\n A mismatch of species thermal preferences to their environment may forewarn that some assemblages will undergo greater reorganization, extirpation, and possibly extinction, than others under climate change. Here, we examined the effects of regional warming on marine benthic species occupancy and assemblage composition over one-million-year time steps during the Early Jurassic. Thermal bias, the difference between modelled regional temperatures and species\u2019 long-term thermal optima, predicted species responses to warming in an escalatory order. Species that became extirpated or extinct tended to have cooler temperature preferences than immigrating species, while regionally persisting species fell midway. Larger regional changes in summer seawater temperatures (maximum\u2009+\u200910\u00b0C) strengthened the relationship between species thermal bias and the escalatory order of responses, which was also stronger for brachiopods than bivalves, but the relationship was overridden by severe seawater deoxygenation. At +\u20093\u00b0C seawater warming, our models estimate that around 5% of an assemblage\u2019s pre-existing benthic species was extirpated, and around one-fourth of the new assemblage being immigrated species. Our results validate thermal bias as an indicator of future extinction, persistence, and immigration of marine species under modern magnitudes of climate change.\n
\n\n \n climate change\n \n
\n\n \n extinction\n \n
\n\n \n community temperature index\n \n
\n\n \n niche\n \n
\n\n \n extirpation\n \n
\n\n A suitable temperature is one of the most commanding habitat requirements for species, especially at broad spatial scales\n \n 1\n \n . Human activity has set isotherms on the move globally\n \n 2,3\n \n , leading to poleward shifts of marine species\u2019 geographical ranges\n \n 4,5\n \n and species departing the tropics\n \n 6\n \n , with substantial repercussions for human well-being and ecosystems\n \n 7\n \n . Warming is projected into the coming centuries\n \n 8\n \n when climate change is anticipated to supplant land use change as the dominant driver of species extinction\n \n 9\n \n . However, species range shifts may leave clues to predict extinction risk\n \n 10\n \n . Range shifts are expected to begin with an extension of the leading edge, as a species arrives into new habitat\n \n 4,11\n \n , while trailing edge populations suffer performance decline. Marine heat waves can cause physiological stress to trailing edge populations\n \n 12\n \n sufficient to cause their extirpation\n \n 10,13\n \n . The proximity to a species\u2019 thermal niche edge should therefore indicate how a given population might react to warming\n \n 14,15\n \n , particularly for marine ectotherms, whose distributions tend to be closely associated to their thermal tolerances\n \n 16\n \n . Additional, future observations will provide greater predictive confidence but a species is irretrievable once extinct and climate-induced extirpations are already widespread\n \n 5\n \n . Rather than waiting for climate-induced extinction to manifest, the rich fossil record has great potential to explore links between climate-induced extirpations and global extinctions\n \n 17\n \n , especially given the recurring Earth system responses to a rapid addition of atmospheric CO\n \n 2\n \n \n 18,19\n \n .\n
\n\n Ecological assemblage change may be the rule over long time scales, allowing the fossil record to elucidate links between species range shifts, turnover, and extinction\n \n 20\n \n . Climate change is consistently tied to organism latitudinal range shifts and regional turnover, covering multiple marine fossil groups and time scales\n \n 21\u201323\n \n . Global warming also encourages seawater deoxygenation in both the modern and the past\n \n 18,24\n \n , which can either make populations more sensitive to warming\n \n 25\n \n or supersede the impacts of warming completely as anoxia\n \n 26\n \n . However, it remains unclear the degree to which fossil thermal preferences or niches can be associated during warming with the regional suitability or vulnerability of populations, species, and assemblages.\n
\n\n Thermal optima can be estimated for a species based on its geographical distribution (species temperature index, STI), which can be combined to estimate assemblage-level net preferences (community temperature index, CTI)\n \n 27,28\n \n . An STI or CTI falling behind environmental change signifies a thermal bias, the difference between species long-term median temperatures (STI) and ambient seawater temperatures\n \n 11,28\n \n . Thermal bias can indicate more populations to be further from their respective species thermal optimum, potentially making the assemblage more vulnerable to species turnover than others\n \n 11,28\n \n . In marine shallow-water fauna, assemblage thermal bias may even be more indicative of species loss than regional warming rates\n \n 28\n \n . Thermal bias, STI, CTI, and thermal niches are commonly used measures for species or community vulnerability under climate change. Although the thermal bias of fish species has been correlated with changes in their local abundance and occupancy\n \n 11\n \n , the wider validity of these metrics is rarely tested, especially at the assemblage level and their links to extinction risk.\n
\n\n We expect that, (A) under warming, species\u2019 occupancy responses are ordered with respect to, and dependent on their thermal bias (regression:\n \n response\n \n ~\n \n thermal_bias\n \n ). This means that regional immigrant species tend to have relatively warm thermal biases i.e., on average they have preferences for warmer waters than the ambient conditions, while extirpated species and those going extinct tend to have relatively cool thermal biases. Finally, persisting species tend to have relatively intermediate thermal biases. Species originating or going extinct could be considered the climaxes of this escalatory ordering of responses (originating\u2009=\u20091, immigrating\u2009=\u20092, persisting\u2009=\u20093, extirpated\u2009=\u20094, extinct\u2009=\u20095), which indicates how well-adapted a species was to the new environment. (B) Thermal determination of species occupancy response is stronger (hypothesis A regression slope becomes steeper) with greater regional climate change and for climate sensitive clades (brachiopods more sensitive to warming than bivalves\n \n e.g. 29\n \n ). (C) If a region is warmer than the thermal optima of many of its species (i.e. regional assemblage is cool-biased), further warming will summon extensive assemblage change. Conversely, a region with little assemblage thermal bias, or occupied by species with warmer optima than ambient temperatures (warm-biased assemblage), will change little under further warming. We test these expectations using mixed effects models to account for nested species, regions, and time zones. To guard inferences against changes in sampling intensity between time bins, we calculate regional rates of species immigration, persistence or extirpation as the numbers of two-timer species (see Methods). Extinctions and, for completeness, originations were identified by dataset-wide last or first appearance dates (LADs or FADs) of two-timer species.\n
\n\n Our study system consists of the bivalve, brachiopod, and gastropod species of the epicontinental seas adjacent to the north-western Tethys during an Early Jurassic extinction event\n \n 30\n \n . We identified major clusters of sampling and focus on these as discrete \u2018regions\u2019 (Fig.\n \n 1\n \n ), being similar in area to modern regions used to investigate thermal bias\n \n 31\n \n . The Late Pliensbachian to Early Toarcian interval of the Early Jurassic covers a transition from cool global temperatures, potentially with polar ice sheets\n \n 32,33\n \n , through rapid global warming with potential modern relevance\n \n 18\n \n , to stabilisation as a greenhouse climate. This can be generalised, at ammonite zone temporal resolution (mean\u2009=\u20091.1 myr), into the following phases (Fig.\n \n 2\n \n A): little change between the two Late Pliensbachian zonal means (\u2018cold stasis\u2019); warming into the earliest Toarcian zone (\u2018warming phase 1\u2019); further warming during the Toarcian Ocean Anoxic Event (T-OAE; \u2018warming phase 2\u2019); an initial continuation of peak warm conditions before cooling slightly (\u2018transitional phase\u2019, having the highest mean temperature); a stable, warm climate (\u2018warm stasis\u2019). We used literature estimates of CO\n \n 2\n \n concentrations, or geochemical proxies to indicate target seawater temperatures, for forcing the climate model, CLIMBER-X\n \n 34\n \n . We focus on the derived spatial variation in summer mean temperatures because maximum temperatures may drive species extirpation\n \n 13\n \n . We apply our models to estimate responses to +\u20093\u00b0C regional warming because, although there are many complications to apply paleobiological models to modern change, this value is projected under high emissions scenarios (RCP 8.5) by the end of the century in the North Sea\n \n 35\n \n .\n
\n\n
\n\n Over the two warming phases and the transitional phase, species\u2019 occupancy responses formed an escalatory order in relation to their thermal bias (Table\n \n 1\n \n ; Fig.\n \n 2\n \n C-E). The significance of this regression coefficient was robust to whether origination and extinction responses were included separately, treated respectively as immigrations and extirpations, or excluded entirely (Fig.\n \n 2\n \n , Tables S1 & S2). During these phases, negative (cool) thermal biases prevailed; immigrating species\u2019 thermal optima approximated ambient water temperatures (species mean thermal bias\u2009=\u2009+\u20090.5\u00b0C), whereas extirpated species had much cooler thermal biases of -4.3\u00b0C. This observed mean fell below the linear model expectation (thermal bias expectation for extirpated species = -3.0\u00b0C, conditional mean 95% CIs = -4.1\u2014-1.9\u00b0C), while all other response levels approximated linear expectations. Persisting species\u2019 thermal optima were significantly below local temperatures during the climate warming and transition phases (mean = -1.1\u00b0C), species going extinct had the coolest biases (mean = -5.0\u00b0C), and originating species had the warmest preferences (mean\u2009=\u2009+\u20091.8\u00b0C).\n
\n\n Thermal bias was a stronger predictor of species occupancy response than the climate model-derived magnitude of regional warming during the warming and transitional phases (Table\n \n 1\n \n ; when individually modelled as fixed effects,\n \n R\n \n \n \n 2\n \n \n \n \n marginal\n \n \n =\u20090.18 vs.\n \n R\n \n \n \n 2\n \n \n \n \n marginal\n \n \n =\u20090.004, respectively). These results were maintained under alternative CO\n \n 2\n \n and paleogeographical scenarios (Table S3), an alternative approach to control for sampling variation (Fig. S1), and no evidence could be found for impacts of changes in habitat substrate (Table S4). Changes in water depth over the first warming phase coincided with an apparent immigration event to Eastern Iberia (Tables S5 and S6) but the effect of thermal bias remained when this was accounted for, including if this region over the first warming phase was removed from analysis (see SM, Table S4).\n
\n\n Support for the escalatory relationship between species occupancy and thermal bias was spatiotemporally and taxonomically variable. Brachiopods were more affected than bivalves by the magnitude of regional warming and marginally so for their thermal bias (Table\n \n 1\n \n ). Greater regional warming strengthened the escalatory response of species to thermal bias, expressed by a steeper regression slope. This was best supported when sample sizes were larger (i.e. more species), representing better sampling but also more oxic conditions (Fig.\u00a03, S2). Specifically, in both phases of climatic stasis, there was no significant relationship between thermal bias and occupancy response (Fig.\n \n 2\n \n ). Support for the relationship was also weak to absent in the British basins and north of Iberia during the widespread bottom anoxia of the transitional phase, and throughout the Toarcian in the Germanic basins because of dwindling occurrences (Fig. S3). This was despite the northern regions (Germanic and British basins and north of Iberia) experiencing the largest warming magnitudes, with climate scenarios estimating\u2009+\u20097\u201410\u00b0C over the two combined warming phases (up to +\u200914\u00b0C in a less likely CO\n \n 2\n \n scenario). Following our climate modelling, the British and Germanic regions were initially the coolest at 19\u201421\u00b0C and warmed the least in the first warming phase (+\u20091.3\u00b0C and +\u20091\u00b0C, respectively), but experienced massive warming over the second warming phase (+\u20096\u20149\u00b0C and +\u20097.5\u00b0C, respectively, vs. +4\u20145\u00b0C in other regions; Fig. S3).\n
\n| \n | \n\n \n Value\n \n | \n \n \n S.E.\n \n | \n \n \n \n t\n \n \n | \n \n \n \n p\n \n \n | \n
|---|---|---|---|---|
| \n \n (Intercept)\n \n | \n \n \n 2.97\n \n | \n \n \n 0.11\n \n | \n \n \n 26.04\n \n | \n \n \n <\u20090.0001\n \n | \n
| \n \n Thermal bias \u00b0C\n \n | \n \n \n -0.09\n \n | \n \n \n 0.01\n \n | \n \n \n -10.00\n \n | \n \n \n <\u20090.0001\n \n | \n
| \n \n Regional temperature change \u00b0C\n \n | \n \n \n -0.03\n \n | \n \n \n 0.03\n \n | \n \n \n -1.19\n \n | \n \n \n 0.268\n \n | \n
| \n \n Clade_Rhynchonellata\n \n | \n \n \n -0.05\n \n | \n \n \n 0.07\n \n | \n \n \n -0.74\n \n | \n \n \n 0.458\n \n | \n
| \n \n Thermal bias:Clade Rhynchonellata\n \n | \n \n \n -0.03\n \n | \n \n \n 0.02\n \n | \n \n \n -1.89\n \n | \n \n \n 0.076\n \n | \n
| \n \n Regional temperature change:Clade Rhynchonellata\n \n | \n \n \n 0.06\n \n | \n \n \n 0.02\n \n | \n \n \n 3.19\n \n | \n \n \n 0.002\n \n | \n
\n Faunal responses to climate change are often measured or projected at the level of assemblage. To assess thermal bias at the assemblage level, we take the mean thermal bias over species regionally present before climate change and correlate it with the proportion of a given assemblage that were extirpated or went extinct, the proportion of species added via immigration or origination, and the overall turnover. Over all climate phases combined, assemblages accumulated thermal bias moderately as the ambient temperature changed, adding \u2212\u20090.41\u00b0C thermal bias (95% Cis = -0.77\u2014-0.05\u00b0C) for each degree of warming, rather than maintaining perfect equilibrium (0\u00b0C thermal bias per degree) or not responding at all (-1\u00b0C thermal bias per degree; calculated by a mixed effect model between regional temperature change and thermal bias). Relationships were not different if assemblage thermal bias was weighted towards cool- or warm-adapted members of the assemblage (Table S7).\n
\n\n Focussing on the climate warming and transition phases, a cooler assemblage thermal bias consistently increased the proportions of species changing, either going extinct, being extirpated, or subsequently immigrating or originating, but was only significantly correlated with an increase in originations (+\u20091.3% in the subsequent assemblage per \u2212\u20091\u00b0C assemblage thermal bias, 95% Cis\u2009=\u20090.6\u20142.0%; Fig.\n \n 4\n \n ). The magnitude of regional warming was significantly correlated with an increase in immigrating species as a proportion of the subsequent assemblage (+\u20098.5% per 1\u00b0C increase in water temperature, 95% Cis\u2009=\u20094.2\u201412.8%; Fig.\n \n 4\n \n ). The influence of regional warming magnitude on the escalating occupancy response (e.g. Figure 3) was supported at the assemblage level by the correlation between the proportion being extirpated and the proportion going extinct increasing to R\u2009=\u20090.73 (P\u2009=\u20090.006) during the climate warming and transition phases, up from R\u2009=\u20090.40 (P\u2009=\u20090.058) across all climatic phases (R values from mixed effect models of standardised variables). Meanwhile, the shares in a new assemblage of immigrated or originated species were moderately correlated both during the climate warming and transition phases (R\u2009=\u20090.49, P\u2009=\u20090.092) and across all phases (R\u2009=\u20090.45, P\u2009=\u20090.033; mixed effect models of standardised variables). No significant effect of changes in broad habitat substrate or water depth was found on assemblage level responses (Fig.\n \n 4\n \n , explored further in SM).\n
\n\n Projecting the models in Fig.\n \n 4\n \n to +\u20093\u00b0C seawater warming estimates that 4.74% (0.03\u20149.45%) of an assemblage\u2019s pre-existing benthic species to be extirpated and 25.5% (95% Cis\u2009=\u200912.5\u201438.4%) of a new assemblage to be newly immigrated (see SM \u2018Application of results\u2019).\n
\n\n Regional species loss is often correlated with climatic changes\n \n 13\n \n but considering climate change relative to species\u2019 thermal niches leverages additional information to assess population vulnerability\n \n 28,31\n \n (this study). We present empirical evidence from the fossil record that immigration, persistence, extirpation, and likely the extinction of species form an escalating response gradient linked to species suitability to regional conditions, as estimated by their thermal bias. A species\u2019 thermal bias is thus a useful attribute to predict its likely response to warming, though it is likely insufficient alone (e.g. here\n \n R\n \n 2\n \n \n = 18%), with magnitude of regional warming and taxonomic membership explaining additional variation. Thermal biases of individual species were highly variable across responses despite the spatiotemporal extent and focal species being well-sampled, which supports the validity of responses such as extirpation. In some times and regions, seawater anoxia likely overruled the importance of temperature in determining assemblage membership. The otherwise consistent temperature\u2013response relationship supports cautious use of habitat suitability models based on temperature, ideally alongside additional niche variables\n \n 31\n \n , to provide evidence on extinction risk as a statistical tendency over multiple species\n \n 38\n \n .\n
\n\n For simplicity, especially given the large time scales, we assumed a linear relationship between a species\u2019 thermal bias and its regional occupancy response, from immigration, through persistence, to extirpation\n \n detailed in 10\n \n , and potentially extinction. This assumed an open thermal landscape over which species could disperse, which was relatively well supported, though sampling was bookended between approximately 15 and 34\u00b0C (Fig. S4), with geographical barriers to the north (discussed below). Non-temperature habitat (and sampling) heterogeneity is likely to dominate at scales beneath our effective spatial resolution of 1000s km. As climate changes, species distributions should move through a region, following shifting isotherms according to their thermal tolerance, other habitat variables permitting (see below)\n \n 10\n \n . Though we observed occupancy responses, these are likely to have additional dimensions that also escalate according to thermal bias, such as larger body sized species being regionally replaced by smaller, opportunist species\n \n 39\n \n and being more likely to go extinct\n \n 29\n \n .\n
\n\n The influence of thermal bias on occupancy response was more pronounced in rhynchonellid brachiopods than bivalves, the latter having a lower mean vulnerability to extinction during rapid warming\n \n 29,39,40\n \n . Thermal performance has ecophysiological underpinnings\n \n 25,41,42\n \n , with some organisms having more specific or limited physiological adaptations. Evidence is mounting that different ecophysiological adaptations among taxa lead to different performance outcomes, including extinction risk\n \n 25\n \n , though quantitative comparisons of the thermal performance of brachiopods and bivalves are scarce\n \n 43\n \n . Our results therefore support the view that ecophysiology predisposes vulnerable taxa and traits to greater species immigration and extirpation at multiple scales, and their extinction risk is predictable via habitat loss\n \n 42,44,45\n \n . Groups vulnerable to warming may thus be more likely to show strong range shift responses, where habitat permits, such as bony fish\n \n 4,25\n \n , relying on escape rather than tolerance. Other vulnerable groups, such as reef corals, may be more restricted in their rate of habitat tracking (though see\n \n 22\n \n ). Identification of vulnerable clades or traits, alongside spatial projections of their habitat loss from physiological principles and environmental factors\n \n 42\n \n , may aid understanding of the pressures regional warming will place on species\n \n 44\u201346\n \n .\n
\n\n Species may avoid climate-induced extinction by colonising newly suitable habitat\n \n 13\n \n , hence marine fauna are expected to consistently trace their thermal preferences during climate change\n \n 21\n \n . Therefore, an escalating occupancy response in line with a species\u2019 thermal bias may be a null expectation for a region under warming\n \n 10,15\n \n , leading species with particularly cool thermal biases to be vulnerable to local and global extinction. However, there are modern examples of how disequilibria between ambient temperatures and a population or assemblage can be stable rather than a dispersal failure, especially when observed at finer spatiotemporal scales than sampled here\n \n 15,28\n \n . Even at our scales, we observed an especially large variation in thermal bias for persisting species, suggesting either that finer scale thermal heterogeneity played a substantial role in permitting species to persist (i.e. refuges), or that many species were temperature generalists. Nevertheless, our finding that regional warming increased the slope of the relationships between thermal bias and response implies that greater magnitudes of warming on average increase the cost of disequilibria between species and climate. Furthermore, the overwhelming negativity of thermal biases across responses during warming phase 2, which coincided with the highest ratio of extirpations and extinctions to persisting species, may also have been amplified by the warming on-top-of warming climatic context, which increases extinction risk\n \n 47\n \n .\n
\n\n The largely overlapping thermal bias values for species being extirpated and going extinct, as observed here, may betray a cause of extinction. The mean thermal bias for extirpated species fell below the expectations of our linear model and instead fell within 95% confidence intervals for species going extinct. Meanwhile, observed thermal biases for immigrating and originating species aligned well with linear expectations. Either the thermal bias values for extirpations were unusually high, or the thermal bias values of the extinctions were unusually low. The first option assumes the true relationship was linear and thus the thermal bias expectations for extinctions were valid, but anomalous values for extirpated species alone are difficult to explain. The second option implies a non-linear true relationship, with the thermal bias values for extirpated species being valid but there being more extinctions observed than expected given their thermal bias. Given the anoxia of northern waters of the north-western Tethys, especially during the T-OAE (discussed below)\n \n 26\n \n , we suspect the anoxia overruled temperature in habitat suitability, leaving species going extinct with unusually low observed thermal bias values. Poor dispersal capabilities and/or dispersal barriers can lead to a species\u2019 failure to lessen its population thermal biases by shifting distributions, thereby shrinking its geographical range\n \n 48\n \n , and making it vulnerable to global extinction\n \n 45,49\n \n . Mechanisms of and limitations to habitat tracking should be explored during other intervals with changes in climate, sea level, and geography\n \n e.g. 49\n \n .\n
\n\n The well-sampled, oceanic-influenced regional clusters of north and east of Iberia best supported thermal determination of assemblage membership, where any deoxygenation prior to the T-OAE\n \n 50\n \n apparently did not preclude a signal of thermal bias. Analyses of well-oxygenated environments such as outcrops from the south-west of Europe implicate Early Toarcian warming as the main regional driver of species loss, changes in bivalve-brachiopod assemblage structure, and their body size\n \n 39,40,51\n \n . During peak T-OAE (\u2018warming 2\u2019 into \u2018transitional\u2019 phases), support for thermal determination of assemblage membership dwindled in the Germanic and British regions alongside the number of occurrences. Although aquatic deoxygenation can amplify the influence of warming on ectotherm performance\n \n 25,52\n \n , bottom water anoxia is likely to supersede the ecological influence of increased temperature. Several regions during the T-OAE are characterized by black shale deposition, where hypoxic and anoxic waters have long been associated with faunal turnover and extinctions\n \n 26\n \n . Accordingly, benthic macrofaunal recovery only began after seafloor ventilation resumed, and remained incomplete in the British region by the end of our study\n \n 53\n \n . During the T-OAE, the northern waters may have essentially been unavailable as habitat for species tracing their thermal niche. This may exemplify how species ranges can be compressed as they trace thermal preferences. Although fully marine (see Table S8), the more restricted northern waters likely had greater terrestrial influence, such that bottom-water anoxia was probably dependent on productivity, as nutrients were delivered from warming-enhanced weathering\n \n 54\n \n , rather than simply temperature-dependent deoxygenation. The HadCM3 model estimated slightly lower salinity in the Germanic and British basins\n \n also 55\n \n , ranging between 33.3 and 34.6ppt across scenarios, than the other regions, while salinity was always highest east of Iberia, ranging between 34 and 35.6ppt (see SM). The semi-enclosed setting, especially of the Germanic and British basins, also likely increased the influence of local processes that global models are unlikely to capture, with the reality likely being warmer and more seasonal than estimated by our models\n \n 56\n \n . Alongside changes in sea-level-dependent seafloor ventilation\n \n 53\n \n , water density differences from freshwater input may have also encouraged stratification\n \n 55,57\n \n . Enhanced capacity to model biochemical processes and extract variables, such as oxygen levels, should expand the ability of predictive models to account for additional niche requirements. While modern oxygen minimum zones continue to spread\n \n 24\n \n , our results show how regional-scale processes can complicate the predictability of assemblage responses to temperature change.\n
\n\n Besides temperature and salinity, other habitat requirements for a benthic species include suitable water depth and substrate conditions, which also dictate the conditions under which a species can be sampled. The northern regions were the only ones dominated by siliciclastic substrates, which could have blocked the immigration (alongside anoxia, see previous paragraph) of carbonate-affinity species. The largest and most consistent non-temperature change occurred at the Spinatum-Tenuicostatum transition, when substantial sea-level rise\n \n 33\n \n led to increases in the frequency of deep habitat occurrences from 20\u201350% to 90\u201396% regional share. However, species thermal bias remained highly significantly associated with its occupancy response through different statistical treatments to explore the importance of this spatiotemporal scenario (see Tables S4 and S5). Being 100s km across, our regions tended to cover substantial substrate and depth variation, such that finer scale analyses may be needed to detect the influence of non-temperature habitat variables. Our focus on two-timer species also emphasised longer-term changes of the more common and better-preserved species, of which our analyses support temperature change being a key driver at broad spatial scales.\n
