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Subfunctionalization of phytochrome B1/B2 leads to differential auxin and photosynthetic responses
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Subfunctionalization of phytochrome B1/B2 leads to differential auxin and photosynthetic responses
Abstract Gene duplication and polyploidization are genetic mechanisms that instantly add genetic material to an organism's genome. Subsequent modification of the duplicated material leads to the evolution of neofunctionalization (new genetic functions), subfunctionalization (differential retention of genetic functions), redundancy, or a decay of duplicated genes to pseudogenes. Phytochromes are light receptors that play a large role in plant development. They are encoded by a small gene family that in tomato is comprised of five members: PHYA, PHYB1, PHYB2, PHYE, and PHYF. The most recent gene duplication within this family was in the ancestral PHYB gene. Using transcriptome profiling, co‐expression network analysis, and physiological and molecular experimentation, we show that tomato SlPHYB1 and SlPHYB2 exhibit both common and non‐redundant functions. Specifically, PHYB1 appears to be the major integrator of light and auxin responses, such as gravitropism and phototropism, while PHYB1 and PHYB2 regulate aspects of photosynthesis antagonistically to each other, suggesting that the genes have subfunctionalized since their duplication.
One important family of plant genes are the phytochromes.
Plants use both internal and external cues as signals to guide their growth and development, and to help them respond to their environment, such as to light quality and light quantity, temperature, moisture, or nutrient availability. Phytochromes (phys) are light-absorbing chromoproteins that consist of a chromophore and an apoprotein, which together transmit light signals and regulate gene expression in response to light (Chen & Chory, 2011;Franklin & Quail, 2010). The phy apoproteins are encoded by a multi-gene family that generally consists of a predominantly far-red (FR) responsive phy, phyA, and one or more predominantly red light (R) responsive phys. In Arabidopsis, the R responsive phys are encoded by four genes: AtPHYB-AtPHYE. Phylogenetically, gene duplication of an ancestral phytochrome gene first separated PHYA/C from the other PHYs. Subsequently, PHYA separated from PHYC, and PHYB/D from PHYE (Li et al., 2015;Mathews & Sharrock, 1997). Eventually, after the divergence of the Brassicales, PHYB/D separated into PHYB and PHYD genes in Arabidopsis (Mathews & Sharrock, 1997).
PHYs in tomato have not undergone the same phylogenetic evolution as in Arabidopsis. For instance, SlPHYB1 and SlPHYB2 (hereafter simply called PHYB1 and PHYB2) are similar to AtPHYB and AtPHYD but these genes arose separately by a gene duplication event after the separation of the Solanales from the Brassicales about 110 Mya (Alba, Kelmenson, Cordonnier-Pratt, & Pratt, 2000;Pratt, Cordonnier-Pratt, Hauser, & Caboche, 1995), suggesting that any functional divergence of the duplicated genes would be unlikely to be the same in the two plant families. In contrast to Arabidopsis, mutation of phyB1 in tomato results only in temporary red light insensitivity at a young seedling stage while phyB1 adults look very similar in phenotype to WT tomato (Lazarova et al., 1998). In Arabidopsis and pea, PHYB plays a role during de-etiolation (Neff & Chory, 1998), chlorophyll production (Foo, Ross, Davies, Reid, & Weller, 2006), photo-reversible seed germination (Shinomura et al., 1996), timing of flowering (Khanna, Kikis, & Quail, 2003), the shade avoidance response (Keller et al., 2011), and the mediation of hormone responses (Borevitz et al., 2002), including lateral root initiation via auxin transport signaling (Salisbury, Hall, Grierson, & Halliday, 2007), polar auxin transport (Liu, Cohen, & Gardner, 2011), and seed germination via the regulation of abscisic acid (ABA) (Seo et al., 2006). Compared to Arabidopsis, much less is known about the functions of phys in the Solanales. In tomato, PHYB1 is involved in hypocotyl inhibition, de-etiolation, and pigment production in R (Kendrick et al., 1994;Kendrick, Kerckhoffs, Tuinen, & Koornneef, 1997; Tuinen, Kerckhoffs, Nagatani, Kendrick, & Koornneef, 1995). PHYB2 plays a role in early seedling development (Hauser, Cordonnier-Pratt, & Pratt, 1998), and, in cooperation with PHYA and PHYB1, in the control of de-etiolation (Weller, Schreuder, Smith, Koornneef, & Kendrick, 2000). Analysis of phyb1;phyb2 double mutants in tomato showed that a high level of redundancy exists between the two genes with respect to hypocotyl elongation during de-etiolation in both white light and R (Weller et al., 2000). Chlorophyll and anthocyanin production, on the other hand, was only reduced in the phyB1 mutant and not in phyB2, but the phyb1;phyb2 double mutant displayed a synergistic phenotype with less of both pigments than found in the phyb1 mutant alone, suggesting that phyB2 contributes to pigment production in a significant manner (Weller et al., 2000).
Subfunctionalization of the B-class phytochromes was also shown in maize, where ZmPHYB1 was the predominant phy to regulate mesocotyl elongation in R, while ZmPHYB2 was mainly responsible for the photoperiod-dependent transition from vegetative to floral development (Sheehan, Kennedy, Costich, & Brutnell, 2007).
To better understand to what degree subfunctionalization has occurred between tomato phyB1 and phyB2, we employed transcriptome profiling and co-expression network analysis. We found that tomato PHYB1 and PHYB2 exhibit both common and non-redundant functions. According to our analysis, two major areas of potential subfunctionalization are the regulation of genes involved in response to auxin and in photosynthesis. To verify the biological relevance of our genomic analyses, we tested phyB1 and phyB2 mutants for classical auxin responses, including phototropism and gravitropism, and for the rate of photosynthetic assimilation. We report here that phyB1 and phyB2 indeed differ in their involvement in some of these phenotypes, suggesting that the recent PHYB duplication in tomato has led to subfunctionalization that is different from those in maize or Arabidopsis.
| Plant materials and growth conditions
Solanum lycopersicum seeds of cultivar Moneymaker (Gourmet Seed, Hollister, CA, United States) and homozygous phyB1 mutants (allele tri 1 ) and phyB2 mutants (allele 2-1 (aka 70F), (Kerckhoffs et al., 1999;Weller et al., 2000) were used in all experiments. Both mutants used in this study were in the Moneymaker background (original source: Tomato Genome Resource Center, Davis, CA, USA). For RNAseq experiments, seeds were surface sterilized using 10% bleach for 15 min in ambient laboratory conditions and then sown on water-saturated, sterile filter paper in light-excluding plastic boxes. Plants were grown in a dark growth chamber at 25°C. Five-day-old seedlings of similar height were harvested under green safe light (522 nm LED), and flash-frozen in liquid nitrogen. Seedling handling and harvesting at room temperature under safelight conditions was limited to a few minutes of indirect exposure. The remaining seedlings were exposed to 60 min of red light (660 nm, using a custom-made LED display, 10 μmol m −2 s −1 ) and then selected, harvested, and frozen as described for the dark-grown seedlings. Specimens were stored at −80°C until RNA was extracted. Tissue was grown in four biological replicates under the same conditions.
| RNA extraction and sequencing
Tissue was flash-frozen in liquid nitrogen and pulverized with a mortar and pestle. About 5 seedlings (~100 mg) were pooled per biological replicate for each genotype and condition. Total RNA was extracted using an RNeasy Plant Mini Kit (Qiagen) according to the manufacturer's instructions. TruSeq stranded mRNA library construction was performed by the Research Technology Support Facility at Michigan State University. Paired end 125 bp reads were obtained using an Illumina Hi-Seq 2500 instrument. All data were uploaded for public use to NCBI's short read archive http://www. ncbi.nlm.nih.gov/sra/SRP10 8371.
| RNAseq differential expression analysis
RNAseq reads were mapped with HISAT2 to the SL3.0 version of the tomato genome with ITAG3.2 genome annotation from SolGenomics (www.solge nomics.org). First, phyB1 experiment reads and phyB2 experiment reads were mapped separately, and then they were mapped together. DESeq was used largely with default parameters to identify differentially expressed genes between wild type in the dark and wild type in R and between phyB mutants in the dark and in R, except that we used an alpha value of 0.05 for the multiple comparison adjustment. Genes identified in the phyB1 experiment alone as significantly differentially expressed (DE) by DESeq and with a abs(log2(fold change)) > 0.63, that is, changed by at least 1.5-fold between dark and R, were then looked at in the phyB1 comparison. If the gene had a log2(FC) that was significantly different from WT, we called the gene phyB1regulated. To be characterized as significantly different, the difference between the log2(FC) in WT and phyB1 had to be greater than the sum of the standard errors of the log2(FC) in WT and in phyB1. The process was repeated with the phyB2 experiment alone to identify phyB2-regulated genes.
| Co-expression analysis with WGCNA
From the data in which phyB1 and phyB2 experiment reads were mapped together, normalized read counts were obtained from DESeq. The variance of normalized expression was calculated across all samples (10 WT-D, 10 WT-R, 5 B1-D, 5 B1-R, 5 B2-D, 5 B2-R), and the top 8,000 most variable genes were identified. Their expression values were log transformed [log2(normalized read count + 1)] and used as input for WGCNA in R to identify co-expression modules.
Beta was set to 10 for the adjacency function. Modules were obtained based on topological overlap and eigenvectors representing average expression of each module were correlated to condition (dark = 0, 60 min R = 1) and genotype (either phyB1 = 1, phyB2 and WT = 0 or phyB2 = 1, phyB1 and WT = 0).
| GO enrichment analysis
To determine which gene ontology (GO) categories were significantly enriched among the differentially regulated or co-expressed genes, we used the R package topGO (Alexa & Rahnenfuhrer, 2010;Alexa, Rahnenführer, & Lengauer, 2006). Only categories with p-values < 0.05 from Fisher's exact tests (weighted models) are reported. For topGO's "gene universe," GO annotations for S. lycopersicum were downloaded from the Panther Classification System (www.panth erdb.org, downloaded May 2017).
| Gravitropism
Wild type, phyB1, and phyB2 seeds were sown at 12p.m., 5p.m., and 1p.m., respectively, to coordinate germination times (age-synchronized) and assure equal developmental stages at the time of experimentation. Seeds were sterilized by stirring for 15 min in 10% bleach in the dark and sown into light-excluding plastic boxes with saturated paper towels and filter paper under green light. Seeds were grown in the dark in a growth chamber at 25°C for 5 days. Age-synchronized seedlings were transferred under green light to 1% agar plates, placed either in dark, under R (135 µE) from the top, or in R from opposite sides (60 µE) and allowed to grow with the same gravity vector for 1 hr. Seedlings were then gravistimulated by rotating plates 90 degrees. Photographs were taken before gravistimulation (0 hr), after 4, 8, and 24 hr.
The angle of bending was measured with ImageJ. A three-way ANOVA (genotype, light condition, time) was performed in R followed by Tukey's post hoc test to determine statistically significant differences between groups.
| Phototropism
For phototropism experiments, age-synchronized seedlings (Moneymaker, phyB1, and phyB2) were grown in individual plastic scintillation vials filled with soil and incubated in the dark at 25°C for 5 days. Seedlings with similar hypocotyl length were then transferred to a black box illuminated with unilateral white light through a slit in the box. The plants were positioned such that their apical hook was facing away from the light source. Every hour over a time period of five hours, a set of plants was removed and scanned. The phototrophic bending angle of these plants was determined by ImageJ analysis, and data were plotted using R software. Data were analyzed by a two-way ANOVA (genotype, time) using the software R.
For qPCR analysis of PHOT genes, tomato seedlings were grown as for the phototropic experiments. Material was harvested and flash-frozen at the indicated times. Total RNA was extracted using an RNeasy kit (Qiagen) according to the manufacturer's instructions.
Reverse transcription was performed using the iScript cDNA Synthesis kit (Bio-Rad) with the recommended incubation times and temperatures as follows: 25°C for 5 min, 46°C for 20 min, and 95°C for 1 min.
QPCR was performed on a Bio-Rad Mastercycler C1000 using iTAQ
Universal SYBR Green Supermix (Bio-Rad) with an incubation at 95°C for 3 min, followed by 40 cycles at 95°C for 10 s, and 60°C for 30 s. SAND (Solyc03g115810) and RPL2 (Solyc10g006580) genes were used for normalization. Primer specificity was verified using the melt curves, and data were analyzed by the 2 -ΔΔCt method (Livak & Schmittgen, 2001). Statistical analysis was performed using ANOVA (R version 3.4.1) on log10 normalized expression values. The primers are listed in Table S6. Three biological replicates were used with five seedlings per genotype and time point per biological replicate.
| Photosynthetic analysis and chlorophyll quantification
Six-week-old Moneymaker, phyB1, and phyB2 plants grown in a growth chamber at 25°C under 16 hr of light were used for photosynthetic analysis and chlorophyll quantification. A LI-COR 6400XT portable photosynthesis system (LI-COR) with a standard leaf chamber and a LI-COR 6400 LED light source was used for photosynthetic efficiency measurement. To ensure best uniformity, we chose for analysis the terminal leaflet of the fourth youngest, fully developed leaf. Single leaflets still attached to the plant were clamped flat into the standard leaf chamber. The conditions in the leaf chamber were set at a reference CO 2 value of 400 mmol and a temperature of 21°C, and CO 2 uptake was measured at two different light intensities: 100 µmol photons m −2 s −1 and 1,500 µmol photons m −2 s −1 . Each leaf was placed in the standard leaf chamber before measurement and exposed to 2 min of light of the mentioned intensities in order to allow the plants to acclimate and CO 2 assimilation was measured.
Matching was done after every plant to minimize errors. After measuring CO 2 assimilation, the leaf was photographed, and the leaf area was measured using ImageJ. Fresh weight of the respective leaf was also recorded, and chlorophyll was extracted in 5 ml of methanol for 72 hr in the dark at 4°C. Methanol extracts were analyzed by spectrophotometry and chlorophyll concentrations determined according to published procedures (Porra et al., 1989). Photosynthetic efficiency was calculated by normalizing the assimilation rate either for area or fresh weight. Three experimental replicates were performed with ~10 plants per genotype per replicate.
| PhyB1 and PhyB2 differentially affect the transcriptome during photomorphogenesis in tomato seedlings
To determine if PHYB1 and PHYB2 have acquired different functions since the divergence from their common single-gene ancestor, we performed RNAseq analysis. We grew WT and phyB1 and phyB2 mutant seedlings for 5 days in the dark and compared them with individuals of the same genotypes and age that were also exposed to red light (R) for 60 min. We then identified genes that were differentially expressed in the mutants between dark and light (Table S1).
Using a threshold value of 1.5-fold upregulation or downregulation, we first filtered the data from the RNAseq analysis for genes that were statistically significantly upregulated or downregulated by light treatment in the WT. Of those genes, we considered a gene to be phyB1 or phyB2 regulated if it was either (a) upregulated or downregulated by light in the WT but not differentially regulated in the mutant, (b) oppositely regulated in the mutant compared to the WT, (c) significantly less strongly regulated in the mutant compared to the WT, or (d) more strongly regulated in the mutant compared to the WT. This data filtration yielded 121 phyB1-regulated genes, and 73 phyB2-regulated genes. In these gene sets, we identified functional enrichment gene ontology (GO) categories ( Figure 1; Table S2). To identify traits possibly subfunctionalized between PHYB1 and PHYB2 mutants, we were particularly interested in GO categories that showed significant enrichment in one phyB-regulated gene set but not the other. GO categories significantly enriched in genes regulated by phyB1 included responses to auxin (GO: 0009733), responses to cytokinins (GO: 0009735), and protein phosphorylation (GO: 0006468). By contrast, phyB2-regulated genes did not fall into these three GO categories, but instead into GO categories such as defense response (GO: 0006952) and processes involving aromatic amino acid metabolism and biosynthesis (GO: 0009095 and GO: 0006558) (Table S2; Figure 1).
To gain additional insight into genes that were differentially affected by their mutations in either PHYB1 or PHYB2, we employed transcriptional co-expression analysis of the top 8,000 most variably expressed genes across all conditions and found modules containing genes that due to their co-expression status were likely to have some degree of functional connectivity ( Figure 2; Table S3). The yellow, blue, red, and light-cyan modules contained genes positively correlated to the phyB1 mutation (i.e., they were more highly expressed in phyB1 than in WT and phyB2) but negatively or not significantly correlated to the phyB2 mutation. The opposite was true for the brown, salmon, turquoise, and green modules, which contained genes positively correlated to the phyB2 mutation (i.e., they were more highly expressed in phyB2 than in WT and phyB1) but not or negatively correlated to the phyB1 mutation.
These opposite expression patterns thus indicated diversified regulation between the two PHYB genes. Such diversified regulation was also seen, albeit not significantly, in the black, green-yellow, and cyan modules.
Modules containing genes that were regulated by light ("condition") included the tan module (negative correlation), and the green-yellow, magenta, green, midnight blue, and pink modules (positive correlation) ( Figure 2). The green module was the only module containing genes that were significantly correlated with light (positively) and were also oppositely correlated with phyB1 and phyB2. We looked for enriched GO functions in each co-expression module (Table S4). Among these F I G U R E 1 phyB1 and phyB2 regulate expression of genes involved in different biological processes. We identified 121 phyB1-regulated genes and 73 phyB2regulated genes. Gene ontology functional enrichment analysis of these gene groups identified biological processes specifically regulated by phyB1 and phyB2. For all significant GO category enrichments, the black bars represent the number of genes with that annotation in that group (Significant) and the gray bars represent the expected number of genes with that annotation if representation was random (Expected) F I G U R E 2 Co-expression modules show phyB1 and phyB2 differently regulate gene networks involved in auxin and photosynthesis related biological processes among others. (a) For each co-expression module (indicated by color) and the genes that did not fall into a coexpression module (gray), the average expression vector (eigenvector) across conditions and genotypes was correlated to condition (dark = 0, 60 min R exposure = 1) and genotype (phyB1 column: WT and phyB2 = 0, phyB1 = 1; phyB2 column: WT and phyB1 = 0, phyB2 = 1). R 2 values from the Pearson correlations are indicated in the heatmap by color according to scale on the right as well as by their printed value in the grid with p-values below in parentheses. (b) Gene ontology functional enrichment analysis identified biological processes central to each co-expression module. Displayed here are four enriched GO biological processes for the brown, green, and blue modules. The black bars represent the number of genes with that annotation in that group (Significant) and the gray bars represent the expected number of genes with that annotation if random (Expected) functions were auxin-related processes, including auxin efflux (GO: 0010329), the auxin-regulated processes of gravitropism and phototropism (GO: 0009959, GO: 0009638), and auxin signaling (GO: 0009734), as well as photosynthesis-related processes (GO: 0009765, GO: 0009773, and GO: 0015979), in addition to a large number of other functional categories (Figure 2b and Table S4).
