate- controlled room maintained at a temperature of \(27 \pm 2^{\circ}\mathrm{C}\) and \(75 \pm 1\%\) relative humidity; the light regime was LD 12:12 h photoperiod. For larval production, batches of four thousand first instar larvae were counted on day 0 into rearing trays (52x32x6 cm) containing 2 L of tap water. Larvae were reared at a room temperature of \(31^{\circ}\mathrm{C}\) and a photoperiod of 12:12 (L:D) and fed with 10,20,25,25 and 20 ml per tray of a solution at \(7.5\%\) (w:v) slurry of diet (50% ground rabbit- food and 50% ground fish- food Tetramin, Tetra, Germany) on days 0,1,2,3 and 4, respectively. Pupae appeared from the fifth to the seventh day. + +### 1.2 Experimental design + +At the IPCL, Ae. aegypti pupae were sexed mechanically using a Fay- Morlan glass plate separator46 as redesigned by Focks (John W. Hock Co., Gainesville, FL)47. With this method, the female contamination in males collected on the first tilting is generally \(1.11 \pm 0.27\%\) on the first day of tilting48. + +Additionally, samples of sorted male pupae were checked under a binocular microscope to measure the desired ratio. Batches of 3,000 male and female pupae were counted and left to emerge inside separate Budgorm cages \((30 \times 30 \times 30 \mathrm{cm})\) . Throughout emergence, the cages were monitored to remove females from the male batches and males from the female batches to achieve complete male and female separation. Adults were maintained with 10% sucrose solution supplied ad libitum in a 150 mL plastic cup containing a sponge. + +To study the sexual harassment of males on Aedes mosquito females without allowing potential density- dependent mortality, batches of 3,000 Ae. aegypti mosquitoes aged 0 to 1 day were placed in the Budgorm cages \((30 \times 30 \times 30 \mathrm{cm})\) at six male to female sex ratios (SR): SR= 3:7, + +<--- Page Split ---> + +SR = 1:3 (control, used in the colony maintained in mass-rearing conditions), SR = 10:1, SR = 23:2, SR = 49:1 and SR = 99:1. Every day at 10 am, these cages were monitored and mortality was recorded during 8 days (13 days in the preliminary trials on non-irradiated males). During preliminary trials, the cumulative mortality rate of females increased for batches with SR of 10:1; 23:2; 49:1; and 99:1 after eight days. It was thus decided to focus on SR of 99:1 and 49:1 to study the effects of sexual harassment in sterilized Aedes mosquitoes. Batches of sterile mosquitoes with a SR of 1:3 were again used as controls. Furthermore, one trial was organized for the Ae. albopictus Rimini strain using a SR of 99:1 and a SR of 1:3 as control (Extended Data Fig. 4). The batches of Ae. aegypti (both sexes) were irradiated at the adult stage at 60 Gy while the batches of Ae. albopictus at the pupal stage at 40 Gy. + +To assess the longevity of harassed females after separation from the males, 20 irradiated females from batches at the SR of 99:1, 20 irradiated females from batches at the SR of 1:3 and 20 irradiated virgin females of the same age were placed into cages \((15 \times 15 \times 15 \mathrm{cm}\) , Bugdorm.com, Taiwan) at the IPCL. Mortality checks were carried out daily over 14 days. + +At IRD, Saint- Denis, Reunion Island, Ae. albopictus pupae were sexed mechanically using standard metal sieves with a square- opening mesh through which males swim upward49. With this method, the female contamination in males collected is \(0.5 \pm 0.7\%\) (unpublished data). + +After sex separation, male pupae were allowed to emerge into Bugdorm cages (30 x 30 x 30 cm) with constant access to a 5% sucrose solution [w/v]. Female pupae were first isolated in tubes (5 per tube) to check the accuracy of the sexing at the emergence and then transferred into cages already containing males. Two treatments were repeated three times, a ratio of 100 : 1 (male : female) and a ratio of 50 : 1 with 3000 males and 30 females and 3000 males and 60 females respectively, in Bugdorm cages (30 x 30 x 30 cm) with constant access to a 5% sucrose solution. Control cages consisted of regular rearing cages with a ratio of 1 : 3 (male : female). + +<--- Page Split ---> + +Each treatment has been done with non-irradiated and irradiated males. Mortality checks were carried out daily and recorded over 8 days. + +To produce irradiated males, male and female pupae of more than 30- h- old were irradiated at 35 Gy during 5 minutes with an X- ray irradiator (Blood X- RAD 13- 19, Cegelec, France) at the Blood bank coordinated by Etablissement Français du Sang (EFS) located at the Bellepierre hospital, St Denis de La Réunion. The irradiated pupae were brought back to the lab and treated as described above. + +## 2. Mosquito recordings + +The strain of Ae. aegypti used for filming originated from Singapore and reared at the National Environment Agency- Environmental Health Institute (NEA- EHI) Singapore, mosquito production facility. The larvae were reared at a High Density Mosquito Rearing System (Orinno Technology, Singapore) at larvae density of 12,000 per tray containing 6 litres of water and maintained at an air temperature of \(29 \pm 1^{\circ}\mathrm{C}\) and \(85 \pm 5\%\) RH with a photoperiod of 12:12h L:D cycle. Aedes aegypti pupae were sexed mechanically using an Auto- Pupae Separation System (Orinno Technology, Singapore). Male and female pupae were placed into two separated Bugdorm cages ( \(30 \times 30 \times 30 \mathrm{cm}\) ) to allow emergence. Adults were supplied with \(10\%\) sucrose solution ad libitum. Adult mosquitoes with age of 5- 6 days post emergence were selected for filming via mouth aspirator. + +All footages were recorded by DJI OSMO pocket and Nikon D750 DSLR camera with Sigma 70mm F2.8 Macro lens. Two Yongnuo YN900 LED panel lights were used as light source. For Movie S1, two female Ae. aegypti adults were introduced into a Bugdorm cage ( \(30 \times 30 \times 30 \mathrm{cm}\) ) with 200 males. Footage was captured by manual tracking at 60 Frame Per Second (FPS) and down speed to 30FPS in the post editing. For Movie S2, a single female was knocked down by exposure to ethyl acetate and carefully sticking it to the head of pin with latex glue. The + +<--- Page Split ---> + +immobilized female was then placed into a Bugdorm cage \((30 \times 30 \times 30 \text{cm})\) with an additional 100 males for filming. Footage was captured at frame rate of 30 FPS. + +## 3. Semi-field trials + +## 3.1 Artificial bait (Austria) + +Mosquito strains, rearing, and irradiation + +Two mosquito laboratory strains of Ae. aegypti (FAO/IAEA, 2017, 2020) were used for these experiments. The strains were maintained following FAO- IAEA guidelines \(^{50}\) . Aedes aegypti strains originating from Brazil (Juazeiro) and Senegal (Dakar) were transferred to the IPCL from the insectary of Biofabrica Moscamed, Juazeiro, Brazil, and from the ISRA- LNERV, Dakar- Hann, Senegal in 2012 and 2021, respectively. + +The larval rearing period had controlled conditions of temperature of \(28 \pm 2^{\circ}\text{C}\) , \(80 \pm 10\%\) RH, and lighting of \(14:10 \text{h L:D}\) , including 1 h of dawn lighting and 1 h of dusk lighting for larval stages. Adults were separately maintained under \(26 \pm 2^{\circ}\text{C}\) , \(60 \pm 10\%\) RH, and \(14:10 \text{h light: dark}\) , including 1 h dawn and 1 h dusk. To perform the experiments, mosquitoes were reared following modified mass- rearing procedures developed at the IPCL \(^{51}\) . Pupae were collected and mechanically sex- separated using a semi- automatic pupal sex sorter (Wolbaki, China). + +Pupae were counted manually and placed in \(30 \times 30 \times 30 \text{cm}\) and \(15 \times 15 \times 15 \text{cm}\) Bugdorm cages for male and female mosquitoes, respectively. Pupae were aliquoted into \(600 \text{mL plastic cups}\) , each holding \(2,100 \text{male pupae}\) and into \(100 \text{mL plastic cups}\) (Medi- Inn, United Kingdom) each holding 25 female pupae. Adults were maintained with ad libitum access to a \(10\%\) (w/v) sucrose solution until the day of the irradiation. Mortality was assessed daily until the day of releases. + +<--- Page Split ---> + +Two- to- three- day- old male adults were exposed to 45 Gy using an X- ray blood irradiator (Raycell MK2) \(^{52}\) . Male adult mosquitoes were held in a cold room at \(4^{\circ}\mathrm{C}\) for ten min in compacted batches of \(100 / \mathrm{cm}^3\) (about 1,000 males / cell) to simulate mass- transport conditions prior to irradiation. Irradiated male mosquitoes were placed back into the cages with ad libitum access to a \(10\%\) (w/v) sucrose solution until testing day (Ecosphere, suppl. mov. 3). Approximately 24h prior to the releases, female mosquitoes were starved by removing the sugar solution from all cages. Two ratios of males to virgin females of 99:1 (1980: 20) and the control ratio 1:1 (20:20) were used with three cages each (technical repeats). + +Sexual harassment assay in large cages + +Experiments were conducted in six large cages \((1.80 \times 1.80 \times 1.80 \mathrm{~m}\) , Live Monarch, Boca Raton, USA) at the FAO/IAEA IPCL climate- controlled Ecosphere in Seibersdorf (Austria) under natural light, average temperatures of \(28 \pm 2^{\circ}\mathrm{C}\) and \(70 \pm 10\%\) RH (suppl. mov. 3). One tray \((30 \times 40 \times 8 \mathrm{~cm})\) containing 1 L tap water was provided in each cage with two \(100 \mathrm{~mL}\) plastic cups of \(10\%\) sugar solution. A stand made of wood was placed inside each cage to hold an Hemotek (Ltd Unit 5 Union Court Great Harwood Business Zone Blackburn BB6 7FD, United Kingdom) blood feeding plate \(^{53}\) as artificial bait. One blooding plate was filled up with \(100 \mathrm{~mL}\) fresh pig blood and was hung upside down. The Hemotek heating system was turned on for 30 min. The plate was placed half- way of the wooden stand at one meter above the floor and allowed females to feed easily. + +Five- to- six- day- old, irradiated males and virgin non- treated female mosquitoes were briefly knocked down for five to ten minutes at \(4^{\circ}\mathrm{C}\) prior to release. Mosquitoes were then transferred into \(100 \mathrm{~mL}\) plastic containers. Each container was labelled according to treatment or control groups. All the containers were then transferred to the Ecosphere and males were released into large cages. Females were released 30 min later where they were allowed to blood feed for two hours starting from 10 am. + +<--- Page Split ---> + +After 2h- exposure time, all females were recaptured separately from the treatment and the control cages using mechanical aspirator device54. The operator wore coverall protective suit and gloves preventing any biting from the females during collection. The number of recaptured females was recorded per cage. To assess the blood- feeding status of females, each recaptured female mosquito was crushed between two pieces of white paper and the visual presence/absence of blood was observed based on the blood stain. The number of blood- fed females was recorded per cage. In total, three technical replicates (cage) were prepared for the control sex ratio (males: virgin females) of 1:1 (20:20) and the treatment sex ratio of 99:1 (1980: 20). + +The full experiment was repeated four times. + +### 3.2 Human bait (China) + +Mosquito strains, rearing, and irradiation + +The female mosquito GUA line was collected from more than 10 field localities of Guangzhou City, China, and has been reared in the laboratory for less than one year ( \(< 12\) generations). The rearing conditions for GUA were described previously55. Briefly, about 300 first- instar larvae were reared in a plastic tray ( \(36 \mathrm{cm} \times 25 \mathrm{cm} \times 5 \mathrm{cm}\) ) with 1.5 L \(\mathrm{dH_2O}\) and bovine liver powder was supplied as larvae food. The establishment, mass- rearing, sex- separation and irradiation of HC mosquitoes were described previously56. + +Human Landing Catch in large cages + +We conducted a second experiment based on Human Landing Catch in China to assess whether male harassment can prevent blood feeding on humans in semi- field conditions. Wild type virgin Ae. albopictus (GUA strain) females were inseminated and 5- 6 days old. They were + +<--- Page Split ---> + +starvied for 24 hours before the experiment start. Irradiated HC males were virgin and 5- 6 days old. Irradiated HC males were released into semi- field cages \((1.80 \times 1.80 \times 1.80 \text{m}\) , containing two sugar water containers). GUA females were released 24 hours later into the semi- field cages. Male and female release numbers were 1980 versus 20 for the 99:1 ratio and 20 versus 20 for the 1:1 ratio. Ten minutes after releasing the females, an adult volunteer entered and sat on a chair in the middle of each cage. The collector exposed one of his legs from foot to knee and killed mosquitoes as soon as they landed on the exposed leg before they started feeding. Mosquito collection was conducted for 15 min for each cage and ratio. All collected females were removed and counted. After 15 min of collection, remaining mosquitoes were collected with an aspirator and females checked to see whether some females had blood meals. Three repeats were conducted with three different collectors managing one 99:1 and one 1:1 cage each. Collectors received appropriate information and gave their informed consent prior to participating in this study. + +## 4. Field trial + +### 4.1 Maintenance of mosquitoes + +We used the Ae. albopictus GT line (without Wolbachia infection) that can be distinguished from the wild Ae. albopictus (wAlbA and wAlbB double infections) via PCR/qPCR assays based on Wolbachia wsp gene. The GT line was maintained as previously described57. For routine colony maintenance, female mosquitoes were blood- fed on mice according to protocols approved by the Ethics Committee on Laboratory Animal Care of the Zhongshan School of Medicine, Sun Yat- sen University (No. 2017- 041). + +### 4.2 Mass-production and irradiation of GT males + +Mass- production of GT males included adult and larval rearing according to protocols described previously with slight modifications58,59. Approximately 15,000 female pupae and 5,000 male + +<--- Page Split ---> + +191 pupae (3:1 ratio of female to male) were placed into an adult cage \((90 \times 90 \times 30 \text{cm})\) . Adults were provided with a \(10\%\) sugar solution ad libitum. Sheep blood mixed with ATP was provided to females twice per rearing cycle. Oviposition cup was provided to the engorged females for laying eggs 48 h after each blood meal. Eggs were collected for 72 h and then matured for at least one week before hatching. After hatching, 4,000- 5,000 larvae were added to each tray \((51.5 \times 36.0 \times 5.5 \text{cm})\) and fed daily with larval food. At day 8, pupae mixed with larvae were collected and then separated by an automatic sex separator (Orinno Technology, Singapore). After sexing, 16,000 male pupae were transferred to a cage \((90 \times 90 \times 30 \text{cm})\) for emergence. The temperature was set at 27- 28 °C. Cotton soaked in \(10\%\) sugar solution was placed on top of the cage for mosquitoes to feed ad libitum. The average female contamination rate was \(0.05\%\) \((n = 30, \text{SE} = 0.02\%)\) in the sterile male release batches (Fig. S6c). Male mosquitoes at 2- 3- day old were immobilized and then packed in plastic dishes (diameter \(10 \text{cm} \times \text{height} 1.2 \text{cm}\) ) in a cooling room set at \(10 \text{°C}\) . Each plastic dish contained 5,000 male mosquitoes and was then placed in a PMMB canister. Each irradiated canister contained 3 dishes and two canisters were irradiated each time. The exposure was done in an X- ray irradiator (XL1606HD, NUCTECH, China) at a dose of 60 Gy with dose rates of 3.74 Gy/min or 7.33 Gy/min. The irradiator was configured with a cooling system to maintain the chamber temperature at \(10 \text{°C}\) , which ensured the immobilization of male mosquitoes during exposure without impacting their quality \(^{60- 62}\) . The irradiated male mosquitoes were recorded as IGT \(^{60 \text{Gy}}\) males. Exposing adult male mosquitoes to 60 Gy resulted in an average of \(99.0\%\) sterility \((n = 30, \text{SE} = 0.22\%)\) , Fig. S6c). + +### 4.3 Quality control + +One of the key quality control parameters for release of sterile males was the female contamination rate (FCR), which was monitored at the adult stage. Each batch of male adults was checked by randomly selecting 800- 1,000 of the mosquitoes for sex identification based + +<--- Page Split ---> + +on morphology. In addition, male sterility was monitored for each batch through egg hatch rate assessment. In details, 100 IGT \(_{60\mathrm {Gy}}\) males were allowed to mate with 100 virgin GT females. Blood feedings and egg collections were the same as mentioned above. Eggs from each blood meal were hatched and egg hatch rate was assessed as previously described \(^{57}\) . Egg hatch rate from crosses between 100 GT males and 100 virgin GT females was considered as fertile control. Male sterility was calculated as: Induced Sterility \((\mathrm {IS}\%=100\%-((\mathrm {Hs}/\mathrm {Hn})*100\%)\) where Hs was the egg hatch rate from the sterile control, and Hn was the egg hatch rate from the fertile control. + +## 4.4 Study area description + +The study site is located at the North Campus of Sun Yat- Sen University in Yuexiu District, Guangzhou, China (Latitude: \(23^{\circ }7^{\prime }39.74^{\prime \prime }\mathrm {N}\) , Longitude: \(113^{\circ }17^{\prime }22.07^{\prime \prime }\mathrm {E}\) ), covering an area of about 20.9 ha (Fig. 1a). The campus has a population of 4,750 people (mainly students and faculty) and is located in a bustling metropolitan area with parks, hospitals, and residential areas nearby. The west and south areas of the campus were selected as the control area (6.55 ha), the northeast was the release area (1.17 ha), and a buffer zone (4.87 ha) was set between the release and the control area (Fig. 1b). The average temperature in the study area was \(24.6^{\circ }\mathrm {C}\) in 2021 (Fig. 1c) and the annual precipitation was 1,511.4 mm with a rainy season between May and October (Fig. 1d). + +## 4.5 Pre-release monitoring of release and control areas + +Before release, Ae. albopictus populations were monitored using ovitraps every two weeks from \(8^{\mathrm {th}}\) March to \(17^{\mathrm {th}}\) August 2021. The number of ovitraps was 17 in the release area, 40 in the control area and 33 in the buffer area, respectively (Fig. 3b). The methods to place and collect ovitraps as well as hatch eggs were the same as described in \(^{56}\) . We also performed Human Landing Catch (HLC) to estimate the mosquito adult populations. There were two positions in + +<--- Page Split ---> + +the release area and 6 positions in the control area (Fig. 3b). The HLC was performed 4 times pre- release of sterile males. Briefly, well- protected volunteers stand in the selected position and used a locally manufactured hand- held electric aspirator to collect the adult mosquitoes fly around the performers for 15 mins. The collected mosquitoes were identified and counted by morphological characteristics. + +### 4.6 Field release of IGT \(_{60Gy}\) males + +IGT \(_{60Gy}\) males were maintained in a mobile- refrigerator set at \(10^{\circ}\mathrm{C}\) and transported from the mass- rearing factory to the study site by a van two times per week. The distance between the factory and the study site was about \(100\mathrm{km}\) . The release was performed at 13:00- 14:00 pm. During release, dishes were opened, and mosquitoes were allowed to fly away freely. Over \(95\%\) of mosquitoes could recover after transportation under chilling conditions. On average, 200,000 mosquitoes were released weekly, and a total of about 3- million mosquitoes were released from mid- August to end of November 2021. + +### 4.7 Monitoring population suppression. + +Throughout the period of IGT \(_{60Gy}\) male release, Ae. albopictus populations were monitored weekly by using ovitraps and BG- Sentinel traps (Biogents, Germany). The number of BG traps was 4 in the release area, 6 in the control area and 5 in the buffer area (Fig. 3b). The methods to place and run BG traps as well as count the number of mosquitoes were the same as described in \(^{56}\) . The average number of hatched eggs per ovitrap, in both release and control areas, was determined and used to measure population suppression efficiency at the larval stage. In addition, the average number of females in both release and control areas per BG trap was determined each week, and used to measure population suppression at the adult stage. Moreover, HLC was repeated three times to estimate the suppression efficiency at 11 weeks' post release of IGT \(_{60Gy}\) males. + +<--- Page Split ---> + +### 4.8 qPCR assays of Wolbachia infection + +Each captured adult mosquito was stored separately in a \(1.5\mathrm{mL}\) tube and maintained at \(- 20^{\circ}\mathrm{C}\) before Wolbachia detection. DNA was extracted according to the protocols of Fast Pure Cell/Tissue DNA Isolation Mini Kit (Vazyme, China). A \(20\mu \mathrm{L}\) qPCR reaction consisted of 1 \(\mu \mathrm{l}\) DNA template, \(10\mu \mathrm{L}\) qPCR 2X mix (Vazyme, China), \(8\mu \mathrm{L}\) nucleic- free water, \(0.5\mu \mathrm{L}\) primer- F, and \(0.5\mu \mathrm{L}\) primer- R. The specific- primers used for the assay were designed for Wolbachia wsp gene and consisted of wAlbB- F: ACGTTGGTGGTGCAACATTG; wAlbB- R: TAACGAGCACCAGCATAAGAC. The qPCR procedures (LightCycler 96, Roche) comprised 10 s at \(95^{\circ}\mathrm{C}\) , followed by 40 cycles of 10 s at \(95^{\circ}\mathrm{C}\) , 10 s at \(50^{\circ}\mathrm{C}\) , 10 s at \(72^{\circ}\mathrm{C}\) , and finally 10 s at \(95^{\circ}\mathrm{C}\) , 60 s at \(65^{\circ}\mathrm{C}\) , 1 s at \(97^{\circ}\mathrm{C}\) , 30 s in \(37^{\circ}\mathrm{C}\) to generate the melting curve for confirmation that the fluorescence detected was for the specific PCR product. The Wolbachia negative samples were considered as \(\mathrm{IGT}_{60\mathrm{Gy}}\) mosquitoes. + +## 5. Statistical analysis + +All statistical analyses were performed using R version 4.2.1 (https://cran.r- project.org) using RStudio 2022.07.1 (RStudio, Inc. Boston, MA, United States, 2016). Shapiro and Bartlett's tests were performed respectively to test the normality and to determine whether the variance in cumulative mortalities was the same for various sex ratios. The relationships between cumulative mortalities and the different sex ratios during the study period were analysed for each Aedes species. For this purpose, binomial linear mixed effect models were used with the assigned sex ratios as response variables and cumulative mortality rates as explanatory variable using the lme4 package63. The various sex ratios were then used as fixed effects and the repetitions as random effects. The generalized linear mixed models were fitted by maximum likelihood. For each species, the cumulative mortality curves were plotted by sex ratios using the ggplot package. The longevity of harassed, non- harassed and virgin Ae. aegypti females was analyzed using Kaplan- Meier survival analyses. The log- rank test (Mantel- Cox) + +<--- Page Split ---> + +was used to compare the level of survival between the different treatments (status of females) using the survival and survminer packages64. Two- tailed Wilcoxon matched- pairs signed rank test was used to compare the hatched eggs and the captured female adults via BG or HLC before and after the release of sterile males, between the released and control areas. The feeding rates and recapture rates of females in semi- field trials were analysed using binomial generalized linear mixed models fit by maximum likelihood (Laplace approximation) with the SR as fix factor and the repeats as random factors65. The odds ratio were computed using the emmeans function (in package emmeans)66. + +## Ethical statement + +The study involving Human Landing Catch in large cages received the approval to the Institutional Ethics Committee from Guangzhou University. The field trial on applying SIT for Aedes albopictus control has been reported to and approved by Zhongshan School of Medicine (ZSSM), SYSU before the release of sterile males in 2021. + +## References + +FAO/IAEA. Guidelines for mass rearing of Aedes mosquitoes. Version 1.0. (2019). Maiga, H. et al. Reducing the cost and assessing the performance of a novel adult mass- rearing cage for the dengue, chikungunya, yellow fever and Zika vector, Aedes aegypti (Linnaeus). PLoS Negl. Trop. Dis. 13, e0007775, doi:10.1371/journal.pntd.0007775 (2019). Zheng, M. L., Zhang, D. J., Damiens, D. D., Yamada, H. & Gilles, J. R. Standard operating procedures for standardized mass rearing of the dengue and chikungunya vectors Aedes aegypti and Aedes albopictus (Diptera: Culicidae) - I - egg quantification. Parasit Vectors 8, 42, doi:10.1186/s13071-014-0631-2 (2015). Mamai, W. et al. Black soldier fly (Hermetia illucens) larvae powder as a larval diet ingredient for mass-rearing Aedes mosquitoes. Parasite 26, 57 (2019). + +<--- Page Split ---> + +Fay, R. W. & Morlan, H. B. A mechanical device for separating the developmental stages, sexes and species of mosquitoes. Mosq. News 19, 144- 147 (1959). + +Focks, D. A. An improved separator for the developmental stages, sexes, and species of mosquitoes (Diptera: Culicidae). J. Med. Entomol. 17, 567- 568 (1980). + +Mamai, W. et al. Aedes aegypti larval development and pupal production in the FAO/IAEA mass- rearing rack and factors influencing sex sorting efficiency. Parasite & Vectors 27, 43, doi:10.1051/parasite/2020041 (2020). + +Sharma, V. P., Patterson, R. S. & Ford, H. R. A device for the rapid separation of male and female mosquito pupae. Bull. World Health Organ. 47, 429- 432 (1972). + +Maiga, H. et al. Guidelines for routine colony maintenance of Aedes mosquito species - Version 1.0. 18 (Vienna, 2017). + +Maiga, H. et al. Standardization of the FAO/IAEA Flight Test for Quality Control of Sterile Mosquitoes. Frontiers in Bioengineering and Biotechnology 10, 876675, doi:10.3389/fbioe.2022.876675 (2022). + +Gómez- Simuta, Y. et al. Characterization and dose- mapping of an X- ray blood irradiator to assess application potential for the sterile insect technique (SIT). Appl. Radiat. Isot. 176, 109859 (2021). + +Damiens, D. et al. Different blood and sugar feeding regimes affect the productivity of Anopheles arabiensis colonies (Diptera: Culicidae). Journal of medical entomology 50, 336- 343 (2013). + +Damiens, D. et al. Different blood and sugar feeding regimes affect the productivity of Anopheles arabiensis colonies (Diptera: Culicidae). Journal of medical entomology 50, 336- 343 (2013). + +Balestrino, F., Puggioli, A., Carrieri, M., Bouyer, J. & Bellini, R. Quality control methods for mosquito Sterile Insect Technique. PloS Negl. Trop. Dis. 11, e0005881 (2017). + +Li, Y. et al. Quality control of long- term mass- reared Aedes albopictus for population suppression. Journal of Pest Science 94, 1531- 1542 (2021). + +Zheng, X. et al. Incompatible and sterile insect techniques combined eliminate mosquitoes. Nature 572, 56- 61, doi:https://doi.org/10.1038/s41586- 019- 1407- 9 (2019). + +<--- Page Split ---> + +Zhang, D., Zheng, X., Xi, Z., Bourtzis, K. & Gilles, J. R. L. Combining the sterile insect technique with the incompatible insect technique: I- impact of Wolbachia infection on the fitness of triple- and double- infected strains of Aedes albopictus. PloS one 10, e0121126 (2015). Zhang, D. et al. Establishment of a medium- scale mosquito facility: optimization of the larval mass- rearing unit for Aedes albopictus (Diptera: Culicidae). Parasites & vectors 10, 569 (2017). + +Zhang, D. et al. Establishment of a medium- scale mosquito facility: tests on mass production cages for Aedes albopictus (Diptera: Culicidae). Parasites & vectors 11, 189 (2018). Culbert, N. J., Gilles, J. R. L. & Bouyer, J. Investigating the impact of chilling temperature on male Aedes aegypti and Aedes albopictus survival. PLoS ONE 14, e0221822, doi:https://doi.org/10.1371/journal.pone.0221822 (2019). + +Culbert, N. J. et al. A rapid quality control test to foster the development of the sterile insect technique against Anopheles arabiensis. Malar. J. 19, 1- 10 (2020). Zhang, D. et al. Toward implementation of combined incompatible and sterile insect techniques for mosquito control: Optimized chilling conditions for handling Aedes albopictus male adults prior to release. PloS Negl. Trop. Dis. 14, e0008561 (2020). + +Ime4 : Linear mixed- effects models using S4 classes, R package version 0.999375- 40/r1308 (2011). + +Kassambara, A., Kosinski, M., Biecek, P. & Fabian, S. Drawing Survival Curves using 'ggplot2'. R Package 'survminer' (2017). + +Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information- theoretic approach. 2nd edn, (Springer- Verlag, 2002). + +Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Emmeans: Estimated marginal means, aka least- squares means. R package version 0.9- 1 1, 3 (2018). + +<--- Page Split ---> +![PLACEHOLDER_39_0] + + +Extended Data Fig. 1. Cumulative mortality rate of non- irradiated Aedes mosquitoes exposed to three sex ratio (SR) (Males/Females) treatments (1:3=control, 49:1 and 99:1 for Aedes aegypti; and 1:3=control, 50:1 and 100:1 for Aedes albopictus) over 8 days. Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (ns = not significant ie p>0.5). + +<--- Page Split ---> +![PLACEHOLDER_40_0] + + +Extended Data Fig. 2. Cumulative mortality rate of non- irradiated Aedes aegypti exposed to six sex ratios (SR) (Males/Females) treatments (3:7, 1:3, 10:1, 23:2, 49:1, and 99:1) over 8 days during preliminary trials. Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (ns = not significant ie p>0.5). Mortality rate of female reached 14.5% (SD=3.9%) after 8 days in the 99:1 batch in comparison to 2.8% (SD=1.2%) in the 1:3 control group. + +<--- Page Split ---> +![PLACEHOLDER_41_0] + + +Extended Data Fig. 3. Cumulative mortality rate of non- irradiated Aedes aegypti exposed to three sex ratio (SR) (Males/Females) treatments (1:3=control, 49:1 and 99:1) over 13 days. Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (ns = not significant ie p>0.5). + +<--- Page Split ---> +![PLACEHOLDER_42_0] + + +Extended Data Fig. 4. Cumulative mortality rate of irradiated Aedes albopictus exposed to two sex ratio (SR) (Males/Females) treatments (1:3=control and 99:1) over 8 days (experiment conducted at ICPL). Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (ns = not significant ie p>0.5). Mortality of females reached 90% (SD=6.1) at 8 days for a ratio of 99:1 as compared to 17.3% (SD=4.1) in the control group. + +<--- Page Split ---> +![PLACEHOLDER_43_0] + + +Extended Data Fig. 5. Survival of female Aedes aegypti exposed to three treatments (harassed female, unharassed female and virgin female) over 14 days. Any difference was observed of survival between females previously exposed to males at a 1:3 or 99:1 ratio after their separation from the males + +<--- Page Split ---> +![PLACEHOLDER_44_0] + + +Extended Data Fig. 6. Mosquito population and the release of sterile males. a, Weekly number of hatched eggs per ovitrap in the release (green dashed lines) and control area (blue dashed lines) before release. A total of 17 ovitraps were used for monitoring in the release area and 40 in the control area. No significant difference was observed in the number of hatched eggs between release and control area (n=10, P=0.1055, Two- tailed Wilcoxon matched- pairs signed rank test). b, Number of female adults captured via HLC in the release (green histogram) and control area (blue histogram) before release. Two positions were selected to perform HLC in the release area and 6 positions in the control area. Four independent HLCs were performed. No significant difference was observed in the captured female adults via HLC (n=4, P>0.9999, Two- tailed Wilcoxon matched- pairs signed rank test). c, Sterile male mosquitoes were released twice per week for a total of about 3 million males. The average female contamination rate (red + +<--- Page Split ---> + +dashed lines) was \(0.053\%\) ( \(n=30\) , \(95\% CI\) : \(0.019\% -0.086\%\) ) and the male sterility (purple dashed lines) \(99.03\%\) ( \(n=30\) , \(95\% CI\) : \(98.59\% -99.47\%\) ) with 30 batches assessed in total. + +**Extended Data Table 1.** Effects of various sex-ratios on the cumulative mortality rates of non-irradiated *Aedes* mosquitoes based on the generalized linear mixed model fit by maximum likelihood using Binomial linear mixed effect models. + +
Aedes speciesSexEstimateStd. Errorz valuePr(>|z|)
Aedes aegyptiFemale(Intercept)4.620.2419.54< 2e-16 ***
Sex Ratio = 3:7-0.590.26-2.310.0211 *
Sex Ratio = 10:1-0.950.24-3.919.25e-05 ***
Sex Ratio = 23:2-1.250.23-5.368.28e-08 ***
Sex Ratio = 49:1-1.130.24-4.781.74e-06 ***
Sex Ratio = 99:1-1.790.22-8.039.92e-16 ***
Male(Intercept)4.300.1824.19<2e-16 ***
Sex Ratio = 3:70.380.281.360.17
Sex Ratio = 10:10.530.291.810.07
Sex Ratio = 23:20.170.260.650.51
Sex Ratio = 49:10.580.301.960.05
Sex Ratio = 99:10.430.281.510.13
Aedes albopictusFemale(Intercept)4.850.4710.39< 2e-16 ***
Sex Ratio = 50:1-2.620.46-5.671.47e-08 ***
Sex Ratio = 100:1-2.910.46-6.342.32e-10 ***
Male(Intercept)4.890.4111.92< 2e-16 ***
Sex Ratio = 50:1-2.260.42-5.368.42e-08 ***
Sex Ratio = 100:1-2.160.42-5.172.36e-07 ***
+ +Values were compared to the Sex Ratio = 1:3 [Control], *p-value < 0.05; ***p-value < 0.001, Std. Error= standard error + +<--- Page Split ---> + +**Extended Data Table 2.** Effects of various sex-ratios on the cumulative mortality rates of irradiated *Aedes* mosquitoes based on the generalized linear mixed model fit by maximum likelihood using Binomial linear mixed effect models. + +
Aedes speciesSexEstimateStd. Errorz valuePr(>|z|)
Aedes aegyptiFemale(Intercept)4.090.1822.93< 2e-16 ***
FemaleSex Ratio = 49:1-0.610.19-3.290.001 **
FemaleSex Ratio = 99:1-1.960.15-13.13< 2e-16 ***
Male(Intercept)3.880.1526.33< 2e-16 ***
MaleSex Ratio = 49:11.010.273.770.00016 ***
MaleSex Ratio = 99:10.270.171.580.115
Aedes albopictusFemale(Intercept)4.830.4710.30< 2e-16 ***
FemaleSex Ratio = 50:1-2.240.46-4.831.40e-06 ***
FemaleSex Ratio = 100:1-2.690.46-5.835.47e-09 ***
Male(Intercept)4.860.4211.67< 2e-16 ***
MaleSex Ratio = 50:1-2.370.42-5.621.91e-08 ***
MaleSex Ratio = 100:1-2.500.42-5.962.51e-09 ***
+ +Values were compared to the Sex Ratio = 1:3 [Control], **p-value < 0.01; ***p-value < + +0.001, Std. Error= standard error + +<--- Page Split ---> + +## Supplementary Information (SI) + +## 1. Mating harassment increases female mortality + +1. Supplementary Results + +At the beginning of the experiment, we monitored some of some non irradiated groups up to 13 days and mortality of females reached \(43.3\%\) (SD=4.7%) in the 99:1 batch in comparison to \(4.1\%\) (SD=1.7%) in the 1:3 control group (Extended Data Fig. 2, p-value \(< 10^{-3}\) ). + +In irradiated mosquitoes, the cumulative mortality rate of female Ae. aegypti increased with sex ratio and was \(26.7\%\) (SD = 14.0%) after 8 days for a male to female ratio of 99:1 as compared with a mortality rate of \(3.9\%\) (SD = 2.4%) in the control group (Fig. 1 and Extended Data Table 2, \(P< 10^{-3}\) ). Male Ae. aegypti mortality after 8 days did not increase with a male to female ratio of 99:1 (Extended Data Table 2, \(P = 0.115\) ) and was even lower than in the control group for a ratio of 49:1. + +The cumulative mortality of Ae. albopictus (Reunion strain) females was even higher as compared with Ae. aegypti and reached \(40.0\%\) (SD = 8.8%) after 8 days with a male to female ratio of 100:1 as compared with \(3.8\%\) in the control group (Fig. 1 and Extended Data Table 2, \(P< 10^{- 3}\) ). Again, the cumulative mortality of males significantly increased with increasing sex ratio in this species, reaching \(24.8\%\) (SD = 0.61%) and \(25.3\%\) (SD = 4.1%) after 8 days with male to female ratios of 50:1 and 100:1, respectively, as compared with \(2.9\%\) in the control group. Comparable results were obtained in a similar trial with another strain of Ae. albopictus (Rimini), except that the mortality of females reached \(90\%\) (SD = 6.1%) after 8 days with a female to male ratio of 99:1 as compared with \(17.3\%\) (SD = 4.1%) in the control group (Extended Data Fig. 4, p <10-3). + +<--- Page Split ---> + +## 2. Supplementary Discussion + +Female mosquitoes are compulsory blood feeders and hence, the pathogen- transmitting sex. Even when irradiated, female mosquitoes require regular blood meals after release and may therefore still contribute to the transmission of diseases despite being sterile'. This can only be avoided if accurate sex- separating systems that remove all female mosquitoes from the release batches are available 2. Different sexing techniques based on biological, genetic and transgenic approaches have been proposed for some mosquito species considered for SIT3,4. While most contemporary SIT programmes use mechanical devices to sex pupae, female contamination rates close to \(1\%\) , a threshold considered as the maximum acceptable contamination rate for release, are common5,6. The sex separation of Aedes mosquitoes is then carried out at the pupal stage, i.e., by using standard metal sieves with a square- opening mesh through which male Aedes swim upward, or by using the glass plate sex separation system. Given the substantial number of mosquitoes required for SIT, such methods are time- costly and require dedicated personnel to manually operate the sorting devices4. More recently, a sex- sorting pipeline including a mechanical pupal sieve, real- time adult visual inspection, a cloud- based machine learning classifier, and non- expert review has been described, but its cost- effectiveness remains uncertain7,8. + +When a predetermined threshold is agreed with the public health authorities, e.g., \(1\%\) , keeping the sterile males for 8 days might be an effective way of eliminating females instead of removing residual females manually or discarding the full batch of sterile males. Nevertheless, this would probably be cost- prohibitive in an operational programme. The feasibility of such action would require for instance, evaluating how long sterile males can be kept in the rearing facility without reducing their competitiveness. On La Réunion island, the competitiveness index of sterile male Ae. albopictus in semi- field conditions increased with the + +<--- Page Split ---> + +age of sterile males, from 0.14 one day after emergence to 0.53 after 5 days9. A similar result was observed in Mauritius10 but this would require field validation. + +## 2. Potential impact of mating harassment on female survival after release + +## 1. Supplementary results + +To assess the potential impact of mating harassment on female survival after release, we monitored female survival after separation from the sterile males in Aedes aegypti. Mating harassment did not reduce the survival of females in the 99:1 group in comparison to the control group (p > 0.05). However, mated females had a much lower survival rate than virgin females (Extended Data Fig. 4, p- value = 0.014). + +## References + +1 Guissou, E. et al. Effect of irradiation on the survival and susceptibility of female Anopheles arabiensis to natural isolates of Plasmodium falciparum. bioRxiv preprint (2020). 2 Lutrat, C. et al. Sex sorting for pest control: it's raining men! Trends Parasitol. 35, 649- 662 (2019). 3 Papathanos, P. A. et al. Sex separation strategies: past experience and new approaches. Malar J. 8(Suppl 2):S5, doi:10.1186/1475- 2875- 8- S2- S5 (2009). 4 Lutrat, C. et al. Combining two Genetic Sexing Strains allows sorting of non- transgenic males for Aedes genetic control. Communications Biology 6, 646, doi:10.1038/s42003- 023- 05030- 7 (2023). 5 WHO & IAEA. Guidance Framework for Testing the Sterile Insect Technique as a Vector Control Tool against Aedes- Borne Diseases, Geneva & Vienna. (2020). + +<--- Page Split ---> + +Bouyer, J., Yamada, H., Pereira, R., Bourtzis, K. & Vreysen, M. J. B. Phased Conditional Approach for Mosquito Management using the Sterile Insect Technique. Trends Parasitol. 36, 325- 336 (2020). + +Crawford, J. E. et al. Efficient production of male Wolbachia- infected Aedes aegypti mosquitoes enables large- scale suppression of wild populations. Nat. Biotechnol. in press: 1- 11. (2020). + +Bouyer, J., Maiga, H. & Vreysen, M. J. B. Assessing the efficiency of Verily's automated process for production and release of male Wolbachia- infected mosquitoes. Nat. Biotechnol., 1- 2 (2022). + +Oliva, C. F., Jacquet, M., Gilles, J., Lemperiere, G. & Maquart, P.- O., et al. The Sterile Insect Technique for Controlling Populations of Aedes albopictus (Diptera: Culicidae) on Reunion Island: Mating Vigour of Sterilized Males. PLoS ONE 7(11): e49414. doi:10.1371/journal.pone.0049414, doi:10.1371/journal.pone.0049414 (2012). + +Iyaloo, D. P., Oliva, C., Facknath, S. & Bheecarry, A. A field cage study of the optimal age for release of radio- sterilized Aedes albopictus mosquitoes in a sterile insect technique program. Entomol. Exp. Appl. in press (2019). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- MovieS1voiceovercut1.wmv- MovieS2voiceovercut1.wmv- MovieS3voiceovercut1.wmv + +<--- Page Split ---> diff --git a/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1_det.mmd b/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..2930b34840fb1876bec8c5b9661202bf765b9323 --- /dev/null +++ b/preprint/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1/preprint__0a660decf706a7bae16eaad20f5cee68c5f0e65b8e6c8aae0702e8860aab4ec1_det.mmd @@ -0,0 +1,824 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 936, 175]]<|/det|> +# Mating harassment may boost the effectiveness of the sterile insect technique for Aedes mosquitoes + +<|ref|>text<|/ref|><|det|>[[44, 195, 222, 240]]<|/det|> +Jeremy Bouyer bouyer@cirad.fr + +<|ref|>text<|/ref|><|det|>[[44, 268, 936, 312]]<|/det|> +Insect Pest Control Sub- Programme, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency (IAEA) https://orcid.org/0000- 0002- 1913- 416X + +<|ref|>text<|/ref|><|det|>[[44, 316, 940, 380]]<|/det|> +Dongjing Zhang Sun Yat- sen University - Michigan State University Joint Center of Vector Control for Tropical Diseases, Sun Yat- Sen University + +<|ref|>text<|/ref|><|det|>[[44, 385, 188, 425]]<|/det|> +Hamidou Maiga IAEA + +<|ref|>text<|/ref|><|det|>[[44, 431, 456, 520]]<|/det|> +Yongjun Li Guangzhou University Mame Thiemo Bakhoum IAEA https://orcid.org/0000- 0001- 6794- 4426 + +<|ref|>text<|/ref|><|det|>[[44, 525, 252, 567]]<|/det|> +Gang Wang Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[44, 572, 252, 612]]<|/det|> +Yan Sun Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[44, 618, 444, 658]]<|/det|> +David Damiens Institut de Recherche pour le Développement + +<|ref|>text<|/ref|><|det|>[[44, 664, 185, 704]]<|/det|> +Wadaka Mamai IAEA + +<|ref|>text<|/ref|><|det|>[[44, 711, 225, 750]]<|/det|> +Nanwintoum Somda IAEA + +<|ref|>text<|/ref|><|det|>[[44, 757, 191, 796]]<|/det|> +Thomas Wallner IAEA + +<|ref|>text<|/ref|><|det|>[[44, 803, 211, 842]]<|/det|> +Odet Bueno Masso IAEA + +<|ref|>text<|/ref|><|det|>[[44, 849, 186, 888]]<|/det|> +Claudia Martina IAEA + +<|ref|>text<|/ref|><|det|>[[44, 895, 160, 933]]<|/det|> +Simran Kotla IAEA + +<|ref|>text<|/ref|><|det|>[[44, 941, 192, 960]]<|/det|> +Hanano Yamada + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 46, 100, 63]]<|/det|> +IAEA + +<|ref|>text<|/ref|><|det|>[[45, 70, 676, 113]]<|/det|> +Lu Deng Environmental Health Institute, National Environment Agency + +<|ref|>text<|/ref|><|det|>[[45, 118, 675, 160]]<|/det|> +Cheong Huat Tan Environmental Health Institute https://orcid.org/0000- 0001- 6263- 9721 + +<|ref|>text<|/ref|><|det|>[[45, 164, 252, 205]]<|/det|> +Jiatian Guo Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[45, 211, 252, 251]]<|/det|> +Qingdeng Feng Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[45, 256, 252, 297]]<|/det|> +Junyan Zhang Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[45, 302, 252, 343]]<|/det|> +Xufei Zhao Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[45, 348, 252, 389]]<|/det|> +Dilinuer Paerhande Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[45, 394, 440, 436]]<|/det|> +Wenjie Pan SYSU Nuclear and Insect Biotechnology Co., + +<|ref|>text<|/ref|><|det|>[[45, 441, 252, 481]]<|/det|> +Yu Wu Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[45, 486, 252, 527]]<|/det|> +Xiaoying Zheng Sun Yat- sen University + +<|ref|>text<|/ref|><|det|>[[45, 532, 775, 575]]<|/det|> +Zhongdao Wu Zhongshan School of Medicine, Sun Yat- sen University, Guangzhou, 510080, China + +<|ref|>text<|/ref|><|det|>[[45, 579, 640, 621]]<|/det|> +Zhiyong Xi Michigan State University https://orcid.org/0000- 0001- 7786- 012X + +<|ref|>text<|/ref|><|det|>[[45, 625, 165, 665]]<|/det|> +Marc Vreysen IAEA + +<|ref|>text<|/ref|><|det|>[[45, 708, 285, 728]]<|/det|> +Biological Sciences - Article + +<|ref|>text<|/ref|><|det|>[[45, 746, 137, 765]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[45, 784, 320, 804]]<|/det|> +Posted Date: August 11th, 2023 + +<|ref|>text<|/ref|><|det|>[[45, 822, 475, 842]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3128571/v1 + +<|ref|>text<|/ref|><|det|>[[45, 860, 910, 903]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[45, 920, 531, 941]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 912, 88]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on March 4th, 2024. See the published version at https://doi.org/10.1038/s41467-024-46268-x. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 84, 880, 142]]<|/det|> +1 Mating harassment may boost the effectiveness of the sterile insect technique for Aedes mosquitoes + +<|ref|>text<|/ref|><|det|>[[75, 155, 870, 375]]<|/det|> +3 Dongjing Zhang \(^{1*}\) , Hamidou Maiga \(^{2*}\) , Yongjun Li \(^{3,4*}\) , Mame Thierno Bakhoum \(^{2,5*}\) , 4 Gang Wang \(^{1*}\) , Yan Sun \(^{1}\) , David Damiens \(^{6}\) , Wadaka Mamai \(^{2}\) , Nanwintoum Séverin 5 Bimbilé Somda \(^{2,7}\) , Thomas Wallner \(^{2}\) , Odet Bueno Masso \(^{2}\) , Claudia Martina \(^{2}\) , Simran 6 Singh Kotla \(^{2}\) , Hanano Yamada \(^{2}\) , Deng Lu \(^{8}\) , Cheong Huat Tan \(^{8}\) , Jiatian Guo \(^{1}\) , Qingdeng 7 Feng \(^{1}\) , Junyan Zhang \(^{1}\) , Xufei Zhao \(^{1}\) , Dilinuer Paerhande \(^{1}\) , Wenjie Pan \(^{9}\) , Yu Wu \(^{1}\) , 8 Xiaoying Zheng \(^{1}\) , Zhongdao Wu \(^{1}\) , Zhiyong Xi \(^{4,10}\) , Marc J.B. Vreysen \(^{2}\) , Jérémy 9 Bouyer \(^{2,11*}\) # + +<|ref|>text<|/ref|><|det|>[[67, 395, 853, 900]]<|/det|> +10 1 Chinese Atomic Energy Agency Center of Excellence on Nuclear Technology 11 Applications for Insect Control, Key Laboratory of Tropical Disease Control of the 12 Ministry of Education, Sun Yat-sen University, Guangzhou, China 13 2 Insect Pest Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in 14 Food and Agriculture, IAEA, Vienna, Austria 15 3 Department of Pathogen Biology, School of Medicine, Jinan University, Guangzhou, 16 China 17 4 Guangzhou Wolbaki Biotech Co., Ltd, Guangzhou, China 18 5 Institut Sénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de 19 Recherches Vétérinaires, BP 2057 Dakar, Sénégal 20 6 Institut de Recherche pour le Développement (IRD), UMR MIVEGEC 21 (CNRS/IRD/Université de Montpellier), IRD Réunion/GIP CYROI (Recherche Santé 22 Bio-innovation), Sainte Clotilde, Reunion Island- France 23 7 Unité de Formation et de Recherche en Science et Technologie (UFR/ST), Université + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 82, 886, 390]]<|/det|> +24 Norbert ZONGO (UNZ), BP 376 Koudougou, Burkina Faso 25 8 National Environment Agency, Singapore 26 9SYSU Nuclear and Insect Biotechnology Co., Ltd., Dongguan, China 27 10Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA 28 11UMR ASTRE, CIRAD, F- 34398 Montpellier, France 29 12These authors contributed equally to this work 30 13corresponding author: j.bouyer@iaea.org + +<|ref|>text<|/ref|><|det|>[[67, 460, 884, 875]]<|/det|> +The sterile insect technique (SIT) is based on the overflooding of a target population with released sterile males inducing sterility in the wild female population. The SIT has proven to be effective against several insect pest species of agricultural and veterinary importance and is under development for Aedes mosquitoes. Here, we show that the release of sterile males in high sterile male to wild female ratios may also impact the target female population through mating harassment. Under laboratory conditions, male to female ratios above 50 to 1 reduced the longevity of female Aedes mosquitoes by reducing their feeding success. Under semi-field conditions, blood uptake of females from an artificial host and biting rates on humans were also strongly reduced. Finally, in a field SIT trial conducted in a 1.17 ha area in China, the female biting rate was reduced by 80%, concurrent to a reduction of female mosquito density of 40% due to the swarming of males around humans attempting to mate with the female mosquitoes. This suggests that the SIT does not only suppress mosquito vector populations through the induction + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 878, 135]]<|/det|> +of sterility, but might also reduce disease transmission due to increased female mortality and lower host contact. + +<|ref|>text<|/ref|><|det|>[[115, 200, 883, 848]]<|/det|> +The SIT is based on the sequential release of sterile male insects over the target area where they will mate with the virgin native female insects1, resulting in the induction of sterility in the wild female population proportionally to the ratio of sterile to wild insects. This impairs the reproduction rate of the female population and as a result, fewer insects will be available in subsequent generations, reducing the density of the target population over time. The SIT has been successfully used to manage populations of various insect pests of agricultural, animal, or human health importance2, and more recently, there has been a renewed interest to develop and implement the SIT against mosquitoes3. Aedes mosquitoes are major vectors of viruses such as dengue, chikungunya, Zika and yellow fever that are severely impacting human health. Traditional vector control strategies such as the use of broad-spectrum insecticides have serious environmental drawbacks and sanitation through reduction or removal of mosquito breeding sites requires the collaboration of the resident human population and has limited impact4,5. In 2023, 42 SIT pilot projects were being implemented worldwide against mosquitoes6. Released males are attracted by hosts, including humans7, and can swarm around them in the search of mates, a behaviour that is exploited to monitor their density through the Human Landing Catch method8. Alternatively, they can be trapped using CO2- baited adult traps9. Continuous, inundative releases of sterile males, like those required for SIT, can lead to high sterile to wild male and male to female ratios, sometimes over 100 to 1, particularly when the target population is suppressed. Could such high sex ratios have some influence on the fitness of females? + +<|ref|>text<|/ref|><|det|>[[115, 857, 880, 907]]<|/det|> +Mating is an essential component of adult life for all species with sexual reproduction. In most insects, a single or a moderate number of matings are sufficient for females to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 81, 883, 563]]<|/det|> +maximize their reproductive success \(^{10 - 12}\) . Therefore, females generally prefer a lower mating rate than males \(^{13}\) and are often resistant or reluctant to re- mate \(^{14}\) . This apparent divergence leads males from a wide range of animal species to compel females to mate by coercion or harassment \(^{15}\) . As a consequence, a ratio of 10 sterile male Aedes aegypti to 1 female resulted in increased mortality of the females but did not impact the fitness of the surviving ones \(^{12}\) . Mating harassment is a form of sexual conflict where repeated attempts to copulate by the male can be costly for the female \(^{15}\) . These costs can be direct (effects on harassed females) or indirect (effects on descendants of harassed females) \(^{12}\) . Harassment behaviours are even more frequent when individuals are confined to closed environments, like a rearing cage in the laboratory. Under mass- rearing conditions for example, a reduced 1:3 male to female ratio is recommended to reduce mating harassment and maximize production in both Ae. albopictus \(^{16,17}\) and Ae. aegypti \(^{18}\) . The same applies to other insects like tsetse flies where a 1:4 male to female ratio increases female fecundity in Glossina fuscipes fuscipes and G. pallidipes \(^{19}\) . However, the effects of large sex ratios such as those observed during an SIT programme are largely unknown. + +<|ref|>text<|/ref|><|det|>[[115, 575, 881, 693]]<|/det|> +Here we explored the impact of mating harassment by sterile male mosquitoes on the survival and feeding success of Ae. albopictus and Ae. aegypti females under laboratory, semi- field and field conditions. We show that both parameters are strongly reduced by mating harassment. + +<|ref|>sub_title<|/ref|><|det|>[[115, 740, 560, 758]]<|/det|> +## Survival of mosquitoes caged at different sex ratios + +<|ref|>text<|/ref|><|det|>[[115, 780, 881, 900]]<|/det|> +We first observed the effect of high fertile male to female ratios in Ae. aegypti and Ae. albopictus in confined laboratory cages. In both species, increased male to female ratios were associated with higher mortality of the females and also of male Ae. albopictus (Extended Data Figs. 1, 2, 3). Even with a male to female ratio of 3:7, which is only slightly higher than the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 883, 333]]<|/det|> +control at 1:3, mortality of female Ae. aegypti significantly increased (Extended Data Fig. 2, Extended Data Table S1, \(P = 0.021\) ). Female mortality reached \(14.5\% \pm 3.9\%\) after 8 days under a male to female ratio of 99:1 as compared with \(2.8\% \pm 1.2\%\) in the control group (male to female ratio of 1:3). The impact of harassment on the survival of female Ae. albopictus was even more pronounced than in Ae. aegypti. A male to female ratio of 50:1 was enough to increase mortality of females significantly after 8 days (Extended Data Fig. 1, Extended Data Table 1, \(P = 1.47\mathrm{e}^{-08}\) ), i.e., \(38.9\% \pm 1.9\%\) , similarly to under a male to female ratio of 100:1, whereas in the control group mortality remained at \(1.5\%\) . + +<|ref|>text<|/ref|><|det|>[[115, 346, 883, 561]]<|/det|> +Fertile male Ae. aegypti did not experience increased mortality with increased sex ratios (Extended Data Figs. 1, 2, Extended Data Table 1, \(P > 0.05\) ). On the contrary, the mortality of male Ae. albopictus also increased with a male to female ratio of 50:1 after 8 days (Extended Data Fig. 1, Extended Data Table 1, \(P = 8.42\mathrm{e} - 08\) ), i.e. \(19.0\% \pm 4.2\%\) , similarly to the batch with a male to female ratio of 100:1, whereas in the control group mortality remained at \(2.9\%\) . This may be related to more male Ae. albopictus being more aggressive, but this will warrant further research. + +<|ref|>text<|/ref|><|det|>[[115, 575, 883, 890]]<|/det|> +A practical application is that the increase in female mortality could be used as an additional process to separate the sterile males from the females by keeping them for some days in the insectary following mechanical separation that results in \(1\%\) or more female contamination of the sterile male batches \(^{20}\) (see Supplementary Information). We thus repeated the same experiments with irradiated mosquitoes to assess whether similar results would be obtained. In general, irradiation exacerbated the negative impact of mating harassment (Fig. 1). Caging of sterile males and females under laboratory conditions at a sex ratio of 100:1 decreased the female contamination of the sterile male batches to \(\sim 0.6\%\) and \(0.7\%\) due mortality for female Ae. aegypti and Ae. albopictus, respectively, within the first eight days. When a predetermined threshold is agreed with the public health authorities, e.g., \(1\%^{20}\) , this + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 83, 881, 169]]<|/det|> +121 might be an effective way of eliminating females instead of removing residual females 122 manually or discarding the full batch of sterile males. Nevertheless, this would probably be 123 cost- prohibitive in an operational programme (see Supplementary Information). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 110, 856, 816]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 849, 856, 900]]<|/det|> +
Fig. 1. Cumulative mortality rate of irradiated Aedes mosquitoes exposed to three sex ratio (SR) (Males/Females) treatments (1:3 = control, 49:1 and 99:1 for Ae. aegypti; and 50:1 and
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 857, 333]]<|/det|> +100:1 for Ae. albopictus) over 8 days. Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (3 repeats). a) Cumulative mortality rate of Ae. aegypti females increased with sex ratio and was \(26.7\% \pm 14.0\%\) at 8 days for a ratio of 99:1 as compared to \(3.9\% \pm 2.4\%\) in the control group. b) In Ae. albopictus, the tendency was even stronger and the cumulative mortality reached \(40.0\% \pm \mathrm{SD} = 8.8\%\) at 8 days for a ratio of 100:1 as compared to \(3.8\%\) in the control group. In Fig (a) and (b), ns represents not significant; \\*\\* represents \(P< 0.01\) ; \\*\\*\\* represents \(P< 0.001\) . + +<|ref|>sub_title<|/ref|><|det|>[[116, 356, 574, 375]]<|/det|> +## What causes mortality in high male to female ratios? + +<|ref|>text<|/ref|><|det|>[[115, 397, 884, 777]]<|/det|> +To better understand the mechanisms leading to increased mortality, we filmed sexual interactions of the mosquitoes at a high resolution (1080P). Females were strongly harassed when sex ratios were biased towards males (see Suppl. Movie S1). At the highest male to female ratio of 99:1, females were completely prevented from feeding and were lying immobile at the bottom of the cage to escape further mating attempts from males who were aggregated around the females by groups of three to five individuals. Any attempt of females to escape attracted more males, probably induced by their wing beat. To verify this hypothesis, some females were glued on their back to a pin (see Suppl. Movie S2), and those females accepted two or three mates, but refused to re- mate thereafter. However, each time they were trying to escape and fly off, new males were attracted and were aggregating around them. In nature, such aggregates may drop to the ground, where they attract immediately predators, and again increase female mortality. + +<|ref|>text<|/ref|><|det|>[[115, 790, 881, 876]]<|/det|> +From these mosquito recordings, it was clear that feeding inhibition was the main factor increasing mortality in females. Although described here for the first time intra- specifically, this finding is consistent with the previous study21 showing feeding inhibition of female Ae. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 878, 135]]<|/det|> +aegypti by male Ae. albopictus. Interspecific mating of male Ae. albopictus with female Ae. aegypti actually occurs and is named satyrization22,23. + +<|ref|>sub_title<|/ref|><|det|>[[115, 182, 622, 201]]<|/det|> +## Mating harassment and feeding success in semi-field cages + +<|ref|>text<|/ref|><|det|>[[115, 223, 883, 406]]<|/det|> +We first explored the impact of a high irradiated males to non- irradiated female ratio on the feeding success of females on an artificial host (Hemotek). A male to female ratio of 99:1, reduced blood feeding success to \(1\% \pm 1\%\) as compared with \(16\% \pm 4\%\) at a male to female ratio of 1:1 (odds ratio 16.50, \(\mathrm{SE} = 9.98\) , \(P < 10^{- 4}\) ) (Fig. 2a). Male mosquitoes were observed forming swarms around the artificial hosts waiting to mate with a female attempting to take a blood meal thus reducing their feeding success (see Suppl. Movie S3). + +<|ref|>text<|/ref|><|det|>[[115, 420, 883, 602]]<|/det|> +A similar experiment was set up but now using a human host. When a collector exposed one of his legs from foot to knee (human bait) in a semi- field cage, and killed the female mosquitoes after landing on the exposed leg but before feeding began, the rate of caught females was reduced to \(38\%\) ( \(\mathrm{SE} = 6\%\) ) at a male to female ratio of 99 to 1 as compared to \(77\%\) ( \(\mathrm{SE} = 6\%\) ) with a male to female ratio of 1:1 (odds ratio 5.30, \(\mathrm{SE} = 2.15\) , \(P < 10^{- 4}\) ) (Fig. 2b). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 85, 873, 468]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[175, 469, 880, 488]]<|/det|> +
Fig. 2. Impact of mating harassment on feeding success in semi-field cages. a. Impact
+ +<|ref|>text<|/ref|><|det|>[[115, 500, 881, 683]]<|/det|> +of the male to female ratio on the engorgement rate of females on an artificial host (Hemotek). Fewer females were engorged in the male: female treatment ratio 99:1 as compared to the control ratio 1:1 \((n = 4\) , odds ratio 16.50, \(\mathrm{SE} = 9.98\) , \(P < 10^{- 4}\) ). b. Impact of the male to female ratio on the engorgement on the catch rate of females by a volunteer collector. Fewer females were collected when attempting to bite a human collector in the male: female treatment ratio of 99:1 as compared to the control ratio 1:1 \((n = 3\) , odds ratio 5.30, \(\mathrm{SE} = 2.15\) , \(P < 10^{- 4}\) ). + +<|ref|>text<|/ref|><|det|>[[115, 729, 880, 814]]<|/det|> +In both semi- field trials, mating harassment thus resulted in feeding inhibition. Aggregation of sterile males around human hosts during mosquito SIT programmes is well- known7,24. + +<|ref|>text<|/ref|><|det|>[[115, 860, 723, 879]]<|/det|> +Mating harassment and human landing catches under field conditions + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 883, 333]]<|/det|> +The data from an Ae. albopictus field trial conducted in the centre of Guangzhou, China, were used to investigate the existence of feeding inhibition in real settings (Fig. 3). Before the release of sterile males, ovitraps were deployed bi- weekly in both the release and the untreated site to collect baseline data from March to August 2021 (Extended Data Fig. 6a). In addition, the density of the adult female populations was estimated with Human Landing Catch (HLC) (Extended Data Fig. 6b). Before the beginning of the release, no significant difference was observed in the number of hatched eggs per ovitrap and number of females caught with HLC in the untreated and release areas (Extended Data Fig. 6a, 6b). + +<|ref|>image<|/ref|><|det|>[[139, 390, 877, 692]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 707, 883, 888]]<|/det|> +
Fig. 3. Study site and climatic conditions. a, Satellite maps of field site in Guangzhou city (map data: Google, DigitalGlobal). Release area outlined with green while control and buffer areas are outlined with blue and orange in the satellite image respectively. b, Spatial distribution of the monitoring tools/methods. Grey points represent ovitraps, blue points represent BG traps, and the purple points represent the positions to perform Human Landing Catch. c, d, Daily average temperature (c) and precipitation (d) in the study area from March to November 2021.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 115, 883, 563]]<|/det|> +On \(13^{\text{th}}\) August 2021, the release of sterile male mosquitoes was initiated at a frequency of twice per week. During a period of 15 weeks, a total of 3 million male mosquitoes were released (Extended Data Fig. 6c). Aedes albopictus populations were monitored weekly with ovitraps, adult-collecting BG traps and irregular HLC. During the release period, the mosquito population was reduced in the release area by \(47.56\%\) and \(35.96\%\) as measured in the ovitraps and the BG traps, respectively, in comparison with the untreated area (Figs. 4a, 4b). From \(6^{\text{th}}\) September to \(8^{\text{th}}\) November, the efficiency of suppression was maintained at an average rate of \(60.53\%\) (min to max: \(39.03\% - 86.07\%\) ) in the ovitrap catches. However, the suppression efficiency showed large variations after 8 November, and this might be attributed to the low ambient temperatures (12- 22 °C) (Fig. 3b) or to possible immigration of fertile females in the release area in view of its small size (1.17 ha). The temporal fluctuations of adult females were similar to the larval samples, i.e., an average suppression of \(47.2\%\) (min to max: \(34.62\% - 92.5\%\) ) for the period 15 September to 2 December (excluding the data collected on 29 to 30 September) (Fig. 4b). + +<|ref|>text<|/ref|><|det|>[[112, 575, 883, 890]]<|/det|> +We compared the sex ratio obtained by BG traps and HLC from \(3^{\text{rd}}\) to \(6^{\text{th}}\) November (11 weeks after the first release of sterile males), and a higher sex ratio was found in HLC than in the BG traps (70.5:1 vs 16.6:1, Fig. 4d). Quantitative polymerase chain reaction (qPCR) targeting Wolbachia wsp gene indicated that over \(95\%\) of caught males with BG traps or HLC were the released sterile males (Fig. 4e). In HLC, the sex ratio was close to the experimental set- up in our lab and semi- field studies presented above. An average of 0.5 adult females were collected in the release area versus 2.8 females in the untreated area using HLC. This indicated a suppression of \(>82.0\%\) of adult females, a much higher suppression rate than what was observed with BG traps during the same period (42.3% during 3rd- 4th November, Fig. 4d). The higher suppression rate obtained with the HLC might possibly be due to the high + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 883, 266]]<|/det|> +overflooding rate of males surrounding the catchers, which could have prevented the approach of female mosquitoes by the sterile males, as was observed in the semi- field trial. In Aedes species, males are known to swarm around the hosts using pheromonal and acoustic cues, presumably to intercept females attempting to feed25- 27. Male Ae. albopictus are particularly attracted to humans7 and our results show that they aggregated in higher numbers around humans than BG traps. + +<|ref|>image<|/ref|><|det|>[[120, 284, 870, 616]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 640, 881, 887]]<|/det|> +
Fig. 4. Suppression efficiency of mosquito populations after sterile male releases. a, Dynamics of larval suppression. Larval reduction is observed in the release area as compared to the control area ( \(n = 14\) , \(P = 0.0023\) , Two-tailed Wilcoxon matched-pairs signed rank test). b, Dynamics of adult female suppression. A total of 4 BG traps in the release area and 6 in the control area. Female reduction is observed in the release area ( \(n = 16\) , \(P = 0.0107\) , Two-tailed Wilcoxon matched-pairs signed rank test). The red dotted lines indicate the suppression efficiency in both (a) and (b). c, Number of female adults captured via Human Landing Catch (HLC) in the release and control areas after 11 weeks of release. Two positions were selected to perform HLC in
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 83, 882, 398]]<|/det|> +the release area and 6 positions in the control area. Three replicates were performed. An average of 0.5 females were collected in the release area while 2.8 females were collected in the control area. d, Relation between the suppression efficiency and ratio of males to females. An average of \(82.86\%\) suppression of adult female was achieved via HLC on \(3^{\text{rd}}\) and \(4^{\text{th}}\) November with a 70.5 ratio of males to females, while \(42.31\%\) reduction of adult females was observed via BG trapping on 3- 4 November (indicated by black arrow in (b)) with a 16.6 ratio of males to females. e, Proportion of sterile males in the collected males via HLC and BG trapping. In both collecting methods, over \(95\%\) of collected males (HLC: 39/40; BG: 88/92) are sterile males, which were identified through qPCR based on the wsp gene of Wolbachia. The Wolbachia- negative samples were considered as the released sterile males. + +<|ref|>text<|/ref|><|det|>[[114, 454, 883, 900]]<|/det|> +In various insect species, mating harassment is associated with costs that negatively affect the physical condition and hence, longevity of females, either through physical damage \(^{28,29}\) or toxic effects from the accessory gland secretions \(^{30,31}\) . In this study, however, females that were exposed to males at a 1:3 or 99:1 ratio and that were separated from the males immediately after the mating (Extended Data Fig. 5), did not show any increase in mortality. This would indicate that depletion of energy reserves and reduced feeding success were the main factors that reduced their longevity, as observed in other studies where reduced fertility was also documented \(^{11,32}\) . Similar results were observed in other species when sex ratios were strongly biased toward males, although to a lesser extent, like in the tsetse fly G. morsitans morsitans \(^{33}\) , in the dung fly Sepsis cynipsea \(^{34}\) , and the field cricket Gryllus bimaculatus \(^{35}\) . Prevention of copulation by blocking or damaging the external genitalia of male tsetse flies resulted in reduced longevity of females caged with them, suggesting that the reduced female survival resulted from the physical aspects of male harassment rather than by components of the ejaculate \(^{33}\) . In addition, male tsetse flies have a shorter lifespan due to being engaged in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 234]]<|/det|> +mating harassment of the females, as was likewise observed in our study in Ae. aegypti. Like in tsetse, Ae. aegypti female mortality was increased equally by caging them with males that had modified claspers to prevent mating or unmodified males12. These authors even suggest potential benefits (higher fitness) obtained from ejaculate components, a common phenomenon in insects that is considered as part of nuptial feeding36. + +<|ref|>text<|/ref|><|det|>[[115, 247, 883, 594]]<|/det|> +The SIT is generally combined with other methods in an integrated pest management approach to first suppress the target population to a level low enough that sufficient sterile to wild male ratios can be obtained to induce enough sterility in the female wild population, e.g. in Aedes mosquitoes6 or tsetse37. Hence, high sex ratios are not uncommon in SIT field trials. In operational tsetse fly SIT programmes, sterile to wild male ratios up to 100 were observed in some cases37,38. The sterile to wild male ratio peaked at 50 to 1 in another successful suppression program against Ae. albopictus in China39. One of the main benefits of the SIT is its inverse density- dependent properties40 or in other words, the sterile to wild male ratio increases with each generation and with the rate of suppression and this can drive an insect population to extinction38. Our data show that feeding inhibition of the females might act synergistically to the induction of sterility in the female population. + +<|ref|>sub_title<|/ref|><|det|>[[117, 642, 217, 658]]<|/det|> +## Conclusion + +<|ref|>text<|/ref|><|det|>[[115, 673, 883, 889]]<|/det|> +Overall, our results allow us to propose two new additional mechanisms contributing to the efficiency of the SIT against mosquito- borne diseases. First, we hypothesize that high male to female ratio increases female mortality through feeding inhibition thus directly reducing female lifespan. Second, at high male to female ratios, males reduce female feeding success and biting rate (and hence transmission rate). The SIT may thus directly reduce disease transmission at high male to female ratios through an impact on two critical components of vectorial capacity, namely female longevity and host contact41. This may as well occur in all + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 83, 882, 202]]<|/det|> +286 genetic control methods based on inundative release of males, like the incompatible insect technique39,42 or RIDL43 or even those driving maleness into wild populations44. These hypotheses warrant more field research to assess the impact of these mechanisms on disease transmission. + +<|ref|>sub_title<|/ref|><|det|>[[111, 258, 312, 276]]<|/det|> +## Authors contributions + +<|ref|>text<|/ref|><|det|>[[110, 298, 872, 648]]<|/det|> +291 J. Bouyer, D.Z., and Z.X. developed the concept and methodology; H. Maiga, M.T.B., D.D., W. M., N.S.B.S., T.W., O.B.M., C.M. and S.S.K performed the lab experiments; Y. Li, H.M., W.M., N.S.B.S. and H.Y. performed the semi-field experiments; D. Zhang, G.W., Y.S., J.G., Q.F., J.Z., X. Zhao, D.P., W.P., Y.W., X. Zheng, and Z.W. performed the field trial, D. Lu, C.H.T. and J.B. performed the movies; J. Bouyer, D.Z., C.H.T., Y.W., Z.X. and M.J.B.V. performed coordination for the project; D. Zhang obtained regulatory approvals for mosquito releases; Z. Xi obtained the ethical permit for the semi-field trial involving human bait; J. Bouyer provided oversight of the project and contributed to all experimental designs, data analysis and data interpretation; J. Bouyer, D.Z., Y.L., D.D., C.M., D.L., Z.X. and M.J.B.V. wrote the manuscript. All authors participated in manuscript editing and final approval. + +<|ref|>text<|/ref|><|det|>[[111, 670, 558, 688]]<|/det|> +Supplementary Information is available for this paper. + +<|ref|>text<|/ref|><|det|>[[111, 711, 794, 730]]<|/det|> +Correspondence and requests for materials should be addressed to JB. Reprints and + +<|ref|>text<|/ref|><|det|>[[111, 745, 650, 763]]<|/det|> +permissions information is available at www.nature.com/reprints. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 231, 104]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[175, 130, 870, 181]]<|/det|> +Dyck, V. A., Hendrichs, J. & Robinson, A. S. Sterile insect technique: principles and practice in area- wide integrated pest management. (CRC press, 2021). + +<|ref|>text<|/ref|><|det|>[[175, 195, 850, 275]]<|/det|> +Vreysen, M. J. B. & Klassen, W. in Sterile Insect Technique. Principles and Practice in Area- Wide Integrated Pest Management (eds A. Dyck, J. Hendrichs, & A.S. Robinson) 75- 112 (CRC Press, 2021). + +<|ref|>text<|/ref|><|det|>[[175, 290, 864, 370]]<|/det|> +Lees, R. S., Carvalho, D. O. & Bouyer, J. in Sterile Insect Technique. Principles and Practice in Area- Wide Integrated Pest Management. (eds A.V. Dyck, J. Hendrichs, & A. S. 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Acta Trop. 132: S12- S19 (2014). + +<|ref|>text<|/ref|><|det|>[[174, 306, 860, 356]]<|/det|> +Jaenson, T. G. Attraction to mammals of male mosquitoes with special reference to Aedes diantaeus in Sweden. J. Am. Mosq. Control Assoc. 1, 195- 198 (1985). + +<|ref|>text<|/ref|><|det|>[[174, 370, 875, 420]]<|/det|> +Cabrera, M. & Jaffe., K. An aggregation pheromone modulates lekking behavior in the vector mosquito Aedes aegypti (Diptera: Culicidae). J. Am. Mosq. Control Assoc. 23, 1- 10 (2007). + +<|ref|>text<|/ref|><|det|>[[174, 433, 870, 512]]<|/det|> +Cator, L. J., Arthur, B. J., Ponlawat, A. & Harrington, L. C. Behavioral observations and sound recordings of free- flight mating swarms of Ae. aegypti (Diptera: Culicidae) in Thailand. J. Med. Entomol. 48, 941- 946 (2011). + +<|ref|>text<|/ref|><|det|>[[174, 528, 864, 578]]<|/det|> +Crudgington, H. S. & Siva- Jothy, M. T. Genital damage, kicking and early death. 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American Naturalist 151, 46- 58 (1998). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[175, 84, 856, 163]]<|/det|> +Clutton- Brock, T. & Langley, P. Persistent courtship reduces male and female longevity in captive tsetse flies Glossina morsitans morsitans Westwood (Diptera: Glossinidae). Behav. Ecol. 8, 392- 395 (1997). + +<|ref|>text<|/ref|><|det|>[[175, 179, 850, 228]]<|/det|> +Muhlhauser, C. & Blanckenhorn, W. U. The cost of avoiding matings in the dung fly Sepsis cynipsea. Behav. Ecol. 13, 359- 365 (2002). + +<|ref|>text<|/ref|><|det|>[[175, 243, 850, 323]]<|/det|> +Bateman, P. W., Ferguson, J. W. H. & Yetman, C. A. Courtship and copulation, but not ejaculate, reduce the longevity of female field crickets (Gryllus bimaculatus). J. Zool. 268, 341- 346 (2006). + +<|ref|>text<|/ref|><|det|>[[175, 339, 869, 387]]<|/det|> +Vahed, K. The function of nuptial feeding in insects: a review of empirical studies. Biological reviews 73, 43- 78 (1998). + +<|ref|>text<|/ref|><|det|>[[175, 403, 857, 454]]<|/det|> +Dicko, A. H. et al. Using species distribution models to optimize vector control: the tsetse eradication campaign in Senegal. Proc. Natl. Acad. Sci. U.S.A. 111, 10149- 10154 (2014). + +<|ref|>text<|/ref|><|det|>[[175, 468, 868, 548]]<|/det|> +Vreysen, M. J. B., Saleh, K. M., Lancelot, R. & Bouyer, J. Factory tsetse flies must behave like wild flies: a prerequisite for the sterile insect technique. PLoS Negl. Trop. Dis. 5(2): e907. (2011). + +<|ref|>text<|/ref|><|det|>[[175, 563, 866, 612]]<|/det|> +Zheng, X. et al. Incompatible and sterile insect techniques combined eliminate mosquitoes. Nature 572, 56- 61, doi:https://doi.org/10.1038/s41586- 019- 1407- 9 (2019). + +<|ref|>text<|/ref|><|det|>[[175, 627, 872, 707]]<|/det|> +Feldmann, U. & Hendrichs, J. Integrating the sterile insect technique as a key component of area- wide tsetse and trypanosomiasis intervention. Vol. 3, Food and Agriculture Organization (2001). + +<|ref|>text<|/ref|><|det|>[[175, 722, 874, 770]]<|/det|> +Kramer, L. D. & Ciota., A. T. Dissecting vectorial capacity for mosquito- borne viruses. Current opinion in virology 15, 112- 118 (2015). + +<|ref|>text<|/ref|><|det|>[[175, 786, 855, 864]]<|/det|> +Crawford, J. E. et al. Efficient production of male Wolbachia- infected Aedes aegypti mosquitoes enables large- scale suppression of wild populations. Nat. Biotechnol. 38, 482- 492 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 84, 878, 199]]<|/det|> +405 43 Carvalho, D. O. et al. Suppression of a field population of Aedes aegypti in Brazil by sustained release of transgenic male mosquitoes. PloS Negl. Trop. Dis. 9, e0003864 (2015). 407 44 Adelman, Z. N. & Tu, Z. Control of Mosquito-Borne Infectious Diseases: Sex and Gene Drive. 408 Trends Parasitol. 32, 219-229 (2016). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[75, 84, 207, 102]]<|/det|> +## 1 Methods + +<|ref|>sub_title<|/ref|><|det|>[[117, 131, 289, 148]]<|/det|> +## 1. Laboratory trials + +<|ref|>text<|/ref|><|det|>[[115, 171, 878, 291]]<|/det|> +All experiments on Ae. aegypti were carried out at the Insect Pest Control Laboratory (ICPL), IAEA, Vienna, Austria whereas experiments on Ae. albopictus were conducted independently at IRD, Saint- Denis, La Reunion Island, except one preliminary experiment on Ae. albopictus also conducted at IPCL (Extended Data Fig. 4). + +<|ref|>sub_title<|/ref|><|det|>[[118, 313, 461, 331]]<|/det|> +### 1.1 Mosquito colonies and mass-rearing + +<|ref|>text<|/ref|><|det|>[[115, 355, 881, 406]]<|/det|> +Three established mosquito colonies of Ae. aegypti and Ae. albopictus were used to perform these experiments. + +<|ref|>text<|/ref|><|det|>[[112, 419, 884, 867]]<|/det|> +At the IPCL, the strain of Ae. aegypti and Ae. albopictus originated respectively from Juazeiro, Brazil in 2012 (provided by Biofabrica Moscamed, IAEA Collaborative Center) and Rimini, Italy in 2018 (provided by Centro Agricoltura Ambiente, IAEA Collaborative Center). These two strains were maintained at the IPCL in a \(264~\mathrm{m}^2\) container- based laboratory under controlled environmental conditions: the larval rearing room was maintained at \(28\pm 2^{\circ}\mathrm{C}\) , \(80\pm 10\%\) RH and the adult rearing room at \(26\pm 2^{\circ}\mathrm{C}\) , \(60\pm 10\%\) RH, with a 14:10 hour light: dark (L:D) photoperiod with 1- hour periods of simulated dawn and dusk in both rooms. Aedes mosquito eggs older than 2 weeks were obtained from mass- rearing procedures developed at the IPCL \(^{42,43}\) . Based on the egg hatch rate calculated from sub- samples of 100 eggs, batches of eggs corresponding to approximately 18,000 first instar larval (L1) were estimated following the method described by Zheng, et al. \(^{44}\) , weighed and then hatched separately in glass jam jars filled with \(700~\mathrm{mL}\) of boiled and cooled reverse osmosis water with the addition of \(10~\mathrm{mL}\) of larval FAO/IAEA diet \(^{42,45}\) . The larvae were reared into mass- rearing trays following the mass- rearing procedures developed by the IPCL \(^{42}\) . Larvae were reared with larval diet (4% w/v) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 82, 883, 130]]<|/det|> +composed of a combination of powdered tuna meal (50%), black soldier fly (35%) and brewer's yeast (15%). + +<|ref|>text<|/ref|><|det|>[[111, 145, 884, 430]]<|/det|> +At IRD, Saint- Denis, Reunion Island, the strain of Aedes albopictus used in the experiments originated from Saint- Benoit, Reunion Island and was reared as adults in a climate- controlled room maintained at a temperature of \(27 \pm 2^{\circ}\mathrm{C}\) and \(75 \pm 1\%\) relative humidity; the light regime was LD 12:12 h photoperiod. For larval production, batches of four thousand first instar larvae were counted on day 0 into rearing trays (52x32x6 cm) containing 2 L of tap water. Larvae were reared at a room temperature of \(31^{\circ}\mathrm{C}\) and a photoperiod of 12:12 (L:D) and fed with 10,20,25,25 and 20 ml per tray of a solution at \(7.5\%\) (w:v) slurry of diet (50% ground rabbit- food and 50% ground fish- food Tetramin, Tetra, Germany) on days 0,1,2,3 and 4, respectively. Pupae appeared from the fifth to the seventh day. + +<|ref|>sub_title<|/ref|><|det|>[[116, 443, 327, 461]]<|/det|> +### 1.2 Experimental design + +<|ref|>text<|/ref|><|det|>[[111, 485, 884, 601]]<|/det|> +At the IPCL, Ae. aegypti pupae were sexed mechanically using a Fay- Morlan glass plate separator46 as redesigned by Focks (John W. Hock Co., Gainesville, FL)47. With this method, the female contamination in males collected on the first tilting is generally \(1.11 \pm 0.27\%\) on the first day of tilting48. + +<|ref|>text<|/ref|><|det|>[[111, 615, 884, 799]]<|/det|> +Additionally, samples of sorted male pupae were checked under a binocular microscope to measure the desired ratio. Batches of 3,000 male and female pupae were counted and left to emerge inside separate Budgorm cages \((30 \times 30 \times 30 \mathrm{cm})\) . Throughout emergence, the cages were monitored to remove females from the male batches and males from the female batches to achieve complete male and female separation. Adults were maintained with 10% sucrose solution supplied ad libitum in a 150 mL plastic cup containing a sponge. + +<|ref|>text<|/ref|><|det|>[[111, 813, 884, 899]]<|/det|> +To study the sexual harassment of males on Aedes mosquito females without allowing potential density- dependent mortality, batches of 3,000 Ae. aegypti mosquitoes aged 0 to 1 day were placed in the Budgorm cages \((30 \times 30 \times 30 \mathrm{cm})\) at six male to female sex ratios (SR): SR= 3:7, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 80, 885, 401]]<|/det|> +SR = 1:3 (control, used in the colony maintained in mass-rearing conditions), SR = 10:1, SR = 23:2, SR = 49:1 and SR = 99:1. Every day at 10 am, these cages were monitored and mortality was recorded during 8 days (13 days in the preliminary trials on non-irradiated males). During preliminary trials, the cumulative mortality rate of females increased for batches with SR of 10:1; 23:2; 49:1; and 99:1 after eight days. It was thus decided to focus on SR of 99:1 and 49:1 to study the effects of sexual harassment in sterilized Aedes mosquitoes. Batches of sterile mosquitoes with a SR of 1:3 were again used as controls. Furthermore, one trial was organized for the Ae. albopictus Rimini strain using a SR of 99:1 and a SR of 1:3 as control (Extended Data Fig. 4). The batches of Ae. aegypti (both sexes) were irradiated at the adult stage at 60 Gy while the batches of Ae. albopictus at the pupal stage at 40 Gy. + +<|ref|>text<|/ref|><|det|>[[110, 410, 885, 525]]<|/det|> +To assess the longevity of harassed females after separation from the males, 20 irradiated females from batches at the SR of 99:1, 20 irradiated females from batches at the SR of 1:3 and 20 irradiated virgin females of the same age were placed into cages \((15 \times 15 \times 15 \mathrm{cm}\) , Bugdorm.com, Taiwan) at the IPCL. Mortality checks were carried out daily over 14 days. + +<|ref|>text<|/ref|><|det|>[[110, 549, 885, 635]]<|/det|> +At IRD, Saint- Denis, Reunion Island, Ae. albopictus pupae were sexed mechanically using standard metal sieves with a square- opening mesh through which males swim upward49. With this method, the female contamination in males collected is \(0.5 \pm 0.7\%\) (unpublished data). + +<|ref|>text<|/ref|><|det|>[[110, 658, 885, 874]]<|/det|> +After sex separation, male pupae were allowed to emerge into Bugdorm cages (30 x 30 x 30 cm) with constant access to a 5% sucrose solution [w/v]. Female pupae were first isolated in tubes (5 per tube) to check the accuracy of the sexing at the emergence and then transferred into cages already containing males. Two treatments were repeated three times, a ratio of 100 : 1 (male : female) and a ratio of 50 : 1 with 3000 males and 30 females and 3000 males and 60 females respectively, in Bugdorm cages (30 x 30 x 30 cm) with constant access to a 5% sucrose solution. Control cages consisted of regular rearing cages with a ratio of 1 : 3 (male : female). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 82, 883, 133]]<|/det|> +Each treatment has been done with non-irradiated and irradiated males. Mortality checks were carried out daily and recorded over 8 days. + +<|ref|>text<|/ref|><|det|>[[111, 156, 884, 308]]<|/det|> +To produce irradiated males, male and female pupae of more than 30- h- old were irradiated at 35 Gy during 5 minutes with an X- ray irradiator (Blood X- RAD 13- 19, Cegelec, France) at the Blood bank coordinated by Etablissement Français du Sang (EFS) located at the Bellepierre hospital, St Denis de La Réunion. The irradiated pupae were brought back to the lab and treated as described above. + +<|ref|>sub_title<|/ref|><|det|>[[115, 331, 318, 349]]<|/det|> +## 2. Mosquito recordings + +<|ref|>text<|/ref|><|det|>[[111, 373, 884, 686]]<|/det|> +The strain of Ae. aegypti used for filming originated from Singapore and reared at the National Environment Agency- Environmental Health Institute (NEA- EHI) Singapore, mosquito production facility. The larvae were reared at a High Density Mosquito Rearing System (Orinno Technology, Singapore) at larvae density of 12,000 per tray containing 6 litres of water and maintained at an air temperature of \(29 \pm 1^{\circ}\mathrm{C}\) and \(85 \pm 5\%\) RH with a photoperiod of 12:12h L:D cycle. Aedes aegypti pupae were sexed mechanically using an Auto- Pupae Separation System (Orinno Technology, Singapore). Male and female pupae were placed into two separated Bugdorm cages ( \(30 \times 30 \times 30 \mathrm{cm}\) ) to allow emergence. Adults were supplied with \(10\%\) sucrose solution ad libitum. Adult mosquitoes with age of 5- 6 days post emergence were selected for filming via mouth aspirator. + +<|ref|>text<|/ref|><|det|>[[111, 709, 884, 892]]<|/det|> +All footages were recorded by DJI OSMO pocket and Nikon D750 DSLR camera with Sigma 70mm F2.8 Macro lens. Two Yongnuo YN900 LED panel lights were used as light source. For Movie S1, two female Ae. aegypti adults were introduced into a Bugdorm cage ( \(30 \times 30 \times 30 \mathrm{cm}\) ) with 200 males. Footage was captured by manual tracking at 60 Frame Per Second (FPS) and down speed to 30FPS in the post editing. For Movie S2, a single female was knocked down by exposure to ethyl acetate and carefully sticking it to the head of pin with latex glue. The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 82, 884, 135]]<|/det|> +immobilized female was then placed into a Bugdorm cage \((30 \times 30 \times 30 \text{cm})\) with an additional 100 males for filming. Footage was captured at frame rate of 30 FPS. + +<|ref|>sub_title<|/ref|><|det|>[[115, 159, 277, 176]]<|/det|> +## 3. Semi-field trials + +<|ref|>sub_title<|/ref|><|det|>[[115, 201, 351, 219]]<|/det|> +## 3.1 Artificial bait (Austria) + +<|ref|>text<|/ref|><|det|>[[115, 243, 450, 260]]<|/det|> +Mosquito strains, rearing, and irradiation + +<|ref|>text<|/ref|><|det|>[[113, 284, 884, 434]]<|/det|> +Two mosquito laboratory strains of Ae. aegypti (FAO/IAEA, 2017, 2020) were used for these experiments. The strains were maintained following FAO- IAEA guidelines \(^{50}\) . Aedes aegypti strains originating from Brazil (Juazeiro) and Senegal (Dakar) were transferred to the IPCL from the insectary of Biofabrica Moscamed, Juazeiro, Brazil, and from the ISRA- LNERV, Dakar- Hann, Senegal in 2012 and 2021, respectively. + +<|ref|>text<|/ref|><|det|>[[113, 457, 884, 641]]<|/det|> +The larval rearing period had controlled conditions of temperature of \(28 \pm 2^{\circ}\text{C}\) , \(80 \pm 10\%\) RH, and lighting of \(14:10 \text{h L:D}\) , including 1 h of dawn lighting and 1 h of dusk lighting for larval stages. Adults were separately maintained under \(26 \pm 2^{\circ}\text{C}\) , \(60 \pm 10\%\) RH, and \(14:10 \text{h light: dark}\) , including 1 h dawn and 1 h dusk. To perform the experiments, mosquitoes were reared following modified mass- rearing procedures developed at the IPCL \(^{51}\) . Pupae were collected and mechanically sex- separated using a semi- automatic pupal sex sorter (Wolbaki, China). + +<|ref|>text<|/ref|><|det|>[[113, 664, 884, 844]]<|/det|> +Pupae were counted manually and placed in \(30 \times 30 \times 30 \text{cm}\) and \(15 \times 15 \times 15 \text{cm}\) Bugdorm cages for male and female mosquitoes, respectively. Pupae were aliquoted into \(600 \text{mL plastic cups}\) , each holding \(2,100 \text{male pupae}\) and into \(100 \text{mL plastic cups}\) (Medi- Inn, United Kingdom) each holding 25 female pupae. Adults were maintained with ad libitum access to a \(10\%\) (w/v) sucrose solution until the day of the irradiation. Mortality was assessed daily until the day of releases. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 82, 884, 330]]<|/det|> +Two- to- three- day- old male adults were exposed to 45 Gy using an X- ray blood irradiator (Raycell MK2) \(^{52}\) . Male adult mosquitoes were held in a cold room at \(4^{\circ}\mathrm{C}\) for ten min in compacted batches of \(100 / \mathrm{cm}^3\) (about 1,000 males / cell) to simulate mass- transport conditions prior to irradiation. Irradiated male mosquitoes were placed back into the cages with ad libitum access to a \(10\%\) (w/v) sucrose solution until testing day (Ecosphere, suppl. mov. 3). Approximately 24h prior to the releases, female mosquitoes were starved by removing the sugar solution from all cages. Two ratios of males to virgin females of 99:1 (1980: 20) and the control ratio 1:1 (20:20) were used with three cages each (technical repeats). + +<|ref|>text<|/ref|><|det|>[[115, 355, 435, 372]]<|/det|> +Sexual harassment assay in large cages + +<|ref|>text<|/ref|><|det|>[[111, 396, 884, 710]]<|/det|> +Experiments were conducted in six large cages \((1.80 \times 1.80 \times 1.80 \mathrm{~m}\) , Live Monarch, Boca Raton, USA) at the FAO/IAEA IPCL climate- controlled Ecosphere in Seibersdorf (Austria) under natural light, average temperatures of \(28 \pm 2^{\circ}\mathrm{C}\) and \(70 \pm 10\%\) RH (suppl. mov. 3). One tray \((30 \times 40 \times 8 \mathrm{~cm})\) containing 1 L tap water was provided in each cage with two \(100 \mathrm{~mL}\) plastic cups of \(10\%\) sugar solution. A stand made of wood was placed inside each cage to hold an Hemotek (Ltd Unit 5 Union Court Great Harwood Business Zone Blackburn BB6 7FD, United Kingdom) blood feeding plate \(^{53}\) as artificial bait. One blooding plate was filled up with \(100 \mathrm{~mL}\) fresh pig blood and was hung upside down. The Hemotek heating system was turned on for 30 min. The plate was placed half- way of the wooden stand at one meter above the floor and allowed females to feed easily. + +<|ref|>text<|/ref|><|det|>[[111, 734, 884, 916]]<|/det|> +Five- to- six- day- old, irradiated males and virgin non- treated female mosquitoes were briefly knocked down for five to ten minutes at \(4^{\circ}\mathrm{C}\) prior to release. Mosquitoes were then transferred into \(100 \mathrm{~mL}\) plastic containers. Each container was labelled according to treatment or control groups. All the containers were then transferred to the Ecosphere and males were released into large cages. Females were released 30 min later where they were allowed to blood feed for two hours starting from 10 am. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 885, 364]]<|/det|> +After 2h- exposure time, all females were recaptured separately from the treatment and the control cages using mechanical aspirator device54. The operator wore coverall protective suit and gloves preventing any biting from the females during collection. The number of recaptured females was recorded per cage. To assess the blood- feeding status of females, each recaptured female mosquito was crushed between two pieces of white paper and the visual presence/absence of blood was observed based on the blood stain. The number of blood- fed females was recorded per cage. In total, three technical replicates (cage) were prepared for the control sex ratio (males: virgin females) of 1:1 (20:20) and the treatment sex ratio of 99:1 (1980: 20). + +<|ref|>text<|/ref|><|det|>[[115, 388, 480, 406]]<|/det|> +The full experiment was repeated four times. + +<|ref|>sub_title<|/ref|><|det|>[[115, 430, 325, 448]]<|/det|> +### 3.2 Human bait (China) + +<|ref|>text<|/ref|><|det|>[[115, 472, 450, 490]]<|/det|> +Mosquito strains, rearing, and irradiation + +<|ref|>text<|/ref|><|det|>[[113, 512, 885, 696]]<|/det|> +The female mosquito GUA line was collected from more than 10 field localities of Guangzhou City, China, and has been reared in the laboratory for less than one year ( \(< 12\) generations). The rearing conditions for GUA were described previously55. Briefly, about 300 first- instar larvae were reared in a plastic tray ( \(36 \mathrm{cm} \times 25 \mathrm{cm} \times 5 \mathrm{cm}\) ) with 1.5 L \(\mathrm{dH_2O}\) and bovine liver powder was supplied as larvae food. The establishment, mass- rearing, sex- separation and irradiation of HC mosquitoes were described previously56. + +<|ref|>text<|/ref|><|det|>[[115, 763, 420, 781]]<|/det|> +Human Landing Catch in large cages + +<|ref|>text<|/ref|><|det|>[[113, 805, 884, 888]]<|/det|> +We conducted a second experiment based on Human Landing Catch in China to assess whether male harassment can prevent blood feeding on humans in semi- field conditions. Wild type virgin Ae. albopictus (GUA strain) females were inseminated and 5- 6 days old. They were + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 82, 885, 496]]<|/det|> +starvied for 24 hours before the experiment start. Irradiated HC males were virgin and 5- 6 days old. Irradiated HC males were released into semi- field cages \((1.80 \times 1.80 \times 1.80 \text{m}\) , containing two sugar water containers). GUA females were released 24 hours later into the semi- field cages. Male and female release numbers were 1980 versus 20 for the 99:1 ratio and 20 versus 20 for the 1:1 ratio. Ten minutes after releasing the females, an adult volunteer entered and sat on a chair in the middle of each cage. The collector exposed one of his legs from foot to knee and killed mosquitoes as soon as they landed on the exposed leg before they started feeding. Mosquito collection was conducted for 15 min for each cage and ratio. All collected females were removed and counted. After 15 min of collection, remaining mosquitoes were collected with an aspirator and females checked to see whether some females had blood meals. Three repeats were conducted with three different collectors managing one 99:1 and one 1:1 cage each. Collectors received appropriate information and gave their informed consent prior to participating in this study. + +<|ref|>sub_title<|/ref|><|det|>[[115, 519, 225, 536]]<|/det|> +## 4. Field trial + +<|ref|>sub_title<|/ref|><|det|>[[115, 561, 385, 578]]<|/det|> +### 4.1 Maintenance of mosquitoes + +<|ref|>text<|/ref|><|det|>[[113, 602, 885, 785]]<|/det|> +We used the Ae. albopictus GT line (without Wolbachia infection) that can be distinguished from the wild Ae. albopictus (wAlbA and wAlbB double infections) via PCR/qPCR assays based on Wolbachia wsp gene. The GT line was maintained as previously described57. For routine colony maintenance, female mosquitoes were blood- fed on mice according to protocols approved by the Ethics Committee on Laboratory Animal Care of the Zhongshan School of Medicine, Sun Yat- sen University (No. 2017- 041). + +<|ref|>sub_title<|/ref|><|det|>[[115, 809, 543, 828]]<|/det|> +### 4.2 Mass-production and irradiation of GT males + +<|ref|>text<|/ref|><|det|>[[115, 850, 884, 901]]<|/det|> +Mass- production of GT males included adult and larval rearing according to protocols described previously with slight modifications58,59. Approximately 15,000 female pupae and 5,000 male + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 78, 885, 760]]<|/det|> +191 pupae (3:1 ratio of female to male) were placed into an adult cage \((90 \times 90 \times 30 \text{cm})\) . Adults were provided with a \(10\%\) sugar solution ad libitum. Sheep blood mixed with ATP was provided to females twice per rearing cycle. Oviposition cup was provided to the engorged females for laying eggs 48 h after each blood meal. Eggs were collected for 72 h and then matured for at least one week before hatching. After hatching, 4,000- 5,000 larvae were added to each tray \((51.5 \times 36.0 \times 5.5 \text{cm})\) and fed daily with larval food. At day 8, pupae mixed with larvae were collected and then separated by an automatic sex separator (Orinno Technology, Singapore). After sexing, 16,000 male pupae were transferred to a cage \((90 \times 90 \times 30 \text{cm})\) for emergence. The temperature was set at 27- 28 °C. Cotton soaked in \(10\%\) sugar solution was placed on top of the cage for mosquitoes to feed ad libitum. The average female contamination rate was \(0.05\%\) \((n = 30, \text{SE} = 0.02\%)\) in the sterile male release batches (Fig. S6c). Male mosquitoes at 2- 3- day old were immobilized and then packed in plastic dishes (diameter \(10 \text{cm} \times \text{height} 1.2 \text{cm}\) ) in a cooling room set at \(10 \text{°C}\) . Each plastic dish contained 5,000 male mosquitoes and was then placed in a PMMB canister. Each irradiated canister contained 3 dishes and two canisters were irradiated each time. The exposure was done in an X- ray irradiator (XL1606HD, NUCTECH, China) at a dose of 60 Gy with dose rates of 3.74 Gy/min or 7.33 Gy/min. The irradiator was configured with a cooling system to maintain the chamber temperature at \(10 \text{°C}\) , which ensured the immobilization of male mosquitoes during exposure without impacting their quality \(^{60- 62}\) . The irradiated male mosquitoes were recorded as IGT \(^{60 \text{Gy}}\) males. Exposing adult male mosquitoes to 60 Gy resulted in an average of \(99.0\%\) sterility \((n = 30, \text{SE} = 0.22\%)\) , Fig. S6c). + +<|ref|>sub_title<|/ref|><|det|>[[115, 780, 281, 798]]<|/det|> +### 4.3 Quality control + +<|ref|>text<|/ref|><|det|>[[113, 821, 884, 905]]<|/det|> +One of the key quality control parameters for release of sterile males was the female contamination rate (FCR), which was monitored at the adult stage. Each batch of male adults was checked by randomly selecting 800- 1,000 of the mosquitoes for sex identification based + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 81, 884, 330]]<|/det|> +on morphology. In addition, male sterility was monitored for each batch through egg hatch rate assessment. In details, 100 IGT \(_{60\mathrm {Gy}}\) males were allowed to mate with 100 virgin GT females. Blood feedings and egg collections were the same as mentioned above. Eggs from each blood meal were hatched and egg hatch rate was assessed as previously described \(^{57}\) . Egg hatch rate from crosses between 100 GT males and 100 virgin GT females was considered as fertile control. Male sterility was calculated as: Induced Sterility \((\mathrm {IS}\%=100\%-((\mathrm {Hs}/\mathrm {Hn})*100\%)\) where Hs was the egg hatch rate from the sterile control, and Hn was the egg hatch rate from the fertile control. + +<|ref|>sub_title<|/ref|><|det|>[[115, 355, 345, 372]]<|/det|> +## 4.4 Study area description + +<|ref|>text<|/ref|><|det|>[[111, 395, 885, 677]]<|/det|> +The study site is located at the North Campus of Sun Yat- Sen University in Yuexiu District, Guangzhou, China (Latitude: \(23^{\circ }7^{\prime }39.74^{\prime \prime }\mathrm {N}\) , Longitude: \(113^{\circ }17^{\prime }22.07^{\prime \prime }\mathrm {E}\) ), covering an area of about 20.9 ha (Fig. 1a). The campus has a population of 4,750 people (mainly students and faculty) and is located in a bustling metropolitan area with parks, hospitals, and residential areas nearby. The west and south areas of the campus were selected as the control area (6.55 ha), the northeast was the release area (1.17 ha), and a buffer zone (4.87 ha) was set between the release and the control area (Fig. 1b). The average temperature in the study area was \(24.6^{\circ }\mathrm {C}\) in 2021 (Fig. 1c) and the annual precipitation was 1,511.4 mm with a rainy season between May and October (Fig. 1d). + +<|ref|>sub_title<|/ref|><|det|>[[115, 701, 588, 719]]<|/det|> +## 4.5 Pre-release monitoring of release and control areas + +<|ref|>text<|/ref|><|det|>[[111, 742, 885, 892]]<|/det|> +Before release, Ae. albopictus populations were monitored using ovitraps every two weeks from \(8^{\mathrm {th}}\) March to \(17^{\mathrm {th}}\) August 2021. The number of ovitraps was 17 in the release area, 40 in the control area and 33 in the buffer area, respectively (Fig. 3b). The methods to place and collect ovitraps as well as hatch eggs were the same as described in \(^{56}\) . We also performed Human Landing Catch (HLC) to estimate the mosquito adult populations. There were two positions in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 233]]<|/det|> +the release area and 6 positions in the control area (Fig. 3b). The HLC was performed 4 times pre- release of sterile males. Briefly, well- protected volunteers stand in the selected position and used a locally manufactured hand- held electric aspirator to collect the adult mosquitoes fly around the performers for 15 mins. The collected mosquitoes were identified and counted by morphological characteristics. + +<|ref|>sub_title<|/ref|><|det|>[[115, 255, 406, 274]]<|/det|> +### 4.6 Field release of IGT \(_{60Gy}\) males + +<|ref|>text<|/ref|><|det|>[[113, 297, 884, 514]]<|/det|> +IGT \(_{60Gy}\) males were maintained in a mobile- refrigerator set at \(10^{\circ}\mathrm{C}\) and transported from the mass- rearing factory to the study site by a van two times per week. The distance between the factory and the study site was about \(100\mathrm{km}\) . The release was performed at 13:00- 14:00 pm. During release, dishes were opened, and mosquitoes were allowed to fly away freely. Over \(95\%\) of mosquitoes could recover after transportation under chilling conditions. On average, 200,000 mosquitoes were released weekly, and a total of about 3- million mosquitoes were released from mid- August to end of November 2021. + +<|ref|>sub_title<|/ref|><|det|>[[115, 538, 457, 556]]<|/det|> +### 4.7 Monitoring population suppression. + +<|ref|>text<|/ref|><|det|>[[113, 579, 884, 893]]<|/det|> +Throughout the period of IGT \(_{60Gy}\) male release, Ae. albopictus populations were monitored weekly by using ovitraps and BG- Sentinel traps (Biogents, Germany). The number of BG traps was 4 in the release area, 6 in the control area and 5 in the buffer area (Fig. 3b). The methods to place and run BG traps as well as count the number of mosquitoes were the same as described in \(^{56}\) . The average number of hatched eggs per ovitrap, in both release and control areas, was determined and used to measure population suppression efficiency at the larval stage. In addition, the average number of females in both release and control areas per BG trap was determined each week, and used to measure population suppression at the adult stage. Moreover, HLC was repeated three times to estimate the suppression efficiency at 11 weeks' post release of IGT \(_{60Gy}\) males. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 457, 101]]<|/det|> +### 4.8 qPCR assays of Wolbachia infection + +<|ref|>text<|/ref|><|det|>[[113, 123, 884, 473]]<|/det|> +Each captured adult mosquito was stored separately in a \(1.5\mathrm{mL}\) tube and maintained at \(- 20^{\circ}\mathrm{C}\) before Wolbachia detection. DNA was extracted according to the protocols of Fast Pure Cell/Tissue DNA Isolation Mini Kit (Vazyme, China). A \(20\mu \mathrm{L}\) qPCR reaction consisted of 1 \(\mu \mathrm{l}\) DNA template, \(10\mu \mathrm{L}\) qPCR 2X mix (Vazyme, China), \(8\mu \mathrm{L}\) nucleic- free water, \(0.5\mu \mathrm{L}\) primer- F, and \(0.5\mu \mathrm{L}\) primer- R. The specific- primers used for the assay were designed for Wolbachia wsp gene and consisted of wAlbB- F: ACGTTGGTGGTGCAACATTG; wAlbB- R: TAACGAGCACCAGCATAAGAC. The qPCR procedures (LightCycler 96, Roche) comprised 10 s at \(95^{\circ}\mathrm{C}\) , followed by 40 cycles of 10 s at \(95^{\circ}\mathrm{C}\) , 10 s at \(50^{\circ}\mathrm{C}\) , 10 s at \(72^{\circ}\mathrm{C}\) , and finally 10 s at \(95^{\circ}\mathrm{C}\) , 60 s at \(65^{\circ}\mathrm{C}\) , 1 s at \(97^{\circ}\mathrm{C}\) , 30 s in \(37^{\circ}\mathrm{C}\) to generate the melting curve for confirmation that the fluorescence detected was for the specific PCR product. The Wolbachia negative samples were considered as \(\mathrm{IGT}_{60\mathrm{Gy}}\) mosquitoes. + +<|ref|>sub_title<|/ref|><|det|>[[115, 496, 298, 513]]<|/det|> +## 5. Statistical analysis + +<|ref|>text<|/ref|><|det|>[[113, 536, 884, 917]]<|/det|> +All statistical analyses were performed using R version 4.2.1 (https://cran.r- project.org) using RStudio 2022.07.1 (RStudio, Inc. Boston, MA, United States, 2016). Shapiro and Bartlett's tests were performed respectively to test the normality and to determine whether the variance in cumulative mortalities was the same for various sex ratios. The relationships between cumulative mortalities and the different sex ratios during the study period were analysed for each Aedes species. For this purpose, binomial linear mixed effect models were used with the assigned sex ratios as response variables and cumulative mortality rates as explanatory variable using the lme4 package63. The various sex ratios were then used as fixed effects and the repetitions as random effects. The generalized linear mixed models were fitted by maximum likelihood. For each species, the cumulative mortality curves were plotted by sex ratios using the ggplot package. The longevity of harassed, non- harassed and virgin Ae. aegypti females was analyzed using Kaplan- Meier survival analyses. The log- rank test (Mantel- Cox) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 81, 885, 330]]<|/det|> +was used to compare the level of survival between the different treatments (status of females) using the survival and survminer packages64. Two- tailed Wilcoxon matched- pairs signed rank test was used to compare the hatched eggs and the captured female adults via BG or HLC before and after the release of sterile males, between the released and control areas. The feeding rates and recapture rates of females in semi- field trials were analysed using binomial generalized linear mixed models fit by maximum likelihood (Laplace approximation) with the SR as fix factor and the repeats as random factors65. The odds ratio were computed using the emmeans function (in package emmeans)66. + +<|ref|>sub_title<|/ref|><|det|>[[115, 355, 270, 372]]<|/det|> +## Ethical statement + +<|ref|>text<|/ref|><|det|>[[114, 396, 858, 512]]<|/det|> +The study involving Human Landing Catch in large cages received the approval to the Institutional Ethics Committee from Guangzhou University. The field trial on applying SIT for Aedes albopictus control has been reported to and approved by Zhongshan School of Medicine (ZSSM), SYSU before the release of sterile males in 2021. + +<|ref|>sub_title<|/ref|><|det|>[[115, 537, 214, 554]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[113, 577, 857, 877]]<|/det|> +FAO/IAEA. Guidelines for mass rearing of Aedes mosquitoes. Version 1.0. (2019). Maiga, H. et al. Reducing the cost and assessing the performance of a novel adult mass- rearing cage for the dengue, chikungunya, yellow fever and Zika vector, Aedes aegypti (Linnaeus). PLoS Negl. Trop. Dis. 13, e0007775, doi:10.1371/journal.pntd.0007775 (2019). Zheng, M. L., Zhang, D. J., Damiens, D. D., Yamada, H. & Gilles, J. R. Standard operating procedures for standardized mass rearing of the dengue and chikungunya vectors Aedes aegypti and Aedes albopictus (Diptera: Culicidae) - I - egg quantification. Parasit Vectors 8, 42, doi:10.1186/s13071-014-0631-2 (2015). Mamai, W. et al. Black soldier fly (Hermetia illucens) larvae powder as a larval diet ingredient for mass-rearing Aedes mosquitoes. Parasite 26, 57 (2019). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[172, 83, 848, 135]]<|/det|> +Fay, R. W. & Morlan, H. B. A mechanical device for separating the developmental stages, sexes and species of mosquitoes. Mosq. News 19, 144- 147 (1959). + +<|ref|>text<|/ref|><|det|>[[172, 145, 840, 196]]<|/det|> +Focks, D. A. An improved separator for the developmental stages, sexes, and species of mosquitoes (Diptera: Culicidae). J. Med. Entomol. 17, 567- 568 (1980). + +<|ref|>text<|/ref|><|det|>[[172, 208, 856, 291]]<|/det|> +Mamai, W. et al. Aedes aegypti larval development and pupal production in the FAO/IAEA mass- rearing rack and factors influencing sex sorting efficiency. Parasite & Vectors 27, 43, doi:10.1051/parasite/2020041 (2020). + +<|ref|>text<|/ref|><|det|>[[172, 304, 847, 356]]<|/det|> +Sharma, V. P., Patterson, R. S. & Ford, H. R. A device for the rapid separation of male and female mosquito pupae. Bull. World Health Organ. 47, 429- 432 (1972). + +<|ref|>text<|/ref|><|det|>[[172, 368, 833, 420]]<|/det|> +Maiga, H. et al. Guidelines for routine colony maintenance of Aedes mosquito species - Version 1.0. 18 (Vienna, 2017). + +<|ref|>text<|/ref|><|det|>[[172, 432, 841, 484]]<|/det|> +Maiga, H. et al. Standardization of the FAO/IAEA Flight Test for Quality Control of Sterile Mosquitoes. Frontiers in Bioengineering and Biotechnology 10, 876675, doi:10.3389/fbioe.2022.876675 (2022). + +<|ref|>text<|/ref|><|det|>[[172, 496, 856, 548]]<|/det|> +Gómez- Simuta, Y. et al. Characterization and dose- mapping of an X- ray blood irradiator to assess application potential for the sterile insect technique (SIT). Appl. Radiat. Isot. 176, 109859 (2021). + +<|ref|>text<|/ref|><|det|>[[172, 560, 840, 612]]<|/det|> +Damiens, D. et al. Different blood and sugar feeding regimes affect the productivity of Anopheles arabiensis colonies (Diptera: Culicidae). Journal of medical entomology 50, 336- 343 (2013). + +<|ref|>text<|/ref|><|det|>[[172, 624, 857, 705]]<|/det|> +Damiens, D. et al. Different blood and sugar feeding regimes affect the productivity of Anopheles arabiensis colonies (Diptera: Culicidae). Journal of medical entomology 50, 336- 343 (2013). + +<|ref|>text<|/ref|><|det|>[[172, 717, 850, 770]]<|/det|> +Balestrino, F., Puggioli, A., Carrieri, M., Bouyer, J. & Bellini, R. Quality control methods for mosquito Sterile Insect Technique. PloS Negl. Trop. Dis. 11, e0005881 (2017). + +<|ref|>text<|/ref|><|det|>[[172, 781, 812, 833]]<|/det|> +Li, Y. et al. Quality control of long- term mass- reared Aedes albopictus for population suppression. Journal of Pest Science 94, 1531- 1542 (2021). + +<|ref|>text<|/ref|><|det|>[[172, 845, 863, 897]]<|/det|> +Zheng, X. et al. Incompatible and sterile insect techniques combined eliminate mosquitoes. Nature 572, 56- 61, doi:https://doi.org/10.1038/s41586- 019- 1407- 9 (2019). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[171, 81, 876, 256]]<|/det|> +Zhang, D., Zheng, X., Xi, Z., Bourtzis, K. & Gilles, J. R. L. Combining the sterile insect technique with the incompatible insect technique: I- impact of Wolbachia infection on the fitness of triple- and double- infected strains of Aedes albopictus. PloS one 10, e0121126 (2015). Zhang, D. et al. Establishment of a medium- scale mosquito facility: optimization of the larval mass- rearing unit for Aedes albopictus (Diptera: Culicidae). Parasites & vectors 10, 569 (2017). + +<|ref|>text<|/ref|><|det|>[[171, 272, 875, 425]]<|/det|> +Zhang, D. et al. Establishment of a medium- scale mosquito facility: tests on mass production cages for Aedes albopictus (Diptera: Culicidae). Parasites & vectors 11, 189 (2018). Culbert, N. J., Gilles, J. R. L. & Bouyer, J. Investigating the impact of chilling temperature on male Aedes aegypti and Aedes albopictus survival. PLoS ONE 14, e0221822, doi:https://doi.org/10.1371/journal.pone.0221822 (2019). + +<|ref|>text<|/ref|><|det|>[[171, 432, 875, 584]]<|/det|> +Culbert, N. J. et al. A rapid quality control test to foster the development of the sterile insect technique against Anopheles arabiensis. Malar. J. 19, 1- 10 (2020). Zhang, D. et al. Toward implementation of combined incompatible and sterile insect techniques for mosquito control: Optimized chilling conditions for handling Aedes albopictus male adults prior to release. PloS Negl. Trop. Dis. 14, e0008561 (2020). + +<|ref|>text<|/ref|><|det|>[[171, 592, 861, 644]]<|/det|> +Ime4 : Linear mixed- effects models using S4 classes, R package version 0.999375- 40/r1308 (2011). + +<|ref|>text<|/ref|><|det|>[[171, 655, 868, 706]]<|/det|> +Kassambara, A., Kosinski, M., Biecek, P. & Fabian, S. Drawing Survival Curves using 'ggplot2'. R Package 'survminer' (2017). + +<|ref|>text<|/ref|><|det|>[[171, 718, 837, 770]]<|/det|> +Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information- theoretic approach. 2nd edn, (Springer- Verlag, 2002). + +<|ref|>text<|/ref|><|det|>[[171, 782, 845, 833]]<|/det|> +Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Emmeans: Estimated marginal means, aka least- squares means. R package version 0.9- 1 1, 3 (2018). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 90, 650, 608]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[114, 634, 885, 787]]<|/det|> +Extended Data Fig. 1. Cumulative mortality rate of non- irradiated Aedes mosquitoes exposed to three sex ratio (SR) (Males/Females) treatments (1:3=control, 49:1 and 99:1 for Aedes aegypti; and 1:3=control, 50:1 and 100:1 for Aedes albopictus) over 8 days. Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (ns = not significant ie p>0.5). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 85, 884, 503]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[114, 518, 885, 702]]<|/det|> +Extended Data Fig. 2. Cumulative mortality rate of non- irradiated Aedes aegypti exposed to six sex ratios (SR) (Males/Females) treatments (3:7, 1:3, 10:1, 23:2, 49:1, and 99:1) over 8 days during preliminary trials. Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (ns = not significant ie p>0.5). Mortality rate of female reached 14.5% (SD=3.9%) after 8 days in the 99:1 batch in comparison to 2.8% (SD=1.2%) in the 1:3 control group. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 110, 880, 498]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[114, 511, 884, 663]]<|/det|> +Extended Data Fig. 3. Cumulative mortality rate of non- irradiated Aedes aegypti exposed to three sex ratio (SR) (Males/Females) treatments (1:3=control, 49:1 and 99:1) over 13 days. Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (ns = not significant ie p>0.5). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 110, 876, 500]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[114, 515, 885, 700]]<|/det|> +Extended Data Fig. 4. Cumulative mortality rate of irradiated Aedes albopictus exposed to two sex ratio (SR) (Males/Females) treatments (1:3=control and 99:1) over 8 days (experiment conducted at ICPL). Probabilities that the observed differences are significant from the control group are presented on the top of each panel, the colour indicating the sex to which it refers (ns = not significant ie p>0.5). Mortality of females reached 90% (SD=6.1) at 8 days for a ratio of 99:1 as compared to 17.3% (SD=4.1) in the control group. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 80, 884, 520]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[115, 558, 885, 675]]<|/det|> +Extended Data Fig. 5. Survival of female Aedes aegypti exposed to three treatments (harassed female, unharassed female and virgin female) over 14 days. Any difference was observed of survival between females previously exposed to males at a 1:3 or 99:1 ratio after their separation from the males + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 92, 825, 512]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 550, 885, 897]]<|/det|> +Extended Data Fig. 6. Mosquito population and the release of sterile males. a, Weekly number of hatched eggs per ovitrap in the release (green dashed lines) and control area (blue dashed lines) before release. A total of 17 ovitraps were used for monitoring in the release area and 40 in the control area. No significant difference was observed in the number of hatched eggs between release and control area (n=10, P=0.1055, Two- tailed Wilcoxon matched- pairs signed rank test). b, Number of female adults captured via HLC in the release (green histogram) and control area (blue histogram) before release. Two positions were selected to perform HLC in the release area and 6 positions in the control area. Four independent HLCs were performed. No significant difference was observed in the captured female adults via HLC (n=4, P>0.9999, Two- tailed Wilcoxon matched- pairs signed rank test). c, Sterile male mosquitoes were released twice per week for a total of about 3 million males. The average female contamination rate (red + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 882, 133]]<|/det|> +dashed lines) was \(0.053\%\) ( \(n=30\) , \(95\% CI\) : \(0.019\% -0.086\%\) ) and the male sterility (purple dashed lines) \(99.03\%\) ( \(n=30\) , \(95\% CI\) : \(98.59\% -99.47\%\) ) with 30 batches assessed in total. + +<|ref|>text<|/ref|><|det|>[[115, 201, 882, 281]]<|/det|> +**Extended Data Table 1.** Effects of various sex-ratios on the cumulative mortality rates of non-irradiated *Aedes* mosquitoes based on the generalized linear mixed model fit by maximum likelihood using Binomial linear mixed effect models. + +<|ref|>table<|/ref|><|det|>[[123, 336, 940, 692]]<|/det|> +
Aedes speciesSexEstimateStd. Errorz valuePr(>|z|)
Aedes aegyptiFemale(Intercept)4.620.2419.54< 2e-16 ***
Sex Ratio = 3:7-0.590.26-2.310.0211 *
Sex Ratio = 10:1-0.950.24-3.919.25e-05 ***
Sex Ratio = 23:2-1.250.23-5.368.28e-08 ***
Sex Ratio = 49:1-1.130.24-4.781.74e-06 ***
Sex Ratio = 99:1-1.790.22-8.039.92e-16 ***
Male(Intercept)4.300.1824.19<2e-16 ***
Sex Ratio = 3:70.380.281.360.17
Sex Ratio = 10:10.530.291.810.07
Sex Ratio = 23:20.170.260.650.51
Sex Ratio = 49:10.580.301.960.05
Sex Ratio = 99:10.430.281.510.13
Aedes albopictusFemale(Intercept)4.850.4710.39< 2e-16 ***
Sex Ratio = 50:1-2.620.46-5.671.47e-08 ***
Sex Ratio = 100:1-2.910.46-6.342.32e-10 ***
Male(Intercept)4.890.4111.92< 2e-16 ***
Sex Ratio = 50:1-2.260.42-5.368.42e-08 ***
Sex Ratio = 100:1-2.160.42-5.172.36e-07 ***
+ +<|ref|>text<|/ref|><|det|>[[115, 705, 882, 755]]<|/det|> +Values were compared to the Sex Ratio = 1:3 [Control], *p-value < 0.05; ***p-value < 0.001, Std. Error= standard error + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 125, 884, 207]]<|/det|> +**Extended Data Table 2.** Effects of various sex-ratios on the cumulative mortality rates of irradiated *Aedes* mosquitoes based on the generalized linear mixed model fit by maximum likelihood using Binomial linear mixed effect models. + +<|ref|>table<|/ref|><|det|>[[123, 245, 956, 490]]<|/det|> +
Aedes speciesSexEstimateStd. Errorz valuePr(>|z|)
Aedes aegyptiFemale(Intercept)4.090.1822.93< 2e-16 ***
FemaleSex Ratio = 49:1-0.610.19-3.290.001 **
FemaleSex Ratio = 99:1-1.960.15-13.13< 2e-16 ***
Male(Intercept)3.880.1526.33< 2e-16 ***
MaleSex Ratio = 49:11.010.273.770.00016 ***
MaleSex Ratio = 99:10.270.171.580.115
Aedes albopictusFemale(Intercept)4.830.4710.30< 2e-16 ***
FemaleSex Ratio = 50:1-2.240.46-4.831.40e-06 ***
FemaleSex Ratio = 100:1-2.690.46-5.835.47e-09 ***
Male(Intercept)4.860.4211.67< 2e-16 ***
MaleSex Ratio = 50:1-2.370.42-5.621.91e-08 ***
MaleSex Ratio = 100:1-2.500.42-5.962.51e-09 ***