\n\n Temporal and spatial resolutions in our study were ~\u20091\u00a0million years and ~\u20092000 km respectively, which need appreciation to compare our results with other studies. Finer scale variations were averaged out, such as the warming at the Pliensbachian\u2013Toarcian stage-boundary\n \n 58\n \n , although permanent ecological changes such as extinctions from short-term pulses remain. Despite the myriad of factors influencing a species occurrence at fine spatial scales, climate is expected to be one of the dominating factors at broader scales\n \n 15,59\n \n . Significant effects of thermal bias have been assessed for modern assemblages at spatial scales from surveyed sites\n \n 11\n \n to biogeographic \u2018ecoregions\u2019, more similarly sized to our regions\n \n 28,59\n \n . At intermediate spatial scales, Flanagan et al.\n \n 31\n \n found larger thermal biases of fish assemblages over decadal scales than inter-annual scales, which might encourage expectations that marine communities rapidly maintain equilibrium with temperature, despite evidence often to the contrary\n \n 31\n \n . At much longer time scales and with spatially coarse temperature estimates, our data also supported equilibrium between assemblage mean thermal optima (CTI) and environment temperatures (Fig. S5). However, geographical context affected observations of thermal equilibrium in a study of planktonic foraminifera over thousands of years: mid latitude assemblages tracked climate change by turnover, but decreasing assemblage turnover at high latitudes under warming and low latitudes under cooling accumulated assemblage thermal bias\n \n 23\n \n . Regions of high climate velocity, such as the tropics and poles, are likely to demand faster species\u2019 niche-tracking than lower climate velocity regions, which is more likely to push populations of multiple species nearer to their thermal niche edges\n \n 45\n \n . However, increasing thermal bias may only increase extirpations and extinctions when changes exceed species recent climatic experience\n \n 47\n \n . Temporal resolution is not a problem per se for the application of paleontological insights to modern issues\n \n 17\n \n , but limits the mechanisms for which we can observe evidence. Future work should be directed to understanding the mechanisms underlying observed palaeontological patterns and the transferability of those mechanisms to modern climate change and the current biodiversity crisis\n \n 17\n \n .\n
\n\n Based on our results for the northwestern Tethys, we expect regional species extirpations and especially immigrations to be already considerable (~\u20095% and ~\u200926%, respectively) at +\u20093\u00b0C warming, such as forecast under high emissions scenarios (RCP 8.5) by the end of the century for the North Sea\n \n 35\n \n . These extirpation and immigration values are similar to projections of a paleo-validated biodiversity model for the shelf seas of Europe by 2100\n \n 60\n \n . Although an application of our results to modern warming ignores the very different time scales (=\u2009observed rates of change), the loss of a species\u2019 thermally suitable habitat can respond directly to the magnitude of warming, regardless of the rate of warming, such as supported by empirical patterns of high latitude extinctions during hyperthermal events\n \n 45\n \n . Rates of ancient climate changes may have been sufficiently slow for most species to track habitat availability but the extremely rapid anthropogenic rates of change are likely to divide response severity between species with greater and lesser dispersal abilities\n \n 45\n \n . This may be especially the case in the tropics where climate velocities are highest\n \n 61\n \n , leaving paleobiological extrapolations most likely as underestimates.\n
\n\n Rare species, both range-restricted or locally uncommon, are unlikely to make it into the fossil record and thereby into our analysis. If rare species are at higher extinction and extirpation risk or tend to have narrower thermal tolerances, the overall magnitude of assemblage change including rare species can be expected to be higher than we predict. Again, this implies that inferences based on paleobiology will tend to give underestimates of whole community responses.\n
\n\n We show a distinct relationship between the thermal suitability of Jurassic benthic species for its occupied region and its occupancy changes in that region during warming, which aggregated to substantial assemblage-level responses. Species thermal bias provides more information than the magnitude of regional warming alone and thus can be a stronger predictor of species extirpation, persistence, or immigration. Temperature-focused models may be less effective at finer (more local) spatial scales, where additional habitat variables may become more important, and in semi-enclosed coastal waters, which may be more inclined to anoxia. Predictions may be further refined by species-specific modelling and using climate models that handle processes at regional or finer scales, such as tidal mixing, where permitted by reliable, high resolution paleogeographic reconstruction. Our results support that greater magnitudes of warming tend to increase the cost of disequilibria between species and climate, increasing the rate of extirpation and extinction, especially if thermal habitat loss is not replaced elsewhere. Meanwhile, ambient warming was most clearly linked to species immigrations. Given potentially unprecedented modern rates of global warming\n \n 62,63\n \n , paleobiology likely presents conservative warnings of future changes in marine species\u2019 regional occupancy.\n
\n\n We focus on the climate changes from the cool Late Pliensbachian to the warm Early and Middle Toarcian (Early Jurassic), covering the hyperthermal Toarcian Ocean Anoxic Event (T-OAE), when some ocean basins became anoxic. We used the finest regionally-consistent temporal resolution for our occurrence data, the ammonite zone (the Serpentinum Zone was further split into Exaratum and Falciferum subzones; Table\n \n 2\n \n ; mean 1.1 myr), at which the literature on temperature proxies was used to estimate local climates (particularly M\u00fcller et al.\n \n 64\n \n and Ullmann et al.\n \n 65\n \n ; more detail in SM Methods, \u2018Climate conditions of our major time steps\u2019). After the cool, low-CO\n \n 2\n \n Late Pliensbachian Margaritatus and Spinatum zones, the Early Toarcian was associated with the release of greenhouse gases from the intense volcanism of the Karoo-Ferrar magmatic province\n \n 66\u201368\n \n . Emplacement of the Karoo-Ferrar large igneous province occurred over ~\u20099\u00a0million years between 183.4 and 176.8 Ma, with bulk magmatism occurring from ~\u2009183.4 to ~\u2009183.0 Ma, coinciding with the T-OAE\n \n 69\n \n . Note that we consider the T-OAE to be equivalent in time to the well-known negative excursion of carbon isotopes (see Erba et al.\n \n 70\n \n for discussion and alternative definitions). Analyses of thallium isotopes suggest that global marine deoxygenation of ocean water started sooner\n \n 50\n \n , alongside rapid, short-lived warming across the Pliensbachian/Toarcian boundary\n \n 66\n \n at ~\u2009184 Ma\n \n 71\n \n . The Tenuicostatum Zone of the earliest Toarcian remained on average warmer than the Late Pliensbachian. Further warming in the T-OAE proper of the Exaratum subzone, possibly as the consequence of a rapid release of thermogenic and/or biogenic methane adding to the volcanic CO\n \n 2\n \n release, is associated with the main extinction phase\n \n 72,73\n \n . After this peak of warming and CO\n \n 2\n \n concentrations, the Falciferum Zone represents a transitional climate, starting warm but later cooling to a level warmer than the Tenuicostatum Zone\n \n 64\n \n , which is maintained into the Bifrons Zone.\n
\n\n Our regional focus follows a roughly north-south trending oceanic transect from Scotland via the western European epicontinental sea to north-western Tethys including Morocco, Tunisia, and Algeria (Fig.\n \n 1\n \n ). Terrestrial influence (nutrients, turbidity, freshwater input) was higher in northern, more restricted water bodies, especially the Cleveland Basin\n \n 26\n \n , with less mixing and less oxygenation of bottom waters\n \n 55,74\n \n . This is particularly expressed during the Exaratum subzone (T-OAE proper) when sites in England and Germany are dominated (though not completely) by hypoxic to anoxic sediments, while other basins were less affected by deoxygenation.\n
\n\n CO\n \n 2\n \n scenarios per ammonite zone were either allocated directly, where CO\n \n 2\n \n estimates were available (Tenuicostatum and Exaratum sub/zones)\n \n 33,72\n \n , or indirectly based on approximating relative temperature change estimates, especially M\u00fcller et al.\n \n 64\n \n and Ullmann et al.\n \n 65\n \n , which together traced relative temperature change via oxygen isotopes over our whole temporal duration. Temperature changes output by the CLIMBER-X climate model were then checked against proxy temperature changes at the appropriate paleocoordinates and water depth (see SM). Secondary CO\n \n 2\n \n scenarios were based on maximum possible temperature changes (Table\n \n 2\n \n ; see SM section \u2018Climate conditions of our major time steps\u2019 for a wider discussion of the evidence).\n
\n\n We ran equilibrium climate simulations at fixed pCO\n \n 2\n \n scenarios using the CLIMBER-X Earth-system model\n \n 34\n \n . CLIMBER-X is particularly useful as a fast and flexible paleoclimate model and provides simulated temperatures in the ocean and atmosphere on a 5\u00b0x5\u00b0 horizontal grid, among other parameters. Early Jurassic boundary conditions were represented by a reduced solar constant (1340.5 W/m\u00b2). For the model paleogeography, we used the bathymetric topography of Kocsis & Scotese\n \n 36\n \n , which matched the coastline to marine occurrences in the Paleobiology Database (see below), primarily using the Toarcian map (180 Ma) and secondarily using the Pliensbachian (185 Ma). Deep seafloor depth was set to -3700m, shallow marine / continental shelf to -200m, and land to +\u2009200m. Local shelf features are not well represented in these reconstructions and the coarse resolution model results are not expected to be perfect, but we expect the derived niche estimates to be better than a simple dependence on paleolatitude. We also downloaded the sea surface temperature maps simulated with the HadCM3 model, though these were limited to CO\n \n 2\n \n scenarios of 560 and 950 ppm\n \n 75\n \n . Despite being affected by similar limitations, HadCM3 is a more complex and highly resolved model than CLIMBER-X, and its outputs were used as a benchmark. This supported the upscaling of the July mean temperature maps from CLIMBER-X to the finer spatial resolution of the HadCM3 maps via bilinear interpolation. In general, correlations between the two models were high (Rho\u2009\u2265\u20090.8) with a root mean square error (RMSE) that increased, as expected, as the modelled CO\n \n 2\n \n scenarios deviated (see SM section \u2018Climate model (dis)agreement\u2019, Tables S9, S10, Figs. S6, S7). While CLIMBER-X has an equilibrium climate sensitivity close to the best estimate of 3\u00b0C per pCO\n \n 2\n \n doubling\n \n 2\n \n , the HadCM3 model is more sensitive and generally yields higher temperatures at elevated CO\n \n 2\n \n levels.\n
\n| \n \n Ammonite (sub)zone\n \n | \n \n \n Main CO\n \n 2\n \n scenario (ppm)\n \n | \n \n \n Secondary CO\n \n 2\n \n scenario (ppm)\n \n | \n \n \n Notes\n \n | \n
|---|---|---|---|
| \n \n Bifrons\n \n | \n \n \n 750\n \n | \n \n \n 750\n \n | \n \n \n Outputs should be warmer than Tenuicostatum*\n \n | \n
| \n \n Falciferum\n \n | \n \n \n 750\n \n | \n \n \n 1000\n \n | \n \n \n Outputs should be warmer than Tenuicostatum*\n \n | \n
| \n \n Exaratum\n \n | \n \n \n 1000\n \n | \n \n \n 1250, 1500\n \n | \n \n \n 1000 as low estimate. 1500 as peak estimate\n \n | \n
| \n \n Tenuicostatum\n \n | \n \n \n 500\n \n | \n \n \n 500\n \n | \n \n \n Secondary scenario with Pliensbachian map\n \n | \n
| \n \n Spinatum\n \n | \n \n \n 400\n \n | \n \n \n 300\n \n | \n \n \n 300 as cold estimate\n \n | \n
| \n \n Margaritatus\n \n | \n \n \n 400\n \n | \n \n \n 300\n \n | \n \n \n 300 as cold estimate\n \n | \n
\n *Following oxygen isotopes covering our temporal range in M\u00fcller et al.\n \n 64\n \n or Ullmann et al.\n \n 65\n \n .\n
\n\n On 24th May 2022, we downloaded marine-only occurrences of bivalves, gastropods and brachiopods from the Paleobiology Database (PaleoDB,\n \n \n https://paleobiodb.org/\n \n \n ), representing benthic assemblages, and binned them to stratigraphic stages using R package divDyn\n \n 76\n \n . Our analyses used species-level occurrences, but occurrences initially had to be accepted at least at the genus level. They also required modern geographical coordinates, which were used for paleogeographical rotation and for isolating north-west Tethys occurrences by a bounding box around modern Europe, east-west from Turkey to Portugal, and north-south from Scotland to the Mediterranean coast of Africa. Confidently identified species names that were taxonomically unaccepted by the PaleoDB underwent automatic checks for spelling mistakes. Of these, persistent unaccepted species names of the Pliensbachian and Toarcian were then taxonomically vetted by M. Aberhan to catch more accepted species occurrences and prevent artifacts in geographic distribution patterns, such as synonymous species names. To achieve ammonite (sub-)zone temporal resolution, we explored PaleoDB download columns \u2018early_interval\u2019, \u2018zone\u2019, and \u2018stratcomments\u2019 for temporal resolution information, especially ammonite zone or subzone allocation. Some data-rich entered references were investigated manually for lacking temporal, paleoenvironmental or lithological information (see R code in SM). A separate, global dataset was used to establish species\u2019 First and Last Appearance Dates (FADs and LADs), ideally at ammonite (sub-)zone resolution, within the Pliensbachian and Toarcian stages.\n
\n\n Determination of species\u2019 thermal preferences may be confounded if species have significant affinities for particular substrate or bathymetric paleoenvironments. Substrate or bathymetric categories were combined using the keys in divDyn, before environmental affinities were tested for using binomial tests with alpha\u2009=\u20090.1 (function \u2018affinity\u2019 in divDyn)\n \n 76\n \n .\n
\n\n Temperature estimates were sampled per taxon occurrence from modeled seawater temperature paleogeographical maps from 180 Ma (Toarcian, primary scenario) and 185 Ma (Pliensbachian, secondary scenario) separately. This avoided switching between maps in the same analytical time series, which could result in a sudden, artificial shift in paleocoordinates and influencing the thermal bias. Accordingly, we reconstructed coordinates and coastlines using the\n \n rgplates\n \n interface\n \n 77\n \n to Gplates v2.3\n \n 78\n \n to both Toarcian and Pliensbachian rotations as separate columns, based on the PaleoMAP model\n \n 36\n \n .\n
\n\n Spatial clusters of sampling, or \u2018regions\u2019, were expected to be more similar in mean temperature and species composition within than among clusters per time zone. The species recorded in each of these clusters per time zone then became the spatiotemporally cohesive \u2018assemblage\u2019 of interest (analogous to quantification of thermal bias for spatiotemporally cohesive sampling transects in\n \n 11\n \n ). Collections were pooled into unique spatial coordinates per time zone. Objective and non-overlapping clusters were identified using hierarchical clustering of Euclidean distance matrices of occurrence paleocoordinates of all time zones pooled. We expected these clusters to arise mainly from sampling patterns, given that they use no ecological data, but separate assemblages should ideally be ecologically distinct, having more differences between them than within them. To assess ecological similarity among the clusters defined by Euclidean distance of coordinates, we also estimated groupings of late Pliensbachian occurrences by hierarchical clustering of Jaccard distance matrices of species presence/\u2019absence\u2019 (i.e. using ecological co-occurrence but ignoring spatial coordinates). Jaccard distance clusters with less than 14 species were removed to balance the tendency of small samples to drive dissimilarity (via species absences) against persistent and more relevant, larger groupings. Ecological clusters validated the use of the separately identified spatial clusters as distinct species assemblages, such as from separate bodies of water or habitat. Adopting ten spatial clusters maximised the agreement between the two approaches.\n
\n\n Finally, practical requirements for spatial clusters included (\n \n 1\n \n ) being sampled in different time steps, ideally throughout, and (\n \n 2\n \n ) having sufficient occurrences. This was the case for four of the ten spatial clusters: the northern and most likely terrestrially influenced British basins cluster, and three clusters surrounding the landmass of Iberia: to the west, to the north, and to the east (likely to be the most pelagic influenced cluster). The benthic fauna of a fifth, Germanic cluster were well-sampled in the late Pliensbachian, but not in the early Toarcian. However, its outcrops are exposed throughout our temporal focus, suggesting that species absences were driven by anoxic bottom waters rather than by poor sampling, so this cluster was also used for analysis. Clusters had different thermal regimes (see Results) and variables like terrestrial influence (see Discussion).\n
\n\n As a precaution against spurious features of sampling patterns (see Fig. S8), we focus on comparing numbers of regional two-timer species, that is, species that were observed in a region for at least two time bins consecutively (Fig.\u00a05)\n \n 79\n \n . These are the better-sampled species, whose observed responses may be more reliable. The same can be done using three-timers (species must be observed in a region for three time bins consecutively; see Supplementary Methods, Fig. S9, and Supplementary Results). However, using two-timers has the advantage that the temporal focus of change is a single boundary between two time bins, which fits understanding of the timing of the climatic changes investigated here, rather than change over a central bin and both of its demarcating boundaries for three-timers. The well-sampled nature of two-timers and high sampling completeness of the focal ammonite (sub)zones of European regions for this interval means the observed times of extinction, extirpation, immigration or origination are relatively reliable (e.g. against Signor-Lipps effect).\n
\n\n Focussing on cluster two-timers (Fig.\u00a05), immigrating species were those observed in the cluster in time i\u2009+\u20091 AND time i\u2009+\u20092 but not in i. Originating species were the same but also had their dataset-wide First Appearance Date (FAD) in time i\u2009+\u20091. Extirpated species were those similarly observed in the cluster in time i AND time i \u2013 1 but not in i\u2009+\u20091, with those having time i as their Last Appearance Date (LAD) were classed as going extinct. Persisting species were observed in the cluster in times i AND time i\u2009+\u20091. There were fewer occurrences before the first time bin (i.e. in the Davoei zone, which preceded Margaritatus) and after the last bin (in the Variabilis zone, which followed Bifrons), limiting the quantity of cluster two-timer species, so their two-timers were simply required to have a presence in times i \u2013 1 and i\u2009+\u20092, respectively, regardless of spatial cluster. Species still needed a cluster occurrence around the focal boundary, either in time i or i\u2009+\u20091, to be assigned a response category (e.g. extirpated), so this step did not artificially increase numbers of species in any response category, but simply allowed more species to pass the sampling threshold in the earliest and latest time bins. Note that in all cases, due to incomplete sampling, extirpation and immigration are probabilistic events rather than definite.\n
\n\n Two-timers without a LAD or FAD have occurrences in the future and past, respectively, of time i, such that their species thermal niche is averaged over past and future distributions. Meanwhile, the thermal niches of extinct and originating species were inherently limited to only past or only future distributions, respectively. To address the potential criticism of extinct and originating species having a fixed thermal niche, we focus our analysis on extirpation, immigration and persistence responses, and only secondarily including extinctions and originations.\n
\n\n Analyses were separate between species and assemblage levels. A species\u2019 thermal bias was defined as the difference between the cluster median temperature for a time zone and the species\u2019 thermal median (temperatures averaged over all zone-level occurrences of the species from the Margaritatus to Bifrons zones, the complete interval when occurrences were matched to temperature maps). An assemblage thermal bias, often assumed to indicate net vulnerability, was thus the difference between the median of the constituent species\u2019 thermal medians\n \n 11,27\n \n and the cluster median temperature for a time zone.\n
\n\n We expected an escalatory order of occupancy responses relative to thermal bias in a warming scenario (Fig.\u00a05), with extinct and extirpated species at one extreme having relatively cool thermal biases, originating or immigrating species at the other extreme having relatively warm thermal biases, and persisting species having relatively intermediate thermal biases. Species-level regressions therefore used species occupancy response as an ordered continuous dependent variable and species thermal bias as a continuous independent variable, Occupancy_response\u2009~\u2009Thermal_bias. Mixed effects accounted for the nested analysis structure, where necessary, with species nested within clusters and clusters nested within time zones (i.e. a single species can have one response and thermal bias per cluster per time zone, a single cluster can occur in multiple time zones). To guard against criticism that originating and extinct species\u2019 thermal niches were pre-decided (e.g. since species going extinct in time i can only have occurrences in the past relative to time i, when climates tended to be relatively colder in our study), we compare regression results with extinction or origination responses left out vs. included. Being at the extremes of the regression line, species originating or going extinct also have a stronger effect on the regression slope than persisting, extirpated (but surviving) or immigrating species (with past occurrences). To assess how much the observed thermal bias values for the different species occupancy responses deviated from linear expectations, the above regression equation was reversed into, Thermal_bias\u2009~\u2009Occupancy_response, to calculate thermal bias confidence intervals.\n
\n\n For assemblage-level analyses, we recorded the percentage of a current assemblage that was categorized at the species escalatory response levels of persisting, extirpated, or extinct, and the percentage of a new assemblage that was categorized as immigrating or originating. The turnover of the current assemblage into the new assemblage (i.e. from time i to i\u2009+\u20091) was also quantified by Jaccard distance. These were each used separately as dependent variables. Independent variables were assemblage thermal bias, regional temperature change magnitude, or the difference in occurrence proportions of occurrences from carbonate or offshore substrates (the most frequent substrate types). Here, mixed effect models nested clusters within time zones, but had a low sample size (5 clusters x 5 time zones\u2009=\u200925 assemblage data points maximum) and thus a weaker potential for inference. These regressions were applied in an exploratory framework akin to a correlation matrix to weigh evidence for further research. Models using assemblage thermal bias as an independent variable were inverse weighted for the standard deviation of species\u2019 thermal bias. We chose to apply these model expectations for regional species responses at a modern-relevant level of warming (+\u20093\u00b0C). All analyses were performed in R\n \n 80\n \n with packages\n \n divDyn\n \n v0.8.2,\n \n corrplot\n \n v0.92 to present assemblage-level analyses,\n \n nlme\n \n v3.1-162 for mixed effects models,\n \n vegan\n \n v2.6-4 for clustering\n \n 76,81,82\n \n .\n
\n\n Supplementary Materials (Supplementary Figures, Supplementary Tables, Supplementary Methods and Supplementary Results) are not available with this version.\n
\n\n Reporting Summary\n
\n \n\n Most animals follow distinct daily activity patterns reflecting their adaptations1, requirements, and interactions2-4. Specific communities provide specific opportunities and constraints to their members that further shape these patterns3,4. Here, we ask whether community-level diel activity patterns among long-separated biogeographic regions differ or converge and whether the resulting patterns indicate top-down (predation risk) or bottom-up processes (prey availability)? We estimated the diel activity of ground-dwelling and scansorial mammals in 16 protected areas across the tropics, using an extensive network of camera traps, and examined the relationship to body mass and trophic guild. We found that mammalian guilds exhibited consistent diel activity patterns across regions, indicating similar responses to similar evolutionary and ecological opportunities and constraints. Larger herbivores tended to be more nocturnal than smaller herbivores, whereas carnivores and omnivores showed the opposite pattern. Insectivores were exceptions, revealing regional differences in which larger insectivorous species were more nocturnal than smaller ones in the Afrotropical and Indo-Malayan regions, while the pattern reversed in the Neotropics. The consistent contrast between predators and prey suggests that diel activity within these communities is primarily determined by large predators and associated risk of predation.\n
\n \n\n Diel activity patterns\n \n \u2014\n \n the distribution of activity throughout the daily cycle\n \n \u2014\n \n are fundamental in animal ecology\n \n \n 5\n \n \n . These patterns reflect when organisms seek food, socialize, and perform other necessary tasks while also accounting for risks\n \n \n 1\n \n ,\n \n 2\n \n \n . Activity patterns vary among species. Some organisms may maintain activity over extended periods while others exhibit brief peaks\n \n \n 6\n \n \n . They may be predominantly active during the night (nocturnal), day (diurnal), twilight (crepuscular), or may lack pronounced peaks with relation to day and night (cathemeral). Furthermore, there can be substantial variation within species and between populations\n \n \n 6\n \n \n . Mammals illustrate a broad range of such behaviours.\n
\n\n While mammals today occupy all temporal niches (day, night, twilight), early mammal species are thought to have been primarily nocturnal to avoid the predation risk imposed by diurnal dinosaurs\u2014an idea known as the \u201cnocturnal bottleneck\u201d hypothesis\n \n \n 7\n \n \n . Following the extinction of the non-avian dinosaurs (66 Ma)\n \n \n 8\n \n \n , mammals diversified and adapted to fill the available temporal niches\n \n \n 7\n \n ,\n \n 9\n \n \n . Physiologic, morphological, and behavioural adaptations\n \n \n 9\n \n \n including endothermy, eye forms\n \n \n 10\n \n \n , and enhanced sensorial systems allowed mammals to thrive under the different illumination and temperatures associated with day and night.\n
\n\n Endothermy permits mammals to exploit multiple temporal niches\n \n \n 11\n \n ,\n \n 12\n \n \n . Nonetheless, species-specific physiological characteristics, in interaction with morphology (e.g., body size), may still favour activity schedules that moderate thermal stress\n \n \n 13\n \n \n . For instance, in the absence of other factors, large species in warm regions may be forced to avoid overheating by avoiding activity in the hottest periods\n \n \n 14\n \n \n . By contrast, small species that can lose heat rapidly may avoid cold and focus activity in warmer periods\n \n \n 15\n \n ,\n \n 16\n \n \n . Small mammals such as mice and rats avoid diurnal predation by favouring nocturnal activity but may nonetheless be active during the daytime due to food scarcity, low nighttime temperatures, or low risks from diurnal predation\n \n \n 1\n \n ,\n \n 17\n \n \n .\n
\n\n Species interactions may influence and control diel activity patterns within communities\n \n \n 3\n \n ,\n \n 4\n \n \n . For instance, predators may favour periods where their prey are active, whereas prey species may avoid periods when their predators are active\n \n \n 5\n \n ,\n \n 18\n \n \n . Potentially, this can involve both top-down or bottom-up processes\n \n \n 19\n \n \u2013\n \n 21\n \n \n . Bottom-up and top-down are key classifiers for the regulation of food web dynamics\n \n \n 19\n \n \u2013\n \n 21\n \n \n and have the potential to influence how species within an assemblage may behave\n \n \n 22\n \n \n . In a top-down process, the temporal activities of certain species (e.g., prey) seek to avoid the time use of others (e.g., predators)\n \n \n 23\n \n \n . For example, small carnivores may avoid activity in periods when they are more likely to encounter larger predators, with similar avoidance expected for prey species to avoid their predators\n \n \n 18\n \n ,\n \n 23\n \n \n . Alternatively, this can be a bottom-up process in which predator species match their activities to that of their prey or competitors\n \n \n 22\n \n \n . For instance, mesopredators in south-western Europe were found to match their activity to that of their prey\n \n \n 24\n \n \n . There is evidence for both bottom-up and top-down determination of activity patterns in a few sympatric species\n \n \n 22\n \n \u2013\n \n 24\n \n \n . Yet, we do not know the degree to which bottom-up and top-down processes operate in nature and whether the resulting patterns are consistent across regions.\n