To determine areas of subfunctionalization between phyB1 and phyB2 in tomato, we combined information from our differential expression, co-expression and GO analyses to choose physiological functions for further testing and verification that transcriptomic differences had measurable effects on phenotypes. These functions were chosen based on (a) frequency of appearance in our data as being differentially regulated by phyB1 and phyB2, (b) statistical significance of our differential and co-expression analyses data, and (c) the number of genes on which individual enrichment analyses were based. Additionally, functions for further study were chosen if they were known from the literature to be regulated, at least in part, by phyB in Arabidopsis.
| PhyB1 and PhyB2 differentially modulate auxin responses in tomato seedlings
To determine if our gene expression analysis had predictive power on the plant's phenotype, we subjected wild type (WT) and phyB1 and phyB2 mutants to a variety of physiological experiments. Given that auxin-related processes had been implicated as differentially regulated by phyB1 and phyB2 in both differential expression and co-expression analyses, we tested if the auxin-related responses phototropism and gravitropism were differentially affected between phyB1 and phyB2 mutants when compared to the WT. Phototropism, the movement of plants toward a light source, is achieved by the perception of blue light via the photoreceptors PHOT1 and PHOT2, eventually leading to unequal distribution of auxin along the hypocotyl of a seedling exposed to unilateral light (Fankhauser & Christie, 2015). Differential auxin concentrations then result in unequal growth on the light versus dark side of the stem or hypocotyl leading to curvature toward the light source (Fankhauser & Christie, 2015).
Indeed, when we exposed 5-day-old seedlings to unilateral white light (WL) over a period of three hours, phyB1 hypocotyls displayed a significantly faster phototropic response (Figure 3) compared to the WT and phyB2 plants, indicating a differential role of phyB1 and phyB2 in the phototropic response in tomato. This suggests that phyB1, but not phyB2, normally inhibits phototropic bending.
Our RNAseq differential gene expression analysis had found PHOT1 to be differentially expressed in the WT dark versus WT red light comparison but the gene was not phyB1 or phyB2 regulated.
PHOT2 was not differentially expressed in either comparison. Since differences in the phototropic phenotype were recorded for seedlings grown under conditions different from those in our RNAseq experiment, we decided to check if gene expression differences of these receptors pivotal to the phototropic response might also be detectable between phyB1 and phyB2 mutants during phototropic stimulation. Testing PHOT1 and PHOT2 expression with qPCR at 0 and 3 hr of treatment with unilateral white light, we observed a decline in PHOT1 and an increase in PHOT2 expression over the 3-hr treatment (Fig. S1), but found no significant differences of gene expression between the two phyB mutants, suggesting that regulation of the PHOT1 and PHOT2 genes does not explain the measured phenotypic differences and instead indicates that the differences are likely due to differential gene regulation downstream of PHOT1 and PHOT2 (Fig. S1).
Since gravitropism, like phototropism, is a typical auxin-regulated response, we decided to test if gravitropism manifests itself differentially in the two phyB mutants in tomato. Five-day-old darkgrown seedlings were transferred to agar plates, either exposed to R or kept in the dark, and grown upright for 1 hr immediately after the transfer. Plates were then reoriented 90 degrees to induce a gravitropic response. We observed that in R the phyB1 mutant responded statistically significantly faster to the altered gravity vector, which was especially obvious around 8 hr postgravistimulation, whereas in darkness the mutants responded to gravity at the same rate as WT ( Figure 4). This experiment suggested that the differential auxin responsiveness between phyB1 and phyB2 also extends to differences in their gravitropic response. Interestingly, when we reduced the light levels from 135 to 60 µmol*m −2 *s −1 , the gravitropic response F I G U R E 3 In white light, phyB1 mutants show significantly faster phototropism than wild type or phyB2 mutants. The average degree to which 5-day-old dark-grown seedlings bent toward unidirectional white light (bend angle) over 3 hr is shown. Error bars represent standard error. Combined data from three biological replicates are shown, n = 5 seedlings per genotype per time point per biological replicate. A two-way ANOVA with time and genotype was performed followed by Tukey's post hoc test using the software R. Shared letters represent no statistically significant difference differences between genotypes disappeared (Fig. S2), suggesting that the phyB1-mediated gravitropic response in tomato is also light intensity-dependent.
Since we only observed significantly greater gravitropic curvature in the phyB1 mutants when the gravitropic experiment was done with high light intensity from the top but not with low light intensity from the side we wanted to exclude the remote possibility that in tomato phototropism can also be triggered by R alone, instead of requiring blue light. We therefore performed a series of control experiments in which we exposed seedlings to unilateral R light and measured their directional growth response over a period of three hours in a similar way to how we had performed the phototropic experiments shown in Figure 3. Not surprisingly (Fankhauser & Christie, 2015), our data showed that, like Arabidopsis, tomato does not have a red light phototropic response (data not shown), confirming that the enhancement in the gravitropic response of phyB1 could not have been due to its enhanced phototropic response.
| PhyB1 and PhyB2 differentially modulate photosynthetic responses in tomato seedlings
Our transcriptional co-expression analysis had shown almost 60 photosynthesis-related genes to be enriched in the blue module, which contains genes with expression positively correlated with the phyB1 mutation, but not significantly correlated with the phyB2 mutation ( Figure 2). We therefore decided to measure a variety of photosynthesis-related physiological parameters to test the hypothesis that gene duplication in PHYB had led to the subfunctionalization of regulation of genes involved in photosynthesis. We measured overall photosynthetic activity and related this activity to leaf size and fresh weight. Measuring overall leaf chlorophyll concentrations, we found differences between the WT and the two mutants but they were not statistically significantly different from each other (data not shown).
Photosynthetic activity was not statistically significantly different between the three genotypes when the photosynthetic rate was normalized by leaf area regardless of light intensity (Figure 5a,c).
However, when we normalized photosynthetic rate by fresh weight of the leaf portion used for the gas exchange analysis, we observed a statistically significant difference between the two phytochrome mutants. These differences between phyB1 and phyB2 were seen both in low and high light intensities (Figure 5b,d). Interestingly, the data suggest that phyB1 and phyB2 act antagonistically to each other and that PHYB1 and PHYB2 have subfunctionalized with respect to the role they play in regulating photosynthesis.
| Subfunctionalization of phyB1 and phyB2 is correlated with differences in the genes' regulatory region
Using the PlantCARE database (Lescot et al., 2002), we compared the 3-kb regulatory region immediately upstream from each gene's transcriptional start site (Table S5) and found a number of differences. Overall, PHYB1 contained 17 recognized light-regulated cisacting elements, while PHYB2 only contained 7 such elements. The type of elements found in each gene's promoter region was also different. For example, the PHYB1 promoter region contained 7 G-Box
F I G U R E 4
In R, phyB1 mutants show significantly faster gravitropism than wild type or phyB2 mutants. The average degree to which 5-day-old dark-grown seedlings bent toward the negative gravity vector (i.e., upwards) after gravistimulation over 24 hr is shown. Seedlings were either gravistimulated in the dark (left), or with 135 µmol photons m −2 s −1 of R. Error bars represent standard error. The dark and R plots each contain data from three biological replicates. N = 20 per genotype per time point per biological replicate. A three-way ANOVA with time, genotype, and light condition was performed followed by Tukey's post hoc test in R. Shared letters represent no statistically significant difference elements, which bind PHYTOCHROME INTERACTING FACOTRs (PIFs) (Pham, Kathare, & Huq, 2018), while in PHYB2, there were only 2.
Several other motifs were found only in one or the other phy gene (Table S5). Overall, the differential occurrence of light regulatory sequences suggests that transcription of these duplicated genes might be differentially regulated.
| D ISCUSS I ON
Gene duplication is a major source of genetic material with the potential for the evolution of novel functions and the development of complexity of responses to the environment (Panchy et al., 2016). Retention of duplicated genes can either indicate that retained genes are positively selected to provide genetic redundancy (Zhang, 2012), that they are required to maintain proper dosage or genetic balance (Birchler & Veitia, 2014;Freeling & Thomas, 2006), or that duplication eventually led to the acquisition of novel or refined functions (Lynch & Conery, 2000;Ohno, 1970). PHY genes, in particular, have been estimated to be evolving at a faster rate (1.52-2.79 times) than the average plant nuclear gene, suggesting that diversification of the PHY gene family might respond either to selective pressure or to the absence of major evolutionary constraints (Alba et al., 2000).
We used differential mRNA expression and co-expression analysis to first evaluate the degree to which the PHY genes PHYB1 and PHYB2 have functionally diversified since their separation from a common ancestor gene and then to identify and verify physiological traits for which phyB has subfunctionalized since its gene duplication event. Our analysis indicated significant differences in the transcriptome of plants mutant in either PHYB1 or PHYB2. On the other hand, after filtering, the overall number of genes that were regulated by phyB1 (121) and phyB2 (73) was relatively modest. Overall, our differential gene expression analysis showed that the group of genes regulated by phyB1 but not phyB2 was enriched in auxin response genes, and our co-expression analysis showed that those genes found in co-expression gene networks and that differentially correlated to phyB1 and phyB2 were enriched in auxin response and photosynthesis genes.
| Regulation of auxin responses by phytochrome B
In Arabidopsis, phototropic curvature is enhanced when plants are pre-treated with R for 2 hr before directional blue light (B) treatment (Janoudi, Konjevic, Apel, & Poff, 1992). This pre-treatment response is phyA-mediated, and not phyB-mediated (Parks, Quail, & Hangarter, 1996), although it has been shown that even without R pre-treatment, Arabidopsis phyA, phyB, and phyD promote phototropism (Whippo & Hangarter, 2004). Specifically for B intensities of greater than 1.0 µmol*m −2 *s −1 of light, phyB and phyD show functional redundancy with phyA, while at fluences of B around 0.01 µmol*m −2 *s −1 , phyA was required for a normal phototropic response (Whippo & Hangarter, 2004). Additionally, Arabidopsis phyB has been shown to inhibit phototropism in shade-free environments (a high R/FR ratio), while mediating the phototropic response in the shade via PHYTOCHROME INTERACTING FACTORs (PIFs) and members of the YUCCA gene family (Goyal et al., 2016).
Furthermore, it was shown that the quadruple mutant for phyB, phyC, phyD, and phyE has a normal phototropic response (Strasser, Sánchez-Lamas, Yanovsky, Casal, & Cerdán, 2010), confirming the notion that phyA is required in Arabidopsis for a normal low-flu- Our data suggest that phototropism is differently regulated between tomato and Arabidopsis. Our genetic analysis shows that phyB1, but not phyB2, negatively regulates the phototropic F I G U R E 5 Photosynthetic activity is enhanced by phyB2 and repressed by phyB1 independent of light intensity. Photosynthetic activity was measured under varying light intensities in 6-week-old WT, phyB1, and phyB2 mutants grown at 25°C (16 hr day/8 hr night) using a LiCOR 6400XT. Three biological replicates were performed with 10 plants per genotype per replicate. Data were normalized in two different ways either by leaf area (a and c) or by leaf area and fresh weight of the leaf tissue that was used for photosynthetic rate measurement. Data were statistically analyzed with a one-way ANOVA followed by a Tukey post hoc test using the software R. In each panel, data points not connected by a shared letter are statistically significantly different response in tomato (Figure 3). This in turn suggests that in tomato, phyB duplication led to a defined split between phyB1 and phyB2 with respect to phototropism, while in Arabidopsis phyB and phyD share redundancy, at least for its control of phototropism in response to R pre-treatment (Whippo & Hangarter, 2004).
Additionally, while Arabidopsis work has shown phyB to be repressing phototropism in shade-free environments (Goyal et al., 2016), we saw that phyB2 in tomato is not involved in that response. Our RNAseq analysis supports the split in function also with respect to expression differences in the PIN genes that Haga and colleagues (2014) had proposed to play a role in phy-mediated phototropism: In tomato, our network analysis placed SlPIN4 into the brown module, which is negatively correlated with the phyB1 mutation but positively correlated with the phyB2 mutation ( Figure 2). Furthermore, SlPIN4 was differentially regulated in response to R only in the phyB2 mutant, but not in the phyB1 mutant (Table S1). This differential sensitivity in auxin response signaling between the two subfunctionalized genes in tomato suggests one possible avenue for the two phy genes in tomato to differentially affect phototropic curvature.
Gravitropism, like phototropism, is an auxin-mediated differential growth response that results in directional elongation with respect to the gravity vector (Morita, 2010). Our data showed that phyB1, but not phyB2, represses gravitropism in R (Figure 4). This response is therefore similar to the phototropic response in that it is enhanced by the phyB1 mutation. The role of phytochrome in the gravitropic response in less well understood than it is for phototropism. In Arabidopsis, but not in tomato, R perceived by both phyA and phyB results in strongly reduced shoot gravitropism (Liscum & Hangarter, 1993;Poppe, Hangarter, Sharrock, Nagy, & Schäfer, 1996) caused by PIFs that in R convert the gravity-sensing amyloplasts in the endodermis into other, non-gravity-sensing types of plastids (Kim et al., 2011). Interestingly, root gravitropism in whitelight-grown Arabidopsis is diminished in phyB but not in phyD mutants (Correll & Kiss, 2005), suggesting subfunctionalization for this trait between the two genes in Arabidopsis. Interestingly, however, in Arabidopsis roots WT phyB promotes gravitropism, whereas in tomato shoots WT phyB1 inhibits it. Since R does not inhibit shoot gravitropism in 5-day-old dark-grown tomato seedlings, gravity sensing in the hypocotyl appears to follow a different signaling route than it does in Arabidopsis, but clearly phytochrome appears to play a role in both.
| Regulation of photosynthesis by phytochrome B
Our co-transcriptional analysis had suggested that photosynthesis genes were differentially affected by mutations in PHYB1 versus PHYB2 of tomato ( Figure 2) and our physiological experiments had supported this finding ( Figure 5). In Arabidopsis, phyB has previously been shown to increase photosynthetic rates, but only at light levels greater than 250 µmol*m −2 *s −1 (Boccalandro et al., 2009). Our data show that photosynthesis is enhanced in the phyB1 mutant and reduced in the phyB2 mutant compared to the WT response ( Figure 5b,d), suggesting that in tomato phyB2, apparently antagonistically to phyB1, plays the role of increasing photosynthetic rates.
Interestingly, it appears that this instance of subfunctionalization did not simply split the two phyB homologs into one serving the function of the parental gene while the other largely lost its participation in the process, but instead led to opposite regulation of the same process. Another difference between the Arabidopsis and tomato responses is that, unlike in Arabidopsis, the effects of phyB1 and phyB2 on photosynthesis are not light intensity-dependent in tomato, at least not at the two light intensities tested here. It is of note that differences in photosynthetic rates were only discernable in our analysis when we normalized carbon assimilation rates by fresh weight and leaf area as opposed to leaf area alone ( Figure 5).
Chlorophyll content in all genotypes was about the same but fresh weight per unit leaf area was highest in phyB2 and lowest in phyB1 among the three genotypes. This indicates that phyB1 promotes leaf thickness, water conservation or both, while phyB2 might promote transpiration (creating a net weight loss) or restrict leaf thickening.
The conflict between gene functions of phyB1 and phyB2 could allow the plant to balance its photosynthetic and water needs depending on environmental conditions. More work is needed, however, to specifically assign those roles to the two phyB homologs in tomato.
| In tomato, subfunctionalization of phyB has led to equally important sister genes
The relatively recent duplication of phyB into separate homologs in different species provides a window into how gene duplication can result in different evolutionary trajectories. PHYB duplications in Arabidopsis and tomato both occurred after divergence of the Solanaceae and Brassicaceae (Li et al., 2015). In Arabidopsis, comparison of the coding sequence shows 48-56% amino acid identity between PHYA, PHYB, PHYC, and PHYE, but 80% identity between PHYB and PHYD (Clack, Mathews, & Sharrock, 1994). Amino acid identities between PHYB and PHYD in Arabidopsis, and between PHYB1 and PHB2 in tomato are similarly high in the two species (Hauser, Cordonnier-Pratt, Daniel-Vedele, & Pratt, 1995). Functional redundancy between PHYB and PHYD in Arabidopsis is high, but mutation in PHYD enhances the phyB mutant response with respect to leaf morphology, rosette leaf number (Franklin et al., 2003) and shade avoidance (Devlin et al., 1999;Franklin et al., 2003). While single mutation of PHYD in Arabidopsis leads to an increase in hypocotyl length in continuous R and white lights, the effect of phyD on the end-of-day (EOD) FR response was negligible until combined with a mutation in PHYB (Aukerman et al., 1997). With respect to leaf morphology and developmental traits, mutation in Arabidopsis PHYD resulted in none or only minor consequences on the phenotype while mutation in PHYB resulted in statistically significant phenotypic change (Aukerman et al., 1997). Analysis of the phyB/D double mutant, however, showed that PHYD contributes residual function to phenotype in a manner redundant and subordinate to PHYB (Aukerman et al., 1997).
In tomato, divergence of the 5′ cis-regulatory regions in PHYB1 and PHYB2 has resulted in variability of the number and type of light response motifs, suggesting that this variation might be part of the reason for the genes' subfunctionalization. Duplication and gene divergence in tomato, in contrast to Arabidopsis, has resulted in two genes that have taken on specialized functions for a variety of developmentally important traits. This situation is not unlike that in maize.
In maize, the two homologs of ZmPHYB showed complete redundancy for involvement in several morphological traits, such as plant height and stem diameter, while regulation of photoperiod-dependent flowering time was regulated only by ZmPHYB2 (Sheehan et al., 2007).
Early work on phyB1 and phyB2 in tomato describing the mutants had already noted that phyB1 and phyB2 played different roles in early seedling development, but described the genes as largely redundant in older plants (Weller et al., 2000). Our data suggest that in tomato, phyB1 inhibits auxin responses of phototropism and gravitropism (and phyB2 does not play a role) while phyB2 promotes and phyB1 inhibits photosynthesis.
We want to caution that in the absence of multiple alleles of phyB1 and phyB2 in our analysis, it is formally possible that unknown, secondary background mutations in the material could contribute to some of the observations we made in this study.
| CON CLUS IONS
Although phys are evolutionarily old genes and found in at least two copies, phyA and phyB, in all angiosperm species (Mathews, 2010), functional diversification is an ongoing process. PhyB is the phy homolog that has most recently duplicated again in some species (Mathews, 2010), including Arabidopsis (phyB/phyD), maize (phyB1/phyB2), and tomato (phyB1/phyB2). This latest round of duplication therefore lends itself well to analysis of variation in subfunctionalization of this important gene between species, and also provides a recent gene duplication event that plants have exploited for further specialization of their responses to light and the environment.