+ +<|ref|>text<|/ref|><|det|>[[115, 490, 832, 506]]<|/det|> +Values were compared to the Sex Ratio = 1:3 [Control], **p-value < 0.01; ***p-value < + +<|ref|>text<|/ref|><|det|>[[115, 523, 386, 539]]<|/det|> +0.001, Std. Error= standard error + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[176, 84, 460, 101]]<|/det|> +## Supplementary Information (SI) + +<|ref|>sub_title<|/ref|><|det|>[[176, 115, 608, 134]]<|/det|> +## 1. Mating harassment increases female mortality + +<|ref|>text<|/ref|><|det|>[[177, 148, 384, 166]]<|/det|> +1. Supplementary Results + +<|ref|>text<|/ref|><|det|>[[115, 180, 883, 266]]<|/det|> +At the beginning of the experiment, we monitored some of some non irradiated groups up to 13 days and mortality of females reached \(43.3\%\) (SD=4.7%) in the 99:1 batch in comparison to \(4.1\%\) (SD=1.7%) in the 1:3 control group (Extended Data Fig. 2, p-value \(< 10^{-3}\) ). + +<|ref|>text<|/ref|><|det|>[[115, 287, 884, 471]]<|/det|> +In irradiated mosquitoes, the cumulative mortality rate of female Ae. aegypti increased with sex ratio and was \(26.7\%\) (SD = 14.0%) after 8 days for a male to female ratio of 99:1 as compared with a mortality rate of \(3.9\%\) (SD = 2.4%) in the control group (Fig. 1 and Extended Data Table 2, \(P< 10^{-3}\) ). Male Ae. aegypti mortality after 8 days did not increase with a male to female ratio of 99:1 (Extended Data Table 2, \(P = 0.115\) ) and was even lower than in the control group for a ratio of 49:1. + +<|ref|>text<|/ref|><|det|>[[113, 493, 884, 810]]<|/det|> +The cumulative mortality of Ae. albopictus (Reunion strain) females was even higher as compared with Ae. aegypti and reached \(40.0\%\) (SD = 8.8%) after 8 days with a male to female ratio of 100:1 as compared with \(3.8\%\) in the control group (Fig. 1 and Extended Data Table 2, \(P< 10^{- 3}\) ). Again, the cumulative mortality of males significantly increased with increasing sex ratio in this species, reaching \(24.8\%\) (SD = 0.61%) and \(25.3\%\) (SD = 4.1%) after 8 days with male to female ratios of 50:1 and 100:1, respectively, as compared with \(2.9\%\) in the control group. Comparable results were obtained in a similar trial with another strain of Ae. albopictus (Rimini), except that the mortality of females reached \(90\%\) (SD = 6.1%) after 8 days with a female to male ratio of 99:1 as compared with \(17.3\%\) (SD = 4.1%) in the control group (Extended Data Fig. 4, p <10-3). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[177, 84, 415, 101]]<|/det|> +## 2. Supplementary Discussion + +<|ref|>text<|/ref|><|det|>[[111, 113, 885, 627]]<|/det|> +Female mosquitoes are compulsory blood feeders and hence, the pathogen- transmitting sex. Even when irradiated, female mosquitoes require regular blood meals after release and may therefore still contribute to the transmission of diseases despite being sterile'. This can only be avoided if accurate sex- separating systems that remove all female mosquitoes from the release batches are available 2. Different sexing techniques based on biological, genetic and transgenic approaches have been proposed for some mosquito species considered for SIT3,4. While most contemporary SIT programmes use mechanical devices to sex pupae, female contamination rates close to \(1\%\) , a threshold considered as the maximum acceptable contamination rate for release, are common5,6. The sex separation of Aedes mosquitoes is then carried out at the pupal stage, i.e., by using standard metal sieves with a square- opening mesh through which male Aedes swim upward, or by using the glass plate sex separation system. Given the substantial number of mosquitoes required for SIT, such methods are time- costly and require dedicated personnel to manually operate the sorting devices4. More recently, a sex- sorting pipeline including a mechanical pupal sieve, real- time adult visual inspection, a cloud- based machine learning classifier, and non- expert review has been described, but its cost- effectiveness remains uncertain7,8. + +<|ref|>text<|/ref|><|det|>[[111, 639, 885, 855]]<|/det|> +When a predetermined threshold is agreed with the public health authorities, e.g., \(1\%\) , keeping the sterile males for 8 days might be an effective way of eliminating females instead of removing residual females manually or discarding the full batch of sterile males. Nevertheless, this would probably be cost- prohibitive in an operational programme. The feasibility of such action would require for instance, evaluating how long sterile males can be kept in the rearing facility without reducing their competitiveness. On La Réunion island, the competitiveness index of sterile male Ae. albopictus in semi- field conditions increased with the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 83, 883, 135]]<|/det|> +age of sterile males, from 0.14 one day after emergence to 0.53 after 5 days9. A similar result was observed in Mauritius10 but this would require field validation. + +<|ref|>sub_title<|/ref|><|det|>[[174, 214, 817, 233]]<|/det|> +## 2. Potential impact of mating harassment on female survival after release + +<|ref|>sub_title<|/ref|><|det|>[[177, 247, 390, 265]]<|/det|> +## 1. Supplementary results + +<|ref|>text<|/ref|><|det|>[[113, 280, 884, 430]]<|/det|> +To assess the potential impact of mating harassment on female survival after release, we monitored female survival after separation from the sterile males in Aedes aegypti. Mating harassment did not reduce the survival of females in the 99:1 group in comparison to the control group (p > 0.05). However, mated females had a much lower survival rate than virgin females (Extended Data Fig. 4, p- value = 0.014). + +<|ref|>sub_title<|/ref|><|det|>[[115, 470, 213, 486]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[113, 508, 884, 860]]<|/det|> +1 Guissou, E. et al. Effect of irradiation on the survival and susceptibility of female Anopheles arabiensis to natural isolates of Plasmodium falciparum. bioRxiv preprint (2020). 2 Lutrat, C. et al. Sex sorting for pest control: it's raining men! Trends Parasitol. 35, 649- 662 (2019). 3 Papathanos, P. A. et al. Sex separation strategies: past experience and new approaches. Malar J. 8(Suppl 2):S5, doi:10.1186/1475- 2875- 8- S2- S5 (2009). 4 Lutrat, C. et al. Combining two Genetic Sexing Strains allows sorting of non- transgenic males for Aedes genetic control. Communications Biology 6, 646, doi:10.1038/s42003- 023- 05030- 7 (2023). 5 WHO & IAEA. Guidance Framework for Testing the Sterile Insect Technique as a Vector Control Tool against Aedes- Borne Diseases, Geneva & Vienna. (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[174, 82, 878, 160]]<|/det|> +Bouyer, J., Yamada, H., Pereira, R., Bourtzis, K. & Vreysen, M. J. B. Phased Conditional Approach for Mosquito Management using the Sterile Insect Technique. Trends Parasitol. 36, 325- 336 (2020). + +<|ref|>text<|/ref|><|det|>[[174, 177, 875, 258]]<|/det|> +Crawford, J. E. et al. Efficient production of male Wolbachia- infected Aedes aegypti mosquitoes enables large- scale suppression of wild populations. Nat. Biotechnol. in press: 1- 11. (2020). + +<|ref|>text<|/ref|><|det|>[[174, 273, 880, 355]]<|/det|> +Bouyer, J., Maiga, H. & Vreysen, M. J. B. Assessing the efficiency of Verily's automated process for production and release of male Wolbachia- infected mosquitoes. Nat. Biotechnol., 1- 2 (2022). + +<|ref|>text<|/ref|><|det|>[[174, 370, 863, 475]]<|/det|> +Oliva, C. F., Jacquet, M., Gilles, J., Lemperiere, G. & Maquart, P.- O., et al. The Sterile Insect Technique for Controlling Populations of Aedes albopictus (Diptera: Culicidae) on Reunion Island: Mating Vigour of Sterilized Males. PLoS ONE 7(11): e49414. doi:10.1371/journal.pone.0049414, doi:10.1371/journal.pone.0049414 (2012). + +<|ref|>text<|/ref|><|det|>[[174, 498, 877, 578]]<|/det|> +Iyaloo, D. P., Oliva, C., Facknath, S. & Bheecarry, A. A field cage study of the optimal age for release of radio- sterilized Aedes albopictus mosquitoes in a sterile insect technique program. Entomol. Exp. Appl. in press (2019). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 43, 311, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 131, 330, 201]]<|/det|> +- MovieS1voiceovercut1.wmv- MovieS2voiceovercut1.wmv- MovieS3voiceovercut1.wmv + +<--- Page Split ---> diff --git a/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/images_list.json b/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b5d0d3a7c081f3a31723d4c8571e618c9e7bd6b2 --- /dev/null +++ b/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/images_list.json @@ -0,0 +1,107 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Graphical abstract", + "footnote": [], + "bbox": [ + [ + 120, + 140, + 684, + 330 + ] + ], + "page_idx": 22 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Immunopeptidomics workflow using Thunder-DDA-PASEF. (a) Sample preparation: 500 million cells of the human JY or Raji cell lines were harvested, then lysed by sonication in 1% CHAPS in PBS buffer (m/v). (b) MHC-ligand peptide enrichment: was performed by immunoaffinity using the W6/32 anti-human-MHC-A, B, C antibody coupled to CNBr-activated agarose beads; after overnight incubation and several washes, peptides were eluted with 0.2% trifluoro-acetic acid, ultrafiltered on molecular weight cutoff filters (MWCO, 10 kDa cutoff) and desalted in HLB plates (Waters Corp.). (c) NanoLC-MS: analysis was performed using a nanoElute coupled to timsTOF-Pro-2 in DDA-PASEF [17] with different parameters to optimize the MS acquisition. (d) Data analysis: Database search was performed in PEAKS XPro using unspecific cleavage. Data analysis was performed in R and predicted MHC-binding affinity was evaluated using NetMHCpan 4.1 [37] and GibbsCluster 2.0 [46] through MhcVizPipe (v0.7.9) [38].", + "footnote": [], + "bbox": [ + [ + 117, + 80, + 880, + 413 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Evaluation of the different fragmentation isolation filters: \"standard\", \"None\" and \"HLAIp-tailored\". (a, b, c): Exemplary heatmaps of ion intensities (gray-scale) across the inversed ion mobility \\((1 / \\mathrm{K}_0)\\) vs m/z dimensions showing fragmentation events (red rhombus). (d - i): Correspondent peptides identified across the \\(1 / \\mathrm{K}_0\\) vs m/z dimensions colored by charge state, including all peptides (d, e, f) or only those with 8 to 13 amino acids (AAs) (g, h, i). (j, l,m): Length distribution and percentage of peptides (pie-charts) with 8 to 13 AAs or other lengths; cut-off at 20 AAs dropping \\(5.4\\%\\) , \\(1.6\\%\\) and \\(0.26\\%\\) of peptides identified for standard, None and HLAIp-tailored, respectively. (m) Average number of unique peptides identified per injection in each method (3 injection replicates, \\(mean \\pm sd\\) ). (m) Average number of MS2 scans triggered per injection in each method (3 injection replicates, \\(mean \\pm sd\\) ). Two-sided t-test, ns: \\(p > 0.05\\) , \\*: \\(p \\leq 0.05\\) , \\*\\*: \\(p \\leq 0.01\\) , \\*\\*\\*: \\(p \\leq 0.001\\) , \\*\\*\\*\\*: \\(p \\leq 0.0001\\) .", + "footnote": [], + "bbox": [ + [ + 145, + 180, + 852, + 675 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Evauation of the original-DDA-PASEF method (original) compared to the optimized Thunder-DDA-PASEF (Thunder) profiling of JY immunopeptides, and the effect of identification rescoring using MS²Rescore (Thunder + MS2R), considering only peptides of 8 to 13 amino acids long. (a) Average number of unique peptides identified per injection in each method (3 injection replicates, mean \\(\\pm s d\\) ; two-sided \\(t\\) -test, \\(\\mathrm{***}\\) : \\(p \\leq 0.0001\\) ). (b) Proportion of peptides (considering modifications) identified in function of their charge state. (c) Dynamic range plot showing the peptides identified (considering modifications), ranked in descending order (x-axis) in function of the average peak area across three replicates (y-axis); the dashed gray line indicates the lowest limit of identification for the original method. (d) Identification data completeness, measured as the proportion of peptides identified across three, two, or only one replicate. (e) Upset plot showing the number (barplot) and percentage (text) of 8-13-mers identified identified uniquely in each method or their combinations; the intersection matrix at the bottom indicates that the same peptides shown above (columns) were detected in the methods (rows) highlighted with a blue dot. (f) Total number of peptides identified in each workflow and the proportion predicted as strong-binders (SB, \\(rank \\leq 0.5\\%\\) ), weak-binders (WB, \\(0.5\\% < rank \\leq 2\\%\\) ) or non-binders (NB, \\(rank > 2\\%\\) ) by NetMHCpan 4.0 [37].", + "footnote": [], + "bbox": [ + [ + 120, + 240, + 880, + 544 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4: HLA class I ligandome of JY and Raji cells employing Thunder-DDA-PASEF, combining wild type and spike-transfected cells. (a) Size distribution of total peptides identified from JY and Raji cells. (b) Number of 8-13-mer peptides identified in each workflow and the proportion predicted as strongbinders (SB, \\(rank \\leq 0.5\\%\\) ), weak-binders (WB, \\(0.5\\% < rank \\leq 2\\%\\) ) or non-binders (NB, \\(rank > 2\\%\\) ) by NetMHCpan 4.0 [37] against the matched HLA alleles expressed by each cell line (JY = HLA-A02:01, B07:02, C07:02; Raji: HLA-A03:01, B15:10, C03:04, C04:01) (c) Charge distribution for the predicted HLA class I binders (HLAIps, SB & WB). (d) Total number of predicted HLAIps (SB & WB) identified (top) and protein groups covered (bottom) for JY, Raji, and in total. (e) Distribution of the number of HLAIps per protein group represented as boxplots (center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range) (top) and histogram (bottom); y-axis cut-off at 12 for simplicity, excluding 0.7% of JY HLAIps (13 to 34 Binders/Protein) and 1.4% of Raji HLAIps (13 to 53 Binders/Protein). (f) Overlap of HLAI ligand peptides (top) and protein groups (bottom) between JY and Raji. (g, h) Supervised clustering (GibbsCluster-2.0 via MhcVizPipe) showing the peptide sequence motifs corresponding to the specific allele motifs for JY and Raji HLAIps, respectively.", + "footnote": [], + "bbox": [ + [ + 216, + 100, + 777, + 685 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5: Spike HLA class I binder peptides (HLAIps) identified in JY and Raji transfected cells. (a, b) Count of protein-specific HLAIps predicted strong-binders (SB, \\(rank\\leq 0.5\\%\\) ) and weak-binders (WB, \\(0.5\\% < rank\\leq 2\\%\\) ) using NetMHCpan 4.0 [37] for spike (a) and the reporter GFP (b). (c) Peptide peak area distribution of the spike peptides (black dots) and all the HLAIps identified in JY (orange) and Raji (purple). (d) Characteristics of spike HLAIps identified in JY (top) and Raji (bottom) transfected cells. From left to right: sequence code name indicating their position within the protein sequence (s[N-ter]-[C-ter], e.g., s0691-0699 for SIIAYTMSL); sequence, with common peptides highlighted in gray; charge state (number of H+); the number of biological replicates (BR) and technical replicates (TR) where the peptide was identified; Log2 of the peptide peak area; Pearson's correlation coefficient (PCC) comparing the fragmentation spectrum of the endogenous peptide against synthetic peptides (S) or Prosit-predicted (P) [56, 34] calculated employing the Universal Spectrum Explorer (USE) [48]; indexed retention times (iRT) ratio (endogenous/synthetic); Immune Epitope Database and Analysis Resource (IEDB) [50] immune response frequency (RF) = proportion of subjects with positive immune response in B-cell or T-cell assays (dots = RF, lines = 95% confidence interval (CI) range, color scale = lower 95% CI, empty = not reported), relative to the total number of subjects tested for the corresponding peptide; binding affinity to JY and Raji HLA alleles predicted by NetMHCpan 4.0 [37], with labels indicating SBs and WBs.", + "footnote": [], + "bbox": [ + [ + 142, + 175, + 860, + 585 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6: Mirrored fragmentation spectra showing the spectrum from endogenous peptides at the top and synthetic or predicted spectra for two spike peptides. (a) SIIAYTMSLs0691-0699 (bottom = synthetic), and (b) TLKSFTVEKs0302-0310, (bottom = Prosit predicted); obtained by USE [48]; PCC = Pearson's correlation coefficient, SA = spectral (contrast) angle.", + "footnote": [], + "bbox": [ + [ + 98, + 357, + 940, + 570 + ] + ], + "page_idx": 28 + } +] \ No newline at end of file diff --git a/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac.mmd b/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac.mmd new file mode 100644 index 0000000000000000000000000000000000000000..7a5946b6f743d203fe1ca832d5fe2fc99df9264e --- /dev/null +++ b/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac.mmd @@ -0,0 +1,402 @@ + +# Thunder-DDA-PASEF enables high-coverage immunopeptidomics and identifies HLA class-I presented SarsCov-2 spike protein epitopes + +David Gomez-Zepeda davidgz.science@gmail.com + +Helmholtz- Institute for Translational Oncology Mainz (HI- TRON) https://orcid.org/0000- 0002- 9467- 1213 + +Danielle Arnold- Schild University Medical Center of the Johannes Gutenberg University Mainz + +Julian Beyrle Helmholtz- Institute for Translational Oncology Mainz (HI- TRON) + +Elena Kumm University Medical Center of the Johannes- Gutenberg- University Mainz + +Ute Distler University Medical Center of the Johannes Gutenberg University Mainz https://orcid.org/0000- 0002- 8031- 6384 + +Hansjorg Schild Johannes Gutenberg- University Medical Center + +Stefan Tenzer University Medical Center of the Johannes- Gutenberg- University Mainz https://orcid.org/0000- 0003- 3034- 0017 + +## Article + +Keywords: + +Posted Date: March 9th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2625909/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on March 13th, 2024. See the published version at https://doi.org/10.1038/s41467-024-46380-y. + +<--- Page Split ---> + +1. Thunder-DDA-PASEF enables high-coverage immunopeptidomics and identifies HLA class-I presented SarsCov-2 spike protein epitopes + +David Gomez- Zepeda \(^{1,2,*}\) , Danielle Arnold- Schild \(^{1}\) , Julian Beyrle \(^{1,2}\) , Elena Kumm \(^{1}\) , Ute Distler \(^{1,3}\) , Hansjörg Schild \(^{1,2,3}\) , Stefan Tenzer \(^{1,2,3,*}\) \(^{1}\) Institute for Immunology, University Medical Center of the Johannes- Gutenberg University, Mainz, 55131, Germany. \(^{2}\) Helmholtz- Institute for Translational Oncology Mainz (HI- TRON), Mainz, 55131, Germany. \(^{3}\) Research Center for Immunotherapy (FZI), University Medical Center of the Johannes- Gutenberg University, Mainz, 55131, Germany. \(^{*}\) To whom correspondence should be addressed: David Gomez- Zepeda, email: david.gomez- zepeda@dkfz- heidelberg.de; Stefan Tenzer, email: tenzer@uni- mainz.de + +## Abstract + +Human leukocyte antigen (HLA) class I peptide ligands (HLAIs) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIs is challenging due to their high diversity, low abundance, and patient- specific profiles. Here, we developed a highly sensitive method for identifying HLAIs using liquid chromatography- ion mobility- tandem mass spectrometry (LC- IMS- MS/MS). The optimized method, Thunder- DDA- PASEF, semi- selectively fragments HLAIs based on their IMS and m/z, thus increasing the coverage of immunopeptidomics analyses. Thunder- DDA- PASEF includes singly- charged peptides, which contributes to more than 35% of the HLAIs identifications. Combined with MS \(^{2}\) Rescore, Thunder- DDA- PASEF improved ligandome coverage by 150% compared to the original- DDA- PASEF method, and enabled in- depth profiling of HLAIs from two human cell lines, JY and Raji, transfected to express the SARS- CoV- 2 spike protein. We identified seventeen spike protein HLAIs, thirteen of which had been reported to elicit immune responses in human patients. + +<--- Page Split ---> + +## 1 Introduction + +Identifying ligands of the major histocompatibility complex (MHC) or human leukocyte antigen (HLA), also called immunopeptides, is key for developing vaccines and immunotherapies (extensively reviewed in [1, 2, 3]). Human HLA class- I complexes bind peptides (HLAIs) of typically 9 to 12 amino acids generated by a multi- step process called antigen processing, which involves multiple proteolytic events by the proteasome and aminopeptidases [4, 5, 6, 7, 8]. Loaded HLA complexes are then displayed on the cell surface, where \(\mathrm{CD8^{+}}\) T- cells scrutinize them. Detection of a "non- self" antigen, e.g., HLAIs derived from viral proteins or mutated cancer- related proteins, leads to the efficient elimination of the presenting cell by cytotoxic T lymphocytes. Thus, non- self HLAIs constitute key targets for developing peptide or mRNA vaccines in the context of personalized immunotherapies, or diagnostic tools. Various in silico tools have been developed to predict HLA- binding peptides from genomic, transcriptomic, or riboSeq data. Still, most predictors are primarily based on HLA binding affinity, thus not fully considering the antigen processing and presentation mechanisms, resulting in discrepancies between predicted and presented HLAIs [9, 10]. Therefore, liquid chromatography mass spectrometry (LC- MS)- based immunopeptidomics is essential for directly identifying HLA class I presented peptides from cells, tissues, and biofluids [9, 11]. + +LC- MS immunopeptidomics faces different challenges than bottom- up proteomics, where proteins are usually digested using trypsin (reviewed in [3, 12]). HLAIs are generated by a complex multi- step process, including various proteolytic events [13, 14]. This results in peptides with restricted size and sequence patterns imprinted by the specificities of TAP transport and HLA binding. While these motifs differ between individual HLA alleles, they restrict the sequence space presented by a single allele. Thus, immunopeptidomics samples are more likely to contain isobaric peptides, potentially co- eluting from the LC, than enzyme- digested samples [2]. Since tryptic peptides are usually multi- charged, typical bottom- up proteomics workflows often omit the fragmentation and identification of singly- charged ions, which are more challenging to identify. In addition, singly- charged peptides are often masked by chemical noise, and their fragmentation generates many uncharged segments not detected by the MS [2]. Moreover, individual HLAIs are low abundant, and the sample preparation recovery yields are low (around 0.5- 3% [15]). These factors demand tailored and high- sensitivity LC- MS methods and have major implications in database searches. The unspecific cleavage of HLAIs increases the search space by up to 2 orders of magnitude compared to tryptic digests. This impairs the discrimination of false positive from true positive peptide- spectrum matches (PSMs), negatively impacting peptide identification yield and confidence [16]. + +Coupling ion mobility separation (IMS) to LC- MS provides an extra dimension of separation, resolving ions in the gas phase by their size and shape. This enhances the signal- to- noise ratio and resolves isobaric ions, thus increasing the number and confidence of peptide identifications. In the timsTOF Pro instruments, a dual trapped ion mobility spectrometry (TIMS) analyzer is employed to perform a parallel accumulation- serial fragmentation (PASEF) of ions, resulting in a high sequencing speed without compromising sensitivity for data- dependent + +<--- Page Split ---> + +53 acquisition (DDA- PASEF) [17, 18], which has already been proven to perform well for immunopeptidomics [19]. 54 During the ongoing Covid- 19 pandemic, there have been significant efforts to identify SARS- CoV- 2 HLAIps, 55 mainly focusing on characterizing the immunogenicity in vitro or in vivo of large libraries of synthetic peptides 56 of in silico predicted HLA- binders (25 studies reviewed in [20]). This has provided important insights into 57 possible immunodominant regions in the viral proteome, HLA allele- dependent responses to SARS- CoV- 2, and 58 the protection capabilities of vaccines (reviewed in [20, 21, 22]). More than 2,000 possible HLA- binding peptides 59 have been predicted from the SARS- CoV- 2 genome [23]. However, only a few SARS- CoV- 2 immunopeptides have 60 been detected by LC- MS until now [24, 25, 26], including less than ten HLAIps for the spike glycoprotein [24, 26], 61 the main target of vaccines and diagnostic tests. This emphasizes the challenges of LC- MS immunopeptidomics 62 and the need for more sensitive and robust methods. + +63 Here, we present Thunder- DDA- PASEF, an optimized LC- IMS- MS method for immunopeptidomics and 64 its application in the discovery of SARS- CoV- 2 spike protein derived HLAIps. The optimized method uses 65 an extended TIMS separation time (300 ms) to improve IMS resolution, and sensitivity [17, 27]. To include 66 singly charged peptides while efficiently using instrument cycle time, precursors are selected by using a tailored 67 isolation polygon for semi- selectively fragmenting potential HLAIps. Compared to the standard method (100 68 ms TIMS, bottom- up proteomics- optimized isolation polygon), Thunder- DDA- PASEF increased the HLAIps 69 identifications from JY cells by 2.3- fold, including more than 35% of identifications derived form singly- charged. 70 Moreover, MS2Rescore- based rescoring [16] further boosted the identification to 3.5- fold relative to the non- 71 rescored standard DDA- PASEF. Subsequently, we employed Thunder- DDA- PASEF to study the HLAIp ligand- 72 dome repertoire of two cell lines recombinantly expressing the canonical spike protein of SARS- CoV- 2. This 73 resulted in deep coverage of 14,313 and 17,806 peptides from JY and Raji cells, respectively, including seventeen 74 HLAIps derived from the SARS- COV- 2 spike protein. Notably, thirteen of these peptides have been previously 75 reported to elicit immune responses in human patients, confirming the potential of our improved method for 76 efficient epitope discovery. In conclusion, optimized Thunder- DDA- PASEF achieved deep and reproducible 77 profiling of the HLA class I ligandome. + +## 2 Results + +### 2.1 General workflow for LC-IMS-MS immunopeptidomics + +For our immunopeptidomics experiments, we followed the general procedure shown in Fig. 1 and described in Material and Methods. The settings used for the LC- MS methods and data processing are fully detailed in Supplementary Material S2a and the ready- to- use MS method for timsTOF Pro instruments is included in Supplementary Material S2b. Briefly, we enriched HLAIps from JY cells by immunoprecipitation (W6/32 antibody), and analyzed them by nanoLC- IMS- MS on a nanoElute coupled to timsTOF- Pro- 2 in DDA- PASEF mode, using PEAKS XPro for subsequent peptide identification. We performed several iterations to optimize + +<--- Page Split ---> + +our LC- IMS- MS method for identifying HLA class I ligands, as described in the following sections. + +### 2.2 An HLAIp-tailored DDA-PASEF fragmentation scheme including singly-charged ions efficiently identified possible HLAIps + +Contrarily to tryptic peptides, HLAIps originate from a large diversity of antigen processing events [13, 14] and do not necessarily contain basic amino acid residues [2]. Thus, many HLAIps can only be detected as singly- charged ions in LC- MS since only their N- ter residue can carry a positive charge ( \(\mathrm{H^{+}}\) ). Although this varies depending on the HLA alleles, up to \(40\%\) of singly- charged ions have been reported for peptides bearing hydrophobic anchor residues such as HLA- B07:02 [28, 29]. In addition, HLAIps have a restricted size of typically 9 to 12 amino acids (AAs) [2], but between 8 to 13 in some instances [30, 31]. For this reason, HLAIp- immunopeptidomics workflows have recently incorporated the fragmentation of singly- charged ions (with \(2^{+}\) and \(3^{+}\) ) within the \(\mathrm{m / z}\) range of possible HLAIps [2, 19, 29, 28, 32, 33, 34, 35, 36]. We hypothesized that the IMS separation and sensitivity of the timsTOF Pro- 2 could provide high- quality MS2 spectra to identify singly- charged peptides confidently. + +First, we tested the original- DDA- PASEF method for proteomics [17] to analyze JY HLAIps samples (Fig. 2a, d, g). DDA- PASEF takes advantage of the charge- state- dependent mobility separation to selectively fragment ions detected within an isolation polygon on the inverse reduced ion mobility ( \(1 / \mathrm{K}_{0}\) ) vs. \(\mathrm{m / z}\) space. Since it was designed for tryptic peptides, the standard isolation polygon covers the multiply- charged ion cloud, clearly separated from the singly- charged ones (Fig. 2a). This resulted in almost 5,000 unique peptides from three injection replicates of JY HLAIps (Fig. 2a), mainly comprising doubly- charged ions ( \(89\%\) , Fig. 2b) and almost \(77\%\) of 8- 13- mers (Fig. 2g, j). As expected, most singly- charged ions were excluded from fragmentation, and only a few were identified due to IMS peak tailing into the isolation polygon. + +Our next step was to remove the isolation polygon (Fig. 2b). Omitting the isolation polygon enabled the fragmentation of singly- charged peptides, representing more than half ( \(54.5\%\) ) of all the peptides identified and \(59.6\%\) of the 8- 13- mers (Fig. 2e, h). Furthermore, the proportion of peptides with 8 to 13 AAs was \(12.4\%\) higher than in the standard- polygon (Fig. 2h, j), corresponding to \(72\%\) more 8- 13- mers identified on average \((p \leq 0.0001\) , Fig. 2m). However, without an isolation polygon, many low \(\mathrm{m / z}\) singly- charged ions and high mass multiply- charged ions were fragmented (Fig. 2b). + +Therefore, we designed fragmentation isolation polygons covering the singly- charged and multiply- charged 8- 13- mer peptides 2c, h) (Table 1). This HLAIp- tailored scheme efficiently identified peptides within the isolation polygon (Fig. 2c, f, i), roughly maintaining the charge distribution of peptides identified, with \(56.4\%\) of all the ions and \(59.7\%\) of the 8- 13- mers being singly- charged. The proportion of 8- 13 mers was almost \(92\%\) , which is \(15\%\) and \(2.6\%\) higher than the standard- and no- polygon, respectively (Fig. 2l, j, k, respectively). As a result, the HLAIp- tailored polygon increased the identification of 8- 13- mers by \(75\%\) relative to the standard \((p \leq 0.0001\) , 2m)). Compared to no polygon, the HLAIp- tailored polygon resulted in \(24\%\) fewer MS2 scans + +<--- Page Split ---> + +\(p \leq 0.001, 2\mathrm{n}\) , but a similar yield of 8- 13- mers identified (Fig. 2m). This \(18\%\) increase in the identification rate shows that the HLAIp- tailored polygon used the cycle time more efficiently to fragment 8- 13- mers. In contrast, without an isolation polygon, a large proportion of the cycle time was used inefficiently to fragment ions that are not of interest for HLAIp profiling. These may include non- peptidic small ions or larger peptides (Fig. 2b, Supplementary Fig. S1a, b) originating from the degradation of HLA proteins, the antibody, or other co- enriched proteins (Supplementary Fig. S1c). Once having established the capabilities of DDA- PASEF with the HLAIp- tailored isolation scheme for immunopeptidomics, we optimized several other parameters of the MS method (detailed in Supplementary Material S2). + +### 2.3 Optimized Thunder-DDA-PASEF enhanced the identification of 8-13-mers by 2.2-fold + +In PASEF methods, each analysis cycle comprises several frames where the trapping TIMS tunnel accumulates a package of ions. Simultaneously, the second TIMS resolves the previous package of ions by ramping down the elution voltage. Increasing TIMS times enhances IMS resolution and accommodates more fragmentation events per MS2 frame while preserving the sensitivity [17]. Raising the TIMS time from 100 to 300 ms resulted in an \(80\%\) increase in peptide identification, while no substantial increase was observed between 400 ms and 300 \((< 5\%\) increase) (Supplementary Fig. S2a, b, c, d). However, the longer cycle times resulted in five- fold fewer MS1 frames and doubled the median coefficient of variation (CV) at 400 ms compared to 100 ms. Since the peak area reproducibility is essential for quantitative comparisons between samples (e.g., diseased vs. control), we compensated for this effect by decreasing the number of MS2 frames/cycle from 10 to 3, and the MS2 cycle overlap from 4 to 1 (Fig. S2e, f, g, h). This resulted in a cycle time of 1.2 s and reduced the median peak area CV from \(19.3\%\) to \(10.3\%\) (Fig. S2d,h). In addition, activating the high- sensitivity mode of the timsTOF Pro- 2, which uses detector voltages optimized for low sample amounts, further increased the number of HLAIps identified by \(30\%\) (Supplementary Fig. S3). + +In summary, the optimized method resolves ions using a 300 ms TIMS ramp, fragmenting mainly ions with \(1^{+}, 2^{+}\) , and \(3^{+}\) charges in 3 MS2 frames per MS1 frame within a 1.2 s cycle time and takes advantage of the high- sensitivity mode. Since the HLAIp- tailored isolation polygon resembles a lighting or thunder icon, we termed the fully optimized method Thunder- DDA- PASEF. In contrast, the original- DDA- PASEF designed for proteomics samples uses 100 ms ramps and selectively fragments multiply- charged ions in 10 MS2 frames per MS1 frame within a 1.2 s cycle time. + +We compared Thunder- DDA- PASEF to the original- DDA- PASEF method by analyzing triplicate injections of JY HLAIps (equivalent to approximately 50 million cells/injection, Supplementary Material S3). Thunder- DDA- PASEF identified 2.2- fold the number of 8- 13- mers than the original method \((p < 0.0001\) , Fig. 3a). This was partly due to the inclusion of singly- charged peptides in Thunder- DDA- PASEF, constituting \(48\%\) of the 8- 13- mers in this data set (Fig. 3b). Thunder- DDA- PASEF improved the dynamic range for identification + +<--- Page Split ---> + +by almost half an order of magnitude towards the low abundant species (Fig. 3c). The number of peptides identified across all three replicates was \(8.4\%\) higher in Thunder- DDA- PASEF than in the original- DDA- PASEF, indicating a slight improvement in the data completeness (Fig. 3d). Although \(8.7\%\) of the peptides were only identified in the original method (Fig. 3e), this could be due to the sampling stochasticity of DDA. Then, we used NetMHCpan- 4.1 [37] via MhcVizPipe [38] to predict peptide HLA- binding, which provides a ranking classifying the peptides into strong- binders (SB, \(rank \leq 0.5\%\) ), weak- binders (WB, \(0.5\% < rank \leq 2\%\) ) or non- binders (NB, \(rank > 2\%\) ). When focusing on the peptides predicted to bind JY HLA alleles, the 8- 13- mers identified comprised \(88.2\%\) SB and \(7.8\%\) WB in the original method and \(85.4\%\) SB and \(9.1\%\) WB in Thunder- DDA- PASEF (Fig. 3f, Supplementary Material S4). Altogether, these results proved a 2.2- fold increase in the coverage of the immunopeptidome using Thunder- DDA- PASEF compared to the original- DDA- PASEF (9,524 and 4,334 HLAIs, respectively). + +### 2.4 Machine learning-based rescoring via MS2Rescore enhanced the identification of HLAIs and data completeness by more than 15% + +Several post- processing tools have shown improvements in immunopeptide identification by rescoring peptide spectrum matches (PSMs) based on characteristics disregarded in the initial search [16, 34, 39, 40]. For instance, MS2rescore (MS2R) [16] integrates the machine learning prediction of retention and fragmentation peak intensity using DeepLC [41] and MS2PIP [42, 43, 44], respectively, with the semi- supervised machine learning- based FDR calculation of Percolator [45]. Since this strategy has shown the potential to boost immunopeptide identification [16], we decided to implement it in our workflow. + +Rescoring the results of Thunder- DDA- PASEF from JY IP- enriched HLAIs (Supplementary Material S3) significantly increased the average number of 8- 13- mer peptides identified per injection by \(29.1\%\) ( \(p < 0.0001\) , Fig. 3a). The proportion of singly- charged peptides decreased (Fig. 3b) not due to a drop in their numbers but because most newly identified peptides were doubly charged ( \(74.5\%\) ). Probably, the performance of MS2Rescore for singly- charged ions was lower due to the fewer singly- charged ions in the MS2PIP immunopeptidomics model training set. Thus, training a predictor model with orthogonal Thunder- DDA- PASEF data could improve its performance. + +Novel identifications were obtained across the whole dynamic range indicating that rescoring performed well even for low- intensity ions (Fig. 3c). Despite applying a stringent confidence filter independently for each file (PSM \(FDR \leq 0.01\) ), \(77.1\%\) of the peptides were consistently identified across all three replicates in the rescored results, meaning a \(16.7\%\) increase in data completeness (Fig. 3d). In addition, only a few peptide identifications were dropped by MS2Rescore ( \(< 1.5\%\) , Fig. 3e), and it also recovered 263 peptides identified in the non- rescored original- DDA- PASEF but not in Thunder. The proportion of SB and WB was not affected by rescoring, indicating that no bias was introduced. The benefits of rescoring Thunder- DDA- PASEF identifications are summarized in a \(14.7\%\) increase in the number of predicted HLAIs identified, yielding a total + +<--- Page Split ---> + +of 10,931 (Fig. 3f). Collectively, the Thunder- DDA- PASEF + MS2R strategy resulted in a 2.5- fold coverage of HLAIps compared to the non- rescored original- DDA- PASEF data for JY HLAIp IP- enriched peptides (Fig. 3f, Supplementary Material S4), with an average of 9,821 HLAIps per injection. + +In summary, combining the optimized Thunder- DDA- PASEF with MS2Rescore resulted in a highly sensitive and reproducible workflow. This level of coverage could enable deep profiling of immunopeptides in patient samples and the comparability between healthy and pathological tissue for the discovery of disease- specific antigens. + +### 2.5 Thunder-DDA-PASEF enabled in-depth characterization of the HLA class I ligandome of JY and Raji cells + +We tested our optimized workflow to characterize the HLA class- I immunopeptidome of JY and Raji cells transfected to express a segment of the SARS- CoV- 2 spike protein (Supplementary Material S5). Thunder- DDA- PASEF + MS2R identified in total 23,147 peptides from JY and 29,397 peptides from Raji, comprising 78% of 8- 13- mers, with a median length of 9 AAs (Fig. 4a), as expected for HLAIps. The reproducibility between biological replicates ranged between 35.8% and 62.7% 8- 13- mers identified in all the samples of the same genotype, and 67.7% to 81.3% regarding the proteins covered (Supplementary Fig. S4). Based on the HLA- binding prediction (NetMHCPan- 4.1 [37] via MhcVizPipe [38], Supplementary Material S6), the 8- 13- mers included 78.9% binders for JY (70% SB, 8.9% WB) and 77.6% for Raji (67.2% SB, 10.4% WB) (Fig. 4b), showing the respective peptide sequence motifs, as indicated by supervised clustering (GibbsCluster- 2.0 [46], Fig. 4g, h). A lower proportion of HLAIps was detected as singly- charged ions in Raji, compared to JY (30.1% vs. 42.9%). This was due to the presence of basic amino acids at the anchor positions for Raji HLA alleles (Fig. 4g), including lysine or arginine at the C- ter (HLA- A03:01) or histidine at the second position (HLA- B15:10, HLA- C04:01). In contrast, the anchor residues binding JY HLA alleles were dominated by apolar amino acids (Fig. 4h). + +Thunder- DDA- PASEF achieved an extensive coverage of protein- HLAIp representation. A total of 14,074 and 17,469 HLAIps were detected in JY and Raji, respectively, summing up to 30,948 peptides (Fig. 4d, top). These peptides corresponded to 5,660 protein groups in JY, 6,170 in Raji, and 8,214 in total (Fig. 4d, bottom). Each protein group was represented by a median of 2 HLAIps per protein group and 75% of them with one to three peptides for both cell lines (Fig. 4e). As a comparison, the DIA analysis of JY HLAIps provided a median of one HLAIp per protein [32] despite a deep coverage of 7,627 peptides. This further shows the potential of our workflow to provide an in- depth characterization of the immunopeptidome, which may unravel novel antigen processing and presentation mechanisms. + +Although only 1.8% of all HLAIps were detected in both JY and Raji, 44% of all the protein groups were covered by the ligandomes of the two cell lines (Fig. 4f, top and bottom, respectively). A gene ontology (GO) enrichment analysis using GOrilla [47] indicated a significant over- representation ( \(FDR \leq 0.001\) ) of proteins + +<--- Page Split ---> + +involved in essential processes, such as the metabolism of nucleic acids (GO:0090304), macromolecule biosynthesis (GO:0034645), macromolecule localization (GO:0033036), and regulation of the cell cycle (GO:0022402) (Supplementary Material S7 and S8). Thus, the cell lines presented complementary peptides for these same crucial proteins due to their different HLA alleles and probably also due to differences in the antigen processing pathway. Because of the large number of HLAIs covered (30,984 binders) (Fig. 4b, d), including more than 11,000 singly-charged peptides (Supplementary Material S3), this combined immunopeptidome of JY and Raji cells constitutes an essential resource for future exploitation. + +### 2.6 Thunder-DDA-PASEF identified seventeen spike HLAIs in JY and Raji spike-transfected cells + +To explore the potential of Thunder- DDA- PASEF on a clinically relevant subject, we focused on the transfected SARS- CoV- 2 spike protein, and the GFP reporter included in the construct. Importantly, peptides from these proteins were only detected in the transfected cells and not in the wild- type cells. Three GFP- derived HLAIs were identified in JY and six in Raji cells (Fig. 5 b), serving as a control for successful antigen processing of the transfected constructs. Five spike HLAIs were identified in JY and thirteen in Raji (Fig. 5a) across a large dynamic range corresponding to four orders of magnitude (Fig. 5c). While the Raji spike HLAIs were distributed across the whole dynamic range, they were mainly in JY's middle to low range. The sequence and characteristics of the spike HLAIs are shown in Fig. 5d and detailed in the Supplementary material S9. Nomenclature in Fig. 5c and d denotes identified spike HLAIs (e.g., SIIAYTMSL0691- 0699) both by peptide sequence and position (N- to C- ter) in the full- length spike protein. Notably, six of the thirteen spike HLAIs were singly charged, showing the advantage of the Thunder HLAIp- tailored isolation polygon for identifying potential clinically relevant immunopeptides. + +In addition to the \(1\%\) FDR threshold applied, the spike HLAIs were assessed based on the number of identifications across biological and technical replicates (n BR, n TR; Fig. 5d, yellow to green scales) and by the similarity of their fragmentation spectra against synthetic peptides or in silico predictions, based on the Pearson correlation coefficient (PCC) [48] (Fig. 5d, blue scale with letters, S = synthetic, P = predicted). The mirrored spectra comparisons are shown in Supplementary Material S10. At the same time, SIIAYTMSL0691- 0699 and TLKSFTVEK0302- 0310 are shown in Fig. 6 as examples of the confident identification of peptides with high and low abundance, respectively. Around \(82\%\) of the reported spike HLAIs were identified in two biological replicates with a PCC \(> = 0.85\) , indicating both robust sample preparation and high- confidence identifications. The synthetic peptides analyzed independently with the same method were eluted at similar indexed retention times (iRT) as the corresponding endogenous peptides (ratio iRT endogenous/synthetic \(> = 0.99\) ). Even though peptides GVLTESNKK0550- 0558 from Raji and RLQSLQTYV1000- 1008 from JY were identified in only one injection replicate in one of the biological replicates, their PCC were 0.94 and 0.96, respectively ( \(FDR < 0.005\) ). Peptide AIHVSGTNGTK0067- 0077 showed a low PCC (0.47) against the predicted spectra but was detected in + +<--- Page Split ---> + +five injection replicates across both biological replicates with an \(FDR< 0.0005\) , thus validating its detection. + +While a large proportion of the HLAIs were predicted to be strong binders (Fig. 5d), there was a deficient number of HLAIs for both the HLA- C alleles in Raji (HLA- C04:01) and JY (HLA- C07:02). This could be due to the low expression of this gene in JY cells, whose effect on its immunopeptidome has been previously reported [49]. Interestingly, some spike HLAIs were predicted to bind to the HLA alleles of both cell lines, but only SIIAYTMSL0691- 0699 was identified in both cell lines. Once more, this highlights the need for direct validation of in silico- predicted HLA class I binders. However, the challenge of LC- MS immunopeptidomics is exemplified here since only one of the seventeen spike HLAIs had been previously reported by MS (SIIAYTMSL0691- 0699)[40]. Moreover, four represent completely novel identifications (AIHVSGTNGTK0067- 0077, YGVSPTKL0380- 0387, RVYSTGSNVFQTR0634- 0646, NRALTGIAV0764- 0772). The remaining thirteen spike HLAIs have been reported to exhibit positive results in T- cell or MHC ligand assays according to the IEDB [50] (December 18, 2022) (Fig. 5d, dot range plot). This shows the capabilities of Thunder- DDA- PASEF for identifying potential HLA class I- restricted immunogenic targets which could be employed for vaccine development. + +In summary, we report seventeen spike peptides identified with high stringency and confidence, which are predicted to bind HLA class I in two cell lines expressing different HLA alleles. Accordingly, this set of peptides constitutes a key resource, comprising novel spike HLAIs, and confirms many previously reported peptides capable of eliciting a T- cell response. + +## 3 Discussion + +Here, we present Thunder- DDA- PASEF, an LC- IMS- MS method tailored and optimized for identifying HLA class I peptide ligands (HLAIs). We showed that the HLAIs- tailored isolation polygon enabled the identification of singly- charged peptides, expanding the universe of identifiable MHC peptide ligands. Thunder- DDAPASEF uses a thunder- shaped isolation polygon (Fig. 2), optimized detector voltages (high sensitivity mode), enhanced IMS resolution (300 ms TIMS), and fewer MS2 frames (3 MS2 frames/cycle, 1 cycle overlap), resulting a cycle time of 1.2 s, compatible with nanoLC peak width (Supplementary Fig. S2h, Supplementary Material S2a and S2b). Altogether, this resulted in more than a 2.2- fold higher number of 8- 13- mers identified from JY cells, compared to the standard DDA- PASEF optimized for proteomics samples (excluding singly- charged ions, 100 ms TIMS ramp, 10 MS2 frames/cycle, 4 overlap) (Fig. 3). MS2Rescore further boosted the identifications up to 2.5- fold compared to the standard, unsecured DDA- PASEF. In addition, Thunder + MS2Rescore improved the identification data completeness, reliably and constantly identifying 77.1% of the peptides across three technical replicates (Fig. 3a). + +Field asymmetric waveform ion mobility spectrometry (FAIMS) has been combined with LC- MS to identify singly- charged HLAIs [29]. However, FAIMS acts as a gas- phase fractionation device, filtering ions in function of their mobility in the electric field. Since only a population of ions can be analyzed simultaneously, identifying + +<--- Page Split ---> + +multiply and singly- charged HLAIps requires dividing the cycle time within an LC- MS run between or performing multiple injections per sample [29]. In contrast, TIMS- MS profiles ions across a \(1 / \mathrm{K}_0\) range. In addition, PASEF maximizes the duty cycle by trapping a package of ions while the previous is being separated and synchronizing ion fragmentation with TIMS elution. We adapted this concept for HLAIps by taking advantage of their size- and charge- dependent separation forming two distinct ion clouds for the singly and multiply- charged 8- 13- mer peptides. Thus, PASEF- MS2 frames are efficiently used to fragment singly- charged ions during the first half of the TIMS ramp and multiply- charged during the second half. + +Additional adaptations could further improve the identification of immunopeptides. For instance, the Thunder isolation polygons could be more restrictive towards 9 to 12- mers to improve fragmentation selectivity for more challenging samples. For example, soluble HLAs enriched from plasma samples tend to include larger peptides resulting from the degradation of proteins adhering non- specifically to the beads, such as blood clotting and other plasma proteins [51]. Here, we decided to employ broad limits to account for variability between HLA alleles and to accommodate slight variations in the instrument (e.g., IMS variations between days). In addition, disease- associated HLAIps can be composed of larger sequences [30, 52, 53] or include modifications that are key for their immunogenicity [1, 54, 55]. + +Sensitivity and reproducibility could be further improved by using a data- independent acquisition (DIA) method including singly- charged ions. Although DIA requires spectral libraries for peptide identification, recent publications have shown its value for immunopeptidomics [32, 40]. For instance, using Orbitrap instruments, more than 97% of the combined identifications from 3 DDA runs used to create the library were identified in each single DIA injection of HLAIp- enriched peptides from cell lines. Using this strategy, Pak et al. [32] identified 7,627 HLAIps per injection of JY cell W6/32 IP- enriched peptides. However, sample fractionation by SPE or in the gas phase, or at least multiple DDA injections, is required to obtain the spectral libraries. In contrast, Thunder- DDA- PASEF can achieve higher HLAIps identification coverage in a single run (10,000 on average). Considering this, we propose a future strategy where a spectral library is acquired using Thunder- DDA- PASEF and then used to identify the peptides for quantitative DIA analysis. + +Thunder- DDA- PASEF enabled the deep profiling of the HLA class I ligandomes from two cell lines with distinct HLA alleles. We detected 14,074 predicted HLAIps from JY and 17,469 from Raji, with a median coverage of two HLAIps per protein, surpassing the number of HLAIps identified for a single cell line in previous publications [32, 40, 49]. In total, 30,984 HLAIps were identified (Fig. 4b, d), including more than 11,000 singly- charged peptides (Supplementary Material S3). Thus, this combined data set constitutes an important resource for future exploitation (data available via ProteomeXchange, identifier: PXD040385). For instance, using the identifications for training DeepLC and MS2PIP prediction models could further improve the performance of MS2Rescore on timsTOF immunopeptidomics data [16], and other prediction algorithms could be explored ([34, 40, 56]). In addition, different strategies for data analysis remain to be evaluated (Fragpipe, MSmill). Besides, a deeper PTM search could be performed using the PTM algorithm from PEAKS [57], or PROMISE + +<--- Page Split ---> + +324 [55]. + +The onset of the ongoing SARS- CoV- 2 pandemic has fueled the discovery of antigen candidates for vaccination, employing in silico prediction algorithms, genetic screens, or peptide library T- cell response assays. Even though immunogenicity testing of hypothesized vaccine candidates yielded some positive outcomes (reviewed in [20, 21, 22]), direct evidence of MHC peptide ligand antigens relies mainly on direct identification by LC- MS. The 17 SARS- CoV- 2 spike HLAIps (Fig. 5d) identified included thirteen peptides with proven immunogenicity (IEDB) and four possibly novel antigens that could be explored as targets for therapy development. Notably, six of the seventeen spike peptides were only identified as singly charged ions, and only the peptide identified in both cell lines (SIAYTMSL0691- 0699) was reported by MS before ([40]). Altogether these results show that Thunder- DDA- PASEF substantially expands the MS- detectable immunopeptidome providing the means for reproducible antigen discovery and direct validation of immunopeptides hypothesized by non- MS methods. + +In summary, Thunder- DDA- PASEF enables an in- depth coverage of HLAIps in a highly reproducible manner. This opens new opportunities to dig deeper into the immunopeptidome in our search to discover novel and specific antigens to target infectious diseases and cancer. + +## 4 Methods + +### 4.1 Cell culture + +The human B lymphoblastoid cell line JY expressing HLA- A02:01, B07:02, C07:02 was purchased from ATCC and the human Burkitt lymphoma cell line Raji expressing HLA- A03:01, B15:10, C03:04, 04:01 was obtained by the DSMZ- German Collection of Microorganisms and Cell Cultures. Both cell lines were maintained in RPMI1640 medium supplemented with 10 % FCS (Gibco), 2 mM glutamine, 1 mM sodium pyruvate, 100 units/ml penicillin and 100 μg/ml streptomycin. Cells were harvested at 220 x g for 10 min and washed three times with 1x PBS prior counting and freezing at - 80°C until further use. + +### 4.2 Cell transfection + +The pcDNA3.1- SARS2- spike vector containing the full- length cDNA encoding for the SARS- CoV2 spike protein was obtained from Fang Li (Addgene plasmid #145032 ; https://www.addgene.org/145032/) [58]. The spike S cDNA was split into S1 (2016 bp) and S2 (1761 bp) subunits for cloning by PCR into the NheI and XhoI restriction sites from the multiple cloning site of the pcDNA3.1+P2AeGFP vector (Genscript). The following oligonucleotides (all purchased by Sigma) were used : GCAT GCT AGC ATG TCT CAG TGC GTG AAC CTG ACT ACT AGA ACC and GCAT CTC GAG ACG GCG AGC CCT CCT TGG GGA GTT GGT CTG GGT CTG for the S1 cDNA and GCAT GCT AGC ATG AGC GTG GCC AGC CAG TCC ATC ATC GCC TAC and GCAT CTC GAG AGC GGG AGC GAC CTG GGA TGT CTC GGT GGA G for the S2 cDNA cloning. To generate stable JY and Raji transfectants expressing either the S1 or the S2 protein fragments + +<--- Page Split ---> + +(Supplementary Material S1, Material and Methods), 2 million cells were exposed to 230 V and \(500 \mu \mathrm{F}\) in the presence of \(10 \mu \mathrm{g}\) plasmid DNA using the Bio- Rad Gene Pulser II. After electroporation, cells were cultured 24 h before starting G418 (Gibco) selection at a concentration of \(400 \mu \mathrm{g / ml}\) for JY cells and \(800 \mu \mathrm{g / ml}\) for Raji cells. G418- resistant and eGFP- expressing cells were selected by three rounds of screening using a FACS Aria (BD Biosciences) at the Core Facility of the Research Center for Immunotherapy (University Medical Center, Johannes Gutenberg University Mainz). + +### 4.3 Immuno-affinity purification of HLA peptide ligands + +HLA class I ligands were enriched by immunoprecipitation as described by [59] with modifications [60]. Briefly, 500 million cells were washed three times with PBS, harvested, flash- frozen, and stored at \(- 80^{\circ} \mathrm{C}\) until further preparation. The cell pellets were thawed and lysed in a non- denaturant buffer (1% CHAPS in PBS (m/v)) aided by sonication. Immunoprecipitation was performed using an anti- panHLA Class I antibody (W6/32, anti- HLA- A, - B, - C), immobilized on CNBr- activated beads. After overnight incubation, the beads were washed once with PBS and once with water before peptide ligands were eluted under acidic conditions (0.2% TFA (v/v)). Next, peptides were ultrafiltered (10 kDa cutoff) and then desalted by SPE on a Hydrophilic- Lipophilic- Balanced sorbent (HLB, Waters Corp.), applying 35% ACN (v/v) + 0.1% TFA (v/v) for elution. Finally, dried peptides were dissolved in \(15 \mu \mathrm{L}\) of water with 0.1% FA (v/v) for subsequent LC- MS/MS analyses. + +### 4.4 LC-MS/MS + +NanoLC- MS analysis was performed using a nanoElute coupled to a timsTOF- Pro- 2 mass spectrometer. The desalted peptides were directly injected in a C18 Reversed- phase (RP) analytical column (Aurora \(25 \mathrm{cm} \times 75 \mu \mathrm{m}\) ID, \(120 \mathrm{A}\) pore size, \(1.7 \mu \mathrm{m}\) particle size, IonOpticks, Australia) and separated using either a \(47 \mathrm{min}\) or \(110 \mathrm{min}\) gradient (Supplementary Material S2a) increasing the proportion of phase B (ACN + 0.1% FA (v/v)) to phase A (water + 0.1% FA (v/v)), as detailed in Supplementary Material S2. A Captive Spray source was used for ionization, with a capillary voltage of \(1600 \mathrm{V}\) , dry gas at \(3.0 \mathrm{L} / \mathrm{min}\) , dry temperature at \(180^{\circ} \mathrm{C}\) , and TIMS- in pressure of \(2.7 \mathrm{mBar}\) . MS data were acquired in DDA- PASEF mode. Different MS parameters were evaluated during method development, as detailed in Supplementary Material S2a. The JY and Raji spike- transfected data set was acquired using the optimized conditions described in the following lines. HLAIp IP- enriched, ultrafiltered, and desalted peptides were analyzed in three injection replicates each, using a volume of \(1.5 \mu \mathrm{L} / \mathrm{injection}\) , equivalent to \(50 \mathrm{million}\) cells from the original sample. Peptides were separated in a \(110 \mathrm{min}\) . gradient from \(2 \%\) to \(37 \%\) of ACN + 0.1% FA (v/v). The MS was configured with the optimized Thunder- DDA- PASEF method, employing an HLAIp- tailored isolation polygon (Fig. 2), a \(300 \mathrm{ms}\) TIMS ramp, three MS2 frames/cycle, one cycle overlap, using the high- sensitivity mode (optimized detector voltages). The settings used for LC- MS are detailed in Supplementary Material S2a and the timsTOF Pro method is included as Supplementary Material S2b. + +<--- Page Split ---> + +### 4.5 Peptidomics database search + +Data analysis was performed in PEAKS XPro (v10.6, build 20201221). Raw LC- MS files were loaded with the configuration for timsTOF DDA- PASEf data with CID fragmentation. The option timstof_feature_min_charge (in file PEAKSStudioXpro\algorithmpara\feature_detection_para.properties) was set to 1 to allow the identification of singly- charged features. The protein database was composed of the UniProtKB (Swiss- Prot) reference proteomes of Homo sapiens (Taxon ID 9606, downloaded 02/Feb/2020), Epstein- Barr virus (strain GD1, Taxon ID 10376, downloaded 06/Feb./2022), GFP from Aequorea victoria (P42212), and SARS- CoV- 2 (Taxon ID 2697049, downloaded 10/March/2021), as well as the SiORF1 reported by [61, 62], supplemented with a list of 172 possible contaminants. For database searches, protein in silico digestion was configured to unspecific cleavage and no enzyme. Methionine oxidation, cysteine cysteinylation, and Protein N- terminal acetylation were set as variable modifications. Peptides were identified with mass accuracy thresholds of 15 ppm for MS1 and 0.03 Da for MS2. Results were filtered at \(FDR \leq 0.01\) for peptides and \(- 10lgP \geq 20\) for proteins. For rescoring, spectra were exported in MGF format and identifications in mzIdentML format, including decoys and without any score filter \((- 10lgP \geq 0\) for peptides and proteins). Identifications were then rescored using MS²Rescore [16] using the Immuno- HCD MS2PIP model and an MS2 mass accuracy tolerance of 0.03 Da. The settings used for data processing are also detailed in Supplementary Material S2a. + +### 4.6 Experiment design + +For method development, pooled samples of IP- enriched HLAlps from JY WT cells were used. For the final JY and Raji data set, the IP protocol was used to enrich the HLAlps from three cultures of each WT cell line (JY_WT, and Raji_WT) and two different cultures of each transfected cell line (JY_S1, JY_S2, Raji_S1, and Raji_S2). In every experiment, each sample was analyzed in three LC- MS injection replicates. + +### 4.7 Data analysis and statistics + +MHC- binding was predicted using NetMHCpan 4.1 [37] and GibbsCluster 2.0 [46] through MhcVizPipe (v0.7.9) [38]. R scripts [63] were used for data analysis, including merging the MS²Rescore [16] output with PEAKS peptide results, as well as with MhcVizPipe output. The main R packages used were as follows; the statistical difference was assessed by two- sided t- test using gppubr (v. 0.4.0) [64]; plots were generated using gpplot2 (v. 3.4.0) [65]; Venn plots with ggvenn (v. 0.1.9) [66]; and upset plots with ggupset (v. 0.3.0) [67]. + +We employed the Universal Spectrum Viewer (USE) [48] to compare the spectra acquired from the cells against spectra obtained from synthetic peptides (n= 7) or predicted in silico (n= 10), based on the similarity Pearson correlation coefficient (PCC). Prosit [34, 56] was used for in silico prediction since it's an orthogonal model to MS²Rescore [16] used for rescoring. + +<--- Page Split ---> + +### 4.8 Data availability + +4.8 Data availabilityThe mass spectrometry immunopeptidomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) [68] via the jPOSTrepo partner repository [69] with the dataset identifiers PXD040385 for ProteomeXchange and JPST002044 for jPOSTrepo. + +### 5 Acknowledgements + +5 AcknowledgementsWe would like to acknowledge Lucas Kleinort (HI- TRON, Mainz) for his technical assistance and contributions to sample preparation, Kristina Marx (Bruker) for the fruitful scientific discussions, Arthur Declercq for his help on adapting MS2Rescore for PEAKS XPro output, and Kevin Kovalchik for his help with MhcVizPipe. We acknowledge the support of the flow cytometry core facility, the mass spectrometry core facility and the sequencing core facility of the Research Center for Immunotherapy (FZI) at the University Medical Center Mainz. The graphical abstract and Figure 1 were designed in part using images from Servier Medical Art (SMART, smart.servier.com). This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) SFB1292 TP13 (H.S.) and TPQ01 (S.T.), the Helmholtz-Institute for Translational Oncology Mainz (HI-TRON Mainz) – a Helmholtz institute by DKFZ, Mainz, Germany. + +### 6 Author contributions + +6 Author contributionsConceptualization: DGZ, DAS, UD, HS, ST. Methodology: DGZ, DAS, HS, ST. Software: DGZ. Validation: DGZ, DAS, JB. Formal analysis: DGZ, DAS, JB. Investigation: DGZ, DAS, JB, EK. Resources: DAS, HS, ST. Data Curation: DGZ, DAS, JB, UD. Writing - Original Draft: DGZ, DAS, JB. Writing - Review & Editing: DGZ, DAS, JB, EK, UD, HS, ST. Visualization: DGZ, JB. Supervision: DGZ, DAS, HS, ST. Project administration: DGZ, DAS, UD, HS, ST. Funding acquisition: HS, ST. + +### 7 Competing interests + +The authors have no conflicts of interest to declare. + +### 8 Materials & Correspondence + +Correspondance should be addressed to: David Gomez- Zepeda, email: david.gomez- zepeda@dkfz- heidelberg.de; Stefan Tenzer, email: tenzer@uni- mainz.de + +<--- Page Split ---> + +## References + +[1] Ramarathinam, S. H., Croft, N. P., Illing, P. T., Faridi, P. & Purcell, A. W. Employing proteomics in the study of antigen presentation: an update. Expert Review of Proteomics 15, 637- 645 (2018). URL https://www.tandfonline.com/doi/full/10.1080/14789450.2018.1509000. [2] Purcell, A. W., Ramarathinam, S. H. & Ternette, N. Mass spectrometry- based identification of MHC- bound peptides for immunopeptidomics. Nature Protocols 14, 1687- 1707 (2019). URL http://dx.doi.org/10.1038/s41596- 019- 0133- y. 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Graphical abstract
+ +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Immunopeptidomics workflow using Thunder-DDA-PASEF. (a) Sample preparation: 500 million cells of the human JY or Raji cell lines were harvested, then lysed by sonication in 1% CHAPS in PBS buffer (m/v). (b) MHC-ligand peptide enrichment: was performed by immunoaffinity using the W6/32 anti-human-MHC-A, B, C antibody coupled to CNBr-activated agarose beads; after overnight incubation and several washes, peptides were eluted with 0.2% trifluoro-acetic acid, ultrafiltered on molecular weight cutoff filters (MWCO, 10 kDa cutoff) and desalted in HLB plates (Waters Corp.). (c) NanoLC-MS: analysis was performed using a nanoElute coupled to timsTOF-Pro-2 in DDA-PASEF [17] with different parameters to optimize the MS acquisition. (d) Data analysis: Database search was performed in PEAKS XPro using unspecific cleavage. Data analysis was performed in R and predicted MHC-binding affinity was evaluated using NetMHCpan 4.1 [37] and GibbsCluster 2.0 [46] through MhcVizPipe (v0.7.9) [38].
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Evaluation of the different fragmentation isolation filters: "standard", "None" and "HLAIp-tailored". (a, b, c): Exemplary heatmaps of ion intensities (gray-scale) across the inversed ion mobility \((1 / \mathrm{K}_0)\) vs m/z dimensions showing fragmentation events (red rhombus). (d - i): Correspondent peptides identified across the \(1 / \mathrm{K}_0\) vs m/z dimensions colored by charge state, including all peptides (d, e, f) or only those with 8 to 13 amino acids (AAs) (g, h, i). (j, l,m): Length distribution and percentage of peptides (pie-charts) with 8 to 13 AAs or other lengths; cut-off at 20 AAs dropping \(5.4\%\) , \(1.6\%\) and \(0.26\%\) of peptides identified for standard, None and HLAIp-tailored, respectively. (m) Average number of unique peptides identified per injection in each method (3 injection replicates, \(mean \pm sd\) ). (m) Average number of MS2 scans triggered per injection in each method (3 injection replicates, \(mean \pm sd\) ). Two-sided t-test, ns: \(p > 0.05\) , \*: \(p \leq 0.05\) , \*\*: \(p \leq 0.01\) , \*\*\*: \(p \leq 0.001\) , \*\*\*\*: \(p \leq 0.0001\) .
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: Evauation of the original-DDA-PASEF method (original) compared to the optimized Thunder-DDA-PASEF (Thunder) profiling of JY immunopeptides, and the effect of identification rescoring using MS²Rescore (Thunder + MS2R), considering only peptides of 8 to 13 amino acids long. (a) Average number of unique peptides identified per injection in each method (3 injection replicates, mean \(\pm s d\) ; two-sided \(t\) -test, \(\mathrm{***}\) : \(p \leq 0.0001\) ). (b) Proportion of peptides (considering modifications) identified in function of their charge state. (c) Dynamic range plot showing the peptides identified (considering modifications), ranked in descending order (x-axis) in function of the average peak area across three replicates (y-axis); the dashed gray line indicates the lowest limit of identification for the original method. (d) Identification data completeness, measured as the proportion of peptides identified across three, two, or only one replicate. (e) Upset plot showing the number (barplot) and percentage (text) of 8-13-mers identified identified uniquely in each method or their combinations; the intersection matrix at the bottom indicates that the same peptides shown above (columns) were detected in the methods (rows) highlighted with a blue dot. (f) Total number of peptides identified in each workflow and the proportion predicted as strong-binders (SB, \(rank \leq 0.5\%\) ), weak-binders (WB, \(0.5\% < rank \leq 2\%\) ) or non-binders (NB, \(rank > 2\%\) ) by NetMHCpan 4.0 [37].
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4: HLA class I ligandome of JY and Raji cells employing Thunder-DDA-PASEF, combining wild type and spike-transfected cells. (a) Size distribution of total peptides identified from JY and Raji cells. (b) Number of 8-13-mer peptides identified in each workflow and the proportion predicted as strongbinders (SB, \(rank \leq 0.5\%\) ), weak-binders (WB, \(0.5\% < rank \leq 2\%\) ) or non-binders (NB, \(rank > 2\%\) ) by NetMHCpan 4.0 [37] against the matched HLA alleles expressed by each cell line (JY = HLA-A02:01, B07:02, C07:02; Raji: HLA-A03:01, B15:10, C03:04, C04:01) (c) Charge distribution for the predicted HLA class I binders (HLAIps, SB & WB). (d) Total number of predicted HLAIps (SB & WB) identified (top) and protein groups covered (bottom) for JY, Raji, and in total. (e) Distribution of the number of HLAIps per protein group represented as boxplots (center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range) (top) and histogram (bottom); y-axis cut-off at 12 for simplicity, excluding 0.7% of JY HLAIps (13 to 34 Binders/Protein) and 1.4% of Raji HLAIps (13 to 53 Binders/Protein). (f) Overlap of HLAI ligand peptides (top) and protein groups (bottom) between JY and Raji. (g, h) Supervised clustering (GibbsCluster-2.0 via MhcVizPipe) showing the peptide sequence motifs corresponding to the specific allele motifs for JY and Raji HLAIps, respectively.
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Figure 5: Spike HLA class I binder peptides (HLAIps) identified in JY and Raji transfected cells. (a, b) Count of protein-specific HLAIps predicted strong-binders (SB, \(rank\leq 0.5\%\) ) and weak-binders (WB, \(0.5\% < rank\leq 2\%\) ) using NetMHCpan 4.0 [37] for spike (a) and the reporter GFP (b). (c) Peptide peak area distribution of the spike peptides (black dots) and all the HLAIps identified in JY (orange) and Raji (purple). (d) Characteristics of spike HLAIps identified in JY (top) and Raji (bottom) transfected cells. From left to right: sequence code name indicating their position within the protein sequence (s[N-ter]-[C-ter], e.g., s0691-0699 for SIIAYTMSL); sequence, with common peptides highlighted in gray; charge state (number of H+); the number of biological replicates (BR) and technical replicates (TR) where the peptide was identified; Log2 of the peptide peak area; Pearson's correlation coefficient (PCC) comparing the fragmentation spectrum of the endogenous peptide against synthetic peptides (S) or Prosit-predicted (P) [56, 34] calculated employing the Universal Spectrum Explorer (USE) [48]; indexed retention times (iRT) ratio (endogenous/synthetic); Immune Epitope Database and Analysis Resource (IEDB) [50] immune response frequency (RF) = proportion of subjects with positive immune response in B-cell or T-cell assays (dots = RF, lines = 95% confidence interval (CI) range, color scale = lower 95% CI, empty = not reported), relative to the total number of subjects tested for the corresponding peptide; binding affinity to JY and Raji HLA alleles predicted by NetMHCpan 4.0 [37], with labels indicating SBs and WBs.
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Figure 6: Mirrored fragmentation spectra showing the spectrum from endogenous peptides at the top and synthetic or predicted spectra for two spike peptides. (a) SIIAYTMSLs0691-0699 (bottom = synthetic), and (b) TLKSFTVEKs0302-0310, (bottom = Prosit predicted); obtained by USE [48]; PCC = Pearson's correlation coefficient, SA = spectral (contrast) angle.
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- S1ThunderDDAPASEFSpike20230224.pdf- S2Extendedmethodssettings.xlsx- S2bThunderDDAPASEF1600V300ms3rampsLSA.m.zip- S3pepall06131.zip- S4hmcpredall06131.zip- S5pepall06133.zip- S6hmcpredall06133.zip- S7gorillajyrajipepbinder.xlsx- S8GOJYRajihLAlpsprotsfromHLAlps.pdf- S9spike06133.xlsx- S10spectraspikeUSEmirror.pdf- nreditorialpolicychecklistthunder.pdf- nrreportingsummarythunder.pdf- RSNCOMMS2308309.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac_det.mmd b/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..600877716c6478c7240d415cfafc2dfb9b10b811 --- /dev/null +++ b/preprint/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac/preprint__0a7d3b5c38049654725a92dddc6647507a06a31feb5a95590bc5e681ea7206ac_det.mmd @@ -0,0 +1,537 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 864, 209]]<|/det|> +# Thunder-DDA-PASEF enables high-coverage immunopeptidomics and identifies HLA class-I presented SarsCov-2 spike protein epitopes + +<|ref|>text<|/ref|><|det|>[[44, 229, 336, 276]]<|/det|> +David Gomez-Zepeda davidgz.science@gmail.com + +<|ref|>text<|/ref|><|det|>[[44, 302, 923, 345]]<|/det|> +Helmholtz- Institute for Translational Oncology Mainz (HI- TRON) https://orcid.org/0000- 0002- 9467- 1213 + +<|ref|>text<|/ref|><|det|>[[44, 350, 666, 393]]<|/det|> +Danielle Arnold- Schild University Medical Center of the Johannes Gutenberg University Mainz + +<|ref|>text<|/ref|><|det|>[[44, 397, 610, 440]]<|/det|> +Julian Beyrle Helmholtz- Institute for Translational Oncology Mainz (HI- TRON) + +<|ref|>text<|/ref|><|det|>[[44, 444, 666, 486]]<|/det|> +Elena Kumm University Medical Center of the Johannes- Gutenberg- University Mainz + +<|ref|>text<|/ref|><|det|>[[44, 490, 930, 555]]<|/det|> +Ute Distler University Medical Center of the Johannes Gutenberg University Mainz https://orcid.org/0000- 0002- 8031- 6384 + +<|ref|>text<|/ref|><|det|>[[44, 560, 462, 602]]<|/det|> +Hansjorg Schild Johannes Gutenberg- University Medical Center + +<|ref|>text<|/ref|><|det|>[[44, 606, 930, 670]]<|/det|> +Stefan Tenzer University Medical Center of the Johannes- Gutenberg- University Mainz https://orcid.org/0000- 0003- 3034- 0017 + +<|ref|>sub_title<|/ref|><|det|>[[44, 710, 102, 727]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 747, 135, 766]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 786, 303, 805]]<|/det|> +Posted Date: March 9th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 823, 474, 843]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2625909/v1 + +<|ref|>text<|/ref|><|det|>[[44, 860, 910, 904]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 921, 530, 941]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 925, 88]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on March 13th, 2024. See the published version at https://doi.org/10.1038/s41467-024-46380-y. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 141, 895, 255]]<|/det|> +1. Thunder-DDA-PASEF enables high-coverage immunopeptidomics and identifies HLA class-I presented SarsCov-2 spike protein epitopes + +<|ref|>text<|/ref|><|det|>[[100, 277, 905, 620]]<|/det|> +David Gomez- Zepeda \(^{1,2,*}\) , Danielle Arnold- Schild \(^{1}\) , Julian Beyrle \(^{1,2}\) , Elena Kumm \(^{1}\) , Ute Distler \(^{1,3}\) , Hansjörg Schild \(^{1,2,3}\) , Stefan Tenzer \(^{1,2,3,*}\) \(^{1}\) Institute for Immunology, University Medical Center of the Johannes- Gutenberg University, Mainz, 55131, Germany. \(^{2}\) Helmholtz- Institute for Translational Oncology Mainz (HI- TRON), Mainz, 55131, Germany. \(^{3}\) Research Center for Immunotherapy (FZI), University Medical Center of the Johannes- Gutenberg University, Mainz, 55131, Germany. \(^{*}\) To whom correspondence should be addressed: David Gomez- Zepeda, email: david.gomez- zepeda@dkfz- heidelberg.de; Stefan Tenzer, email: tenzer@uni- mainz.de + +<|ref|>sub_title<|/ref|><|det|>[[464, 664, 533, 678]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[132, 685, 867, 918]]<|/det|> +Human leukocyte antigen (HLA) class I peptide ligands (HLAIs) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIs is challenging due to their high diversity, low abundance, and patient- specific profiles. Here, we developed a highly sensitive method for identifying HLAIs using liquid chromatography- ion mobility- tandem mass spectrometry (LC- IMS- MS/MS). The optimized method, Thunder- DDA- PASEF, semi- selectively fragments HLAIs based on their IMS and m/z, thus increasing the coverage of immunopeptidomics analyses. Thunder- DDA- PASEF includes singly- charged peptides, which contributes to more than 35% of the HLAIs identifications. Combined with MS \(^{2}\) Rescore, Thunder- DDA- PASEF improved ligandome coverage by 150% compared to the original- DDA- PASEF method, and enabled in- depth profiling of HLAIs from two human cell lines, JY and Raji, transfected to express the SARS- CoV- 2 spike protein. We identified seventeen spike protein HLAIs, thirteen of which had been reported to elicit immune responses in human patients. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[66, 80, 280, 99]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[66, 115, 908, 444]]<|/det|> +Identifying ligands of the major histocompatibility complex (MHC) or human leukocyte antigen (HLA), also called immunopeptides, is key for developing vaccines and immunotherapies (extensively reviewed in [1, 2, 3]). Human HLA class- I complexes bind peptides (HLAIs) of typically 9 to 12 amino acids generated by a multi- step process called antigen processing, which involves multiple proteolytic events by the proteasome and aminopeptidases [4, 5, 6, 7, 8]. Loaded HLA complexes are then displayed on the cell surface, where \(\mathrm{CD8^{+}}\) T- cells scrutinize them. Detection of a "non- self" antigen, e.g., HLAIs derived from viral proteins or mutated cancer- related proteins, leads to the efficient elimination of the presenting cell by cytotoxic T lymphocytes. Thus, non- self HLAIs constitute key targets for developing peptide or mRNA vaccines in the context of personalized immunotherapies, or diagnostic tools. Various in silico tools have been developed to predict HLA- binding peptides from genomic, transcriptomic, or riboSeq data. Still, most predictors are primarily based on HLA binding affinity, thus not fully considering the antigen processing and presentation mechanisms, resulting in discrepancies between predicted and presented HLAIs [9, 10]. Therefore, liquid chromatography mass spectrometry (LC- MS)- based immunopeptidomics is essential for directly identifying HLA class I presented peptides from cells, tissues, and biofluids [9, 11]. + +<|ref|>text<|/ref|><|det|>[[66, 448, 908, 796]]<|/det|> +LC- MS immunopeptidomics faces different challenges than bottom- up proteomics, where proteins are usually digested using trypsin (reviewed in [3, 12]). HLAIs are generated by a complex multi- step process, including various proteolytic events [13, 14]. This results in peptides with restricted size and sequence patterns imprinted by the specificities of TAP transport and HLA binding. While these motifs differ between individual HLA alleles, they restrict the sequence space presented by a single allele. Thus, immunopeptidomics samples are more likely to contain isobaric peptides, potentially co- eluting from the LC, than enzyme- digested samples [2]. Since tryptic peptides are usually multi- charged, typical bottom- up proteomics workflows often omit the fragmentation and identification of singly- charged ions, which are more challenging to identify. In addition, singly- charged peptides are often masked by chemical noise, and their fragmentation generates many uncharged segments not detected by the MS [2]. Moreover, individual HLAIs are low abundant, and the sample preparation recovery yields are low (around 0.5- 3% [15]). These factors demand tailored and high- sensitivity LC- MS methods and have major implications in database searches. The unspecific cleavage of HLAIs increases the search space by up to 2 orders of magnitude compared to tryptic digests. This impairs the discrimination of false positive from true positive peptide- spectrum matches (PSMs), negatively impacting peptide identification yield and confidence [16]. + +<|ref|>text<|/ref|><|det|>[[66, 804, 907, 916]]<|/det|> +Coupling ion mobility separation (IMS) to LC- MS provides an extra dimension of separation, resolving ions in the gas phase by their size and shape. This enhances the signal- to- noise ratio and resolves isobaric ions, thus increasing the number and confidence of peptide identifications. In the timsTOF Pro instruments, a dual trapped ion mobility spectrometry (TIMS) analyzer is employed to perform a parallel accumulation- serial fragmentation (PASEF) of ions, resulting in a high sequencing speed without compromising sensitivity for data- dependent + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 81, 908, 312]]<|/det|> +53 acquisition (DDA- PASEF) [17, 18], which has already been proven to perform well for immunopeptidomics [19]. 54 During the ongoing Covid- 19 pandemic, there have been significant efforts to identify SARS- CoV- 2 HLAIps, 55 mainly focusing on characterizing the immunogenicity in vitro or in vivo of large libraries of synthetic peptides 56 of in silico predicted HLA- binders (25 studies reviewed in [20]). This has provided important insights into 57 possible immunodominant regions in the viral proteome, HLA allele- dependent responses to SARS- CoV- 2, and 58 the protection capabilities of vaccines (reviewed in [20, 21, 22]). More than 2,000 possible HLA- binding peptides 59 have been predicted from the SARS- CoV- 2 genome [23]. However, only a few SARS- CoV- 2 immunopeptides have 60 been detected by LC- MS until now [24, 25, 26], including less than ten HLAIps for the spike glycoprotein [24, 26], 61 the main target of vaccines and diagnostic tests. This emphasizes the challenges of LC- MS immunopeptidomics 62 and the need for more sensitive and robust methods. + +<|ref|>text<|/ref|><|det|>[[60, 319, 908, 666]]<|/det|> +63 Here, we present Thunder- DDA- PASEF, an optimized LC- IMS- MS method for immunopeptidomics and 64 its application in the discovery of SARS- CoV- 2 spike protein derived HLAIps. The optimized method uses 65 an extended TIMS separation time (300 ms) to improve IMS resolution, and sensitivity [17, 27]. To include 66 singly charged peptides while efficiently using instrument cycle time, precursors are selected by using a tailored 67 isolation polygon for semi- selectively fragmenting potential HLAIps. Compared to the standard method (100 68 ms TIMS, bottom- up proteomics- optimized isolation polygon), Thunder- DDA- PASEF increased the HLAIps 69 identifications from JY cells by 2.3- fold, including more than 35% of identifications derived form singly- charged. 70 Moreover, MS2Rescore- based rescoring [16] further boosted the identification to 3.5- fold relative to the non- 71 rescored standard DDA- PASEF. Subsequently, we employed Thunder- DDA- PASEF to study the HLAIp ligand- 72 dome repertoire of two cell lines recombinantly expressing the canonical spike protein of SARS- CoV- 2. This 73 resulted in deep coverage of 14,313 and 17,806 peptides from JY and Raji cells, respectively, including seventeen 74 HLAIps derived from the SARS- COV- 2 spike protein. Notably, thirteen of these peptides have been previously 75 reported to elicit immune responses in human patients, confirming the potential of our improved method for 76 efficient epitope discovery. In conclusion, optimized Thunder- DDA- PASEF achieved deep and reproducible 77 profiling of the HLA class I ligandome. + +<|ref|>sub_title<|/ref|><|det|>[[66, 700, 220, 720]]<|/det|> +## 2 Results + +<|ref|>sub_title<|/ref|><|det|>[[66, 740, 689, 760]]<|/det|> +### 2.1 General workflow for LC-IMS-MS immunopeptidomics + +<|ref|>text<|/ref|><|det|>[[66, 774, 908, 910]]<|/det|> +For our immunopeptidomics experiments, we followed the general procedure shown in Fig. 1 and described in Material and Methods. The settings used for the LC- MS methods and data processing are fully detailed in Supplementary Material S2a and the ready- to- use MS method for timsTOF Pro instruments is included in Supplementary Material S2b. Briefly, we enriched HLAIps from JY cells by immunoprecipitation (W6/32 antibody), and analyzed them by nanoLC- IMS- MS on a nanoElute coupled to timsTOF- Pro- 2 in DDA- PASEF mode, using PEAKS XPro for subsequent peptide identification. We performed several iterations to optimize + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 83, 816, 99]]<|/det|> +our LC- IMS- MS method for identifying HLA class I ligands, as described in the following sections. + +<|ref|>sub_title<|/ref|><|det|>[[66, 125, 930, 171]]<|/det|> +### 2.2 An HLAIp-tailored DDA-PASEF fragmentation scheme including singly-charged ions efficiently identified possible HLAIps + +<|ref|>text<|/ref|><|det|>[[66, 185, 907, 415]]<|/det|> +Contrarily to tryptic peptides, HLAIps originate from a large diversity of antigen processing events [13, 14] and do not necessarily contain basic amino acid residues [2]. Thus, many HLAIps can only be detected as singly- charged ions in LC- MS since only their N- ter residue can carry a positive charge ( \(\mathrm{H^{+}}\) ). Although this varies depending on the HLA alleles, up to \(40\%\) of singly- charged ions have been reported for peptides bearing hydrophobic anchor residues such as HLA- B07:02 [28, 29]. In addition, HLAIps have a restricted size of typically 9 to 12 amino acids (AAs) [2], but between 8 to 13 in some instances [30, 31]. For this reason, HLAIp- immunopeptidomics workflows have recently incorporated the fragmentation of singly- charged ions (with \(2^{+}\) and \(3^{+}\) ) within the \(\mathrm{m / z}\) range of possible HLAIps [2, 19, 29, 28, 32, 33, 34, 35, 36]. We hypothesized that the IMS separation and sensitivity of the timsTOF Pro- 2 could provide high- quality MS2 spectra to identify singly- charged peptides confidently. + +<|ref|>text<|/ref|><|det|>[[66, 422, 907, 604]]<|/det|> +First, we tested the original- DDA- PASEF method for proteomics [17] to analyze JY HLAIps samples (Fig. 2a, d, g). DDA- PASEF takes advantage of the charge- state- dependent mobility separation to selectively fragment ions detected within an isolation polygon on the inverse reduced ion mobility ( \(1 / \mathrm{K}_{0}\) ) vs. \(\mathrm{m / z}\) space. Since it was designed for tryptic peptides, the standard isolation polygon covers the multiply- charged ion cloud, clearly separated from the singly- charged ones (Fig. 2a). This resulted in almost 5,000 unique peptides from three injection replicates of JY HLAIps (Fig. 2a), mainly comprising doubly- charged ions ( \(89\%\) , Fig. 2b) and almost \(77\%\) of 8- 13- mers (Fig. 2g, j). As expected, most singly- charged ions were excluded from fragmentation, and only a few were identified due to IMS peak tailing into the isolation polygon. + +<|ref|>text<|/ref|><|det|>[[66, 611, 907, 748]]<|/det|> +Our next step was to remove the isolation polygon (Fig. 2b). Omitting the isolation polygon enabled the fragmentation of singly- charged peptides, representing more than half ( \(54.5\%\) ) of all the peptides identified and \(59.6\%\) of the 8- 13- mers (Fig. 2e, h). Furthermore, the proportion of peptides with 8 to 13 AAs was \(12.4\%\) higher than in the standard- polygon (Fig. 2h, j), corresponding to \(72\%\) more 8- 13- mers identified on average \((p \leq 0.0001\) , Fig. 2m). However, without an isolation polygon, many low \(\mathrm{m / z}\) singly- charged ions and high mass multiply- charged ions were fragmented (Fig. 2b). + +<|ref|>text<|/ref|><|det|>[[66, 754, 907, 913]]<|/det|> +Therefore, we designed fragmentation isolation polygons covering the singly- charged and multiply- charged 8- 13- mer peptides 2c, h) (Table 1). This HLAIp- tailored scheme efficiently identified peptides within the isolation polygon (Fig. 2c, f, i), roughly maintaining the charge distribution of peptides identified, with \(56.4\%\) of all the ions and \(59.7\%\) of the 8- 13- mers being singly- charged. The proportion of 8- 13 mers was almost \(92\%\) , which is \(15\%\) and \(2.6\%\) higher than the standard- and no- polygon, respectively (Fig. 2l, j, k, respectively). As a result, the HLAIp- tailored polygon increased the identification of 8- 13- mers by \(75\%\) relative to the standard \((p \leq 0.0001\) , 2m)). Compared to no polygon, the HLAIp- tailored polygon resulted in \(24\%\) fewer MS2 scans + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 82, 907, 265]]<|/det|> +\(p \leq 0.001, 2\mathrm{n}\) , but a similar yield of 8- 13- mers identified (Fig. 2m). This \(18\%\) increase in the identification rate shows that the HLAIp- tailored polygon used the cycle time more efficiently to fragment 8- 13- mers. In contrast, without an isolation polygon, a large proportion of the cycle time was used inefficiently to fragment ions that are not of interest for HLAIp profiling. These may include non- peptidic small ions or larger peptides (Fig. 2b, Supplementary Fig. S1a, b) originating from the degradation of HLA proteins, the antibody, or other co- enriched proteins (Supplementary Fig. S1c). Once having established the capabilities of DDA- PASEF with the HLAIp- tailored isolation scheme for immunopeptidomics, we optimized several other parameters of the MS method (detailed in Supplementary Material S2). + +<|ref|>sub_title<|/ref|><|det|>[[60, 290, 904, 335]]<|/det|> +### 2.3 Optimized Thunder-DDA-PASEF enhanced the identification of 8-13-mers by 2.2-fold + +<|ref|>text<|/ref|><|det|>[[58, 350, 907, 653]]<|/det|> +In PASEF methods, each analysis cycle comprises several frames where the trapping TIMS tunnel accumulates a package of ions. Simultaneously, the second TIMS resolves the previous package of ions by ramping down the elution voltage. Increasing TIMS times enhances IMS resolution and accommodates more fragmentation events per MS2 frame while preserving the sensitivity [17]. Raising the TIMS time from 100 to 300 ms resulted in an \(80\%\) increase in peptide identification, while no substantial increase was observed between 400 ms and 300 \((< 5\%\) increase) (Supplementary Fig. S2a, b, c, d). However, the longer cycle times resulted in five- fold fewer MS1 frames and doubled the median coefficient of variation (CV) at 400 ms compared to 100 ms. Since the peak area reproducibility is essential for quantitative comparisons between samples (e.g., diseased vs. control), we compensated for this effect by decreasing the number of MS2 frames/cycle from 10 to 3, and the MS2 cycle overlap from 4 to 1 (Fig. S2e, f, g, h). This resulted in a cycle time of 1.2 s and reduced the median peak area CV from \(19.3\%\) to \(10.3\%\) (Fig. S2d,h). In addition, activating the high- sensitivity mode of the timsTOF Pro- 2, which uses detector voltages optimized for low sample amounts, further increased the number of HLAIps identified by \(30\%\) (Supplementary Fig. S3). + +<|ref|>text<|/ref|><|det|>[[58, 659, 907, 793]]<|/det|> +In summary, the optimized method resolves ions using a 300 ms TIMS ramp, fragmenting mainly ions with \(1^{+}, 2^{+}\) , and \(3^{+}\) charges in 3 MS2 frames per MS1 frame within a 1.2 s cycle time and takes advantage of the high- sensitivity mode. Since the HLAIp- tailored isolation polygon resembles a lighting or thunder icon, we termed the fully optimized method Thunder- DDA- PASEF. In contrast, the original- DDA- PASEF designed for proteomics samples uses 100 ms ramps and selectively fragments multiply- charged ions in 10 MS2 frames per MS1 frame within a 1.2 s cycle time. + +<|ref|>text<|/ref|><|det|>[[58, 800, 907, 912]]<|/det|> +We compared Thunder- DDA- PASEF to the original- DDA- PASEF method by analyzing triplicate injections of JY HLAIps (equivalent to approximately 50 million cells/injection, Supplementary Material S3). Thunder- DDA- PASEF identified 2.2- fold the number of 8- 13- mers than the original method \((p < 0.0001\) , Fig. 3a). This was partly due to the inclusion of singly- charged peptides in Thunder- DDA- PASEF, constituting \(48\%\) of the 8- 13- mers in this data set (Fig. 3b). Thunder- DDA- PASEF improved the dynamic range for identification + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 81, 909, 337]]<|/det|> +by almost half an order of magnitude towards the low abundant species (Fig. 3c). The number of peptides identified across all three replicates was \(8.4\%\) higher in Thunder- DDA- PASEF than in the original- DDA- PASEF, indicating a slight improvement in the data completeness (Fig. 3d). Although \(8.7\%\) of the peptides were only identified in the original method (Fig. 3e), this could be due to the sampling stochasticity of DDA. Then, we used NetMHCpan- 4.1 [37] via MhcVizPipe [38] to predict peptide HLA- binding, which provides a ranking classifying the peptides into strong- binders (SB, \(rank \leq 0.5\%\) ), weak- binders (WB, \(0.5\% < rank \leq 2\%\) ) or non- binders (NB, \(rank > 2\%\) ). When focusing on the peptides predicted to bind JY HLA alleles, the 8- 13- mers identified comprised \(88.2\%\) SB and \(7.8\%\) WB in the original method and \(85.4\%\) SB and \(9.1\%\) WB in Thunder- DDA- PASEF (Fig. 3f, Supplementary Material S4). Altogether, these results proved a 2.2- fold increase in the coverage of the immunopeptidome using Thunder- DDA- PASEF compared to the original- DDA- PASEF (9,524 and 4,334 HLAIs, respectively). + +<|ref|>sub_title<|/ref|><|det|>[[58, 360, 905, 408]]<|/det|> +### 2.4 Machine learning-based rescoring via MS2Rescore enhanced the identification of HLAIs and data completeness by more than 15% + +<|ref|>text<|/ref|><|det|>[[58, 420, 907, 558]]<|/det|> +Several post- processing tools have shown improvements in immunopeptide identification by rescoring peptide spectrum matches (PSMs) based on characteristics disregarded in the initial search [16, 34, 39, 40]. For instance, MS2rescore (MS2R) [16] integrates the machine learning prediction of retention and fragmentation peak intensity using DeepLC [41] and MS2PIP [42, 43, 44], respectively, with the semi- supervised machine learning- based FDR calculation of Percolator [45]. Since this strategy has shown the potential to boost immunopeptide identification [16], we decided to implement it in our workflow. + +<|ref|>text<|/ref|><|det|>[[58, 563, 907, 722]]<|/det|> +Rescoring the results of Thunder- DDA- PASEF from JY IP- enriched HLAIs (Supplementary Material S3) significantly increased the average number of 8- 13- mer peptides identified per injection by \(29.1\%\) ( \(p < 0.0001\) , Fig. 3a). The proportion of singly- charged peptides decreased (Fig. 3b) not due to a drop in their numbers but because most newly identified peptides were doubly charged ( \(74.5\%\) ). Probably, the performance of MS2Rescore for singly- charged ions was lower due to the fewer singly- charged ions in the MS2PIP immunopeptidomics model training set. Thus, training a predictor model with orthogonal Thunder- DDA- PASEF data could improve its performance. + +<|ref|>text<|/ref|><|det|>[[58, 728, 907, 913]]<|/det|> +Novel identifications were obtained across the whole dynamic range indicating that rescoring performed well even for low- intensity ions (Fig. 3c). Despite applying a stringent confidence filter independently for each file (PSM \(FDR \leq 0.01\) ), \(77.1\%\) of the peptides were consistently identified across all three replicates in the rescored results, meaning a \(16.7\%\) increase in data completeness (Fig. 3d). In addition, only a few peptide identifications were dropped by MS2Rescore ( \(< 1.5\%\) , Fig. 3e), and it also recovered 263 peptides identified in the non- rescored original- DDA- PASEF but not in Thunder. The proportion of SB and WB was not affected by rescoring, indicating that no bias was introduced. The benefits of rescoring Thunder- DDA- PASEF identifications are summarized in a \(14.7\%\) increase in the number of predicted HLAIs identified, yielding a total + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 82, 907, 147]]<|/det|> +of 10,931 (Fig. 3f). Collectively, the Thunder- DDA- PASEF + MS2R strategy resulted in a 2.5- fold coverage of HLAIps compared to the non- rescored original- DDA- PASEF data for JY HLAIp IP- enriched peptides (Fig. 3f, Supplementary Material S4), with an average of 9,821 HLAIps per injection. + +<|ref|>text<|/ref|><|det|>[[57, 153, 907, 240]]<|/det|> +In summary, combining the optimized Thunder- DDA- PASEF with MS2Rescore resulted in a highly sensitive and reproducible workflow. This level of coverage could enable deep profiling of immunopeptides in patient samples and the comparability between healthy and pathological tissue for the discovery of disease- specific antigens. + +<|ref|>sub_title<|/ref|><|det|>[[57, 266, 905, 312]]<|/det|> +### 2.5 Thunder-DDA-PASEF enabled in-depth characterization of the HLA class I ligandome of JY and Raji cells + +<|ref|>text<|/ref|><|det|>[[55, 325, 907, 652]]<|/det|> +We tested our optimized workflow to characterize the HLA class- I immunopeptidome of JY and Raji cells transfected to express a segment of the SARS- CoV- 2 spike protein (Supplementary Material S5). Thunder- DDA- PASEF + MS2R identified in total 23,147 peptides from JY and 29,397 peptides from Raji, comprising 78% of 8- 13- mers, with a median length of 9 AAs (Fig. 4a), as expected for HLAIps. The reproducibility between biological replicates ranged between 35.8% and 62.7% 8- 13- mers identified in all the samples of the same genotype, and 67.7% to 81.3% regarding the proteins covered (Supplementary Fig. S4). Based on the HLA- binding prediction (NetMHCPan- 4.1 [37] via MhcVizPipe [38], Supplementary Material S6), the 8- 13- mers included 78.9% binders for JY (70% SB, 8.9% WB) and 77.6% for Raji (67.2% SB, 10.4% WB) (Fig. 4b), showing the respective peptide sequence motifs, as indicated by supervised clustering (GibbsCluster- 2.0 [46], Fig. 4g, h). A lower proportion of HLAIps was detected as singly- charged ions in Raji, compared to JY (30.1% vs. 42.9%). This was due to the presence of basic amino acids at the anchor positions for Raji HLA alleles (Fig. 4g), including lysine or arginine at the C- ter (HLA- A03:01) or histidine at the second position (HLA- B15:10, HLA- C04:01). In contrast, the anchor residues binding JY HLA alleles were dominated by apolar amino acids (Fig. 4h). + +<|ref|>text<|/ref|><|det|>[[57, 658, 907, 840]]<|/det|> +Thunder- DDA- PASEF achieved an extensive coverage of protein- HLAIp representation. A total of 14,074 and 17,469 HLAIps were detected in JY and Raji, respectively, summing up to 30,948 peptides (Fig. 4d, top). These peptides corresponded to 5,660 protein groups in JY, 6,170 in Raji, and 8,214 in total (Fig. 4d, bottom). Each protein group was represented by a median of 2 HLAIps per protein group and 75% of them with one to three peptides for both cell lines (Fig. 4e). As a comparison, the DIA analysis of JY HLAIps provided a median of one HLAIp per protein [32] despite a deep coverage of 7,627 peptides. This further shows the potential of our workflow to provide an in- depth characterization of the immunopeptidome, which may unravel novel antigen processing and presentation mechanisms. + +<|ref|>text<|/ref|><|det|>[[57, 847, 905, 911]]<|/det|> +Although only 1.8% of all HLAIps were detected in both JY and Raji, 44% of all the protein groups were covered by the ligandomes of the two cell lines (Fig. 4f, top and bottom, respectively). A gene ontology (GO) enrichment analysis using GOrilla [47] indicated a significant over- representation ( \(FDR \leq 0.001\) ) of proteins + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 82, 907, 241]]<|/det|> +involved in essential processes, such as the metabolism of nucleic acids (GO:0090304), macromolecule biosynthesis (GO:0034645), macromolecule localization (GO:0033036), and regulation of the cell cycle (GO:0022402) (Supplementary Material S7 and S8). Thus, the cell lines presented complementary peptides for these same crucial proteins due to their different HLA alleles and probably also due to differences in the antigen processing pathway. Because of the large number of HLAIs covered (30,984 binders) (Fig. 4b, d), including more than 11,000 singly-charged peptides (Supplementary Material S3), this combined immunopeptidome of JY and Raji cells constitutes an essential resource for future exploitation. + +<|ref|>sub_title<|/ref|><|det|>[[57, 266, 905, 313]]<|/det|> +### 2.6 Thunder-DDA-PASEF identified seventeen spike HLAIs in JY and Raji spike-transfected cells + +<|ref|>text<|/ref|><|det|>[[57, 325, 907, 604]]<|/det|> +To explore the potential of Thunder- DDA- PASEF on a clinically relevant subject, we focused on the transfected SARS- CoV- 2 spike protein, and the GFP reporter included in the construct. Importantly, peptides from these proteins were only detected in the transfected cells and not in the wild- type cells. Three GFP- derived HLAIs were identified in JY and six in Raji cells (Fig. 5 b), serving as a control for successful antigen processing of the transfected constructs. Five spike HLAIs were identified in JY and thirteen in Raji (Fig. 5a) across a large dynamic range corresponding to four orders of magnitude (Fig. 5c). While the Raji spike HLAIs were distributed across the whole dynamic range, they were mainly in JY's middle to low range. The sequence and characteristics of the spike HLAIs are shown in Fig. 5d and detailed in the Supplementary material S9. Nomenclature in Fig. 5c and d denotes identified spike HLAIs (e.g., SIIAYTMSL0691- 0699) both by peptide sequence and position (N- to C- ter) in the full- length spike protein. Notably, six of the thirteen spike HLAIs were singly charged, showing the advantage of the Thunder HLAIp- tailored isolation polygon for identifying potential clinically relevant immunopeptides. + +<|ref|>text<|/ref|><|det|>[[57, 611, 907, 912]]<|/det|> +In addition to the \(1\%\) FDR threshold applied, the spike HLAIs were assessed based on the number of identifications across biological and technical replicates (n BR, n TR; Fig. 5d, yellow to green scales) and by the similarity of their fragmentation spectra against synthetic peptides or in silico predictions, based on the Pearson correlation coefficient (PCC) [48] (Fig. 5d, blue scale with letters, S = synthetic, P = predicted). The mirrored spectra comparisons are shown in Supplementary Material S10. At the same time, SIIAYTMSL0691- 0699 and TLKSFTVEK0302- 0310 are shown in Fig. 6 as examples of the confident identification of peptides with high and low abundance, respectively. Around \(82\%\) of the reported spike HLAIs were identified in two biological replicates with a PCC \(> = 0.85\) , indicating both robust sample preparation and high- confidence identifications. The synthetic peptides analyzed independently with the same method were eluted at similar indexed retention times (iRT) as the corresponding endogenous peptides (ratio iRT endogenous/synthetic \(> = 0.99\) ). Even though peptides GVLTESNKK0550- 0558 from Raji and RLQSLQTYV1000- 1008 from JY were identified in only one injection replicate in one of the biological replicates, their PCC were 0.94 and 0.96, respectively ( \(FDR < 0.005\) ). Peptide AIHVSGTNGTK0067- 0077 showed a low PCC (0.47) against the predicted spectra but was detected in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 82, 905, 101]]<|/det|> +five injection replicates across both biological replicates with an \(FDR< 0.0005\) , thus validating its detection. + +<|ref|>text<|/ref|><|det|>[[55, 106, 907, 380]]<|/det|> +While a large proportion of the HLAIs were predicted to be strong binders (Fig. 5d), there was a deficient number of HLAIs for both the HLA- C alleles in Raji (HLA- C04:01) and JY (HLA- C07:02). This could be due to the low expression of this gene in JY cells, whose effect on its immunopeptidome has been previously reported [49]. Interestingly, some spike HLAIs were predicted to bind to the HLA alleles of both cell lines, but only SIIAYTMSL0691- 0699 was identified in both cell lines. Once more, this highlights the need for direct validation of in silico- predicted HLA class I binders. However, the challenge of LC- MS immunopeptidomics is exemplified here since only one of the seventeen spike HLAIs had been previously reported by MS (SIIAYTMSL0691- 0699)[40]. Moreover, four represent completely novel identifications (AIHVSGTNGTK0067- 0077, YGVSPTKL0380- 0387, RVYSTGSNVFQTR0634- 0646, NRALTGIAV0764- 0772). The remaining thirteen spike HLAIs have been reported to exhibit positive results in T- cell or MHC ligand assays according to the IEDB [50] (December 18, 2022) (Fig. 5d, dot range plot). This shows the capabilities of Thunder- DDA- PASEF for identifying potential HLA class I- restricted immunogenic targets which could be employed for vaccine development. + +<|ref|>text<|/ref|><|det|>[[55, 388, 907, 478]]<|/det|> +In summary, we report seventeen spike peptides identified with high stringency and confidence, which are predicted to bind HLA class I in two cell lines expressing different HLA alleles. Accordingly, this set of peptides constitutes a key resource, comprising novel spike HLAIs, and confirms many previously reported peptides capable of eliciting a T- cell response. + +<|ref|>sub_title<|/ref|><|det|>[[55, 510, 256, 530]]<|/det|> +## 3 Discussion + +<|ref|>text<|/ref|><|det|>[[55, 548, 907, 827]]<|/det|> +Here, we present Thunder- DDA- PASEF, an LC- IMS- MS method tailored and optimized for identifying HLA class I peptide ligands (HLAIs). We showed that the HLAIs- tailored isolation polygon enabled the identification of singly- charged peptides, expanding the universe of identifiable MHC peptide ligands. Thunder- DDAPASEF uses a thunder- shaped isolation polygon (Fig. 2), optimized detector voltages (high sensitivity mode), enhanced IMS resolution (300 ms TIMS), and fewer MS2 frames (3 MS2 frames/cycle, 1 cycle overlap), resulting a cycle time of 1.2 s, compatible with nanoLC peak width (Supplementary Fig. S2h, Supplementary Material S2a and S2b). Altogether, this resulted in more than a 2.2- fold higher number of 8- 13- mers identified from JY cells, compared to the standard DDA- PASEF optimized for proteomics samples (excluding singly- charged ions, 100 ms TIMS ramp, 10 MS2 frames/cycle, 4 overlap) (Fig. 3). MS2Rescore further boosted the identifications up to 2.5- fold compared to the standard, unsecured DDA- PASEF. In addition, Thunder + MS2Rescore improved the identification data completeness, reliably and constantly identifying 77.1% of the peptides across three technical replicates (Fig. 3a). + +<|ref|>text<|/ref|><|det|>[[55, 833, 907, 898]]<|/det|> +Field asymmetric waveform ion mobility spectrometry (FAIMS) has been combined with LC- MS to identify singly- charged HLAIs [29]. However, FAIMS acts as a gas- phase fractionation device, filtering ions in function of their mobility in the electric field. Since only a population of ions can be analyzed simultaneously, identifying + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 81, 907, 240]]<|/det|> +multiply and singly- charged HLAIps requires dividing the cycle time within an LC- MS run between or performing multiple injections per sample [29]. In contrast, TIMS- MS profiles ions across a \(1 / \mathrm{K}_0\) range. In addition, PASEF maximizes the duty cycle by trapping a package of ions while the previous is being separated and synchronizing ion fragmentation with TIMS elution. We adapted this concept for HLAIps by taking advantage of their size- and charge- dependent separation forming two distinct ion clouds for the singly and multiply- charged 8- 13- mer peptides. Thus, PASEF- MS2 frames are efficiently used to fragment singly- charged ions during the first half of the TIMS ramp and multiply- charged during the second half. + +<|ref|>text<|/ref|><|det|>[[90, 247, 907, 430]]<|/det|> +Additional adaptations could further improve the identification of immunopeptides. For instance, the Thunder isolation polygons could be more restrictive towards 9 to 12- mers to improve fragmentation selectivity for more challenging samples. For example, soluble HLAs enriched from plasma samples tend to include larger peptides resulting from the degradation of proteins adhering non- specifically to the beads, such as blood clotting and other plasma proteins [51]. Here, we decided to employ broad limits to account for variability between HLA alleles and to accommodate slight variations in the instrument (e.g., IMS variations between days). In addition, disease- associated HLAIps can be composed of larger sequences [30, 52, 53] or include modifications that are key for their immunogenicity [1, 54, 55]. + +<|ref|>text<|/ref|><|det|>[[90, 436, 907, 666]]<|/det|> +Sensitivity and reproducibility could be further improved by using a data- independent acquisition (DIA) method including singly- charged ions. Although DIA requires spectral libraries for peptide identification, recent publications have shown its value for immunopeptidomics [32, 40]. For instance, using Orbitrap instruments, more than 97% of the combined identifications from 3 DDA runs used to create the library were identified in each single DIA injection of HLAIp- enriched peptides from cell lines. Using this strategy, Pak et al. [32] identified 7,627 HLAIps per injection of JY cell W6/32 IP- enriched peptides. However, sample fractionation by SPE or in the gas phase, or at least multiple DDA injections, is required to obtain the spectral libraries. In contrast, Thunder- DDA- PASEF can achieve higher HLAIps identification coverage in a single run (10,000 on average). Considering this, we propose a future strategy where a spectral library is acquired using Thunder- DDA- PASEF and then used to identify the peptides for quantitative DIA analysis. + +<|ref|>text<|/ref|><|det|>[[90, 673, 907, 904]]<|/det|> +Thunder- DDA- PASEF enabled the deep profiling of the HLA class I ligandomes from two cell lines with distinct HLA alleles. We detected 14,074 predicted HLAIps from JY and 17,469 from Raji, with a median coverage of two HLAIps per protein, surpassing the number of HLAIps identified for a single cell line in previous publications [32, 40, 49]. In total, 30,984 HLAIps were identified (Fig. 4b, d), including more than 11,000 singly- charged peptides (Supplementary Material S3). Thus, this combined data set constitutes an important resource for future exploitation (data available via ProteomeXchange, identifier: PXD040385). For instance, using the identifications for training DeepLC and MS2PIP prediction models could further improve the performance of MS2Rescore on timsTOF immunopeptidomics data [16], and other prediction algorithms could be explored ([34, 40, 56]). In addition, different strategies for data analysis remain to be evaluated (Fragpipe, MSmill). Besides, a deeper PTM search could be performed using the PTM algorithm from PEAKS [57], or PROMISE + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 83, 125, 98]]<|/det|> +324 [55]. + +<|ref|>text<|/ref|><|det|>[[60, 106, 907, 335]]<|/det|> +The onset of the ongoing SARS- CoV- 2 pandemic has fueled the discovery of antigen candidates for vaccination, employing in silico prediction algorithms, genetic screens, or peptide library T- cell response assays. Even though immunogenicity testing of hypothesized vaccine candidates yielded some positive outcomes (reviewed in [20, 21, 22]), direct evidence of MHC peptide ligand antigens relies mainly on direct identification by LC- MS. The 17 SARS- CoV- 2 spike HLAIps (Fig. 5d) identified included thirteen peptides with proven immunogenicity (IEDB) and four possibly novel antigens that could be explored as targets for therapy development. Notably, six of the seventeen spike peptides were only identified as singly charged ions, and only the peptide identified in both cell lines (SIAYTMSL0691- 0699) was reported by MS before ([40]). Altogether these results show that Thunder- DDA- PASEF substantially expands the MS- detectable immunopeptidome providing the means for reproducible antigen discovery and direct validation of immunopeptides hypothesized by non- MS methods. + +<|ref|>text<|/ref|><|det|>[[60, 343, 905, 405]]<|/det|> +In summary, Thunder- DDA- PASEF enables an in- depth coverage of HLAIps in a highly reproducible manner. This opens new opportunities to dig deeper into the immunopeptidome in our search to discover novel and specific antigens to target infectious diseases and cancer. + +<|ref|>sub_title<|/ref|><|det|>[[60, 440, 236, 459]]<|/det|> +## 4 Methods + +<|ref|>sub_title<|/ref|><|det|>[[60, 482, 263, 500]]<|/det|> +### 4.1 Cell culture + +<|ref|>text<|/ref|><|det|>[[60, 513, 907, 649]]<|/det|> +The human B lymphoblastoid cell line JY expressing HLA- A02:01, B07:02, C07:02 was purchased from ATCC and the human Burkitt lymphoma cell line Raji expressing HLA- A03:01, B15:10, C03:04, 04:01 was obtained by the DSMZ- German Collection of Microorganisms and Cell Cultures. Both cell lines were maintained in RPMI1640 medium supplemented with 10 % FCS (Gibco), 2 mM glutamine, 1 mM sodium pyruvate, 100 units/ml penicillin and 100 μg/ml streptomycin. Cells were harvested at 220 x g for 10 min and washed three times with 1x PBS prior counting and freezing at - 80°C until further use. + +<|ref|>sub_title<|/ref|><|det|>[[60, 675, 310, 692]]<|/det|> +### 4.2 Cell transfection + +<|ref|>text<|/ref|><|det|>[[60, 707, 907, 914]]<|/det|> +The pcDNA3.1- SARS2- spike vector containing the full- length cDNA encoding for the SARS- CoV2 spike protein was obtained from Fang Li (Addgene plasmid #145032 ; https://www.addgene.org/145032/) [58]. The spike S cDNA was split into S1 (2016 bp) and S2 (1761 bp) subunits for cloning by PCR into the NheI and XhoI restriction sites from the multiple cloning site of the pcDNA3.1+P2AeGFP vector (Genscript). The following oligonucleotides (all purchased by Sigma) were used : GCAT GCT AGC ATG TCT CAG TGC GTG AAC CTG ACT ACT AGA ACC and GCAT CTC GAG ACG GCG AGC CCT CCT TGG GGA GTT GGT CTG GGT CTG for the S1 cDNA and GCAT GCT AGC ATG AGC GTG GCC AGC CAG TCC ATC ATC GCC TAC and GCAT CTC GAG AGC GGG AGC GAC CTG GGA TGT CTC GGT GGA G for the S2 cDNA cloning. To generate stable JY and Raji transfectants expressing either the S1 or the S2 protein fragments + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 82, 907, 217]]<|/det|> +(Supplementary Material S1, Material and Methods), 2 million cells were exposed to 230 V and \(500 \mu \mathrm{F}\) in the presence of \(10 \mu \mathrm{g}\) plasmid DNA using the Bio- Rad Gene Pulser II. After electroporation, cells were cultured 24 h before starting G418 (Gibco) selection at a concentration of \(400 \mu \mathrm{g / ml}\) for JY cells and \(800 \mu \mathrm{g / ml}\) for Raji cells. G418- resistant and eGFP- expressing cells were selected by three rounds of screening using a FACS Aria (BD Biosciences) at the Core Facility of the Research Center for Immunotherapy (University Medical Center, Johannes Gutenberg University Mainz). + +<|ref|>sub_title<|/ref|><|det|>[[57, 243, 664, 262]]<|/det|> +### 4.3 Immuno-affinity purification of HLA peptide ligands + +<|ref|>text<|/ref|><|det|>[[57, 275, 907, 483]]<|/det|> +HLA class I ligands were enriched by immunoprecipitation as described by [59] with modifications [60]. Briefly, 500 million cells were washed three times with PBS, harvested, flash- frozen, and stored at \(- 80^{\circ} \mathrm{C}\) until further preparation. The cell pellets were thawed and lysed in a non- denaturant buffer (1% CHAPS in PBS (m/v)) aided by sonication. Immunoprecipitation was performed using an anti- panHLA Class I antibody (W6/32, anti- HLA- A, - B, - C), immobilized on CNBr- activated beads. After overnight incubation, the beads were washed once with PBS and once with water before peptide ligands were eluted under acidic conditions (0.2% TFA (v/v)). Next, peptides were ultrafiltered (10 kDa cutoff) and then desalted by SPE on a Hydrophilic- Lipophilic- Balanced sorbent (HLB, Waters Corp.), applying 35% ACN (v/v) + 0.1% TFA (v/v) for elution. Finally, dried peptides were dissolved in \(15 \mu \mathrm{L}\) of water with 0.1% FA (v/v) for subsequent LC- MS/MS analyses. + +<|ref|>sub_title<|/ref|><|det|>[[57, 508, 263, 526]]<|/det|> +### 4.4 LC-MS/MS + +<|ref|>text<|/ref|><|det|>[[57, 540, 907, 914]]<|/det|> +NanoLC- MS analysis was performed using a nanoElute coupled to a timsTOF- Pro- 2 mass spectrometer. The desalted peptides were directly injected in a C18 Reversed- phase (RP) analytical column (Aurora \(25 \mathrm{cm} \times 75 \mu \mathrm{m}\) ID, \(120 \mathrm{A}\) pore size, \(1.7 \mu \mathrm{m}\) particle size, IonOpticks, Australia) and separated using either a \(47 \mathrm{min}\) or \(110 \mathrm{min}\) gradient (Supplementary Material S2a) increasing the proportion of phase B (ACN + 0.1% FA (v/v)) to phase A (water + 0.1% FA (v/v)), as detailed in Supplementary Material S2. A Captive Spray source was used for ionization, with a capillary voltage of \(1600 \mathrm{V}\) , dry gas at \(3.0 \mathrm{L} / \mathrm{min}\) , dry temperature at \(180^{\circ} \mathrm{C}\) , and TIMS- in pressure of \(2.7 \mathrm{mBar}\) . MS data were acquired in DDA- PASEF mode. Different MS parameters were evaluated during method development, as detailed in Supplementary Material S2a. The JY and Raji spike- transfected data set was acquired using the optimized conditions described in the following lines. HLAIp IP- enriched, ultrafiltered, and desalted peptides were analyzed in three injection replicates each, using a volume of \(1.5 \mu \mathrm{L} / \mathrm{injection}\) , equivalent to \(50 \mathrm{million}\) cells from the original sample. Peptides were separated in a \(110 \mathrm{min}\) . gradient from \(2 \%\) to \(37 \%\) of ACN + 0.1% FA (v/v). The MS was configured with the optimized Thunder- DDA- PASEF method, employing an HLAIp- tailored isolation polygon (Fig. 2), a \(300 \mathrm{ms}\) TIMS ramp, three MS2 frames/cycle, one cycle overlap, using the high- sensitivity mode (optimized detector voltages). The settings used for LC- MS are detailed in Supplementary Material S2a and the timsTOF Pro method is included as Supplementary Material S2b. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[58, 81, 430, 100]]<|/det|> +### 4.5 Peptidomics database search + +<|ref|>text<|/ref|><|det|>[[58, 111, 910, 463]]<|/det|> +Data analysis was performed in PEAKS XPro (v10.6, build 20201221). Raw LC- MS files were loaded with the configuration for timsTOF DDA- PASEf data with CID fragmentation. The option timstof_feature_min_charge (in file PEAKSStudioXpro\algorithmpara\feature_detection_para.properties) was set to 1 to allow the identification of singly- charged features. The protein database was composed of the UniProtKB (Swiss- Prot) reference proteomes of Homo sapiens (Taxon ID 9606, downloaded 02/Feb/2020), Epstein- Barr virus (strain GD1, Taxon ID 10376, downloaded 06/Feb./2022), GFP from Aequorea victoria (P42212), and SARS- CoV- 2 (Taxon ID 2697049, downloaded 10/March/2021), as well as the SiORF1 reported by [61, 62], supplemented with a list of 172 possible contaminants. For database searches, protein in silico digestion was configured to unspecific cleavage and no enzyme. Methionine oxidation, cysteine cysteinylation, and Protein N- terminal acetylation were set as variable modifications. Peptides were identified with mass accuracy thresholds of 15 ppm for MS1 and 0.03 Da for MS2. Results were filtered at \(FDR \leq 0.01\) for peptides and \(- 10lgP \geq 20\) for proteins. For rescoring, spectra were exported in MGF format and identifications in mzIdentML format, including decoys and without any score filter \((- 10lgP \geq 0\) for peptides and proteins). Identifications were then rescored using MS²Rescore [16] using the Immuno- HCD MS2PIP model and an MS2 mass accuracy tolerance of 0.03 Da. The settings used for data processing are also detailed in Supplementary Material S2a. + +<|ref|>sub_title<|/ref|><|det|>[[60, 488, 331, 506]]<|/det|> +### 4.6 Experiment design + +<|ref|>text<|/ref|><|det|>[[60, 519, 907, 608]]<|/det|> +For method development, pooled samples of IP- enriched HLAlps from JY WT cells were used. For the final JY and Raji data set, the IP protocol was used to enrich the HLAlps from three cultures of each WT cell line (JY_WT, and Raji_WT) and two different cultures of each transfected cell line (JY_S1, JY_S2, Raji_S1, and Raji_S2). In every experiment, each sample was analyzed in three LC- MS injection replicates. + +<|ref|>sub_title<|/ref|><|det|>[[60, 634, 418, 652]]<|/det|> +### 4.7 Data analysis and statistics + +<|ref|>text<|/ref|><|det|>[[60, 665, 907, 781]]<|/det|> +MHC- binding was predicted using NetMHCpan 4.1 [37] and GibbsCluster 2.0 [46] through MhcVizPipe (v0.7.9) [38]. R scripts [63] were used for data analysis, including merging the MS²Rescore [16] output with PEAKS peptide results, as well as with MhcVizPipe output. The main R packages used were as follows; the statistical difference was assessed by two- sided t- test using gppubr (v. 0.4.0) [64]; plots were generated using gpplot2 (v. 3.4.0) [65]; Venn plots with ggvenn (v. 0.1.9) [66]; and upset plots with ggupset (v. 0.3.0) [67]. + +<|ref|>text<|/ref|><|det|>[[60, 785, 907, 874]]<|/det|> +We employed the Universal Spectrum Viewer (USE) [48] to compare the spectra acquired from the cells against spectra obtained from synthetic peptides (n= 7) or predicted in silico (n= 10), based on the similarity Pearson correlation coefficient (PCC). Prosit [34, 56] was used for in silico prediction since it's an orthogonal model to MS²Rescore [16] used for rescoring. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[60, 81, 309, 100]]<|/det|> +### 4.8 Data availability + +<|ref|>text<|/ref|><|det|>[[60, 113, 905, 177]]<|/det|> +4.8 Data availabilityThe mass spectrometry immunopeptidomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) [68] via the jPOSTrepo partner repository [69] with the dataset identifiers PXD040385 for ProteomeXchange and JPST002044 for jPOSTrepo. + +<|ref|>sub_title<|/ref|><|det|>[[60, 211, 354, 231]]<|/det|> +### 5 Acknowledgements + +<|ref|>text<|/ref|><|det|>[[60, 248, 907, 456]]<|/det|> +5 AcknowledgementsWe would like to acknowledge Lucas Kleinort (HI- TRON, Mainz) for his technical assistance and contributions to sample preparation, Kristina Marx (Bruker) for the fruitful scientific discussions, Arthur Declercq for his help on adapting MS2Rescore for PEAKS XPro output, and Kevin Kovalchik for his help with MhcVizPipe. We acknowledge the support of the flow cytometry core facility, the mass spectrometry core facility and the sequencing core facility of the Research Center for Immunotherapy (FZI) at the University Medical Center Mainz. The graphical abstract and Figure 1 were designed in part using images from Servier Medical Art (SMART, smart.servier.com). This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) SFB1292 TP13 (H.S.) and TPQ01 (S.T.), the Helmholtz-Institute for Translational Oncology Mainz (HI-TRON Mainz) – a Helmholtz institute by DKFZ, Mainz, Germany. + +<|ref|>sub_title<|/ref|><|det|>[[60, 488, 380, 508]]<|/det|> +### 6 Author contributions + +<|ref|>text<|/ref|><|det|>[[60, 526, 907, 639]]<|/det|> +6 Author contributionsConceptualization: DGZ, DAS, UD, HS, ST. Methodology: DGZ, DAS, HS, ST. Software: DGZ. Validation: DGZ, DAS, JB. Formal analysis: DGZ, DAS, JB. Investigation: DGZ, DAS, JB, EK. Resources: DAS, HS, ST. Data Curation: DGZ, DAS, JB, UD. Writing - Original Draft: DGZ, DAS, JB. Writing - Review & Editing: DGZ, DAS, JB, EK, UD, HS, ST. Visualization: DGZ, JB. Supervision: DGZ, DAS, HS, ST. Project administration: DGZ, DAS, UD, HS, ST. Funding acquisition: HS, ST. + +<|ref|>sub_title<|/ref|><|det|>[[60, 672, 370, 693]]<|/det|> +### 7 Competing interests + +<|ref|>text<|/ref|><|det|>[[60, 712, 472, 728]]<|/det|> +The authors have no conflicts of interest to declare. + +<|ref|>sub_title<|/ref|><|det|>[[60, 760, 468, 781]]<|/det|> +### 8 Materials & Correspondence + +<|ref|>text<|/ref|><|det|>[[60, 800, 610, 862]]<|/det|> +Correspondance should be addressed to: David Gomez- Zepeda, email: david.gomez- zepeda@dkfz- heidelberg.de; Stefan Tenzer, email: tenzer@uni- mainz.de + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[60, 80, 220, 99]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[50, 113, 909, 920]]<|/det|> +[1] Ramarathinam, S. H., Croft, N. P., Illing, P. 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Graphical abstract
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Figure 1: Immunopeptidomics workflow using Thunder-DDA-PASEF. (a) Sample preparation: 500 million cells of the human JY or Raji cell lines were harvested, then lysed by sonication in 1% CHAPS in PBS buffer (m/v). (b) MHC-ligand peptide enrichment: was performed by immunoaffinity using the W6/32 anti-human-MHC-A, B, C antibody coupled to CNBr-activated agarose beads; after overnight incubation and several washes, peptides were eluted with 0.2% trifluoro-acetic acid, ultrafiltered on molecular weight cutoff filters (MWCO, 10 kDa cutoff) and desalted in HLB plates (Waters Corp.). (c) NanoLC-MS: analysis was performed using a nanoElute coupled to timsTOF-Pro-2 in DDA-PASEF [17] with different parameters to optimize the MS acquisition. (d) Data analysis: Database search was performed in PEAKS XPro using unspecific cleavage. Data analysis was performed in R and predicted MHC-binding affinity was evaluated using NetMHCpan 4.1 [37] and GibbsCluster 2.0 [46] through MhcVizPipe (v0.7.9) [38].
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 180, 852, 675]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[91, 687, 905, 833]]<|/det|> +
Figure 2: Evaluation of the different fragmentation isolation filters: "standard", "None" and "HLAIp-tailored". (a, b, c): Exemplary heatmaps of ion intensities (gray-scale) across the inversed ion mobility \((1 / \mathrm{K}_0)\) vs m/z dimensions showing fragmentation events (red rhombus). (d - i): Correspondent peptides identified across the \(1 / \mathrm{K}_0\) vs m/z dimensions colored by charge state, including all peptides (d, e, f) or only those with 8 to 13 amino acids (AAs) (g, h, i). (j, l,m): Length distribution and percentage of peptides (pie-charts) with 8 to 13 AAs or other lengths; cut-off at 20 AAs dropping \(5.4\%\) , \(1.6\%\) and \(0.26\%\) of peptides identified for standard, None and HLAIp-tailored, respectively. (m) Average number of unique peptides identified per injection in each method (3 injection replicates, \(mean \pm sd\) ). (m) Average number of MS2 scans triggered per injection in each method (3 injection replicates, \(mean \pm sd\) ). Two-sided t-test, ns: \(p > 0.05\) , \*: \(p \leq 0.05\) , \*\*: \(p \leq 0.01\) , \*\*\*: \(p \leq 0.001\) , \*\*\*\*: \(p \leq 0.0001\) .
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 240, 880, 544]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[91, 558, 905, 757]]<|/det|> +
Figure 3: Evauation of the original-DDA-PASEF method (original) compared to the optimized Thunder-DDA-PASEF (Thunder) profiling of JY immunopeptides, and the effect of identification rescoring using MS²Rescore (Thunder + MS2R), considering only peptides of 8 to 13 amino acids long. (a) Average number of unique peptides identified per injection in each method (3 injection replicates, mean \(\pm s d\) ; two-sided \(t\) -test, \(\mathrm{***}\) : \(p \leq 0.0001\) ). (b) Proportion of peptides (considering modifications) identified in function of their charge state. (c) Dynamic range plot showing the peptides identified (considering modifications), ranked in descending order (x-axis) in function of the average peak area across three replicates (y-axis); the dashed gray line indicates the lowest limit of identification for the original method. (d) Identification data completeness, measured as the proportion of peptides identified across three, two, or only one replicate. (e) Upset plot showing the number (barplot) and percentage (text) of 8-13-mers identified identified uniquely in each method or their combinations; the intersection matrix at the bottom indicates that the same peptides shown above (columns) were detected in the methods (rows) highlighted with a blue dot. (f) Total number of peptides identified in each workflow and the proportion predicted as strong-binders (SB, \(rank \leq 0.5\%\) ), weak-binders (WB, \(0.5\% < rank \leq 2\%\) ) or non-binders (NB, \(rank > 2\%\) ) by NetMHCpan 4.0 [37].