\n\n Humid tropical forests provide a useful context for exploring these questions as the influence of seasonality is low and similar environmental conditions are found in biogeographically distinct regions\n \n \n 13\n \n \n . These forests encompass many of the most diverse and rich terrestrial biomes on earth and the maintenance of such diversity likely involves biotic interactions\n \n \n 25\n \n \n . Trophic composition of tropical forest mammal communities appears relatively consistent across tropical regions\n \n \n 26\n \n \n and has been attributed to convergent evolution, likely due to similarities in environment and adaptations across distant forests\n \n \n 27\n \n \n . We expect that the processes that shape trophic interactions and composition may also influence diel activity patterns.\n
\n\n We studied the daily activity patterns of ground-dwelling and scansorial mammals inhabiting protected tropical forests across the Neotropics, Afrotropics, and Indo-Malayan tropics (Fig.\n \n 1\n \n ). We used time-stamped images from standardized large-scale camera-trap surveys implemented by the Tropical Ecology Assessment and Monitoring (TEAM) Network in 16 protected areas (Table S1)\n \n \n 28\n \n \n . Using multinomial analysis, we investigated how diurnal, nocturnal, and crepuscular activity was related to trophic guild and body size and whether any such patterns were consistent among regions.\n
\n\n We tested three hypotheses (Fig.\n \n 2\n \n ). First, if top-down processes regulate the diel activity of animals in a community (H1), we predicted (1a) that prey species (e.g., herbivores) should exhibit diel activity patterns that avoid those of predators (e.g., carnivores and omnivores) of a similar size (interguild avoidance), and (1b) smaller members of a trophic guild (especially carnivores and omnivores) should exhibit diel activity patterns that avoid that of larger members of the same guild (intraguild avoidance). If bottom-up processes regulate the diel activity of animals in a community (H2), then (2) diel activity patterns of predators should match that of prey species (herbivores, insectivores, and small omnivores). Finally, if the energetic cost of thermoregulation constrains diel activity of tropical mammals (H3), then (3) large mammals should be more active during the night when it is colder and small mammals more active during the day when it is warmer.\n
\n\n
\n\n
\n\n We extracted the probability for the activity (0\u20131) during day, night, and twilight, and the correspondent upper (UCI) and lower (LCI) 95% confidence intervals for the given range of body mass and trophic guild derived from the multinomial model in every region with the lowest AIC. Diel activity was best modelled when including body mass, trophic guild, and their interaction for the three regions (Fig.\u00a03, Table S2).\n
\n\n \n Fig.\u00a03. Distribution of daily activity in relation to body size and trophic guilds of tropical ground-dwelling and scansorial mammals in three regions.\n \n Estimates correspond to the probability of activity during the day, night, and twilight extracted from the model fitted to TEAM camera-trap data. Tick marks above the x-axis indicate the typical body mass of species analysed. Colour hue indicates where the model interpolates among observations of the sizes presented (darker) versus extrapolates beyond values in the data for that trophic guild and region (lighter).\n
\n\n We found consistent patterns of diel activity in relation to trophic guild and body mass across regions (Fig.\u00a03, Fig. S1, Table S2) indicative of top-down processes playing a dominant role in shaping community activity patterns (H1). Following our prediction 1a, the interguild relationships between nocturnality and body mass showed contrasting patterns for predators (i.e., carnivores, omnivores) and prey (i.e., herbivores, and perhaps insectivores) indicating avoidance of predators by prey across regions. In general, larger prey species were nocturnal whereas larger predators were diurnal (Fig.\u00a03). For example, In the Neotropics, the highest diurnal probability for large predators was 0.64 (LCI:0.63, UCI:0.74, body mass\u2009=\u200996 kg). Herbivores (i.e., prey) were more likely to exhibit a high nocturnal activity as the body mass increased to a maximum probability of 0.60 (LCI:0.48, UCI:0.71, body mass\u2009=\u2009210 kg).\n
\n\n Among carnivores, we found a negative relationship between body mass and nocturnality supporting prediction 1b. Thus, small carnivores, which risk predation by larger carnivores\n \n \n 29\n \n \n , were more likely to be nocturnal than larger carnivores. For example, carnivores in the Afrotropics decreased nocturnality probability from 0.81 (LCI: 0.74, UCI:0.87, body mass\u2009=\u20091 kg) to 0.21 (LCI: 0.14, UCI: 0.28, body mass\u2009=\u200961 kg) as size increased. Such temporal partitioning has previously been identified as a strategy for mitigating intraguild predation among carnivores, thus aiding their coexistence\n \n 3,4,18,22,29\u221231\n \n . Finally, our analyses indicate that among both herbivores and insectivores, smaller species were more likely to be diurnal than larger species which we suggest is likely a consequence of avoiding small and medium-sized predators.\n
\n\n The high degree of diurnality among large carnivores evident in our study sites contrasts with reports from other forests, as in Madagascar and North America where carnivores were largely active at night\n \n \n 32\n \n ,\n \n 33\n \n \n . These previous studies focused on more anthropogenic landscapes, where carnivores appear to avoid interacting with humans by becoming more nocturnal\n \n \n 32\n \n \u2013\n \n 34\n \n \n . Our sites are within protected areas and therefore suffer lower human impacts than elsewhere and may permit greater diurnality.\n
\n\n While top-down processes appear to shape overall activity patterns within each community, notable variation among species persists, even within the same trophic guild and for comparable body sizes (Fig. S4). Species-specific diel activity patterns likely arise from a combination of bottom-up and top-down processes, and other influences (e.g., habitat features, environmental conditions, intra-specific dynamics, etc.). Furthermore, some patterns cannot be attributed unambiguously to one process or factor, for example, the nocturnal activity of small omnivores may reflect avoidance to top predators (top-down) and/or following of omnivore prey (bottom-up, Prediction 2, Fig.\n \n 2\n \n , Fig.\u00a03). Both explanations have merits when we consider better-known species such as the ocelot (\n \n Leopardus pardalis\n \n ), a neotropical felid, which is known to prey on various species including nocturnal omnivores such as opossums and racoons\n \n \n 35\n \n \n , and is also known to avoid jaguars. Although bottom-up regulation can influence the abundance of species\n \n \n 36\n \n \n , we did not find further evidence for this process in the activity of other trophic groups.\n
\n\n Larger-bodied herbivores and insectivores tended to be more nocturnal consistent with the thermoregulatory constraint hypothesis (H3). For example, for Afrotropical herbivores, nocturnality probability increased from 0.09 (LCI: 0.06, UCI: 0.11, body mass\u2009=\u20090.70 kg) to 0.60 (LCI: 0.51, UCI: 0.69, body mass\u2009=\u20094334 kg) as the body mass increased (Fig.\u00a03). Similarly, the probability of being nocturnal among insectivores in the Indo-Malayan increased with body mass from 0.01 to 0.98 (Fig.\u00a03). While daily temperature is more stable in tropical rainforests than in many other ecosystems, it does vary\n \n \n 37\n \n \n . Most tropical mammals are adapted to survive in a narrow thermal tolerance range\n \n \n 38\n \n ,\n \n 39\n \n \n , thus both high and low temperatures can increase energy expenditure\n \n \n 40\n \n \n . Small-bodied species can reduce energy loss by being active during warmer periods of the day\n \n \n 15\n \n \n , while large-bodied animals (e.g., tapirs\n \n \n 41\n \n \n , aardvark\n \n \n 42\n \n \n ) can reduce thermal stress by focusing activity during cooler periods of the day\n \n \n 14\n \n ,\n \n 41\n \n ,\n \n 43\n \n \n . For example, in the Neotropics the probability of being active during the night was two times higher for a 290 kg herbivore (e.g.,\n \n Tapirus bairdii\n \n ) than for one of 1 kg (e.g.,\n \n Myopracta acouchy\n \n ). In contrast, we found a positive relationship between size and diurnality for carnivores, omnivores and neotropical insectivores. If thermoregulatory constraints were sufficiently powerful, we might anticipate it to manifest across all trophic guilds. Perhaps this was not apparent because interactions may be more influential than other factors (eg., physiology) in tropical forests compared to other biomes\n \n \n 25\n \n \n due to more stable climatic conditions. Megafaunal species were also scarce among non-herbivores and thus thermal stress may be less influential.\n
\n\n Although all our study areas are relatively well-protected none are completely free of human impacts\n \n \n 28\n \n \n raising the question of how this may influence the observed patterns. Clearly, human presence influences animal activity patterns too; for example, some species have become more nocturnal to avoid hunters\n \n \n 44\n \n \n . This was recognised in one of our study sites, where ungulates became more nocturnal as hunting increased\n \n \n 45\n \n \n . In this context, it is remarkable that the general patterns were so robust and remained consistent across sites despite variation in hunting pressure. We acknowledge the inability of our study to clarify the role of large carnivores and hunters in determining the specific details of the patterns reported. However, simple approaches using human activity may be misleading as evasive responses among mammals are not universal and can change over time (for example, the gorillas in Bwindi have been habituated to humans), and in some locations, animals favour human settlements to access certain foods or avoid predation. At some of our sites, certain large predators (e.g., leopards in Biwindi\n \n \n 46\n \n \n ) are now absent due to earlier extinctions and more recent losses\n \n \n 47\n \n ,\n \n 48\n \n \n . This, however, does not necessarily mean release from diurnal risks and disturbance from omnivorous mammals (e.g., chimpanzees), birds of prey, reptiles (e.g., pythons, anacondas), and humans (tourists and hunters). Furthermore, current activity patterns may reflect the anachronistic top-down regulation by \u201cghosts of predators past\u201d. Further work is needed to explore these nuances. To ensure we are not misunderstood, we underline that the robust and consistent patterns we observed in these comparatively well protected forest communities do not contradict past work indicating that widespread species decline and loss can have a devastating impact on ecosystems\n \n \n 49\n \n \u2013\n \n 51\n \n \n .\n
\n\n Insectivores were an exception to the consistent patterns across regions: while Afrotropical and Indo-Malayan species revealed a positive relationship between greater body mass and the likelihood of nocturnal activity (e.g., Afrotropical increased from 0.01 to 0.91), a negative relation was found in the Neotropics with a decrease of nocturnality with greater body mass, from a probability of 0.99 (LCI: 0.99, UCI: 0.99, body mass\u2009=\u20090.12 kg) to 0.32 (LCI: 0.22, UCI: 0.44, body mass\u2009=\u200943.30 kg). We do not know the cause for this exception but can speculate. The pattern reported for insectivores in Afrotropical and Indo-Malaya regions is consistent with the thermoregulatory constraints hypothesis (H3). However, the higher diurnality of large insectivore species than small ones in the Neotropics, was mostly driven by three species (\n \n Myrmecophaga tridactyla, Tamandua tetradactyla, and Tamandua mexicana\n \n ) which may derive from the distinct biogeographic history of the Neotropics, where insectivores are among the few native lineages that persisted after the great interchange\n \n \n 52\n \n \n . In any case, the difference may reflect different characteristic requirements (e.g., African aardvarks dig burrows, whereas neotropical anteaters live above ground).\n
\n\n Despite their distinct origins, biogeographic histories, and taxonomic compositions, community level diel activity patterns for tropical forest mammals, examined by trophic guild and body size, are remarkably consistent across 16 sites and three tropical regions. As shown previously for trophic structures\n \n \n 47\n \n \n , diel activity patterns appear shaped by common processes regardless of biogeography. Convergent evolution across regions appears manifested in many ways including, as we see here for the first time, diel activity strategies. These community-level activity patterns appear shaped primarily by larger predators through top-down processes\n
\n\n \n 1)\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Study areas and camera trapping\n \n
\n\n We used camera-trap data from the Tropical Ecology Assessment and Monitoring (TEAM) Network\n \n 47\n \n . TEAM data comprise data from three tropical biogeographic regions (Neotropics, Afrotropics and Indo-Malayan tropics) and 16 protected areas (TEAM Network, 2011) (Fig. 1). Camera-traps were deployed following a standardized protocol through all protected areas during the dry season between 2008 and 2017. At each protected area the monitoring run from two to ten years with the deployment of 60 to 90 cameras. Camera-traps were placed at a density of 0.5 - 1 camera/km\n \n 2\n \n (1 camera every km\n \n 2\n \n or 1 camera every 2 km\n \n 2\n \n ) and remained active for ~30 consecutive days\n \n 28,47\n \n . We excluded data from camera-trap sites with inconsistent date-time stamps, yielding a total of 60-89 cameras per protected area (Fig. 1, Table S1).\n
\n\n \n 2)\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Data\n \n
\n\n A total of 2 312 635 camera-trap pictures corresponded to mammals. We further filtered the dataset to delimitate our study for species with a body mass greater than 75 g (smaller species have high uncertainty of identification and are difficult to detect) and species strictly terrestrial or scansorial (i.e., we excluded all arboreal and aquatic species)\n \n 26,53\n \n . A total of 166 species, 38 families, and 15 orders of ground-dwelling and scansorial species were detected (Table S1). Since camera-traps usually take consecutive pictures, we avoided pseudo-replication of individuals by establishing independent events (time interval between pictures > 1-hour per camera for a given species). This resulted in a total of 126 382 independent events (Supplementary Material 2). To analyse diel activity, we used the time-stamp recorded in each independent event\n \n 54\n \n and summarized the number of events for each of the following three categories 1) day, 2) twilight, or 3) night. Each event was classified by protected area, location, time, and date to specify the sunrise, sunset, nautical dawn, and dusk using the R library \u2018maptools\u2019\n \n 55\n \n . Twilight was defined as the interval between dawn and sunrise and between sunset and \u201cnautical dusk\u201d\n \n 56\n \n . Day was defined as the interval between sunrise and sunset. Night was the interval between nautical dusk and nautical dawn.\n
\n\n As species characteristics we used 1) trophic guild and 2) body mass(g) which we extracted from the PHYLACINE database\n \n 57\n \n (Fig. S2). We classified each mammal species into four trophic guilds: carnivore, herbivore, insectivore, or omnivore. Categories were based on diet reported in the PHYLACINE database and we classified as carnivore species feeding on \u2265 80% vertebrates, herbivore species feeding on \u2265 80 % plant materials, insectivore feeding on \u2265 80 % insects, the remaining species were categorized as omnivores (e.g., feeding on vertebrates and fruits)\n \n 57,58\n \n .\n
\n\n \n 3)\u00a0 \u00a0 \u00a0 \u00a0\u00a0Analysis\n \n
\n\n To test how trophic guild (carnivores, herbivorous, insectivores, and omnivores) and body mass (log-transformed) is associated with the number of independent events of each diel activity (day, night, twilight) of tropical ground-dwelling and scansorial mammals we fitted a multinomial logit model\n \n 59\n \n using package \u2018mclogit\u2019\n \n 60\n \n . Multinomial modelling allowed us to assess three instead of two response classes (day, night, and twilight). We built a set of candidate models for each tropical region using maximum likelihood (ML) and with a convergence tolerance (\u0190) of 1e-6 (Table S1). To account for the variability between the activity of species in different protected areas we include protected areas as a random effect within all models. We selected the best model for each tropical region using Akaike information criterion (AIC). We ranked models using \u0394AIC and considered models with a \u0394AIC <2 to equally be supported. Once we selected the best models, we run the models with a restricted maximum likelihood (REML) to arrive at final estimates for each tropical region. We predicted relative activity with the package \u2018mpred\u2019\n \n 60\n \n . This allowed us to extract the predicted probability of activity in each diel category for the range of body mass in each trophic guild and region.\n
\n\n To show the diversity of activity patterns we characterized species-specific activity patterns when the number of independent events was 25 or more\n \n 61\n \n . We gathered the data of all protected areas in each biogeographic region to display species activity patterns (Fig. 1, Fig. S3). To correct for diel differences on the delimitation of day, night and twilights between protected areas and distinct dates of the year of sampling we anchored activity patterns to sunrise and sunset\n \n 62\n \n using the \u2018activity\u2019 package\n \n 63\n \n (Fig. S3). Then we plotted species activity with the package \u2018overlap\u2019, which employs kernel density estimation that circumvents the conflation of data required for histograms\n \n 61\n \n .\n
\n\n Alzheimer\u2019s disease (AD) is often accompanied by early non-cognitive symptoms, including olfactory deficits, such as hyposmia and anosmia\n \n 1\n \n . These have emerged as solid predictors of cognitive decline, but the underlying mechanisms of hyposmia in early AD remain elusive\n \n 2\n \n . Pathologically, one of the brain regions affected earliest is the brainstem locus coeruleus (LC), the main source of the neurotransmitter noradrenalin (NA) and, a well-known neuromodulator of olfactory information processing\n \n 3\n \n . Here we show that early and distinct loss of noradrenergic input to the olfactory bulb (OB) coincides with impaired olfaction in a mouse model of AD, even before pronounced appearance of extracellular amyloid plaques. Mechanistically, OB microglia detect externalized phosphatidylserine and MFG-E8 on hyperactive LC axons and subsequently initiate their clearance. Translocator protein 18 kDa (TSPO) knockout reduces phagocytosis, preserving LC axons and olfaction. Importantly, patients with prodromal AD display elevated TSPO-PET signals in the OB, similarly to APP\n \n NL-G-F\n \n mice. We further confirm early LC axon degeneration in post-mortem OBs in patients with early AD. Collectively, we uncover an underlying mechanism linking early LC system damage and hyposmia in AD. Our work may help to improve early diagnosis of AD by olfactory testing and neurocircuit analysis and consequently enable early intervention.\n
\n\n Alzheimer\u2019s disease is currently the most prevalent and devastating form of dementia, affecting millions of people worldwide\n \n 4\n \n . Extracellular deposition of \u03b2-amyloid (A\u03b2), the formation of A\u03b2-plaques and the aggregation of microtubule-associated protein tau forming neurofibrillary tangles are the pathological hallmarks of AD\n \n 5\n \n . While causal therapies are still not available, recent A\u03b2 targeting antibody-therapies moderately improve cognitive decline in patients at early AD stages\n \n 6,7\n \n . However, therapeutic success critically depends on the earliest possible diagnosis, warranting a detailed understanding of the mechanisms prior to the onset of first cognitive symptoms. The locus coeruleus (LC) noradrenergic (NA) system is affected particularly early in AD. It is the first site where aberrant tau hyperphosphorylation (pTau) is detected, putatively kickstarting the spread of tau throughout the CNS\n \n 8\n \n . Consequently, past research has focused intensely on the effects of pTau on LC physiology, while the role of A\u03b2 in LC dysfunction has attracted only scant attention. Forebrain NA is almost solely derived from the LC and, as a function of its widespread axonal projections, regulates a variety of physiological processes including arousal and attention, sleep-wake-cycles, memory, energy homeostasis, cerebral blood flow and sensory processing, all of which are impaired in the progression of AD, though with differences in temporal progression\n \n 3\n \n . Symptomatically, early olfactory dysfunction frequently marks the early onset of AD with prospective patients remaining cognitively normal and otherwise healthy\n \n 9,10\n \n . Although decreased olfactory sensitivity is apparent in ~85% of AD cases the underlying mechanisms remain a conundrum\n \n 1,11\n \n . Here, we ventured for a multifaceted approach to study the neural correlate of olfactory dysfunction in a mouse model of amyloidosis using a plethora of steady-state systems neuroscience techniques both\n \n ex vivo\n \n and\n \n in vivo\n \n and studied human post-mortem brain tissue to validate our mechanistic findings.\n
\n\n \n Early LC axon loss exclusive to the OB\n \n
\n\n LC axon loss has been reported at late disease stages in the APP\n \n NL-G-F\n \n mouse model\n \n 12\n \n . By systematic comparison of multiple brain areas,\u00a0we set out to analyse if LC axon loss might already be detected earlier in these animals (Fig. 1a). Surprisingly, we discovered an early LC axon degeneration exclusive to the OB starting between 1 and 2 months in APP\n \n NL-G-F\n \n mice (Fig. 1a-d). While in 1-month-old animals, the LC axon density was unaltered compared to WT animals, we observed a 14% fibre loss at 2 months of age. This loss further progressed to 27% at 3 months, and 33% at 6 months. Notably, LC axons started to degenerate in other regions such as the hippocampus, piriform cortex and medial prefrontal cortex between 6 and 12 months at the earliest (Extended Data Fig. 1a,b). Similar to the cortex, the OB is composed of different layers, which are disparately innervated by the LC-NA system. We thus analysed layer-specific axon loss and identified the most densely innervated region, the internal plexiform layer\u00a0to be the site of most prominent axon loss, followed by the external plexiform layer\u00a0(Fig. 1d). OB microglia increased between 2 and 3 months of age without significant A\u03b2 plaque deposition (Fig. 1f,g). We excluded NA cell loss in the LC to underlie axonal demise as we did not observe differences in LC neuron number in APP\n \n NL-G-F\n \n mice when compared to WT animals at 12 months (Fig. 1h,i). We next asked whether early deposition of extracellular A\u03b2 correlates with LC axonal damage. Intriguingly, we found LC fibre loss to be independent of the amount of extracellular A\u03b2 (Extended Data Fig. 2a).\n
\n\n \n LC axon loss drives hyposmia\n \n
\n\n Early sensory manifestations such as hyposmia have been well described in prodromal AD (pAD), as have the contributions of NA to olfaction\n \n 13\n \n . Thus, we set out to analyse whether LC axon loss results in impaired olfaction. We employed the buried food test, a well-established olfactory task to measure the ability of an animal to detect volatile odours\n \n 14\n \n (Fig. 1j). Food deprived WT animals rapidly started exploring the arena and usually uncovered the hidden food pellet within ~40 s. In contrast, 3-month-old APP\n \n NL-G-F\n \n mice needed 60% more time to find the buried food pellet. The same phenotype was reproduced in 6 months old animals (Fig. 1k). We did not observe any differences when testing animals at 1 month of age which is consistent with the lack of LC axon degeneration at that time point (Fig. 1b,c,k). To rule out task-specific confounders we aimed to recapitulate our findings in a second olfactory task. To this end, we subjected 3-month-old WT and APP\n \n NL-G-F\n \n mice to an odour sensitivity test (Fig. 1l-n). We exposed the animals to ascending concentrations of vanilla, a pleasant odour, and measured the time the animals spent interacting with the odour delivery stick (Fig. 1m,n). WT animals were readily attracted by a low odour concentration (dilution 1:1000) and repeatedly interacted with the odour stick, while APP\n \n NL-G-F\n \n mice visited the interaction zone considerably later and less often. The same behaviour was observed when testing a high vanilla concentration (dilution 1:1; Fig. 1m,n). Collectively, these data reveal a consistent olfactory phenotype in APP\n \n NL-G-F\n \n mice, starting at 3 months of age, which is hitherto the earliest behavioural manifestation described in this mouse model.\n
\n\n \n Impaired NA release links to hyposmia\n \n
\n\n Neurocircuit-homeostasis is able to partially balance molecular and structural changes or loss in case of neuropathological insults\n \n 15\n \n . We thus aimed to understand whether LC axon loss translates into decreased NA release in the OB. In order to investigate potential changes in the concentration of NA in the OB of APP\n \n NL-G-F\n \n animals, we performed NA ELISA. Interestingly, we did not observe a significantly different concentration of baseline NA in these animals compared to WT mice (Extended Data Fig. 3a). We thus hypothesized that a change in LC-NA would be more pronounced in stimulus-related NA release. We transduced the OB of 2\u2011month\u2011old WT and APP\n \n NL-G-F\n \n animals with the NA sensitive biosensor GRAB\n \n NE\n \n (G-protein-coupled receptor-activation-based sensor for noradrenaline) and implanted a chronic cranial window over the olfactory bulb\n \n 16\n \n (Fig. 2a). At 3 months of age, we performed\n \n in vivo\n \n acousto-optical 2-photon (AO-2P) microscopy in awake animals paired with olfactory stimulation by 10s long vanilla puffs (Fig. 2b-g). WT animals reliably and repeatedly responded to the odour delivery with a strong and long-lasting increase of fluorescence. In contrast, delivering odour to APP\n \n NL-G-F\n \n animals revealed a drastically decreased response (Fig. 2b-g). As a control, neither an air puff only nor NA measurements in the cortex coupled to odour delivery elicited coherent changes in fluorescence (Fig. 2d, Extended Data Fig. 3b). Immunohistochemical validation revealed a solid transduction of the tissue in the OB of all animals and NA fibre loss in\n
\n APP\n \n NL-G-F\n \n mice (Fig. 2h,i). To exclude the possibility of dysfunctional mitral cells, the first order projection neurons of the OB, driving impaired olfaction, we performed perforated patch-clamp recordings of mitral cells in acute OB slices. In line with previous studies, we found mitral cells to be spontaneously active, but we did not detect alterations of intrinsic properties between genotypes at 6 months of age at which hyposmia is well manifested in these animals (Extended Data Fig. 4a-f)\n \n 17\n \n . The structure-to-function relationship of the LC-NA system and olfaction led us to further probe whether persistent activation of remaining LC axons by chemogenetics would be sufficient to reinstate olfaction (Extended Data Fig. 5a-c). We bilaterally injected an AAV transducing LC neurons of APP\n \n NL-G-F\n \n x Dbh-Cre animals with an excitatory ligand-gated G-protein-coupled receptor (h3MDGs, designer-receptor exclusively activated by designed drugs, DREADD). In patch-clamp recordings, we confirmed that the application of Clozapine-N-Oxide (CNO) readily activates LC neurons (Extended Data Fig. 5a,b), however systemic CNO-injection to activate excitatory DREADDs\n \n in vivo\n \n failed to accelerate the time to find the buried food pellet in these animals (Extended Data Fig. 5c). This strongly suggests a structure-to-function-relationship of LC axons in the OB in the context of olfaction.\n
\n \n OB microglia clear LC axons\n \n