ACK N OWLED G EM ENTS
We acknowledge funding from the National Science Foundation (IOS-1339222, to AM, PRFB 1523917 to KDC). We thank Bob Peaslee and Amy Replogle for technical help and critical discussions.
CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interest associated with the work described in this manuscript.
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Evaluation of Proinflammatory, NF-kappaB Dependent Cytokines: IL-1α, IL-6, IL-8, and TNF-α in Tissue Specimens and Saliva of Patients with Oral Squamous Cell Carcinoma and Oral Potentially Malignant Disorders
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Evaluation of Proinflammatory, NF-kappaB Dependent Cytokines: IL-1α, IL-6, IL-8, and TNF-α in Tissue Specimens and Saliva of Patients with Oral Squamous Cell Carcinoma and Oral Potentially Malignant Disorders
Background: Oral squamous cell carcinoma (OSCC) is a life-threatening disease. It could be preceded by oral potentially malignant disorders (OPMDs). It was confirmed that chronic inflammation can promote carcinogenesis. Cytokines play a crucial role in this process. The aim of the study was to evaluate interleukin-1alpha (IL-1α), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor alpha (TNF-α) in tissue specimens and saliva of patients with OSCC and OPMDs. Methods: Cytokines were evaluated in 60 tissue specimens of pathological lesions (OSCCs or OPMDs) and in 7 controls (normal oral mucosa, NOM) by immunohistochemistry and in saliva of 45 patients with OSCC or OPMDs and 9 controls (healthy volunteers) by enzyme-linked immunosorbent assays. Results: Immunohistochemical analysis revealed significantly higher expression of IL-8 in OSCC specimens and TNF-α in OSCCs and OPMDs with dysplasia as compared to NOM. Moreover, expression of TNF-α was significantly higher in oral leukoplakia and oral lichen planus without dysplasia, whereas expression of IL-8 only in oral leukoplakia without dysplasia in comparison with NOM. Salivary concentrations of all evaluated cytokines were significantly higher in patients with OSCC than in controls. Moreover, levels of IL-8 were significantly higher in saliva of patients with OPMDs with dysplasia as compared to controls and in OSCC patients as compared to patients with dysplastic lesions. There was also significant increase in salivary concentrations of IL-6, IL-8 and TNF-α in patients with OSCC as compared to patients with OPMDs without dysplasia. Conclusion: The study confirmed that proinflammatory, NF-kappaB dependent cytokines are involved in pathogenesis of OPMDs and OSCC. The most important biomarker of malignant transformation process within oral mucosa among all assessed cytokines seems to be IL-8. Further studies on a larger sample size are needed to corroborate these results.
Introduction
Oral cancer is quite common disease and has an increasing worldwide trend [1]. Histologically, over 90% of malignancies affecting this region are diagnosed as oral squamous cell carcinoma (OSCC) [2]. OSCC is responsible for approximately 4% of all malignancies [3]. It has invasive behavior and high risk of metastases. The mortality rate associated to OSCC is high and has remained unchanged over the past decades [4]. The main reason is too late diagnosis.
There are some clinically defined precursor lesions, such as oral erythroplakia, oral leukoplakia, oral submucous fibrosis, and oral lichen planus, that can precede cancer development within the oral mucosa. All these lesions should be called oral potentially malignant disorders (OPMDs) [5]. The term was recommended in year 2005 during one of the WHO workshops [6].
The identification of OPMDs with higher risk of malignant transformation and OSCCs at the early stage of development seems to be a matter of great importance and the best way to improve OSCC statistics.
Oral carcinogenesis is a complex process in which genetic events result in successive molecular changes that lead to the disruption of cell proliferation, growth, and differentiation [7]. The kinetics of this event is a result of the interactions between tumor and host, especially the immune system [8]. The role of inflammation in carcinogenesis was suggested for the first time by Rudolf Virchow more than 150 years ago [9]. Various studies have confirmed that chronic inflammation can influence cell homeostasis and various metabolic processes, inducing changes at the genomic level, which can promote carcinogenesis [10]. Inflammation stimulates the activation of cytotoxic mediators, such as reactive oxygen species (ROS) and reactive nitrogen species (RNS), which play a major role in DNA damages. DNA damage accumulation is responsible for the initiation of carcinogenesis through the enhancement of genomic instabilities. Moreover, several inflammatory factors can facilitate the migration and invasion of neoplastic cells, leading to cancer progression [11].
Cytokines are critical regulators of tumor microenvironment and chronic pro-tumorigenic inflammation [12]. They are soluble, low molecular weight, multifunctional polypeptides that are produced mainly by cells of the innate and adaptive immune system but also by resident tissue and tumor cells [8]. They influence many aspects of cellular behaviors, such as growth, differentiation, and function. Their physiological activities are dysregulated during inflammation and carcinogenesis. The studies confirmed the crucial role of proinflammatory cytokines in carcinogenesis process, including the development of lung cancer [13,14], hepatocarcinoma [15], colorectal cancer [16], as well as OSCC [17].
The transcription factor, nuclear factor-kappaB (NF-kB) is an early response gene promoting the expression of a series of cytokines with proinflammatory, proangiogenic, and immunoregulatory activity which play an important role in carcinogenesis. Aberrant NF-kB regulation has been observed in many cancers [18,19]. Studies have demonstrated the activation of NF-kB in OSCC and elevated expression of its downstream proinflammatory cytokines in tissues, serum, tissue infiltrating lymphocytes (TIL) and cell lines of OSCC, including interleukin-1alpha (IL-1α), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor alpha (TNF-α) [20].
IL-1α modulates various growth-promoting pathways, including anti-apoptotic signaling and cellular proliferation [21]. It was also observed that IL-1α released from OSCC cells stimulates carcinoma-associated fibroblasts (CAFs) to secrete CCL7, CXCL1, and IL-8, thereby facilitating cancer invasion [22]. IL-6 is a multifunctional cytokine with growth-promoting and anti-apoptotic activity [18,23]. There is evidence that IL-6 regulates activation of the Janus kinases (JAK) and signal transducers and activators of transcription (STATs), which then stimulate pathways involving mitogen-activated protein kinase (MAPK), which in turn supports cancer development [24].
IL-8, a member of the chemokine family, acts on two receptors, namely CRCX-1 and CRCX-2, that are located on tumor-associated macrophages, neutrophils, and cancer cells. Their presence on cancer cells strongly suggests that IL-8 is an important chemokine for cancer cells environment. The carcinogenic potential of IL-8 results from its ability to neutrophil recruitment, angiogenic potential, proliferation and survival promotion, as well as protection from apoptosis [24]. TNF-α is a pleiotropic cytokine. It is known that the TNF-TNF receptor system plays an important role in inflammation, angiogenesis, programmed cell death, and proliferation, which are all crucial components in malignant transformation process [25]. It was also discovered that TNF-α can directly damage DNA of cells and lead to their malignant transformation through induction of reactive oxygen species (ROS) [26]. Additionally, TNF family members contribute to immune suppression [18].
There are some immunohistochemical studies that confirmed the role of pro-inflammatory, NF-kB dependent cytokines in malignant transformation process within oral mucosa, however evidence is rather scarce. It was revealed that IL-6 and TNF-α can promote malignant transformation in patients with oral submucous fibrosis [27] and with oral lichen planus [28]. Moreover, it was reported that the expression of TNF-α is significantly increased in lesions exhibiting epithelial dysplasia [29].
In recent years, the role of saliva for early detection of oral cancer has been intensively studied [30][31][32][33]. Due to the fact that saliva can be collected in an easy and noninvasive way, it seems to be a very attractive diagnostic material [34]. Proinflammatory cytokines have also been investigated in saliva as potential biomarkers of OPMDs and OSCC, and the current results are encouraging [24,[35][36][37].
The aim of the presented study was to evaluate IL-1α, IL-6, IL-8, and TNF-α in tissue specimens and saliva of patients with oral squamous cell carcinoma and oral potentially malignant disorders such as oral leukoplakia and oral lichen planus to confirm the potential of proinflammatory, NF-kappaB dependent cytokines as biomarkers of malignant transformation process within the oral mucosa.
Study Group
Sixty patients with diagnosis of OSCC or OPMDs such as oral leukoplakia and oral lichen planus were included into the study. They were diagnosed in the Chair of Periodontology and Clinical Oral Pathology and Department of Oral Surgery, Institute of Dentistry, Jagiellonian University Medical College in Krakow between 2011 and 2015. The diagnosis was made on the basis of clinical and histopathological examination using the WHO criteria [38]. The approval of the Bioethics Committee of the Jagiellonian University (KBET/290/B/2011, KBET/122.6120.183.2015) and the informed consent of the patients were obtained before collection of saliva and evaluation of tissue specimens. The study was performed in accordance with the Helsinki Declaration of 2008.
Histopathology and Immunohistochemistry
The formalin-fixed, paraffin-embedded blocks of 60 tissue samples collected from patients of the study group were sectioned (2 µm). Normal oral mucosa (NOM) in margins of 7 formalin-fixed, paraffin-embedded archival blocks of fibromas were used as controls. For histopathological examination, the sections were stained with hematoxylin and eosin (H&E). OSCCs were graded as well, moderately, and poorly differentiated using the standard WHO criteria [39]. The criterion for judging the malignant potential of OPMDs is mainly the presence and degree of dysplasia [39]. OPMDs were classified histologically into stages with increasing risk of developing into OSCC, namely as mild, moderate, and severe epithelial dysplasia according to the WHO criteria [6,40].
For immunohistochemistry, the sections were deparaffinized and rehydrated. After antigen retrieval, slides were incubated with antibodies: Anti-IL-1α rabbit polyclonal IgG (30 min., room temp.) and Anti-IL-6 mouse monoclonal IgG (30 min., room temp.) purchased from Santa Cruz Biotechnology Inc. (Dallas, TX, USA) and Anti-IL-8 mouse monoclonal IgG (60 min., room temp.) and Anti-TNF-α rabbit polyclonal IgG (60 min., room temp.) purchased from Abcam Plc. Taking into account that epithelium and stroma contain completely different cells, we analyzed them separately. Depend on the proportion of the positively stained cells and intensity of staining, a semiquantitative immunoreactive score from 0 to 6 was calculated separately for epithelial/cancer cells and stromal cells ( Table 1). The overall score was not calculated. The score was elaborated by authors based on the literature [41][42][43].
Laboratory Tests
Whole unstimulated saliva (WUS) was collected from 45 subjects of the study group: 9 patients with OSCC, 7 with oral epithelial dysplasia (OED), 16 with oral leukoplakia without dysplasia (OL), and 13 with oral lichen planus without dysplasia (OLP). Individuals with a history of any systemic inflammatory disease, individuals suffering from inflammatory conditions in the oral cavity (e.g., dental abscess, pericoronitis, gingivitis, periodontitis), patients treated because of OSCC in the past, individuals taking drugs that induced hyposalivation (e.g., anticholinergics, antihistamines, antihypertensives, and beta adrenal blockers), and individuals using secretagogues were excluded from this part of the study. None of the lesions had been treated in any manner prior to sample collection. Samples of WUS of nine volunteers without any systemic diseases and without any pathological lesion within oral mucosa were used as controls.
WUS samples were collected between 9.00 and 11.00 a.m. The subjects were instructed to refrain from eating, drinking, using chewing gum, and smoking for at least 90 min before collection of saliva. Samples were obtained by requesting the subjects to swallow first, tilt their head forwards, and expectorate the saliva into plastic vials for 10 min [44]. Samples were stored at −80 • C and centrifuged at 6000 rpm for 20 min to remove squamous cells and debris before the biochemical analysis.
All laboratory tests were conducted in the Diagnostic Department, Chair of Clinical Biochemistry, Jagiellonian University Medical College, Krakow, Poland.
Statistical Analysis
For categorical variables, frequency and percentage were calculated. For continuous variables, minimum (Min), maximum (Max), median (Me) and interquartile range (IQR) were calculated. For qualitative data, differences between groups were analyzed by Fisher's exact test. For quantitative data, differences between groups were analyzed by Kruskal-Wallis test and post-hoc analyses were performed with Dunn's test. p-values less than 0.05 were considered significant. Analyses were performed using the Statistical Package for Social Sciences (SPSS, version 19.0) and the R Project for Statistical Computing (www.R-project.org).
Histopathology and Immunohistochemistry
On the basis of histopathological examination and clinical data, 14 tissue samples were diagnosed as OSCC, 21 as oral leukoplakia (hyper-and/or parakeratosis) without dysplasia (OL), 15 as oral lichen planus without dysplasia (OLP), and 10 as OED (5 cases as hyper-and/or parakeratosis with dysplasia and 5 cases as lichenoid dysplasia). Using the standard WHO criteria, 6 cases of OSCC were classified as well differentiated, another 6 cases as moderately and 2 cases as poorly differentiated. Among OED cases all but only one were classified as mild and one as severe epithelial dysplasia according to the WHO criteria.
Immunohistochemical staining revealed differences in distribution of particular cytokines within the epithelium between different types of lesions ( Table 2). IL-1α was present more often within all layers of the epithelium in OSCCs than in OPMDs and it was present within all layers of the epithelium in none of the specimens assessed as normal oral mucosa. In turn, TNF-α was present within all layers of the epithelium in almost all cases of OED and OSCC specimens and only in one third cases of specimens assessed as NOM. Moreover, TNF-α was not present in any layer of the epithelium of almost one third of NOM specimens. When only OSCC, OED, and NOM cases were included in the statistical analysis, significant differences in distribution of IL-8 within the epithelium between compared lesions were confirmed. IL-8 was present within all layers of the epithelium in almost 65% of OSCC cases and in none layer of the epithelium of all specimens assessed as NOM. Analysis of immunoreactive scores confirmed significant differences in immunoreactivity for IL-8 and TNF-α (Tables 3 and 4). When only OSCC, OED, and NOM cases were compared, immunoreactivity for IL-8 was significantly higher in epithelial/cancer cells and in stroma of OSCCs in comparison with NOM specimens (p = 0.0073 and 0.032, respectively), whereas immunoreactivity for TNF-α was markedly higher in epithelium and stroma of OEDs in comparison with NOM cases (p = 0.019 and 0.0038, respectively) and in epithelium/cancer cells of OSCCs as compared to NOM specimens (p = 0.011). Moreover, immunoreactivity for TNF-α was significantly higher in stroma of OED cases than in OSCCs (p = 0.0102). When all types of specimens were included into statistical analysis, significant differences in immunoreactivity for IL-8 in stroma and for TNF-α in epithelium and stroma between oral leukoplakia without dysplasia and NOM cases (p = 0.022, p = 0.0017, and 0.047, respectively) as well as for TNF-α in epithelium between oral lichen planus without dysplasia and NOM specimens (p = 0.0071) were also revealed. Below we present photos of immunohistochemical staining for IL-1α, IL-8, and TNF-α in selected specimens of OSCCs and OPMDs (Figures 1-8).
Laboratory Tests
Subjects characteristics are given in Table 5. There were no significant differences in age, sex, as well as cigarette use and alcohol consumption between compared groups. Figure 8. TNF-α-strong brown staining in epithelium (basal and parabasal layer) and stroma of oral lichen planus without dysplasia (OLP) (20x).
Laboratory Tests
Subjects characteristics are given in Table 5. There were no significant differences in age, sex, as well as cigarette use and alcohol consumption between compared groups. Statistical analysis revealed significant differences in levels of all measured cytokines when only patients with OSCC, OED, and healthy volunteers were compared. Concentrations of IL-1α, IL-6, IL-8, and TNF-α were markedly higher in saliva of patients with OSCC in comparison with healthy volunteers (p = 0.017, 0.0012, 0.0001, and 0.0012, respectively). Moreover, levels of IL-8 were significantly higher in saliva of patients with OED as compared to controls (p = 0.0492) and in OSCC patients as compared to patients with OED (p = 0.0345).
However, when all groups were analyzed, only levels of IL-6, IL-8, and TNF-α were markedly higher in patients with OSCC as compared to controls (p = 0.0041, 0.0004, and 0.0041, respectively). Concentrations of IL-6, IL-8, and TNF-α were also markedly higher in OSCC group as compared to subjects with oral leukoplakia without dysplasia (p = 0.0012, 0.0000, and 0.0492, respectively) and oral lichen planus without dysplasia (p = 0.0084, 0.0002, and 0.0212, respectively) ( Figure 9). J. Clin. Med. 2020, 9, x FOR PEER REVIEW 12 of 18 significantly higher in saliva of patients with OED as compared to controls (p = 0.0492) and in OSCC patients as compared to patients with OED (p = 0.0345). However, when all groups were analyzed, only levels of IL-6, IL-8, and TNF-α were markedly higher in patients with OSCC as compared to controls (p = 0.0041, 0.0004, and 0.0041, respectively). Concentrations of IL-6, IL-8, and TNF-α were also markedly higher in OSCC group as compared to subjects with oral leukoplakia without dysplasia (p = 0.0012, 0.0000, and 0.0492, respectively) and oral lichen planus without dysplasia (p = 0.0084, 0.0002, and 0.0212, respectively) ( Figure 9).
Discussion
Alterations in host immunity, inflammation, angiogenesis, and metabolism have been noted as the prominent pathological features in patients with oral cancer [45]. NF-kappaB dependent
Discussion
Alterations in host immunity, inflammation, angiogenesis, and metabolism have been noted as the prominent pathological features in patients with oral cancer [45]. NF-kappaB dependent cytokines are molecular messengers highly involved in all these processes [24]. Altered levels of proinflammatory, NF-kappaB dependent cytokines have been reported not only in patients with OSCC but also in patients with OPMDs, such as oral leukoplakia, oral lichen planus, and OSF [46]. There are numerous studies in which levels of proinflammatory cytokines were assessed in body fluids of patients with OSCC or OPMDs, however, in most of them only one cytokine and one type of OPMDs was considered. Moreover, in some of the previous studies exclusion criteria were not restrictive. In turn, the evidence on the expression of proinflammatory, NF-kappaB dependent cytokines in tissue samples of OSCCs and OPMDs is very limited, especially in OPMDs. Thus, the present study is unique. We decided to evaluate the panel of four proinflammatory, NF-kappaB dependent cytokines (IL-1α, IL-6, IL-8, and TNF-α) not only in saliva, but also in tissue specimens of OSCCs and OPMDs such as oral leukoplakia and oral lichen planus. We compared the expression of IL-1α, IL-6, IL-8, and TNF-α in epithelial and stromal cells between different types of tissue specimens implementing the immunoreactive score. To the best of our knowledge, this is the first study designed in this way. Moreover, to reduce the risk of interfering variables affecting salivary concentrations of assessed cytokines we implemented strict exclusion criteria. Subjects with acute or chronic inflammatory conditions in the oral cavity, such as dental abscess, pericoronitis, gingivitis, or periodontitis, patients with systemic inflammatory diseases and patients taking medications that can alter salivary flow were not included into the salivary analysis. All analyzed groups were also comparable in terms of age, gender, cigarette smoking and alcohol drinking. It should be also underline that this is the first such study carried out in the Polish population.