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[216, 100, 777, 685]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[91, 693, 905, 892]]<|/det|> +
Figure 4: HLA class I ligandome of JY and Raji cells employing Thunder-DDA-PASEF, combining wild type and spike-transfected cells. (a) Size distribution of total peptides identified from JY and Raji cells. (b) Number of 8-13-mer peptides identified in each workflow and the proportion predicted as strongbinders (SB, \(rank \leq 0.5\%\) ), weak-binders (WB, \(0.5\% < rank \leq 2\%\) ) or non-binders (NB, \(rank > 2\%\) ) by NetMHCpan 4.0 [37] against the matched HLA alleles expressed by each cell line (JY = HLA-A02:01, B07:02, C07:02; Raji: HLA-A03:01, B15:10, C03:04, C04:01) (c) Charge distribution for the predicted HLA class I binders (HLAIps, SB & WB). (d) Total number of predicted HLAIps (SB & WB) identified (top) and protein groups covered (bottom) for JY, Raji, and in total. (e) Distribution of the number of HLAIps per protein group represented as boxplots (center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range) (top) and histogram (bottom); y-axis cut-off at 12 for simplicity, excluding 0.7% of JY HLAIps (13 to 34 Binders/Protein) and 1.4% of Raji HLAIps (13 to 53 Binders/Protein). (f) Overlap of HLAI ligand peptides (top) and protein groups (bottom) between JY and Raji. (g, h) Supervised clustering (GibbsCluster-2.0 via MhcVizPipe) showing the peptide sequence motifs corresponding to the specific allele motifs for JY and Raji HLAIps, respectively.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[142, 175, 860, 585]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[92, 597, 905, 826]]<|/det|> +
Figure 5: Spike HLA class I binder peptides (HLAIps) identified in JY and Raji transfected cells. (a, b) Count of protein-specific HLAIps predicted strong-binders (SB, \(rank\leq 0.5\%\) ) and weak-binders (WB, \(0.5\% < rank\leq 2\%\) ) using NetMHCpan 4.0 [37] for spike (a) and the reporter GFP (b). (c) Peptide peak area distribution of the spike peptides (black dots) and all the HLAIps identified in JY (orange) and Raji (purple). (d) Characteristics of spike HLAIps identified in JY (top) and Raji (bottom) transfected cells. From left to right: sequence code name indicating their position within the protein sequence (s[N-ter]-[C-ter], e.g., s0691-0699 for SIIAYTMSL); sequence, with common peptides highlighted in gray; charge state (number of H+); the number of biological replicates (BR) and technical replicates (TR) where the peptide was identified; Log2 of the peptide peak area; Pearson's correlation coefficient (PCC) comparing the fragmentation spectrum of the endogenous peptide against synthetic peptides (S) or Prosit-predicted (P) [56, 34] calculated employing the Universal Spectrum Explorer (USE) [48]; indexed retention times (iRT) ratio (endogenous/synthetic); Immune Epitope Database and Analysis Resource (IEDB) [50] immune response frequency (RF) = proportion of subjects with positive immune response in B-cell or T-cell assays (dots = RF, lines = 95% confidence interval (CI) range, color scale = lower 95% CI, empty = not reported), relative to the total number of subjects tested for the corresponding peptide; binding affinity to JY and Raji HLA alleles predicted by NetMHCpan 4.0 [37], with labels indicating SBs and WBs.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[98, 357, 940, 570]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[90, 587, 904, 646]]<|/det|> +
Figure 6: Mirrored fragmentation spectra showing the spectrum from endogenous peptides at the top and synthetic or predicted spectra for two spike peptides. (a) SIIAYTMSLs0691-0699 (bottom = synthetic), and (b) TLKSFTVEKs0302-0310, (bottom = Prosit predicted); obtained by USE [48]; PCC = Pearson's correlation coefficient, SA = spectral (contrast) angle.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 92, 765, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[58, 129, 561, 496]]<|/det|> +- S1ThunderDDAPASEFSpike20230224.pdf- S2Extendedmethodssettings.xlsx- S2bThunderDDAPASEF1600V300ms3rampsLSA.m.zip- S3pepall06131.zip- S4hmcpredall06131.zip- S5pepall06133.zip- S6hmcpredall06133.zip- S7gorillajyrajipepbinder.xlsx- S8GOJYRajihLAlpsprotsfromHLAlps.pdf- S9spike06133.xlsx- S10spectraspikeUSEmirror.pdf- nreditorialpolicychecklistthunder.pdf- nrreportingsummarythunder.pdf- RSNCOMMS2308309.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/images_list.json b/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..52edc1ca8ec34c86f34c96a70de4e91a83927ef6 --- /dev/null +++ b/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/images_list.json @@ -0,0 +1,115 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 GWAS and fine mapping of the major locus that underlies grain chalkiness variation. a, The genome-wide association signals for chalky grain rate (CGR) and degree of chalkiness (DC) in the region at 18–21 Mb on chromosome 9 (x axis) across two years. Negative \\(\\log_{10}\\) -transformed \\(P\\) values from the linear mixed model are plotted", + "footnote": [], + "bbox": [ + [ + 144, + 103, + 850, + 777 + ] + ], + "page_idx": 36 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 Chalk9 negatively regulates grain chalkiness in rice. a, Grain chalkiness in ZH11, ZH11-OE1, ZH11-OE2, ZH11-RNAi-1, and ZH11-RNAi-2 plants. Scale bar: 5 mm. b, Expression analysis of Chalk9 in ZH11 and transgenic plants. Data show means \\(\\pm\\) SD \\((n = 3\\) biological replicates). c,d, Chalky grain rate (e) and degree of chalkiness (d) in ZH11 and transgenic plants. Data show means \\(\\pm\\) SD \\((n = 16\\) plants). e, Grain chalkiness in Nip, chalk9-1, and chalk9-2 plants. Scale bar: 5 mm. f,g, Chalky grain rate (f) and degree of chalkiness (g) in Nip, chalk9-1, and chalk9-2 plants. Data show means \\(\\pm\\) SD \\((n = 16\\) plants). In b, c, d, f, and g, different letters indicate significant differences \\((P< 0.05\\) , one-way ANOVA with Tukey's multiple comparison test); for \\(P\\) values, see Source Data.", + "footnote": [], + "bbox": [ + [ + 145, + 111, + 850, + 550 + ] + ], + "page_idx": 38 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 A 64-bp indel in the Chalk9 promoter confers different grain chalkiness in rice. a, Major haplotypes of Chalk9. v4, v5, v10, v12, v14 and v15 indicate the variants, and their positions relative to ATG are shown in the table. b, c, The distribution of chalky grain rate (b) and degree of chalkiness (c) in haplotype H ( \\(n = 45\\) accessions) and", + "footnote": [], + "bbox": [ + [ + 144, + 113, + 846, + 800 + ] + ], + "page_idx": 39 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 Chalk9 is an E3 ubiquitin ligase that interacts with OsEBP89. a, Subcellular localization of Chalk9-GFP fusion protein in rice protoplasts. IPA1-mCherry was used as a nuclear marker. Scale bars: \\(5 \\mu \\mathrm{m}\\) . b, Quantitative PCR with reverse transcription (qRT-PCR)-based transcript abundance analysis of Chalk9 in various tissues. R, root;", + "footnote": [], + "bbox": [ + [ + 145, + 112, + 850, + 800 + ] + ], + "page_idx": 41 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 Chalk9 ubiquitinates OsEBP89 and regulates its stability. a, In vitro ubiquitination of OsEBP89 by Chalk9. Ubiquitinated proteins were detected using anti-GST and anti-Ub antibodies. b, Cell-free degradation of GST-OsEBP89 in the protein extracts from Nip and chalk9-1 seedlings. Protein levels of GST-OsEBP89 were detected using anti-GST antibody, and Actin was used as a loading control for total protein extraction. Relative fold changes of GST-OsEBP89 to Actin loading controls", + "footnote": [], + "bbox": [ + [ + 145, + 111, + 835, + 730 + ] + ], + "page_idx": 43 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Fig. 6 Chalk9-OsEBP89 module regulates rice grain chalkiness by influencing seed", + "footnote": [], + "bbox": [ + [ + 145, + 115, + 845, + 870 + ] + ], + "page_idx": 45 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Fig. 7 Geographical distribution, genomic differentiation, and genomic selection of Chalk9 between japonica and indica subspecies. a,b, The relative ratio of nucleotide diversity (a) and Tajima's \\(D\\) (b) analyses in the whole chromosome 9 of cultivated and wild rice. Red dashed line indicates the Chalk9 locus. c,d Phylogeny (c) and haplotype networks (d) generated from the genomic sequences of Chalk9 in both cultivated and wild rice varieties. Outer circle of the tree indicates various rice populations. Circle size of the network is proportional to the number of samples for each haplotype. Black spots on the lines indicate mutational steps between two", + "footnote": [], + "bbox": [ + [ + 145, + 111, + 848, + 660 + ] + ], + "page_idx": 47 + }, + { + "type": "image", + "img_path": "images/Extended_Data_Figure_3.jpg", + "caption": "Extended Data Fig. 3 OsEBP89 positively regulates grain chalkiness in rice. a,", + "footnote": [], + "bbox": [], + "page_idx": 49 + } +] \ No newline at end of file diff --git a/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828.mmd b/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6910df7b148db67dd2b17163fb5a88bb552e5d64 --- /dev/null +++ b/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828.mmd @@ -0,0 +1,601 @@ + +# Natural Variation of Chalk9 Regulates Grain Chalkiness in Rice + +Changjie Yan c.jyan@yzu.edu.cn + +Yangzhou University Zhi Hu Yangzhou University Hongchun Liu Yangzhou University Min Guo Yangzhou University Xiang Han Yangzhou University Youguang Li Yangzhou University Rujia Chen Yangzhou University https://orcid.org/0000- 0001- 6744- 3509 Yifan Guo Yangzhou University Yihao Yang Yangzhou University Shengyuan Sun Huazhong Agricultural University Yong Zhou Yangzhou University Minghong Gu Yangzhou University + +# Article + +# Keywords: + +Posted Date: January 29th, 2025 + +<--- Page Split ---> + +DOI: https://doi.org/10.21203/rs.3.rs- 5850266/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on July 19th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 61683- 4. + +<--- Page Split ---> + +# Natural Variation of Chalk9 Regulates Grain Chalkiness in Rice + +Zhi \(\mathrm{Hu}^{1,2\#}\) , Hongchun Liu \(^{1\#}\) , Min Guo \(^{1\#}\) , Xiang Han \(^{1}\) , Youguang Li \(^{1}\) , Rujia Chen \(^{1}\) , Yifan Guo \(^{1}\) , Yihao Yang \(^{1}\) , Shengyuan Sun \(^{1}\) , Yong Zhou \(^{1}\) , Minghong Gu \(^{1}\) and Changjie Yan \(^{1,2*}\) \(^{1}\) Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Zhongshan Biological Breeding Laboratory/Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009 Jiangsu, China \(^{2}\) Co-Innovation Center for Modern Production Technology of Grain Crops of Jiangsu Province, Yangzhou University, Yangzhou 225009 Jiangsu, China # These authors contribute equally to this work. \*Correspondence to: C.Y. (cjyan@yzu.edu.cn) + +<--- Page Split ---> + +## Abstract + +Grain chalkiness is an undesirable trait affecting rice quality, concerning both consumers and breeders. However, the genetic mechanisms underlying rice chalkiness remain largely elusive. Here, we identified Chalk9 as a major gene associated with grain chalkiness in a natural population, explaining \(28\%\) of the observed variance. Chalk9 encodes an E3 ubiquitin ligase that targets OsEBP89 for its ubiquitination and degradation during the post- milk stage to balance storage component accumulation in the endosperm. However, low expression of Chalk9 results in excessive accumulation of OsEBP89, disrupting the homeostasis of storage components and leading to the chalkiness phenotype. A 64- bp insertion/deletion in the Chalk9 promoter contributes to its differential transcriptional levels, thus causing chalkiness variation among rice varieties. Moreover, the introgression of the elite allele Chalk9- L into a high- chalkiness rice variety reduced the chalky grain rate by up to \(20\%\) and the degree of chalkiness by up to \(40\%\) , without compromising yield. Chalk9- L was strongly selected during japonica rice domestication and gradually incorporated into modern indica breeding programs. Our findings reveal novel molecular and genetic mechanisms underlying chalkiness and provide a potential strategy for breeding novel rice variety with improved quality. + +<--- Page Split ---> + +## Introduction + +Rice (Oryza sativa L.) is a staple food for over half of the global population, but enhancing its grain quality remains a significant challenge as living standards rise1,2. Chalkiness, a major determinant of rice quality, severely reduces the appearance quality of rice and negatively affects milling, eating and cooking, thereby diminishing its commercial value3,4. Chalkiness is an undesirable trait for consumers and marketing4. Preventing grain chalkiness formation is thus a critical goal in rice breeding. + +Crop breeding is a dynamic and continuous process that strongly reflects human preferences5. Over the past century, rice breeding efforts have primarily focused on enhancing rice productivity by developing high- yield varieties6. However, these increased yields often come at the cost of poor quality, particularly high chalkiness2,7. Seed storage proteins (SSPs) and starch, the predominant components in rice grains, determine both yield and quality. The negative correlation between yield and quality is likely arises from the disruption of their coordinated synthesis8,9. Breaking this trade- off between yield and quality represents a breakthrough opportunity for rice breeders. + +Chalkiness, which refers to opaque regions in the endosperm, is a complex quantitative trait influenced by polygenes and environmental factors, such as high temperature and nutrient availability10- 12. Extensive efforts have been made to dissect the genetic basis of chalkiness in rice, and numerous quantitative trait loci (QTLs) related to chalkiness have been identified on all 12 rice chromosomes using biparental mapping and natural populations13- 19. Several genes have been functionally cloned and characterized. For example, Chalk5 influences rice grain chalkiness by regulating pH homeostasis in developing seeds20. Natural variation in WCR1 regulates redox homeostasis in rice endosperm to affect grain chalkiness21. Recent studies also identified WBR7 and LCG1 as regulators of rice chalkiness through their effects on the accumulation of grain storage components22,23. Despite these advancements, the genetic and molecular mechanisms underlying rice grain chalkiness remain unclear. + +<--- Page Split ---> + +E3 ligases are critical components of the ubiquitin–proteasome system determining the substrate specificity of the cascade by covalent attachment of ubiquitin to target proteins24,25. RING-finger proteins, a major family of E3 ligases characterized by a 40- 60 residue RING domain, confer substrate specificity through direct interaction with target proteins26. The RING domain, stabilized by zinc ions coordinated by cysteine and histidine residues, is essential for E3 activity. Mutations in these zinc-binding residues can disrupt the domain structure and abolish ligase activity26. E3 ligases play significant roles in plant growth, stress resistance, and signaling27- 29; however, their role in regulating grain chalkiness remains unknown. + +In this study, we identified Chalk9 as a major gene controlling chalkiness variation through genome- wide association studies (GWAS) in indica rice germplasm and elucidated the molecular mechanism of Chalk9- mediated chalkiness regulation. For breeding applications, we identified the elite haplotype Chalk9- L, which improves rice appearance quality without yield penalty. Our findings provide novel insight into the molecular mechanisms underlying rice chalkiness and offer promising strategies for breeding rice varieties with high quality. + +## Results + +## Chalk9 is a major locus associated with grain chalkiness in indica rice + +To investigate the genetic basis of grain chalkiness, we collected 175 indica rice varieties from a global population with high phenotypic diversity in chalky grain rate (CGR) and degree of chalkiness (DC) (Supplementary Fig. 1a–d and Supplementary Table 1). Whole- genome sequencing of these varieties generated a final set of 2,290,145 high- quality single- nucleotide polymorphisms (SNPs) after filtering. Principal component analysis showed that the score plot of principal components had continuous distribution without any distinct clusters (Supplementary Fig. 1e), indicating that these indica varieties did not represent a highly structured population. In addition, the average decay of linkage disequilibrium (LD) distance was estimated about 180 kb in this + +<--- Page Split ---> + +population \((r^{2} = 0.1)\) (Supplementary Fig. 1f), consistent with the previous estimation in cultivated rice30. + +Using a linear mixed model, we identified a major locus on chromosome 9, Chalk9, associated with CGR and DC in the 2- year trials through GWAS. This locus explained \(\sim 28\%\) of the total phenotypic variation (Extended Data Fig. 1). In the overlapped peak, the top two SNPs associated with CGR were located at 19,506,938 bp \((P = 8.12 \times 10^{- 10})\) and 19,536,079 bp \((P = 7.25 \times 10^{- 12})\) , while the top two SNPs associated with DC were located at 19,586,699 bp \((P = 2.65 \times 10^{- 10})\) and 19,536,079 bp \((P = 4.39 \times 10^{- 11})\) (Fig. 1a). LD analysis delimited the candidate region within an approximately 200- kb block from 19.43 to 19.63 Mb (Fig. 1b). Interestingly, Chalk9 was located within the previously reported chalkiness- associated QTL regions, such as qWBR9- 1 and qCR9- 117. + +Using a relatively strict \(P\) value threshold \((P < 1 \times 10^{- 6})\) , we identified 76 SNPs that were significantly associated with chalkiness (Supplementary Table 2). Of these, 11 caused missense mutations, one SNP caused a synonymous mutation were in gene coding regions, 20 were in regulatory regions. These SNPs were assigned to 15 genes (Supplementary Table 2). The others were in the intergenic regions (14 SNPs) or gene introns (30 SNPs). For these 15 genes, three genes were annotated as either transposon- related or expressed proteins, the remaining 12 candidate genes were annotated as putative functional proteins (Supplementary Table 3). + +## LOC_Os09g32730 is the candidate of Chalk9 + +To identify the candidate gene for Chalk9, we first evaluated SNPs causing amino acid substitutions in the 12 putative functional proteins. Only one SNP affected a functional domain (Supplementary Fig. 2a), but it was not conserved across plant species (Supplementary Fig. 2b), suggesting the missense SNP was unlikely to affect protein function. We then randomly selected eight lines from both high chalky- grain and low chalky- grain varieties to measure the expression levels of these 12 genes in endosperms + +<--- Page Split ---> + +and leaves by quantitative RT- PCR (qRT- PCR). Of the 12 genes, 11 showed no significant differences in the expression levels in the endosperms between the low chalky- grain and high chalky- grain varieties (Fig. 1c). Only gene III (LOC_Os09g32730) showed significantly higher expression in low chalky- grain varieties compared to high chalky- grain varieties (Fig. 1c, d). In contrast, these 12 genes exhibited similar expression levels of expression in leaves between high chalky- grain and low chalky- grain varieties (Fig. 1e). Notably, LOC_Os09g32730 was preferentially expressed in the developing endosperm, compared to the other candidate genes (Supplementary Fig. 2c). Since grain chalkiness is closely associated with endosperm development, LOC_Os09g32730 was identified as a potential candidate gene for the Chalk9 locus. Hence, we designated this gene as Chalk9. + +To further validate LOC_Os09g32730 as the candidate gene, we generated transgenic lines that either overexpressed Chalk9 (OE) using the constitutive CaMV 35S promoter or interfered Chalk9 using RNA interference (RNAi) in the Zhonghua11 (ZH11) background. Two Chalk9- overexpression lines (OE1 and OE2) displayed decreased chalkiness with lower CGR and DC values, whereas two Chalk9- RNAi lines exhibited increased chalkiness with higher CGR and DC values (Fig. 2a–d). The RANi lines developed in the variety Nipponbare (Nip) or Yangdao 6 (93- 11) also displayed increased chalkiness (Supplementary Fig. 3a–h). Additionally, the CRISPR/Cas9 system was used to specifically disrupt the Chalk9 gene in Nip (Supplementary Fig. 3i–k). Two knockout lines (chalk9- 1 and chalk9- 2) demonstrated increased chalkiness (Fig. 2e–g). Collectively, these results strongly suggest that LOC_Os09g32730 is the candidate of Chalk9, acting as a negative regulator of grain chalkiness in rice. + +## An indel in the Chalk9 promoter confers grain chalkiness variation + +To address potential limitations in identifying DNA sequence variations in Chalk9 from low- coverage genome sequencing, we resequenced Chalk9 and conducted an association analysis with the identified variants (Supplementary Table 4). Two indels (−1331, 64- bp and −791, 1- bp; referred to as v5 and v12) and four SNPs (−1355G>A, + +<--- Page Split ---> + +-817A>G, -749G>A and -634G>C; referred to as v4, v10, v14 and v15) in the promoter region of Chalk9 exhibited stronger associations with grain chalkiness than the top SNP (Extended Data Fig. 2a; Supplementary Table 4). However, a missense SNP in the coding region was not significantly associated with grain chalkiness (Extended Data Fig. 2a). + +Based on the identified variants, we classified Chalk9 variations into two haplotypes: one associated with high- chalky varieties [haplotype H (Chalk9- H)] and one with low- chalky varieties [haplotype L (Chalk9- L)] (Fig. 3a- c, Supplementary Table 5). Chalk9- L accessions showed significantly higher Chalk9 expression in the endosperm compared to Chalk9- H accessions (Fig. 3d). We further developed a near- isogenic line (NIL) carrying the Chalk9- H allele from the indica variety Kasalath in the japonica variety Nip, which had the Chalk9- L allele based on the known reference genome (Extended Data Fig. 2b, c). Compared to Nip, NILChalk9-H plants exhibited significantly higher grain chalkiness with reduced Chalk9 expression (Fig. 3e- h). These results suggest that the two Chalk9 haplotypes confer different expression levels and variations in grain chalkiness in rice. + +To investigate whether the functional differences between the two Chalk9 haplotypes arise from the promoter variants, we created three transgenic constructs (pChalk9- L::Chalk9- L, pChalk9- H::Chalk9- H, and pChalk9- L::Chalk9- H), and used them to generate transgenic plants (see Methods). Compared to wild- type Guichao2 plants (Chalk9- H type), pChalk9- L::Chalk9- L and pChalk9- L::Chalk9- H transgenic lines showed significantly reduced grain chalkiness, with approximately \(20\%\) and \(40\%\) decreases in CGR and DC values, respectively (Fig. 3i- k). However, pChalk9- H::Chalk9- H transgenic plants showed no significant difference in grain chalkiness relative to wild- type Guichao2 (Fig. 3i- k). Consistent with the phenotypes of these transgenic lines, pChalk9- L::Chalk9- L and pChalk9- L::Chalk9- H transgenic lines showed higher Chalk9 transcript levels than wild- type Guichao2 and pChalk9- H::Chalk9- H transgenic plants (Fig. 3l). These findings indicate that the variants in the + +<--- Page Split ---> + +Chalk9 promoter are responsible for the differences in grain chalkiness. + +To further pinpoint the functional variations, we mutated the Chalk9- L promoter by introducing each of six variations individually (v4, v5, v10, v12, v14, and v15) from the Chalk9- H promoter. Transient expression assays showed that the activity of the Chalk9- L promoter was significantly reduced by deleting the 64- bp indel, to a level that was comparable to that of the Chalk9- H promoter (Fig. 3m, Extended Data Fig. 2d). By contrast, none of the other five mutations affected the activity of the Chalk9- L promoter (Fig. 3m, Extended Data Fig. 2d). We also generated gene- edited plants with a deletion in this 64- bp indel region in Nip (Chalk9- L type) (Extended Data Fig. 2e). The Chalk9- L gene- edited (D52) plants exhibited reduced Chalk9 expression and increased chalkiness (Fig. 3n- p, Extended Data Fig. 2f), further confirming that the 64- bp indel in the promoter as the causal variant. + +To understand why the 64- bp indel resulted in the different expression, we further analyzed its sequence and identified binding sites for some conserved transcription factors, including AT- Hook, TCR, B3, and ZF- HD families (Supplementary Table 6). Among these, a rice B3 domain transcription factor (OsB3), highly expressed in endosperms and homologous to ABI3 (essential for seed maturation in Arabidopsis \(^{31}\) ), was found (Supplementary Fig. 4a, b). We found that OsB3 protein activated the promoter of Chalk9- L (Fig. 3q, Extended Data Fig. 2g). In the absence of the 64- bp sequence of the Chalk9- L promoter, the activation of OsB3 protein was significantly reduced (Fig. 3q, Extended Data Fig. 2g). These results demonstrate that the 64- bp sequence in the Chalk9- L promoter contains the DNA binding elements by the OsB3 protein in rice. + +## Chalk9 exhibits E3 ubiquitin ligase activity + +To investigate the molecular function of Chalk9, we first analyzed its localization and expression pattern. The results showed that Chalk9 was localized in the nucleus and highly expressed in the developing endosperm with gradually increasing during grain + +<--- Page Split ---> + +filling (Fig. 4a, b). Similar results were observed through GUS staining (Supplementary Fig. 5a). Chalk9 is predicted to be a RING- C3HC4 type E3 ubiquitin ligase. To confirm its E3 ligase activity, we produced recombinant MBP- Chalk9 protein in Escherichia coli (E. coli) for in vitro ubiquitination assays. When ubiquitin, ubiquitin- activating enzyme (E1), and ubiquitin- conjugating enzyme (E2) were present, Chalk9 underwent auto- ubiquitination, whereas no ubiquitination was detected when E1, E2, or MBP- Chalk9 was absent (Fig. 4c). We mutated the conserved cysteine at position 189 to serine, creating the MBP- Chalk9C189S mutant (Supplementary Fig. 5b). The self- ubiquitination was abolished by the substitution in the RING finger domain (Fig. 4c), confirming that Chalk9 is a functional RING finger E3 ligase. + +Using yeast two- hybrid assays to screen the substrate of Chalk9, we successfully identified OsEBP89, a transcription factor involved in amylose biosynthesis32- 34, that interacts with Chalk9 (Fig. 4d). The C- terminal domain of OsEBP89 was found to be critical for this interaction (Supplementary Fig. 6a). This interaction was further validated by in vitro pull- down (Fig. 4e) and coimmunoprecipitation (CoIP) assays in rice protoplasts (Fig. 4f). Co- localization and bimolecular fluorescence complementation (BiFC) assays confirmed that the interaction between Chalk9 and OsEBP89 occurred in the nucleus (Fig. 4g, Supplementary Fig. 6b). + +Given that Chalk9 functions as an active RING finger E3 ligase and interacts with OsEBP89 (Fig. 4), we hypothesized that OsEBP89 is the direct substrate of Chalk9. In vitro ubiquitination assay was performed using MBP- Chalk9 and GST- OsEBP89. In the presence of E1, E2, ubiquitin, GST- OsEBP89 was ubiquitinated by MBP- Chalk9 (Fig. 5a). In contrast, no polyubiquitination was observed in the absence of E1, E2, ubiquitin or MBP- Chalk9 (Fig. 5a). Furthermore, replacing MBP- Chalk9 with the MBP- Chalk9C189S mutant failed to ubiquitinate GST- OsEBP89 (Fig. 5a), confirming that Chalk9 targeted OsEBP89 for ubiquitination. + +Since ubiquitination often leads to 26S proteasome- dependent degradation of target + +<--- Page Split ---> + +proteins, we tested whether Chalk9 influences the protein stability of OsEBP89 in a rice cell- free system. GST- OsEBP89 protein was expressed in E. coli, and purified protein was incubated in cell- free extracts from Nip and chalk9- 1 seedlings. The GST- OsEBP89 protein was found to be more stable in the chalk9- 1 mutant extract compared to Nip (Fig. 5b, c). The addition of MG132 significantly inhibited GST- OsEBP89 degradation in both Nip and chalk9- 1 extracts (Fig. 5b), indicating that Chalk9 mediated the stability of OsEBP89 in vivo through the 26S proteasome system. + +To further determine OsEBP89 abundance in seeds from Nip and chalk9 mutants, we generated a specific antibody against OsEBP89 (Supplementary Fig. 6c). OsEBP89 showed more abundant in chalk9 mutants than in Nip, although the transcript levels of OsEBP89 remained unchanged (Fig. 5d- f). This suggests that the loss of Chalk9 function leads to the accumulation of OsEBP89 protein in rice. We also compared OsEBP89 protein levels between Nip (Chalk9- L type) and NILChalk9-H plants. The OsEBP89 protein level in seeds was higher in NILChalk9-H plants than in Nip (Fig. 5g, h), while OsEBP89 expression was similar (Fig. 5i), suggesting that Chalk9- L promotes more degradation of OsEBP89 than Chalk9- H. + +## Chalk9–OsEBP89 module regulates grain chalkiness through regulation of the storage components in endosperm + +We observed that the chalk9 mutants produced white- belly endosperms (Supplementary Fig. 7a). Scanning electron microscopy revealed that the chalky endosperm of the chalk9 mutants contained loosely packed spherical starch granules interspersed with large air spaces, whereas the non- chalky endosperm of Nip consisted of densely and regularly packed polyhedral starch granules (Fig. 6a), which is consistent with previous studies35,36. Although the total starch content remained unchanged, the chalk9 mutants exhibited significantly higher amylose content (Fig. 6b, c). Transmission electron microscopy further showed that chalky endosperm cells of the chalk9 mutants contained increased numbers and larger mean areas of spherical protein body I (PBI) and irregularly shaped PBII compared to Nip (Fig. 6d–f). This observation aligned with + +<--- Page Split ---> + +the greatly increased levels of seed storage proteins in chalk9 mutants, including glutelin, prolamin, and albumin (Fig. 6g- k). We further performed a transcriptome deep sequencing (RNA- seq) analysis on seeds from Nip and chalk9- 1 (Supplementary Fig. 7b). A total of 2,658 differentially expressed genes were identified in chalk9- 1 compared to Nip (Supplementary Fig. 7c, Supplementary Data 1). We found that the Waxy (Wx) gene for amylose synthesis and some genes for seed storage protein (SSP) exhibited significantly higher expression levels in chalk9- 1 compared to Nip (Supplementary Fig. 7d and Supplementary Table 7). These findings were further validated by qRT- PCR analysis, which confirmed the increased expression of related genes in chalk9- 1 (Supplementary Fig. 8). + +To investigate whether OsEBP89 is involved in chalkiness regulation, we generated OsEBP89 knockout plants (osebp89- 1 and osebp89- 2) using CRISPR/Cas9 (Extended Data Fig. 3a), and OsEBP89 overexpression lines (OsEBP89- OE1 and OsEBP89- OE2) driven by the constitutive CaMV 35S promoter (Extended Data Fig. 3b). Compared to the wild- type Nip, the OsEBP89 knockout plants showed a slight yet significant reduction in both CGR and DC values, whereas the OsEBP89- overexpression lines exhibited markedly increased chalkiness with higher CGR and DC values (Extended Data Fig. 3c- e). These results indicate that OsEBP89 positively regulates chalkiness in rice. Notably, a significant decrease in Wx expression was detected in OsEBP89 knockout mutants, whereas its expression increased in OsEBP89- overexpressing plants (Extended Data Fig. 3f), which is consistent with previous studies showing that OsEBP89 binds to the GCC box and GCC box- like sequences in the Wx promoter, thereby promoting its expression32- 34. Several such binding sites were also identified in the promoters of SSP genes (Supplementary Table 8). The expression of SSP genes was greatly repressed in OsEBP89 knockout mutants but upregulated in OsEBP89- overexpressing plants (Extended Data Fig. 3g- l). Furthermore, yeast one- hybrid assays demonstrated that OsEBP89 directly bound to the promoters of SSP genes (Extended Data Fig. 3m). + +<--- Page Split ---> + +We crossed the chalk9- 1 mutant with the osebp89- 1 mutant to generate double mutant plants (chalk9- 1/osebp89- 1). While the chalk9- 1 mutant showed increased chalkiness, the chalk9- 1/osebp89- 1 double mutant exhibited a reduced chalkiness, resembling the phenotype of osebp89- 1 (Fig. 6l- n). In addition, the chalk9- 1/osebp89- 1 double mutant showed decreased amylose and total protein similar to osebp89- 1 mutants, while the chalk9- 1 mutant contained increased levels of both (Fig. 6o, p). Taken together, these results reveal that Chalk9- OsEBP89 module regulated the synthesis of grain storage components by modulating the expression of genes involved in storage components, thereby influencing chalkiness formation in rice. + +In addition, based on our whole- genome sequencing data, we observed that OsEBP89 had a single major haplotype in indica rice (Supplementary Table 9). Extending this analysis to 4,726 accessions of cultivated rice \(^{38 - 40}\) , this major haplotype occupied \(97.5\%\) of indica (Supplementary Table 10), indicating the strong genetic conservation and unlikely contribution to chalkiness variation in indica varieties. This result clearly demonstrates that Chalk9 plays an important role in determining the grain chalkiness. + +## Chalk9-L is artificially selected in cultivated rice during domestication and breeding + +We performed a geographic distribution analysis of haplotypes in 1,424 cultivated varieties from the 3K Rice Genomes Project \(^{37}\) . The distribution of rice accessions carrying either Chalk9- L or Chalk9- H was variable across Asia regions relative to other areas (Extended Data Fig. 4a). The frequency of Chalk9- L was nearly \(100\%\) in Southeast Asia (e.g., Myanmar, Philippines, Laos, and Thailand), but it was relatively lower in China (71.1%) and South Asia, including Bangladesh (62%), Nepal (68.1%), Pakistan (70%), and India (76.2%) (Extended Data Fig. 4a). We further performed haplotype analysis in 4,726 accessions of cultivated rice \(^{38 - 40}\) . Eight out of 9 unique high- confidence haplotypes belonged to the Chalk9- L group, while only one belonged to the Chalk9- H group (Supplementary Table 11). Chalk9- L was present in \(12.3\%\) of Aus, \(85.3\%\) of aromatic, \(99.9\%\) of japonica, and \(80.1\%\) of indica varieties (Supplementary + +<--- Page Split ---> + +Table 12). Within the indica subgroups, its frequency was \(40.9\%\) in indica I, \(96.6\%\) in indica II, \(94\%\) in indica III, and \(84\%\) in indica intermediate (Supplementary Table 12). In 445 accessions of the wild ancestor Oryza rufipogon (O. rufipogon) \(^{38}\) , O. rufipogon had a high frequency of Chalk9- L (89.4%) (Supplementary Table 13). These results suggest that the allele distribution of Chalk9 in different rice subgroups may be correlated to their evolution and selection. + +A selective sweep surrounding the Chalk9 locus was observed between japonica and wild rice, with significantly reduced nucleotide diversity in japonica compared to wild rice (Fig. 7a), indicating a strong artificial selection in Chalk9 locus of japonica. Tajima's \(D\) values in the Chalk9 locus was significantly negative in japonica (Fig. 7b), reflecting directional selection across this region. In contrast, no obvious selection was detected in indica because the relative ratio of nucleotide diversity in indica to wild rice was higher than that in japonica to wild rice in Chalk9 locus (Fig. 7a). Further phylogenetic analysis showed that the Chalk9- L haplotype in japonica rice formed a tight cluster, while in indica rice, Chalk9- L was more widely distributed and genetically diverse (Fig. 7c). Haplotype network also showed that Chalk9- L in japonica was closely related to O. rufipogon, with few mutational differences, whereas Chalk9- L in indica exhibited more complex connections and mutational steps (Fig. 7d), suggesting that Chalk9- L in japonica evolved from O. rufipogon through a single lineage, while Chalk9- L in indica had a more complex evolution history with multiple origins. + +To trace the selection of Chalk9- L during indica rice breeding, we developed a 64- bp InDel marker in the Chalk9 promoter and genotyped Chalk9 in 127 indica varieties from the 1950s to the 2000s. The frequency of Chalk9- L in varieties prior to 1990 was relatively low, but it increased significantly thereafter (Extended Data Fig. 4b). This trend aligns with the significant reduction of chalkiness observed in indica varieties post- 1990 (Extended Data Fig. 4c, d), indicating that Chalk9- L has been artificially selected in modern indica rice breeding programs. All 123 japonica varieties carried Chalk9- L (Extended Data Fig. 4b), consistent with the lower chalkiness observed + +<--- Page Split ---> + +(Extended Data Fig. 4e, f). These findings suggest that Chalk9- L might have been under artificial selection to reduce chalkiness. + +## Chalk9-L holds the potential for breeding low-chalkiness rice cultivars without yield penalty + +We further investigated the effect of Chalk9 on yield. Chalk9 knockout plants displayed no significant differences from Nip in major agronomic traits, including heading date, tiller number, plant height, grain size and weight, as well as yield per plant and yield per plot (Supplementary Fig. 9). These results suggest that Chalk9 has no impact on rice yield. NILChalk9-H plants showed no significant differences in grain weight or yield per plant compared to Nip (Chalk9- L type) (Supplementary Fig. 10a, b). Furthermore, introducing the Chalk9- L transgene into the high- yield variety Guichao2 significantly reduced chalkiness without affecting other agronomic traits, particularly yield per plant (Supplementary Fig. 10c- h), demonstrating the potential of Chalk9- L to reduce chalkiness in high- yield rice cultivars without compromising productivity. + +## Discussion + +To date, little progress has been made in understanding the genetic and molecular mechanisms underlying natural variation associated with chalkiness in rice. Here we reported that Chalk9 is the major gene controlling chalkiness variation in indica rice. A 64- bp indel variant in Chalk9 promoter leads to differing expression levels, conferring chalkiness variation among rice varieties. Moreover, we deciphered a Chalk9- OsEBP89- Wx/SSP regulatory module that mediates chalkiness variation (Fig. 7e). These findings deepen our understanding of the genetic and molecular mechanisms underlying grain chalkiness variation in rice. + +Developing high- yielding rice with superior quality is challenging for rice breeding due to the trade- off between these traits2. One notable reason is that many QTLs associated with chalkiness are closely linked to yield- associated genes12,20. Fortunately, Chalk9 + +<--- Page Split ---> + +does not exhibit such a linkage drag, as the yield in its near-isogenic lines shows no significant difference compared to the wild type (Supplementary Fig. 10a, b). Chalk9- L as an elite haplotype showed increased Chalk9 expression, conferring reduced chalkiness (Fig. 3a- h). By introducing this favorable allele into a well- known high- yielding indica variety but with high chalkiness, the chalkiness in the new lines was significantly decreased but did not compromise yield (Fig. 3i- k and Supplementary Fig. 10g, h). + +The distribution of Chalk9- L in cultivated rice appears to have been influenced by evolution and artificial selection during domestication and breeding. Our evolutionary analysis revealed that Chalk9 originated from wild rice but diverged significantly between japonica and indica rice (Fig. 7b- e). In japonica rice, Chalk9- L is likely derived from a single origin in O. rufipogon, while, in indica rice, Chalk9- L has multiple origins and exhibits greater genetic diversity. Moreover, the increasing incorporation of Chalk9- L in modern indica breeding programs has contributed to a significant reduction of chalkiness. In the light of that approximately \(30\%\) of indica varieties lack Chalk9- L and that Chalk9 explains \(28\%\) of the variance in chalkiness phenotype, our results strongly indicate that Chalk9- L is a key target for improving rice appearance quality of indica rice. + +The accumulated knowledge showed that the regulatory regions of genes involved in starch and storage protein biosynthesis usually share common motifs, which facilitates their co- regulation by common transcription factors, such as OsNAC20 and OsNAC26 in rice41. Similarly, OsEBP89 not only influences \(Wx\) expression but also regulates the expression of part of SSP genes, thereby coordinating the synthesis of amylose and storage proteins (Extended Data Fig. 3). In addition, Chalk9 acts as an E3 ubiquitin ligase, targeting OsEBP89 for ubiquitination and subsequent degradation via the 26S proteasome pathway (Figs. 4 and 5). This discovery underscores the critical role of the 26S proteasome in maintaining OsEBP89 protein homeostasis. Notably, recent research showed that OsSK41 phosphorylates OsEBP89, thereby reducing its stability34. + +<--- Page Split ---> + +Whether this phosphorylation is involved in Chalk9- mediated degradation of OsEBP89 remains to be elucidated. + +We propose that OsEBP89 is a positive regulator of chalkiness in rice. Genetic analysis demonstrates that Chalk9 operates in an OsEBP89- dependent manner to modulate the expression of genes involved in the biosynthesis of storage substances, thereby influencing chalkiness (Fig. 6, Supplementary Figs. 7 and 8). Notably, OsEBP89 exhibits a single major haplotype in indica varieties, highlighting its high conservation in indica rice. Consequently, the variation in chalkiness observed in indica rice is largely attributed to genetic variation in Chalk9. Moreover, our findings suggest that OsB3 acts as a potential upstream regulator of Chalk9, mediating its differential expression in response to the 64- bp indel. Future studies should aim to elucidate the role of OsB3 in regulating chalkiness and its contribution to chalkiness variation in rice. These efforts will help elucidate the OsB3- Chalk9- OsEBP89- Wx/SSP pathway in chalkiness regulation. + +Endosperm development involves the coordinated synthesis and accumulation of storage substances, a process closely associated with chalkiness. This developmental progress begins in the pre- milk stage, peaks during mid- milk, and tapers off in the post- milk stage42- 44. Similarly, \(Wx\) and SSP genes, which are central to this process, exhibit finely tuned temporal expression patterns that align with the synthesis of storage compounds45,46. This coordination is crucial for optimizing grain quality by balancing biosynthetic processes that determine grain texture and appearance. Our findings reveal that Chalk9 expression gradually increases during endosperm development, reaching its peak in the post- milk stage (Fig. 4b), a period when the synthesis of storage substances naturally declines. At this stage, Chalk9 functions as a regulatory “brake”, limiting storage substance accumulation by promoting OsEBP89 degradation. This regulatory mechanism aligns with the natural decline in storage substance synthesis, supporting seed maturation and contributing to the formation of translucent grains. Thus, we propose a model in which the Chalk9- OsEBP89 regulatory module governs + +<--- Page Split ---> + +chalkiness variation in rice (Fig. 7e). In rice varieties carrying the Chalk9- H allele, reduced Chalk9 expression leads to OsEBP89 stabilization, which subsequently upregulates the expression of \(Wx\) and SSP genes. This increased synthesis of storage compounds disrupts the natural decline in their accumulation during the post- milk stage, resulting in the formation of chalky endosperm. In contrast, the Chalk9- L allele enhances Chalk9 expression, promoting OsEBP89 degradation. This reduction in OsEBP89 levels downregulates the expression of \(Wx\) and SSP genes, reducing storage product synthesis during the post- milk stage, leading to translucent grains and improved grain quality. + +## Methods + +## Plant materials and genotyping + +All 175 indica accessions, obtained from germplasm banks and breeders around the world, are listed in Supplementary Table 1. The japonica rice varieties (Nip and ZH11) and the indica rice varieties (93- 11 and Guichao2) were used in this study. All rice materials used in this study were cultivated simultaneously during the summer in paddy fields at the experimental station of Yangzhou University, located in Yangzhou, China. The plants were grown under standardized crop management practices. + +Total genomic DNA was extracted from the samples and used to generate DNA sequencing libraries. Sequencing was performed, and the resulting libraries were size- checked using an Agilent 2100 Bioanalyzer system. The library preparations were ultimately sequenced on an Illumina Xten platform, producing 150 bp paired- end reads. After removing nucleotide variations with missing rates \(\geq 0.25\) and minor allele frequency \(< 0.05\) , all nucleotide polymorphisms were categorized based on their location in the reference genome. + +## Measurements of grain chalkiness and storage components + +Seeds harvested after full maturation were air- dried, stored at room temperature for + +<--- Page Split ---> + +three months. Images of 200- 300 polished rice grains, randomly selected from each plant, were captured using a ScanWizard EZ scanner and analyzed with the rice quality TS- G automated analysis system (Hangzhou Shansheng Testing Technology Co., China). For chalkiness traits, the chalky grain rate (CGR) refers to the proportion of chalky grains among all rice grains, while the degree of chalkiness (DC) represents the extent of chalkiness in the rice grains. Total starch, amylose, total protein, and storage protein fractions were measured based on previously published methods47. + +## Genome-wide association study + +GCR and DC were surveyed in 175 indica varieties over two years (2021 and 2023) and subsequently used for genome- wide association studies (GWAS). The analysis was performed using GEMMA (version 0.941), which fits a linear mixed model48. The \(P\) - value threshold for significance was set at \(1 \times 10^{- 5}\) using the Bonferroni correction method49, and the leading SNP was determined to be the SNP with the minimum \(P\) - value in the associated signal. Linkage disequilibrium (LD), evaluated as \(r^2\), between SNPs in the 175 varieties was calculated using plink v1.950, and The LD heatmap surrounding the peak region was constructed using the LDBlockShow v1.4051. + +## Constructs for genetic transformation + +For the Chalk9 RNA- interference vector, Chalk9- specific sequences from the coding region were amplified, and inserted in both sense and antisense orientations into a modified pTAC303- RNAi vector. For the Chalk9 overexpression vectors, the full- length coding sequence of Chalk9 from Nip was inserted into pCAMBIA2300- 35S vector to generate the pCAMBIA2300- 35S:Chalk9 construct. For the Chalk9 knockout vectors, two small- guide RNA (sgRNA) sequences targeting the Chalk9 coding region were cloned into pYLCRISPR/Cas9- MH vector to generate the Chalk9 CRISPR- Cas9 construct. Additionally, two sgRNA sequences from the Chalk9 promoter surrounding the 64- bp indel were designed and inserted into pYLCRISPR/Cas9- MH vector to generate the Chalk9 promoter- editing construct. For the Chalk9 promoter- GUS vector, a 2- kb genomic upstream region of Chalk9 was amplified and cloned into the + +<--- Page Split ---> + +pCAMBIA1381z vector. + +For the pChalk9- L::Chalk9- L vector, the 2,645- bp genomic region including the 2- kb upstream sequence and 645- bp coding sequence was amplified from low chalky- variety IR72 (Chalk9- L type) genomic sequence and cloned into plant binary vector pCAMBIA2300. The construct pChalk9- H::Chalk9- H contains the 2- kb upstream sequence and 645- bp coding sequence from high chalky- variety Guichao2 (Chalk9- H type). The 645- bp coding sequence from Guichao2 was driven by the 2- kb promoter sequence from IR72 to generate the pChalk9- L::Chalk9- H construct. + +For the OsEBP89 knockout vector, two sgRNA sequences targeting the OsEBP89 coding region were cloned into pYLCRISPR/Cas9- MH vector to generate the OsEBP89 CRISPR- Cas9 construct. For the OsEBP89 overexpression vector, the full- length coding sequence of OsEBP89 from Nip was inserted into pCAMBIA2300- 35S vector to generate the pCAMBIA2300- 35S:OsEBP89 construct. Agrobacterium- mediated transformation was used to generate transgenic rice plants. Primer sequences used in this study are listed in Supplementary Table 14. + +## GUS analysis + +Various rice tissues, including young roots, stems, leaf sheaths, leaves, young panicles, and developing seeds from proChalk9::GUS transgenic plants, were stained with a GUS staining kit (Coolaber Biotech, Beijing, China) at \(37^{\circ}\mathrm{C}\) in the dark for 12 hours, and then decolorized with \(100\%\) ethanol and imaged using a microscope (OLYMPUS, MVX10, Japan). + +## Gene expression analysis + +After synthesizing first- strand cDNA from total RNA extracted from rice samples, quantitative PCR was performed using ChamQ SYBR qPCR Master Mix (Vazyme Biotech, Nanjing, China). Data analysis was conducted from three replicates for each experiment, using the rice OsActin (LOC_Os10g36650) gene as the internal reference. + +<--- Page Split ---> + +Gene- specific primers are listed in Supplementary Table 14. + +## Transcriptome deep sequencing (RNA-seq) analysis + +Total RNA was isolated from seeds at 20 days after flowering (DAF), and RNA- seq libraries were prepared in triplicate from wild- type Nip and chalk9- 1 mutant samples. RNA- seq and gene transcript abundance analysis were performed by the Bioacme Biotechnology Co., Ltd. (Wuhan, China). Differentially expressed genes were identified using DESeq2 with a \(P\) - value \(< 0.05\) and \(|\log_2\mathrm{FoldChange}| > 1\) . Correlation analysis, heatmap plotting, and volcano plot analysis were performed as previously described52. + +## Transmission electron microscopy + +Seeds from WT and chalk9- 1 mutant plants at 18 DAF were collected and used for transmission electron microscopy (TEM) samples, which were fixed and prepared as previously described52. Micrographs of the endosperm cells were captured on 80- nm ultra- thin sections using a transmission electron microscope. + +## Scanning electron microscopy + +Brown rice grains were naturally broken from the middle and then coated with gold using an E- 100 ion sputter coater. The morphology of starch granules was observed using a scanning electron microscope as previously described21. At least three biological replicates from different mature grains were analyzed. + +## Subcellular colocalization assay + +The coding regions of \(IPAl\) and Chalk9 were amplified by PCR and individually cloned into the 163- mCherry plasmid and the 163- GFP plasmid, respectively. Protoplasts isolated from 10- day- old Nip rice seedlings were transfected with the constructs as described previously53. GFP and mCherry were excited with 488- nm and 543- nm laser lines, respectively, and all fluorescence signals were detected at 500- 580 nm and 565- 615 nm using confocal laser- scanning microscopy. Images presented in the figures are + +<--- Page Split ---> + +representative of at least five protoplasts. + +## Y2H assay + +For the Y2H screening, developing seeds at the reproductive stage were combined to construct a two- hybrid library by Shanghai OE Biotech Company. The coding sequence of Chalk9 was cloned into the pGBKT7 vector and used as the bait. The yeast strain Y2H Gold was employed for transformation. To verify the interaction between OsEBP89 and Chalk9 in yeast, the coding sequences of OsEBP89 and mutated OsEBP89 variants (OsEBP89 [1- 119], OsEBP89 [120- 201], and OsEBP89 [202- 326]) were separately cloned into the pGADT7 vector. Y2H assays were performed according to the manufacturer's instructions (Clontech). Primers used for construction are listed in Supplementary Table 14. + +## BiFC assay + +The coding regions of OsEBP89 and Chalk9 were amplified and cloned into the pUC- SPYCE and pUC- SPYNE vectors, respectively. The IPA1- mCherry vector served as a nuclear marker. The transfected protoplasts with the indicated constructs were observed using a fluorescence microscope. Yellow fluorescent protein (YFP) and mCherry were excited with 514- nm and 543- nm laser line, respectively, and detected at 522- 555 nm and 565- 615 nm. Images presented in the figures are representative of at least five protoplasts. Primers used for construction are listed in Supplementary Table 14. + +## Co-IP assay + +The coding sequences of OsEBP89 and Chalk9 were amplified and cloned into the 163- GFP and pUC35S- HA vectors, respectively, to generate the OsEBP89- GFP and Chalk9- HA constructs. Total proteins for the Co- IP assay were extracted from protoplasts isolated from 10- day- old rice seedlings, which were transfected with the indicated constructs. Chalk9- HA was immunoprecipitated using anti- HA beads at \(4^{\circ}\mathrm{C}\) for 2 hours. The eluted proteins were analyzed by immunoblotting with anti- HA (1:3000, ab9110, Abcam) and anti- GFP (1:3000, ab290, Abcam) antibodies. + +<--- Page Split ---> + +## In vitro GST pull-down assays + +The coding sequences of OsEBP89 and Chalk9 were cloned into the pGEX- 5X- 1 and pMAL- c5X vectors, respectively, to produce GST- OsEBP89 and MBP- Chalk9. The constructs were transformed into E. coli BL21 and induced with 0.2 mM IPTG for 12 hours at \(16^{\circ}\mathrm{C}\) to generate GST- OsEBP89 and MBP- Chalk9 recombinant proteins. GST- OsEBP89 and MBP- Chalk9 were purified using glutathione- sepharose resins (CW0190S; CWBlO) and amylose resins (E8021V; NEB), respectively, for Pull- down assays as described previously52. The eluted proteins were analyzed by immunoblotting with anti- GST (CW0084M; CWBlO) and anti- MBP (HT701; Transgene) antibodies. + +## In vitro self-ubiquitination and substrate ubiquitination analyses + +Recombinant MBP- Chalk9 and its single amino acid substitution mutant (MBP- Chalk9C189S) were expressed in E. coli and purified using amylose resins (E8021V; NEB) for in vitro self- ubiquitination analyses. The ubiquitination assay was performed as previously described, with some modifications54. 400 \(\mu \mathrm{g}\) of MBP- Chalk9, MBP- Chalk9C189S, or MBP protein was incubated in a 50- \(\mu \mathrm{L}\) reaction mixture containing ubiquitination buffer (50 mM Tris- HCl, pH7.5, 5 mM MgCl2, 2 mM DTT, 4 mM ATP, 15 \(\mu \mathrm{g}\) ubiquitin). The reaction was carried out at \(30^{\circ}\mathrm{C}\) for 2 hours in the presence or absence of 50 ng E1 (Beyotime, Shanghai, China) and 100 ng E2 (Beyotime). The reactions were stopped by the addition of 5×SDS sample buffer and heated at 95 \(^\circ \mathrm{C}\) for 5 minutes. The reaction products were separated on SDS- PAGE, followed by immunoblot analysis using an anti- MBP antibody (1:5000; HT701; Transgene) and a polyclonal anti- ubiquitin antibody (1:1,000, RM4934; Biodragon). + +For in vitro substrate ubiquitination assays, GST- OsEBP89 was used as the target substrate. 300 ng of the GST- OsEBP89 fusion protein was mixed with an equal amount of MBP- Chalk9 or MBP- Chalk9C189S in the presence or absence of the following: 50 ng of E1, 100 ng of E2, and 5 \(\mu \mathrm{g}\) of ubiquitin. The reaction was performed in a 50 \(\mu \mathrm{L}\) total mixture containing ubiquitination buffer at \(30^{\circ}\mathrm{C}\) for 3 hours. Ubiquitination levels + +<--- Page Split ---> + +of proteins were determined by Western blotting using a polyclonal anti- ubiquitin antibody (1:1,000, RM4934; Biodragon) and an anti- GST antibody (CW0084M; CWBI0). + +## Cell-free degradation assays + +The leaf powder, frozen in liquid nitrogen from Nip and chalk9- 1 plants, was suspended in extraction buffer (5 mM \(\mathrm{MgCl}_2\) , 40 mM Tris- HCl pH 7.5, 5 mM NaCl, 1 mM DTT, and 10 mM ATP) and vigorously vortexed at \(4^{\circ}\mathrm{C}\) for 1 hour. After centrifugation at 16,000 g at \(4^{\circ}\mathrm{C}\) for 30 minutes, the supernatant was collected for the cell- free degradation assay. The GST- OsEBP89 recombinant protein was incubated with the supernatant at \(30^{\circ}\mathrm{C}\) for different periods, with or without the addition of 50 mM MG132 (Beyotime). The reactions were terminated by adding \(5 \times \mathrm{SDS}\) sample buffer and then immunoblotted using anti- GST (CW0084M; CWBI0) and anti- Actin (CW0264M; CWBI0) antibodies. The protein levels were quantified using ImageJ software (http://rsb.info.nih.gov/ij). + +## Yeast one-hybrid assay + +Yeast one- hybrid (Y1H) assays were performed using the Matchmaker™ Gold Yeast One- Hybrid System (Clontech). The coding sequence of OsEBP89 was fused to the activation domain of the GAL4 protein in the pGADT7 vector, generating the prey construct pGADT7- OsEBP89. 2- kb promoter sequences from GluB1a, GluB2, GluB4, PROLM20, PROLM22, and PROLM23 were individually inserted into the pAbAi vector, generating the bait constructs. The minimal inhibitory concentration of aureobasidin A (AbA) for the bait strains was determined for yeast one- hybrid assay. The prey construct pGADT7- OsEBP89 was then transformed into the recombinant bait- reporter strains. The interaction between the empty pGADT7 and the corresponding bait plasmid was considered a negative control. Yeast cells were grown on SD/- Leu culture media with or without AbA for 3- 5 days at \(30^{\circ}\mathrm{C}\) . Primer sequences are shown in Supplementary Table 14. + +<--- Page Split ---> + +## Luciferase activity assay in rice protoplasts + +To investigate the regulatory effect of the Chalk9 promoter on gene expression, approximately 2- kb promoter sequences of Chalk9 were amplified from Nip and Guichao2 and inserted into pGreenII 0800- LUC vector to generate proChalk9- L:LUC and proChalk9- H:LUC, respectively. Six variants were mutated based on proChalk9- L:LUC using a Fast Mutagenesis System (FM111, Transgen Biotech). All the vectors were transformed into protoplasts, respectively. Afterwards, all the protoplasts were incubated in W5 solution for 12 h at \(28^{\circ}\mathrm{C}\) . Activities of firefly luciferase (LUC) and Renilla luciferase (REN) were examined using a dual luciferase assay kit (Vazyme Biotech, Jiangsu, China). The primers used for PCR amplification and mutation are listed in Supplementary Table 14. + +To test the transcriptional activity of OsEBP89 proteins on SSP genes, 2- kb promoter sequences of GluB1a, GluB2, GluB4, PROLM20, PROLM22, and PROLM23 were cloned into the pGreenII 0800- LUC vector to create reporter constructs. The coding sequence of OsEBP89 was cloned into the pGreenII 62- SK vector to generate effector construct. For analyzing the transcriptional activity of the OsB3 protein on the Chalk9 alleles, 2- kb promoter sequences of Chalk9- L and Chalk9- L v5m were cloned into the pGreenII 0800- LUC vector to create reporter constructs, respectively. The coding sequence of OsB3 was cloned into the pGreenII 62- SK vector to generate the effector construct. The empty pGreenII 62- SK vector was used as a negative control. Plasmid combinations were co- transformed into rice protoplasts for transcriptional activity analysis. The transformed cells were incubated in the dark at \(28^{\circ}\mathrm{C}\) for 12 hours and then used to measure transcriptional activity using a dual luciferase assay kit (Vazyme Biotech, Jiangsu, China). The relevant primers are listed in Supplementary Table 14. + +## Immunoblot analysis + +Developing seeds were homogenized in protein extraction buffer (2 mM EDTA, 100 mM NaCl, 20 mM Tris- HCl, pH 7.5, \(0.1\%\) [v/v] Triton X- 100, 1 mM PMSF, and a \(1 \times\) proteinase inhibitor cocktail). Total proteins were collected after the homogenate was + +<--- Page Split ---> + +centrifuged at 16,000 g at 4 °C for 20 min. Western blotting was performed as previously described. Briefly, Protein samples were separated by 10% (w/v) SDS- PAGE and transferred to PVDF membranes (Immobilon- P, USA). Protein signals were detected using the eECL Western Blot Kit (CW0049S; CWBIO) after being probed with specific primary antibodies, followed by incubation with the appropriate secondary antibodies. + +## OsEBP89 polyclonal antibody preparation + +To generate a specific antibody against OsEBP89, we chosen a truncated sequence (residues 1- 120) for recombinant protein production. The corresponding coding sequence was amplified and cloned into the pET28a vector with an N- terminal His- tag. The recombinant protein was expressed in E. coli strain BL21 (DE3) transformed with the resulting construct and then purified using a Ni- NTI agarose resin matrix (Qiagen). The purified recombinant protein served as the antigen to raise antibodies in two rabbits, a process conducted by GenScript. The antibody against OsEBP89 was further affinity- purified from serum using immobilized recombinant protein and specifically detected endogenous OsEBP89. + +## Population genetic and evolutionary analyses + +The geographical information and genomic sequences of 1,424 cultivated varieties were obtained from the 3K Rice Genomes Project37, and marked on map to observe geographic distribution of the two types of Chalk9. Using VCFtools v0.1.1655, the nucleotide diversity (π) and Neutral test (Tajima's D) were calculated in 50- kb windows for each japonica, indica, and wild rice population. For all sites in the Chalk9 locus with a minor allele frequency ≥ 0.01, phylogenetic and haplotypes network analyses were constructed following previously established methods56. + +## The spatiotemporal gene expression and TFBS enrichment analysis + +The spatio- temporal gene expression pattern was analyzed by RiceXPro57. Additionally, the 64- bp sequence of the Chalk9 promoter was examined for transcription factor + +<--- Page Split ---> + +binding site (TFBS) enrichment using PlantPan v4.058. + +## Statistical analysis + +Prism v.6.0 (GraphPad) software was used for all statistical tests and data visualization. The individual figures and figure legends indicated the sample sizes \((n)\) and \(P\) values. For two groups, statistical significance was determined using two- tailed paired Student's \(t\) - test. For more than two groups, statistical significance was determined using one- way analysis of variance (ANOVA) with Tukey's multiple comparisons test. + +## Accession numbers + +Sequence data related to this article can be obtained from the Rice Database (https://www.ricedata.cn/gene) under following accession numbers LOC_Os09g32730 for Chalk9, LOC_Os03g08460 for OsEBP89, LOC_Os06g04200 for Wx, LOC_Os02g15178 for GluB1a, LOC_Os02g15150 for GluB2, LOC_Os02g16830 for GluB4, LOC_Os02g16820 for GluB5, LOC_Os02g14600 for GluB7, LOC_Os02g25640 for GluC, LOC_Os02g15090 for GluD, LOC_Os05g26350 for PROLM4, LOC_Os05g26460 for PROLM11, LOC_Os05g26368 for PROLM13, LOC_Os05g26720 for PROLM16, LOC_Os07g11910 for PROLM20, LOC_Os07g11920 for PROLM22, and LOC_Os06g31060 for PROLM23. + +## Acknowledgements + +We thank Prof. L. Yan (Oklahoma State University, USA) for revising the paper. The pTAC303- RNAi vector was provided by K. Chong; The pYLCRISPR/Cas9- MH vector was provided by Y. Liu; and the pUC- SPYCE and pUC- SPYNE vectors were provided by R. Lin. This work was supported by the grants from the National Natural Sciences Foundation of China (32301828), the Biological Breeding- National Science and Technology Major Project (2023ZD04068), the programs of the Jiangsu Province Government (BE2022335, JBGS [2021]001, and BE2021334- 1), and the Project of Zhongshan Biological Breeding Laboratory (ZSBBL- KY2023- 01). + +<--- Page Split ---> + +## Author contributions + +Z.H., H.L. and M.G. performed experiments and analyzed the data. Z.H. and X.H. collected phenotype data of rice varieties and genetic materials in the field. Y.L. conducted the GWAS analysis. R.C. performed the evolutionary analysis. Y.G., Y.Y., S.S., Y.Z. and M.G. participated in the experiments. Z.H. and C.Y. designed research and wrote the manuscript. C.Y. supervised the project. 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Nucleic Acids Res. 52, D1569–D1578 (2024). + +<--- Page Split ---> + +## Figure legends + +Fig. 1 GWAS and fine mapping of the major locus that underlies grain chalkiness variation. + +Fig. 2 Chalk9 negatively regulates grain chalkiness in rice. + +Fig. 3 A 64- bp indel in the Chalk9 promoter confers different grain chalkiness in rice. + +Fig. 4 Chalk9 is an E3 ubiquitin ligase that interacts with OsEBP89. + +Fig. 5 Chalk9 ubiquitinates OsEBP89 and regulates its stability. + +Fig. 6 Chalk9- OsEBP89 module regulates rice grain chalkiness by influencing seed storage substance biosynthesis. + +Fig. 7 Geographical distribution, genomic differentiation, and genomic selection of Chalk9 between japonica and indica subspecies. + +Extended Data Fig. 1 The genome- wide association study for rice grain chalkiness. + +Extended Data Fig. 2 The 64- bp indel in the Chalk9 promoter contributes to grain chalkiness variation. + +Extended Data Fig. 3 OsEBP89 positively regulates grain chalkiness in rice. + +Extended Data Fig. 4 Temporal and geographic patterns of Chalk9- L distribution and its impact on chalkiness in cultivated rice varieties. + +Supplementary Fig. 1 Variations of chalky grain rate and degree of chalkiness in 175 indica varieties. + +Supplementary Fig. 2 Functional importance estimation of SNPs located in the coding region. + +Supplementary Fig. 3 Identification of Chalk9 RNAi and knockout plants. + +<--- Page Split ---> + +Supplementary Fig. 4 Identification of the candidate genes in transcriptional factors analysis. + +Supplementary Fig. 5 The amino acid sequence of RING domain in Chalk9 is highly conserved in plants. + +Supplementary Fig. 6 Functional analysis of OsEBP89 and identification of the anti- OsEBP89 antibody. + +Supplementary Fig. 7 Transcript levels of storage substance- related genes in seeds of Nip and chalk9- 1 plants from RNA- seq data. + +Supplementary Fig. 8 Transcript levels of storage substance- related genes in the seeds form Nip and chalk9- 1 plants. + +Supplementary Fig. 9 Agronomic traits for chalk9 mutants. + +Supplementary Fig. 10 Agronomic traits for near- isogenic lines and transgenic plants. + +Supplementary Table 1. Chalky grain rate and degree of chalkiness of 175 indica accessions in two years. + +Supplementary Table 2. Annotation of significant SNPs associated with the grain chalkiness in the candidate region. + +Supplementary Table 3. Annotation of candidate genes on Chromosome 9 associated with the grain chalkiness by MSU Rice Genome Annotation Project Release 7. + +Supplementary Table 4. All variations of Chalk9 in 149 indica accessions were identified by re- sequencing based on PCR amplification. + +Supplementary Table 5. Major haplotypes of Chalk9 were identified from significant variations in indica accessions. + +Supplementary Table 6. Prediction of transcription factors binding to the 64- bp sequence in Chalk9. + +<--- Page Split ---> + +Supplementary Table 7. Expression analysis of starch and SSP genes generated from chalk9- 1 vs Nip by RNA- seq. + +Supplementary Table 8. Putative cis-regulatory elements identified in the promoters of genes involved in starch and storage protein biosynthesis. + +Supplementary Table 9. The SNPs at the OsEBP89 gene region define one major haplotype in indica varieties. + +Supplementary Table 10. The SNPs at the OsEBP89 gene region from sequencing data of 4,726 rice accessions. + +Supplementary Table 11. The SNPs at the Chalk9 promoter region in 3K Rice Genomes Project. + +Supplementary Table 12. Allele frequency of Chalk9-L from sequencing data of 4,726 rice accessions. + +Supplementary Table 13. Allele frequency of Chalk9-L in common wild rice (O. rufipogon). + +Supplementary Table 14. Primers (5'-3') used in this study. + +Supplementary Data 1. 2,658 differentially expressed genes generated from chalk9-1 vs Nip. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 GWAS and fine mapping of the major locus that underlies grain chalkiness variation. a, The genome-wide association signals for chalky grain rate (CGR) and degree of chalkiness (DC) in the region at 18–21 Mb on chromosome 9 (x axis) across two years. Negative \(\log_{10}\) -transformed \(P\) values from the linear mixed model are plotted
+ +<--- Page Split ---> + +on the y- axis. The horizontal dashed line indicates the genome- wide significance threshold \((P = 1\times 10^{- 6})\) . b, Linkage disequilibrium (LD) heatmap of the Chalk9 locus region. Pairwise linkage disequilibrium was determined by calculating \(r^2\) (the square of the correlation coefficient between SNPs). c, Relative expression level of the 12 candidate genes in the endosperm of eight high- chalk varieties and eight low- chalk varieties at 20 days after flowering (DAF). The 12 predicted genes in the Chalk9 locus region are labeled by I to XII. Data show means \(\pm \mathrm{SD}\) ( \(n = 8\) varieties). d, Relative expression level of the candidate gene III (Chalk9) in the endosperm from the selected varieties at 20 DAF. Data show means \(\pm \mathrm{SD}\) ( \(n = 3\) biological replicates). e, Relative expression level of the 12 candidate genes in the leaves of high- chalk varieties and low- chalk varieties. Data show means \(\pm \mathrm{SD}\) ( \(n = 8\) varieties). f, Expression analysis of the candidate genes from GWAS in various tissues. The result of two genes (II and XI) was not found in RiceXPro database. L, leaf blade; LS, leaf sheath; R, root; S, stem; I, inflorescence; A, anther; P, pistil; L/P, lemma/palea; O, ovary; Em, embryo; En, endosperm. In c and e, statistical analysis was performed by two- tailed Student's \(t\) - test; for \(P\) values, see Source Data. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 Chalk9 negatively regulates grain chalkiness in rice. a, Grain chalkiness in ZH11, ZH11-OE1, ZH11-OE2, ZH11-RNAi-1, and ZH11-RNAi-2 plants. Scale bar: 5 mm. b, Expression analysis of Chalk9 in ZH11 and transgenic plants. Data show means \(\pm\) SD \((n = 3\) biological replicates). c,d, Chalky grain rate (e) and degree of chalkiness (d) in ZH11 and transgenic plants. Data show means \(\pm\) SD \((n = 16\) plants). e, Grain chalkiness in Nip, chalk9-1, and chalk9-2 plants. Scale bar: 5 mm. f,g, Chalky grain rate (f) and degree of chalkiness (g) in Nip, chalk9-1, and chalk9-2 plants. Data show means \(\pm\) SD \((n = 16\) plants). In b, c, d, f, and g, different letters indicate significant differences \((P< 0.05\) , one-way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 A 64-bp indel in the Chalk9 promoter confers different grain chalkiness in rice. a, Major haplotypes of Chalk9. v4, v5, v10, v12, v14 and v15 indicate the variants, and their positions relative to ATG are shown in the table. b, c, The distribution of chalky grain rate (b) and degree of chalkiness (c) in haplotype H ( \(n = 45\) accessions) and
+ +<--- Page Split ---> + +haplotype L \((n = 102\) accessions). d, Expression analysis of Chalk9 in haplotype H \((n\) \(= 24\) accessions) and haplotype L \((n = 24\) accessions) in endosperms. e, Grain chalkiness of Nip and NIL \(^{\text{Chalk9 - H}}\) plants. Scale bar: \(5\mathrm{mm}\) . f,g, Chalky grain rate (f) and degree of chalkiness (g) of Nip and NIL \(^{\text{Chalk9 - H}}\) plants. Data show means \(\pm\) SD \((n = 16\) plants). h, Relative Chalk9 expression levels of Nip and NIL \(^{\text{Chalk9 - H}}\) plants in endosperms. Data show means \(\pm\) SD \((n = 3\) biological replicates). i, Grain chalkiness of wild-type Guichao2, pChalk9- H::Chalk9- H, pChalk9- L::Chalk9- L, and pChalk9- L::Chalk9- H plants. Scale bar: \(6\mathrm{mm}\) . j,k, Chalky grain rate (j) and degree of chalkiness (k) of Guichao2 and different transgenic plants. Data show means \(\pm\) SD \((n = 10\) plants). l, Relative Chalk9 expression levels of Guichao2 and different transgenic plants in endosperms. Data show means \(\pm\) SD \((n = 3\) biological replicates). m, Transient expression assays of the effect of different variations on gene expression, shown by firefly luciferase/Renilla luciferase activity ratio (LUC/REN). v4m, v5m, v10m, v12m, v14m and v15m represent the mutations introduced into the promoter of Chalk9- L. Data show means \(\pm\) SD \((n = 3\) biological replicates). n,o, Degree of chalkiness (n) and chalky grain rate (o) in Nip and D52 plants. Data show means \(\pm\) SD \((n = 16\) plants). p, Relative Chalk9 expression levels of Nip and D52 plants in endosperms. Data show means \(\pm\) SD \((n = 3\) biological replicates). q, Indel of 64 bp resulted in divergent activation of the OsB3 protein to Chalk9 promoter, as shown by LUC/REN. Data show means \(\pm\) SD \((n\) \(= 3\) biologically replicates). In b- d, the bars within violin plots represent 25th percentiles, medians, and 75th percentiles. In b- d, f- h, and n- p, statistical analysis was performed by two- tailed Student's \(t\) - test. In j- m and q, different letters indicate significant differences \((P< 0.05\) , one- way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 Chalk9 is an E3 ubiquitin ligase that interacts with OsEBP89. a, Subcellular localization of Chalk9-GFP fusion protein in rice protoplasts. IPA1-mCherry was used as a nuclear marker. Scale bars: \(5 \mu \mathrm{m}\) . b, Quantitative PCR with reverse transcription (qRT-PCR)-based transcript abundance analysis of Chalk9 in various tissues. R, root;
+ +<--- Page Split ---> + +S, stem; L, leaf; LS, leaf sheath; P, panicle; DAF, days after flowering. Data show means \(\pm \mathrm{SD}\) ( \(n = 3\) biological replicates). c, Ubiquitin ligase activity of Chalk9. MBP- Chalk9 was expressed in E. coli strain BL21, and ubiquitinated proteins were detected using both anti- MBP and anti- ubiquitin (Ub) antibodies. d, Yeast two- hybrid (Y2H) assay showing the interaction between Chalk9 and OsEBP89. Strains carrying the indicated constructs were grown on synthetic dropout medium. DDO and QDO/X represent SD/- Trp- Leu and SD/- Trp- Leu- His- Ade + X- Gal selection medium, respectively. e, Pull- down assay. GST- OsEBP89 was used as baits, and the pull down of MBP- Chalk9 was detected by the anti- MBP antibody. f, Co- immunoprecipitation (Co- IP) assay of rice protoplasts co- expressing Chalk9- HA and GFP- OsEBP89. Total proteins were incubated with magnetic agarose beads conjugated to HA- tag antibody. The immunoprecipitants were probed with antibodies against HA and GFP. IP, immunoprecipitation. g, Interaction between the Chalk9 and OsEBP89 demonstrated by bimolecular fluorescence complementation (BiFC) assays in rice protoplasts. N- terminal fragment of YFP (nYFP) fused with Chalk9 and the C- terminal fragment of YFP (cYFP) fused with OsEBP89 were co- expressed in rice protoplasts. IPA1- mCherry was used as a nuclear control. + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5 Chalk9 ubiquitinates OsEBP89 and regulates its stability. a, In vitro ubiquitination of OsEBP89 by Chalk9. Ubiquitinated proteins were detected using anti-GST and anti-Ub antibodies. b, Cell-free degradation of GST-OsEBP89 in the protein extracts from Nip and chalk9-1 seedlings. Protein levels of GST-OsEBP89 were detected using anti-GST antibody, and Actin was used as a loading control for total protein extraction. Relative fold changes of GST-OsEBP89 to Actin loading controls
+ +<--- Page Split ---> + +were quantified by ImageJ and marked on bottom of the lanes. The protein level at time point 0 min was marked as 1. c, GST- OsEBP89 degradation rate in Nip and chalk9- 1 seedlings. d, Detection of OsEBP89 protein abundance in Nip and chalk9- 1 plants. Total proteins were extracted from seeds at 18 DAF. OsEBP89 protein abundance was determined by immunoblotting. e, Relative quantification of OsEBP89 protein abundance in Nip, chalk9- 1, and chalk9- 2 plants. OsEBP89 protein levels were quantified relative to Actin by ImageJ. The protein level at time point 0 min was set as 1. f, OsEBP89 mRNA level in Nip, chalk9- 1, and chalk9- 2 plants. g, Detection of OsEBP89 protein abundance in Nip and NILChalk9-H plants. Total proteins were extracted from seeds at 15 DAF. OsEBP89 protein abundance was determined by immunoblotting. h, Relative quantification of OsEBP89 protein abundance in the Nip and NILChalk9-H plants. i, OsEBP89 mRNA level in Nip and NILChalk9-H plants. In c, e, f, h, and i, data show means ± SD (n = 3 biological replicates). In c, h, and i, statistical analysis was performed by two- tailed Student's \(t\) - test ( \(**P < 0.01\) ); for \(P\) values, see Source Data.. In e and f, different letters indicate significant differences ( \(P < 0.05\) , one-way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data. + +<--- Page Split ---> +![](images/Figure_6.jpg) + + + +
Fig. 6 Chalk9-OsEBP89 module regulates rice grain chalkiness by influencing seed
+ +<--- Page Split ---> + +storage substance biosynthesis. a, The scanning electron microscopy observation of transverse sections of mature seeds from Nip, chalk9- 1, and chalk9- 2 plants. Scale bars: 0.8 mm (upper), 5 μm (down). b,c, Starch (b) and amylose (c) contents of Nip, chalk9- 1, and chalk9- 2 plants. d, Transmission electron microscopy analysis of the endosperm cells from Nip, chalk9- 1, and chalk9- 2 plants at 18 DAF. Scale bars: 5 μm (upper), 2 μm (down). White asterisk indicates PBI; red asterisk indicates PBII. e,f, Number (per 400 μm²) of protein bodies (e) and mean area of protein bodies (f) in the endosperms from Nip, chalk9- 1, and chalk9- 2 plants. g-k, Total protein (g), glutelin (h), prolamin (i), albumin (j), and globulin (k) contents of Nip, chalk9- 1, and chalk9- 2 plants. l, Grain chalkiness of Nip, chalk9- 1, osebp89- 1 and chalk9- 1/osebp89- 1 plants. Scale bar: 5 mm. m-p, Degree of chalkiness (m), chalky grain rate (n), amylose (o), and total protein (p) in Nip, chalk9- 1, osebp89- 1, and chalk9- 1/osebp89- 1 plants. In b, c, g-k, and m-p, data show means ± SD (n = 9 plants); different letters indicate significant differences (P < 0.05, one- way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data. In e and f, data show means ± SD (n = 3); statistical analysis was performed by two- tailed Student's \(t\) - test (** \(P < 0.01\) ; * \(P < 0.05\) ); for \(P\) values, see Source Data. + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Fig. 7 Geographical distribution, genomic differentiation, and genomic selection of Chalk9 between japonica and indica subspecies. a,b, The relative ratio of nucleotide diversity (a) and Tajima's \(D\) (b) analyses in the whole chromosome 9 of cultivated and wild rice. Red dashed line indicates the Chalk9 locus. c,d Phylogeny (c) and haplotype networks (d) generated from the genomic sequences of Chalk9 in both cultivated and wild rice varieties. Outer circle of the tree indicates various rice populations. Circle size of the network is proportional to the number of samples for each haplotype. Black spots on the lines indicate mutational steps between two
+ +<--- Page Split ---> + +haplotypes. e, A proposed model for the Chalk9- OsEBP89 module in the regulation of grain chalkiness. In rice varieties with the Chalk9- H allele, Chalk9 expression in the endosperm is relatively lower, which reduces the degradation of OsEBP89. This accumulation of OsEBP89 leads to the upregulation of \(Wx\) and SSP genes, resulting in increased levels of amylose and storage protein in the endosperm. This elevated synthesis of storage compound during the post- milk stage contributes to the formation of chalky grains. Conversely, in rice varieties with the Chalk9- L allele, Chalk9 is highly expressed, which accelerates OsEBP89 degradation. The reduction in OsEBP89 levels leads to the downregulation of \(Wx\) and SSP genes in endosperm, resulting in decreased synthesis of storage substances during the post- milk stage and the formation of translucent grains. + +<--- Page Split ---> +![](images/Extended_Data_Figure_3.jpg) + + +Extended Data Fig. 1 The genome- wide association study for rice grain chalkiness. a,b, Manhattan plots and Quantile- quantile plots for chalky grain rate (a) and degree of chalkiness (b) in 175 indica varieties in 2021. c,d, Manhattan plots and Quantile- quantile plots for chalky grain rate (c) and degree of chalkiness (d) in 175 indica varieties in 2023. The horizontal dash- dot line indicates the genome- wide significant threshold \((P = 1 \times 10^{- 5})\) . + +<--- Page Split ---> +![PLACEHOLDER_50_0] + + +Extended Data Fig. 2 The 64- bp indel in the Chalk9 promoter contributes to grain chalkiness variation. a, Structure of Chalk9 and association mapping with 28 variants. Red dots connected with the dashed lines indicate the six variants that are significantly associated with chalkiness. x axis, position relative to ATG (0 bp). b, Identification and diagram of the \(\mathrm{NIL}^{Chalk9 - H}\) line. The numbering above the line represents the molecular markers used for the construction of \(\mathrm{NIL}^{Chalk9 - H}\) . The green bar indicates the genomic + +<--- Page Split ---> + +region from Kasalah (Chalk9- H type). The double- headed arrow shows the length of the substitution segment. c, Representative photographs of Nip and NILChalk9-H plants at the mature stage in the field. Scale bar: 10 cm. d, Schematic representation of the reporter constructs for the luciferase assay in Fig. 3m. v4m, v5m, v10m, v12m, v14m and v15m represent the mutations introduced into the promoter of Chalk9- L. e, Diagram of the Chalk9 promoter sequences of Nip and D52 plants. Red box indicates the position of the 64- bp indel. f, Grain chalkiness of Nip and D52 plants. Scale bar: 5 mm. g, Schematic diagrams of the effector and reporter plasmids used in the luciferase assays from Fig. 3q. + +<--- Page Split ---> +![PLACEHOLDER_52_0] + +
Extended Data Fig. 3 OsEBP89 positively regulates grain chalkiness in rice. a,
+ +Loss-of-function mutants (osebp89- 1 and osebp89- 2) of OsEBP89 generated using + +<--- Page Split ---> + +CRISPR/Cas9 on the wild-type Nip (WT). The 20-bp target sequence for CRISPR/Cas9-mediated editing is underlined. b, Expression analysis of OsEBP89 in WT, OsEBP89-OE1, and OsEBP89-OE2 plants. Data show means \(\pm\) SD \((n = 3\) biological replicates). c, Grain chalkiness of WT, osebp89-1, osebp89-2, OsEBP89- OE1, and OsEBP89-OE2 plants. Scale bar: \(5\mathrm{mm}\) . d, e, Chalky grain rate (d) and degree of chalkiness (e) in WT, osebp89-1, osebp89-2, OsEBP89-OE1, and OsEBP89-OE2 plants. Data show means \(\pm\) SD \((n = 16\) plants). f-1, Expression analysis of Wx (f), GluB1a (g), GluB2 (h), GluB4 (i), PROLM20 (j), PROLM22 (k), and PROLM23 (l) genes from WT, osebp89-1, osebp89-2, OsEBP89-OE1, and OsEBP89-OE2 plants. Data show means \(\pm\) SD \((n = 3\) biological replicates). m, Y1H assay demonstrated the interaction between OsEBP89 and the promoters of GluB1a, GluB2, GluB4, PROLM20, PROLM22, and PROLM23 genes. In b and d-1, different letters indicate significant differences \((P< 0.05\) , one- way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data. + +<--- Page Split ---> +![PLACEHOLDER_54_0] + + +Extended Data Fig. 4 Temporal and geographic patterns of Chalk9- L distribution and its impact on chalkiness in cultivated rice varieties. a, Geographic distributions of 1,424 cultivated rice varieties. blue and orange circles indicate the Chalk9- L and Chalk9- H type, respectively. b, The frequency of Chalk9- L in the cultivated indica varieties from different years ( \(\sim 1960 - 2000\) s) and the cultivated japonica varieties. c, d, The Chalky grain rate (c) and degree of chalkiness (d) in indica varieties from two periods: the 1980s and earlier versus the 1990s and later. e, f, Chalky grain rate (e) and degree of chalkiness (f) in japonica varieties compared with indica varieties. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryTable.xlsx SupplementaryData.xlsx SupplementaryFigure.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828_det.mmd b/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1174c557737880e20f311b14d8d813b96151fb6f --- /dev/null +++ b/preprint/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828/preprint__0a8f1ca999301a2ab4d52f4b89787c7ef42bdb88f6c6bbf60d2710384ff32828_det.mmd @@ -0,0 +1,772 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 800, 175]]<|/det|> +# Natural Variation of Chalk9 Regulates Grain Chalkiness in Rice + +<|ref|>text<|/ref|><|det|>[[44, 196, 235, 243]]<|/det|> +Changjie Yan c.jyan@yzu.edu.cn + +<|ref|>text<|/ref|><|det|>[[42, 270, 597, 808]]<|/det|> +Yangzhou University Zhi Hu Yangzhou University Hongchun Liu Yangzhou University Min Guo Yangzhou University Xiang Han Yangzhou University Youguang Li Yangzhou University Rujia Chen Yangzhou University https://orcid.org/0000- 0001- 6744- 3509 Yifan Guo Yangzhou University Yihao Yang Yangzhou University Shengyuan Sun Huazhong Agricultural University Yong Zhou Yangzhou University Minghong Gu Yangzhou University + +<|ref|>title<|/ref|><|det|>[[44, 840, 103, 857]]<|/det|> +# Article + +<|ref|>title<|/ref|><|det|>[[44, 878, 135, 896]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 915, 330, 934]]<|/det|> +Posted Date: January 29th, 2025 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 475, 64]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5850266/v1 + +<|ref|>text<|/ref|><|det|>[[42, 82, 912, 125]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 143, 535, 163]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 198, 910, 242]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 19th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 61683- 4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[110, 88, 702, 108]]<|/det|> +# Natural Variation of Chalk9 Regulates Grain Chalkiness in Rice + +<|ref|>text<|/ref|><|det|>[[103, 120, 852, 490]]<|/det|> +Zhi \(\mathrm{Hu}^{1,2\#}\) , Hongchun Liu \(^{1\#}\) , Min Guo \(^{1\#}\) , Xiang Han \(^{1}\) , Youguang Li \(^{1}\) , Rujia Chen \(^{1}\) , Yifan Guo \(^{1}\) , Yihao Yang \(^{1}\) , Shengyuan Sun \(^{1}\) , Yong Zhou \(^{1}\) , Minghong Gu \(^{1}\) and Changjie Yan \(^{1,2*}\) \(^{1}\) Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Zhongshan Biological Breeding Laboratory/Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009 Jiangsu, China \(^{2}\) Co-Innovation Center for Modern Production Technology of Grain Crops of Jiangsu Province, Yangzhou University, Yangzhou 225009 Jiangsu, China # These authors contribute equally to this work. \*Correspondence to: C.Y. (cjyan@yzu.edu.cn) + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[148, 91, 227, 107]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[140, 115, 852, 583]]<|/det|> +Grain chalkiness is an undesirable trait affecting rice quality, concerning both consumers and breeders. However, the genetic mechanisms underlying rice chalkiness remain largely elusive. Here, we identified Chalk9 as a major gene associated with grain chalkiness in a natural population, explaining \(28\%\) of the observed variance. Chalk9 encodes an E3 ubiquitin ligase that targets OsEBP89 for its ubiquitination and degradation during the post- milk stage to balance storage component accumulation in the endosperm. However, low expression of Chalk9 results in excessive accumulation of OsEBP89, disrupting the homeostasis of storage components and leading to the chalkiness phenotype. A 64- bp insertion/deletion in the Chalk9 promoter contributes to its differential transcriptional levels, thus causing chalkiness variation among rice varieties. Moreover, the introgression of the elite allele Chalk9- L into a high- chalkiness rice variety reduced the chalky grain rate by up to \(20\%\) and the degree of chalkiness by up to \(40\%\) , without compromising yield. Chalk9- L was strongly selected during japonica rice domestication and gradually incorporated into modern indica breeding programs. Our findings reveal novel molecular and genetic mechanisms underlying chalkiness and provide a potential strategy for breeding novel rice variety with improved quality. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 91, 260, 107]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[145, 116, 852, 275]]<|/det|> +Rice (Oryza sativa L.) is a staple food for over half of the global population, but enhancing its grain quality remains a significant challenge as living standards rise1,2. Chalkiness, a major determinant of rice quality, severely reduces the appearance quality of rice and negatively affects milling, eating and cooking, thereby diminishing its commercial value3,4. Chalkiness is an undesirable trait for consumers and marketing4. Preventing grain chalkiness formation is thus a critical goal in rice breeding. + +<|ref|>text<|/ref|><|det|>[[145, 311, 852, 526]]<|/det|> +Crop breeding is a dynamic and continuous process that strongly reflects human preferences5. Over the past century, rice breeding efforts have primarily focused on enhancing rice productivity by developing high- yield varieties6. However, these increased yields often come at the cost of poor quality, particularly high chalkiness2,7. Seed storage proteins (SSPs) and starch, the predominant components in rice grains, determine both yield and quality. The negative correlation between yield and quality is likely arises from the disruption of their coordinated synthesis8,9. Breaking this trade- off between yield and quality represents a breakthrough opportunity for rice breeders. + +<|ref|>text<|/ref|><|det|>[[145, 561, 852, 885]]<|/det|> +Chalkiness, which refers to opaque regions in the endosperm, is a complex quantitative trait influenced by polygenes and environmental factors, such as high temperature and nutrient availability10- 12. Extensive efforts have been made to dissect the genetic basis of chalkiness in rice, and numerous quantitative trait loci (QTLs) related to chalkiness have been identified on all 12 rice chromosomes using biparental mapping and natural populations13- 19. Several genes have been functionally cloned and characterized. For example, Chalk5 influences rice grain chalkiness by regulating pH homeostasis in developing seeds20. Natural variation in WCR1 regulates redox homeostasis in rice endosperm to affect grain chalkiness21. Recent studies also identified WBR7 and LCG1 as regulators of rice chalkiness through their effects on the accumulation of grain storage components22,23. Despite these advancements, the genetic and molecular mechanisms underlying rice grain chalkiness remain unclear. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 330]]<|/det|> +E3 ligases are critical components of the ubiquitin–proteasome system determining the substrate specificity of the cascade by covalent attachment of ubiquitin to target proteins24,25. RING-finger proteins, a major family of E3 ligases characterized by a 40- 60 residue RING domain, confer substrate specificity through direct interaction with target proteins26. The RING domain, stabilized by zinc ions coordinated by cysteine and histidine residues, is essential for E3 activity. Mutations in these zinc-binding residues can disrupt the domain structure and abolish ligase activity26. E3 ligases play significant roles in plant growth, stress resistance, and signaling27- 29; however, their role in regulating grain chalkiness remains unknown. + +<|ref|>text<|/ref|><|det|>[[144, 366, 852, 555]]<|/det|> +In this study, we identified Chalk9 as a major gene controlling chalkiness variation through genome- wide association studies (GWAS) in indica rice germplasm and elucidated the molecular mechanism of Chalk9- mediated chalkiness regulation. For breeding applications, we identified the elite haplotype Chalk9- L, which improves rice appearance quality without yield penalty. Our findings provide novel insight into the molecular mechanisms underlying rice chalkiness and offer promising strategies for breeding rice varieties with high quality. + +<|ref|>sub_title<|/ref|><|det|>[[147, 590, 214, 606]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[147, 644, 748, 663]]<|/det|> +## Chalk9 is a major locus associated with grain chalkiness in indica rice + +<|ref|>text<|/ref|><|det|>[[144, 671, 852, 914]]<|/det|> +To investigate the genetic basis of grain chalkiness, we collected 175 indica rice varieties from a global population with high phenotypic diversity in chalky grain rate (CGR) and degree of chalkiness (DC) (Supplementary Fig. 1a–d and Supplementary Table 1). Whole- genome sequencing of these varieties generated a final set of 2,290,145 high- quality single- nucleotide polymorphisms (SNPs) after filtering. Principal component analysis showed that the score plot of principal components had continuous distribution without any distinct clusters (Supplementary Fig. 1e), indicating that these indica varieties did not represent a highly structured population. In addition, the average decay of linkage disequilibrium (LD) distance was estimated about 180 kb in this + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 850, 135]]<|/det|> +population \((r^{2} = 0.1)\) (Supplementary Fig. 1f), consistent with the previous estimation in cultivated rice30. + +<|ref|>text<|/ref|><|det|>[[144, 172, 852, 440]]<|/det|> +Using a linear mixed model, we identified a major locus on chromosome 9, Chalk9, associated with CGR and DC in the 2- year trials through GWAS. This locus explained \(\sim 28\%\) of the total phenotypic variation (Extended Data Fig. 1). In the overlapped peak, the top two SNPs associated with CGR were located at 19,506,938 bp \((P = 8.12 \times 10^{- 10})\) and 19,536,079 bp \((P = 7.25 \times 10^{- 12})\) , while the top two SNPs associated with DC were located at 19,586,699 bp \((P = 2.65 \times 10^{- 10})\) and 19,536,079 bp \((P = 4.39 \times 10^{- 11})\) (Fig. 1a). LD analysis delimited the candidate region within an approximately 200- kb block from 19.43 to 19.63 Mb (Fig. 1b). Interestingly, Chalk9 was located within the previously reported chalkiness- associated QTL regions, such as qWBR9- 1 and qCR9- 117. + +<|ref|>text<|/ref|><|det|>[[144, 477, 852, 690]]<|/det|> +Using a relatively strict \(P\) value threshold \((P < 1 \times 10^{- 6})\) , we identified 76 SNPs that were significantly associated with chalkiness (Supplementary Table 2). Of these, 11 caused missense mutations, one SNP caused a synonymous mutation were in gene coding regions, 20 were in regulatory regions. These SNPs were assigned to 15 genes (Supplementary Table 2). The others were in the intergenic regions (14 SNPs) or gene introns (30 SNPs). For these 15 genes, three genes were annotated as either transposon- related or expressed proteins, the remaining 12 candidate genes were annotated as putative functional proteins (Supplementary Table 3). + +<|ref|>sub_title<|/ref|><|det|>[[145, 728, 533, 746]]<|/det|> +## LOC_Os09g32730 is the candidate of Chalk9 + +<|ref|>text<|/ref|><|det|>[[144, 754, 852, 914]]<|/det|> +To identify the candidate gene for Chalk9, we first evaluated SNPs causing amino acid substitutions in the 12 putative functional proteins. Only one SNP affected a functional domain (Supplementary Fig. 2a), but it was not conserved across plant species (Supplementary Fig. 2b), suggesting the missense SNP was unlikely to affect protein function. We then randomly selected eight lines from both high chalky- grain and low chalky- grain varieties to measure the expression levels of these 12 genes in endosperms + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 388]]<|/det|> +and leaves by quantitative RT- PCR (qRT- PCR). Of the 12 genes, 11 showed no significant differences in the expression levels in the endosperms between the low chalky- grain and high chalky- grain varieties (Fig. 1c). Only gene III (LOC_Os09g32730) showed significantly higher expression in low chalky- grain varieties compared to high chalky- grain varieties (Fig. 1c, d). In contrast, these 12 genes exhibited similar expression levels of expression in leaves between high chalky- grain and low chalky- grain varieties (Fig. 1e). Notably, LOC_Os09g32730 was preferentially expressed in the developing endosperm, compared to the other candidate genes (Supplementary Fig. 2c). Since grain chalkiness is closely associated with endosperm development, LOC_Os09g32730 was identified as a potential candidate gene for the Chalk9 locus. Hence, we designated this gene as Chalk9. + +<|ref|>text<|/ref|><|det|>[[144, 421, 852, 749]]<|/det|> +To further validate LOC_Os09g32730 as the candidate gene, we generated transgenic lines that either overexpressed Chalk9 (OE) using the constitutive CaMV 35S promoter or interfered Chalk9 using RNA interference (RNAi) in the Zhonghua11 (ZH11) background. Two Chalk9- overexpression lines (OE1 and OE2) displayed decreased chalkiness with lower CGR and DC values, whereas two Chalk9- RNAi lines exhibited increased chalkiness with higher CGR and DC values (Fig. 2a–d). The RANi lines developed in the variety Nipponbare (Nip) or Yangdao 6 (93- 11) also displayed increased chalkiness (Supplementary Fig. 3a–h). Additionally, the CRISPR/Cas9 system was used to specifically disrupt the Chalk9 gene in Nip (Supplementary Fig. 3i–k). Two knockout lines (chalk9- 1 and chalk9- 2) demonstrated increased chalkiness (Fig. 2e–g). Collectively, these results strongly suggest that LOC_Os09g32730 is the candidate of Chalk9, acting as a negative regulator of grain chalkiness in rice. + +<|ref|>sub_title<|/ref|><|det|>[[145, 784, 725, 803]]<|/det|> +## An indel in the Chalk9 promoter confers grain chalkiness variation + +<|ref|>text<|/ref|><|det|>[[144, 811, 852, 914]]<|/det|> +To address potential limitations in identifying DNA sequence variations in Chalk9 from low- coverage genome sequencing, we resequenced Chalk9 and conducted an association analysis with the identified variants (Supplementary Table 4). Two indels (−1331, 64- bp and −791, 1- bp; referred to as v5 and v12) and four SNPs (−1355G>A, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 851, 220]]<|/det|> +-817A>G, -749G>A and -634G>C; referred to as v4, v10, v14 and v15) in the promoter region of Chalk9 exhibited stronger associations with grain chalkiness than the top SNP (Extended Data Fig. 2a; Supplementary Table 4). However, a missense SNP in the coding region was not significantly associated with grain chalkiness (Extended Data Fig. 2a). + +<|ref|>text<|/ref|><|det|>[[144, 255, 852, 555]]<|/det|> +Based on the identified variants, we classified Chalk9 variations into two haplotypes: one associated with high- chalky varieties [haplotype H (Chalk9- H)] and one with low- chalky varieties [haplotype L (Chalk9- L)] (Fig. 3a- c, Supplementary Table 5). Chalk9- L accessions showed significantly higher Chalk9 expression in the endosperm compared to Chalk9- H accessions (Fig. 3d). We further developed a near- isogenic line (NIL) carrying the Chalk9- H allele from the indica variety Kasalath in the japonica variety Nip, which had the Chalk9- L allele based on the known reference genome (Extended Data Fig. 2b, c). Compared to Nip, NILChalk9-H plants exhibited significantly higher grain chalkiness with reduced Chalk9 expression (Fig. 3e- h). These results suggest that the two Chalk9 haplotypes confer different expression levels and variations in grain chalkiness in rice. + +<|ref|>text<|/ref|><|det|>[[144, 589, 852, 914]]<|/det|> +To investigate whether the functional differences between the two Chalk9 haplotypes arise from the promoter variants, we created three transgenic constructs (pChalk9- L::Chalk9- L, pChalk9- H::Chalk9- H, and pChalk9- L::Chalk9- H), and used them to generate transgenic plants (see Methods). Compared to wild- type Guichao2 plants (Chalk9- H type), pChalk9- L::Chalk9- L and pChalk9- L::Chalk9- H transgenic lines showed significantly reduced grain chalkiness, with approximately \(20\%\) and \(40\%\) decreases in CGR and DC values, respectively (Fig. 3i- k). However, pChalk9- H::Chalk9- H transgenic plants showed no significant difference in grain chalkiness relative to wild- type Guichao2 (Fig. 3i- k). Consistent with the phenotypes of these transgenic lines, pChalk9- L::Chalk9- L and pChalk9- L::Chalk9- H transgenic lines showed higher Chalk9 transcript levels than wild- type Guichao2 and pChalk9- H::Chalk9- H transgenic plants (Fig. 3l). These findings indicate that the variants in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 89, 728, 108]]<|/det|> +Chalk9 promoter are responsible for the differences in grain chalkiness. + +<|ref|>text<|/ref|><|det|>[[145, 145, 852, 442]]<|/det|> +To further pinpoint the functional variations, we mutated the Chalk9- L promoter by introducing each of six variations individually (v4, v5, v10, v12, v14, and v15) from the Chalk9- H promoter. Transient expression assays showed that the activity of the Chalk9- L promoter was significantly reduced by deleting the 64- bp indel, to a level that was comparable to that of the Chalk9- H promoter (Fig. 3m, Extended Data Fig. 2d). By contrast, none of the other five mutations affected the activity of the Chalk9- L promoter (Fig. 3m, Extended Data Fig. 2d). We also generated gene- edited plants with a deletion in this 64- bp indel region in Nip (Chalk9- L type) (Extended Data Fig. 2e). The Chalk9- L gene- edited (D52) plants exhibited reduced Chalk9 expression and increased chalkiness (Fig. 3n- p, Extended Data Fig. 2f), further confirming that the 64- bp indel in the promoter as the causal variant. + +<|ref|>text<|/ref|><|det|>[[145, 478, 852, 775]]<|/det|> +To understand why the 64- bp indel resulted in the different expression, we further analyzed its sequence and identified binding sites for some conserved transcription factors, including AT- Hook, TCR, B3, and ZF- HD families (Supplementary Table 6). Among these, a rice B3 domain transcription factor (OsB3), highly expressed in endosperms and homologous to ABI3 (essential for seed maturation in Arabidopsis \(^{31}\) ), was found (Supplementary Fig. 4a, b). We found that OsB3 protein activated the promoter of Chalk9- L (Fig. 3q, Extended Data Fig. 2g). In the absence of the 64- bp sequence of the Chalk9- L promoter, the activation of OsB3 protein was significantly reduced (Fig. 3q, Extended Data Fig. 2g). These results demonstrate that the 64- bp sequence in the Chalk9- L promoter contains the DNA binding elements by the OsB3 protein in rice. + +<|ref|>sub_title<|/ref|><|det|>[[147, 813, 521, 831]]<|/det|> +## Chalk9 exhibits E3 ubiquitin ligase activity + +<|ref|>text<|/ref|><|det|>[[147, 840, 851, 914]]<|/det|> +To investigate the molecular function of Chalk9, we first analyzed its localization and expression pattern. The results showed that Chalk9 was localized in the nucleus and highly expressed in the developing endosperm with gradually increasing during grain + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 359]]<|/det|> +filling (Fig. 4a, b). Similar results were observed through GUS staining (Supplementary Fig. 5a). Chalk9 is predicted to be a RING- C3HC4 type E3 ubiquitin ligase. To confirm its E3 ligase activity, we produced recombinant MBP- Chalk9 protein in Escherichia coli (E. coli) for in vitro ubiquitination assays. When ubiquitin, ubiquitin- activating enzyme (E1), and ubiquitin- conjugating enzyme (E2) were present, Chalk9 underwent auto- ubiquitination, whereas no ubiquitination was detected when E1, E2, or MBP- Chalk9 was absent (Fig. 4c). We mutated the conserved cysteine at position 189 to serine, creating the MBP- Chalk9C189S mutant (Supplementary Fig. 5b). The self- ubiquitination was abolished by the substitution in the RING finger domain (Fig. 4c), confirming that Chalk9 is a functional RING finger E3 ligase. + +<|ref|>text<|/ref|><|det|>[[144, 394, 852, 609]]<|/det|> +Using yeast two- hybrid assays to screen the substrate of Chalk9, we successfully identified OsEBP89, a transcription factor involved in amylose biosynthesis32- 34, that interacts with Chalk9 (Fig. 4d). The C- terminal domain of OsEBP89 was found to be critical for this interaction (Supplementary Fig. 6a). This interaction was further validated by in vitro pull- down (Fig. 4e) and coimmunoprecipitation (CoIP) assays in rice protoplasts (Fig. 4f). Co- localization and bimolecular fluorescence complementation (BiFC) assays confirmed that the interaction between Chalk9 and OsEBP89 occurred in the nucleus (Fig. 4g, Supplementary Fig. 6b). + +<|ref|>text<|/ref|><|det|>[[144, 644, 852, 858]]<|/det|> +Given that Chalk9 functions as an active RING finger E3 ligase and interacts with OsEBP89 (Fig. 4), we hypothesized that OsEBP89 is the direct substrate of Chalk9. In vitro ubiquitination assay was performed using MBP- Chalk9 and GST- OsEBP89. In the presence of E1, E2, ubiquitin, GST- OsEBP89 was ubiquitinated by MBP- Chalk9 (Fig. 5a). In contrast, no polyubiquitination was observed in the absence of E1, E2, ubiquitin or MBP- Chalk9 (Fig. 5a). Furthermore, replacing MBP- Chalk9 with the MBP- Chalk9C189S mutant failed to ubiquitinate GST- OsEBP89 (Fig. 5a), confirming that Chalk9 targeted OsEBP89 for ubiquitination. + +<|ref|>text<|/ref|><|det|>[[144, 895, 850, 914]]<|/det|> +Since ubiquitination often leads to 26S proteasome- dependent degradation of target + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 88, 852, 275]]<|/det|> +proteins, we tested whether Chalk9 influences the protein stability of OsEBP89 in a rice cell- free system. GST- OsEBP89 protein was expressed in E. coli, and purified protein was incubated in cell- free extracts from Nip and chalk9- 1 seedlings. The GST- OsEBP89 protein was found to be more stable in the chalk9- 1 mutant extract compared to Nip (Fig. 5b, c). The addition of MG132 significantly inhibited GST- OsEBP89 degradation in both Nip and chalk9- 1 extracts (Fig. 5b), indicating that Chalk9 mediated the stability of OsEBP89 in vivo through the 26S proteasome system. + +<|ref|>text<|/ref|><|det|>[[145, 311, 852, 554]]<|/det|> +To further determine OsEBP89 abundance in seeds from Nip and chalk9 mutants, we generated a specific antibody against OsEBP89 (Supplementary Fig. 6c). OsEBP89 showed more abundant in chalk9 mutants than in Nip, although the transcript levels of OsEBP89 remained unchanged (Fig. 5d- f). This suggests that the loss of Chalk9 function leads to the accumulation of OsEBP89 protein in rice. We also compared OsEBP89 protein levels between Nip (Chalk9- L type) and NILChalk9-H plants. The OsEBP89 protein level in seeds was higher in NILChalk9-H plants than in Nip (Fig. 5g, h), while OsEBP89 expression was similar (Fig. 5i), suggesting that Chalk9- L promotes more degradation of OsEBP89 than Chalk9- H. + +<|ref|>sub_title<|/ref|><|det|>[[147, 589, 850, 635]]<|/det|> +## Chalk9–OsEBP89 module regulates grain chalkiness through regulation of the storage components in endosperm + +<|ref|>text<|/ref|><|det|>[[145, 644, 852, 914]]<|/det|> +We observed that the chalk9 mutants produced white- belly endosperms (Supplementary Fig. 7a). Scanning electron microscopy revealed that the chalky endosperm of the chalk9 mutants contained loosely packed spherical starch granules interspersed with large air spaces, whereas the non- chalky endosperm of Nip consisted of densely and regularly packed polyhedral starch granules (Fig. 6a), which is consistent with previous studies35,36. Although the total starch content remained unchanged, the chalk9 mutants exhibited significantly higher amylose content (Fig. 6b, c). Transmission electron microscopy further showed that chalky endosperm cells of the chalk9 mutants contained increased numbers and larger mean areas of spherical protein body I (PBI) and irregularly shaped PBII compared to Nip (Fig. 6d–f). This observation aligned with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 360]]<|/det|> +the greatly increased levels of seed storage proteins in chalk9 mutants, including glutelin, prolamin, and albumin (Fig. 6g- k). We further performed a transcriptome deep sequencing (RNA- seq) analysis on seeds from Nip and chalk9- 1 (Supplementary Fig. 7b). A total of 2,658 differentially expressed genes were identified in chalk9- 1 compared to Nip (Supplementary Fig. 7c, Supplementary Data 1). We found that the Waxy (Wx) gene for amylose synthesis and some genes for seed storage protein (SSP) exhibited significantly higher expression levels in chalk9- 1 compared to Nip (Supplementary Fig. 7d and Supplementary Table 7). These findings were further validated by qRT- PCR analysis, which confirmed the increased expression of related genes in chalk9- 1 (Supplementary Fig. 8). + +<|ref|>text<|/ref|><|det|>[[144, 393, 852, 888]]<|/det|> +To investigate whether OsEBP89 is involved in chalkiness regulation, we generated OsEBP89 knockout plants (osebp89- 1 and osebp89- 2) using CRISPR/Cas9 (Extended Data Fig. 3a), and OsEBP89 overexpression lines (OsEBP89- OE1 and OsEBP89- OE2) driven by the constitutive CaMV 35S promoter (Extended Data Fig. 3b). Compared to the wild- type Nip, the OsEBP89 knockout plants showed a slight yet significant reduction in both CGR and DC values, whereas the OsEBP89- overexpression lines exhibited markedly increased chalkiness with higher CGR and DC values (Extended Data Fig. 3c- e). These results indicate that OsEBP89 positively regulates chalkiness in rice. Notably, a significant decrease in Wx expression was detected in OsEBP89 knockout mutants, whereas its expression increased in OsEBP89- overexpressing plants (Extended Data Fig. 3f), which is consistent with previous studies showing that OsEBP89 binds to the GCC box and GCC box- like sequences in the Wx promoter, thereby promoting its expression32- 34. Several such binding sites were also identified in the promoters of SSP genes (Supplementary Table 8). The expression of SSP genes was greatly repressed in OsEBP89 knockout mutants but upregulated in OsEBP89- overexpressing plants (Extended Data Fig. 3g- l). Furthermore, yeast one- hybrid assays demonstrated that OsEBP89 directly bound to the promoters of SSP genes (Extended Data Fig. 3m). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 330]]<|/det|> +We crossed the chalk9- 1 mutant with the osebp89- 1 mutant to generate double mutant plants (chalk9- 1/osebp89- 1). While the chalk9- 1 mutant showed increased chalkiness, the chalk9- 1/osebp89- 1 double mutant exhibited a reduced chalkiness, resembling the phenotype of osebp89- 1 (Fig. 6l- n). In addition, the chalk9- 1/osebp89- 1 double mutant showed decreased amylose and total protein similar to osebp89- 1 mutants, while the chalk9- 1 mutant contained increased levels of both (Fig. 6o, p). Taken together, these results reveal that Chalk9- OsEBP89 module regulated the synthesis of grain storage components by modulating the expression of genes involved in storage components, thereby influencing chalkiness formation in rice. + +<|ref|>text<|/ref|><|det|>[[144, 366, 852, 526]]<|/det|> +In addition, based on our whole- genome sequencing data, we observed that OsEBP89 had a single major haplotype in indica rice (Supplementary Table 9). Extending this analysis to 4,726 accessions of cultivated rice \(^{38 - 40}\) , this major haplotype occupied \(97.5\%\) of indica (Supplementary Table 10), indicating the strong genetic conservation and unlikely contribution to chalkiness variation in indica varieties. This result clearly demonstrates that Chalk9 plays an important role in determining the grain chalkiness. + +<|ref|>sub_title<|/ref|><|det|>[[147, 562, 849, 608]]<|/det|> +## Chalk9-L is artificially selected in cultivated rice during domestication and breeding + +<|ref|>text<|/ref|><|det|>[[144, 616, 852, 914]]<|/det|> +We performed a geographic distribution analysis of haplotypes in 1,424 cultivated varieties from the 3K Rice Genomes Project \(^{37}\) . The distribution of rice accessions carrying either Chalk9- L or Chalk9- H was variable across Asia regions relative to other areas (Extended Data Fig. 4a). The frequency of Chalk9- L was nearly \(100\%\) in Southeast Asia (e.g., Myanmar, Philippines, Laos, and Thailand), but it was relatively lower in China (71.1%) and South Asia, including Bangladesh (62%), Nepal (68.1%), Pakistan (70%), and India (76.2%) (Extended Data Fig. 4a). We further performed haplotype analysis in 4,726 accessions of cultivated rice \(^{38 - 40}\) . Eight out of 9 unique high- confidence haplotypes belonged to the Chalk9- L group, while only one belonged to the Chalk9- H group (Supplementary Table 11). Chalk9- L was present in \(12.3\%\) of Aus, \(85.3\%\) of aromatic, \(99.9\%\) of japonica, and \(80.1\%\) of indica varieties (Supplementary + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 248]]<|/det|> +Table 12). Within the indica subgroups, its frequency was \(40.9\%\) in indica I, \(96.6\%\) in indica II, \(94\%\) in indica III, and \(84\%\) in indica intermediate (Supplementary Table 12). In 445 accessions of the wild ancestor Oryza rufipogon (O. rufipogon) \(^{38}\) , O. rufipogon had a high frequency of Chalk9- L (89.4%) (Supplementary Table 13). These results suggest that the allele distribution of Chalk9 in different rice subgroups may be correlated to their evolution and selection. + +<|ref|>text<|/ref|><|det|>[[144, 283, 852, 666]]<|/det|> +A selective sweep surrounding the Chalk9 locus was observed between japonica and wild rice, with significantly reduced nucleotide diversity in japonica compared to wild rice (Fig. 7a), indicating a strong artificial selection in Chalk9 locus of japonica. Tajima's \(D\) values in the Chalk9 locus was significantly negative in japonica (Fig. 7b), reflecting directional selection across this region. In contrast, no obvious selection was detected in indica because the relative ratio of nucleotide diversity in indica to wild rice was higher than that in japonica to wild rice in Chalk9 locus (Fig. 7a). Further phylogenetic analysis showed that the Chalk9- L haplotype in japonica rice formed a tight cluster, while in indica rice, Chalk9- L was more widely distributed and genetically diverse (Fig. 7c). Haplotype network also showed that Chalk9- L in japonica was closely related to O. rufipogon, with few mutational differences, whereas Chalk9- L in indica exhibited more complex connections and mutational steps (Fig. 7d), suggesting that Chalk9- L in japonica evolved from O. rufipogon through a single lineage, while Chalk9- L in indica had a more complex evolution history with multiple origins. + +<|ref|>text<|/ref|><|det|>[[144, 700, 852, 914]]<|/det|> +To trace the selection of Chalk9- L during indica rice breeding, we developed a 64- bp InDel marker in the Chalk9 promoter and genotyped Chalk9 in 127 indica varieties from the 1950s to the 2000s. The frequency of Chalk9- L in varieties prior to 1990 was relatively low, but it increased significantly thereafter (Extended Data Fig. 4b). This trend aligns with the significant reduction of chalkiness observed in indica varieties post- 1990 (Extended Data Fig. 4c, d), indicating that Chalk9- L has been artificially selected in modern indica rice breeding programs. All 123 japonica varieties carried Chalk9- L (Extended Data Fig. 4b), consistent with the lower chalkiness observed + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 89, 850, 135]]<|/det|> +(Extended Data Fig. 4e, f). These findings suggest that Chalk9- L might have been under artificial selection to reduce chalkiness. + +<|ref|>sub_title<|/ref|><|det|>[[147, 173, 850, 219]]<|/det|> +## Chalk9-L holds the potential for breeding low-chalkiness rice cultivars without yield penalty + +<|ref|>text<|/ref|><|det|>[[145, 227, 852, 499]]<|/det|> +We further investigated the effect of Chalk9 on yield. Chalk9 knockout plants displayed no significant differences from Nip in major agronomic traits, including heading date, tiller number, plant height, grain size and weight, as well as yield per plant and yield per plot (Supplementary Fig. 9). These results suggest that Chalk9 has no impact on rice yield. NILChalk9-H plants showed no significant differences in grain weight or yield per plant compared to Nip (Chalk9- L type) (Supplementary Fig. 10a, b). Furthermore, introducing the Chalk9- L transgene into the high- yield variety Guichao2 significantly reduced chalkiness without affecting other agronomic traits, particularly yield per plant (Supplementary Fig. 10c- h), demonstrating the potential of Chalk9- L to reduce chalkiness in high- yield rice cultivars without compromising productivity. + +<|ref|>sub_title<|/ref|><|det|>[[148, 535, 242, 551]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[145, 589, 852, 802]]<|/det|> +To date, little progress has been made in understanding the genetic and molecular mechanisms underlying natural variation associated with chalkiness in rice. Here we reported that Chalk9 is the major gene controlling chalkiness variation in indica rice. A 64- bp indel variant in Chalk9 promoter leads to differing expression levels, conferring chalkiness variation among rice varieties. Moreover, we deciphered a Chalk9- OsEBP89- Wx/SSP regulatory module that mediates chalkiness variation (Fig. 7e). These findings deepen our understanding of the genetic and molecular mechanisms underlying grain chalkiness variation in rice. + +<|ref|>text<|/ref|><|det|>[[147, 839, 852, 914]]<|/det|> +Developing high- yielding rice with superior quality is challenging for rice breeding due to the trade- off between these traits2. One notable reason is that many QTLs associated with chalkiness are closely linked to yield- associated genes12,20. Fortunately, Chalk9 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 88, 852, 276]]<|/det|> +does not exhibit such a linkage drag, as the yield in its near-isogenic lines shows no significant difference compared to the wild type (Supplementary Fig. 10a, b). Chalk9- L as an elite haplotype showed increased Chalk9 expression, conferring reduced chalkiness (Fig. 3a- h). By introducing this favorable allele into a well- known high- yielding indica variety but with high chalkiness, the chalkiness in the new lines was significantly decreased but did not compromise yield (Fig. 3i- k and Supplementary Fig. 10g, h). + +<|ref|>text<|/ref|><|det|>[[145, 310, 852, 608]]<|/det|> +The distribution of Chalk9- L in cultivated rice appears to have been influenced by evolution and artificial selection during domestication and breeding. Our evolutionary analysis revealed that Chalk9 originated from wild rice but diverged significantly between japonica and indica rice (Fig. 7b- e). In japonica rice, Chalk9- L is likely derived from a single origin in O. rufipogon, while, in indica rice, Chalk9- L has multiple origins and exhibits greater genetic diversity. Moreover, the increasing incorporation of Chalk9- L in modern indica breeding programs has contributed to a significant reduction of chalkiness. In the light of that approximately \(30\%\) of indica varieties lack Chalk9- L and that Chalk9 explains \(28\%\) of the variance in chalkiness phenotype, our results strongly indicate that Chalk9- L is a key target for improving rice appearance quality of indica rice. + +<|ref|>text<|/ref|><|det|>[[145, 644, 852, 914]]<|/det|> +The accumulated knowledge showed that the regulatory regions of genes involved in starch and storage protein biosynthesis usually share common motifs, which facilitates their co- regulation by common transcription factors, such as OsNAC20 and OsNAC26 in rice41. Similarly, OsEBP89 not only influences \(Wx\) expression but also regulates the expression of part of SSP genes, thereby coordinating the synthesis of amylose and storage proteins (Extended Data Fig. 3). In addition, Chalk9 acts as an E3 ubiquitin ligase, targeting OsEBP89 for ubiquitination and subsequent degradation via the 26S proteasome pathway (Figs. 4 and 5). This discovery underscores the critical role of the 26S proteasome in maintaining OsEBP89 protein homeostasis. Notably, recent research showed that OsSK41 phosphorylates OsEBP89, thereby reducing its stability34. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 89, 850, 135]]<|/det|> +Whether this phosphorylation is involved in Chalk9- mediated degradation of OsEBP89 remains to be elucidated. + +<|ref|>text<|/ref|><|det|>[[145, 172, 852, 499]]<|/det|> +We propose that OsEBP89 is a positive regulator of chalkiness in rice. Genetic analysis demonstrates that Chalk9 operates in an OsEBP89- dependent manner to modulate the expression of genes involved in the biosynthesis of storage substances, thereby influencing chalkiness (Fig. 6, Supplementary Figs. 7 and 8). Notably, OsEBP89 exhibits a single major haplotype in indica varieties, highlighting its high conservation in indica rice. Consequently, the variation in chalkiness observed in indica rice is largely attributed to genetic variation in Chalk9. Moreover, our findings suggest that OsB3 acts as a potential upstream regulator of Chalk9, mediating its differential expression in response to the 64- bp indel. Future studies should aim to elucidate the role of OsB3 in regulating chalkiness and its contribution to chalkiness variation in rice. These efforts will help elucidate the OsB3- Chalk9- OsEBP89- Wx/SSP pathway in chalkiness regulation. + +<|ref|>text<|/ref|><|det|>[[145, 533, 852, 914]]<|/det|> +Endosperm development involves the coordinated synthesis and accumulation of storage substances, a process closely associated with chalkiness. This developmental progress begins in the pre- milk stage, peaks during mid- milk, and tapers off in the post- milk stage42- 44. Similarly, \(Wx\) and SSP genes, which are central to this process, exhibit finely tuned temporal expression patterns that align with the synthesis of storage compounds45,46. This coordination is crucial for optimizing grain quality by balancing biosynthetic processes that determine grain texture and appearance. Our findings reveal that Chalk9 expression gradually increases during endosperm development, reaching its peak in the post- milk stage (Fig. 4b), a period when the synthesis of storage substances naturally declines. At this stage, Chalk9 functions as a regulatory “brake”, limiting storage substance accumulation by promoting OsEBP89 degradation. This regulatory mechanism aligns with the natural decline in storage substance synthesis, supporting seed maturation and contributing to the formation of translucent grains. Thus, we propose a model in which the Chalk9- OsEBP89 regulatory module governs + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 855, 330]]<|/det|> +chalkiness variation in rice (Fig. 7e). In rice varieties carrying the Chalk9- H allele, reduced Chalk9 expression leads to OsEBP89 stabilization, which subsequently upregulates the expression of \(Wx\) and SSP genes. This increased synthesis of storage compounds disrupts the natural decline in their accumulation during the post- milk stage, resulting in the formation of chalky endosperm. In contrast, the Chalk9- L allele enhances Chalk9 expression, promoting OsEBP89 degradation. This reduction in OsEBP89 levels downregulates the expression of \(Wx\) and SSP genes, reducing storage product synthesis during the post- milk stage, leading to translucent grains and improved grain quality. + +<|ref|>sub_title<|/ref|><|det|>[[148, 369, 226, 385]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[148, 423, 421, 440]]<|/det|> +## Plant materials and genotyping + +<|ref|>text<|/ref|><|det|>[[147, 449, 852, 607]]<|/det|> +All 175 indica accessions, obtained from germplasm banks and breeders around the world, are listed in Supplementary Table 1. The japonica rice varieties (Nip and ZH11) and the indica rice varieties (93- 11 and Guichao2) were used in this study. All rice materials used in this study were cultivated simultaneously during the summer in paddy fields at the experimental station of Yangzhou University, located in Yangzhou, China. The plants were grown under standardized crop management practices. + +<|ref|>text<|/ref|><|det|>[[147, 645, 852, 830]]<|/det|> +Total genomic DNA was extracted from the samples and used to generate DNA sequencing libraries. Sequencing was performed, and the resulting libraries were size- checked using an Agilent 2100 Bioanalyzer system. The library preparations were ultimately sequenced on an Illumina Xten platform, producing 150 bp paired- end reads. After removing nucleotide variations with missing rates \(\geq 0.25\) and minor allele frequency \(< 0.05\) , all nucleotide polymorphisms were categorized based on their location in the reference genome. + +<|ref|>sub_title<|/ref|><|det|>[[147, 868, 657, 886]]<|/det|> +## Measurements of grain chalkiness and storage components + +<|ref|>text<|/ref|><|det|>[[145, 895, 850, 913]]<|/det|> +Seeds harvested after full maturation were air- dried, stored at room temperature for + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 275]]<|/det|> +three months. Images of 200- 300 polished rice grains, randomly selected from each plant, were captured using a ScanWizard EZ scanner and analyzed with the rice quality TS- G automated analysis system (Hangzhou Shansheng Testing Technology Co., China). For chalkiness traits, the chalky grain rate (CGR) refers to the proportion of chalky grains among all rice grains, while the degree of chalkiness (DC) represents the extent of chalkiness in the rice grains. Total starch, amylose, total protein, and storage protein fractions were measured based on previously published methods47. + +<|ref|>sub_title<|/ref|><|det|>[[148, 312, 421, 329]]<|/det|> +## Genome-wide association study + +<|ref|>text<|/ref|><|det|>[[144, 338, 852, 554]]<|/det|> +GCR and DC were surveyed in 175 indica varieties over two years (2021 and 2023) and subsequently used for genome- wide association studies (GWAS). The analysis was performed using GEMMA (version 0.941), which fits a linear mixed model48. The \(P\) - value threshold for significance was set at \(1 \times 10^{- 5}\) using the Bonferroni correction method49, and the leading SNP was determined to be the SNP with the minimum \(P\) - value in the associated signal. Linkage disequilibrium (LD), evaluated as \(r^2\), between SNPs in the 175 varieties was calculated using plink v1.950, and The LD heatmap surrounding the peak region was constructed using the LDBlockShow v1.4051. + +<|ref|>sub_title<|/ref|><|det|>[[148, 590, 476, 607]]<|/det|> +## Constructs for genetic transformation + +<|ref|>text<|/ref|><|det|>[[144, 615, 852, 914]]<|/det|> +For the Chalk9 RNA- interference vector, Chalk9- specific sequences from the coding region were amplified, and inserted in both sense and antisense orientations into a modified pTAC303- RNAi vector. For the Chalk9 overexpression vectors, the full- length coding sequence of Chalk9 from Nip was inserted into pCAMBIA2300- 35S vector to generate the pCAMBIA2300- 35S:Chalk9 construct. For the Chalk9 knockout vectors, two small- guide RNA (sgRNA) sequences targeting the Chalk9 coding region were cloned into pYLCRISPR/Cas9- MH vector to generate the Chalk9 CRISPR- Cas9 construct. Additionally, two sgRNA sequences from the Chalk9 promoter surrounding the 64- bp indel were designed and inserted into pYLCRISPR/Cas9- MH vector to generate the Chalk9 promoter- editing construct. For the Chalk9 promoter- GUS vector, a 2- kb genomic upstream region of Chalk9 was amplified and cloned into the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 90, 350, 108]]<|/det|> +pCAMBIA1381z vector. + +<|ref|>text<|/ref|><|det|>[[147, 145, 852, 330]]<|/det|> +For the pChalk9- L::Chalk9- L vector, the 2,645- bp genomic region including the 2- kb upstream sequence and 645- bp coding sequence was amplified from low chalky- variety IR72 (Chalk9- L type) genomic sequence and cloned into plant binary vector pCAMBIA2300. The construct pChalk9- H::Chalk9- H contains the 2- kb upstream sequence and 645- bp coding sequence from high chalky- variety Guichao2 (Chalk9- H type). The 645- bp coding sequence from Guichao2 was driven by the 2- kb promoter sequence from IR72 to generate the pChalk9- L::Chalk9- H construct. + +<|ref|>text<|/ref|><|det|>[[147, 367, 852, 553]]<|/det|> +For the OsEBP89 knockout vector, two sgRNA sequences targeting the OsEBP89 coding region were cloned into pYLCRISPR/Cas9- MH vector to generate the OsEBP89 CRISPR- Cas9 construct. For the OsEBP89 overexpression vector, the full- length coding sequence of OsEBP89 from Nip was inserted into pCAMBIA2300- 35S vector to generate the pCAMBIA2300- 35S:OsEBP89 construct. Agrobacterium- mediated transformation was used to generate transgenic rice plants. Primer sequences used in this study are listed in Supplementary Table 14. + +<|ref|>sub_title<|/ref|><|det|>[[148, 590, 266, 607]]<|/det|> +## GUS analysis + +<|ref|>text<|/ref|><|det|>[[147, 616, 852, 747]]<|/det|> +Various rice tissues, including young roots, stems, leaf sheaths, leaves, young panicles, and developing seeds from proChalk9::GUS transgenic plants, were stained with a GUS staining kit (Coolaber Biotech, Beijing, China) at \(37^{\circ}\mathrm{C}\) in the dark for 12 hours, and then decolorized with \(100\%\) ethanol and imaged using a microscope (OLYMPUS, MVX10, Japan). + +<|ref|>sub_title<|/ref|><|det|>[[148, 785, 365, 802]]<|/det|> +## Gene expression analysis + +<|ref|>text<|/ref|><|det|>[[147, 811, 852, 914]]<|/det|> +After synthesizing first- strand cDNA from total RNA extracted from rice samples, quantitative PCR was performed using ChamQ SYBR qPCR Master Mix (Vazyme Biotech, Nanjing, China). Data analysis was conducted from three replicates for each experiment, using the rice OsActin (LOC_Os10g36650) gene as the internal reference. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 90, 634, 108]]<|/det|> +Gene- specific primers are listed in Supplementary Table 14. + +<|ref|>sub_title<|/ref|><|det|>[[148, 145, 595, 164]]<|/det|> +## Transcriptome deep sequencing (RNA-seq) analysis + +<|ref|>text<|/ref|><|det|>[[147, 171, 852, 357]]<|/det|> +Total RNA was isolated from seeds at 20 days after flowering (DAF), and RNA- seq libraries were prepared in triplicate from wild- type Nip and chalk9- 1 mutant samples. RNA- seq and gene transcript abundance analysis were performed by the Bioacme Biotechnology Co., Ltd. (Wuhan, China). Differentially expressed genes were identified using DESeq2 with a \(P\) - value \(< 0.05\) and \(|\log_2\mathrm{FoldChange}| > 1\) . Correlation analysis, heatmap plotting, and volcano plot analysis were performed as previously described52. + +<|ref|>sub_title<|/ref|><|det|>[[148, 395, 444, 413]]<|/det|> +## Transmission electron microscopy + +<|ref|>text<|/ref|><|det|>[[147, 421, 852, 524]]<|/det|> +Seeds from WT and chalk9- 1 mutant plants at 18 DAF were collected and used for transmission electron microscopy (TEM) samples, which were fixed and prepared as previously described52. Micrographs of the endosperm cells were captured on 80- nm ultra- thin sections using a transmission electron microscope. + +<|ref|>sub_title<|/ref|><|det|>[[148, 562, 408, 580]]<|/det|> +## Scanning electron microscopy + +<|ref|>text<|/ref|><|det|>[[147, 588, 852, 690]]<|/det|> +Brown rice grains were naturally broken from the middle and then coated with gold using an E- 100 ion sputter coater. The morphology of starch granules was observed using a scanning electron microscope as previously described21. At least three biological replicates from different mature grains were analyzed. + +<|ref|>sub_title<|/ref|><|det|>[[148, 728, 424, 746]]<|/det|> +## Subcellular colocalization assay + +<|ref|>text<|/ref|><|det|>[[147, 754, 852, 913]]<|/det|> +The coding regions of \(IPAl\) and Chalk9 were amplified by PCR and individually cloned into the 163- mCherry plasmid and the 163- GFP plasmid, respectively. Protoplasts isolated from 10- day- old Nip rice seedlings were transfected with the constructs as described previously53. GFP and mCherry were excited with 488- nm and 543- nm laser lines, respectively, and all fluorescence signals were detected at 500- 580 nm and 565- 615 nm using confocal laser- scanning microscopy. Images presented in the figures are + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 90, 481, 108]]<|/det|> +representative of at least five protoplasts. + +<|ref|>sub_title<|/ref|><|det|>[[148, 145, 242, 162]]<|/det|> +## Y2H assay + +<|ref|>text<|/ref|><|det|>[[147, 171, 852, 413]]<|/det|> +For the Y2H screening, developing seeds at the reproductive stage were combined to construct a two- hybrid library by Shanghai OE Biotech Company. The coding sequence of Chalk9 was cloned into the pGBKT7 vector and used as the bait. The yeast strain Y2H Gold was employed for transformation. To verify the interaction between OsEBP89 and Chalk9 in yeast, the coding sequences of OsEBP89 and mutated OsEBP89 variants (OsEBP89 [1- 119], OsEBP89 [120- 201], and OsEBP89 [202- 326]) were separately cloned into the pGADT7 vector. Y2H assays were performed according to the manufacturer's instructions (Clontech). Primers used for construction are listed in Supplementary Table 14. + +<|ref|>sub_title<|/ref|><|det|>[[148, 452, 247, 469]]<|/det|> +## BiFC assay + +<|ref|>text<|/ref|><|det|>[[147, 478, 852, 665]]<|/det|> +The coding regions of OsEBP89 and Chalk9 were amplified and cloned into the pUC- SPYCE and pUC- SPYNE vectors, respectively. The IPA1- mCherry vector served as a nuclear marker. The transfected protoplasts with the indicated constructs were observed using a fluorescence microscope. Yellow fluorescent protein (YFP) and mCherry were excited with 514- nm and 543- nm laser line, respectively, and detected at 522- 555 nm and 565- 615 nm. Images presented in the figures are representative of at least five protoplasts. Primers used for construction are listed in Supplementary Table 14. + +<|ref|>sub_title<|/ref|><|det|>[[148, 702, 252, 718]]<|/det|> +## Co-IP assay + +<|ref|>text<|/ref|><|det|>[[147, 728, 852, 915]]<|/det|> +The coding sequences of OsEBP89 and Chalk9 were amplified and cloned into the 163- GFP and pUC35S- HA vectors, respectively, to generate the OsEBP89- GFP and Chalk9- HA constructs. Total proteins for the Co- IP assay were extracted from protoplasts isolated from 10- day- old rice seedlings, which were transfected with the indicated constructs. Chalk9- HA was immunoprecipitated using anti- HA beads at \(4^{\circ}\mathrm{C}\) for 2 hours. The eluted proteins were analyzed by immunoblotting with anti- HA (1:3000, ab9110, Abcam) and anti- GFP (1:3000, ab290, Abcam) antibodies. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[148, 118, 414, 136]]<|/det|> +## In vitro GST pull-down assays + +<|ref|>text<|/ref|><|det|>[[147, 144, 852, 359]]<|/det|> +The coding sequences of OsEBP89 and Chalk9 were cloned into the pGEX- 5X- 1 and pMAL- c5X vectors, respectively, to produce GST- OsEBP89 and MBP- Chalk9. The constructs were transformed into E. coli BL21 and induced with 0.2 mM IPTG for 12 hours at \(16^{\circ}\mathrm{C}\) to generate GST- OsEBP89 and MBP- Chalk9 recombinant proteins. GST- OsEBP89 and MBP- Chalk9 were purified using glutathione- sepharose resins (CW0190S; CWBlO) and amylose resins (E8021V; NEB), respectively, for Pull- down assays as described previously52. The eluted proteins were analyzed by immunoblotting with anti- GST (CW0084M; CWBlO) and anti- MBP (HT701; Transgene) antibodies. + +<|ref|>sub_title<|/ref|><|det|>[[148, 395, 707, 414]]<|/det|> +## In vitro self-ubiquitination and substrate ubiquitination analyses + +<|ref|>text<|/ref|><|det|>[[147, 421, 852, 750]]<|/det|> +Recombinant MBP- Chalk9 and its single amino acid substitution mutant (MBP- Chalk9C189S) were expressed in E. coli and purified using amylose resins (E8021V; NEB) for in vitro self- ubiquitination analyses. The ubiquitination assay was performed as previously described, with some modifications54. 400 \(\mu \mathrm{g}\) of MBP- Chalk9, MBP- Chalk9C189S, or MBP protein was incubated in a 50- \(\mu \mathrm{L}\) reaction mixture containing ubiquitination buffer (50 mM Tris- HCl, pH7.5, 5 mM MgCl2, 2 mM DTT, 4 mM ATP, 15 \(\mu \mathrm{g}\) ubiquitin). The reaction was carried out at \(30^{\circ}\mathrm{C}\) for 2 hours in the presence or absence of 50 ng E1 (Beyotime, Shanghai, China) and 100 ng E2 (Beyotime). The reactions were stopped by the addition of 5×SDS sample buffer and heated at 95 \(^\circ \mathrm{C}\) for 5 minutes. The reaction products were separated on SDS- PAGE, followed by immunoblot analysis using an anti- MBP antibody (1:5000; HT701; Transgene) and a polyclonal anti- ubiquitin antibody (1:1,000, RM4934; Biodragon). + +<|ref|>text<|/ref|><|det|>[[147, 785, 852, 914]]<|/det|> +For in vitro substrate ubiquitination assays, GST- OsEBP89 was used as the target substrate. 300 ng of the GST- OsEBP89 fusion protein was mixed with an equal amount of MBP- Chalk9 or MBP- Chalk9C189S in the presence or absence of the following: 50 ng of E1, 100 ng of E2, and 5 \(\mu \mathrm{g}\) of ubiquitin. The reaction was performed in a 50 \(\mu \mathrm{L}\) total mixture containing ubiquitination buffer at \(30^{\circ}\mathrm{C}\) for 3 hours. Ubiquitination levels + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 88, 850, 163]]<|/det|> +of proteins were determined by Western blotting using a polyclonal anti- ubiquitin antibody (1:1,000, RM4934; Biodragon) and an anti- GST antibody (CW0084M; CWBI0). + +<|ref|>sub_title<|/ref|><|det|>[[148, 201, 394, 218]]<|/det|> +## Cell-free degradation assays + +<|ref|>text<|/ref|><|det|>[[145, 227, 852, 499]]<|/det|> +The leaf powder, frozen in liquid nitrogen from Nip and chalk9- 1 plants, was suspended in extraction buffer (5 mM \(\mathrm{MgCl}_2\) , 40 mM Tris- HCl pH 7.5, 5 mM NaCl, 1 mM DTT, and 10 mM ATP) and vigorously vortexed at \(4^{\circ}\mathrm{C}\) for 1 hour. After centrifugation at 16,000 g at \(4^{\circ}\mathrm{C}\) for 30 minutes, the supernatant was collected for the cell- free degradation assay. The GST- OsEBP89 recombinant protein was incubated with the supernatant at \(30^{\circ}\mathrm{C}\) for different periods, with or without the addition of 50 mM MG132 (Beyotime). The reactions were terminated by adding \(5 \times \mathrm{SDS}\) sample buffer and then immunoblotted using anti- GST (CW0084M; CWBI0) and anti- Actin (CW0264M; CWBI0) antibodies. The protein levels were quantified using ImageJ software (http://rsb.info.nih.gov/ij). + +<|ref|>sub_title<|/ref|><|det|>[[148, 535, 348, 552]]<|/det|> +## Yeast one-hybrid assay + +<|ref|>text<|/ref|><|det|>[[145, 560, 852, 888]]<|/det|> +Yeast one- hybrid (Y1H) assays were performed using the Matchmaker™ Gold Yeast One- Hybrid System (Clontech). The coding sequence of OsEBP89 was fused to the activation domain of the GAL4 protein in the pGADT7 vector, generating the prey construct pGADT7- OsEBP89. 2- kb promoter sequences from GluB1a, GluB2, GluB4, PROLM20, PROLM22, and PROLM23 were individually inserted into the pAbAi vector, generating the bait constructs. The minimal inhibitory concentration of aureobasidin A (AbA) for the bait strains was determined for yeast one- hybrid assay. The prey construct pGADT7- OsEBP89 was then transformed into the recombinant bait- reporter strains. The interaction between the empty pGADT7 and the corresponding bait plasmid was considered a negative control. Yeast cells were grown on SD/- Leu culture media with or without AbA for 3- 5 days at \(30^{\circ}\mathrm{C}\) . Primer sequences are shown in Supplementary Table 14. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 91, 523, 108]]<|/det|> +## Luciferase activity assay in rice protoplasts + +<|ref|>text<|/ref|><|det|>[[145, 117, 852, 388]]<|/det|> +To investigate the regulatory effect of the Chalk9 promoter on gene expression, approximately 2- kb promoter sequences of Chalk9 were amplified from Nip and Guichao2 and inserted into pGreenII 0800- LUC vector to generate proChalk9- L:LUC and proChalk9- H:LUC, respectively. Six variants were mutated based on proChalk9- L:LUC using a Fast Mutagenesis System (FM111, Transgen Biotech). All the vectors were transformed into protoplasts, respectively. Afterwards, all the protoplasts were incubated in W5 solution for 12 h at \(28^{\circ}\mathrm{C}\) . Activities of firefly luciferase (LUC) and Renilla luciferase (REN) were examined using a dual luciferase assay kit (Vazyme Biotech, Jiangsu, China). The primers used for PCR amplification and mutation are listed in Supplementary Table 14. + +<|ref|>text<|/ref|><|det|>[[145, 421, 852, 775]]<|/det|> +To test the transcriptional activity of OsEBP89 proteins on SSP genes, 2- kb promoter sequences of GluB1a, GluB2, GluB4, PROLM20, PROLM22, and PROLM23 were cloned into the pGreenII 0800- LUC vector to create reporter constructs. The coding sequence of OsEBP89 was cloned into the pGreenII 62- SK vector to generate effector construct. For analyzing the transcriptional activity of the OsB3 protein on the Chalk9 alleles, 2- kb promoter sequences of Chalk9- L and Chalk9- L v5m were cloned into the pGreenII 0800- LUC vector to create reporter constructs, respectively. The coding sequence of OsB3 was cloned into the pGreenII 62- SK vector to generate the effector construct. The empty pGreenII 62- SK vector was used as a negative control. Plasmid combinations were co- transformed into rice protoplasts for transcriptional activity analysis. The transformed cells were incubated in the dark at \(28^{\circ}\mathrm{C}\) for 12 hours and then used to measure transcriptional activity using a dual luciferase assay kit (Vazyme Biotech, Jiangsu, China). The relevant primers are listed in Supplementary Table 14. + +<|ref|>sub_title<|/ref|><|det|>[[148, 812, 333, 829]]<|/det|> +## Immunoblot analysis + +<|ref|>text<|/ref|><|det|>[[147, 838, 850, 914]]<|/det|> +Developing seeds were homogenized in protein extraction buffer (2 mM EDTA, 100 mM NaCl, 20 mM Tris- HCl, pH 7.5, \(0.1\%\) [v/v] Triton X- 100, 1 mM PMSF, and a \(1 \times\) proteinase inhibitor cocktail). Total proteins were collected after the homogenate was + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 88, 852, 247]]<|/det|> +centrifuged at 16,000 g at 4 °C for 20 min. Western blotting was performed as previously described. Briefly, Protein samples were separated by 10% (w/v) SDS- PAGE and transferred to PVDF membranes (Immobilon- P, USA). Protein signals were detected using the eECL Western Blot Kit (CW0049S; CWBIO) after being probed with specific primary antibodies, followed by incubation with the appropriate secondary antibodies. + +<|ref|>sub_title<|/ref|><|det|>[[147, 284, 518, 303]]<|/det|> +## OsEBP89 polyclonal antibody preparation + +<|ref|>text<|/ref|><|det|>[[145, 310, 855, 553]]<|/det|> +To generate a specific antibody against OsEBP89, we chosen a truncated sequence (residues 1- 120) for recombinant protein production. The corresponding coding sequence was amplified and cloned into the pET28a vector with an N- terminal His- tag. The recombinant protein was expressed in E. coli strain BL21 (DE3) transformed with the resulting construct and then purified using a Ni- NTI agarose resin matrix (Qiagen). The purified recombinant protein served as the antigen to raise antibodies in two rabbits, a process conducted by GenScript. The antibody against OsEBP89 was further affinity- purified from serum using immobilized recombinant protein and specifically detected endogenous OsEBP89. + +<|ref|>sub_title<|/ref|><|det|>[[147, 589, 540, 608]]<|/det|> +## Population genetic and evolutionary analyses + +<|ref|>text<|/ref|><|det|>[[145, 615, 853, 802]]<|/det|> +The geographical information and genomic sequences of 1,424 cultivated varieties were obtained from the 3K Rice Genomes Project37, and marked on map to observe geographic distribution of the two types of Chalk9. Using VCFtools v0.1.1655, the nucleotide diversity (π) and Neutral test (Tajima's D) were calculated in 50- kb windows for each japonica, indica, and wild rice population. For all sites in the Chalk9 locus with a minor allele frequency ≥ 0.01, phylogenetic and haplotypes network analyses were constructed following previously established methods56. + +<|ref|>sub_title<|/ref|><|det|>[[147, 839, 730, 858]]<|/det|> +## The spatiotemporal gene expression and TFBS enrichment analysis + +<|ref|>text<|/ref|><|det|>[[147, 866, 855, 912]]<|/det|> +The spatio- temporal gene expression pattern was analyzed by RiceXPro57. Additionally, the 64- bp sequence of the Chalk9 promoter was examined for transcription factor + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 89, 593, 108]]<|/det|> +binding site (TFBS) enrichment using PlantPan v4.058. + +<|ref|>sub_title<|/ref|><|det|>[[148, 147, 310, 163]]<|/det|> +## Statistical analysis + +<|ref|>text<|/ref|><|det|>[[147, 172, 851, 303]]<|/det|> +Prism v.6.0 (GraphPad) software was used for all statistical tests and data visualization. The individual figures and figure legends indicated the sample sizes \((n)\) and \(P\) values. For two groups, statistical significance was determined using two- tailed paired Student's \(t\) - test. For more than two groups, statistical significance was determined using one- way analysis of variance (ANOVA) with Tukey's multiple comparisons test. + +<|ref|>sub_title<|/ref|><|det|>[[148, 340, 316, 356]]<|/det|> +## Accession numbers + +<|ref|>text<|/ref|><|det|>[[147, 366, 852, 608]]<|/det|> +Sequence data related to this article can be obtained from the Rice Database (https://www.ricedata.cn/gene) under following accession numbers LOC_Os09g32730 for Chalk9, LOC_Os03g08460 for OsEBP89, LOC_Os06g04200 for Wx, LOC_Os02g15178 for GluB1a, LOC_Os02g15150 for GluB2, LOC_Os02g16830 for GluB4, LOC_Os02g16820 for GluB5, LOC_Os02g14600 for GluB7, LOC_Os02g25640 for GluC, LOC_Os02g15090 for GluD, LOC_Os05g26350 for PROLM4, LOC_Os05g26460 for PROLM11, LOC_Os05g26368 for PROLM13, LOC_Os05g26720 for PROLM16, LOC_Os07g11910 for PROLM20, LOC_Os07g11920 for PROLM22, and LOC_Os06g31060 for PROLM23. + +<|ref|>sub_title<|/ref|><|det|>[[148, 646, 318, 662]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[147, 672, 852, 887]]<|/det|> +We thank Prof. L. Yan (Oklahoma State University, USA) for revising the paper. The pTAC303- RNAi vector was provided by K. Chong; The pYLCRISPR/Cas9- MH vector was provided by Y. Liu; and the pUC- SPYCE and pUC- SPYNE vectors were provided by R. Lin. This work was supported by the grants from the National Natural Sciences Foundation of China (32301828), the Biological Breeding- National Science and Technology Major Project (2023ZD04068), the programs of the Jiangsu Province Government (BE2022335, JBGS [2021]001, and BE2021334- 1), and the Project of Zhongshan Biological Breeding Laboratory (ZSBBL- KY2023- 01). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[148, 92, 335, 108]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[147, 111, 852, 254]]<|/det|> +Z.H., H.L. and M.G. performed experiments and analyzed the data. Z.H. and X.H. collected phenotype data of rice varieties and genetic materials in the field. Y.L. conducted the GWAS analysis. R.C. performed the evolutionary analysis. Y.G., Y.Y., S.S., Y.Z. and M.G. participated in the experiments. Z.H. and C.Y. designed research and wrote the manuscript. C.Y. supervised the project. All authors reviewed the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[148, 289, 325, 306]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[148, 309, 501, 326]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[148, 357, 245, 374]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[144, 384, 852, 903]]<|/det|> +1. Tian, Z. et al. Allelic diversities in rice starch biosynthesis lead to a diverse array of rice eating and cooking qualities. Proc. Natl Acad. Sci. USA 106, 21760–21765 (2009). +2. Zeng, D. et al. Rational design of high-yield and superior-quality rice. Nat. Plants 3, 17031 (2017). +3. Zhao, D., Zhang, C., Li, Q. & Liu, Q. Genetic control of grain appearance quality in rice. Biotechnol. Adv. 60, 108014 (2022). +4. 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Sun, J., Sun, Y., Ahmed, R. I., Ren, A. & Xie, A. M. Research progress on plant RING-finger proteins. Genes 10, 973 (2019).30. Huang, X. et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42, 961-7 (2010).31. Gazzarrini, S. & Song, L. LAFL Factors in Seed Development and Phase Transitions. Annu. Rev. Plant Biol. 75, 459-488 (2024).32. Yang, H. J. et al. The OsEBP-89 gene of rice encodes a putative EREBP transcription factor and is temporally expressed in developing endosperm and intercalary meristem. Plant Mol. Biol. 50, 379-391 (2002).33. Zhu, Y., Cai, X. L., Wang, Z. Y. & Hong, M. M. An interaction between a MYC protein and an EREBP protein is involved in transcriptional regulation of the rice Wx gene. J. Biol. Chem. 278, 47803-47811 (2003).34. Hu, Z. et al. The kinase OsSK41/OsGSK5 negatively regulates amylose content + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[202, 88, 850, 135]]<|/det|> +in rice endosperm by affecting the interaction between OsEBP89 and OsBP5. J. Integr. Plant Biol. 65, 1782- 1793 (2023). + +<|ref|>text<|/ref|><|det|>[[202, 144, 850, 218]]<|/det|> +Yan, H. et al. Rice LIKE EARLY STARVATION1 cooperates with FLOURY ENDOSPERM6 to modulate starch biosynthesis and endosperm development. Plant Cell 36, 1892- 1912 (2024). + +<|ref|>text<|/ref|><|det|>[[202, 227, 850, 275]]<|/det|> +Wang, Y. et al. OsRab5a regulates endomembrane organization and storage protein trafficking in rice endosperm cells. Plant J. 64, 812- 824 (2010). + +<|ref|>text<|/ref|><|det|>[[202, 283, 850, 330]]<|/det|> +Alexandrov, N. et al. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res. 43, D1023- D1027 (2015). + +<|ref|>text<|/ref|><|det|>[[202, 339, 850, 386]]<|/det|> +Huang, X. et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497- 501 (2012). + +<|ref|>text<|/ref|><|det|>[[202, 395, 850, 442]]<|/det|> +Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43- 49 (2018). + +<|ref|>text<|/ref|><|det|>[[202, 450, 850, 498]]<|/det|> +Zhao, H. et al. RiceVarMap: a comprehensive database of rice genomic variations. Nucleic Acids Res. 43, D1018- D1022 (2015). + +<|ref|>text<|/ref|><|det|>[[202, 506, 850, 582]]<|/det|> +Wang, J., Chen, Z., Zhang, Q., Meng, S. & Wei, C. The NAC transcription factors OsNAC20 and OsNAC26 regulate starch and storage protein synthesis. Plant Physiol. 184, 1775- 1791 (2020). + +<|ref|>text<|/ref|><|det|>[[202, 590, 850, 637]]<|/det|> +Yamagata, H., Sugimoto, T., Tanaka, K. & Kasai, Z. Biosynthesis of storage proteins in developing rice seeds. Plant Physiol. 70, 1094- 1100 (1982). + +<|ref|>text<|/ref|><|det|>[[202, 645, 853, 691]]<|/det|> +Yang, J. et al. Changes in activities of three enzymes associated with starch synthesis in rice grains during grain filling. Acta Agron. Sin. 27, 157- 164 (2001). + +<|ref|>text<|/ref|><|det|>[[202, 700, 850, 775]]<|/det|> +Zhong, L. & Cheng, F. Varietal differences in amylose accumulation and activities of major enzymes associated with starch synthesis during grain filling in rice. Acta Agron. Sin. 29, 452- 456 (2003). + +<|ref|>text<|/ref|><|det|>[[202, 784, 850, 831]]<|/det|> +Duan, M. & Sun, S. S. M. Profiling the expression of genes controlling rice grain quality. Plant Mol. Biol. 59, 165- 178 (2005). + +<|ref|>text<|/ref|><|det|>[[202, 840, 850, 915]]<|/det|> +Liu, X. et al. Transcriptome analysis of grain- filling caryopses reveals involvement of multiple regulatory pathways in chalky grain formation in rice. BMC Genomics 11, 730 (2010). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 90, 855, 870]]<|/det|> +47. Yang, Y. et al. Natural variation of OsGluA2 is involved in grain protein content regulation in rice. Nat. Commun. 10, 1949 (2019). +48. Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012). +49. Si, L. et al. OsSPL13 controls grain size in cultivated rice. Nat. Genet. 48, 447–456 (2016). +50. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007). +51. Dong, S. S. et al. LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files. Brief. Bioinform. 22, bbaa227 (2021). +52. Hu, Z. et al. Autophagy targets Hd1 for vacuolar degradation to regulate rice flowering. Mol. Plant 15, 1137–1156 (2022). +53. Zhang, Y. et al. A highly efficient rice green tissue protoplast system for transient gene expression and studying light/chloroplast-related processes. Plant Methods 7, 30 (2011). +54. Huang, L. et al. The LARGE2-APO1/APO2 regulatory module controls panicle size and grain number in rice. Plant Cell 33, 1212–1228 (2021). +55. Danceck, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011). +56. Chen, R. et al. A de novo evolved gene contributes to rice grain shape difference between indica and japonica. Nat. Commun. 14, 5906 (2023). +57. Sato, Y. et al. RiceXPro: a platform for monitoring gene expression in japonica rice grown under natural field conditions. Nucleic Acids Res. 39, D1141–D1148 (2011). +58. Chow, C. N. et al. PlantPAN 4.0: updated database for identifying conserved non-coding sequences and exploring dynamic transcriptional regulation in plant promoters. Nucleic Acids Res. 52, D1569–D1578 (2024). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 121, 309, 142]]<|/det|> +## Figure legends + +<|ref|>text<|/ref|><|det|>[[147, 163, 850, 208]]<|/det|> +Fig. 1 GWAS and fine mapping of the major locus that underlies grain chalkiness variation. + +<|ref|>text<|/ref|><|det|>[[147, 228, 650, 247]]<|/det|> +Fig. 2 Chalk9 negatively regulates grain chalkiness in rice. + +<|ref|>text<|/ref|><|det|>[[147, 265, 850, 310]]<|/det|> +Fig. 3 A 64- bp indel in the Chalk9 promoter confers different grain chalkiness in rice. + +<|ref|>text<|/ref|><|det|>[[147, 330, 735, 349]]<|/det|> +Fig. 4 Chalk9 is an E3 ubiquitin ligase that interacts with OsEBP89. + +<|ref|>text<|/ref|><|det|>[[147, 368, 697, 386]]<|/det|> +Fig. 5 Chalk9 ubiquitinates OsEBP89 and regulates its stability. + +<|ref|>text<|/ref|><|det|>[[147, 405, 850, 450]]<|/det|> +Fig. 6 Chalk9- OsEBP89 module regulates rice grain chalkiness by influencing seed storage substance biosynthesis. + +<|ref|>text<|/ref|><|det|>[[147, 469, 850, 514]]<|/det|> +Fig. 7 Geographical distribution, genomic differentiation, and genomic selection of Chalk9 between japonica and indica subspecies. + +<|ref|>text<|/ref|><|det|>[[147, 533, 850, 551]]<|/det|> +Extended Data Fig. 1 The genome- wide association study for rice grain chalkiness. + +<|ref|>text<|/ref|><|det|>[[147, 570, 850, 615]]<|/det|> +Extended Data Fig. 2 The 64- bp indel in the Chalk9 promoter contributes to grain chalkiness variation. + +<|ref|>text<|/ref|><|det|>[[147, 635, 797, 654]]<|/det|> +Extended Data Fig. 3 OsEBP89 positively regulates grain chalkiness in rice. + +<|ref|>text<|/ref|><|det|>[[147, 672, 850, 717]]<|/det|> +Extended Data Fig. 4 Temporal and geographic patterns of Chalk9- L distribution and its impact on chalkiness in cultivated rice varieties. + +<|ref|>text<|/ref|><|det|>[[147, 736, 850, 781]]<|/det|> +Supplementary Fig. 1 Variations of chalky grain rate and degree of chalkiness in 175 indica varieties. + +<|ref|>text<|/ref|><|det|>[[147, 801, 850, 847]]<|/det|> +Supplementary Fig. 2 Functional importance estimation of SNPs located in the coding region. + +<|ref|>text<|/ref|><|det|>[[147, 866, 785, 885]]<|/det|> +Supplementary Fig. 3 Identification of Chalk9 RNAi and knockout plants. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 89, 850, 137]]<|/det|> +Supplementary Fig. 4 Identification of the candidate genes in transcriptional factors analysis. + +<|ref|>text<|/ref|><|det|>[[144, 153, 850, 202]]<|/det|> +Supplementary Fig. 5 The amino acid sequence of RING domain in Chalk9 is highly conserved in plants. + +<|ref|>text<|/ref|><|det|>[[144, 217, 850, 265]]<|/det|> +Supplementary Fig. 6 Functional analysis of OsEBP89 and identification of the anti- OsEBP89 antibody. + +<|ref|>text<|/ref|><|det|>[[144, 282, 850, 330]]<|/det|> +Supplementary Fig. 7 Transcript levels of storage substance- related genes in seeds of Nip and chalk9- 1 plants from RNA- seq data. + +<|ref|>text<|/ref|><|det|>[[144, 346, 850, 395]]<|/det|> +Supplementary Fig. 8 Transcript levels of storage substance- related genes in the seeds form Nip and chalk9- 1 plants. + +<|ref|>text<|/ref|><|det|>[[144, 411, 660, 432]]<|/det|> +Supplementary Fig. 9 Agronomic traits for chalk9 mutants. + +<|ref|>text<|/ref|><|det|>[[144, 448, 850, 496]]<|/det|> +Supplementary Fig. 10 Agronomic traits for near- isogenic lines and transgenic plants. + +<|ref|>text<|/ref|><|det|>[[144, 513, 850, 561]]<|/det|> +Supplementary Table 1. Chalky grain rate and degree of chalkiness of 175 indica accessions in two years. + +<|ref|>text<|/ref|><|det|>[[144, 578, 850, 626]]<|/det|> +Supplementary Table 2. Annotation of significant SNPs associated with the grain chalkiness in the candidate region. + +<|ref|>text<|/ref|><|det|>[[144, 642, 850, 717]]<|/det|> +Supplementary Table 3. Annotation of candidate genes on Chromosome 9 associated with the grain chalkiness by MSU Rice Genome Annotation Project Release 7. + +<|ref|>text<|/ref|><|det|>[[144, 734, 850, 783]]<|/det|> +Supplementary Table 4. All variations of Chalk9 in 149 indica accessions were identified by re- sequencing based on PCR amplification. + +<|ref|>text<|/ref|><|det|>[[144, 799, 850, 848]]<|/det|> +Supplementary Table 5. Major haplotypes of Chalk9 were identified from significant variations in indica accessions. + +<|ref|>text<|/ref|><|det|>[[144, 865, 850, 914]]<|/det|> +Supplementary Table 6. Prediction of transcription factors binding to the 64- bp sequence in Chalk9. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[92, 88, 852, 135]]<|/det|> +Supplementary Table 7. Expression analysis of starch and SSP genes generated from chalk9- 1 vs Nip by RNA- seq. + +<|ref|>text<|/ref|><|det|>[[92, 154, 852, 200]]<|/det|> +Supplementary Table 8. Putative cis-regulatory elements identified in the promoters of genes involved in starch and storage protein biosynthesis. + +<|ref|>text<|/ref|><|det|>[[92, 219, 852, 265]]<|/det|> +Supplementary Table 9. The SNPs at the OsEBP89 gene region define one major haplotype in indica varieties. + +<|ref|>text<|/ref|><|det|>[[92, 284, 852, 330]]<|/det|> +Supplementary Table 10. The SNPs at the OsEBP89 gene region from sequencing data of 4,726 rice accessions. + +<|ref|>text<|/ref|><|det|>[[92, 349, 852, 395]]<|/det|> +Supplementary Table 11. The SNPs at the Chalk9 promoter region in 3K Rice Genomes Project. + +<|ref|>text<|/ref|><|det|>[[92, 414, 852, 460]]<|/det|> +Supplementary Table 12. Allele frequency of Chalk9-L from sequencing data of 4,726 rice accessions. + +<|ref|>text<|/ref|><|det|>[[92, 479, 852, 526]]<|/det|> +Supplementary Table 13. Allele frequency of Chalk9-L in common wild rice (O. rufipogon). + +<|ref|>text<|/ref|><|det|>[[92, 544, 658, 562]]<|/det|> +Supplementary Table 14. Primers (5'-3') used in this study. + +<|ref|>text<|/ref|><|det|>[[92, 581, 852, 627]]<|/det|> +Supplementary Data 1. 2,658 differentially expressed genes generated from chalk9-1 vs Nip. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[144, 103, 850, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 794, 850, 896]]<|/det|> +
Fig. 1 GWAS and fine mapping of the major locus that underlies grain chalkiness variation. a, The genome-wide association signals for chalky grain rate (CGR) and degree of chalkiness (DC) in the region at 18–21 Mb on chromosome 9 (x axis) across two years. Negative \(\log_{10}\) -transformed \(P\) values from the linear mixed model are plotted
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 528]]<|/det|> +on the y- axis. The horizontal dashed line indicates the genome- wide significance threshold \((P = 1\times 10^{- 6})\) . b, Linkage disequilibrium (LD) heatmap of the Chalk9 locus region. Pairwise linkage disequilibrium was determined by calculating \(r^2\) (the square of the correlation coefficient between SNPs). c, Relative expression level of the 12 candidate genes in the endosperm of eight high- chalk varieties and eight low- chalk varieties at 20 days after flowering (DAF). The 12 predicted genes in the Chalk9 locus region are labeled by I to XII. Data show means \(\pm \mathrm{SD}\) ( \(n = 8\) varieties). d, Relative expression level of the candidate gene III (Chalk9) in the endosperm from the selected varieties at 20 DAF. Data show means \(\pm \mathrm{SD}\) ( \(n = 3\) biological replicates). e, Relative expression level of the 12 candidate genes in the leaves of high- chalk varieties and low- chalk varieties. Data show means \(\pm \mathrm{SD}\) ( \(n = 8\) varieties). f, Expression analysis of the candidate genes from GWAS in various tissues. The result of two genes (II and XI) was not found in RiceXPro database. L, leaf blade; LS, leaf sheath; R, root; S, stem; I, inflorescence; A, anther; P, pistil; L/P, lemma/palea; O, ovary; Em, embryo; En, endosperm. In c and e, statistical analysis was performed by two- tailed Student's \(t\) - test; for \(P\) values, see Source Data. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 111, 850, 550]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[144, 570, 852, 840]]<|/det|> +
Fig. 2 Chalk9 negatively regulates grain chalkiness in rice. a, Grain chalkiness in ZH11, ZH11-OE1, ZH11-OE2, ZH11-RNAi-1, and ZH11-RNAi-2 plants. Scale bar: 5 mm. b, Expression analysis of Chalk9 in ZH11 and transgenic plants. Data show means \(\pm\) SD \((n = 3\) biological replicates). c,d, Chalky grain rate (e) and degree of chalkiness (d) in ZH11 and transgenic plants. Data show means \(\pm\) SD \((n = 16\) plants). e, Grain chalkiness in Nip, chalk9-1, and chalk9-2 plants. Scale bar: 5 mm. f,g, Chalky grain rate (f) and degree of chalkiness (g) in Nip, chalk9-1, and chalk9-2 plants. Data show means \(\pm\) SD \((n = 16\) plants). In b, c, d, f, and g, different letters indicate significant differences \((P< 0.05\) , one-way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[144, 113, 846, 800]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[144, 812, 850, 916]]<|/det|> +
Fig. 3 A 64-bp indel in the Chalk9 promoter confers different grain chalkiness in rice. a, Major haplotypes of Chalk9. v4, v5, v10, v12, v14 and v15 indicate the variants, and their positions relative to ATG are shown in the table. b, c, The distribution of chalky grain rate (b) and degree of chalkiness (c) in haplotype H ( \(n = 45\) accessions) and
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 87, 853, 750]]<|/det|> +haplotype L \((n = 102\) accessions). d, Expression analysis of Chalk9 in haplotype H \((n\) \(= 24\) accessions) and haplotype L \((n = 24\) accessions) in endosperms. e, Grain chalkiness of Nip and NIL \(^{\text{Chalk9 - H}}\) plants. Scale bar: \(5\mathrm{mm}\) . f,g, Chalky grain rate (f) and degree of chalkiness (g) of Nip and NIL \(^{\text{Chalk9 - H}}\) plants. Data show means \(\pm\) SD \((n = 16\) plants). h, Relative Chalk9 expression levels of Nip and NIL \(^{\text{Chalk9 - H}}\) plants in endosperms. Data show means \(\pm\) SD \((n = 3\) biological replicates). i, Grain chalkiness of wild-type Guichao2, pChalk9- H::Chalk9- H, pChalk9- L::Chalk9- L, and pChalk9- L::Chalk9- H plants. Scale bar: \(6\mathrm{mm}\) . j,k, Chalky grain rate (j) and degree of chalkiness (k) of Guichao2 and different transgenic plants. Data show means \(\pm\) SD \((n = 10\) plants). l, Relative Chalk9 expression levels of Guichao2 and different transgenic plants in endosperms. Data show means \(\pm\) SD \((n = 3\) biological replicates). m, Transient expression assays of the effect of different variations on gene expression, shown by firefly luciferase/Renilla luciferase activity ratio (LUC/REN). v4m, v5m, v10m, v12m, v14m and v15m represent the mutations introduced into the promoter of Chalk9- L. Data show means \(\pm\) SD \((n = 3\) biological replicates). n,o, Degree of chalkiness (n) and chalky grain rate (o) in Nip and D52 plants. Data show means \(\pm\) SD \((n = 16\) plants). p, Relative Chalk9 expression levels of Nip and D52 plants in endosperms. Data show means \(\pm\) SD \((n = 3\) biological replicates). q, Indel of 64 bp resulted in divergent activation of the OsB3 protein to Chalk9 promoter, as shown by LUC/REN. Data show means \(\pm\) SD \((n\) \(= 3\) biologically replicates). In b- d, the bars within violin plots represent 25th percentiles, medians, and 75th percentiles. In b- d, f- h, and n- p, statistical analysis was performed by two- tailed Student's \(t\) - test. In j- m and q, different letters indicate significant differences \((P< 0.05\) , one- way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 112, 850, 800]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 812, 851, 914]]<|/det|> +
Fig. 4 Chalk9 is an E3 ubiquitin ligase that interacts with OsEBP89. a, Subcellular localization of Chalk9-GFP fusion protein in rice protoplasts. IPA1-mCherry was used as a nuclear marker. Scale bars: \(5 \mu \mathrm{m}\) . b, Quantitative PCR with reverse transcription (qRT-PCR)-based transcript abundance analysis of Chalk9 in various tissues. R, root;
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 853, 555]]<|/det|> +S, stem; L, leaf; LS, leaf sheath; P, panicle; DAF, days after flowering. Data show means \(\pm \mathrm{SD}\) ( \(n = 3\) biological replicates). c, Ubiquitin ligase activity of Chalk9. MBP- Chalk9 was expressed in E. coli strain BL21, and ubiquitinated proteins were detected using both anti- MBP and anti- ubiquitin (Ub) antibodies. d, Yeast two- hybrid (Y2H) assay showing the interaction between Chalk9 and OsEBP89. Strains carrying the indicated constructs were grown on synthetic dropout medium. DDO and QDO/X represent SD/- Trp- Leu and SD/- Trp- Leu- His- Ade + X- Gal selection medium, respectively. e, Pull- down assay. GST- OsEBP89 was used as baits, and the pull down of MBP- Chalk9 was detected by the anti- MBP antibody. f, Co- immunoprecipitation (Co- IP) assay of rice protoplasts co- expressing Chalk9- HA and GFP- OsEBP89. Total proteins were incubated with magnetic agarose beads conjugated to HA- tag antibody. The immunoprecipitants were probed with antibodies against HA and GFP. IP, immunoprecipitation. g, Interaction between the Chalk9 and OsEBP89 demonstrated by bimolecular fluorescence complementation (BiFC) assays in rice protoplasts. N- terminal fragment of YFP (nYFP) fused with Chalk9 and the C- terminal fragment of YFP (cYFP) fused with OsEBP89 were co- expressed in rice protoplasts. IPA1- mCherry was used as a nuclear control. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 111, 835, 730]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 738, 850, 897]]<|/det|> +
Fig. 5 Chalk9 ubiquitinates OsEBP89 and regulates its stability. a, In vitro ubiquitination of OsEBP89 by Chalk9. Ubiquitinated proteins were detected using anti-GST and anti-Ub antibodies. b, Cell-free degradation of GST-OsEBP89 in the protein extracts from Nip and chalk9-1 seedlings. Protein levels of GST-OsEBP89 were detected using anti-GST antibody, and Actin was used as a loading control for total protein extraction. Relative fold changes of GST-OsEBP89 to Actin loading controls
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 853, 533]]<|/det|> +were quantified by ImageJ and marked on bottom of the lanes. The protein level at time point 0 min was marked as 1. c, GST- OsEBP89 degradation rate in Nip and chalk9- 1 seedlings. d, Detection of OsEBP89 protein abundance in Nip and chalk9- 1 plants. Total proteins were extracted from seeds at 18 DAF. OsEBP89 protein abundance was determined by immunoblotting. e, Relative quantification of OsEBP89 protein abundance in Nip, chalk9- 1, and chalk9- 2 plants. OsEBP89 protein levels were quantified relative to Actin by ImageJ. The protein level at time point 0 min was set as 1. f, OsEBP89 mRNA level in Nip, chalk9- 1, and chalk9- 2 plants. g, Detection of OsEBP89 protein abundance in Nip and NILChalk9-H plants. Total proteins were extracted from seeds at 15 DAF. OsEBP89 protein abundance was determined by immunoblotting. h, Relative quantification of OsEBP89 protein abundance in the Nip and NILChalk9-H plants. i, OsEBP89 mRNA level in Nip and NILChalk9-H plants. In c, e, f, h, and i, data show means ± SD (n = 3 biological replicates). In c, h, and i, statistical analysis was performed by two- tailed Student's \(t\) - test ( \(**P < 0.01\) ); for \(P\) values, see Source Data.. In e and f, different letters indicate significant differences ( \(P < 0.05\) , one-way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 115, 845, 870]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[144, 888, 847, 905]]<|/det|> +
Fig. 6 Chalk9-OsEBP89 module regulates rice grain chalkiness by influencing seed
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[90, 88, 855, 559]]<|/det|> +storage substance biosynthesis. a, The scanning electron microscopy observation of transverse sections of mature seeds from Nip, chalk9- 1, and chalk9- 2 plants. Scale bars: 0.8 mm (upper), 5 μm (down). b,c, Starch (b) and amylose (c) contents of Nip, chalk9- 1, and chalk9- 2 plants. d, Transmission electron microscopy analysis of the endosperm cells from Nip, chalk9- 1, and chalk9- 2 plants at 18 DAF. Scale bars: 5 μm (upper), 2 μm (down). White asterisk indicates PBI; red asterisk indicates PBII. e,f, Number (per 400 μm²) of protein bodies (e) and mean area of protein bodies (f) in the endosperms from Nip, chalk9- 1, and chalk9- 2 plants. g-k, Total protein (g), glutelin (h), prolamin (i), albumin (j), and globulin (k) contents of Nip, chalk9- 1, and chalk9- 2 plants. l, Grain chalkiness of Nip, chalk9- 1, osebp89- 1 and chalk9- 1/osebp89- 1 plants. Scale bar: 5 mm. m-p, Degree of chalkiness (m), chalky grain rate (n), amylose (o), and total protein (p) in Nip, chalk9- 1, osebp89- 1, and chalk9- 1/osebp89- 1 plants. In b, c, g-k, and m-p, data show means ± SD (n = 9 plants); different letters indicate significant differences (P < 0.05, one- way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data. In e and f, data show means ± SD (n = 3); statistical analysis was performed by two- tailed Student's \(t\) - test (** \(P < 0.01\) ; * \(P < 0.05\) ); for \(P\) values, see Source Data. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 111, 848, 660]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 682, 852, 897]]<|/det|> +
Fig. 7 Geographical distribution, genomic differentiation, and genomic selection of Chalk9 between japonica and indica subspecies. a,b, The relative ratio of nucleotide diversity (a) and Tajima's \(D\) (b) analyses in the whole chromosome 9 of cultivated and wild rice. Red dashed line indicates the Chalk9 locus. c,d Phylogeny (c) and haplotype networks (d) generated from the genomic sequences of Chalk9 in both cultivated and wild rice varieties. Outer circle of the tree indicates various rice populations. Circle size of the network is proportional to the number of samples for each haplotype. Black spots on the lines indicate mutational steps between two
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 853, 386]]<|/det|> +haplotypes. e, A proposed model for the Chalk9- OsEBP89 module in the regulation of grain chalkiness. In rice varieties with the Chalk9- H allele, Chalk9 expression in the endosperm is relatively lower, which reduces the degradation of OsEBP89. This accumulation of OsEBP89 leads to the upregulation of \(Wx\) and SSP genes, resulting in increased levels of amylose and storage protein in the endosperm. This elevated synthesis of storage compound during the post- milk stage contributes to the formation of chalky grains. Conversely, in rice varieties with the Chalk9- L allele, Chalk9 is highly expressed, which accelerates OsEBP89 degradation. The reduction in OsEBP89 levels leads to the downregulation of \(Wx\) and SSP genes in endosperm, resulting in decreased synthesis of storage substances during the post- milk stage and the formation of translucent grains. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 110, 850, 720]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[145, 740, 850, 897]]<|/det|> +Extended Data Fig. 1 The genome- wide association study for rice grain chalkiness. a,b, Manhattan plots and Quantile- quantile plots for chalky grain rate (a) and degree of chalkiness (b) in 175 indica varieties in 2021. c,d, Manhattan plots and Quantile- quantile plots for chalky grain rate (c) and degree of chalkiness (d) in 175 indica varieties in 2023. The horizontal dash- dot line indicates the genome- wide significant threshold \((P = 1 \times 10^{- 5})\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 110, 848, 720]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[145, 755, 851, 915]]<|/det|> +Extended Data Fig. 2 The 64- bp indel in the Chalk9 promoter contributes to grain chalkiness variation. a, Structure of Chalk9 and association mapping with 28 variants. Red dots connected with the dashed lines indicate the six variants that are significantly associated with chalkiness. x axis, position relative to ATG (0 bp). b, Identification and diagram of the \(\mathrm{NIL}^{Chalk9 - H}\) line. The numbering above the line represents the molecular markers used for the construction of \(\mathrm{NIL}^{Chalk9 - H}\) . The green bar indicates the genomic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 333]]<|/det|> +region from Kasalah (Chalk9- H type). The double- headed arrow shows the length of the substitution segment. c, Representative photographs of Nip and NILChalk9-H plants at the mature stage in the field. Scale bar: 10 cm. d, Schematic representation of the reporter constructs for the luciferase assay in Fig. 3m. v4m, v5m, v10m, v12m, v14m and v15m represent the mutations introduced into the promoter of Chalk9- L. e, Diagram of the Chalk9 promoter sequences of Nip and D52 plants. Red box indicates the position of the 64- bp indel. f, Grain chalkiness of Nip and D52 plants. Scale bar: 5 mm. g, Schematic diagrams of the effector and reporter plasmids used in the luciferase assays from Fig. 3q. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 108, 850, 820]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 850, 850, 870]]<|/det|> +
Extended Data Fig. 3 OsEBP89 positively regulates grain chalkiness in rice. a,
+ +<|ref|>text<|/ref|><|det|>[[145, 879, 850, 898]]<|/det|> +Loss-of-function mutants (osebp89- 1 and osebp89- 2) of OsEBP89 generated using + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[90, 88, 855, 469]]<|/det|> +CRISPR/Cas9 on the wild-type Nip (WT). The 20-bp target sequence for CRISPR/Cas9-mediated editing is underlined. b, Expression analysis of OsEBP89 in WT, OsEBP89-OE1, and OsEBP89-OE2 plants. Data show means \(\pm\) SD \((n = 3\) biological replicates). c, Grain chalkiness of WT, osebp89-1, osebp89-2, OsEBP89- OE1, and OsEBP89-OE2 plants. Scale bar: \(5\mathrm{mm}\) . d, e, Chalky grain rate (d) and degree of chalkiness (e) in WT, osebp89-1, osebp89-2, OsEBP89-OE1, and OsEBP89-OE2 plants. Data show means \(\pm\) SD \((n = 16\) plants). f-1, Expression analysis of Wx (f), GluB1a (g), GluB2 (h), GluB4 (i), PROLM20 (j), PROLM22 (k), and PROLM23 (l) genes from WT, osebp89-1, osebp89-2, OsEBP89-OE1, and OsEBP89-OE2 plants. Data show means \(\pm\) SD \((n = 3\) biological replicates). m, Y1H assay demonstrated the interaction between OsEBP89 and the promoters of GluB1a, GluB2, GluB4, PROLM20, PROLM22, and PROLM23 genes. In b and d-1, different letters indicate significant differences \((P< 0.05\) , one- way ANOVA with Tukey's multiple comparison test); for \(P\) values, see Source Data. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[142, 110, 850, 633]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[145, 664, 852, 878]]<|/det|> +Extended Data Fig. 4 Temporal and geographic patterns of Chalk9- L distribution and its impact on chalkiness in cultivated rice varieties. a, Geographic distributions of 1,424 cultivated rice varieties. blue and orange circles indicate the Chalk9- L and Chalk9- H type, respectively. b, The frequency of Chalk9- L in the cultivated indica varieties from different years ( \(\sim 1960 - 2000\) s) and the cultivated japonica varieties. c, d, The Chalky grain rate (c) and degree of chalkiness (d) in indica varieties from two periods: the 1980s and earlier versus the 1990s and later. e, f, Chalky grain rate (e) and degree of chalkiness (f) in japonica varieties compared with indica varieties. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 43, 312, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 131, 306, 204]]<|/det|> +SupplementaryTable.xlsx SupplementaryData.xlsx SupplementaryFigure.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/images_list.json b/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0d08c4f94f32f02d860bbe3940ebccafc83c8bc6 --- /dev/null +++ b/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/images_list.json @@ -0,0 +1,47 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. Changes in cloud profile due to deforestation. (a) Zonal mean of the cloud", + "footnote": [], + "bbox": [ + [ + 155, + 90, + 805, + 540 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Changes in surface turbulent heat flux due to deforestation. (a) Global pattern of the surface turbulent heat flux (latent heat (LH) + sensible heat (SH)) difference between the deforest-glob and piControl simulations (deforest-glob minus piControl). The diagonal grids indicate four or more of the five models showing the same symbol. (b) Box plots of the CMIP6 surface turbulent heat flux (LH+SH, LH and SH) differences between the deforest-glob and piControl simulations over both tropical and boreal areas. (c) ERA5 surface turbulent heat flux (LH+SH) variations due to deforestation using the space-for-time substitution (see Methods). (d) Box plots of the ERA5 surface turbulent heat flux (LH+SH, LH and SH) variations due to deforestation. The data in (a-b) is the ensemble mean of the local effect extracted from CMIP6 model simulations (see Methods). Boxes in (b and d) show the 25th to 75th percentiles of the data, whiskers display the 5th to 95th percentiles, horizontal yellow lines in the boxes represent the median values, and red dots are the mean values.", + "footnote": [], + "bbox": [ + [ + 152, + 85, + 844, + 430 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. Changes in outgoing radiation at the top of atmosphere (TOA) due to deforestation. (a, c, and e) Global pattern of the TOA outgoing radiation (shortwave", + "footnote": [], + "bbox": [ + [ + 160, + 180, + 820, + 840 + ] + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422.mmd b/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422.mmd new file mode 100644 index 0000000000000000000000000000000000000000..eb13b7eacb1ccc623efd250bdd3807ede2b68a4a --- /dev/null +++ b/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422.mmd @@ -0,0 +1,260 @@ + +# Decreased cloud cover partially offsets the cooling effects of surface albedo change due to deforestation + +Hao Luo + +luoh93@mail2. sysu.edu.cn + +Sun Yat- sen University https://orcid.org/0000- 0002- 6648- 4234 Johannes Quaas Universitaet Leipzig https://orcid.org/0000- 0001- 7057- 194X Yong Han Sun Yat- sen University https://orcid.org/0000- 0002- 3297- 2782 + +## Article + +# Keywords: + +Posted Date: July 23rd, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4019501/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on August 26th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 51783- y. + +<--- Page Split ---> + +# Decreased cloud cover partially offsets the cooling effects of + +# surface albedo change due to deforestation + +Hao Luo \(^{1,2*}\) , Johannes Quaas \(^{2,3}\) , Yong Han \(^{1,4*}\) + +\(^{1}\) Advanced Science & Technology of Space and Atmospheric Physics Group (ASAG), + +\(^{5}\) School of Atmospheric Sciences, Sun Yat- sen University, 519082 Zhuhai, China + +\(^{2}\) Leipzig Institute for Meteorology, Leipzig University, 04103 Leipzig, Germany + +\(^{3}\) German Centre for Integrative Biodiversity Research (iDiv) Halle- Jena- Leipzig, + +\(^{4}\) 04103 Leipzig, Germany + +\(^{4}\) Key Laboratory of Tropical Atmosphere- Ocean System (Sun Yat- sen University), + +\(^{10}\) Ministry of Education, 519082 Zhuhai, China + +\(^{*}\) Corresponding author(s). Email(s): luoh93@mail2.sysu.edu.cn (Hao Luo); + +hany66@mail.sysu.edu.cn (Yong Han) + +## 13 Abstract + +Biophysical processes of forests affect climate through the regulation of surface water and heat fluxes, which leads to further effects through the adjustment of clouds and water cycles. These indirect biophysical effects of forests on clouds and their radiative forcing are poorly understood but highly relevant in the context of large- scale deforestation or afforestation, respectively. Here, we provide evidence for local decreases in global low- level clouds and tropical high- level clouds from deforestation through both idealized deforestation simulations with climate models and from observations- driven reanalysis using space- for- time substitution. The decreased cloud cover can be explained by alterations in surface turbulent heat flux, which diminishes uplift and moisture to varying extents. Deforestation- induced reduction in cloud cover warms the climate, partially counteracting the cooling effects of increased surface albedo. The findings from idealized deforestation experiments and space- for- time substitution exhibit disparities, with global average offsets of, respectively, approximately \(44\%\) and \(26\%\) , suggesting the necessity for further constraints. + +<--- Page Split ---> + +## 28 Introduction + +Forests have the capacity to buffer global warming by storing large amounts of carbon from the atmosphere via photosynthesis \(^{1 - 3}\) . Alongside the biochemical effects, forests can influence the local and regional climate through biophysical processes, including alterations in land surface water and energy balance \(^{4 - 7}\) . On the local scale, the higher albedo and lower evapotranspiration (ET) following deforestation cause either surface cooling or warming, depending on which process holds dominance \(^{8 - 10}\) . These cooling or warming impacts have the potential to offset or intensify, respectively, the warming effects connected to the released carbon caused by deforestation \(^{11 - 16}\) . Extensive studies on the direct biophysical effects of deforestation on surface temperature have unveiled a latitudinal shift from tropical warming to boreal cooling \(^{8,9,17 - 19}\) . Nevertheless, globally, alterations in surface albedo are more prevalent in the direct biophysical temperature response than ET because of its wider- scale impact \(^{17}\) . This suggests that the global warming attributed to the biochemical effects of deforestation could potentially be mitigated by the cooling effects resulting from increased surface albedo and consequently altered radiative balance \(^{12,14,16}\) . Yet, the impact of forest indirect biophysical processes on clouds and their associated radiative balance has not been well addressed, and the assessment of how changes in cloud radiative effects interact with the surface albedo effects remains unquantified. Understanding the response of clouds and their radiative effects to deforestation, however, is crucial due to the overwhelming effect clouds play for the Earth energy budget. It stands as a major challenge in evaluating land- use- change- driven climate change \(^{20 - 24}\) . + +Observational studies allow for the conclusion that deforestation may predominantly reduce global cloud cover \(^{22,23,25}\) , but with contrasting impacts across various regions \(^{21}\) . These studies mostly compare clouds above forests and open land in adjacent geographical units (i.e. space- for- time substitution) and find larger cloudiness over forests. This commonly adopted method assumes that forests and + +<--- Page Split ---> + +neighboring land units share the same climate background, thereby deducing local effects through distinctions in land surface conditions. Apart from observations- based studies, general circulation models (GCMs) have been widely employed to quantify the impacts of deforestation \(^{26 - 28}\) . GCMs show a global average enhancement in cloud cover with deforestation \(^{29}\) . Unlike the observational studies that concentrate solely on local effects, GCMs probably possess the ability to encompass both local and non- local effects of deforestation. Hence, separating local and non- local effects could facilitate comparisons between these two distinct methods and enhance comprehension of the biophysical mechanisms of deforestation on clouds \(^{24}\) . + +Given the essential roles of cloud vertical structures in influencing radiative processes \(^{30,31}\) , a sole concentration on overall cloud cover may be insufficient for a comprehensive analysis of the changes in cloud radiative effects from deforestation. Typically, low, highly reflective clouds have a cooling effect as they reflect solar radiation. In contrast, high, semi- transparent clouds contribute to warming by allowing shortwave radiation to pass through while impeding longwave radiation \(^{32,33}\) . The alterations in cloud vertical profiles following deforestation have not received adequate attention, and addressing this gap is essential for gaining a deeper understanding of the consequent changes in cloud radiative effects. + +In this study, we approach the evaluations of cloud profiles and associated radiative response to deforestation from two distinct viewpoints: the space- for- time substitution method and the idealized deforestation experiments available from GCM simulations. Given that the outcomes from GCMs contain both local and non- local signals, we then isolate the local signals using a chessboard- like method \(^{24,34}\) , enabling a comparative analysis between the two distinct ways. Using both methods, this work consistently indicates a global reduction in low- level clouds and a decline in high- level clouds over tropical regions in response to deforestation. In addition, we explore the potential physical mechanisms through which deforestation induces alterations in cloud profiles, suggesting that changes in turbulent heat flux could be a crucial factor. + +<--- Page Split ---> + +Finally, we quantify the impact of deforestation on cloud radiative forcing within the Earth- atmosphere system, with findings indicating that the warming effects of clouds to substantial extent counterbalance the cooling effects of surface albedo at a global scale. + +## Results + +## Cloud profile changes + +Two distinct approaches (see Methods) are employed in this study to assess the potential impact of deforestation on cloud fraction profiles. The first method draws upon five available GCMs (Table S1) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) \(^{35}\) . It entails analyzing the idealized global deforestation simulations (deforest- glob) conducted in the Land Use Model Intercomparison Project (LUMIP) \(^{36}\) , and comparing them against the pre- industrial control simulations (piControl). The second method uses the space- for- time substitution to contrast the multi- year average cloud fraction profiles between the neighboring unaltered forested and unaltered open land grids. In this approach, the potential effects of deforestation on cloud profiles are measured by land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and cloud profiles from the European Centre for Medium- Range Weather Forecasts (ECMWF) fifth reanalysis (ERA5). One significant drawback of the cloud profile data from active satellites is that they have relatively small footprint and sample sizes. As a result, data from numerous satellite passes must be averaged or combined to create a product with sufficient coverage. Given the finer spatial resolution of ERA5 cloud profiles, and their much larger coverage, in comparison to the available gridded data derived from active satellite observations, along with the strong correlation exhibited between ERA5 and the observations (Fig. S1), we employ long- term ERA5 data instead. As GCMs contain both local and no- local effects, we extract the local effects from the total signals (see Methods). Isolating local effects can aid in understanding the biophysical mechanisms of deforestation on clouds. Despite the differing + +<--- Page Split ---> + +principles behind the two methods, it is noted that the space- for- time substitution also solely considers local effects, allowing for a comparison between these two approaches. + +While Boysen, et al. \(^{28}\) outlined diverse spatial patterns in how cloud cover responds to deforestation across GCMs in LUMIP, once the local effects are isolated, they reveal consistent spatial patterns (Fig. 1a). This implies that the inconsistencies across models documented by Boysen, et al. \(^{28}\) primarily arise from discrepancies in non- local effects. Even with distinct principles, both methods demonstrate consistent spatial signals regarding cloud vertical profile responses to deforestation (Fig. 1). The results are consistent in terms of sign, albeit with different magnitudes that can be explained by the differences between the two methods. Globally, cloud cover below 700 hPa decreases in response to deforestation, showing consistency with satellite observations \(^{21- 23}\) . The decrease in tropical cloud cover is restricted to relatively low altitudes according to the ERA5 space- for- time substitution method. The response to deforestation is most pronounced in tropical low- level clouds, with additional reductions found for tropical high- level clouds (>500 hPa). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1. Changes in cloud profile due to deforestation. (a) Zonal mean of the cloud
+ +fraction profile difference between the deforest- glob and piControl simulations (deforest- glob minus piControl). The data is the ensemble mean of the local effect extracted from CMIP6 model simulations (see Methods). The stippling represents four or more of the five models showing the same sign. (b) Zonal mean ERA5 cloud fraction profile variations that deforestation would imply using the space- for- time substitution (open land minus forest; see Methods). Only latitudes possessing more than 10 data are considered to ensure representativeness. + +## Discussion of physical mechanisms of forest-cloud impacts + +Various biophysical processes are engaged in the interactions between forests and clouds, yet identifying the factors that dictate where cloud enhancement or reduction occurs across global deforested areas has remained unclear \(^{21,22,29}\) . In terms + +<--- Page Split ---> + +of the thermodynamics and moisture factors involved in cloud formation, cloud cover in certain areas might be restricted by the heating needed for uplift \(^{37}\) . In others, it might be restricted by the availability of moisture \(^{38}\) . In the following, we explore these two fundamental factors. + +In comparison to forests, open land typically exhibits higher surface albedo (Fig. S2) and lower ET (Fig. S3). Increased surface albedo from deforestation causes cooling by reflecting more shortwave radiation. This cooling effect is counterbalanced by lower ET \(^{8}\) . Both the cooling caused by the surface albedo difference and the warming due to ET difference vary across latitudes, indicating that the magnitude and even the sign of local land surface temperature (LST) changes resulting from alterations in forests differ across climate regions. When examining LST changes in deforested areas, shifting from forests to open land induces surface warming in tropical regions (Fig. S4). This is primarily due to the prevailing impact of ET on the temperature signal, although alterations in surface albedo partially counteract this surface warming. In contrast, the overall biophysical effect of deforestation leads to cooling in the boreal zone (Fig. S4). Notably, the impact of surface albedo becomes more pronounced as latitude increases, while the influence of evapotranspiration tends to diminish with higher latitudes. Hence, in boreal regions, increased surface albedo emerges as the predominant factor of surface cooling. Moreover, the reduction in incoming solar radiation and the drop in LST caused by the higher surface albedo results in a substantial decrease in sensible heat flux (SH) within the boreal zone; however, in the tropics, the decline in the surface turbulent heat flux primarily stems from the reduction in latent heat flux (LH) due to the dominant role of ET (Fig. S5 and Fig. 2). Thus, when combining the alterations in LH and SH, the decrease in surface turbulent heat flux depicted in Fig. 2 is evident globally. In conclusion, the response of cloud cover to the reduction in turbulent heat flux is illustrated through the decrease in water vapor supply due to decreased LH in the tropics and the weakening in uplifting process caused by decreased SH in the boreal regions. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2. Changes in surface turbulent heat flux due to deforestation. (a) Global pattern of the surface turbulent heat flux (latent heat (LH) + sensible heat (SH)) difference between the deforest-glob and piControl simulations (deforest-glob minus piControl). The diagonal grids indicate four or more of the five models showing the same symbol. (b) Box plots of the CMIP6 surface turbulent heat flux (LH+SH, LH and SH) differences between the deforest-glob and piControl simulations over both tropical and boreal areas. (c) ERA5 surface turbulent heat flux (LH+SH) variations due to deforestation using the space-for-time substitution (see Methods). (d) Box plots of the ERA5 surface turbulent heat flux (LH+SH, LH and SH) variations due to deforestation. The data in (a-b) is the ensemble mean of the local effect extracted from CMIP6 model simulations (see Methods). Boxes in (b and d) show the 25th to 75th percentiles of the data, whiskers display the 5th to 95th percentiles, horizontal yellow lines in the boxes represent the median values, and red dots are the mean values.