\n\n Microglia have been attracting considerable attention in the pathogenesis of AD\n \n 18\n \n .\u00a0Their remarkable heterogeneity has been revealed recently, highlighting the complex nature of microglia and their influence on brain functions\n \n 19\n \n . Since early LC axon loss coincides with an increased number of microglia, we set out to investigate whether microglia could account for LC axon loss.\u00a0Thus, we performed bulk RNA sequencing (RNA-seq) of microglia isolated from OBs of WT and APP\n \n NL-G-F\n \n mice at the age of 2 months, the very onset of LC axon loss (Fig. 3a). In line with our immunohistological data, we observed an increased number of microglia cells isolated from bulbi of APP\n \n NL-G-F\n \n animals (Fig. 3b). After appropriate quality control (Extended Data Fig. 6), we performed differential expression testing using negative binomial models while controlling for sex. This revealed that 2.344 genes (of a total of 17.840) were differentially expressed, with a slight majority of them (1.283) being upregulated in APP\n \n NL-G-F\n \n animals (Fig. 3c, Extended Data Table 1). Previous work has demonstrated a so-called \u201cdisease-associated\u201d microglia response (DAM) in AD mouse models and humans alike\n \n 20,21\n \n . To test whether this phenotype was visible in our data, we directly compared our microglia OB RNA-seq data to a publicly available cortical microglia RNA-seq dataset taken from 8\u2011month\u2011old APP\n \n NL-G-F\n \n mice\n \n 22\n \n . Linear regression of log-fold changes in fact revealed a significant negative relationship (R = -0.44, p < 2e-16), suggesting that no such DAM response is seen in 2-month-old OBs and that these microglia did not yet acquire a similar response to pathological stressors (Fig. 3d). A crucial function of microglia is the removal of debris or apoptotic cells from the parenchyma as well as synaptic remodelling\n \n 23\n \n . Interestingly, gene ontology (GO) term analysis revealed the 20 most enriched terms relate to neuronal function and synaptic or neuronal plasticity. We thus hypothesized that microglia phagocytosis, a component of synaptic pruning, might be responsible for the selective clearance of LC axons in the olfactory bulb. We compared all identified transcripts annotated to the GO term \u201cphagocytosis\u201d. Here, we identified 121 transcripts, of which only 2 were differentially expressed in our data\u00a0set (Extended Data Fig. 7a). However, when analysing gene modules related to the GO-term \u201csynapse\u201d, we observed an overarching upregulation of 73 genes, suggesting an increased plastic environment, potentially indicating increased synaptic pruning (Fig. 3e). Thus,\u00a0we conducted an automated phagocytosis assay from primary OB microglia of WT and APP\n \n NL-G-F\n \n mice, aged 2 months (Fig. 3g). Microglia were incubated with pHrodo-labelled synaptosomes to measure their phagocytic uptake over the course of 24 hours (Fig. 3f\u2011i). Our data revealed an increased efficiency of APP\n \n NL-G-F\n \n microglia to phagocytose fluorescently labelled synaptosomes, with OB microglia of APP\n \n NL-G-F\n \n mice showing a 33% higher phagocytic capacity already after 12 hours. As expected, Cytochalasin-D application completely abolished phagocytosis in both genotypes (Fig. 3h,i). Based on their increased phagocytic activity, we hypothesized that microglia might indeed be phagocytosing LC axons in OBs from APP\n \n NL-G-F\n \n mice. To test this directly, we performed high-resolution imaging of NET fibres together with microglia and the lysosomal marker CD68 and subsequently performed 3D-reconstructions of these images (Fig. 3j). We found a higher volume of NET\n \n +\n \n immunosignal in single microglia cells from APP\n \n NL-G-F\n \n mice compared to WT animals, as well as increased volumes of lysosomal CD68 (Fig. 3k), corroborating the increase in phagocytic activity observed in vitro. Notably, we did not see significant differences in the cellular volumes of single microglia between groups. Collectively, our data show no overt disease-associated activation of microglia, but a strikingly increased phagocytic activity compared to WT animals of the same age. Consequently, we hypothesized that an inhibition of phagocytosis could prevent the loss of NA axons in the OB. Translocator protein 18 kDa (TSPO) has recently been identified as a key-protein in fuelling synaptic pruning and microglial phagocytosis\n \n 24,25\n \n .\u00a0We sought to investigate if TSPO elimination would be sufficient to halt or decelerate the loss of LC axons. To this end, we bred mice with a global knockout of TSPO\n \n 26\n \n to APP\n \n NL-G-F\n \n . We again harvested OBs from these animals at 2-6 months of age and stained for NET\n \n +\n \n LCaxons. Indeed, lack of TSPO in APP\n \n NL-G-F\n \n mice abrogated the loss of NA axons in these animals up to an age of 6 months (Fig. 4a,b). This correlated with a decreased uptake of NET\n \n +\n \n axons in microglia of TSPO-KO x APP\n \n NL-G-F\n \n mice (Fig. 4d,e). We then exposed the TSPO-KO x APP\n \n NL\u2011G-F\n \n animals to the buried food task. Importantly, the preservation of LC axons in the OB resulted in a retained ability to find the buried food pellet indistinguishable from WT animals (Fig. 4c).\n
\n\n \n PS labels LC axons for phagocytosis\n \n
\n\n A plethora of \u201cfind-me\u201d- and \u201ceat-me\u201d-signals attracting microglia to their phagocytic targets have been revealed within the last years\n \n 27\n \n . The complement cascade has emerged as one key player of synaptic removal in AD\n \n 28\n \n . We thus aimed to analyse whether LC\u00a0axons from APP\n \n NL\u2011G\u2011F\n \n mice would be decorated by Complement component 1q (C1q) as a possible underlying cause of axonal clearance. As expected, staining for C1q resulted in a dense punctate pattern. However, we did not observe any significant changes of C1q colocalisation to NET\n \n +\n \n axons in the OBs of APP\n \n NL-G-F\n \n mice compared to WT mice (Extended Data Fig. 8a,b). In both healthy and diseased brains, the highly coordinated local externalization of phosphatidylserine (PS) leads to the targeted engulfment of neuronal material by microglia and has similarly been described to contribute to synapse loss in AD mouse models\n \n 29,30\n \n . A variety of microglial receptors are known to recognize exposed PS, such as triggering receptor expressed in myeloid cells 2 (TREM2) and milk fat globule-EGF factor 8 protein (MFG-E8), which in turn binds to microglial vitronectin receptors (the \u03b1\n \n v\n \n \u03b23/5 integrins), both of which play major roles in the aetiology of AD\n \n 29,31,32\n \n . While PS recognized by TREM2 was shown to contribute to synapse loss in APP\n \n NL-F\n \n mice, PS and MFG-E8 are important physiological mediators of microglia-dependent synaptic pruning during adult neurogenesis in the OB of mice\n \n 29\n \n . Considering the increase of mRNAs associated with synaptic plasticity (Fig. 3e), we hypothesized that increased PS externalization might be the underlying cause of LC axon phagocytosis by microglia. To test this, we performed\n \n in vivo\n \n PS labelling by injecting PSVue550 in the OBs of WT and APP\n \n NL-G-F\n \n mice\u00a0at the age of 5 months. Importantly, as shown previously and in line with its physiological function, we could visualize externalized PS in the OB, both in WT and APP\n \n NL-G-F\n \n mice. In order to assess whether PS externalization can be detected on NET\n \n +\n \n axons, we conducted a colocalisation analysis using 3D reconstruction. When adjusting for the fibre density, we found an elevated colocalisation of PS on NET\n \n +\n \n axons in APP\n \n NL-G-F\n \n mice (Fig. 4f,g). Intriguingly, flipped PS was often accompanied by Iba1\n \n +\n \n microglia directly contacting LC axons. However, when analysing the contact points between microglia and LC axons, no difference in colocalised volume was found between the genotypes (Fig. 4h,i). Further investigating the possible link, we could show that PS is capped with MFG-E8, serving as the adaptor protein between PS and the microglial integrin receptor (Extended Fig. 9a). Using 3D reconstruction, we found more MFG-E8 colocalised to LC axons of APP\n \n NL-G-F\n \n mice than on LC axons from WT animals (Fig. 4j,k). Given the TSPO-KO mediated rescue of LC axons and olfaction, we hypothesized that MFG-E8 decoration should similarly be increased in APP\n \n NL-G-F\n \n x TSPO-KO mice. We stained OB tissue from these animals for LC axons and MFG-E8 and again reconstructed both signals. Intriguingly, MFG-E8 decoration of LC axons was clearly increased compared to WT animals and even showed a trend towards an increase compared to APP\n \n NL-G-F\n \n mice (Fig. 4j,k). Overall, we conclude that local PS externalization in conjunction with MFG-E8 decoration constitutes a major \u201ceat-me\u201d signal for microglia interaction with LC axons and subsequent phagocytosis. We finally ventured to elucidate mechanistically as to why PS is externalized on LC axons. In neurons, the protein\u00a0TMEM16F\u00a0constitutes a Ca\n \n 2+\n \n -dependent scramblase responsible for PS externalization. Earlier work has put much emphasis on the firing properties of LC neurons and the Ca\n \n 2+\n \n -dependence of their intrinsic pacemaker, especially in the context of neurodegeneration\n \n 33\n \n . During pacemaking activity of LC neurons, each action potential (AP) is accompanied by a Ca\n \n 2+\n \n -driven supra-threshold oscillation, which leads to the activation of voltage-gated sodium channels underlying the super-threshold AP. We thus hypothesized that increased firing in LC neurons may underlie Ca\n \n 2+\n \n -triggered scramblase to flip PS to the outside of the plasma membrane. We performed perforated patch-clamp recordings of LC neurons from WT and APP\n \n NL-G-F\n \n mice at the age of 6 months (Fig. 4l-q). Indeed, we found an overall increase in spontaneous AP frequency in acute brain slices from APP\n \n NL-G-F\n \n mice (Fig. 4m,n). We did not observe a change in input resistance during hyperpolarization but a slightly decreased intrinsic excitability in response to depolarizing stimuli, likely reflecting an increased activation of Ca\n \n 2+\n \n -dependent potassium channels (Fig.\u00a04o-q). We thus conclude that spontaneous hyperactivity in LC neurons and consequently elevated Ca\n \n 2+\n \n -signalling instigates Ca\n \n 2+\n \n -dependent scramblase/flippase, leading to the externalization of PS and a microglia-mediated removal of hyperactive LC originating axons. In summary, we clearly pinpoint microglial phagocytosis of NA axons in the OB to be the underlying cause of the progressive early axon loss in APP\n \n NL-G-F\n \n mice.\n
\n\n \n LC-APP\n \n NL-G-F\n \n expression induces hyposmia\n \n
\n\n In APP\n \n NL-G-F\n \n mice, every\n \n APP\n \n expressing cell harbours three mutations, limiting conclusion about the relative effect of LC axon loss\n \n 34\n \n . Thus, we asked whether APP\n \n NL-G-F\n \n expression restricted to the LC would be sufficient to recapitulate the neuroanatomical and behavioural findings. We engineered a custom-built Cre-dependent AAV to specifically transduce LC neurons of Dbh-Cre mice with the human APP\n \n NL-G-F\n \n (Dbh-hAPP\n \n NL-G-F\n \n ) or a control virus leading to the expression of a fluorophore only (Dbh-EYPF; Fig. 5a). Three-months post injection, we performed a buried food test. Of note, Dbh-hAPP\n \n NL-G-F\n \n mice needed more time to find the buried food compared to the control injected Dbh-EYPFmice (Fig. 5d,e). Immunohistochemical validation revealed an LC axon degeneration of 15% in the OB of Dbh\u2011hAPP\n \n NL-G-F\n \n mice compared to Dbh-EYPFmice (Fig. 5b,c), without LC neuron loss (Extended Data Fig. 10a,b). We thus asked next, whether again microglia in the OB would phagocytose LC axons and performed the same set of immunohistological staining to assess NET protein within CD68\n \n +\n \n lysosomes of microglia. Indeed, we observed an increase in the volume of NET\n \n +\n \n signal inside the lysosomes of microglia (Fig. 5f,g). Collectively, our approach to induce Dbh-hAPP\n \n NL-G-F\n \n expression specifically in LC neurons illustrates that this is sufficient to recapitulate both early behavioural and neuropathological phenotypes observed in the APP\n \n NL-G-F\n \n mouse line.\n
\n\n \n LC axon loss and hyposmia in human pAD\n \n
\n\n Early impairment of the LC-NA system in humans has recently been in the spotlight of several multimodal imaging studies\n \n 35\n \n . While at the level of the brainstem, LC volume decreases over time and levels of LC integrity predict cognitive outcome in elderly subjects, it is not yet clear whether axon loss also precedes late-phase occurring cell loss in the LC of humans\n \n 36\n \n . Interestingly, both hyposmia and LC integrity are predictors of cognitive decline in humans\n \n 9,10\n \n . We thus ventured to decipher whether LC axon degeneration is evident in post-mortem tissue from OBs of early AD cases, staged by A\u03b2 and tau\u00a0immunostainings (Thal-phase 1-2, Braak stage 1-2)\u00a0and healthy controls. Strikingly, in the OB tissue from early AD cases, we revealed a pronounced degeneration of NET\n \n +\n \n fibres compared to healthy, age-matched controls, which did not further decline in progressive AD cases (Fig. 6a-c). Moreover, we hypothesized that LC axon loss in humans, similar to mice, may correlate with an increased number of microglia. To this end, we performed TSPO-PET imaging in 16 patients with subjective cognitive decline (SCD)/ mild cognitive impairment (MCI), 16 AD patients and 14 healthy controls, staged by A\u03b2 and tau cerebrospinal fluid (CSF) levels, and investigated their TSPO signal in the respective OBs. We identified increased TSPO signals in the OBs of patients with prodromal AD, indicative of increased numbers or activation of microglia. Interestingly, even transitioning into AD diagnosis did not further elevate OB TSPO signals significantly (Fig. 6d,e). A number of independent longitudinal studies have highlighted olfactory deficits as a predictor of cognitive decline\n \n 2,37\u201340\n \n . Thus, we analysed the data of our cohort for signs of hyposmia. While the prodromal AD group showed a trend towards olfactory deficits, patients transitioned into AD indeed revealed a significant decrease in the ability to identify common odours (Fig. 6f). Consequently, we asked whether these findings could be back-translated to APP\n \n NL-G-F\n \n mice. Indeed, TSPO-PET imaging in these animals revealed an early elevated signal in the OB compared to WT mice at 2-3 months of age, while the signal in the cortex of the same animals at that age remained unaltered (Fig. 6g-j). Thus, these translational data highlight and assign TSPO-PET imaging of the OB and hyposmia as a potential early bio-marker of AD and LC-NA system dysfunction.\n
\n\n We reveal LC-NA system degeneration as an impaired neuronal network to account for olfactory deficits in AD\n \n 1\n \n . In humans,\u00a0~85% of AD patients exhibit early sensory deficits including hyposmia and anosmia, predicting cognitive decline\n \n 1,2,11,37\u201340\n \n . Similarly, LC integrity is established as an early biomarker predicting cognitive decline in ageing and neurodegenerative diseases\n \n 35,36\n \n . Interestingly, hyposmia is well documented in Parkinson\u2019s disease (PD) and LC dysfunction has been implicated to drive prodromal symptoms in PD. In contrast to the LC in AD, the OB and the dorsal motor nucleus of the vagus are the first sites to display \u03b1-synuclein pathology, likely suggesting an impairment of first-order olfactory neurons\n \n 41\n \n . The well-established modulation of olfaction by LC-derived NA, especially in olfactory memory, underscores a possible link from LC vulnerability to hyposmia\n \n 42\n \n . In our study, we detected LC axon loss in post-mortem OB tissue from prodromal AD patients. Notably, this pronounced early degeneration of LC axons did not progress further at later stages. Similarly, microgliosis detected by an elevated TSPO-PET signal in the OBs of SCD/MCI patients did not continue to increase in diagnosed AD patients. The same AD patients showed a strong olfactory deficit, while we could only assign a slight trend to the prodromal AD group. Based on the substantial evidence of several independent studies that highlight hyposmia as a common early symptom in AD, we believe that this is likely due to our small cohort size\n \n 2,37\u201340,43\u201345\n \n . It is reasonable to hypothesize that hyposmia and LC integrity as independent predictors of cognitive decline may not only be correlating but may be causally linked. Indeed, early sophisticated work suggested that pharmacotoxic lesion of the LC exaggerates olfactory problems in APPPS1 mice, however, the experiments were conducted after nine months of consecutive toxin administration in 12-month-old animals\n \n 46\n \n . We here provide the first causal link between the LC and olfactory deficits in mice. While we clearly provide translational data, more research is needed to further confirm this in human patients. The fast progress in MRI resolution and the sophisticated identification of the LC will enable a more detailed examination of the causal link between these two phenomena. Functional connectivity in live patients together with resting-state activity may then be able to delineate putative interconnections between these two widely separated anatomical regions.\n
\n\n LC dysfunction has classically been viewed as a consequence of tau-pathology. It is considered to be the first region positive for hyperphosphorylated tau\n \n 47\n \n . Due to this tau-centric view of LC dysfunction, the role of APP and A\u03b2 pathology in the LC in the aetiology of AD has only attracted little attention, although A\u03b2 increases as a function of LC connectivity in rats\n \n 48\n \n . In line, we provide evidence for an APP mutation-dependent axon loss underlying early olfactory deficits\n \n 47,49\n \n , marking the earliest described phenotype in this widely used AD mouse model to date. Functionally, the pronounced reduction of NA-release in APP\n \n NL-G-F\n \n mice upon odour stimulation can be considered a strong driver of the olfactory phenotype. With our cell-type specific expression of APP\n \n NL-G-F\n \n in LC neurons, we were able to demonstrate a coherent relationship between LC axon loss and olfactory deficits. Mechanistically, we present clear evidence that the expression of mutant human APP\n \n NL-G-F\n \n instigates the externalization of PS on LC axons. The Ca\n \n 2+\n \n -dependence of this externalization is in line with the hyperactivity observed in our study. Moreover, similar AP frequency elevations have been recorded in APPPS1 animals\n \n 29,50\n \n . In the olfactory bulb, PS-dependent microglial phagocytosis plays a crucial role in both physiology and pathology. During development and adult neurogenesis, microglia mediate synaptic pruning via PS detection which serves as a key mechanism to integrate newborn neurons into functional neuronal networks. Thus, PS located on hyperactive LC axons may be detected with a higher probability and fidelity compared to other regions, providing a rationale for the early axon loss preceding all other highly LC-innervated regions. This is additionally reflected in the lack of an amyloid-driven DAM response in microglia extracted from the OB and the lack of changes in microglia contacts to NET\n \n +\n \n axons. PS has recently been recognized as an opsonin in AD that marks neuronal structures for removal\n \n 28\n \n . A variety of different receptors or effector-proteins subsequently trigger microglia-dependent clearance, including TREM-2 and MFG-E8. In line with the physiological role of PS-dependent microglia-driven synaptic remodelling, we reveal MFG-E8 as a mediator of microglia-dependent phagocytosis of LC axons. Our data supports the hypothesis that the OB is an anatomical region prone to detection of PS-MFG-E8 complexes by microglia and thus axons of hyperactive LC neurons are cleared with a higher fidelity compared to other regions involving PS-MFG-E8 driven synaptic remodelling. In summary, we provide the first underlying mechanism for hyposmia, a so far underappreciated sensory deficit in AD. Coordinated assessment of structural and functional connectivity, olfactory testing, together with CSF and blood biomarkers could facilitate earlier AD diagnosis and be employed as solid predictors of disease progression and outcome. Ultimately, this may open the window for the earliest treatment to halt or decelerate disease progression.\n
\n\n \n Animals.\n \n Mice, both male and female (1-6 months of age) were used and held on a 12-h light/dark cycle with food and water ad libitum. The APP\n \n NL-G-F\n \n mouse line is a knock-in model, were pathogenic A\u03b2 is elevated by inserting 3 different mutations, associated with AD\n \n 34\n \n . Crossing APP\n \n NL-G-F\n \n mice with Dbh-Cre was used to manipulate the locus coeruleus-noradrenergic system. Dbh-Cre mice express the Cre recombinase under the\n \n dbh\n \n (dopamine beta hydroxylase) promotor\n \n 51\n \n . APP\n \n NL-G-F\n \n mice were also crossed with TSPO-KO\n \n 26\n \n mice to access the effect of a TSPO knock-out on the noradrenergic system. \u00a0As control animals, C57BL/6J mice were used, purchased from the Jackson Laboratory (Maine, United States). All animal experiments were approved by the Government of Upper Bavaria and followed the regulations of the Ludwig Maximilian University of Munich.\n
\n\n \n Immunostaining: Mouse brain tissue.\n \n Mice were deeply anesthetized and transcardially perfused with phosphate-buffered saline (PBS) and 4% paraformaldehyde (PFA). Brains got fixed by immersion in PFA at 4\u00b0C for 16 h. 50 \u00b5m thick slices were cut in a coronal plane using a vibratome (VT1200S, Leica Biosystems). Each 4 slices per animal containing the olfactory bulb, piriform cortex, hippocampus and locus coeruleus were used for an immunostaining analysis. Staining was performed on free-floating sections. Slices were blocked with blocking solution (10 % normal goat serum and 10 % normal donkey serum in 0.3 %Triton and PBS) for 2 hours at RT. Primary antibodies were incubated over-night at 4\u00b0C, followed by washing and secondary antibody incubation for 2 hours at RT, protected against light. Slices were mounted and cover slipped with mounting medium, containing DAPI (Dako, Santa Clara, USA).Primary antibodies used were: rabbit anti-NET (1:500, Abcam, ab254361), mouse anti-NET (1:1000, Thermo Fisher, MA5-24547), guinea pig anti-Iba1 (1:500, Synaptic Systems, 234308), chicken anti-TH (1:1000, Abcam, ab76442), mouse anti-A\u00df (NAB228) (1:500, Santa Cruz, sc-3277), rat anti-CD68 (1:500, BioRad, MCA1957), goat anti-MFG-E8 (1:500, R&D Systems, AF2805), rabbit anti-C1q (1:1000, Abcam, ab182451), chicken anti-GFP (1:1000, Abcam, ab13970), rabbit anti-GFP (1:1000, Thermo Fisher, A21311), rabbit, HA-tag (1:500, Sigma, H6908), Streptavidin 488 (1:1000, Invitrogen, S32354), Streptavidin 647 (1:1000, Invitrogen, S32357).\n
\n\n \n Image acquisition.\n \n Three-dimensional\u00a0images were acquired with a Zeiss LSM900 confocal microscope (Carl Zeiss, Oberkochen).\n
\n\n \n NET fibre quantification.\n \n For the quantification of the NET fibre density as well as Iba1-microglia and NAB288-A\u03b2-plaque area, a 10x objective (8-bit stacks of 101.41 \u00b5m x 101.41 \u00b5m x 25 \u00b5m) was used. The staining density (area %) was analysed with ImageJ. After a manual brightness/contrast adjustment, a threshold was set to calculate the perceptual area of NET-positive LC fibres, Iba1-positive microglia and NAB288-positive A\u03b2 plaques. Results from 4 sections per animal from 4-8 animals per groups were averaged and reported as\u00a0mean \u00b1 s.e.m.\n
\n\n \n Colocalisation analysis.\n \n For the engulfment of NET in microglia, airyscan images were taken with a 63x/1.4x NA oil immersion objective. Z-stack images were acquired of 8 microglia per mouse from 3 animals per group in the external plexiform layer, covering 30 \u00b5m at 0.14 \u00b5m intervals. Colocalisation of Iba1\n \n +\n \n microglia- NET\n \n +\n \n LC axon contact points was analysed on 15 \u00b5m z-stack images (40x/1.3x magnification, 0.3 \u00b5m intervals) of 6 pictures per mouse, 3 mice per genotype. Colocalisation of PS on NET\n \n +\n \n LC axon was analysed on 15 \u00b5m z-stack images (40x/0.7x magnification, 0.3 \u00b5m intervals) of 7 pictures per mouse, 3 mice per genotype. Colocalisation of C1q on NET\n \n +\n \n LC axon was analysed on 6 \u00b5m z-stack images (63x/1.4x magnification, 0.18 \u00b5m intervals) of 5 pictures per mouse, 2 mice per genotype. Colocalisation of MFG-E8 on NET\n \n +\n \n LC axon was analysed on 15 \u00b5m z-stack images (40x/0.7x magnification, 0.3 \u00b5m intervals) of 6 pictures per mouse, 4 mice per genotype. All images were 3-D reconstruction in IMARIS (Bitplane, 9.6.1) using the Surface module. Colocalisation was measured in volume and normalized to the NET axon density.\n
\n\n \n Staining: Human brain tissue.\n \n Human brain tissue from 7 healthy control subjects, 7 prodromal AD subjects and 6 AD patients was provided from the Munich brain bank. Demographic details of the subjects are listed in Supplementary Table 3. Paraffin embedded brain sections (5 \u00b5m) of the olfactory bulb were cut in a horizontal plane, using a microtome (Leica SM2010R) and mounted on glass slides until further processing. Sections were deparaffinized with xylene and rehydrated through a series of descending alcohol concentrations. For the DAB staining, an automated IHC/SH slice staining system (Ventana BenchMark ULTRA) was used. On separate slices, NET 1:200, A\u00df 1:5000 and Tau 1:400 was stained and visualized with an upright Bridgefield microscope. Each 4 pictures per subject (20x magnification) were acquired and analysed regarding their perceptual density of NET\n \n +\n \n LC axons.\n
\n\n \n Microglia isolation.\n \n Primary microglia were isolated from the olfactory bulb of 2-month-old C57BL/6J and APP\n \n NL-G-F\n \n mice using MACS technology (Miltenyi Biotec) according to manufacturer\u2019s instructions. Briefly, mice were perfused with PBS and the brain washed in ice cold HBSS (Gibco) supplemented with 7 mM HEPES (Gibco). Chopped tissue pieces were incubated with digestion medium D-MEM/GlutaMax high glucose and pyruvate (Gibco) supplemented with 20 U papain per ml (Sigma P3125) and 0.01 \\% L-Cysteine (Sigma) for 15 min at 37 C in a water bath. Subsequently, enzymatic digestion was stopped using blocking medium 10 \\% heat-inactivated FBS (Sigma) in D-MEM/GlutaMax high glucose and pyruvate. Mechanical dissociation was gently but thoroughly performed by using three fire-polished, BSA-coated glass Pasteur pipettes with decreasing diameter. Subsequently, microglia were magnetically labelled with CD11b microbeads (Miltenyi Biotec, 130-097-678) in MACS buffer (0.5 \\% BSA, 2 mM EDTA in 1x PBS, sterile filtered) and the suspension loaded onto a pre-washed LS-column (Miltenyi Biotec, 130-042-401). Following washing with 3x1 ml MACS buffer, magnetic separation resulted in a CD11b enriched and a CD11b depleted fraction. To increase purity further, the microglia-enriched fraction was loaded onto another LS-column. Total numbers of obtained microglia fractions were quantified using C-Chip chambers (Nano EnTek, DHC-N01). Isolated primary microglia were washed twice with 1x PBS (Gibco) and immediately processed for sequencing or plated for a phagocytosis assay.\n
\n\n \n Phagocytosis assay.\n \n Synaptic Protein was enriched using the Syn-PER\u2122 Synaptic Protein Extraction Reagent (Thermo Fisher) according to manufacturer\u2019s protocol and published previously\n \n 52\n \n . In brief, fresh brains from C57BL/6J mice at 4 months of age were isolated and homogenized in 10mL/g of brain tissue of Syn-PER\u2122 reagent substituted with protease and phosphatase inhibitor. The homogenate was then centrifuged at 1200 x g at 4\u00b0C for 10 minutes. The supernatant containing the synaptic fraction was then transferred into a new tube and spun at 15.000 x g at 4\u00b0C for 20 minutes. The supernatant was aspirated and the pellet of synaptic protein was resuspended in 1mL of Syn-PER\u2122 reagent containing 5 \\% (v/v) DMSO per gram tissue originally used. Synaptosome extracts were then stored at -80\u00b0 before further usage.Synaptic Protein was labelled with the pHrodo\u2122 Red succinimidyl ester (Thermo Fisher Scientific), which emits a red fluorescent signal only in acidic environments. Labelling was performed as previously described\n \n 53\n \n . In brief, synaptic protein was washed in 100 mM sodium bicarbonate, pH 8.5 and spun down (17,000 x g for 4 min at 4C). pHrodo\u2122 dye was dissolved in 150 \u00b5L DMSO per 1 mg dye to a concentration of 10 mM. The pHrodo\u2122 stock solution was added to the synaptic protein at a concentration 1 \u00b5l pHrodo per 1 mg of synaptic protein. After incubating at room temperature for 2 hours, protected from light, the labelled protein was washed twice in DPBS and spun down (at 17,000 x g for 4 min at 4C). After resuspending synaptic protein with 100 mM sodium bicarbonate, pH 8.5 to a concentration of 1000 \u00b5g/ml, it was aliquoted and stored at -80\u00b0C before usage.Primary microglia were cultured in tissue culture treated 96-well plates in microglia-medium adding freshly 10 ng/ml GM-CSF (R&D Systems) for three days in vitro (DIV) at 37\u00b0C, 5 \\% CO2, changing medium at DIV 1. For the phagocytic uptake assay, medium was replaced with medium in which pHrodo\u2122 labelled synaptic protein was resuspended at the desired concentration (2.5 \u00b5g/mL). For the Cytochalasin D (CytoD) control, cells were treated with 10 \u00b5M CytoD (Sigma) for 30 minutes, before adding medium with labelled synaptic protein and CytoD. Immediately after adding the substrates the cells were placed in an Incucyte\u2122 S3 Live-Cell Analysis System (Sartorius). Scans were performed every hour with 20x magnification and both phase contrast and red fluorescent channels, acquiring a minimum of three images per well and scan. Quantification was done using the cell-by-cell adherent analysis. Phagocytic index was calculated using the total integrated intensity (RCU x \u00b5m2/Image) normalized to the number of cells per image.\n