The results of immunohistochemical staining confirmed the expression of IL-1α, IL-6, IL-8, and TNF-α in OSCCs. Analyzed cytokines were observed within epithelial/cancer cells of most OSCC cases and in stroma of all OSCC tissue specimens. Likewise, Woods et al. confirmed intracellular production of IL-1 and IL-6 in all analyzed invasive OSCCs, whereas Chen et al. detected IL-1α, IL-6, and IL-8 within keratin-positive malignant epithelium of all analyzed OSCCs in situ [47,48]. In turn, de Oliveira et al. revealed presence of IL-6 and IL-8 in inflammatory cells in invasive front of all analyzed OSCCs [8]. These results showed that proinflammatory, NF-kappaB dependent cytokines, which regulate innate and adaptive immune response, are produced not only by inflammatory cells in the tumor microenvironment, but also by tumor cells. Expression of these proinflammatory and proangiogenic cytokines in OSCCs indicate that they may play a role in the increased pathogenicity of OSCC by providing a growth advantage.
The present study also confirmed the expression of IL-1α, IL-6, IL-8, and TNF-α in oral leukoplakia and oral lichen planus specimens. All assessed cytokines were observed in stroma of every OPMD and in epithelial cells of most analyzed cases. However, IL-8 was present in the smallest number of OPMDs samples as compared to other assessed cytokines. Haque et al. confirmed the expression of IL-1α and IL-6 in stroma and in epithelial cells of specimens of oral submucous fibrosis, whereas Sclavounou et al. reported the expression of TNF-α in epithelial cells and proinflammatory cells of most analyzed oral lichen planus specimens [49,50]. These results indicate that proinflammatory cytokines, especially IL-1α, IL-6, and TNF-α could play an important role in the pathogenesis of OPMDs.
The comparison of immunoreactivity for particular cytokines between different types of tissue specimens analyzed in this study revealed that the expression of IL-8 and TNF-α was markedly increased in OSCCs in comparison with tissue specimens assessed as normal oral mucosa, whereas in OED specimens the expression of TNF-α was notably altered. These results together with the fact that IL-8 was not present in epithelial cells of specimens assessed as normal oral mucosa, whereas it was present within all layers of the epithelium in most cases of OSCCs indicate that IL-8 and TNF-α could play a leading role among proinflammatory, NF-kappaB dependent cytokines in malignant transformation process within the oral mucosa.
Because of too small samples and a large disproportion in numbers between subgroups with different grade of OED and with different differentiation grade of OSCC, it was not possible to check whether the grade of OED (mild, moderate, and severe) and the differentiation grade of OSCC (well, moderate, and poor) significantly influence the expression of cytokines.
Analysis of saliva revealed markedly higher levels of IL-1α, IL-6, IL-8, and TNF-α in OSCC patients in comparison with healthy individuals when only OSCC, OED, and controls were taken into consideration, whereas only IL-6, IL-8, and TNF-α when all groups were analyzed. These results are in line with other studies. Rajkumar et al. and Lee at al. reported significantly higher salivary levels of IL-6, IL-8, and TNF-α in OSCC patients in comparison with controls [51,52], whereas Rhodus et al. observed significantly higher levels of IL-1α, IL-6, IL-8, and TNF-α in saliva of patients with OSCC as compared to healthy individuals [20,52]. SahebJamee et al. also observed higher levels of IL-1α, IL-6, IL-8, and TNF-α in saliva of patients with OSCC in comparison with healthy subjects, however only differences in IL-6 concentration were statistically significant [53]. In turn, Punyani and Sathavane stated significantly higher salivary concentrations of IL-8 in OSCC patients in comparison with controls. The levels of IL-8 were also compared as per the TNM stage and histopathologic grading. The levels were the highest for stage IV disease, however, the difference between stages was statistically non-significant. The mean salivary IL-8 concentration was higher in patients with moderately differentiated squamous cell carcinoma than in patients with well-differentiated squamous cell carcinoma, however, the difference was also not statistically significant [45]. Korostoff et al. analyzed salivary levels of IL-1α, IL-6, IL-8, and TNF-α in patients with exophytic and endophytic tongue squamous cell carcinoma (TSCC) [54]. They observed an increasing trend of all assessed cytokines from controls to TSCC subjects. All cytokines were markedly elevated in saliva of patients with endophytic TSCC. Moreover, patients with endophytic TSCC and elevated IL-8 had a shorter lifespan after diagnosis.
In the present study levels of all analyzed cytokines were higher in saliva of patients with OPMDs with dysplasia than in subjects without oral mucosal lesions, however only differences in IL-8 concentrations were statistically significant (when only OSCC, OED, and healthy controls were taken into consideration). Rhodus et al. observed markedly higher salivary levels of IL-1α, IL-6, IL-8, and TNF-α in patients with oral lichen planus with dysplasia in comparison with the control group [20,53]. Sharma et al. reported markedly higher levels of IL-6 in patients with oral leukoplakia with dysplasia as compared to controls. Moreover, within the leukoplakia group IL-6 level was found to be increased with increase in the severity of dysplasia [55]. Lack of statistical significance in differences of IL-1α, IL-6, and TNF-α concentrations between patients with OED and controls in the present study could be related to the fact that all but only one case of OED were classified as mild, whereas in the study of Rhodus et al. all cases were classified as moderate or severe dysplasia. Similar to Rhodus et al., Kaur and Jacobs reported significantly higher levels of IL-6, IL-8, and TNF-α in saliva of patients with OPMDs (oral leukoplakia, oral lichen planus, and oral submucous fibrosis) in comparison with the control group [46]. They also observed that salivary levels of IL-6, IL-8, and TNF-α were markedly higher in the advanced stages of OPMDs as compared to the early stages. In turn, the study of Rajkumar et al. revealed significantly higher levels of IL-6 and TNF-α in patients with oral leukoplakia and oral submucous fibrosis in comparison with healthy individuals [52], whereas differences in IL-8 concentrations between patients with OPMDs and controls were not significant. Unfortunately, the authors of both mentioned studies did not give information about epithelial dysplasia of analyzed cases of OPMDs. In the study of Punyani and Sathavane, salivary levels of IL-8 were higher in patients with oral submucous fibrosis and oral leukoplakia in comparison with the controls, but the difference was statistically non-significant. It should be underlined that there was no histological evidence of dysplasia in all cases of oral submucous fibrosis and only in five of twelve cases of oral leukoplakia mild dysplasia was stated [45]. The results of the present study did not reveal significant differences in salivary concentration of IL-1α, IL-6, IL-8, and TNF-α between patients with OPMDs without dysplasia and healthy individuals.
In turn, we observed markedly higher levels of IL-8 in OSCC patients in comparison with OED cases. Rhodus et al. reported significantly higher levels of IL-1α, IL-6, IL-8, and TNF-α in OSCC patients as compared to patients with oral lichen planus with dysplasia, whereas Punyani and Sathavane stated markedly higher levels of IL-8 in the OSCC group in comparison with patients with oral leukoplakia, but only in five of twelve cases mild dysplasia were described [20,45,52]. Rajkumar et al. reported significantly higher levels of IL-6, IL-8, and TNF-α in patients with OSCC as compared to patients with oral leukoplakia and oral submucous fibrosis, however they gave no information about epithelial dysplasia [56].
There are two large scale studies in which diagnostic value of IL-8 and IL-6 for OSCC was assessed. Rajkumar et at. analyzed IL-8 levels in saliva of patients with OSCC and OPMDs (oral leukoplakia and oral submucous fibrosis). This study revealed significantly higher levels of IL-8 in patients with OSCC and OPMDs as compared to healthy individuals [57]. Moreover, they observed a significant increase in levels of salivary IL-8 in OSCC patients in comparison with OPMDs. Most cases of OPMDs were dysplastic. Receiver operating characteristic curve analysis found salivary IL-8 to have superior sensitivity in detecting OSCC. A significant increase in IL-8 levels based on the histologic grading of OSCC was also observed. Analogical results in case of IL-6 were reported by Dineshkumar et al. [58].
Conclusions
The present study confirmed that proinflammatory, NF-kappaB dependent cytokines are involved in pathogenesis of OPMDs and OSCC. The increase in salivary levels of IL-6, IL-8, and TNF-α could be a useful indicator of malignant transformation process within the oral mucosa. The higher levels of proinflammatory, NF-kappaB dependent cytokines, especially IL-8 and TNF-α in saliva of patients with OSCC or OPMDs, could be caused by increased expression of these cytokines in pathological tissues. The most important biomarker of malignant transformation process within oral mucosa among all assessed cytokines seems to be IL-8. It was present within all layers of the epithelium in most OSCCs, was not present in epithelial cells of specimens assessed as normal oral mucosa and was observed in the smallest number of OPMDs samples. Moreover, its salivary concentration was significantly higher in patients with OSCC as compared not only to healthy subjects but also to patients with OPMDs with dysplasia. Further studies on a large sample size are required to confirm the utility of proinflammatory, NF-kappaB dependent cytokines as screening/diagnostic markers for routine use at clinical practice.
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2020-03-26T10:30:11.884Z
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2020-03-01T00:00:00.000Z
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Reliability of task‐evoked neural activation during face‐emotion paradigms: Effects of scanner and psychological processes
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Reliability of task‐evoked neural activation during face‐emotion paradigms: Effects of scanner and psychological processes
Abstract Assessing and improving test–retest reliability is critical to efforts to address concerns about replicability of task‐based functional magnetic resonance imaging. The current study uses two statistical approaches to examine how scanner and task‐related factors influence reliability of neural response to face‐emotion viewing. Forty healthy adult participants completed two face‐emotion paradigms at up to three scanning sessions across two scanners of the same build over approximately 2 months. We examined reliability across the main task contrasts using Bayesian linear mixed‐effects models performed voxel‐wise across the brain. We also used a novel Bayesian hierarchical model across a predefined whole‐brain parcellation scheme and subcortical anatomical regions. Scanner differences accounted for minimal variance in temporal signal‐to‐noise ratio and task contrast maps. Regions activated during task at the group level showed higher reliability relative to regions not activated significantly at the group level. Greater reliability was found for contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. nonface discrimination) compared to conditions with more similar demands (e.g., angry vs. happy face discrimination). Voxel‐wise reliability estimates tended to be higher than those based on predefined anatomical regions. This work informs attempts to improve reliability in the context of task activation patterns and specific task contrasts. Our study provides a new method to estimate reliability across a large number of regions of interest and can inform researchers' selection of task conditions and analytic contrasts.
| INTRODUCTION
Concerns about replicability (Open Science Collaboration, 2015) in functional magnetic resonance imaging (fMRI) work are growing (e.g., Poldrack et al., 2017). Improving test-retest reliability is a cornerstone of addressing these concerns. A recent meta-analysis (Elliott et al., 2019) suggests that test-retest reliability of fMRI task contrasts is often relatively poor (e.g., intra-class correlation coefficients [ICCs] < .4). The current study uses two statistical approaches to examine how scanner effects and task-related factors influence reliability. The study focuses specifically on task-evoked activation during two faceemotion-viewing paradigms.
Across experimental paradigms, several factors are known to influence fMRI reliability. These include scanner-or site-related factors, participant-related factors, and time-related change. Several studies have shown that only small proportions of variance tend to be affected by scanner differences (e.g., Gountouna et al., 2010;Gradin et al., 2010;Yendiki et al., 2010). However, as such studies can often confound scanner and practice effects, we use a pseudo-random assignment to two scanners across three time points, thereby separating scanner-and time-related variance.
Face-emotion paradigms are often used in studies of individual differences as affect-evoking stimuli. In prior studies, the reliability of fMRI face-emotion paradigms varied by task condition. For example, prior work typically finds moderate reliability for face vs. baseline contrasts, but poor reliability for contrasts between specific face-emotion types, for example, angry vs. neutral Plichta et al., 2012;Sauder, Hajcak, Angstadt, & Phan, 2013;van den Bulk et al., 2013;White et al., 2016). The current study utilizes two tasks that differ in their cognitive demands. One task involves implicit faceemotion processing, such that face-emotion monitoring is irrelevant to task performance; the other involves explicit face-emotion judgments.
Many earlier reliability studies focused on a priori regions-ofinterest (ROIs), whereas newer statistical methods have become available for whole-brain reliability analyses. That said, common approaches to multiple comparisons correction for whole-brain analyses, for example, cluster-correction, rely on profound data reduction that may reduce reliability (Chen et al., 2019;Woo, Krishnan, & Wager, 2014). Significance tests are conducted independently per voxel; this massive multiplicity is accounted for by estimating the probability of a number of contiguous voxels all exhibiting significant effects. A complementary approach is to leverage the substantial information present in fMRI scans by using rational, Bayesian principles that mitigate data reduction by accounting for uncertainty (Chen, Taylor, Cox, & Pessoa, 2020). Therefore, this study includes a recent translation of Bayesian methods for group-level fMRI analysis, measuring reliability through two approaches. First, we examined a conventional, voxel-wise linear mixed-effects model with cluster-based correction. Second, we used a hierarchical Bayesian approach that examines ROIs across the whole brain, defined independently of the study data. For this second approach, results are reported based on an open-source, publicly available Bayesian hierarchical model developed for fMRI (Chen et al., 2019). This method enables test-retest analyses that incorporate all ROIs into one model to mitigate the issue of multiple testing over many units.
The current study examines 40 healthy adult participants using two face-emotion paradigms, one requiring explicit face-emotion labeling and one involving implicit, task-irrelevant face-emotion processing. Participants completed up to three scanning sessions over approximately 2 months. We examine reliability using Bayesian linear mixed-effects models performed voxel-wise across the brain and a novel Bayesian hierarchical model in predefined ROIs. Participants were pseudo-randomized and scanned across two comparable 3T GE MRIs, as would be common in single-site or harmonized multi-site studies. We expect scanner to account for minimal variance in temporal signal-to-noise ratio (tSNR) and fMRI task contrast maps. Moreover, we expect higher reliability among regions activated during the task at the group level (i.e., regions showing significant task contrast activity at the first scan session) relative to regions not activated significantly at the group level. Finally, we expect to see greater reliability for contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. nonface discrimination) compared to conditions with more similar demands (e.g., angry vs. happy discrimination).
| Participants
Forty-five participants enrolled in an institutional review boardapproved protocol at the National Institute of Mental Health in Bethesda, MD. Participants provided written informed consent. All participants were >18 years old (Age: M = 31.95 years, SD = 9.39; 58% female). Participants were excluded for any current psychiatric conditions, as determined by the Structured Clinical Interview for DSM-IV Disorders (Spitzer, Williams, Gibbon, & First, 1992 Figure S1).
| Task paradigms
Participants completed up to three MRI sessions, across two scanners in an ABA or BAB order, pseudo-randomized across participants.
During each scan, participants completed two tasks, a visual search with emotional distractors, and an explicit labeling emotional face task. The order of task completion was counterbalanced across participants (but consistent within-participant across session).
| Visual search task
This task, which was modified and adapted for fMRI from a previously used paradigm (Haas, Amso, & Fox, 2017), required participants to find a target stimulus in search array following an emotional face image. Each trial consisted of a grayscale face stimulus (angry, happy, or scrambled control) presented for 300 ms, then a 600 ms fixation cross, followed by a visual search array with one black bar target slanted left or right and 0, 4, or 29 distractors (slanted or vertical white bars and vertical black bars) displayed for a 2,000 ms response window ( Figure 1). Participants were required to find the target bar and indicate the direction that it was slanted (left or right) via a response box button press. Emotional face stimuli were images from 16 actors displaying angry or happy expression drawn from an available stimulus set (Tottenham et al., 2009). Face stimuli were cropped to a face-shaped oval and set to grayscale. The pixels of a face stimulus were scrambled to create a control stimulus matched on visual properties but without any face properties. A fixation cross was presented between trials for a jittered inter-trial interval (ITI; min = 500 ms, ITI distribution followed an exponential decay curve). 2.2.2 | Face-emotion labeling task This task was adapted for fMRI from a previously used behavioral paradigm (Stoddard et al., 2016). Participants were required to judge the emotion of a composite male face drawn from the Karolinska Directed Emotional Faces (Lundqvist & Litton, 1998). Stimuli were 15 faceemotion expressions equally spaced/morphed on a continuum from prototypically angry to prototypically happy. On each trial, a face morph was presented for 150 ms followed by a 250 ms white noise mask, and then a response screen with a fixation cross for 2,000 ms ( Figure 1).
Participants had to indicate whether the briefly presented face displayed an angry or happy expression via a button box press. A fixation cross was presented for a jittered ITI between trials (min 500 ms, ITI distribution followed an exponential decay curve). Stimulus presentation and jitter orders were optimized and pseudo-randomized using AFNI's make_random_timing.py program. Participants completed a total of 540 trials across four runs, including 90 fixation trials (i.e., each morph was presented 30 times). Each run was 412 s long with $10 s of fixation at the beginning and end of each run.
| Behavioral data
Accuracy and reaction time data were examined for each task, see details by task below.
| Visual search task
Accuracy and mean reaction time (to identify the slant of the target bar) were calculated as a function of condition: face-emotion (angry, happy, scrambled control) and search array size (1, 5, and 30 bars).
Sessions with accuracy <70% and/or >15% nonresponses were excluded (2 sessions for 1 participant). The effect of emotion, search array size, and their interaction were of interest here.
| Face-emotion labeling task
As in prior work (Stoddard et al., 2016), a four-parameter logistic curve was fit to each participant's choice-response data (parameters F I G U R E 1 Schematics of inscanner tasks. Left panel: Visual search task. Right panel: Faceemotion labeling task included: upper limit, lower limit, slope, and inflection point of the logistic curve, that is, the morph/emotional intensity where judgments switch from predominantly happy to angry, adjusted for the maximum probability of either judgment). An inflection point of 8 indicates no bias (middle of morphs 1-15), whereas a lower inflection point indicates a hostile interpretation bias, that is, a tendency to judge ambiguous faces as angry, rather than happy. We examined both inflection point and slope from the logistic regressor for the behavioral data.