+ +## Implications for radiation and climate + +Previous studies have concentrated on alterations in surface albedo following deforestation, yet there is a lack of quantitative analysis on changes in cloud albedo + +<--- Page Split ---> + +subsequent to deforestation \(^{22}\) . Clouds on average exert a cooling effect on climate \(^{39}\) . The decrease in cloud cover with deforestation therefore implies a warming effect on climate. The increase in surface albedo resulting from deforestation, in turn, contributes to a cooler climate \(^{17}\) . Hence, clarifying the competitive relationship between these two elements is essential to the area of forest biophysical effects. + +For a complete analysis, we also examine the disturbance of the outgoing radiation at the top of the atmosphere (TOA). The perturbation of outgoing radiation under all- sky conditions reflects the combined impacts of alterations in both surface and cloud properties from deforestation. Under clear- sky conditions, the radiation perturbations solely arise from alterations in surface properties. Thus, the alterations in TOA outgoing radiation due to cloud cover changes can be obtained through the difference between all- sky and clear- sky conditions (all- sky minus clear- sky, also known as cloud radiative effect). As denoted in Fig. 3, a universal pattern prevails worldwide: alterations in surface properties largely govern the overall outgoing radiation changes, with changes in cloud cover acting as a buffer. When comparing the shortwave and longwave components (Figs. S6 and S7), however, it becomes evident that the perturbations to the climate come mainly from shortwave, further indicating that changes in surface and cloud albedo are the most main causes. From a global average standpoint, the quantitative competition between clouds and surface albedo becomes apparent. On average, from the CMIP6 idealized deforestation experiments, reduced cloud cover offsets approximately \(44\%\) of the surface albedo cooling effect; while from the space- for- time substitution method based on ERA5, the relative offset is about \(26\%\) (Fig. 3g). The disparities in numerical outcomes primarily result from methodological differences. Nonetheless, both methods lead to consistent conclusions. Given the saturation of CMIP6 latitudinal data, we proceed to examine the zonal disparities (Fig. 3h). The discernible result reveals that the compensatory impact of cloud cover compared to the surface albedo change is stable across latitudes, at roughly \(50\%\) . Considering that alterations in cloud cover following deforestation + +<--- Page Split ---> + +214 approximately counterbalance half of the cooling effect caused by changes in surface 215 albedo, neglecting the shifts in cloud- climate interactions introduces a large bias when 216 investigating the biophysical effects of forests in the future. + +![](images/Figure_3.jpg) + +
Fig. 3. Changes in outgoing radiation at the top of atmosphere (TOA) due to deforestation. (a, c, and e) Global pattern of the TOA outgoing radiation (shortwave
+ +<--- Page Split ---> + ++ longwave) difference between the deforest- glob and piControl simulations (deforest- glob minus piControl), respectively, under all- sky, clear- sky, and all- sky minus clear- sky circumstances. The diagonal grids indicate four or more of the five models showing the same symbol. (b, d, and f) ERA5 TOA outgoing radiation (shortwave + longwave) variations due to deforestation using the space- for- time substitution (see Methods). Global mean values and standard errors for (a- f) are shown in (g). The offset ratio is the proportion of all- sky minus clear- sky to the all- sky value. (h) Zonal mean of the TOA outgoing radiation (shortwave + longwave) difference between the deforest- glob and piControl simulations under both clear- sky and all- sky minus clear- sky circumstances. The black line indicates the zonal mean offset ratio and the dashed yellow line is the ratio equal to \(- 0.5\) . The CMIP6 data is the ensemble mean of the local effect extracted from multi- model simulations (see Methods). + +## Methods + +## CMIP6 simulations + +Cloud fraction profile, tree cover fraction, surface latent heat flux (LH), sensible heat flux (SH), air temperature, radiation fluxes and evapotranspiration (ET), as well as radiation fluxes at the top of atmosphere (TOA) from five available general climate models (GCMs) (Table S1) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are adopted in this study \(^{38}\) . The idealized global deforestation simulations (deforest- glob) from the Land Use Model Intercomparison Project (LUMIP) \(^{39}\) are analyzed in comparison to the pre- industrial control simulations (piControl). The deforest- glob setup assumes that a total forest area of 20 million \(\mathrm{km}^2\) is linearly removed from the top \(30\%\) forested area with a fixed rate of \(400 000 \mathrm{km}^2 \mathrm{yr}^{- 1}\) over a period of 50 years across the globe. This is then followed by at least a 30- year simulation with a constant land cover to achieve stable conditions. The last 30 years of the deforest- glob and piControl simulations are compared (deforest- glob minus piControl) to derive the mean response to deforestation \(^{28}\) . Due + +<--- Page Split ---> + +to differences in resolution among GCMs, the ensemble mean statistics are calculated by bilinear remapping of diagnostics from individual GCMs to a \(2^{\circ} \times 2^{\circ}\) grid, and vertically to 27 pressure levels from 1000 to 100 hPa. + +## Reanalysis datasets + +From the European Centre for Medium- Range Weather Forecasts (ECMWF) ERA5 \(^{40}\) , we utilize the ERA5 cloud fraction profiles data alongside elevation, surface LH, SH, air temperature, radiation fluxes, ET, and TOA radiation fluxes to examine the impacts of deforestation. Datasets spanning from 2001 to 2021, featuring a spatial resolution of \(0.25^{\circ} \times 0.25^{\circ}\) and encompassing 28 vertical pressure levels from 1000 to 100 hPa, are employed for the analysis. + +## Observed land cover + +For delineating forested and open land areas, we use land cover data from the Moderate resolution imaging spectroradiometer (MODIS) dataset (MCD12C1, version 6.1) \(^{41}\) , relying on the International Geosphere- Biosphere Program (IGBP) classification layer to define the land cover types. Annual data for the years 2001–2021 with a spatial resolution of \(0.05^{\circ} \times 0.05^{\circ}\) are adopted. Here, five forest types (evergreen needleleaf forest, evergreen broadleaf forest, deciduous needleleaf forest, deciduous broadleaf forest and mixed forest) are merged into a single forest classification. The forest fraction is bilinearly gridded spatially into \(0.25^{\circ} \times 0.25^{\circ}\) to align with the ERA5 data. + +## Observed cloud profile + +In assessing the accuracy of ERA5 cloud profiles, we analyse active satellite- observed cloud profiles. The cloud profile retrievals from Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat between 2007 and 2010, are aggregated to a spatial resolution of \(2^{\circ} \times 2^{\circ}\) and a vertical resolution of 480 meters \(^{42}\) . The fusion of data from both sensors facilitates an extensive depiction of the vertical cloud structure. This comprehensive view is achieved by leveraging the + +<--- Page Split ---> + +275 distinct wavelengths each sensor employs (CloudSat: approximately \(2\mathrm{mm}\) , CALIPSO: 276 532 nm and \(1064\mathrm{nm}\) ), catering to various cloud and precipitation particles in both 277 liquid and solid phases. + +## Climate zones + +In this study, climate zones are defined according to the global maps of the Köppen- Geiger climate classification (Version 1) \(^{43}\) . The Köppen- Geiger historical map contains 30 climate zones at a resolution of \(1\mathrm{km}\) . Tropical and boreal regions are each merged from corresponding subdivided climate zones. + +## Extracting local effect from GCMs + +Deforestation exerts a local impact on the climate within deforested areas (local effect) by modifying land surface characteristics such as albedo, roughness, and ET. Additionally, it affects both deforested and open land grids by altering the advection of heat and moisture, as well as influencing atmospheric circulation (non- local effect) \(^{44}\) . Distinguishing between local and non- local effects within GCMs is crucial as coupled models encompass the complete climate response to deforestation, incorporating both local and non- local impacts. Moreover, it allows to develop a more profound insight into the mechanisms influencing the local effects in comparison to those governing the non- local effects. + +Here, we use a chessboard method as outlined by Winckler, et al. \(^{34}\) to assess the local effect. This method assumes that the unaltered and adjacent deforested grids share the same non- local effect \(^{21,44}\) . To generate a global map of the non- local effect, we spatially interpolate the non- local signal to the adjacent deforested regions, maintaining the original values over the unaltered grids unchanged. The local effect over the deforested grids thus can be derived by subtracting the interpolated non- local effect from the total effect. Notably, employing a chessboard- like method introduces horizontal interpolation errors, given that the local effect relies solely on interpolation from neighboring, unaltered grids. However, our study is centered on idealized deforestation scenarios and prior a study \(^{24}\) has demonstrated the possibility of + +<--- Page Split ---> + +isolating local effects using similar methodologies and datasets. Winckler, et al. \(^{34}\) conducted comparisons between simulations involving both sparse and extensive idealized deforestation, finding small differences in derived local effects from spatial interpolation. + +## Space-for-time substitution + +In addition to idealized deforestation simulations, this study employs a space- for- time substitution method to assess the impacts of deforestation combining MODIS land cover and ERA5 reanalysis datasets. Such an approach has previously been applied in various studies to evaluate the effect of alterations in land cover on temperature \(^{8,26}\) , the surface energy budget \(^{5,45}\) , or cloud cover \(^{21,22}\) . The fundamental premise of this method is that neighboring land patches share the same climatic background and variations in their characteristics can act as a proxy for temporal changes. This method exclusively includes the local effects, making it well- suited for assessing the isolated local effects derived from GCMs. + +Areas designated as unaltered forested (or unaltered open land) are identified as pixels where the initial (in 2001) tree cover fraction exceeds \(60\%\) (or is below \(40\%\) ) and with a net change in forest cover \(< 10\%\) from 2001 to 2021. Pixels with water coverage \(>10\%\) are excluded. We use a moving window approach to search for comparison samples between unaltered forested and unaltered open land pixels. We choose for each moving window a size of \(7 \times 7\) pixels \((1.75^{\circ} \times 1.75^{\circ})\) . To reduce the influence of topography, we calculate the standard deviation (s.d.) of elevation within specific moving windows and omit samples where this s.d. exceeds \(100 \mathrm{~m}\) following Xu, et al. \(^{21}\) . Finally, the potential effect of deforestation on a specific variable ( \(\Delta \mathrm{Var}\) ) is quantified as: + +\[\Delta \mathrm{Var} = \mathrm{Var}_{\mathrm{open~land}} - \mathrm{Var}_{\mathrm{surrounding~forests}} \quad (1)\] + +or + +\[\Delta \mathrm{Var} = \mathrm{Var}_{\mathrm{surrounding~open~lands}} - \mathrm{Var}_{\mathrm{forest}} \quad (2)\] + +<--- Page Split ---> + +where equations (1) and (2) are applicable to the case where the central pixel of the moving window is unaltered open land and unaltered forest, respectively. \(\mathrm{Var}_{\mathrm{open~land}}\) and \(\mathrm{Var}_{\mathrm{forest}}\) are multi- year mean variables over unaltered open land and unaltered forest pixels, respectively. \(\overline{\mathrm{Var}_{\mathrm{surrounding~forests}}}\) and \(\overline{\mathrm{Var}_{\mathrm{surrounding~open~lands}}}\) are the average values of the surrounding \(\mathrm{Var}_{\mathrm{forest}}\) and \(\mathrm{Var}_{\mathrm{open~land}}\) within a moving window when the central pixel is unaltered open land and unaltered forest, respectively. + +## Data availability + +The data that support the findings of this study are publicly available. The CMIP6 data are taken from https://esgf- data.dkrz.de/search/cmip6- dkrz/. The ERA5 cloud fraction profile data are obtained from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis- era5- pressure- levels- monthly- means?tab=overview. Other ERA5 datasets are available from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis- era5- single- levels- monthly- means?tab=overview. MODIS land cover data are obtained from https://lpdaac.usgs.gov/products/mcd12c1v061/. CALIPSO- CloudSat cloud profile data are taken from https://www.cen.uni- hamburg.de/en/icdc/data/atmosphere/calipso- cloudsat- cloudcover.html. The Köppen- Geiger historical map is available from https://figshare.com/articles/dataset/Present_and_future_K_ppen- Geiger_climate_classification_maps_at_1- km_resolution/6396959/2. + +## Code availability + +The code used in the work can be obtained upon request from the corresponding author. + +## Acknowledgments + +This research has been supported by the National Natural Science Foundation of China (grant nos. 42027804, 41775026, 41075012). + +## Author contributions + +<--- Page Split ---> + +356 J.Q., H.L. and Y.H. designed the research. H.L. performed the research and drafted the paper. H.L., J.Q. and Y.H. contributed to analysis and interpretation of the results, as well as revising the paper. + +## 359 Competing interests + +360 The authors declare no competing interests. + +## 361 References + +362 1 Bonan, G. B. Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. 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Present and future Köppen- Geiger climate classification 486 maps at 1- km resolution. Scientific Data 5, 180214, 487 doi:10.1038/sdata.2018.214 (2018). 488 44 Pongratz, J. et al. Land Use Effects on Climate: Current State, Recent Progress, 489 and Emerging Topics. Current Climate Change Reports 7, 99- 120, 490 doi:10.1007/s40641- 021- 00178- y (2021). 491 45 Liu, Z., Ballantyne, A. P. & Cooper, L. A. Biophysical feedback of global 492 forest fires on surface temperature. Nature Communications 10, 214, 493 doi:10.1038/s41467- 018- 08237- z (2019). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- forestcloudSupplementary.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422_det.mmd b/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d3ff89c375d5509f4f68ea069de071d3213a5434 --- /dev/null +++ b/preprint/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422/preprint__0ab7b0bb9760a2d34a3e8c55df78035e6e4b32c3b6a2028b02979e3615445422_det.mmd @@ -0,0 +1,345 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 928, 207]]<|/det|> +# Decreased cloud cover partially offsets the cooling effects of surface albedo change due to deforestation + +<|ref|>text<|/ref|><|det|>[[44, 230, 120, 247]]<|/det|> +Hao Luo + +<|ref|>text<|/ref|><|det|>[[55, 257, 323, 274]]<|/det|> +luoh93@mail2. sysu.edu.cn + +<|ref|>text<|/ref|><|det|>[[44, 303, 610, 415]]<|/det|> +Sun Yat- sen University https://orcid.org/0000- 0002- 6648- 4234 Johannes Quaas Universitaet Leipzig https://orcid.org/0000- 0001- 7057- 194X Yong Han Sun Yat- sen University https://orcid.org/0000- 0002- 3297- 2782 + +<|ref|>sub_title<|/ref|><|det|>[[44, 456, 103, 473]]<|/det|> +## Article + +<|ref|>title<|/ref|><|det|>[[44, 494, 135, 512]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 531, 295, 550]]<|/det|> +Posted Date: July 23rd, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 570, 474, 588]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4019501/v1 + +<|ref|>text<|/ref|><|det|>[[42, 607, 914, 650]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 668, 533, 687]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 724, 935, 767]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on August 26th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 51783- y. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[217, 95, 821, 115]]<|/det|> +# Decreased cloud cover partially offsets the cooling effects of + +<|ref|>title<|/ref|><|det|>[[281, 133, 714, 152]]<|/det|> +# surface albedo change due to deforestation + +<|ref|>text<|/ref|><|det|>[[312, 165, 682, 184]]<|/det|> +Hao Luo \(^{1,2*}\) , Johannes Quaas \(^{2,3}\) , Yong Han \(^{1,4*}\) + +<|ref|>text<|/ref|><|det|>[[150, 195, 850, 214]]<|/det|> +\(^{1}\) Advanced Science & Technology of Space and Atmospheric Physics Group (ASAG), + +<|ref|>text<|/ref|><|det|>[[150, 225, 800, 243]]<|/det|> +\(^{5}\) School of Atmospheric Sciences, Sun Yat- sen University, 519082 Zhuhai, China + +<|ref|>text<|/ref|><|det|>[[150, 255, 800, 273]]<|/det|> +\(^{2}\) Leipzig Institute for Meteorology, Leipzig University, 04103 Leipzig, Germany + +<|ref|>text<|/ref|><|det|>[[150, 284, 848, 302]]<|/det|> +\(^{3}\) German Centre for Integrative Biodiversity Research (iDiv) Halle- Jena- Leipzig, + +<|ref|>text<|/ref|><|det|>[[150, 314, 355, 331]]<|/det|> +\(^{4}\) 04103 Leipzig, Germany + +<|ref|>text<|/ref|><|det|>[[150, 342, 848, 361]]<|/det|> +\(^{4}\) Key Laboratory of Tropical Atmosphere- Ocean System (Sun Yat- sen University), + +<|ref|>text<|/ref|><|det|>[[150, 372, 521, 389]]<|/det|> +\(^{10}\) Ministry of Education, 519082 Zhuhai, China + +<|ref|>text<|/ref|><|det|>[[150, 401, 848, 420]]<|/det|> +\(^{*}\) Corresponding author(s). Email(s): luoh93@mail2.sysu.edu.cn (Hao Luo); + +<|ref|>text<|/ref|><|det|>[[150, 430, 464, 448]]<|/det|> +hany66@mail.sysu.edu.cn (Yong Han) + +<|ref|>sub_title<|/ref|><|det|>[[150, 464, 227, 480]]<|/det|> +## 13 Abstract + +<|ref|>text<|/ref|><|det|>[[147, 496, 852, 893]]<|/det|> +Biophysical processes of forests affect climate through the regulation of surface water and heat fluxes, which leads to further effects through the adjustment of clouds and water cycles. These indirect biophysical effects of forests on clouds and their radiative forcing are poorly understood but highly relevant in the context of large- scale deforestation or afforestation, respectively. Here, we provide evidence for local decreases in global low- level clouds and tropical high- level clouds from deforestation through both idealized deforestation simulations with climate models and from observations- driven reanalysis using space- for- time substitution. The decreased cloud cover can be explained by alterations in surface turbulent heat flux, which diminishes uplift and moisture to varying extents. Deforestation- induced reduction in cloud cover warms the climate, partially counteracting the cooling effects of increased surface albedo. The findings from idealized deforestation experiments and space- for- time substitution exhibit disparities, with global average offsets of, respectively, approximately \(44\%\) and \(26\%\) , suggesting the necessity for further constraints. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 96, 260, 112]]<|/det|> +## 28 Introduction + +<|ref|>text<|/ref|><|det|>[[144, 125, 852, 760]]<|/det|> +Forests have the capacity to buffer global warming by storing large amounts of carbon from the atmosphere via photosynthesis \(^{1 - 3}\) . Alongside the biochemical effects, forests can influence the local and regional climate through biophysical processes, including alterations in land surface water and energy balance \(^{4 - 7}\) . On the local scale, the higher albedo and lower evapotranspiration (ET) following deforestation cause either surface cooling or warming, depending on which process holds dominance \(^{8 - 10}\) . These cooling or warming impacts have the potential to offset or intensify, respectively, the warming effects connected to the released carbon caused by deforestation \(^{11 - 16}\) . Extensive studies on the direct biophysical effects of deforestation on surface temperature have unveiled a latitudinal shift from tropical warming to boreal cooling \(^{8,9,17 - 19}\) . Nevertheless, globally, alterations in surface albedo are more prevalent in the direct biophysical temperature response than ET because of its wider- scale impact \(^{17}\) . This suggests that the global warming attributed to the biochemical effects of deforestation could potentially be mitigated by the cooling effects resulting from increased surface albedo and consequently altered radiative balance \(^{12,14,16}\) . Yet, the impact of forest indirect biophysical processes on clouds and their associated radiative balance has not been well addressed, and the assessment of how changes in cloud radiative effects interact with the surface albedo effects remains unquantified. Understanding the response of clouds and their radiative effects to deforestation, however, is crucial due to the overwhelming effect clouds play for the Earth energy budget. It stands as a major challenge in evaluating land- use- change- driven climate change \(^{20 - 24}\) . + +<|ref|>text<|/ref|><|det|>[[148, 768, 851, 902]]<|/det|> +Observational studies allow for the conclusion that deforestation may predominantly reduce global cloud cover \(^{22,23,25}\) , but with contrasting impacts across various regions \(^{21}\) . These studies mostly compare clouds above forests and open land in adjacent geographical units (i.e. space- for- time substitution) and find larger cloudiness over forests. This commonly adopted method assumes that forests and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 88, 852, 343]]<|/det|> +neighboring land units share the same climate background, thereby deducing local effects through distinctions in land surface conditions. Apart from observations- based studies, general circulation models (GCMs) have been widely employed to quantify the impacts of deforestation \(^{26 - 28}\) . GCMs show a global average enhancement in cloud cover with deforestation \(^{29}\) . Unlike the observational studies that concentrate solely on local effects, GCMs probably possess the ability to encompass both local and non- local effects of deforestation. Hence, separating local and non- local effects could facilitate comparisons between these two distinct methods and enhance comprehension of the biophysical mechanisms of deforestation on clouds \(^{24}\) . + +<|ref|>text<|/ref|><|det|>[[144, 351, 852, 602]]<|/det|> +Given the essential roles of cloud vertical structures in influencing radiative processes \(^{30,31}\) , a sole concentration on overall cloud cover may be insufficient for a comprehensive analysis of the changes in cloud radiative effects from deforestation. Typically, low, highly reflective clouds have a cooling effect as they reflect solar radiation. In contrast, high, semi- transparent clouds contribute to warming by allowing shortwave radiation to pass through while impeding longwave radiation \(^{32,33}\) . The alterations in cloud vertical profiles following deforestation have not received adequate attention, and addressing this gap is essential for gaining a deeper understanding of the consequent changes in cloud radiative effects. + +<|ref|>text<|/ref|><|det|>[[144, 611, 852, 893]]<|/det|> +In this study, we approach the evaluations of cloud profiles and associated radiative response to deforestation from two distinct viewpoints: the space- for- time substitution method and the idealized deforestation experiments available from GCM simulations. Given that the outcomes from GCMs contain both local and non- local signals, we then isolate the local signals using a chessboard- like method \(^{24,34}\) , enabling a comparative analysis between the two distinct ways. Using both methods, this work consistently indicates a global reduction in low- level clouds and a decline in high- level clouds over tropical regions in response to deforestation. In addition, we explore the potential physical mechanisms through which deforestation induces alterations in cloud profiles, suggesting that changes in turbulent heat flux could be a crucial factor. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 88, 852, 197]]<|/det|> +Finally, we quantify the impact of deforestation on cloud radiative forcing within the Earth- atmosphere system, with findings indicating that the warming effects of clouds to substantial extent counterbalance the cooling effects of surface albedo at a global scale. + +<|ref|>sub_title<|/ref|><|det|>[[101, 212, 214, 229]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[101, 250, 339, 268]]<|/det|> +## Cloud profile changes + +<|ref|>text<|/ref|><|det|>[[140, 279, 852, 917]]<|/det|> +Two distinct approaches (see Methods) are employed in this study to assess the potential impact of deforestation on cloud fraction profiles. The first method draws upon five available GCMs (Table S1) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) \(^{35}\) . It entails analyzing the idealized global deforestation simulations (deforest- glob) conducted in the Land Use Model Intercomparison Project (LUMIP) \(^{36}\) , and comparing them against the pre- industrial control simulations (piControl). The second method uses the space- for- time substitution to contrast the multi- year average cloud fraction profiles between the neighboring unaltered forested and unaltered open land grids. In this approach, the potential effects of deforestation on cloud profiles are measured by land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and cloud profiles from the European Centre for Medium- Range Weather Forecasts (ECMWF) fifth reanalysis (ERA5). One significant drawback of the cloud profile data from active satellites is that they have relatively small footprint and sample sizes. As a result, data from numerous satellite passes must be averaged or combined to create a product with sufficient coverage. Given the finer spatial resolution of ERA5 cloud profiles, and their much larger coverage, in comparison to the available gridded data derived from active satellite observations, along with the strong correlation exhibited between ERA5 and the observations (Fig. S1), we employ long- term ERA5 data instead. As GCMs contain both local and no- local effects, we extract the local effects from the total signals (see Methods). Isolating local effects can aid in understanding the biophysical mechanisms of deforestation on clouds. Despite the differing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 89, 850, 166]]<|/det|> +principles behind the two methods, it is noted that the space- for- time substitution also solely considers local effects, allowing for a comparison between these two approaches. + +<|ref|>text<|/ref|><|det|>[[144, 175, 852, 545]]<|/det|> +While Boysen, et al. \(^{28}\) outlined diverse spatial patterns in how cloud cover responds to deforestation across GCMs in LUMIP, once the local effects are isolated, they reveal consistent spatial patterns (Fig. 1a). This implies that the inconsistencies across models documented by Boysen, et al. \(^{28}\) primarily arise from discrepancies in non- local effects. Even with distinct principles, both methods demonstrate consistent spatial signals regarding cloud vertical profile responses to deforestation (Fig. 1). The results are consistent in terms of sign, albeit with different magnitudes that can be explained by the differences between the two methods. Globally, cloud cover below 700 hPa decreases in response to deforestation, showing consistency with satellite observations \(^{21- 23}\) . The decrease in tropical cloud cover is restricted to relatively low altitudes according to the ERA5 space- for- time substitution method. The response to deforestation is most pronounced in tropical low- level clouds, with additional reductions found for tropical high- level clouds (>500 hPa). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[155, 90, 805, 540]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 556, 852, 575]]<|/det|> +
Fig. 1. Changes in cloud profile due to deforestation. (a) Zonal mean of the cloud
+ +<|ref|>text<|/ref|><|det|>[[144, 584, 852, 777]]<|/det|> +fraction profile difference between the deforest- glob and piControl simulations (deforest- glob minus piControl). The data is the ensemble mean of the local effect extracted from CMIP6 model simulations (see Methods). The stippling represents four or more of the five models showing the same sign. (b) Zonal mean ERA5 cloud fraction profile variations that deforestation would imply using the space- for- time substitution (open land minus forest; see Methods). Only latitudes possessing more than 10 data are considered to ensure representativeness. + +<|ref|>sub_title<|/ref|><|det|>[[145, 792, 652, 811]]<|/det|> +## Discussion of physical mechanisms of forest-cloud impacts + +<|ref|>text<|/ref|><|det|>[[144, 825, 852, 902]]<|/det|> +Various biophysical processes are engaged in the interactions between forests and clouds, yet identifying the factors that dictate where cloud enhancement or reduction occurs across global deforested areas has remained unclear \(^{21,22,29}\) . In terms + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 89, 851, 197]]<|/det|> +of the thermodynamics and moisture factors involved in cloud formation, cloud cover in certain areas might be restricted by the heating needed for uplift \(^{37}\) . In others, it might be restricted by the availability of moisture \(^{38}\) . In the following, we explore these two fundamental factors. + +<|ref|>text<|/ref|><|det|>[[144, 203, 852, 896]]<|/det|> +In comparison to forests, open land typically exhibits higher surface albedo (Fig. S2) and lower ET (Fig. S3). Increased surface albedo from deforestation causes cooling by reflecting more shortwave radiation. This cooling effect is counterbalanced by lower ET \(^{8}\) . Both the cooling caused by the surface albedo difference and the warming due to ET difference vary across latitudes, indicating that the magnitude and even the sign of local land surface temperature (LST) changes resulting from alterations in forests differ across climate regions. When examining LST changes in deforested areas, shifting from forests to open land induces surface warming in tropical regions (Fig. S4). This is primarily due to the prevailing impact of ET on the temperature signal, although alterations in surface albedo partially counteract this surface warming. In contrast, the overall biophysical effect of deforestation leads to cooling in the boreal zone (Fig. S4). Notably, the impact of surface albedo becomes more pronounced as latitude increases, while the influence of evapotranspiration tends to diminish with higher latitudes. Hence, in boreal regions, increased surface albedo emerges as the predominant factor of surface cooling. Moreover, the reduction in incoming solar radiation and the drop in LST caused by the higher surface albedo results in a substantial decrease in sensible heat flux (SH) within the boreal zone; however, in the tropics, the decline in the surface turbulent heat flux primarily stems from the reduction in latent heat flux (LH) due to the dominant role of ET (Fig. S5 and Fig. 2). Thus, when combining the alterations in LH and SH, the decrease in surface turbulent heat flux depicted in Fig. 2 is evident globally. In conclusion, the response of cloud cover to the reduction in turbulent heat flux is illustrated through the decrease in water vapor supply due to decreased LH in the tropics and the weakening in uplifting process caused by decreased SH in the boreal regions. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[152, 85, 844, 430]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 439, 852, 808]]<|/det|> +
Fig. 2. Changes in surface turbulent heat flux due to deforestation. (a) Global pattern of the surface turbulent heat flux (latent heat (LH) + sensible heat (SH)) difference between the deforest-glob and piControl simulations (deforest-glob minus piControl). The diagonal grids indicate four or more of the five models showing the same symbol. (b) Box plots of the CMIP6 surface turbulent heat flux (LH+SH, LH and SH) differences between the deforest-glob and piControl simulations over both tropical and boreal areas. (c) ERA5 surface turbulent heat flux (LH+SH) variations due to deforestation using the space-for-time substitution (see Methods). (d) Box plots of the ERA5 surface turbulent heat flux (LH+SH, LH and SH) variations due to deforestation. The data in (a-b) is the ensemble mean of the local effect extracted from CMIP6 model simulations (see Methods). Boxes in (b and d) show the 25th to 75th percentiles of the data, whiskers display the 5th to 95th percentiles, horizontal yellow lines in the boxes represent the median values, and red dots are the mean values.
+ +<|ref|>sub_title<|/ref|><|det|>[[147, 822, 479, 840]]<|/det|> +## Implications for radiation and climate + +<|ref|>text<|/ref|><|det|>[[147, 855, 850, 902]]<|/det|> +Previous studies have concentrated on alterations in surface albedo following deforestation, yet there is a lack of quantitative analysis on changes in cloud albedo + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 851, 225]]<|/det|> +subsequent to deforestation \(^{22}\) . Clouds on average exert a cooling effect on climate \(^{39}\) . The decrease in cloud cover with deforestation therefore implies a warming effect on climate. The increase in surface albedo resulting from deforestation, in turn, contributes to a cooler climate \(^{17}\) . Hence, clarifying the competitive relationship between these two elements is essential to the area of forest biophysical effects. + +<|ref|>text<|/ref|><|det|>[[144, 234, 853, 897]]<|/det|> +For a complete analysis, we also examine the disturbance of the outgoing radiation at the top of the atmosphere (TOA). The perturbation of outgoing radiation under all- sky conditions reflects the combined impacts of alterations in both surface and cloud properties from deforestation. Under clear- sky conditions, the radiation perturbations solely arise from alterations in surface properties. Thus, the alterations in TOA outgoing radiation due to cloud cover changes can be obtained through the difference between all- sky and clear- sky conditions (all- sky minus clear- sky, also known as cloud radiative effect). As denoted in Fig. 3, a universal pattern prevails worldwide: alterations in surface properties largely govern the overall outgoing radiation changes, with changes in cloud cover acting as a buffer. When comparing the shortwave and longwave components (Figs. S6 and S7), however, it becomes evident that the perturbations to the climate come mainly from shortwave, further indicating that changes in surface and cloud albedo are the most main causes. From a global average standpoint, the quantitative competition between clouds and surface albedo becomes apparent. On average, from the CMIP6 idealized deforestation experiments, reduced cloud cover offsets approximately \(44\%\) of the surface albedo cooling effect; while from the space- for- time substitution method based on ERA5, the relative offset is about \(26\%\) (Fig. 3g). The disparities in numerical outcomes primarily result from methodological differences. Nonetheless, both methods lead to consistent conclusions. Given the saturation of CMIP6 latitudinal data, we proceed to examine the zonal disparities (Fig. 3h). The discernible result reveals that the compensatory impact of cloud cover compared to the surface albedo change is stable across latitudes, at roughly \(50\%\) . Considering that alterations in cloud cover following deforestation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[92, 88, 852, 167]]<|/det|> +214 approximately counterbalance half of the cooling effect caused by changes in surface 215 albedo, neglecting the shifts in cloud- climate interactions introduces a large bias when 216 investigating the biophysical effects of forests in the future. + +<|ref|>image<|/ref|><|det|>[[160, 180, 820, 840]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[89, 855, 852, 904]]<|/det|> +
Fig. 3. Changes in outgoing radiation at the top of atmosphere (TOA) due to deforestation. (a, c, and e) Global pattern of the TOA outgoing radiation (shortwave
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 458]]<|/det|> ++ longwave) difference between the deforest- glob and piControl simulations (deforest- glob minus piControl), respectively, under all- sky, clear- sky, and all- sky minus clear- sky circumstances. The diagonal grids indicate four or more of the five models showing the same symbol. (b, d, and f) ERA5 TOA outgoing radiation (shortwave + longwave) variations due to deforestation using the space- for- time substitution (see Methods). Global mean values and standard errors for (a- f) are shown in (g). The offset ratio is the proportion of all- sky minus clear- sky to the all- sky value. (h) Zonal mean of the TOA outgoing radiation (shortwave + longwave) difference between the deforest- glob and piControl simulations under both clear- sky and all- sky minus clear- sky circumstances. The black line indicates the zonal mean offset ratio and the dashed yellow line is the ratio equal to \(- 0.5\) . The CMIP6 data is the ensemble mean of the local effect extracted from multi- model simulations (see Methods). + +<|ref|>sub_title<|/ref|><|det|>[[147, 473, 226, 490]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[147, 511, 318, 529]]<|/det|> +## CMIP6 simulations + +<|ref|>text<|/ref|><|det|>[[144, 544, 852, 912]]<|/det|> +Cloud fraction profile, tree cover fraction, surface latent heat flux (LH), sensible heat flux (SH), air temperature, radiation fluxes and evapotranspiration (ET), as well as radiation fluxes at the top of atmosphere (TOA) from five available general climate models (GCMs) (Table S1) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are adopted in this study \(^{38}\) . The idealized global deforestation simulations (deforest- glob) from the Land Use Model Intercomparison Project (LUMIP) \(^{39}\) are analyzed in comparison to the pre- industrial control simulations (piControl). The deforest- glob setup assumes that a total forest area of 20 million \(\mathrm{km}^2\) is linearly removed from the top \(30\%\) forested area with a fixed rate of \(400 000 \mathrm{km}^2 \mathrm{yr}^{- 1}\) over a period of 50 years across the globe. This is then followed by at least a 30- year simulation with a constant land cover to achieve stable conditions. The last 30 years of the deforest- glob and piControl simulations are compared (deforest- glob minus piControl) to derive the mean response to deforestation \(^{28}\) . Due + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 90, 850, 165]]<|/det|> +to differences in resolution among GCMs, the ensemble mean statistics are calculated by bilinear remapping of diagnostics from individual GCMs to a \(2^{\circ} \times 2^{\circ}\) grid, and vertically to 27 pressure levels from 1000 to 100 hPa. + +<|ref|>sub_title<|/ref|><|det|>[[148, 181, 318, 199]]<|/det|> +## Reanalysis datasets + +<|ref|>text<|/ref|><|det|>[[144, 215, 852, 380]]<|/det|> +From the European Centre for Medium- Range Weather Forecasts (ECMWF) ERA5 \(^{40}\) , we utilize the ERA5 cloud fraction profiles data alongside elevation, surface LH, SH, air temperature, radiation fluxes, ET, and TOA radiation fluxes to examine the impacts of deforestation. Datasets spanning from 2001 to 2021, featuring a spatial resolution of \(0.25^{\circ} \times 0.25^{\circ}\) and encompassing 28 vertical pressure levels from 1000 to 100 hPa, are employed for the analysis. + +<|ref|>sub_title<|/ref|><|det|>[[148, 395, 330, 412]]<|/det|> +## Observed land cover + +<|ref|>text<|/ref|><|det|>[[144, 428, 852, 679]]<|/det|> +For delineating forested and open land areas, we use land cover data from the Moderate resolution imaging spectroradiometer (MODIS) dataset (MCD12C1, version 6.1) \(^{41}\) , relying on the International Geosphere- Biosphere Program (IGBP) classification layer to define the land cover types. Annual data for the years 2001–2021 with a spatial resolution of \(0.05^{\circ} \times 0.05^{\circ}\) are adopted. Here, five forest types (evergreen needleleaf forest, evergreen broadleaf forest, deciduous needleleaf forest, deciduous broadleaf forest and mixed forest) are merged into a single forest classification. The forest fraction is bilinearly gridded spatially into \(0.25^{\circ} \times 0.25^{\circ}\) to align with the ERA5 data. + +<|ref|>sub_title<|/ref|><|det|>[[148, 694, 349, 712]]<|/det|> +## Observed cloud profile + +<|ref|>text<|/ref|><|det|>[[144, 728, 852, 893]]<|/det|> +In assessing the accuracy of ERA5 cloud profiles, we analyse active satellite- observed cloud profiles. The cloud profile retrievals from Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat between 2007 and 2010, are aggregated to a spatial resolution of \(2^{\circ} \times 2^{\circ}\) and a vertical resolution of 480 meters \(^{42}\) . The fusion of data from both sensors facilitates an extensive depiction of the vertical cloud structure. This comprehensive view is achieved by leveraging the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 90, 854, 166]]<|/det|> +275 distinct wavelengths each sensor employs (CloudSat: approximately \(2\mathrm{mm}\) , CALIPSO: 276 532 nm and \(1064\mathrm{nm}\) ), catering to various cloud and precipitation particles in both 277 liquid and solid phases. + +<|ref|>sub_title<|/ref|><|det|>[[148, 182, 271, 199]]<|/det|> +## Climate zones + +<|ref|>text<|/ref|><|det|>[[147, 215, 851, 321]]<|/det|> +In this study, climate zones are defined according to the global maps of the Köppen- Geiger climate classification (Version 1) \(^{43}\) . The Köppen- Geiger historical map contains 30 climate zones at a resolution of \(1\mathrm{km}\) . Tropical and boreal regions are each merged from corresponding subdivided climate zones. + +<|ref|>sub_title<|/ref|><|det|>[[147, 336, 450, 355]]<|/det|> +## Extracting local effect from GCMs + +<|ref|>text<|/ref|><|det|>[[145, 370, 852, 620]]<|/det|> +Deforestation exerts a local impact on the climate within deforested areas (local effect) by modifying land surface characteristics such as albedo, roughness, and ET. Additionally, it affects both deforested and open land grids by altering the advection of heat and moisture, as well as influencing atmospheric circulation (non- local effect) \(^{44}\) . Distinguishing between local and non- local effects within GCMs is crucial as coupled models encompass the complete climate response to deforestation, incorporating both local and non- local impacts. Moreover, it allows to develop a more profound insight into the mechanisms influencing the local effects in comparison to those governing the non- local effects. + +<|ref|>text<|/ref|><|det|>[[144, 631, 852, 912]]<|/det|> +Here, we use a chessboard method as outlined by Winckler, et al. \(^{34}\) to assess the local effect. This method assumes that the unaltered and adjacent deforested grids share the same non- local effect \(^{21,44}\) . To generate a global map of the non- local effect, we spatially interpolate the non- local signal to the adjacent deforested regions, maintaining the original values over the unaltered grids unchanged. The local effect over the deforested grids thus can be derived by subtracting the interpolated non- local effect from the total effect. Notably, employing a chessboard- like method introduces horizontal interpolation errors, given that the local effect relies solely on interpolation from neighboring, unaltered grids. However, our study is centered on idealized deforestation scenarios and prior a study \(^{24}\) has demonstrated the possibility of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 851, 197]]<|/det|> +isolating local effects using similar methodologies and datasets. Winckler, et al. \(^{34}\) conducted comparisons between simulations involving both sparse and extensive idealized deforestation, finding small differences in derived local effects from spatial interpolation. + +<|ref|>sub_title<|/ref|><|det|>[[148, 211, 385, 229]]<|/det|> +## Space-for-time substitution + +<|ref|>text<|/ref|><|det|>[[144, 243, 852, 496]]<|/det|> +In addition to idealized deforestation simulations, this study employs a space- for- time substitution method to assess the impacts of deforestation combining MODIS land cover and ERA5 reanalysis datasets. Such an approach has previously been applied in various studies to evaluate the effect of alterations in land cover on temperature \(^{8,26}\) , the surface energy budget \(^{5,45}\) , or cloud cover \(^{21,22}\) . The fundamental premise of this method is that neighboring land patches share the same climatic background and variations in their characteristics can act as a proxy for temporal changes. This method exclusively includes the local effects, making it well- suited for assessing the isolated local effects derived from GCMs. + +<|ref|>text<|/ref|><|det|>[[144, 505, 852, 787]]<|/det|> +Areas designated as unaltered forested (or unaltered open land) are identified as pixels where the initial (in 2001) tree cover fraction exceeds \(60\%\) (or is below \(40\%\) ) and with a net change in forest cover \(< 10\%\) from 2001 to 2021. Pixels with water coverage \(>10\%\) are excluded. We use a moving window approach to search for comparison samples between unaltered forested and unaltered open land pixels. We choose for each moving window a size of \(7 \times 7\) pixels \((1.75^{\circ} \times 1.75^{\circ})\) . To reduce the influence of topography, we calculate the standard deviation (s.d.) of elevation within specific moving windows and omit samples where this s.d. exceeds \(100 \mathrm{~m}\) following Xu, et al. \(^{21}\) . Finally, the potential effect of deforestation on a specific variable ( \(\Delta \mathrm{Var}\) ) is quantified as: + +<|ref|>equation<|/ref|><|det|>[[343, 797, 847, 821]]<|/det|> +\[\Delta \mathrm{Var} = \mathrm{Var}_{\mathrm{open~land}} - \mathrm{Var}_{\mathrm{surrounding~forests}} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[144, 839, 170, 853]]<|/det|> +or + +<|ref|>equation<|/ref|><|det|>[[343, 864, 847, 888]]<|/det|> +\[\Delta \mathrm{Var} = \mathrm{Var}_{\mathrm{surrounding~open~lands}} - \mathrm{Var}_{\mathrm{forest}} \quad (2)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 852, 293]]<|/det|> +where equations (1) and (2) are applicable to the case where the central pixel of the moving window is unaltered open land and unaltered forest, respectively. \(\mathrm{Var}_{\mathrm{open~land}}\) and \(\mathrm{Var}_{\mathrm{forest}}\) are multi- year mean variables over unaltered open land and unaltered forest pixels, respectively. \(\overline{\mathrm{Var}_{\mathrm{surrounding~forests}}}\) and \(\overline{\mathrm{Var}_{\mathrm{surrounding~open~lands}}}\) are the average values of the surrounding \(\mathrm{Var}_{\mathrm{forest}}\) and \(\mathrm{Var}_{\mathrm{open~land}}\) within a moving window when the central pixel is unaltered open land and unaltered forest, respectively. + +<|ref|>sub_title<|/ref|><|det|>[[147, 308, 294, 325]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[144, 340, 852, 682]]<|/det|> +The data that support the findings of this study are publicly available. The CMIP6 data are taken from https://esgf- data.dkrz.de/search/cmip6- dkrz/. The ERA5 cloud fraction profile data are obtained from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis- era5- pressure- levels- monthly- means?tab=overview. Other ERA5 datasets are available from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis- era5- single- levels- monthly- means?tab=overview. MODIS land cover data are obtained from https://lpdaac.usgs.gov/products/mcd12c1v061/. CALIPSO- CloudSat cloud profile data are taken from https://www.cen.uni- hamburg.de/en/icdc/data/atmosphere/calipso- cloudsat- cloudcover.html. The Köppen- Geiger historical map is available from https://figshare.com/articles/dataset/Present_and_future_K_ppen- Geiger_climate_classification_maps_at_1- km_resolution/6396959/2. + +<|ref|>sub_title<|/ref|><|det|>[[147, 695, 297, 712]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[147, 728, 850, 776]]<|/det|> +The code used in the work can be obtained upon request from the corresponding author. + +<|ref|>sub_title<|/ref|><|det|>[[147, 792, 309, 809]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[147, 824, 852, 872]]<|/det|> +This research has been supported by the National Natural Science Foundation of China (grant nos. 42027804, 41775026, 41075012). + +<|ref|>sub_title<|/ref|><|det|>[[147, 888, 334, 905]]<|/det|> +## Author contributions + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[95, 90, 850, 167]]<|/det|> +356 J.Q., H.L. and Y.H. designed the research. H.L. performed the research and drafted the paper. H.L., J.Q. and Y.H. contributed to analysis and interpretation of the results, as well as revising the paper. + +<|ref|>sub_title<|/ref|><|det|>[[95, 183, 323, 200]]<|/det|> +## 359 Competing interests + +<|ref|>text<|/ref|><|det|>[[95, 216, 542, 234]]<|/det|> +360 The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[95, 250, 245, 267]]<|/det|> +## 361 References + +<|ref|>text<|/ref|><|det|>[[95, 275, 852, 903]]<|/det|> +362 1 Bonan, G. B. Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. Science 320, 1444- 1449, doi:10.1126/science.1155121 (2008). 363 2 Nabuurs, G.- J. et al. First signs of carbon sink saturation in European forest biomass. Nature Climate Change 3, 792- 796, doi:10.1038/nclimate1853 (2013). 364 3 Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon- density maps. Nature Climate Change 2, 182- 185, doi:10.1038/nclimate1354 (2012). 365 4 Runyan, C. 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Influence of cirrus cloud radiative forcing on climate and climate sensitivity in a general circulation model. Journal of Geophysical Research: Atmospheres 100, 16305-16323, doi:10.1029/95JD01383 (1995). + +<|ref|>text<|/ref|><|det|>[[201, 532, 850, 586]]<|/det|> +Winckler, J., Reick, C. H. & Pongratz, J. Robust Identification of Local Biogeophysical Effects of Land-Cover Change in a Global Climate Model. Journal of Climate 30, 1159-1176, doi:10.1175/JCLI-D-16-0067.1 (2017). + +<|ref|>text<|/ref|><|det|>[[201, 590, 850, 644]]<|/det|> +Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937-1958, doi:10.5194/gmd-9-1937-2016 (2016). + +<|ref|>text<|/ref|><|det|>[[201, 648, 850, 702]]<|/det|> +Lawrence, D. M. et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. 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Quarterly Journal of the Royal Meteorological Society 146, 1999-2049, doi:10.1002/qj.3803 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[92, 85, 852, 400]]<|/det|> +478 41 Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P. & Friedl, M. A. 479 Hierarchical mapping of annual global land cover 2001 to present: The 480 MODIS Collection 6 Land Cover product. Remote Sensing of Environment 481 222, 183- 194, doi:10.1016/j.rse.2018.12.013 (2019). 482 42 Kay, J. E. & Gettelman, A. Cloud influence on and response to seasonal Arctic 483 sea ice loss. Journal of Geophysical Research: Atmospheres 114, 484 doi:10.1029/2009JD011773 (2009). 485 43 Beck, H. E. et al. Present and future Köppen- Geiger climate classification 486 maps at 1- km resolution. Scientific Data 5, 180214, 487 doi:10.1038/sdata.2018.214 (2018). 488 44 Pongratz, J. et al. Land Use Effects on Climate: Current State, Recent Progress, 489 and Emerging Topics. Current Climate Change Reports 7, 99- 120, 490 doi:10.1007/s40641- 021- 00178- y (2021). 491 45 Liu, Z., Ballantyne, A. P. & Cooper, L. A. Biophysical feedback of global 492 forest fires on surface temperature. Nature Communications 10, 214, 493 doi:10.1038/s41467- 018- 08237- z (2019). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 92, 768, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 350, 150]]<|/det|> +- forestcloudSupplementary.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/images_list.json b/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..e846dfc841d05775d089bce91c221c187f2ced3c --- /dev/null +++ b/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/images_list.json @@ -0,0 +1,138 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Table 1. Ligand screening in the asymmetric hydroformylation of 1a[al]", + "footnote": [], + "bbox": [], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Ligands evaluation for asymmetric hydroformylation of 1a.", + "footnote": [], + "bbox": [ + [ + 115, + 370, + 871, + 825 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. Scope of 1-substituted cyclopent-3-en-1-ols and 1-phenylcyclohept-4-en-1-ol.", + "footnote": [], + "bbox": [ + [ + 145, + 88, + 846, + 825 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. Substrates for synthesis of chiral aldehydes with an all-carbon quaternary stereocenter. The dr value of 5a-5c were determined by \\(^1\\mathrm{H}\\) NMR spectroscopy.", + "footnote": [], + "bbox": [ + [ + 186, + 308, + 810, + 522 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6. Gram-scale reaction and transformations of oxo-products and ring-open reaction of bridged [2,2,1] bicyclic lactones.", + "footnote": [], + "bbox": [ + [ + 186, + 88, + 830, + 523 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 75, + 108, + 900, + 490 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 55, + 53, + 720, + 777 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5", + "footnote": [], + "bbox": [ + [ + 55, + 50, + 940, + 375 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6", + "footnote": [], + "bbox": [ + [ + 70, + 60, + 920, + 690 + ] + ], + "page_idx": 20 + } +] \ No newline at end of file diff --git a/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594.mmd b/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594.mmd new file mode 100644 index 0000000000000000000000000000000000000000..61e738e8ccabbc890167613587580769fe35750c --- /dev/null +++ b/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594.mmd @@ -0,0 +1,332 @@ + +# Stereospecific Hydroformylation of 1-Substituted Cyclopent-3-en-1-ols: A Concise Access to Bridged [2, 2, 1] Bicyclic Lactones With A Quaternary Stereocenter + +Shuaiong Li Wuhan University Zhuangxing Li Wuhan University Mingzheng Li Wuhan University Lin He Shihezi University Xumu Zhang Southern University of Science and Technology https://orcid.org/0000- 0001- 5700- 0608 Hui Lv ( \(\boxed{\bullet}\) huilv@whu.edu.cn) Wuhan University https://orcid.org/0000- 0003- 1378- 1945 + +## Article + +Keywords: bridged [2, 2, 1] bicyclic lactones, Rh- catalyzed asymmetric hydroformylation /intramolecular cyclization/PCC oxidation + +Posted Date: January 19th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 145121/v1 + +License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on September 6th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 25569- 5. + +<--- Page Split ---> + +# Stereospecific Hydroformylation of 1-Substituted Cyclopent-3-en-1-ols: A Concise Access to Bridged [2,2,1] Bicyclic Lactones With A Quaternary Stereocenter + +Shuailong \(Li^{a}\) , Zhuangxing \(Li^{a}\) , Mingcheng \(Li^{a}\) , Lin He, \(^{b}\) Xumu Zhang \(^{a}\) , Hui \(Lv^{a,b}\) + +\(^{a}\) Key Laboratory of Biomedical Polymers of Ministry of Education & College of Chemistry and Molecular Sciences, Engineering Research Center of Organosilicon Compounds & Materials, Ministry of Education, Sauvage Center for Molecular Sciences, Wuhan University, Wuhan 430072, China. + +\(^{b}\) Key Laboratory for Green Processing of Chemical Engineering of Xinjiang Bingtuan, Schoo 1 of Chemistry and Chemical Engineering, Shihezi University, Xinjiang Uygur Autonomous Region, 832000, China. + +\(^{c}\) Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, Guangdong 518000, China + +E- mail: huilv@whu.edu.cn, zhangxm@sustc.edu.cn + +<--- Page Split ---> + +Abstract: An efficient method for enantioselective construction of bridged [2,2,1] bicyclic lactones bearing a quaternary stereocenter was achieved by Rh- catalyzed asymmetric hydroformylation /intramolecular cyclization/PCC oxidation. By employing a hybrid phosphine- phosphite chiral ligand, a series of cyclopent- 3- en- 1- ols were transformed into their corresponding \(\gamma\) - hydroxyl aldehydes with specific syn- selectivity, then hemiacetal formed in situ and oxidized by PCC in one- pot, affording bridged [2,2,1] bicyclic lactones in high yields and excellent enantiomeric excess. Replacing the hydroxyl group by an ester group, cyclopentanecarbaldehyde with a chiral all- carbon quaternary stereocenter in the \(\gamma\) - position can be generated efficiently. Gram- scale reaction and several transformations to corresponding amide, alcohol and acid demonstrated the practical value of this methodology. + +<--- Page Split ---> + +Enantiomeric bridged [2,2,1] bicyclic lactones and their ring- open products, cyclopentanols bearing two chiral centers, are important scaffolds widely occurring in both pharmaceutics and biology active compounds (Figure 1).1, 2, 3, 4, 5 Consequently, the synthesis of bridged[2,2,1] bicyclic lactones received wide attentions and several approaches have been developed. The typical methods including Baeyer- Villiger oxidation,6 esterification,7, 8 halolactonization,9, 10 electrocatalytic reaction11 and others.12, 13, 14 However, most of these approaches were focused on the synthesis of racemic bridged [2,2,1] bicyclic lactones and multi- step synthesis were necessary to achieve these transformations. To date, there are only two examples on the construction of chiral bridged [2,2,1] bicyclic lactones in an enantioselective manner. In 2015, Dominguez developed a new synthetic route to chiral bridged [2,2,1] bicyclic lactones by using chiral alcohol as starting material (Figure 2, a).15 In 2018, Zhu and co- workers developed a copper- catalyzed enantioselective arylative desymmetrization of prochiral cyclopentenes, and then followed by hydrolysis and intramolecular iodolactonlization to generate bridged [2,2,1] bicyclic lactones. However, the installation and removal of an amide direction group were essential to this synthetic route, which resulted in relatively low atom economy (Figure 2, b).16 Therefore, the development of a concise and efficient method to produce bridged [2,2,1] bicyclic lactones is highly desirable. + +![](images/Figure_unknown_0.jpg) + + +<--- Page Split ---> + +Figure 1. Pharmaceuticals and bioactive compounds containing bridged [2,2,1] bicyclic lactones and it's alcohol derivatives. + +Asymmetric hydroformylation (AHF) represents an efficient approach for asymmetric formation of C- C bond in an atomic economic manner, \(^{17,18,19,20,21,22,23,24,25}\) and the aldehyde products can be easily converted to versatile functional compounds, such as chiral alcohols, acids, amines and esters, \(^{26,27,28,29,30,31,32,33,34}\) thus asymmetric hydroformylation has been widely investigated and some significant progress have been made. \(^{35,36,37,38,39,40,41,42,43,44,45}\) However, asymmetric hydroformylation is very sensitive to the steric hindrance of substrate, which make it difficult to tolerate tri- or tetrasubstituted alkenes. As a result, the construction of chiral aldehydes with a quaternary stereocenter and a tertiary stereocenter by asymmetric hydroformylation is rarely exploited. To the best of our knowledge, there was only one report achieved this transformation by using desymmetric hydroformylation strategy, but the substrate scope was limited to cyclopropenes with high ring strain. Furthermore, only moderate to good enantioselectivities were obtained ( \(\leq 83\%\) ee). \(^{46}\) Consequently, highly efficient synthesis of multichiral aldehydes bearing a quaternary stereocenter is still a problematic issue in this field. + +Recently, our group developed a Rh- catalyzed asymmetric hydroformylation of 1,1- disubstituted allyl alcohols to createcc \(\gamma\) - butyrolactones. \(^{47}\) We envision that the similar transformation might occur if 1- substituted cyclopent- 3- en- 1- ols were used as starting material, providing efficient access to bridged [2,2,1] bicyclic lactones with a quaternary stereocenter. However, this transformation faces several challenges (Figure 2, c). First, it is very difficult to generate chiral aldehydes with exclusive syn- selectivity through asymmetric hydroformylation of 1- substituted cyclopent- 3- en- 1- ols, but it's an essential factor to form bridged [2,2,1] bicyclic lactones in high yield. Second, the generation of the hemiacetals is unfavourable in this transformation because the large steric hindrance of tertiary alcohols greatly decreased the nucleophilic ability of hydroxy group to aldehydes. In addition, the relatively small steric difference between the two prochiral faces makes it difficult to obtain high enantioselectivity. Thus, the development of a highly efficient method for asymmetric synthesis of bridged [2,2,1] bicyclic lactones containing a quaternary stereocenter is still a challenge. Herein, we report one- pot synthesis of chiral bridged [2,2,1] bicyclic lactones from readily available cyclopent- 3- en- 1- ols. + +<--- Page Split ---> + +a) Preparation of bridge[2,2,1]lactones from chiral alcohols + +![](images/Figure_3.jpg) + + +b) Desymmetrization of prochiral cyclopentenes and iodolactonization to bridge[2,2,1]lactones + +![](images/Figure_4.jpg) + + +c) This work: + +![](images/Figure_5.jpg) + + +Challenges: + +1) Stereospecific formation of syn chiral aldehydes +2) The formation of hemiacetal is unfavorable due to the steric effect of tertiary alcohol +3) The relatively small steric difference make it difficult to differentiate the two prochiral faces + +Figure 2. Methods for synthesis of chiral bridged [2,2,1] bicyclic lactones: (a), use chiral materials to build bridged [2,2,1] bicyclic lactones; (b), install a chiral center beforehand and iodolactonization to generate bridged [2,2,1] bicyclic lactones; (c), specific plane syn- selective hydroformylation and lactonization to form bridged [2,2,1] bicyclic lactones. + +## Results + +Reaction development and optimizations. Initially, considering only syn oxo- products can be transfered to corresponding bridged [2,2,1] bicyclic lactones, asymmetric hydroformylation of 1a was investigated to obtain 2a stereospecifically. When (S,S)- Ph- BPE, the representative + +<--- Page Split ---> + +ligand in AHF, \(^{48}\) was employed, 1a was transformed into oxo-product with high conversion and excellent ee, along with good diastereoselectivity (table 1, entry 1). \((Rc,Sp)\) - Duanphos showed low activity in this transformaiton albeit with good stereocontrol (entry 2). \((R,R)\) - Quinoxp, which performed well in asymmetric hydrogenation reactions, \(^{49, 50, 51, 52, 53}\) afforded taget product in low yield with moderate enantioselectivity (entry 3). The reaction was totally inhibited when \((S,S)\) - Me- Duphos and \((S)\) - Segphos were employed. In order to obtain higher enantio- and diastereoselectivity, a series of YanPhos with different axial chirality, which were developed by our group, were evaluated. \(^{54, 55, 56, 57}\) The results showed that all YanPhos type ligands had good catalytic activity for this transformation, but there were big differences in the control of enantioselectivity and diastereoselectivity. Generally, YanPhos containing \((S,R)\) axial chirality had better perfomance than that of YanPhos with \((S,S)\) axial chirality (entries 6- 13). When \((S,R)\) - DM- YanPhos was employed (entry 11), the target product was obtained with the best diastereo- and enenatioselectivity. + +![](images/Figure_6.jpg) + +
Table 1. Ligand screening in the asymmetric hydroformylation of 1a[al]
+ +
EntryLigandConv. (%)[b]Ee (%) of 3a[c](2a+2a')/2a' [b]
1L1909412.5
2L243905.3
3L337-732.9
4L4TraceNDND
5L5TraceNDND
6L6&gt;99454.2
7L7&gt;99505.3
8L8&gt;9979&gt;20
9L9&gt;99707.7
+ +<--- Page Split ---> + + +
10L10&gt;999611.6
11L11&gt;99(61)[d]94&gt;20
12L12&gt;998811.1
13L13&gt;99392.4
+ +[a]The reaction of 1a (0.2 mmol) was performed in the presence of \(\mathrm{Rh}(\mathrm{acac})(\mathrm{CO})_2\) (2 mol%), L (4 mol%), \(\mathrm{H}_2 / \mathrm{CO} = 5 / 5\) bar in toluene (1 mL) at \(70^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) , then PCC (0.5 mmol) in DCM (4 mL) \(25^{\circ}\mathrm{C}\) for \(12\mathrm{h}\) . After the completion of AHF, partial of reaction solution was took out for \(^1\mathrm{H}\) NMR to detect the conversion of AHF and the ratio of \((2\mathbf{a} + 2\mathbf{a}') / 2\mathbf{a}'\) , the rest of solution was treated with PCC to give target product 3a. [b]Determined by \(^1\mathrm{H}\) NMR spectroscopy. [c]Determined by HPLC analysis on a chiral stationary phase. [d]Isolated yield. \(\mathrm{ND} =\) not detected. + +![](images/Figure_1.jpg) + +
Figure 3. Ligands evaluation for asymmetric hydroformylation of 1a.
+ +<--- Page Split ---> + +Having established the optimized reaction condition for asymmetric hydroformylation of 1a, we attempt to synthesize bridged [2,2,1] bicyclic lactone 3a in one pot by sequential AHF / intramolecular cyclization /dehydrogenation oxidation (Table 2). Based on our previous work, \(^{58}\) PCC (pyridinium chlorochromate) was selected as oxidant and delivered target product 3a with moderate yield (entry 1). Increasing reaction temperature could not improve the yield (entry 2). Considering the bulky steric hindrance greatly decreased the nucleophilicity of tertiary alcohol, \(^{59}\) several additives were screened to promote the cyclization of 2a. Acetic acid lead to a significant drop in yield, NaOAc resulted in the decrease of yield to some extent. To our delight, \(\mathrm{K}_2\mathrm{CO}_3\) , \(\mathrm{Cs}_2\mathrm{CO}_3\) and \(\mathrm{NEt}_3\) can promote this reaction, affording target product in high yield without compromising the enantioselectivity (entries 5- 7). However, a racemization occurred when NaOH was used, resulting in the decrease of ee and dr values (entry 8, \(40\%\) yield, \(80\%\) ee). Thus, one practical method for synthesis of bridged [2,2,1] bicyclic lactones was most effective with \((S,R)\) - DM- YanPhos as the ligand in AHF and \(\mathrm{NEt}_3\) as additive in PCC oxidation. + +Table 2 Additive screening in the PCC oxidation [a] + +
OH
Ph		CO/H2
Rh(acac)(CO)2
(S,R)-DM-YanPhos		then PCC/additive, DCM
r.t. overnight		Ph
then PCC/additive, DCM
r.t. overnight
EntryAdditiveYield (%)Ee (%) [b]
1-6194
2[c]-5694
3AcOH2694
4NaOAc·3H2O4994
5K2CO38294
6Cs2CO38594
7NEt39094
8NaOH4080
+ +[a]The reaction of 1a (0.2 mmol) was performed in the presence of \(\mathrm{Rh(acac)(CO)_2(2 mol\%)}\) , L11 (4 mol%), \(\mathrm{H}_2 / \mathrm{CO} = 5 / 5\) bar in toluene (1 mL) at \(70^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) , The reaction was cooled to room temperature and the pressure was carefully released in a well-ventilated hood, then the mixture was treated with PCC (0.5 mmol), + +<--- Page Split ---> + +additive (0.1 mmol) in DCM (4 mL) 25 °C for 12 h in one pot. \(^{[b]}\) Determined by HPLC analysis on a chiral stationary phase. \(^{[c]}\) Performed at 40 °C. + +Under the optimal conditions, we investigated the substrate scope. All of the bridged [2,2,1] bicyclic lactones were prepared in good yields with excellent enantioselectivities (Figure 4). Substrates bearing halides on the phenyl ring performed well in this transformation, giving target products with high yields and excellent ee's (3b- 3f). The absolute configuration of 3d was confirmed by X- ray crystallographic analysis. Electron- donating and electron- withdrawing substituted groups on the phenyl ring were also tolerated, furnishing 3f, 3g, 3h, 3i, 3j with high yields and excellent enantioselectivities, respectively. The yield of 3k was dropped sharply due to the ortho effect of methoxy group, but the high enantioselectivity was remained. In addition, functional groups, such as trifloromethyl, phenyl and borate (3l- 3n) on the para- position of the benzene ring were compatible, and the corresponding products were afforded with moderate to good yields and high ee's. Replacing phenyl by a naphthyl group (3o), the reaction also proceeded smoothly, providing the desired compound with high yield and excellent ee. Notably, alkyl substituents, such as benzyl, \(n\) - hexyl, isopropyl, cyclopropyl, cyclopentyl and cyclohexyl were also well tolerated in this transformation, delivering bridged [2,2,1] bicyclic lactones with excellent ee's and high yields (3p- 3u). Cyclopent- 3- en- 1- ol bearing a bulky sterically hindered damantlyl group also proceeded effectively, affording target product with high yield (3v). Moreover, the oxo- product 2w was produced with high diastereoselectivity and excellent enantioselectivity. \(^{60}\) Interestingly, 1- phenylcyclohept- 4- en- 1- ol, a challenge substrate for AHF because of the substituent far away from reaction site, which made it difficult to control the stereoselectivity, also worked very well in this transformation, delivering 6- oxabicyclo[3.2.2]nonan- 7- one 3x with high yield and good enantioselectivity. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 4. Scope of 1-substituted cyclopent-3-en-1-ols and 1-phenylcyclohept-4-en-1-ol.
+ +<--- Page Split ---> + +Encouraged by the success of desymmetric strategy for construction of chiral bridged [2,2,1] bicyclic lactones with a O- substituted quaternary center, primary exploration on efficient synthesis of cyclopentanecarbaldehyde with an all- carbon quaternary stereocenter was conducted. As shown in Figure 5, when symmetric cyclopentene with phenyl and ester substituents was employed, the desired chiral aldehyde 5a was generated in good yield with high diastereo- and enantioselectivity. Moreover, all- carbon substituted chiral spiro- lactones could also be efficiently synthesized by this strategy, delivering target products with good yields and high enantioselectivities (5b, 5c). + +![](images/Figure_4.jpg) + +
Figure 5. Substrates for synthesis of chiral aldehydes with an all-carbon quaternary stereocenter. The dr value of 5a-5c were determined by \(^1\mathrm{H}\) NMR spectroscopy.
+ +To further demonstrate the practical utility of this methodology, a gram- scale reaction of 1d were conducted in the presence of 0.2 mol% catalyst under 3/3 bar syngas pressure at \(70^{\circ}\mathrm{C}\) for 72 hours, then treated with \(\mathrm{NEt}_3\) and PCC, 3d was generated with high yield, without any loss in enantioselectivity (Figure 6, a). Treating 3d with methanol solution of ammonia, the ring- open reaction occurred, furnishing chiral amide 6 with high yield and excellent ee (Figure 6, b). The hydroformylation product 2a can be efficiently reduced by \(\mathrm{NaBH}_4\) , affording chiral dual alcohol 7 in high yield (Figure 6, c). Under a mild condition, the bioactive chiral acid 8 was readily prepared by oxidation of 2m with \(\mathrm{H}_2\mathrm{O}_2\) and \(\mathrm{NaClO}_2\) (Figure 6, d). \(^{61}\) + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 6. Gram-scale reaction and transformations of oxo-products and ring-open reaction of bridged [2,2,1] bicyclic lactones.
+ +## Conclusions + +In summary, we have developed an efficient method for synthesizing bridged [2,2,1] bicyclic lactones bearing a quaternary stereocenter by one- pot sequential asymmetric hydroformylation/intramolecular cyclization/PCC oxidation. This methodology showed excellent substrate compatibility and excellent stereocontrol, giving target products with high yields and excellent enantioselectivities. In addition, this protocol also provided a useful strategy for construction of chiral aldehydes with an all- carbon quaternary stereocenter. Gram- scale reaction and diverse transformations of the oxo- products and bridged [2,2,1] bicyclic lactones demonstrated the utility of this method in synthetic chemistry. Further exportation on the construction of quaternary chiral center by asymmetric hydroformylation is ongoing in our laboratory. + +<--- Page Split ---> + +## Methods + +See the Supplementary Methods for experimental details as well as characterization data, supplementary item 4- 8 for the results of functional group tolerance of reactions for the results of additional reactions. NMR and HPLC spectra can be found in the Supplementary Information. + +General procedure for Rh- catalyzed hydroformylation of 1- substituted cyclopent- 3- en- 1- olc and PCC oxidation. In a glovebox filled with argon, to a \(5\mathrm{mL}\) vial equipped with a magnetic bar was added \((S,R)\) - DM- YanPhos (0.004 mmol) and \(\mathrm{Rh(acac)(CO)_2}\) (0.002 mmol in \(1\mathrm{mL}\) toluene). After stirring for 10 minutes, the mixture was charged to substrate (0.2 mmol). The vial was transferred into an autoclave and taken out of the glovebox. The argon gas was replacement with hydrogen gas for three times, and then hydrogen (2.5 bar) and carbon monoxide (2.5 bar) were charged in sequence. The reaction mixture was stirred at \(70^{\circ}\mathrm{C}\) (oil bath) for \(48\mathrm{h}\) . The reaction was cooled to room temperature and the pressure was carefully released in a well- ventilated hood. The solution was transferred into a solution of pyridinium chlorochromate (PCC) (0.5 mmol) and triethylamine (0.1 mmol) in \(4\mathrm{mL}\) dichloromethane, the reaction mixture was stirred at \(25^{\circ}\mathrm{C}\) (oil bath) overnight. The solution was concentrated and the product was isolated by column chromatography using petrol ether/EtOAc (30:1- 10:1) as eluent to give the desired product. The enantiomeric excesses of 3a- 3p, 3x, 5a- 5c, 6, and 8 were determined by HPLC analysis using a chiral stationary phase. The enantiomeric excesses of 3q- 3u, 2w and 7 were determined by SHIMADZU gas chromatography using chiral capillary columns. + +And the racemate bridged [2,2,1] bicyclic lactones were prepared with \(\mathrm{PPH_3}\) as the ligand according to the general procedure described below: In a glovebox filled with argon, to a \(5\mathrm{mL}\) vial equipped with a magnetic bar was added \(\mathrm{PPH_3}\) (0.004 mmol) and \(\mathrm{Rh(acac)(CO)_2}\) (0.002 mmol in \(1\mathrm{mL}\) toluene). After stirring for 10 minutes, the mixture was charged to substrate (0.2 mmol). The vial was transferred into an autoclave and taken out of the glovebox. The argon gas was replacement with hydrogen gas for three times, and then hydrogen (10 bar) and carbon monoxide (10 bar) were charged in sequence. The reaction mixture was stirred at \(110^{\circ}\mathrm{C}\) (oil + +<--- Page Split ---> + +bath) for \(24\mathrm{h}\) . The reaction was cooled to room temperature and the pressure was carefully released in a well- ventilated hood. The solution was transferred into a solution of pyridinium chlorochromate (PCC) (0.5 mmol) and triethylamine (0.1 mmol) in \(4\mathrm{mL}\) dichloromethane, the reaction mixture was stirred at \(25^{\circ}\mathrm{C}\) (oil bath) overnight. The solution was concentrated and the product was isolated by column chromatography using petrol ether/EtOAc (30:1- 10:1) as eluent to give the desired product. + +Measurement of enantiomeric excess (ee). The ee value was determined by chiral HPLC (CHIRALPAK AD- H and AS- H and CHIRALCEL OD- H and OJ- H column) and chiral GC ( \(\beta\) - dex225). + +Data availability. Crystallographic data for the structure 3d reported in this paper have been deposited at the Cambridge Crystallographic Data Centre under deposition number CCDC 2034549. Copies of the data can be obtained free of charge via www.ccdc.cam.ac.uk/data_request/cif. All other data supporting the findings of this study, including experimental procedures and compound characterization, are available within the paper and its Supplementary Information, or from the corresponding author upon reasonable request. + +## Author contributions + +H. L. and X.Z. directed the project. S. L. and H. L. contributed to the concept and design of the experiments. S. L., Z. L., M. L. and L. H. performed the experiments and data analysis. S. L. wrote the manuscript with feedback and guidance from H. L. and X.Z. All authors discussed the experimental results and commented on the manuscript. + +## Additional information + +Supplementary information and chemical compound information are available in the online version of the paper. Reprints and permissions information is available online at + +<--- Page Split ---> + +www.nature.com/reprints. Correspondence and requests for materials should be addressed to H. L. or to X. Z. + +## Competing financial interests + +The authors declare no competing financial or non- financial interests. + +## References + +1. Xie L, Guo H-F, Lu H, Zhang X-M, Zhang A-M, Wu G, Ruan J-X, Zhou T, Yu D, Qian K, Lee K-H, Jiang S. Development and Preclinical Studies of Broad-Spectrum Anti-HIV Agent \((3^{\prime}R,4^{\prime}R)\) -3-Cyanomethyl-4-methyl-3',4'-di-O-(S)-camphanoyl-\((+)\) -cis-khellactone (3-Cyanomethyl-4-methyl-DCK). J. Med. Chem. 51, 7689-7696 (2008).2. Xie L, Allaway G, Wild C, Kilgore N, Lee K-H. Anti-AIDS Agents. Part 47: Synthesis and Anti-HIV Activity of 3-Substituted \(3^{\prime},4^{\prime}\) -Di-O-(S)-camphanoyl-(3'R,4'R)-\((+)\) -cis-khellactone Derivatives. Bioorg. Med. Chem. Lett. 11, 2291-2293 (2001).3. Patterson B D, Lu Q, Aggen J B, Dozzo P, Kasar R A, Linsell M S, Kane T R, Gliedt M J, Hildebrandt D J, Mcenroe G A, Cohen F. World patent WO2013170030 (2013).4. Brubaker J D, Dipietro L V. World patent WO2018022761 (2018).5. 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The result is different with that of our previous work on AHF initiated cascade reaction to form stable hemiacetal (ref. 47), only small amount of hemiacetal was detected on crude \(^1\mathrm{H}\) NMR in this transformation, which was unstable on silicon gel column and transformed to aldehyde, giving aldehyde 2a in 94% isolated yield. + +<--- Page Split ---> + +60. The target product 3w can be prepared under standard reaction conditions, but it was difficult to obtain pure 3w due to the low boiling point, thus only the data of 2w was provided. + +61. Krall J, Jensen C H, Bavo F, Falk-Petersen C B, Haugaard A S, CVogensen S B, Tian Y, Nittegaard-Nielsen M, Sigurdardottir S B, Kehler J, Kongstad K T, Gloriam D E, Clausen R P, Harpsoe K, Wellendorph P, Frølund B. Molecular Hybridization of Potent and Selective γ-Hydroxybutyric Acid (GHB) Ligands: Design, Synthesis, Binding Studies, and Molecular Modeling of Novel 3-Hydroxycyclopent-1-encarboxylic Acid (HOCPCA) and trans-γ-Hydroxycrotonic Acid (T-HCA) Analogs. J. Med. Chem. 60, 9022-9039 (2017). + +<--- Page Split ---> + +## Figures + +![](images/Figure_6.jpg) + +
Figure 1
+ +Pharmaceutics and bioactive compounds containing bridged [2,2,1] bicyclic lactones and it's alcohol derivatives. + +<--- Page Split ---> + +a) Preparation of bridge[2,2,1]lactones from chiral alcohols + +![PLACEHOLDER_21_0] + + + +b) Desymmetrization of prochiral cyclopentenes and iodolactonization to bridge[2,2,1]lactones + +![PLACEHOLDER_21_1] + + + +c) This work: + +![PLACEHOLDER_21_2] + + + +Challenges: + +1) Stereospecific formation of syn chiral aldehydes + +2) The formation of hemiacetal is unfavorable due to the steric effect of tertiary alcohol + +3) The relatively small steric difference make it difficult to differentiate the two prochiral faces + +
Figure 2
+ +Methods for synthesis of chiral bridged [2,2,1] bicyclic lactones: (a), use chiral meterials to build bridged [2,2,1] bicyclic lactones; (b), install a chiral center beforehand and iodolactonization to generate bridged [2,2,1] bicyclic lactones; (c), specific plane syn-selective hydroformylation and lactonization to form bridged [2,2,1] bicyclic lactones. + +<--- Page Split ---> +![PLACEHOLDER_22_0] + +
Figure 3
+ +Ligands evaluation for asymmetric hydroformylation of 1a. + +<--- Page Split ---> +![PLACEHOLDER_23_0] + +
Figure 4
+ +Scope of 1- substituted cyclopent- 3- en- 1- ols and 1- phenylcyclohept- 4- en- 1- ol. + +<--- Page Split ---> +![PLACEHOLDER_24_0] + +
Figure 5
+ +Substrates for synthesis of chiral aldehydes with an all- carbon quaternary stereocenter. The dr value of 5a- 5c were determined by 1H NMR spectroscopy. + +<--- Page Split ---> +![PLACEHOLDER_25_0] + +
Figure 6
+ +Gram- scale reaction and transformations of oxo- products and ring- open reaction of bridged [2,2,1] bicyclic lactones. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SupportingInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594_det.mmd b/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..00f72006b8f090e0366ebe836e808aa8cdb85130 --- /dev/null +++ b/preprint/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594/preprint__0ac71c3e953761955eed4f4f3011b6796dcbd115cf727488da33f12f3e33e594_det.mmd @@ -0,0 +1,401 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 932, 242]]<|/det|> +# Stereospecific Hydroformylation of 1-Substituted Cyclopent-3-en-1-ols: A Concise Access to Bridged [2, 2, 1] Bicyclic Lactones With A Quaternary Stereocenter + +<|ref|>text<|/ref|><|det|>[[44, 262, 825, 540]]<|/det|> +Shuaiong Li Wuhan University Zhuangxing Li Wuhan University Mingzheng Li Wuhan University Lin He Shihezi University Xumu Zhang Southern University of Science and Technology https://orcid.org/0000- 0001- 5700- 0608 Hui Lv ( \(\boxed{\bullet}\) huilv@whu.edu.cn) Wuhan University https://orcid.org/0000- 0003- 1378- 1945 + +<|ref|>sub_title<|/ref|><|det|>[[44, 579, 102, 596]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 616, 940, 658]]<|/det|> +Keywords: bridged [2, 2, 1] bicyclic lactones, Rh- catalyzed asymmetric hydroformylation /intramolecular cyclization/PCC oxidation + +<|ref|>text<|/ref|><|det|>[[44, 677, 328, 696]]<|/det|> +Posted Date: January 19th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 715, 463, 734]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 145121/v1 + +<|ref|>text<|/ref|><|det|>[[44, 752, 910, 794]]<|/det|> +License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 830, 950, 873]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on September 6th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 25569- 5. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[117, 97, 881, 175]]<|/det|> +# Stereospecific Hydroformylation of 1-Substituted Cyclopent-3-en-1-ols: A Concise Access to Bridged [2,2,1] Bicyclic Lactones With A Quaternary Stereocenter + +<|ref|>text<|/ref|><|det|>[[189, 230, 808, 249]]<|/det|> +Shuailong \(Li^{a}\) , Zhuangxing \(Li^{a}\) , Mingcheng \(Li^{a}\) , Lin He, \(^{b}\) Xumu Zhang \(^{a}\) , Hui \(Lv^{a,b}\) + +<|ref|>text<|/ref|><|det|>[[119, 275, 880, 348]]<|/det|> +\(^{a}\) Key Laboratory of Biomedical Polymers of Ministry of Education & College of Chemistry and Molecular Sciences, Engineering Research Center of Organosilicon Compounds & Materials, Ministry of Education, Sauvage Center for Molecular Sciences, Wuhan University, Wuhan 430072, China. + +<|ref|>text<|/ref|><|det|>[[120, 354, 880, 410]]<|/det|> +\(^{b}\) Key Laboratory for Green Processing of Chemical Engineering of Xinjiang Bingtuan, Schoo 1 of Chemistry and Chemical Engineering, Shihezi University, Xinjiang Uygur Autonomous Region, 832000, China. + +<|ref|>text<|/ref|><|det|>[[157, 415, 840, 453]]<|/det|> +\(^{c}\) Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, Guangdong 518000, China + +<|ref|>text<|/ref|><|det|>[[286, 491, 710, 508]]<|/det|> +E- mail: huilv@whu.edu.cn, zhangxm@sustc.edu.cn + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 884, 358]]<|/det|> +Abstract: An efficient method for enantioselective construction of bridged [2,2,1] bicyclic lactones bearing a quaternary stereocenter was achieved by Rh- catalyzed asymmetric hydroformylation /intramolecular cyclization/PCC oxidation. By employing a hybrid phosphine- phosphite chiral ligand, a series of cyclopent- 3- en- 1- ols were transformed into their corresponding \(\gamma\) - hydroxyl aldehydes with specific syn- selectivity, then hemiacetal formed in situ and oxidized by PCC in one- pot, affording bridged [2,2,1] bicyclic lactones in high yields and excellent enantiomeric excess. Replacing the hydroxyl group by an ester group, cyclopentanecarbaldehyde with a chiral all- carbon quaternary stereocenter in the \(\gamma\) - position can be generated efficiently. Gram- scale reaction and several transformations to corresponding amide, alcohol and acid demonstrated the practical value of this methodology. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 87, 884, 555]]<|/det|> +Enantiomeric bridged [2,2,1] bicyclic lactones and their ring- open products, cyclopentanols bearing two chiral centers, are important scaffolds widely occurring in both pharmaceutics and biology active compounds (Figure 1).1, 2, 3, 4, 5 Consequently, the synthesis of bridged[2,2,1] bicyclic lactones received wide attentions and several approaches have been developed. The typical methods including Baeyer- Villiger oxidation,6 esterification,7, 8 halolactonization,9, 10 electrocatalytic reaction11 and others.12, 13, 14 However, most of these approaches were focused on the synthesis of racemic bridged [2,2,1] bicyclic lactones and multi- step synthesis were necessary to achieve these transformations. To date, there are only two examples on the construction of chiral bridged [2,2,1] bicyclic lactones in an enantioselective manner. In 2015, Dominguez developed a new synthetic route to chiral bridged [2,2,1] bicyclic lactones by using chiral alcohol as starting material (Figure 2, a).15 In 2018, Zhu and co- workers developed a copper- catalyzed enantioselective arylative desymmetrization of prochiral cyclopentenes, and then followed by hydrolysis and intramolecular iodolactonlization to generate bridged [2,2,1] bicyclic lactones. However, the installation and removal of an amide direction group were essential to this synthetic route, which resulted in relatively low atom economy (Figure 2, b).16 Therefore, the development of a concise and efficient method to produce bridged [2,2,1] bicyclic lactones is highly desirable. + +<|ref|>image<|/ref|><|det|>[[139, 585, 850, 875]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 89, 881, 136]]<|/det|> +Figure 1. Pharmaceuticals and bioactive compounds containing bridged [2,2,1] bicyclic lactones and it's alcohol derivatives. + +<|ref|>text<|/ref|><|det|>[[115, 144, 884, 500]]<|/det|> +Asymmetric hydroformylation (AHF) represents an efficient approach for asymmetric formation of C- C bond in an atomic economic manner, \(^{17,18,19,20,21,22,23,24,25}\) and the aldehyde products can be easily converted to versatile functional compounds, such as chiral alcohols, acids, amines and esters, \(^{26,27,28,29,30,31,32,33,34}\) thus asymmetric hydroformylation has been widely investigated and some significant progress have been made. \(^{35,36,37,38,39,40,41,42,43,44,45}\) However, asymmetric hydroformylation is very sensitive to the steric hindrance of substrate, which make it difficult to tolerate tri- or tetrasubstituted alkenes. As a result, the construction of chiral aldehydes with a quaternary stereocenter and a tertiary stereocenter by asymmetric hydroformylation is rarely exploited. To the best of our knowledge, there was only one report achieved this transformation by using desymmetric hydroformylation strategy, but the substrate scope was limited to cyclopropenes with high ring strain. Furthermore, only moderate to good enantioselectivities were obtained ( \(\leq 83\%\) ee). \(^{46}\) Consequently, highly efficient synthesis of multichiral aldehydes bearing a quaternary stereocenter is still a problematic issue in this field. + +<|ref|>text<|/ref|><|det|>[[115, 507, 884, 912]]<|/det|> +Recently, our group developed a Rh- catalyzed asymmetric hydroformylation of 1,1- disubstituted allyl alcohols to createcc \(\gamma\) - butyrolactones. \(^{47}\) We envision that the similar transformation might occur if 1- substituted cyclopent- 3- en- 1- ols were used as starting material, providing efficient access to bridged [2,2,1] bicyclic lactones with a quaternary stereocenter. However, this transformation faces several challenges (Figure 2, c). First, it is very difficult to generate chiral aldehydes with exclusive syn- selectivity through asymmetric hydroformylation of 1- substituted cyclopent- 3- en- 1- ols, but it's an essential factor to form bridged [2,2,1] bicyclic lactones in high yield. Second, the generation of the hemiacetals is unfavourable in this transformation because the large steric hindrance of tertiary alcohols greatly decreased the nucleophilic ability of hydroxy group to aldehydes. In addition, the relatively small steric difference between the two prochiral faces makes it difficult to obtain high enantioselectivity. Thus, the development of a highly efficient method for asymmetric synthesis of bridged [2,2,1] bicyclic lactones containing a quaternary stereocenter is still a challenge. Herein, we report one- pot synthesis of chiral bridged [2,2,1] bicyclic lactones from readily available cyclopent- 3- en- 1- ols. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[140, 120, 580, 138]]<|/det|> +a) Preparation of bridge[2,2,1]lactones from chiral alcohols + +<|ref|>image<|/ref|><|det|>[[150, 145, 825, 222]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[140, 236, 836, 253]]<|/det|> +b) Desymmetrization of prochiral cyclopentenes and iodolactonization to bridge[2,2,1]lactones + +<|ref|>image<|/ref|><|det|>[[150, 270, 840, 360]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[140, 370, 240, 385]]<|/det|> +c) This work: + +<|ref|>image<|/ref|><|det|>[[145, 384, 825, 608]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[148, 599, 239, 612]]<|/det|> +Challenges: + +<|ref|>text<|/ref|><|det|>[[147, 612, 838, 655]]<|/det|> +1) Stereospecific formation of syn chiral aldehydes +2) The formation of hemiacetal is unfavorable due to the steric effect of tertiary alcohol +3) The relatively small steric difference make it difficult to differentiate the two prochiral faces + +<|ref|>text<|/ref|><|det|>[[117, 672, 883, 775]]<|/det|> +Figure 2. Methods for synthesis of chiral bridged [2,2,1] bicyclic lactones: (a), use chiral materials to build bridged [2,2,1] bicyclic lactones; (b), install a chiral center beforehand and iodolactonization to generate bridged [2,2,1] bicyclic lactones; (c), specific plane syn- selective hydroformylation and lactonization to form bridged [2,2,1] bicyclic lactones. + +<|ref|>sub_title<|/ref|><|det|>[[118, 812, 185, 828]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[117, 839, 883, 914]]<|/det|> +Reaction development and optimizations. Initially, considering only syn oxo- products can be transfered to corresponding bridged [2,2,1] bicyclic lactones, asymmetric hydroformylation of 1a was investigated to obtain 2a stereospecifically. When (S,S)- Ph- BPE, the representative + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 87, 884, 442]]<|/det|> +ligand in AHF, \(^{48}\) was employed, 1a was transformed into oxo-product with high conversion and excellent ee, along with good diastereoselectivity (table 1, entry 1). \((Rc,Sp)\) - Duanphos showed low activity in this transformaiton albeit with good stereocontrol (entry 2). \((R,R)\) - Quinoxp, which performed well in asymmetric hydrogenation reactions, \(^{49, 50, 51, 52, 53}\) afforded taget product in low yield with moderate enantioselectivity (entry 3). The reaction was totally inhibited when \((S,S)\) - Me- Duphos and \((S)\) - Segphos were employed. In order to obtain higher enantio- and diastereoselectivity, a series of YanPhos with different axial chirality, which were developed by our group, were evaluated. \(^{54, 55, 56, 57}\) The results showed that all YanPhos type ligands had good catalytic activity for this transformation, but there were big differences in the control of enantioselectivity and diastereoselectivity. Generally, YanPhos containing \((S,R)\) axial chirality had better perfomance than that of YanPhos with \((S,S)\) axial chirality (entries 6- 13). When \((S,R)\) - DM- YanPhos was employed (entry 11), the target product was obtained with the best diastereo- and enenatioselectivity. + +<|ref|>image<|/ref|><|det|>[[156, 510, 828, 675]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 478, 725, 497]]<|/det|> +
Table 1. Ligand screening in the asymmetric hydroformylation of 1a[al]
+ +<|ref|>table<|/ref|><|det|>[[115, 686, 856, 901]]<|/det|> + +
EntryLigandConv. (%)[b]Ee (%) of 3a[c](2a+2a')/2a' [b]
1L1909412.5
2L243905.3
3L337-732.9
4L4TraceNDND
5L5TraceNDND
6L6&gt;99454.2
7L7&gt;99505.3
8L8&gt;9979&gt;20
9L9&gt;99707.7
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 81, 860, 170]]<|/det|> + +
10L10&gt;999611.6
11L11&gt;99(61)[d]94&gt;20
12L12&gt;998811.1
13L13&gt;99392.4
+ +<|ref|>text<|/ref|><|det|>[[115, 176, 883, 333]]<|/det|> +[a]The reaction of 1a (0.2 mmol) was performed in the presence of \(\mathrm{Rh}(\mathrm{acac})(\mathrm{CO})_2\) (2 mol%), L (4 mol%), \(\mathrm{H}_2 / \mathrm{CO} = 5 / 5\) bar in toluene (1 mL) at \(70^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) , then PCC (0.5 mmol) in DCM (4 mL) \(25^{\circ}\mathrm{C}\) for \(12\mathrm{h}\) . After the completion of AHF, partial of reaction solution was took out for \(^1\mathrm{H}\) NMR to detect the conversion of AHF and the ratio of \((2\mathbf{a} + 2\mathbf{a}') / 2\mathbf{a}'\) , the rest of solution was treated with PCC to give target product 3a. [b]Determined by \(^1\mathrm{H}\) NMR spectroscopy. [c]Determined by HPLC analysis on a chiral stationary phase. [d]Isolated yield. \(\mathrm{ND} =\) not detected. + +<|ref|>image<|/ref|><|det|>[[115, 370, 871, 825]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 834, 684, 852]]<|/det|> +
Figure 3. Ligands evaluation for asymmetric hydroformylation of 1a.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 884, 469]]<|/det|> +Having established the optimized reaction condition for asymmetric hydroformylation of 1a, we attempt to synthesize bridged [2,2,1] bicyclic lactone 3a in one pot by sequential AHF / intramolecular cyclization /dehydrogenation oxidation (Table 2). Based on our previous work, \(^{58}\) PCC (pyridinium chlorochromate) was selected as oxidant and delivered target product 3a with moderate yield (entry 1). Increasing reaction temperature could not improve the yield (entry 2). Considering the bulky steric hindrance greatly decreased the nucleophilicity of tertiary alcohol, \(^{59}\) several additives were screened to promote the cyclization of 2a. Acetic acid lead to a significant drop in yield, NaOAc resulted in the decrease of yield to some extent. To our delight, \(\mathrm{K}_2\mathrm{CO}_3\) , \(\mathrm{Cs}_2\mathrm{CO}_3\) and \(\mathrm{NEt}_3\) can promote this reaction, affording target product in high yield without compromising the enantioselectivity (entries 5- 7). However, a racemization occurred when NaOH was used, resulting in the decrease of ee and dr values (entry 8, \(40\%\) yield, \(80\%\) ee). Thus, one practical method for synthesis of bridged [2,2,1] bicyclic lactones was most effective with \((S,R)\) - DM- YanPhos as the ligand in AHF and \(\mathrm{NEt}_3\) as additive in PCC oxidation. + +<|ref|>table<|/ref|><|det|>[[203, 530, 790, 836]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[117, 505, 537, 524]]<|/det|> +Table 2 Additive screening in the PCC oxidation [a] + +
OH
Ph		CO/H2
Rh(acac)(CO)2
(S,R)-DM-YanPhos		then PCC/additive, DCM
r.t. overnight		Ph
then PCC/additive, DCM
r.t. overnight
EntryAdditiveYield (%)Ee (%) [b]
1-6194
2[c]-5694
3AcOH2694
4NaOAc·3H2O4994
5K2CO38294
6Cs2CO38594
7NEt39094
8NaOH4080
+ +<|ref|>table_footnote<|/ref|><|det|>[[115, 841, 881, 914]]<|/det|> +[a]The reaction of 1a (0.2 mmol) was performed in the presence of \(\mathrm{Rh(acac)(CO)_2(2 mol\%)}\) , L11 (4 mol%), \(\mathrm{H}_2 / \mathrm{CO} = 5 / 5\) bar in toluene (1 mL) at \(70^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) , The reaction was cooled to room temperature and the pressure was carefully released in a well-ventilated hood, then the mixture was treated with PCC (0.5 mmol), + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 89, 881, 136]]<|/det|> +additive (0.1 mmol) in DCM (4 mL) 25 °C for 12 h in one pot. \(^{[b]}\) Determined by HPLC analysis on a chiral stationary phase. \(^{[c]}\) Performed at 40 °C. + +<|ref|>text<|/ref|><|det|>[[115, 170, 884, 777]]<|/det|> +Under the optimal conditions, we investigated the substrate scope. All of the bridged [2,2,1] bicyclic lactones were prepared in good yields with excellent enantioselectivities (Figure 4). Substrates bearing halides on the phenyl ring performed well in this transformation, giving target products with high yields and excellent ee's (3b- 3f). The absolute configuration of 3d was confirmed by X- ray crystallographic analysis. Electron- donating and electron- withdrawing substituted groups on the phenyl ring were also tolerated, furnishing 3f, 3g, 3h, 3i, 3j with high yields and excellent enantioselectivities, respectively. The yield of 3k was dropped sharply due to the ortho effect of methoxy group, but the high enantioselectivity was remained. In addition, functional groups, such as trifloromethyl, phenyl and borate (3l- 3n) on the para- position of the benzene ring were compatible, and the corresponding products were afforded with moderate to good yields and high ee's. Replacing phenyl by a naphthyl group (3o), the reaction also proceeded smoothly, providing the desired compound with high yield and excellent ee. Notably, alkyl substituents, such as benzyl, \(n\) - hexyl, isopropyl, cyclopropyl, cyclopentyl and cyclohexyl were also well tolerated in this transformation, delivering bridged [2,2,1] bicyclic lactones with excellent ee's and high yields (3p- 3u). Cyclopent- 3- en- 1- ol bearing a bulky sterically hindered damantlyl group also proceeded effectively, affording target product with high yield (3v). Moreover, the oxo- product 2w was produced with high diastereoselectivity and excellent enantioselectivity. \(^{60}\) Interestingly, 1- phenylcyclohept- 4- en- 1- ol, a challenge substrate for AHF because of the substituent far away from reaction site, which made it difficult to control the stereoselectivity, also worked very well in this transformation, delivering 6- oxabicyclo[3.2.2]nonan- 7- one 3x with high yield and good enantioselectivity. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 88, 846, 825]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[140, 832, 856, 850]]<|/det|> +
Figure 4. Scope of 1-substituted cyclopent-3-en-1-ols and 1-phenylcyclohept-4-en-1-ol.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 884, 303]]<|/det|> +Encouraged by the success of desymmetric strategy for construction of chiral bridged [2,2,1] bicyclic lactones with a O- substituted quaternary center, primary exploration on efficient synthesis of cyclopentanecarbaldehyde with an all- carbon quaternary stereocenter was conducted. As shown in Figure 5, when symmetric cyclopentene with phenyl and ester substituents was employed, the desired chiral aldehyde 5a was generated in good yield with high diastereo- and enantioselectivity. Moreover, all- carbon substituted chiral spiro- lactones could also be efficiently synthesized by this strategy, delivering target products with good yields and high enantioselectivities (5b, 5c). + +<|ref|>image<|/ref|><|det|>[[186, 308, 810, 522]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 533, 881, 581]]<|/det|> +
Figure 5. Substrates for synthesis of chiral aldehydes with an all-carbon quaternary stereocenter. The dr value of 5a-5c were determined by \(^1\mathrm{H}\) NMR spectroscopy.
+ +<|ref|>text<|/ref|><|det|>[[115, 616, 884, 831]]<|/det|> +To further demonstrate the practical utility of this methodology, a gram- scale reaction of 1d were conducted in the presence of 0.2 mol% catalyst under 3/3 bar syngas pressure at \(70^{\circ}\mathrm{C}\) for 72 hours, then treated with \(\mathrm{NEt}_3\) and PCC, 3d was generated with high yield, without any loss in enantioselectivity (Figure 6, a). Treating 3d with methanol solution of ammonia, the ring- open reaction occurred, furnishing chiral amide 6 with high yield and excellent ee (Figure 6, b). The hydroformylation product 2a can be efficiently reduced by \(\mathrm{NaBH}_4\) , affording chiral dual alcohol 7 in high yield (Figure 6, c). Under a mild condition, the bioactive chiral acid 8 was readily prepared by oxidation of 2m with \(\mathrm{H}_2\mathrm{O}_2\) and \(\mathrm{NaClO}_2\) (Figure 6, d). \(^{61}\) + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[186, 88, 830, 523]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 535, 876, 580]]<|/det|> +
Figure 6. Gram-scale reaction and transformations of oxo-products and ring-open reaction of bridged [2,2,1] bicyclic lactones.