\n\n \n NA Elisa.\n \n In order to measure potential difference in the noradrenaline concentration between C57BL/6J mice and APP\n \n NL-G-F\n \n mice, a noradrenaline ELISA was carried out. Mice were deeply anesthetized and perfused with PBS and their brains got rapidly removed. The olfactory bulb was dissected and snap frozen using liquid nitrogen. The tissue was homogenized in 0.01M HCl in the presence of 0.15 mM EDTA and 4 mM sodium metabisulfite, before being processed with an ELISA kit (BA E-5200) according to the manufacturer\u2019s protocol.\n
\n\n \n RNA sequencing and Bioinformatics.\n \n RNA was isolated from microglial cell pellets using the RNeasy Plus Micro kit (Qiagen, 74034). Briefly, samples were lysed with RLT Plus lysis buffer containing beta-Mercaptoethanol, genomic DNA was removed by passing the lysate through gDNA eliminator columns, and the eluate was applied to RNeasy spin columns. Contaminants were removed with repeated Ethanol washes before RNA was eluted with 20 \u00b5L molecular grade water. All steps were carried out automatically on a Qiacube machine. RNA was quantified on a Qubit Fluorometer (Invitrogen, Q33230) and 6 ng of total RNA were used as input for library preparation with the Takara SMART-seq Stranded kit (Takara, 634444) following the manufacturer\u2019s instructions. Fragmentation time was kept at 6 minutes and AMPure XP beads (Beckman Coulter, A63880) were used for all clean-up steps. Library QC using a Bioanalyzer revealed average insert sizes around 350 bps. The molarity of each of the 16 libraries was determined by using the ddPCR Library Quantification Kit for Illumina TruSeq (Bio-Rad, 1863040) according to the manufacturer\u2019s instructions. Libraries were then diluted to 4 nM and pooled in an equimolar fashion. Paired-end sequencing was carried out for 150 cycles on a NextSeq 550 sequencer (Illumina, 20024907) using a High-Output flow cell. After sample demultiplexing, reads were aligned using STAR v2.7.8 to a customized genome based on the GRCm39 assembly and the gencode vM32 primary annotation that additionally contained sequences and annotations for the human\n \n APP\n \n gene. Group assignments were verified by manually inspecting alignments to the (human) APP sequence and checking for presence of the NL-, G- and F- mutations in transgenic animals. The count matrix produced by STAR v2.7.8 was used as an input for differential expression testing using edgeR. The count matrix was filtered to retain genes with at least 5 counts in at least 50% of samples and quasi-likelihood tests were conducted after fitting appropriate binomial models. Differential expression was considered significant if FDR < 0.1 and if the absolute log-fold-change exceeded 0.5. Gene lists were annotated with the enrichR package. All analyses made heavy use of the tidyverse and ggplot2 packages and were performed on a server running Arch Linux, R version 4.3.2 and Rstudio Server 2023.03.0.\n
\n\n \n Behavioural olfactory tests.\n \n All behavioural experiments were conducted during the light-phase of the animals and were performed in a blinded manner. To evaluate possible differences in odour performance, C57BL/6J and APP\n \n NL-G-F\n \n mice at 1, 3 and 6 months of age underwent a buried food test. One day before the test, animals got food deprived for 18 hours. On the test day, animals got acclimated to the new environment for at least 30 minutes in a fresh cage with increased bedding volume. The test begins with placing the animal in the test cage with a food pellet buried in the bedding. The time it takes for the animals to reach the food pellet was analysed based on a video recording. The mean search time that the two groups took to find the food pellet was calculated and compared by an unpaired student\u2019s\n \n t\n \n -test. The sensitivity test evaluates whether mice can perceive odours even at weak concentrations. At the beginning of the experiment, the animals got acclimated to the odour applicator (a dry cotton swab without odour) for 30 minutes to exclude the applicator itself as a potential source of error and a new, interesting object. For the test, a pleasant-smelling odour \u201cvanilla\u201d got applied to a cotton swab in two ascending concentrations (1:1000 and 1:1 in water), and each concentration got presented to the mouse for 2 minutes consecutively, with 1 min break in between to change the odorant. Water, in which all odours are dissolved, was used as a control. Mice were filmed from the top and side with 2 synchronized cameras, and their nose was segmented and tracked offline in both videos using 2 S.L.E.A.P. networks (PMID:\n \n 35379947\n \n ). A python code was used to track the 3D position of the nose relative to the odour dispersing cotton tip, and to quantify the time spent interacting with the different odour concentrations (investigation zone < 2 cm nose to cotton tip).\n
\n\n \n Virus injections.\n \n Different viral injection into the LC region or olfactory bulb were carried out in this study. For injections into the olfactory bulb the following coordinates were used: right OB (AP: 5.00, ML: -1.07, DV: 2.57) and left OB (AP: 4.28, ML: 0.41, DV: 2.45), while injection into the LC region were made using the following coordinate: left LC (AP: -5.44, ML: -0.89, DV: 4.07) and right LC (AP:-5.44, ML: -0.99, DV: 3.99). Adjustments were made if blood vessels were right on top of the injection location. AAV-hSyn-DIO-h3MDGs / AAV1-Syn-GCamp8f; Chemogenetic activation of LC neurons was carried out to investigate if an increase in noradrenaline release could rescue the impaired olfaction in APP\n \n NL-G-F\n \n x Dbh-Cre mice. 5-month-old mice were bilaterally injected in the LC with AAV-hSyn-DIO-h3MDGs or the control AAV1-Syn-GCamp8f. \u00a0To activate H3MDGs 1 month post injection, mice were injected i.p. with 1 mg/kg CNO 30 min before undergoing the buried food test. For patch clamp recordings, a concentration of 3 \u00b5M was used. AAV5-Flex-hSyn1-APP\n \n NL-G-F\n \n -P2A-HA / AAV-5-Flex-Ef1\u03b1-EYFP; To investigate APP\n \n NL-G-F\n \n expression exclusively in the LC, we designed a custom-build Cre-dependent AAV virus. It is a mammalian FLEX conditional gene expression AAV virus (Cre-on) with the full vector name: pAAV[FLEXon]-SYN1>LL:rev({hAPP(KM670/671NL,I716F)}/P2A/HA):rev(LL):WPRE \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (Vector ID: VB230525-1787fff). The virus is flagged with an HA-tag for post-hoc virus expression validation.\n
\n\n \n Chronic olfactory bulb window implantation.\n \n To study pathology dependent norepinephrine release in the olfactory bulb, 2-month-old\u00a0APP\n \n NL-G-F\n \n mice (n=3) and C57BL/6J (n=3) control animals were fitted with cranial windows. In short, mice were anesthetized with a mixture of Medetomidin, Midazolam and Fentanyl at 0.5, 5 and 0.05 mg/kg bodyweight respectively. Dexamethason was injected i.p. at 100 mg/kg to reduce inflammatory responses and the animal got headfixed in a stereotactic frame. The skin was cut vertically to expose lambda, bregma and the olfactory bulb and give adequate adherence space for the headbar. Surface edging was performed by scoring the skull lightly with a scalpel and applying a UV light curing mildly corrosive agent (IBond Self Etch, Kulzer 66046243). After locating the rostral rhinal vein, running just posterior of the olfactory bulb, a 3mm biopsy punch was used to indicate the craniotomy location just anterior of the vein. The Neurostar surgical robot was the used to drill the marked circle until the skull disk could be removed. The dura mater was removed on the exposed part of the left olfactory bulb. The norepinephrine sensor pAAV-hSyn-GRAB_NE1m was injected into the centre of the bulb (450 nl at 45 nl/min) at a depth of 400 \u00b5m. After injection the area was cleaned and a 3mm circular cover slip fitted over the craniotomy area. The window was fixed in place with tissue adhesive glue (Surgibond tissue adhesive, Praxisdienst, 190740). The entire area with exposed skull was subsequently filled with dental cement (Gradia Direct Flo BW, Spree Dental, 2485494) and a headbar suitable for the later utilized 2P-microscope quickly placed over the window. The cement was cured with UV. After surgery the mice received 5 mg/kg Enrofloxacin as an antibiotic, 25 mg/kg Carprofen to reduce inflammation and 0.1 mg/kg Buprenorphin as an analgesic. A mixture of Atipamezol and Flumazenil (2.5 and 0.5 mg/kg) was used to antagonize the anaesthesia.\n
\n\n \n 2-photon imaging.\n \n One month after surgery all mice were trained on the wheel used for awake\n \n in vivo\n \n imaging, their windows cleaned and the injection site checked for expression. A delivery method for a vanilla scent was established by combining a tube connected to a picospritzer system (PSES-02DX) with a vial containing vanilla aroma (Butter-Vanille, Dr. Oetker, 60-1-01-144800). The tube opening was placed at a fixed distance of roughly 4cm in front of the mouse and a vacuum pump placed slightly behind the head to ensure quick dispersion of the scent after an airpuff was delivered. The two photon microscope system was the Femptonix system ATLAS with a Coherent Chameleon tunable laser set at 920nm. Three locations were imaged per mouse at depths between 30 and 60 \u00b5m below the surface with an 16x objective. Over three minutes a z-stack of 120x120x30 \u00b5m with a pixel size of 0.22 \u00b5m and a z step of 1 \u00b5m was recorded at 1.13 Hz. After one minute of baseline recording, 10 seconds of a vanilla delivering airpuff were administered. After each three-minute recording 20 minutes of waiting time separated the subsequent recording and ensured the dispersion of the odour inside of the imaging setup.For an additional long term trial, one WT mouse was imaged for 18 minutes with the above mentioned settings. Here, vanilla airpuffs at 10 seconds of length were applied at 5, 10 and 15 minutes. The recordings were loaded into Fiji and each z-stack projected with a summation of all 30 slices. Afterwards the EZCalcium Motion Correction (based on NoRMCorre) (PMID: 32499682) was used to reduce motion artefacts. For each individual recording the frame brightness was normalized to the average of the baseline frames 20-67 before the vanilla airpuff and the average of the three adjusted curves calculated. The first 20 frames were removed to account for inconsistencies at the start of each recording, such as startling of the animal. For the 18 minute recording the average was taken from frames 20-300. Heatmaps were created with the Python Seaborn distribution.\n
\n\n \n Acute slice electrophysiology (perforated-patch-clamp).\n \n Acute brain slice recordings were performed as previously described\n \n 54\u201356\n \n . Mice were anaesthetized with isoflurane and subsequently decapitated, before the brain was rapidly removed and stored in cold (4\u00b0C) glycerol aCSF. 300 \u00b5m thick slices containing the region of the locus coeruleus and the olfactory bulb were cut in carbogenated (95% O2 and 5% CO2) glycerol aCSF (230 mM Glycerol, 2.5 mM KCl, 1.2 mM NaH2PO4, 10 mM HEPES, 21 mM NaHCO3, 5 mM glucose, 2 mM MgCl2, 2 mM CaCl2 (pH 7.2, 300-310 mOsm), using a vibration microtome (Leica VT1200S, Leica Biosystems, Wetzlar, Germany). Slices were immediately transferred into a maintenance chamber with warm (36\u00b0C) carbogenated aCSF (125 mM NaCl, 2.5 mM KCl, 1.2 mM NaH2PO4, 10 mM HEPES, 21 mM NaHCO3, 5 mM glucose, 2 mM MgCl2, 2 mM CaCl2 (pH 7.2, 300-310 mOsm)). After 50 min recovery, slices were kept at room temperature (~22\u00b0C) waiting for recordings. For electrophysiological recordings, slices were individually transferred into a recording chamber and perfused with carbogenated aCSF at a flow rate of 2.5 ml/min. The temperature was controlled with a heat controller and set to 26 \u00b0C. Perforated patch-clamp recordings were obtained from LC neurons and OB mitral cells visualized with an upright microscope, using a 60x water immersion objective. Biocytin labelling and post-hoc immunohistochemistry was used to confirm the right cell type. Patch pipettes were fabricated from borosilicate glass capillaries (outer diameter: 1.5 mm, inner diameter: 0.86 mm, length: 100 mm, Harvard Apparatus) with a vertical pipette puller (Narishige PC-10, Narishige Int. Ltd., London, UK). When filled with internal solution (tip-filled with potassium-D-gluconate intracellular pipette solution 1: 140 mM potassium-D-gluconate, 10 mM KCl, 10 mM HEPES, 0.1 mM EGTA, 2 mM MgCl2 (pH 7.2, ~290 mOsm) and back-filled with potassium-D-gluconate intracellular pipette solution 2: 140 mM potassium-D-gluconate, 10 mM KCl, 10 mM HEPES, 0.1 mM EGTA, 2 mM MgCl2, 0.02% Rhodamine Dextran, ~200 mg/ml Amphotericin B (dissolved in DMSO) and if needed 1% biocytin (pH 7.2, ~ 290 mOsm), they had a resistance of 4-5 MOhm. All experiments were performed using an EPC10 patch clamp (HEKA, Lambrecht, Germany) and controlled with the software PatchMaster (version 2.32; HEKA). The liquid junction potential (~14.6 mV) was compensated prior to seal formation and recordings were always compensated for series resistance and capacity. All executed protocols were recorded with Spike 2 (version 10a, Cambridge Electronic Design, Cambridge, UK). Data were sampled with 10 to 25 kHz and low-pass filtered with a 2 kHz Bessel filter.\n
\n\n \n Human TSPO-PET imaging acquisition and analysis.\n \n For PET imaging an established standardized protocol was used\n \n 57\u201359\n \n . All participants were scanned at the Department of Nuclear Medicine, LMU Munich, using a Biograph 64 PET/CT scanner (Siemens, Erlangen, Germany). Before each PET acquisition, a low-dose CT scan was performed for attenuation correction. Emission data of TSPO-PET were acquired from 60 to 80 minutesafter the injection of 187 \u00b1 11 MBq [\n \n 18\n \n F]GE-180 as an intravenous bolus, with some patients receiving dynamic PET imaging over 90 minutes. The specific activity was >1500 GBq/\u03bcmol at the end of radiosynthesis, and the injected mass was 0.13 \u00b1 0.05 nmol. All participants provided written informed consent before the PET scans. Images were consistently reconstructed using a 3-dimensional ordered subsets expectation maximization algorithm (16 iterations, 4 subsets, 4 mm gaussian filter) with a matrix size of 336 \u00d7 336 \u00d7 109, and a voxel size of 1.018 \u00d7 1.018 \u00d7 2.027 mm. Standard corrections for attenuation, scatter, decay, and random counts were applied. The 60-80 min p.i. images of all patients and controls were analysed.\n
\n\n \n Small animal TSPO \u03bcPET.\n \n All small animal positron emission tomography (\u03bcPET) procedures followed an established standardized protocol for radiochemistry, acquisition and post-processing\n \n 60,61\n \n . In brief, [\n \n 18\n \n F]GE-180 TSPO \u03bcPET with an emission window of 60-90 mins post injection was used to measure cerebral microglial activity. APP\n \n NL-G-F\n \n and age-matched C57BL/6 mice were studied at ages between two and twelve months. The TSPO \u00b5PET signal in the cortex and the hippocampus was previously reported in other studies\n \n 62\u201364\n \n . All analyses were performed by PMOD (V3.5, PMOD technologies, Basel, Switzerland).Normalization of injected activity was performed by the previously validated myocardium correction method\n \n 65\n \n . TSPO \u03bcPET estimates deriving from predefined volumes of interest of the Mirrione atlas\n \n 66\n \n were used: olfactory bulb (xx mm\u00b3) and cortical composite (xx mm\u00b3). Associations of TSPO \u00b5PET estimates with age and genotype as well as the interaction of age*genotype were tested by a linear regression model.\u00a0We performed all PET data analyses using PMOD (V3.9; PMOD Technologies LLC; Zurich; Switzerland). The primary analysis used static emission recordings which were coregistered to the Montreal Neurology Institute (MNI) space using non-linear warping (16 iterations, frequency cutoff 25, transient input smoothing 8x8x8 mm\u00b3) to a tracer-specific template acquired in previous in-house studies. Intensity normalization of all PET images was performed by calculation of standardized uptake value ratios (SUVr) using the cerebellum as an established pseudo-reference tissue for TSPO-PET (9).\n
\n\n \n Human olfactory test.\n \n For detecting decreased olfactory performance due to neurodegenerative diseases, the \"Sniffin' Sticks - Screening 12\" test was employed. Developed in collaboration with the Working Group \"Olfactology and Gustology\" of the German Society for Otorhinolaryngology, Head and Neck Surgery, the test provides a preliminary diagnostic orientation and can be conveniently used in everyday settings. It classifies individuals as anosmics (no olfactory ability), hyposmics (reduced olfactory ability), or normosmics (normal olfactory ability)\n \n 67\n \n . The participants are presented with 12 familiar scents (health-safe aromas, mostly used in food as flavourings) separately, in succession. Both nostrils are assessed simultaneously. Each scent is presented with a multiple-choice format, where participants choose one of four terms that best describe the scent, even if they perceive no smell. During testing, no feedback is provided to ensure unbiased responses. Demographic details of the subjects are listed in Supplementary Table 3.\n
\n\n \n Statistics\n \n
\n\n All statistical analyses were performed in GraphPadPrism (version 10.1.1). Data are reported as mean \u00b1 s.e.m. Significance was set at\n \n P\n \n <\u20090.05 and expressed as\n \n *P\n \n <\u20090.05,\n \n **P\n \n <\u20090.01,\n \n ***P\n \n <\u20090.001 and ****P<0.0001. Statistical details of every experiment are explained in Supplementary Table 1 and 2.\n
\n\n Dataset 1\n
\n \n\n Dataset 2\n
\n \n\n Dataset 3\n
\n \n\n Extended Data Table 1\n
\n \n\n How dysregulation of neurodevelopment relates to medulloblastoma (MB), the most common pediatric brain tumor, remains elusive. Here, we uncovered a neurodevelopmental epigenomic program being hijacked to induce MB metastatic dissemination. Unsupervised analyses by integrating publicly available datasets with our newly generated data revealed that SMARCD3/BAF60C regulates DAB1-mediated Reelin signaling in Purkinje cell migration and MB metastasis by orchestrating\n \n cis\n \n -regulatory elements (CREs) at the\n \n DAB1\n \n locus. We further identified that a core set of transcription factors, enhancer of zeste homolog 2 (EZH2) and nuclear factor I X (NFIX), coordinates with the CREs at the\n \n SMARCD3\n \n locus to form a chromatin hub for controlling SMARCD3 expression in the developing cerebellum and metastatic MB. Elevated SMARCD3 activates Reelin/DAB1-mediated Src kinase signaling, resulting in MB response to Src inhibition. These data deepen our understanding of how neurodevelopmental programming influences disease progression and provide a potential therapeutic option for MB patients.\n
\n\n The development of an organism is a precisely orchestrated temporal and spatial process, in which dysregulation of every biological factor may be related to diseases, such as medulloblastoma (MB), the most common brain cancer of childhood. MB is classified as an embryonal tumor arising in the cerebellum and causes a high rate of morbidity and mortality in children\n \n \n 1\n \n ,\n \n 2\n \n \n . Molecular characterization of MB revealed the disease heterogeneity associated with four major subgroups, WNT, SHH, Group 3, and Group 4\n \n 3,4\n \n . Notably, Group 3 MB (G3 hereafter), accounting for 25%-30% of all MBs, is the most aggressive and malignant, characterized by frequent metastasis at diagnosis and the worst prognosis\n \n \n 5\n \n \n . While surgical resection, radiation, and chemotherapy are effective at eliminating some forms of MBs, patients with high-risk tumors (\n \n e\n \n .\n \n g\n \n ., G3) are more likely to suffer disease progression after initial therapy.\n
\n\n Metastatic tumors, rather than primary tumors or recurrent tumors at the primary sites, have a particularly high mortality rate in MB patients\n \n \n 6\n \n ,\n \n 7\n \n \n . Despite rarely spreading to extraneural organs, MB metastasizes almost exclusively to the spinal and intracranial leptomeninges through the cerebrospinal fluid and/or the bloodstream\n \n \n 6\n \n ,\n \n 8\n \n ,\n \n 9\n \n \n . However, how MB cells acquire the capability of mobility for metastatic dissemination is poorly understood.\n
\n\n G3 is thought to arise from Nestin\n \n +\n \n early neural stem cells that give rise to GABAergic and glutamatergic neurons, the two major lineages of the cerebellum\n \n \n 10\n \n \n . Over the past decades, the morphological, cellular, and molecular features of the developing cerebellum have been extensively explored, implicating that abnormal cerebellar development is a major determining factor for neurological diseases, including MB\n \n \n 11\n \n \u2013\n \n 13\n \n \n . Although MB is linked to aberrant cerebellar development, cellular and molecular mechanisms of tumor metastatic dissemination remain elusive.\n
\n\n In this study, we identified a novel molecular circuit to regulate the migration and positioning of Purkinje cells (PCs), a principal GABAergic neuron in cerebellar development. Interestingly, MB hijacks this molecular circuit using an abnormal epigenetic program to promote tumor metastatic dissemination. These findings shed light on the mechanisms associated with tumor dissemination and potential new targeted therapies for this devastating brain cancer in children.\n
\n\n Given that epigenetic deregulation plays a critical role in the development and progression of MB\n \n \n 14\n \n \n , we explored epigenetic regulators involved in the oncobiology of G3. We first defined G3-associated differentially expressed genes (DEGs) by analyzing transcriptomic data from 1,350 patient MB and 291 normal cerebellum samples\n \n \n 15\n \n \n (Fig.\n \n 1\n \n a). Second, the G3-associated DEGs were intersected with epigenetic-related genes from the EpiFactors database containing 720 DNA/RNA-, histone-, and chromatin-modifying enzymes and their cofactors\n \n \n 16\n \n \n . Surprisingly,\n \n SMARCD3\n \n was the sole G3-associated DEG related to epigenetic modifications (Fig.\n \n 1\n \n b). Analysis of two transcriptomic datasets\n \n \n 15\n \n ,\n \n 17\n \n \n revealed that\n \n SMARCD3\n \n mRNA expression levels were significantly higher in G3 than those in other MB subgroups and normal tissues (Fig.\n \n 1\n \n c and\n \n Extended Data\n \n Fig.\n \n 1\n \n a). Analysis of single-cell RNA sequencing (scRNAseq) data\n \n \n 18\n \n \n demonstrated that the majority of G3 cells (40.98%) expressed\n \n SMARCD3\n \n compared with cells in other subgroups (G4: 15.67%; SHH: 5.43%; WNT: 13.14%) (Fig.\n \n 1\n \n d and\n \n Extended Data\n \n Fig.\n \n 1\n \n b). Consistently, higher levels of SMARCD3 protein expression were observed in G3 compared with other MB subgroups in a proteomic dataset\n \n \n 19\n \n \n (Fig.\n \n 1\n \n e). Higher levels of\n \n SMARCD3\n \n mRNA expression were significantly correlated with poorer prognosis of MB patients, which was independent of age and sex (Fig.\n \n 1\n \n f and\n \n Extended Data\n \n Fig.\n \n 1\n \n c). Immunohistochemistry (IHC) analysis using human MB tissue microarrays revealed that high levels of SMARCD3 protein were also associated with worse patient outcomes (Fig.\n \n 1\n \n g). These results suggest that SMARCD3 may play a critical role in G3 development and progression.\n
\n\n
\n\n To determine SMARCD3 functions, we performed gene ontology (GO) analysis based on SMARCD3-associated genes in MB using a transcriptomics dataset\n \n \n 4\n \n \n (\n \n Supplementary Table 1\n \n ) and identified that SMARCD3 was involved in biological processes for regulating cell membrane projection and organization related to cell motility and migration (Fig.\n \n 1\n \n h). Thus, we hypothesized a positive correlation between high levels of SMARCD3 expression and increased tumor metastasis. To this end, analysis of transcriptomic and proteomic datasets\n \n \n 4\n \n ,\n \n 19\n \n \n revealed that patients with metastases from all MB subgroups and G3 only exhibited higher levels of SMARCD3 mRNA and protein expression than those in patients without metastases (Fig.\n \n 1\n \n i and\n \n Extended Data\n \n Fig.\n \n 1\n \n d), respectively. Consistently, patients with higher SMARCD3 levels had a higher frequency of tumor metastasis (\n \n Extended Data\n \n Fig.\n \n 1\n \n e, f). Experimentally, G3 cell lines with higher SMARCD3 expression levels exhibited increased migratory abilities in transwell assay and a higher metastatic capacity in the brain and spine of xenograft MB mice (Fig.\n \n 1\n \n j, k, and\n \n Extended Data\n \n Fig.\n \n 1\n \n g). Together, these data demonstrate a strong correlation between SMARCD3 expression and tumor migration and metastasis in MB.\n
\n\n \n SMARCD3 drives MB cell migration and tumor metastatic dissemination\n \n
\n\n To examine if SMARCD3 promotes MB cell migration\n \n in vitro\n \n and\n \n in vivo\n \n , we generated CRISPR/Cas9-mediated SMARCD3 knockout (KO) G3 cell lines and found that\u00a0SMARCD3 deletion significantly decreased cell migration in MED8A and D341 cells by scratch-wound healing and transwell assays\u00a0(\n \n Fig. 2a\n \n ,\n \n b\n \n ,\n \n \n and\n \n Extended Data Fig.\u00a02a\n \n -\n \n d\n \n ).\u00a0Bioluminescence imaging (BLI) of orthotopic xenograft mice bearing MED8A with SMARCD3 KO showed a decreasing percentage of spinal metastasis compared with control (WT) (\n \n Fig. 2c\n \n and\n \n Extended Data Fig.\u00a02e\n \n ). Notably, we observed that SMARCD3 was highly expressed in the tumor margin compared with the tumor center (\n \n Fig. 2d\n \n ), suggesting that MB cells with high levels of SMARCD3 tend to spread from the primary tumor site.\n
\n\n Of note, SMARCD3 expression levels in the metastatic tumor cell line D458 were higher than those in the matched primary tumor cell line D425\n \n 20\n \n (\n \n Fig. 1j\n \n ). To further test the SMARCD3 function in determining MB metastatic dissemination, we performed loss- and gain-of-function studies using these paired cell lines. SMARCD3 deletion significantly decreased D458 cell migration and spinal metastasis in\u00a0orthotopic\u00a0xenograft mice (\n \n Fig. 2e\n \n ,\n \n f\n \n ,\n \n Extended Data Fig.\u00a02f\n \n ,\n \n g\n \n ). Circulating tumor cells (CTCs) in peripheral blood are considered to mediate MB leptomeningeal metastasis\n \n 6\n \n . Therefore, we generated the\u00a0orthotopic\u00a0xenograft mice bearing GFP-labeled D458 cells with SMARCD3 KO or WT (\n \n Fig. 2g\n \n ) and observed fewer mice with CTCs (at least more than 1 GFP\n \n +\n \n cell in 10,000 total peripheral blood mononuclear cells) after SMARCD3 deletion (\n \n Fig. 2h\n \n ). Conversely,\u00a0overexpression (OE) of SMARCD3 in D425 significantly increased cell migration, spinal metastasis, and the percentage of tumor-bearing mice with CTCs (\n \n Fig. 2i\n \n -\n \n k\n \n ,\n \n Extended Data Fig.\u00a02h\n \n ,\n \n i\n \n ).\u00a0Moreover, SMARCD3-enhanced tumor dissemination was visualized in the local brain cortex and the spinal cord by assessing D425 (WT\n \n vs\n \n SMARCD3 OE)-derived GFP\n \n +\n \n xenograft mice (\n \n Fig. 2l\n \n ,\n \n m\n \n ). These results suggest a pivotal role of SMARCD3 in the phenotypic determination of MB cell migration and metastasis.\n
\n\n We observed moderate survival differences in mice bearing orthotopic xenograft tumors with SMARCD3 deletion or overexpression compared with the controls (\n \n Extended Data Fig.\n \n \n 2j\n \n ). This could be explained by a mechanism whereby SMARCD3 moderately influences tumor cell proliferation, leading to the continuing growth of the primary tumors. However, we grouped these mice to increase cohort size and found a significantly decreased survival in mice with high levels of SMARCD3 expression (MED8A, D458, and D425-SMARCD3 OE) compared with mice with low levels of SMARCD3 expression (MED8A-SMARCD3 KO, D458-SMARCD3 KO, and D425) (\n \n Fig. 2n\n \n ). These data support that SMARCD3-induced metastasis, rather than proliferation, predominantly contributes to a worse prognosis in these mouse models, further supported by the evidence of no correlation between proliferating cell nuclear antigen (PCNA) and SMARCD3 expression in MB patients (\n \n Extended Data Fig.\n \n \n 2k\n \n ). Collectively,\n \n in vitro\n \n and\n \n in vivo\n \n loss/gain-of-function studies aligning with patient data analysis suggest that SMARCD3 acts as the main driver in MB metastatic dissemination.\n
\n\n \n SMARCD3 upregulates DAB1-mediated Reelin signaling to promote MB cell migration\n \n
\n\n To delineate molecular mechanisms of SMARCD3 promoting MB metastasis, we performed RNAseq on SMARCD3 KO\n \n vs\n \n WT MED8A cells. Ingenuity pathway analysis (IPA) based on the 44 downregulated and 67 upregulated DEGs (4-fold change;\n \n P\n \n < 0.05) showed the most significant enrichment of Reelin signaling in neurons (\n \n Fig. 3a\n \n and\n \n Supplementary Table 2\n \n ). Reelin\u00a0plays a critical role in cell migration and positioning throughout the central nervous system by\u00a0binding to its receptors, the very-low-density lipoprotein receptor (VLDLR) and/or the apolipoprotein E receptor-2 (ApoER2, encoded by LRP8 gene), and\u00a0promoting downstream activation of\u00a0Disabled-1 (DAB1) signaling\n \n 21\n \n . Notably, decreased gene expression of the key components of Reelin signaling, such as\n \n RELN,\n \n \n VLDLR\n \n ,\n \n DAB1\n \n , and\n \n DCC\n \n , was observed in SMARCD3 KO MED8A cells (\n \n Fig. 3b\n \n ).\n