Reaction time was examined as linear slope (coding emotion intensity from angry to happy) and quadratic slope (coding ambiguity from ambiguous to overt) across face morphs. Additional reaction time indices (i.e., reaction time difference scores) are presented in the Supporting Information. Data from participants who failed to correctly identify at least 70% of the emotional expression of the overtly angry and happy facial expressions or had more than 15% of missed responses were excluded (8 sessions across 6 participants).
| Behavioral data
Test-retest reliability of task behavior across three scanning sessions was tested in a Bayesian framework using linear mixed-effects models in R v3.5.0 (R Core Team, 2015) using the blme package (Chung, Rabe-Hesketh, Dorie, Gelman, & Liu, 2013). This included a random effect for participant modeled with Gamma priors (shape = 2, rate = 0.5) and three fixed effects, one for scanner, one for visit and one for the order of task acquisition in the scanner. Intraclass correlation coefficients were estimated as the proportion of participant-specific variance out of total variance (Bartko, 1966;Shrout & Fleiss, 1979). This approach mirrored that used at the voxel-level in the fMRI analyses described below. ICCs were calculated for task contrasts of interest (see below).
| Visual search task
ICCs were calculated for two reaction time contrasts: faces vs. scramble control difference scores and log-transformed slope across search array size for each emotion and scramble control stimuli.
Supporting Information additionally contains ICCs for array size 30 vs.
1 and happy versus angry differences scores and log-transformed slope across all emotions, as well as the ICC for the difference in accuracy for search array size 30 vs. 1.
| Face-emotion labeling task
ICCs were calculated for inflection point and slope of the choice response data as well as for linear and quadratic slopes of reaction time data across face emotion morphs. Refer to Supporting Information for additional contrasts (e.g., ambiguous vs. overt and happy vs. angry faces).
| Acquisition
Neuroimaging data were collected on two 3T General Electric Signa 750 scanners each using a 32-channel head coil with identical acquisition sequences. After a sagittal localizer scan, an automated shim calibrated the magnetic field to reduce signal dropout due to susceptibility artifact. BOLD signal was measured by T2*-weighted echo-planar imaging at a voxel resolution of 2.5 Â 2.5 Â 3.0 mm (
| Imaging preprocessing
Neuroimaging data were analyzed using Analysis of Functional NeuroImages (AFNI; http://afni.nimh.nih.gov/afni/; Cox, 1996) v18.3.03 with standard preprocessing, including despiking, slicetiming correction, distortion correction, alignment of all volumes to a base volume (MIN_OUTLIER), nonlinear registration to the MNI template, spatial smoothing to 6.5 mm FWHM kernel (using blur_to_fwhm flag), masking, and intensity scaling. Spatial smoothing to a desired blur size assures that a similar smoothness is achieved across scanners and sessions, rather than adding a set blur kernel to acquired images that may vary in initial smoothness. First-level models were created with generalized least squares time series fit with restricted maximum likelihood estimation of the temporal autocorrelation structure (3dREMLfit). This work utilized the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). This processing and first-level general linear models (GLM) controlled for head motion. Specifically, we regressed any pair of successive TRs where the sum head displacement (Euclidean norm of the derivative of the translation and rotation parameters) between those TRs exceeded 0.5 mm. TRs, where more than 10% of voxels were timeseries signal outliers, were also excluded. Sessions were excluded if the average motion per TR after censoring was >0.25 mm or if >15% of TRs were censored for motion/outliers. Additionally, six head motion parameters were included as nuisance regressors in individuallevel models. Temporal signal-to-noise ratio (tSNR = average signal/ standard deviation of noise [GLM residuals]) maps were created from the first-level model output.
Visual search task
Regressors for nine trial types of interest (3 emotion by 3 search array size) and error trials were included in first-level GLMs. These were modeled with a block hemodynamic response function (BLOCK (2.9,1)). Four first-level contrasts were created for each participant to examine: task vs. fixation, faces vs. scrambled control, search array 30 vs. 1, and a log-linear slope across search array size. Figure S2 displays additional contrasts of angry versus happy, a log slope per emotion, and 30 versus 1 search array.
Face-emotion labeling task
Fifteen regressors of interest were included to represent the 15 face emotion morphs, modeled with a block hemodynamic response function (BLOCK (0.15,1)). Separately, two amplitude-modulated regressors as a function of face morph weighed in a linear and quadratic fashion (AM2), as well as error trials modeled without amplitude modulation, were coded. Three first-level contrasts were created for each participant: task (15 face-emotion regressors) vs. fixation, amplitudemodulated linear slope across morphs (coding emotion intensity from angry to happy), amplitude modulated quadratic slope across morphs (coding ambiguity from ambiguous to overt). Additional contrasts of subtraction values: ambiguous vs. overt faces and angry vs. happy faces are presented in the Supporting Information.
| Imaging analysis
Activation at Session 1 Linear mixed-effects models (3dLME; Chen et al., 2013) with participant as a random effect were computed for the first scan session to examine group average activity for each task condition. Models included scanner and task order (to indicate which behavioral task was performed first in the scanner) as fixed-effects covariates. Monte Carlo simulations were performed using AFNI's 3dClustSim to correct for multiple comparisons. All analyses were restricted to a whole-brain mask of 98,386 voxels where 90% of participants (completing either/ both tasks) had useable data at Session 1. Smoothness of the residuals was estimated based on a Gaussian plus mono-exponential spatial autocorrelation function (3dFWHMx with -acf flag) for all participants and averaged yielding an effective smoothness of FWHM = 9.14 mm (ACF parameters, a = 0.61, b = 3.37, c = 10.88). Two-sided thresholding was examined for whole-brain tests with first-nearest neighbor clustering (NN = 1). To obtain a whole-brain family-wise error correction of p < .05, all results were thresholded at a voxel-wise p < .001 and a cluster extent of k = 20 voxels.
Voxel-wise test-retest assessment
Bayesian linear mixed-effects models (3dLME; Chen et al., 2018) were used to compute voxel-wise ICC of BOLD activation across the three MRI sessions. The Bayesian ICC approach has been demonstrated to address potential issues in traditional ICC estimates (e.g., negative ICC values, missing data, confounding effects). Linear mixed-effects models included a fixed effect for task order and visit and random effects with Gamma priors (Chen, Saad, Britton, Pine, & Cox, 2013) for participant and scanner, to estimate of the proportion of participant, scanner, and residual error variance per voxel for tSNR and task versus baseline (ICCs with absolute agreement (ICC[2,1]; Shrout & Fleiss, 1979). For task contrasts, we used ICCs with consistency formulation (ICC[3,1]; examining the consistency in rank rather than absolute value, which accounts for systematic changes over time, such as practice effects) with participant as a random effect and fixed effects for scanner, task order, and visit. For display purposes, ICC maps of participant-specific variance were binned into color schemes representing "poor" (ICC < 0.4), "fair" (ICC = .4-.6), "good" (ICC = .6-.75), and "excellent" (ICC > .75) test-retest reliability.
Conjunction maps ( Figure S2) were created for display purposes to illustrate the overlap in brain regions that were robustly activated by the task (at the first scanning session; cluster-corrected) and reliably activated across scanner and time (ICC > 0.4). To statistically test whether more active regions are also more reliable, we use AFNI's 3ddot function to examine whole-brain voxel-wise correlations between first scanning session tSNR or task activation and their associated ICC maps. Additionally, we examined associations between mean tSNR at the first scanning session and the task versus baseline ICC maps to assess how tSNR may influence task reliability. AFNI's 3ddot provides a single correlation coefficient describing the association between two voxel-wise maps.
ROI-based test-retest assessment
As an alternative to voxel-wise testing with cluster-based multiple comparisons correction, we conducted ICC analyses across 214 ROIs covering the whole brain (defined independently of our reliability estimates). These included 200 parcels from a published cortical parcellation (Schaefer et al., 2018) and 14 subcortical ROIs from the Harvard-Oxford probabilistic atlas (75% probability for defining the hippocampus; 50% probability for defining other regions). Contrast activity was extracted from all ROIs across the three scanning sessions for both tasks and used in these analyses. ICCs at the ROI level was inferred through a Bayesian multilevel model that integrated all regions (Chen et al., 2019(Chen et al., , 2020. Specifically, each effect was decomposed into three components that are associated with the variability across subjects, visits, and regions while the scanner and task effects were modeled as covariates with the following Bayesian multilevel formulation, where a 0 , a 1 , and a 2 code the intercept, scanner, and task effects, respectively; ξ 0i , ξ 1i , and ξ 2i represent the intercept, scanner, and task effects during the ith session (visit); η j models the effect of jth subject; γ ij characterizes the effect of jth subject during ith session; ζ 0k , ζ 0k , and ζ 0k are the intercept, scanner, and task effects at the kth ROI; the μ and μ terms are the intercept, scanner, and task effects of the ith session at the kth ROI and the jth subject at the kth ROI, respectively; finally, ε is the residual term. With a Gaussian assumption for crosssession, cross-participant, cross-ROI effects, their interactions, and residuals, the Bayesian model is numerically solved through Markov Chain Monte Carlo simulations using the R package brms (Bürkner, 2017) in Stan (Carpenter, 2017). The ICC at the kth ROI was assessed through the mean, standard error, and quantile interval based on the variances (σ 2 ) of the corresponding posterior density: 3.2 | Imaging data
| Scanner effects on tSNR
We first investigated scanner effects on tSNR using both voxel-wise analyses and ROIs covering the whole brain.
Voxel-wise analysis
Average tSNR (at the first scan) for the visual search task was M = 212.84 (SD = 28.58) and for the face-emotion labeling task M = 223.33 (SD = 28.01). For both tasks, we found participantspecific variance in tSNR to be highly reliable across the three scanning sessions (Figure 2a). Higher ICCs for scanner-specific relative to participant-specific effects were only seen in white matter. The mean tSNR map (at the first session) was highly correlated with voxel-wise participant-specific ICCs for both paradigms (visual search: r = .92; face-emotion labeling: r = .89), that is, voxels with higher tSNR were more reliable over scans. Refer to Tables S11 and S13 for a list of participant-specific ICCs for each of the 200 cortical parcels and 14 subcortical ROIs for each task, presented alongside the conventional linear mixed-effects approach for each parcel.
| Reliability of task contrasts
We next investigated reliability of the task contrasts for each paradigm utilizing both voxel-wise and ROI analyses. Table 1 contains "ata-glance" summaries of reliability estimates of the main behavioral indices and fMRI contrasts for each task; detailed tables can be found in Tables S3-S6.
Voxel-wise analysis of visual search task First, for the task vs. baseline contrast, scanner-associated variance was minimal, with no scanner-associated variance surpassing the threshold of ICC > .4 (Figure 2b). Reliable participant-specific variance was observed in visual, parietal, and prefrontal cortices, including the inferior-frontal and middle frontal gyri. ICCs for the task vs. baseline contrast correlated positively with both mean tSNR (r = .82) and task vs. baseline activity (r = .60) at the voxel-wise level.
Next, two task contrasts of interest were examined (Figure 2c).
Faces vs. scrambled contrast signal in visual cortex/fusiform gyrus was both active at the first session and reliable at ICC > .4. Note that the right amygdala was also active at the first session but was not reliable at a threshold of ICC > .4. Average faces vs. scrambled contrast activity at the first session was weakly correlated with the associated reliability map (r = .29) at the voxel-wise level.
The log-transformed slope analysis revealed contrast signal in visual, parietal, and bilateral dorsal lateral prefrontal cortex (dlPFC) regions, signal that was also reliable across time. The anterior insular showed significant activation at the first session, but was not reliable at ICC > .4. Average log-transformed slope activity at the first session was correlated with the associated reliability map (r = .55). Additional contrasts (angry vs. happy and log slopes per emotion) and tables detailing group-level activation at the first scanning session and clusters of ICC > .4 are presented in Figure S2.
Voxel-wise analysis of face-emotion labeling task As above, scanner-associated variance was minimal for the task vs. baseline contrast, and reliable participant-specific variance was F I G U R E 2 Voxel-wise analysis. (a) Scanner effects on temporal signal-to-noise ratio (tSNR) and (b) task versus baseline contrasts. For both tasks, participant-specific variance in tSNR was highly reliable over time. Higher ICCs for scanner-specific relative to participant-specific effects were only seen in white matter. (c) Conjunction maps between the first group-level activation for main task contrast at a corrected significance level of .05 based on voxel-wise p < .001 and ICC maps at a threshold of ICC > 0.4 observed in visual, motor, parietal, and prefrontal cortices (Figure 2b).
Task vs. baseline ICCs positively correlated with both mean tSNR (r = .84) and the task versus baseline activity at the first session (r = .65).
Next, two task contrasts of interest were examined (Figure 2c).
The linear slope across face-morphs (coding emotion intensity from angry to happy) reliably tracked motor response in the bilateral motor cortex. Average linear slope activity at the first session was correlated with the associated reliability map (r = .61).
For the quadratic slope across morphs (coding ambiguity from ambiguous to overt), reliable signal (ICC > .4) overlapped with regions of significant activation at the first session in the bilateral dlPFC/ anterior insula, supplementary motor area/anterior cingulate cortex (ACC). Average quadratic slope activity at the first session was correlated with the associated reliability map (r = .48).
Additional contrasts (i.e., difference value: ambiguous vs. overt faces, happy vs. angry faces) and tables detailing group-level F I G U R E 3 Surface renderings of unthresholded maps of ROI ICCs for both tasks using a Bayesian hierarchical model. (a) tSNR and (b) task versus baseline contrasts showed reliable participant-specific variance, while reliability of main task contrasts faces versus scrambled contrast signal and log-transformed slope exhibited patterns of largely "poor" reliability activation at the first scanning session and clusters of ICC > .4 are presented in Figure S2.
ROI-based analysis
Similar test-restest results were observed in Bayesian multi-level analyses of ROIs covering the whole-brain for both tasks. Figure 3b,c displays surface renderings of maps of ROI ICC for each contrast. Refer to Tables S12 and S14 for the full list of ICCs for each of the 200 cortical parcels and 14 subcortical ROIs.
| DISCUSSION
This study examined test-retest reliability of neural responses during two face-emotion paradigms, one requiring explicit, task-directed face-emotion labeling, and one involving implicit, task-irrelevant faceemotion processing. Three key findings emerged. First, scanner effects accounted for minimal variance in temporal signal-to-noise ratio (tSNR) and fMRI activity maps. Second, regions showing significant task-contrast activity showed higher reliability than regions that T A B L E 1 Summary of reliability estimates for main behavioral indices and fMRI task contrasts Note: This table provides an "at-a-glance" summary of behavioral and neural reliability findings alongside group-level voxel-wise activation patterns at the first scan session for the visual search and emotion labeling tasks. The test-retest reliability of main behavioral indices is noted, that is, the intra-class correlation coefficient (ICC) of participant-specific variance. Full behavioral reliability results are presented in Tables S1 and S2. A brief descriptive summary of regions exhibiting at least "fair" reliability (ICC > .4) in voxel-wise analyses for main tasks contrasts is also presented, full results are presented in Tables S3-S6. did not show strong task-related activity at the group level. Finally, across both tasks, we found greater reliability for task contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. non-face discrimination) compared to conditions with more similar demands (e.g., angry vs. happy discrimination).
Variability in tSNR and activation across scanners is undesirable for multi-scanner/multi-site studies. Previous work has generally reported relatively little systematic variability in fMRI signal across scanners (Noble et al., 2017), specifically in subtraction contrasts (Nielson et al., 2018). However, some studies combining data across scanners of different field strength and/or from different vendors or models find larger scanner effects (Friedman, Glover, Krenz, Magnotta, & First, 2006). Although we found substantial white matter variance to be scanner-specific, we found little variance accounted for by scanner in gray matter. Our study employed scanners from the same vendor as is typical for single-site or harmonized multi-site studies; nonetheless, it is likely that effects would be larger for studies with less consistent hardware. Continuing to examine possible systematic scanner differences is important as differences may also be vendor-specific. Different software solutions can be adopted to harmonize systematic scanner differences without removing other variance of interest. Alternatively, including scanner as a covariate can help partition scanner-associated variance.
Past studies have typically found reliability estimates of taskbased imaging to be relatively poor across commonly used tasks is not stable within an individual over time (i.e., activity is robust but not reliable). In contrast, other regions may show stronger betweenindividual differences (leading to lower/less robust mean signal) that allows for variability that is consistent across time (Chen et al., 2021b;Hedge, Powell, & Sumner, 2018). Hence, sub-optimal levels of reliability may, for some tasks, derive from design features that aim to maximize group-level activation (thereby implicitly minimizing individual differences; Hedge et al., 2018;Lissek, Pine, & Grillon, 2006 Although using an a priori whole-brain atlas allows us to define ROIs independent of the current data and avoid circularity, a predefined anatomical or parcellation atlas may not best capture the most reliable functional units for a given task (and estimates may vary based on the chosen atlas). Nonetheless, our two statistical approaches largely converged, but ICCs were generally higher in the voxel-wise approach.
This is in part due to a "global calibration" across spatial units in the hierarchical model. Leveraging the distribution of estimates across ROIs helps to minimize outlier values and ideally yields a better estimate of true reliability. In our data, this partial pooling/shrinkage generally decreased regional ICC estimates given overall low reliability estimates across the brain. Furthermore, trial-wise modeling approaches using amplitude modulation or hierarchical models (Chen et al., 2021) can also be helpful in modeling cognitive processes precisely and circumvent subtraction contrasts. Additional modeling approaches, including structural equation (Cooper, Jackson, Barch, & Braver, 2019) and computational modeling, are growing and hold promise to increase reliability. For new data collection, it will be important to make design choices to improve reliability, for example by increasing the potency of stimuli or making conditions more distinct (i.e., less correlated), while still isolating the cognitive process of interest. Additionally, selecting sequences with improved tSNR will result in increased statistical power; similarly, increasing the number of trials can optimize the statistical efficiency of designs (Chen et al., 2021c).
This study provides strong data on reliability in healthy adults and has several strengths. These include assessing reliability across three points in time, which allowed us to separate scanner-and time-related variance components. We also examine two different face-emotion tasks requiring different attentional demands, and we compare two statistical approaches to reliability estimates. However, the current study also has several limitations. First, the generalizability of these estimates will need to be tested in individuals with psychopathology and pediatric samples. Second, there was variability in the time between sessions across participants, though this was constrained to 2-6 weeks. This timeframe would be similar to pre-/post-scanning for many psychiatric treatment trials, but reliability over longer time frames would still need to be examined, for example, to match developmental studies over years. Third, our sample size was larger than many reliability studies but is still relatively limited, especially given only moderately reliable behavioral effects. Fourth, scanners were of the same build and tasks were run with identical sequences across scanners to minimize scanner-related variance. This reflects an ideal scenario and may not be the case for many large multi-site projects.
The current report adds to the small but growing corpus of work on test-retest reliability of task-based fMRI activation by examining the influence of specific scanners and task-related factors on estimates of reliability. Greater reliability was found in regions activated during the task at the group level and for contrasts involving conditions with clearly distinct cognitive demands. This work highlights the importance of assessing reliability in the context of task activation patterns and specific task contrasts. K23MH113731). The funding source was not involved in study design; the collection, analysis, or interpretation of data; writing of the report; or the decision to submit the article for publication.