+ +<|ref|>sub_title<|/ref|><|det|>[[118, 618, 226, 635]]<|/det|> +## Conclusions + +<|ref|>text<|/ref|><|det|>[[116, 644, 884, 914]]<|/det|> +In summary, we have developed an efficient method for synthesizing bridged [2,2,1] bicyclic lactones bearing a quaternary stereocenter by one- pot sequential asymmetric hydroformylation/intramolecular cyclization/PCC oxidation. This methodology showed excellent substrate compatibility and excellent stereocontrol, giving target products with high yields and excellent enantioselectivities. In addition, this protocol also provided a useful strategy for construction of chiral aldehydes with an all- carbon quaternary stereocenter. Gram- scale reaction and diverse transformations of the oxo- products and bridged [2,2,1] bicyclic lactones demonstrated the utility of this method in synthetic chemistry. Further exportation on the construction of quaternary chiral center by asymmetric hydroformylation is ongoing in our laboratory. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 118, 197, 135]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[117, 144, 883, 247]]<|/det|> +See the Supplementary Methods for experimental details as well as characterization data, supplementary item 4- 8 for the results of functional group tolerance of reactions for the results of additional reactions. NMR and HPLC spectra can be found in the Supplementary Information. + +<|ref|>text<|/ref|><|det|>[[115, 281, 884, 720]]<|/det|> +General procedure for Rh- catalyzed hydroformylation of 1- substituted cyclopent- 3- en- 1- olc and PCC oxidation. In a glovebox filled with argon, to a \(5\mathrm{mL}\) vial equipped with a magnetic bar was added \((S,R)\) - DM- YanPhos (0.004 mmol) and \(\mathrm{Rh(acac)(CO)_2}\) (0.002 mmol in \(1\mathrm{mL}\) toluene). After stirring for 10 minutes, the mixture was charged to substrate (0.2 mmol). The vial was transferred into an autoclave and taken out of the glovebox. The argon gas was replacement with hydrogen gas for three times, and then hydrogen (2.5 bar) and carbon monoxide (2.5 bar) were charged in sequence. The reaction mixture was stirred at \(70^{\circ}\mathrm{C}\) (oil bath) for \(48\mathrm{h}\) . The reaction was cooled to room temperature and the pressure was carefully released in a well- ventilated hood. The solution was transferred into a solution of pyridinium chlorochromate (PCC) (0.5 mmol) and triethylamine (0.1 mmol) in \(4\mathrm{mL}\) dichloromethane, the reaction mixture was stirred at \(25^{\circ}\mathrm{C}\) (oil bath) overnight. The solution was concentrated and the product was isolated by column chromatography using petrol ether/EtOAc (30:1- 10:1) as eluent to give the desired product. The enantiomeric excesses of 3a- 3p, 3x, 5a- 5c, 6, and 8 were determined by HPLC analysis using a chiral stationary phase. The enantiomeric excesses of 3q- 3u, 2w and 7 were determined by SHIMADZU gas chromatography using chiral capillary columns. + +<|ref|>text<|/ref|><|det|>[[117, 728, 884, 914]]<|/det|> +And the racemate bridged [2,2,1] bicyclic lactones were prepared with \(\mathrm{PPH_3}\) as the ligand according to the general procedure described below: In a glovebox filled with argon, to a \(5\mathrm{mL}\) vial equipped with a magnetic bar was added \(\mathrm{PPH_3}\) (0.004 mmol) and \(\mathrm{Rh(acac)(CO)_2}\) (0.002 mmol in \(1\mathrm{mL}\) toluene). After stirring for 10 minutes, the mixture was charged to substrate (0.2 mmol). The vial was transferred into an autoclave and taken out of the glovebox. The argon gas was replacement with hydrogen gas for three times, and then hydrogen (10 bar) and carbon monoxide (10 bar) were charged in sequence. The reaction mixture was stirred at \(110^{\circ}\mathrm{C}\) (oil + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 88, 883, 247]]<|/det|> +bath) for \(24\mathrm{h}\) . The reaction was cooled to room temperature and the pressure was carefully released in a well- ventilated hood. The solution was transferred into a solution of pyridinium chlorochromate (PCC) (0.5 mmol) and triethylamine (0.1 mmol) in \(4\mathrm{mL}\) dichloromethane, the reaction mixture was stirred at \(25^{\circ}\mathrm{C}\) (oil bath) overnight. The solution was concentrated and the product was isolated by column chromatography using petrol ether/EtOAc (30:1- 10:1) as eluent to give the desired product. + +<|ref|>text<|/ref|><|det|>[[117, 283, 882, 358]]<|/det|> +Measurement of enantiomeric excess (ee). The ee value was determined by chiral HPLC (CHIRALPAK AD- H and AS- H and CHIRALCEL OD- H and OJ- H column) and chiral GC ( \(\beta\) - dex225). + +<|ref|>text<|/ref|><|det|>[[116, 394, 883, 580]]<|/det|> +Data availability. Crystallographic data for the structure 3d reported in this paper have been deposited at the Cambridge Crystallographic Data Centre under deposition number CCDC 2034549. Copies of the data can be obtained free of charge via www.ccdc.cam.ac.uk/data_request/cif. All other data supporting the findings of this study, including experimental procedures and compound characterization, are available within the paper and its Supplementary Information, or from the corresponding author upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[118, 618, 304, 635]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[117, 653, 883, 755]]<|/det|> +H. L. and X.Z. directed the project. S. L. and H. L. contributed to the concept and design of the experiments. S. L., Z. L., M. L. and L. H. performed the experiments and data analysis. S. L. wrote the manuscript with feedback and guidance from H. L. and X.Z. All authors discussed the experimental results and commented on the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[118, 812, 320, 829]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[117, 848, 882, 895]]<|/det|> +Supplementary information and chemical compound information are available in the online version of the paper. Reprints and permissions information is available online at + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 880, 135]]<|/det|> +www.nature.com/reprints. Correspondence and requests for materials should be addressed to H. L. or to X. Z. + +<|ref|>sub_title<|/ref|><|det|>[[119, 192, 373, 209]]<|/det|> +## Competing financial interests + +<|ref|>text<|/ref|><|det|>[[118, 229, 681, 246]]<|/det|> +The authors declare no competing financial or non- financial interests. + +<|ref|>sub_title<|/ref|><|det|>[[118, 303, 214, 319]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[115, 323, 884, 867]]<|/det|> +1. Xie L, Guo H-F, Lu H, Zhang X-M, Zhang A-M, Wu G, Ruan J-X, Zhou T, Yu D, Qian K, Lee K-H, Jiang S. Development and Preclinical Studies of Broad-Spectrum Anti-HIV Agent \((3^{\prime}R,4^{\prime}R)\) -3-Cyanomethyl-4-methyl-3',4'-di-O-(S)-camphanoyl-\((+)\) -cis-khellactone (3-Cyanomethyl-4-methyl-DCK). J. Med. Chem. 51, 7689-7696 (2008).2. Xie L, Allaway G, Wild C, Kilgore N, Lee K-H. Anti-AIDS Agents. Part 47: Synthesis and Anti-HIV Activity of 3-Substituted \(3^{\prime},4^{\prime}\) -Di-O-(S)-camphanoyl-(3'R,4'R)-\((+)\) -cis-khellactone Derivatives. Bioorg. Med. Chem. Lett. 11, 2291-2293 (2001).3. 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Application of a Chiral Scaffolding Ligand in Catalytic Enantioselective Hydroformylation. J. Am. Chem. Soc. 132,14757-14759 (2010). + +<|ref|>text<|/ref|><|det|>[[115, 143, 880, 179]]<|/det|> +31. Joe C L, Blaisdell T P, Geoghan A F, Tan K L. Distal-Selective Hydroformylation using Scaffolding Catalysis. J. Am. Chem. Soc. 136, 8556-8559 (2014). + +<|ref|>text<|/ref|><|det|>[[115, 180, 880, 233]]<|/det|> +32. You C, Li X, Yang Y, Yang Y-S, Tan X, Li S, Wei B, Lv H, Chung L-W, Zhang X. Silicon-oriented regio- and enantioselective rhodium-catalyzed hydroformylation. Nat. Commun. 9, 2045 (2018). + +<|ref|>text<|/ref|><|det|>[[115, 234, 880, 270]]<|/det|> +33. Wong G W, Landis C R. Iterative Asymmetric Hydroformylation/Wittig Olefination Sequence. Angew. Chem., Int. Ed. 52, 1564-1567 (2013). + +<|ref|>text<|/ref|><|det|>[[115, 271, 880, 324]]<|/det|> +34. Li S, Li Z, You C, Li X, Yang J, Lv H, Zhang X. 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The result is different with that of our previous work on AHF initiated cascade reaction to form stable hemiacetal (ref. 47), only small amount of hemiacetal was detected on crude \(^1\mathrm{H}\) NMR in this transformation, which was unstable on silicon gel column and transformed to aldehyde, giving aldehyde 2a in 94% isolated yield. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 84, 881, 140]]<|/det|> +60. The target product 3w can be prepared under standard reaction conditions, but it was difficult to obtain pure 3w due to the low boiling point, thus only the data of 2w was provided. + +<|ref|>text<|/ref|><|det|>[[118, 140, 881, 252]]<|/det|> +61. Krall J, Jensen C H, Bavo F, Falk-Petersen C B, Haugaard A S, CVogensen S B, Tian Y, Nittegaard-Nielsen M, Sigurdardottir S B, Kehler J, Kongstad K T, Gloriam D E, Clausen R P, Harpsoe K, Wellendorph P, Frølund B. Molecular Hybridization of Potent and Selective γ-Hydroxybutyric Acid (GHB) Ligands: Design, Synthesis, Binding Studies, and Molecular Modeling of Novel 3-Hydroxycyclopent-1-encarboxylic Acid (HOCPCA) and trans-γ-Hydroxycrotonic Acid (T-HCA) Analogs. J. Med. Chem. 60, 9022-9039 (2017). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 44, 143, 69]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[75, 108, 900, 490]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 539, 115, 559]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 580, 911, 624]]<|/det|> +Pharmaceutics and bioactive compounds containing bridged [2,2,1] bicyclic lactones and it's alcohol derivatives. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[75, 58, 600, 80]]<|/det|> +a) Preparation of bridge[2,2,1]lactones from chiral alcohols + +<|ref|>image<|/ref|><|det|>[[90, 90, 900, 192]]<|/det|> + + +<|ref|>text<|/ref|><|det|>[[75, 207, 912, 230]]<|/det|> +b) Desymmetrization of prochiral cyclopentenes and iodolactonization to bridge[2,2,1]lactones + +<|ref|>image<|/ref|><|det|>[[95, 259, 900, 362]]<|/det|> + + +<|ref|>text<|/ref|><|det|>[[75, 384, 201, 404]]<|/det|> +c) This work: + +<|ref|>image<|/ref|><|det|>[[90, 400, 900, 693]]<|/det|> + + +<|ref|>text<|/ref|><|det|>[[85, 683, 198, 701]]<|/det|> +Challenges: + +<|ref|>text<|/ref|><|det|>[[85, 701, 536, 720]]<|/det|> +1) Stereospecific formation of syn chiral aldehydes + +<|ref|>text<|/ref|><|det|>[[85, 720, 857, 738]]<|/det|> +2) The formation of hemiacetal is unfavorable due to the steric effect of tertiary alcohol + +<|ref|>text<|/ref|><|det|>[[85, 738, 911, 757]]<|/det|> +3) The relatively small steric difference make it difficult to differentiate the two prochiral faces + +<|ref|>image_caption<|/ref|><|det|>[[44, 799, 123, 819]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[44, 841, 940, 930]]<|/det|> +Methods for synthesis of chiral bridged [2,2,1] bicyclic lactones: (a), use chiral meterials to build bridged [2,2,1] bicyclic lactones; (b), install a chiral center beforehand and iodolactonization to generate bridged [2,2,1] bicyclic lactones; (c), specific plane syn-selective hydroformylation and lactonization to form bridged [2,2,1] bicyclic lactones. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 50, 936, 647]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 667, 116, 688]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[44, 710, 557, 731]]<|/det|> +Ligands evaluation for asymmetric hydroformylation of 1a. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 53, 720, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 800, 117, 820]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[44, 842, 700, 863]]<|/det|> +Scope of 1- substituted cyclopent- 3- en- 1- ols and 1- phenylcyclohept- 4- en- 1- ol. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 50, 940, 375]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 406, 118, 426]]<|/det|> +
Figure 5
+ +<|ref|>text<|/ref|><|det|>[[42, 449, 933, 494]]<|/det|> +Substrates for synthesis of chiral aldehydes with an all- carbon quaternary stereocenter. The dr value of 5a- 5c were determined by 1H NMR spectroscopy. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[70, 60, 920, 690]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 721, 118, 741]]<|/det|> +
Figure 6
+ +<|ref|>text<|/ref|><|det|>[[42, 763, 890, 807]]<|/det|> +Gram- scale reaction and transformations of oxo- products and ring- open reaction of bridged [2,2,1] bicyclic lactones. + +<|ref|>sub_title<|/ref|><|det|>[[44, 830, 311, 858]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 880, 765, 901]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 919, 318, 939]]<|/det|> +- SupportingInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/images_list.json b/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..7aa383b6a7fe5be7c40ea94bf075ca8cb0da6ad7 --- /dev/null +++ b/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Probing Er 4f electron spins through a Ti spin sensor. a, Schematic of the experimental set-up for ESR-STM measurement of an Er-Ti dimer built on MgO/(100)/Ag(100). The Ti atom (purple) is positioned close to the Er atom (orange) and located under a spin-polarized (SP) STM tip. The external magnetic field (B) defines the z-direction and is applied at an angle \\(\\theta\\) from the out-of-plane direction. A dc voltage \\(V_{dc}\\) is applied to the STM junction while the radio-frequency (rf) voltage is applied to the tip or to the antenna with an amplitude \\(V_{rf}\\). b, The projected total angular momentum of Er \\((J_{z})\\) onto the B field direction as a function of \\(\\theta\\) . The strong magnetic anisotropy favors an in-plane alignment of \\(J_{Er}\\) . c, ESR spectra of the Ti atom placed 0.928 nm apart from the Er atom at different \\(\\theta\\) . At \\(\\theta = 8^{\\circ}\\) , a single ESR peak is visible (pink) while, at \\(\\theta = 68^{\\circ}\\) (purple), the two ESR peaks are separated due to the magnetic interactions between the Er and Ti (set-point: \\(V_{dc} = 50 \\text{mV}\\) , \\(I_{dc} = 20 \\text{pA}\\) , \\(V_{rf} = 12 \\text{mV}\\) , \\(B = 0.3 \\text{T}\\) ). The spectrum at \\(\\theta = 8^{\\circ}\\) (pink) was normalized at its maximum intensity while the spectrum at \\(\\theta = 68^{\\circ}\\) (purple) was normalized to the sum of the intensities of its two peaks. The frequency detuning is defined with respect to 9.1 GHz (8.1 GHz) for the spectrum at \\(\\theta = 8^{\\circ}\\) ( \\(\\theta = 68^{\\circ}\\) ). d, ESR peak separation, \\(\\Delta f\\) , as a function of \\(\\theta\\) . The experimental points (black dots) were acquired at different set-points ( \\(V_{dc}\\)", + "footnote": [], + "bbox": [ + [ + 130, + 85, + 857, + 696 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | Measurement of Er ESR transitions through a strongly coupled Ti atom. a, Constant-current STM image of the engineered Er-Ti dimer with the atomic separation of 0.72 nm. The intersection of grids represents the oxygen sites of MgO. The Er atom (circled in yellow) is adsorbed on the oxygen site of MgO, while the Ti atom (circled in purple) is adsorbed on the bridge site (setpoint: \\(V_{dc} = 100 \\text{mV}\\) , \\(I_{dc} = 20 \\text{pA}\\) ). b, ESR spectra of the dimer given in a. When the STM tip is located on top of Er, no peaks are observed (yellow) (set-point: \\(V_{dc} = 50 \\text{mV}\\) , \\(I_{dc} = 20 \\text{pA}\\) , \\(V_{rf} = 20 \\text{mV}\\) , \\(B = 0.28 \\text{T}\\) , \\(\\theta = 97^{\\circ}\\) ). When the STM tip is located on top of Ti, 5 ESR peaks are detected \\((f_{1,2}^{\\text{Ti}}, f_{3,4}^{\\text{Er}}\\) and \\(f_{5}^{\\text{TE1Er}}\\) ) with \\(\\theta = 52^{\\circ}\\) (pink), while 4 ESR peaks are detected \\((f_{1,2}^{\\text{Ti}}\\) and \\(f_{3,4}^{\\text{Er}}\\) ) with \\(\\theta = 97^{\\circ}\\) (purple) (set-point: \\(V_{dc} = 70, 60 \\text{mV}\\) , \\(I_{dc} = 30, 40 \\text{pA}\\) , \\(V_{rf} = 20, 15 \\text{mV}\\) , \\(B = 0.3 \\text{T}\\) ). The spectra measured on Ti at \\(\\theta = 52^{\\circ}\\) and at \\(\\theta = 97^{\\circ}\\) were normalized at their respective maxima while the spectrum measured on top of Er was rescaled by the same amount used for the spectrum measured on Ti at \\(\\theta = 97^{\\circ}\\) . The spectra measured on Ti at \\(\\theta = 52^{\\circ}\\) and on Er are offset for clarity. c, ESR resonance frequencies as a function of \\(\\theta\\) at \\(B = 0.32 \\text{T}\\) . The ESR frequencies obtained from each measurement are given as black dots alongside the transition energies predicted from the model Hamiltonian for \\(f_{1}^{\\text{Ti}}\\) (blue line), \\(f_{2}^{\\text{Ti}}\\) (light blue line), \\(f_{3}^{\\text{Er}}\\) (red line), \\(f_{4}^{\\text{Er}}\\) (orange line), \\(f_{5}^{\\text{TE1Er}}\\) (green line) and flip-flop transition (dashed gray line). The experimental points were obtained at different set-points \\((V_{dc} = 60 - 70 \\text{mV}\\) , \\(I_{dc} = 12 - 40 \\text{pA}\\) , \\(V_{rf} = 15 - 25 \\text{mV}\\) , \\(B = 0.28 - 0.8 \\text{T}\\) ); the resonance frequencies were rescaled by 0.32 T/B. d, e, Four-level schemes corresponding to the energies of the 4 spin states of the Er-Ti dimer and the corresponding transitions depicted as colored arrows at \\(B = 0.32 \\text{T}\\) with different \\(\\theta\\) (90° and 0°, respectively). At \\(\\theta = 90^{\\circ}\\) (d) the spin states are given by the Zeeman products states, while at \\(\\theta = 0^{\\circ}\\) (e), a linear combination of the Zeeman product states is needed to describe the levels.", + "footnote": [], + "bbox": [ + [ + 120, + 92, + 876, + 465 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Detection and driving mechanisms of Er ESR transitions. a, ESR spectra showing \\(f_{3,4}^{\\mathrm{Er}}\\) for two different STM tips: negative peaks related to negative spin-pumping (yellow line) and positive peaks related to positive spin-pumping (orange line) (set-point: \\(I_{dc} = 12\\) , 20 pA, \\(V_{dc} = 70 \\mathrm{mV}\\) , \\(V_{rf} = 25 \\mathrm{mV}\\) , \\(B = 0.28\\) , 0.32 T, \\(\\theta = 67^{\\circ}\\) ). b, ESR peak intensities as a function of \\(V_{rf}\\) . The measured values for \\(f_{1}^{\\mathrm{Ti}}\\) and \\(f_{3}^{\\mathrm{Er}}\\) are given by black dots while the intensities predicted from the rate equation model for \\(f_{1,2}^{\\mathrm{Ti}}\\) and \\(f_{3,4}^{\\mathrm{Er}}\\) are given as blue, light blue, red solid lines and an orange dashed line, respectively (set-point: \\(I_{dc} = 40 \\mathrm{pA}\\) , \\(V_{dc} = 70 \\mathrm{mV}\\) , \\(B = 0.28 \\mathrm{T}\\) , \\(\\theta = 97^{\\circ}\\) ). c, Four-level scheme explaining the rate equation model while driving \\(f_{3}^{\\mathrm{Er}}\\) (red arrow). The Ti's spin relaxation rates \\(f_{1}^{\\mathrm{Ti}}\\) and \\(f_{2}^{\\mathrm{Ti}}\\) are depicted as purple arrows while the Er's spin relaxation rates \\(f_{3}^{\\mathrm{Er}}\\) and \\(f_{4}^{\\mathrm{Er}}\\) are given as dashed yellow arrows. The negative spin pumping effect is represented as blue double arrows. d, Normalized ESR peak intensities \\((\\Delta I / I_{dc})\\) for \\(f_{1}^{\\mathrm{Ti}}\\) (blue circles) and for \\(f_{4}^{\\mathrm{Er}}\\) (orange circles) at different tip heights. Here, the tip height is controlled by the set-point current \\(I_{dc}\\) (set-point: \\(V_{dc} = 70 \\mathrm{mV}\\) , \\(V_{rf} = 10 \\mathrm{mV}\\) , \\(B = 0.28 \\mathrm{T}\\) , \\(\\theta = 97^{\\circ}\\) ). The blue and the orange lines serve as guides for the eye. The insets show two different tip-Ti distances: larger for low \\(I_{dc}\\) and smaller for higher \\(I_{dc}\\) .", + "footnote": [], + "bbox": [ + [ + 122, + 92, + 876, + 515 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | Determination of Er spin relaxation time. a,b, Double resonance spectra in the frequency range covering Ti ESR transitions \\(f_{1,2}^{\\mathrm{Er}}\\) (a) without and (b) with simultaneous driving of Er at the ESR frequency of \\(f_{3}^{\\mathrm{Er}}\\) . The peak intensities of \\(f_{1,2}^{\\mathrm{Er}}\\) are related to the relative population of the Er spin states (insets). The spectra were normalized to the sum of their peak intensity. c, ESR intensity ratios between \\(\\Delta I_{f_2}^{\\mathrm{Er}}\\) and \\(\\Delta I_{f_1}^{\\mathrm{Er}}\\) as a function of the driving strength \\(V_{\\mathrm{rf2}}\\) at different Er ESR transition states (red, orange, and grey circles for \\(f_{3}^{\\mathrm{Er}}\\) , \\(f_{4}^{\\mathrm{Er}}\\) , and off-resonance, respectively). The solid curves show the correspondent simulation results by the rate equation model for \\(f_{3}^{\\mathrm{Er}}\\) (red line), \\(f_{4}^{\\mathrm{Er}}\\) (orange line) and at an off-resonance frequency (grey line). Set-point: \\(I_{dc} = 15 \\mathrm{pA}\\) , \\(V_{dc} = 70 \\mathrm{mV}\\) , \\(V_{\\mathrm{rf}} = 30 \\mathrm{mV}\\) , \\(V_{\\mathrm{rf2}} = 1\\) , \\(30 \\mathrm{mV}\\) , \\(B = 0.28 \\mathrm{T}\\) , \\(\\theta = 97^{\\circ}\\) . d, Schematics of the inversion recovery measurement in a pump-probe pulse scheme to determine the Er spin relaxation time \\(T_{1}^{\\mathrm{Er}}\\) . Each sequence is composed of a pump pulse at the resonance frequency of \\(f_{3}^{\\mathrm{Er}}\\) (red box) and a probe pulse at the resonance frequency of \\(f_{1}^{\\mathrm{Er}}\\) (blue box). The probe pulse follows the pump pulse after a delay time \\(\\Delta t\\) . The population of the Er states after the pump pulse relaxes back to the thermal state following its \\(T_{1}\\) . e, The experimental data for the inversion recovery measurement (blue circles) show the intensity of the ESR signal at the probe pulse \\(f_{1}\\) as a function of the delay time. The black line shows the fit using an exponential function with \\(T_{1}^{\\mathrm{Er}}\\) of about \\(1 \\mu \\mathrm{s}\\) . Set-point: \\(I_{dc} = 50 \\mathrm{pA}\\) , \\(V_{dc} = 70 \\mathrm{mV}\\) , \\(V_{\\mathrm{rf pump}} = 60 \\mathrm{mV}\\) , \\(V_{\\mathrm{rf probe}} = 100 \\mathrm{mV}\\) , \\(B = 0.28 \\mathrm{T}\\) , \\(\\theta = 97^{\\circ}\\) .", + "footnote": [], + "bbox": [ + [ + 122, + 90, + 879, + 465 + ] + ], + "page_idx": 12 + } +] \ No newline at end of file diff --git a/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e.mmd b/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e.mmd new file mode 100644 index 0000000000000000000000000000000000000000..15cd60f91be504066a418f24f57b0344ca25baa4 --- /dev/null +++ b/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e.mmd @@ -0,0 +1,205 @@ + +# Electrically Driven Spin Resonance of 4f Electrons in a Single Atom on a Surface + +Yujeong Bae bae.yu.jeong@qns.science + +IBS Center for Quantum Nanoscience https://orcid.org/0000- 0002- 9983- 8529 + +Stefano Reale IBS Center for Quantum Nanoscience + +Jiyoon Hwang IBS Center for Quantum Nanoscience + +Jeongmin Oh IBS Center for Quantum Nanoscience + +Harald Brune Ecole Polytechnique Fédérale de Lausanne (EPFL) https://orcid.org/0000- 0003- 4459- 3111 + +Andreas Heinrich Institute for Basic Science at Ewha Womans University https://orcid.org/0000- 0001- 6204- 471X + +Fabio Donati IBS Center for Quantum Nanoscience https://orcid.org/0000- 0002- 3932- 2889 + +## Article + +Keywords: + +Posted Date: November 7th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3385164/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on June 20th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 49447- y. + +<--- Page Split ---> + +# Electrically Driven Spin Resonance of \(4f\) Electrons in a Single Atom on a Surface + +Stefano Reale \(^{1,2,3}\) , Jiyoon Hwang \(^{1,4}\) , Jeongmin Oh \(^{1,4}\) , Harald Brune \(^{5}\) , Andreas J. Heinrich \(^{1,4}\) , Fabio Donati \(^{1,4*}\) , and Yujeong Bae \(^{1,4*}\) + +\(^{1}\) Center for Quantum Nanoscience (QNS), Institute for Basic Science (IBS), Seoul 03760, Republic of Korea \(^{2}\) Ewha Womans University, Seoul 03760, Republic of Korea \(^{3}\) Department of Energy, Politecnico di Milano, Milano 20133, Italy \(^{4}\) Department of Physics, Ewha Womans University, Seoul 03760, Republic of Korea \(^{5}\) Institute of Physics, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland \*Corresponding authors: F.D. (donati.fabio@qns.science), Y.B. (bae.yujeong@qns.science) + +A pivotal challenge in quantum technologies lies in reconciling long coherence times with efficient manipulation of the quantum states of a system. Lanthanide atoms, with their well- localized \(4f\) electrons, emerge as a promising solution to this dilemma if provided with a rational design for manipulation and detection. Here we construct tailored spin structures to perform electron spin resonance on a single lanthanide atom using a scanning tunneling microscope. A magnetically coupled structure made of an erbium and a titanium atom enables us to both drive the erbium's \(4f\) electron spins and indirectly probe them through the titanium's \(3d\) electrons. In this coupled configuration, the erbium spin states exhibit a five- fold increase in the spin relaxation time and a two- fold increase in the driving efficiency compared to the \(3d\) electron counterparts. Our work provides a new approach to accessing highly protected spin states, enabling their coherent control in an all- electric fashion. + +The last two decades have witnessed a rising focus on the control and application of quantum coherent effects, marking the advent of the so- called "second quantum revolution". Utilizing quantum coherent functionalities of materials for novel technologies, such as imaging, information processing, and communications, requires robustness of their quantum coherence, addressability, and scalability \(^{1}\) . However, these requirements often clash since decoupling the quantum states from the environment prolongs the quantum coherent properties but hinders the possibility of efficient state manipulation. + +<--- Page Split ---> + +Lanthanide atoms represent a promising platform to tackle this dilemma. Their well- localized \(4f\) electrons show long spin relaxation \(T_{1}^{2,3}\) and coherence times \(T_{2}^{4,5}\) . In addition, their strong hyperfine interaction facilitates the read- out of nuclear spins \(^{6,7}\) . In bulk insulators, exceedingly long \(T_{1}\) and \(T_{2}\) have been demonstrated using optical control and detection \(^{8,11}\) down to the single atom level \(^{12,13}\) . While hybrid optical- electrical approaches have been developed to access individual lanthanide atom's spins embedded in a silicon transistor \(^{14}\) , it is still challenging to achieve efficient control of the quantum states using electrical transport methods. This necessitates the rational design of a quantum platform capable of tackling both control and detection schemes, along with their interactions with local environments. In this context, single crystal surfaces constitute an advantageous framework both for building atomically engineered nanostructures and addressing individual spin centers, in particular using probe techniques \(^{15 - 18}\) . However, coherent manipulation and detection of surface- adsorbed lanthanide atoms have so far remained elusive. + +In this work, we demonstrate the control and detection of \(4f\) electron spins by building atomic- scale structures on a surface using a scanning tunneling microscope (STM) with electron spin resonance (ESR) capabilities \(^{19 - 22}\) . The atomic structures are composed of an erbium (Er) atom as the target spin system and a magnetically coupled titanium (Ti) atom as the sensor spin. This architecture allows us to drive ESR transitions on the Er \(4f\) electrons with a projected angular momentum of \(1 / 2^{23}\) and to probe them indirectly through Ti. We observed an Er \(T_{1}\) of close to \(1\mu s\) , 5 times longer than what was previously measured in \(3d\) electrons with spin \(1 / 2\) on the same surface \(^{18}\) . This novel platform allows for the ESR driving and read- out of the well- screened \(4f\) electron spin states, paving the way to integrate lanthanide atoms in quantum architectures. + +## Sensing Er Spin States through a Ti Atom + +Erbium atoms on a few monolayer- thick MgO(100) on Ag(100) present a \(4f^{11}\) configuration with no unpaired electrons in the \(5d\) and \(6s\) shells \(^{23}\) . The atomic- like spin and orbital momenta are coupled through the large spin- orbit interaction into a total angular momentum \(J_{\mathrm{Er}}\) with magnitude of \(15\hbar /2^{23}\) . When adsorbed on the oxygen site of MgO (Fig. 1a), the crystal field leads to a strong hard- axis magneto- crystalline anisotropy that stabilizes a doubly- degenerate ground state with an out- of- plane component of the angular momentum \(\pm \hbar /2^{23}\) , which splits into two singlets when an external magnetic field (B) is applied. As found in a previous work \(^{23}\) , the component of \(J_{\mathrm{Er}}\) along the magnetic field direction (z), defined as \(J_{z}\) , increases from \(\pm \hbar /2\) to \(\pm 4\hbar\) by rotating B from the out- of- plane ( \(\theta = 0^{\circ}\) ) to the in- plane ( \(\theta = 90^{\circ}\) ) direction + +<--- Page Split ---> + +(Fig. 1b), while retaining a large probability for spin dipole transitions. Given these properties, Er can be regarded as a highly tunable two- level system allowing for efficient ESR driving. To characterize the magnetic states and anisotropy of Er, we utilized the dipole field sensing technique24 with a Ti atom on the bridge binding site of MgO as a well- known spin sensor. On this binding site, Ti has a spin \(S_{\mathrm{Ti}}\) of magnitude \(\hbar /2\) and a relatively weak g- factor anisotropy25 with respect to the oxygen binding site26. + +We deposited Er and Ti at cryogenic temperatures ( \(\sim 10 \text{K}\) ) on 2 monolayers of MgO grown on Ag(100) (Methods and Fig. S1a). Their binding sites on the surface can be changed by atom manipulation (Supplementary Section 2). Figure 1c shows the ESR spectra obtained on Ti in an Er- Ti dimer with the atomic separation of 0.928 nm (Fig. S2b). For \(\theta = 8^{\circ}\) , we observed one ESR peak at the resonance frequency of Ti which splits into two peaks separated by \(\Delta f = 334 \pm 3 \text{MHz}\) when rotating \(B\) close to the in- plane direction ( \(\theta = 68^{\circ}\) ). The two ESR peaks stem from the magnetic interaction with the Er spin fluctuating between two states24 during the measurement, with the relative peak intensity being proportional to the time- averaged population of the Er states. The pronounced difference in the relative intensity of the ESR peaks indicates a large imbalance in the Er state occupation even at \(B = 0.3 \text{T}\) and \(1.3 \text{K}\) , which reflects the large \(J_{z}\) of Er at \(\theta = 68^{\circ}\) (Fig. 1b). The sign of this asymmetry depends on the character of the magnetic interactions between the two atoms. In Fig. 1c, the peak at the lower frequency is less intense than the one at the higher frequency and, hence, the interaction can be regarded as ferromagnetic27. + +The angle dependence of \(\Delta f\) (Fig. 1d) gives a direct measurement of the Er- Ti interaction energy and of its anisotropy24,27. To interpret it, we model the system through a spin- Hamiltonian including both the single atom Zeeman and anisotropy terms, as well as the interaction between the two spins: + +\[H = \mu_{B}g_{\mathrm{Er}}\boldsymbol {B}\cdot \boldsymbol {J}_{\mathrm{Er}} + D\dot{J}_{\perp}^{2} + \mu_{B}\boldsymbol {B}\cdot \boldsymbol {g}_{\mathrm{Ti}}\cdot \boldsymbol {S}_{\mathrm{Ti}} + H_{\mathrm{dip}} + H_{\mathrm{exc}}. \quad (1)\] + +Here, \(\mu_{B}\) is the Bohr magneton, \(J_{\perp}\) is the out- of- plane component of \(J_{\mathrm{Er}}\) , \(g_{\mathrm{Er}} = 1.2\) is the Er g- factor, and \(g_{\mathrm{Ti}}\) is the Ti anisotropic g- tensor25. We use a magnetic anisotropy parameter \(D = 2.4 \text{meV}\) to match the Er energy splitting found in a previous study23. The magnetic coupling consists of dipolar \((H_{\mathrm{dip}})\) and Heisenberg exchange interactions \((H_{\mathrm{exc}})\) : + +\[H_{\mathrm{dip}} = \frac{\mu_0\mu_B^2}{4\pi\hbar^2r^3}\left[g_{\mathrm{Er}}J_{\mathrm{Er}}\cdot \mathbf{g}_{\mathrm{Ti}}\cdot \mathbf{S}_{\mathrm{Ti}} - 3(\hat{\mathbf{r}}\cdot g_{\mathrm{Er}}J_{\mathrm{Er}})(\hat{\mathbf{r}}\cdot \mathbf{g}_{\mathrm{Ti}}\cdot \mathbf{S}_{\mathrm{Ti}})\right],\] \[H_{\mathrm{exc}} = \frac{J_{\mathrm{exc}}}{\hbar^2} (J_{\mathrm{Er}}\cdot \mathbf{S}_{\mathrm{Ti}}),\] + +<--- Page Split ---> + +where \(\mu_0\) is the vacuum permittivity, \(r\) the separation between the two atoms, \(\hat{r}\) the unit vector connecting them \(^{24}\) , and \(J_{\mathrm{exc}}\) the exchange interaction energy expressed in terms of \(J_{\mathrm{Er}}\) \(^{28}\) . In our model, \(J_{\mathrm{exc}}\) is the only free parameter for the fit. As shown in Fig. 1d, our model accurately reproduces the data for \(J_{\mathrm{exc}} / \mathrm{h} = - 48 \mathrm{MHz}\) , where the negative sign indicates an antiferromagnetic coupling. This value is more than 20 times smaller than that observed for a Ti- Ti dimer at the same distance \((- 1.16 \mathrm{GHz})\) \(^{29}\) . We ascribe the smaller Er- Ti coupling to the localization of the \(4f\) orbitals near the atom's core, which limits the overlap between Er and Ti orbitals when compared to the Ti- Ti case. + +The strong angle dependence of \(\Delta f\) can be understood by considering the large magneto- crystalline anisotropy of \(J_{\mathrm{Er}}\) . At \(\theta = 90^{\circ}\) , \(J_{z}\) is largest (4h) and the angular momenta of both atoms are parallel to \(\hat{r}\) (Fig. 1e), which maximizes the contribution of the dipolar coupling with a positive sign (ferromagnetic). When rotating \(B\) away from the in- plane direction, \(S_{\mathrm{Ti}}\) follows the direction of \(B\) , while the anisotropy of Er preserves a large component of \(J_{\mathrm{Er}}\) mainly aligned along the in- plane direction (Fig. 1f). This misalignment between the two angular momenta reduces the dipolar interaction. Finally, as \(B\) approaches the surface normal (Fig. 1g), \(J_{\mathrm{Er}}\) turns towards the out- of- plane direction with a much smaller value of \(J_{z} = \hbar /2\) . With the two momenta being perpendicular to \(\hat{r}\) , the dipolar contribution is minimal and negative (antiferromagnetic). Conversely, the mutual projection of \(S_{\mathrm{Ti}}\) and \(J_{\mathrm{Er}}\) is the only factor modulating the exchange interaction term, which remains negative (antiferromagnetic) at all angles. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 | Probing Er 4f electron spins through a Ti spin sensor. a, Schematic of the experimental set-up for ESR-STM measurement of an Er-Ti dimer built on MgO/(100)/Ag(100). The Ti atom (purple) is positioned close to the Er atom (orange) and located under a spin-polarized (SP) STM tip. The external magnetic field (B) defines the z-direction and is applied at an angle \(\theta\) from the out-of-plane direction. A dc voltage \(V_{dc}\) is applied to the STM junction while the radio-frequency (rf) voltage is applied to the tip or to the antenna with an amplitude \(V_{rf}\). b, The projected total angular momentum of Er \((J_{z})\) onto the B field direction as a function of \(\theta\) . The strong magnetic anisotropy favors an in-plane alignment of \(J_{Er}\) . c, ESR spectra of the Ti atom placed 0.928 nm apart from the Er atom at different \(\theta\) . At \(\theta = 8^{\circ}\) , a single ESR peak is visible (pink) while, at \(\theta = 68^{\circ}\) (purple), the two ESR peaks are separated due to the magnetic interactions between the Er and Ti (set-point: \(V_{dc} = 50 \text{mV}\) , \(I_{dc} = 20 \text{pA}\) , \(V_{rf} = 12 \text{mV}\) , \(B = 0.3 \text{T}\) ). The spectrum at \(\theta = 8^{\circ}\) (pink) was normalized at its maximum intensity while the spectrum at \(\theta = 68^{\circ}\) (purple) was normalized to the sum of the intensities of its two peaks. The frequency detuning is defined with respect to 9.1 GHz (8.1 GHz) for the spectrum at \(\theta = 8^{\circ}\) ( \(\theta = 68^{\circ}\) ). d, ESR peak separation, \(\Delta f\) , as a function of \(\theta\) . The experimental points (black dots) were acquired at different set-points ( \(V_{dc}\)
+ +<--- Page Split ---> + +\(= 50 \text{mV}, I_{\text{dc}} = 12 - 30 \text{pA}, V_{\text{rf}} = 12 - 20 \text{mV}, B = 0.3 \text{T}\) . The total interaction (solid purple line) calculated by the model Hamiltonian is composed of a dipolar contribution (dashed blue line) and an exchange contribution (dashed pink line). \(\mathbf{e} - \mathbf{g}\) , Schematic of the angular momenta of Er and Ti on \(\text{MgO / Ag(100)}\) . The dipolar fields induced by Er are depicted as black curved arrows. When \(\mathbf{B}\) is applied along the in-plane direction \((\theta = 90^{\circ})\) , the \(J_{z}\) is maximum and aligned with the spin of Ti giving the largest ferromagnetic interaction. When \(\mathbf{B}\) is rotated, the spin of Ti follows the direction of \(\mathbf{B}\) while the total angular momentum of Er is aligned preferentially in-plane (f). In the out-of-plane direction \((\theta = 0^{\circ})\) , \(J_{z}\) is minimum and aligned with the spin of Ti (g) giving a small antiferromagnetic interaction. + +## Spin Resonance of Er 4f Electrons + +The direct drive of ESR in STM requires positioning the tip directly on top of the target atom19. However, we observed no ESR when positioning the tip over an Er atom (Fig. S3b), which we attribute to the small polarization of the 5d and 6s shells of Er and to the weak interaction between the 4f and tunneling electrons. These factors were found to limit the tunneling magnetoresistance at the STM junction in other lanthanide atoms30,31, possibly hindering both the ESR drive and detection23. + +To overcome this limitation, we built a strongly interacting Er- Ti dimer by positioning Ti at 0.72 nm from Er through atom manipulation (Fig. 2a and Supplementary Section 2). Similar to the isolated atom, we observed no ESR peaks at the Er position in the dimer (yellow curve in Fig. 2b). However, when the tip was positioned on Ti, we observed up to 5 peaks (pink curve in Fig. 2b). The first two peaks below 10 GHz with \(\Delta f = 2.70 \pm 0.01 \text{GHz}\) correspond to the ESR transitions of Ti that were similarly found in the dimer with larger atomic separations (Fig. 1c). Hence, we label them as \(f_{1}^{\text{Ti}}\) and \(f_{2}^{\text{Ti}}\), respectively. In this dimer, we observed that \(f_{1}^{\text{Ti}}\) shows a higher intensity than \(f_{2}^{\text{Ti}}\), indicating an antiferromagnetic exchange interaction27 dominating over the dipolar coupling at this atomic separation. At higher frequencies, we further observed two peaks that are significantly blue- shifted when rotating \(\mathbf{B}\) from \(\theta = 52^{\circ}\) (pink curve in Fig. 2b) to \(97^{\circ}\) (purple). The higher resonance frequencies and pronounced angle dependence indicate that those transitions involve the large and anisotropic angular momentum of Er, and, thus, we label them as \(f_{3}^{\text{Er}}\) and \(f_{4}^{\text{Er}}\). In addition, their frequency separation exactly matches the one between \(f_{1}^{\text{Ti}}\) and \(f_{2}^{\text{Ti}}\), reflecting the same Er- Ti interaction. On the other hand, \(f_{3}^{\text{Er}}\) and \(f_{4}^{\text{Er}}\) are approximately equal in intensity, indicating that Ti fluctuates between two spin states with almost equal occupations. The comparable Ti states' occupation stems from the scattering with tunneling electrons and from the Zeeman splitting of Ti (~7 GHz) being smaller than the thermal energy at the measurement temperature of 1.3 K (~27 GHz). With \(\mathbf{B}\) at \(\theta = 52^{\circ}\), we observed one more peak at even higher frequencies. Its frequency exactly matches the sum of \(f_{1}^{\text{Ti}}\) and \(f_{4}^{\text{Er}}\) (or equivalently \(f_{2}^{\text{Ti}}\) and \(f_{3}^{\text{Er}}\)), which suggests an ESR transition + +<--- Page Split ---> + +involving both Ti and Er spins. We label this peak as \(f_{3}^{\mathrm{TiEr}}\) . Remarkably, the sign of \(f_{3}^{\mathrm{Er}}\) , \(f_{4}^{\mathrm{Er}}\) and \(f_{5}^{\mathrm{TiEr}}\) is opposite to that of \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) , indicating a different detection mechanism for the transitions involving the Er spin, which will be discussed below. Finally, we observed an energy level crossing between Er and Ti transitions at \(\theta \sim 12^{\circ}\) , with the Er resonance frequencies further shifting below the Ti transitions at \(\theta \sim 0^{\circ}\) (Fig. 2c and Fig. S4). This peculiar behavior is a consequence of the large difference in magnetic anisotropy between Er and \(\mathrm{Ti^{23}}\) . + +As shown in Fig. 2c, the angular dependence of the ESR frequencies is well reproduced by using Eq. 1 with \(J_{\mathrm{exc}} / \mathrm{h} = - 326 \mathrm{MHz}\) . We observed small deviations for \(f_{1}^{\mathrm{Ti}}\) , \(f_{2}^{\mathrm{Ti}}\) and \(f_{5}^{\mathrm{TiEr}}\) , which we ascribe to different experimental conditions and magnetic interaction of Ti with the tip, which is not included in our model. Diagonalizing Eq. 1 allows us to analyze the quantum states of the Er- Ti dimer in terms of individual Er and Ti spin states. For an in- plane \(B = 0.3 \mathrm{T}\) , the energy detuning between the Er and Ti spins (30 GHz) is much larger than the interaction energy (about 3 GHz). Therefore, the Er- Ti dimer can be modeled with the 4 Zeeman product states of the Er and Ti spins. Following this picture, we can support the assignment of \(f_{1,2}^{\mathrm{Ti}}\) as Ti spin transitions occurring with no changes in the Er state, while \(f_{3,4}^{\mathrm{Er}}\) correspond to Er spin transitions without altering Ti. Finally, we attribute \(f_{5}^{\mathrm{TiEr}}\) to a double- flip transition involving both Er and Ti spins. Even though a \(|\Delta m| > 1 \mathrm{~h}\) process is generally forbidden to first order, anisotropic terms in the magnetic interaction can give rise to higher order matrix elements connecting states with \(\Delta m = \pm 2 \mathrm{~h}^{32}\) . + +When the field is oriented at \(\theta = 0^{\circ}\) , both \(J_{\mathrm{Er}}\) and \(S_{\mathrm{Ti}}\) show an expectation value of \(\hbar /2\) , but a detuning still occurs due to the difference between the g- factors, \(g_{\mathrm{Er}} = 1.2\) and \(g_{\mathrm{Ti}} = 1.989^{25}\) . This detuning is comparable to their interaction energy and, thus, the two middle levels are no longer described by Zeeman product states (Fig. 2e). Finally, at the level crossing angle \((\theta \sim 12^{\circ})\) , the two Er and Ti middle levels become singlet and triplet states \(^{29}\) . However, measuring ESR spectra under these conditions becomes challenging (Fig. S5), possibly due to the limitation in our detection as discussed in the following. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 | Measurement of Er ESR transitions through a strongly coupled Ti atom. a, Constant-current STM image of the engineered Er-Ti dimer with the atomic separation of 0.72 nm. The intersection of grids represents the oxygen sites of MgO. The Er atom (circled in yellow) is adsorbed on the oxygen site of MgO, while the Ti atom (circled in purple) is adsorbed on the bridge site (setpoint: \(V_{dc} = 100 \text{mV}\) , \(I_{dc} = 20 \text{pA}\) ). b, ESR spectra of the dimer given in a. When the STM tip is located on top of Er, no peaks are observed (yellow) (set-point: \(V_{dc} = 50 \text{mV}\) , \(I_{dc} = 20 \text{pA}\) , \(V_{rf} = 20 \text{mV}\) , \(B = 0.28 \text{T}\) , \(\theta = 97^{\circ}\) ). When the STM tip is located on top of Ti, 5 ESR peaks are detected \((f_{1,2}^{\text{Ti}}, f_{3,4}^{\text{Er}}\) and \(f_{5}^{\text{TE1Er}}\) ) with \(\theta = 52^{\circ}\) (pink), while 4 ESR peaks are detected \((f_{1,2}^{\text{Ti}}\) and \(f_{3,4}^{\text{Er}}\) ) with \(\theta = 97^{\circ}\) (purple) (set-point: \(V_{dc} = 70, 60 \text{mV}\) , \(I_{dc} = 30, 40 \text{pA}\) , \(V_{rf} = 20, 15 \text{mV}\) , \(B = 0.3 \text{T}\) ). The spectra measured on Ti at \(\theta = 52^{\circ}\) and at \(\theta = 97^{\circ}\) were normalized at their respective maxima while the spectrum measured on top of Er was rescaled by the same amount used for the spectrum measured on Ti at \(\theta = 97^{\circ}\) . The spectra measured on Ti at \(\theta = 52^{\circ}\) and on Er are offset for clarity. c, ESR resonance frequencies as a function of \(\theta\) at \(B = 0.32 \text{T}\) . The ESR frequencies obtained from each measurement are given as black dots alongside the transition energies predicted from the model Hamiltonian for \(f_{1}^{\text{Ti}}\) (blue line), \(f_{2}^{\text{Ti}}\) (light blue line), \(f_{3}^{\text{Er}}\) (red line), \(f_{4}^{\text{Er}}\) (orange line), \(f_{5}^{\text{TE1Er}}\) (green line) and flip-flop transition (dashed gray line). The experimental points were obtained at different set-points \((V_{dc} = 60 - 70 \text{mV}\) , \(I_{dc} = 12 - 40 \text{pA}\) , \(V_{rf} = 15 - 25 \text{mV}\) , \(B = 0.28 - 0.8 \text{T}\) ); the resonance frequencies were rescaled by 0.32 T/B. d, e, Four-level schemes corresponding to the energies of the 4 spin states of the Er-Ti dimer and the corresponding transitions depicted as colored arrows at \(B = 0.32 \text{T}\) with different \(\theta\) (90° and 0°, respectively). At \(\theta = 90^{\circ}\) (d) the spin states are given by the Zeeman products states, while at \(\theta = 0^{\circ}\) (e), a linear combination of the Zeeman product states is needed to describe the levels.
+ +## Erbium ESR Detection and Driving Mechanisms + +The detection of ESR peaks exclusively occurs when the tip is positioned on top of Ti. Moving the tip from Ti to Er, the intensities of \(f_{3}^{\text{Er}}\) and \(f_{4}^{\text{Er}}\) gradually decrease and eventually vanish at \(\sim 0.3 \text{nm}\) from the Ti + +<--- Page Split ---> + +center (Fig. S6). This behavior indicates that driving an ESR transition on Er must induce a change in the Ti state occupation, subsequently modifying the spin polarization of the tunnel junction. In addition, Er ESR signals differ depending on specific tip conditions, i.e., different tips show positive or negative sign for \(f_{3,4}^{\mathrm{Er}}\) (Fig. 3a). + +To further delve into the driving and detection mechanisms of the Er spin, we measured the intensities of \(f_{1}^{\mathrm{Ti}}\) and \(f_{3}^{\mathrm{Er}}\) as a function of \(V_{\mathrm{rf}}\) using a tip that shows negative Er peaks (Fig. 3b). While \(f_{1}^{\mathrm{Ti}}\) exhibits a continuous increase in intensity with increasing \(V_{\mathrm{rf}}\) , \(f_{3}^{\mathrm{Er}}\) reaches saturation at \(V_{\mathrm{rf}} \sim 20 \mathrm{mV}\) . The result for \(f_{1}^{\mathrm{Ti}}\) aligns with previous measurements on \(\mathrm{Ti}^{29}\) , while the low- power saturation of Er is comparable to that of Fe, which might reflect a long \(T_{1}\) and/or a high Rabi rate \((\Omega)^{33}\) . To understand this \(V_{\mathrm{rf}}\) - dependence as well as the signs of ESR signals, we developed a rate equation model (Supplementary Section 7) based on the four- level scheme depicted in Fig. 3c. When driving \(f_{3}^{\mathrm{Er}}\) , the populations of the initial and final states involved in the transition tend to equalize through a population transfer34. The changes in population are counteracted by the relaxation rates of each state \((I_{1,2}^{\mathrm{Ti}}\) and \(I_{3,4}^{\mathrm{Er}}\) ), which tend to repopulate the depleted states. These rates are inversely proportional to the \(T_{1}\) of the atom involved in the spin flip. Since Ti located under the tip is strongly influenced by tunneling electrons, relaxation events occur on a much shorter timescale than for \(\mathrm{Er}^{35}\) , providing a more efficient pathway to attain the steady state. In addition, to account for the tip- dependent sign and intensity of Er ESR signals, we included a spin- pumping term originating from the spin- polarized current that can shift the Ti spin occupation (Fig. 3c for a negatively polarized tip)17,36. The proposed detection scheme based on the change of Ti state population accurately describes the \(V_{\mathrm{rf}}\) - dependence (Fig. 3b) and the tip- dependent sign variations of the ESR signals (Fig. S7). + +Finally, to identify the ESR driving source of the Er spin, we follow the relative peak intensity \((\Delta l / l_{\mathrm{dc}})\) at different tip heights, as controlled by \(l_{\mathrm{dc}}\) . As shown in Fig. 3d, \(\Delta l / l_{\mathrm{dc}}\) of \(f_{1}^{\mathrm{Ti}}\) increases with reducing the tip- sample distance, indicating that the main driving term for Ti arises from the exchange interaction with the spin- polarized tip37,38. On the other hand, \(\Delta l / l_{\mathrm{dc}}\) for \(f_{4}^{\mathrm{Ti}}\) remains independent of \(l_{\mathrm{dc}}\) , which identifies the modulation of the magnetic interaction with Ti as the ESR driving source of \(\mathrm{Er}^{39}\) . The modulation of the magnetic coupling40, in combination with anisotropic interaction terms32, additionally explains the drive of the double- flip transition \(f_{5}^{\mathrm{TiEr}}\) . + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 | Detection and driving mechanisms of Er ESR transitions. a, ESR spectra showing \(f_{3,4}^{\mathrm{Er}}\) for two different STM tips: negative peaks related to negative spin-pumping (yellow line) and positive peaks related to positive spin-pumping (orange line) (set-point: \(I_{dc} = 12\) , 20 pA, \(V_{dc} = 70 \mathrm{mV}\) , \(V_{rf} = 25 \mathrm{mV}\) , \(B = 0.28\) , 0.32 T, \(\theta = 67^{\circ}\) ). b, ESR peak intensities as a function of \(V_{rf}\) . The measured values for \(f_{1}^{\mathrm{Ti}}\) and \(f_{3}^{\mathrm{Er}}\) are given by black dots while the intensities predicted from the rate equation model for \(f_{1,2}^{\mathrm{Ti}}\) and \(f_{3,4}^{\mathrm{Er}}\) are given as blue, light blue, red solid lines and an orange dashed line, respectively (set-point: \(I_{dc} = 40 \mathrm{pA}\) , \(V_{dc} = 70 \mathrm{mV}\) , \(B = 0.28 \mathrm{T}\) , \(\theta = 97^{\circ}\) ). c, Four-level scheme explaining the rate equation model while driving \(f_{3}^{\mathrm{Er}}\) (red arrow). The Ti's spin relaxation rates \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) are depicted as purple arrows while the Er's spin relaxation rates \(f_{3}^{\mathrm{Er}}\) and \(f_{4}^{\mathrm{Er}}\) are given as dashed yellow arrows. The negative spin pumping effect is represented as blue double arrows. d, Normalized ESR peak intensities \((\Delta I / I_{dc})\) for \(f_{1}^{\mathrm{Ti}}\) (blue circles) and for \(f_{4}^{\mathrm{Er}}\) (orange circles) at different tip heights. Here, the tip height is controlled by the set-point current \(I_{dc}\) (set-point: \(V_{dc} = 70 \mathrm{mV}\) , \(V_{rf} = 10 \mathrm{mV}\) , \(B = 0.28 \mathrm{T}\) , \(\theta = 97^{\circ}\) ). The blue and the orange lines serve as guides for the eye. The insets show two different tip-Ti distances: larger for low \(I_{dc}\) and smaller for higher \(I_{dc}\) .
+ +<--- Page Split ---> + +## Relaxation Time Measurement through Electron-Electron Double Resonance + +By applying an additional rf voltage \((V_{\mathrm{rf2}})\) , Ti and Er spins can be simultaneously driven in the so- called "electron- electron double resonance" scheme \(^{41}\) . In a single- frequency ESR sweep, the relative intensities of \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) (Fig. 4a) reflect the thermal population of the Er spin. Instead, in double resonance the relative intensities of \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) are equalized when \(f_{3}^{\mathrm{Er}}\) is simultaneously driven (Fig. 4b). As shown in Fig. 4c, the intensity ratio of \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) \((\Delta I_{f_{2}}^{\mathrm{Ti}} / \Delta I_{f_{1}}^{\mathrm{Ti}})\) increases with increasing \(V_{\mathrm{rf}}\) only when \(V_{\mathrm{rf2}}\) is applied at the resonance frequency of \(f_{3}^{\mathrm{Er}}\) or \(f_{4}^{\mathrm{Er}}\) , enabling selective modulation of the Er states to an out- of- equilibrium configuration. + +Taking advantage of this selective driving mechanism, we implemented an inversion recovery measurement to estimate the spin relaxation time of Er \((T_{1}^{\mathrm{Er}})\) in a pump- probe scheme (Fig. 4d). After exciting \(f_{3}^{\mathrm{Er}}\) with a pumping rf pulse of \(200~\mathrm{ns}\) duration that equalized the Er population, we applied a probe pulse of \(500~\mathrm{ns}\) for \(f_{1}^{\mathrm{Ti}}\) after a delay time \(\Delta t\) . Using this sequence, we monitored the time evolution of the intensity of \(f_{1}\) as a function of \(\Delta t\) from the out- of- equilibrium to the thermal state (Fig. 4e). The fit to an exponential function (Fig. 4e) gives \(T_{1}^{\mathrm{Er}} = 0.818 \pm 0.115 \mu \mathrm{s}\) , which is five times longer than what previously measured in Fe- Ti dimers in the absence of tunnel current \(^{18}\) . We attribute this enhancement to the efficient decoupling of \(4f\) electrons from the environment, which reduces the relaxation events arising from the scattering with substrate electrons. + +The large \(T_{1}^{\mathrm{Er}}\) measured through Ti indicates that the rapid spin fluctuations of Ti occurring on the timescale of a few \(\mathrm{ns}^{35}\) do not significantly perturb the stability of the Er states. This property partially originates from the large energy detuning between Er and Ti levels, which prevents the energy exchange required for spin- flip events. Using the experimentally obtained value of \(T_{1}^{\mathrm{Er}}\) in the rate equation model, we extract a driving term \(W = \Omega^{2}T_{2} / 2\) for Er that is two times larger than for Ti in the same dimer (Supplementary Section 7). Despite the long spin lifetime and large driving term, attempts to drive Er Rabi oscillations through Ti do not yield a complete cycle (Fig. S8b), preventing a direct measure of the Er \(T_{2}\) . This is most likely due to a relatively low Rabi rate \(\Omega\) provided by the moderate Er- Ti exchange coupling, which is about 2–3 times smaller than in the Fe- Ti dimer \(^{39}\) . In turn, a low value of \(\Omega\) together with a large driving term \(W\) would imply much longer \(T_{2}\) for Er than previous \(3d\) elements, highlighting the potential of \(4f\) electrons to realize higher performance atomic- scale qubits. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 | Determination of Er spin relaxation time. a,b, Double resonance spectra in the frequency range covering Ti ESR transitions \(f_{1,2}^{\mathrm{Er}}\) (a) without and (b) with simultaneous driving of Er at the ESR frequency of \(f_{3}^{\mathrm{Er}}\) . The peak intensities of \(f_{1,2}^{\mathrm{Er}}\) are related to the relative population of the Er spin states (insets). The spectra were normalized to the sum of their peak intensity. c, ESR intensity ratios between \(\Delta I_{f_2}^{\mathrm{Er}}\) and \(\Delta I_{f_1}^{\mathrm{Er}}\) as a function of the driving strength \(V_{\mathrm{rf2}}\) at different Er ESR transition states (red, orange, and grey circles for \(f_{3}^{\mathrm{Er}}\) , \(f_{4}^{\mathrm{Er}}\) , and off-resonance, respectively). The solid curves show the correspondent simulation results by the rate equation model for \(f_{3}^{\mathrm{Er}}\) (red line), \(f_{4}^{\mathrm{Er}}\) (orange line) and at an off-resonance frequency (grey line). Set-point: \(I_{dc} = 15 \mathrm{pA}\) , \(V_{dc} = 70 \mathrm{mV}\) , \(V_{\mathrm{rf}} = 30 \mathrm{mV}\) , \(V_{\mathrm{rf2}} = 1\) , \(30 \mathrm{mV}\) , \(B = 0.28 \mathrm{T}\) , \(\theta = 97^{\circ}\) . d, Schematics of the inversion recovery measurement in a pump-probe pulse scheme to determine the Er spin relaxation time \(T_{1}^{\mathrm{Er}}\) . Each sequence is composed of a pump pulse at the resonance frequency of \(f_{3}^{\mathrm{Er}}\) (red box) and a probe pulse at the resonance frequency of \(f_{1}^{\mathrm{Er}}\) (blue box). The probe pulse follows the pump pulse after a delay time \(\Delta t\) . The population of the Er states after the pump pulse relaxes back to the thermal state following its \(T_{1}\) . e, The experimental data for the inversion recovery measurement (blue circles) show the intensity of the ESR signal at the probe pulse \(f_{1}\) as a function of the delay time. The black line shows the fit using an exponential function with \(T_{1}^{\mathrm{Er}}\) of about \(1 \mu \mathrm{s}\) . Set-point: \(I_{dc} = 50 \mathrm{pA}\) , \(V_{dc} = 70 \mathrm{mV}\) , \(V_{\mathrm{rf pump}} = 60 \mathrm{mV}\) , \(V_{\mathrm{rf probe}} = 100 \mathrm{mV}\) , \(B = 0.28 \mathrm{T}\) , \(\theta = 97^{\circ}\) .
+ +## Conclusions + +We demonstrated a new experimental approach to electrically drive ESR on the elusive \(4f\) electrons in a surface- adsorbed lanthanide atom with long spin relaxation time. Given the reduced scattering with the substrate electrons, it is reasonable to anticipate an enhancement in the coherence time of Er in comparison to \(3d\) elements. We expect that, by employing a similar approach in different atomic structures, the ESR driving on the \(4f\) electrons can be amplified, enabling the use of lanthanide atoms as surface spin qubits with superior properties compared to the routinely adopted \(3d\) elements. + +<--- Page Split ---> + +## Methods + +## STM measurements + +Our experiment was performed in a home- built STM operating at the cryogenic temperature of \(\sim 1.3 \text{K}\) in an ultrahigh vacuum environment \((< 1 \times 10^{- 9} \text{Torr})^{42}\) . Using a two- axis vector magnet (6 T in- plane/4 T out- of- plane), the magnetic fields were varied from 0.28 T to 0.9 T at different angles from the surface \(^{42}\) . To allow atom deposition on the sample kept in the STM stage, the sample is slightly tilted from the axis of the magnet by \(\sim 7^{\circ}\) as estimated from the fit to the data shown in Fig. 1d. Considering this misalignment, all our experimental \(\theta\) were offset by that amount accordingly. The magnetic tips used in our measurements were prepared by picking up \(\sim 4 - 9 \text{Fe}\) atoms from the MgO surface until the tips presented good ESR signals on isolated Ti atoms. + +## ESR measurements + +We used two different schemes to apply \(V_{\text{rf}}\) to the STM junction: one through the tip and one through an antenna (rf generators: Keysight E8257D and E8267D) \(^{42}\) . In all our measurement involving a single rf sweep, we applied the \(V_{\text{rf}}\) using an antenna located near the STM tip except for the data in Fig. 3b, where the \(V_{\text{rf}}\) was combined with the dc bias voltage \(V_{\text{dc}}\) using a diplexer at room temperature and then applied to the STM tip. The data in Fig. 4a–c were acquired by applying \(V_{\text{rf1}}\) to the tip and simultaneously \(V_{\text{rf2}}\) to the antenna. For the measurements reported in Fig. 4e and Fig. S8, the two rf voltages \((V_{\text{rf1}}\) and \(V_{\text{rf2}}\) ) were combined through a power splitter (minicircuits ZC2PD- K0244+) and applied to the STM tip. For these measurements, both rf generators were gated by an arbitrary waveform generator (Tektronix, AWG 70002B). + +## Sample preparation + +The surface of a Ag(100) substrate was cleaned by repeated cycles of Ar+ sputtering and annealing (700 K). We grew atomically thin layers of MgO(100) on the Ag(100) following a procedure described in a previous work \(^{43}\) . We deposited Fe, Ti and Er atoms \((< 1\%\) of monolayer) from high purity rods \((>99\%)\) using an e- beam evaporator. During the deposition the sample was held at \(\sim 10 \text{K}\) in order to have well- isolated single atoms on the surface. + +## Analysis of ESR spectra + +<--- Page Split ---> + +We fit the ESR spectra using a model given in \(^{29}\) in order to extract the resonance frequency, peak intensity, and peak width for the data shown in Fig. 1d, Fig. 2c, Fig. 3b,d and Fig. 4c. + +## Acknowledgements + +We thank Taehong Ahn and Leonard Edens for their support at the initial stage of the experiment and Yi Chen, Arzhang Ardavan, and Joaquín Fernández- Rossier for fruitful discussions. We acknowledge support from the Institute for Basic Science (IBS- R027- D1). 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ACS Nano 17, 14144- 14151 (2023). https://doi.org:10.1021/acsnano.3c0475442 Hwang, J. et al. Development of a scanning tunneling microscope for variable temperature electron spin resonance. Rev. Sci. Instrum. 93 (2022). https://doi.org:10.1063/5.009608143 Paul, W. et al. Control of the millisecond spin lifetime of an electrically probed atom. Nature Physics 13, 403- 407 (2017). https://doi.org:10.1038/nphys3965 + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- ErMgOESRSIsubmission.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e_det.mmd b/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..87b194925bc3db7d79b1a4b392632d3b3182079b --- /dev/null +++ b/preprint/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e/preprint__0acd819abe48c47a84cbc21e7054100bd0d1363bbc114db43e3926604a5e536e_det.mmd @@ -0,0 +1,258 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 949, 175]]<|/det|> +# Electrically Driven Spin Resonance of 4f Electrons in a Single Atom on a Surface + +<|ref|>text<|/ref|><|det|>[[44, 195, 310, 240]]<|/det|> +Yujeong Bae bae.yu.jeong@qns.science + +<|ref|>text<|/ref|><|det|>[[44, 268, 741, 288]]<|/det|> +IBS Center for Quantum Nanoscience https://orcid.org/0000- 0002- 9983- 8529 + +<|ref|>text<|/ref|><|det|>[[44, 293, 383, 333]]<|/det|> +Stefano Reale IBS Center for Quantum Nanoscience + +<|ref|>text<|/ref|><|det|>[[44, 340, 383, 380]]<|/det|> +Jiyoon Hwang IBS Center for Quantum Nanoscience + +<|ref|>text<|/ref|><|det|>[[44, 386, 383, 426]]<|/det|> +Jeongmin Oh IBS Center for Quantum Nanoscience + +<|ref|>text<|/ref|><|det|>[[44, 432, 850, 473]]<|/det|> +Harald Brune Ecole Polytechnique Fédérale de Lausanne (EPFL) https://orcid.org/0000- 0003- 4459- 3111 + +<|ref|>text<|/ref|><|det|>[[44, 478, 891, 519]]<|/det|> +Andreas Heinrich Institute for Basic Science at Ewha Womans University https://orcid.org/0000- 0001- 6204- 471X + +<|ref|>text<|/ref|><|det|>[[44, 524, 750, 565]]<|/det|> +Fabio Donati IBS Center for Quantum Nanoscience https://orcid.org/0000- 0002- 3932- 2889 + +<|ref|>sub_title<|/ref|><|det|>[[44, 608, 104, 625]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 647, 135, 665]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 683, 338, 702]]<|/det|> +Posted Date: November 7th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 721, 475, 740]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3385164/v1 + +<|ref|>text<|/ref|><|det|>[[44, 758, 914, 801]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 819, 534, 839]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 875, 916, 917]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on June 20th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 49447- y. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[205, 91, 793, 144]]<|/det|> +# Electrically Driven Spin Resonance of \(4f\) Electrons in a Single Atom on a Surface + +<|ref|>text<|/ref|><|det|>[[113, 177, 884, 216]]<|/det|> +Stefano Reale \(^{1,2,3}\) , Jiyoon Hwang \(^{1,4}\) , Jeongmin Oh \(^{1,4}\) , Harald Brune \(^{5}\) , Andreas J. Heinrich \(^{1,4}\) , Fabio Donati \(^{1,4*}\) , and Yujeong Bae \(^{1,4*}\) + +<|ref|>text<|/ref|><|det|>[[113, 228, 816, 344]]<|/det|> +\(^{1}\) Center for Quantum Nanoscience (QNS), Institute for Basic Science (IBS), Seoul 03760, Republic of Korea \(^{2}\) Ewha Womans University, Seoul 03760, Republic of Korea \(^{3}\) Department of Energy, Politecnico di Milano, Milano 20133, Italy \(^{4}\) Department of Physics, Ewha Womans University, Seoul 03760, Republic of Korea \(^{5}\) Institute of Physics, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland \*Corresponding authors: F.D. (donati.fabio@qns.science), Y.B. (bae.yujeong@qns.science) + +<|ref|>text<|/ref|><|det|>[[113, 394, 886, 642]]<|/det|> +A pivotal challenge in quantum technologies lies in reconciling long coherence times with efficient manipulation of the quantum states of a system. Lanthanide atoms, with their well- localized \(4f\) electrons, emerge as a promising solution to this dilemma if provided with a rational design for manipulation and detection. Here we construct tailored spin structures to perform electron spin resonance on a single lanthanide atom using a scanning tunneling microscope. A magnetically coupled structure made of an erbium and a titanium atom enables us to both drive the erbium's \(4f\) electron spins and indirectly probe them through the titanium's \(3d\) electrons. In this coupled configuration, the erbium spin states exhibit a five- fold increase in the spin relaxation time and a two- fold increase in the driving efficiency compared to the \(3d\) electron counterparts. Our work provides a new approach to accessing highly protected spin states, enabling their coherent control in an all- electric fashion. + +<|ref|>text<|/ref|><|det|>[[114, 693, 885, 840]]<|/det|> +The last two decades have witnessed a rising focus on the control and application of quantum coherent effects, marking the advent of the so- called "second quantum revolution". Utilizing quantum coherent functionalities of materials for novel technologies, such as imaging, information processing, and communications, requires robustness of their quantum coherence, addressability, and scalability \(^{1}\) . However, these requirements often clash since decoupling the quantum states from the environment prolongs the quantum coherent properties but hinders the possibility of efficient state manipulation. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 885, 387]]<|/det|> +Lanthanide atoms represent a promising platform to tackle this dilemma. Their well- localized \(4f\) electrons show long spin relaxation \(T_{1}^{2,3}\) and coherence times \(T_{2}^{4,5}\) . In addition, their strong hyperfine interaction facilitates the read- out of nuclear spins \(^{6,7}\) . In bulk insulators, exceedingly long \(T_{1}\) and \(T_{2}\) have been demonstrated using optical control and detection \(^{8,11}\) down to the single atom level \(^{12,13}\) . While hybrid optical- electrical approaches have been developed to access individual lanthanide atom's spins embedded in a silicon transistor \(^{14}\) , it is still challenging to achieve efficient control of the quantum states using electrical transport methods. This necessitates the rational design of a quantum platform capable of tackling both control and detection schemes, along with their interactions with local environments. In this context, single crystal surfaces constitute an advantageous framework both for building atomically engineered nanostructures and addressing individual spin centers, in particular using probe techniques \(^{15 - 18}\) . However, coherent manipulation and detection of surface- adsorbed lanthanide atoms have so far remained elusive. + +<|ref|>text<|/ref|><|det|>[[113, 404, 885, 625]]<|/det|> +In this work, we demonstrate the control and detection of \(4f\) electron spins by building atomic- scale structures on a surface using a scanning tunneling microscope (STM) with electron spin resonance (ESR) capabilities \(^{19 - 22}\) . The atomic structures are composed of an erbium (Er) atom as the target spin system and a magnetically coupled titanium (Ti) atom as the sensor spin. This architecture allows us to drive ESR transitions on the Er \(4f\) electrons with a projected angular momentum of \(1 / 2^{23}\) and to probe them indirectly through Ti. We observed an Er \(T_{1}\) of close to \(1\mu s\) , 5 times longer than what was previously measured in \(3d\) electrons with spin \(1 / 2\) on the same surface \(^{18}\) . This novel platform allows for the ESR driving and read- out of the well- screened \(4f\) electron spin states, paving the way to integrate lanthanide atoms in quantum architectures. + +<|ref|>sub_title<|/ref|><|det|>[[115, 644, 419, 661]]<|/det|> +## Sensing Er Spin States through a Ti Atom + +<|ref|>text<|/ref|><|det|>[[113, 678, 886, 876]]<|/det|> +Erbium atoms on a few monolayer- thick MgO(100) on Ag(100) present a \(4f^{11}\) configuration with no unpaired electrons in the \(5d\) and \(6s\) shells \(^{23}\) . The atomic- like spin and orbital momenta are coupled through the large spin- orbit interaction into a total angular momentum \(J_{\mathrm{Er}}\) with magnitude of \(15\hbar /2^{23}\) . When adsorbed on the oxygen site of MgO (Fig. 1a), the crystal field leads to a strong hard- axis magneto- crystalline anisotropy that stabilizes a doubly- degenerate ground state with an out- of- plane component of the angular momentum \(\pm \hbar /2^{23}\) , which splits into two singlets when an external magnetic field (B) is applied. As found in a previous work \(^{23}\) , the component of \(J_{\mathrm{Er}}\) along the magnetic field direction (z), defined as \(J_{z}\) , increases from \(\pm \hbar /2\) to \(\pm 4\hbar\) by rotating B from the out- of- plane ( \(\theta = 0^{\circ}\) ) to the in- plane ( \(\theta = 90^{\circ}\) ) direction + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 210]]<|/det|> +(Fig. 1b), while retaining a large probability for spin dipole transitions. Given these properties, Er can be regarded as a highly tunable two- level system allowing for efficient ESR driving. To characterize the magnetic states and anisotropy of Er, we utilized the dipole field sensing technique24 with a Ti atom on the bridge binding site of MgO as a well- known spin sensor. On this binding site, Ti has a spin \(S_{\mathrm{Ti}}\) of magnitude \(\hbar /2\) and a relatively weak g- factor anisotropy25 with respect to the oxygen binding site26. + +<|ref|>text<|/ref|><|det|>[[112, 225, 886, 536]]<|/det|> +We deposited Er and Ti at cryogenic temperatures ( \(\sim 10 \text{K}\) ) on 2 monolayers of MgO grown on Ag(100) (Methods and Fig. S1a). Their binding sites on the surface can be changed by atom manipulation (Supplementary Section 2). Figure 1c shows the ESR spectra obtained on Ti in an Er- Ti dimer with the atomic separation of 0.928 nm (Fig. S2b). For \(\theta = 8^{\circ}\) , we observed one ESR peak at the resonance frequency of Ti which splits into two peaks separated by \(\Delta f = 334 \pm 3 \text{MHz}\) when rotating \(B\) close to the in- plane direction ( \(\theta = 68^{\circ}\) ). The two ESR peaks stem from the magnetic interaction with the Er spin fluctuating between two states24 during the measurement, with the relative peak intensity being proportional to the time- averaged population of the Er states. The pronounced difference in the relative intensity of the ESR peaks indicates a large imbalance in the Er state occupation even at \(B = 0.3 \text{T}\) and \(1.3 \text{K}\) , which reflects the large \(J_{z}\) of Er at \(\theta = 68^{\circ}\) (Fig. 1b). The sign of this asymmetry depends on the character of the magnetic interactions between the two atoms. In Fig. 1c, the peak at the lower frequency is less intense than the one at the higher frequency and, hence, the interaction can be regarded as ferromagnetic27. + +<|ref|>text<|/ref|><|det|>[[113, 556, 884, 631]]<|/det|> +The angle dependence of \(\Delta f\) (Fig. 1d) gives a direct measurement of the Er- Ti interaction energy and of its anisotropy24,27. To interpret it, we model the system through a spin- Hamiltonian including both the single atom Zeeman and anisotropy terms, as well as the interaction between the two spins: + +<|ref|>equation<|/ref|><|det|>[[113, 647, 660, 669]]<|/det|> +\[H = \mu_{B}g_{\mathrm{Er}}\boldsymbol {B}\cdot \boldsymbol {J}_{\mathrm{Er}} + D\dot{J}_{\perp}^{2} + \mu_{B}\boldsymbol {B}\cdot \boldsymbol {g}_{\mathrm{Ti}}\cdot \boldsymbol {S}_{\mathrm{Ti}} + H_{\mathrm{dip}} + H_{\mathrm{exc}}. \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[113, 685, 884, 783]]<|/det|> +Here, \(\mu_{B}\) is the Bohr magneton, \(J_{\perp}\) is the out- of- plane component of \(J_{\mathrm{Er}}\) , \(g_{\mathrm{Er}} = 1.2\) is the Er g- factor, and \(g_{\mathrm{Ti}}\) is the Ti anisotropic g- tensor25. We use a magnetic anisotropy parameter \(D = 2.4 \text{meV}\) to match the Er energy splitting found in a previous study23. The magnetic coupling consists of dipolar \((H_{\mathrm{dip}})\) and Heisenberg exchange interactions \((H_{\mathrm{exc}})\) : + +<|ref|>equation<|/ref|><|det|>[[113, 799, 575, 870]]<|/det|> +\[H_{\mathrm{dip}} = \frac{\mu_0\mu_B^2}{4\pi\hbar^2r^3}\left[g_{\mathrm{Er}}J_{\mathrm{Er}}\cdot \mathbf{g}_{\mathrm{Ti}}\cdot \mathbf{S}_{\mathrm{Ti}} - 3(\hat{\mathbf{r}}\cdot g_{\mathrm{Er}}J_{\mathrm{Er}})(\hat{\mathbf{r}}\cdot \mathbf{g}_{\mathrm{Ti}}\cdot \mathbf{S}_{\mathrm{Ti}})\right],\] \[H_{\mathrm{exc}} = \frac{J_{\mathrm{exc}}}{\hbar^2} (J_{\mathrm{Er}}\cdot \mathbf{S}_{\mathrm{Ti}}),\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 261]]<|/det|> +where \(\mu_0\) is the vacuum permittivity, \(r\) the separation between the two atoms, \(\hat{r}\) the unit vector connecting them \(^{24}\) , and \(J_{\mathrm{exc}}\) the exchange interaction energy expressed in terms of \(J_{\mathrm{Er}}\) \(^{28}\) . In our model, \(J_{\mathrm{exc}}\) is the only free parameter for the fit. As shown in Fig. 1d, our model accurately reproduces the data for \(J_{\mathrm{exc}} / \mathrm{h} = - 48 \mathrm{MHz}\) , where the negative sign indicates an antiferromagnetic coupling. This value is more than 20 times smaller than that observed for a Ti- Ti dimer at the same distance \((- 1.16 \mathrm{GHz})\) \(^{29}\) . We ascribe the smaller Er- Ti coupling to the localization of the \(4f\) orbitals near the atom's core, which limits the overlap between Er and Ti orbitals when compared to the Ti- Ti case. + +<|ref|>text<|/ref|><|det|>[[112, 277, 886, 525]]<|/det|> +The strong angle dependence of \(\Delta f\) can be understood by considering the large magneto- crystalline anisotropy of \(J_{\mathrm{Er}}\) . At \(\theta = 90^{\circ}\) , \(J_{z}\) is largest (4h) and the angular momenta of both atoms are parallel to \(\hat{r}\) (Fig. 1e), which maximizes the contribution of the dipolar coupling with a positive sign (ferromagnetic). When rotating \(B\) away from the in- plane direction, \(S_{\mathrm{Ti}}\) follows the direction of \(B\) , while the anisotropy of Er preserves a large component of \(J_{\mathrm{Er}}\) mainly aligned along the in- plane direction (Fig. 1f). This misalignment between the two angular momenta reduces the dipolar interaction. Finally, as \(B\) approaches the surface normal (Fig. 1g), \(J_{\mathrm{Er}}\) turns towards the out- of- plane direction with a much smaller value of \(J_{z} = \hbar /2\) . With the two momenta being perpendicular to \(\hat{r}\) , the dipolar contribution is minimal and negative (antiferromagnetic). Conversely, the mutual projection of \(S_{\mathrm{Ti}}\) and \(J_{\mathrm{Er}}\) is the only factor modulating the exchange interaction term, which remains negative (antiferromagnetic) at all angles. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 85, 857, 696]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 705, 884, 888]]<|/det|> +
Fig. 1 | Probing Er 4f electron spins through a Ti spin sensor. a, Schematic of the experimental set-up for ESR-STM measurement of an Er-Ti dimer built on MgO/(100)/Ag(100). The Ti atom (purple) is positioned close to the Er atom (orange) and located under a spin-polarized (SP) STM tip. The external magnetic field (B) defines the z-direction and is applied at an angle \(\theta\) from the out-of-plane direction. A dc voltage \(V_{dc}\) is applied to the STM junction while the radio-frequency (rf) voltage is applied to the tip or to the antenna with an amplitude \(V_{rf}\). b, The projected total angular momentum of Er \((J_{z})\) onto the B field direction as a function of \(\theta\) . The strong magnetic anisotropy favors an in-plane alignment of \(J_{Er}\) . c, ESR spectra of the Ti atom placed 0.928 nm apart from the Er atom at different \(\theta\) . At \(\theta = 8^{\circ}\) , a single ESR peak is visible (pink) while, at \(\theta = 68^{\circ}\) (purple), the two ESR peaks are separated due to the magnetic interactions between the Er and Ti (set-point: \(V_{dc} = 50 \text{mV}\) , \(I_{dc} = 20 \text{pA}\) , \(V_{rf} = 12 \text{mV}\) , \(B = 0.3 \text{T}\) ). The spectrum at \(\theta = 8^{\circ}\) (pink) was normalized at its maximum intensity while the spectrum at \(\theta = 68^{\circ}\) (purple) was normalized to the sum of the intensities of its two peaks. The frequency detuning is defined with respect to 9.1 GHz (8.1 GHz) for the spectrum at \(\theta = 8^{\circ}\) ( \(\theta = 68^{\circ}\) ). d, ESR peak separation, \(\Delta f\) , as a function of \(\theta\) . The experimental points (black dots) were acquired at different set-points ( \(V_{dc}\)
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 203]]<|/det|> +\(= 50 \text{mV}, I_{\text{dc}} = 12 - 30 \text{pA}, V_{\text{rf}} = 12 - 20 \text{mV}, B = 0.3 \text{T}\) . The total interaction (solid purple line) calculated by the model Hamiltonian is composed of a dipolar contribution (dashed blue line) and an exchange contribution (dashed pink line). \(\mathbf{e} - \mathbf{g}\) , Schematic of the angular momenta of Er and Ti on \(\text{MgO / Ag(100)}\) . The dipolar fields induced by Er are depicted as black curved arrows. When \(\mathbf{B}\) is applied along the in-plane direction \((\theta = 90^{\circ})\) , the \(J_{z}\) is maximum and aligned with the spin of Ti giving the largest ferromagnetic interaction. When \(\mathbf{B}\) is rotated, the spin of Ti follows the direction of \(\mathbf{B}\) while the total angular momentum of Er is aligned preferentially in-plane (f). In the out-of-plane direction \((\theta = 0^{\circ})\) , \(J_{z}\) is minimum and aligned with the spin of Ti (g) giving a small antiferromagnetic interaction. + +<|ref|>sub_title<|/ref|><|det|>[[115, 254, 365, 272]]<|/det|> +## Spin Resonance of Er 4f Electrons + +<|ref|>text<|/ref|><|det|>[[114, 288, 884, 410]]<|/det|> +The direct drive of ESR in STM requires positioning the tip directly on top of the target atom19. However, we observed no ESR when positioning the tip over an Er atom (Fig. S3b), which we attribute to the small polarization of the 5d and 6s shells of Er and to the weak interaction between the 4f and tunneling electrons. These factors were found to limit the tunneling magnetoresistance at the STM junction in other lanthanide atoms30,31, possibly hindering both the ESR drive and detection23. + +<|ref|>text<|/ref|><|det|>[[112, 425, 884, 883]]<|/det|> +To overcome this limitation, we built a strongly interacting Er- Ti dimer by positioning Ti at 0.72 nm from Er through atom manipulation (Fig. 2a and Supplementary Section 2). Similar to the isolated atom, we observed no ESR peaks at the Er position in the dimer (yellow curve in Fig. 2b). However, when the tip was positioned on Ti, we observed up to 5 peaks (pink curve in Fig. 2b). The first two peaks below 10 GHz with \(\Delta f = 2.70 \pm 0.01 \text{GHz}\) correspond to the ESR transitions of Ti that were similarly found in the dimer with larger atomic separations (Fig. 1c). Hence, we label them as \(f_{1}^{\text{Ti}}\) and \(f_{2}^{\text{Ti}}\), respectively. In this dimer, we observed that \(f_{1}^{\text{Ti}}\) shows a higher intensity than \(f_{2}^{\text{Ti}}\), indicating an antiferromagnetic exchange interaction27 dominating over the dipolar coupling at this atomic separation. At higher frequencies, we further observed two peaks that are significantly blue- shifted when rotating \(\mathbf{B}\) from \(\theta = 52^{\circ}\) (pink curve in Fig. 2b) to \(97^{\circ}\) (purple). The higher resonance frequencies and pronounced angle dependence indicate that those transitions involve the large and anisotropic angular momentum of Er, and, thus, we label them as \(f_{3}^{\text{Er}}\) and \(f_{4}^{\text{Er}}\). In addition, their frequency separation exactly matches the one between \(f_{1}^{\text{Ti}}\) and \(f_{2}^{\text{Ti}}\), reflecting the same Er- Ti interaction. On the other hand, \(f_{3}^{\text{Er}}\) and \(f_{4}^{\text{Er}}\) are approximately equal in intensity, indicating that Ti fluctuates between two spin states with almost equal occupations. The comparable Ti states' occupation stems from the scattering with tunneling electrons and from the Zeeman splitting of Ti (~7 GHz) being smaller than the thermal energy at the measurement temperature of 1.3 K (~27 GHz). With \(\mathbf{B}\) at \(\theta = 52^{\circ}\), we observed one more peak at even higher frequencies. Its frequency exactly matches the sum of \(f_{1}^{\text{Ti}}\) and \(f_{4}^{\text{Er}}\) (or equivalently \(f_{2}^{\text{Ti}}\) and \(f_{3}^{\text{Er}}\)), which suggests an ESR transition + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 86, 885, 237]]<|/det|> +involving both Ti and Er spins. We label this peak as \(f_{3}^{\mathrm{TiEr}}\) . Remarkably, the sign of \(f_{3}^{\mathrm{Er}}\) , \(f_{4}^{\mathrm{Er}}\) and \(f_{5}^{\mathrm{TiEr}}\) is opposite to that of \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) , indicating a different detection mechanism for the transitions involving the Er spin, which will be discussed below. Finally, we observed an energy level crossing between Er and Ti transitions at \(\theta \sim 12^{\circ}\) , with the Er resonance frequencies further shifting below the Ti transitions at \(\theta \sim 0^{\circ}\) (Fig. 2c and Fig. S4). This peculiar behavior is a consequence of the large difference in magnetic anisotropy between Er and \(\mathrm{Ti^{23}}\) . + +<|ref|>text<|/ref|><|det|>[[112, 253, 886, 532]]<|/det|> +As shown in Fig. 2c, the angular dependence of the ESR frequencies is well reproduced by using Eq. 1 with \(J_{\mathrm{exc}} / \mathrm{h} = - 326 \mathrm{MHz}\) . We observed small deviations for \(f_{1}^{\mathrm{Ti}}\) , \(f_{2}^{\mathrm{Ti}}\) and \(f_{5}^{\mathrm{TiEr}}\) , which we ascribe to different experimental conditions and magnetic interaction of Ti with the tip, which is not included in our model. Diagonalizing Eq. 1 allows us to analyze the quantum states of the Er- Ti dimer in terms of individual Er and Ti spin states. For an in- plane \(B = 0.3 \mathrm{T}\) , the energy detuning between the Er and Ti spins (30 GHz) is much larger than the interaction energy (about 3 GHz). Therefore, the Er- Ti dimer can be modeled with the 4 Zeeman product states of the Er and Ti spins. Following this picture, we can support the assignment of \(f_{1,2}^{\mathrm{Ti}}\) as Ti spin transitions occurring with no changes in the Er state, while \(f_{3,4}^{\mathrm{Er}}\) correspond to Er spin transitions without altering Ti. Finally, we attribute \(f_{5}^{\mathrm{TiEr}}\) to a double- flip transition involving both Er and Ti spins. Even though a \(|\Delta m| > 1 \mathrm{~h}\) process is generally forbidden to first order, anisotropic terms in the magnetic interaction can give rise to higher order matrix elements connecting states with \(\Delta m = \pm 2 \mathrm{~h}^{32}\) . + +<|ref|>text<|/ref|><|det|>[[113, 547, 886, 695]]<|/det|> +When the field is oriented at \(\theta = 0^{\circ}\) , both \(J_{\mathrm{Er}}\) and \(S_{\mathrm{Ti}}\) show an expectation value of \(\hbar /2\) , but a detuning still occurs due to the difference between the g- factors, \(g_{\mathrm{Er}} = 1.2\) and \(g_{\mathrm{Ti}} = 1.989^{25}\) . This detuning is comparable to their interaction energy and, thus, the two middle levels are no longer described by Zeeman product states (Fig. 2e). Finally, at the level crossing angle \((\theta \sim 12^{\circ})\) , the two Er and Ti middle levels become singlet and triplet states \(^{29}\) . However, measuring ESR spectra under these conditions becomes challenging (Fig. S5), possibly due to the limitation in our detection as discussed in the following. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 92, 876, 465]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 479, 884, 765]]<|/det|> +
Fig. 2 | Measurement of Er ESR transitions through a strongly coupled Ti atom. a, Constant-current STM image of the engineered Er-Ti dimer with the atomic separation of 0.72 nm. The intersection of grids represents the oxygen sites of MgO. The Er atom (circled in yellow) is adsorbed on the oxygen site of MgO, while the Ti atom (circled in purple) is adsorbed on the bridge site (setpoint: \(V_{dc} = 100 \text{mV}\) , \(I_{dc} = 20 \text{pA}\) ). b, ESR spectra of the dimer given in a. When the STM tip is located on top of Er, no peaks are observed (yellow) (set-point: \(V_{dc} = 50 \text{mV}\) , \(I_{dc} = 20 \text{pA}\) , \(V_{rf} = 20 \text{mV}\) , \(B = 0.28 \text{T}\) , \(\theta = 97^{\circ}\) ). When the STM tip is located on top of Ti, 5 ESR peaks are detected \((f_{1,2}^{\text{Ti}}, f_{3,4}^{\text{Er}}\) and \(f_{5}^{\text{TE1Er}}\) ) with \(\theta = 52^{\circ}\) (pink), while 4 ESR peaks are detected \((f_{1,2}^{\text{Ti}}\) and \(f_{3,4}^{\text{Er}}\) ) with \(\theta = 97^{\circ}\) (purple) (set-point: \(V_{dc} = 70, 60 \text{mV}\) , \(I_{dc} = 30, 40 \text{pA}\) , \(V_{rf} = 20, 15 \text{mV}\) , \(B = 0.3 \text{T}\) ). The spectra measured on Ti at \(\theta = 52^{\circ}\) and at \(\theta = 97^{\circ}\) were normalized at their respective maxima while the spectrum measured on top of Er was rescaled by the same amount used for the spectrum measured on Ti at \(\theta = 97^{\circ}\) . The spectra measured on Ti at \(\theta = 52^{\circ}\) and on Er are offset for clarity. c, ESR resonance frequencies as a function of \(\theta\) at \(B = 0.32 \text{T}\) . The ESR frequencies obtained from each measurement are given as black dots alongside the transition energies predicted from the model Hamiltonian for \(f_{1}^{\text{Ti}}\) (blue line), \(f_{2}^{\text{Ti}}\) (light blue line), \(f_{3}^{\text{Er}}\) (red line), \(f_{4}^{\text{Er}}\) (orange line), \(f_{5}^{\text{TE1Er}}\) (green line) and flip-flop transition (dashed gray line). The experimental points were obtained at different set-points \((V_{dc} = 60 - 70 \text{mV}\) , \(I_{dc} = 12 - 40 \text{pA}\) , \(V_{rf} = 15 - 25 \text{mV}\) , \(B = 0.28 - 0.8 \text{T}\) ); the resonance frequencies were rescaled by 0.32 T/B. d, e, Four-level schemes corresponding to the energies of the 4 spin states of the Er-Ti dimer and the corresponding transitions depicted as colored arrows at \(B = 0.32 \text{T}\) with different \(\theta\) (90° and 0°, respectively). At \(\theta = 90^{\circ}\) (d) the spin states are given by the Zeeman products states, while at \(\theta = 0^{\circ}\) (e), a linear combination of the Zeeman product states is needed to describe the levels.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 811, 466, 828]]<|/det|> +## Erbium ESR Detection and Driving Mechanisms + +<|ref|>text<|/ref|><|det|>[[115, 846, 884, 891]]<|/det|> +The detection of ESR peaks exclusively occurs when the tip is positioned on top of Ti. Moving the tip from Ti to Er, the intensities of \(f_{3}^{\text{Er}}\) and \(f_{4}^{\text{Er}}\) gradually decrease and eventually vanish at \(\sim 0.3 \text{nm}\) from the Ti + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 186]]<|/det|> +center (Fig. S6). This behavior indicates that driving an ESR transition on Er must induce a change in the Ti state occupation, subsequently modifying the spin polarization of the tunnel junction. In addition, Er ESR signals differ depending on specific tip conditions, i.e., different tips show positive or negative sign for \(f_{3,4}^{\mathrm{Er}}\) (Fig. 3a). + +<|ref|>text<|/ref|><|det|>[[112, 201, 886, 634]]<|/det|> +To further delve into the driving and detection mechanisms of the Er spin, we measured the intensities of \(f_{1}^{\mathrm{Ti}}\) and \(f_{3}^{\mathrm{Er}}\) as a function of \(V_{\mathrm{rf}}\) using a tip that shows negative Er peaks (Fig. 3b). While \(f_{1}^{\mathrm{Ti}}\) exhibits a continuous increase in intensity with increasing \(V_{\mathrm{rf}}\) , \(f_{3}^{\mathrm{Er}}\) reaches saturation at \(V_{\mathrm{rf}} \sim 20 \mathrm{mV}\) . The result for \(f_{1}^{\mathrm{Ti}}\) aligns with previous measurements on \(\mathrm{Ti}^{29}\) , while the low- power saturation of Er is comparable to that of Fe, which might reflect a long \(T_{1}\) and/or a high Rabi rate \((\Omega)^{33}\) . To understand this \(V_{\mathrm{rf}}\) - dependence as well as the signs of ESR signals, we developed a rate equation model (Supplementary Section 7) based on the four- level scheme depicted in Fig. 3c. When driving \(f_{3}^{\mathrm{Er}}\) , the populations of the initial and final states involved in the transition tend to equalize through a population transfer34. The changes in population are counteracted by the relaxation rates of each state \((I_{1,2}^{\mathrm{Ti}}\) and \(I_{3,4}^{\mathrm{Er}}\) ), which tend to repopulate the depleted states. These rates are inversely proportional to the \(T_{1}\) of the atom involved in the spin flip. Since Ti located under the tip is strongly influenced by tunneling electrons, relaxation events occur on a much shorter timescale than for \(\mathrm{Er}^{35}\) , providing a more efficient pathway to attain the steady state. In addition, to account for the tip- dependent sign and intensity of Er ESR signals, we included a spin- pumping term originating from the spin- polarized current that can shift the Ti spin occupation (Fig. 3c for a negatively polarized tip)17,36. The proposed detection scheme based on the change of Ti state population accurately describes the \(V_{\mathrm{rf}}\) - dependence (Fig. 3b) and the tip- dependent sign variations of the ESR signals (Fig. S7). + +<|ref|>text<|/ref|><|det|>[[113, 649, 886, 826]]<|/det|> +Finally, to identify the ESR driving source of the Er spin, we follow the relative peak intensity \((\Delta l / l_{\mathrm{dc}})\) at different tip heights, as controlled by \(l_{\mathrm{dc}}\) . As shown in Fig. 3d, \(\Delta l / l_{\mathrm{dc}}\) of \(f_{1}^{\mathrm{Ti}}\) increases with reducing the tip- sample distance, indicating that the main driving term for Ti arises from the exchange interaction with the spin- polarized tip37,38. On the other hand, \(\Delta l / l_{\mathrm{dc}}\) for \(f_{4}^{\mathrm{Ti}}\) remains independent of \(l_{\mathrm{dc}}\) , which identifies the modulation of the magnetic interaction with Ti as the ESR driving source of \(\mathrm{Er}^{39}\) . The modulation of the magnetic coupling40, in combination with anisotropic interaction terms32, additionally explains the drive of the double- flip transition \(f_{5}^{\mathrm{TiEr}}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 92, 876, 515]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 536, 884, 725]]<|/det|> +
Fig. 3 | Detection and driving mechanisms of Er ESR transitions. a, ESR spectra showing \(f_{3,4}^{\mathrm{Er}}\) for two different STM tips: negative peaks related to negative spin-pumping (yellow line) and positive peaks related to positive spin-pumping (orange line) (set-point: \(I_{dc} = 12\) , 20 pA, \(V_{dc} = 70 \mathrm{mV}\) , \(V_{rf} = 25 \mathrm{mV}\) , \(B = 0.28\) , 0.32 T, \(\theta = 67^{\circ}\) ). b, ESR peak intensities as a function of \(V_{rf}\) . The measured values for \(f_{1}^{\mathrm{Ti}}\) and \(f_{3}^{\mathrm{Er}}\) are given by black dots while the intensities predicted from the rate equation model for \(f_{1,2}^{\mathrm{Ti}}\) and \(f_{3,4}^{\mathrm{Er}}\) are given as blue, light blue, red solid lines and an orange dashed line, respectively (set-point: \(I_{dc} = 40 \mathrm{pA}\) , \(V_{dc} = 70 \mathrm{mV}\) , \(B = 0.28 \mathrm{T}\) , \(\theta = 97^{\circ}\) ). c, Four-level scheme explaining the rate equation model while driving \(f_{3}^{\mathrm{Er}}\) (red arrow). The Ti's spin relaxation rates \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) are depicted as purple arrows while the Er's spin relaxation rates \(f_{3}^{\mathrm{Er}}\) and \(f_{4}^{\mathrm{Er}}\) are given as dashed yellow arrows. The negative spin pumping effect is represented as blue double arrows. d, Normalized ESR peak intensities \((\Delta I / I_{dc})\) for \(f_{1}^{\mathrm{Ti}}\) (blue circles) and for \(f_{4}^{\mathrm{Er}}\) (orange circles) at different tip heights. Here, the tip height is controlled by the set-point current \(I_{dc}\) (set-point: \(V_{dc} = 70 \mathrm{mV}\) , \(V_{rf} = 10 \mathrm{mV}\) , \(B = 0.28 \mathrm{T}\) , \(\theta = 97^{\circ}\) ). The blue and the orange lines serve as guides for the eye. The insets show two different tip-Ti distances: larger for low \(I_{dc}\) and smaller for higher \(I_{dc}\) .
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[114, 90, 685, 108]]<|/det|> +## Relaxation Time Measurement through Electron-Electron Double Resonance + +<|ref|>text<|/ref|><|det|>[[113, 124, 885, 303]]<|/det|> +By applying an additional rf voltage \((V_{\mathrm{rf2}})\) , Ti and Er spins can be simultaneously driven in the so- called "electron- electron double resonance" scheme \(^{41}\) . In a single- frequency ESR sweep, the relative intensities of \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) (Fig. 4a) reflect the thermal population of the Er spin. Instead, in double resonance the relative intensities of \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) are equalized when \(f_{3}^{\mathrm{Er}}\) is simultaneously driven (Fig. 4b). As shown in Fig. 4c, the intensity ratio of \(f_{1}^{\mathrm{Ti}}\) and \(f_{2}^{\mathrm{Ti}}\) \((\Delta I_{f_{2}}^{\mathrm{Ti}} / \Delta I_{f_{1}}^{\mathrm{Ti}})\) increases with increasing \(V_{\mathrm{rf}}\) only when \(V_{\mathrm{rf2}}\) is applied at the resonance frequency of \(f_{3}^{\mathrm{Er}}\) or \(f_{4}^{\mathrm{Er}}\) , enabling selective modulation of the Er states to an out- of- equilibrium configuration. + +<|ref|>text<|/ref|><|det|>[[113, 319, 886, 545]]<|/det|> +Taking advantage of this selective driving mechanism, we implemented an inversion recovery measurement to estimate the spin relaxation time of Er \((T_{1}^{\mathrm{Er}})\) in a pump- probe scheme (Fig. 4d). After exciting \(f_{3}^{\mathrm{Er}}\) with a pumping rf pulse of \(200~\mathrm{ns}\) duration that equalized the Er population, we applied a probe pulse of \(500~\mathrm{ns}\) for \(f_{1}^{\mathrm{Ti}}\) after a delay time \(\Delta t\) . Using this sequence, we monitored the time evolution of the intensity of \(f_{1}\) as a function of \(\Delta t\) from the out- of- equilibrium to the thermal state (Fig. 4e). The fit to an exponential function (Fig. 4e) gives \(T_{1}^{\mathrm{Er}} = 0.818 \pm 0.115 \mu \mathrm{s}\) , which is five times longer than what previously measured in Fe- Ti dimers in the absence of tunnel current \(^{18}\) . We attribute this enhancement to the efficient decoupling of \(4f\) electrons from the environment, which reduces the relaxation events arising from the scattering with substrate electrons. + +<|ref|>text<|/ref|><|det|>[[113, 561, 886, 836]]<|/det|> +The large \(T_{1}^{\mathrm{Er}}\) measured through Ti indicates that the rapid spin fluctuations of Ti occurring on the timescale of a few \(\mathrm{ns}^{35}\) do not significantly perturb the stability of the Er states. This property partially originates from the large energy detuning between Er and Ti levels, which prevents the energy exchange required for spin- flip events. Using the experimentally obtained value of \(T_{1}^{\mathrm{Er}}\) in the rate equation model, we extract a driving term \(W = \Omega^{2}T_{2} / 2\) for Er that is two times larger than for Ti in the same dimer (Supplementary Section 7). Despite the long spin lifetime and large driving term, attempts to drive Er Rabi oscillations through Ti do not yield a complete cycle (Fig. S8b), preventing a direct measure of the Er \(T_{2}\) . This is most likely due to a relatively low Rabi rate \(\Omega\) provided by the moderate Er- Ti exchange coupling, which is about 2–3 times smaller than in the Fe- Ti dimer \(^{39}\) . In turn, a low value of \(\Omega\) together with a large driving term \(W\) would imply much longer \(T_{2}\) for Er than previous \(3d\) elements, highlighting the potential of \(4f\) electrons to realize higher performance atomic- scale qubits. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 90, 879, 465]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 476, 884, 700]]<|/det|> +
Fig. 4 | Determination of Er spin relaxation time. a,b, Double resonance spectra in the frequency range covering Ti ESR transitions \(f_{1,2}^{\mathrm{Er}}\) (a) without and (b) with simultaneous driving of Er at the ESR frequency of \(f_{3}^{\mathrm{Er}}\) . The peak intensities of \(f_{1,2}^{\mathrm{Er}}\) are related to the relative population of the Er spin states (insets). The spectra were normalized to the sum of their peak intensity. c, ESR intensity ratios between \(\Delta I_{f_2}^{\mathrm{Er}}\) and \(\Delta I_{f_1}^{\mathrm{Er}}\) as a function of the driving strength \(V_{\mathrm{rf2}}\) at different Er ESR transition states (red, orange, and grey circles for \(f_{3}^{\mathrm{Er}}\) , \(f_{4}^{\mathrm{Er}}\) , and off-resonance, respectively). The solid curves show the correspondent simulation results by the rate equation model for \(f_{3}^{\mathrm{Er}}\) (red line), \(f_{4}^{\mathrm{Er}}\) (orange line) and at an off-resonance frequency (grey line). Set-point: \(I_{dc} = 15 \mathrm{pA}\) , \(V_{dc} = 70 \mathrm{mV}\) , \(V_{\mathrm{rf}} = 30 \mathrm{mV}\) , \(V_{\mathrm{rf2}} = 1\) , \(30 \mathrm{mV}\) , \(B = 0.28 \mathrm{T}\) , \(\theta = 97^{\circ}\) . d, Schematics of the inversion recovery measurement in a pump-probe pulse scheme to determine the Er spin relaxation time \(T_{1}^{\mathrm{Er}}\) . Each sequence is composed of a pump pulse at the resonance frequency of \(f_{3}^{\mathrm{Er}}\) (red box) and a probe pulse at the resonance frequency of \(f_{1}^{\mathrm{Er}}\) (blue box). The probe pulse follows the pump pulse after a delay time \(\Delta t\) . The population of the Er states after the pump pulse relaxes back to the thermal state following its \(T_{1}\) . e, The experimental data for the inversion recovery measurement (blue circles) show the intensity of the ESR signal at the probe pulse \(f_{1}\) as a function of the delay time. The black line shows the fit using an exponential function with \(T_{1}^{\mathrm{Er}}\) of about \(1 \mu \mathrm{s}\) . Set-point: \(I_{dc} = 50 \mathrm{pA}\) , \(V_{dc} = 70 \mathrm{mV}\) , \(V_{\mathrm{rf pump}} = 60 \mathrm{mV}\) , \(V_{\mathrm{rf probe}} = 100 \mathrm{mV}\) , \(B = 0.28 \mathrm{T}\) , \(\theta = 97^{\circ}\) .
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 714, 206, 729]]<|/det|> +## Conclusions + +<|ref|>text<|/ref|><|det|>[[114, 746, 886, 894]]<|/det|> +We demonstrated a new experimental approach to electrically drive ESR on the elusive \(4f\) electrons in a surface- adsorbed lanthanide atom with long spin relaxation time. Given the reduced scattering with the substrate electrons, it is reasonable to anticipate an enhancement in the coherence time of Er in comparison to \(3d\) elements. We expect that, by employing a similar approach in different atomic structures, the ESR driving on the \(4f\) electrons can be amplified, enabling the use of lanthanide atoms as surface spin qubits with superior properties compared to the routinely adopted \(3d\) elements. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 125, 185, 140]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 161, 266, 177]]<|/det|> +## STM measurements + +<|ref|>text<|/ref|><|det|>[[113, 196, 886, 392]]<|/det|> +Our experiment was performed in a home- built STM operating at the cryogenic temperature of \(\sim 1.3 \text{K}\) in an ultrahigh vacuum environment \((< 1 \times 10^{- 9} \text{Torr})^{42}\) . Using a two- axis vector magnet (6 T in- plane/4 T out- of- plane), the magnetic fields were varied from 0.28 T to 0.9 T at different angles from the surface \(^{42}\) . To allow atom deposition on the sample kept in the STM stage, the sample is slightly tilted from the axis of the magnet by \(\sim 7^{\circ}\) as estimated from the fit to the data shown in Fig. 1d. Considering this misalignment, all our experimental \(\theta\) were offset by that amount accordingly. The magnetic tips used in our measurements were prepared by picking up \(\sim 4 - 9 \text{Fe}\) atoms from the MgO surface until the tips presented good ESR signals on isolated Ti atoms. + +<|ref|>sub_title<|/ref|><|det|>[[115, 410, 260, 426]]<|/det|> +## ESR measurements + +<|ref|>text<|/ref|><|det|>[[113, 444, 886, 666]]<|/det|> +We used two different schemes to apply \(V_{\text{rf}}\) to the STM junction: one through the tip and one through an antenna (rf generators: Keysight E8257D and E8267D) \(^{42}\) . In all our measurement involving a single rf sweep, we applied the \(V_{\text{rf}}\) using an antenna located near the STM tip except for the data in Fig. 3b, where the \(V_{\text{rf}}\) was combined with the dc bias voltage \(V_{\text{dc}}\) using a diplexer at room temperature and then applied to the STM tip. The data in Fig. 4a–c were acquired by applying \(V_{\text{rf1}}\) to the tip and simultaneously \(V_{\text{rf2}}\) to the antenna. For the measurements reported in Fig. 4e and Fig. S8, the two rf voltages \((V_{\text{rf1}}\) and \(V_{\text{rf2}}\) ) were combined through a power splitter (minicircuits ZC2PD- K0244+) and applied to the STM tip. For these measurements, both rf generators were gated by an arbitrary waveform generator (Tektronix, AWG 70002B). + +<|ref|>sub_title<|/ref|><|det|>[[115, 685, 265, 701]]<|/det|> +## Sample preparation + +<|ref|>text<|/ref|><|det|>[[114, 718, 885, 839]]<|/det|> +The surface of a Ag(100) substrate was cleaned by repeated cycles of Ar+ sputtering and annealing (700 K). We grew atomically thin layers of MgO(100) on the Ag(100) following a procedure described in a previous work \(^{43}\) . We deposited Fe, Ti and Er atoms \((< 1\%\) of monolayer) from high purity rods \((>99\%)\) using an e- beam evaporator. During the deposition the sample was held at \(\sim 10 \text{K}\) in order to have well- isolated single atoms on the surface. + +<|ref|>sub_title<|/ref|><|det|>[[115, 857, 288, 874]]<|/det|> +## Analysis of ESR spectra + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 883, 133]]<|/det|> +We fit the ESR spectra using a model given in \(^{29}\) in order to extract the resonance frequency, peak intensity, and peak width for the data shown in Fig. 1d, Fig. 2c, Fig. 3b,d and Fig. 4c. + +<|ref|>sub_title<|/ref|><|det|>[[115, 187, 265, 203]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[114, 221, 885, 342]]<|/det|> +We thank Taehong Ahn and Leonard Edens for their support at the initial stage of the experiment and Yi Chen, Arzhang Ardavan, and Joaquín Fernández- Rossier for fruitful discussions. We acknowledge support from the Institute for Basic Science (IBS- R027- D1). 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ACS Nano 17, 14144- 14151 (2023). https://doi.org:10.1021/acsnano.3c0475442 Hwang, J. et al. Development of a scanning tunneling microscope for variable temperature electron spin resonance. Rev. Sci. Instrum. 93 (2022). https://doi.org:10.1063/5.009608143 Paul, W. et al. Control of the millisecond spin lifetime of an electrically probed atom. Nature Physics 13, 403- 407 (2017). https://doi.org:10.1038/nphys3965 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 345, 149]]<|/det|> +- ErMgOESRSIsubmission.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/images_list.json b/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..146853dece3a06f495f84924d81da97ea975277c --- /dev/null +++ b/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. | Cross section and crystal structure of the Sn/β-Fe₂O₃ photoanode.", + "footnote": [], + "bbox": [ + [ + 156, + 92, + 844, + 468 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. | PEC properties of the \\(\\beta\\) -Fe2O3 photoanode in simulated seawater.", + "footnote": [], + "bbox": [ + [ + 157, + 87, + 848, + 370 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. | Evolution of the \\(\\beta\\) -Fe₂O₃ surface during the long-term seawater decomposition reaction.", + "footnote": [], + "bbox": [ + [ + 175, + 100, + 853, + 420 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4. | Effect of Sn atoms in \\(\\beta\\) -Fe₂O₃ on resistance to seawater corrosion.", + "footnote": [], + "bbox": [ + [ + 169, + 88, + 828, + 555 + ] + ], + "page_idx": 15 + } +] \ No newline at end of file diff --git a/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c.mmd b/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ca4ea89a82664af17e27ec93024d01fedaf91fd7 --- /dev/null +++ b/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c.mmd @@ -0,0 +1,310 @@ + +# Ultradurability of metastable \(\beta\) -Fe2O3 photoanodes in highly corrosive seawater + +Changhao Liu Nanjing University + +Ningsi Zhang Nanjing University + +Yang Li Nanjing University + +Rongli Fan Nanjing University + +Wenjing Wang Nanjing University + +Jianyong Feng Nanjing University + +C. Liu Institute of High Energy Physics, Chinese Academy of Sciences + +Jiaou Wang Chinese Academy of Sciences https://orcid.org/0000- 0002- 4686- 1821 + +Weichang Hao Beihang University https://orcid.org/0000- 0002- 1597- 7151 + +Zhaosheng Li ( zsli@nju.edu.cn ) Nanjing University https://orcid.org/0000- 0001- 8114- 0432 + +Zhigang Zou Nanjing University https://orcid.org/0000- 0003- 2092- 8335 + +Article + +Keywords: + +Posted Date: December 12th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 2096634/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on July 17th, 2023. See the published version at https://doi.org/10.1038/s41467-023-40010-9. + +<--- Page Split ---> + +# Ultradurability of metastable \(\beta\) -Fe₂O₃ photoanodes in highly + +## corrosive seawater + +Changhao Liu1, 2, Ningsi Zhang1, 2, Yang Li1, Rongli Fan1, Wenjing Wang1, Jianyong Feng1, *, Chen Liu3, Jiaou Wang3, Weichang Hao4, Zhaosheng Li1, 2, *, Zhigang Zou1, 2 + +## Affiliations: + +1 Collaborative Innovation Center of Advanced Microstructures, National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Nanjing University; 22 Hankou Road, Nanjing 210093, China + +University; 22 Hankou Road, Nanjing 210093, China + +2 Jiangsu Key Laboratory for Nano Technology, Nanjing University; 22 Hankou Road, Nanjing 210093, China + +3 Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + +4 School of Physics and Centre of Quantum and Matter Sciences, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China + +\*Corresponding authors. Email: zsli@nju.edu.cn; fengjianyong@nju.edu.cn. + +Abstract: Durability is one prerequisite for material application. Photoelectrochemical (PEC) decomposition of seawater is a promising approach to produce clean hydrogen by using solar energy, but it always suffers from serious \(\mathrm{Cl}^{- }\) corrosion. We found that the main deactivation mechanism of the photoanodes is oxide surface reconstruction accompanied by the coordination of \(\mathrm{Cl}^{- }\) during seawater splitting, and the stability of the photoanodes can be greatly improved by enhancing the metal- oxygen interaction. Taking the metastable \(\beta\) - Fe₂O₃ photoanode as an example, Sn added to the lattice can enhance the M- O bonding energy and hinder the transfer of protons to lattice oxygen, thereby inhibiting excessive surface hydration and \(\mathrm{Cl}^{- }\) coordination. Therefore, the \(\mathrm{Sn} / \beta\) - Fe₂O₃ photoanode without any extra electrocatalyst or protective overlayer delivered a record durability for PEC seawater splitting over 1440 h. + +<--- Page Split ---> + +## Main Text: + +The use of PEC water splitting to produce hydrogen can realize the conversion of solar energy to hydrogen energy in one step, which is a very promising solution for building a low- carbon society \(^{1 - 5}\) . The long- term stability of photoelectrodes is an essential prerequisite for the practical application of PEC water splitting for hydrogen production \(^{6}\) . However, except for iron oxide, almost all bare photoelectrodes show unsatisfactory stability in water splitting for hydrogen production, let alone in highly corrosive seawater \(^{7 - 10}\) . Some strategies, such as protective layers, electrocatalysts, and tuning electrolyte composition, have been used to improve the durability of photoelectrodes in aqueous electrolytes without Cl \(^{- }\) ions \(^{11 - 13}\) . Little attention has been given to improving the stability of photoelectrodes in aqueous electrolytes with Cl \(^{- }\) ions \(^{14, 15}\) since Cl \(^{- }\) ions easily corrode photoelectrode materials and may participate in the competitive oxidation reaction to produce Cl \(_{2}\) or ClO \(^{- 16 - 19}\) . + +Herein, we studied the effect of Cl \(^{- }\) ions on the stability of a photoelectrode such as \(\beta\) - Fe \(_2\) O \(_3\) . Recently, \(\beta\) - Fe \(_2\) O \(_3\) , as a metastable phase of iron oxide, has entered our research horizon due to a theoretical solar- to- hydrogen efficiency of \(20.9\%\) , showing good stability of PEC water splitting in aqueous electrolytes without Cl \(^{- }\) ions \(^{6, 7}\) . We have revealed that the Cl \(^{- }\) ions in seawater will damage the surface hydrated layer of \(\beta\) - Fe \(_2\) O \(_3\) photoanodes, thus remarkably reducing their stability. Dispersed Sn single atoms in the lattice were found to endow the \(\beta\) - Fe \(_2\) O \(_3\) photoanodes with good inhibition of hydration and resistance to Cl \(^{- }\) attack in seawater. As a result, the bare Sn/ \(\beta\) - Fe \(_2\) O \(_3\) shows excellent durability in seawater splitting over 1440 h and is by far the most stable + +<--- Page Split ---> + +photoanode. This study may ignite the dawn of application for PEC seawater splitting for hydrogen production and deepen the understanding of the seawater corrosion of oxides. + +## Material characterization of the \(\beta\) -Fe2O3 photoanode + +Metastable \(\beta\) - Fe2O3 photoanodes doped with Sn were prepared by the spray pyrolysis method, and their phases were accurately determined (Supplementary Fig. 1). The Sn/ \(\beta\) - Fe2O3 film is composed of blocks arranged vertically with a thickness of approximately 400 nm (Fig. 1a). A large area of lattice stripes indicates good crystallinity of \(\beta\) - Fe2O3 (Fig. 1b). Many bright spots with high contrast in the (1 1 0) crystal plane in Fig. 1c correspond to the Sn single atom in the \(\beta\) - Fe2O3 lattice. One of the regions was selected for three- dimensional modelling, which shows the contrast difference between Sn atoms and surrounding Fe atoms (Fig. 1d). It was confirmed that the lattice position of Fe was substituted by Sn. A clear atomic image of the (1 1 1) crystal plane taken from another region of \(\beta\) - Fe2O3 and fast Fourier transform (FFT) patterns of the (1 1 0) and (1 1 1) planes were obtained (Supplementary Fig. 1). These lattice atomic images and FFT patterns are completely consistent with the atomic arrangement of the corresponding crystal plane in the theoretical model. + +## PEC properties and seawater splitting stability + +The as- prepared \(\beta\) - Fe2O3 photoanodes were tested for PEC simulated seawater splitting. The saturated photocurrent density of the \(2\%\) Sn/ \(\beta\) - Fe2O3 photoanodes reaches 2.21 mA cm \(^{- 2}\) at 1.6 V \(_{\mathrm{RHE}}\) , which is 8.5 times that of \(\beta\) - Fe2O3 photoanodes + +<--- Page Split ---> + +(Supplementary Fig. 2a). The Sn dopants do not affect the light absorption of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes, while the PEC performance improvement is partly due to the increased carrier concentration (Supplementary Fig. 3). Sn can simultaneously adjust the chemical field at the semiconductor/electrolyte interface, which significantly reduces the AC impedance of the interface (Supplementary Fig. 2b). + +The most important role of Sn dispersed in the lattice is to surprisingly promote its durability in simulated seawater with \(\mathrm{Cl}^-\) ions. Specifically, in Fig. 2a, the stability of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes is good in 1 M KOH electrolyte within \(100\mathrm{h}\) , while its photocurrent density decreased obviously in \(1\mathrm{M}\mathrm{KOH} + 0.5\mathrm{M}\mathrm{NaCl}\) electrolyte. This indicates that \(\mathrm{Cl}^-\) significantly reduced its PEC stability. In contrast, the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes could still maintain stable performance even in a saturated \(\mathrm{NaCl}\) electrolyte within \(100\mathrm{h}\) without decay, which was much better than the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes. In the stability test, the photocurrent increased slightly in a period of time after the beginning of the reaction due to the change in the state of Fe and O on the surface20-22. Correspondingly, the AC impedance of the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanode in the first \(50\mathrm{h}\) gradually decreases (Fig. 2b). The HADDF image of the photoanode also shows that an amorphous hydrated layer was formed on the surface of \(\beta\) - \(\mathrm{Fe_2O_3}\) (Fig. 2c), which indicates that the FeOOH hydrated layer spontaneously formed on the surface during the reaction process. The specific process and impact of surface reconstruction will be further discussed below. Furthermore, the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanode shows excellent stability over \(1440\mathrm{h}\) in simulated seawater (Fig. 2d). After \(1440\mathrm{h}\) , the photocurrent maintains \(90.5\%\) of the initial value. The \(\mathrm{Sn / \beta - Fe_2O_3}\) + +<--- Page Split ---> + +photoanode has achieved the longest durability in research on PEC seawater splitting over the years, as shown in Fig. 2e, even without any extra electrocatalyst or protective overlayer. Additionally, the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes showed excellent stability in alkaline natural seawater (Supplementary Fig. 4). + +## Effect of Sn on the resistance of photoanode to \(\mathrm{Cl}^-\) corrosion + +The evolution process of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode surface in seawater splitting is explored here. XPS analysis can be used to obtain the state of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode surface elements in contact with the electrolyte during the reaction. As shown in the XPS spectrum of O in the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode (Supplementary Fig. 5a), the peak of \(\mathrm{M} - \mathrm{O}\) at \(529.4\mathrm{eV}\) decreases after the reaction. It transforms to \(\mathrm{M} - \mathrm{OH}\) at \(531.7\mathrm{eV}\) , with a significant shift from lattice oxygen to hydroxyl oxygen on the surface23. This corresponds to the reconstruction of the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface in alkaline electrolytes. In the XPS plot of \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes with \(100\mathrm{h}\) of reaction in Fig. 3a, a large number of O atoms can remain in the form of lattice oxygen when Sn is present on the surface. After \(100\mathrm{h}\) of reaction, no Cl signal was detected on the surface of the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes (Fig. 3b). In contrast, the Cl signal was detected on the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface (Supplementary Fig. 5b), which indicates that the process of lattice oxygen reconstruction was accompanied by the adsorption or implantation of \(\mathrm{Cl}^-\) in the electrolyte. During surface hydration and lattice reformation, \(\beta\) - \(\mathrm{Fe_2O_3}\) slowly dissolves, which can be determined by inductive coupled plasma emission spectrometry (Supplementary Table 2). The amount of dissolved Fe atoms was also significantly reduced when Sn acted as an anchor at the surface. However, due to the intense + +<--- Page Split ---> + +hydration, the surface lattice was still continuously attacked after a long reaction time, accompanied by O remodelling and loss of metal elements. The XPS peak of the Sn 3d signal disappeared after 1000 h of reaction (Fig. 3g), indicating that Sn is also slowly lost during lattice reconstruction by surface hydration. Cations are also involved in surface hydration and embedded in the hydrated layer, such as \(\mathrm{Na}^+\) , and after 1000 h, cations were also detected on the photoanode surface. As shown in the Raman spectra of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode surface (Fig. 3c), after 100 h and 1000 h of seawater splitting, there are two peaks of M- OOH at approximately \(470~\mathrm{cm^{- 1}}\) and \(550~\mathrm{cm^{- 1}24,25}\) , which echo the change in the O 1 s XPS peak. The \(\alpha\) - \(\mathrm{Fe_2O_3}\) peak can be observed when the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes are further calcined at \(600^{\circ}\mathrm{C}\) . Here, \(\alpha\) - \(\mathrm{Fe_2O_3}\) was transformed from FeOOH generated by surface reconstruction during heat treatment. A NaCl peak appeared on the surface after 1000 h of reaction, indicating that the crystallization of anions and cations diffused into the hydration layer. + +To further confirm the reconstruction of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes and the exchange of atoms at the interface during the reaction in the electrolyte, the surface element distribution measurement was probed by time- of- flight secondary ion mass spectrometry (TOF- SIMS). In Fig. 3e, the signal of Cl can be detected on the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface after the reaction in simulated seawater for \(100\mathrm{h}\) . The Cl content of the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface is much higher than that of the \(\mathrm{Sn / \beta - Fe_2O_3}\) surface. This confirmed that the presence of Sn can significantly improve the rejection of \(\mathrm{Cl}^-\) in the electrolyte. Meanwhile, \(\mathrm{H_2^{18}O}\) was added to explore electrolyte participation in the surface reconstruction of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode. \(^{18}\mathrm{O}\) in the electrolyte participates in the + +<--- Page Split ---> + +formation of a hydration layer, so the signal of \(^{18}\mathrm{OH}\) with surface \(\mathrm{m / z} = 19.005\) can be detected. The signal intensity of \(^{18}\mathrm{OH}\) on the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanode surface is much weaker than that on the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface, indicating that the Sn dopants weaken lattice oxygen reconstruction. In the depth profiling in Fig. 3f, the content of both Cl and \(^{18}\mathrm{OH}\) in the \(\mathrm{Sn / \beta - Fe_2O_3}\) surface decays faster with depth than without Sn dopants. This reveals that surface reconstruction and \(\mathrm{Cl}^-\) erosion occur simultaneously. The \(^{18}\mathrm{O}\) added to the electrolyte participates in the reconstruction of the lattice oxygen of \(\beta\) - \(\mathrm{Fe_2O_3}\) and forms \(\mathrm{M}^{- 18}\mathrm{OH}\) . At the same time, \(\mathrm{Cl}^-\) in the electrolyte would also first be adsorbed on the surface and gradually infiltrate into the bulk with surface reconstruction. The \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode undergoes excessive surface reconstruction, resulting in a thicker hydrated layer. \(\mathrm{Cl}^-\) shuttles and infiltrates into it, which may destroy the structure of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode and affect the interface water oxidation reaction. Sn inhibits the exchange of \(^{18}\mathrm{O}\) and lattice oxygen and suppresses the erosion of \(\mathrm{Cl}^-\) , thus obtaining a more stable photoanode surface. + +The O K- edge of the soft X- ray absorption near- edge structure (XANES) spectrum shows the change in the lattice oxygen state before and after adding Sn to the lattice. The spectrogram of \(\beta\) - \(\mathrm{Fe_2O_3}\) is similar to the O K- edge of standard iron oxide \(^{27}\) . After adding Sn, the X- ray absorption peak of oxygen shifts to the direction of high energy, which reflects that the addition of Sn effectively improves the bonding energy of O in the lattice. A shoulder peak at 532.3 eV corresponds to the contribution of the Sn 5s orbit \(^{28,29}\) . This shows that the Sn atoms dispersed in the lattice change the average chemical environment of O and play an anchor role in lattice oxygen. + +<--- Page Split ---> + +The enhanced metal- oxygen interaction in the surface chemical reaction is specifically manifested in that the lattice oxygen at the semiconductor electrolyte interface has more difficulty accepting protons, which can be confirmed by the proton- coupled electron transfer process analyzed by the H/D kinetic isotope effect30- 32. The OER on the photoanode surface is a proton- coupled electron transfer process involving four electrons. Specifically, the reaction intermediate species \(*OH\) and \(*OOH\) transfer one electron to the semiconductor and discard one proton33,34. The isotope effect is particularly significant at low pH. The \(\mathrm{j}_{\mathrm{H2O}}/\mathrm{j}_{\mathrm{D2O}}\) value of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode is always lower than that of \(\mathrm{Sn}/\beta\) - \(\mathrm{Fe_2O_3}\) , indicating that the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface has a stronger affinity for protons. The lattice oxygen on the surface of \(\beta\) - \(\mathrm{Fe_2O_3}\) easily acts as a proton acceptor, which to some extent accelerates the proton coupling process in the reaction process. However, lattice oxygen as a proton acceptor will bring about the problem of structural stability being destroyed. As demonstrated in Fig. 4c, protons transferred to nearby locations will combine with lattice oxygen, break the M- O bond and generate an FeOOH hydrated layer. When the M- O bond breaks, oxygen in solution will exchange with lattice oxygen, and \(\mathrm{Cl}^-\) will also coordinate with Fe and destroy the surface structure. On the other hand, hydrated FeOOH is a loose amorphous or layered structure, which is also prone to the insertion and adsorption of \(\mathrm{Cl}^-\) , thus affecting the activity of water splitting. The Sn atoms dispersed in the lattice play a role in anchoring the lattice oxygen to prevent proton coupling between the reaction intermediate and the lattice oxygen, which shows that the proton transfer process will have a greater impact on the reaction kinetics. When the pH rises, proton transfer is no longer the rate- + +<--- Page Split ---> + +determining step. The advantages of high valence cation- doped Sn dispersed in the bulk phase in increasing the carrier concentration and conductivity can also be fully demonstrated. Therefore, the photocurrent increases to 8.5 times that of the \(\beta\) - Fe₂O₃ photoanode. Although alkaline electrolytes are used in PEC tests, local pH will decrease in the water oxidation reaction, and protons with higher local concentrations will also exist. These protons attack lattice oxygen, causing surface reconstruction. Sn in the lattice enhances the metal oxygen interaction, thus inhibiting the wrong proton transfer path and avoiding surface hydration and Cl⁻ corrosion. + +The advantage of uniformly dispersed Sn in the bulk phase is that when the surface is hydrated and peeled by corrosion, the exposed Sn/β- Fe₂O₃ is still corrosion resistant. During the annealing process of the Sn/β- Fe₂O₃ photoanode, Sn atoms tend to diffuse to the surface²⁶, but the Sn in the bulk is still relatively uniform (Supplementary Fig. 7). In contrast, we covered a layer of efficient OER electrocatalyst CoFe- LDH on the surface of Sn/β- Fe₂O₃ as a protective passivation layer. However, a significant downwards trend of photocurrent was observed in the first 50 h of the reaction, and after a long time, the current gradually decreased to the level without loading the electrocatalyst (Supplementary Fig. 9). This shows that the surface modification of the electrocatalyst cannot resist the corrosion of Cl⁻ in seawater. Metal hydroxide itself will also be reconstructed in the OER reaction, which will also be accompanied by the problems of Cl⁻ coordination and structural collapse. Finally, the structure is destroyed and gradually dissolved and peeled off. This extra loaded electrocatalyst protective + +<--- Page Split ---> + +layer often protects the photoanode by its own corrosion and consumption, which cannot fundamentally solve the long- term stability problem. + +## Conclusion + +Here, we revealed that excessive hydration reconstruction of the surface will corrode the surface of the oxide photoanode with the corrosion of \(\mathrm{Cl}^{- }\) ions in the solution. The anchoring of the surface lattice by Sn hinders the transfer of protons to lattice oxygen, and the probability of oxygen hydrogen bonding will decrease due to the strong M- O bond, thereby suppressing the surface reconstruction and coordination of \(\mathrm{Cl}^{- }\) . The \(\mathrm{Sn} / \beta \mathrm{- Fe}_2\mathrm{O}_3\) photoanode constitutes by far the most durable photoanode for seawater splitting. This strategy can also improve the durability of other photoanodes, such as \(\alpha \mathrm{- Fe}_2\mathrm{O}_3\) (Supplementary Fig. 10). This study will pave a new path to solving the problem of the long- term durability of photoelectrodes in energy conversion. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1. | Cross section and crystal structure of the Sn/β-Fe₂O₃ photoanode.
+ +a, b, HAADF images of the Sn/β-Fe₂O₃ film cross-section at different magnifications. + +c, Atomic image of the (1 1 0) plane of β-Fe₂O₃. d, Local enlargement near doped Sn + +atoms and three-dimensional modelling of surface contrast. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2. | PEC properties of the \(\beta\) -Fe2O3 photoanode in simulated seawater.
+ +a, Stability test of \(\beta\) - Fe2O3 and Sn/β- Fe2O3 in 1 M KOH with and without 0.5 M NaCl for 100 h. b, AC electrochemical impedance spectra of Sn/β- Fe2O3 at 1.6 VRE after the reaction. c, HAADF images of the Sn/β- Fe2O3 photoanode after 100 h of reaction in simulated seawater. d, Stability test of Sn/and \(\beta\) - Fe2O3 in saturated NaCl solution for 1440 h. e, Summary of the photoanode stability of PEC (simulated) seawater splitting over the years. Detailed information can be found in Supplementary Table 1. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3. | Evolution of the \(\beta\) -Fe₂O₃ surface during the long-term seawater decomposition reaction.
+ +a- c, XPS spectra of O 1s, Cl 2p and Sn 3d of Sn/β- Fe₂O₃ before, after 100 h, and after 1000 h of seawater splitting reaction. d, Raman spectra of β- Fe₂O₃ photoanodes with different reaction times and annealing treatments. e, f, TOF- SIMS of the distributions of Cl and \(^{18}\mathrm{OH}\) on the surface and depth profiling of the β- Fe₂O₃ photoanode before and after Sn doping after the 100- h reaction in simulated seawater with 20 wt.% H₂¹⁸O. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4. | Effect of Sn atoms in \(\beta\) -Fe₂O₃ on resistance to seawater corrosion.