\n\n DAB1 plays an essential role in Reelin signaling activation, which is mediated by phosphorylation of key tyrosine residues (\n \n e.g.,\n \n Y232) when Reelin binds to VLDLR/ApoER2\n \n 21,22\n \n . To test our hypothesis that SMARCD3 upregulates the\n \n DAB1\n \n transcription activity, we validated that DAB1 expression was significantly decreased in SMARCD3 KO MED8A and D458 cells but increased in SMARCD3-overexpressed \u00a0MED8A, D425, and D556 cells (\n \n Fig. 3c\n \n ,\n \n d\n \n ). Integrated analysis of transcriptomic and proteomic data from MB patient samples\n \n 19\n \n revealed that the\n \n DAB1\n \n mRNA expression was strongly correlated with translational and post-translational modifications of DAB1 protein, including phosphorylation on\n \n serine\n \n ,\n \n threonine\n \n , or\n \n tyrosine\n \n (pSTY), particularly Y232 (\n \n Extended Data Fig.\n \n \n 3a\n \n ). Based on analysis of MB patient datasets\n \n 15,19\n \n ,\n \n DAB1\n \n mRNA levels were significantly higher in G3 than those in other MB subgroups and normal cerebellum tissues (\n \n Fig. 3e\n \n ); and DAB1 protein levels also tended to be higher in G3 compared with other MB subgroups (\n \n Fig. 3f\n \n and\n \n Extended Data Fig.\n \n \n 3b\n \n ). Furthermore, we found positive correlations between SMARCD3 and DAB1 in transcriptional, translational, and post-translational levels (\n \n Fig. 3g\n \n ,\n \n h\n \n ,\n \n \n and\n \n \n \n Extended Data Fig.\n \n \n 3c\n \n ) using MB patient datasets\n \n 4,19\n \n . Functional validations revealed that DAB1 deletion significantly decreased cell migration in MED8A (\n \n Fig. 3i\n \n ,\n \n j\n \n ). Analysis of a patient dataset\n \n 4\n \n revealed that\n \n DAB1\n \n expression was associated with MB metastasis (\n \n Fig. 3k\n \n ,\n \n l\n \n ).\u00a0Together, these results suggest that SMARCD3 transcriptionally regulates Reelin-DAB1 signaling to promote cell migration and MB metastasis.\n
\n\n \n Spatiotemporal expression patterns of SMARCD3 relate to Reelin-DAB1 signaling in cerebellar development\n \n
\n\n Given a positive correlation between SMARCD3 and DAB1, we asked whether this association exists in other human cancers or\u00a0normal organs. Pan-cancer analyses using\u00a0The Cancer Genome Atlas (TCGA) datasets revealed that the\u00a0levels of\n \n SMARCD3\n \n and\n \n DAB1\n \n mRNA expression were not correlated (\n \n R\n \n = 0.17,\n \n P\n \n < 2.2e-16) (\n \n Extended Data Fig. 3d\n \n ). While both SMARCD3 and DAB1 were highly expressed in low-grade glioma and glioblastoma, their expression levels were not positively correlated in these tumors (\n \n R\n \n = -0.11,\n \n P\n \n = 0.0023) (\n \n Extended Data Fig. 3e\n \n ). Gene expression correlation analysis in various human normal organs revealed that SMARCD3 and DAB1 were significantly correlated and highly expressed in the brain compared with other organs and in the cerebellar hemisphere/cerebellum compared with other parts of the brain, respectively (\n \n Extended Data Fig. 3f\n \n ,\n \n g\n \n ). Analysis of gene-specific patterns of expression variation across organs and species\n \n 23\n \n revealed that SMARCD3 and DAB1 expression varied considerably across organs and little across species (\n \n Extended Data Fig. 3h\n \n ), indicating potential evolutionary conservation of organ-specific gene expression throughout vertebrates. These data suggest that SMARCD3 regulating DAB1-mediated Reelin signaling is unique to the cerebellum in physiological conditions and MB in pathological conditions.\n
\n\n Reelin signaling is known to critically control PC radial migration and cerebellar circuit function in brain development\n \n 13\n \n . Thus, we asked whether SMARCD3 expression is positively correlated with Reelin signaling in the developmental trajectory of the cerebellum. We analyzed scRNAseq data from the\u00a0developing murine cerebellum\n \n 24\n \n , and found that\n \n Smarcd3\n \n ,\n \n Dab1\n \n ,\n \n Vldlr\n \n , and\n \n Lrp8\n \n mRNA were highly expressed in PCs (\n \n Fig. 4a\n \n ,\n \n b\n \n ,\n \n \n and\n \n Extended Data Fig. 4a\n \n ). PCs emerge in the ventricular zone (VZ) from embryonic day 10.5 (E10.5) to E13.5 in mice and from gestation week (GW) 7 to GW13 in humans\n \n 25,26\n \n (\n \n Extended Data Fig. 4b\n \n ). Then, PCs migrate toward the outer surface of the cerebellar cortex to subsequently form the Purkinje cell layer (PCL) from E12.5 to the early postnatal days in mice and during GW16-GW28 in humans\n \n 13,27,28\n \n . Reelin secreted by glutamatergic neurons (granule cells, GCs) acts on PCs, and activates its downstream VLDLR/ApoER2-DAB1 signaling pathway to control PC migration\n \n 29,30\n \n . We found low levels of\n \n Smarcd3,\n \n \n Dab1\n \n ,\n \n Vldlr\n \n , and\n \n Lrp8\n \n but\n \n \n high levels of\n \n Reln\n \n expression in GCs (\n \n Fig. 4b\n \n ). Further analysis of spatial-temporal gene expression revealed a similar trajectory of\n \n Smarcd3\n \n expression and Reelin signaling, particularly\n \n Dab1\n \n expression in PCs; and high levels of\n \n Reln\n \n expression in GCs from E13.5 to the postnatal stages (\n \n Fig. 4c\n \n and\n \n Extended Data Fig. 4c\n \n ). Moreover, we performed immunofluorescence (IF) staining of SMARCD3 and the PC-specific markers FOXP2 and Calbindin 1 (CALB1), respectively, using mouse cerebellar tissues. Notably, we observed increased levels of SMARCD3 protein expression that colocalize with FOXP2 and CALB1 at E15.5 and postnatal day 0 (P0), respectively; and dramatically decreased levels of SMARCD3 after P0 that remain low or undetectable at P7, P28, and P84 in the mouse cerebellum (\n \n Fig. 4d\n \n ,\n \n e\n \n ).\n
\n\n To validate expression patterns of SMARCD3 and Reelin signaling in the human cerebellum, we analyzed single-nucleus RNA sequencing (snRNAseq) data of\u00a013 human\u00a0cerebella ranging in age from 9 to 21 post-conceptional weeks\n \n 31\n \n . After defining cell types and assembling cell-type-specific transcriptomes (\n \n Extended Data Fig.\n \n \n 4d\n \n ,\n \n e\n \n ), we found that\n \n SMARCD3\n \n was highly expressed and associated with\n \n DAB1\n \n ,\n \n VLDLR\n \n , and\n \n LRP8\n \n expression in PCs; that\n \n RELN\n \n was exclusively expressed in glutamatergic neurons, including precursor, cerebellar nuclei, and GCs (\n \n Fig. 4f\n \n ). We further analyzed the normalized gene expression data of 291 normal cerebellum samples over four age groups: fetal (year \u2264 0), infants (\u20600 < year\u2009\u2009\u2264 3), children (\u20603 < year < 18\u2060), and adults (\u2265 18\u2009\u2009years\u2060)\n \n 15\n \n .\n \n SMARCD3\n \n mRNA expression was increased from ~GW13 to ~GW28, then dramatically decreased during 1 year postnatal, and maintained at low levels in infant, children, and adult age groups (\n \n Fig. 4g\n \n ,\n \n h\n \n ). Together, transcriptomic analysis of mouse and human developing cerebellum demonstrates that spatiotemporal expression patterns of SMARCD3 are associated with Reelin signaling in controlling PC migration during cerebellar development.\u00a0Furthermore, GO-term analysis based on the genes that were positively related to SMARCD3 during human cerebellum development revealed enrichment of cell projection assembly and organization, brain development, and response to wounding (\n \n Supplementary\n \n \n Table 3\n \n and\n \n \n \n Extended Data Fig.\n \n \n 4f\n \n ). Furthermore, gene-disease network analysis revealed enrichment of childhood and adult MB using these SMARCD3-associated developmental genes in\u00a0DisGeNET\u00a0(\n \n Extended Data Fig.\u00a04g\n \n ). Collectively,\u00a0these results indicate that MB hijacks SMARCD3-Reelin-DAB1 mediated cell migration, a neurodevelopmental program in the cerebellum, to promote tumor metastatic dissemination in MB.\n
\n\n \n SMARCD3 modulates chromatin accessibility and\n \n cis\n \n -transcription elements controlling\n \n DAB1\n \n expression in neurodevelopment and MB\n \n
\n\n SMARCD3, also known as BAF60C, a subunit of the BRG1/BRM-associated factor complexes, modulates chromatin accessibility and thereby regulates temporal gene expression programs in cardiogenesis\n \n 32\n \n . To determine the functions of SMARCD3 in genome architecture for regulating gene expression involved in cell migration and tumor metastasis, we performed Assay for Transposase-Accessible Chromatin using sequencing (ATACseq) to examine chromatin accessibility genome-wide in SMARCD3 KO\n \n vs\n \n WT\n \n \n MED8A cells\u00a0(\n \n Extended Data Fig.\n \n \n 5a\n \n ). Analysis of accessibility using the nucleosome-free fragments (<100 base pairs) and mononucleosome fragments (180-247 base pairs)\n \n 33\n \n revealed global changes in chromatin accessibility in the absence of SMARCD3 (\n \n Fig. 5a\n \n and\n \n \n \n Extended Data Fig.\n \n \n 5b\n \n ).\n \n \n We found 20,578 ATACseq peaks with increased accessibility and 10,131 peaks with decreased accessibility in SMARCD3 KO\n \n vs\n \n WT controls out of 144,432 total accessible regions identified (\n \n Fig. 5a\n \n ). Genes (n=725) proximal to these less accessible peaks (positive correlation with SMARCD3) were involved in cellular movement, assembly, and organization by IPA analysis (\n \n Fig. 5b\n \n ). These data suggest that SMARCD3 regulates chromatin remodeling for promoting cell migration and tumor dissemination.\n
\n\n We next assigned these differentially accessible regions to the nearest genes that could be regulated by the\n \n cis\n \n -regulatory elements (CREs). Of note, changes of most genes (90.29%) in chromatin accessibility corresponded to changes in gene expression by RNAseq (\n \n Fig. 5c\n \n ). Specifically, the decreased accessibility of\n \n DAB1\n \n in the absence of SMARCD3 was consistent with its decreased levels of mRNA expression (\n \n Fig. 5c\n \n and\n \n 3b\n \n ). To identify the specific CREs in the genome controlling SMARCD3-mediated DAB1 gene regulation, we first defined the topologically associating domain (TAD) regions that were enriched in the\n \n DAB1\n \n locus using available Hi-C data\n \n 34\n \n (\n \n Extended Data Fig. 5c\n \n ). Second, we analyzed ATACseq data between MED8A SMARCD3 KO\n \n vs\n \n WT and found 4 decreased accessibility regions within the DAB1 locus-containing TAD in the absence of SMARCD3 (\n \n Fig. 5d\n \n ). To explore the functions of these CREs, we performed cleavage under targets and release using nuclease (CUT&RUN)\n \n 35,36\n \n in SMARCD3 KO\n \n vs\n \n WT MED8A (\n \n Extended Data Fig. 5d\n \n ). The 4 CREs (CRE1, CRE2, CRE3, and CRE4) were enriched for chromatin accessibility, H3K4me1, H3K4me3, and H3K27ac, which were attenuated in the absence of SMARCD3 (\n \n Fig. 5d\n \n ). Notably, there were obvious changes of CRE2 for accessibility and H3K4me3 at the transcription start site (TSS) of\n \n DAB1\n \n between SMARCD3 KO and WT (\n \n Fig. 5d\n \n ), indicating a key function of CRE2 in SMARCD3-mediated DAB1 transcriptional activity.\n
\n\n To validate these CREs involved in DAB1 regulation in cerebellar development and MB, we analyzed a dataset of\u00a0chromatin immunoprecipitation sequencing (ChIPseq)\u00a0chromatin modification profiles\u00a0and RNAseq-based transcriptomics from 5 human G3 MB samples\n \n 37\n \n .\u00a0We first classified the 5 tumors into the\n \n SMARCD\n \n 3 mRNA expression high h, and low l, groups (\n \n Extended Data Fig. 5e\n \n ). Second, the ChIPseq enrichment data from the 4 CREs proximal to the\n \n DAB1\n \n locus in each tumor were pooled into H and L groups, respectively. Thus, we observed histone mark enrichment (H3K4me1, H3K4me3, and H3K27ac) at these CREs, particularly CRE2, from the high group compared with the low group (\n \n Fig. 5e\n \n ). We analyzed the ChIPseq datasets from mouse cerebellum\n \n 38\n \n and found increased H3K4me3 and H3K27ac signals from E12.5 to P0, but a decreased H3K4me3 and H3K27ac signals at P56, localizing at these CREs of the\n \n Dab1\n \n locus, particularly CRE2 (\n \n Fig. 5f\n \n and\n \n Extended Data Fig. 5f\n \n ), which corresponded to\n \n \n \n Dab1\n \n expression during mouse cerebellar development (\n \n Extended Data Fig. 5g\n \n ). These data suggest that SMARCD3 epigenetically regulates DAB1 transcriptional activity by controlling chromatin accessibility and histone modifications at\n \n cis\n \n -regulatory elements in the developing cerebellum and MB.\n
\n\n \n Spatiotemporal chromatin architecture regulates SMARCD3 transcription in MB and the developmental trajectory of the cerebellum\n \n
\n\n To examine the epigenetic regulation of SMARCD3 in MB and the cerebellum, we analyzed ATACseq data of MED8A and identified the 7 accessible regions (CRE1-7) proximal to the\n \n SMARCD3\n \n locus (\n \n Fig. 6a\n \n ). To define these open chromatin regions as putative CREs regulating\n \n SMARCD3\n \n transcriptional activity, we performed CUT&RUN\u00a0on H3K4me1, H3K4me3, H3K27ac, H3K27me3, and H3K9me3 in MED8A cells and assessed the histone modification abundance at these CREs. Notably, these chromatin regions were enriched with peaks of H3K4me1, H3K4me3, and/or H3K27ac as hallmarks of active or poised enhancers. To verify these CREs involved in SMARCD3 regulation in MB, we analyzed ChIPseq and RNAseq datasets of 5 patient samples\u00a0(Boulay et al., 2017)\u00a0and found enrichment of H3K4me1, H3K4me3, and H3K27ac at these CREs in the SMARCD3 expression high h, group compared with the low l, group (\n \n Fig. 6b\n \n and\n \n Extended Data Fig. 5e\n \n ). Particularly, H3K27ac, a marker of active enhancers and TSS, was significantly enriched at these CREs in G3 compared with other MB subgroups, which corresponded to\n \n SMARCD3\n \n expression based on analysis of a previously published RNAseq dataset\n \n 39\n \n (\n \n Extended Data Fig. 6a\n \n ,\n \n b\n \n ). To explore the functions of these CREs in mammalian development, we analyzed the temporal expression of the\n \n Smarcd3\n \n and the corresponding histone modifications in mouse cerebellum using publicly available datasets\n \n 38\n \n . We first analyzed Hi-C data to map the regulatory regions of the mouse\n \n Smarcd3\n \n locus\n \n \n in the genome (\n \n Fig. 6c\n \n ). Then, we analyzed the enrichment of histone modifications, H3K4me1, H3K4me3, H3K27ac, and H3K27me3, during cerebellar development based on the ChIPseq data. We observed higher enrichment of H3K4me3 and H3K27ac around these CREs in E16.5 and P0 compared with E12.5 and P56, which corresponded to the levels of\n \n Smarcd3\n \n mRNA expression at these time points (\n \n Fig. 6c\n \n ,\n \n d\n \n ). These results suggest that the CREs play a crucial role in regulating SMARCD3 transcription through controlling chromatin architecture.\n
\n\n To functionally evaluate the CREs in SMARCD3 regulation, we employed CRISPR/Cas9-mediated\n \n in situ\n \n genome excision to remove these CREs, leading to transcriptional inactivation of targeted genes (\n \n Fig. 6e\n \n ). qRT-PCR analysis revealed that site-specific excision of CRE1, CRE4, CRE5, CRE6, and CRE7, but not CRE2 and CRE3, resulted in a significant decrease of the\n \n SMARCD\n \n 3 mRNA expression in MED8A cells (\n \n Fig. 6\n \n \n f\n \n ). Of note, two isoforms of the SMARCD3 gene shared the CRE4-7 but not CRE1, indicating divergence in transcriptional regulation whereby we observed decreased\n \n SMARCD\n \n 3 mRNA expression after site-specific excision of CRE4-7 but not CRE1 in D458 cells (\n \n Extended Data Fig.\u00a06c\n \n ). This observation was supported by higher enrichment of H3K4me3 and H3K27ac around CRE1 in MED8A but not in D458 cells (\n \n Fig. 6a\n \n and\n \n Extended Data Fig.\u00a06d\n \n ). We further found a higher signal of H3K4me3 and H3K27ac enrichment around CRE4-7 regions in metastatic tumor-derived D458 compared with the paired primary tumor-derived D425 cells (\n \n Extended Data Fig.\u00a06d\n \n ), indicating that these CREs are involved in transcriptional activation of the SMARCD3-mediated tumor metastatic dissemination in MB.\n
\n\n To define how these CREs cooperate to regulate SMARCD3 transcription, we analyzed available datasets of the single-cell combinatorial indexing (sci) assay for profiling chromatin accessibility (sci-ATACseq) in the human fetal cerebellum\n \n 40\n \n . Analysis of these sci-ATACseq data revealed higher levels of the\n \n SMARCD3\n \n expression in the PCs compared with astrocytes, GCs, and inhibitory interneurons, which were concordant with a more open chromatin structure leading to a higher gene activity score by Cicero, an algorithm for quantitative measurement of how changes in chromatin accessibility relate to changes in the expression of nearby genes based on single-cell data\n \n 41\n \n (\n \n Extended Data Fig.\u00a06e\n \n ,\n \n f\n \n ). We further found that Cicero links were heavily enriched around the CRE4-7 at the\n \n SMARCD3\n \n locus in the PCs compared with the other three cell types (\n \n Fig. 6g\n \n and\n \n Extended Data Fig.\u00a06g\n \n ). These data suggest that the CRE1-7, particularly CRE4-7, can form chromatin hubs that physically and functionally control SMARCD3 transcriptional regulation.\n
\n\n The chromatin hubs are enriched for physical proximity, interaction with a common set of transcription factors (TFs), and orchestration of histone modifications in gene expression\n \n 41\n \n . Therefore, we generated a list of the putative TFs that should meet the following four criteria: 1) they should be differentially expressed in the human fetal cerebellum compared with infants, children, and adults (absolute log\n \n 2\n \n fold change >0.5,\n \n P\n \n <0.05); 2) they should be positively or negatively correlated to\n \n SMARCD3\n \n mRNA expression in the human normal cerebellum (\n \n R\n \n > 0.25,\n \n P\n \n < 0.05); 3) they should be positively or negatively correlated to the\n \n SMARCD3\n \n mRNA expression in G3 only or all MBs (\n \n R\n \n > 0.25,\n \n P\n \n < 0.05); 4) they are defined in the human TF database\n \n 42\n \n . CENPA, CSRNP3, EZH2, FOXN3, NFIX, NR2F2, TEF, and ZFHX4 satisfied the above criteria, which were validated by using CRISPR/Cas9-mediated gene deletion in MB cells. qRT-PCR analysis revealed that deletion of EZH2 and NFIX most significantly decreased and increased the\n \n SMARCD3\n \n mRNA expression in MED8A cells, respectively (\n \n Fig. 6h\n \n ). Conversely, overexpression of EZH2 significantly increased\n \n SMARCD3\n \n mRNA expression in MED8A and D458 cells (\n \n Extended Data Fig.\u00a06h\n \n ). \u00a0Analysis of transcriptomic data from normal human brain showed that\n \n SMARCD3\n \n was positively correlated with\n \n EZH2\n \n (R = 0.38,\n \n P\n \n = 3.1e-06) but negatively correlated with\n \n NFIX\n \n (R = - 0.33,\n \n P\n \n = 0.0004) (\n \n Extended Data Fig.\u00a06i\n \n ).\n \n EZH2\n \n expression was significantly increased from about 19GW to 29 GW and then decreased and maintained at a low level in infants, children, and adults (\n \n Extended Data Fig. 6j\n \n ,\n \n k\n \n ); however, the changes of\n \n NFIX\n \n expression are opposite during cerebellar development (\n \n Extended Data Fig. 6l\n \n ,\n \n m\n \n ).\u00a0Taken together, these results demonstrate a comprehensive map of a chromatin hub that orchestrates CREs, chromatin accessibility, TFs, and histone modifications in regulating SMARCD3 transcription in the developing cerebellum and MB metastasis (\n \n Fig. 6i\n \n ).\n
\n\n \n Inhibition of Src kinase activity attenuates SMARCD3-induced metastatic dissemination\n \n
\n\n We identified an epigenetic program wherein the EZH2/NFIX-SMARCD3-Reelin/DAB1 signaling regulates spatiotemporal developmental trajectories of PCs in the cerebellum, which is hijacked by MB to promote tumor metastatic dissemination. The Reelin-activated Src family tyrosine kinases (SFKs) are required for the phosphorylation of DAB1 that in turn potentiates SFK activation in a positive feedback manner, which plays a central role in the activation of its downstream signaling cascades during cerebellar development\n \n 43,44\n \n . We asked whether SMARCD3 expression levels are elevated in metastatic tumors, leading to activation of SFK and response to SFK inhibitor treatment for clinical application (\n \n Fig.7a\n \n ). To this end, we assessed the protein levels of SMARCD3 and phosphorylated Src (p-Src) in 10 patient-matched primary and metastatic MBs (\n \n Fig. 7b\n \n and\n \n Supplementary Table 4\n \n ). IHC analysis revealed a positive correlation between SMARCD3 and p-Src (Y416), both of which were highly elevated in metastatic tumors compared with the paired primary tumors (\n \n Fig. 7c\n \n -\n \n e\n \n ). To further verify Src activation induced by elevated SMARCD3, we observed that deletion of SMARCD3 reduced the protein levels of p-Src in MED8A and D458 cells and these cell-derived xenograft tumors (\n \n Fig. 7f\n \n ,\n \n g\n \n ,\n \n \n and\n \n Extended Data Fig.\u00a07a\n \n ). Just as SMARCD3 expression patterns, we observed higher levels of p-Src in the tumor margin than in the tumor center (\n \n Fig. 2d\n \n and\n \n 7h\n \n ).\n
\n\n To test our hypothesis that SFK inhibition can reduce metastatic dissemination, we first examined\n \n in vitro\n \n attenuation of cell migration at the lower concentration of Dasatinib, an FDA-approved inhibitor of SFKs for leukemia. Transwell assays revealed that 50 nM Dasatinib significantly decreased cell migration of MED8A and D458 cells (\n \n Fig. 7i\n \n and\n \n \n \n Extended Data Fig.\u00a07b\n \n ). Next, Dasatinib was administered orally once daily at the standard dose of 15 mg/kg and a low dose of 7.5 mg/kg for mice bearing D458-derived orthotopic xenograft MB, respectively. BLI and flow cytometry analyses revealed that both standard and low dose Dasatinib decreased spinal metastasis and\u00a0the percentage of\u00a0mice carrying CTCs compared with placebo (\n \n Fig. 7j\n \n ,\n \n k\n \n , and\n \n Extended Data Fig.\u00a07c\n \n ). However, assessment of tumor cell proliferation and apoptosis in these mice revealed that administration with low dose Dasatinib did not significantly decrease the levels of Ki67 and cleaved caspase-3 (\n \n Fig. 7l\n \n and\n \n \n \n Extended Data Fig.\u00a07d\n \n ). The data indicate that inhibition of SFK activity mainly influences cell migration rather than cell proliferation and apoptosis. Together, these results suggest that SFK inhibition may reduce tumor cell migration and metastatic dissemination at a lower and safe dose in MB, indicating a potential repurposing of this drug for the treatment of pediatric brain tumor metastasis in clinical studies.\n
\n\n The most critical challenge in designing therapies for children with MB is to reduce tumor metastasis. How tumor cells gain motility and migration capacity to detach from the primary site remains largely unknown. In this study, we identified that G3 MB cells hijack a neurodevelopmental epigenetic program to promote metastatic dissemination whereby abnormally elevated SMARCD3 activates the Reelin/DAB1/Src signaling-mediated cell migration. Our findings provide the first evidence that SMARCD3 plays a central role in cerebellar development and G3 MB metastatic dissemination, which sheds light on the development of antimetastatic therapy for MB patients.\n
\n\n Based on unbiased analyses of MB subgroup-specific gene expression, we uncovered higher expression levels of\n \n SMARCD3\n \n mRNA and protein in the G3 subgroup, which is strongly associated with tumor metastasis and worse patient prognosis. SMARCD3, a subunit of the SWI/SNF chromatin remodeling complex, regulates gene expression programs that are essential for heart development and function\n \n \n 45\n \n ,\n \n 46\n \n \n . Under pathological conditions, SMARCD3 was reported to regulate epithelial-mesenchymal transition (EMT) in breast cancer by inducing WNT5A signaling\n \n \n 47\n \n \n . Our previous study demonstrated epigenetic upregulation of WNT5A contributing to glioblastoma invasiveness and recurrence\n \n \n 48\n \n \n . These previous studies indicate a relationship between SMARCD3 and tumor aggressiveness. However, in this study, we discovered that SMARCD3 epigenetically regulates Reelin/DAB1 signaling that plays a central role in cell migration and positioning throughout cerebellar development\n \n \n 49\n \n \n . Moreover, we identified that a positive correlation between SMARCD3 and DAB1 is evolutionarily conserved and unique in the cerebellum and MB, supporting our hypothesis that tumor cells hijack developmental signaling to promote tumor progression.\n
\n\n Our data showed that the spatiotemporal expression pattern of SMARCD3 in the developing cerebellum is strongly associated with PC migration. SMARCD3 expression is dramatically decreased at the late stage of PC development when there is no migratory activity after birth in the human and mouse cerebellum, which is regulated by the Reelin/DAB1 signaling pathway\n \n \n 30\n \n ,\n \n 50\n \n \n . These findings suggest that the SMARCD3-Reelin/DAB1 pathway acts as a modulator in the balance of \u201cGo\u201d and \u201cStop\u201d signaling in orchestrating cerebellar development. However, SMARCD3-DAB1 signaling is highly activated in MB, leading to tumor metastatic dissemination. We further defined that EZH2 and NFIX regulate SMARCD3 transcriptional activation in opposite ways through a chromatin hub. The roles of EZH2 in MB are controversial and its mechanisms of action are incompletely understood. Previous studies reported that targeting EZH2 has significant antitumor effects in medulloblastoma, including an aggressive G3 MB\n \n \n 51\n \n \u2013\n \n 54\n \n \n . Conversely, the inactivation of EZH2 accelerates MB development and progression by upregulating GFI1 and DAB2IP\n \n \n 55\n \n ,\n \n 56\n \n \n . Besides its histone methyltransferase activity, EZH2 also acts as a transcriptional co-activator in gene regulation involved in aggressive castration-resistant prostate cancer and breast cancer\n \n \n 57\n \n \u2013\n \n 59\n \n \n . NFIX, as a member of the nuclear factor I family (including NFIA and NFIB), plays a critical role in regulating granule precursor cell proliferation and differentiation within the postnatal cerebellum\n \n \n 60\n \n \n . NFIB was reported to repress\n \n Ezh2\n \n expression within the neocortex and hippocampus\n \n \n 61\n \n \n , indicating negative regulation of these TFs in brain development. Our data show that EZH2 and NFIX serve as a core set of TFs for binding to the CREs proximal to the\n \n SMARCD3\n \n locus to form a chromatin hub, which controls spatiotemporal gene expression in the cerebellum and MB metastasis. Our findings further suggest that targeting EZH2 for MB therapy is complex and challenging although multiple EZH2 inhibitors are currently active in clinical trials.\n
\n\n This study also provides new perspectives on the development of antimetastatic therapy for MB patients by testing the inhibitory effects of Dasatinib on tumor cell migration and metastatic dissemination. Although good tolerability of Dasatinib was observed in a pediatric phase I trial for patients with leukemia and other solid tumors\n \n \n 62\n \n \n , another phase I trial study reported that administration of Dasatinib at 50mg/m\n \n 2\n \n twice daily resulted in poor tolerance with significant toxicities in combination with crizotinib (an oral c-Met inhibitor) in children with recurrent or progressive high-grade and diffuse intrinsic pontine glioma\n \n \n 63\n \n \n . Failures in clinical trials for glioblastoma treatment were also observed after administering dasatinib combined with other drugs including erlotinib and bevacizumab\n \n \n 64\n \n \u2013\n \n 66\n \n \n . These clinical studies indicate that targeting SFK activation may need more specific context-dependent mechanisms to exert optimal efficacy in brain tumor treatment. In this study, we identified a cerebellum-specific developmental program that spatiotemporally regulates Purkinje cell migration cerebellar development, depending on SMARCD3-DAB1-mediated Src tyrosine kinase activation. MB hijacking this developmental program provides a strong rationale to target its downstream Src activation for reducing tumor metastatic dissemination. We showed that even lower doses of Dasatinib can reach antimetastatic effects, hopefully causing less toxicity in this specific context. Our findings provide a rationale for combining SFK inhibition, particularly low-lose Dasatinib, with other standard cytotoxic agents in the treatment of patients with G3 MB.\n
\n\n See supplementary materials\n
\n\n Suppl. text - updated 01/24\n
\n \n\n Extended Data Figures\n
\n \n\n Supplementary Table 1 The genes are associated with SMARCD3 in MB\n
\n \n\n Supplementary Table 2 The downregulated and upregulated DEGs after SMARCD3 KO\n
\n \n\n Supplementary Table 3 The genes are positively correlated to SMARCD3 during human cerebellum development\n
\n \n\n Supplementary Table 4 Clinical information of the 10 MB patients\n
\n \n\n Supplementary Table 5 sgRNA sequences targeting genes and CREs\n
\n \n\n Supplementary Table 6 Primers used in qRT-PCR\n
\n \n\n Supplementary Table 7 Genotyping primers\n