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2022-02-16T06:24:10.380Z
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2022-02-15T00:00:00.000Z
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SIRT1 Activity Is Linked to Its Brain Region-Specific Phosphorylation and Is Impaired in Huntington’s Disease Mice
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SIRT1 Activity Is Linked to Its Brain Region-Specific Phosphorylation and Is Impaired in Huntington’s Disease Mice
Huntington’s disease (HD) is a neurodegenerative disorder for which there are no disease-modifying treatments. SIRT1 is a NAD+-dependent protein deacetylase that is implicated in maintaining neuronal health during development, differentiation and ageing. Previous studies suggested that the modulation of SIRT1 activity is neuroprotective in HD mouse models, however, the mechanisms controlling SIRT1 activity are unknown. We have identified a striatum-specific phosphorylation-dependent regulatory mechanism of SIRT1 induction under normal physiological conditions, which is impaired in HD. We demonstrate that SIRT1 activity is down-regulated in the brains of two complementary HD mouse models, which correlated with altered SIRT1 phosphorylation levels. This SIRT1 impairment could not be rescued by the ablation of DBC1, a negative regulator of SIRT1, but was linked to changes in the sub-cellular distribution of AMPK-α1, a positive regulator of SIRT1 function. This work provides insights into the regulation of SIRT1 activity with the potential for the development of novel therapeutic strategies.
Introduction
Huntington's disease (HD) is a devastating neurodegenerative disorder caused by a CAG repeat expansion within exon 1 of the huntingtin gene (HTT), which encodes for an expanded polyglutamine (polyQ) tract in the huntingtin protein (HTT) [1]. Symptoms usually appear in mid-life, comprise personality changes, problems with motor coordination and cognitive decline; disease duration lasts between 15 and 20 years and there are no disease-modifying treatments [2]. The neuropathology of HD is characterised by neuronal cell death in the striatum, cortex and other brain regions and the accumulation of cytoplasmic and nuclear aggregates [3].
Mouse models of HD include those that are transgenic for N-terminal fragments of HTT (e.g. R6/2) or the full length HTT protein or are knock-in models in which the HD mutation has been introduced into mouse Htt (e.g. HdhQ150) [4]. The R6/2 mouse is transgenic for an exon 1 HTT protein [5] and is a model of the aberrant splicing that occurs in HD [6]. The HdhQ150 model had a 150 CAG repeat knocked into the mouse Htt gene [7]. In addition to the full length protein, HdhQ150 mice express mutant exon 1 HTT through aberrant splicing [6] and many other N-terminal HTT fragments generated through proteolysis [8]. At late stage disease (14 weeks for R6/2 and 22 months for homozygous HdhQ150 mice) these models exhibit remarkably similar phenotypes [9][10][11][12][13][14] the main difference between these two models being the age of disease onset and rate of disease progression. SIRT1, a mammalian orthologue of the yeast Sir2 protein, is a NAD + -dependent deacetylase that plays a critical role in multiple biological processes including apoptosis [15], ageing [16], metabolism [17] and various stress responses [18]. It has been demonstrated that DBC1 (deleted in breast cancer 1) inhibits SIRT1 via a direct interaction with its catalytic domain [19]. This dynamic interaction is sensitive to the energetic state of the cell and involves the activity of AMPK (AMP-activated protein kinase), an important cellular energy sensor [20]. In circumstances of low cellular energy, AMPK stimulates compensatory processes, including the activation of SIRT1, resulting in the restoration of ATP levels [21]. However, the complexity of SIRT1 functions in the mammalian brain and the mechanisms involved in SIRT1 regulation are not fully understood.
SIRT1 has been shown to participate in neuronal protection and survival in various mouse models of neurodegenerative disorders through a number of substrates such as P53 [22] and HSF1 [23]. With relevance to HD, the activation of Sir2 was protective against mutant phenotypes in a C. elegans model [24]. Increased expression of Sirt1 attenuated neurodegeneration and improved motor function in N171-82Q and BACHD mice [25] and attenuated brain atrophy and reduced mutant HTT aggregation in R6/2 mice without prolonging lifespan [26]. More recently, SRT2104, a SIRT1 activator was reported to have beneficial effects in an HD mouse model [27] with the potential for interrogating SIRT1 activity in the clinic [28]. In contrast, a SIRT1 inhibitor, selisistat, has been reported to alleviate HD-related phenotypes in multiple HD models [29] and has been found to be safe in clinical trials [30]. Based on these findings, the mis-regulation of SIRT1 could have important implications in the development and progression of HD.
In this study we describe a striatum-specific phosphorylation-dependent regulatory mechanism that controls SIRT1 activity under normal physiological conditions that is impaired in HD. We show that SIRT1 activity is decreased in the brains of R6/2 and HdhQ150 mice, and that this is not caused by the sequestration of SIRT1 into HTT inclusions. We demonstrate that the presence of mutant HTT in the striatum and cerebellum of HD mice alters the phosphorylation status of SIRT1 and that these effects are related to the abnormal expression and cellular localization of AMPK-α1. Finally, we show that the ablation of DBC1, a negative regulator of SIRT1 [31] does not rescue the deficit in SIRT1 activity in HD mouse models. These results provide new insights into the mechanisms that regulate SIRT1 function and may lead to the development of new strategies by which SIRT1 can be manipulated for therapeutic benefit.
(CBA × C57BL/6) F1 background were generated by intercrossing Hdh Q150/Q7 heterozygous CBA/Ca and C57BL/6J congenic lines (inbred lines from Harlan Olac). R6/2 and Hdh Q150/Q150 homozygous mice were genotyped and the CAG repeat was sized as previously described [32]. The mean repeat size (± SD) for all mice used in the entire study was 165 ± 10 for Hdh Q150/Q150 homozygous mice and 204 ± 7 for R6/2 mice. Dbc1 heterozygous mice were obtained from the Eduardo Chini at the Mayo Foundation, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. PCR conditions for genotyping Dbc1 knock-out mice have been previously described [19]. SirT1 floxed homozygous (SirT1 Fl/Fl) mice were obtained from the JAX Laboratory (Mouse Strain: B6;129-SirT1tm1Ygu/J) [33] and were bred with β-actin/Cre heterozygous mice to generate complete Sirt1KO mice. Sirt1 transgenic mice (CBA×C57BL/6J) [34] were obtained from David Holzman's laboratory at Washington University, Missouri, USA Animals were housed under 12 h light/12 h dark cycle, with unlimited access to water and food (Special Diet Service, Witham, UK) in a conventional Unit. Cages were environmentally enriched with a cardboard tube. R6/2 mice and all mice in phenotypic assessment trials were always given mash food consisting of powered chow mixed with water from 12 weeks of age until sacrificed. Upon sacrifice, dissected brain regions, whole brains or peripheral tissues were snap frozen in liquid nitrogen and stored at -80°C until use.
Mouse behavioural analysis
At 4 weeks of age, mice were weaned into cages of 5-6 animals. Each cage contained at least one representative of each genotype from mixed litters. The analysis of mice of different genotypes was distributed equally throughout the assessment period on any given day and all behavioural tests were performed blind to the investigator. Mice were weighed weekly and rotarod performance and grip strength were assessed as previously reported [35][36][37]. The statistical power of these tests was calculated as previously described [37]. The data were analysed by repeated measures general linear model ANOVA using SPSS software [37].
Protein extraction for SDS PAGE, Immunoblotting and Immunoprecipitation
Frozen mouse brain tissue was homogenized in 1 volume of ice cold NETN buffer (20 mM Tris-HCl pH 8, 100 mM NaCl, 1 mM EDTA, 0.5% NP-40, complete protease inhibitors and phosphatase inhibitors) using a polytron homogenizing probe. Samples were sonicated on ice with a vibracell sonicator (10 x 1 s 20 kHz pulses) and spun at 13,000 x g for 10 min at 4°C. The supernatant was retained and protein concentration was determined for each sample by the BCA assay (Thermo Scientific).
SDS PAGE and Immunoblotting
Protein lysates were diluted with 2x Leammli Buffer, denatured for 10 min at 95°C, loaded onto SDS polyacrylamide gels and subjected to western blot as previously described [8]. Membranes were blocked in 5% non-fat dried milk in PBS-0.2% Tween 20 (PBS-T) or 4% BSA for 2 h at RT. Primary antibodies were added overnight at 4°C in 5% non-fat dried milk in PBS-T (DBC1, SIRT1, HTT, AMPK-α1,) or 4% BSA (MpM2). β-actin, ATP5B, α-tubulin and histone pan-H3 were incubated for 20 min at RT in 5% non-fat dried milk in PBS-T. Blots were washed three times for 10 min in 0.2% PBS-T, incubated with the appropriate secondary antibody for 1 h at RT, washed three times for 10 min in 0.2% PBS-T and exposed to ECL according to manufacturer's instruction (Amersham). The signal was developed using Amersham hyperfilm and Xenograph developer. Densitometry of western blots was performed using a Bio-Rad GS-800 densitometer. Developed films were scanned and the average pixel optical density (OD) for each band was measured using QuantityONE software. The OD of an area devoid of bands was subtracted from the values obtained for bands of interest in order to normalize the OD against background. Relative expression was determined by dividing the normalized OD of bands of interest by the OD of the appropriate loading control for each sample. For full details of primary antibodies see S1 Table. Immunoprecipitation Protein lysates were prepared for immunoprecipitation (IP) as described above. For IP from striatal lysates, striata were pooled from two animals. IP reactions were performed in 1 ml of NETN buffer containing from 400 to 1000 μg protein and 1 μg of antibody and normal rabbit IgG (#2729; Cell Signaling) was used as a negative control. Reactions were left on a rotating wheel at 4°C for 90 min (AMPK-α1) or 4 h (SIRT1) and 15 μl of protein G-coupled Dynabeads (10004D; Life Technologies) were added for the last 45 min. Following IP, protein G-coupled Dynabeads were briefly spun at 13,000 x g for 30 sec, put on a magnetic rack, washed with 1 ml of NETN buffer (4x) and re-suspended in 15 μl of 2x Leammli buffer. Immuno-precipitated complexes were eluted from the beads by denaturation at 100°C for 10 min and immediately loaded for SDS-PAGE analysis.
Nuclear/cytoplasmic fractionation
All steps were performed on ice. Half brain tissue or liver was cut into small pieces and homogenized with a Dounce homogenizer in TKM buffer (0.25 M sucrose; 50 mM Tris-HCl, pH 7.4; 25 mM KCl; 5 mM MgCl2 and 1 mM PMSF) and nuclear and cytoplasmic fractions were prepared as previously described [19]. For the nuclear and cytoplasmic preparations from brain regions, striata were pooled from four animals, whereas a single cerebellum was used. The final pellet containing the purified nuclei was resuspended in 4% PFA for immunohistochemistry or in NETN buffer for protein analysis and protein concentration was determined by the BCA assay (Thermo Scientific).
Immunohistochemistry
The isolation of nuclei from brain or liver was as described above. Nuclei were extracted from 4 mice per genotype from half brain or liver and for brain regions, from two pools each containing specific regions from five mice. Samples were fixed on the slide for 30 min with 4% paraformaldehyde prepared in PBS, permeabilized with 0.1% Triton X-100 in PBS for 15 min, washed 3X with PBS, and incubated for 1 h at RT in blocking buffer (PBS with 0.1% Triton and 1% BSA). Nuclei were incubated with the primary antibody in blocking buffer (DBC1, SIRT1, P53 and Ac-P53) overnight at 4°C, washed 3x with PBS at RT and then incubated with the secondary antibody and DAPI in PBS-0.1% Triton for 1 h at RT. Samples were mounted using VECTA-SHIELD mounting medium. Nuclei were visualized using a TCS SP2 Leica confocal microscope. Fluorescence intensity was quantified from 50 nuclei per sample imaged from 10 fields of view per slide using ImageJ. Ac-P53 levels were normalised to the P53 intensity level. Fluorescent intensity levels were presented as a fold change from WT levels as indicated in the figures. The direction of the fold change was inverted to depict the comparative deacetylase activity.
Fluor de Lys assay SIRT1 activity was determined with a SIRT1 Fluorometric Kit (BML-AK555) according to the manufacturer's instructions. Protein extraction was performed as described above. Homogenates were then incubated for 10 min at 37°C to allow degradation of any contaminant NAD + . 10 mM DTT was added to the medium, and homogenates were incubated again for 10 min at 37°C. The homogenates (20-30 μg protein/well) were then incubated in SIRT1 assay buffer in the presence of 50, 100 or 200 μM Fluor de Lys-SIRT1 substrate (Enzo Life Sciences), 5 μM TSA and 200 μM NAD + . After 0-, 20-, 40-and 60 minutes of incubation at 37°C, the reaction was terminated by adding a solution containing Fluor de Lys Developer (Enzo Life Sciences) and 2 mM nicotinamide. After 1 h the values were determined by reading fluorescence on a fluorometric plate reader (Spectramax Gemini XPS; Molecular Devices) with an excitation wavelength of 360 nm and an emission wavelength of 460 nm.
Taqman RT-qPCR RNA extraction, cDNA sysnthesis, Taqman RT-qPCR and ΔCt analysis were performed as described previously [38]. The Taqman qPCR assays were purchased from Primer Design and ABI. For a list of primers and probes, see S2 Table. Statistical Analysis Statistical analysis was performed with SPSS (repeated measures ANOVA General Linear Model) or Microsoft Excel (Student's t-test) software. p-values of <0.05 were considered significant. Graphs were constructed using Prism Ver.5.0b (GraphPad Software).
SIRT1 function becomes compromised in the brains of HD mice
There is considerable evidence to support the beneficial effect of SIRT1 manipulation in HD mouse models. However, the impact of mutant HTT on SIRT1 function has not been fully elucidated. As such, we set out to analyse SIRT1 activity and the mechanisms involved in its regulation in two different mouse models of HD: R6/2 transgenic and HdhQ150 knock-in homozygous mice.
SIRT1 regulates the activity of several transcription factors including P53 [39]. It deacetylates P53 on Lys382 thereby inhibiting its function [40]. There are a number of commercial kits that use the deacetylation of this P53 lysine residue to assess SIRT 1 activity. In order to have a direct measurement of SIRT1 activity, we applied the Fluor-de-Lys fluorometric activity assay (Enzo Laboratories). The specificity of the kit was evaluated on lysates from the brains of SIRT1 knock-out (Sirt1KO) [33] mice at 4 weeks of age, but unfortunately we found that this kit was not specific for SIRT1 in these brain lysates (S1 Fig). Therefore we tested an alternative published method to assess the steady-state levels of SIRT1 activity on endogenous P53 in mouse brains that makes use of nuclei purified from mouse tissues [19]. The genotypes of the mice used for the experiment were verified by western blot (Fig 1A). Nuclei were isolated from the brains of Sirt1KO and Sirt1Tg mice [34] at 4 weeks of age and immunostained for P53 and acetylated-P53 (AcP53) at Lys382, and counterstained with DAPI ( Fig 1B). P53 levels were equivalent between the Sirt1KO and Sirt1Tg lines and the corresponding wild type (WT) littermates ( Fig 1C). The acetylation of P53 Lys 382 was considerably increased in the Sirt1KO nuclei and decreased in those from the Sirt1Tg mice, consistent with a decrease in SIRT1 activity in the knock-out line and an increase in SIRT1 activity in the transgenic line respectively (Fig 1C), demonstrating that this approach could be used to monitor the steady-state level of SIRT1 activity in mouse brain.
To monitor the level of SIRT1 activity in HD mouse models, we isolated cell nuclei from the brains of R6/2 mice at 4, 9 and 14 weeks of age and HdhQ150 homozygous mice at 2 and 22 months together with their aged-matched WT littermates. Nuclei were immunostained for SIRT1, P53 and acetylated-P53 (AcP53), and counterstained with DAPI (Fig 2A and S2A Fig). We did not detect any variation in the intensity level of SIRT1 and P53 staining between HD mouse samples and their corresponding WT controls at each age of analysis ( Fig 2B and S2B Fig). In contrast, whilst we found that the acetylation levels of endogenous P53 were equivalent in HD as compared to WT littermate brains in presymptomatic mice (i.e. 4 week R6/2 and 2 month HdhQ150 homozygotes) (Fig 2A and 2B), the level of AcP53 was significantly higher (! 1.5 fold) in samples from early symptomatic R6/2 mice (9 weeks) and late stage symptomatic R6/2 (14 weeks) and HdhQ150 homozygous (22 months) mice (
SIRT1 does not co-localize with mutant HTT inclusions and is aberrantly phosphorylated in HD mice
Previous studies have shown that SIRT1 interacts with HTT in vitro [26]. To investigate whether the altered SIRT1 activity is caused by the sequestration of SIRT1 into HTT inclusions, we performed a double staining for SIRT1 and HTT (EM48) on nuclei isolated from the brains of 14-week R6/2 and 22-month HdhQ150 homozygous mice, together with their age-matched WT littermates. Interestingly, SIRT1 did not co-localize with HTT inclusions (Fig 3A). To further support this finding, the levels of SIRT1 protein were not decreased in HD brains as judged by western blot (Fig 3B).
The role of post-translational modifications (PTMs) in the regulation of SIRT1 activity has been the subject of several studies and phosphorylation has been described as a major control SIRT1 Phosphorylation, Impaired Activity and Huntington's Disease mechanism [41]. Therefore, to understand how mutant HTT reduces SIRT1 activity, we monitored the phosphorylation status of SIRT1 in HD mice. We performed SIRT1 immunoprecipitation from the brains of R6/2 mice at 9 weeks of age and HdhQ150 homozygous mice at 22 months and probed the phosphorylation level of SIRT1 by western blot using the mitotic phosphoprotein monoclonal 2 (MpM2) antibody [42]. This antibody detects the phosphorylation of serine and threonine residues when they are followed by a proline (S/T-P sites) and it is not specific for a SIRT1 phosphorylation site (S3 Fig). Interestingly, a higher level of phosphorylated SIRT1 was found in the brains of both R6/2 and HdhQ150 homozygotes as compared to their WT littermates ( Fig 3C). As previously shown in vitro [26], we were able to co-immunoprecipitate endogenous HTT from R6/2 lysates and mutant HTT and WT HTT from HdhQ150 homozygous and WT lysates respectively (Fig 3C). These results suggest that the impairment in SIRT1 function in the brains of HD mice is not related to its sequestration into HTT inclusions, but rather to an alteration in its phosphorylation profile.