+ +a, XANES spectra of the O K-edge in \(\beta\) - Fe₂O₃. b, The steady- state photocurrent \(\mathrm{j}_{\mathrm{H2O}} / \mathrm{j}_{\mathrm{D2O}}\) and \(\mathrm{j}_{\mathrm{Sn}} / \mathrm{j}_{\mathrm{Pure}}\) values at different pH values. c, Schematic diagram of doped Sn atoms against \(\mathrm{Cl}^{-}\) corrosion in seawater splitting. Sn enhances the M- O bonds, prevents the hydrated surface reconstruction caused by the transfer of \(\mathrm{H}^{+}\) to lattice oxygen, and weakens the coordination of \(\mathrm{Cl}^{-}\) . + +<--- Page Split ---> + +References1. Fujishima, A. & Honda, K. Electrochemical Photolysis of Water at Semiconductor Electrode. Nature 238, 37–38 (1972).2. Fang, T. et al. Reactive inorganic vapor deposition of perovskite oxynitride films for solar energy conversion. Research 2019, 9 (2019).3. De Luna, P. et al. What would it take for renewably powered electrosynthesis to displace petrochemical processes? Science 364, eaav3506 (2020).4. Lewis, N. S. Research opportunities to advance solar energy utilization. Science 351, aad1920 (2016).5. Zhang, Y. et al. Homogeneous solution assembled Turing structures with near zero strain semi-coherence interface. Nat. Commun. 13, 2942 (2022).6. Kibsgaard, J. & Chorkendorff, I. Considerations for the scaling-up of water splitting catalysts. Nat. Energy 4, 430–433 (2019).7. Jadwiszczak, M., Jakubow-Piotrowska, K., Kedzierzawski, P., Bienkowski, K. & Augustynski, J. Highly efficient sunlight-driven seawater splitting in a photoelectrochemical cell with chlorine evolved at nanostructured \(\mathrm{WO}_3\) photoanode and hydrogen stored as hydride within metallic cathode. Adv. Energy Mater. 10, 1903213 (2020).8. Li, Y. G. et al., Photoelectrochemical splitting of natural seawater with \(\alpha\) - \(\mathrm{Fe}_2\mathrm{O}_3 / \mathrm{WO}_3\) nanorod arrays. Int. J. Hydrogen Energy 41, 4096–4105 (2016).9. Yang, J. S. & Wu, J. J. Toward eco-friendly and highly efficient solar water splitting using \(\mathrm{In}_2\mathrm{S}_3\) /anatase/rutile \(\mathrm{TiO}_2\) dual-staggered-heterojunction + +<--- Page Split ---> + +nanodendrite array photoanode. ACS Appl. Mater. Interfaces 10, 3714- 3722 (2018). + +Farras, P., Strasser, P. & Cowan, A. J. Water electrolysis: Direct from the sea or not to be? Joule 5, 1921- 1923, (2021). + +Lee, D. K. & Choi, K. S. Enhancing long- term photostability of \(\mathrm{BiVO_4}\) photoanodes for solar water splitting by tuning electrolyte composition. Nat. Energy 3, 53- 60 (2018). + +Kuang, Y. B. et al. Ultrastable low- bias water splitting photoanodes via photocorrosion inhibition and in situ catalyst regeneration. Nat. Energy 2, 16191 (2017). + +Hu, S. et al. Amorphous \(\mathrm{TiO_2}\) coatings stabilize Si, GaAs, and GaP photoanodes for efficient water oxidation. Science 344, 1005- 1009 (2014). + +Luo, W. J. et al. Solar hydrogen generation from seawater with a modified \(\mathrm{BiVO_4}\) photoanode. Energy Environ. Sci. 4, 4046- 4051 (2011). + +Zhong, D. K. & Gamelin, D. R. Photoelectrochemical water oxidation by cobalt catalyst ("Co- \(\mathrm{Pi}\) ") \(\alpha\) - \(\mathrm{Fe_2O_3}\) composite photoanodes: oxygen evolution and resolution of a kinetic bottleneck. J. Am. Chem. Soc. 132, 4202- 4207 (2010). + +Li, Z. S., Luo, W. J., Zhang, M. L., Feng, J. Y. & Zou, Z. G. Photoelectrochemical cells for solar hydrogen production: current state of promising photoelectrodes, methods to improve their properties, and outlook. Energy Environ. Sci. 6, 347- 370 (2013). + +Hausmann, J. N., Schlogl, R., Menezes, P. W. & Driess, M. Is direct seawater + +<--- Page Split ---> + +splitting economically meaningful? Energy Environ. Sci. 14, 3679–3685 (2021). + +18. Kuang, Y. et al. Solar-driven, highly sustained splitting of seawater into hydrogen and oxygen fuels. Proc. Natl. Acad. Sci. U. S. A. 116, 6624–6629 (2019). + +19. Tong, W. M. et al. Electrolysis of low-grade and saline surface water. Nat. Energy 6, 935–935 (2021). + +20. Feng, C. et al. A self-healing catalyst for electrocatalytic and photoelectrochemical oxygen evolution in highly alkaline conditions. Nat. Commun. 12, 5980 (2021). + +21. Chung, D. Y. et al. Dynamic stability of active sites in hydr(oxy)oxides for the oxygen evolution reaction. Nat. Energy 5, 550–550 (2020). + +22. Hunter, B. M. et al. Trapping an iron(VI) water-splitting intermediate in nonaqueous media. Joule 2, 747–763 (2018). + +23. Kim, J. Y., Youn, D. H., Kang, K. & Lee, J. S. Highly conformal deposition of an ultrathin FeOOH layer on a hematite nanostructure for efficient solar water splitting. Angew. Chem. Int. Ed. 55, 10854–10858 (2016). + +24. Tang, F. Liu, T., Jiang, W. L. & Gan, L. Windowless thin layer electrochemical Raman spectroscopy of Ni-Fe oxide electrocatalysts during oxygen evolution reaction. J. Electroanal. Chem. 871, 6 (2020). + +25. Duan, Y. et al. Scaled-up synthesis of amorphous NiFeMo oxides and their rapid surface reconstruction for superior oxygen evolution catalysis. Angew. Chem. Int. Ed. 58, 15772–15777 (2019). + +<--- Page Split ---> + +Zhang, H. M. et al. Gradient tantalum-doped hematite homojunction photoanode improves both photocurrents and turn-on voltage for solar water splitting. Nat. Commun. 11, 4622 (2020). + +Frati, F., Hunault, M. & de Groot, F. M. F. Oxygen K-edge X-ray absorption spectra. Chem. Rev. 120, 4056- 4110, (2020). + +McLeod, J. A. et al. Band gaps and electronic structure of alkaline-earth and post- transition- metal oxides. Phys. Rev. B 81, 245123 (2010). + +McLeod, J. A. et al. Chemical bonding and hybridization in 5p binary oxide. J. Phys. Chem. C 116, 24248- 24254, (2012). + +Burke, M. S., Kast, M. G., Trotochaud, L., Smith, A. M. & Boettcher, S. W. Cobalt- iron (Oxy)hydroxide oxygen evolution electrocatalysts: the role of structure and composition on activity, stability, and mechanism. J. Am. Chem. Soc. 137, 3638- 3648 (2015). + +Dau, H. et al. The Mechanism of water oxidation: from electrolysis via homogeneous to biological catalysis. ChemCatChem 2, 724- 761 (2010). + +Chen, J., Li, Y. F., Sit, P. & Selloni, A. Chemical dynamics of the first proton- coupled electron transfer of water oxidation on \(\mathrm{TiO_2}\) anatase. J. Am. Chem. Soc. 135, 18774- 18777 (2013). + +Iandolo, B. & Hellman, A. The role of surface states in the oxygen evolution reaction on hematite. Angew. Chem. Int. Ed. 53, 13404- 13408 (2014). + +Li, Y. F., Liu, Z. P., Liu, L. L. & Gao, W. G. Mechanism and activity of photocatalytic oxygen evolution on titania anatase in aqueous surroundings. J. + +<--- Page Split ---> + +Am. Chem. Soc. 132, 13008–13015 (2010). + +Li, Y. G. et al. Efficient and stable photoelectrochemical seawater splitting with \(\mathrm{TiO_2@g - C_3N_4}\) nanorod arrays decorated by Co- Pi. J. Phys. Chem. C 119, 20283–20292 (2015). + +Li, Y. G. et al. Construction of inorganic- organic 2D/2D \(\mathrm{WO_3 / g - C_3N_4}\) nanosheet arrays toward efficient photoelectrochemical splitting of natural seawater. Phys. Chem. Chem. Phys. 18, 10255–10261 (2016). + +Sharma, M. D., Mahala, C. & Basu, M. Photoelectrochemical water splitting by \(\mathrm{In_2S_3 / In_2O_3}\) composite nanopyramids. ACS Appl. Nano Mater. 3, 11638–11649 (2020). + +Sahoo, P., Sharma, A., Padhan, S. & Thangavel, R. Visible light driven photosplitting of water using one dimensional Mg doped ZnO nanorod arrays. Int. J. Hydrogen Energy 45, 22576–22588 (2020). + +Gao, R. T. et al. Ultrastable and high- performance seawater- based photoelectrolysis system for solar hydrogen generation. Appl. Catal. B- Environ. 304, 120883 (2022). + +Guo, X. T., Liu, X. H. & Wang, L. \(\mathrm{NiMoO_x}\) as a highly protective layer against photocorrosion for solar seawater splitting. J Mater. Chem. A 10, 1270–1277 (2022). + +She, X. F. et al. Floc- like CNTs jointed with \(\mathrm{Bi_xFe_{(1-x)VO_4}}\) nanoparticles for high efficient and stable photoelectrochemical seawater splitting. J. Alloys Compd. 893, 162146 (2022). + +<--- Page Split ---> + +42. Seenivasan, S., Moon, H. & Kim, D. H. Multilayer strategy for photoelectrochemical hydrogen generation: new electrode architecture that alleviates multiple bottlenecks. Nano-Micro Lett. 14, 78 (2022). + +## Methods + +## Preparation of photoanode + +Typically, in an experiment, 0.01 mol of iron acetylacetonate (AcAcFe) was dissolved in \(500~\mathrm{mL}\) ethanol by stirring with a magnetic force for over \(48~\mathrm{h}\) . Fluorine- doped tin oxide (FTO) conductive glass was cut into dimensions of \(2\mathrm{cm}\times 1\mathrm{cm}\) , wrapped with aluminum foil to make a deposition area of \(1\mathrm{cm}\times 1\mathrm{cm}\) and then placed in a tube furnace with a set temperature of \(480~^\circ \mathrm{C}\) . The precursor solution was added to the injection pump and dispersed into droplets by using an ultrasonic atomizer. During the experiment, \(40~\mathrm{mL}\) of precursor solution was injected at a speed of \(1.6~\mathrm{mL}\) \(\mathrm{min}^{- 1}\) , which equally matched the power of the ultrasonic atomizer. Using air as the carrier gas, the precursor was fed into a tubular furnace. After deposition, the film was annealed in a muffle furnace at \(600~^\circ \mathrm{C}\) for \(3\mathrm{h}\) at a heating rate of \(10~^\circ \mathrm{C}\mathrm{min}^{- 1}\) . The \(\mathrm{Sn / \beta - Fe_2O_3}\) films were prepared using the same spray pyrolysis method by adding a certain amount of tetrabutyltin ( \(\mathrm{C_{16}H_{36}Sn}\) , analytical reagent, Aladdin) ethanol solution to the precursor solution so that the Sn atom concentration accounted for \(1\%\) , \(2\%\) , \(3\%\) , and \(4\%\) of the total Sn and Fe atoms. The CoFe-LDH @ \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes were prepared by a hydrothermal method. The as-prepared \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes were put + +<--- Page Split ---> + +into a \(100~\mathrm{mL}\) hydrothermal kettle, \(50~\mathrm{mL}\) of a solution containing \(0.002\mathrm{mol}\mathrm{L}^{- 1}\) cobalt nitrate hexahydrate \(\mathrm{(Co(NO_3)_2\cdot 6H_2O}\) , Sinopharm Chemical Reagent), \(0.002\mathrm{mol}\mathrm{L}^{- 1}\) iron(III) nitrate nonahydrate \(\mathrm{(Fe(NO_3)_3\cdot 9H_2O}\) , analytical reagent, Aladdin)), \(0.005\mathrm{mol}\) \(\mathrm{L}^{- 1}\) urea (Aladdin) and \(0.001\mathrm{mol}\mathrm{L}^{- 1}\) trisodium citrate was added, and the reaction was carried out in an oven at \(120^{\circ}\mathrm{C}\) for \(5\mathrm{h}\) . + +## Characterization + +To identify the crystal structures of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes, they were measured by powder X- ray diffraction (XRD, Rigaku Ultima III, Cu Kα radiation, \(\lambda = 1.54178\) Å) at \(40\mathrm{kV}\) and \(40\mathrm{mA}\) . The surface morphology of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes was examined by a high- resolution scanning electron microscope (HRSEM, ZEISS ULTRA 55 at an accelerating voltage of \(5\mathrm{kV}\) ). Raman spectra of \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes were characterized with a confocal laser Raman spectrometer (Japan, Horiba, LabRAM Aramis). X- ray photoemission spectroscopy (XPS, PHI 5000 VersaProbe) was used to characterize the content and valence of Sn, O, Fe and Co, and the binding energy was calibrated by the adventitious carbon 1 s line at \(284.8\mathrm{eV}\) . The optical absorption spectra of the photoanode were tested on a UV- Visible- NIR (near- infrared) spectrophotometer (PerkinElmer, UV3600 UV- Vis- NIR spectrophotometer). Transmission electron microscopy (TEM) and high- resolution transmission electron microscopy (HRTEM) images were obtained on an FEI Tecnai G2 F30. High- angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) images were obtained + +<--- Page Split ---> + +by a JEOL JEM- ARM200F microscope incorporated with a spherical aberration correction system for STEM. + +## PEC measurements + +The PEC measurements were carried out in a PEC cell with an electrochemical analyser (CHI- 760E, CH Instrument, Shanghai) in a three- electrode system including a reference electrode consisting of \(\mathrm{Ag / AgCl}\) placed in a saturated KCl solution, Pt foil as the counter electrode, and \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes as working electrodes. The electrolyte was a 1 M KOH aqueous solution for freshwater and 1 M KOH with 0.5 M NaCl for simulated seawater. The potential was reported vs. the reversible hydrogen electrode (RHE) with \(\mathrm{E_{RHE}} = \mathrm{E_{Ag / AgCl}} + 0.197 + 0.0591\) pH. The photocurrent density was measured under AM 1.5 G light source, and the light intensity was \(100\mathrm{mWcm^{- 2}}\) . A Newport 91150 V standard silicon cell was used as the reference standard for calibration. Mott- Schottky analysis was performed at bias potentials from 0.5 V to 1.5 V vs. RHE. AC electrochemical impedance was obtained at a bias of \(1.6\mathrm{V_{RHE}}\) over the frequency range of \(100\mathrm{kHz}\) to \(1\mathrm{Hz}\) . The PEC stabilities were tested at a constant potential of \(1.6\mathrm{V_{RHE}}\) under LED- simulated sunlight sources through illumination from the front side. + +In the PEC test of the H/D kinetic isotope effect, the electrolyte was measured with a pH meter to keep the concentrations of \(\mathrm{OH^- }\) and \(\mathrm{OD^- }\) in the solution the same ( \(\mathrm{pD} = \mathrm{pH}_{\mathrm{read}} + 0.4\) ). \(\mathrm{D_2O}\) was purchased from Bide Pharmatech Ltd. (99.9% atom \(\% \mathrm{D}\) ). The \(\mathrm{pD}\) values were adjusted by \(\mathrm{NaOH}\) (Aladdin, 30 wt.% solution in \(\mathrm{D_2O}\) , 99.5%). In the + +<--- Page Split ---> + +current time curve, the photocurrent density value after 50 s of reaction was selected as the steady- state value for the calculation of \(\mathrm{j}_{\mathrm{H2O}}/\mathrm{j}_{\mathrm{D2O}}\) and \(\mathrm{j}_{\mathrm{SN}}/\mathrm{j}_{\mathrm{Pure}}\) (Supplementary Fig. 8). + +## Time-of-flight secondary ion mass spectrometry (TOF-SIMS) tests + +TOF- SIMS tests were carried out by PHI nanoTOF II Time- of- Flight SIMS. \(\mathrm{Bi}_{3}^{++}\) with an energy of \(30 \mathrm{eV}\) was used in the acquisition phase in high mass resolution mode. An Ar ion gun with an energy of \(4 \mathrm{kV}\) was used in the sputter phase with a sputter rate of \(0.4 \mathrm{nm / s}\) on \(\mathrm{SiO}_{2}\) . Before the \(\beta\) - \(\mathrm{Fe}_{2} \mathrm{O}_{3}\) photoanode was tested, the reactions in the electrolyte with \(1 \mathrm{M} \mathrm{KOH} + 0.5 \mathrm{M} \mathrm{NaCl}\) and \(20 \mathrm{wt.} \% \mathrm{H}_{2}^{18} \mathrm{O}\) for \(100 \mathrm{h}\) were carried out. + +## X-ray absorption near-edge structure (XANES) tests + +Soft X- ray absorption near- edge structure (XANES) measurements were performed at the Beijing Synchrotron Radiation Facility (BSRF), 4B9B beamline. The O- K edge and Fe- L edge spectra were collected in total electron yield (TEY) mode by measuring the sample current with an amperemeter. All spectra were normalized to the intensity of the incident beam (10), which was measured simultaneously with the current emitted from a gold mesh located after the last optical elements of the beamline. The photon energy was calibrated using the Au- 4f core level at \(84.0 \mathrm{eV}\) in binding energy by measuring a clean polycrystalline gold foil that is electrically connected to the sample. + +## Computational processing + +<--- Page Split ---> + +The calculations on pure and \(\mathrm{Sn} / \beta \mathrm{- Fe}_2\mathrm{O}_3\) were implemented in the VASP (Vienna Ab initio Simulation Package) based on density functional theory, with a projected- augmented- wave method in the scheme of generalized- gradient approximation. The strong on- site Coulomb repulsion among the localized Fe 3d electrons was described with the generalized- gradient approximation + U approach (U is the strength of the onsite Coulomb interaction). The exchange- correlation effects were treated using the generalized gradient approximation (GGA) in the Perdew- Burke- Ernzerhof parametrization, with spin- polarized effects considered. + +Acknowledgements: We are indebted to Prof. Yixin Zhao (Shanghai Jiaotong University) for discussions. + +Funding: The authors thank the National Science Fund for Distinguished Young Scholars [No. 22025202], National Key Research and Development Program of China [Nos. 2018YFA0209303 and 2021YFA1502100], and National Natural Science Foundation of China [Nos. 51972165 and 51902153] for financial support. + +Author contributions: Z.L. constructed the concept and designed the project. Z.L. supervised the study. N.Z., Y.L., J.F., W.W., C.L., J.W., W.H. and Z.Z. advised on the research. C.H.L. and R.F. collected and analysed the experimental data. C.H.L. and Z.L. wrote the manuscript. Z.L. and J.F. revised the manuscript. All the authors contributed to the discussions about the manuscript. + +<--- Page Split ---> + +Competing interests: The authors declare that they have no competing interests. + +Data and materials availability: All data are available in the main text or the + +Supplementary Information. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupportingInformationLiuCH.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c_det.mmd b/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..0e6f7cb1f4486f5087d8e6fb5e07fb4843753dbd --- /dev/null +++ b/preprint/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c/preprint__0ae031331c2d266697df1ca15bce36f1cb2333132590b2a9b295f659d1dffe9c_det.mmd @@ -0,0 +1,407 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 936, 177]]<|/det|> +# Ultradurability of metastable \(\beta\) -Fe2O3 photoanodes in highly corrosive seawater + +<|ref|>text<|/ref|><|det|>[[44, 196, 216, 238]]<|/det|> +Changhao Liu Nanjing University + +<|ref|>text<|/ref|><|det|>[[44, 244, 216, 285]]<|/det|> +Ningsi Zhang Nanjing University + +<|ref|>text<|/ref|><|det|>[[44, 290, 216, 331]]<|/det|> +Yang Li Nanjing University + +<|ref|>text<|/ref|><|det|>[[44, 336, 216, 377]]<|/det|> +Rongli Fan Nanjing University + +<|ref|>text<|/ref|><|det|>[[44, 382, 216, 424]]<|/det|> +Wenjing Wang Nanjing University + +<|ref|>text<|/ref|><|det|>[[44, 429, 216, 470]]<|/det|> +Jianyong Feng Nanjing University + +<|ref|>text<|/ref|><|det|>[[44, 475, 602, 517]]<|/det|> +C. Liu Institute of High Energy Physics, Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[44, 521, 674, 563]]<|/det|> +Jiaou Wang Chinese Academy of Sciences https://orcid.org/0000- 0002- 4686- 1821 + +<|ref|>text<|/ref|><|det|>[[44, 567, 576, 609]]<|/det|> +Weichang Hao Beihang University https://orcid.org/0000- 0002- 1597- 7151 + +<|ref|>text<|/ref|><|det|>[[44, 613, 576, 655]]<|/det|> +Zhaosheng Li ( zsli@nju.edu.cn ) Nanjing University https://orcid.org/0000- 0001- 8114- 0432 + +<|ref|>text<|/ref|><|det|>[[44, 660, 576, 702]]<|/det|> +Zhigang Zou Nanjing University https://orcid.org/0000- 0003- 2092- 8335 + +<|ref|>text<|/ref|><|det|>[[44, 742, 101, 760]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 780, 135, 799]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 818, 346, 838]]<|/det|> +Posted Date: December 12th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 857, 474, 876]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2096634/v1 + +<|ref|>text<|/ref|><|det|>[[44, 894, 909, 937]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 100, 907, 142]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 17th, 2023. See the published version at https://doi.org/10.1038/s41467-023-40010-9. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[193, 93, 802, 115]]<|/det|> +# Ultradurability of metastable \(\beta\) -Fe₂O₃ photoanodes in highly + +<|ref|>sub_title<|/ref|><|det|>[[402, 131, 593, 150]]<|/det|> +## corrosive seawater + +<|ref|>text<|/ref|><|det|>[[147, 167, 848, 220]]<|/det|> +Changhao Liu1, 2, Ningsi Zhang1, 2, Yang Li1, Rongli Fan1, Wenjing Wang1, Jianyong Feng1, *, Chen Liu3, Jiaou Wang3, Weichang Hao4, Zhaosheng Li1, 2, *, Zhigang Zou1, 2 + +<|ref|>sub_title<|/ref|><|det|>[[147, 243, 253, 259]]<|/det|> +## Affiliations: + +<|ref|>text<|/ref|><|det|>[[175, 275, 833, 323]]<|/det|> +1 Collaborative Innovation Center of Advanced Microstructures, National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Nanjing University; 22 Hankou Road, Nanjing 210093, China + +<|ref|>text<|/ref|><|det|>[[175, 333, 579, 350]]<|/det|> +University; 22 Hankou Road, Nanjing 210093, China + +<|ref|>text<|/ref|><|det|>[[175, 360, 825, 405]]<|/det|> +2 Jiangsu Key Laboratory for Nano Technology, Nanjing University; 22 Hankou Road, Nanjing 210093, China + +<|ref|>text<|/ref|><|det|>[[175, 414, 799, 460]]<|/det|> +3 Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + +<|ref|>text<|/ref|><|det|>[[175, 470, 835, 516]]<|/det|> +4 School of Physics and Centre of Quantum and Matter Sciences, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China + +<|ref|>text<|/ref|><|det|>[[175, 533, 747, 551]]<|/det|> +\*Corresponding authors. Email: zsli@nju.edu.cn; fengjianyong@nju.edu.cn. + +<|ref|>text<|/ref|><|det|>[[144, 560, 852, 907]]<|/det|> +Abstract: Durability is one prerequisite for material application. Photoelectrochemical (PEC) decomposition of seawater is a promising approach to produce clean hydrogen by using solar energy, but it always suffers from serious \(\mathrm{Cl}^{- }\) corrosion. We found that the main deactivation mechanism of the photoanodes is oxide surface reconstruction accompanied by the coordination of \(\mathrm{Cl}^{- }\) during seawater splitting, and the stability of the photoanodes can be greatly improved by enhancing the metal- oxygen interaction. Taking the metastable \(\beta\) - Fe₂O₃ photoanode as an example, Sn added to the lattice can enhance the M- O bonding energy and hinder the transfer of protons to lattice oxygen, thereby inhibiting excessive surface hydration and \(\mathrm{Cl}^{- }\) coordination. Therefore, the \(\mathrm{Sn} / \beta\) - Fe₂O₃ photoanode without any extra electrocatalyst or protective overlayer delivered a record durability for PEC seawater splitting over 1440 h. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 96, 247, 112]]<|/det|> +## Main Text: + +<|ref|>text<|/ref|><|det|>[[144, 130, 852, 560]]<|/det|> +The use of PEC water splitting to produce hydrogen can realize the conversion of solar energy to hydrogen energy in one step, which is a very promising solution for building a low- carbon society \(^{1 - 5}\) . The long- term stability of photoelectrodes is an essential prerequisite for the practical application of PEC water splitting for hydrogen production \(^{6}\) . However, except for iron oxide, almost all bare photoelectrodes show unsatisfactory stability in water splitting for hydrogen production, let alone in highly corrosive seawater \(^{7 - 10}\) . Some strategies, such as protective layers, electrocatalysts, and tuning electrolyte composition, have been used to improve the durability of photoelectrodes in aqueous electrolytes without Cl \(^{- }\) ions \(^{11 - 13}\) . Little attention has been given to improving the stability of photoelectrodes in aqueous electrolytes with Cl \(^{- }\) ions \(^{14, 15}\) since Cl \(^{- }\) ions easily corrode photoelectrode materials and may participate in the competitive oxidation reaction to produce Cl \(_{2}\) or ClO \(^{- 16 - 19}\) . + +<|ref|>text<|/ref|><|det|>[[144, 585, 852, 900]]<|/det|> +Herein, we studied the effect of Cl \(^{- }\) ions on the stability of a photoelectrode such as \(\beta\) - Fe \(_2\) O \(_3\) . Recently, \(\beta\) - Fe \(_2\) O \(_3\) , as a metastable phase of iron oxide, has entered our research horizon due to a theoretical solar- to- hydrogen efficiency of \(20.9\%\) , showing good stability of PEC water splitting in aqueous electrolytes without Cl \(^{- }\) ions \(^{6, 7}\) . We have revealed that the Cl \(^{- }\) ions in seawater will damage the surface hydrated layer of \(\beta\) - Fe \(_2\) O \(_3\) photoanodes, thus remarkably reducing their stability. Dispersed Sn single atoms in the lattice were found to endow the \(\beta\) - Fe \(_2\) O \(_3\) photoanodes with good inhibition of hydration and resistance to Cl \(^{- }\) attack in seawater. As a result, the bare Sn/ \(\beta\) - Fe \(_2\) O \(_3\) shows excellent durability in seawater splitting over 1440 h and is by far the most stable + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 852, 187]]<|/det|> +photoanode. This study may ignite the dawn of application for PEC seawater splitting for hydrogen production and deepen the understanding of the seawater corrosion of oxides. + +<|ref|>sub_title<|/ref|><|det|>[[186, 215, 644, 234]]<|/det|> +## Material characterization of the \(\beta\) -Fe2O3 photoanode + +<|ref|>text<|/ref|><|det|>[[144, 258, 852, 725]]<|/det|> +Metastable \(\beta\) - Fe2O3 photoanodes doped with Sn were prepared by the spray pyrolysis method, and their phases were accurately determined (Supplementary Fig. 1). The Sn/ \(\beta\) - Fe2O3 film is composed of blocks arranged vertically with a thickness of approximately 400 nm (Fig. 1a). A large area of lattice stripes indicates good crystallinity of \(\beta\) - Fe2O3 (Fig. 1b). Many bright spots with high contrast in the (1 1 0) crystal plane in Fig. 1c correspond to the Sn single atom in the \(\beta\) - Fe2O3 lattice. One of the regions was selected for three- dimensional modelling, which shows the contrast difference between Sn atoms and surrounding Fe atoms (Fig. 1d). It was confirmed that the lattice position of Fe was substituted by Sn. A clear atomic image of the (1 1 1) crystal plane taken from another region of \(\beta\) - Fe2O3 and fast Fourier transform (FFT) patterns of the (1 1 0) and (1 1 1) planes were obtained (Supplementary Fig. 1). These lattice atomic images and FFT patterns are completely consistent with the atomic arrangement of the corresponding crystal plane in the theoretical model. + +<|ref|>sub_title<|/ref|><|det|>[[186, 748, 594, 767]]<|/det|> +## PEC properties and seawater splitting stability + +<|ref|>text<|/ref|><|det|>[[144, 792, 850, 885]]<|/det|> +The as- prepared \(\beta\) - Fe2O3 photoanodes were tested for PEC simulated seawater splitting. The saturated photocurrent density of the \(2\%\) Sn/ \(\beta\) - Fe2O3 photoanodes reaches 2.21 mA cm \(^{- 2}\) at 1.6 V \(_{\mathrm{RHE}}\) , which is 8.5 times that of \(\beta\) - Fe2O3 photoanodes + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 852, 262]]<|/det|> +(Supplementary Fig. 2a). The Sn dopants do not affect the light absorption of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes, while the PEC performance improvement is partly due to the increased carrier concentration (Supplementary Fig. 3). Sn can simultaneously adjust the chemical field at the semiconductor/electrolyte interface, which significantly reduces the AC impedance of the interface (Supplementary Fig. 2b). + +<|ref|>text<|/ref|><|det|>[[144, 285, 852, 901]]<|/det|> +The most important role of Sn dispersed in the lattice is to surprisingly promote its durability in simulated seawater with \(\mathrm{Cl}^-\) ions. Specifically, in Fig. 2a, the stability of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes is good in 1 M KOH electrolyte within \(100\mathrm{h}\) , while its photocurrent density decreased obviously in \(1\mathrm{M}\mathrm{KOH} + 0.5\mathrm{M}\mathrm{NaCl}\) electrolyte. This indicates that \(\mathrm{Cl}^-\) significantly reduced its PEC stability. In contrast, the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes could still maintain stable performance even in a saturated \(\mathrm{NaCl}\) electrolyte within \(100\mathrm{h}\) without decay, which was much better than the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes. In the stability test, the photocurrent increased slightly in a period of time after the beginning of the reaction due to the change in the state of Fe and O on the surface20-22. Correspondingly, the AC impedance of the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanode in the first \(50\mathrm{h}\) gradually decreases (Fig. 2b). The HADDF image of the photoanode also shows that an amorphous hydrated layer was formed on the surface of \(\beta\) - \(\mathrm{Fe_2O_3}\) (Fig. 2c), which indicates that the FeOOH hydrated layer spontaneously formed on the surface during the reaction process. The specific process and impact of surface reconstruction will be further discussed below. Furthermore, the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanode shows excellent stability over \(1440\mathrm{h}\) in simulated seawater (Fig. 2d). After \(1440\mathrm{h}\) , the photocurrent maintains \(90.5\%\) of the initial value. The \(\mathrm{Sn / \beta - Fe_2O_3}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 852, 223]]<|/det|> +photoanode has achieved the longest durability in research on PEC seawater splitting over the years, as shown in Fig. 2e, even without any extra electrocatalyst or protective overlayer. Additionally, the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes showed excellent stability in alkaline natural seawater (Supplementary Fig. 4). + +<|ref|>sub_title<|/ref|><|det|>[[186, 249, 710, 268]]<|/det|> +## Effect of Sn on the resistance of photoanode to \(\mathrm{Cl}^-\) corrosion + +<|ref|>text<|/ref|><|det|>[[144, 290, 852, 910]]<|/det|> +The evolution process of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode surface in seawater splitting is explored here. XPS analysis can be used to obtain the state of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode surface elements in contact with the electrolyte during the reaction. As shown in the XPS spectrum of O in the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode (Supplementary Fig. 5a), the peak of \(\mathrm{M} - \mathrm{O}\) at \(529.4\mathrm{eV}\) decreases after the reaction. It transforms to \(\mathrm{M} - \mathrm{OH}\) at \(531.7\mathrm{eV}\) , with a significant shift from lattice oxygen to hydroxyl oxygen on the surface23. This corresponds to the reconstruction of the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface in alkaline electrolytes. In the XPS plot of \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes with \(100\mathrm{h}\) of reaction in Fig. 3a, a large number of O atoms can remain in the form of lattice oxygen when Sn is present on the surface. After \(100\mathrm{h}\) of reaction, no Cl signal was detected on the surface of the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes (Fig. 3b). In contrast, the Cl signal was detected on the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface (Supplementary Fig. 5b), which indicates that the process of lattice oxygen reconstruction was accompanied by the adsorption or implantation of \(\mathrm{Cl}^-\) in the electrolyte. During surface hydration and lattice reformation, \(\beta\) - \(\mathrm{Fe_2O_3}\) slowly dissolves, which can be determined by inductive coupled plasma emission spectrometry (Supplementary Table 2). The amount of dissolved Fe atoms was also significantly reduced when Sn acted as an anchor at the surface. However, due to the intense + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 93, 853, 560]]<|/det|> +hydration, the surface lattice was still continuously attacked after a long reaction time, accompanied by O remodelling and loss of metal elements. The XPS peak of the Sn 3d signal disappeared after 1000 h of reaction (Fig. 3g), indicating that Sn is also slowly lost during lattice reconstruction by surface hydration. Cations are also involved in surface hydration and embedded in the hydrated layer, such as \(\mathrm{Na}^+\) , and after 1000 h, cations were also detected on the photoanode surface. As shown in the Raman spectra of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode surface (Fig. 3c), after 100 h and 1000 h of seawater splitting, there are two peaks of M- OOH at approximately \(470~\mathrm{cm^{- 1}}\) and \(550~\mathrm{cm^{- 1}24,25}\) , which echo the change in the O 1 s XPS peak. The \(\alpha\) - \(\mathrm{Fe_2O_3}\) peak can be observed when the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes are further calcined at \(600^{\circ}\mathrm{C}\) . Here, \(\alpha\) - \(\mathrm{Fe_2O_3}\) was transformed from FeOOH generated by surface reconstruction during heat treatment. A NaCl peak appeared on the surface after 1000 h of reaction, indicating that the crystallization of anions and cations diffused into the hydration layer. + +<|ref|>text<|/ref|><|det|>[[144, 583, 852, 899]]<|/det|> +To further confirm the reconstruction of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes and the exchange of atoms at the interface during the reaction in the electrolyte, the surface element distribution measurement was probed by time- of- flight secondary ion mass spectrometry (TOF- SIMS). In Fig. 3e, the signal of Cl can be detected on the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface after the reaction in simulated seawater for \(100\mathrm{h}\) . The Cl content of the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface is much higher than that of the \(\mathrm{Sn / \beta - Fe_2O_3}\) surface. This confirmed that the presence of Sn can significantly improve the rejection of \(\mathrm{Cl}^-\) in the electrolyte. Meanwhile, \(\mathrm{H_2^{18}O}\) was added to explore electrolyte participation in the surface reconstruction of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode. \(^{18}\mathrm{O}\) in the electrolyte participates in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 92, 852, 596]]<|/det|> +formation of a hydration layer, so the signal of \(^{18}\mathrm{OH}\) with surface \(\mathrm{m / z} = 19.005\) can be detected. The signal intensity of \(^{18}\mathrm{OH}\) on the \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanode surface is much weaker than that on the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface, indicating that the Sn dopants weaken lattice oxygen reconstruction. In the depth profiling in Fig. 3f, the content of both Cl and \(^{18}\mathrm{OH}\) in the \(\mathrm{Sn / \beta - Fe_2O_3}\) surface decays faster with depth than without Sn dopants. This reveals that surface reconstruction and \(\mathrm{Cl}^-\) erosion occur simultaneously. The \(^{18}\mathrm{O}\) added to the electrolyte participates in the reconstruction of the lattice oxygen of \(\beta\) - \(\mathrm{Fe_2O_3}\) and forms \(\mathrm{M}^{- 18}\mathrm{OH}\) . At the same time, \(\mathrm{Cl}^-\) in the electrolyte would also first be adsorbed on the surface and gradually infiltrate into the bulk with surface reconstruction. The \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode undergoes excessive surface reconstruction, resulting in a thicker hydrated layer. \(\mathrm{Cl}^-\) shuttles and infiltrates into it, which may destroy the structure of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode and affect the interface water oxidation reaction. Sn inhibits the exchange of \(^{18}\mathrm{O}\) and lattice oxygen and suppresses the erosion of \(\mathrm{Cl}^-\) , thus obtaining a more stable photoanode surface. + +<|ref|>text<|/ref|><|det|>[[144, 619, 852, 897]]<|/det|> +The O K- edge of the soft X- ray absorption near- edge structure (XANES) spectrum shows the change in the lattice oxygen state before and after adding Sn to the lattice. The spectrogram of \(\beta\) - \(\mathrm{Fe_2O_3}\) is similar to the O K- edge of standard iron oxide \(^{27}\) . After adding Sn, the X- ray absorption peak of oxygen shifts to the direction of high energy, which reflects that the addition of Sn effectively improves the bonding energy of O in the lattice. A shoulder peak at 532.3 eV corresponds to the contribution of the Sn 5s orbit \(^{28,29}\) . This shows that the Sn atoms dispersed in the lattice change the average chemical environment of O and play an anchor role in lattice oxygen. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 90, 853, 895]]<|/det|> +The enhanced metal- oxygen interaction in the surface chemical reaction is specifically manifested in that the lattice oxygen at the semiconductor electrolyte interface has more difficulty accepting protons, which can be confirmed by the proton- coupled electron transfer process analyzed by the H/D kinetic isotope effect30- 32. The OER on the photoanode surface is a proton- coupled electron transfer process involving four electrons. Specifically, the reaction intermediate species \(*OH\) and \(*OOH\) transfer one electron to the semiconductor and discard one proton33,34. The isotope effect is particularly significant at low pH. The \(\mathrm{j}_{\mathrm{H2O}}/\mathrm{j}_{\mathrm{D2O}}\) value of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanode is always lower than that of \(\mathrm{Sn}/\beta\) - \(\mathrm{Fe_2O_3}\) , indicating that the \(\beta\) - \(\mathrm{Fe_2O_3}\) surface has a stronger affinity for protons. The lattice oxygen on the surface of \(\beta\) - \(\mathrm{Fe_2O_3}\) easily acts as a proton acceptor, which to some extent accelerates the proton coupling process in the reaction process. However, lattice oxygen as a proton acceptor will bring about the problem of structural stability being destroyed. As demonstrated in Fig. 4c, protons transferred to nearby locations will combine with lattice oxygen, break the M- O bond and generate an FeOOH hydrated layer. When the M- O bond breaks, oxygen in solution will exchange with lattice oxygen, and \(\mathrm{Cl}^-\) will also coordinate with Fe and destroy the surface structure. On the other hand, hydrated FeOOH is a loose amorphous or layered structure, which is also prone to the insertion and adsorption of \(\mathrm{Cl}^-\) , thus affecting the activity of water splitting. The Sn atoms dispersed in the lattice play a role in anchoring the lattice oxygen to prevent proton coupling between the reaction intermediate and the lattice oxygen, which shows that the proton transfer process will have a greater impact on the reaction kinetics. When the pH rises, proton transfer is no longer the rate- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 93, 852, 373]]<|/det|> +determining step. The advantages of high valence cation- doped Sn dispersed in the bulk phase in increasing the carrier concentration and conductivity can also be fully demonstrated. Therefore, the photocurrent increases to 8.5 times that of the \(\beta\) - Fe₂O₃ photoanode. Although alkaline electrolytes are used in PEC tests, local pH will decrease in the water oxidation reaction, and protons with higher local concentrations will also exist. These protons attack lattice oxygen, causing surface reconstruction. Sn in the lattice enhances the metal oxygen interaction, thus inhibiting the wrong proton transfer path and avoiding surface hydration and Cl⁻ corrosion. + +<|ref|>text<|/ref|><|det|>[[144, 396, 852, 865]]<|/det|> +The advantage of uniformly dispersed Sn in the bulk phase is that when the surface is hydrated and peeled by corrosion, the exposed Sn/β- Fe₂O₃ is still corrosion resistant. During the annealing process of the Sn/β- Fe₂O₃ photoanode, Sn atoms tend to diffuse to the surface²⁶, but the Sn in the bulk is still relatively uniform (Supplementary Fig. 7). In contrast, we covered a layer of efficient OER electrocatalyst CoFe- LDH on the surface of Sn/β- Fe₂O₃ as a protective passivation layer. However, a significant downwards trend of photocurrent was observed in the first 50 h of the reaction, and after a long time, the current gradually decreased to the level without loading the electrocatalyst (Supplementary Fig. 9). This shows that the surface modification of the electrocatalyst cannot resist the corrosion of Cl⁻ in seawater. Metal hydroxide itself will also be reconstructed in the OER reaction, which will also be accompanied by the problems of Cl⁻ coordination and structural collapse. Finally, the structure is destroyed and gradually dissolved and peeled off. This extra loaded electrocatalyst protective + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 850, 150]]<|/det|> +layer often protects the photoanode by its own corrosion and consumption, which cannot fundamentally solve the long- term stability problem. + +<|ref|>sub_title<|/ref|><|det|>[[188, 175, 288, 193]]<|/det|> +## Conclusion + +<|ref|>text<|/ref|><|det|>[[144, 219, 852, 535]]<|/det|> +Here, we revealed that excessive hydration reconstruction of the surface will corrode the surface of the oxide photoanode with the corrosion of \(\mathrm{Cl}^{- }\) ions in the solution. The anchoring of the surface lattice by Sn hinders the transfer of protons to lattice oxygen, and the probability of oxygen hydrogen bonding will decrease due to the strong M- O bond, thereby suppressing the surface reconstruction and coordination of \(\mathrm{Cl}^{- }\) . The \(\mathrm{Sn} / \beta \mathrm{- Fe}_2\mathrm{O}_3\) photoanode constitutes by far the most durable photoanode for seawater splitting. This strategy can also improve the durability of other photoanodes, such as \(\alpha \mathrm{- Fe}_2\mathrm{O}_3\) (Supplementary Fig. 10). This study will pave a new path to solving the problem of the long- term durability of photoelectrodes in energy conversion. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[156, 92, 844, 468]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 483, 784, 503]]<|/det|> +
Fig. 1. | Cross section and crystal structure of the Sn/β-Fe₂O₃ photoanode.
+ +<|ref|>text<|/ref|><|det|>[[145, 520, 848, 540]]<|/det|> +a, b, HAADF images of the Sn/β-Fe₂O₃ film cross-section at different magnifications. + +<|ref|>text<|/ref|><|det|>[[145, 557, 848, 577]]<|/det|> +c, Atomic image of the (1 1 0) plane of β-Fe₂O₃. d, Local enlargement near doped Sn + +<|ref|>text<|/ref|><|det|>[[145, 594, 630, 612]]<|/det|> +atoms and three-dimensional modelling of surface contrast. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[157, 87, 848, 370]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 393, 781, 411]]<|/det|> +
Fig. 2. | PEC properties of the \(\beta\) -Fe2O3 photoanode in simulated seawater.
+ +<|ref|>text<|/ref|><|det|>[[145, 427, 852, 632]]<|/det|> +a, Stability test of \(\beta\) - Fe2O3 and Sn/β- Fe2O3 in 1 M KOH with and without 0.5 M NaCl for 100 h. b, AC electrochemical impedance spectra of Sn/β- Fe2O3 at 1.6 VRE after the reaction. c, HAADF images of the Sn/β- Fe2O3 photoanode after 100 h of reaction in simulated seawater. d, Stability test of Sn/and \(\beta\) - Fe2O3 in saturated NaCl solution for 1440 h. e, Summary of the photoanode stability of PEC (simulated) seawater splitting over the years. Detailed information can be found in Supplementary Table 1. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[175, 100, 853, 420]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 446, 850, 501]]<|/det|> +
Fig. 3. | Evolution of the \(\beta\) -Fe₂O₃ surface during the long-term seawater decomposition reaction.
+ +<|ref|>text<|/ref|><|det|>[[147, 519, 852, 689]]<|/det|> +a- c, XPS spectra of O 1s, Cl 2p and Sn 3d of Sn/β- Fe₂O₃ before, after 100 h, and after 1000 h of seawater splitting reaction. d, Raman spectra of β- Fe₂O₃ photoanodes with different reaction times and annealing treatments. e, f, TOF- SIMS of the distributions of Cl and \(^{18}\mathrm{OH}\) on the surface and depth profiling of the β- Fe₂O₃ photoanode before and after Sn doping after the 100- h reaction in simulated seawater with 20 wt.% H₂¹⁸O. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[169, 88, 828, 555]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 593, 781, 612]]<|/det|> +
Fig. 4. | Effect of Sn atoms in \(\beta\) -Fe₂O₃ on resistance to seawater corrosion.
+ +<|ref|>text<|/ref|><|det|>[[144, 629, 851, 799]]<|/det|> +a, XANES spectra of the O K-edge in \(\beta\) - Fe₂O₃. b, The steady- state photocurrent \(\mathrm{j}_{\mathrm{H2O}} / \mathrm{j}_{\mathrm{D2O}}\) and \(\mathrm{j}_{\mathrm{Sn}} / \mathrm{j}_{\mathrm{Pure}}\) values at different pH values. c, Schematic diagram of doped Sn atoms against \(\mathrm{Cl}^{-}\) corrosion in seawater splitting. Sn enhances the M- O bonds, prevents the hydrated surface reconstruction caused by the transfer of \(\mathrm{H}^{+}\) to lattice oxygen, and weakens the coordination of \(\mathrm{Cl}^{-}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[89, 90, 855, 900]]<|/det|> +References1. Fujishima, A. & Honda, K. Electrochemical Photolysis of Water at Semiconductor Electrode. Nature 238, 37–38 (1972).2. Fang, T. et al. Reactive inorganic vapor deposition of perovskite oxynitride films for solar energy conversion. Research 2019, 9 (2019).3. De Luna, P. et al. What would it take for renewably powered electrosynthesis to displace petrochemical processes? Science 364, eaav3506 (2020).4. Lewis, N. S. Research opportunities to advance solar energy utilization. Science 351, aad1920 (2016).5. Zhang, Y. et al. Homogeneous solution assembled Turing structures with near zero strain semi-coherence interface. Nat. Commun. 13, 2942 (2022).6. Kibsgaard, J. & Chorkendorff, I. Considerations for the scaling-up of water splitting catalysts. Nat. Energy 4, 430–433 (2019).7. Jadwiszczak, M., Jakubow-Piotrowska, K., Kedzierzawski, P., Bienkowski, K. & Augustynski, J. Highly efficient sunlight-driven seawater splitting in a photoelectrochemical cell with chlorine evolved at nanostructured \(\mathrm{WO}_3\) photoanode and hydrogen stored as hydride within metallic cathode. Adv. Energy Mater. 10, 1903213 (2020).8. Li, Y. G. et al., Photoelectrochemical splitting of natural seawater with \(\alpha\) - \(\mathrm{Fe}_2\mathrm{O}_3 / \mathrm{WO}_3\) nanorod arrays. 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Ultrastable low- bias water splitting photoanodes via photocorrosion inhibition and in situ catalyst regeneration. Nat. Energy 2, 16191 (2017). + +<|ref|>text<|/ref|><|det|>[[205, 464, 850, 520]]<|/det|> +Hu, S. et al. Amorphous \(\mathrm{TiO_2}\) coatings stabilize Si, GaAs, and GaP photoanodes for efficient water oxidation. Science 344, 1005- 1009 (2014). + +<|ref|>text<|/ref|><|det|>[[205, 537, 850, 593]]<|/det|> +Luo, W. J. et al. Solar hydrogen generation from seawater with a modified \(\mathrm{BiVO_4}\) photoanode. Energy Environ. Sci. 4, 4046- 4051 (2011). + +<|ref|>text<|/ref|><|det|>[[205, 610, 850, 704]]<|/det|> +Zhong, D. K. & Gamelin, D. R. Photoelectrochemical water oxidation by cobalt catalyst ("Co- \(\mathrm{Pi}\) ") \(\alpha\) - \(\mathrm{Fe_2O_3}\) composite photoanodes: oxygen evolution and resolution of a kinetic bottleneck. J. Am. Chem. Soc. 132, 4202- 4207 (2010). + +<|ref|>text<|/ref|><|det|>[[205, 722, 850, 852]]<|/det|> +Li, Z. S., Luo, W. J., Zhang, M. L., Feng, J. Y. & Zou, Z. G. Photoelectrochemical cells for solar hydrogen production: current state of promising photoelectrodes, methods to improve their properties, and outlook. Energy Environ. Sci. 6, 347- 370 (2013). + +<|ref|>text<|/ref|><|det|>[[205, 870, 850, 889]]<|/det|> +Hausmann, J. N., Schlogl, R., Menezes, P. W. & Driess, M. Is direct seawater + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[204, 94, 850, 114]]<|/det|> +splitting economically meaningful? Energy Environ. Sci. 14, 3679–3685 (2021). + +<|ref|>text<|/ref|><|det|>[[206, 132, 850, 223]]<|/det|> +18. Kuang, Y. et al. Solar-driven, highly sustained splitting of seawater into hydrogen and oxygen fuels. Proc. Natl. Acad. Sci. U. S. A. 116, 6624–6629 (2019). + +<|ref|>text<|/ref|><|det|>[[206, 243, 850, 298]]<|/det|> +19. Tong, W. M. et al. Electrolysis of low-grade and saline surface water. Nat. Energy 6, 935–935 (2021). + +<|ref|>text<|/ref|><|det|>[[206, 317, 850, 409]]<|/det|> +20. Feng, C. et al. A self-healing catalyst for electrocatalytic and photoelectrochemical oxygen evolution in highly alkaline conditions. Nat. Commun. 12, 5980 (2021). + +<|ref|>text<|/ref|><|det|>[[206, 428, 850, 483]]<|/det|> +21. Chung, D. Y. et al. Dynamic stability of active sites in hydr(oxy)oxides for the oxygen evolution reaction. Nat. Energy 5, 550–550 (2020). + +<|ref|>text<|/ref|><|det|>[[206, 502, 850, 557]]<|/det|> +22. Hunter, B. M. et al. Trapping an iron(VI) water-splitting intermediate in nonaqueous media. Joule 2, 747–763 (2018). + +<|ref|>text<|/ref|><|det|>[[206, 576, 850, 668]]<|/det|> +23. Kim, J. Y., Youn, D. H., Kang, K. & Lee, J. S. Highly conformal deposition of an ultrathin FeOOH layer on a hematite nanostructure for efficient solar water splitting. Angew. Chem. Int. Ed. 55, 10854–10858 (2016). + +<|ref|>text<|/ref|><|det|>[[206, 688, 850, 780]]<|/det|> +24. Tang, F. Liu, T., Jiang, W. L. & Gan, L. Windowless thin layer electrochemical Raman spectroscopy of Ni-Fe oxide electrocatalysts during oxygen evolution reaction. J. Electroanal. Chem. 871, 6 (2020). + +<|ref|>text<|/ref|><|det|>[[206, 800, 850, 890]]<|/det|> +25. Duan, Y. et al. Scaled-up synthesis of amorphous NiFeMo oxides and their rapid surface reconstruction for superior oxygen evolution catalysis. Angew. Chem. Int. Ed. 58, 15772–15777 (2019). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[201, 93, 850, 186]]<|/det|> +Zhang, H. M. et al. Gradient tantalum-doped hematite homojunction photoanode improves both photocurrents and turn-on voltage for solar water splitting. Nat. Commun. 11, 4622 (2020). + +<|ref|>text<|/ref|><|det|>[[201, 204, 850, 259]]<|/det|> +Frati, F., Hunault, M. & de Groot, F. M. F. Oxygen K-edge X-ray absorption spectra. Chem. Rev. 120, 4056- 4110, (2020). + +<|ref|>text<|/ref|><|det|>[[201, 278, 850, 334]]<|/det|> +McLeod, J. A. et al. Band gaps and electronic structure of alkaline-earth and post- transition- metal oxides. Phys. Rev. B 81, 245123 (2010). + +<|ref|>text<|/ref|><|det|>[[201, 352, 848, 408]]<|/det|> +McLeod, J. A. et al. Chemical bonding and hybridization in 5p binary oxide. J. Phys. Chem. C 116, 24248- 24254, (2012). + +<|ref|>text<|/ref|><|det|>[[201, 426, 850, 558]]<|/det|> +Burke, M. S., Kast, M. G., Trotochaud, L., Smith, A. M. & Boettcher, S. W. Cobalt- iron (Oxy)hydroxide oxygen evolution electrocatalysts: the role of structure and composition on activity, stability, and mechanism. J. Am. Chem. Soc. 137, 3638- 3648 (2015). + +<|ref|>text<|/ref|><|det|>[[201, 576, 850, 632]]<|/det|> +Dau, H. et al. The Mechanism of water oxidation: from electrolysis via homogeneous to biological catalysis. ChemCatChem 2, 724- 761 (2010). + +<|ref|>text<|/ref|><|det|>[[201, 650, 850, 743]]<|/det|> +Chen, J., Li, Y. F., Sit, P. & Selloni, A. Chemical dynamics of the first proton- coupled electron transfer of water oxidation on \(\mathrm{TiO_2}\) anatase. J. Am. Chem. Soc. 135, 18774- 18777 (2013). + +<|ref|>text<|/ref|><|det|>[[201, 761, 850, 816]]<|/det|> +Iandolo, B. & Hellman, A. The role of surface states in the oxygen evolution reaction on hematite. Angew. Chem. Int. Ed. 53, 13404- 13408 (2014). + +<|ref|>text<|/ref|><|det|>[[201, 834, 850, 890]]<|/det|> +Li, Y. F., Liu, Z. P., Liu, L. L. & Gao, W. G. Mechanism and activity of photocatalytic oxygen evolution on titania anatase in aqueous surroundings. J. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[205, 95, 560, 112]]<|/det|> +Am. Chem. Soc. 132, 13008–13015 (2010). + +<|ref|>text<|/ref|><|det|>[[205, 130, 850, 225]]<|/det|> +Li, Y. G. et al. Efficient and stable photoelectrochemical seawater splitting with \(\mathrm{TiO_2@g - C_3N_4}\) nanorod arrays decorated by Co- Pi. J. Phys. Chem. C 119, 20283–20292 (2015). + +<|ref|>text<|/ref|><|det|>[[205, 241, 850, 335]]<|/det|> +Li, Y. G. et al. Construction of inorganic- organic 2D/2D \(\mathrm{WO_3 / g - C_3N_4}\) nanosheet arrays toward efficient photoelectrochemical splitting of natural seawater. Phys. Chem. Chem. Phys. 18, 10255–10261 (2016). + +<|ref|>text<|/ref|><|det|>[[205, 352, 850, 447]]<|/det|> +Sharma, M. D., Mahala, C. & Basu, M. Photoelectrochemical water splitting by \(\mathrm{In_2S_3 / In_2O_3}\) composite nanopyramids. ACS Appl. Nano Mater. 3, 11638–11649 (2020). + +<|ref|>text<|/ref|><|det|>[[205, 464, 850, 556]]<|/det|> +Sahoo, P., Sharma, A., Padhan, S. & Thangavel, R. Visible light driven photosplitting of water using one dimensional Mg doped ZnO nanorod arrays. Int. J. Hydrogen Energy 45, 22576–22588 (2020). + +<|ref|>text<|/ref|><|det|>[[205, 573, 850, 668]]<|/det|> +Gao, R. T. et al. Ultrastable and high- performance seawater- based photoelectrolysis system for solar hydrogen generation. Appl. Catal. B- Environ. 304, 120883 (2022). + +<|ref|>text<|/ref|><|det|>[[205, 685, 850, 780]]<|/det|> +Guo, X. T., Liu, X. H. & Wang, L. \(\mathrm{NiMoO_x}\) as a highly protective layer against photocorrosion for solar seawater splitting. J Mater. Chem. A 10, 1270–1277 (2022). + +<|ref|>text<|/ref|><|det|>[[205, 797, 850, 891]]<|/det|> +She, X. F. et al. Floc- like CNTs jointed with \(\mathrm{Bi_xFe_{(1-x)VO_4}}\) nanoparticles for high efficient and stable photoelectrochemical seawater splitting. J. Alloys Compd. 893, 162146 (2022). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[201, 93, 852, 188]]<|/det|> +42. Seenivasan, S., Moon, H. & Kim, D. H. Multilayer strategy for photoelectrochemical hydrogen generation: new electrode architecture that alleviates multiple bottlenecks. Nano-Micro Lett. 14, 78 (2022). + +<|ref|>sub_title<|/ref|><|det|>[[147, 301, 226, 318]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[147, 337, 384, 355]]<|/det|> +## Preparation of photoanode + +<|ref|>text<|/ref|><|det|>[[144, 371, 852, 916]]<|/det|> +Typically, in an experiment, 0.01 mol of iron acetylacetonate (AcAcFe) was dissolved in \(500~\mathrm{mL}\) ethanol by stirring with a magnetic force for over \(48~\mathrm{h}\) . Fluorine- doped tin oxide (FTO) conductive glass was cut into dimensions of \(2\mathrm{cm}\times 1\mathrm{cm}\) , wrapped with aluminum foil to make a deposition area of \(1\mathrm{cm}\times 1\mathrm{cm}\) and then placed in a tube furnace with a set temperature of \(480~^\circ \mathrm{C}\) . The precursor solution was added to the injection pump and dispersed into droplets by using an ultrasonic atomizer. During the experiment, \(40~\mathrm{mL}\) of precursor solution was injected at a speed of \(1.6~\mathrm{mL}\) \(\mathrm{min}^{- 1}\) , which equally matched the power of the ultrasonic atomizer. Using air as the carrier gas, the precursor was fed into a tubular furnace. After deposition, the film was annealed in a muffle furnace at \(600~^\circ \mathrm{C}\) for \(3\mathrm{h}\) at a heating rate of \(10~^\circ \mathrm{C}\mathrm{min}^{- 1}\) . The \(\mathrm{Sn / \beta - Fe_2O_3}\) films were prepared using the same spray pyrolysis method by adding a certain amount of tetrabutyltin ( \(\mathrm{C_{16}H_{36}Sn}\) , analytical reagent, Aladdin) ethanol solution to the precursor solution so that the Sn atom concentration accounted for \(1\%\) , \(2\%\) , \(3\%\) , and \(4\%\) of the total Sn and Fe atoms. The CoFe-LDH @ \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes were prepared by a hydrothermal method. The as-prepared \(\mathrm{Sn / \beta - Fe_2O_3}\) photoanodes were put + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 92, 851, 261]]<|/det|> +into a \(100~\mathrm{mL}\) hydrothermal kettle, \(50~\mathrm{mL}\) of a solution containing \(0.002\mathrm{mol}\mathrm{L}^{- 1}\) cobalt nitrate hexahydrate \(\mathrm{(Co(NO_3)_2\cdot 6H_2O}\) , Sinopharm Chemical Reagent), \(0.002\mathrm{mol}\mathrm{L}^{- 1}\) iron(III) nitrate nonahydrate \(\mathrm{(Fe(NO_3)_3\cdot 9H_2O}\) , analytical reagent, Aladdin)), \(0.005\mathrm{mol}\) \(\mathrm{L}^{- 1}\) urea (Aladdin) and \(0.001\mathrm{mol}\mathrm{L}^{- 1}\) trisodium citrate was added, and the reaction was carried out in an oven at \(120^{\circ}\mathrm{C}\) for \(5\mathrm{h}\) . + +<|ref|>sub_title<|/ref|><|det|>[[148, 316, 298, 333]]<|/det|> +## Characterization + +<|ref|>text<|/ref|><|det|>[[144, 352, 852, 858]]<|/det|> +To identify the crystal structures of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes, they were measured by powder X- ray diffraction (XRD, Rigaku Ultima III, Cu Kα radiation, \(\lambda = 1.54178\) Å) at \(40\mathrm{kV}\) and \(40\mathrm{mA}\) . The surface morphology of the \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes was examined by a high- resolution scanning electron microscope (HRSEM, ZEISS ULTRA 55 at an accelerating voltage of \(5\mathrm{kV}\) ). Raman spectra of \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes were characterized with a confocal laser Raman spectrometer (Japan, Horiba, LabRAM Aramis). X- ray photoemission spectroscopy (XPS, PHI 5000 VersaProbe) was used to characterize the content and valence of Sn, O, Fe and Co, and the binding energy was calibrated by the adventitious carbon 1 s line at \(284.8\mathrm{eV}\) . The optical absorption spectra of the photoanode were tested on a UV- Visible- NIR (near- infrared) spectrophotometer (PerkinElmer, UV3600 UV- Vis- NIR spectrophotometer). Transmission electron microscopy (TEM) and high- resolution transmission electron microscopy (HRTEM) images were obtained on an FEI Tecnai G2 F30. High- angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) images were obtained + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 94, 850, 149]]<|/det|> +by a JEOL JEM- ARM200F microscope incorporated with a spherical aberration correction system for STEM. + +<|ref|>sub_title<|/ref|><|det|>[[148, 206, 321, 222]]<|/det|> +## PEC measurements + +<|ref|>text<|/ref|><|det|>[[144, 240, 852, 744]]<|/det|> +The PEC measurements were carried out in a PEC cell with an electrochemical analyser (CHI- 760E, CH Instrument, Shanghai) in a three- electrode system including a reference electrode consisting of \(\mathrm{Ag / AgCl}\) placed in a saturated KCl solution, Pt foil as the counter electrode, and \(\beta\) - \(\mathrm{Fe_2O_3}\) photoanodes as working electrodes. The electrolyte was a 1 M KOH aqueous solution for freshwater and 1 M KOH with 0.5 M NaCl for simulated seawater. The potential was reported vs. the reversible hydrogen electrode (RHE) with \(\mathrm{E_{RHE}} = \mathrm{E_{Ag / AgCl}} + 0.197 + 0.0591\) pH. The photocurrent density was measured under AM 1.5 G light source, and the light intensity was \(100\mathrm{mWcm^{- 2}}\) . A Newport 91150 V standard silicon cell was used as the reference standard for calibration. Mott- Schottky analysis was performed at bias potentials from 0.5 V to 1.5 V vs. RHE. AC electrochemical impedance was obtained at a bias of \(1.6\mathrm{V_{RHE}}\) over the frequency range of \(100\mathrm{kHz}\) to \(1\mathrm{Hz}\) . The PEC stabilities were tested at a constant potential of \(1.6\mathrm{V_{RHE}}\) under LED- simulated sunlight sources through illumination from the front side. + +<|ref|>text<|/ref|><|det|>[[145, 761, 851, 891]]<|/det|> +In the PEC test of the H/D kinetic isotope effect, the electrolyte was measured with a pH meter to keep the concentrations of \(\mathrm{OH^- }\) and \(\mathrm{OD^- }\) in the solution the same ( \(\mathrm{pD} = \mathrm{pH}_{\mathrm{read}} + 0.4\) ). \(\mathrm{D_2O}\) was purchased from Bide Pharmatech Ltd. (99.9% atom \(\% \mathrm{D}\) ). The \(\mathrm{pD}\) values were adjusted by \(\mathrm{NaOH}\) (Aladdin, 30 wt.% solution in \(\mathrm{D_2O}\) , 99.5%). In the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 850, 188]]<|/det|> +current time curve, the photocurrent density value after 50 s of reaction was selected as the steady- state value for the calculation of \(\mathrm{j}_{\mathrm{H2O}}/\mathrm{j}_{\mathrm{D2O}}\) and \(\mathrm{j}_{\mathrm{SN}}/\mathrm{j}_{\mathrm{Pure}}\) (Supplementary Fig. 8). + +<|ref|>sub_title<|/ref|><|det|>[[148, 242, 722, 261]]<|/det|> +## Time-of-flight secondary ion mass spectrometry (TOF-SIMS) tests + +<|ref|>text<|/ref|><|det|>[[144, 278, 852, 445]]<|/det|> +TOF- SIMS tests were carried out by PHI nanoTOF II Time- of- Flight SIMS. \(\mathrm{Bi}_{3}^{++}\) with an energy of \(30 \mathrm{eV}\) was used in the acquisition phase in high mass resolution mode. An Ar ion gun with an energy of \(4 \mathrm{kV}\) was used in the sputter phase with a sputter rate of \(0.4 \mathrm{nm / s}\) on \(\mathrm{SiO}_{2}\) . Before the \(\beta\) - \(\mathrm{Fe}_{2} \mathrm{O}_{3}\) photoanode was tested, the reactions in the electrolyte with \(1 \mathrm{M} \mathrm{KOH} + 0.5 \mathrm{M} \mathrm{NaCl}\) and \(20 \mathrm{wt.} \% \mathrm{H}_{2}^{18} \mathrm{O}\) for \(100 \mathrm{h}\) were carried out. + +<|ref|>sub_title<|/ref|><|det|>[[147, 502, 604, 520]]<|/det|> +## X-ray absorption near-edge structure (XANES) tests + +<|ref|>text<|/ref|><|det|>[[144, 536, 852, 817]]<|/det|> +Soft X- ray absorption near- edge structure (XANES) measurements were performed at the Beijing Synchrotron Radiation Facility (BSRF), 4B9B beamline. The O- K edge and Fe- L edge spectra were collected in total electron yield (TEY) mode by measuring the sample current with an amperemeter. All spectra were normalized to the intensity of the incident beam (10), which was measured simultaneously with the current emitted from a gold mesh located after the last optical elements of the beamline. The photon energy was calibrated using the Au- 4f core level at \(84.0 \mathrm{eV}\) in binding energy by measuring a clean polycrystalline gold foil that is electrically connected to the sample. + +<|ref|>sub_title<|/ref|><|det|>[[148, 873, 378, 890]]<|/det|> +## Computational processing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 93, 852, 373]]<|/det|> +The calculations on pure and \(\mathrm{Sn} / \beta \mathrm{- Fe}_2\mathrm{O}_3\) were implemented in the VASP (Vienna Ab initio Simulation Package) based on density functional theory, with a projected- augmented- wave method in the scheme of generalized- gradient approximation. The strong on- site Coulomb repulsion among the localized Fe 3d electrons was described with the generalized- gradient approximation + U approach (U is the strength of the onsite Coulomb interaction). The exchange- correlation effects were treated using the generalized gradient approximation (GGA) in the Perdew- Burke- Ernzerhof parametrization, with spin- polarized effects considered. + +<|ref|>text<|/ref|><|det|>[[147, 471, 850, 528]]<|/det|> +Acknowledgements: We are indebted to Prof. Yixin Zhao (Shanghai Jiaotong University) for discussions. + +<|ref|>text<|/ref|><|det|>[[147, 552, 851, 683]]<|/det|> +Funding: The authors thank the National Science Fund for Distinguished Young Scholars [No. 22025202], National Key Research and Development Program of China [Nos. 2018YFA0209303 and 2021YFA1502100], and National Natural Science Foundation of China [Nos. 51972165 and 51902153] for financial support. + +<|ref|>text<|/ref|><|det|>[[147, 707, 851, 876]]<|/det|> +Author contributions: Z.L. constructed the concept and designed the project. Z.L. supervised the study. N.Z., Y.L., J.F., W.W., C.L., J.W., W.H. and Z.Z. advised on the research. C.H.L. and R.F. collected and analysed the experimental data. C.H.L. and Z.L. wrote the manuscript. Z.L. and J.F. revised the manuscript. All the authors contributed to the discussions about the manuscript. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[89, 94, 805, 112]]<|/det|> +Competing interests: The authors declare that they have no competing interests. + +<|ref|>text<|/ref|><|det|>[[89, 137, 850, 155]]<|/det|> +Data and materials availability: All data are available in the main text or the + +<|ref|>text<|/ref|><|det|>[[89, 177, 379, 193]]<|/det|> +Supplementary Information. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 370, 150]]<|/det|> +SupportingInformationLiuCH.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/images_list.json b/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..656bfb71decd0618ecadee4055b54e4380403e0f --- /dev/null +++ b/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 Transcriptional kissing loop (KL) condensates form different synthetic nuclear patterns in DNA protonuclei (PNs). PolyA strands with barcode p (T7 promoter sequence), polyA strands with dummy barcode (o), and polyT with k barcode are used for LLPS process to form PNs with an incorporated promoter region. The promoter barcodes inside the PNs recruit DNA templates, T7 RNA Polymerase (T7 RNAP), and nucleotide triphosphate (NTP) monomers to induce a localized transcription and enrichment of KL sequences, forming distinct nuclear patterns via different nucleation and condensation processes.", + "footnote": [], + "bbox": [ + [ + 111, + 128, + 884, + 315 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 Transcriptional KL condensates in PNs show different nuclear patterns. a, Scheme showing transcription in PNs characterized by CLSM and plate reader. For CLSM experiment, UTP-Att0488 is added to the NTP mix for transcript labeling. For plate reader experiments, dsDNA reporters with fluorophore-quencher pairs (R) are present in solution to react with transcribed RNA by strand displacement reaction (SDR), generating fluorescent signals. b, Representative CLSM images showing the localized transcription of fluorescent \\(\\mathbf{x}^*\\) (green, labeled by UTP-Att0488 during transcription) inside PNs at different times ([NTP] : [T \\(\\mathbf{x}^*\\) ] : [p] = 250 : 1: 1, 30 °C, 30 mM Mg \\(^{2 + }\\) , 2.5 U/μL T7 RNAP). The whole process is recorded in Supplementary Video 1. c, Transcription kinetics inside PNs with embedded poly(A \\(\\mathbf{\\cdot}_{20} - \\mathbf{p})\\) n, in solution with free poly(A \\(\\mathbf{\\cdot}_{20} - \\mathbf{p})\\) n, and in solution with free promoter (p) oligonucleotide, monitored by SDR of the R in a plate reader ([NTP] : [R] : [T \\(\\mathbf{\\cdot}_{R}\\mathbf{p}^*\\) ] : [p] = 200 : 10 : 1 : 1, 30 °C, 6 mM Mg \\(^{2 + }\\) , 2.5 U/μL T7 RNAP). d, Scheme for the formation of transcriptional KL1 condensates in PNs and in solution, with KL1 labeled by UTP-Att0488 during transcription. e, CLSM images with maximum intensity projection of z-stacked images showing the formation of transcriptional KL1 condensates in PNs containing embedded poly(A \\(\\mathbf{\\cdot}_{20} - \\mathbf{p})\\) n ([NTP] : [T \\(\\mathbf{\\cdot}_{KL1}\\) ] = 3.6), and in solution with free promoter (p) oligonucleotide ([NTP] : [T \\(\\mathbf{\\cdot}_{KL1}\\) ] = 3.6 : 1 or 14.4 : 1, 30 °C, 30 mM Mg \\(^{2 + }\\) , 2.5 U/μL T7 RNAP, 18 h for in-PN and in-solution transcription). f, Diameter distributions of KL1 condensates in PNs and in solution at different [NTP]. g, Normalized fluorescence recovery kinetics in the bleached areas in (h) during FRAP experiments on", + "footnote": [], + "bbox": [ + [ + 112, + 120, + 880, + 636 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 Mechanism of co-condensation of transcriptional KL1 with the PN matrix. a, Scheme for formation of single co-condensates of transcriptional KL1 and polyA in PNs through peripherical initiation of transcription, re-organization, and co-condensation. b, Representative CLSM images of PNs conducting KL1 transcription over \\(24\\mathrm{h}\\left(\\mathrm{[NTP]}:[\\mathrm{T}_{\\mathrm{KL1}}] = 3.6:1,1\\mathrm{mol}\\% \\mathrm{UTP - Atto}488,30^{\\circ}\\mathrm{C},2.5\\mathrm{U} / \\mu \\mathrm{L} T7\\mathrm{RNAP},30\\mathrm{mM}\\mathrm{Mg}^{2 + }\\right)\\) . Note a slight slow-down of co-condensation kinetics of KL1 compared with results shown in Fig. 2e, j after \\(18\\mathrm{h}\\) , due to the interruption of shaking during incubation for the CLSM imaging. Green channel: KL1 condensate labeled by UTP-Attot488; Magenta channel: PN shell (poly( \\(\\mathrm{T}_{20}\\mathrm{-k}\\) ), labeled with \\(\\mathrm{k}^{*}\\) -Attot647). c, Space-time plot analysis corresponding to the two dashed lines in (b) over \\(24\\mathrm{h}\\) shows the KL1 transcription, condensation, and reorganization process. d, Normalized fluorescence intensity change in the KL1 condensate channel over \\(24\\mathrm{h}\\) in the two white dashed circle in (b), normalized to the intensity at \\(24\\mathrm{h}\\) . e, Normalized radius change of KL1 condensate and PN as measured from (b) over \\(24\\mathrm{h}\\) . f, g Representative CLSM images of fluorescent PNs (magenta) containing KL1 transcripts (green) at \\(12\\mathrm{h}\\) (f) and \\(24\\mathrm{h}\\) (g). The right plots correspond to the line segment analysis of the white line in the CLSM images, showing fluorescence distribution for KL1-Attot488 and poly(A20-0)n-Attot643. The KL1 transcripts show a peripheral distribution in the PN matrix at \\(12\\mathrm{h}\\) (f), while colocalization and co-condensation between KL1 and PN matrix occur after \\(24\\mathrm{h}\\) (g). Error bars and error areas represent standard deviation. Scale bars are all \\(5\\mu \\mathrm{m}\\) .", + "footnote": [], + "bbox": [ + [ + 113, + 123, + 884, + 562 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 Hybridization of the polyA matrix of PNs induces disassembly of KL1-PN co-condensate. a, Representative CLSM images of transcriptional KL1 condensates (Atto488, green channel) in PNs (Atto647, magenta channel) 60 min after adding 10%, 50%, 80% and 300% o\\*- Atto647 as an invader strand. b, Schematic", + "footnote": [], + "bbox": [ + [ + 111, + 275, + 880, + 814 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 Formation of orthogonal transcriptional KL condensates in PNs. a, Scheme showing orthogonal transcription and condensation of KL1-R1 and KL2-R2 in the same PN at different salinity. KL1-R1 is identical to KL1 in its nanostar framework, but with an additional recognition tail (R1) for R1\\*-Atto647 labelling. KL2-R2 shares the same stem sequence as KL1 but has orthogonal kissing loop sequences and a distinct recognition tail (R2) for R2\\*-Atto488 labeling. R1\\*-Atto647 and R2\\*-Atto488 are added during transcription. b, Representative single-plane CLSM image and maximum intensity projection of z-stacked CLSM image showing orthogonal transcriptional condensates of KL1-R1 (green channel) and KL2-R2 (magenta channel) in PNs at 30 mM \\(\\mathrm{Mg^{2 + }}\\) ([NTP] : [R1\\*] : [R2\\*] : [TKL1-R1] : [TKL2-R2] : [p] = 3.6 : 1.8 : 1.8 : 0.5 : 0.5 : 1, 30 °C, 30", + "footnote": [], + "bbox": [ + [ + 110, + 311, + 884, + 744 + ] + ], + "page_idx": 13 + } +] \ No newline at end of file diff --git a/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221.mmd b/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ddfb26ac4741d5db126760a59c911e464ce06a28 --- /dev/null +++ b/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221.mmd @@ -0,0 +1,275 @@ + +# Constructing synthetic nuclear architectures via transcriptional condensates in a DNA protonucleus + +Andreas Walther + +andreas.walther@uni- mainz.de + +University of Mainz https://orcid.org/0000- 0003- 2170- 3306 + +Miao Xie + +University of Mainz + +Weixiang Chen + +University of Mainz https://orcid.org/0009- 0000- 5518- 0799 + +Maria Roy + +University of Mainz + +## Article + +Keywords: + +Posted Date: February 7th, 2025 + +DOI: https://doi.org/10.21203/rs.3.rs- 5959823/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on September 10th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 63445- 8. + +<--- Page Split ---> + +# Constructing synthetic nuclear architectures via transcriptional condensates in a DNA protonucleus + +Miao Xie \(^{1,2,\#}\) , Weixiang Chen \(^{1,2,\#}\) , Maria de Roy \(^{1}\) , Andreas Walther \(^{1,2,*}\) + +## Affiliations + +\(^{1}\) Life- Like Materials and Systems, University of Mainz, Duesbergweg 10- 14, 55128 Mainz, Germany. + +\(^{2}\) Max Planck Institute for Polymer Research, 55128 Mainz, Germany. + +\(^{*}\) Corresponding author. Email: andreas.walther@uni- mainz.de + +\(^{\#}\) These authors contributed equally. + +## Abstract + +Nuclear biomolecular condensates are essential sub- compartments within the cell nucleus and play key roles in transcription and RNA processing. Bottom- up construction of nuclear architectures in synthetic settings is non- trivial but vital for understanding the mechanisms of condensates in real cellular systems. Here, we present a facile and versatile synthetic DNA protonucleus (PN) platform that facilitates localized transcription of branched RNA motifs with kissing loops (KLs) for subsequent condensation into complex condensate architectures. We identify salinity, monomer feeding, and KL- PN interactions as key parameters to control co- transcriptional condensation of these KLs into diverse artificial nuclear patterns, including single and multiple condensates, interface condensates, and biphasic condensates. Over time, KL transcripts co- condense with the PN matrix, with the final architecture determined by their interactions, which can be precisely modulated using a short DNA invader strand that outcompetes these interactions. Our findings deepen the understanding of RNA condensation in nuclear environments and provide new strategies for designing functional nucleus- mimetic systems with precise architectural control. + +<--- Page Split ---> + +## Introduction + +In eukaryotic cells, the nucleus provides a compartment for essential processes such as transcription, mRNA pre- splicing, and ribosome assembly1. To ensure precise spatial and temporal regulation of these biochemical processes2, membrane- less organelles such as nucleolus, Cajal bodies, and nuclear speckles form sub- compartments within the nucleus, which are biomolecular condensates that concentrate specific nucleic acids, enzymes, and metabolites3- 6. Beyond regulating these crucial processes, unique nuclear patterns formed by biomolecular condensates vary across cell types, adapting to specific demands and functional cell states7. Importantly, dysfunctions in nuclear condensates have been implicated in diseases such as cancer, ribosomopathy, and neurodegeneration6, 8, 9. Thus, understanding and reconstructing nuclear biomolecular condensates is not only essential for uncovering their mechanisms but also holds significant potential for therapeutic applications. + +Despite considerable advances in studying natural biomolecular condensates and attempts to engineer transcriptional condensates within the nucleus8, 10- 12 based on specific or non- specific interactions of protein- protein, protein- nucleic acid, and RNA- RNA pairs2, 13, 14, much still remains unknown about their formation mechanisms and the involved kinetic processes. Specifically, the mechanisms by which these condensates concentrate molecules, maintain structural integrity, regulate composition, and modulate internal biochemical activities remain elusive, largely due to the complexity of in vivo environments. In contrast, in vitro models of biomolecular condensates allow for precise control over composition in a simplified setting11, enabling detailed mechanism assessment through experiments and computational modeling15. Here, studies presently however rely on plain solutions that are far from the conditions in a nucleus. + +Transcriptional RNAs with specific sequences have been identified to play a key role in many biomolecular condensation processes15. However, achieving control in synthetic nuclear architectures and functions requires more advanced RNA designs capable of forming higher- order structures. In nature, the self- complementary kissing loop sequence in type 1 human immunodeficiency virus (HIV- 1) virions has been identified as framework for systematically manipulating genomic dimerization16. Similar kissing loop interactions have been shown to facilitate condensation in bacterial riboswitches13, 17. Inspired by the sequence- dependent interaction of kissing loops, which enables specific pairing between internally folded RNAs18, 19, the groups of Takinoue20, di Michele21, and Franco22 have recently introduced programmable condensates in solution formed by nanostar- like RNA motifs. The latter two groups have further shown that RNA nanostars with kissing loops at the end of each arm (KLs) could croscentrically condense into condensates with controlled size, number, morphology, and composition either in solution or confined within water- in- oil emulsions21, 22. Through integration of RNA aptamers into KLs, such condensates can mimic natural membrane- less organelles capable of selective capture of client molecules with biofunctions21. However, it remains unexplored whether RNA condensates can form in crowded conditions and how they may interact with DNA- rich environments resembling the cellular nucleus, where intricate RNA- DNA interactions occur. + +<--- Page Split ---> + +64 How such DNA environments influence the organizational principles of such designer condensates is unknown. + +65 We have recently introduced core- shell DNA coacervates, formed by single- stranded DNA (ssDNA) polymers, with a highly concentrated DNA- enriched core \(^{23}\) , that can flexibly recruit molecules and proteins for enzymatic functions \(^{24, 25}\) and chemical reactions \(^{26}\) . These DNA coacervates closely resemble the crowded environment of the cellular nucleus, making them an ideal platform for constructing nucleus mimics \(^{27}\) . Therefore, we term them protonuclei (PNs) in this study. As the internal composition of the PNs can be flexibly tuned based on the ssDNA polymer selection, we incorporate T7 promoter sequences into the DNA core to recruit transcription templates and facilitate localized in- protonucleo transcription. We demonstrate that KL can be transcribed within these PNs, leading to the formation of co- transcriptional KL condensates with various morphologies. We demonstrate a range of synthetic nuclear architectures, including single condensates, multiple condensates, interfacial condensates formed through secondary nucleation, and biphasic condensates of orthogonal KLs, all controlled by salinity, PN- KL affinity, and competing PN- KL interactions, respectively. Given the design flexibility of transcriptional KLs and the tunable condensate patterns in our crowded PN system, we believe this artificial nucleus platform will significantly advance the field of synthetic biology, in particular synthetic cells, providing a powerful toolkit for designing and constructing synthetic nuclear architectures with unprecedented control and precision. + +## Results + +Figure 1 shows an overview of our entire approach. It consists of constructing a modular PNs platform using DNA nanoscience approaches, followed by immobilization of short KL templates to initiate transcription therein. The transcribed KLs are designed to undergo phase separation by complementary interactions. By precisely controlling KL- PN interactions and environmental conditions, we study structure formation and response in detail through easily accessible pathways. In more detail, the DNA PNs are derived from our previous work on DNA protocells \(^{23, 24}\) , where we have identified that temperature ramps of mixtures of long poly(A \(_{20}\) - m) \(_{n}\) ssDNA and long poly(T \(_{20}\) - k) \(_{n}\) ssDNA form micron- sized core- shell coacervates with an adenine- rich ssDNA polymer (polyA) core and a thymine- rich ssDNA polymer (polyT) shell \(^{23 - 25, 28}\) . This process features a selective liquid- liquid phase separation (LLPS) of polyA during heating, forming polyA droplets at high temperature, which are then stabilized by polyT with A \(_{20}\) /T \(_{20}\) hybridization during cooling, forming a thin and crosslinked hydrogel shell. This ultimately furnishes a highly concentrated polyA core of around 10 g/L \(^{29}\) . The dynamic properties of the PNs can be regulated from an arrested state to a liquid- like state by tuning the salinity. Additional ssDNA barcode sequences (o, p, k) can be modularly incorporated into the ssDNA polymers for integrating functionalities into the core and the shell (Fig. 1). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 Transcriptional kissing loop (KL) condensates form different synthetic nuclear patterns in DNA protonuclei (PNs). PolyA strands with barcode p (T7 promoter sequence), polyA strands with dummy barcode (o), and polyT with k barcode are used for LLPS process to form PNs with an incorporated promoter region. The promoter barcodes inside the PNs recruit DNA templates, T7 RNA Polymerase (T7 RNAP), and nucleotide triphosphate (NTP) monomers to induce a localized transcription and enrichment of KL sequences, forming distinct nuclear patterns via different nucleation and condensation processes.
+ +We synthesized several ssDNA polymers using rolling circle amplification (details in Supplementary Table 1), including poly(A20- p)n, poly(A20- o)n, and poly(T20- k)n with n ranging roughly from 10 to 60 repeating units23. The barcodes p, o, and k serve specific functions. The most critical part is the p barcode in poly(A20- p)n, which is the T7 RNA polymerase (T7 RNAP) promoter sequence that allows for the flexible integration of ssDNA templates (short genes) amenable to transcription of RNA in the PNs through simple addition of the templates after formation of the PNs. Poly(A20- o)n serves to homogeneously dilute the p barcode and provides an addressable matrix barcode to tune properties and (as we will see below) adjust the affinity to the transcribed RNA, which regulates the subsequent growth of the transcriptional condensates. Following our established protocols23- 25, 28, we prepared a set of core- shell PNs by mixing poly(A20- p)n and poly(A20- o)n for the core, and poly(T20- k)n for the shell, using a temperature ramp in TE buffer at 50 mM \(\mathrm{Mg^{2 + }}\) . Functionalization of the p and o barcodes with complementary day- appended ssDNA confirms a homogeneous integration of both polyAs in the PN cores (confocal laser scanning microscopy (CLSM) images in Supplementary Fig. 1). The PNs can be conditioned to different salinity after preparation. We focus on a PN system where 10% of promoter sequences (poly(A20- p)n) are diluted with 90% of a matrix (poly(A20- o)n). + +To verify transcription to occur inside the PNs, we hybridized a transcription template \(\mathrm{Tx^*}\) containing \(\mathrm{p}^*\) for hybridization with the promoter sequence p and an active transcription region \(\mathrm{x^*}\) at stoichiometric ratio into the PNs (sequences in Supplementary Table 2). \(\mathrm{x^*}\) codes for a simple RNA not amenable to undergo condensation. Subsequent addition of T7 RNAP and a nucleotide triphosphate (NTP) monomer mix containing 1% fluorescent monomer (UTP- Atto488) induces transcription with local formation of fluorescent RNA strands (Fig. 2a, b, and Supplementary Video 1). + +To better quantify the transcription efficiency and kinetics inside the PNs and compare it to free + +<--- Page Split ---> + +transcription in solution, we further designed a reporter (R) containing a fluorophore- quencher pair, which is a partially complementary double- stranded DNA (dsDNA; Rep/Rep' sequences in Supplementary Table 2; ' denotes a partially complementary sequence with a toehold). The transcribed Rep\* from template \(\mathrm{T_{Rep^*}}\) will trigger a strand displacement reaction (SDR) with the R by fully hybridizing with the Rep strand, generating a fluorescent signal, which can be monitored by fluorescence measurements using a plate reader. In more detail, we compared the transcription kinetics between PNs with embedded promoter sequence of poly \((\mathrm{A}_{20} - \mathrm{p})_{\mathrm{n}}\) , pure poly \((\mathrm{A}_{20} - \mathrm{p})_{\mathrm{n}}\) ssDNA in solution, and short p ssDNA in solution - all at identical p concentration and otherwise identical transcription conditions (Fig. 2a, c). All systems show relatively similar kinetic profiles, with the free promoter in solution being the most active transcription system, and the PN showing a slightly lower activity compared to the free poly \((\mathrm{A}_{20} - \mathrm{p})_{\mathrm{n}}\) in solution. The slightly lower activity can be understood considering constraints on the diffusion of NTPs into the PNs and RNA strands out of the PNs. + +After confirming successful transcription in the PNs, we turn to KL- condensate formation by transcriptional control in the PNs versus in solution. As a proof of concept, we first focus on a three- armed singled- stranded RNA (ssRNA) nanostar with a wildtype palindromic KL sequence \(^{20 - 22}\) at the tip of each arm (KL1 in Fig. 2d; Template = hybridized \(\mathrm{T_{KL1} / T_{KL1}}\) , where \(\mathrm{T_{KL1}}\) contains a \(\mathrm{p}^*\) ssDNA sequence for hybridization to poly \((\mathrm{A}_{20} - \mathrm{p})_{\mathrm{n}}\) inside the PNs, Supplementary Table 2). We compared differences in KL1 condensate formation at low \([\mathrm{NTP}] = 3.6 \times [\mathrm{T_{KL1}}]\) after \(18\mathrm{h}\) transcription ([NTP] is defined as the maximum amount of KL1 transcripts that can be produced per template). The PN system clearly shows a single KL1 condensate in every PN with an average diameter of approximately \(4\mu \mathrm{m}\) for PNs with an average diameter of around \(6.7\mu \mathrm{m}\) (Fig. 2e, f). In striking contrast, no KL1 condensates can be found in solution due to the limited concentration of RNA transcripts (Fig. 2e, f). Transcriptional KL1 condensates in solution start to appear with diameter of \(\sim 4.5\mu \mathrm{m}\) at increased [NTP] ([NTP] = 14.4 x; Fig. 2e, f). The size of the transcriptional KL1 condensate in solution increases with [NTP] due to the increased amount of RNA transcripts (Supplementary Fig. 2). This comparison demonstrates that the spatial transcription of the KL1 in PNs leads to locally high concentrations sufficient for condensation, similar to the enrichment mechanism in natural nuclear condensates \(^2\) . + +Interestingly, one single KL1 condensate forms in each PN, confirming sufficient dynamics within the PN to follow energy minimization constraints to yield a minimum surface area (Fig. 2e and Supplementary Fig. 3). We further performed fluorescence recovery after photobleaching (FRAP) experiments on the KL1 condensates in PNs and in solution to study their dynamic properties. Strikingly, their fluorescence recovery kinetics differ substantially. Whereas KL1 condensates in solution show near full recovery overnight, KL1 condensates in PNs only show limited recovery, highlighting much better diffusion dynamics of KL1 condensate in solution than in PNs (Fig. 2g, h). A complementary half- bleaching experiment shows a bright edge of transcriptional KL1 condensates in solution during recovery, indicating a dynamic exchange of soluble KL1 transcripts from the solution with the condensate phase (Supplementary Fig. 4). In contrast, half- bleached KL1 condensates in PNs show less recovery and lack the bright edge, likely due to their restricted + +<--- Page Split ---> + +dynamics in a DNA- crowded environment and interactions between PN matrix and the KL1 transcripts, as we will further discuss below. + +Next, we discuss the effects of [NTP] and \([\mathrm{Mg}^{2 + }]\) on transcriptional KL1 condensate formation inside the PNs. KL1 transcription with [NTP] varying from \(2.4 \times\) to \(3.6 \times\) show a morphological transition from peripheral localization of KL1 transcripts to reorganization and compaction into a single condensate (Fig. 2i). The formation of peripheral KL1 transcripts at low [NTP] shows that incoming NTPs are converted to RNA as they reach the embedded transcription templates in the outer PN parts. The lack of a centrally compacted condensate points to the fact that, at this low concentration of KL1 transcripts, phase segregation is at least not very pronounced. The remaining ring indicates an interaction between the PN matrix and the KL1 transcripts. At higher [NTP], KL1 transcripts are homogeneously produced throughout the PN, and phase segregation drives the formation of the KL1 condensate. + +\([\mathrm{Mg}^{2 + }]\) shows a profound impact on nucleation and condensate morphology. Multiple small condensates can be observed at \(15 \mathrm{mM} \mathrm{Mg}^{2 + }\) , whereas \([\mathrm{Mg}^{2 + }] > 20 \mathrm{mM}\) leads to the formation of a single condensate droplet. \(20 \mathrm{mM} \mathrm{Mg}^{2 + }\) corresponds to a transition point. Interestingly, a transition in the condensate formation process is visible. Whereas isolated nucleation events dominate at \(15 \mathrm{mM} \mathrm{Mg}^{2 + }\) , co- continuous phase separation is visible above \(25 \mathrm{mM} \mathrm{Mg}^{2 + }\) with a sponge- like structure. At \(40 \mathrm{mM} \mathrm{Mg}^{2 + }\) , condensate formation in PNs is no longer visible (Fig. 2j). Such distinct condensate formation in PNs is associated with multiple influences of \(\mathrm{Mg}^{2 + }\) on the system: First, higher \([\mathrm{Mg}^{2 + }]\) leads to reduced dynamics in the crowded environment of PNs, as previously studied by us in detail \(^{23,24}\) . Second, higher \([\mathrm{Mg}^{2 + }]\) also assists in tighter condensation of the KL condensates and potentially increases non- specific interactions between the KL condensates and the PN matrix \(^{22}\) . Third, increasing \([\mathrm{Mg}^{2 + }]\) leads to a continuous decrease of the transcription efficiency as depicted in Fig. 2k. Thus, multiple isolated nucleation events and binodal phase separation occur at \(15 \mathrm{mM} \mathrm{Mg}^{2 + }\) , driven by the high dynamics of the PN core and the high transcription efficiency. In contrast, spinodal or viscoelastic phase separation \(^{26,30,31}\) is favored at high \(\mathrm{Mg}^{2 + }\) concentrations, where the dynamics of the PNs become more arrested. Further, charge screening increases the propensity for non- specific interactions between nucleic acids (RNA and DNA). The transcription is strongly suppressed at \(40 \mathrm{mM} \mathrm{Mg}^{2 + }\) with limited KL1 transcripts so that condensation of KL1 cannot take place (Fig. 2j, k). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 Transcriptional KL condensates in PNs show different nuclear patterns. a, Scheme showing transcription in PNs characterized by CLSM and plate reader. For CLSM experiment, UTP-Att0488 is added to the NTP mix for transcript labeling. For plate reader experiments, dsDNA reporters with fluorophore-quencher pairs (R) are present in solution to react with transcribed RNA by strand displacement reaction (SDR), generating fluorescent signals. b, Representative CLSM images showing the localized transcription of fluorescent \(\mathbf{x}^*\) (green, labeled by UTP-Att0488 during transcription) inside PNs at different times ([NTP] : [T \(\mathbf{x}^*\) ] : [p] = 250 : 1: 1, 30 °C, 30 mM Mg \(^{2 + }\) , 2.5 U/μL T7 RNAP). The whole process is recorded in Supplementary Video 1. c, Transcription kinetics inside PNs with embedded poly(A \(\mathbf{\cdot}_{20} - \mathbf{p})\) n, in solution with free poly(A \(\mathbf{\cdot}_{20} - \mathbf{p})\) n, and in solution with free promoter (p) oligonucleotide, monitored by SDR of the R in a plate reader ([NTP] : [R] : [T \(\mathbf{\cdot}_{R}\mathbf{p}^*\) ] : [p] = 200 : 10 : 1 : 1, 30 °C, 6 mM Mg \(^{2 + }\) , 2.5 U/μL T7 RNAP). d, Scheme for the formation of transcriptional KL1 condensates in PNs and in solution, with KL1 labeled by UTP-Att0488 during transcription. e, CLSM images with maximum intensity projection of z-stacked images showing the formation of transcriptional KL1 condensates in PNs containing embedded poly(A \(\mathbf{\cdot}_{20} - \mathbf{p})\) n ([NTP] : [T \(\mathbf{\cdot}_{KL1}\) ] = 3.6), and in solution with free promoter (p) oligonucleotide ([NTP] : [T \(\mathbf{\cdot}_{KL1}\) ] = 3.6 : 1 or 14.4 : 1, 30 °C, 30 mM Mg \(^{2 + }\) , 2.5 U/μL T7 RNAP, 18 h for in-PN and in-solution transcription). f, Diameter distributions of KL1 condensates in PNs and in solution at different [NTP]. g, Normalized fluorescence recovery kinetics in the bleached areas in (h) during FRAP experiments on
+ +<--- Page Split ---> + +KL1 condensates in PNs and in solution at \(30^{\circ}\mathrm{C}\) . Intensity values were normalized to pre- bleached levels. \(n =\) 3. h, Time- lapse CLSM images for FRAP experiments on KL1 condensates in PNs and in solution at \(30^{\circ}\mathrm{C}\) . White dashed circles indicate the bleached regions. i, Representative CLSM images showing the effect of [NTP] on transcriptional KL1 condensate formation in PNs after \(18\mathrm{h}\) at \(30^{\circ}\mathrm{C}\) . Note that the left half of the \(3.6\times [\mathrm{NTP}]\) sample is a maximum intensity projection of z- stacked images. Green channel: KL1- Atto488; Magenta channel: \(\mathrm{k^{*} - Atto647 / poly(A_{20} - k)_{n}}\) . j, Representative CLSM images showing the effects of \([\mathrm{Mg}^{2 + }]\) on transcriptional KL1 condensate formation in PNs after \(18\) and \(48\mathrm{h}\) reaction. Note that a hyperstack image generated from z- stacked images is used for KL1 condensates in PNs at \(15\mathrm{mM}\mathrm{Mg}^{2 + }\) after 48 hours of transcription to visualize condensates formed in different planes with a corresponding color- coded z- scale. k, Effects of \([\mathrm{Mg}^{2 + }]\) on transcription efficiency in PNs, monitored via RNA- triggered SDR of the R. \([\mathrm{NTP}]:[\mathrm{R}]:[\mathrm{T}_{\mathrm{Rep}}^{*}]:[\mathrm{p}] = 200:\) \(10:1:1\) , \(30^{\circ}\mathrm{C}\) , \(2.5\mathrm{U / \mu L}\) T7 RNAP at indicated \([\mathrm{Mg}^{2 + }]\) , measured by a plate reader. In the box plot (f), the central line marks the median, the box represents the interquartile range (IQR) from Q1 (first quartile) to Q3 (third quartile), and the whiskers enclose all data points from the minimum to the maximum. This applies to all box plots shown in this paper. Error areas represent standard deviation. Scale bars are \(10\mu \mathrm{m}\) for (b) and (e), 5 \(\mu \mathrm{m}\) for (h), (i), and (j). + +To get a deeper understanding of the morphological development of single KL condensates in the PNs at \(30\mathrm{mM}\mathrm{Mg}^{2 + }\) , we monitored the whole process over \(24\mathrm{h}\) through CLSM (Fig. 3a, b). Two distinct stages occur. In the first \(12\mathrm{h}\) , transcription takes place from the edge of the PNs to their center, and the entire structures reach maximum fluorescence intensities at \(12\mathrm{h}\) (Fig. 3b- d). Spongy structures of KL1 condensates during phase separation start to appear at ca. \(8\mathrm{- }10\mathrm{h}\) , whereas significant coarsening and compaction into single spherical condensates follows in the later \(12\mathrm{- }24\mathrm{h}\) (Fig. 3b- e). Interestingly, we can observe a relatively slow and continuous increase of the PN dimensions, as facilitated by the relaxation of polyA/polyT shell as a result of the increasing negative charge density inside the PNs during localized RNA production and condensation (Fig. 3c, e). + +For further probing the universality of this single condensate formation phenomena for various KL structures, we adapted a KL1 condensate with an RNA light- up broccoli aptamer (BrA) as one of the arms, termed KL1- BrA (NUPACK- simulated structure shown in Supplementary Fig. 5a). After \(12\mathrm{- }24\mathrm{h}\) transcription, single condensates are formed in each PN. In contrast, KL1- BrA only forms irregular aggregates in solution. Here, the interaction between the PN matrix and the KL1- BrA condensate may facilitate better relaxation and stabilization of KL1- BrA condensate within the PNs (Supplementary Fig. 5). Taken together, KL condensation in solution and in- PN differ profoundly in both the kinetic formation process and the formed final structures at what could be considered closer to the thermodynamic equilibrium. The system can be easily tuned by adjusting the NTP and \(\mathrm{Mg}^{2 + }\) concentrations and is robust to changes in the KL components. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 Mechanism of co-condensation of transcriptional KL1 with the PN matrix. a, Scheme for formation of single co-condensates of transcriptional KL1 and polyA in PNs through peripherical initiation of transcription, re-organization, and co-condensation. b, Representative CLSM images of PNs conducting KL1 transcription over \(24\mathrm{h}\left(\mathrm{[NTP]}:[\mathrm{T}_{\mathrm{KL1}}] = 3.6:1,1\mathrm{mol}\% \mathrm{UTP - Atto}488,30^{\circ}\mathrm{C},2.5\mathrm{U} / \mu \mathrm{L} T7\mathrm{RNAP},30\mathrm{mM}\mathrm{Mg}^{2 + }\right)\) . Note a slight slow-down of co-condensation kinetics of KL1 compared with results shown in Fig. 2e, j after \(18\mathrm{h}\) , due to the interruption of shaking during incubation for the CLSM imaging. Green channel: KL1 condensate labeled by UTP-Attot488; Magenta channel: PN shell (poly( \(\mathrm{T}_{20}\mathrm{-k}\) ), labeled with \(\mathrm{k}^{*}\) -Attot647). c, Space-time plot analysis corresponding to the two dashed lines in (b) over \(24\mathrm{h}\) shows the KL1 transcription, condensation, and reorganization process. d, Normalized fluorescence intensity change in the KL1 condensate channel over \(24\mathrm{h}\) in the two white dashed circle in (b), normalized to the intensity at \(24\mathrm{h}\) . e, Normalized radius change of KL1 condensate and PN as measured from (b) over \(24\mathrm{h}\) . f, g Representative CLSM images of fluorescent PNs (magenta) containing KL1 transcripts (green) at \(12\mathrm{h}\) (f) and \(24\mathrm{h}\) (g). The right plots correspond to the line segment analysis of the white line in the CLSM images, showing fluorescence distribution for KL1-Attot488 and poly(A20-0)n-Attot643. The KL1 transcripts show a peripheral distribution in the PN matrix at \(12\mathrm{h}\) (f), while colocalization and co-condensation between KL1 and PN matrix occur after \(24\mathrm{h}\) (g). Error bars and error areas represent standard deviation. Scale bars are all \(5\mu \mathrm{m}\) .
+ +To study the behavior and aforementioned interactions of the PN DNA matrix with the KL1 RNA condensates, we covalently labeled poly(A20- 0)n with Attot643 to prepare fluorescent PNs and used these new PNs to initiate localized KL1 transcription. As expected, the initial production and localization of KL1 transcripts occur at the periphery of the PNs (Fig. 3f). Unexpectedly, + +<--- Page Split ---> + +co- condensation of the PN matrix with the KL1 condensates occurs over time. These co- condensates deposit at the bottom of PNs after \(24\mathrm{h}\) in the imaging chamber, highlighting their higher density and compactness (Fig. 3g, Supplementary Fig. 6, and Supplementary Video 2). FRAP experiments reveal a better recovery for the KL1 components compared to the PN matrix, corresponding to higher dynamics for the KL1 condensate part composed of small RNAs than the PN matrix composed of long ssDNA polymers (Supplementary Fig. 7). This demonstrates the molecular level diffusivity of the RNA nanostars in this co- condensate structure. + +Overall, this co- condensation between PN and KL1 condensate comes unexpectedly because the KL1 condensate was not designed to have any specific interactions with the PN matrix. Indeed, a NUPACK simulation suggests no specific hybridization between the \(\mathrm{A}_{20} - \mathrm{o}\) repeats and the KL1 sequence (Supplementary Fig. 8). Experimentally, we probed interactions between mature KL1 condensates and poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) inside PNs by adding different quantities of \(\mathrm{o}^{*} - \mathrm{Atto}647\) (from \(10\%\) - \(300\%\) ) that can bind to the majority phase of poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) in the PNs. We hypothesized that the hybridization between \(\mathrm{o} / \mathrm{o}^{*}\) may break non- specific KL1- PN interactions (Fig. 4a, b). A gradual invasion of \(\mathrm{o}^{*} - \mathrm{Atto}647\) into the KL1- poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) co- condensates occurs as the amount of \(\mathrm{o}^{*} - \mathrm{Atto}647\) increases. This process leads to continuous surface erosion of the co- condensates (Fig. 4c- e and Supplementary Video 3). A sharp interface defined by a bright ring of \(\mathrm{o}^{*} - \mathrm{Atto}647\) with a locally high concentration appears. The poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}} / \mathrm{o}^{*} - \mathrm{Atto}647\) thereafter occupies the space within the entire PN, whereas the KL1 transcripts are squeezed to the PN periphery and eventually dissolve into solution to equilibrate to their low concentration there. This process verifies that the interaction between KL1 and poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) PN matrix promotes the formation of the KL1- poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) co- condensate. + +Seeing such a profound impact, we then investigated KL1 transcription in PNs with a poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) matrix pre- hybridized by different amounts of \(\mathrm{o}^{*} - \mathrm{Atto}647\) (from \(0\% - 300\%\) ) to provide weakened affinity between PN matrix and KL1 transcripts. In analogy with the above result, single KL1 condensates form in pristine PNs (Fig. 4f). When applying \(10\% \mathrm{o}^{*} - \mathrm{Atto}647\) , the KL1 transcripts form single condensates with irregular secondary nucleation on its surface inside the PN, along with multiple tiny nuclei outside the PN shell (Fig. 4f). The brighter green parts are condensates purely enriched with KL1 transcripts that remain inside the PN due to relatively sufficient affinity. Increasing the content of pre- hybridized \(\mathrm{o}^{*} - \mathrm{Atto}647\) domains from \(10\%\) to \(300\%\) gradually prevents KL1 condensate formation inside the PNs due to weakened PN- KL1 interaction, which likely becomes even repulsive at higher pre- hybridization degrees. As a result, the KL1 transcripts formed inside the PNs do not yield condensates insides the PNs, instead, multiple small transcriptional KL1 condensates form in the PN surroundings. These results highlight the importance of the interaction between the DNA matrix of the PNs and the KL1 transcripts in both the formation and the maintenance of the condensates within the PNs. Hence, modulating the DNA- RNA interaction is a way for regulating nucleus condensate architectures. + +To directly study the affinity between polyA sequence of the PN matrix and KL1 condensate, we prepared transcriptional KL1 condensates in solution and added \(\mathrm{A}_{20} - \mathrm{o} - \mathrm{Atto}647\) ssDNA, and + +<--- Page Split ---> + +A20- o/o\*- Atto565 dsDNA. Such pure KL1 condensates sequester A20- o- Atto647 whereas A20- o/o\*- Atto565 is excluded (Fig. 4g, h). Such marked differences among interactions between KL1- to- ssDNA versus KL1- to- dsDNA confirm some level of unspecific interaction between the KL1 transcript and the o region, which is removed through hybridization into o/o\*. Furthermore, electrostatic repulsion from increased negative charge density after dsDNA formation could also play a role, as in analogy to re- entrant phenomena in living cells, where transcriptional condensate formation is promoted at low rates of RNA synthesis up to a point of charge imbalance, beyond which higher rates of RNA synthesis disfavors condensate formation11, 32. + +![](images/Figure_4.jpg) + +
Fig. 4 Hybridization of the polyA matrix of PNs induces disassembly of KL1-PN co-condensate. a, Representative CLSM images of transcriptional KL1 condensates (Atto488, green channel) in PNs (Atto647, magenta channel) 60 min after adding 10%, 50%, 80% and 300% o\*- Atto647 as an invader strand. b, Schematic
+ +<--- Page Split ---> + +illustration of the o\\*- Atto647 invasion process. Hybridization of o\\*- Atto647 with poly \(\mathrm{(A_{20} - o)_n}\) starts from the edge of the KL1- polyA co- condensate with a bright and sharp invading front to final dissolution of the whole co- condensate. c, Representative CLSM images showing the process of co- condensate (Atto488, green channel) dissolution by adding \(300\%\) o\\*- Atto647 (magenta channel) to hybridize to the poly \(\mathrm{(A_{20} - o)_n}\) of the PNs. See also Supplementary Video 3. d, Space- time plot analysis along the white dashed line in (c) shows the gradual dissolution of the condensate. e, Normalized fluorescence intensities of KL1 condensates (KL1- Atto488) and invader strand (o\\*- Atto647) measured in the white dashed circle in (c) during the invasion process. f, Scheme and representative CLSM images of KL1 transcription and condensation (Atto488, green channel) after \(18\mathrm{h}\) in pre- hybridized PNs with \(0\%\) \(10\%\) \(50\%\) \(80\%\) , and \(300\%\) o\\*- Atto647 (magenta channel). g, Scheme shows the attractive interaction between KL1 condensate and ssDNA \(\mathrm{A_{20} - o}\) - Atto647, and repulsive interaction between KL1 condensate and dsDNA \(\mathrm{A_{20} - o}\) - Atto647/o\\*- Atto565. h, Representative CLSM images of pure transcriptional KL1 condensates (Atto488, green channel) prepared in solution, with the addition of (top) ssDNA \(\mathrm{A_{20} - o}\) - Atto647 (magenta channel) for \(1\mathrm{h}\) , showing preferential partitioning, or (bottom) dsDNA \(\mathrm{A_{20} - o}\) - Atto647/o\\*- Atto565 (red channel) showing rejection. Shaded areas represent standard deviations. Scale bars are \(5\mu \mathrm{m}\) for (a), (c), and (f), and \(10\mu \mathrm{m}\) for (h). + +Finally, we attempted to integrate orthogonal KL transcription systems into PNs for constructing more complex structures to mimic multiple RNA condensates in the crowded environment of natural cell nuclei. We adapted two KL nanostars (KL1- R1 and KL2- R2) with orthogonal kissing loop sequences at the end of their arms, and distinct tail regions (R1 and R2) for specific labeling by R1\\*- Atto488 and R2\\*- Atto647, respectively (Fig. 5a). We firstly confirmed the transcription and the formation of centrally located condensates for both KL1- R1 or KL2- R2 inside PNs (Supplementary Fig. 9). Hence, both systems form similar condensate structure as the original KL1 system and the KL1- BrA system studied above. + +Since \(\mathrm{[Mg^{2 + }]}\) can control the condensate morphology (Fig. 2j), we conducted co- transcription of both KLs in the same PN at 15 and \(30\mathrm{mM}\mathrm{Mg}^{2 + }\) , respectively (Fig. 5a). At \(30\mathrm{mM}\mathrm{Mg}^{2 + }\) , KL1- R1 assembles to a large single condensate ( \(\sim 0.7\) - fold the diameter of the host) at the PN center, while KL2- R2 forms small condensates, budding at the PN shell, with diameters less than 0.2- fold of the host PN (Fig. 5b- d). This suggests a preferred interaction between KL1- R1 and PN matrix, retaining the KL1- R1 condensate inside the PN, whereas KL2- R2 gets obviously expelled. KL1- R1 dominates the interaction with the PN matrix in this competitive system, whereas pure KL2- R2- PN would form a single central condensate (Supplementary Fig. 9). At \(15\mathrm{mM}\mathrm{Mg}^{2 + }\) , the transcriptional KL1- R1 occupies the major PN space, while KL2- R2 forms multiple condensates in the PNs (Fig. 5e- g). This can be attributed to weakened interactions between KL1- R1 and the PN at low salinity, allowing KL2- R2 to occupy some of the available volume in the PN to form condensates. Hence, the combined effect of \(\mathrm{Mg}^{2 + }\) on changing the viscoelastic properties and modulating KL interactions as well as KL- to- PN interactions again shows a profound effect. We can conclude that phase segregation of KL1- R1 is energetically favored to be retained in the PNs. A mixing of both KL phases does not occur. + +To verify the competitive interaction between KL1- R1 and KL2- R2 with the polyA in the PN matrix, we performed competitive partition experiments of \(\mathrm{A_{20} - o}\) or \(\mathrm{A_{20} - o / o^*}\) with pure transcriptional KL1- R1 and KL2- R2 condensates grown in solution. The results show preferential partitioning of \(\mathrm{A_{20} - o}\) into the KL1- R1 condensates, whereas \(\mathrm{A_{20} - o / o^*}\) is excluded by both condensates (Supplementary Fig. 10). This confirms a higher affinity of KL1- R1 condensates to + +<--- Page Split ---> + +the PN matrix and explains the different condensate architectures formed in the PNs. Additionally, transcriptional KL2- R2 condensates show a less spherical structure compared with transcriptional KL1- R1 ones (Supplementary Fig. 10), suggesting stronger condensation interactions for KL2- R2 than KL1- R1, consistent with the higher melting temperature of KL2 interactions than KL1 interactions provided in literature21. This helps to explain that the KL2- R2 could still form condensates, whether expelled from PN at 30 mM \(\mathrm{Mg^{2 + }}\) or remained in PN at 15 mM \(\mathrm{Mg^{2 + }}\) . In summary, these results reveal that, in addition to salinity effect, subtle variations in RNA composition and sequence modulate their interaction with the DNA matrix of PN in a competitive environment, leading to profoundly different condensation processes and resulting in distinct multi- phase co- condensate architectures in PNs. + +![](images/Figure_5.jpg) + +
Fig. 5 Formation of orthogonal transcriptional KL condensates in PNs. a, Scheme showing orthogonal transcription and condensation of KL1-R1 and KL2-R2 in the same PN at different salinity. KL1-R1 is identical to KL1 in its nanostar framework, but with an additional recognition tail (R1) for R1\*-Atto647 labelling. KL2-R2 shares the same stem sequence as KL1 but has orthogonal kissing loop sequences and a distinct recognition tail (R2) for R2\*-Atto488 labeling. R1\*-Atto647 and R2\*-Atto488 are added during transcription. b, Representative single-plane CLSM image and maximum intensity projection of z-stacked CLSM image showing orthogonal transcriptional condensates of KL1-R1 (green channel) and KL2-R2 (magenta channel) in PNs at 30 mM \(\mathrm{Mg^{2 + }}\) ([NTP] : [R1\*] : [R2\*] : [TKL1-R1] : [TKL2-R2] : [p] = 3.6 : 1.8 : 1.8 : 0.5 : 0.5 : 1, 30 °C, 30
+ +<--- Page Split ---> + +\(\mathrm{mM Mg^{2 + }}\) , 2.5 U/μL T7 RNAP). e, Normalized intensity profiles of line segment analyses corresponding to the white line in (b) for both channels. d, Diameter of formed orthogonal condensates at \(30\mathrm{mM}\mathrm{Mg}^{2 + }\) , normalized to the diameter of the host PNs. e, Representative single-plane CLSM image and maximum intensity projection of z- stacked CLSM image showing orthogonal transcriptional condensates of KL1- R1 and KL2- R2 in PNs at 15 \(\mathrm{mM}\mathrm{Mg}^{2 + }\) ([NTP]: [R1\\*]: [R2\\*]: [TKL1- R1]: [TKL2- R2]: [p] = 3.6 : 1.8 : 1.8 : 0.5 : 0.5 : 1, 2.5 U/μL T7 RNAP, \(15\mathrm{mM}\mathrm{Mg}^{2 + }\) , \(30^{\circ}\mathrm{C}\) , 18 h reaction). f, Normalized intensity profiles of line segment analyses corresponding to the white line in (e) for both channels. g, Diameter of formed KL2- R2 condensates at \(15\mathrm{mM}\mathrm{Mg}^{2 + }\) , normalized to the diameter of host PNs. Note that the diameter of KL1- R1 condensates cannot be quantified due to their hollow shape. Scale bars are all \(5\mu \mathrm{m}\) . + +## Discussion + +We have introduced a versatile nucleus- mimicking DNA condensate platform - a protonucleus - that enables localized transcription and the study of phase- separation of transcribed RNA nanostars in crowded and highly concentrated DNA environments. Since the strategy builds on our previous work on all- DNA synthetic cells23- 25, 28, our approach shows how specific components from a completely different area of research, that is synthetic artificial cell research, can be effectively repurposed into new application domains. These protonuclei offer a highly programmable platform for introducing short genes for transcription while also enabling control over properties such as gene density and the dynamic behavior of the matrix23, 24. Transcription inside these crowded PNs proceeds with satisfying efficiency up to high salt concentration. To study transcriptional folding and phase segregation in the crowded, nuclear- mimetic environment, we focused on KL condensates formed by ssRNA nanostars. We identified that ionic strength is one key parameter for cross- regulating transcription efficiency, viscoelasticity of the PNs, and KL- PN affinity. These effects in turn affect the nucleation of condensates from binodal to spinodal or viscoelastic phase separation26, 30, 31, resulting in tunable artificial nuclear architectures inside the PNs. The non- specific interactions between KL and PN matrix turned out to be crucial for retaining KL transcripts inside PNs via KL- PN co- condensation. We showed how such interactions can be efficiently modulated using DNA nanoscience approaches in such synthetic settings, ultimately leading to a repulsion and exclusion of the KL condensates from the PNs. + +We further studied co- transcription and condensation of orthogonal KLS systems within the same PN, which resulted in distinct structures arising from competitive interactions between different RNA nanostars and the PN matrix. This highlights the potential of using our PN platform to study subtle interactions between RNA and DNA, as well as competitive interactions among RNAs in a DNA- enriched environment. Finally, at proper conditions, multiphase condensate structures can be built, which are further regulated by salinity through the cross- regulation of the viscoelastic environment, transcription efficiency, and competitive KL- PN interaction. + +Looking into the future, our work opens new perspectives for constructing artificial nuclear architectures in synthetic model systems with DNA nanoscience tools. While we focused on a rather artificial and well controllable system of KL condensates, this work lays an important cornerstone to study more sophisticated phase separation processes, such as in case of polymerase II that forms rich condensate architectures with helper proteins, and those which are implicated in + +<--- Page Split ---> + +disease and ageing33, 34. In addition, the modulable KL- PN interactions within protonucleus could serve as simplified models for transcriptional condensates in living cells, which are dynamically forming and dissolving, and essential for transcription regulations11, 32. Moreover, from the perspectives of molecular systems engineering, synthetic biology, and artificial cell research, we have identified important pathways to transcriptionally regulate structure formation processes towards multiscale condensates that can be selectively addressed in their compartments. We anticipate that this system will serve as a valuable platform and toolkit for DNA nanoscience and synthetic biology. + +## Methods + +## Materials + +ssDNA were purchased from Biomers and Integrated DNA Technologies (IDT). Supplementary Table 1 and 2 summarize all sequences used in this study. T4 DNA Ligase (2 U/μL), Exonuclease I (40 U/μL), Exonuclease III (200 U/μL), and \(\Phi_{29}\) polymerase (10 U/μL) were purchased from Lucigen. Thermostable Inorganic Pyrophosphatase (2 U/μL), T7 polymerase (50000 U/mL) and nuclease- free water were bought from New England BioLabs (NEB). Deoxynucleotide triphosphate (dATP, dTTP, dGTP and dCTP) (100 mM), Aminoallyl- dUTP- XX- ATTO- 643 (1 mM), Aminoallyl- UTP- Atto488 (1 mM), and Aminoallyl- UTP- Atto630 (1 mM) were purchased from Jena Bioscience. Hexadecane, sodium chloride, magnesium chloride, Tris(hydroxymethyl)- aminomethane hydrochloride (Tris- HCl), Trizma base, acetic acid and Ethylenediaminetetraacetic acid disodium salt dihydrate (EDTA), were purchased (as bioreagent grade if available) from Sigma- Aldrich. RNase Inhibitor (40 U/μL), RNase- free TE buffer (Invitrogen, 10 mM Tris and 1 mM EDTA, pH 8.0, 500 mL), 384- well high- content imaging glass bottom microplates were purchased from Corning. + +## Instruments + +All thermal annealing and heating ramps were performed on a TPersonal Thermocycler (Analytik Jena). Incubation with shaking was carried out on an Eppendorf ThermoMixer C with heated lid. DNA concentration was determined by a DS- 11 Spectrophotometer (DeNovix). Confocal laser scanning microscopy (CLSM) was performed on a Leica Stellaris 5. + +## Synthesis of circular ssDNA templates and long ssDNA polymers + +The synthesis of the circular DNA template and its corresponding ssDNA polymer can be found in our previous reports23. In short, the linear ssDNA template and the corresponding ligation strand were firstly mixed at concentration of 1 μM in 100 μL TE buffer containing 100 mM NaCl. The solution was heated to 85 °C for 5 min before cooling to 25 °C with a cooling rate of 0.01 °C/s for hybridization. Afterwards, 20 μL of 10× Ligase buffer (500 mM Tris- HCl, 100 mM MgCl2, 50 mM dithiothreitol and 10 mM ATP (Lucigen)), 70 μL of nuclease- free water and 10 μL of T4 DNA Ligase (2 U/μL (Lucigen)) were introduced into the reaction mixture at room temperature for 3 h reaction. The solution was then heated to 70 °C for 20 min to deactivate the enzyme. Then, 10 μL of Exonuclease I (40 U/μL (Lucigen)) and 10 μL of Exonuclease III (200 U/μL (Lucigen)) were added into the reaction mixture for further overnight reaction at 37 °C to remove the ligation strands and any non- circularized templates in solution. Afterwards, the reaction mixture was heated to 80 °C for 40 min to deactivate the enzymes. To obtain the final circular ssDNA templates, + +<--- Page Split ---> + +the reaction mixture was washed with \(400~\mu \mathrm{L}\) TE buffer and filtrated using Amicon Ultra- centrifugal filters with a \(10\mathrm{kDa}\) cut- off (Merck Millipore) for three times. The concentrations of the collected circular ssDNA templates were measured by the DS- 11 Spectrophotometer (DeNovix), and the templates were stored in TE buffer at \(- 20^{\circ}\mathrm{C}\) . + +For the synthesis of the long ssDNA polymers, we used rolling circle amplification (RCA). \(5\mu \mathrm{L}\) of circular template ( \(1\mu \mathrm{M}\) in TE buffer) and \(1\mu \mathrm{L}\) of exonuclease resistant primer ( \(10\mu \mathrm{M}\) in TE buffer) were mixed with \(76\mu \mathrm{L}\) nuclease- free water, \(10\mu \mathrm{L}\) of commercial \(10\times\) polymerase buffer ( \(500\mathrm{mM}\) Tris- HCl, \(100\mathrm{mM}\) ( \(\mathrm{NH_4)_2SO_4}\) , \(40\mathrm{mM}\) dithiothreitol, \(100\mathrm{mM}\) MgCl2 (Lucigen)), \(2\mu \mathrm{L}\) of \(\Phi_{29}\) DNA polymerase ( \(10\mathrm{U / \mu L}\) (Lucigen)), \(1\mu \mathrm{L}\) of thermal stable inorganic pyrophosphatase ( \(2\mathrm{U / \mu L}\) (NEB)) and \(5\mu \mathrm{L}\) of adjusted deoxyribose nucleoside \(5^{\prime}\) - triphosphate mix ( \(100\mathrm{mM}\) , the mix contains pure dATP, dTTP, dCTP, and dGTP solutions mixed in corresponding proportions of the exact composition of the desired ssDNA polymer repeating units (Jena Bioscience)). Note that for the synthesis of ssDNA polymers with in- chain fluorophores of Atto643, we replaced \(2\mathrm{mol}\%\) of the dTTP in the mix with Aminoallyl- dUTP- XX- ATTO- 643 for random insertion of the dye into the ssDNA chains during RCA. The reaction mixture was kept at \(30^{\circ}\mathrm{C}\) for \(50\mathrm{h}\) before thermal cleavage at \(95^{\circ}\mathrm{C}\) for \(15\mathrm{min}\) to shorten the ultrahigh molecular weight of the synthesized DNA polymer \(^{23}\) . The final products were purified by rinsing with \(400\mu \mathrm{L}\) TE buffer and filtration in Amicon Ultra- centrifugal filters with \(30\mathrm{kDa}\) cut- off (Merck Millipore) three times. The concentrations of the collected final ssDNA polymers were measured using the DS- 11 Spectrophotometer (DeNovix), and the DNA polymers were stored in TE buffer at \(- 20^{\circ}\mathrm{C}\) . + +## Preparation of all-DNA PNs embedded with T7 promoter sequence. + +The preparation of the PNs is adapted from our previous reports \(^{23}\) with modifications for the formation of PNs containing T7 promoter sequence. Adenine- rich DNA polymers (poly(A \(_{20}\) - p) \(_n\) + poly(A \(_{20}\) - o) \(_n\) in a ratio of 1:9) (0.5556 g/L) and poly(T \(_{20}\) - k) \(_n\) (0.0694 g/L) were mixed in TE buffer without any salt at a final volume of \(9\mu \mathrm{L}\) . The solution mixture was heated at \(95^{\circ}\mathrm{C}\) for \(15\mathrm{min}\) for thermal cleavage to further reduce the chain length of the ssDNA polymers. Afterwards, \(1\mu \mathrm{L}\) of TE buffer containing \(500\mathrm{mM}\) MgCl \(_2\) was introduced into the reaction mixture. The solution containing finally \(0.5\mathrm{g / L}\) mixture of polyA and \(0.0625\mathrm{g / L}\) poly(T \(_{20}\) - k) \(_n\) with \(50\mathrm{mM}\) MgCl \(_2\) was heated to \(95^{\circ}\mathrm{C}\) for \(20\mathrm{min}\) ( \(3^{\circ}\mathrm{C / s}\) ) and cooled down to room temperature ( \(3^{\circ}\mathrm{C / s}\) ), yielding core- shell PNs. Finally, the \(10\mu \mathrm{L}\) solution containing the PNs was diluted 5 times by adding \(40\mu \mathrm{L}\) TE buffer containing various amounts of MgCl \(_2\) to reach desired salinity. The obtained \(50\mu \mathrm{L}\) DNA condensates solution (as \(5\times\) diluted) has \(0.1\mathrm{g / L}\) polyA mixture and \(0.0125\mathrm{g / L}\) poly(T \(_{20}\) - k) \(_n\) , corresponding to ca. \(0.8\mu \mathrm{M}\) p barcode, ca. \(7.2\mu \mathrm{M}\) o barcode and ca. \(1\mu \mathrm{M}\) k barcode, respectively, in total solution. The solution was then stored in a fridge at \(4^{\circ}\mathrm{C}\) for 1 week for equilibration before usage. + +## Spatially controlled transcription assay in PN. + +For transcription in PNs monitored by plate reader, \(3.125\mu \mathrm{L}\) of \(5\times\) diluted PNs ( \(90\%\) o barcode + \(10\%\) p barcode) is further diluted into \(25\mu \mathrm{L}\) solution containing \(1\times\) RNA polymerase buffer (40 mM Tris- HCl, \(6\mathrm{mM}\) MgCl \(_2\) , \(1\mathrm{mM}\) DTT, \(2\mathrm{mM}\) spermidine), \(100\mathrm{mM}\) template ( \(\mathrm{T_{Rep}^*}\) ), \(1\mu \mathrm{M}\) prehybridized fluorophore- quencher reporter (Rep/Rep' dsDNA), \(2.5\mathrm{U / \mu L}\) T7 RNAP, \(0.02\mathrm{U / \mu L}\) Thermostable Inorganic Pyrophosphatase, \(1\mathrm{U / \mu L}\) RNase Inhibitor. \(\mathrm{MgCl_2}\) concentration was adjusted in different settings as noted in each figure caption. At the end, \(2\mu \mathrm{L}\) of NTP mix (to reach \(2\mathrm{mM}\) of ATP, GTP, CTP, and UTP each) was added into the solution to trigger the transcription reaction at different temperatures ranging from \(25 - 30^{\circ}\mathrm{C}\) . The final promoter sequence + +<--- Page Split ---> + +concentration in the solution is at \(100~\mathrm{nM}\) . As for control, transcription with pure promoter ssDNA (p) and poly(A₂₀-p)ₙ ssDNA polymer was also performed to compare the transcription efficiency. For kinetic experiments under CLSM, \(\mathrm{T_x^*}\) is loaded into the PNs at a final concentration of 100 nM. Reporter is not used, instead, we further added \(0.0833~\mathrm{mM}\) Aminoallyl- UTP- Atto488 so that the transcribed RNA can be fluorescently labeled and observed under CLSM. + +## Transcriptional KLs condensates formation. + +2.5 \(\mu \mathrm{L}\) of \(5\times\) diluted PNs ( \(90\%\) o barcode \(^{+10\%}\) p barcode) is further diluted into \(20~\mu \mathrm{L}\) solution containing \(1\times\) RNA polymerase buffer ( \(40~\mathrm{mM}\) Tris- HCl, \(6\mathrm{mM}\) MgCl₂, \(1\mathrm{mM}\) DTT, \(2\mathrm{mM}\) spermidine), \(100\mathrm{nM}\) dsDNA template \((\mathrm{T}_{\mathrm{KL1}} / \mathrm{T}_{\mathrm{KL1}}^{*}\) , \(\mathrm{T}_{\mathrm{KL1 - BrA}} / \mathrm{T}_{\mathrm{KL1 - BrA}}^{*}\) , \(\mathrm{T}_{\mathrm{KL1 - RI}} / \mathrm{T}_{\mathrm{KL1 - RI}}^{*}\) , or \(\mathrm{T}_{\mathrm{KL2 - R2}} / \mathrm{T}_{\mathrm{KL2 - R2}}^{*}\) ; or \(50\mathrm{nM}\mathrm{T}_{\mathrm{KL1 - RI}} / \mathrm{T}_{\mathrm{KL1 - RI}}^{*} + 50\mathrm{nM}\mathrm{T}_{\mathrm{KL2 - R2}} / \mathrm{T}_{\mathrm{KL2 - R2}}^{*}\) ), \(2.5\mathrm{U} / \mu \mathrm{L}\) T7 RNAP, 0.02 \(\mathrm{U} / \mu \mathrm{L}\) Thermostable Inorganic Pyrophosphatase, \(1\mathrm{U} / \mu \mathrm{L}\) RNase Inhibitor, and \(0.048\mathrm{mM}\) NTP mix ( \(0.048\mathrm{mM}\) of ATP, GTP, CTP, and UTP each at \([\mathrm{NTP}]:[\mathrm{T}_{\mathrm{KL1}}] = 3.6:1\) , for maximum amount of KL1 produced, which is \(3.6\) - fold of \(\mathrm{T}_{\mathrm{KL1}}\) , adjusted in different settings as noted in figure captions). \(\mathrm{MgCl}_2\) concentration is adjusted in different settings as noted in each figure caption. The mixture is incubated with shaking at \(30^{\circ}\mathrm{C}\) for \(18\mathrm{h}\) reaction. The final promoter sequence concentration in the solution is at \(100~\mathrm{nM}\) . As control, transcription of KLs with pure promoter oligo was also performed with corresponding NTP concentration. For transcription of KLs with covalent label, \(1\mathrm{mol}\%\) of UTP is replaced by either Aminoallyl- UTP- Atto488, or Aminoallyl- UTP- Atto630 in transcription system. For KL1- BrA transcription, \(0.05\mathrm{mM}\) DFHBI is added to the solution. For KL1- R1 or KL2- R2 transcription, \(360\mathrm{nM}\mathrm{R}1^{*}\) - Atto647 or \(\mathrm{R}2^{*}\) - Atto488 sequence is added to the system, respectively. For transcriptional KL1 condensate formed in solution as a control, \(100\mathrm{nM}\) promoter ssDNA is added to system, instead of PNs. + +## Fluorescence recovery after photobleaching (FRAP) experiments. + +FRAP experiments were performed by applying 3 times bleaching in a small circular region of interest (ROI) with diameter of \(2\mu \mathrm{m}\) by \(100\%\) laser intensity. Post- bleaching images were recorded over different periods. The intensities within the circular ROI \((I_{\mathrm{ROI}})\) , and intensities in a circular region of the same size away from bleached region within the condensates \((I_{\mathrm{ref}})\) , in pre- and post- bleaching images were measured in ImageJ for performing double normalization in bleached regions by: + +\[I_{\mathrm{Norm}}(t) = \frac{I_{\mathrm{ROI}}(t)}{I_{\mathrm{ROI}}(t_0)}\times \frac{I_{\mathrm{ref}}(t_0)}{I_{\mathrm{ref}}(t)} \quad (1)\] + +to quantify the recovery kinetics over time. Note that \(I(t_0)\) represents the intensity measured in the first image before bleaching. + +## Data availability + +Additional supporting data are available from the corresponding author upon request. Source data are provided with this paper. + +## Acknowledgments + +We would like to thank Dr. Siyu Song and Tao Xu for their helpful discussion about data analysis and plotting. M.X. acknowledges the support of the Alexander von Humboldt Foundation. W.C. acknowledges support from the Max Planck Graduate Center with the Johannes Gutenberg + +<--- Page Split ---> + +University of Mainz (MPGC), and the RTG 2516 "Structure Formation of Soft Matter at Interfaces". This research was funded by the German Research Foundation (DFG) in the framework of the CRC 1552; Project No. 465145163. A.W. acknowledges funding from the Gutenberg Research Council Mainz underpinning his Life- Like Materials Program, the German Research Foundation grant WA 3084/19- 1, the Max Planck Fellowship, and from the EU in the framework of the ERC Consolidator Grant to AW – M3ALI (101001638). + +## Author contributions + +M.X. and A.W. conceived the project. M.X. and W.C. designed and performed all the experiments. M.R. helped with the initial transcription experiments. M.X. prepared the draft manuscript. M.X., W.C., and A.W. reviewed and edited the manuscript. A.W. supervised the project. M.X. and W.C. contributed equally. + +## Competing interest + +The authors declare no competing interests. + +## Additional information + +Supplementary Information is available for this paper. + +Correspondence and requests for materials should be addressed to Andreas Walther. + +## Reference + +1. Lamond, A.I. & Earnshaw, W.C. Structure and function in the nucleus. Science 280, 547-553 (1998). +2. 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Patel, A. et al. A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation. Cell 162, 1066-1077 (2015). +10. Wu, J.H. et al. Self-Assembly of Intracellular Multivalent RNA Complexes Using Dimeric Corn and Beetroot Aptamers. J. Am. Chem. Soc. 144, 5471-5477 (2022). +11. Henninger, J.E. et al. RNA-Mediated Feedback Control of Transcriptional Condensates. Cell 184, 207-225 (2021). + +<--- Page Split ---> + +606 12. Mao, Y.S., Sunwoo, H., Zhang, B. & Spector, D.L. Direct visualization of the co-transcriptional 607 assembly of a nuclear body by noncoding RNAs. Nat. Cell Biol. 13, 95-101 (2011). 608 13. Bevilacqua, P.C., Williams, A.M., Chou, H.- L. & Assmann, S.M. RNA multimerization as an organizing 609 force for liquid- liquid phase separation. RNA 28, 16-26 (2022). 610 14. Dai, Y., You, L. & Chilkoti, A. Engineering synthetic biomolecular condensates. Nat. Rev. Bioeng. 1, 466-480 (2023). 611 15. Roden, C. & Gladfelter, A.S. RNA contributions to the form and function of biomolecular 612 condensates. Nat. Rev. Mol. Cell Biol. 22, 183-195 (2021). 613 16. Clever, J.L., Wong, M.L. & Parslow, T.G. Requirements for kissing- loop- mediated dimerization of 614 human immunodeficiency virus RNA. J. Virol. 70, 5902-5908 (1996). 615 17. Poudyal, R.R., Sieg, J.P., Portz, B., Keating, C.D. & Bevilacqua, P.C. RNA sequence and structure 616 control assembly and function of RNA condensates. RNA 27, 1589-1601 (2021). 617 18. Guo, P. The emerging field of RNA nanotechnology. Nat. Nanotechnol. 5, 833-842 (2010). 618 19. Geary, C., Rothemund, P.W. & Andersen, E.S. A single- stranded architecture for cotranscriptional 619 folding of RNA nanostructures. Science 345, 799-804 (2014). 620 20. Udono, H. et al. Programmable Computational RNA Droplets Assembled via Kissing- Loop 621 Interaction. Acs Nano 18, 15477-15486 (2024). 622 21. Fabrini, G. et al. Co- transcriptional production of programmable RNA condensates and synthetic 623 organelles. Nat. Nanotechnol. 19, 1665-1673 (2024). 624 22. Stewart, J.M. et al. Modular RNA motifs for orthogonal phase separated compartments. Nat. 625 Commun. 15, 6244 (2024). 626 23. Merindol, R., Loescher, S., Samanta, A. & Walther, A. Pathway- controlled formation of 627 mesostructured all- DNA colloids and superstructures. Nat. Nanotechnol. 13, 730-738 (2018). 628 24. Samanta, A., Sabatino, V., Ward, T.R. & Walther, A. Functional and morphological adaptation in 629 DNA protocells via signal processing prompted by artificial metalloenzymes. Nat. Nanotechnol. 15, 914-921 (2020). 630 25. Samanta, A., Horner, M., Liu, W., Weber, W. & Walther, A. Signal- processing and adaptive 631 prototissue formation in metabolic DNA protocells. Nat. Commun. 13, 3968 (2022). 632 26. Liu, W., Lupfer, C., Samanta, A., Sarkar, A. & Walther, A. Switchable Hydrophobic Pockets in DNA 633 Protocells Enhance Chemical Conversion. J. Am. Chem. Soc. 145, 7090-7094 (2023). 634 27. Samanta, A., Pellejero, L.B., Masukawa, M. & Walther, A. DNA- empowered synthetic cells as 635 minimalistic life forms. Nat. Rev. Chem. 8, 454-470 (2024). 636 28. Liu, W., Samanta, A., Deng, J., Akintayo, C.O. & Walther, A. Mechanistic Insights into the Phase 637 Separation Behavior and Pathway- Directed Information Exchange in all- DNA Droplets. Angew. 638 Chem. Int. Ed. 61, e202208951 (2022). 639 29. Walther, A. et al. Growing functional artificial cytoskeletons in the viscoelastic confinement of DNA 640 synthetic cells. Preprint at 10.21203/rs.3.rs-5048001/v1 (2024). 641 30. Alberti, S., Gladfelter, A. & Mittag, T. Considerations and Challenges in Studying Liquid- Liquid Phase 642 Separation and Biomolecular Condensates. Cell 176, 419-434 (2019). 643 31. André, A.A.M. & Spruijt, E. Liquid- Liquid Phase Separation in Crowded Environments. Int. J. Mol. 644 Sci. 21, 5908 (2020). + +<--- Page Split ---> + +647 32. Pei, G.F., Lyons, H., Li, P.L. & Sabari, B.R. Transcription regulation by biomolecular condensates. Nat. Rev. Mol. Cell Bio. (2024).649 33. Changiarath, A. et al. Promoter and Gene-Body RNA-Polymerase II co-exist in partial demixed condensates. Preprint at 10.1101/2024.03.16.585180 (2024).651 34. Delaney, C.E. et al. SETDB1-like MET-2 promotes transcriptional silencing and development independently of its H3K9me-associated catalytic activity. Nat. Struct. Mol. Biol. 29, 85-96 (2022).652653 + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryInformation.pdf MovieS1.mp4 MovieS2.mp4 MovieS3.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221_det.mmd b/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..8b67ba80fbae66388b5d74cdbb51620ab0ad647e --- /dev/null +++ b/preprint/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221/preprint__0ae7f8f6cbad98f78d95595f0fd34157385af5879d5ea6b07e3d14e42afd8221_det.mmd @@ -0,0 +1,358 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 931, 177]]<|/det|> +# Constructing synthetic nuclear architectures via transcriptional condensates in a DNA protonucleus + +<|ref|>text<|/ref|><|det|>[[44, 196, 191, 214]]<|/det|> +Andreas Walther + +<|ref|>text<|/ref|><|det|>[[55, 223, 366, 240]]<|/det|> +andreas.walther@uni- mainz.de + +<|ref|>text<|/ref|><|det|>[[44, 269, 585, 289]]<|/det|> +University of Mainz https://orcid.org/0000- 0003- 2170- 3306 + +<|ref|>text<|/ref|><|det|>[[44, 294, 123, 311]]<|/det|> +Miao Xie + +<|ref|>text<|/ref|><|det|>[[55, 316, 224, 333]]<|/det|> +University of Mainz + +<|ref|>text<|/ref|><|det|>[[44, 340, 175, 357]]<|/det|> +Weixiang Chen + +<|ref|>text<|/ref|><|det|>[[55, 362, 585, 381]]<|/det|> +University of Mainz https://orcid.org/0009- 0000- 5518- 0799 + +<|ref|>text<|/ref|><|det|>[[44, 386, 133, 403]]<|/det|> +Maria Roy + +<|ref|>text<|/ref|><|det|>[[55, 408, 224, 426]]<|/det|> +University of Mainz + +<|ref|>sub_title<|/ref|><|det|>[[44, 468, 103, 486]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 506, 137, 524]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 544, 325, 563]]<|/det|> +Posted Date: February 7th, 2025 + +<|ref|>text<|/ref|><|det|>[[44, 582, 475, 601]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5959823/v1 + +<|ref|>text<|/ref|><|det|>[[42, 619, 916, 662]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 680, 535, 700]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 736, 920, 779]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on September 10th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 63445- 8. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[77, 124, 872, 163]]<|/det|> +# Constructing synthetic nuclear architectures via transcriptional condensates in a DNA protonucleus + +<|ref|>text<|/ref|><|det|>[[220, 168, 773, 188]]<|/det|> +Miao Xie \(^{1,2,\#}\) , Weixiang Chen \(^{1,2,\#}\) , Maria de Roy \(^{1}\) , Andreas Walther \(^{1,2,*}\) + +<|ref|>sub_title<|/ref|><|det|>[[115, 196, 211, 211]]<|/det|> +## Affiliations + +<|ref|>text<|/ref|><|det|>[[115, 220, 881, 255]]<|/det|> +\(^{1}\) Life- Like Materials and Systems, University of Mainz, Duesbergweg 10- 14, 55128 Mainz, Germany. + +<|ref|>text<|/ref|><|det|>[[115, 262, 662, 280]]<|/det|> +\(^{2}\) Max Planck Institute for Polymer Research, 55128 Mainz, Germany. + +<|ref|>text<|/ref|><|det|>[[115, 288, 610, 305]]<|/det|> +\(^{*}\) Corresponding author. Email: andreas.walther@uni- mainz.de + +<|ref|>text<|/ref|><|det|>[[115, 314, 393, 330]]<|/det|> +\(^{\#}\) These authors contributed equally. + +<|ref|>sub_title<|/ref|><|det|>[[115, 362, 191, 377]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[112, 386, 884, 623]]<|/det|> +Nuclear biomolecular condensates are essential sub- compartments within the cell nucleus and play key roles in transcription and RNA processing. Bottom- up construction of nuclear architectures in synthetic settings is non- trivial but vital for understanding the mechanisms of condensates in real cellular systems. Here, we present a facile and versatile synthetic DNA protonucleus (PN) platform that facilitates localized transcription of branched RNA motifs with kissing loops (KLs) for subsequent condensation into complex condensate architectures. We identify salinity, monomer feeding, and KL- PN interactions as key parameters to control co- transcriptional condensation of these KLs into diverse artificial nuclear patterns, including single and multiple condensates, interface condensates, and biphasic condensates. Over time, KL transcripts co- condense with the PN matrix, with the final architecture determined by their interactions, which can be precisely modulated using a short DNA invader strand that outcompetes these interactions. Our findings deepen the understanding of RNA condensation in nuclear environments and provide new strategies for designing functional nucleus- mimetic systems with precise architectural control. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 125, 223, 140]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[112, 149, 883, 351]]<|/det|> +In eukaryotic cells, the nucleus provides a compartment for essential processes such as transcription, mRNA pre- splicing, and ribosome assembly1. To ensure precise spatial and temporal regulation of these biochemical processes2, membrane- less organelles such as nucleolus, Cajal bodies, and nuclear speckles form sub- compartments within the nucleus, which are biomolecular condensates that concentrate specific nucleic acids, enzymes, and metabolites3- 6. Beyond regulating these crucial processes, unique nuclear patterns formed by biomolecular condensates vary across cell types, adapting to specific demands and functional cell states7. Importantly, dysfunctions in nuclear condensates have been implicated in diseases such as cancer, ribosomopathy, and neurodegeneration6, 8, 9. Thus, understanding and reconstructing nuclear biomolecular condensates is not only essential for uncovering their mechanisms but also holds significant potential for therapeutic applications. + +<|ref|>text<|/ref|><|det|>[[112, 358, 883, 540]]<|/det|> +Despite considerable advances in studying natural biomolecular condensates and attempts to engineer transcriptional condensates within the nucleus8, 10- 12 based on specific or non- specific interactions of protein- protein, protein- nucleic acid, and RNA- RNA pairs2, 13, 14, much still remains unknown about their formation mechanisms and the involved kinetic processes. Specifically, the mechanisms by which these condensates concentrate molecules, maintain structural integrity, regulate composition, and modulate internal biochemical activities remain elusive, largely due to the complexity of in vivo environments. In contrast, in vitro models of biomolecular condensates allow for precise control over composition in a simplified setting11, enabling detailed mechanism assessment through experiments and computational modeling15. Here, studies presently however rely on plain solutions that are far from the conditions in a nucleus. + +<|ref|>text<|/ref|><|det|>[[111, 548, 883, 858]]<|/det|> +Transcriptional RNAs with specific sequences have been identified to play a key role in many biomolecular condensation processes15. However, achieving control in synthetic nuclear architectures and functions requires more advanced RNA designs capable of forming higher- order structures. In nature, the self- complementary kissing loop sequence in type 1 human immunodeficiency virus (HIV- 1) virions has been identified as framework for systematically manipulating genomic dimerization16. Similar kissing loop interactions have been shown to facilitate condensation in bacterial riboswitches13, 17. Inspired by the sequence- dependent interaction of kissing loops, which enables specific pairing between internally folded RNAs18, 19, the groups of Takinoue20, di Michele21, and Franco22 have recently introduced programmable condensates in solution formed by nanostar- like RNA motifs. The latter two groups have further shown that RNA nanostars with kissing loops at the end of each arm (KLs) could croscentrically condense into condensates with controlled size, number, morphology, and composition either in solution or confined within water- in- oil emulsions21, 22. Through integration of RNA aptamers into KLs, such condensates can mimic natural membrane- less organelles capable of selective capture of client molecules with biofunctions21. However, it remains unexplored whether RNA condensates can form in crowded conditions and how they may interact with DNA- rich environments resembling the cellular nucleus, where intricate RNA- DNA interactions occur. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 123, 884, 160]]<|/det|> +64 How such DNA environments influence the organizational principles of such designer condensates is unknown. + +<|ref|>text<|/ref|><|det|>[[110, 167, 884, 479]]<|/det|> +65 We have recently introduced core- shell DNA coacervates, formed by single- stranded DNA (ssDNA) polymers, with a highly concentrated DNA- enriched core \(^{23}\) , that can flexibly recruit molecules and proteins for enzymatic functions \(^{24, 25}\) and chemical reactions \(^{26}\) . These DNA coacervates closely resemble the crowded environment of the cellular nucleus, making them an ideal platform for constructing nucleus mimics \(^{27}\) . Therefore, we term them protonuclei (PNs) in this study. As the internal composition of the PNs can be flexibly tuned based on the ssDNA polymer selection, we incorporate T7 promoter sequences into the DNA core to recruit transcription templates and facilitate localized in- protonucleo transcription. We demonstrate that KL can be transcribed within these PNs, leading to the formation of co- transcriptional KL condensates with various morphologies. We demonstrate a range of synthetic nuclear architectures, including single condensates, multiple condensates, interfacial condensates formed through secondary nucleation, and biphasic condensates of orthogonal KLs, all controlled by salinity, PN- KL affinity, and competing PN- KL interactions, respectively. Given the design flexibility of transcriptional KLs and the tunable condensate patterns in our crowded PN system, we believe this artificial nucleus platform will significantly advance the field of synthetic biology, in particular synthetic cells, providing a powerful toolkit for designing and constructing synthetic nuclear architectures with unprecedented control and precision. + +<|ref|>sub_title<|/ref|><|det|>[[115, 512, 179, 527]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[110, 536, 884, 829]]<|/det|> +Figure 1 shows an overview of our entire approach. It consists of constructing a modular PNs platform using DNA nanoscience approaches, followed by immobilization of short KL templates to initiate transcription therein. The transcribed KLs are designed to undergo phase separation by complementary interactions. By precisely controlling KL- PN interactions and environmental conditions, we study structure formation and response in detail through easily accessible pathways. In more detail, the DNA PNs are derived from our previous work on DNA protocells \(^{23, 24}\) , where we have identified that temperature ramps of mixtures of long poly(A \(_{20}\) - m) \(_{n}\) ssDNA and long poly(T \(_{20}\) - k) \(_{n}\) ssDNA form micron- sized core- shell coacervates with an adenine- rich ssDNA polymer (polyA) core and a thymine- rich ssDNA polymer (polyT) shell \(^{23 - 25, 28}\) . This process features a selective liquid- liquid phase separation (LLPS) of polyA during heating, forming polyA droplets at high temperature, which are then stabilized by polyT with A \(_{20}\) /T \(_{20}\) hybridization during cooling, forming a thin and crosslinked hydrogel shell. This ultimately furnishes a highly concentrated polyA core of around 10 g/L \(^{29}\) . The dynamic properties of the PNs can be regulated from an arrested state to a liquid- like state by tuning the salinity. Additional ssDNA barcode sequences (o, p, k) can be modularly incorporated into the ssDNA polymers for integrating functionalities into the core and the shell (Fig. 1). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[111, 128, 884, 315]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[111, 323, 884, 408]]<|/det|> +
Fig. 1 Transcriptional kissing loop (KL) condensates form different synthetic nuclear patterns in DNA protonuclei (PNs). PolyA strands with barcode p (T7 promoter sequence), polyA strands with dummy barcode (o), and polyT with k barcode are used for LLPS process to form PNs with an incorporated promoter region. The promoter barcodes inside the PNs recruit DNA templates, T7 RNA Polymerase (T7 RNAP), and nucleotide triphosphate (NTP) monomers to induce a localized transcription and enrichment of KL sequences, forming distinct nuclear patterns via different nucleation and condensation processes.