\n \n\n Supplementary Table 8 List of primary antibodies\n
\n \n\n Supplementary Table 9 List of key chemicals\n
\n \n\n Supplementary Table 10 Table containing mouse models\n
\n \n\n Supplementary Table 11 List of plasmids\n
\n \n\n Transport diffusivity of molecules in a porous solid is constricted by the rate at which molecules move from one pore to the other, along the concentration gradient, i.e. by following Fickian diffusion. In heterogeneous porous materials, i.e. in the presence of pores of different sizes and chemical environments, diffusion rate and directionality remain tricky to estimate and adjust. In such a porous system, we have realized that molecular diffusion direction can be orthogonal to the concentration gradient. To experimentally determine this complex diffusion rate dependency and get insight of the microscopic diffusion pathway, we have designed a model nanoporous structure, metal-organic framework (MOF). In this model two chemically and geometrically distinct nanopores are spatially oriented by an epitaxial layer-by-layer growth method. The specific design of the nonporous channels and quantitative mass uptake rate measurements have indicated that the mass uptake is governed by the interpore diffusion along the direction orthogonal to the concentration gradient. This revelation allows chemically carving the nanopores, and accelerating the interpore diffusion and kinetic diffusion selectivity.\n
\n\n Molecular diffusion in a nanoporous solid, e.g. zeolite, porous carbon, metal-organic framework (MOF) and covalent organic framework (COF),\n \n \n 1\n \n \u2013\n \n 5\n \n \n is an important process with regard to chemical separation\n \n \n 6\n \n ,\n \n 7\n \n \n and catalysis.\n \n \n 8\n \n ,\n \n 9\n \n \n For separating chemicals, state-of-the art nanoporous membranes\n \n \n 6\n \n \n require faster diffusion or permeation of the separated chemicals across the membrane layer, so that the production efficacy increases and cost is reduced. In heterogeneous catalysis using the nanoporous solids, reactant diffusion to the active site is the rate-determining step.\n \n 8,10\u221212\n \n Hence, for both of the applications, efficacy of the process is controlled by the diffusivity (\n \n D\n \n ). In the case of perfect molecular sieving (i.e. size-based exclusion),\n \n \n 13\n \n ,\n \n 14\n \n \n exclusively selective molecular diffusion occurs while in the case of competitive diffusion of the molecules in the pores, selectivity is decreased. However, the selectivity can be improved by specific pore environment design at different length-scales; few \u00c5ngstrom to nanometer sized pores can be geometrically and chemically tuned or the nanoporous channels can be oriented in a specific direction at micron scale to accelerate diffusion.\n \n \n 15\n \n \u2013\n \n 21\n \n \n To formulate these strategies that can accelerate diffusion and consequently the selectivity, insight into the rate determining step is necessary.\n
\n\n In the nanoporous materials, following physical processes take place during the permeation of the chemicals along the concentration gradient: A) adsorbate-pore surface interaction, B) surface to pore diffusion and C) interpore diffusion. The surface barrier phenomenon\n \n \n 22\n \n ,\n \n 23\n \n \n is related to the steps A and B, while step C is usually the rate limiting factor.\n \n \n 6\n \n ,\n \n 24\n \n \n As the permeation is directly proportional to the diffusivity and adsorbate solubility, managing the interpore diffusion is the key in case of nanoporous solids. Earlier studies revealed that the diffusion in the nanoporous solids, e.g. MOFs, can be modelled and estimated using the Fick\u2019s law.\n \n \n 25\n \n \u2013\n \n 27\n \n \n However, in the case of nonlinearity in diffusion (i.e. diffusivity as a function of mass loading in step C), an appropriate model is difficult to define.\n \n \n 26\n \n ,\n \n 27\n \n \n This nonlinearity increases with increasing mass loading, as adsorbate-adsorbate interaction also comes into play.\n \n \n 28\n \n \n Hence, a rationale to experimentally map interpore diffusion becomes more difficult. The validity of the Fickian model becomes more problematic in the case of the nonhomogeneous pores (i.e. more than one types of pore sizes and functionalities),\n \n \n 26\n \n ,\n \n 29\n \n \n which is commonly the case for nanoporous MOFs and COFs. In this communication we postulate a chemically derived path to guide and control the interpore diffusion at this nonlinear regime of mass loading.\n
\n\n The exact estimation of the molecular diffusion path (and tortuosity)\n \n \n 30\n \n \n in a nanoporous solid may not be straight forward in the presence of structural defects and disordered crystalline domains.\n \n \n 27\n \n \n Molecular simulations can be useful to understand the complex, pore topology dependent diffusion characteristics.\n \n \n 31\n \n \u2013\n \n 34\n \n \n By experimental route, it is rather more useful to assess the factors that control the interpore diffusion and find out a convenient way to tune those factors. One way to do so is to make a model porous structure and carefully analyze the mass uptake rate (M\n \n \n rate\n \n \n ). As a proof of concept, we have chosen a nanoporous system in which the pores are highly ordered; one type of the pores is aligned along the concentration gradient and another type is orthogonal to the gradient (Scheme\n \n 1\n \n ). The oriented pores are created by metal-organic ligand coordination in a layer-by-layer (lbl) liquid-phase epitaxy (LPE) method, i.e. surface-anchored MOF thin films\n \n \n 35\n \n \n and solvent vapors are used to probe the mass uptake rate. Presence of two chemically and geometrically distinct pores that are perfectly aligned orthogonal to each other helps to realize the interpore diffusion directionalities. It is revealed that the interpore diffusion in the selected structure is actually controlled by the orthogonally positioned (to the concentration gradient) pores, but not those which are aligned along the concentration gradient. This finding assists to tune the molecular diffusion process using a chemically derived route. We have used an isoreticular MOF design strategy\n \n \n 36\n \n \n to introduce different chemical functionalities in two isostructural MOFs, and in the following discussion we demonstrate its impact on the molecular diffusivities with supporting mass uptake rate experiments and simulations.\n
\n\n While considering a model porous structure, we have set the following criteria: i) preconceivable nanometer pore size and geometry, ii) periodically arranged pores with specific orientation, iii) chemical tunability, and iv) ease of assembly as a thin film at micrometer length scale (so that it can be related to a membrane-type structure). Among the contemporary porous materials, MOFs qualify with these criteria. MOFs consist of inorganic metal or metaloxo nodes and functionalized organic linkers,\n \n \n 37\n \n ,\n \n 38\n \n \n which are linked by reversible and directional coordination bonds. The choices for metal and linkers are virtually infinite, and the possible structural topologies are also numerous. To name a few benchmark examples where molecular diffusion and gas adsorption selectivities have been studied in details and possible applications for membrane based gas separation have also been performed, are ZIFs (Zeolitic imidazolate frameworks), UiOs (University of Oslo), MILs (Material Institute Lavoisier).\n \n 35,39\u221243\n \n In our present approach, we have considered a rather simple PCU topology that can afford biporous (two different pores), 3D connected nanochannels. One advantage of this type of topology, otherwise also known as pillared-layer MOFs,\n \n \n 44\n \n \u2013\n \n 46\n \n \n is that these structures can be grown as a thin film in an oriented fashion\n \n \n 47\n \n \u2013\n \n 49\n \n \n and two different types of pores can be arranged in a preconceived orientation.\n
\n\n The selected model structure is Cu(BDC)(pillar) MOF, where the pillars are Py-X\u2009=\u2009X-Py (Py\u2009=\u2009pyridyl, X\u2009=\u2009CH and N) (Fig.\n \n 1\n \n a). The Cu(BDC) 2D square grids are formed by linking Cu-paddle-wheels with benzenedicarboxylic acid (BDC) linker along the\n \n ab\n \n plane and these 2D sheets are pillared by Py-X\u2009=\u2009X-Py along the\n \n c\n \n -axis (along [001]) forming an extended pillared-layer structure (Fig.\n \n 1\n \n a). This 3D structure features two types of pores, one of them has a window size of ~\u20097.3 \u00d7 4.3 \u00c5 along the\n \n c\n \n -axis while the other one comes with a pore size of ~\u20099.7 \u00d7 6.9 \u00c5 along the\n \n ab\n \n plane. These pore sizes are estimated by adding van der Waals radii of the atoms in the simulated structures (see computational details). Herein, we have two types of pillared-layer (PL) structures, denoted as PL\n \n C=C\n \n and PL\n \n N=N\n \n . The only difference between the two structures is their pillar linker functionality, one having -C\u2009=\u2009C- while the other one with -N\u2009=\u2009N-. Note that the smaller pores are chemically equivalent but different chemical functionalities are present at the larger pore (2-times larger), (see Fig.\n \n 1\n \n a).\n
\n\n To synthesize the model structures and perform the molecular diffusion studies, we have grown oriented thin films of both PL\n \n C=C\n \n and PL\n \n N=N\n \n using well-known LPE method in an lbl fashion. The surface functionalization with \u2013OH end groups is used to grow the oriented thin films. Each cycle progresses by alternately exposing the substrate surface to a solution of copper (II) acetate (1 mM) and mixed organic linkers (BDC (0.2 mM) and one of the pillar linkers (0.2 mM)) using an automatized pump system (see experimental section for details). By repeating the number of cycles we could obtain homogenous and pinhole free\u2009~\u2009250 nm thick films (Fig.\n \n 1\n \n a, Figure S1). These synthesized films were characterized using powder X-ray diffraction (PXRD) and Raman spectroscopy (Figures S2 and S3). Figure\n \n 1\n \n b shows the out-of-plane (OP) PXRD along with the simulated PXRD patterns. In the OP PXRD, the diffraction peaks appear at ~\u20095.4, 10.8, and 16.3\u00b0. Comparison of these peaks with simulated PXRD suggests that these peaks are related to (00l) planes of the PL\n \n C=C\n \n . In the in-plane PXRD, we have observed the diffraction peaks corresponding to the orthogonal planes ((100), (010) and (110), see Figure S2). This observation confirms that the PL\n \n C=C\n \n structure is oriented along the (001) or\n \n c\n \n -direction where the smaller pores are vertically aligned, (hereafter called as W\n \n V\n \n ) and the larger pores are parallel to the substrate plane (along the\n \n ab\n \n plane, hereafter called as W\n \n H\n \n ). PL\n \n N=N\n \n thin film also exhibits similar growth orientation, as can be confirmed from the PXRD patterns (Figs.\n \n 1\n \n b and S2).\n
\n\n Because of the crystal growth preference along the\n \n c\n \n -axis, the surfaces of both thin films are populated with the W\n \n V\n \n pore windows as shown in Fig.\n \n 1\n \n a. Hence, during the molecular diffusion into the thin film steps A and B (\n \n vide supra\n \n ) should be similar for both PL\n \n C=C\n \n and PL\n \n N=N\n \n . Diffusivity will differ, only if the different chemical functionalities come into play or the larger pores W\n \n H\n \n controls the diffusivity. To study this, we have measured mass uptake rates of the PL thin films grown on quartz crystal microbalance (QCM)\n \n \n 25\n \n \n sensors with \u2013OH functionalized Au-surface. Methanol (kinetic diameter\u2009~\u20093.6 \u00c5) is used as a probe molecule because it is compatible with the pore size of the W\n \n V\n \n pores and has high vapour pressure at ambient temperature. The QCM sensors coated with the PL thin films were mounted in a fluidic cell in a temperature controlled environment. The saturated methanol vapor (~\u200915.8 kPa) uptake profiles were recorded at 298 K by monitoring the fundamental frequency change (\u0394\n \n f\n \n ) over time (t). The mass change (\u0394\n \n m\n \n ) per area is calculated using the Sauerbrey equation:\n
\n\n \n \n \\(\\varDelta m= -c\\frac{\\varDelta f}{n}\\)\n \n \n \u2026Eq.\u00a0(1)\n
\n\n where\n \n n\n \n denotes the overtone order and\n \n c\n \n is the mass sensitivity constant.\n \n \n 25\n \n \n
\n\n In Fig.\n \n 2\n \n a, the fractional mass uptake is plotted against the uptake time in linear and logarithmic scale. At lower fractional uptake (<\u200920%; molecules entering from the vapour phase into the pore, i.e. steps A and B) both PL\n \n C=C\n \n and PL\n \n N=N\n \n shows linear uptake behavior and almost no difference in the uptake rate. But beyond this regime, when the interpore diffusion step C, dominates the mass uptake rate, the uptake rate slowed down for PL\n \n N=N\n \n as compared to that of PL\n \n C=C\n \n for similar thickness of the films (~\u2009250 nm, saturation mass uptake time is ~\u20092x slower for PL\n \n N=N\n \n compared to that of PL\n \n C=C\n \n ) (see Figure S4). For a larger molecule (1-butanol; kinetic diameter\u2009~\u20094.6 \u00c5) also we observed the uptake rate difference at the higher mass loading regime only (see Figure S5). These observations indicate that at lower mass loading, i.e. when methanol molecules are mostly near the surface, diffusivity rates are controlled by the pore windows which are similar in PL\n \n C=C\n \n and PL\n \n N=N\n \n , i.e. W\n \n V\n \n . Note that at this regime of mass uptake, concentration gradient is maximum, and it is probably obvious that methanol molecules will diffuse along the gradient through W\n \n V\n \n . The surprising difference in the uptake saturation time indicates that the larger pores W\n \n H\n \n do play an important role even though diffusion through these pores are orthogonal to the concentration gradient. Moreover, the W\n \n H\n \n sizes are similar for PL\n \n C=C\n \n and PL\n \n N=N\n \n , hence it must be the different chemical functionalities that are controlling the diffusion rate. In the following discussion, we reveal that diffusion through W\n \n H\n \n pores is indeed rate limiting for the interpore diffusion and can be tuned by chemical design.\n
\n\n To ascertain that the dominating diffusion path for steps A and B involves W\n \n V\n \n pores only, we have compared the methanol diffusivities of the different types of PLs (designed by using different pillars; 1,4-diazabicyclo[2.2.2]octane or DABCO, Py-S\u2013S-Py, Py-N\u2009=\u2009N-Py and Py-CH\u2009=\u2009CH-Py with Cu(BDC) 2D layer) at lower mass uptake regime (Figs.\n \n 2\n \n b and S4). In these PLs, W\n \n V\n \n dimensions, orientations and chemical functionalities are identical (see the out and in-plane XRDs in Figures S6, S7). For PL\n \n DABCO\n \n , W\n \n H\n \n pore dimension (~\u20093.1 \u00d7 4.0 \u00c5) is smaller than the other PLs. The estimated diffusivity values at 298 K are found to be similar;\n \n D\n \n =\u20091 \u00d7 10\n \n \u2212\u200916\n \n for PL\n \n C=C\n \n , 1.6 \u00d7 10\n \n \u2212\u200916\n \n for PL\n \n N=N\n \n , 1.8 \u00d7 10\n \n \u2212\u200916\n \n for PL\n \n DABCO\n \n and 1.7 \u00d7 10\n \n \u2212\u200916\n \n m\n \n 2\n \n s\n \n \u2212\u20091\n \n for PL\n \n S\u2212S\n \n (see Figure S8). Note, that change in the pore structure and chemical functionalities can change the diffusivities by an order of magnitude or higher, as we observed for ZIF-8 methanol diffusivity (Fig.\n \n 2\n \n b, Figures S9 and S10, ZIF-8 is a cage like 3D porous structure having pore window dimension of ~\u20093.5 \u00c5). We have also compared the activation energy (\n \n E\n \n \n A\n \n ) and enthalpy of adsorption (\u0394\n \n H\n \n ) for the PL\n \n C=C\n \n and PL\n \n N=N\n \n for methanol (see Figures S11, S12) by measuring mass uptake at different temperatures. We found that the differences are very small (\u0394\n \n H\n \n =\u2009~\u200924.3 (\u00b1\u20092.1) and 27.4 (\u00b1\u20093.2) kJ/mol and \u0394\n \n E\n \n \n A\n \n \n =\n \n 26.9 (\u00b1\u20092.4) and 30.8 (\u00b1\u20092.7) kJ/mol for PL\n \n N=N\n \n and PL\n \n C=C\n \n ), Fig.\n \n 2\n \n c and\n \n 2\n \n d). The\n \n E\n \n \n A\n \n is estimated by the diffusivities at lower uptake regime, hence similar\n \n E\n \n \n A\n \n values confirms the hypothesis that steps A and B involve mostly W\n \n V\n \n pores. Similar \u0394\n \n H\n \n values indicates that the adsorbate-adsorbent interaction differences are small enough, to be identified by the present experimental setup.\n
\n\n The PLs presented in Fig.\n \n 1\n \n a do exhibit distinct time differences in the saturation uptake, and this can be attributed to the following features: i) structural defect densities, ii) cooperative effect between adsorbed molecules, iii) lateral diffusion through W\n \n H\n \n pores with different functionalities. We rule out the defect densities, because in that case mass uptake rate will be affected also at the lower mass loading (steps A and B). To test the impact of cooperative effect, we have compared the percentage change in the rate of mass uptake (slope %) vs. fractional mass loading at two different temperatures (298 and 315 K, Fig.\n \n 2\n \n e). We observed that with increasing mass uptake, rate increases. It indicates that the methanol-methanol cooperative interaction at higher loading accelerates mass uptake. Furthermore, at lower temperature the change in the slope percentage is higher for both the PLs. This is probably due to the stronger methanol-methanol interaction at the lower temperature, indicating presence of similar cooperativity in both the PLs. Hence, methanol cooperative interaction is not rate limiting at higher mass loading.\n
\n\n In light of the dependence of interpore diffusivities the W\n \n H\n \n pores, which are stationed orthogonal to the concentration gradient, we postulate that the effective diffusion is governed not only by the concentration gradient but also by the pore window size. At the lower uptake regime, during steps A and B, concentration gradient is highest and hence, it dominates the mass uptake rate. At higher mass loading (when the interpore diffusion dominates) concentration gradient continuously decreases, and the pore window size becomes the rate limiting factor. In the present case 2x larger size of W\n \n H\n \n , as compared to W\n \n V\n \n , dictates the diffusion path during interpore diffusion at low concentration gradient. This is contrary to the common notion of Fickian diffusion, and can be generalized to any 3D porous structure, in which more than one type of pore window exists. Evidently, as the chemical functionalities of the W\n \n H\n \n are changed, uptake rates change sharply. Comparing the mass uptake time of methanol and 1-butanol, for the different PLs with different pillar functionalities indicate that (size-based) selectivities are higher at saturation, compared to the lower mass loading region (Table S1). Using this approach, the permeation and selectivity of the chemical mixtures can be regulated rationally, in a preconceived manner.\n
\n\n To get an insight into the energy barriers along the W\n \n V\n \n and W\n \n H\n \n pores for methanol, we performed force field based molecular dynamics simulations (see computational section). The comparative free energy profiles are illustrated in Fig.\n \n 3\n \n . It is evident from these simulations that during the diffusion along the W\n \n V\n \n pores (from A to A'), the free energy changes are similar for PL\n \n C=C\n \n and PL\n \n N=N\n \n . On the contrary, it is energetically uphill to traverse along the W\n \n H\n \n pores (from B to B') for PL\n \n N=N\n \n but energetically downhill for PL\n \n C=C\n \n . This finding is in tune with the observed the higher mass uptake rate in PL\n \n C=C\n \n . The preferential interaction between methanol and the pillar functionalities is clearly visible at the energy minimum (see Figures S13-S15) ascertaining the hypothesis of chemically controlled interpore diffusion.\n
\n\n Complexity in microscopic mechanism for interpore diffusion, which is the rate determining step during the permeation through a porous membrane or during a catalysis process, is challenging to resolve. This lack in clarity is due to the fact that the simplistic model of concentration gradient dependent diffusion does not strictly apply. By careful analyses of the mass uptake rates in the oriented nanochannels of the pillared-layer MOFs, we could reveal the diffusion path and rate limiting parameters. Different types of pillared-layer MOFs were grown as oriented thin films, and this allowed correlating the mass uptake rates with chemical functionalities and pore orientation. The experimental observations indicate that in spite of the presence of concentration gradient, diffusivity is controlled by the large pores aligned along the direction orthogonal to the gradient. The changes in chemical functionality in these pore windows, realized by changing the pillar functionalities of the MOFs, drastically modulate the uptake time, resulting in a chemical control of the overall molecular diffusion. In the present case, we have found that ethylene (-C\u2009=\u2009C-) functionality, in comparison to -N\u2009=\u2009N- and -S\u2013S-, helps to accelerate the mass uptake rate. The applicability of this diffusion mechanism can be extended to other adsorbate molecules and porous solids, in which the pores are 3D. However, nature of adsorbate-adsorbent interactions can vary in a nonlinear fashion and hence diffusivity rates will change accordingly. The insight of the diffusion path and the chemical route to modulate diffusion can be applied further to designing of nanoporous membranes for chemical separation, e.g. aliphatic and aromatic hydrocarbons, pollutant gases and volatile organic compounds.\n \n \n 50\n \n \n Also the chemical reactions carried out in the confined spaces of porous catalysts can be tuned using the findings presented here.\n
\n\n \n Synthesis of 4,4'-Azopyridine\n \n
\n\n 4,4'-azopyridine was synthesized following a reported method.\n \n \n 51\n \n \n
\n\n \n Synthesis of pillared-layer MOF thin films on QCM substrate\n \n : 5 MHz (gold coated) QCM-sensors were dipped in an ethanolic solution (20 mM) of 11-mercapto-1-undecanol (MUD) for 24 h to obtain \u2013OH functionalized surface. These substrates were then thoroughly washed with absolute ethanol (99.99%), dried and used for thin films synthesis. SiO\n \n 2\n \n /Si substrates were cleaned by isopropanol and then by UV-ozone cleaner, to remove organic impurities and to create free \u2013OH groups on the surface. The MOF thin films were prepared on those functionalized substrate\n \n via\n \n a well-known layer-by-layer (lbl) liquid-phase epitaxial (LPE) method.\n \n \n 45\n \n \n The method consists of four steps to complete a cycle at 60 \u00baC as: i) dipped in 1 mM copper acetate ethanol solution for 10 min, ii) drained the metal solution and washed with fresh ethanol, iii) dipped in 0.2 mM linker solution (mixture of two linkers) in ethanol for 20 min and iv) drained the linker solution and washed with fresh ethanol. This cycle is repeated for 40 times to get substrates coated with pillared-layer MOF thin films. 1,4-Benzene dicarboxylic acid is the primary linker used with different pillar linkers (4,4'-azopyridine, 1,2-di(4-pyridyl)ethylene, 4,4'-dithiodipyridine and 1,4-diazabicyclo[2.2.2]octane) for MOF films.\n
\n\n \n Syntheses of ZIF-8\n \n oriented\n \n thin films\n \n
\n\n Gold (5 MHz) coated QCM sensors with \u2013OH functionalized surface were used to grow ZIF-8\n \n oriented\n \n thin films by following an earlier developed method with slight modification.\n \n \n 18\n \n \n The functionalized substrates were dipped in a mixture of zinc nitrate (25 mM) and 2-methylimidazole (50 mM) solution in methanol for 30 minutes at room temperature to complete one cycle. By repeating the cycles, thicker ZIF-8\n \n oriented\n \n thin films were obtained.\n
\n\n Powder X-ray diffractometer (XRD) patterns of thin films were recorded on a Rigaku XDS 2000 diffractometer using nickel-filtered Cu K\n \n \u03b1\n \n radiation (\n \n \u03bb\n \n =\u20091.5418 \u00c5) ranging from 5 to 20 \u00b0 at room temperature (voltage 40 kV, current 200 mA). Out-of plane PXRD was recorded in 2\n \n \u03b8\n \n /\n \n \u03b8\n \n (step size 0.01, scan rate 0.2 \u00ba/s), in-plane in 2\n \n \u03b8\n \n /\n \n \u03c6\n \n geometry with grazing incident angle (\n \n \u03c9\n \n ) at 0.3 \u00ba and step size of 0.01 with scan rate 0.1 \u00ba/s.\n
\n\n Surface morphology of samples were characterized using field emission scanning electron microscopy (FESEM), JEOL JSM-7200F instrument with a cold emission gun operating at 30 kV. Energy-Dispersive X-ray spectroscopy (EDS) elemental analysis and mapping were also done on the FESEM.\n
\n\n The vibrational Raman spectra were recorded by using a Renishaw inVia Raman microscope (532 nm excitation).\n
\n\n The adsorption profiles were measured using a quartz crystal microbalance (QCM) from open QCM, Italy. Thickness for all the thin films was calculated using J.A. Wollam ellipsometer (alpha-SE). The data was fitted using a B-Spline model including surface roughness.\n
\n\n \n QCM experiments\n \n
\n\n The MOF samples were activated preceding the measurements by heating the QCM sensors at 65\u00b0C for 12 h under vacuum (10\n \n \u2212\u20094\n \n bar). Mass uptake experiments were carried out using a constant flow rate (50 sccm) of dry N\n \n 2\n \n , passing through saturated solvent vapors (methanol, 1-butanol).\n
\n\n \n Analyses of uptake kinetics\n \n
\n\n Mass-frequency relationship for the QCM measurements is given by Sauerbrey Eq.\u00a02\n \n 5\n \n ;\n
\n\n Where n denotes the overtone order (n\u2009=\u20093, 5, and 7) and c is the mass sensitivity constant. For a 5 MHz crystal, c has value of 17.7 ng/cm\n \n 2\n \n .\n
\n\n Diffusivity, D, can be obtained by fitting the mass uptake vs. the square root of adsorption time using this Eq.\u00a02\n \n 5\n \n :\n
\n\n \n \n \\(\\frac{{\\text{M}}_{\\text{t}}\\left(\\text{t}\\right)}{{\\text{M}}_{{\\infty }}}\\)\n \n \n \n \n \\(\\approx \\frac{8}{\\surd {\\pi }}\\surd \\frac{\\text{D}\\text{t}}{{\\text{L}}^{2}}\\)\n \n \n
\n\n \n Computational details\n \n
\n\n All the periodic DFT calculations were performed using PBE functional along with empirical D3 correction as implemented in CP2K software package that employs Gaussian plane waves. Double zeta quality basis sets were employed for all the atoms (DZVP-GTH-qn for all non-metallic atoms and DZVP-MOLOPT-SR-GTH for the Cu centers) along with GTH-PBE pseudopotentials. For the PL\n \n C=C\n \n and PL\n \n N=N\n \n structures geometry and cell parameters were optimized simultaneously. In order to run force field based molecular dynamics simulations RESP fitted partial charges were computed with REPEAT method using Bloechl charges as initial guess. Structure coordinates are provided in the supporting information.\n
\n\n \n Molecular Dynamics\n \n
\n\n The MOF structures were constructed by multiplying the ab-initio optimized 1x1x1 unit cell, to create 3x3x3 cages that are periodic along\n \n ab\n \n and terminated along\n \n c\n \n with a vacuum. UFF LJ\n \n \n 52\n \n \n parameters and ab-initio computed charges (Table S2) are used to simulate the non-bonding interactions of the MOF system, while the MOF is considered frozen. For the methanol molecule, CHARMM parameters are calculated using CHARMM-GUI.\n \n 53\n \n All simulations are performed in the open source program GROMACS.\n \n \n 54\n \n \n
\n\n 100 methanol molecules are added to the MOF system and the system is equilibrated. NVT ensemble simulations are performed, with a temperature of 300 K maintained using the V-rescale method.\n \n \n 55\n \n \n In case of 100 methanol molecules, a longer (1 microsecond) simulation is performed to generate the density distribution shown in Figure S17. For the free energy profiles, umbrella sampling simulations\n \n \n 56\n \n \n are performed that bias the perpendicular distance (along\n \n a\n \n or\n \n c\n \n axis) between the pore (W\n \n H\n \n or W\n \n V\n \n ) and the methanol molecule. The W\n \n V\n \n and W\n \n H\n \n profiles contain 11 (0.0 to 1.0 nm) and 7 (0.0 to 0.6 nm) sampling windows each (of 100 ns each) that are 0.1 nm apart, and apply a bias of 100\u2013300 kJ/mol. A smaller bias (50 kJ/mol) is also added to the parallel direction to restrict greater movement of the methanol molecule.\n
\n\n Scheme 1 is available in the Supplementary Files section\n
\n\n An illustration of the molecular diffusion along the concentration gradient in a crystalline, porous structure, in which mass uptake is controlled by the channels orthogonal to the concentration gradient; (molecules are methanol, presented in space fill model); concentration gradient is along\n \n c\n \n -axis.\n
\n \n\n Table of Content\n