SIRT1 phosphorylation becomes decreased in the striatum and increased in the cerebellum of HD mice
The analysis of total brain samples might mask or dilute any regional pathological changes. Therefore, we extended the analysis of SIRT1 phosphorylation to the striatum, cortex and cerebellum of R6/2 mice at 4, 9, and 14 weeks of age. We did not detect any difference in the phosphorylation status of SIRT1 at presymptomatic stages of the disease (i.e. 4-week-old R6/2) as compared to WT littermates in any brain region (Fig 4A and 4C and S4 Fig). In keeping with our functional data from total brain, the levels of phosphorylated SIRT1 were altered in the striatum and cerebellum of R6/2 mice by 9 weeks of age (Fig 4A and 4C). Surprisingly, the level of phosphorylation of SIRT1 remained unchanged in the R6/2 cortex at these later stages (S4 Fig), but notably was decreased in the striatum ( Fig 4A) and increased in the cerebellum ( Fig 4C) as compared to WT littermates. These data were replicated in the HdhQ150 homozygous mice: there was no difference in the SIRT1 phosphorylation level at 2 months of age (Fig 4B and 4D) whereas SIRT1 phosphorylation was decreased in the striatum and increased in the cerebellum of 22-month-old HdhQ150 homozygous mice (Fig 4B and 4D). Taken together, these results demonstrate that the presence of mutant HTT alters the phosphorylation status of SIRT1 in opposing directions for the striatum and cerebellum as the disease progresses.
Induction of SIRT1 activity is blocked in the striatum
Phosphorylation plays a central role in controlling protein activity, cellular localization and degradation [43]. To determine whether the differentially altered phosphorylation profile of SIRT1 in striatum and cerebellum corresponded to a compromised SIRT1 function in these brain regions, we immunostained for SIRT1, P53 and AcP53 in nuclei from the striatum and cerebellum of R6/2 and WT mice at 4, 9 and 14 weeks of age. Consistent with the total brain data, we did not detect a change in the intensity level of SIRT1 and P53 staining in either the striatum or cerebellum of R6/2 and WT mice, at any of the ages studied (S5A and S5B, S6A and S6B Figs). Interestingly, we observed a significant reduction in the level of AcP53 in the striatum of WT mice, corresponding to an increase in SIRT1 activity, between 4 and 9 weeks of age, which was absent in the striatum of R6/2 mice (Fig 5A and 5B). In contrast, when we analysed SIRT1 activity in the cerebellum, we detected no change in the level of AcP53 in WT samples at these ages and there was a significant increase in AcP53 in the cerebellum of R6/2 mice from 4 to 14 weeks of age (Fig 5C and 5D), corresponding to an impairment in SIRT1 activity. These data highlight that SIRT1 activity is regulated by different mechanisms in the striatum and cerebellum of WT mice between 4 and 14 weeks of age; SIRT1 activity is induced in the striatum between 4 and 9 weeks, whereas it remains constant in the cerebellum. The presence of mutant HTT can block this induction process in the striatum and cause a reduction in normal SIRT1 function in the cerebellum resulting in an impairment of SIRT1 activity in both brain regions (Fig 5).
SIRT1 induction in the striatum correlates with age-dependent phosphorylation
The comparison of SIRT1 activity in the striatum and cerebellum revealed that SIRT1 function is controlled by different mechanisms in these two brain regions in WT mice. In the striatum, SIRT1 is activated with age, a process that does not occur in the cerebellum. To monitor changes in the phosphorylation status of SIRT1 under normal physiological conditions we immunoprecipitated SIRT1 Phosphorylation, Impaired Activity and Huntington's Disease SIRT1 from striatal and cerebellar lysates of WT mice at 4, 9 and 14 weeks and immunoprobed with the MpM2 antibody. Notably, SIRT1 phosphorylation levels decreased in the striatum between 4 and 9 weeks of age (Fig 6A), a time at which the functional data revealed an increase in SIRT1 activity (Fig 5A and 5B). However, it then dramatically increased at 14 weeks (Fig 6A), a stage at which SIRT1 activity remains constant as compared to 9 weeks (Fig 5A and 5B). The MpM2 antibody detects phosphorylation on serine and threonine residues followed by proline (S/ T-P sites) and is not specific for a SIRT1 phosphorylation site; therefore, the increased phosphorylation signal at 14 weeks may correspond to the phosphorylation of different SIRT1 residues to those detected a 4 and 9 weeks of age. Conversely, SIRT1 activity remains constant during these ages in the cerebellum (Fig 5C and 5D) and this is reflected by a phosphorylation level that does not change (Fig 6B). Taken together these data provide a link between the phosphorylation status of SIRT1 and its function, suggesting that in the striatum changes in the SIRT1 phosphorylation with age might be related to the induction of SIRT1 activity.
The sub-cellular distribution of SIRT1 is not altered in R6/2 mice
Previous studies suggested that the phosphorylation of human SIRT1 can increase its nuclear localization and enzymatic activity [44]. To assess whether the mis-regulation of SIRT1 phosphorylation could affect its nuclear localization we prepared nuclear and cytoplasmic fractions from the striatum and cerebellum of R6/2 and WT mice at 9 and 14 weeks of age. Notably, we did not detect any difference in the distribution of SIRT1 at these ages between R6/2 and WT mice in either brain region (Fig 7A and 7B). However, the level of SIRT1 in the nuclear fraction was more pronounced at 14 weeks as compared to 9 weeks of age in the striatum and cerebellum of both R6/2 and WT mice (Fig 7A and 7B). We went on to analyse the phosphorylation level of SIRT1 in these two cellular compartments from the cerebellum by immunoprecipitation. This was not possible from the striatum due to limiting quantities of the extracts. Interestingly, a strong phosphorylation signal was detected in the nuclear fraction, that was absent from the cytoplasm, for both R6/2 and WT samples and, as previously shown on total lysates, the level of phosphorylation was much higher in R6/2 as compared to WT mice ( Fig 7C). These results demonstrate that the sub-cellular distribution of SIRT1 is not affected by the presence of mutant HTT and suggests, once again, that the phosphorylation levels might be directly linked to the regulation of SIRT1 activity.
Tissue specific alteration of the subcellular distribution of AMPK-α1 with disease progression
Previous studies showed that DBC1 directly interacts with the catalytic domain of SIRT1 inhibiting its activity both in vitro and in vivo [19]. This dynamic interaction is sensitive to the energetic state of the cell [19]. Activation of AMP-activated protein kinase-α1 (AMPK-α1), an important energy sensor in circumstances of low cellular energy, was recently shown to induce the activation of SIRT1 through the dissociation of SIRT1 and DBC1 [20,45]. To identify the possible role of AMPK-α1 and DBC1 in the molecular phenotypes described so far, we decided to study the interaction between these two opposing modulators of SIRT1 using co-immunoprecipitation. We immunoprecipitated AMPK-α1 from the striatum and cerebellum of 9-week R6/2, 22-month HdhQ150 homozygous and WT littermates and detected the co-immunoprecipitated DBC1. Interestingly, we observed a stronger interaction between AMPK-α1 and DBC1 in the striatum of HD as compared to WT mice (Fig 8A), whereas equivalent amounts of DBC1 were co-immunoprecipitated with AMPK-α1 from cerebellar extracts of HD and WT samples (Fig 8B). These data suggest two possible scenarios: either the increased interaction of AMPK-α1 with the SIRT1-DBC1 complex might attempt to promote SIRT1 activation in the striatum of HD mice through the dissociation from DBC1, or the inability to induce SIRT1 in R6/2 mice might be due to an inhibitory retention of AMPK-α1 via DBC1.
To gain insight into the molecular events involved in this process we examined the cellular distribution of AMPK-α1 and DBC1 in the striatum and cerebellum of R6/2 and WT mice at 9 and 14 weeks of age. Consistent with the phenotypes described so far, the distributions AMPK-α1 and DBC1 were different in the striatum and cerebellum. Interestingly, using western blots of nuclear and cytoplasmic fractions, we were able to detect DBC1 in both cellular compartments and, although at 9 weeks of age DBC1 was slightly more abundant in the striatal cytoplasmic fraction, this balance was reverted by 14 weeks of age for both R6/2 and WT mice ( Fig 8C). In contrast cerebellar DBC1 remained constant between the two cellular compartments at 9 and 14 weeks of age for both R6/2 and WT mice (Fig 8D). We next monitored the distribution of AMPK-α1 by immunostaining nuclei isolated from the striata of R6/2 and WT mice at 4, 9 and 14 weeks of age. At 9 weeks, AMPK-α1 was present in the nuclei from the WT striatum, whereas it could not be detected in nuclei from the striatum of R6/2 mice until 14 weeks of age ( Fig 8E). Conversely, cerebellar extracts showed an early nuclear accumulation of AMPK-α1 in R6/2 at 9 weeks of age as compared to WT mice, where AMPK-α1 could only be detected in the nucleus at 14 weeks of age ( Fig 8F). These data were confirmed by western blot (Fig 8C and 8D). The nuclear accumulation of AMPK-α1 at 9 weeks of age in the striatum of WT mice, in conjunction with an induction of SIRT1 activity, might indeed support a role for this kinase in the activation of SIRT1. This mechanism appears to be compromised in R6/2 mice and in this case, AMPK-α1 does not reach the nucleus until 14 weeks. Therefore, the increased interaction between AMPK-α1 and DBC1 might result in the retention of AMPK-α1 in the cytoplasm inhibiting the activation of SIRT1, and/or attempting to rescue SIRT1 activity by preventing DBC1 from binding to SIRT1. Conversely, the early nuclear accumulation of AMPK-α1 in the cerebellum of R6/2 at 9 weeks of age as compared to WT mice with the concomitant alteration in SIRT1 function might be an attempt to increase impaired SIRT1 activity. Taken together these data suggest that the inhibition of SIRT1 function in the striatum of R6/2 might arise through an altered functionality of AMPK-α1 and that AMPK-α1 might be involved in rescuing a deficient SIRT1 function both in the striatum and cerebellum, although through different molecular mechanisms. SIRT1, AMPK-α1 and DBC1 act as partners in the same regulatory circuit to control SIRT1 activity in the striatum Our data suggest that AMPK-α1 may play an active role in attempting to rescue SIRT1 deficiency in both the striatum and cerebellum of R6/2 mice. To obtain further evidence for a regulatory circuit involving these three proteins, we went on to compare the expression levels of SIRT1, DBC1 and AMPK-α1 in the striatum and cerebellum of R6/2 at 4, 9 and 14 weeks of age and HdhQ150 homozygotes at 2 and 22 months as compared to their WT littermates. Strikingly, there was a synchronised, statistically significant down-regulation (35-40%) of all three genes at the mRNA level from 4 to 9 weeks of age in the striatum of WT mice (Fig 9A). We detected the same significant reduction in the striatum of R6/2 mice for Dbc1 and Ampk-α1, and there was a weak trend for Sirt1 (Fig 9A). Notably, the presence of this regulatory circuit in the cerebellum was not supported by the same co-ordinated changes in expression levels, although the expression level of Sirt1 was significantly higher in WT mice at 9 and 14 weeks as compared to 4 weeks of age (Fig 9D). These mRNA changes did not result in concomitant alterations in the levels of the SIRT1, DBC1, and AMPK-α1 proteins (Fig 9B, 9C, 9E and 9F). The mRNA changes in the striatum may be the result of a stabilisation of these proteins between 4 and 9 weeks of age. Indeed, the levels of all three proteins were equivalent at 2 and 22 months in the HdhQ150 homozygotes and WT mice (S7A and S7B Fig). Interestingly, we observed a significant upregulation of AMPK-α1 in the striatum of WT mice between 4 and 9 weeks occurring in conjunction with the increase in SIRT1 activity, neither of which took place in R6/2 mice (Fig 9B and 9C). The only change that occurred in the cerebellum was a reduction in the level of SIRT1 in both WT and R6/2 at 14 weeks of age (Fig 9E and 9F).
Dbc1 ablation does not improve HD-related phenotypes
Our data indicate that brain region specific dysregulated cellular processes result in a reduction in SIRT1 activity in the brains of HD mouse models. We hypothesis that this is related to the phosphorylation status of SIRT1 and that the AMPK-α1 kinase attempts to rescue SIRT1 function. As DBC1 is a negative regulator of SIRT1, and Dbc1 knock-out mice are viable and healthy [19], we elected to use a genetic approach to ablate DBC1 levels in R6/2 mice and investigate whether the dissociation between SIRT1 and DBC1 could increase SIRT1 activity and improve HD phenotypes. We crossed R6/2 transgenic mice with Dbc1 heterozygous knock-out mice (Dbc1 +/-) to obtain Dbc1 +/-::R6/2 males that were then crossed with Dbc1 +/females to generate WT, Dbc1 +/-, Dbc1 -/-, R6/2, Dbc1 +/-::R6/2 and Dbc1 -/-::R6/2 mice. As predicted, genetic ablation of Dbc1 resulted in a significant decrease in Dbc1 mRNA and DBC1 protein levels, and we found that this did not alter the expression of SIRT1 (S8A Fig). To confirm that the removal of DBC1 resulted in an increase in SIRT1 activity, we immunostained nuclei extracted from the brains of WT, Dbc1 -/-, R6/2 and Dbc1 -/-::R6/2 mice at 9 weeks of age for SIRT1, P53, AcP53 and DBC1 and counterstained with DAPI. The absence of DBC1 did not affect the level and nuclear accumulation of SIRT1 and/or P53 (Fig 10A and 10B). As expected, the ablation of DBC1 in WT mice resulted in an increase in SIRT1 activity as indicated by a significant reduction (~65%) in the signal intensity for AcP53 in Dbc1 -/as compared to WT mice (Fig 10B). SIRT1 activity was decreased in R6/2 mice (consistent with Fig 2) and surprisingly the absence of DBC1 did not ameliorate this impairment (Fig 10). In line with these results, we did not detect improvements in the onset and progression of specific behavioural HD-related phenotypes such as body weight, grip strength and rotarod impairment (S8B, S8C and S8D Fig). Taken together these results suggest that the negative effect of mutant HTT on SIRT1 activity might be multifactorial and/or operate outside the inhibitory circuit controlled by DBC1.
Discussion
The involvement of SIRT1 in lifespan extension and cellular protection from aggregationprone proteins http://jcb.rupress.org/content/190/5/719.full-ref-91 has made it a promising therapeutic target for neurodegenerative disorders [46][47][48]. In the context of HD, the manipulation of SIRT1 activity has not generated results that are easy to interpret. On the one hand, over expression of SIRT1 has been shown to reduce mutant HTT-induced toxicity in HD mouse models, improving motor function and reducing brain atrophy [25,26]. In contrast to this, the pharmacological inhibition of SIRT1 has been shown to have beneficial effects in drosophila and mouse models of HD [29] and on the basis of these results, selisistat was assessed for safety and tolerability in a clinical trial aimed at the development of HD pharmacodynamic biomarkers [30]. Despite this interest, the integrity of SIRT1 function in HD has not been comprehensively investigated. In the present study, we have shown that SIRT1 activity is impaired in different brain regions from two distinct mouse models of HD and that this is linked to an altered SIRT1 phosphorylation status. Furthermore, we provide insights into the temporal tissue-specific regulation of SIRT1 activity in different brain regions from WT mice.
To monitor SIRT1 activity in the brain, we analysed P53 acetylation by performing immunohistochemistry on nuclei isolated from both R6/2 and HdhQ150 mice as compared to their WT SIRT1 Phosphorylation, Impaired Activity and Huntington's Disease littermates. We did not detect an alteration in SIRT1 function at the presymptomatic stage in either model. However, SIRT1 activity was overtly compromised by 9 weeks of age in the R6/2 mice with a comparable impairment at late stage disease in both models. This was not caused by SIRT1 sequestration into HTT inclusions and we did not detect any variation in either the level or sub-cellular distribution of SIRT1 between HD and WT mice. We also showed that this impairment occurred in liver and therefore extends to peripheral tissues. We would not expect the increase in P53 acetylation to be caused by the HD-related dysregulation of acetyltransferases as it has been shown that the P53 acetyltransferases: CREB binding protein, P300 and P300/CBP associated factor, are inhibited with disease progression in HD [49] and would therefore be expected to result in a reduction in P53 acetylation, the opposite to that observed in this study. We were unable to replicate these results using an independent measure of SIRT1 activity as the commercial kit that we tested was not specific for SIRT1 in mouse brain lysates.
The role of post-translational modifications in the regulation of SIRT1 activity has been the subject of several studies and phosphorylation has been described as a major control mechanism [41]. It has been shown that kinases such as JNK1 and CK2 can phosphorylate SIRT1 thereby increasing its nuclear deacetylase activity [44,50]. The phosphorylation of SIRT1 by JNK1 has also been shown to induce SIRT1 ubiquitination and proteasomal degradation [44]. In this study, although we detected an impaired SIRT1 activity in both the striatum and cerebellum of HD mice, the phosphorylation level of SIRT1 changed in opposite directions in these two brain regions, indicative of a tissue-specific SIRT1 regulation. Our further investigations led us to identify a striatum-specific phosphorylation-dependent induction of SIRT1 activity with age in WT mice, which does not occur in the cerebellum. Taken together, our findings suggest that it is the induction of SIRT1 function that is compromised by mutant HTT in the striatum of HD mice (Fig 11A), whereas in the cerebellum, mutant HTT impairs an already established SIRT1 activity.
In an attempt to rescue this SIRT1 deficiency, we crossed R6/2 mice with Dbc1 -/mice, as DBC1 negatively regulates SIRT1 via the direct interaction with its deacetylase domain [19]. Strikingly, despite a significant upregulation of SIRT1 activity in Dbc1 -/mice, the ablation of DBC1 from R6/2 mice had no effect on the impairment of SIRT1 activity, suggesting that mutant HTT alters key regulatory events that lie outside the inhibitory circuit controlled by DBC1. Consistent with this, the absence of DBC1 did not lead to improvements in the onset and progression of several behavioural HD-related phenotypes.