+ +<|ref|>text<|/ref|><|det|>[[111, 414, 884, 708]]<|/det|> +We synthesized several ssDNA polymers using rolling circle amplification (details in Supplementary Table 1), including poly(A20- p)n, poly(A20- o)n, and poly(T20- k)n with n ranging roughly from 10 to 60 repeating units23. The barcodes p, o, and k serve specific functions. The most critical part is the p barcode in poly(A20- p)n, which is the T7 RNA polymerase (T7 RNAP) promoter sequence that allows for the flexible integration of ssDNA templates (short genes) amenable to transcription of RNA in the PNs through simple addition of the templates after formation of the PNs. Poly(A20- o)n serves to homogeneously dilute the p barcode and provides an addressable matrix barcode to tune properties and (as we will see below) adjust the affinity to the transcribed RNA, which regulates the subsequent growth of the transcriptional condensates. Following our established protocols23- 25, 28, we prepared a set of core- shell PNs by mixing poly(A20- p)n and poly(A20- o)n for the core, and poly(T20- k)n for the shell, using a temperature ramp in TE buffer at 50 mM \(\mathrm{Mg^{2 + }}\) . Functionalization of the p and o barcodes with complementary day- appended ssDNA confirms a homogeneous integration of both polyAs in the PN cores (confocal laser scanning microscopy (CLSM) images in Supplementary Fig. 1). The PNs can be conditioned to different salinity after preparation. We focus on a PN system where 10% of promoter sequences (poly(A20- p)n) are diluted with 90% of a matrix (poly(A20- o)n). + +<|ref|>text<|/ref|><|det|>[[111, 714, 884, 841]]<|/det|> +To verify transcription to occur inside the PNs, we hybridized a transcription template \(\mathrm{Tx^*}\) containing \(\mathrm{p}^*\) for hybridization with the promoter sequence p and an active transcription region \(\mathrm{x^*}\) at stoichiometric ratio into the PNs (sequences in Supplementary Table 2). \(\mathrm{x^*}\) codes for a simple RNA not amenable to undergo condensation. Subsequent addition of T7 RNAP and a nucleotide triphosphate (NTP) monomer mix containing 1% fluorescent monomer (UTP- Atto488) induces transcription with local formation of fluorescent RNA strands (Fig. 2a, b, and Supplementary Video 1). + +<|ref|>text<|/ref|><|det|>[[110, 849, 884, 867]]<|/det|> +To better quantify the transcription efficiency and kinetics inside the PNs and compare it to free + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 123, 884, 361]]<|/det|> +transcription in solution, we further designed a reporter (R) containing a fluorophore- quencher pair, which is a partially complementary double- stranded DNA (dsDNA; Rep/Rep' sequences in Supplementary Table 2; ' denotes a partially complementary sequence with a toehold). The transcribed Rep\* from template \(\mathrm{T_{Rep^*}}\) will trigger a strand displacement reaction (SDR) with the R by fully hybridizing with the Rep strand, generating a fluorescent signal, which can be monitored by fluorescence measurements using a plate reader. In more detail, we compared the transcription kinetics between PNs with embedded promoter sequence of poly \((\mathrm{A}_{20} - \mathrm{p})_{\mathrm{n}}\) , pure poly \((\mathrm{A}_{20} - \mathrm{p})_{\mathrm{n}}\) ssDNA in solution, and short p ssDNA in solution - all at identical p concentration and otherwise identical transcription conditions (Fig. 2a, c). All systems show relatively similar kinetic profiles, with the free promoter in solution being the most active transcription system, and the PN showing a slightly lower activity compared to the free poly \((\mathrm{A}_{20} - \mathrm{p})_{\mathrm{n}}\) in solution. The slightly lower activity can be understood considering constraints on the diffusion of NTPs into the PNs and RNA strands out of the PNs. + +<|ref|>text<|/ref|><|det|>[[111, 368, 884, 660]]<|/det|> +After confirming successful transcription in the PNs, we turn to KL- condensate formation by transcriptional control in the PNs versus in solution. As a proof of concept, we first focus on a three- armed singled- stranded RNA (ssRNA) nanostar with a wildtype palindromic KL sequence \(^{20 - 22}\) at the tip of each arm (KL1 in Fig. 2d; Template = hybridized \(\mathrm{T_{KL1} / T_{KL1}}\) , where \(\mathrm{T_{KL1}}\) contains a \(\mathrm{p}^*\) ssDNA sequence for hybridization to poly \((\mathrm{A}_{20} - \mathrm{p})_{\mathrm{n}}\) inside the PNs, Supplementary Table 2). We compared differences in KL1 condensate formation at low \([\mathrm{NTP}] = 3.6 \times [\mathrm{T_{KL1}}]\) after \(18\mathrm{h}\) transcription ([NTP] is defined as the maximum amount of KL1 transcripts that can be produced per template). The PN system clearly shows a single KL1 condensate in every PN with an average diameter of approximately \(4\mu \mathrm{m}\) for PNs with an average diameter of around \(6.7\mu \mathrm{m}\) (Fig. 2e, f). In striking contrast, no KL1 condensates can be found in solution due to the limited concentration of RNA transcripts (Fig. 2e, f). Transcriptional KL1 condensates in solution start to appear with diameter of \(\sim 4.5\mu \mathrm{m}\) at increased [NTP] ([NTP] = 14.4 x; Fig. 2e, f). The size of the transcriptional KL1 condensate in solution increases with [NTP] due to the increased amount of RNA transcripts (Supplementary Fig. 2). This comparison demonstrates that the spatial transcription of the KL1 in PNs leads to locally high concentrations sufficient for condensation, similar to the enrichment mechanism in natural nuclear condensates \(^2\) . + +<|ref|>text<|/ref|><|det|>[[111, 668, 884, 870]]<|/det|> +Interestingly, one single KL1 condensate forms in each PN, confirming sufficient dynamics within the PN to follow energy minimization constraints to yield a minimum surface area (Fig. 2e and Supplementary Fig. 3). We further performed fluorescence recovery after photobleaching (FRAP) experiments on the KL1 condensates in PNs and in solution to study their dynamic properties. Strikingly, their fluorescence recovery kinetics differ substantially. Whereas KL1 condensates in solution show near full recovery overnight, KL1 condensates in PNs only show limited recovery, highlighting much better diffusion dynamics of KL1 condensate in solution than in PNs (Fig. 2g, h). A complementary half- bleaching experiment shows a bright edge of transcriptional KL1 condensates in solution during recovery, indicating a dynamic exchange of soluble KL1 transcripts from the solution with the condensate phase (Supplementary Fig. 4). In contrast, half- bleached KL1 condensates in PNs show less recovery and lack the bright edge, likely due to their restricted + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 123, 880, 159]]<|/det|> +dynamics in a DNA- crowded environment and interactions between PN matrix and the KL1 transcripts, as we will further discuss below. + +<|ref|>text<|/ref|><|det|>[[112, 167, 884, 350]]<|/det|> +Next, we discuss the effects of [NTP] and \([\mathrm{Mg}^{2 + }]\) on transcriptional KL1 condensate formation inside the PNs. KL1 transcription with [NTP] varying from \(2.4 \times\) to \(3.6 \times\) show a morphological transition from peripheral localization of KL1 transcripts to reorganization and compaction into a single condensate (Fig. 2i). The formation of peripheral KL1 transcripts at low [NTP] shows that incoming NTPs are converted to RNA as they reach the embedded transcription templates in the outer PN parts. The lack of a centrally compacted condensate points to the fact that, at this low concentration of KL1 transcripts, phase segregation is at least not very pronounced. The remaining ring indicates an interaction between the PN matrix and the KL1 transcripts. At higher [NTP], KL1 transcripts are homogeneously produced throughout the PN, and phase segregation drives the formation of the KL1 condensate. + +<|ref|>text<|/ref|><|det|>[[111, 357, 884, 686]]<|/det|> +\([\mathrm{Mg}^{2 + }]\) shows a profound impact on nucleation and condensate morphology. Multiple small condensates can be observed at \(15 \mathrm{mM} \mathrm{Mg}^{2 + }\) , whereas \([\mathrm{Mg}^{2 + }] > 20 \mathrm{mM}\) leads to the formation of a single condensate droplet. \(20 \mathrm{mM} \mathrm{Mg}^{2 + }\) corresponds to a transition point. Interestingly, a transition in the condensate formation process is visible. Whereas isolated nucleation events dominate at \(15 \mathrm{mM} \mathrm{Mg}^{2 + }\) , co- continuous phase separation is visible above \(25 \mathrm{mM} \mathrm{Mg}^{2 + }\) with a sponge- like structure. At \(40 \mathrm{mM} \mathrm{Mg}^{2 + }\) , condensate formation in PNs is no longer visible (Fig. 2j). Such distinct condensate formation in PNs is associated with multiple influences of \(\mathrm{Mg}^{2 + }\) on the system: First, higher \([\mathrm{Mg}^{2 + }]\) leads to reduced dynamics in the crowded environment of PNs, as previously studied by us in detail \(^{23,24}\) . Second, higher \([\mathrm{Mg}^{2 + }]\) also assists in tighter condensation of the KL condensates and potentially increases non- specific interactions between the KL condensates and the PN matrix \(^{22}\) . Third, increasing \([\mathrm{Mg}^{2 + }]\) leads to a continuous decrease of the transcription efficiency as depicted in Fig. 2k. Thus, multiple isolated nucleation events and binodal phase separation occur at \(15 \mathrm{mM} \mathrm{Mg}^{2 + }\) , driven by the high dynamics of the PN core and the high transcription efficiency. In contrast, spinodal or viscoelastic phase separation \(^{26,30,31}\) is favored at high \(\mathrm{Mg}^{2 + }\) concentrations, where the dynamics of the PNs become more arrested. Further, charge screening increases the propensity for non- specific interactions between nucleic acids (RNA and DNA). The transcription is strongly suppressed at \(40 \mathrm{mM} \mathrm{Mg}^{2 + }\) with limited KL1 transcripts so that condensation of KL1 cannot take place (Fig. 2j, k). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 120, 880, 636]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[111, 640, 884, 867]]<|/det|> +
Fig. 2 Transcriptional KL condensates in PNs show different nuclear patterns. a, Scheme showing transcription in PNs characterized by CLSM and plate reader. For CLSM experiment, UTP-Att0488 is added to the NTP mix for transcript labeling. For plate reader experiments, dsDNA reporters with fluorophore-quencher pairs (R) are present in solution to react with transcribed RNA by strand displacement reaction (SDR), generating fluorescent signals. b, Representative CLSM images showing the localized transcription of fluorescent \(\mathbf{x}^*\) (green, labeled by UTP-Att0488 during transcription) inside PNs at different times ([NTP] : [T \(\mathbf{x}^*\) ] : [p] = 250 : 1: 1, 30 °C, 30 mM Mg \(^{2 + }\) , 2.5 U/μL T7 RNAP). The whole process is recorded in Supplementary Video 1. c, Transcription kinetics inside PNs with embedded poly(A \(\mathbf{\cdot}_{20} - \mathbf{p})\) n, in solution with free poly(A \(\mathbf{\cdot}_{20} - \mathbf{p})\) n, and in solution with free promoter (p) oligonucleotide, monitored by SDR of the R in a plate reader ([NTP] : [R] : [T \(\mathbf{\cdot}_{R}\mathbf{p}^*\) ] : [p] = 200 : 10 : 1 : 1, 30 °C, 6 mM Mg \(^{2 + }\) , 2.5 U/μL T7 RNAP). d, Scheme for the formation of transcriptional KL1 condensates in PNs and in solution, with KL1 labeled by UTP-Att0488 during transcription. e, CLSM images with maximum intensity projection of z-stacked images showing the formation of transcriptional KL1 condensates in PNs containing embedded poly(A \(\mathbf{\cdot}_{20} - \mathbf{p})\) n ([NTP] : [T \(\mathbf{\cdot}_{KL1}\) ] = 3.6), and in solution with free promoter (p) oligonucleotide ([NTP] : [T \(\mathbf{\cdot}_{KL1}\) ] = 3.6 : 1 or 14.4 : 1, 30 °C, 30 mM Mg \(^{2 + }\) , 2.5 U/μL T7 RNAP, 18 h for in-PN and in-solution transcription). f, Diameter distributions of KL1 condensates in PNs and in solution at different [NTP]. g, Normalized fluorescence recovery kinetics in the bleached areas in (h) during FRAP experiments on
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 123, 884, 336]]<|/det|> +KL1 condensates in PNs and in solution at \(30^{\circ}\mathrm{C}\) . Intensity values were normalized to pre- bleached levels. \(n =\) 3. h, Time- lapse CLSM images for FRAP experiments on KL1 condensates in PNs and in solution at \(30^{\circ}\mathrm{C}\) . White dashed circles indicate the bleached regions. i, Representative CLSM images showing the effect of [NTP] on transcriptional KL1 condensate formation in PNs after \(18\mathrm{h}\) at \(30^{\circ}\mathrm{C}\) . Note that the left half of the \(3.6\times [\mathrm{NTP}]\) sample is a maximum intensity projection of z- stacked images. Green channel: KL1- Atto488; Magenta channel: \(\mathrm{k^{*} - Atto647 / poly(A_{20} - k)_{n}}\) . j, Representative CLSM images showing the effects of \([\mathrm{Mg}^{2 + }]\) on transcriptional KL1 condensate formation in PNs after \(18\) and \(48\mathrm{h}\) reaction. Note that a hyperstack image generated from z- stacked images is used for KL1 condensates in PNs at \(15\mathrm{mM}\mathrm{Mg}^{2 + }\) after 48 hours of transcription to visualize condensates formed in different planes with a corresponding color- coded z- scale. k, Effects of \([\mathrm{Mg}^{2 + }]\) on transcription efficiency in PNs, monitored via RNA- triggered SDR of the R. \([\mathrm{NTP}]:[\mathrm{R}]:[\mathrm{T}_{\mathrm{Rep}}^{*}]:[\mathrm{p}] = 200:\) \(10:1:1\) , \(30^{\circ}\mathrm{C}\) , \(2.5\mathrm{U / \mu L}\) T7 RNAP at indicated \([\mathrm{Mg}^{2 + }]\) , measured by a plate reader. In the box plot (f), the central line marks the median, the box represents the interquartile range (IQR) from Q1 (first quartile) to Q3 (third quartile), and the whiskers enclose all data points from the minimum to the maximum. This applies to all box plots shown in this paper. Error areas represent standard deviation. Scale bars are \(10\mu \mathrm{m}\) for (b) and (e), 5 \(\mu \mathrm{m}\) for (h), (i), and (j). + +<|ref|>text<|/ref|><|det|>[[111, 340, 884, 523]]<|/det|> +To get a deeper understanding of the morphological development of single KL condensates in the PNs at \(30\mathrm{mM}\mathrm{Mg}^{2 + }\) , we monitored the whole process over \(24\mathrm{h}\) through CLSM (Fig. 3a, b). Two distinct stages occur. In the first \(12\mathrm{h}\) , transcription takes place from the edge of the PNs to their center, and the entire structures reach maximum fluorescence intensities at \(12\mathrm{h}\) (Fig. 3b- d). Spongy structures of KL1 condensates during phase separation start to appear at ca. \(8\mathrm{- }10\mathrm{h}\) , whereas significant coarsening and compaction into single spherical condensates follows in the later \(12\mathrm{- }24\mathrm{h}\) (Fig. 3b- e). Interestingly, we can observe a relatively slow and continuous increase of the PN dimensions, as facilitated by the relaxation of polyA/polyT shell as a result of the increasing negative charge density inside the PNs during localized RNA production and condensation (Fig. 3c, e). + +<|ref|>text<|/ref|><|det|>[[111, 529, 884, 712]]<|/det|> +For further probing the universality of this single condensate formation phenomena for various KL structures, we adapted a KL1 condensate with an RNA light- up broccoli aptamer (BrA) as one of the arms, termed KL1- BrA (NUPACK- simulated structure shown in Supplementary Fig. 5a). After \(12\mathrm{- }24\mathrm{h}\) transcription, single condensates are formed in each PN. In contrast, KL1- BrA only forms irregular aggregates in solution. Here, the interaction between the PN matrix and the KL1- BrA condensate may facilitate better relaxation and stabilization of KL1- BrA condensate within the PNs (Supplementary Fig. 5). Taken together, KL condensation in solution and in- PN differ profoundly in both the kinetic formation process and the formed final structures at what could be considered closer to the thermodynamic equilibrium. The system can be easily tuned by adjusting the NTP and \(\mathrm{Mg}^{2 + }\) concentrations and is robust to changes in the KL components. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 123, 884, 562]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 568, 883, 793]]<|/det|> +
Fig. 3 Mechanism of co-condensation of transcriptional KL1 with the PN matrix. a, Scheme for formation of single co-condensates of transcriptional KL1 and polyA in PNs through peripherical initiation of transcription, re-organization, and co-condensation. b, Representative CLSM images of PNs conducting KL1 transcription over \(24\mathrm{h}\left(\mathrm{[NTP]}:[\mathrm{T}_{\mathrm{KL1}}] = 3.6:1,1\mathrm{mol}\% \mathrm{UTP - Atto}488,30^{\circ}\mathrm{C},2.5\mathrm{U} / \mu \mathrm{L} T7\mathrm{RNAP},30\mathrm{mM}\mathrm{Mg}^{2 + }\right)\) . Note a slight slow-down of co-condensation kinetics of KL1 compared with results shown in Fig. 2e, j after \(18\mathrm{h}\) , due to the interruption of shaking during incubation for the CLSM imaging. Green channel: KL1 condensate labeled by UTP-Attot488; Magenta channel: PN shell (poly( \(\mathrm{T}_{20}\mathrm{-k}\) ), labeled with \(\mathrm{k}^{*}\) -Attot647). c, Space-time plot analysis corresponding to the two dashed lines in (b) over \(24\mathrm{h}\) shows the KL1 transcription, condensation, and reorganization process. d, Normalized fluorescence intensity change in the KL1 condensate channel over \(24\mathrm{h}\) in the two white dashed circle in (b), normalized to the intensity at \(24\mathrm{h}\) . e, Normalized radius change of KL1 condensate and PN as measured from (b) over \(24\mathrm{h}\) . f, g Representative CLSM images of fluorescent PNs (magenta) containing KL1 transcripts (green) at \(12\mathrm{h}\) (f) and \(24\mathrm{h}\) (g). The right plots correspond to the line segment analysis of the white line in the CLSM images, showing fluorescence distribution for KL1-Attot488 and poly(A20-0)n-Attot643. The KL1 transcripts show a peripheral distribution in the PN matrix at \(12\mathrm{h}\) (f), while colocalization and co-condensation between KL1 and PN matrix occur after \(24\mathrm{h}\) (g). Error bars and error areas represent standard deviation. Scale bars are all \(5\mu \mathrm{m}\) .
+ +<|ref|>text<|/ref|><|det|>[[114, 800, 883, 871]]<|/det|> +To study the behavior and aforementioned interactions of the PN DNA matrix with the KL1 RNA condensates, we covalently labeled poly(A20- 0)n with Attot643 to prepare fluorescent PNs and used these new PNs to initiate localized KL1 transcription. As expected, the initial production and localization of KL1 transcripts occur at the periphery of the PNs (Fig. 3f). Unexpectedly, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 123, 884, 252]]<|/det|> +co- condensation of the PN matrix with the KL1 condensates occurs over time. These co- condensates deposit at the bottom of PNs after \(24\mathrm{h}\) in the imaging chamber, highlighting their higher density and compactness (Fig. 3g, Supplementary Fig. 6, and Supplementary Video 2). FRAP experiments reveal a better recovery for the KL1 components compared to the PN matrix, corresponding to higher dynamics for the KL1 condensate part composed of small RNAs than the PN matrix composed of long ssDNA polymers (Supplementary Fig. 7). This demonstrates the molecular level diffusivity of the RNA nanostars in this co- condensate structure. + +<|ref|>text<|/ref|><|det|>[[112, 259, 884, 533]]<|/det|> +Overall, this co- condensation between PN and KL1 condensate comes unexpectedly because the KL1 condensate was not designed to have any specific interactions with the PN matrix. Indeed, a NUPACK simulation suggests no specific hybridization between the \(\mathrm{A}_{20} - \mathrm{o}\) repeats and the KL1 sequence (Supplementary Fig. 8). Experimentally, we probed interactions between mature KL1 condensates and poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) inside PNs by adding different quantities of \(\mathrm{o}^{*} - \mathrm{Atto}647\) (from \(10\%\) - \(300\%\) ) that can bind to the majority phase of poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) in the PNs. We hypothesized that the hybridization between \(\mathrm{o} / \mathrm{o}^{*}\) may break non- specific KL1- PN interactions (Fig. 4a, b). A gradual invasion of \(\mathrm{o}^{*} - \mathrm{Atto}647\) into the KL1- poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) co- condensates occurs as the amount of \(\mathrm{o}^{*} - \mathrm{Atto}647\) increases. This process leads to continuous surface erosion of the co- condensates (Fig. 4c- e and Supplementary Video 3). A sharp interface defined by a bright ring of \(\mathrm{o}^{*} - \mathrm{Atto}647\) with a locally high concentration appears. The poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}} / \mathrm{o}^{*} - \mathrm{Atto}647\) thereafter occupies the space within the entire PN, whereas the KL1 transcripts are squeezed to the PN periphery and eventually dissolve into solution to equilibrate to their low concentration there. This process verifies that the interaction between KL1 and poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) PN matrix promotes the formation of the KL1- poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) co- condensate. + +<|ref|>text<|/ref|><|det|>[[112, 540, 884, 815]]<|/det|> +Seeing such a profound impact, we then investigated KL1 transcription in PNs with a poly \((\mathrm{A}_{20} - \mathrm{o})_{\mathrm{n}}\) matrix pre- hybridized by different amounts of \(\mathrm{o}^{*} - \mathrm{Atto}647\) (from \(0\% - 300\%\) ) to provide weakened affinity between PN matrix and KL1 transcripts. In analogy with the above result, single KL1 condensates form in pristine PNs (Fig. 4f). When applying \(10\% \mathrm{o}^{*} - \mathrm{Atto}647\) , the KL1 transcripts form single condensates with irregular secondary nucleation on its surface inside the PN, along with multiple tiny nuclei outside the PN shell (Fig. 4f). The brighter green parts are condensates purely enriched with KL1 transcripts that remain inside the PN due to relatively sufficient affinity. Increasing the content of pre- hybridized \(\mathrm{o}^{*} - \mathrm{Atto}647\) domains from \(10\%\) to \(300\%\) gradually prevents KL1 condensate formation inside the PNs due to weakened PN- KL1 interaction, which likely becomes even repulsive at higher pre- hybridization degrees. As a result, the KL1 transcripts formed inside the PNs do not yield condensates insides the PNs, instead, multiple small transcriptional KL1 condensates form in the PN surroundings. These results highlight the importance of the interaction between the DNA matrix of the PNs and the KL1 transcripts in both the formation and the maintenance of the condensates within the PNs. Hence, modulating the DNA- RNA interaction is a way for regulating nucleus condensate architectures. + +<|ref|>text<|/ref|><|det|>[[112, 822, 884, 858]]<|/det|> +To directly study the affinity between polyA sequence of the PN matrix and KL1 condensate, we prepared transcriptional KL1 condensates in solution and added \(\mathrm{A}_{20} - \mathrm{o} - \mathrm{Atto}647\) ssDNA, and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 123, 884, 270]]<|/det|> +A20- o/o\*- Atto565 dsDNA. Such pure KL1 condensates sequester A20- o- Atto647 whereas A20- o/o\*- Atto565 is excluded (Fig. 4g, h). Such marked differences among interactions between KL1- to- ssDNA versus KL1- to- dsDNA confirm some level of unspecific interaction between the KL1 transcript and the o region, which is removed through hybridization into o/o\*. Furthermore, electrostatic repulsion from increased negative charge density after dsDNA formation could also play a role, as in analogy to re- entrant phenomena in living cells, where transcriptional condensate formation is promoted at low rates of RNA synthesis up to a point of charge imbalance, beyond which higher rates of RNA synthesis disfavors condensate formation11, 32. + +<|ref|>image<|/ref|><|det|>[[111, 275, 880, 814]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 817, 884, 861]]<|/det|> +
Fig. 4 Hybridization of the polyA matrix of PNs induces disassembly of KL1-PN co-condensate. a, Representative CLSM images of transcriptional KL1 condensates (Atto488, green channel) in PNs (Atto647, magenta channel) 60 min after adding 10%, 50%, 80% and 300% o\*- Atto647 as an invader strand. b, Schematic
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 123, 883, 334]]<|/det|> +illustration of the o\\*- Atto647 invasion process. Hybridization of o\\*- Atto647 with poly \(\mathrm{(A_{20} - o)_n}\) starts from the edge of the KL1- polyA co- condensate with a bright and sharp invading front to final dissolution of the whole co- condensate. c, Representative CLSM images showing the process of co- condensate (Atto488, green channel) dissolution by adding \(300\%\) o\\*- Atto647 (magenta channel) to hybridize to the poly \(\mathrm{(A_{20} - o)_n}\) of the PNs. See also Supplementary Video 3. d, Space- time plot analysis along the white dashed line in (c) shows the gradual dissolution of the condensate. e, Normalized fluorescence intensities of KL1 condensates (KL1- Atto488) and invader strand (o\\*- Atto647) measured in the white dashed circle in (c) during the invasion process. f, Scheme and representative CLSM images of KL1 transcription and condensation (Atto488, green channel) after \(18\mathrm{h}\) in pre- hybridized PNs with \(0\%\) \(10\%\) \(50\%\) \(80\%\) , and \(300\%\) o\\*- Atto647 (magenta channel). g, Scheme shows the attractive interaction between KL1 condensate and ssDNA \(\mathrm{A_{20} - o}\) - Atto647, and repulsive interaction between KL1 condensate and dsDNA \(\mathrm{A_{20} - o}\) - Atto647/o\\*- Atto565. h, Representative CLSM images of pure transcriptional KL1 condensates (Atto488, green channel) prepared in solution, with the addition of (top) ssDNA \(\mathrm{A_{20} - o}\) - Atto647 (magenta channel) for \(1\mathrm{h}\) , showing preferential partitioning, or (bottom) dsDNA \(\mathrm{A_{20} - o}\) - Atto647/o\\*- Atto565 (red channel) showing rejection. Shaded areas represent standard deviations. Scale bars are \(5\mu \mathrm{m}\) for (a), (c), and (f), and \(10\mu \mathrm{m}\) for (h). + +<|ref|>text<|/ref|><|det|>[[112, 339, 883, 485]]<|/det|> +Finally, we attempted to integrate orthogonal KL transcription systems into PNs for constructing more complex structures to mimic multiple RNA condensates in the crowded environment of natural cell nuclei. We adapted two KL nanostars (KL1- R1 and KL2- R2) with orthogonal kissing loop sequences at the end of their arms, and distinct tail regions (R1 and R2) for specific labeling by R1\\*- Atto488 and R2\\*- Atto647, respectively (Fig. 5a). We firstly confirmed the transcription and the formation of centrally located condensates for both KL1- R1 or KL2- R2 inside PNs (Supplementary Fig. 9). Hence, both systems form similar condensate structure as the original KL1 system and the KL1- BrA system studied above. + +<|ref|>text<|/ref|><|det|>[[111, 492, 883, 767]]<|/det|> +Since \(\mathrm{[Mg^{2 + }]}\) can control the condensate morphology (Fig. 2j), we conducted co- transcription of both KLs in the same PN at 15 and \(30\mathrm{mM}\mathrm{Mg}^{2 + }\) , respectively (Fig. 5a). At \(30\mathrm{mM}\mathrm{Mg}^{2 + }\) , KL1- R1 assembles to a large single condensate ( \(\sim 0.7\) - fold the diameter of the host) at the PN center, while KL2- R2 forms small condensates, budding at the PN shell, with diameters less than 0.2- fold of the host PN (Fig. 5b- d). This suggests a preferred interaction between KL1- R1 and PN matrix, retaining the KL1- R1 condensate inside the PN, whereas KL2- R2 gets obviously expelled. KL1- R1 dominates the interaction with the PN matrix in this competitive system, whereas pure KL2- R2- PN would form a single central condensate (Supplementary Fig. 9). At \(15\mathrm{mM}\mathrm{Mg}^{2 + }\) , the transcriptional KL1- R1 occupies the major PN space, while KL2- R2 forms multiple condensates in the PNs (Fig. 5e- g). This can be attributed to weakened interactions between KL1- R1 and the PN at low salinity, allowing KL2- R2 to occupy some of the available volume in the PN to form condensates. Hence, the combined effect of \(\mathrm{Mg}^{2 + }\) on changing the viscoelastic properties and modulating KL interactions as well as KL- to- PN interactions again shows a profound effect. We can conclude that phase segregation of KL1- R1 is energetically favored to be retained in the PNs. A mixing of both KL phases does not occur. + +<|ref|>text<|/ref|><|det|>[[112, 774, 882, 866]]<|/det|> +To verify the competitive interaction between KL1- R1 and KL2- R2 with the polyA in the PN matrix, we performed competitive partition experiments of \(\mathrm{A_{20} - o}\) or \(\mathrm{A_{20} - o / o^*}\) with pure transcriptional KL1- R1 and KL2- R2 condensates grown in solution. The results show preferential partitioning of \(\mathrm{A_{20} - o}\) into the KL1- R1 condensates, whereas \(\mathrm{A_{20} - o / o^*}\) is excluded by both condensates (Supplementary Fig. 10). This confirms a higher affinity of KL1- R1 condensates to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 124, 883, 306]]<|/det|> +the PN matrix and explains the different condensate architectures formed in the PNs. Additionally, transcriptional KL2- R2 condensates show a less spherical structure compared with transcriptional KL1- R1 ones (Supplementary Fig. 10), suggesting stronger condensation interactions for KL2- R2 than KL1- R1, consistent with the higher melting temperature of KL2 interactions than KL1 interactions provided in literature21. This helps to explain that the KL2- R2 could still form condensates, whether expelled from PN at 30 mM \(\mathrm{Mg^{2 + }}\) or remained in PN at 15 mM \(\mathrm{Mg^{2 + }}\) . In summary, these results reveal that, in addition to salinity effect, subtle variations in RNA composition and sequence modulate their interaction with the DNA matrix of PN in a competitive environment, leading to profoundly different condensation processes and resulting in distinct multi- phase co- condensate architectures in PNs. + +<|ref|>image<|/ref|><|det|>[[110, 311, 884, 744]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 748, 884, 860]]<|/det|> +
Fig. 5 Formation of orthogonal transcriptional KL condensates in PNs. a, Scheme showing orthogonal transcription and condensation of KL1-R1 and KL2-R2 in the same PN at different salinity. KL1-R1 is identical to KL1 in its nanostar framework, but with an additional recognition tail (R1) for R1\*-Atto647 labelling. KL2-R2 shares the same stem sequence as KL1 but has orthogonal kissing loop sequences and a distinct recognition tail (R2) for R2\*-Atto488 labeling. R1\*-Atto647 and R2\*-Atto488 are added during transcription. b, Representative single-plane CLSM image and maximum intensity projection of z-stacked CLSM image showing orthogonal transcriptional condensates of KL1-R1 (green channel) and KL2-R2 (magenta channel) in PNs at 30 mM \(\mathrm{Mg^{2 + }}\) ([NTP] : [R1\*] : [R2\*] : [TKL1-R1] : [TKL2-R2] : [p] = 3.6 : 1.8 : 1.8 : 0.5 : 0.5 : 1, 30 °C, 30
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 123, 883, 252]]<|/det|> +\(\mathrm{mM Mg^{2 + }}\) , 2.5 U/μL T7 RNAP). e, Normalized intensity profiles of line segment analyses corresponding to the white line in (b) for both channels. d, Diameter of formed orthogonal condensates at \(30\mathrm{mM}\mathrm{Mg}^{2 + }\) , normalized to the diameter of the host PNs. e, Representative single-plane CLSM image and maximum intensity projection of z- stacked CLSM image showing orthogonal transcriptional condensates of KL1- R1 and KL2- R2 in PNs at 15 \(\mathrm{mM}\mathrm{Mg}^{2 + }\) ([NTP]: [R1\\*]: [R2\\*]: [TKL1- R1]: [TKL2- R2]: [p] = 3.6 : 1.8 : 1.8 : 0.5 : 0.5 : 1, 2.5 U/μL T7 RNAP, \(15\mathrm{mM}\mathrm{Mg}^{2 + }\) , \(30^{\circ}\mathrm{C}\) , 18 h reaction). f, Normalized intensity profiles of line segment analyses corresponding to the white line in (e) for both channels. g, Diameter of formed KL2- R2 condensates at \(15\mathrm{mM}\mathrm{Mg}^{2 + }\) , normalized to the diameter of host PNs. Note that the diameter of KL1- R1 condensates cannot be quantified due to their hollow shape. Scale bars are all \(5\mu \mathrm{m}\) . + +<|ref|>sub_title<|/ref|><|det|>[[113, 281, 205, 297]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[112, 306, 883, 636]]<|/det|> +We have introduced a versatile nucleus- mimicking DNA condensate platform - a protonucleus - that enables localized transcription and the study of phase- separation of transcribed RNA nanostars in crowded and highly concentrated DNA environments. Since the strategy builds on our previous work on all- DNA synthetic cells23- 25, 28, our approach shows how specific components from a completely different area of research, that is synthetic artificial cell research, can be effectively repurposed into new application domains. These protonuclei offer a highly programmable platform for introducing short genes for transcription while also enabling control over properties such as gene density and the dynamic behavior of the matrix23, 24. Transcription inside these crowded PNs proceeds with satisfying efficiency up to high salt concentration. To study transcriptional folding and phase segregation in the crowded, nuclear- mimetic environment, we focused on KL condensates formed by ssRNA nanostars. We identified that ionic strength is one key parameter for cross- regulating transcription efficiency, viscoelasticity of the PNs, and KL- PN affinity. These effects in turn affect the nucleation of condensates from binodal to spinodal or viscoelastic phase separation26, 30, 31, resulting in tunable artificial nuclear architectures inside the PNs. The non- specific interactions between KL and PN matrix turned out to be crucial for retaining KL transcripts inside PNs via KL- PN co- condensation. We showed how such interactions can be efficiently modulated using DNA nanoscience approaches in such synthetic settings, ultimately leading to a repulsion and exclusion of the KL condensates from the PNs. + +<|ref|>text<|/ref|><|det|>[[112, 644, 883, 771]]<|/det|> +We further studied co- transcription and condensation of orthogonal KLS systems within the same PN, which resulted in distinct structures arising from competitive interactions between different RNA nanostars and the PN matrix. This highlights the potential of using our PN platform to study subtle interactions between RNA and DNA, as well as competitive interactions among RNAs in a DNA- enriched environment. Finally, at proper conditions, multiphase condensate structures can be built, which are further regulated by salinity through the cross- regulation of the viscoelastic environment, transcription efficiency, and competitive KL- PN interaction. + +<|ref|>text<|/ref|><|det|>[[112, 779, 883, 868]]<|/det|> +Looking into the future, our work opens new perspectives for constructing artificial nuclear architectures in synthetic model systems with DNA nanoscience tools. While we focused on a rather artificial and well controllable system of KL condensates, this work lays an important cornerstone to study more sophisticated phase separation processes, such as in case of polymerase II that forms rich condensate architectures with helper proteins, and those which are implicated in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 123, 883, 270]]<|/det|> +disease and ageing33, 34. In addition, the modulable KL- PN interactions within protonucleus could serve as simplified models for transcriptional condensates in living cells, which are dynamically forming and dissolving, and essential for transcription regulations11, 32. Moreover, from the perspectives of molecular systems engineering, synthetic biology, and artificial cell research, we have identified important pathways to transcriptionally regulate structure formation processes towards multiscale condensates that can be selectively addressed in their compartments. We anticipate that this system will serve as a valuable platform and toolkit for DNA nanoscience and synthetic biology. + +<|ref|>sub_title<|/ref|><|det|>[[113, 304, 192, 319]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[113, 323, 198, 337]]<|/det|> +## Materials + +<|ref|>text<|/ref|><|det|>[[112, 338, 883, 546]]<|/det|> +ssDNA were purchased from Biomers and Integrated DNA Technologies (IDT). Supplementary Table 1 and 2 summarize all sequences used in this study. T4 DNA Ligase (2 U/μL), Exonuclease I (40 U/μL), Exonuclease III (200 U/μL), and \(\Phi_{29}\) polymerase (10 U/μL) were purchased from Lucigen. Thermostable Inorganic Pyrophosphatase (2 U/μL), T7 polymerase (50000 U/mL) and nuclease- free water were bought from New England BioLabs (NEB). Deoxynucleotide triphosphate (dATP, dTTP, dGTP and dCTP) (100 mM), Aminoallyl- dUTP- XX- ATTO- 643 (1 mM), Aminoallyl- UTP- Atto488 (1 mM), and Aminoallyl- UTP- Atto630 (1 mM) were purchased from Jena Bioscience. Hexadecane, sodium chloride, magnesium chloride, Tris(hydroxymethyl)- aminomethane hydrochloride (Tris- HCl), Trizma base, acetic acid and Ethylenediaminetetraacetic acid disodium salt dihydrate (EDTA), were purchased (as bioreagent grade if available) from Sigma- Aldrich. RNase Inhibitor (40 U/μL), RNase- free TE buffer (Invitrogen, 10 mM Tris and 1 mM EDTA, pH 8.0, 500 mL), 384- well high- content imaging glass bottom microplates were purchased from Corning. + +<|ref|>sub_title<|/ref|><|det|>[[113, 567, 220, 580]]<|/det|> +## Instruments + +<|ref|>text<|/ref|><|det|>[[113, 581, 881, 645]]<|/det|> +All thermal annealing and heating ramps were performed on a TPersonal Thermocycler (Analytik Jena). Incubation with shaking was carried out on an Eppendorf ThermoMixer C with heated lid. DNA concentration was determined by a DS- 11 Spectrophotometer (DeNovix). Confocal laser scanning microscopy (CLSM) was performed on a Leica Stellaris 5. + +<|ref|>sub_title<|/ref|><|det|>[[113, 664, 660, 680]]<|/det|> +## Synthesis of circular ssDNA templates and long ssDNA polymers + +<|ref|>text<|/ref|><|det|>[[112, 680, 883, 870]]<|/det|> +The synthesis of the circular DNA template and its corresponding ssDNA polymer can be found in our previous reports23. In short, the linear ssDNA template and the corresponding ligation strand were firstly mixed at concentration of 1 μM in 100 μL TE buffer containing 100 mM NaCl. The solution was heated to 85 °C for 5 min before cooling to 25 °C with a cooling rate of 0.01 °C/s for hybridization. Afterwards, 20 μL of 10× Ligase buffer (500 mM Tris- HCl, 100 mM MgCl2, 50 mM dithiothreitol and 10 mM ATP (Lucigen)), 70 μL of nuclease- free water and 10 μL of T4 DNA Ligase (2 U/μL (Lucigen)) were introduced into the reaction mixture at room temperature for 3 h reaction. The solution was then heated to 70 °C for 20 min to deactivate the enzyme. Then, 10 μL of Exonuclease I (40 U/μL (Lucigen)) and 10 μL of Exonuclease III (200 U/μL (Lucigen)) were added into the reaction mixture for further overnight reaction at 37 °C to remove the ligation strands and any non- circularized templates in solution. Afterwards, the reaction mixture was heated to 80 °C for 40 min to deactivate the enzymes. To obtain the final circular ssDNA templates, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 123, 881, 189]]<|/det|> +the reaction mixture was washed with \(400~\mu \mathrm{L}\) TE buffer and filtrated using Amicon Ultra- centrifugal filters with a \(10\mathrm{kDa}\) cut- off (Merck Millipore) for three times. The concentrations of the collected circular ssDNA templates were measured by the DS- 11 Spectrophotometer (DeNovix), and the templates were stored in TE buffer at \(- 20^{\circ}\mathrm{C}\) . + +<|ref|>text<|/ref|><|det|>[[111, 192, 883, 448]]<|/det|> +For the synthesis of the long ssDNA polymers, we used rolling circle amplification (RCA). \(5\mu \mathrm{L}\) of circular template ( \(1\mu \mathrm{M}\) in TE buffer) and \(1\mu \mathrm{L}\) of exonuclease resistant primer ( \(10\mu \mathrm{M}\) in TE buffer) were mixed with \(76\mu \mathrm{L}\) nuclease- free water, \(10\mu \mathrm{L}\) of commercial \(10\times\) polymerase buffer ( \(500\mathrm{mM}\) Tris- HCl, \(100\mathrm{mM}\) ( \(\mathrm{NH_4)_2SO_4}\) , \(40\mathrm{mM}\) dithiothreitol, \(100\mathrm{mM}\) MgCl2 (Lucigen)), \(2\mu \mathrm{L}\) of \(\Phi_{29}\) DNA polymerase ( \(10\mathrm{U / \mu L}\) (Lucigen)), \(1\mu \mathrm{L}\) of thermal stable inorganic pyrophosphatase ( \(2\mathrm{U / \mu L}\) (NEB)) and \(5\mu \mathrm{L}\) of adjusted deoxyribose nucleoside \(5^{\prime}\) - triphosphate mix ( \(100\mathrm{mM}\) , the mix contains pure dATP, dTTP, dCTP, and dGTP solutions mixed in corresponding proportions of the exact composition of the desired ssDNA polymer repeating units (Jena Bioscience)). Note that for the synthesis of ssDNA polymers with in- chain fluorophores of Atto643, we replaced \(2\mathrm{mol}\%\) of the dTTP in the mix with Aminoallyl- dUTP- XX- ATTO- 643 for random insertion of the dye into the ssDNA chains during RCA. The reaction mixture was kept at \(30^{\circ}\mathrm{C}\) for \(50\mathrm{h}\) before thermal cleavage at \(95^{\circ}\mathrm{C}\) for \(15\mathrm{min}\) to shorten the ultrahigh molecular weight of the synthesized DNA polymer \(^{23}\) . The final products were purified by rinsing with \(400\mu \mathrm{L}\) TE buffer and filtration in Amicon Ultra- centrifugal filters with \(30\mathrm{kDa}\) cut- off (Merck Millipore) three times. The concentrations of the collected final ssDNA polymers were measured using the DS- 11 Spectrophotometer (DeNovix), and the DNA polymers were stored in TE buffer at \(- 20^{\circ}\mathrm{C}\) . + +<|ref|>sub_title<|/ref|><|det|>[[113, 466, 680, 483]]<|/det|> +## Preparation of all-DNA PNs embedded with T7 promoter sequence. + +<|ref|>text<|/ref|><|det|>[[111, 483, 883, 705]]<|/det|> +The preparation of the PNs is adapted from our previous reports \(^{23}\) with modifications for the formation of PNs containing T7 promoter sequence. Adenine- rich DNA polymers (poly(A \(_{20}\) - p) \(_n\) + poly(A \(_{20}\) - o) \(_n\) in a ratio of 1:9) (0.5556 g/L) and poly(T \(_{20}\) - k) \(_n\) (0.0694 g/L) were mixed in TE buffer without any salt at a final volume of \(9\mu \mathrm{L}\) . The solution mixture was heated at \(95^{\circ}\mathrm{C}\) for \(15\mathrm{min}\) for thermal cleavage to further reduce the chain length of the ssDNA polymers. Afterwards, \(1\mu \mathrm{L}\) of TE buffer containing \(500\mathrm{mM}\) MgCl \(_2\) was introduced into the reaction mixture. The solution containing finally \(0.5\mathrm{g / L}\) mixture of polyA and \(0.0625\mathrm{g / L}\) poly(T \(_{20}\) - k) \(_n\) with \(50\mathrm{mM}\) MgCl \(_2\) was heated to \(95^{\circ}\mathrm{C}\) for \(20\mathrm{min}\) ( \(3^{\circ}\mathrm{C / s}\) ) and cooled down to room temperature ( \(3^{\circ}\mathrm{C / s}\) ), yielding core- shell PNs. Finally, the \(10\mu \mathrm{L}\) solution containing the PNs was diluted 5 times by adding \(40\mu \mathrm{L}\) TE buffer containing various amounts of MgCl \(_2\) to reach desired salinity. The obtained \(50\mu \mathrm{L}\) DNA condensates solution (as \(5\times\) diluted) has \(0.1\mathrm{g / L}\) polyA mixture and \(0.0125\mathrm{g / L}\) poly(T \(_{20}\) - k) \(_n\) , corresponding to ca. \(0.8\mu \mathrm{M}\) p barcode, ca. \(7.2\mu \mathrm{M}\) o barcode and ca. \(1\mu \mathrm{M}\) k barcode, respectively, in total solution. The solution was then stored in a fridge at \(4^{\circ}\mathrm{C}\) for 1 week for equilibration before usage. + +<|ref|>sub_title<|/ref|><|det|>[[113, 729, 502, 745]]<|/det|> +## Spatially controlled transcription assay in PN. + +<|ref|>text<|/ref|><|det|>[[113, 745, 883, 873]]<|/det|> +For transcription in PNs monitored by plate reader, \(3.125\mu \mathrm{L}\) of \(5\times\) diluted PNs ( \(90\%\) o barcode + \(10\%\) p barcode) is further diluted into \(25\mu \mathrm{L}\) solution containing \(1\times\) RNA polymerase buffer (40 mM Tris- HCl, \(6\mathrm{mM}\) MgCl \(_2\) , \(1\mathrm{mM}\) DTT, \(2\mathrm{mM}\) spermidine), \(100\mathrm{mM}\) template ( \(\mathrm{T_{Rep}^*}\) ), \(1\mu \mathrm{M}\) prehybridized fluorophore- quencher reporter (Rep/Rep' dsDNA), \(2.5\mathrm{U / \mu L}\) T7 RNAP, \(0.02\mathrm{U / \mu L}\) Thermostable Inorganic Pyrophosphatase, \(1\mathrm{U / \mu L}\) RNase Inhibitor. \(\mathrm{MgCl_2}\) concentration was adjusted in different settings as noted in each figure caption. At the end, \(2\mu \mathrm{L}\) of NTP mix (to reach \(2\mathrm{mM}\) of ATP, GTP, CTP, and UTP each) was added into the solution to trigger the transcription reaction at different temperatures ranging from \(25 - 30^{\circ}\mathrm{C}\) . The final promoter sequence + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 123, 883, 210]]<|/det|> +concentration in the solution is at \(100~\mathrm{nM}\) . As for control, transcription with pure promoter ssDNA (p) and poly(A₂₀-p)ₙ ssDNA polymer was also performed to compare the transcription efficiency. For kinetic experiments under CLSM, \(\mathrm{T_x^*}\) is loaded into the PNs at a final concentration of 100 nM. Reporter is not used, instead, we further added \(0.0833~\mathrm{mM}\) Aminoallyl- UTP- Atto488 so that the transcribed RNA can be fluorescently labeled and observed under CLSM. + +<|ref|>sub_title<|/ref|><|det|>[[115, 231, 488, 247]]<|/det|> +## Transcriptional KLs condensates formation. + +<|ref|>text<|/ref|><|det|>[[112, 247, 883, 502]]<|/det|> +2.5 \(\mu \mathrm{L}\) of \(5\times\) diluted PNs ( \(90\%\) o barcode \(^{+10\%}\) p barcode) is further diluted into \(20~\mu \mathrm{L}\) solution containing \(1\times\) RNA polymerase buffer ( \(40~\mathrm{mM}\) Tris- HCl, \(6\mathrm{mM}\) MgCl₂, \(1\mathrm{mM}\) DTT, \(2\mathrm{mM}\) spermidine), \(100\mathrm{nM}\) dsDNA template \((\mathrm{T}_{\mathrm{KL1}} / \mathrm{T}_{\mathrm{KL1}}^{*}\) , \(\mathrm{T}_{\mathrm{KL1 - BrA}} / \mathrm{T}_{\mathrm{KL1 - BrA}}^{*}\) , \(\mathrm{T}_{\mathrm{KL1 - RI}} / \mathrm{T}_{\mathrm{KL1 - RI}}^{*}\) , or \(\mathrm{T}_{\mathrm{KL2 - R2}} / \mathrm{T}_{\mathrm{KL2 - R2}}^{*}\) ; or \(50\mathrm{nM}\mathrm{T}_{\mathrm{KL1 - RI}} / \mathrm{T}_{\mathrm{KL1 - RI}}^{*} + 50\mathrm{nM}\mathrm{T}_{\mathrm{KL2 - R2}} / \mathrm{T}_{\mathrm{KL2 - R2}}^{*}\) ), \(2.5\mathrm{U} / \mu \mathrm{L}\) T7 RNAP, 0.02 \(\mathrm{U} / \mu \mathrm{L}\) Thermostable Inorganic Pyrophosphatase, \(1\mathrm{U} / \mu \mathrm{L}\) RNase Inhibitor, and \(0.048\mathrm{mM}\) NTP mix ( \(0.048\mathrm{mM}\) of ATP, GTP, CTP, and UTP each at \([\mathrm{NTP}]:[\mathrm{T}_{\mathrm{KL1}}] = 3.6:1\) , for maximum amount of KL1 produced, which is \(3.6\) - fold of \(\mathrm{T}_{\mathrm{KL1}}\) , adjusted in different settings as noted in figure captions). \(\mathrm{MgCl}_2\) concentration is adjusted in different settings as noted in each figure caption. The mixture is incubated with shaking at \(30^{\circ}\mathrm{C}\) for \(18\mathrm{h}\) reaction. The final promoter sequence concentration in the solution is at \(100~\mathrm{nM}\) . As control, transcription of KLs with pure promoter oligo was also performed with corresponding NTP concentration. For transcription of KLs with covalent label, \(1\mathrm{mol}\%\) of UTP is replaced by either Aminoallyl- UTP- Atto488, or Aminoallyl- UTP- Atto630 in transcription system. For KL1- BrA transcription, \(0.05\mathrm{mM}\) DFHBI is added to the solution. For KL1- R1 or KL2- R2 transcription, \(360\mathrm{nM}\mathrm{R}1^{*}\) - Atto647 or \(\mathrm{R}2^{*}\) - Atto488 sequence is added to the system, respectively. For transcriptional KL1 condensate formed in solution as a control, \(100\mathrm{nM}\) promoter ssDNA is added to system, instead of PNs. + +<|ref|>sub_title<|/ref|><|det|>[[113, 519, 663, 536]]<|/det|> +## Fluorescence recovery after photobleaching (FRAP) experiments. + +<|ref|>text<|/ref|><|det|>[[113, 536, 883, 634]]<|/det|> +FRAP experiments were performed by applying 3 times bleaching in a small circular region of interest (ROI) with diameter of \(2\mu \mathrm{m}\) by \(100\%\) laser intensity. Post- bleaching images were recorded over different periods. The intensities within the circular ROI \((I_{\mathrm{ROI}})\) , and intensities in a circular region of the same size away from bleached region within the condensates \((I_{\mathrm{ref}})\) , in pre- and post- bleaching images were measured in ImageJ for performing double normalization in bleached regions by: + +<|ref|>equation<|/ref|><|det|>[[390, 648, 877, 680]]<|/det|> +\[I_{\mathrm{Norm}}(t) = \frac{I_{\mathrm{ROI}}(t)}{I_{\mathrm{ROI}}(t_0)}\times \frac{I_{\mathrm{ref}}(t_0)}{I_{\mathrm{ref}}(t)} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[113, 694, 883, 728]]<|/det|> +to quantify the recovery kinetics over time. Note that \(I(t_0)\) represents the intensity measured in the first image before bleaching. + +<|ref|>sub_title<|/ref|><|det|>[[115, 745, 256, 760]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[113, 761, 883, 794]]<|/det|> +Additional supporting data are available from the corresponding author upon request. Source data are provided with this paper. + +<|ref|>sub_title<|/ref|><|det|>[[115, 810, 270, 825]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[115, 826, 883, 874]]<|/det|> +We would like to thank Dr. Siyu Song and Tao Xu for their helpful discussion about data analysis and plotting. M.X. acknowledges the support of the Alexander von Humboldt Foundation. W.C. acknowledges support from the Max Planck Graduate Center with the Johannes Gutenberg + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 123, 884, 221]]<|/det|> +University of Mainz (MPGC), and the RTG 2516 "Structure Formation of Soft Matter at Interfaces". This research was funded by the German Research Foundation (DFG) in the framework of the CRC 1552; Project No. 465145163. A.W. acknowledges funding from the Gutenberg Research Council Mainz underpinning his Life- Like Materials Program, the German Research Foundation grant WA 3084/19- 1, the Max Planck Fellowship, and from the EU in the framework of the ERC Consolidator Grant to AW – M3ALI (101001638). + +<|ref|>sub_title<|/ref|><|det|>[[115, 237, 296, 252]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[115, 253, 883, 318]]<|/det|> +M.X. and A.W. conceived the project. M.X. and W.C. designed and performed all the experiments. M.R. helped with the initial transcription experiments. M.X. prepared the draft manuscript. M.X., W.C., and A.W. reviewed and edited the manuscript. A.W. supervised the project. M.X. and W.C. contributed equally. + +<|ref|>sub_title<|/ref|><|det|>[[115, 333, 279, 348]]<|/det|> +## Competing interest + +<|ref|>text<|/ref|><|det|>[[115, 349, 460, 365]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[115, 380, 312, 395]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[115, 397, 541, 414]]<|/det|> +Supplementary Information is available for this paper. + +<|ref|>text<|/ref|><|det|>[[115, 418, 772, 435]]<|/det|> +Correspondence and requests for materials should be addressed to Andreas Walther. + +<|ref|>sub_title<|/ref|><|det|>[[115, 466, 201, 480]]<|/det|> +## Reference + +<|ref|>text<|/ref|><|det|>[[112, 483, 884, 860]]<|/det|> +1. Lamond, A.I. & Earnshaw, W.C. Structure and function in the nucleus. Science 280, 547-553 (1998). +2. Sabari, B.R., Dall'Agnese, A. & Young, R.A. Biomolecular Condensates in the Nucleus. Trends Biochem. Sci. 45, 961-977 (2020). +3. Sawyer, I.A., Sturgill, D. & Dundr, M. 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Transcription regulation by biomolecular condensates. Nat. Rev. Mol. Cell Bio. (2024).649 33. Changiarath, A. et al. Promoter and Gene-Body RNA-Polymerase II co-exist in partial demixed condensates. Preprint at 10.1101/2024.03.16.585180 (2024).651 34. Delaney, C.E. et al. SETDB1-like MET-2 promotes transcriptional silencing and development independently of its H3K9me-associated catalytic activity. Nat. Struct. Mol. Biol. 29, 85-96 (2022).652653 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 130, 353, 230]]<|/det|> +SupplementaryInformation.pdf MovieS1.mp4 MovieS2.mp4 MovieS3.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/images_list.json b/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..9c46b1afe976c3c59cb75cad5116a46f8c9e8650 --- /dev/null +++ b/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Available Gibbs Free Energy Posterior Distributions Inferred from Simulated Reflected Light Observations for Different Proterozoic Earth Models. a, The marginal posterior distribution of the log of the available Gibbs free energy for the high abundance case, derived from 20 (hatched), 30 (un-filled) and 50 (solid fill) SNR simulated reflected light observations. Vertical dashed orange, red, and blue lines in all three panels represent the previously reported values for the available Gibbs free energy of modern Earth (atmosphere only case), Mars, and modern Earth (ME) (atmosphere-ocean case) respectively (Krissansen-Totton et al., 2016). b, Same as a but for the medium abundance case. c, Same as a but for the low abundance case.", + "footnote": [], + "bbox": [ + [ + 92, + 200, + 877, + 437 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Posterior Distributions for Key Retrieved Atmospheric Parameters for Different Noise Levels and Proterozoic Earth Models. a, The marginal posterior probability distributions for the retrieved log abundance of \\(\\mathrm{O_2}\\) at the high (green), medium (purple), and low (blue) abundance cases. Each distribution is inferred from simulated reflected light observations at SNRs of 20 (hatched), 30 (unfilled), and 50 (solid fill). A vertical black dashed line represents the input value for each parameter (the input is calculated via column integrated mass mixing ratio profiles for each gas phase species). b, Same as a except now showcasing methane constraints. c, Same as a except now showcasing temperature constraints.", + "footnote": [], + "bbox": [ + [ + 151, + 108, + 820, + 630 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Simulated Reflected Light Spectra for Proterozoic Earth Cases. a, Simulated reflected light spectrum for the high (green) abundance case. Absorption features for \\(\\mathrm{O_3}\\) (brown), \\(\\mathrm{CO_2}\\) (blue), \\(\\mathrm{CH_4}\\) (yellow), and \\(\\mathrm{O_2}\\) (red) are shown and their input abundances are multiplied by a factor of two \\((\\times 2)\\) . Water vapor absorption features are labeled with text as well. The grey error bar legend shows scaling with each noise instance (20, 30, and 50 SNR). b, The simulated reflected light spectrum for the medium (purple) abundance case. Each input abundance is multiplied by a factor of two \\((\\times 2)\\) to show its effect. c, The simulated reflected light spectrum for the low (blue) abundance case. The denoted species absorption features are shown and their input abundances are multiplied by a factor of two \\((\\times 2)\\) .", + "footnote": [], + "bbox": [ + [ + 256, + 156, + 723, + 672 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Figure S1: Thermodynamic Calculation for High Proterozoic Earth Case. Blue bars represent the observed (or input) abundance for each species labeled across the bottom axis. Red bars indicate the equilibrium abundance (calculated from the Gibbs free energy minimization). Yellow bars indicate the absolute value of the difference between the observed and equilibrium abundance for each species. Note that the molecules with a substantially large abundance and large difference (i.e., the \\(\\mathrm{O}_{2}\\) and \\(\\mathrm{CH}_{4}\\) ) are major contributors to the atmospheric chemical disequilibrium signal.", + "footnote": [], + "bbox": [ + [ + 160, + 268, + 825, + 658 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "Figure S2: Reflected Light Retrieval Parameters Pertinent to Computing The Available Gibbs Free Energy (Non-essential). a, Marginal posterior distributions for the high (green), medium (purple), and low (blue) abundance cases for the log abundance of \\(\\mathrm{H}_2\\mathrm{O}\\) . Included are distributions for observations simulated at 20 (hatched), 30 (un-filled), and 50 (solid fill) SNR. b Marginal Posterior distribution for the log mixing ratio of \\(\\mathrm{CO}_2\\) . c Marginal posterior distributions for the log mixing ratio of \\(\\mathrm{O}_3\\) . d, Marginal posterior distributions for the global surface pressure. Note that each of these parameters are included in the overall Gibbs free energy calculation, but their uncertainties do not substantially impact the overall available Gibbs free energy.", + "footnote": [], + "bbox": [ + [ + 152, + 90, + 840, + 610 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_2.jpg", + "caption": "Figure S3: \\(\\mathbf{O}_2\\) Posterior Distributions Derived From Simulated JWST/NIRSpec Observations. a, Marginal Posterior distribution of \\(\\mathrm{O_2}\\) for the high Proterozoic Earth abundance case. The vertical black dashed line denotes the input value for the \\(\\mathrm{O_2}\\) abundance. Each distribution is inferred from simulated transit observations at noise levels of 5 ppm (un-filled) and 10 ppm (hatched). b, Same as a but for the medium abundance case. c, Same as a but for the low abundance case. Note the lack of constraints for all abundance cases at both noise levels, which indicates the chemical disequilibrium cannot be adequately constrained at these abundances via JWST/NIRSpec transit observations.", + "footnote": [], + "bbox": [ + [ + 152, + 218, + 825, + 476 + ] + ], + "page_idx": 17 + } +] \ No newline at end of file diff --git a/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc.mmd b/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc.mmd new file mode 100644 index 0000000000000000000000000000000000000000..3946d916cba8d9085ddf36bd273cb5084d1a8237 --- /dev/null +++ b/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc.mmd @@ -0,0 +1,271 @@ + +# Constraining Chemical Disequilibrium Biosignatures for Proterozoic Earth-like Exoplanets Using Reflectance Spectra + +Amber Young ( ☑ amberastro12@gmail.com ) Northern Arizona University https://orcid.org/0000- 0003- 3099- 1506 + +Tyler Robinson University of Arizona + +Joshua Krissansen- Totton University of Washington + +Edward W. Schwieterman https://orcid.org/0000- 0002- 2949- 2163 + +Nicholas Wogan University of Washington + +Michael Way NASA Goddard Institute for Space Studies + +Linda Sohl Columbia University https://orcid.org/0000- 0002- 6673- 2007 + +Giada Amey Goddard Space Flight Center + +Christopher Reinhard Georgia Institute of Technology https://orcid.org/0000- 0002- 2632- 1027 + +Michael Line Arizona State University https://orcid.org/0000- 0001- 6247- 8323 + +David Catling University of Washington https://orcid.org/0000- 0001- 5646- 120X + +James Windsor Northern Arizona University + +## Article + +Keywords: + +Posted Date: January 4th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2335028/v1 + +<--- Page Split ---> + +License: © ① This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Astronomy on January 22nd, 2024. See the published version at https://doi.org/10.1038/s41550-023-02145- z. + +<--- Page Split ---> + +# Constraining Chemical Disequilibrium Biosignatures for Proterozoic Earth-Like Exoplanets Using Reflectance Spectra + +3 Amber V. Young, \(^{1*}\) Tyler D. Robinson, \(^{2}\) Joshua Krissansen- Toton, \(^{3}\) Edward W. Schwieterman, \(^{4}\) Nicholas F. Wogan, \(^{5}\) Michael J. Way, \(^{6,10}\) Linda E. Sohl, \(^{11,6}\) Giada N. Arney, \(^{7}\) Christopher T. Reinhard, \(^{8}\) Michael R. Line, \(^{9}\) David C. Catling, \(^{3,5}\) James D. Windsor \(^{1}\) \(^{1}\) Department of Astronomy and Planetary Sciences, Northern Arizona University, Physical Sciences Building, 527 S Beaver St, Flagstaff, AZ 86011 \(^{2}\) Lunar and Planetary Laboratory, University of Arizona, Tucson AZ 85721 \(^{3}\) Earth and Space Sciences, University of Washington, Seattle WA 98195 \(^{4}\) Department of Earth and Planetary Sciences, University of California Riverside, Riverside CA 92521 \(^{5}\) Department of Astrobiology, University of Washington, Seattle WA 98195 \(^{6}\) NASA Goddard Institute for Space Studies, New York NY 10025 \(^{7}\) NASA Goddard Space Flight Center, Greenbelt MD 20771 \(^{8}\) Earth and Atmospheric Sciences, Georgia Tech, Atlanta GA 30332 \(^{9}\) School of Earth and Space Exploration, Arizona State University, Tempe AZ 85281 \(^{10}\) Department of Physics and Astronomy, Uppsala University, Uppsala, SE- 75120, Sweden \(^{11}\) Center for Climate Systems Research, Columbia University, New York, New York, 10025 \(^{*}\) To whom correspondence should be addressed: Amber.Young86@nau.edu. + +November 21, 2022 + +Chemical disequilibrium quantified via available free energy has previously been proposed as a potential planetary biosignature. However, little work has been done that links anticipated observational uncertainties to our ability to infer the available Gibbs free energy from remote observations. Planetary properties indicative of the atmospheric state (and pertinent to constraining the available Gibbs free energy) can be inferred via spectroscopic analyses and thus the available Gibbs free energy could be a potentially useful biosignature for exoplanets. Simulated reflected light atmospheric retrievals (Robinson and Salvador, 2022) were coupled with thermodynamics modeling (Krissansen- Toton et al., 2016; Krissansen- Toton et al., 2018b) to assess the predicted chemical disequilibrium signatures of Earth- like exoplanets. The Proterozoic Earth is a long period (2 Gyr) in Earth's history where the atmospheric abundance of the biogenic oxygen ( \(\mathrm{O_2}\) )- methane ( \(\mathrm{CH_4}\) ) disequilibrium pair may have been relatively high (Krissansen- Toton et al., 2018b). Retrieval models applied across a range of "High", "Medium", and "Low" biosignature gas abundance scenarios for methane and oxygen show that spectral observations spanning the ultraviolet through near- infrared wavelengths at characteristic visual band signal- to- noise ratio (SNR) of 20–30 provide either very weak or upper limit constraints on the available Gibbs free energy for all abundance scenarios while spectra at SNR of 50 or larger could provide order- of- magnitude constraints on the disequilibrium biosignatures for the high- abundance scenario. Constraints on the atmospheric available Gibbs free energy are heavily driven by the posterior distributions inferred for \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) from the simulated spectral observations. Furthermore, the disequilibrium energy constraints are improved by modest atmospheric temperature constraints encoded in molecular opacities at optical and near- infrared wavelengths. These results have important implications for continuing to develop biosignature search strategies in preparation for future direct imaging exoplanet characterization missions. + +<--- Page Split ---> + +## 29 Introduction + +Exoplanet exploration science is making rapid progress toward the detection and characterization of potentially habitable worlds (Gardner et al., 2006). Ongoing (Greene et al., 2016; The JWST Transiting Exoplanet Community Early Release Science Team et al., 2022) and near- future exoplanet strategies (of Sciences Engineering and Medicine, 2019) will emphasize the search for atmospheric gases, including the chemical signatures of life (or biosignatures) (Schwieterman et al., 2018; Meadows et al., 2018; Madhusudhan, 2019). Recently, the Decadal Survey on Astronomy and Astrophysics 2020 report (Gaudi et al., 2015) recommended space- based high contrast imaging of potentially life- bearing exoplanets as a leading priority for the coming decades. When attempting to infer if a distant world is inhabited, chemical disequilibrium is a potential indicator of life that has a long history of study in solar system planetary environments (Lovelock, 1965; Hitchcock and Lovelock, 1967; Lovelock, 1975). A key example is the coexistence of \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) in Earth's atmosphere where the strong biological production rates of \(\mathrm{CH_4}\) are able to maintain this gas at appreciable levels despite its relatively short chemical lifetime in an oxidizing atmosphere. + +A primary metric for quantifying chemical disequilibrium involves calculating the difference in chemical energy associated with an observed system and that system's theoretical equilibrium state. Recent work has explored the application of one such metric—the available Gibbs free energy—to solar system worlds and to Earth's planetary evolution (Krissansen- Totton et al., 2016; Krissansen- Totton et al., 2018b; Wogan and Catling, 2020). Although the available Gibbs free energy is a promising metric for interpreting chemical disequilibrium biosignatures, little is known about how observational uncertainties will impact our ability to constrain the available Gibbs free energy for Earth- like exoplanets. + +Exoplanet atmospheric characterization, including the search for biosignature gases, proceeds through retrieval analysis or atmospheric inference (e.g., Madhusudhan and Seager (2009); Benneke and Seager (2012); Line et al. (2013); Feng et al. (2018); Barstow et al. (2020)). In short, a retrieval framework enables the statistical exploration of atmospheric states that are consistent with a given set of spectral observations, be these real or simulated. While retrieval models do not directly constrain quantities like the available Gibbs free energy, pairing an exoplanet atmospheric retrieval model with a thermochemical tool—as detailed in Methods—enables inferences of both atmospheric chemical abundances and the associated disequilibrium state of the atmosphere. As described in Methods, simulated observations of an Earth- like planet are created with uncertainties specified by the V- band SNR where the V- band is centered at .551 \(\mu \mathrm{m}\) , with .088 \(\mu \mathrm{m}\) full width at half maximum. Applying inverse modeling techniques to these simulated observations (for multiple randomized observational noise realizations) then maps observational quality to expected constraints on the available Gibbs free energy. + +Analyses presented here emphasize directly- imaged Proterozoic Earth analogs with reflectance spectral data spanning ultraviolet through near- infrared wavelengths (motivated by Decadal Survey mission concept reports (Gaudi et al., 2018a; Roberge and Moustakas, 2018a)). This eon in Earth's history is notable for its oxygenated atmosphere that also may have had enhanced atmospheric methane concentrations (as compared to modern Earth), thereby presenting an ideal time period for detecting \(\mathrm{O_2 - CH_4}\) disequilibrium. Retrieval studies below explore a range of concentrations for key gases in Proterozoic Earth's atmosphere and were adopted from a span of Earth evolutionary scenarios summarized in a review (Robinson and Reinhard, 2020). High and low concentration scenarios here are identical to mid- Proterozoic extremes from this review, and an intermediate concentration case was generated by computing the logarithmic geometric mean of the high and low cases. + +## 71 Results + +Fig. 1 shows modeled constraints on the atmospheric available Gibbs free energy (in Joules per mole of atmosphere) that would be expected from observations of Proterozoic Earth analogs in reflected light. Each result is broken up into three atmospheric composition categories of "high", "medium", and "low" biosignature gas abundances and simulated observations were conducted at several SNRs for each abundance category. Most of these reflected light cases present available Gibbs free energy posteriors that are consistent with an upper- limit constraint. In our simulations, the log available Gibbs free energy is found to be no larger than \(1.30 / 1.13 / 1.21 \mathrm{J mol^{- 1}}\) at \(95\%\) confidence for SNRs of \(20 / 30 / 50\) for the medium abundance case and \(0.47 / 0.68 / 0.03 \mathrm{J mol^{- 1}}\) for the low abundance case. Additionally, the uncertainty on the available Gibbs free + +<--- Page Split ---> + +energy goes down to as low as an order of magnitude for the SNR 50 observational case. In the high abundance scenario (Fig. 1a), the posteriors derived from the simulated SNR 20 and 30 observations exhibit a dual peak in the distribution. Given the randomization of the simulated observational data points, retrievals at these SNRs could occasionally constrain the \(\mathrm{O_2}\) abundance. Thus, one peak corresponds to cases where \(\mathrm{O_2}\) was well- detected and the other peak corresponds to non- detections. + +![](images/Figure_1.jpg) + +
Figure 1: Available Gibbs Free Energy Posterior Distributions Inferred from Simulated Reflected Light Observations for Different Proterozoic Earth Models. a, The marginal posterior distribution of the log of the available Gibbs free energy for the high abundance case, derived from 20 (hatched), 30 (un-filled) and 50 (solid fill) SNR simulated reflected light observations. Vertical dashed orange, red, and blue lines in all three panels represent the previously reported values for the available Gibbs free energy of modern Earth (atmosphere only case), Mars, and modern Earth (ME) (atmosphere-ocean case) respectively (Krissansen-Totton et al., 2016). b, Same as a but for the medium abundance case. c, Same as a but for the low abundance case.
+ +The constraints on the available Gibbs free energy are most- strongly dependent on the quality of the inferences for \(\mathrm{O_2}\) , \(\mathrm{CH_4}\) , and temperature, which are shown in Fig. 2. This is consistent with thermodynamic theory, which has shown that the Gibbs free energy is strongly dependent on temperature and only weakly dependent on pressure (Engel and Reid, 2019). In the high abundance case at SNR 20, results show a large uncertainty on the inferred \(\mathrm{O_2}\) abundance which ranged from \(10^{- 10}\) to 0.1. This introduced a significant uncertainty on the available Gibbs free energy for this particular case. However, higher observational SNRs of 30 and 50 at high abundance showed better constraints on \(\mathrm{O_2}\) which led to improved constraints on the available Gibbs free energy. These trends also held true for the \(\mathrm{CH_4}\) posteriors in the high abundance case. The retrieval analyses for the medium and low cases largely resulted in upper- limit constraints for \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) at each of the observational signal- to- noise scenarios tested here. Reasonable temperature constraints were seen for all abundance cases and observing scenarios, which stemmed from adequate constraints on the shape of atmospheric water vapor bands across the spectral range for all modeled scenarios. Table 1 details the \(16^{\mathrm{th}}\) , \(50^{\mathrm{th}}\) , and \(84^{\mathrm{th}}\) - percentile values for the marginal \(\mathrm{O_2}\) , \(\mathrm{CH_4}\) , and temperature distributions (corresponding to the 1- sigma values for a Gaussian distribution). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Posterior Distributions for Key Retrieved Atmospheric Parameters for Different Noise Levels and Proterozoic Earth Models. a, The marginal posterior probability distributions for the retrieved log abundance of \(\mathrm{O_2}\) at the high (green), medium (purple), and low (blue) abundance cases. Each distribution is inferred from simulated reflected light observations at SNRs of 20 (hatched), 30 (unfilled), and 50 (solid fill). A vertical black dashed line represents the input value for each parameter (the input is calculated via column integrated mass mixing ratio profiles for each gas phase species). b, Same as a except now showcasing methane constraints. c, Same as a except now showcasing temperature constraints.
+ +Most fundamentally, results demonstrated that spectra with strongly- detected \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) absorption features lead to tight constraints on the resulting available Gibbs free energy, thereby enabling an inference of the extent of chemical disequilibrium in the atmosphere of an Earth- like exoplanet analog. Fig. 3 highlights spectral features of several species including \(\mathrm{O_2}\) , \(\mathrm{CH_4}\) , \(\mathrm{CO_2}\) , and \(\mathrm{H_2O}\) across the range of modeled Proterozoic Earth scenarios. In the near- infrared/optical/ultra- violet spectral range explored in this work, the strongest \(\mathrm{O_2}\) feature is the oxygen A- band at \(0.762 \mu \mathrm{m}\) . There are several \(\mathrm{CH_4}\) features within the 1.0 - \(1.8 \mu \mathrm{m}\) wavelength range, indicated in orange. Each color coded absorption feature for \(\mathrm{O_3}\) , \(\mathrm{CO_2}\) , \(\mathrm{CH_4}\) , and \(\mathrm{O_2}\) is accentuated by a factor of two relative to the original input abundance in order to highlight the precision needed to observe each species. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: Simulated Reflected Light Spectra for Proterozoic Earth Cases. a, Simulated reflected light spectrum for the high (green) abundance case. Absorption features for \(\mathrm{O_3}\) (brown), \(\mathrm{CO_2}\) (blue), \(\mathrm{CH_4}\) (yellow), and \(\mathrm{O_2}\) (red) are shown and their input abundances are multiplied by a factor of two \((\times 2)\) . Water vapor absorption features are labeled with text as well. The grey error bar legend shows scaling with each noise instance (20, 30, and 50 SNR). b, The simulated reflected light spectrum for the medium (purple) abundance case. Each input abundance is multiplied by a factor of two \((\times 2)\) to show its effect. c, The simulated reflected light spectrum for the low (blue) abundance case. The denoted species absorption features are shown and their input abundances are multiplied by a factor of two \((\times 2)\) .
+ +<--- Page Split ---> + +## Discussion and Conclusion + +The Proterozoic Eon is a potentially ideal Earth- like context for constraining the atmospheric \(\mathrm{O_2}\) - CH4 chemical disequilibrium gas pair for an Earth- like planet around a G- type star due to a likely higher abundance of \(\mathrm{CH_4}\) , a rise in \(\mathrm{O_2}\) relative to the Archean Earth, and the longevity of this signal over a 2 Gyr time period. An Archean Earth- like atmosphere may have a modest atmospheric chemical disequilibrium signature driven by the \(\mathrm{CH_4}\) - \(\mathrm{CO_2}\) gas pair. However, abiotic sources for \(\mathrm{CH_4}\) production would need to be explored (Krissansen- Totton et al., 2018b). modern Earth has substantially less atmospheric \(\mathrm{CH_4}\) , making detection of the \(\mathrm{O_2}\) - \(\mathrm{CH_4}\) atmospheric signature challenging, although the photochemistry for a modern Earth- like planet around an M- or K- dwarf may generate substantially more \(\mathrm{CH_4}\) in atmospheres with modern levels of \(\mathrm{O_2}\) (e.g., Segura et al. (2005); Arney (2019)). For any exoplanet, the stellar photochemical context will be vital to consider when evaluating potential biosignatures. Nevertheless, our results offer a window into the characterization of an Earth- sun twin as an analog for similar exoplanets. + +The thermodynamic systems that were modeled here are only characteristic of the chemical disequilibrium present in the atmosphere of these systems. Oceans can provide an additional source for chemical disequilibrium to arise and, in fact, the maintenance of \(\mathrm{N_2}\) and \(\mathrm{O_2}\) in the presence of liquid water (for Proterozoic and modern Earth) and the maintenance of \(\mathrm{CO_2}\) , \(\mathrm{N_2}\) , and \(\mathrm{CH_4}\) in the presence of liquid water (for Archean Earth) are major contributors to disequilibrium energy over time (Krissansen- Totton et al., 2018b). However, it is unlikely exoplanet data will be sensitive enough to characterize the oceanic state and its dissolved species. Further characterization would also require obtaining constraints on the planet's ocean volume in order to infer the chemical disequilibrium of an atmosphere- ocean system. Thus, atmospheric disequilibrium constraints are likely to be conservative, at least for ocean- bearing worlds. In general, the inability to easily constrain ocean volume (and/or its dissolved species) or the inability of retrieval to fully detect all species in the atmosphere makes the available Gibbs free energy constraints likely to be conservative. + +Constraining the available Gibbs free energy is a promising characterization strategy that synergizes well with established techniques for biosignature gas detection. In practice, it is possible to determine an upper limit on the available Gibbs free energy and, for more optimistic cases, proper constraints can be obtained on the free energy for Proterozoic Earth- like planets. Particularly for high abundance cases, it is possible to place constraints on the available Gibbs free energy to within an order of magnitude with SNR 50 observations. This could be feasible with a future exoplanet direct imaging mission, but may require significant integration time to reach this level of signal- to- noise and such an observation may be limited to the most promising of targets. It is also worth noting that the detection of the \(\mathrm{O_2}\) - \(\mathrm{CH_4}\) disequilibrium is, in part, sensitive to temperature constraints and highly sensitive to the near- infrared spectral features that drive the quality of the \(\mathrm{CH_4}\) abundance constraints. Such spectral features may not be observable for all targets as the inner working angle for high contrast imaging systems, especially coronagraphs, expands with wavelength. Nevertheless, performing a baseline analysis at lower SNRs may help us identify potentially exciting targets for more detailed follow- up observations and provide upper limit constraints on potential chemical disequilibrium signals for a subset of targets. + +The successful launch of the James Webb Space Telescope (JWST) and the prospects for characterizing Earth- like planets in the habitable zone of M dwarf stars motivated attempts to constrain the available Gibbs free energy of a Proterozoic Earth- like planet orbiting the M dwarf TRAPPIST- 1. For all three atmospheric cases, and simulated observations with the NIRSpec instrument, results (Fig. S3, which highlight the \(\mathrm{O_2}\) posteriors) indicate that it is extremely challenging to constrain the available Gibbs free energy given the weaker spectral features of \(\mathrm{O_2}\) (as compared to modern Earth). However, this outcome was to be expected given that detecting biogenic \(\mathrm{O_2}\) abundances with JWST is a known challenge (Fauchez et al., 2020; Krissansen- Totton et al., 2018a; Lustig- Yaeger et al., 2019; Wunderlich et al., 2019). A chemical disequilibrium detection would likely require less than 5 ppm noise, which is smaller than predicted estimates of the noise floor (Greene et al., 2016; Rustamkulov et al., 2022). It is therefore unlikely that JWST could constrain the available Gibbs free energy for a Proterozoic Earth- like planet. + +Discriminating false positives, where an abiotically driven signal could mimic a true biosignature, is especially crucial for interpreting chemical disequilibrium signatures considering false positives for \(\mathrm{O_2}\) have been heavily studied (Harman et al., 2018; Luger and Barnes, 2015; Domagal- Goldman et al., 2014; Wordsworth and Pierrehumbert, 2014; Krissansen- Totton et al., 2021). The holistic interpretation of a given chemical disequilibrium signal will not only require quantifying the extent of the signal, but also inferring the chemical + +<--- Page Split ---> + +161 species driving the signature and their production/loss mechanisms (Meadows et al., 2018). A previous study by Wogan and Catling (2020) outlined the potential abiotic mechanisms for generating free energy, and even cases where a lack of free energy could lead to a false negative scenario. A known environment with an excess amount of abiotically produced free energy is Mars and in fact estimates of chemical disequilibrium for the Proterozoic Earth are lower than the modern Mars case (136 J mol \(^{- 1}\) versus 24 J mol \(^{- 1}\) ). However, the available Gibbs free energy produced on Mars is driven by the \(\mathrm{O_2}\) - CO gas pair (abiotically generated by \(\mathrm{CO_2}\) photolysis). In addition, Mars exhibits very cold, dry atmospheric conditions with a majority of its atmosphere comprised of \(\mathrm{CO_2}\) . Given this context, a Proterozoic Earth-like case would be distinguishable with enough contextual information on the atmospheric water vapor abundance, the planetary surface temperature, gas phase species abundance constraints, and incident host stellar spectrum (especially at ultraviolet wavelengths). In any given search, a planet's retrieved chemical disequilibrium must be considered alongside other contextual information to establish biogenicity. For example, a disequilibrium that requires gas fluxes incompatible with abiotic explanations is more likely to be due to life. Additionally, planets with "edible" disequilibria (i.e., easily surmountable kinetic barriers) that might be expected to be readily consumed as a metabolic fuel by a resident biosphere may serve as "anti-biosignatures" (Wogan and Catling, 2020). Modern Earth has a very big atmosphere-ocean chemical disequilibrium, but an atmospheric disequilibrium that is minor in comparison (and challenging to detect, as mentioned earlier). In general, detection of atmospheric chemical disequilibrium would require that the associated chemical species be well-mixed in the atmosphere while more-localized, non-global signatures would be much more difficult to constrain. + +In summary, searching for chemical disequilibrium biosignatures will be a promising endeavor for exoplanet characterization efforts and future missions should consider this approach when developing their observational strategies. For Earth-like analogs, access to \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) spectral features is key and will hinge on sufficient resolving power and wide enough wavelength range to retrieve relevant spectral features. Maximizing the potential to place tight constraints on these chemical species will likely require high SNR observations, so this technique may be most relevant to the best exoplanetary candidates. Alongside understanding planets and stars as systems (Meadows et al., 2018), chemical disequilibrium biosignatures will be a powerful tool for future observations. + +## Methods + +This study incorporated an exoplanet atmospheric retrieval model that was coupled to a thermodynamics Gibbs free energy tool to explore how observational quality influences our ability to interpret and quantify chemical disequilibrium signals from simulated reflected light observations. The photochemical model, Atmos, was used to explore a range of atmospheric compositions spanning high, medium, and low biosignature gas abundance and were then used to generate simulated spectra according to each abundance case. Thereafter a spectral retrieval model, rfast, was used to map out the posterior distributions of relevant atmospheric and planetary parameters consistent with a given simulated observation. The rfast and the Gibbs free energy tools were then coupled via randomly sampling the atmospheric state posterior distribution for relevant parameters and passing those randomized instances as inputs to the Gibbs free energy tool. Repeating this process thousands of times generates a posterior distribution for the available Gibbs free energy that is consistent with the simulated observation. The resulting posterior distribution allowed us asses how observational uncertainty influences our ability to constrain and interpret chemical disequilibrium biosignatures. + +## The Retrieval Model + +The rfast model (Robinson and Salvador, 2022) was adopted for the atmospheric retrievals. It incorporates a radiative transfer forward model, an instrument noise model, and a retrieval tool to enable rapid investigations of exoplanet atmospheric remote sensing scenarios. The radiative transfer forward model is capable of simulating (1) both 1- D and 3- D views of an exoplanet in reflected light, (2) emission spectra, and (3) transit spectra, and takes as input the atmospheric chemical and thermal state (including profiles of cloud properties). The incorporated noise model is based on Robinson et al. (2016), and provides wavelength- dependent noise estimates for a variety of observing scenarios. Finally, the retrieval package uses a Bayesian + +<--- Page Split ---> + +sampling package (emcee) to call the aforementioned radiative transfer model while mapping out the posterior distribution for the atmospheric parameters used to fit a noisy observation (Foreman- Mackey et al., 2013). + +## 213 Gibbs Free Energy Model + +The thermodynamics Gibbs free energy model from Krissansen- Totton et al. (2016) and Krissansen- Totton et al. (2018b) was adopted to calculate chemical disequilibrium biosignatures. The Gibbs free energy is a thermodynamic state function that describes the maximum amount of work a chemical process can produce at constant pressure/temperature (Engel and Reid, 2019): + +\[G = \sum_{i}^{N}\left(\frac{\partial G}{\partial n_{i}}\right)n_{i} \quad (1)\] + +where \(\mathfrak{n}_{i}\) is the moles of species ' \(i\) , \(\partial G / \partial n_{i}\) is the change in Gibbs free energy with respect to the moles of a given species, and the sum is over the total number of species in the system ( \(N\) ). The change in Gibbs free energy with respect to a given species can be rewritten in terms of thermodynamic activity and the standard Gibbs free energy of formation for a given species: + +\[\Delta G_{(T,P)} = \sum_{i}^{N}\left(\Delta_{f}G_{i(T,P_{r})}^{o} + RT\ln \left(\frac{P_{\mathrm{ni}}}{n_{T}}\gamma_{fi}\right)\right)n_{i} \quad (2)\] + +The utility of Gibbs free energy is that it is minimized at thermodynamic equilibrium, which allows us to model the theoretical equilibrium state at a given temperature and pressure without explicitly considering individual chemical reactions. + +Each observational scenario that was simulated considers the target as a closed, well- mixed system at constant global surface pressure and constant characteristic global temperature (Krissansen- Totton et al., 2016). The Gibbs free energy model first computes the Gibbs free energy of the system given an input atmospheric state (e.g., usually an instance of gas mixing ratios, atmospheric temperature, and surface pressure from the atmospheric retrieval model) and subsequently solves for the equilibrium species mixing ratios, where the Gibbs free energy is minimized and atoms are conserved. An interior points method, implemented using Matlab's fmincon function, was used to minimize and solve for the equilibrium state. The difference between the Gibbs energy of the input atmospheric state and the equilibrium state quantifies the "available Gibbs free energy": + +\[\Phi \equiv G_{(T,P)}(n_{\mathrm{initial}}) - G_{(T,P)}(n_{\mathrm{final}}) \quad (3)\] + +The available Gibbs free energy ( \(\Phi\) ) is henceforth used to quantify chemical disequilibrium, and is measured in Joules of available free energy per mole of atmosphere (Krissansen- Totton et al., 2016; Krissansen- Totton et al., 2018b). A large available Gibbs free energy indicates strong planetary chemical disequilibrium (e.g., the modern Earth atmosphere- ocean system at 2,326 J mol \(^{- 1}\) ). Conversely, low available Gibbs free energy indicates weak chemical disequilibrium. Most planetary bodies in our solar system like Jupiter, Venus, and Uranus, all have well below 1 J mol \(^{- 1}\) of available free energy. + +## 240 Proterozoic Earth Atmospheric Modeling + +Self- consistent atmospheric models were generated using the photochemical model component of the Atmos tool (Arney et al., 2016; Arney et al., 2017). Atmos is a coupled photochemical- climate model that uses planetary inputs (e.g., chemical species mixing ratios, associated chemical reactions, gravity, surface pressure, surface temperature, and stellar spectrum) to calculate the steady- state profiles of chemical species present in the atmosphere. In the overall analysis, the default solar spectrum was used to model Earth- like cases relevant to reflected light observations (Thuillier et al., 2004). In the model, this solar spectrum was then adjusted with an input parameter (TIMEGA) to scale the solar flux to its appropriate insolation 1.3 Gyrs ago. The TRAPPIST- 1 spectrum (Peacock et al., 2019) was used for the TRAPPIST- 1 simulations that were mentioned in the text. All the atmospheric cases modeled in this study assumed a total fixed surface + +<--- Page Split ---> + +250 pressure of 1 bar. A suite of cases spanning low to high concentrations in \(\mathrm{O}_2\) , \(\mathrm{CH}_4\) , and \(\mathrm{CO}_2\) (input values outlined in Table 1) were explored to capture broad uncertainty in the abundance of certain atmospheric species during the Proterozoic eon and referenced to the “model low” and “model high” values from Table 1 in Robinson and Reinhard (2020). The med. abundance case was computed taking the logarithmic geometric average of the high and low values. Note that the changes in \(\mathrm{CO}_2\) abundance did not have a significant impact on the available Gibbs free energy uncertainty. + +## Simulated rfast Observations + +Reflected light observations in this study were modeled after direct imaging Decadal Survey mission concepts with ultra- violet \((0.2 - 0.4\mu \mathrm{m})\) , optical \((0.4 - 1.0\mu \mathrm{m})\) , and near- infrared \((1.0 - 1.8\mu \mathrm{m})\) bandpasses at resolving powers of 7, 140, and 70, respectively (Roberge and Moustakas, 2018b; Gaudi et al., 2018b). Each instance of a simulated noisy spectrum was produced with randomized error bars and with the prescribed SNR taken to apply at \(0.55\mu \mathrm{m}\) (consistent with earlier exo- Earth studies (Feng et al., 2018)). + +
InputSNR 20SNR 30SNR 50
log O2High-2.01-2.92+1.45
-4.76		-2.07+0.49
-4.54		-1.66+0.24
-0.23
Med.-3.01-6.08+2.65
-2.67		-6.02+2.68
-2.71		-6.29+2.54
-2.52
Low-4.01-6.60+2.37
-2.31		-6.73+2.25
-2.23		-7.00+2.05
-2.05
log CH4High-4.54-4.36+0.62
-3.04		-4.20+0.35
-1.28		-4.11+0.27
-0.32
Med.-4.94-6.07+1.52
-2.66		-6.29+1.57
-2.52		-5.31+0.71
-2.94
Low-5.53-7.19+1.92
-1.90		-6.87+1.92
-2.12		-7.44+1.76
-1.75
log CO2High-1.16-0.65+0.36
-0.39		-0.79+0.24
-0.26		-0.75+0.22
-0.20
Med.-2.23-2.28+0.48
-3.27		-2.14+0.27
-0.34		-2.19+0.22
-0.27
Low-3.31-6.36+2.54
-2.48		-6.40+2.44
-2.46		-6.75+2.25
-2.21
T0 (K)High288.259.91+32.18
-26.39		272.23+21.41
-20.62		266.86+12.44
-12.24
Med.288.267.84+30.15
-27.70		273.84+22.47
-28.01		266.13+14.21
-12.44
Low288.255.31+25.06
-19.79		258.34+23.07
-17.31		263.09+15.18
-13.10
log Avail. GibbsHigh1.36870.17+1.69
-0.95		1.28+0.59
-1.72		1.76+0.28
-0.32
Med.0.9739-1.24+1.59
-0.65		-1.23+1.54
-0.60		-1.16+1.74
-0.62
Low0.5853-1.65+1.11
-0.55		-1.63+1.18
-0.60		-1.74+0.81
-0.57
+ +Table 1: Summary of the parameters, input values, and \(1\sigma\) confidence intervals (taken from the \(16^{\mathrm{th}}\) \(50^{\mathrm{th}}\) and \(84^{\mathrm{th}}\) percentile values) for the high, med., and low atmospheric abundance scenarios that were modeled. + +## References + +Giada Arney, Shawn D. Domagal- Goldman, Victoria S. Meadows, Eric T. Wolf, Edward Schwieterman, Benjamin Charnay, Mark Claire, Eric Hebrard, and Melissa G. Trainer. The Pale Orange Dot: The Spectrum and Habitability of Hazy Archean Earth. Astrobiology, 16(11):873- 899, October 2016. ISSN 1531- 1074. doi: 10.1089/ast.2015.1422. URL https://www.liebertpub.com/doi/abs/10.1089/ast.2015.1422. Giada N. Arney. The K Dwarf Advantage for Biosignatures on Directly Imaged Exoplanets. Astrophysical Journal Letters, 873(1):L7, March 2019. doi: 10.3847/2041- 8213/ab0651. Giada N. Arney, Victoria S. Meadows, Shawn D. Domagal- Goldman, Drake Deming, Tyler D. Robinson, Guadalupe Tovar, Eric T. 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Schwieterman, Shawn D. Domagal- Goldman, Andrew P. Lincowski, Karl R. Stapelfeldt, Heike Rauer, Shiladitya DasSarma, Siddharth Hegde, Norio Narita, Russell Deitrick, Jacob Lustig- Yaeger, Timothy W. Lyons, Nicholas Siegler, and J. Lee Grenfell. Exoplanet Biosignatures: Understanding Oxygen as a Biosignature in the Context of Its Environment. Astrobiology, 18(6):630- 662, June 2018. doi: 10.1089/ast.2017.1727. + +<--- Page Split ---> + +National Academies of Sciences Engineering and Medicine. An Astrobiology Strategy for the Search for Life in the Universe. The National Academies Press, October 2019. ISBN 978- 0- 309- 48416- 9. doi: 10.17226/25252. URL https://doi.org/10.17226/25252. + +<--- Page Split ---> + +Nixon, Kevin Ortiz Ceballos, Anjali A. A. Piette, Diana Powell, Benjamin V. Rackham, Lakeisha Ramos- Rosado, Emily Rauscher, Seth Redfield, Laura K. Rogers, Michael T. Roman, Gael M. Roudier, Nicholas Scarsdale, Evgenya L. Shkolnik, John Southworth, Jessica J. 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The Astrophysical Journal, 785(2):L20, apr 2014. doi: 10.1088/2041-8205/785/2/120. URL https://doi.org/10.1088/2041-8205/785/2/120. + +Fabian Wunderlich, Mareike Godolt, John Lee Grenfell, Steffen Städt, Alexis MS Smith, Stefanie Gebauer, Franz Schreier, Pascal Hedelt, and Heike Rauer. Detectability of atmospheric features of earth-like planets in the habitable zone around m dwarfs. Astronomy & Astrophysics, 624:A49, 2019. + +## Acknowledgements + +All authors would like to acknowledge support from the NASA Exobiology Program, Grant No. 80NSSC18K0349. AVY, TDR, EWS, and DCC also acknowledge support from NASA's Nexus for Exoplanet System Science Virtual Planetary Laboratory (No. 80NSSC18K0829). TDR acknowledges support from NASA's Exoplanets Research Program (No. 80NSSC18K0349) and Habitable Worlds Program (No. 80NSSC20K0226) as well as the Cottrell Scholar Program administered by the Research Corporation for Science Advancement. EWS and CTR acknowledge additional support from the NASA Interdisciplinary Consortia for Astrobiology Research (ICAR) Program via the Alternative Earths team with funding issued under grant No. 80NSSC21K059. M.J.W. acknowledges support from the GSFC Sellers Exoplanet Environments Collaboration (SEEC) and ROCKE- 3D: The evolution of solar system worlds through time, both of which are funded by the NASA Planetary Science Divisions Internal Scientist Funding Model. + +## Author Contributions + +AVY conducted all the atmospheric retrievals outlined in this work, performed code development to couple the retrieval results to the Gibbs free energy model, and computed the available Gibbs free energy posteriors for all the simulated cases. TDR performed code adaptations and development of rfast in correspondence with the retrieval analyses performed in this work and is the principal investigator of the project. JKT distributed the most up to date version of thermodynamics model used in this work and contributed to necessary code development and adaptation for the thermodynamic simulations. ES provided Atmos generated atmospheric compositions for each of the abundance scenarios outlined in this work. All authors contributed to both project development and the writing and/or editing of the paper and approved the scientific content. + +<--- Page Split ---> + +Supplementary Information + +Fig. S1 shows an equilibrium calculation for the high Proterozoic abundance case presented in the main text. The total available Gibbs free energy for this scenario is \(24.24 \mathrm{J} \mathrm{mol}^{- 1}\) . The blue bars show the observed mixing ratios of each species, the red bars represent the equilibrium mixing ratios of each species, and the yellow bars show the change in mixing ratio between the equilibrium and observed abundances. \(\mathrm{O}_{2}\) and \(\mathrm{CH}_{4}\) have significantly large observed mixing ratios and also have large differences between their observed and equilibrium abundances. This indicates that these two species are the main drivers of chemical disequilibrium in the atmosphere. Using these results, chemical species like \(\mathrm{NH}_{3}\) , \(\mathrm{H}_{2}\) , and \(\mathrm{N}_{2} \mathrm{O}\) where excluded from the retrieval analysis and held at their equilibrium abundances when computing the available Gibbs free energy posterior distribution. + +![](images/Figure_unknown_0.jpg) + +
Figure S1: Thermodynamic Calculation for High Proterozoic Earth Case. Blue bars represent the observed (or input) abundance for each species labeled across the bottom axis. Red bars indicate the equilibrium abundance (calculated from the Gibbs free energy minimization). Yellow bars indicate the absolute value of the difference between the observed and equilibrium abundance for each species. Note that the molecules with a substantially large abundance and large difference (i.e., the \(\mathrm{O}_{2}\) and \(\mathrm{CH}_{4}\) ) are major contributors to the atmospheric chemical disequilibrium signal.
+ +Outlined in Fig. S2 are the marginal posterior distributions for the mixing ratios of \(\mathrm{H}_{2} \mathrm{O}\) , Fig. S2a, \(\mathrm{CO}_{2}\) , Fig. S2b, and \(\mathrm{O}_{3}\) , Fig. S2c, along with the retrieved atmospheric pressure, Fig. S2d, for the high (green) med., (purple), low (blue) abundance scenarios. These retrieved parameters were included in the random sampling used to compute the available Gibbs free energy posteriors (Fig. 1 in the main text), but did not have a significant influence on the uncertainty. However, constraining parameters like \(\mathrm{H}_{2} \mathrm{O}\) , \(\mathrm{CO}_{2}\) , and surface pressure are essential for inferring the climate and habitability of exoplanets. Additionally, \(\mathrm{O}_{3}\) can be used as a proxy for determining the \(\mathrm{O}_{2}\) abundance for less oxygenated atmospheres since \(\mathrm{O}_{3}\) can remain detectable even at low \(\mathrm{O}_{2}\) concentrations (Meadows, 2017; Meadows et al., 2018; Schwieterman et al., 2018). + +<--- Page Split ---> +![](images/Figure_unknown_1.jpg) + +
Figure S2: Reflected Light Retrieval Parameters Pertinent to Computing The Available Gibbs Free Energy (Non-essential). a, Marginal posterior distributions for the high (green), medium (purple), and low (blue) abundance cases for the log abundance of \(\mathrm{H}_2\mathrm{O}\) . Included are distributions for observations simulated at 20 (hatched), 30 (un-filled), and 50 (solid fill) SNR. b Marginal Posterior distribution for the log mixing ratio of \(\mathrm{CO}_2\) . c Marginal posterior distributions for the log mixing ratio of \(\mathrm{O}_3\) . d, Marginal posterior distributions for the global surface pressure. Note that each of these parameters are included in the overall Gibbs free energy calculation, but their uncertainties do not substantially impact the overall available Gibbs free energy.
+ +<--- Page Split ---> + +In Fig. S3, the marginal posterior distributions for the \(\mathrm{O_2}\) abundance are outlined at the high (Fig. S3a), med (Fig. S3b), and low (Fig. S3c) abundance cases for a Proterozoic Earth- like planet orbiting an M dwarf and inferred from simulated transit observations with the JWST NIRSpec instrument. These results show it is very difficult to constrain the atmospheric abundance of \(\mathrm{O_2}\) at each of the observational noise levels (5 ppm and 10 ppm), either of which would be challenging for JWST to achieve for current best- case targets. The lack of constraints on the \(\mathrm{O_2}\) abundance likely make inferring the chemical disequilibrium energy of a Proterozoic Earth- like exoplanet orbiting a late- type star too challenging for JWST. + +![](images/Figure_unknown_2.jpg) + +
Figure S3: \(\mathbf{O}_2\) Posterior Distributions Derived From Simulated JWST/NIRSpec Observations. a, Marginal Posterior distribution of \(\mathrm{O_2}\) for the high Proterozoic Earth abundance case. The vertical black dashed line denotes the input value for the \(\mathrm{O_2}\) abundance. Each distribution is inferred from simulated transit observations at noise levels of 5 ppm (un-filled) and 10 ppm (hatched). b, Same as a but for the medium abundance case. c, Same as a but for the low abundance case. Note the lack of constraints for all abundance cases at both noise levels, which indicates the chemical disequilibrium cannot be adequately constrained at these abundances via JWST/NIRSpec transit observations.
+ +<--- Page Split ---> diff --git a/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc_det.mmd b/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..814a4e1d28be23eea86fbd112fd0c5f8e2395631 --- /dev/null +++ b/preprint/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc/preprint__0b133dd94eaf8e8de770997072bdc3a75b3370dc3352359bd89046e5f72ed4bc_det.mmd @@ -0,0 +1,364 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 955, 208]]<|/det|> +# Constraining Chemical Disequilibrium Biosignatures for Proterozoic Earth-like Exoplanets Using Reflectance Spectra + +<|ref|>text<|/ref|><|det|>[[44, 230, 650, 271]]<|/det|> +Amber Young ( ☑ amberastro12@gmail.com ) Northern Arizona University https://orcid.org/0000- 0003- 3099- 1506 + +<|ref|>text<|/ref|><|det|>[[44, 277, 290, 316]]<|/det|> +Tyler Robinson University of Arizona + +<|ref|>text<|/ref|><|det|>[[44, 324, 275, 363]]<|/det|> +Joshua Krissansen- Totton University of Washington + +<|ref|>text<|/ref|><|det|>[[44, 370, 400, 410]]<|/det|> +Edward W. Schwieterman https://orcid.org/0000- 0002- 2949- 2163 + +<|ref|>text<|/ref|><|det|>[[44, 416, 275, 456]]<|/det|> +Nicholas Wogan University of Washington + +<|ref|>text<|/ref|><|det|>[[44, 463, 423, 502]]<|/det|> +Michael Way NASA Goddard Institute for Space Studies + +<|ref|>text<|/ref|><|det|>[[44, 508, 588, 548]]<|/det|> +Linda Sohl Columbia University https://orcid.org/0000- 0002- 6673- 2007 + +<|ref|>text<|/ref|><|det|>[[44, 555, 308, 594]]<|/det|> +Giada Amey Goddard Space Flight Center + +<|ref|>text<|/ref|><|det|>[[44, 601, 686, 641]]<|/det|> +Christopher Reinhard Georgia Institute of Technology https://orcid.org/0000- 0002- 2632- 1027 + +<|ref|>text<|/ref|><|det|>[[44, 647, 623, 687]]<|/det|> +Michael Line Arizona State University https://orcid.org/0000- 0001- 6247- 8323 + +<|ref|>text<|/ref|><|det|>[[44, 693, 635, 733]]<|/det|> +David Catling University of Washington https://orcid.org/0000- 0001- 5646- 120X + +<|ref|>text<|/ref|><|det|>[[44, 739, 293, 779]]<|/det|> +James Windsor Northern Arizona University + +<|ref|>sub_title<|/ref|><|det|>[[44, 824, 101, 841]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 861, 135, 878]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 897, 319, 916]]<|/det|> +Posted Date: January 4th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 936, 473, 954]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2335028/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 911, 87]]<|/det|> +License: © ① This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 105, 530, 125]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 160, 944, 204]]<|/det|> +Version of Record: A version of this preprint was published at Nature Astronomy on January 22nd, 2024. See the published version at https://doi.org/10.1038/s41550-023-02145- z. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[92, 135, 884, 190]]<|/det|> +# Constraining Chemical Disequilibrium Biosignatures for Proterozoic Earth-Like Exoplanets Using Reflectance Spectra + +<|ref|>text<|/ref|><|det|>[[92, 203, 920, 490]]<|/det|> +3 Amber V. Young, \(^{1*}\) Tyler D. Robinson, \(^{2}\) Joshua Krissansen- Toton, \(^{3}\) Edward W. Schwieterman, \(^{4}\) Nicholas F. Wogan, \(^{5}\) Michael J. Way, \(^{6,10}\) Linda E. Sohl, \(^{11,6}\) Giada N. Arney, \(^{7}\) Christopher T. Reinhard, \(^{8}\) Michael R. Line, \(^{9}\) David C. Catling, \(^{3,5}\) James D. Windsor \(^{1}\) \(^{1}\) Department of Astronomy and Planetary Sciences, Northern Arizona University, Physical Sciences Building, 527 S Beaver St, Flagstaff, AZ 86011 \(^{2}\) Lunar and Planetary Laboratory, University of Arizona, Tucson AZ 85721 \(^{3}\) Earth and Space Sciences, University of Washington, Seattle WA 98195 \(^{4}\) Department of Earth and Planetary Sciences, University of California Riverside, Riverside CA 92521 \(^{5}\) Department of Astrobiology, University of Washington, Seattle WA 98195 \(^{6}\) NASA Goddard Institute for Space Studies, New York NY 10025 \(^{7}\) NASA Goddard Space Flight Center, Greenbelt MD 20771 \(^{8}\) Earth and Atmospheric Sciences, Georgia Tech, Atlanta GA 30332 \(^{9}\) School of Earth and Space Exploration, Arizona State University, Tempe AZ 85281 \(^{10}\) Department of Physics and Astronomy, Uppsala University, Uppsala, SE- 75120, Sweden \(^{11}\) Center for Climate Systems Research, Columbia University, New York, New York, 10025 \(^{*}\) To whom correspondence should be addressed: Amber.Young86@nau.edu. + +<|ref|>text<|/ref|><|det|>[[418, 500, 579, 516]]<|/det|> +November 21, 2022 + +<|ref|>text<|/ref|><|det|>[[90, 546, 883, 910]]<|/det|> +Chemical disequilibrium quantified via available free energy has previously been proposed as a potential planetary biosignature. However, little work has been done that links anticipated observational uncertainties to our ability to infer the available Gibbs free energy from remote observations. Planetary properties indicative of the atmospheric state (and pertinent to constraining the available Gibbs free energy) can be inferred via spectroscopic analyses and thus the available Gibbs free energy could be a potentially useful biosignature for exoplanets. Simulated reflected light atmospheric retrievals (Robinson and Salvador, 2022) were coupled with thermodynamics modeling (Krissansen- Toton et al., 2016; Krissansen- Toton et al., 2018b) to assess the predicted chemical disequilibrium signatures of Earth- like exoplanets. The Proterozoic Earth is a long period (2 Gyr) in Earth's history where the atmospheric abundance of the biogenic oxygen ( \(\mathrm{O_2}\) )- methane ( \(\mathrm{CH_4}\) ) disequilibrium pair may have been relatively high (Krissansen- Toton et al., 2018b). Retrieval models applied across a range of "High", "Medium", and "Low" biosignature gas abundance scenarios for methane and oxygen show that spectral observations spanning the ultraviolet through near- infrared wavelengths at characteristic visual band signal- to- noise ratio (SNR) of 20–30 provide either very weak or upper limit constraints on the available Gibbs free energy for all abundance scenarios while spectra at SNR of 50 or larger could provide order- of- magnitude constraints on the disequilibrium biosignatures for the high- abundance scenario. Constraints on the atmospheric available Gibbs free energy are heavily driven by the posterior distributions inferred for \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) from the simulated spectral observations. Furthermore, the disequilibrium energy constraints are improved by modest atmospheric temperature constraints encoded in molecular opacities at optical and near- infrared wavelengths. These results have important implications for continuing to develop biosignature search strategies in preparation for future direct imaging exoplanet characterization missions. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[86, 87, 261, 105]]<|/det|> +## 29 Introduction + +<|ref|>text<|/ref|><|det|>[[86, 117, 882, 300]]<|/det|> +Exoplanet exploration science is making rapid progress toward the detection and characterization of potentially habitable worlds (Gardner et al., 2006). Ongoing (Greene et al., 2016; The JWST Transiting Exoplanet Community Early Release Science Team et al., 2022) and near- future exoplanet strategies (of Sciences Engineering and Medicine, 2019) will emphasize the search for atmospheric gases, including the chemical signatures of life (or biosignatures) (Schwieterman et al., 2018; Meadows et al., 2018; Madhusudhan, 2019). Recently, the Decadal Survey on Astronomy and Astrophysics 2020 report (Gaudi et al., 2015) recommended space- based high contrast imaging of potentially life- bearing exoplanets as a leading priority for the coming decades. When attempting to infer if a distant world is inhabited, chemical disequilibrium is a potential indicator of life that has a long history of study in solar system planetary environments (Lovelock, 1965; Hitchcock and Lovelock, 1967; Lovelock, 1975). A key example is the coexistence of \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) in Earth's atmosphere where the strong biological production rates of \(\mathrm{CH_4}\) are able to maintain this gas at appreciable levels despite its relatively short chemical lifetime in an oxidizing atmosphere. + +<|ref|>text<|/ref|><|det|>[[87, 300, 882, 404]]<|/det|> +A primary metric for quantifying chemical disequilibrium involves calculating the difference in chemical energy associated with an observed system and that system's theoretical equilibrium state. Recent work has explored the application of one such metric—the available Gibbs free energy—to solar system worlds and to Earth's planetary evolution (Krissansen- Totton et al., 2016; Krissansen- Totton et al., 2018b; Wogan and Catling, 2020). Although the available Gibbs free energy is a promising metric for interpreting chemical disequilibrium biosignatures, little is known about how observational uncertainties will impact our ability to constrain the available Gibbs free energy for Earth- like exoplanets. + +<|ref|>text<|/ref|><|det|>[[87, 404, 882, 585]]<|/det|> +Exoplanet atmospheric characterization, including the search for biosignature gases, proceeds through retrieval analysis or atmospheric inference (e.g., Madhusudhan and Seager (2009); Benneke and Seager (2012); Line et al. (2013); Feng et al. (2018); Barstow et al. (2020)). In short, a retrieval framework enables the statistical exploration of atmospheric states that are consistent with a given set of spectral observations, be these real or simulated. While retrieval models do not directly constrain quantities like the available Gibbs free energy, pairing an exoplanet atmospheric retrieval model with a thermochemical tool—as detailed in Methods—enables inferences of both atmospheric chemical abundances and the associated disequilibrium state of the atmosphere. As described in Methods, simulated observations of an Earth- like planet are created with uncertainties specified by the V- band SNR where the V- band is centered at .551 \(\mu \mathrm{m}\) , with .088 \(\mu \mathrm{m}\) full width at half maximum. Applying inverse modeling techniques to these simulated observations (for multiple randomized observational noise realizations) then maps observational quality to expected constraints on the available Gibbs free energy. + +<|ref|>text<|/ref|><|det|>[[88, 585, 882, 737]]<|/det|> +Analyses presented here emphasize directly- imaged Proterozoic Earth analogs with reflectance spectral data spanning ultraviolet through near- infrared wavelengths (motivated by Decadal Survey mission concept reports (Gaudi et al., 2018a; Roberge and Moustakas, 2018a)). This eon in Earth's history is notable for its oxygenated atmosphere that also may have had enhanced atmospheric methane concentrations (as compared to modern Earth), thereby presenting an ideal time period for detecting \(\mathrm{O_2 - CH_4}\) disequilibrium. Retrieval studies below explore a range of concentrations for key gases in Proterozoic Earth's atmosphere and were adopted from a span of Earth evolutionary scenarios summarized in a review (Robinson and Reinhard, 2020). High and low concentration scenarios here are identical to mid- Proterozoic extremes from this review, and an intermediate concentration case was generated by computing the logarithmic geometric mean of the high and low cases. + +<|ref|>sub_title<|/ref|><|det|>[[89, 759, 201, 778]]<|/det|> +## 71 Results + +<|ref|>text<|/ref|><|det|>[[88, 789, 882, 910]]<|/det|> +Fig. 1 shows modeled constraints on the atmospheric available Gibbs free energy (in Joules per mole of atmosphere) that would be expected from observations of Proterozoic Earth analogs in reflected light. Each result is broken up into three atmospheric composition categories of "high", "medium", and "low" biosignature gas abundances and simulated observations were conducted at several SNRs for each abundance category. Most of these reflected light cases present available Gibbs free energy posteriors that are consistent with an upper- limit constraint. In our simulations, the log available Gibbs free energy is found to be no larger than \(1.30 / 1.13 / 1.21 \mathrm{J mol^{- 1}}\) at \(95\%\) confidence for SNRs of \(20 / 30 / 50\) for the medium abundance case and \(0.47 / 0.68 / 0.03 \mathrm{J mol^{- 1}}\) for the low abundance case. Additionally, the uncertainty on the available Gibbs free + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 90, 883, 167]]<|/det|> +energy goes down to as low as an order of magnitude for the SNR 50 observational case. In the high abundance scenario (Fig. 1a), the posteriors derived from the simulated SNR 20 and 30 observations exhibit a dual peak in the distribution. Given the randomization of the simulated observational data points, retrievals at these SNRs could occasionally constrain the \(\mathrm{O_2}\) abundance. Thus, one peak corresponds to cases where \(\mathrm{O_2}\) was well- detected and the other peak corresponds to non- detections. + +<|ref|>image<|/ref|><|det|>[[92, 200, 877, 437]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 469, 883, 590]]<|/det|> +
Figure 1: Available Gibbs Free Energy Posterior Distributions Inferred from Simulated Reflected Light Observations for Different Proterozoic Earth Models. a, The marginal posterior distribution of the log of the available Gibbs free energy for the high abundance case, derived from 20 (hatched), 30 (un-filled) and 50 (solid fill) SNR simulated reflected light observations. Vertical dashed orange, red, and blue lines in all three panels represent the previously reported values for the available Gibbs free energy of modern Earth (atmosphere only case), Mars, and modern Earth (ME) (atmosphere-ocean case) respectively (Krissansen-Totton et al., 2016). b, Same as a but for the medium abundance case. c, Same as a but for the low abundance case.
+ +<|ref|>text<|/ref|><|det|>[[88, 604, 883, 817]]<|/det|> +The constraints on the available Gibbs free energy are most- strongly dependent on the quality of the inferences for \(\mathrm{O_2}\) , \(\mathrm{CH_4}\) , and temperature, which are shown in Fig. 2. This is consistent with thermodynamic theory, which has shown that the Gibbs free energy is strongly dependent on temperature and only weakly dependent on pressure (Engel and Reid, 2019). In the high abundance case at SNR 20, results show a large uncertainty on the inferred \(\mathrm{O_2}\) abundance which ranged from \(10^{- 10}\) to 0.1. This introduced a significant uncertainty on the available Gibbs free energy for this particular case. However, higher observational SNRs of 30 and 50 at high abundance showed better constraints on \(\mathrm{O_2}\) which led to improved constraints on the available Gibbs free energy. These trends also held true for the \(\mathrm{CH_4}\) posteriors in the high abundance case. The retrieval analyses for the medium and low cases largely resulted in upper- limit constraints for \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) at each of the observational signal- to- noise scenarios tested here. Reasonable temperature constraints were seen for all abundance cases and observing scenarios, which stemmed from adequate constraints on the shape of atmospheric water vapor bands across the spectral range for all modeled scenarios. Table 1 details the \(16^{\mathrm{th}}\) , \(50^{\mathrm{th}}\) , and \(84^{\mathrm{th}}\) - percentile values for the marginal \(\mathrm{O_2}\) , \(\mathrm{CH_4}\) , and temperature distributions (corresponding to the 1- sigma values for a Gaussian distribution). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[151, 108, 820, 630]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 650, 883, 756]]<|/det|> +
Figure 2: Posterior Distributions for Key Retrieved Atmospheric Parameters for Different Noise Levels and Proterozoic Earth Models. a, The marginal posterior probability distributions for the retrieved log abundance of \(\mathrm{O_2}\) at the high (green), medium (purple), and low (blue) abundance cases. Each distribution is inferred from simulated reflected light observations at SNRs of 20 (hatched), 30 (unfilled), and 50 (solid fill). A vertical black dashed line represents the input value for each parameter (the input is calculated via column integrated mass mixing ratio profiles for each gas phase species). b, Same as a except now showcasing methane constraints. c, Same as a except now showcasing temperature constraints.
+ +<|ref|>text<|/ref|><|det|>[[85, 772, 883, 910]]<|/det|> +Most fundamentally, results demonstrated that spectra with strongly- detected \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) absorption features lead to tight constraints on the resulting available Gibbs free energy, thereby enabling an inference of the extent of chemical disequilibrium in the atmosphere of an Earth- like exoplanet analog. Fig. 3 highlights spectral features of several species including \(\mathrm{O_2}\) , \(\mathrm{CH_4}\) , \(\mathrm{CO_2}\) , and \(\mathrm{H_2O}\) across the range of modeled Proterozoic Earth scenarios. In the near- infrared/optical/ultra- violet spectral range explored in this work, the strongest \(\mathrm{O_2}\) feature is the oxygen A- band at \(0.762 \mu \mathrm{m}\) . There are several \(\mathrm{CH_4}\) features within the 1.0 - \(1.8 \mu \mathrm{m}\) wavelength range, indicated in orange. Each color coded absorption feature for \(\mathrm{O_3}\) , \(\mathrm{CO_2}\) , \(\mathrm{CH_4}\) , and \(\mathrm{O_2}\) is accentuated by a factor of two relative to the original input abundance in order to highlight the precision needed to observe each species. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[256, 156, 723, 672]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 744, 883, 866]]<|/det|> +
Figure 3: Simulated Reflected Light Spectra for Proterozoic Earth Cases. a, Simulated reflected light spectrum for the high (green) abundance case. Absorption features for \(\mathrm{O_3}\) (brown), \(\mathrm{CO_2}\) (blue), \(\mathrm{CH_4}\) (yellow), and \(\mathrm{O_2}\) (red) are shown and their input abundances are multiplied by a factor of two \((\times 2)\) . Water vapor absorption features are labeled with text as well. The grey error bar legend shows scaling with each noise instance (20, 30, and 50 SNR). b, The simulated reflected light spectrum for the medium (purple) abundance case. Each input abundance is multiplied by a factor of two \((\times 2)\) to show its effect. c, The simulated reflected light spectrum for the low (blue) abundance case. The denoted species absorption features are shown and their input abundances are multiplied by a factor of two \((\times 2)\) .
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 87, 423, 107]]<|/det|> +## Discussion and Conclusion + +<|ref|>text<|/ref|><|det|>[[115, 118, 882, 284]]<|/det|> +The Proterozoic Eon is a potentially ideal Earth- like context for constraining the atmospheric \(\mathrm{O_2}\) - CH4 chemical disequilibrium gas pair for an Earth- like planet around a G- type star due to a likely higher abundance of \(\mathrm{CH_4}\) , a rise in \(\mathrm{O_2}\) relative to the Archean Earth, and the longevity of this signal over a 2 Gyr time period. An Archean Earth- like atmosphere may have a modest atmospheric chemical disequilibrium signature driven by the \(\mathrm{CH_4}\) - \(\mathrm{CO_2}\) gas pair. However, abiotic sources for \(\mathrm{CH_4}\) production would need to be explored (Krissansen- Totton et al., 2018b). modern Earth has substantially less atmospheric \(\mathrm{CH_4}\) , making detection of the \(\mathrm{O_2}\) - \(\mathrm{CH_4}\) atmospheric signature challenging, although the photochemistry for a modern Earth- like planet around an M- or K- dwarf may generate substantially more \(\mathrm{CH_4}\) in atmospheres with modern levels of \(\mathrm{O_2}\) (e.g., Segura et al. (2005); Arney (2019)). For any exoplanet, the stellar photochemical context will be vital to consider when evaluating potential biosignatures. Nevertheless, our results offer a window into the characterization of an Earth- sun twin as an analog for similar exoplanets. + +<|ref|>text<|/ref|><|det|>[[115, 284, 882, 450]]<|/det|> +The thermodynamic systems that were modeled here are only characteristic of the chemical disequilibrium present in the atmosphere of these systems. Oceans can provide an additional source for chemical disequilibrium to arise and, in fact, the maintenance of \(\mathrm{N_2}\) and \(\mathrm{O_2}\) in the presence of liquid water (for Proterozoic and modern Earth) and the maintenance of \(\mathrm{CO_2}\) , \(\mathrm{N_2}\) , and \(\mathrm{CH_4}\) in the presence of liquid water (for Archean Earth) are major contributors to disequilibrium energy over time (Krissansen- Totton et al., 2018b). However, it is unlikely exoplanet data will be sensitive enough to characterize the oceanic state and its dissolved species. Further characterization would also require obtaining constraints on the planet's ocean volume in order to infer the chemical disequilibrium of an atmosphere- ocean system. Thus, atmospheric disequilibrium constraints are likely to be conservative, at least for ocean- bearing worlds. In general, the inability to easily constrain ocean volume (and/or its dissolved species) or the inability of retrieval to fully detect all species in the atmosphere makes the available Gibbs free energy constraints likely to be conservative. + +<|ref|>text<|/ref|><|det|>[[115, 450, 882, 660]]<|/det|> +Constraining the available Gibbs free energy is a promising characterization strategy that synergizes well with established techniques for biosignature gas detection. In practice, it is possible to determine an upper limit on the available Gibbs free energy and, for more optimistic cases, proper constraints can be obtained on the free energy for Proterozoic Earth- like planets. Particularly for high abundance cases, it is possible to place constraints on the available Gibbs free energy to within an order of magnitude with SNR 50 observations. This could be feasible with a future exoplanet direct imaging mission, but may require significant integration time to reach this level of signal- to- noise and such an observation may be limited to the most promising of targets. It is also worth noting that the detection of the \(\mathrm{O_2}\) - \(\mathrm{CH_4}\) disequilibrium is, in part, sensitive to temperature constraints and highly sensitive to the near- infrared spectral features that drive the quality of the \(\mathrm{CH_4}\) abundance constraints. Such spectral features may not be observable for all targets as the inner working angle for high contrast imaging systems, especially coronagraphs, expands with wavelength. Nevertheless, performing a baseline analysis at lower SNRs may help us identify potentially exciting targets for more detailed follow- up observations and provide upper limit constraints on potential chemical disequilibrium signals for a subset of targets. + +<|ref|>text<|/ref|><|det|>[[115, 660, 882, 826]]<|/det|> +The successful launch of the James Webb Space Telescope (JWST) and the prospects for characterizing Earth- like planets in the habitable zone of M dwarf stars motivated attempts to constrain the available Gibbs free energy of a Proterozoic Earth- like planet orbiting the M dwarf TRAPPIST- 1. For all three atmospheric cases, and simulated observations with the NIRSpec instrument, results (Fig. S3, which highlight the \(\mathrm{O_2}\) posteriors) indicate that it is extremely challenging to constrain the available Gibbs free energy given the weaker spectral features of \(\mathrm{O_2}\) (as compared to modern Earth). However, this outcome was to be expected given that detecting biogenic \(\mathrm{O_2}\) abundances with JWST is a known challenge (Fauchez et al., 2020; Krissansen- Totton et al., 2018a; Lustig- Yaeger et al., 2019; Wunderlich et al., 2019). A chemical disequilibrium detection would likely require less than 5 ppm noise, which is smaller than predicted estimates of the noise floor (Greene et al., 2016; Rustamkulov et al., 2022). It is therefore unlikely that JWST could constrain the available Gibbs free energy for a Proterozoic Earth- like planet. + +<|ref|>text<|/ref|><|det|>[[115, 827, 882, 902]]<|/det|> +Discriminating false positives, where an abiotically driven signal could mimic a true biosignature, is especially crucial for interpreting chemical disequilibrium signatures considering false positives for \(\mathrm{O_2}\) have been heavily studied (Harman et al., 2018; Luger and Barnes, 2015; Domagal- Goldman et al., 2014; Wordsworth and Pierrehumbert, 2014; Krissansen- Totton et al., 2021). The holistic interpretation of a given chemical disequilibrium signal will not only require quantifying the extent of the signal, but also inferring the chemical + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 90, 883, 377]]<|/det|> +161 species driving the signature and their production/loss mechanisms (Meadows et al., 2018). A previous study by Wogan and Catling (2020) outlined the potential abiotic mechanisms for generating free energy, and even cases where a lack of free energy could lead to a false negative scenario. A known environment with an excess amount of abiotically produced free energy is Mars and in fact estimates of chemical disequilibrium for the Proterozoic Earth are lower than the modern Mars case (136 J mol \(^{- 1}\) versus 24 J mol \(^{- 1}\) ). However, the available Gibbs free energy produced on Mars is driven by the \(\mathrm{O_2}\) - CO gas pair (abiotically generated by \(\mathrm{CO_2}\) photolysis). In addition, Mars exhibits very cold, dry atmospheric conditions with a majority of its atmosphere comprised of \(\mathrm{CO_2}\) . Given this context, a Proterozoic Earth-like case would be distinguishable with enough contextual information on the atmospheric water vapor abundance, the planetary surface temperature, gas phase species abundance constraints, and incident host stellar spectrum (especially at ultraviolet wavelengths). In any given search, a planet's retrieved chemical disequilibrium must be considered alongside other contextual information to establish biogenicity. For example, a disequilibrium that requires gas fluxes incompatible with abiotic explanations is more likely to be due to life. Additionally, planets with "edible" disequilibria (i.e., easily surmountable kinetic barriers) that might be expected to be readily consumed as a metabolic fuel by a resident biosphere may serve as "anti-biosignatures" (Wogan and Catling, 2020). Modern Earth has a very big atmosphere-ocean chemical disequilibrium, but an atmospheric disequilibrium that is minor in comparison (and challenging to detect, as mentioned earlier). In general, detection of atmospheric chemical disequilibrium would require that the associated chemical species be well-mixed in the atmosphere while more-localized, non-global signatures would be much more difficult to constrain. + +<|ref|>text<|/ref|><|det|>[[86, 377, 882, 498]]<|/det|> +In summary, searching for chemical disequilibrium biosignatures will be a promising endeavor for exoplanet characterization efforts and future missions should consider this approach when developing their observational strategies. For Earth-like analogs, access to \(\mathrm{O_2}\) and \(\mathrm{CH_4}\) spectral features is key and will hinge on sufficient resolving power and wide enough wavelength range to retrieve relevant spectral features. Maximizing the potential to place tight constraints on these chemical species will likely require high SNR observations, so this technique may be most relevant to the best exoplanetary candidates. Alongside understanding planets and stars as systems (Meadows et al., 2018), chemical disequilibrium biosignatures will be a powerful tool for future observations. + +<|ref|>sub_title<|/ref|><|det|>[[87, 519, 217, 539]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[86, 551, 882, 749]]<|/det|> +This study incorporated an exoplanet atmospheric retrieval model that was coupled to a thermodynamics Gibbs free energy tool to explore how observational quality influences our ability to interpret and quantify chemical disequilibrium signals from simulated reflected light observations. The photochemical model, Atmos, was used to explore a range of atmospheric compositions spanning high, medium, and low biosignature gas abundance and were then used to generate simulated spectra according to each abundance case. Thereafter a spectral retrieval model, rfast, was used to map out the posterior distributions of relevant atmospheric and planetary parameters consistent with a given simulated observation. The rfast and the Gibbs free energy tools were then coupled via randomly sampling the atmospheric state posterior distribution for relevant parameters and passing those randomized instances as inputs to the Gibbs free energy tool. Repeating this process thousands of times generates a posterior distribution for the available Gibbs free energy that is consistent with the simulated observation. The resulting posterior distribution allowed us asses how observational uncertainty influences our ability to constrain and interpret chemical disequilibrium biosignatures. + +<|ref|>sub_title<|/ref|><|det|>[[87, 766, 316, 784]]<|/det|> +## The Retrieval Model + +<|ref|>text<|/ref|><|det|>[[86, 791, 882, 899]]<|/det|> +The rfast model (Robinson and Salvador, 2022) was adopted for the atmospheric retrievals. It incorporates a radiative transfer forward model, an instrument noise model, and a retrieval tool to enable rapid investigations of exoplanet atmospheric remote sensing scenarios. The radiative transfer forward model is capable of simulating (1) both 1- D and 3- D views of an exoplanet in reflected light, (2) emission spectra, and (3) transit spectra, and takes as input the atmospheric chemical and thermal state (including profiles of cloud properties). The incorporated noise model is based on Robinson et al. (2016), and provides wavelength- dependent noise estimates for a variety of observing scenarios. Finally, the retrieval package uses a Bayesian + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[84, 90, 882, 137]]<|/det|> +sampling package (emcee) to call the aforementioned radiative transfer model while mapping out the posterior distribution for the atmospheric parameters used to fit a noisy observation (Foreman- Mackey et al., 2013). + +<|ref|>sub_title<|/ref|><|det|>[[86, 154, 366, 172]]<|/det|> +## 213 Gibbs Free Energy Model + +<|ref|>text<|/ref|><|det|>[[85, 179, 882, 241]]<|/det|> +The thermodynamics Gibbs free energy model from Krissansen- Totton et al. (2016) and Krissansen- Totton et al. (2018b) was adopted to calculate chemical disequilibrium biosignatures. The Gibbs free energy is a thermodynamic state function that describes the maximum amount of work a chemical process can produce at constant pressure/temperature (Engel and Reid, 2019): + +<|ref|>equation<|/ref|><|det|>[[430, 250, 880, 291]]<|/det|> +\[G = \sum_{i}^{N}\left(\frac{\partial G}{\partial n_{i}}\right)n_{i} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[85, 296, 882, 358]]<|/det|> +where \(\mathfrak{n}_{i}\) is the moles of species ' \(i\) , \(\partial G / \partial n_{i}\) is the change in Gibbs free energy with respect to the moles of a given species, and the sum is over the total number of species in the system ( \(N\) ). The change in Gibbs free energy with respect to a given species can be rewritten in terms of thermodynamic activity and the standard Gibbs free energy of formation for a given species: + +<|ref|>equation<|/ref|><|det|>[[313, 368, 880, 411]]<|/det|> +\[\Delta G_{(T,P)} = \sum_{i}^{N}\left(\Delta_{f}G_{i(T,P_{r})}^{o} + RT\ln \left(\frac{P_{\mathrm{ni}}}{n_{T}}\gamma_{fi}\right)\right)n_{i} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[85, 415, 882, 461]]<|/det|> +The utility of Gibbs free energy is that it is minimized at thermodynamic equilibrium, which allows us to model the theoretical equilibrium state at a given temperature and pressure without explicitly considering individual chemical reactions. + +<|ref|>text<|/ref|><|det|>[[85, 461, 882, 598]]<|/det|> +Each observational scenario that was simulated considers the target as a closed, well- mixed system at constant global surface pressure and constant characteristic global temperature (Krissansen- Totton et al., 2016). The Gibbs free energy model first computes the Gibbs free energy of the system given an input atmospheric state (e.g., usually an instance of gas mixing ratios, atmospheric temperature, and surface pressure from the atmospheric retrieval model) and subsequently solves for the equilibrium species mixing ratios, where the Gibbs free energy is minimized and atoms are conserved. An interior points method, implemented using Matlab's fmincon function, was used to minimize and solve for the equilibrium state. The difference between the Gibbs energy of the input atmospheric state and the equilibrium state quantifies the "available Gibbs free energy": + +<|ref|>equation<|/ref|><|det|>[[371, 612, 880, 628]]<|/det|> +\[\Phi \equiv G_{(T,P)}(n_{\mathrm{initial}}) - G_{(T,P)}(n_{\mathrm{final}}) \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[85, 634, 882, 726]]<|/det|> +The available Gibbs free energy ( \(\Phi\) ) is henceforth used to quantify chemical disequilibrium, and is measured in Joules of available free energy per mole of atmosphere (Krissansen- Totton et al., 2016; Krissansen- Totton et al., 2018b). A large available Gibbs free energy indicates strong planetary chemical disequilibrium (e.g., the modern Earth atmosphere- ocean system at 2,326 J mol \(^{- 1}\) ). Conversely, low available Gibbs free energy indicates weak chemical disequilibrium. Most planetary bodies in our solar system like Jupiter, Venus, and Uranus, all have well below 1 J mol \(^{- 1}\) of available free energy. + +<|ref|>sub_title<|/ref|><|det|>[[85, 743, 517, 761]]<|/det|> +## 240 Proterozoic Earth Atmospheric Modeling + +<|ref|>text<|/ref|><|det|>[[85, 768, 882, 904]]<|/det|> +Self- consistent atmospheric models were generated using the photochemical model component of the Atmos tool (Arney et al., 2016; Arney et al., 2017). Atmos is a coupled photochemical- climate model that uses planetary inputs (e.g., chemical species mixing ratios, associated chemical reactions, gravity, surface pressure, surface temperature, and stellar spectrum) to calculate the steady- state profiles of chemical species present in the atmosphere. In the overall analysis, the default solar spectrum was used to model Earth- like cases relevant to reflected light observations (Thuillier et al., 2004). In the model, this solar spectrum was then adjusted with an input parameter (TIMEGA) to scale the solar flux to its appropriate insolation 1.3 Gyrs ago. The TRAPPIST- 1 spectrum (Peacock et al., 2019) was used for the TRAPPIST- 1 simulations that were mentioned in the text. All the atmospheric cases modeled in this study assumed a total fixed surface + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 90, 882, 181]]<|/det|> +250 pressure of 1 bar. A suite of cases spanning low to high concentrations in \(\mathrm{O}_2\) , \(\mathrm{CH}_4\) , and \(\mathrm{CO}_2\) (input values outlined in Table 1) were explored to capture broad uncertainty in the abundance of certain atmospheric species during the Proterozoic eon and referenced to the “model low” and “model high” values from Table 1 in Robinson and Reinhard (2020). The med. abundance case was computed taking the logarithmic geometric average of the high and low values. Note that the changes in \(\mathrm{CO}_2\) abundance did not have a significant impact on the available Gibbs free energy uncertainty. + +<|ref|>sub_title<|/ref|><|det|>[[86, 199, 402, 216]]<|/det|> +## Simulated rfast Observations + +<|ref|>text<|/ref|><|det|>[[85, 223, 882, 300]]<|/det|> +Reflected light observations in this study were modeled after direct imaging Decadal Survey mission concepts with ultra- violet \((0.2 - 0.4\mu \mathrm{m})\) , optical \((0.4 - 1.0\mu \mathrm{m})\) , and near- infrared \((1.0 - 1.8\mu \mathrm{m})\) bandpasses at resolving powers of 7, 140, and 70, respectively (Roberge and Moustakas, 2018b; Gaudi et al., 2018b). Each instance of a simulated noisy spectrum was produced with randomized error bars and with the prescribed SNR taken to apply at \(0.55\mu \mathrm{m}\) (consistent with earlier exo- Earth studies (Feng et al., 2018)). + +<|ref|>table<|/ref|><|det|>[[207, 315, 785, 666]]<|/det|> + +
InputSNR 20SNR 30SNR 50
log O2High-2.01-2.92+1.45
-4.76		-2.07+0.49
-4.54		-1.66+0.24
-0.23
Med.-3.01-6.08+2.65
-2.67		-6.02+2.68
-2.71		-6.29+2.54
-2.52
Low-4.01-6.60+2.37
-2.31		-6.73+2.25
-2.23		-7.00+2.05
-2.05
log CH4High-4.54-4.36+0.62
-3.04		-4.20+0.35
-1.28		-4.11+0.27
-0.32
Med.-4.94-6.07+1.52
-2.66		-6.29+1.57
-2.52		-5.31+0.71
-2.94
Low-5.53-7.19+1.92
-1.90		-6.87+1.92
-2.12		-7.44+1.76
-1.75
log CO2High-1.16-0.65+0.36
-0.39		-0.79+0.24
-0.26		-0.75+0.22
-0.20
Med.-2.23-2.28+0.48
-3.27		-2.14+0.27
-0.34		-2.19+0.22
-0.27
Low-3.31-6.36+2.54
-2.48		-6.40+2.44
-2.46		-6.75+2.25
-2.21
T0 (K)High288.259.91+32.18
-26.39		272.23+21.41
-20.62		266.86+12.44
-12.24
Med.288.267.84+30.15
-27.70		273.84+22.47
-28.01		266.13+14.21
-12.44
Low288.255.31+25.06
-19.79		258.34+23.07
-17.31		263.09+15.18
-13.10
log Avail. GibbsHigh1.36870.17+1.69
-0.95		1.28+0.59
-1.72		1.76+0.28
-0.32
Med.0.9739-1.24+1.59
-0.65		-1.23+1.54
-0.60		-1.16+1.74
-0.62
Low0.5853-1.65+1.11
-0.55		-1.63+1.18
-0.60		-1.74+0.81
-0.57
+ +<|ref|>table_footnote<|/ref|><|det|>[[113, 675, 882, 707]]<|/det|> +Table 1: Summary of the parameters, input values, and \(1\sigma\) confidence intervals (taken from the \(16^{\mathrm{th}}\) \(50^{\mathrm{th}}\) and \(84^{\mathrm{th}}\) percentile values) for the high, med., and low atmospheric abundance scenarios that were modeled. + +<|ref|>sub_title<|/ref|><|det|>[[86, 732, 240, 750]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[86, 762, 884, 911]]<|/det|> +Giada Arney, Shawn D. Domagal- Goldman, Victoria S. Meadows, Eric T. Wolf, Edward Schwieterman, Benjamin Charnay, Mark Claire, Eric Hebrard, and Melissa G. Trainer. The Pale Orange Dot: The Spectrum and Habitability of Hazy Archean Earth. Astrobiology, 16(11):873- 899, October 2016. ISSN 1531- 1074. doi: 10.1089/ast.2015.1422. URL https://www.liebertpub.com/doi/abs/10.1089/ast.2015.1422. Giada N. Arney. The K Dwarf Advantage for Biosignatures on Directly Imaged Exoplanets. 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Atmospheric Retrieval for Super- Earths: Uniquely Constraining the Atmospheric Composition with Transmission Spectroscopy. Astrophysical Journal, 753(2):100, July 2012. doi: 10.1088/0004- 637X/753/2/100. + +<|ref|>text<|/ref|><|det|>[[83, 240, 883, 287]]<|/det|> +Shawn D. Domagal- Goldman, Antigona Segura, Mark W. Claire, Tyler D. Robinson, and Victoria S. Meadows. Abiotic Ozone and Oxygen in Atmospheres Similar to Prebiotic Earth. The Astrophysical Journal, 792(2):90, September 2014. doi: 10.1088/0004- 637X/792/2/90. + +<|ref|>text<|/ref|><|det|>[[83, 295, 883, 327]]<|/det|> +Thomas Engel and Philip Reid. Thermodynamics, Statistical Thermodynamics, and Kinetics. Pearson Education, New York, 4th edition, 2019. ISBN 978- 0- 13- 480458- 3. + +<|ref|>text<|/ref|><|det|>[[83, 335, 883, 397]]<|/det|> +Thomas J. Fauchez, Geronimo L. Villanueva, Edward W. Schwieterman, Martin Turbet, Giada Arney, Daria Pidhorodetska, Ravi K. Kopparapu, Avi Mandell, and Shawn D. Domagal- Goldman. 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Hutchings, Peter Jakobsen, Simon J. Lilly, Knox S. Long, Jonathan I. Lunine, Mark J. McCaughrean, Matt Mountain, John Nella, George H. Rieke, Marcia J. Rieke, Hans- Walter Rix, Eric P. Smith, George Sonneborn, Massimo Stiavelli, H. S. Stockman, Rogier A. Windhorst, and Gillian S. Wright. The James Webb Space Telescope. Space Science Reviews, 123(4):485- 606, April 2006. doi: 10.1007/s11214- 006- 8315- 7. + +<|ref|>text<|/ref|><|det|>[[83, 632, 883, 771]]<|/det|> +B. Scott Gaudi, Eric Agol, Daniel Apai, Eduardo Bendek, Alan Boss, James B. Breckinridge, David R. Ciardi, Nicolas B. Cowan, William C. Danchi, Shawn Domagal-Goldman, Jonathan J. Fortney, Thomas P. Greene, Lisa Kaltenegger, James F. Kasting, David T. Leisawitz, Alain Leger, Charles F. Lille, Douglas P. Lisman, Amy S. Lo, Fabian Malbet, Avi M. Mandell, Victoria S. Meadows, Bertrand Mennesson, Bijan Nemati, Peter P. Plavchan, Stephen A. Rinehart, Aki Roberge, Eugene Serabyn, Stuart B. Shaklan, Michael Shao, Karl R. Stapelfeldt, Christopher C. Stark, Mark Swain, Stuart F. Taylor, Margaret C. Turnbull, Neal J. Turner, Slava G. Turyshev, Stephen C. Unwin, and Lucianne M. Walkowicz. Exoplanet exploration program analysis group (exopag) report to paul hertz regarding large mission concepts to study for the 2020 decadal survey, 2015. + +<|ref|>text<|/ref|><|det|>[[83, 780, 883, 826]]<|/det|> +B. Scott Gaudi, Sara Seager, Bertrand Mennesson, Alina Kiessling, Keith R. Warfield, Habitable Exoplanet Observatory Science, and Technology Definition Team. The Habitable Exoplanet Observatory. Nature Astronomy, 2:600-604, August 2018a. doi: 10.1038/s41550-018-0549-2. + +<|ref|>text<|/ref|><|det|>[[83, 835, 883, 881]]<|/det|> +B. Scott Gaudi, Sara Seager, Bertrand Mennesson, Alina Kiessling, Keith R. Warfield, Habitable Exoplanet Observatory Science, and Technology Definition Team. The Habitable Exoplanet Observatory. Nature Astronomy, 2:600-604, August 2018b. doi: 10.1038/s41550-018-0549-2. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[83, 90, 884, 141]]<|/det|> +Thomas P. Greene, Michael R. Line, Cezar Montero, Jonathan J. Fortney, Jacob Lustig- Yaeger, and Kyle Luther. Characterizing Transiting Exoplanet Atmospheres with JWST. The Astrophysical Journal, 817 (1):17, January 2016. doi: 10.3847/0004- 637X/817/1/17. + +<|ref|>text<|/ref|><|det|>[[84, 143, 883, 206]]<|/det|> +C. E. Harman, R. Felton, R. Hu, S. D. Domagal-Goldman, A. Segura, F. Tian, and J. F. Kasting. Abiotic \(\mathrm{O}_{2}\) Levels on Planets around F, G, K, and M Stars: Effects of Lightning-produced Catalysts in Eliminating Oxygen False Positives. The Astrophysical Journal, 866(1):56, October 2018. doi: 10.3847/1538- 4357/aadd9b. + +<|ref|>text<|/ref|><|det|>[[84, 213, 883, 245]]<|/det|> +Dian R. Hitchcock and James E. Lovelock. Life detection by atmospheric analysis. Icarus, 7(1- 3):149- 159, January 1967. doi: 10.1016/0019- 1035(67)90059- 0. + +<|ref|>text<|/ref|><|det|>[[84, 251, 883, 313]]<|/det|> +Joshua Krissansen- Tutton, David S. Bergsman, and David C. Catling. On Detecting Biospheres from Chemical Thermodynamic Disequilibrium in Planetary Atmospheres. Astrobiology, 16(1):39- 67, January 2016. ISSN 1531- 1074. doi: 10.1089/ast.2015.1327. URL https://www.liebertpub.com/doi/full/10.1089/ast.2015.1327. + +<|ref|>text<|/ref|><|det|>[[84, 320, 883, 367]]<|/det|> +Joshua Krissansen- Tutton, Ryan Garland, Patrick Irwin, and David C. Catling. Detectability of Biosignatures in Anoxic Atmospheres with the James Webb Space Telescope: A TRAPPIST- 1e Case Study. Astronomical Journal, 156(3):114, September 2018a. doi: 10.3847/1538- 3881/aad564. + +<|ref|>text<|/ref|><|det|>[[84, 373, 883, 420]]<|/det|> +Joshua Krissansen- Tutton, Stephanie Olson, and David C. Catling. Disequilibrium biosignatures over Earth history and implications for detecting exoplanet life. Science Advances, 4(1):eaao5747, January 2018b. doi: 10.1126/sciadv.aoa5747. + +<|ref|>text<|/ref|><|det|>[[84, 427, 883, 473]]<|/det|> +Joshua Krissansen- Tutton, Jonathan J. Fortney, Francis Nimmo, and Nicholas Wogan. Oxygen False Positives on Habitable Zone Planets Around Sun- Like Stars. AGU Advances, 2(2):e00294, June 2021. doi: 10.1029/2020AV000294. + +<|ref|>text<|/ref|><|det|>[[84, 480, 883, 542]]<|/det|> +Michael R. Line, Aaron S. Wolf, Xi Zhang, Heather Knutson, Joshua A. Kammer, Elias Ellison, Pieter Deroo, Dave Crisp, and Yuk L. Yung. A Systematic Retrieval Analysis of Secondary Eclipse Spectra. I. A Comparison of Atmospheric Retrieval Techniques. Astrophysical Journal, 775(2):137, October 2013. doi: 10.1088/0004- 637X/775/2/137. + +<|ref|>text<|/ref|><|det|>[[84, 548, 883, 580]]<|/det|> +J. E. Lovelock. A Physical Basis for Life Detection Experiments. Nature, 207(4997):568- 570, August 1965. ISSN 1476- 4687. doi: 10.1038/207568a0. URL https://www.nature.com/articles/207568a0. + +<|ref|>text<|/ref|><|det|>[[84, 586, 883, 618]]<|/det|> +J. E. Lovelock. Thermodynamics and the Recognition of Alien Biospheres. Proceedings of the Royal Society of London Series B, 189(1095):167- 180, May 1975. doi: 10.1098/rspb.1975.0051. + +<|ref|>text<|/ref|><|det|>[[84, 625, 883, 657]]<|/det|> +R. Luger and R. Barnes. Extreme Water Loss and Abiotic O2Buildup on Planets Throughout the Habitable Zones of M Dwarfs. Astrobiology, 15(2):119- 143, Feb 2015. doi: 10.1089/ast.2014.1231. + +<|ref|>text<|/ref|><|det|>[[84, 664, 883, 710]]<|/det|> +Jacob Lustig- Yaeger, Victoria S. Meadows, and Andrew P. Lincowski. The Detectability and Characterization of the TRAPPIST- 1 Exoplanet Atmospheres with JWST. Astrophysical Journal, 158(1):27, July 2019. doi: 10.3847/1538- 3881/ab21e0. + +<|ref|>text<|/ref|><|det|>[[84, 718, 883, 749]]<|/det|> +N. Madhusudhan and S. Seager. A Temperature and Abundance Retrieval Method for Exoplanet Atmospheres. The Astrophysical Journal, 707(1):24- 39, December 2009. doi: 10.1088/0004- 637X/707/1/24. + +<|ref|>text<|/ref|><|det|>[[84, 756, 883, 788]]<|/det|> +Nikko Madhusudhan. Exoplanetary Atmospheres: Key Insights, Challenges, and Prospects. Annual Review of Astronomy and Astrophysics, 57:617- 663, August 2019. doi: 10.1146/annurev- astro- 081817- 051846. + +<|ref|>text<|/ref|><|det|>[[84, 795, 883, 826]]<|/det|> +Victoria S. Meadows. Reflections on \(\mathrm{O}_{2}\) as a Biosignature in Exoplanetary Atmospheres. Astrobiology, 17 (10):1022- 1052, October 2017. doi: 10.1089/ast.2016.1578. + +<|ref|>text<|/ref|><|det|>[[84, 834, 883, 910]]<|/det|> +Victoria S. Meadows, Christopher T. Reinhard, Giada N. Arney, Mary N. Parenteau, Edward W. Schwieterman, Shawn D. Domagal- Goldman, Andrew P. Lincowski, Karl R. Stapelfeldt, Heike Rauer, Shiladitya DasSarma, Siddharth Hegde, Norio Narita, Russell Deitrick, Jacob Lustig- Yaeger, Timothy W. Lyons, Nicholas Siegler, and J. Lee Grenfell. Exoplanet Biosignatures: Understanding Oxygen as a Biosignature in the Context of Its Environment. Astrobiology, 18(6):630- 662, June 2018. doi: 10.1089/ast.2017.1727. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 90, 884, 137]]<|/det|> +National Academies of Sciences Engineering and Medicine. An Astrobiology Strategy for the Search for Life in the Universe. The National Academies Press, October 2019. ISBN 978- 0- 309- 48416- 9. doi: 10.17226/25252. URL https://doi.org/10.17226/25252. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 88, 884, 181]]<|/det|> +Nixon, Kevin Ortiz Ceballos, Anjali A. A. Piette, Diana Powell, Benjamin V. Rackham, Lakeisha Ramos- Rosado, Emily Rauscher, Seth Redfield, Laura K. Rogers, Michael T. Roman, Gael M. Roudier, Nicholas Scarsdale, Evgenya L. Shkolnik, John Southworth, Jessica J. Spake, Maria E Steinrueck, Xianyu Tan, Johanna K. Teske, Pascal Tremblin, Shang- Min Tsai, Gregory S. Tucker, Jake D. Turner, Jeff A. Valenti, Olivia Venot, Ingo P. Waldmann, Nicole L. Wallack, Xi Zhang, and Sebastian Zieba. Identification of carbon dioxide in an exoplanet atmosphere. arXiv e- prints, art. arXiv:2208.11692, August 2022. + +<|ref|>text<|/ref|><|det|>[[85, 189, 883, 237]]<|/det|> +G. Thuillier, L. Floyd, T. N. Woods, R. Cebula, E. Hilsenrath, M. Herse, and D. Labs. Solar irradiance reference spectra for two solar active levels. Advances in Space Research, 34(2):256-261, January 2004. doi: 10.1016/j.asr.2002.12.004. + +<|ref|>text<|/ref|><|det|>[[85, 245, 883, 293]]<|/det|> +Nicholas F. Wogan and David C. Catling. When is Chemical Disequilibrium in Earth-like Planetary Atmosphere a Biosignature versus an Anti-biosignature? Disequilibria from Dead to Living Worlds. Astrophysical Journal, 892(2):127, April 2020. doi: 10.3847/1538-4357/ab7b81. + +<|ref|>text<|/ref|><|det|>[[85, 300, 883, 348]]<|/det|> +Robin Wordsworth and Raymond Pierrehumbert. ABIOTIC OXYGEN-DOMINATED ATMOSPHERES ON TERRESTRIAL HABITABLE ZONE PLANETS. The Astrophysical Journal, 785(2):L20, apr 2014. doi: 10.1088/2041-8205/785/2/120. URL https://doi.org/10.1088/2041-8205/785/2/120. + +<|ref|>text<|/ref|><|det|>[[85, 355, 883, 403]]<|/det|> +Fabian Wunderlich, Mareike Godolt, John Lee Grenfell, Steffen Städt, Alexis MS Smith, Stefanie Gebauer, Franz Schreier, Pascal Hedelt, and Heike Rauer. Detectability of atmospheric features of earth-like planets in the habitable zone around m dwarfs. Astronomy & Astrophysics, 624:A49, 2019. + +<|ref|>sub_title<|/ref|><|det|>[[86, 437, 336, 457]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[85, 468, 904, 620]]<|/det|> +All authors would like to acknowledge support from the NASA Exobiology Program, Grant No. 80NSSC18K0349. AVY, TDR, EWS, and DCC also acknowledge support from NASA's Nexus for Exoplanet System Science Virtual Planetary Laboratory (No. 80NSSC18K0829). TDR acknowledges support from NASA's Exoplanets Research Program (No. 80NSSC18K0349) and Habitable Worlds Program (No. 80NSSC20K0226) as well as the Cottrell Scholar Program administered by the Research Corporation for Science Advancement. EWS and CTR acknowledge additional support from the NASA Interdisciplinary Consortia for Astrobiology Research (ICAR) Program via the Alternative Earths team with funding issued under grant No. 80NSSC21K059. M.J.W. acknowledges support from the GSFC Sellers Exoplanet Environments Collaboration (SEEC) and ROCKE- 3D: The evolution of solar system worlds through time, both of which are funded by the NASA Planetary Science Divisions Internal Scientist Funding Model. + +<|ref|>sub_title<|/ref|><|det|>[[86, 643, 368, 663]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[85, 672, 883, 795]]<|/det|> +AVY conducted all the atmospheric retrievals outlined in this work, performed code development to couple the retrieval results to the Gibbs free energy model, and computed the available Gibbs free energy posteriors for all the simulated cases. TDR performed code adaptations and development of rfast in correspondence with the retrieval analyses performed in this work and is the principal investigator of the project. JKT distributed the most up to date version of thermodynamics model used in this work and contributed to necessary code development and adaptation for the thermodynamic simulations. ES provided Atmos generated atmospheric compositions for each of the abundance scenarios outlined in this work. All authors contributed to both project development and the writing and/or editing of the paper and approved the scientific content. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 86, 436, 107]]<|/det|> +Supplementary Information + +<|ref|>text<|/ref|><|det|>[[115, 117, 882, 256]]<|/det|> +Fig. S1 shows an equilibrium calculation for the high Proterozoic abundance case presented in the main text. The total available Gibbs free energy for this scenario is \(24.24 \mathrm{J} \mathrm{mol}^{- 1}\) . The blue bars show the observed mixing ratios of each species, the red bars represent the equilibrium mixing ratios of each species, and the yellow bars show the change in mixing ratio between the equilibrium and observed abundances. \(\mathrm{O}_{2}\) and \(\mathrm{CH}_{4}\) have significantly large observed mixing ratios and also have large differences between their observed and equilibrium abundances. This indicates that these two species are the main drivers of chemical disequilibrium in the atmosphere. Using these results, chemical species like \(\mathrm{NH}_{3}\) , \(\mathrm{H}_{2}\) , and \(\mathrm{N}_{2} \mathrm{O}\) where excluded from the retrieval analysis and held at their equilibrium abundances when computing the available Gibbs free energy posterior distribution. + +<|ref|>image<|/ref|><|det|>[[160, 268, 825, 658]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 670, 882, 761]]<|/det|> +
Figure S1: Thermodynamic Calculation for High Proterozoic Earth Case. Blue bars represent the observed (or input) abundance for each species labeled across the bottom axis. Red bars indicate the equilibrium abundance (calculated from the Gibbs free energy minimization). Yellow bars indicate the absolute value of the difference between the observed and equilibrium abundance for each species. Note that the molecules with a substantially large abundance and large difference (i.e., the \(\mathrm{O}_{2}\) and \(\mathrm{CH}_{4}\) ) are major contributors to the atmospheric chemical disequilibrium signal.
+ +<|ref|>text<|/ref|><|det|>[[115, 777, 882, 899]]<|/det|> +Outlined in Fig. S2 are the marginal posterior distributions for the mixing ratios of \(\mathrm{H}_{2} \mathrm{O}\) , Fig. S2a, \(\mathrm{CO}_{2}\) , Fig. S2b, and \(\mathrm{O}_{3}\) , Fig. S2c, along with the retrieved atmospheric pressure, Fig. S2d, for the high (green) med., (purple), low (blue) abundance scenarios. These retrieved parameters were included in the random sampling used to compute the available Gibbs free energy posteriors (Fig. 1 in the main text), but did not have a significant influence on the uncertainty. However, constraining parameters like \(\mathrm{H}_{2} \mathrm{O}\) , \(\mathrm{CO}_{2}\) , and surface pressure are essential for inferring the climate and habitability of exoplanets. Additionally, \(\mathrm{O}_{3}\) can be used as a proxy for determining the \(\mathrm{O}_{2}\) abundance for less oxygenated atmospheres since \(\mathrm{O}_{3}\) can remain detectable even at low \(\mathrm{O}_{2}\) concentrations (Meadows, 2017; Meadows et al., 2018; Schwieterman et al., 2018). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[152, 90, 840, 610]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 627, 883, 750]]<|/det|> +
Figure S2: Reflected Light Retrieval Parameters Pertinent to Computing The Available Gibbs Free Energy (Non-essential). a, Marginal posterior distributions for the high (green), medium (purple), and low (blue) abundance cases for the log abundance of \(\mathrm{H}_2\mathrm{O}\) . Included are distributions for observations simulated at 20 (hatched), 30 (un-filled), and 50 (solid fill) SNR. b Marginal Posterior distribution for the log mixing ratio of \(\mathrm{CO}_2\) . c Marginal posterior distributions for the log mixing ratio of \(\mathrm{O}_3\) . d, Marginal posterior distributions for the global surface pressure. Note that each of these parameters are included in the overall Gibbs free energy calculation, but their uncertainties do not substantially impact the overall available Gibbs free energy.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 90, 883, 196]]<|/det|> +In Fig. S3, the marginal posterior distributions for the \(\mathrm{O_2}\) abundance are outlined at the high (Fig. S3a), med (Fig. S3b), and low (Fig. S3c) abundance cases for a Proterozoic Earth- like planet orbiting an M dwarf and inferred from simulated transit observations with the JWST NIRSpec instrument. These results show it is very difficult to constrain the atmospheric abundance of \(\mathrm{O_2}\) at each of the observational noise levels (5 ppm and 10 ppm), either of which would be challenging for JWST to achieve for current best- case targets. The lack of constraints on the \(\mathrm{O_2}\) abundance likely make inferring the chemical disequilibrium energy of a Proterozoic Earth- like exoplanet orbiting a late- type star too challenging for JWST. + +<|ref|>image<|/ref|><|det|>[[152, 218, 825, 476]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 499, 883, 606]]<|/det|> +
Figure S3: \(\mathbf{O}_2\) Posterior Distributions Derived From Simulated JWST/NIRSpec Observations. a, Marginal Posterior distribution of \(\mathrm{O_2}\) for the high Proterozoic Earth abundance case. The vertical black dashed line denotes the input value for the \(\mathrm{O_2}\) abundance. Each distribution is inferred from simulated transit observations at noise levels of 5 ppm (un-filled) and 10 ppm (hatched). b, Same as a but for the medium abundance case. c, Same as a but for the low abundance case. Note the lack of constraints for all abundance cases at both noise levels, which indicates the chemical disequilibrium cannot be adequately constrained at these abundances via JWST/NIRSpec transit observations.
+ +<--- Page Split ---> diff --git a/preprint/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91/images_list.json b/preprint/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..2b299dfde8ac0c16430b56dc315213263c047942 --- /dev/null +++ b/preprint/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91/images_list.json @@ -0,0 +1,528 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. The pipeline of cell morphology reconstruction for C. elegans embryogenesis by CMap. (A) The data processing pipeline of CMap. Time-lapse 3D (4D) images of GFP-labeled cell nuclei and mCherry-labeled cell membranes are used for cell lineage tracing and morphology segmentation respectively, with output of cell identity (with information on cell lineage and cell fate), cell shape, volume,", + "footnote": [], + "bbox": [], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Statistics of the cells with resolved cell lineage and morphology up to the 550-cell stage of \\(C\\) . elegans embryogenesis. (A) The embryonic cell lineage tree averaged over the eight \\(C\\) . elegans wild-type embryos up to the 550-cell stage. Cell fates are differentially color-coded as indicated. The kidney and the sole body-wall muscle cell derived from the AB lineage are indicated with black and gray arrowhead respectively. The cells with consistent failures in segmentation in all embryo samples are indicated with black dots. Developmental time is shown on the left, with the last time point of the four-cell stage set as the time zero. (B) Cell count dynamics across developmental stages for the eight embryos, with the average cell number represented by dots (black for surviving cells and red for apoptotic ones) and standard deviation by vertical lines. The duration of significant developmental landmarks is indicated by differential shading47. (C-D) Comparison of average cell volume (C) and cell surface area (D) with individual measurements from the eight \\(C\\) . elegans embryos. Data points represent individual cell comparisons, with the average across embryos on the horizontal axis and individual embryo measurements on the vertical axis. Cells present before and after the \\(\\sim 350\\) -cell stage are color-coded in blue and yellow, respectively. Insets show the distribution of correlation coefficients for these comparisons.", + "footnote": [], + "bbox": [ + [ + 66, + 199, + 930, + 550 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. Cell shape dynamics across tissue formation and organogenesis during late embryogenesis. (A)", + "footnote": [], + "bbox": [ + [ + 75, + 45, + 896, + 820 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4. Notch signaling promotes directional asymmetry in cell volume between anterior and posterior daughters of the target cell and its sibling. (A) Illustrations of asymmetric cell division producing two representative apoptotic cells, MSpaapp (left) and ABprpppps (right). \\(T_{\\mathrm{C}}\\) denotes the last time point of cytokinesis. (B) Reconstructed 3D morphologies of contacting cell pairs engaged in the previously identified Notch signaling events during C. elegans embryogenesis. Cells expressing Notch ligands are highlighted in green, while those with the receptor are in red. (C) Plots showing the volume asymmetry", + "footnote": [], + "bbox": [ + [ + 75, + 45, + 920, + 760 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5. Consecutive asymmetric divisions in terms of cell volume lead to a disproportionately large size of the kidney cell, ABplappaaap. (A) The “H”-shaped kidney cell labeled by GFP (left) or its merge with differential interference contrast microscopy (DIC) (right) in an adult. (B) Quantification of kidney cell volume changes over embryogenesis. The graph shows the mean cell volumes (line) and their standard deviations (shaded area) for the kidney cell and its progenitors from eight wild-type embryos in red, and for their sister cells in green. The time of ABplapp's birth is used as the reference point (time zero). (C) Quantification of volume change over embryogenesis for all cells derived from AB (blue) and E (gray) with that for the kidney cell and its progenitors (red, same data as in (B)). The time of ABplapp's birth is used as the reference point (time zero). (D) Asymmetric division of the kidney’s grandparent (top) and parent (bottom) in an exemplary embryo, with the resulting daughters differentially color-coded as indicated. \\(T_{\\mathrm{C}}\\) denotes the last time point of cytokinesis. (E) Comparison of the apoptotic cell ABplappap's differential engulfment by two cells, ABarapppp (top) and ABplappaa (bottom), across two different embryos (WT_Sample1 and WT_Sample5). \\(T_{\\mathrm{E}}\\) denotes a chosen time point before the apoptotic cell is engulfed.", + "footnote": [], + "bbox": [ + [ + 80, + 50, + 912, + 475 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Fig. 6. Identification of new Notch signaling interactions with size effect on the kidney progenitors and a symmetric cell. (A) Lineal expression (redness on cell lineage tree) of two ligands, lag-1 and apx-1, and one receptor, lin-12, of the Notch signaling pathway. The relationship between gene expression level", + "footnote": [], + "bbox": [ + [ + 80, + 48, + 911, + 475 + ] + ], + "page_idx": 18 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Fig. 7. Multiple factors contribute to the cell volume asymmetry between daughter cells. (A) The distribution of cell volume asymmetry between daughter cells without positional bias \\(\\left(\\frac{|V_{D1} - V_{D2}|}{|V_{D1} + V_{D2}|}\\right)\\) in the wild-type and pop-1- (pop-1 RNAi) embryos. The statistical significance is obtained by the one-sided Wilcoxon rank-sum test and is listed at the top. (B) The negative correlation between the shift of cell volume asymmetry with \\(\\left(\\delta_{\\mathrm{U} - \\mathrm{C}}\\left[\\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\\right]\\right)\\) and without mechanical compression \\(\\left(\\frac{|V_{D1} - V_{D2}|}{|V_{D1} + V_{D2}|}\\right)\\) . The result of proportional fitting between \\(\\left[\\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\\right]_{\\mathrm{U}}\\) and \\(\\delta_{\\mathrm{U} - \\mathrm{C}}\\left[\\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\\right]\\) is shown with a solid line, with the proportional coefficient \\((K)\\) and goodness of fit \\((G)\\) listed in the top right corner. The statistical significance", + "footnote": [], + "bbox": [ + [ + 66, + 210, + 926, + 711 + ] + ], + "page_idx": 20 + }, + { + "type": "image", + "img_path": "images/Figure_8.jpg", + "caption": "Fig. 8. ITK-SNAP-CVE: A customized software tool for the visualization and interactive analysis of embryonic cell morphologies. (A) The main graphical user interface of ITK-SNAP-CVE, showcasing the layout and available tools. (B) The visual representations of all cells in embryo using the software's \"Show all cells\" display mode, with 2D views (top) and 3D reconstructions (bottom). (C-E) The detailed visualization of a selected individual cell, i.e., the somatic founder cell \"C\", within an embryo, as seen through different viewing options: (C) \"Show master cells only\" display mode, highlighting the \"C\" cell alone. (D) \"Show master cells and neighbors\" display mode, highlighting the \"C\" cell along with its immediate neighboring cells. (E) \"Show master cells and other cells\" display mode, where the \"C\" cell is visible in the context of the entire cell population. (F) A comprehensive view of all cells derived from the same lineage, exemplified here by the MS lineage, demonstrating the lineage-specific visualization capabilities of the software. (G) A display of all cells that are destined to become part of the same organ,", + "footnote": [], + "bbox": [ + [ + 72, + 150, + 899, + 644 + ] + ], + "page_idx": 22 + }, + { + "type": "image", + "img_path": "images/Figure_9.jpg", + "caption": "Fig. 9. An interactive web platform for visualizing embryonic cell morphologies, intercellular contacts, and cell-resolved lineal gene expressions. (A) The lineage-specific expression of the transcription factor, ceh-36, over approximately four hours from the four-cell stage. The relationship between gene expression level and color is displayed on the right. (B) The 3D views of an exemplary embryo at specified developmental stages (t, imaging time) with an overlay of ceh-36 expression (color-", + "footnote": [], + "bbox": [ + [ + 66, + 70, + 930, + 803 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "fig. S1: The comparison of Hausdorff distance (left) and Dice score (right) between CShaper and CMap implemented at different developmental stages (represented by cell numbers), using the manually annotated ground truth images as a benchmark. This displays that CMap performs better than CShaper in both metrics (smaller Hausdorff distance and larger Dice score). For the Hausdorff distance, the lower and upper quartiles of a data group are shown with a colored box, with its median labeled inside and two whiskers extended to the maximum and minimum data value; for the Dice score, the average of a data group is shown by a colored column, with all the data values plotted with gray dots. The statistical significance is obtained by the one-sided Student’s \\(t\\) -test and is listed at the top.", + "footnote": [], + "bbox": [ + [ + 70, + 469, + 920, + 628 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "Fig. S2: The comparison of 3D segmentation performances between ground truth (left column), CShaper (middle column), and CMap (right column), using WT_Sample1. The snapshots at around the 100-, 200-, 300-, 400-, 500-, and 550-cell stages are shown from top to bottom, respectively; segmentation defects in CShaper but not in CMap outputs are indicated by red arrows.", + "footnote": [], + "bbox": [ + [ + 152, + 60, + 840, + 799 + ] + ], + "page_idx": 46 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_2.jpg", + "caption": "fig. S3: The comparison of 3D segmentation performances between ground truth (left column), CShaper (middle column), and CMap (right column), using WT_Sample7. The snapshots at around the 100-, 200-, 300-, 400-, 500-, and 550-cell stages are shown from top to bottom, respectively; segmentation defects in CShaper but not in CMap outputs are indicated by red arrows.", + "footnote": [], + "bbox": [ + [ + 152, + 65, + 840, + 796 + ] + ], + "page_idx": 47 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_3.jpg", + "caption": "fig. S4: The comparison of Hausdorff distance (top) and Dice score (bottom) between the state-of-the-art cell segmentation algorithms implemented at different developmental stages (represented by cell numbers), using the manually annotated ground truth images as a benchmark. This displays that CMap performs better than the other algorithms in both metrics (smaller Hausdorff distance and larger Dice score). For the Hausdorff distance, the lower and upper quartiles of a data group are shown with a colored box, with its median labeled inside and two whiskers extended to the maximum and minimum data value; for the Dice score, the average of a data group is shown by a colored column, with all the data values plotted with gray dots. The statistical significance is obtained by the one-sided Student's \\(t\\) -test and is listed at the top.", + "footnote": [], + "bbox": [ + [ + 90, + 48, + 860, + 547 + ] + ], + "page_idx": 48 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_4.jpg", + "caption": "fig. S5: The micrographs showing the ubiquitous postembryonic expression of two transgenes (GFP for cell nuclei and mCherry for cell membranes) in a transgenic C. elegans strain. The images for L1, L2, L3, L4, and adult stages are placed from left to right. The images obtained from differential interference contrast microscopy (DIC), GFP (labeling cell nuclei), and mCherry (labeling cell membranes) channels, along with their merged ones, are shown from top to bottom respectively.", + "footnote": [], + "bbox": [ + [ + 52, + 68, + 944, + 333 + ] + ], + "page_idx": 49 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_5.jpg", + "caption": "fig. S6: The detailed convolutional neural network structure and data processing flow of CMap segmentation. (A) The overall U-shape structure of EDT-DMFNet with the channel number of each 3D deep convolutional layer marked. The model conducts semantic segmentation on the \\(z\\) -stack of 3D image data and generates segmented 3D cell objects by cell nucleus marker-based watershed algorithm (EDT map segmentation). (B) The detailed inner data flow of DMFUnit of the encoders (the left part of the EDT- DMFNet shown in (A)), where dilated convolution cores and multiple fibers are used for exchanging information. (C) The detailed inner data flow of MFUnit of the decoders (the right part of the EDT-DMFNet shown in (A)).", + "footnote": [], + "bbox": [ + [ + 85, + 55, + 914, + 714 + ] + ], + "page_idx": 50 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_6.jpg", + "caption": "fig S7. The 3D snapshots (bottom left of both panels) for the uncompressed (left; exemplified by the embryo sample WT_Sample5) and compressed (right; exemplified by the embryo sample Sample20) embryos within the \\(350\\pm 5\\) -cell stage, along with their cross-sections observed along the \\(z\\) (top left), \\(y\\) (top right), and \\(x\\) (bottom right) axes, respectively. Illustrations are realized with the aid of ITK-SNAP.", + "footnote": [], + "bbox": [ + [ + 72, + 75, + 925, + 344 + ] + ], + "page_idx": 51 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_7.jpg", + "caption": "fig. S9: The 2D and 3D snapshots for the exemplary cell objects filtered by CPR (top), VIR (middle), and VVR (bottom). The illustrations are realized with the aid of ITK-SNAP. The definition and calculation for CPR, VIR, and VVR are detailed in Materials and Methods.", + "footnote": [], + "bbox": [], + "page_idx": 52 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_8.jpg", + "caption": "fig. S10: The 2D and 3D snapshots for the exemplary cell objects filtered by \\(V\\) (top), \\(SR\\) (middle), and \\(FRL\\) (bottom). The illustrations are realized with the aid of ITK-SNAP. The definition and calculation for \\(V\\) , \\(SR\\) , and \\(FRL\\) are detailed in Materials and Methods.", + "footnote": [], + "bbox": [ + [ + 140, + 72, + 860, + 604 + ] + ], + "page_idx": 53 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_9.jpg", + "caption": "fig. S11: The 2D snapshots for the exemplary non-apoptotic cell objects with defective automatic segmentation (left; unsmooth boundary) that can be corrected by manual annotation (right; smooth boundary). Illustrations are realized with the aid of ITK-SNAP.", + "footnote": [], + "bbox": [ + [ + 135, + 48, + 860, + 360 + ] + ], + "page_idx": 54 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_10.jpg", + "caption": "fig. S12: The 2D snapshots for the exemplary apoptotic cell objects with defective automatic segmentation (left; uninflated boundary) that can be corrected by manual annotation (right; inflated boundary). Illustrations are realized with the aid of ITK-SNAP.", + "footnote": [], + "bbox": [ + [ + 135, + 461, + 860, + 760 + ] + ], + "page_idx": 55 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_11.jpg", + "caption": "fig. S13: The 3D snapshots of cell shapes from ten consecutive time points from different lineages with various fates indicated on the left. For the P3 and P4 cells, the cell shapes of the last consecutive ten time points before their completion of cytokinesis (included) are shown from left to right; for the remaining cells, their first consecutive ten time points since the \\(\\geq 550\\) -cell stage are shown from left to right. All the sequential snapshots for a cell are collected from the same embryo sample without data-point loss. Cells are randomly colored.", + "footnote": [], + "bbox": [ + [ + 66, + 45, + 905, + 718 + ] + ], + "page_idx": 56 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_12.jpg", + "caption": "fig. S14: The 3D snapshots for different tissues and organs within the \\(100\\pm 10\\) , \\(200\\pm 10\\) , \\(300\\pm 10\\) , \\(400\\pm 10\\) , \\(500\\pm 10\\) , and \\(>550\\) -cell stages. The sequential snapshots for a tissue or organ are collected from the same embryo sample. Note that the data-point loss rate is always lower than \\(5\\%\\) for all the snapshots.", + "footnote": [], + "bbox": [ + [ + 80, + 48, + 918, + 388 + ] + ], + "page_idx": 56 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_13.jpg", + "caption": "fig. S15: The qualitative and quantitative characterization of the shape and migration of intestinal cells during intestine morphogenesis in the C. elegans embryo. (A) The average (solid dot) and standard", + "footnote": [], + "bbox": [ + [ + 140, + 52, + 856, + 864 + ] + ], + "page_idx": 57 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_14.jpg", + "caption": "fig. S16: The qualitative and quantitative characterization of the only AB progeny, cell ABprppppaa, that forms body-wall muscle in the C. elegans embryo. (A) The average (solid dot) and standard deviation (solid line) of irregularity of cell ABprppppaa over developmental time \\((t)\\) in one embryo (WT_Sample1), with the last time point of the four-cell stage set as the time zero. The maximums and minimums are indicated by triangles, where the correlation coefficients of the monotonic \\(\\eta -t\\) curves are labeled at the top. (B) The 3D shape snapshots for cell ABprppppaa during body-wall muscle assembly at the six time points labeled in (A).", + "footnote": [], + "bbox": [ + [ + 255, + 50, + 737, + 560 + ] + ], + "page_idx": 58 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_15.jpg", + "caption": "fig. S17: The Anti-correlation between cell size asymmetry and cell cycle length asymmetry (division asynchrony). (A) The anti-correlation between cell volume asymmetry \\(\\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\\) and division asynchrony \\(\\frac{L_{D1} - L_{D2}}{L_{D1} + L_{D2}}\\) . The result of proportional fitting between \\(\\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\\) and \\(\\frac{L_{D1} - L_{D2}}{L_{D1} + L_{D2}}\\) is shown with a solid line, with the", + "footnote": [], + "bbox": [ + [ + 140, + 46, + 860, + 298 + ] + ], + "page_idx": 59 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_16.jpg", + "caption": "fig. S18: The plots showing the surface area asymmetry ratio, calculated as the net surface area difference over the combined surface area of anterior and posterior daughter cells from six sister-cell pairs in eight wild-type embryos. Data for conditions with and without active Notch signaling are shown in red and blue respectively.", + "footnote": [], + "bbox": [ + [ + 222, + 48, + 720, + 390 + ] + ], + "page_idx": 61 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_17.jpg", + "caption": "fig. S19: The confocal micrographs of an \"H\"-shaped kidney cell labeled by GFP. Shown are kidney cells at the adult, L4, and L1 stages with scale bars individually indicated. Fluorescence, DIC, and merged micrographs are shown from the top, middle, and bottom, respectively at each panel.", + "footnote": [], + "bbox": [ + [ + 82, + 523, + 914, + 661 + ] + ], + "page_idx": 62 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_18.jpg", + "caption": "fig. S20: The lineage-specific expression of the the Notch ligand lag-2, ligand apx-1, receptor lin-12, and receptor glp-1. The relationship between gene expression level and color is displayed on the right.", + "footnote": [], + "bbox": [ + [ + 70, + 52, + 930, + 512 + ] + ], + "page_idx": 63 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_19.jpg", + "caption": "fig. S21. The comparison of morphological changes between the Notch-responsive ABplapppa cell", + "footnote": [], + "bbox": [ + [ + 230, + 592, + 760, + 909 + ] + ], + "page_idx": 63 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_20.jpg", + "caption": "fig. S22: The apoptotic cells (vertical) are mostly smaller in surface area compared to their sisters (horizontal) upon their birth. Shown are 93 non-apoptotic and apoptotic sister-cell pairs, 79 of which show a relatively smaller surface area for the apoptotic cells.", + "footnote": [], + "bbox": [ + [ + 306, + 197, + 689, + 466 + ] + ], + "page_idx": 64 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_21.jpg", + "caption": "fig. S23: The direct way to illustrate the new gene expressions and cell morphology map by CMOS.", + "footnote": [], + "bbox": [ + [ + 120, + 46, + 880, + 740 + ] + ], + "page_idx": 64 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_22.jpg", + "caption": "fig. S24: The time-lapse monitoring of tissue formation and organogenesis using both gene expression and cell morphology. (A) The pharynx assembly remarked by pha-4. (B) The body-wall muscle assembly remarked by hll-1. The cell-resolved gene expression profiles of pha-4 (top) and hll-1 (bottom) are differentially color-coded, as indicated. The embryo is oriented in a ventral view with the anterior to the left.", + "footnote": [], + "bbox": [ + [ + 66, + 66, + 930, + 214 + ] + ], + "page_idx": 65 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_23.jpg", + "caption": "fig. S25: The time-lapse monitoring of gene expression in an up-down manner, shown with the embryonic cell lineage tree averaged over the eight C. elegans wild-type embryos. The cell-resolved gene expression profiles of pal-1 (top) and end-1 (bottom) are differentially color-coded, as indicated. The sublineage with substantially high and low expression sequentially is indicated by arrows.", + "footnote": [], + "bbox": [ + [ + 140, + 370, + 857, + 526 + ] + ], + "page_idx": 66 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_24.jpg", + "caption": "fig. S26: The segmentation accuracy of CMap beyond the 550-cell stage. Shown are the comparison of segmentation results before and after manual curation for WT_Sample1 (A) and WT_Sample7 (B), respectively, with the absolute imaging time and total cell number listed on the top.", + "footnote": [], + "bbox": [ + [ + 68, + 48, + 904, + 345 + ] + ], + "page_idx": 67 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_25.jpg", + "caption": "fig. S27: The examples of specific image issues that complicate image segmentation. Shown are the three types of issues in terms of image quality that complicate image segmentation (membrane images collected at the edge of embryos; those collected with attenuated laser power due to sample depth; those associated with apoptosis). Images from which cell surfaces are manually distinguishable or indistinguishable are in the left and right two columns. A magnified view of the highlighted region is shown on the right.", + "footnote": [], + "bbox": [ + [ + 87, + 448, + 905, + 738 + ] + ], + "page_idx": 67 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_26.jpg", + "caption": "fig. S28: The issues associated with live-cell imaging of cell membranes. (A) The comparison of qualities of images acquired through single-shot (top) or actual 4D imaging (bottom) from roughly the same focal plane at four different developmental stages. Top: still single-shot micrographs of cell nuclei and cell membranes labeled by GFP and mCherry respectively taken from four different embryo samples. GFP, mCherry, and merged micrographs are shown from the top to the bottom row. Bottom: actual micrographs", + "footnote": [], + "bbox": [ + [ + 75, + 48, + 920, + 812 + ] + ], + "page_idx": 68 + } +] \ No newline at end of file diff --git a/preprint/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91.mmd b/preprint/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91.mmd new file mode 100644 index 0000000000000000000000000000000000000000..8d75e1261f01c3b2f92d6de136533b31135c941a --- /dev/null +++ b/preprint/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91/preprint__0b1a0b4aec6a90ace8891c1807e0877f3f0920fddeddfa564eaec78950d02f91.mmd @@ -0,0 +1,767 @@ + +# Cell lineage-resolved embryonic morphological map reveals novel signaling regulating cell fate and size asymmetry + +Zhongying Zhao zyzhao@hkbu.edu.hk + +Hong Kong Baptist University https://orcid.org/0000- 0003- 2743- 9008 + +Guoye Guan Peking University https://orcid.org/0000- 0003- 4479- 4722 + +Zelin Li City University of Hong Kong + +Yiming Ma Hong Kong Baptist University + +Pohao Ye Hong Kong Baptist University + +Jianfeng Cao City University of Hong Kong + +Ming- Kin Wong Hong Kong Baptist University + +Vinyc Wing Sze Ho Hong Kong Baptist University + +Lu- Yan Chan Hong Kong Baptist University + +Hong Yan City University of Hong Kong + +Chao Tang + +Academy for Advanced Interdisciplinary Studies, Peking University https://orcid.org/0000- 0003- 1474- 3705 + +Article + +Keywords: + +Posted Date: August 30th, 2024 + +<--- Page Split ---> + +DOI: https://doi.org/10.21203/rs.3.rs- 4664717/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on April 18th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 58878- 0. + +<--- Page Split ---> + +# Cell lineage-resolved embryonic morphological map reveals novel signaling regulating cell fate and size asymmetry + +Guoye Guan \(^{1,\mathrm{a,b}}\) , Zelin Li \(^{2,3}\) †, Yiming Ma \(^{4}\) †, Pobao Ye \(^{4}\) †, Jianfeng Cao \(^{2,3,c}\) , Ming- Kin Wong \(^{4,\mathrm{d}}\) , Vincy Wing Sze Ho \(^{4,\mathrm{e}}\) , Lu- Yan Chan \(^{4,\mathrm{f,g}}\) , Hong Yan \(^{2,3,*}\) , Chao Tang \(^{1,5,6,*}\) , Zhongying Zhao \(^{4,7*}\) + +1. Center for Quantitative Biology, Peking University, Beijing, China +2. Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China +3. Centre for Intelligent Multidimensional Data Analysis, Hong Kong Science Park, Hong Kong, China +4. Department of Biology, Hong Kong Baptist University, Hong Kong, China +5. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China +6. School of Physics, Peking University, Beijing, China +7. State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China +a Present address: Department of Systems Biology, Harvard Medical School, Boston, USA +b Present address: Department of Data Science, Dana-Farber Cancer Institute, Boston, USA +c Present address: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China +d Present address: Schulich School of Medicine and Dentistry, Western University, Ontario, Canada +e Present address: Department of Surgery, Chinese University of Hong Kong, Hong Kong, China +f Present address: Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China +g Present address: Center for Epigenomics Research, Hong Kong University of Science and Technology, Hong Kong, China +† These authors contributed equally to this work. \* Corresponding author. Email: h.yan@cityu.edu.hk (H.Y.); tangc@pku.edu.cn (C.T.); zyzhao@hkbu.edu.hk (Z.Z.) + +<--- Page Split ---> + +## ABSTRACT + +Understanding the dynamic evolutions of cellular morphology throughout development is crucial for elucidating the mechanisms of morphogenesis and organogenesis. However, a systematic and detailed characterization of these processes poses a significant challenge. In this study, we introduce a comprehensive real- time morphological map covering over \(95\%\) of the cells produced during Caenorhabditis elegans embryogenesis, constituted by nearly 400,000 3D cell regions. Our map integrates information about identity, lineage, fate, shape, volume, surface area, and contact area for each individual cell, together with lineage- specific gene expression profiles, all of which are accessible through our custom- designed software and website. This morphological map enables vivid and precise characterization of key morphogenetic events, such as dorsal intercalation, intestinal morphogenesis, and body- wall muscle assembly. Furthermore, we reveal that the Notch and Wnt signaling pathways, in concert with mechanical forces from cell- cell interactions, orchestrate the asymmetry of both cell fate and cell size. Additionally, our analysis of cell- cell contact maps and lineage- specific gene expression profiles uncovers a cascade of Notch signaling events that drive the cell size asymmetries critical for kidney development. This resource lays the foundation for in- depth studies of the regulatory networks that determine cell fate, size, and cell cycle length throughout C. elegans embryogenesis. + +## ONE SENTENCE SUMMARY + +A new and comprehensive cellular morphological map in C. elegans embryogenesis with exceptional spatiotemporal resolution leads to the discovery of cell- cell signaling interactions regulating the asymmetries of cell size as well as cell fate and the underlying intercellular signaling networks. + +<--- Page Split ---> +![](images/Figure_1.jpg) + + +## POTENTIAL COVER ILLUSTRATION + +A beguiling smile 'face' of a Caenorhabditis elegans embryo, depicted at the center in a dorsal view with the anterior oriented downward. The image showcases primarily skin cells (red) within a \(\sim 400\) - cell- stage embryo. A mosaic of other cell types is interwoven and colored in gray, green, and blue, or presented in a translucent manner for visual emphasis. Encircling this central image are confocal fluorescence snapshots showcasing GFP- tagged cell nuclei and mCherry- highlighted cell membranes (internal) and reconstructed cellular morphologies (external). These concentric frames capture the transformative journey of the \(C\) . elegans embryo, progressing from mere two cells to over 550 cells. Bridging the developmental timeline is a series of merged GFP and mCherry fluorescence images that narrate the transition to the intricate anatomy of a mature \(C\) . elegans adult. + +<--- Page Split ---> + +## Introduction + +The systematic tracking of key cellular behaviors in real time, including the dynamics of cell shape, volume, surface and neighborhood, is crucial for understanding various developmental processes but remains technically challenging, especially during animal embryogenesis. This is because in response to cell autonomous or non- autonomous regulation, embryonic cells undergo rapid division and migration along with cell fate specification and differentiation to ensure proper tissue formation and organogenesis1. On top of this, the precise delineation of cell lineage provides further insurance on each cell's identity and developmental trajectory, enabling the dissection of the developmental process with unprecedented precision and spatiotemporal and cellular resolution. However, achieving such a resolution is impractical in higher animals, especially in mammals, due to the excessive number of cells and difficulties in culturing such embryos in vitro for live cell imaging2-5. For example, cell shape changes dramatically during compaction of earlier embryogenesis6. Single- cell analyses in mice or human usually focus on very early stage of embryogenesis with inferred cell lineage that come with inherent uncertainties7,8. In addition, all these single- cell analyses deduce a cell state mainly dictated by molecular signatures, but ignore important cellular features, i.e., cellular morphology, including the dynamics of cell shape, volume, surface and neighborhood. Integration of quantitative data on these parameters with single- cell omics data is expected to significantly improve the power to draw more biologically relevant conclusions in terms of the progression of cell fate specification and differentiation. + +Cellular morphology, such as cell shape, volume, surface area, and contact between neighboring cells, plays a fundamental role in morphogenesis and tissue formation in various species9- 14. Moreover, the spatial distribution and fate specification of cells in animals rely on the mechanical and signaling interactions between neighboring cells. For example, in C. elegans, a change in the division geometry in the four- cell- stage embryo disrupts Notch signaling from the P2 to the AB cell, leading to the failure of fate specification15. In addition, such fate induction through Notch signaling is time- dependent, and thus, may have an opposite effect at different developmental times16. Most of these signaling interactions have been mapped either by genetic screening or by the expression of ligands and receptors of signaling interactions15,17- 19. The identification of such interactions is becoming increasingly challenging over development due to the difficulties in resolving cell identity and the lack of expression profiles of ligands and receptors with cellular resolution, especially during late embryogenesis. In addition to cell shape, cell size is also vital for the proper development of tissues and organs. For example, in C. elegans, heterogeneous cell size is correlated with heterogeneous cell cycle length during embryogenesis, which + +<--- Page Split ---> + +helps to coordinate the migrations, positions, and contacts of all cells20- 26. Unfortunately, information on cell shape and size is commonly neglected along with cell cycle length during studies of gene regulation of embryogenesis. Equipped with light- sheet microscopy and a newly developed algorithm, systematic mapping of cell shape and contact was performed in early- to- mid- stage ascidian embryos, thereby revealing the invariance of ascidian embryogenesis and that the contact areas between signaling and responding cells are indicative of the embryonic inductions required for fate specification27. However, a complete picture of the cellular morphology of ascidian embryos has yet to be obtained due to technical difficulties in the segmentation of late- stage embryos. + +Control of cell size, including cell volume and surface area, is critical for proper tissue formation and organogenesis. Attempts have been made to systematically map the cellular morphology of a few species27- 29. Numerous studies have investigated the role of cell size accuracy in the control of development in both biological and physical contexts12,20,22,23. However, a comprehensive map of cellular morphology with resolved cell lineage and fate as well as other quantitative morphological features (e.g., cell volume, cell surface area, and cell- cell contact area) throughout embryogenesis has not been constructed in any species. The organism of choice for systematic mapping of cellular morphology is C. elegans, due to its invariant development and transparent body30, the well- established methods for automated cell lineage tracing31- 33, and the abundance of genetic and molecular tools34,35. On top of this, the availability of lineal expression profiles of numerous genes especially that of transcription factors36,37 enables the study of molecular and cellular controls of embryogenesis with unprecedented precision. As such, numerous attempts have been made to reconstruct the cellular morphology of a developing C. elegans embryo with or without cell identity29,38,39. However, these methods can only produce cellular morphology during early embryogenesis in C. elegans, due to the high density and small size of the cells during late embryogenesis. For example, a C. elegans cell is roughly 100 times smaller than that of an ascidian when the number of cells is comparable between the embryos of the two species40. We have previously attempted to reconstruct the cellular morphology of C. elegans embryos using nucleus- based modeling41 or nucleus- independent deep learning (CShaper)12,42. However, both the modeling- based method and CShaper were only able to produce cellular morphology for the first half of embryogenesis (i.e., up to approximately the 350- cell stage), due to the difficulties in modeling or segmenting membranes of the highly crowded cells at a stage beyond the 350- cell stage. In addition, the expression intensity of the transgenic membrane markers becomes dimmer than the early stage, making it impractical to reconstruct the cellular morphology map beyond the 350- cell stage. However, most embryonic cells at this stage have not completed their final round of embryonic division, and have yet differentiated to their terminal fate, which prevents the study of the gene regulations that control the fate asymmetry for most embryonic cells. + +<--- Page Split ---> + +In this study, we established a new platform that allows qualitative and quantitative analysis of threedimensional (3D) cell shape, volume, surface area, and contact area as well as lineal expression of various genes with defined cell lineage in C. elegans embryos up to the 550-cell stage, when most embryonic cells complete their final round of division with terminal fate. We first built a segmentation method that performs significantly better than the state-of-the-art methods in segmenting cell membranes beyond the 350-cell stage of the C. elegans embryo. We next generated a comprehensive cellular morphological map up to the comma stage of embryos at around 1.5-minute intervals, which consists of cell shape, size, and contact between neighboring cells for all cells with resolved cell lineage. We then demonstrated the power of the platform by analyzing the effect of Notch signaling on not only breaking the symmetry of cell fate, but also cell size, which led to a surprising finding that Notch signaling interaction between neighboring cells not only regulates fate asymmetry, but also controls the size asymmetry of the same cell pair in a division orientation-dependent manner, i.e., such interaction invariably enlarges the anterior daughter cell at the cost of the posterior cell. To further demonstrate the power of the platform, we integrated lineal expression of Notch ligands and receptors with the morphological map and found that in addition to an existing Notch interaction targeting the cell "ABplapp" at around the 80-cell stage \(^{17,43,44}\) , we observed that four more rounds of consecutive Notch interactions that target itself, its daughter, and its granddaughter by different ligand-expressing cells that drive asymmetric divisions in terms of both cell fate and size, leading to its final differentiation into the C. elegans excretory cell, an equivalent of kidney, which has the largest size in the adult. We finally make our data accessible both locally as standalone software or online through interactive query and vivid visualization. + +## A new method for automatic segmentation of cell membranes labeled by fluorescent protein up to the 550-cell stage + +To enable systematic reconstruction of the cellular morphologies up to the 550- cell stage of C. elegans embryogenesis, we developed an automated pipeline, CMap, which shows a superior segmentation accuracy (Fig. 1A; figs. S1 to S4; table S1) and a reasonably high computational speed compared with the existing ones (table S2). The improved performance is partially contributed by a novel C. elegans transgenic strain that was built with biolistic bombardment to facilitate membrane segmentation. The strain showed a higher intensity of membrane fluorescence than the one we previously used \(^{12}\) , especially at the late stage of the embryo (Fig. 1B and fig. S5). We used the pipeline to reconstruct cellular morphologies for nearly all embryonic cells up to the comma stage at \(\sim 1.5\) - minute intervals for a total of eight wild- type embryos ("WT_Sample1" to "WT_Sample8"), along with the complete cell identities and lineages produced by StarryNite and AceTree \(^{30,32,33}\) (movie S1; Materials and Methods). CMap not only outputs cell shape, but also computes cell volume, surface area, and contact area (Fig. 1A). Nuclei were ubiquitously labeled with the green fluorescent protein (GFP) to enable cell lineage tracing \(^{32}\) , which produced cell nuclei positions + +<--- Page Split ---> + +that were used as alternative seeds to facilitate CMap to reconstruct the morphology of individual cells, especially those at late-stage embryos where cells are densely packed with small size. This reconstruction was achieved by using an advanced adaptive deep convolutional neural network, denoted the Euclidean distance transform dilated multifiber network (EDT- DMFNet), for cell membrane recognition (fig. S6). As a result, the EDT- DMFNet segments the fluorescently labeled cell membranes up to the 550- cell stage of the embryo with a high accuracy. At this stage, cells are approximately half the size they are at the 350- cell stage. (Fig. 1B). The segmentation results serve as an input for downstream analyses whereby cell lineage- and fate- wise 3D cell objects, and morphological features of individual cells are automatically extracted for an entire embryo (movies S2 and S3). On average, it takes CMap about 3 hours to implement automatic cell segmentation for a C. elegans embryo from the four- to 550- cell stages (table S2), allowing processing of wild- type embryos or embryos perturbed with RNA interference (RNAi), mechanical compression, eggshell removal, or laser ablation25,41,45,46. + +![](images/Figure_2.jpg) + +
Fig. 1. The pipeline of cell morphology reconstruction for C. elegans embryogenesis by CMap. (A) The data processing pipeline of CMap. Time-lapse 3D (4D) images of GFP-labeled cell nuclei and mCherry-labeled cell membranes are used for cell lineage tracing and morphology segmentation respectively, with output of cell identity (with information on cell lineage and cell fate), cell shape, volume,
+ +<--- Page Split ---> + +surface area, and contact area over embryogenesis. (B) Top: 3D projections of a dually labeled embryo at representative developmental stages indicated above; middle: outputs of cell nucleus tracing; bottom: outputs of cell membrane segmentation. Nuclei and membranes are differentially colored based on their lineal origins as indicated. + +## A quantitative morphological map with resolved lineage for nearly all cells produced during C. elegans embryogenesis + +Equipped with the CMap, we established a comprehensive morphological map with resolved cell identity, cell lineage, and cell fate up to the 550- cell stage of C. elegans embryogenesis with both statistical reliability and data completeness. We took the following measures to ensure the comprehensiveness, reliability, and accuracy of our data. First, we collected time- lapse 3D images for eight wild- type C. elegans embryos with GFP- labeled cell nuclei and mCherry- labeled cell membranes from no later than the four- cell stage to no earlier than the 550- cell stage at intervals of \(\sim 1.5\) minutes (table S3). Next, to minimize the deformation of cell shape, unlike the embryo mounting techniques we used previously, in which the embryo was immobilized by applying some pressure from the cover slide, leading to a severely deformed embryo shape \(^{12}\) (fig. S7), we applied no pressure (without external mechanical compression) on the mounted embryos (Materials and Methods). Third, we performed automated cell lineage analysis for all embryos with manual curation up to the time point when all 48 progeny cells of the somatic founder cell "C" were present for at least five time points (more than 7 minutes) when all hypodermis cells are produced \(^{30,47}\) (Fig. 2, A and B). Therefore, over 95% of all cells in C. elegans embryogenesis were successfully segmented in at least one embryo sample \(^{48}\) (table S4). More specifically, we segmented the membranes of a total of 1,292 unique cells in the eight embryos, including 1,188 non- apoptotic cells and 104 apoptotic cells, with the latter representing 92.04% of all embryonic apoptotic cells. Moreover, 1,190 unique cells were reproducibly segmented and recorded in all eight embryos, with 589 having a complete lifespan (cell cycle length) and 79 being apoptotic. This was almost twice the number of cells that were previously segmented by CShaper \(^{12}\) (table S5), enabling morphological characterization of embryonic cells from the four- to 550- cell stages. This is significant because at this point most embryonic cells complete their final round of division with terminal fate, allowing the study of gene network regulating development at cellular resolution with defined cell lineage and fate for nearly every cell throughout embryogenesis. Specifically, for a 3D cell region (defined as a unique compartment formed by cell membrane) or a so- called "data point" (c, j, T) denoted by cell identity c, embryo sample j, and time point T, a total of 395,741 cell regions (covering 99.99% out of all the existing cell nuclei recorded) were effectively segmented by CMap, with 5,905 containing two cell nuclei inside, which were usually observed at the time points immediately before cytokinesis (table S4). Subsequently, we filtered out 10.89% of the cell regions due to their abnormal volume or shape (figs. + +<--- Page Split ---> + +S8 to S10; Materials and Methods). \(69.10\%\) of these regions are non-apoptotic cells that are presumed to have a nearly identical volume and space occupation over time. Thus, these were removed from our reported dataset. The remaining 13,320 regions are the apoptotic ones that exhibited a sudden decrease in volume over time, and therefore we retained them in our final dataset along with a subset manually checked and corrected49 (figs. S11 and S12). + +![](images/Figure_3.jpg) + +
Fig. 2. Statistics of the cells with resolved cell lineage and morphology up to the 550-cell stage of \(C\) . elegans embryogenesis. (A) The embryonic cell lineage tree averaged over the eight \(C\) . elegans wild-type embryos up to the 550-cell stage. Cell fates are differentially color-coded as indicated. The kidney and the sole body-wall muscle cell derived from the AB lineage are indicated with black and gray arrowhead respectively. The cells with consistent failures in segmentation in all embryo samples are indicated with black dots. Developmental time is shown on the left, with the last time point of the four-cell stage set as the time zero. (B) Cell count dynamics across developmental stages for the eight embryos, with the average cell number represented by dots (black for surviving cells and red for apoptotic ones) and standard deviation by vertical lines. The duration of significant developmental landmarks is indicated by differential shading47. (C-D) Comparison of average cell volume (C) and cell surface area (D) with individual measurements from the eight \(C\) . elegans embryos. Data points represent individual cell comparisons, with the average across embryos on the horizontal axis and individual embryo measurements on the vertical axis. Cells present before and after the \(\sim 350\) -cell stage are color-coded in blue and yellow, respectively. Insets show the distribution of correlation coefficients for these comparisons.
+ +<--- Page Split ---> + +In summary, CMap accurately produces the shape of nearly all cells from the four- to 550- cell stages with resolved cell identity, cell lineage, and cell fate, and with reproducibility and statistical support provided by the eight wild- type embryos (fig. S13). It outputs three other quantitative morphological features: cell volume \((V)\) , cell surface area \((A_{S})\) , and cell- cell contact area \((A_{C})\) (Fig. 1A). For each \(C\) . elegans wild- type embryo in our dataset, in addition to the 322 unique cells prior to the \(\sim 350\) - cell stage with a complete lifespan that were correctly segmented as we did before \(^{12}\) , we segmented another 267 unique cells beyond the 350- cell stage with a complete lifespan; such requirement on a complete lifespan allows the study on the cellular morphological dynamics at the time scale within the cell cycle length (fig. S13), for instance, the study on the consecutive rounding and elongation realized by cytoskeleton remodeling during cell division \(^{50 - 52}\) . Statistical analyses of cell volume and surface area demonstrated that both cellular parameters are tightly controlled throughout embryogenesis (Fig. 2, C and D). As a result, a comprehensive cellular morphological map of \(C\) . elegans embryo was established with a missing rate of less than \(8\%\) for total time- lapse 3D data points and with coverage for over \(95\%\) of all cells (recorded with at least one data point) in \(C\) . elegans embryogenesis (tables S6 to S8), which provides an invaluable resource for studying cellular behaviors for nearly all cells with defined cell lineage and identity throughout \(C\) . elegans embryogenesis. + +## Characterization of morphogenesis and organogenesis with cellular resolution using the morphological map + +Our morphological map with defined cell lineage and identity allowed us to reconstruct cell deformations and migrations for a specific tissue or organ as well as their lineal origin over embryogenesis (Fig. 3A; movies S2 to S4; fig. S14). We categorize cells as “unspecified” or “determined” according to the documentation described previously \(^{37}\) , i.e., if the two daughters of a cell have different or the same fates, the cells are categorized as “unspecified” or “determined”, respectively (table S4). For example, the morphology of cells forming various tissues and organs shows an apparent bilateral symmetry (Fig. 3A). The pharynx and intestine form a tube along the anterior- posterior axis of the embryo, where the intestinal tube bends precisely over its contact with the germline progenitors, i.e., the Z2 and Z3 cells, which may provide physical protection or produce a unique position for the engulfment and degradation of large lobes extended by the Z2 and Z3 cells during embryogenesis \(^{53}\) (Fig. 3A; movie S5). The cell- resolved morphological map enables the reconstruction and vivid visualization of both early and late morphogenetic events in \(C\) . elegans embryogenesis, including gastrulation (movie S6), dorsal intercalation \(^{47,54}\) (Fig. 3B), intestine morphogenesis \(^{55,56}\) (Fig. 3C) and body- wall muscle assembly \(^{57,58}\) (Fig. 3D). + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 3. Cell shape dynamics across tissue formation and organogenesis during late embryogenesis. (A)
+ +Visualization of cell shapes within the entire embryo or within specific tissues/organ from various perspectives. (B- D) Depiction of dynamic cell shape changes in different tissues during late embryogenesis: skin cells (hypodermis) during dorsal intercalation (B), intestine cells during intestinal twisting and + +<--- Page Split ---> + +elongation (C), and body- wall muscle cells during the ingression of the AB- derived body- wall muscle cell (ABprppppaa), which is indicated by an arrow (D). Developmental time and stage are shown on the left, with the last time point of the four- cell stage set as the time zero. (E- G) Quantification of cell irregularity \((\eta)\) for the processes shown in (B- D). (E) presents the average (solid dot) and standard deviation (solid line) of the irregularity for 9 skin cells, as indicated by arrowheads in (B), during the developmental timeline \((t)\) in an exemplary embryo with the last time point of the four- cell stage set as the time zero. (F) presents similar data for all cells during the developmental timeline \((t)\) in an exemplary embryo. (G) presents the average (solid dot) and standard deviation (solid line) of cell shape irregularity \((\eta)\) for the cell ABprppppaa during the developmental timeline \((t)\) in eight embryos. In (E- G), the maximums and minimums are denoted by green and pink triangles respectively, and the correlation coefficients for the monotonic \(\eta - t\) curves are shown at the top. + +The dimensionless surface- to- volume ratio \((\eta = \frac{2\sqrt{A_5}}{3\sqrt{V}})\) was used to evaluate the irregularity of cell shape, which revealed the roles played by cell migration and lineal origin in determining cell irregularity \(^{12,52}\) . For example, during dorsal intercalation, two rows of skin cells, i.e., hypodermis, originally located on each dorsal side of the embryo move toward the dorsal mid- line and eventually form a single row, with cell irregularity increasing gradually during squeezing and narrowing of the relevant cells (Fig. 3E; movie S7). Intestine cells also show an apparent low- high- low pattern in cell irregularity over the course of morphogenesis in the first transition punctuated with the intercalation (Fig. 3C and F) and insertion of the E cells (the Ealpa, Earpa, Ealpp, and Earpp cells), and the latter transition corresponds to elongation and twisting (Fig. S15; movie S8). Body- wall muscle cells are derived from various lineal origins (i.e., the AB, MS, C, and D sublineages) and assembled into two bilaterally symmetric stripes (Fig. 3D; movie S9). This raises an interesting question, i.e., how cells with different lineal origins are able to “locate” each other and assemble into two coherent tubular stripes, particularly in the case of ABprppppaa, which is the sole body- wall muscle cell derived from the AB sublineage. Intriguingly, ABprppppaa is initially located outside the stripe formed by the remaining muscle cells but is eventually inserted into the stripe reproducibly in different embryos, suggesting that its insertion is genetically programmed (movie S10). Three reproducible peak–valley pairs are present in the ABprppppaa cell irregularity curve as the cell ingresses from the periphery of the body-wall muscle stripe, during which its shape changes first from spherical to oblate and then to spherical (Fig. 3G and fig. S16). Taken together, our 550-cell-stage morphological map allows not only qualitative but also quantitative analyses of cell deformations and migrations during tissue formation and organogenesis, permitting characterization of embryogenesis with exceptional precision and spatiotemporal resolution in the C. elegans embryo. + +<--- Page Split ---> + +## Roles of Notch signaling in promoting the size asymmetry between anterior and posterior daughters of its target cell + +Given the important roles of Notch signaling in inducing asymmetry in cell fates in \(C\) . elegans embryo \(^{59}\) , we examined whether the Notch signaling that induced the fate asymmetry also produced asymmetry in the cell cycle length or cell size. Intriguingly, we observed that there is a negative correlation between cell size asymmetry and cell cycle length asymmetry \(^{25}\) (also referred to as division asynchrony) (fig. S17; table S9), which is consistent with the previous finding that cell- cycle- related factors are positively correlated with cell- volume partitioning during cytokinesis \(^{22,60}\) . Our morphological map also shows that smaller cell size is more frequently associated with apoptotic cells, which take a unique cell fate, than with their sisters \(^{61,62}\) . For example, MSpaapp proceeds to apoptosis immediately after its birth, whereas ABprpppppa is involved in the development of the spike tail and proceeds to apoptosis at a very late stage \(^{30}\) . However, despite MSpaapp and ABprpppppa having different apoptotic onset times, they are both smaller than their sister cells right after their birth (with an average volume proportion of 0.15 and 0.59 respectively) (Fig. 4A), which suggests that a small initial size destines them for apoptosis. + +To systematically investigate the effect of Notch signaling on the cell fate and size asymmetry, we focused on the five rounds of Notch signaling events reported before but using actual cell size \(^{41,59}\) (Fig. 4B). For each pair of sister cells, we defined the cell size asymmetry as the ratio of net volume difference between the anterior and posterior daughter to their total volume, i.e., \(\frac{V_{\mathrm{D1}} - V_{\mathrm{D2}}}{V_{\mathrm{D1}} + V_{\mathrm{D2}}}\) , where D1 denotes the anterior daughter cell, D2 the posterior daughter cell, and \(V\) the cell volume. Surprisingly, we found that in addition to the fate asymmetry between the daughter cells of Notch target cells, the cell size asymmetries of their daughters are invariably increased significantly between the anterior and posterior daughter of the Notch target cell than daughters of the sister cell of the Notch target cell, and this change is directional, i.e., the Notch signaling always enlarges the anterior daughter at the cost of the posterior one (one- sided Wilcoxon rank- sum test, \(p \leq 0.001\) , for all sister- cell pairs) (Fig. 4C). Such a significant shift also occurs in cell surface area asymmetry (one- sided Wilcoxon rank- sum test, \(p \leq 0.01\) for all sister- cell pairs) (Fig. S18). As a result, the size asymmetry between the daughters of the cells receiving the first, second, and fourth Notch signals is completely reversed, whereas that of the third Notch signal is decreased, and the initial size asymmetry between the anterior and posterior daughters of cells receiving the fifth Notch signal is further increased (Fig. 4D). Notably, these shifts were reproducibly observed in the sister- cell pairs of all eight wild- type embryos, which indicates that Notch signaling not only induces fate asymmetry but also promotes directional change in cell size. + +We functionally validated the effect of Notch signaling on the size asymmetry of its target cells by comparing the size asymmetry between the daughters of Notch target cells before and after the RNAi + +<--- Page Split ---> + +1 against lag- 1, which encodes the terminal effector of the Notch signaling pathway \(^{63}\) (table S3; Materials and Methods). We found that the difference in the value of \(\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\) between the anterior and posterior sister- cell pairs (i.e., \(\Delta \left[\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\right]\) , which is always positive in a wild- type animal), in which one receives Notch signaling and the other does not, is significantly reduced in four out of the six Notch signaling events (Fig. 4E). The results indicate that Notch signaling plays a major role in enlarging the anterior but shrinking the posterior daughter of its target cells (Fig. 4F). + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 4. Notch signaling promotes directional asymmetry in cell volume between anterior and posterior daughters of the target cell and its sibling. (A) Illustrations of asymmetric cell division producing two representative apoptotic cells, MSpaapp (left) and ABprpppps (right). \(T_{\mathrm{C}}\) denotes the last time point of cytokinesis. (B) Reconstructed 3D morphologies of contacting cell pairs engaged in the previously identified Notch signaling events during C. elegans embryogenesis. Cells expressing Notch ligands are highlighted in green, while those with the receptor are in red. (C) Plots showing the volume asymmetry
+ +<--- Page Split ---> + +ratio, calculated as the net volume difference over the combined volume of anterior and posterior daughter cells from six sister- cell pairs in eight wild- type embryos. Data for conditions with and without active Notch signaling are shown in red and blue respectively. (D) A summary graph showing the alteration in cell volume asymmetry between sister- cell pairs under conditions with (red) or without (blue) Notch signaling, corresponding to the color scheme in (C). (E) Comparison of volume asymmetries of daughters of Notch target cells between wild- type (horizontal) and perturbed (vertical) embryos by RNAi against lag- 1. (F) Comparison of morphological changes between the Notch- responsive ABplpapp cell (middle) that receives the fourth Notch signal and its sibling (top), which does not receive the Notch signal, in the wild- type embryo or the ABplpapp cell in the perturbed embryos by RNA against lag- 1 (bottom). Note that the directional size asymmetry in the division of ABplpapp (middle) in contrast to its sister (top), and its perturbed state in embryos with RNAi targeting lag- 1 (bottom). \(T_{\mathrm{C}}\) denotes the last time point of cytokinesis. + +## Multiple rounds of asymmetric divisions leading to the disproportionately large size of the C. elegans kidney cell + +The C. elegans kidney cell ABplpappaaq, also called the excretory cell, is one of the largest cells in the late- stage embryo (the largest among AB cells and slightly smaller than E cells) and the largest cell in an adult, and plays a vital function in osmotic and ionic regulation, and waste elimination30,64- 66 (Fig. 5A and fig. S19). The cell ABplpapp, the great- grandparent cell of ABplpappaaq, was reported to receive the fourth Notch signal, which is essential for the terminal fate specification and size asymmetry giving rise to the kidney cell ABplpappaaq43 (Fig. 4, C and E), we wondered whether the unusual size of ABplpappaaq is solely due to this Notch signal. We explored this possibility by using our cell lineage and cell volume data from eight embryos to plot the volumes of ABplpapp and its progenies over its subsequent three rounds of division that lead to its terminal fate differentiation. We found that the unusually large size of ABplpappaaq is caused not only by the fourth Notch signaling event that induces asymmetric division in ABplpapp (Fig. 4, C and E), but also by the subsequent two rounds of asymmetric division (Fig. 5, B and C; movies S11 and S12). Strikingly, both wild- type and Notch- blocked embryos support that the general role of Notch signaling in promoting directional volume asymmetry is still at work (Fig. 5, B and D, and fig. S20). More than 80% of the parental cell volume is allocated to the kidney cell parent ABplpappaaq and itself during the second and third rounds of asymmetric division with the second round producing an apoptotic cell (Fig. 5, B and D). Specifically, the kidney cell’s aunt and sister, i.e., ABplpappaq and ABplpappaaa have a similar volume, but the latter develops into a neuron while the former proceeds to apoptosis, after which its corpse is randomly engulfed and digested by either ABarapapp or ABplpappaa61 (Fig. 5E; movies S11 and S12). + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Fig. 5. Consecutive asymmetric divisions in terms of cell volume lead to a disproportionately large size of the kidney cell, ABplappaaap. (A) The “H”-shaped kidney cell labeled by GFP (left) or its merge with differential interference contrast microscopy (DIC) (right) in an adult. (B) Quantification of kidney cell volume changes over embryogenesis. The graph shows the mean cell volumes (line) and their standard deviations (shaded area) for the kidney cell and its progenitors from eight wild-type embryos in red, and for their sister cells in green. The time of ABplapp's birth is used as the reference point (time zero). (C) Quantification of volume change over embryogenesis for all cells derived from AB (blue) and E (gray) with that for the kidney cell and its progenitors (red, same data as in (B)). The time of ABplapp's birth is used as the reference point (time zero). (D) Asymmetric division of the kidney’s grandparent (top) and parent (bottom) in an exemplary embryo, with the resulting daughters differentially color-coded as indicated. \(T_{\mathrm{C}}\) denotes the last time point of cytokinesis. (E) Comparison of the apoptotic cell ABplappap's differential engulfment by two cells, ABarapppp (top) and ABplappaa (bottom), across two different embryos (WT_Sample1 and WT_Sample5). \(T_{\mathrm{E}}\) denotes a chosen time point before the apoptotic cell is engulfed.
+ +## Identification of novel asymmetric divisions regulated by cell signaling + +Availability of the comprehensive cell- cell contact map throughout C. elegans embryogenesis allows not only the confirmation of the existing signaling interactions (Fig. 4B), but also the inference of novel + +<--- Page Split ---> + +interactions that drive cell fate or size asymmetry if the map is integrated with lineal expression of the components of signaling pathways. For example, if a cell expressing a Notch receptor bears a direct contact with its neighbor that expresses a Notch ligand, and the cell undergoes a more asymmetric division in the anterior- posterior direction than its sister, the Notch signaling interaction is likely responsible for breaking the division symmetry. To this end, we collected lineal expression profiles from a total of nine existing or newly generated C. elegans transgenic embryos that express Notch ligands, i.e., lag- 2 and apx- 1, or receptors, i.e., lin- 12 and glp- 141 (Fig. 6A and fig. S20). By superimposing cell pairs that demonstrate a reproducible cell- cell contact and expression of the ligands and receptors respectively in each cell, we were able to identify six more putative Notch signaling events (Fig. 6B). All these cells belong to the ABplapp and ABprapp sublineages, which have substantial directional size amplification of their anterior daughters compared to the size asymmetry of their sisters' daughters, ranking in the top \(7\%\) within all sister- cell pairs with daughter volume available. To verify the effect of the inferred Notch signaling interaction, we compared the size asymmetries of the daughters of Notch target cells and their respective sisters. We observed a significantly decreased size asymmetry in all their daughters in the perturbed embryos with RNAi against lag- 1 (one- sided Wilcoxon rank- sum test, \(p \leq 0.05\) ) (Fig. 4F, Fig. 6C, and fig. S21). Specifically, following the fourth Notch signaling interaction that targets ABplapp by MSapp (Fig. 4B), we observed that the ABplapp cell continues to receive Notch signaling by both daughters of MSapp and MSapap, which we referred to as the sixth Notch signaling event as judged by their direct contact and the lineal expression profile of a Notch receptor, lin- 12, and a ligand, lag- 2 (Fig. 4F and Fig. 6, A, B, and D) in the two cells respectively. Similarly, the anterior daughter of ABplapp, i.e., the grandparent of the kidney, also receives Notch signaling from three cells, MSaapa, MSaapp, and MSappa, which we referred to as the seventh Notch signaling event; and then the same cell receives Notch signaling from another three cells, MSaapap, MSaappa, and MSaappp, which we referred to as the eighth Notch signaling event (Fig. 6, A, B, and D, and fig. S20). Finally, the cell ABplappaaa, the parent of the kidney, also receives Notch signaling from another two cells, MSaappa and MSaappp, which we referred to as the ninth Notch signaling event (Fig. 6, A, B, and D). In addition, we observed that the ABprapp cell, the symmetric cell of the ABplapp that receives the fourth Notch signaling, is also targeted by Notch signaling from cells derived from the MSpp sublineage that shows expression of another Notch ligand, apx- 1, whereas itself shows expression of a Notch receptor, lin- 12 (Fig. 6, A to D). Taken together, equipped with our comprehensive map of intercellular contact in combination with lineal expression of ligand and receptor of signaling pathways, we were able to not only confirm the existing signaling interactions, but also identify novel signaling interactions that are responsible for inducing size asymmetry that is often coupled with fate asymmetry. + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Fig. 6. Identification of new Notch signaling interactions with size effect on the kidney progenitors and a symmetric cell. (A) Lineal expression (redness on cell lineage tree) of two ligands, lag-1 and apx-1, and one receptor, lin-12, of the Notch signaling pathway. The relationship between gene expression level
+ +and a symmetric cell. (A) Lineal expression (redness on cell lineage tree) of two ligands, lag- 1 and apx- 1, and one receptor, lin- 12, of the Notch signaling pathway. The relationship between gene expression level and color is displayed on the right. Cells involved in the sixth- eleventh signaling events are indicated with arrows. Apoptosis is marked with an "X". (B) Reconstructed 3D morphologies of contacting cell pairs engaged in the newly identified Notch signaling events during C. elegans embryogenesis. Cells expressing Notch ligands are highlighted in green, while those with the receptor are in red. (C) Comparison of morphological changes between the Notch- responsive ABprpapp cell (middle) that receives the tenth and eleventh Notch signals and its sibling (top), which does not receive the Notch signal, in the wild- type embryo or the ABprpapp cell in the perturbed embryos by RNA against lag- 1 (bottom). Note that the directional size asymmetry in the division of ABprpapp (middle) in contrast to its sister (top), and its perturbed state in embryos with RNAi targeting lag- 1 (bottom). \(T_{\mathrm{C}}\) denotes the last time point of cytokinesis. (D) Positions (illustrated with the cell nuclei positions) of the Notch- responsive cells (red), Notch- signaling cells (green), and others (semi- transparent gray) at the moments when the sixth- eleventh signaling events take place. + +## Extensive regulation of cell size asymmetries by multiple factors + +<--- Page Split ---> + +Given that the fate and size asymmetries of a subset of cells can be regulated by the Notch signaling interactions (Figs. 4 to 6), we wonder whether other factors are also involved in breaking of the size symmetry of other cells. It is known that Wnt signaling contributes to the specification of C. elegans embryonic cell fate \(^{55,67,68}\) , and the nuclear level of the T cell factor (TCF) protein POP- 1 (the terminal effector of the Wnt signaling pathway) is lowered in all posterior cells \(^{69,70}\) . Therefore, we did RNAi against pop- 1 followed by cell size analysis to investigate whether Wnt signaling regulates division asymmetry in terms of cell size (table S3). RNAi on the Wnt signaling pathway can alter cell division orientation, which hinders the unambiguous assignment of cell identities based on division axes. Therefore, we focused on only cell size asymmetry between sister cells, regardless of their position, by calculating the absolute value of cell volume asymmetry, i.e., \(\left|\frac{V_{\mathrm{D1}} - V_{\mathrm{D2}}}{V_{\mathrm{D1}} + V_{\mathrm{D2}}}\right|\) . We analyzed 257 cell divisions that were present in both wild-type and RNAi- treated embryos, for which a complete lifespan for a mother cell and two daughter cells was available. There was a significant decrease in cell volume asymmetry in the RNAi- treated embryos, i.e., the average value decreased from 0.1194 (wild- type) to 0.0877 (RNAi- treated) (one- sided Wilcoxon rank- sum test, \(p = 1.44 \times 10^{- 4}\) ) (Fig. 7A). When Wnt signaling was perturbed, the number of cells whose daughter cells exhibited low volume asymmetry (<0.1) increased substantially in the RNAi- treated embryos, and vice versa (Fig. 7A). These results indicate that Wnt signaling plays a role in breaking cell size symmetry during embryogenesis. + +In C. elegans embryogenesis up to the 28- cell stage, a proportion of cell division events decreased cell volume asymmetry between daughter cells when the eggshell was removed \(^{46}\) . We wondered whether this indicates that external mechanical compression, which is believed to increase internal pressure and change cell positions in an embryo, increased cell size asymmetry. We investigated this by using the 17 C. elegans wild- type embryos under external mechanical compression (applied by cover slide), which deformed the originally ellipsoidal shape of the eggshell into an elliptical cylinder with an approximate width- to- height ratio of 1:2 ( \(9.4675 \pm 0.2693 \mu \mathrm{m}\) to \(18.2534 \pm 0.0373 \mu \mathrm{m}\) ) in its cross- section parallel to the direction of imaging \(^{12}\) (table S10). These embryo samples were segmented for the first half of C. elegans embryogenesis (approximately from the four- to 350- cell stages), such that 285 cells whose daughter cells had a complete lifespan were analyzed (table S11). We regarded a change in cell volume asymmetry ( \(\delta_{\mathrm{U - C}} \left[\frac{V_{\mathrm{D1}} - V_{\mathrm{D2}}}{V_{\mathrm{D1}} + V_{\mathrm{D2}}}\right]\) ) relative to the default in the mechanically uncompressed state \(\left(\frac{V_{\mathrm{D1}} - V_{\mathrm{D2}}}{V_{\mathrm{D1}} + V_{\mathrm{D2}}}\right)_{\mathrm{U}}\) as positive if the volume of the anterior/left/dorsal daughter cell (D1) decreased under external mechanical compression, and negative if this volume increased under external mechanical compression. Intriguingly, we found that there was a modest negative correlation between the two variables, with 151 cell division events (~50%) exhibiting significantly different cell volume asymmetry (one- sided Wilcoxon rank- sum test, \(p \leq 0.1\) ) and more than half being in the second quadrant \(\left(\frac{V_{\mathrm{D1}} - V_{\mathrm{D2}}}{V_{\mathrm{D1}} + V_{\mathrm{D2}}}\right)_{\mathrm{U}} < 0\) , \(\delta_{\mathrm{U - C}} \left[\frac{V_{\mathrm{D1}} - V_{\mathrm{D2}}}{V_{\mathrm{D1}} + V_{\mathrm{D2}}}\right] > 0\) , which means a collective increased volume asymmetry under external mechanical compression (Fig. 7B; table S11). This is + +<--- Page Split ---> + +consistent with the results of a comparison of the daughter cells of ABpl between mechanically uncompressed and compressed embryos reported previously39. Overall, our evaluation of multiple cells in multiple generations indicates that external mechanical compression amplifies the effect of Wnt signaling on cell volume asymmetry, i.e., most cell pairs exhibited significantly higher asymmetry when they were under mechanical compression than when they were not (Fig. 7B). + +![](images/Figure_8.jpg) + +
Fig. 7. Multiple factors contribute to the cell volume asymmetry between daughter cells. (A) The distribution of cell volume asymmetry between daughter cells without positional bias \(\left(\frac{|V_{D1} - V_{D2}|}{|V_{D1} + V_{D2}|}\right)\) in the wild-type and pop-1- (pop-1 RNAi) embryos. The statistical significance is obtained by the one-sided Wilcoxon rank-sum test and is listed at the top. (B) The negative correlation between the shift of cell volume asymmetry with \(\left(\delta_{\mathrm{U} - \mathrm{C}}\left[\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\right]\right)\) and without mechanical compression \(\left(\frac{|V_{D1} - V_{D2}|}{|V_{D1} + V_{D2}|}\right)\) . The result of proportional fitting between \(\left[\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\right]_{\mathrm{U}}\) and \(\delta_{\mathrm{U} - \mathrm{C}}\left[\frac{V_{D1} - V_{D2}}{V_{D1} + V_{D2}}\right]\) is shown with a solid line, with the proportional coefficient \((K)\) and goodness of fit \((G)\) listed in the top right corner. The statistical significance
+ +<--- Page Split ---> + +is obtained by the one- sided Wilcoxon rank- sum test and is listed in the bottom left corner. (C) The apoptotic cells (vertical) are mostly smaller in volume compared to their sisters (horizontal) upon their birth. Shown are 93 non- apoptotic and apoptotic sister- cell pairs, 80 of which have a relatively smaller volume for the apoptotic cells. (D) The illustration for asymmetric divisions of three representative parents of apoptotic cells from the AB (left), C (middle), and MS (right) lineages. For each cell, only cellular morphology at the time points before and after cytokinesis is shown. + +Cell division involving an apoptotic daughter is often asymmetric in terms of cell size \(^{61,62,71}\) . We explored whether all such divisions are associated with an asymmetric division by comparing the cell sizes immediately after cytokinesis of 93 sister- cell pairs in our dataset that contained one apoptotic cell and one non- apoptotic cell. Most apoptotic cells (78 out of 93 sister- cell pairs, \(>80\%\) ) demonstrated an average smaller volume and surface area than their sister cells, regardless of their lineal origin (Fig. 7, C and D, and fig. S22; table S12). The result shows that the size asymmetry between an apoptotic cell and its sister cell that has been reported previously in specific cell types \(^{61,62,71}\) (e.g., neuroblasts) is a global feature of cell division involving apoptosis. However, it remains to be determined why a subset of divisions producing an apoptotic daughter undergoes nearly symmetric division, or why an apoptotic daughter is destined to die even if it is born with a bigger size than its non- apoptotic sister (Fig. 7C and fig. S22; table S12). + +## A customized software tool for the visualization and interactive analysis of embryonic cell morphologies + +To facilitate access to our cell morphological data, we employed the public software ITK- SNAP to visualize a 3D image of both raw and segmented cells within three orthogonal cross- sections and generate rendered objects \(^{72}\) , and built a new version of the software, named ITK- SNAP- CVE (where “CVE” stands for “C. elegans virtual embryogenesis”), which allows customized visualization and analysis of C. elegans cell morphological data from multiple embryo samples, including cell identities, shapes, and quantitative morphological features (i.e., cell volume, surface area, and contact area) over embryogenesis (Fig. 8A; movie S13). To facilitate the use of the software, we reformatted all the raw images and processed images of the eight uncompressed wild- type embryos and four uncompressed RNAi- treated embryos used in this study, and those from the 17 compressed wild- type embryos described previously \(^{12}\) (table S13). We also developed several display modes, namely, “Show all cells”, “Show master cells only”, “Show master cells and neighbors”, and “Show master cells and other cells” (Fig. 8, B to E). Master cells can be arbitrarily selected by the user through inputting or selecting a cell name (cell identity) from cells with certain lineage (Fig. 8F) or fate (Fig. 8G), and their neighboring cells as well as the remaining embryonic cells can be shown with tunable opacity. A main menu selection option (on the top right of the interface) and a submenu (on the bottom right of the interface) enable the quantitative morphological features of a cell to be tracked. + +<--- Page Split ---> + +Thus, ITK-SNAP-CVE is an integrative tool that allows researchers to navigate cell-resolved C. elegans embryonic morphological maps interactively, thereby facilitating comprehensive visualization and analysis of this informative data. + +![](images/Figure_9.jpg) + +
Fig. 8. ITK-SNAP-CVE: A customized software tool for the visualization and interactive analysis of embryonic cell morphologies. (A) The main graphical user interface of ITK-SNAP-CVE, showcasing the layout and available tools. (B) The visual representations of all cells in embryo using the software's "Show all cells" display mode, with 2D views (top) and 3D reconstructions (bottom). (C-E) The detailed visualization of a selected individual cell, i.e., the somatic founder cell "C", within an embryo, as seen through different viewing options: (C) "Show master cells only" display mode, highlighting the "C" cell alone. (D) "Show master cells and neighbors" display mode, highlighting the "C" cell along with its immediate neighboring cells. (E) "Show master cells and other cells" display mode, where the "C" cell is visible in the context of the entire cell population. (F) A comprehensive view of all cells derived from the same lineage, exemplified here by the MS lineage, demonstrating the lineage-specific visualization capabilities of the software. (G) A display of all cells that are destined to become part of the same organ,
+ +embryonic cell morphologies. (A) The main graphical user interface of ITK- SNAP- CVE, showcasing the layout and available tools. (B) The visual representations of all cells in embryo using the software's "Show all cells" display mode, with 2D views (top) and 3D reconstructions (bottom). (C- E) The detailed visualization of a selected individual cell, i.e., the somatic founder cell "C", within an embryo, as seen through different viewing options: (C) "Show master cells only" display mode, highlighting the "C" cell alone. (D) "Show master cells and neighbors" display mode, highlighting the "C" cell along with its immediate neighboring cells. (E) "Show master cells and other cells" display mode, where the "C" cell is visible in the context of the entire cell population. (F) A comprehensive view of all cells derived from the same lineage, exemplified here by the MS lineage, demonstrating the lineage-specific visualization capabilities of the software. (G) A display of all cells that are destined to become part of the same organ, + +<--- Page Split ---> + +in this case, the intestine, illustrating the software's functionality to group cells by their developmental fate. + +## An interactive web platform for visualizing embryonic cell morphologies, intercellular contacts, and cell-resolved lineal gene expressions + +The gene expression is substantially associated with cell morphology, bridging the molecular scale and the cellular scale. The effect is mutual: in one way, activities of specific genes as well as their protein products control the cell morphology through intracellular to intercellular mechanics, by mechanisms like cytoskeleton remodeling, cell adhesion, and gap junction \(^{73 - 75}\) ; in another way, intracellular and intercellular mechanics control the cell morphology, such as the cell- cell contact area, then the signaling transduction that regulates fate specification and division orientation would be affected \(^{59,76,77}\) . Actually, this effect is even more complex, given the existence of various mechanosensitive pathways and gene interactions, making the comprehensive understanding of development a difficult task as it is crossing multiple scales and dimensions that influence each other \(^{58,78 - 80}\) . + +To further facilitate access to our data, we developed a website, CMOS (where "CMOS" stands for "cellular morphology of C. elegans embryo"; https://bcc.ee.cityu.edu.hk/cmos), that allows interactive access to the cell morphological data for the eight wild- type embryos generated in this study, including cell shape, volume, surface area and contact area as well as cell identity, lineage, and fate of all cells from the 4- to 550- cell stages (Fig. 9; movie S14). To enhance the usability of our data for gene- related research, we integrated our morphological data with the existing and newly generated lineal expression profiles of various genes, mostly consisting of transcription factors \(^{4,36,37,41,58,80}\) , which involved expression profiles of 412 unique genes derived from 1055 individual embryos (table S14). including those of tads- 1 and snfc- 5 that were newly generated in this study (fig. S23). A gene's lineal expression intensity can be projected onto all embryonic cells or onto cells in tissue-, organ- or lineage- specific manner (Fig. 9, A to C). This will permit in- depth interpretation of a gene's function in the context of cells' lineal history, fate, and position on top of quantitative morphological data, which is not practical in any other species. + +The website allows the visualization of cell- cell contact maps either systematically or by focusing on a cell of interest with detailed quantitative data on cell morphology and intercellular contact displayed (Fig. 9, D and E). The details on all the data source of each embryo, name of profiled genes, time point subjected to manual curation, and construct type are listed in table S14 (Materials and Methods). Cell morphological data, including cell identities, shapes, and quantitative morphological features (i.e., cell volume, surface area, and contact area), can be visualized along with the expression profile of a gene of interest over embryogenesis through navigating different time points, permitting time- lapse monitoring of gene expression and cell morphology as exemplified by pha- 4 for pharynx assembly, hlh- 1 for body- wall muscle assembly, and pal- 1 and end- 1 for transient expression, in other words, in an up- down manner (figs. S24 and S25). + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Fig. 9. An interactive web platform for visualizing embryonic cell morphologies, intercellular contacts, and cell-resolved lineal gene expressions. (A) The lineage-specific expression of the transcription factor, ceh-36, over approximately four hours from the four-cell stage. The relationship between gene expression level and color is displayed on the right. (B) The 3D views of an exemplary embryo at specified developmental stages (t, imaging time) with an overlay of ceh-36 expression (color-
+ +<--- Page Split ---> + +coded as in (A)). The embryo is oriented in a dorsal view with the anterior to the left. (C) The 3D views of different tissues and organs with highlighted expression of corresponding specific cell fate markers (colored as in (A)). The embryo is oriented in a ventral view with the anterior to the left. (D) The comparative views of a cell-cell contact map in an over 200-minute-old embryo (as seen in (B)): a global (left) and a cell-centric perspective (right). Intercellular contacts can be further examined in detail via an interactive table that appears upon clicking on a cell of interest. The thickness of the connecting lines corresponds to the cell-cell contact area. Expression levels for ceh- 36 are superimposed on relevant cells, consistent with the visualization in (B). (E) The visualization of intercellular contacts for the sixth-eleventh Notch signaling events (Fig. 6B) through the website. + +## Discussion + +Systematic and quantitative characterization of cellular morphology over development is critical for an in- depth understanding of regulatory control of embryogenesis. Here we developed a platform that allows the systematic reconstruction and quantification of the cellular morphology of \(C\) . elegans embryogenesis up to beyond the 550- cell stage, when most embryonic cells complete their final round of division and thus differentiate into their terminal fate (Figs. 1 and 2). Specifically, it is the improvement in the following features that make this platform significantly outperform the previous one we built earlier12,42. The first is the development of a novel cell segmentation algorithm, \(CMap\) , which allows the segmentation of time- lapse 3D cell images up to beyond the 550- cell stage with a much higher accuracy. This was achieved by a combination of integration of cell nuclei information to guide segmentation and a rigorous quality control of data post segmentation. The second is the generation of a novel transgenic strain of \(C\) . elegans that shows bright and ubiquitous expression on embryonic cell membranes, especially in late- stage embryos, which contributes to improved segmentation accuracy during late embryogenesis. The third is the development of a worm mounting method that allows long- duration imaging without applying any pressure on the imaged embryo, preventing artificial embryonic or cellular shape deformation. The fourth is the development of customized standalone software, ITK- SNAP- CVE, and a customized online website, CMOS, which allows local and remote access to our cell morphological data, permitting interactive visualization of 3D cell shape, intercellular cell contact, their superimposition with lineal expression of genes, and so forth (Figs. 8 and 9). Finally, our comprehensive morphological map covering over 95% of all cells that are present during \(C\) . elegans embryogenesis (table S4), including cell shape, volume, surface area, and contact area in qualitative and quantitative format, together with their cell identity, lineage, and fate as well as lineal expression of about 400 genes, forms an invaluable resource for the study of the regulation of embryogenesis with unprecedented spatiotemporal resolution and depth. For example, our data allows the vivid examination of gene expression in specific tissues, organs, or cell lineages with various cell shapes and sizes throughout embryogenesis (Fig. 9 and figs. S23 and S24). + +Through the integration with lineal expression of ligands and receptors, our morphological map not + +<--- Page Split ---> + +only allows the confirmation of existing Notch signaling interactions, but also permits the identification of novel signaling interactions that drive asymmetry of cell size that is coupled with cell fate (Figs. 4- 6 and Fig. 7A). It also enables the study of the regulation of cell size by external mechanical compression and apoptosis (Fig. 7, B to D). Quantification of cell irregularity permits the characterization of major morphogenetic events such as dorsal intercalation, intestinal morphogenesis, and body- wall muscle assembly (Fig. 3). Furthermore, the map could be used to answer many other key questions. For example, why are some divisions involving apoptosis asymmetric, whereas others are symmetric in terms of cell size? Is an apoptotic body engulfed randomly by any of its neighbors or by a specific neighbor? Whether a division asynchrony is co- regulated with fate or size asymmetry? To what extent does a shift in cell size asymmetry caused by external mechanical compression induce lethality? How does the asymmetry in cell fate or size correlate with gene expression at the cellular level, for example, by integrating with existing single- cell RNA sequencing data \(^{4}\) ? Answering these questions may reveal compensatory or fail- safe mechanisms that underpin C. elegans embryogenesis \(^{22,45}\) . In summary, the morphological map of C. elegans embryonic cells and the associated methodology and tools developed in this study are expected to facilitate addressing these questions among many others, which would be difficult otherwise. + +Despite the comprehensiveness of our morphological map, it still suffers from data missing for certain cells. For example, we were unable to recover data on cellular morphology for all embryonic cells that appear during C. elegans embryogenesis. The total data- point loss rate showed an increase gradually as embryogenesis proceeded (table S7), and the highest cell loss rate was seen for the cells at the surface of late- stage embryos, where skin cells exhibited an overall data- point loss rate of \(17.3\%\) , with \(19.5\%\) of the C- derived skins (a major contributor to skin formation) being lost (fig. S26; table S8). This segmentation failure may be attributable to both experimental and computational factors. First, the boundaries of the cells located inside the embryo are mostly defined by two layers of cell membranes due to cell- cell contact between neighboring cells, whereas those of cells located on the surface of the embryo consist of a single layer of their fluorescently labeled cell membranes. Thus, the cell membrane fluorescence intensity of the embryo surface would have been half that inside the embryo, leading to a higher segmentation failure rate. This would have occurred because the extraembryonic space is treated as a dilatable region by CMap and thus invades the embryo when there is an insufficient number of pixels of membrane fluorescence on the surface of the embryo. Second, attenuation of laser intensity through the \(z\) axis of the embryo causes the signal- to- noise ratio of cell membrane fluorescence images collected from the top focal planes to be less than that of the images from the bottom focal planes (fig. S27). This is on top of the insufficient \(z\) - axis resolution relative to that of the \(x / y\) axis, i.e., \(0.42 \mu \mathrm{m}\) versus \(0.09 \mu \mathrm{m}\) . Third, the parallelism between a cell membrane and an image focal plane prevents sampling of the membrane image due to the low \(z\) - axis resolution. This is a problem even in the segmentation of an early embryo \(^{29}\) . Fourth, apoptosis frequently leads to cell shrinkage, which leads to the aggregation of cell membrane fluorescence signals into a “solid” ball (fig. S27), making it impractical to segment the membrane boundary. Finally, given that our platform + +<--- Page Split ---> + +demands long- duration (over six hours) live- cell imaging with highly frequent image sampling (92 focal planes for each channel at \(\sim 1.5\) - minute intervals), phototoxicity and photobleaching prevent us from obtaining high- quality fluorescence images. This necessitates a tradeoff between the laser intensity and animal viability to ensure the viability of imaged animals and minimize the bleaching of fluorescence signals, which means that the quality of our actual images used for segmentation is much lower than that of still, single- shot images (fig. S28). Therefore, future work should focus on the development of a more robust segmentation algorithm to deal with image deficiencies, alternative imaging methods to compensate for signal loss over laser passages or insufficient \(z\) - axis resolution, and new transgenic strains that demonstrate uniform expression of membrane signals with a higher signal- to- noise ratio. + +## METHOD SUMMARY + +All methods used in this study are recapitulated in detail in the supplementary material. + +## DATA AVAILABILITY + +The data, including the ITK- SNAP- CVE software, are accessible at https://doi.org/10.6084/m9.figshare.24768921. v2. The strain is available upon request. + +## CODE AVAILABILITY + +The code of the CMap algorithm is accessible at https://github.com/cao13jf/CMap. + +## REFERENCES + +1 Arendt, D. et al. The origin and evolution of cell types. Nat. Rev. Genet. 17, 744- 757 (2016). 2 Domcke, S. & Shendure, J. A reference cell tree will serve science better than a reference cell atlas. 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A high- resolution C. elegans essential gene network based on phenotypic profiling of a complex tissue. Cell 145, 470- 482 (2011). 5 Sönnichsen, B. et al. Full- genome RNAi profiling of early embryogenesis in Caenorhabditis elegans. Nature 434, 462- 469 (2005). 6 Community, B. O. Blender - A 3D modelling and rendering package (2023). url: https://www.blender.org 7 Schindelin, J. et al. Fiji: An open- source platform for biological- image analysis. Nat. Methods 9, 676- 682 (2012). 8 Murray, J. I. et al. Automated analysis of embryonic gene expression with cellular resolution in C. elegans. Nat. Methods 5, 703- 709 (2008). 9 Guan, G. et al. System- level quantification and phenotyping of early embryonic morphogenesis of Caenorhabditis elegans. Preprint on bioRxiv (2019). doi: 10.1101/776062 + +## ACKNOWLEDGMENTS + +We thank all the members of Yan Lab, Tang Lab, and Zhao Lab for the fruitful discussion and constructive comments. We appreciate the assistance of Dr. David Smith, Yixuan Chen, Qianli Zhang, Tongshu Wen, Yiqing Liu, and Cunnin Zhao for improving the paper materials, and Ahjol Hyrat for the preliminary analysis of C. elegans developmental symmetry breaking, Kai Kang for the preliminary analysis of gene expression profile and 3D illustration, and Dr. Hung Chim for the assistance in building stable server and website. We are grateful to Prof. Zhuo Du, Dr. Xuehua Ma, and Dr. Zhiguang Zhao for providing the summarized information on C. elegans cell identity and cell fate. Gratitude is extended to Prof. Robert Hugh Waterston and Prof. John Isaac Murray for instructing the data collection from EPIC. The computation of the quantitative and statistical analyses was performed in part on the High-Performance Computing Platform at Peking University. The strains used for the newly generated 97 embryos for profiling gene expression were provided by the Caenorhabditis Genetics Center (CGC), which is funded by the National Institutes of Health (NIH) Office of Research Infrastructure Programs (P40 OD010440). Funding: This work was supported by the National Natural Science Foundation of China (12090053, 32088101) to C.T.; by the Hong Kong Innovation and Technology Commission (ITC) (InnoHK Project CIMDA) and Hong Kong Research Grants Council (RGC) (11204821) to H.Y.; and by the Hong Kong Innovation and Technology Commission (ITC) (GHP/176/21SZ) and Hong Kong Research Grants Council (RGC) (HKBU12101520, HKBU12101522, and HKBU12101323) to Z.Z. Author contributions: H.Y., C.T., and Z.Z. conceived and supervised this project; Y.M., M.- K.W., V.W.S.H, and L.- Y.C. cultured, + +<--- Page Split ---> + +1 imaged, and curated the C. elegans; Z.L. and J.C. devised the CMap algorithm, performed cell segmentation, and extracted morphological features; G.G., Z.L., and J.C. arranged the manual annotation for ground truth, performed data quality evaluation and control, and organized the datasets; G.G. carried out the quantitative and statistical analyses; Z.L., G.G., and P.Y. generated the 3D illustration; G.G., Z.L., Y.M., P.Y., J.C., M.- K.W., and L.- Y.C. designed and improved the CVE software; P.Y., G.G., and Z.L. designed and improved the CMOS website; G.G., Z.L., Y.M., and P.Y. wrote the manuscript; H.Y., C.T., and Z.Z. revised the manuscript. Competing interests: Authors declare that they have no competing interests. + +## DECLARATION OF INTERESTS + +The authors declare no competing interests. + +## SUPPLEMENTARY MATERIAL + +### (i) Materials and Methods + +## Worm strains, maintenance, and transgenesis + +All the animals were maintained on nematode growth media (NGM) plates seeded with Escherichia coli OP50 at a constant ambient temperature of \(20^{\circ}\mathrm{C}\) . To build a transgenic strain that shows bright and ubiquitous expression on cell membrane especially beyond the 350- cell stage, a transgenic strain, ZZY0855, with an insertion allele zzyIs139 [(Phis- 72::PH(PLC1delta1)::mCherry::pie- 1 3' UTR+unc- 119(+)), was generated using biolistic bombardment81. The allele zzyI139 was backcrossed with the N2 strain for five generations and crossed into another transgenic strain ZZY0535 with insertion alleles of zulS178 [his- 72(1kb 5' UTR)::HIS- 72::SRPVAT::GFP::his- 72 (1KB 3' UTR)+5.7 kb XbaI - HindIII unc- 119(+)] and stIs10024 [pie- 1::H2B::GFP::pie- 1 3' UTR+unc- 119(+)], lts44 [Ppie- 1::mCherry::PH(PLC1delta1)+unc- 119(+)]41. All alleles were rendered homozygous to generate the strain, ZZY0861, for all live- cell imaging in this study. The resulting strain shows bright and ubiquitous expressions of two fusions. One is between histone (HIS- 72) and GFP that is expressed in all cell nuclei for automated cell tracing and lineageing; the other is between a membrane- specific domain PH(PLC1delta1) and mCherry that is expressed in all cell membranes, enabling automated membrane segmentation. The transgenic strain, BC10210, dpy- 5(e907), sIs10089[dpy- 5(+)+rCes- pgp- 12- GFP+pCes361]66, was used for imaging the C. elegans excretory cell at postembryonic stages. + +Fluorescence microscopy for wild- type and RNAi- treated embryos subjected to cell membrane segmentation + +<--- Page Split ---> + +Time- lapse 3D imaging was conducted similarly as described in our previous study12 with the following modifications to eliminate mechanical compression applied to the embryos. Embryos were mounted on a polylysine- pretreated glass slide to immobilize them during imaging. Instead of using glass beads that have a slightly smaller diameter than that of embryos, Vaseline was dotted outside the four corners of the polylysine pad to support the cover slip without touching the embryos to be imaged. One- to four- celled embryos were retrieved from wild- type young adults (hermaphrodite) using a mouth pipette for mounting. For RNAi- treated embryos, double- stranded RNAs for lag- 1 and pop- 1 were synthesized in the test tube and injected into both gonads of young adults. One- to four- celled embryos were retrieved from the injected animals 12 to 24 hours after injection. \(12\mu \mathrm{L}\) of Boyd's buffer was added on the polylysine pad before embryo transfer. The coverslip was gently placed on top of the buffer and pushed to the proximity of embryos by applying gentle pressure on the Vaseline on each corner of the polylysine pad. The edges of the coverslip were sealed with melted Vaseline before imaging. + +Imaging was performed with the SP8 confocal microscope (Leica) at a constant ambient temperature of \(20^{\circ}\mathrm{C}\) . Images were acquired from both GFP and mCherry channels with a frame size of \(712\times 512\) pixels (x/y- axis resolution: \(0.09\mu \mathrm{m}\) ) and a scanning speed of \(8000\mathrm{Hz}\) , using a water immersion objective. The excitation laser beams used for GFP and mCherry were \(488\mathrm{nm}\) and \(594\mathrm{nm}\) , respectively. Fluorescence images from 92 focal planes were consecutively collected for four embryos per imaging session, with a z- axis resolution of \(0.42\mu \mathrm{m}\) , at \(\sim 1.5\) - minute intervals (table S3). Images were continuously collected for at least a total of 240 time points, during which the cell count would reach up to 550 and 330 in wild- type and RNAi- treated embryos, respectively. The entire imaging duration was divided into five blocks to accommodate fluorescence signal variation over development (1- 60, 61- 130, 131- 160, 161- 200, and 201- 240 time points). The z- axis compensation was \(0.5 - 3\%\) for the \(488\mathrm{nm}\) laser and \(20 - 95\%\) for the \(594\mathrm{nm}\) laser. The pinhole sizes for the four blocks were 2.3, 2.0, 1.6, 1.6, and 1.3 Airy Units, respectively. Prior to image analysis, all images were subjected to deconvolution and renaming for automated tracing and lineageing, and membrane segmentation. + +## Fluorescence microscopy for postembryonic animals + +Micrographs were acquired using the SP5 confocal microscope (Leica) with tile scanning and merged using the software LAS X (Leica). Intact animals were mounted on an agarose pad in Boyd's buffer/methyl cellulose31 containing 0.1 M sodium azide for imaging with a scanning speed of 200- 400 Hz depending on the size of the animals. For the acquisition of 3D stack images, imaging settings were the same as those used for the embryo, except that the z- axis resolution is \(1\mu \mathrm{m}\) for the adult and \(0.42\mu \mathrm{m}\) for the embryo. + +## Automated cell tracing and lineageing + +<--- Page Split ---> + +Using GFP- labeled cell nuclei images as input, automated cell tracing and lineageing as well as lineage curation were performed as described before \(^{31 - 33}\) . The automated tracing and lineageing results were manually curated up to beyond the 550- cell stage when all the progeny of the founder cell "C" were born and present for at least five time points. The cell nucleus information, including spatial position and cell identity at each time point, was output as a separate file to be used as input by \(CMap\) . + +## Calculation of cell volume, cell surface area, and cell-cell contact area + +For the segmented 3D cell objects in \(C\) . elegans embryos, the cell volume \(V\) was calculated by summing the corresponding cell object's pixels. Moreover, the Alpha shape mesh algorithm \(^{82}\) , which is commonly used to create a 3D triangular mesh from the surface of a 3D object (in voxels format), was employed to extract the surface area \(A_{S}\) of a cell and the contact area \(A_{C}\) between two cell objects. The detailed procedure is as follows: (1) Dilation with the thickness of a pixel added was executed on each cell object, followed by the generation of a 3D triangular mesh from the dilated surface; (2) The cell surface area was calculated by summing the areas of all the triangles on the mesh; (3) The pixels contacted and between any two cell objects were detected and recorded; (4) For each of a pair of contacting cells, the partial area of its surface mesh that contains those pixels was calculated; (5) Among two cells that contact each other, the larger value of the abovementioned partial area (shared pixels between two cells), was adopted as the contact area between them. The quantification of the three morphological properties was jointly validated by two evaluations on each cell object of the eight wild- type embryos and four RNAi- treated embryos presented in this study: (1) The dimensionless cell irregularity defined as \(\frac{3 / A_{S}}{3 / V}\) is always larger than the theoretical minimum \(\sqrt[3]{6} \cdot \sqrt[6]{\pi} \approx 2.1991\) in a perfect sphere \(^{12}\) ; (2) The sum of the contact areas with neighboring cells never exceeds the surface area of any contacting cell with a relative discrepancy of \(20\%\) . + +## Automated cell membrane segmentation + +In this study, an integrated method, \(CMap\) , was proposed for automatically segmenting 3D fluorescence images, also known as cellular volumetric segmentation. The proposed method includes three successive parts: (1) Interpolation is performed for the 2D TIFF images (x/y- axis resolution: \(0.09 \mu \mathrm{m}\) ) collected \(0.42 \mu \mathrm{m}\) per plane, transforming them into 3D NIfTI images with designed sizes and equal resolutions in all the \(x, y\) , and \(z\) directions; (2) A deep 3D neural network, the Euclidean distance transform dilated multifiber network (EDT- DMFNet), predicts the probability that a single pixel belongs to cell membrane; (3) With the probability map, the segmented instances corresponding to multiple separate 3D cell objects are generated. The details of these three parts are elaborated below. + +<--- Page Split ---> + +1. Image preprocessing + +For every embryo, the fluorescence images at each time point were originally collected from \(xy\) focal planes along the \(z\) axis (i.e., the depth direction \(z\) is perpendicular to the focal plane, \(xy\) ) as a stack of 2D images, namely, a 3D stack image. Its corresponding pixel number along \(x\) (the width of the focal plane) and \(y\) (the height of the focal plane) axes are 712 and 512, with a total of 92 focal planes, respectively. For each raw 3D image, the pixels whose fluorescence intensity is within the highest and lowest \(1\%\) of the global range were discarded. Then, the 3D image underwent spatial resampling with downsampling along the \(x\) and \(y\) axes from \(712 \times 512\) to \(356 \times 256\) and upsampling along the \(z\) axis from 92 to 214, using bilinear interpolation. + +2. Euclidean distance map prediction + +The volumes of the rescaled 3D image were processed by EDT- DMFNet, which follows the structure of U- Net \(^{83}\) , and were transformed into a membrane- centered 3D Euclidean distance map. To increase the perceptive field, within which the communications between convoluted channels are made in our small- scale network (a tiny fully connected network), inspired by DMFNet \(^{84}\) , a 3D dilated regression network EDT- DMFNet was deployed to incorporate the information across multiple neighboring images in different convoluted channels with high computational speed (fig S6; table S2). EDT- DMFNet utilized weighted fully- dilated convolution to summarize the features at different scales adaptively. While group convolution was used to implement channel- wise convolution for a small network, a multiple- stage multiplexer, which is composed of several \(1 \times 1 \times 1\) filters, was applied to route the information among groups. + +EDT- DMFNet transforms the segmentation task from a pixel classification problem to a pixel probability map regression problem. The designed network output, namely the inference on the probability map of pixels belonging to cell membrane \(P \in [0,1]^{W \times H \times D}\) according to the input image \(I_0 \in [0,255]^{W \times H \times D}\) , allows multiple cell objects segmentation with small computational resource and high speed. Here, \(W = 356\) , \(H = 256\) , and \(D = 214\) are the width (along \(x\) axis), height (along \(y\) axis), and depth (along \(z\) axis) of 3D stack images. Therefore, the Softmax activation function at the output layer was replaced with the Sigmoid activation function for probability prediction. An adaptive membrane- centered weighted loss \(L\) was used to measure the difference between the target map (the ground- truth data) \(I\) and the predicted probability map \(\hat{I}\) . The loss \(L\) of a 3D volumetric image was calculated by taking the weighted average of the mean squared error (MSE) between the target map \(I\) and the probability map \(\hat{I}\) . Here, the adaptive membrane- centered weighted mask \(W_{\text{mask}}\) is defined as \(W_{\text{mask}} = \mu \cdot I + \text{avg}\{I\}\) , where \(\mu\) is a constant (0.2) that scales the probability map \(\hat{I}\) , and \(\text{avg}\{I\}\) is the average value of \(I\) over the entire 3D volumetric image. In training, the pixels of the predicted probability map \(\hat{I}\) nearby cell membrane become more significant with the weighted mean squared error. Thus, the weighted mask substantially enhanced the contributions of non- zero pixels in the loss function because the non- zero value represents the probability of a pixel belonging to cell membrane in the predicted probability map \(\hat{I}\) . Meanwhile, the pixels of the target map \(I\) surrounding the cell membrane should accordingly generate larger values in the + +<--- Page Split ---> + +weighted mask \(W_{\mathrm{mask}}\) . As a result, the loss grows rapidly if the EDT- DMFNet makes the wrong prediction for the pixels near cell membrane, effectively encouraging the network to focus on the cell membrane and its surrounding pixels. In training, the input image \(I_0\) was imposed with random noises, cropped into a \(128 \times 128 \times 128\) volume, and flipped, which realized the augmentation of the training data and ensured the robustness of the network. The Adam optimizer was employed to update the network with an initial learning rate of \(5 \times 10^{- 3}\) and a weight decay rate of \(1 \times 10^{- 5}\) , using AMSGrad gradient descent optimization. The model was trained for 50 epochs with a batch size of eight on an NVIDIA 2080 Ti GPU. The output of EDT- DMFNet, \(\hat{I}\) , is a probability map as well as a Euclidean distance map for a 3D volumetric image, which will be subjected to the subsequent multiple cell objects segmentation. + +## 3. Cell region generation + +Based on the nucleus marker- seeded watershed algorithm as described before \(^{85}\) , CMap pre- inserts the experimentally established positions of cell nuclei into the Euclidean distance map \(\hat{I}\) . This strategy confers solid cell position information to cell membrane segmentation as guidance cues. The novel cell nucleus marker- based watershed algorithm successfully improved the segmentation performance of CMap for \(C\) . elegans embryos, avoiding under- and over- segmentation problems, especially at the stage with over 350 cells in the embryo when cells become smaller and more crowded with a deteriorated signal- to- noise ratio. Such panoramic cell segmentation was executed up to the last time point with manual curation of automated lineage in each embryo. + +## Training data augmentation + +Due to the rapidly changing cell morphology during embryogenesis and the uneven distribution of fluorescence signal, the 3D images captured in the experiment often have unexpected noise and indistinct intensity, which are harmful to cell membrane recognition (semantic segmentation) correctness to varying degrees. Importantly, by adding the low convoluted feature images into the upsampling layers, the U- shaped network accepts spatially operated images as different training data, which allows the deep convolutional neural networks to run effectively on a small medical or biological training dataset \(^{83}\) . Thus, data augmentation is critical for the training of EDT- DMFNet. Appropriate and valid augmentation acquired manually not only guarantees the robustness of the network but also improves its universality, avoiding the potential over- fitting and under- fitting. Because of the small amount of training data (54 3D stack images reconstructed with two embryos), the augmentation part helps to improve the extent to which the training dataset simulates real- world data \(^{12}\) . By perturbing the pixels' intensity and randomly flipping and cropping an image, the 54 3D stack images in the training dataset were augmented to 21,600 effective training \(128 \times 128 \times 128\) cube images, which significantly improves the robustness of the trained EDT- DMFNet for other wild- type embryonic images. The protocols for data augmentation in training are described below. + +<--- Page Split ---> + +Random intensity scale change: the intensity of all pixels in each training image was scaled with a uniform distribution, the half- open interval [1, 1.1). + +Random intensity shift change: the intensity of all pixels in each training image was shifted with a uniform distribution, the half- open interval [0, 0.1). + +Random flip: each training image had a \(50\%\) chance to be flipped along the \(x, y,\) and \(z\) axes, respectively. + +Random crop: each training cube image was cropped randomly as [128, 128, 128] from the original size [205, 285, 134]. + +## Manual annotation + +The ground truth of cell morphology is necessary for evaluating the performance of machine segmentation algorithms. In this study, the cell morphology of two embryos, WT_Sample1 and WT_Sample7 (fig. S11), was annotated by ten well- trained experts based on the segmentation results from CMap and the raw fluorescence images. The segmentation results were strictly checked and corrected slice by slice and cell by cell, ensuring the correctness of the ground truth established. Specifically, we gained two sets of ground truth data in different dimensions. First, the middle slice at each imaging time point throughout embryogenesis (255 2D images for WT_Sample1 and 205 for WT_Sample7) was annotated for 2D comparison, providing the cross- section of 30,509 cell objects in total. Second, the whole 3D stack within \(100 \pm 5\) , \(200 \pm 5\) , \(300 \pm 5\) , \(400 \pm 5\) , \(500 \pm 5\) , and \(550 \pm 5\) - cell stages (six 3D images each of WT_Sample1 or WT_Sample7) were annotated for 3D comparison, providing the full morphology of 4,046 cell objects in total. + +## Performance comparison between CMap and other state-of-the-art algorithms + +The convolutional- neural- network- based method proposed in this study, CMap (Fig. 1A), consists of two algorithmic advances for processing the denoised 3D fluorescence images: (1) the deep learning network with optimal loss function and network structure (fig. S6); (2) manually curated cell nuclei of automated tracked cells that provides seeds for cell membrane recognition. To evaluate how close the cell morphology reconstructed by CMap is to the real one, we manually segmented the 3D images with fluorescently labeled cell membranes at six different time points (at around 100-, 200-, 300-, 400-, 500-, and 550- cell stages) in two embryos (WT_Sample1 and WT_Sample7). Besides CShaper, using those ground truths (4,046 cell objects in total) as a reference, we compared the performance between CMap and other four state- of- the- art cell segmentation algorithms: 3DCellSeg86, CellPose3D87,88, StarDist3D89,90, and VNet91, in order to study the advantages and disadvantages of each algorithm. + +In comparison to the manually annotated ground truth, the segmentation performance is quantitatively evaluated by the Hausdorff distance (defined as the largest of all distances from a pixel in one region to the + +<--- Page Split ---> + +closest pixel in the other region) and Dice score (the ratio between the overlapping region and the overall region) for each segmented 3D cell object. Among the other four general cell segmentation algorithms (3DCellSeg, CellPose3D, StarDist3D, and VNet), StarDist3D and VNet have a Hausdorff distance approaching the ones of CShaper and CMap at up to the \(\sim 300\) - and \(\sim 200\) - cell stage respectively, revealing their applicability for the first half of \(C\) elegans embryogenesis even though it is not customized for this system (one- sided Student's \(t\) - test, \(p > 0.1\) ). Nonetheless, CMap still exhibits a significantly better performance than all the other algorithms at the \(\sim 400\) , \(\sim 500\) , and \(\sim 550\) - cell stages, no matter for Hausdorff distance or Dice score (one- sided Student's \(t\) - test, \(p \leq 0.05\) ) (fig. S4; table S1). Note that VNet is a semantic segmentation network so we integrated the instance segmentation part of CShaper into its binary output for subsequent performance evaluation. All six algorithms were trained through the same pipeline. + +Each algorithm generated multiple cell objects for 12 3D images (WT_Sample1 at 90/123/132/166/178/185 time points with 101/201/300/400/495/551 cells respectively and WT_Sample7 at 78/114/123/157/172/181 time points with 100/203/304/400/505/552 cells respectively). Then the segmentation outputs were quantitatively evaluated with the manually annotated ground truth by two metrics, Hausdorff distance and Dice score (fig. S4). To avoid the complications associated with extreme cases and achieve a fair comparison with the Hausdorff distance, we excluded the data values smaller than 1 and larger than 20. For the Dice score, we excluded the data values smaller than 0.1 and larger than 0.9. The detailed results of each 3D image and each algorithm are listed in table S1. + +## Quality control on embryo imaging and cell tracing and lineaging + +Every \(C\) . elegans wild- type embryo sample was imaged for a duration with the following criteria: + +1. The last moment of the four-cell stage must be recorded. + +2. The whole C lineage must be generated, namely, with 8 progenies in the Caa sublineage, 8 progenies in the Cpa sublineage, 16 progenies in the Cap sublineage, and 16 progenies in the Cpp sublineage. Lastly, there are 48 C progenies in total, each of which has at least five time points recorded. + +3. The final recognized cell number must be over 570 and the total edited time points must be no less than \(185 (\approx 265\) minutes). + +For the time-lapse 3D images, all the non-apoptotic cells have to be tracked by StarryNite and AceTree till their division or the last edited time point, while the apoptotic cells are tracked to their last time point when they are still distinguishable by the naked eye \(^{31 - 33}\) . The results of cell tracing and lineaging were subjected to manual editing according to the procedures: + +1. For each cell with a complete lifespan recorded in all embryo samples, its normalized cell cycle lengths in all embryos are noted as \([L_1, L_2, \ldots , L_8]\) . The top \(10\%\) cells with the largest \(\max ([L_1, L_2, \ldots , L_8]) / \text{mean}([L_1, L_2, \ldots , L_8])\) and the bottom \(10\%\) cells with the smallest \(\min ([L_1, L_2, \ldots , L_8]) / \text{mean}([L_1, + +<--- Page Split ---> + +\(L_{2}, \ldots , L_{8}]\) were subjected to manual check. + +2. For each 3D region marked by cell identity \(c\) , embryo sample \(j\) , and time point \(T\) , we characterized its volume dynamics with three characteristics: + +- The relative difference in volume between daughter cells and their mother is defined as + +\[CPR(c_{1},c_{2},j,T) = \frac{|V(c_{1},j,T) + V(c_{2},j,T) - \hat{V}(p_{0},j)|}{\hat{V}(p_{0},j)} \quad (1)\] + +where \(V(c_{1},j,t)\) and \(V(c_{2},j,t)\) are the volumes of two daughter cells \(c_{1}\) and \(c_{2}\) at time point \(T\) ; \(\hat{V}(p_{0},j)\) is the median volume of the mother cell \(p_{0}\) throughout its lifespan. + +- The volume inconsistency of cell \(c\) at two consecutive time points \(T + 1\) and \(T\) is defined as + +\[VIR(c,j,T) = \frac{|V(c,j,T + 1) - V(c,j,T)|}{\hat{V}(c,j)} \quad (2)\] + +- The volume variation of cell \(c\) at a specific time point \(T\) is defined as + +\[VVR(c,j,T) = \frac{|V(c,j,T) - \hat{V}(c,j)|}{\hat{V}(c,j)} \quad (3)\] + +For an embryo, if a specific cell at a specific time point belongs to the top \(1 / 30 \approx 3.33\%\) outliers in all three criteria, it was subjected to manual check. + +## RNA interference + +Gene knockdown was performed by RNA interference (RNAi) through microinjection. Primers for the amplification of the double- stranded RNA (dsRNA) template were as described before \(^{92,93}\) . For dsRNA production, the T7 promoter was included at the \(5'\) ends of both forward and reverse. The primers are as follows: lag- 1: forward: TAATACGACTCACTATAGGG TCAGTCTCTTGCAAACCACG; reverse: TAATACGACTCACTATAGGG ATGCTGCAATCGAAGATGA; pop- 1: forward: TAATACGACTCACTATAGGG TTCCCAGGAAAGTTAGGCA; reverse: TAATACGACTCACTATAGGG AAACCGACACCCGTATGAAG. Polymerase Chain Reaction (PCR) was performed using \(C\) . elegans N2 genomic DNA as a template in \(20 \mu \mathrm{L}\) volume using ExTaq DNA polymerase (Cat# RR001Q, TaKaRa). After checking the PCR product on \(1\%\) agarose gel, \(1 \mu \mathrm{L}\) of the PCR product was used as a template for dsRNA production with NEB HiScribe T7 Quick High Yield RNA Synthesis Kit according to the manufacturer's description. The reaction mixture was incubated at \(75^{\circ}\mathrm{C}\) for 15 minutes in a water bath followed by turning off the heating power and incubating overnight in the same water bath for annealing of dsRNAs. The dsRNA was diluted to a concentration of \(300 \mathrm{ng / \mu L}\) in TE buffer for microinjection. At last, the automated tracing and lineageing results were manually corrected up to the stage when embryos were arrested, i.e., up to the 500- cell stage for two embryo samples with RNAi against lag- 1 (the nucleus effector of the Notch signaling pathway) and up to the 330- cell stage for two embryo samples with RNAi against pop- 1 (the nucleus effector of the Wnt signaling pathway) (table S3). + +<--- Page Split ---> + +## Data filtering based on abnormal cell volume and cell shape + +As a subset of the 3D cell objects segmented by CMap were apparently wrong, we devised a pipeline to filter them out from the last time point of the four- cell stage to the last edited time point for every embryo sample. Six strategies were applied as listed below. + +1. The volume \(V\) of the cell objects: \(V\) satisfies a bimodal distribution, where the extremely small cell volume is shown to result from the cell region that fails to dilate toward the distinguishable cell membrane and is close to the area of the cell nucleus (figs. S8A and S10). A cell region is filtered out if it has a volume on the left of the valley of the bimodal distribution. + +2. The relative difference in volume between daughter cells and their mother CPR: A cell region is filtered out if it has a value over \(99\%\) of cell objects in the first half of \(C\) . elegans embryogenesis recorded by the CShaper dataset12, considering its high quality validated (figs. S8B and S9). + +3. The volume inconsistency VIR of cell \(c\) at two consecutive time points \(T + 1\) and \(T\) : A cell region is filtered out if it has a value over \(99\%\) of cell objects in the first half of \(C\) . elegans embryogenesis recorded by the CShaper dataset, considering its high quality validated before12 (figs. S8C and S9). + +4. The volume variation VVR of cell \(c\) at a specific time point \(T\) : A cell region is filtered out if it has a value over \(99\%\) of cell objects in the first half of \(C\) . elegans embryogenesis recorded by the CShaper dataset12, considering its high quality validated (figs. S8D and S9). + +5. The existence of separate regions \(SR\) , which disobeys the space continuity of cell shape: A cell region is filtered out if it contains two cell nuclei inside but neither the middle point nor the two quarter points between them belong to the cell region (fig. S10). + +6. The filter rate FRL of lifespan recorded: A cell region is filtered out if over half of the time points within the lifespan of this cell have been filtered out by the five criteria above (figs. S8E and S10). + +## Systematic checks for data consistency + +To ensure that the massive data brought to the public is in the correct format, we wrote independent code to check its different aspects, including but not limited to: + +1. Every cell recorded in "SegCell\\_