In contrast to DBC1, AMPK-α1 has been reported to positively regulate the activity of SIRT1 by inducing SIRT1 activation through its dissociation from DBC1 [20,45]. In addition, there is evidence to indicate that AMPK-α1 and SIRT1 can regulate each other [21]. Interestingly, our co-immunoprecipitation experiments revealed an increased interaction between DBC1 and AMPK-α1 in the striatum of HD mice, which might point to an attempt to rescue SIRT1 function. On the other hand, the nuclear accumulation of AMPK-α1 is delayed in the striatal nuclei of R6/2 mice and its retention in the cytoplasm through an interaction with DBC1 might impede SIRT1 activation. In contrast, the nuclear accumulation of AMPK-α1 in the cerebellum of R6/2 mice occurs earlier than in WT mice, indicating that it might be attempting to relieve the SIRT1 inhibition imposed by mutant HTT. In support of the existence of a striatum-specific regulatory circuit linking these three proteins in the induction of SIRT1 activity, we found that the downregulation of Sirt1, Dbc1 and Ampk-α1 is co-ordinated in WT mice between 4 and 9 weeks of age. The protein level of AMPK-α1 increases in WT mice during the same time frame, which correlates with the induction of SIRT1 activity, neither of which occurs in R6/2 mice. We propose a model whereby disease progression leads to an altered SIRT1 phosphorylation status. As a consequence, the decrease in SIRT1 activity leads to a reduction in the deacetylation of P53 and other SIRT1 substrates, modifications that may contribute to neuronal Proposed model for the striatum-specific regulation of SIRT1 via phosphorylation in WT mice and for the impairment in SIRT1 activity in HD brain. (A) The change in SIRT1 phosphorylation status in the striatum of WT mice between 4 and 9 weeks of age induces an increase in SIRT1 activity followed by a reduction in acetylated P53. The nuclear accumulation of AMPK-α1 in WT striatum at 9 weeks supports a role for this kinase in the activation of SIRT1 (AMPK-α1 is not the kinase involved in the change in SIRT1 phosphorylation detected here, as the MpM2 antibody only recognises Ser/Thr-Pro residues and AMPK-α1 does not phosphorylate Ser/Thr residues that are followed by proline). AMPK-α1 is present in the nucleus at 9 dysfunction ( Fig 11B). This would be consistent with previous data showing that the ablation of P53 from an HD mouse model had beneficial consequences [51]. In conclusion, our data provide two major new findings. First, we have shown that mechanisms controlling the tissuespecific regulation of SIRT1 activity differ between brain regions, and we have identified a novel striatum-specific phosphorylation-dependent mechanism of SIRT1 induction in WT mice. Second, we demonstrate that SIRT1 activity is impaired in two distinct HD mouse models. Given that SIRT1 plays a central role in metabolism, longevity and neurodegeneration, loss of SIRT1 activity may contribute significantly to disease progression in HD. These results provide new insights into the mechanisms that regulate SIRT1 function and may lead to the development of new strategies by which SIRT1 can be manipulated for therapeutic benefit. weeks and activates SIRT1 through a mechanism independent of DBC1. The down-regulation of Sirt1, Dbc1 and Ampk-α1 at the mRNA level between 4 and 9 weeks of age is consistent with these three proteins being partners in the same regulatory circuit. In the context of HD, the marked reduction in SIRT1 phosphorylation impedes the induction of SIRT1 activity. The greater interaction between AMPK-α1 and DBC1 may result in the cytoplasmic retention of AMPK-α1, inhibiting the activation of SIRT1, and/or promoting a futile rescue attempt by preventing DBC1 from binding to SIRT1. (B) The HD pathogenic process leads to an alteration in the phosphorylation status of SIRT1, resulting in an impairment in SIRT1 activity which modulated the function of SIRT1 targets that include P53 and may contribute to neuronal dysfunction.
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Circulation microRNA expression profiles in patients with complete responses to chemoradiotherapy in nasopharyngeal carcinoma
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Circulation microRNA expression profiles in patients with complete responses to chemoradiotherapy in nasopharyngeal carcinoma
Background Nasopharyngeal carcinoma (NPC) is endemic cancer in Southeast Asia with a relatively poor prognosis. Chemoradiotherapy is a primary treatment that advantages certain patients, particularly in the early stages. New predictive and prognostic biomarkers are required to guide and select the best treatment. Aims To evaluate the circulation expression profile of microRNAs (miRNAs) associated with responses to chemoradiotherapy in nasopharyngeal carcinoma. Methods Peripheral blood from 17 patients was collected before and after chemotherapy and radiotherapy. Differential expression circulating miRNAs were analyzed using microRNA Cancer Panels and were compared among patients with complete responses. Differential expression analysis using GenEx 7 Multid, statistic represented by GraphPad Prism 9. Alterations mechanism signaling pathways and biological function using IPA (Ingenuity Pathways Analysis). Results Using microRNAs Cancer Plate consisting of 116 miRNAs, we identified ten circulating miRNAs that were differentially expressed in NPC patients after chemoradiotherapy. Unsupervised clustering and confirmation using qRT-PCR showed that miR-483-5p, miR-584-5p, miR-122-5p, miR-7-5p, miR-150-5p were overexpressed and miRNA are miR-421, miR-133a-3p, miR-18a-5p, miR-106b-3p, miR-339-5p were significantly downregulated after chemoradiotherapy (p < 0.0001). In addition, ROC analysis through AUC (Area Under Curve) with 99% confidence interval (CI) p value < 0.0001. Gene enrichment analysis of microRNAs and the targeted proteins revealed that the main involved pathways for chemoradiotherapy in NPC were cell death and survival signaling pathways. Conclusion qPCR profiling in circulating blood compared before and after chemoradiotherapy in nasopharyngeal carcinoma can identify pathways involved in treatment responses. miR-483-5p, miR-584-5p, miR-122-5p, miR-7-5p, miR-150-5p, miR-421, miR-133a-3p, miR-18a-5p, miR-106b-3p, miR-339-5p are differentially regulated after chemoradiotherapy in NPC.
Introduction
Nasopharyngeal cancer (NPC) is a head and neck cancer with relatively high incidence, mortality, and low survival rates in Southeast Asia, including Indonesia [1]. Many are found in Southeast Asia and are associated with particular ethnicities, so this cancer is unique [2]. The cause of cancer is still unclear. Although it is more commonly found in men, the relationship with gender has not been explained. Delay in diagnosis worsens the patient's condition. The anatomical location and small size are difficult to detect early on, so it is considered the cause of the low cure rate [3][4][5]. One of the biggest challenges in the treatment is complete response and a high rate of cancer progression after treatment.
The core clinical management is early diagnosis using nasopharyngeal tissue biopsy followed by radiotherapy with or without concomitant chemotherapy. In low-and middle-income countries, NPCs are usually diagnosed in late stages due to the difficulty of recognizing the disease until manifestation in the cervical lymph nodes [6][7][8][9]. In patients with advanced stages, disease recurrences are relatively high after a particular time of clinical responses with chemoradiotherapy. Clinical biomarkers to predict disease progression in NPC are still lacking.
Tumor biomarkers are essential to guide treatment, calculate disease progression risk, and design surveillance [10]. MicroRNAs (miRNAs) are small RNA (20-25 bp) involved in the modulation of gene expression through post-translational mechanisms [11,12]. Differences in miRNA expression profiles in primary tissue samples have been used in differentiating pathophysiology risk factors [13], therapeutics response [14], and prognosis of NPC [15].
Chemoradiotherapy causes apoptosis of cancer cells, thereby affecting changes in miRNA expression. Several miRNAs have been involved in chemotherapy response after receiving 5-fluorouracil + cisplatin of NPC and 5-fluorouracil sensitivity in breast cancer [16,17]. We also previously investigated the association between the expressions of chemotherapy responses f NPC and Breast cancer using cell lines and primary tissue samples [18,19]. Chemoradiotherapy might also affect circulating tumor cells, protein, free-DNA, and RNAs. This study investigated altered miRNA expression in the plasma in NPC patients with complete responses after chemoradiotherapy.
Study subjects and ethics statement
17 NPC patients who received chemotherapy based on cisplatin and radiotherapy were involved. Patients come to health facilities with complaints of nasal obstruction, ear problems, nosebleeds, headaches, and lumps in the neck. Four patients were initially diagnosed in stage II B, and 4 and 9 patients were diagnosed in stage III and IV. Plasma samples were collected before treatment 3-17 months after the chemoradiotherapy. Other eligibility criteria for this study were Early Antigen (EA) > 1, Viral Capsid Antigen (VCA) > 2, and EBNA >1.6. Response to treatment was evaluated 12 weeks after using nasopharyngeal endoscopy and CT scan. Complete response (CR) was defined as no residual disease in the smooth nasopharyngeal mucosa, no mass, and no lymph nodes with confirmation of biopsy. This study was performed after approval from Jenderal Soedirman University Ethics Committee (number 898/EC/2016). Furthermore, all participants were older than 18 years old and could provide an informed consent form when recruited, which entails using samples, acquisition, and clinical data.
Chemoradiotherapy
A combination of radiation and chemotherapy is utilized to treat progressed locoregional NPC. Chemotherapy is classified as neoadjuvant, contemporaneous, or adjuvant, whether given some time recently, amid, or after radiation. Chemotherapy is chosen separately based on the patient's characteristics. Concurrent cisplatin with illumination is the standard treatment for chemoradiation in nearby maladies. In the meantime, with cisplatin + radiotherapy taken after cisplatin/5-FU or carboplatin/5-FU, chemoradiation taken after adjuvant chemotherapy may be utilized. The docetaxel/cisplatin/5 FI, docetaxel/cisplatin regimen is utilized for neoadjuvant chemotherapy. Cisplatin/5 FU, cisplatin/epirubicin/paclitaxel, and concordant coordination with week-by-week cisplatin or carboplatin organization. The radiation measurements endorsed were 69-74 Gy to PGTVnx, 66-70 Gy to PGTVnd, 60-66 Gy to PTV1, and 50-54 Gy to PTV2, conveyed in 30 or 33 divisions. Radiation is given once day by day, five divisions per week, for 6-6.5 weeks for IMRT arranging.
Plasma sample collection and miRNA isolation
Whole blood from patients (5 mL each) before and after therapy was collected using an EDTA vacutainer. Plasma was separated using centrifugation (1500 rpm for 10 min) and was stored at − 80 • C until analysis. 200 mL of plasma was used for total RNA extraction using RNA Isolation Kit miRCURY-Biofluid (Cat No. 300 112, Exiqon). cDNA synthesis was performed using 50 mL of total RNA with cDNA Synthesis Universal kit II, 8-64 rxns (Cat No. 203 301, Exiqon) in Biorad C1000 thermal cycler (42 • C for 60 min, 95 • C for 5 min, and 4 • C). All procedures were performed following the manufacturer's recommended protocol.
Quantification microRNA panel
MicroRNA profiling was performed using real-time PCR using Cancer Focus microRNA PCR Panel. ExiLent SYBR Green master mix, 2.5 mL (Cat No. 203 402, Exiqon) consisting of 196 target primer miRNAs based on LNA (Locked Nucleic Acid). All protocols were performed following the recommended protocols provided by the manufacturer.
Data analysis
The analyses were performed using Genex 6 Pro with Exiqon qPCR wizard software MultiD. Expression analysis was performed using relative quantification of − 2 ΔΔCt [20]. Gene enrichment analysis of the differential miRNA expression was performed using Ineguinity Pathway Analysis (IPA). GraphPad Prism 9 software was used for data analysis and Figure configuration to represent the mean, standard deviation (SD), and the student t-test. ROC sensitivity and specificity analysis was constructed with a 99% confidence interval and p < 0.05 as a statistically significant value.
Patients characteristics
In this study of patients, 13 males and 4 females with a median age of 51. Staging I-II data was performed on 4 patients and 13 at III-IV. These patients received completed chemo and radiotherapy, as shown in Table 1. Based on titer EBV infection, data showed positive EBV EA (n = 15), EBV-EBNA (n = 15) and EBV-VCA (n = 17). From the histology status, the participants were dominated by WHO type III with 14 patients.
Differential expression
Clinical and pathological patient characteristics at diagnosis are summarized in Table 1. Analysis of relative expression using GenEx identified 20 of the most differentially deregulated miRNAs (details see Table 2). From these results, 10 miRNAs were down expressions (p < 0.0001), and 10 were up expressions (p < 0001). The heatmap of differentially expressed microRNAs is presented in Fig. 1.
miRNAs expression in the circulation of 17 NPC patients with complete response after chemoradiotherapy showed an inverse expression of 20 microRNAs before and after receiving therapy (Fig. 2). Five miRNAs including miR-483-5p, miR-584-5p, miR-122-5p, miR-7-5p, and miR150-5p were upregulated. Another 5 miRNAs including miR-421, miR-133a-5p, miR-18a-5p, miR-106b-3p, and miR-339-5p were downregulated. Sensitivity and specificity analyzes were performed on 10 miRNAs that consistently experienced changes in expression after receiving chemotherapy. The analysis showed that 10 miRNAs could be suggested as candidates for assessing the response to chemoradiotherapy in NPC patients using circulating miRNAs. ROC analysis shows the AUC (Area Under Curve) value > 0.9 with a 99% confidence interval with a significance value of p < 0.0001 (can be seen in Fig. 3).
Mechanism signaling pathways
The identification of potential biological mechanisms affected by the miRNAs expression dysregulation after chemoradiotherapy was carried out using IPA (Ingenuity Analysis Pathways). Differential expression of Fig. 1. Profile expression with a high significance p-value (p < 0.0001) using cancer focus microRNAs panel from circulating pretreatment (non-chemo-radiotherapy) and chemo-radiotherapy in NPC.
deregulated miRNAs by analysis using IPA showed several impacts of cellular mechanisms with p-value <0.01 category with activation zscore of − 1.131 -2.256. Significant changes due to the impact of chemotherapy and radiotherapy affect the mechanism of cellular death and survival involved in the necrosis, apoptosis, cell viability, and cell death of carcinoma cell lines (Table 3). 13 downregulated miRNAs were involved in the cell viability processes. Six miRNAs were involved in cell death regulation, 23 miRNAs were associated with the biological pathways of apoptosis, and 25 miRNAs were involved in cell necrosis processes.
Table 3
Mechanism cellular analysis from profiling circulating expression of microRNAs in NPC after receiving chemo-radio therapy. potential targeted treatment [21]. The low number of patients to achieve a complete response makes it interesting to know the molecular changes. One molecule that is known to be differentially regulated in response to the changes in cellular activities such as therapy is microRNA (miRNA). MicroRNA is responsive to the stress-like effect on hypoxia. Biological processes such as cell proliferation, apoptosis, and tumorigenesis may provide a general overview of the microenvironment affecting miRNA expression [22,23]. Therefore, discovering candidate biomarkers based on minimally invasive is expected to provide a new approach to assessing the success of treatment to increase efforts for treatment success [24]. miRNAs are molecules that play an essential role as posttranscriptional regulators by targeting hundreds of mRNAs and are involved in many disease cases, including cancer. Previous studies have reported that miRNA expression is associated with several chemotherapy responses and resistance events in esophageal cancer [14], Oral squamous cell carcinoma [25], breast cancer [19], and lung cancer [26]. In another report on nasopharyngeal carcinoma, miR-324-3p and miR-519d are deregulated by inhibiting gene translation targeting WNT2B [27] and PDRG1 [28] toward radiotherapy sensitivity. In addition, mIR-29c is known to be jointly sensitive to cisplatin-based radiotherapy and chemotherapy [18,29,30].
This study found 10 miRNAs that stably and consistently changed circulating expression after achieving a complete response to chemoradiotherapy ( Figs. 1 and 2). It consisted of 5 miRNAs that significantly increased expression, namely miR-483-5p, miR-584-5p, miR-122-5p, miR-7-5p, and miR-150-5p, and 5 miRNAs that had decreased expression. Significantly, namely miR-421, miR-133a-3p, miR-18a-5p, miR-106b-3p and miR-339-5p. In the previous study, Changes in the expression response of miRNAs to therapy are closely related to the type of therapeutic agent given to influence cellular mechanisms and the response by the body [31]. It is shown by giving 5-FU to chemosensitive affect miR-494 expression and chemoresistance to miR-200c in colorectal cancer.
To further investigate the mechanism regulation of miRNAs of this nasopharyngeal carcinoma, expressions with significance miRNAs 4 cellular regulation of cell death and survival related to chemotherapy and radiotherapy treatment. We found alterations of miRNAs related to necrosis, apoptosis, cell viability, and cell death of carcinoma cell lines. Although the mechanism analysis is related to the chemoradiotherapy response in nasopharyngeal carcinoma cases, the direct relationship with miRNAs is unclear.
There are several limitations to this study. We used a small sample size for miRNAs profiling: low survival rate and irregular treatment schedule due to unvalidated with a large cohort. Alterations expression of miRNAs on circulating can be detected by bioinformatics approaches and correlated with molecular biology change after receiving the chemoradiotherapy. Even though confirmation using the biological model and validating the differential expression of miRNAs is necessary for evaluation. Therefore, further research is needed to explain more comprehensively the mechanism and validation with a large number of samples. Meanwhile, few studies are studying the function of microRNA and circulation in the sensitivity of tumor treatment and the complete response to chemoradiotherapy for nasopharyngeal carcinoma.
Conclusions
In conclusion, research focused on studying changes in circulating miRNA expression in response to a combination of radiotherapy and chemotherapy is relatively limited. In this study, we created a signature profile associated with these conditions. As a result we found increased expression of miR-483-5p, miR-584-5p, miR-122-5p, miR-7-5p and miR-150-5p. In contrast, decreased expression was found in miR-421, miR-133a-3p, miR-18a-5p, miR-106b-3p and miR-339-5p.In the future, our study will validate with a larger sample size to determine the sensitivity and specificity of the obtained chemoradiotherapy biomarker candidate miRNAs.
Declaration of competing interest
The authors state that there is no conflict of interest in this study.
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Assessing Consumer Willingness to Pay for Nutritional Information Using a Dietary App
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Double Soft Graviton Theorems and BMS Symmetries
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"Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-S(...TRUNCATED)
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Sex differences in the human peripheral blood transcriptome
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"Occupational radiation exposure to nursing staff during cardiovascular fluoroscopic procedures: A r(...TRUNCATED)
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End of preview. Expand
in Data Studio
Introducing Pes2oX Full Text, a transformed dataset derived from the original Allen AI's Pes2o dataset. Our focus in this dataset was to restructure and reorganize the original Pes2o dataset. This was done to make it more accessible to research groups in terms of using it for training Artificial Intelligence models and fine-tuning for specific tasks within a particular domain.
Why was restructuring necessary?
After examining the original Pes2o dataset's structure, we found it necessary to restructure it. The full text was located immediately after 30 million abstracts, making it computationally intensive for anyone to extract the full text from Pes2o and use it for further training.
By our restructuring efforts, we've simplified the process of using the dataset, providing an out-of-the-box solution. Research groups now have the option to either stream the dataset from Hugging Face (HF) or download it directly, eliminating the need for a tedious extraction process. This streamlined approach allows researchers to get started quickly and efficiently.
Is the dataset similar to the original pes2o dataset?
Yes, we preserved the original pes2o dataset's structure and content. We avoided preprocessing for textual data cleaning to prevent unicode disruption, as some papers in the dataset are not in English.
Regrettably, due to schema and data-type discrepancies, 162 rows are absent from this dataset.
Dataset information
Index: Pes2o v2 dataset 2023
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8.2M
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