\n \n\n The enantioselective domino Heck/cross-coupling has emerged as a powerful tool in modern chemical synthesis for decades. Despite significant progress in relative rigid skeleton substrates, the implementation of asymmetric Heck/cross-coupling cascades of highly flexible haloalkene substrates remains a challenging and and long-standing goal. Here we report an efficient asymmetric tandem Heck/Tsuji\u2212Trost reaction of highly flexible vinylic halides with 1,3-dienes enabled by palladium catalysis. A variety of functional cyclic isoprenoids, which are key structural motifs of numerous natural products family, were delivered in good yields with excellent regio-, diastereo- and enantioselectivity. Specifically, the Heck insertion of stereodetermining step to form \u019e\n \n 3\n \n \u03c0-allyl palladium complex and in situ trapping with nucleophiles enable efficient Heck/etherification in a formal (4+2) cycloaddition manner. Engineering the Sadphos bearing androgynous non-C\n \n 2\n \n -symmetric chiral sulfinamide phosphine ligands were vital component in achieving excellent catalytic reactivity and enantioselectivity. This strategy offers a general, modular and divergent platform for rapidly upgrading feedstock flexible vinylic halides and dienes to various value-added molecules and is expected to inspire the development of other challenging enantioselective domino Heck/cross-couplings.\n
\n\n Catalytic asymmetric\u00a0tandem\u00a0Heck/cross-coupling\u00a0in the past forty years were broad attraction and applications in the functionalization of C\u2013C \u03c0-Bonds.\n \n 1-3\n \n Relying on a relative rigid skeleton substrate that is provided in aryl halides, these versatile domino reactions proved its powerfulness allowing high control in regio-, diastereo- and enantio-selectivities (\n \n Fig. 1a\n \n ). In contrast to the underdeveloped highly flexible haloalkene substrates,\n \n 4-7\n \n the substrates of rigid skeleton frequently exhibited an enhanced conformational stability involving elementary reactions, to make the reaction more beneficial to generate the desired product and inhibit the side reaction.\n \n 8\n \n Indeed, as one of elegant reactions, the tandem Heck/Tsuji-Trost reactions\n \n 9-11\n \n would permit the formation of multiple stereocenters in mono- and polycycles with high atom- and step-economic efficiency (\n \n Fig. 1b\n \n ).\n \n 12-17\n \n In this regard, the enantioselective formal Heck/amination,\n \n 18-23\n \n Heck/etherification\n \n 24-26\n \n and Heck/alkylation\n \n 27-29\n \n with 1,3\u2011dienes were established by Shibasaki, Luan, Gong, Overman, Han and Zhang, independently, opening a new era for asymmetric domino Heck/functionalization of conjugated dienes with rigid ambiphilic substrates. Specifically, pioneering studies were disclosed by Shibasaki\n \n 26\n \n in 1991. Subsequently, Overman\n \n 22\n \n group reported the enantioselective total synthesis of the fungal natural product (\u2212)-spirotryprostatin B in 2000. With the development of novel chiral ligands, by utilizing the BINOL-based phosphine ligand, Gong\n \n 16\n \n described elegant enantioselective redox-neutral difunctionalization of dienes in 2015. More recently, we\n \n 18-20\n \n also developed the use of adaptive Sadphos ligand, enabling this cascade pathway through a stereoselective olefin insertion.\n
\n\n According to these seminal researchs, which suggest: (1) a satisfying enantioselective protocol for the highly flexible haloalkene substrates and homologues (\n \n Fig.\n \n \n 1a\n \n ) is especially challenging and still waiting to be developed.\n \n 8\n \n (2) the orderly activation of reactive site requires precise control at every stage in catalytic asymmetric cascade process while avoiding transition metal-catalysed direct allylic functionalization (\n \n Fig.\n \n \n 1a\n \n ).\n \n 30-33\n \n (3) the traditional approach to such stereodetermining step relies on Tsuji\u2212Trost nucleophilic attack step rather than Heck insertion step (\n \n Fig.\n \n \n 1b\n \n ).\n
\n\n As part of our ongoing research into transition-metal/Sadphos-catalyzed\n \n 34,35\n \n asymmetric annulation/cyclization reaction,\n \n 36-38\n \n herein, we envisaged that ambiphilic halogenated allylic alcohols\n \n 1\n \n with readily available cyclic 1,3-dienes\n \n 39-41\n \n \n 2\n \n via the more challenging asymmetric tandem Heck/Tsuji-Trost reaction to produce the enantioenriched sp\n \n 3\n \n -rich cyclic isoprenoids (\n \n Fig.\n \n \n 1c\n \n ). If successful, a variety of valuable chiral functional cyclic iso-prenoids (\n \n Fig.\n \n \n 2a\n \n ) could be easily prepared, which are key structural motifs\n \n 42\n \n of numerous\u00a0natural product family, pharmaceutical agents, and carbohydrates but remain challenging to access via asymmetric catalysis. Besides,\n \n \n several challenges would be encountered in this scenario: (1) Unactivated allylic alcohol substrates\n \n 1\n \n may be direct activated via Tsuji-Trost reaction leading to electrophilic\n \n \u03c0\n \n -allyl palladiumintermediates.\n \n 43-45\n \n (2) How to get high regioselectivity and enantioselectivity via the key stereodetermining step of Heck insertion.\n \n 46\n \n (3) As yet, the development of catalytic asymmetric reaction with readily available and ambiphilic vinylic halides\n \n 1\n \n had not been explored. Actually, we propose that the chiral ligand is crucial for overcoming these challenges.\n
\n\n With these considerations in mind, an initial attempt that the Xu-Phos (\n \n Xu1\n \n , one family member of Sadphos) could indeed enable the catalytic asymmetric tandem Heck/Tsuji-Trost model reaction of flexible halogenated allylic alcohol\n \n 1a\n \n with conjugated dienes\n \n 2a\n \n or\n \n 2a\u00b4\n \n to access chiral sp\n \n 3\n \n -rich cyclic isoprenoids (\n \n Fig.\n \n \n 2b\n \n ). It's worth noting that the cyclic diene\n \n 2a\n \n give the desired product in 23% yield with 81% ee, while the acyclic 1,3-diene\n \n 2a\u00b4\n \n led to higher yield but with almost no ee, indicating that the stereodeterming step for this reaction is attrivute to the Heck insertion step. And, this cascade reactions occurred chemo-, regio- and enantio- selectively at the less-hindered olefin of diene. To our delight, amide-type solvents and silver salt as the base could lead to desired product\n \n 3aa\n \n up to 96% ee (Supplementary Information (SI) for details,\n \n Figure S2\n \n and\n \n Figure S3\n \n ). Additionally, switching the counterion of palladium catalyst precursor from acetate to pivalate is beneficial to this transformation (SI for details,\n \n Figure S5\n \n ). With these preliminary results, we then turned our investigation on the asymmetric tandem Heck/Tsuji-Trost reaction of\n \n 1a\n \n with cyclic 1,3-dienes\n \n 2a\n \n by using Pd(CO\n \n 2\n \n \n \n t\n \n \n Bu)\n \n 2\n \n as a precatalyst and Ag\n \n 2\n \n SO\n \n 4\n \n as the base in N,N-dimethylacetamide (DMAc) at 70\n \n o\n \n C. A series of commercially available chiral rigid-flexible ligands (DIOP, Trost\u2019s ligand, BOX, Josiphos, Segphos, BINAP and other family members of Sadphos), which also have shown good performance in asymmetric\n \n \u03c0\n \n -allylpalladium chemistry, were first investigated (\n \n Fig.\n \n \n 2c\n \n and SI for details,\n \n Figure S1\n \n ), these results once again revealed the fact that adaptive Sadphos ligand is the key involved in regulating the domino Heck/cross-coupling. Inspired by the previous findings that tuning the electron-nature of the backbone could affect the catalytic activity and enantioselectivity,\n \n 47-48\n \n Xu-Phos (\n \n Xu2\n \n \u2212\n \n Xu5\n \n ) bearing electron-donating group on the benzene backbone were then synthesized and subjexted to the reaction. To our delight, employing\n \n Xu3\n \n as ligand, the yield was indeed significantly improved from 56% to 83% with the enantioselectivity increased from 65% to 99% ee.\n
\n\n With the optimal reaction conditions in hand, the generality of substrates in this asymmetric tandem Heck/Tsuji-Trost reaction of ambiphilic and flexible vinylic halides\n \n 1\n \n with conjugated dienes\n \n 2\n \n was then investigated as depicted in\n \n Fig. 3\n \n and\n \n Fig 4\n \n . Notably, flexible vinylic halides\n \n 1\n \n are easily synthesized by the nucleophilic addition of propargyl alcohol (PA), with a large range of substituted alkenes.\n \n 49\n \n The structure and configuration of (\n \n R,S\n \n )-\n \n 3aa\n \n was unambiguously determined\n
\n\n via its X-ray analysis (CCDC: 2323645). Initially, the results demonstrated that vinylic halides\n \n 1\n \n bearing halogens (fluorine, chlorine), electron-donating groups (tertiary butyl, methyl, methoxyl) at various positions of the phenyl ring were compatible, delivering corresponding products\n \n 3aa\n \n \u2013\n \n 3ag\n \n in good to high yields with 84\u201399% ee. To our delight, various substituents and functional groups on the flexible vinylic halides\n \n 1\n \n could be tolerated. For example, 2-naphthyl, 2-allyl, terminal\n \n n\n \n -butenyl and\n \n n\n \n -pentenyl could also produce the corresponding target products\n \n 3ah\n \n \u2013\n \n 3ak\n \n in high yields with 93-97% ee. It is particularly worth mentioning that a series of more flexible straight chain alkyl, branched chain alkyl and cycloalkyl, all can deliver the cyclic isoprenoids\n \n 3al\n \n \u2013\n \n 3ax\n \n in good to excellent yields with 85\u201398% ee as a single regioisomer and diasteroisomer. The bicyclic isoprenoid compound\n \n 3\n \n shares the core structure with several monoterpene lactones, making it a promising synthetic intermediate for the production of these bioactive natural substances.\n \n 42\n \n
\n\n On the other hand, substituted cyclohexadienes\n \n 2\n \n are also easily synthesized by the 1,4-dehydration of allyl alcohols. The locked\n \n s-cis\n \n conformation of double bonds makes the generation of the \u03c0-allyl palladium intermediates much easier, with lowered entropy of activation (\n \n Fig. 4\n \n ).\n \n 50\n \n The applicability of this protocol toward various substituents and functional groups on the cyclohexadienes scope was investigated. For instance, substituents such as fluorine, chlorine, methyl, methoxyl, trifluoromethoxy, trifluoromethyl, trimethylsilyl on the aryl moiety of 1-aryl-cyclohexa-1,3-dienes\n \n 2\n \n are compatible, delivering the desired\n \n 3ba\n \n \u2013\n \n 3bj\n \n in 57\u201393% yields with 90\u201399% ee. Moreover, 1-naphthyl, 2-naphthyl, dioxa-phthyl, 5-benzothienyl, 3-thienyl, 1-vinyl, 1-phenylethynyl and\n \n n\n \n -butyl derived cyclohexa-1,3-dienes could also produce\n \n 3bk\n \n \u2013\n \n 3bs\n \n in 52\u221294% yields with 83\u221297% ee. Specifically, the 1-vinyl and 1-phenylethynyl groups act as versatile handles for subsequent modifications of the bicyclic rings. And 1-vinylcyclohexadiene, which functions as a conjugated triene containing mono-, di-, and trisubstituted olefins, selectively cyclized at the cyclic and less-substituted olefin portion. This selectivity is likely due to the more effective orbital overlap of the cyclic diene. With the derivatives of pharmaceuticals (Menthol and Perillyl alcohol) as the dienes, the corresponding products\n \n 3bt\n \n and\n \n 3bu\n \n could be obtained in moderate yields with excellent diastereoselectivity.\n
\n\n To demonstrate the practical utility of our protocol, a gram scale reaction was carried out under standard reaction conditions, furnishing 1.14 g of\n \n 3aa\n \n in 79 % yield with 99% ee (\n \n Fig. 5a\n \n ). Moreover, the unsaturated bonds present in the cyclic products\n \n 3\n \n offer opportunities for further diverse modifications. For instance, the selective dihydroxylation of\n \n 3aa\n \n with K\n \n 2\n \n OsO\n \n 4\n \n delivered the the target products\n \n 4\n \n in 69% yield with 99% ee. The hydrogenation of\n \n 3aa\n \n in the presence of Pd/C furnished octahydro-2H-chromene product\n \n 5\n \n in 87% yield with 99% ee. The selective difluorocyclopropanation of\n \n 3aa\n \n led to the highly functionalized product\n \n 6\n \n in 74% yield with 96%\n \n ee\n \n . The selective epoxidation of the two olefin moieties of\n \n 3aa\n \n with\n \n m\n \n -CPBA delivered the the target products\n \n 7\n \n in 77% yield with 99% ee. In light of the structures of the chiral Pd/Sadphos catalyst\n \n 35\n \n and the product\n \n 3\n \n , a catalytic chirality-induction model was proposed for the reaction (\n \n Fig. 5b\n \n ). The 8-membered ring of O,P-chaleting complex, the less-hindered olefin coordinate to the Pd(II) center and the\n \n Re\n \n -face of alkene is shielded by the 3,5-ditert-butyl-4-methoxy-phenyl group of the ligand leads to intermediate\n \n Int-l\n \n . Because of these, the\n \n syn\n \n -migration insertion of 1,3-diene\n \n 2\n \n into the C\u2212Pd bond would deliver a palladium complex\n \n Int-ll\n \n . The intramolecular nucleophilic attack takes place at the\n \n Si\n \n -face to form the\n \n cis\n \n -product.\n
\n\n In summary, we have developed a highly chemo-, regio-, and enantio-selective palladium-catalyzed asymmetric tandem Heck/Tsuji-Trost reaction of flexible halogenated allylic halides with cyclic 1,3-dienes. This reaction serves as a promising tool for the modular synthesis of enantioenriched sp3-rich cyclic isoprenoids. The androgyne\n \n Xu-Phos\n \n ligand plays a crucial role in regulating catalytic activity and selectivity of this domino Heck/cross-coupling. Further studies will focus on the application of Sadphos in asymmetric metal catalysis, particularly in tandem Heck/Tsuji-Trost reactions involving other challenging reactions and substrates.\n
\n\n \n General procedure for asymmetric tandem Heck/Tsuji\u2212Trost of flexible vinylic halides with 1,3-dienes\n \n
\n\n To a sealed tube was added Palladium pivalate (10 mol%, 15.5 mg, CAS: 106224-36-6,\n \n Bide\n \n ) and\n \n Xu3\n \n (20 mol%, 68.4 mg,) in 2.5 mL dry DMAc and stirred at room temperature for 1 h under nitrogen atmosphere. Then,\n \n 1\n \n (0.5 mmol, 1.0 eq),\n \n 2\n \n (2.0 mmol, 4.0 eq) and Ag\n \n 2\n \n SO\n \n 4\n \n (93.5 mg, 0.6 equiv) were added to the tube under nitrogen atmosphere, and stirred at 70 \u00b0C for 48 h. After the reaction was complete (monitored by TLC), dilute with saturated salt water and EA, then extracted with EA (twice), dried over anhydrous Na\n \n 2\n \n SO\n \n 4\n \n , the solvent was removed under reduced pressure. The crude product was purified by column chromatography (\n \n n\n \n -Hexane/EA, 50:1 to 30:1) to give\n \n 3\n \n as a white solid or colourless liquid.\n
\n\n Complex autoimmune diseases are sexually dimorphic. An interplay between predisposing genetics and sex-related factors likely determines the sex discrepancy in the immune response, but conclusive evidence is lacking regarding the underlying molecular mechanisms. Using forward genetics, we positionally identified a polymorphic estrogen receptor binding site that regulates\n \n CD2\n \n expression, leading to female-specific differences in mouse models of T cell-dependent autoimmunity. Female mice with reduced CD2 levels displayed diminished expansion of autoreactive T cells. Mechanistically, CD2 affected T cell activation by inhibiting LAG-3 expression. Our findings explain the sexual dimorphism in human autoimmunity, as CD2 associated with rheumatoid arthritis and its regulation through 17-\u03b2-estradiol was conserved in human T cells. Hormonal regulation of CD2 has implications for CD2-targeted therapy. Indeed, anti-CD2 treatment was more potent in female mice. In conclusion, our results demonstrate the relevance of sex-genotype interactions and provide strong evidence for CD2 as a sex-sensitive predisposing factor in autoimmunity.\n
\n\n \n \n Immunology\n \n \n
\n\n \n Immunogenetics\n \n
\n\n \n Gene regulation\n \n
\n\n \n autoimmune diseases\n \n
\n\n \n Polymorphic estrogen receptor\n \n
\n\n \n CD2\n \n
\n\n Women mount a more vigorous immune response and are more susceptible to most autoimmune diseases\n \n \n 1\n \n ,\n \n 2\n \n \n . These diseases have a strong but complex genetic component, and it has been difficult to identify the underlying polymorphisms\n \n \n 3\n \n \u2013\n \n 5\n \n \n . The female preponderance in autoimmunity is sex hormone related\n \n \n 6\n \n \n but could also be genetically dependent\n \n \n 7\n \n \n . Not only through sex chromosomes but also through distinct sex hormone regulated expression of autosomal genes. However, conclusive evidence is still lacking, as it is difficult to positionally identify the underlying polymorphisms controlling complex traits in a sex-dependent manner.\n
\n\n Analysis of genetically segregated inbred animal strains dramatically enhances the power to isolate polymorphisms underlying complex diseases. Compared with association studies of human cohorts, studies in mice reduce environmental variability and allow for proof-of-concept experiments in biologically relevant systems, making it possible to conclusively identify genes underlying complex traits. In the context of previous such work to identify genetic loci that regulate autoimmune arthritis\n \n \n 8\n \n \u2013\n \n 10\n \n \n , we have identified a locus on mouse chromosome 3 (Cia21) that affects expression of the T cell activation marker\n \n CD2\n \n and regulates arthritis severity in females, but not in males\n \n \n 9\n \n \n . We herein find the cause of the effect to be a polymorphic estrogen receptor binding site (ERBS) within Cia21 that recapitulates the phenotypic properties of its parent locus. This polymorphic ERBS orchestrates expression of surrounding genes in a sex-specific manner, including CD2. We isolated these polymorphisms in a congenic mouse line (D3-31) and used these mice to study the consequences of estrogen-mediated regulation of CD2 for T cell-dependent autoimmunity. In addition, we found estrogen regulation of CD2 expression to be a conserved mechanism in humans that likely contributes to the sexual dimorphism in T cell-mediated autoimmune diseases.\n
\n\n We have set out to identify major genetic polymorphisms underlying the development of autoimmune arthritis, using animal models. As part of these efforts we previously described a major quantitative trait locus (QTL) on chromosome 3 qF2.2, which we termed Cia21\n \n 9\n \n . Cia21 was identified from an inter-cross between the collagen-induced arthritis (CIA)-susceptible C57BL/10.RIII (BR) and the CIA-resistant RIIIS/J (R3) mouse strains\n \n \n 11\n \n \n . Cia21 contains several differentially expressed genes, including\n \n CD2\n \n and\n \n PTPN22\n \n \n 9\n \n . Both CD2 and PTPN22 play a key role in T cell activation and were proposed as strong candidate genes. The aim of the present study is to identify the polymorphisms underlying the Cia21 QTL.\n
\n\n \n A minimal non-coding genetic interval proximal to\n \n \n CD2\n \n \n recapitulates the arthritis-regulating properties of Cia21\n \n
\n\n To dissect the Cia21 QTL, we bred heterozygous Cia21 mice and evaluated the resulting recombinant mice (shown in Fig.\n \n 1\n \n a) using CIA (Fig.\n \n 1\n \n b). Out of all the evaluated recombinants, only two, numbers 1 and 5, recapitulated the protective arthritis phenotype previously observed in Cia21 mice\n \n \n 9\n \n \n . Thus, the Cia21 QTL results from individual contributions of these two sub-QTLs. Importantly, the phenotype driving recombinant regions 1 and 5 mapped to the previously proposed\n \n \n 9\n \n \n candidate genes\n \n CD2\n \n and\n \n PTPN22\n \n , respectively. Recombinant fragment 1 (proximal to\n \n CD2\n \n ), however, was significantly smaller than fragment 5 providing better conditions for the positional identification of underlying polymorphisms. Therefore, we focused our efforts on the former.\n
\n\n Recombinant fragment 1 stretched from markers D3KV1 to MF31 (Fig.\n \n 1\n \n a, ca. 0.2 Mbp), but could be further redefined to the significantly smaller D3KV1-MF96 interval (ca. 0.02 Mbp) through a recombination assisted breeding strategy. Although recombinant fragments 1, 2, and 3 overlapped significantly, only fragment 1 regulated arthritis. Thus, we concluded that the causative polymorphisms must be positioned between markers D3KV1 and MF96 (Fig.\n \n 1\n \n a, highlighted yellow). D3KV1-MF96 is a non-coding 0.02 Mbp region proximal to\n \n CD2\n \n , located in-between the genes\n \n ATP1A1\n \n and\n \n IGSF3\n \n (Fig.\n \n 1\n \n c). We isolated the D3KV1-MF31 recombinant fragment (termed D3-31) in a congenic mouse line for further investigations. D3-31 congenic mice carry the parental R3 allele of D3-31 on an otherwise BR background. For simplicity, we hereon refer to the congenic line as D3-31 and to wild type littermates as BR.\n
\n\n \n D3-31 congenic mice are protected from several T cell-dependent models of autoimmunity in a sex-specific manner\n \n
\n\n In accordance with our previous data on Cia21, the R3 allele of D3-31 protected congenic mice in T cell-dependent\n \n \n 12\n \n \u2013\n \n 14\n \n \n autoimmune inflammatory models, including collagen induced arthritis (CIA), experimental autoimmune encephalomyelitis (EAE), and delayed type hypersensitivity (DTH) (Fig.\n \n 2\n \n a-f). We also investigated the T cell-independent\n \n \n 15\n \n \n collagen antibody-induced arthritis (CAIA) model, but observed no phenotypic differences (supplementary fig. S1). As the DTH model does not depend on B cells\n \n \n 12\n \n \n , these results indicated a critical role for T cells. Interestingly, and as previously described for Cia21\n \n 9\n \n , only female D3-31 mice were protected from T cell mediated autoimmunity (Figs.\n \n 2\n \n a-f). Thus, we concluded that D3-31 regulates T cell dependent autoimmune phenotypes, and likely T cells, in a sex-specific manner.\n
\n\n To discriminate between influence of sex chromosomes versus hormones, we performed CIA and EAE experiments in castrated female mice (Fig.\n \n 3\n \n a-c). Castration of female mice depletes gonadal production of 17-\u03b2-estradiol (E2)\n \n \n 16\n \n \n , which constitutes the major circulating estrogenic compound in females. Castration reverted the protective effect of the D3-31 fragment both in CIA and EAE (Fig.\n \n 3\n \n a-c), which demonstrated the crucial contribution of female sex hormones, most likely E2, to the protective phenotype in female D3-31 mice. We next defined the genetic mechanisms underlying this sexually dimorphic immune phenotype by sequencing the D3-31 fragment.\n
\n\n DNA sequencing of the D3-31 BR and R3 alleles revealed four single nucleotide polymorphisms (SNPs) in the critical D3KV1-MF96 interval (Fig.\n \n 4\n \n a and b). None of the variants affected the coding region of known genes, indicating distal (cis) regulation of gene expression, likely by interfering with regulatory elements. Given our previous observations, we speculated that the identified polymorphisms could be located within an ERBS, interfering with sex-dependent regulation of gene expression.\n
\n\n Estrogen receptors (ER\u03b1 and ER\u03b2) are nuclear hormone receptors that translate E2-mediated signalling. Both ER\u03b1 and ER\u03b2 are expressed in immune cells\n \n \n 17\n \n \n , and act as transcription factors regulating the expression of proximal and distant genes\n \n \n 18\n \n ,\n \n 19\n \n \n . To test our hypothesis, we screened publicly available ChIP-seq data for ER\u03b1 binding sites overlapping with one or more of the sequenced SNPs within D3KV1-MF96 interval. Indeed, one of the SNPs, AC\u2009>\u2009GG on chr3:101310478-479 (termed SNP478), clearly overlapped with an ER\u03b1 binding site (Fig.\n \n 4\n \n c). In fact, bioinformatic analysis also revealed an estrogen response element (i.e. an ER core binding motif) in close proximity to SNP478. We sought to verify this finding, and confirmed binding of ER\u03b1 to SNP478 in spleen cells using ChIP-qPCR (Fig.\n \n 4\n \n d). Comparison of SNP478 between mouse inbred strains revealed that this SNP is in fact part of a highly polymorphic AC/GT simple repeat (supplementary fig. S2, extracted from\n \n \n 20\n \n \n ).\n
\n\n To address whether SNP478 had functional consequences for E2-mediated transcriptional activity (i.e. interfered with the binding of ER\u03b1 to the DNA), we cloned the candidate D3KV1 ERBS (\u00b1\u2009100 bp) in its two variant forms (AC and GG) into luciferase reporter constructs. We assessed transcriptional activity of these constructs in transfected ER\u03b1\u2009+\u2009MCF-7 cells treated with increasing concentrations of E2 (Fig.\n \n 4\n \n e). In the context of the reporter construct, an increased occupancy of the ERBS by ER\u03b1 (as a function of increasing E2) resulted in suppression of transcriptional activity. Although surprising, similar observations have been reported elsewhere\n \n \n 21\n \n \n . Given the stronger transcriptional inhibition when using the BR derived construct, we concluded that ER\u03b1 has a higher affinity for the BR allele than for the D3-31 allele. Importantly, these data demonstrate that SNP478 has functional consequences for E2-mediated transcriptional activity.\n
\n\n \n Polymorphism in an ERBS leads to female-specific changes in\n \n \n CD2\n \n \n expression\n \n
\n\n Next, we tested the biological relevance of our findings by comparing the gene expression profile in lymphoid tissue from male and female D3-31 and BR mice. We observed female-specific changes in the expression of three genes adjacent to the polymorphic ERBS, namely\n \n CD2\n \n ,\n \n IGSF3\n \n and\n \n MAB21L3\n \n (Fig.\n \n 5\n \n a). We also investigated the expression of\n \n ATP1A1\n \n as well as more distal genes (\n \n CD101\n \n and\n \n SLC22A15\n \n ) previously implicated in the non-obese diabetic (NOD) mouse model of type 1 diabetes\n \n \n 22\n \n \n , but found no changes in their expression level. Notably, the female-specific reduction of CD2 expression in D3-31 mice was also evident at protein level (Fig.\n \n 5\n \n b), and correlated with our previously reported gene expression results\n \n \n 9\n \n \n .\n
\n\n Out of the differentially expressed genes,\n \n CD2\n \n was the only gene predominantly expressed in lymphoid tissue (Fig.\n \n 5\n \n c), particularly in activated CD4\n \n +\n \n T cells (Fig.\n \n 5\n \n d).\n \n IGSF3\n \n and\n \n MAB21L3\n \n regulate neural\n \n \n 23\n \n \n and ocular\n \n \n 24\n \n \n development, whereas CD2 has been involved in immune function\n \n \n 25\n \n \n and associated with human autoimmune conditions\n \n \n 4\n \n ,\n \n 26\n \n \n . Indeed, treatment of lymph node cells with anti-CD2 mAb inhibited T cell activation as demonstrated by reduced secretion of pro-inflammatory cytokines (Fig.\n \n 5\n \n e). Considering these data and normal development of D3-31 mice, we concluded that CD2 is driving the T cell-dependent immune phenotype observed in D3-31 mice.\n
\n\n Given the sex-specific differences in gene expression, we next investigated the relation between E2 and CD2 expression. T cells cultured in the presence of E2 up-regulated CD2 in a dose-dependent manner (Fig.\n \n 5\n \n f). Conversely, use of E2 depleted medium (achieved by using charcoal-stripped serum) reduced the expression of CD2, and, more importantly, neutralized the observed differences in CD2 expression between BR and D3-31 mice. Additionally, differences in CD2 expression could be re-established by reintroducing E2 to the medium (Fig.\n \n 5\n \n g). This not only demonstrates direct regulation of E2 on CD2 expression, but also proves that the identified polymorphisms interfere with this regulation. Consequently, we speculated that E2-mediated regulation of CD2 was contributing to sex-specific differences in the T cell responses. A sex-dependent reduction of CD2 expression in female D3-31 mice could likely limit the T cell responses.\n
\n\n To investigate the impact of sex hormone-dependent alterations in CD2 expression on the T cell responses, we compared the activation of T cells between BR and D3-31 female mice. In a first set of\n \n in vitro\n \n experiments, we found an impaired response in D3-31 T cells to TCR stimulation, as evidenced by reduced proliferation and IL-2 production (Fig.\n \n 6\n \n a). Importantly, the difference in T cell proliferation between BR and D3-31 mice could be enhanced in a dose-dependent manner by E2 (Fig.\n \n 6\n \n b), much like the E2-dependent expression differences observed for CD2 (Fig.\n \n 5\n \n g).\n
\n\n A diminished T cell response in D3-31 mice was also evident\n \n in vivo\n \n . Compared to BR mice, D3-31 mice showed a lower level of antigen specific T cells responses 10 days after immunization with CIA antigen bovine collagen type II, as demonstrated by reduced secretion of proinflammatory cytokines in lymph node cultures recalled with antigen (Fig.\n \n 6\n \n c). Flow cytometry analysis of D3-31 draining lymph nodes revealed lower numbers of antigen experienced CD40L\n \n +\n \n CD4\n \n +\n \n T cells (Fig.\n \n 6\n \n d), which expressed reduced levels of CD2 and L-17A after\n \n ex vivo\n \n restimulation with PMA (Fig.\n \n 6\n \n d and e). Differences in T cell activation status were also evident by lower numbers of induced regulatory T cells after immunization (Fig.\n \n 6\n \n f). Importantly, the observed differences in T cell activation were strictly sex-specific (Fig.\n \n 6\n \n d-f), mirroring sex-specific differences in CD2 expression. Treatment with anti-CD2 strongly reduced the expression of IL-17 in na\u00efve and autoreactive CD4\n \n +\n \n T cells (Fig.\n \n 6\n \n g and e, respectively), as well as FOXP3 in na\u00efve T cells (Fig.\n \n 6\n \n g), demonstrating a key role for CD2 in the differentiation of Th17 and Treg cells. Consequently, we concluded that reduced CD2 expression in female D3-31 mice limits T cell activation in a sex-specific manner.\n
\n\n Although CD2 can regulate TCR signalling by increasing the stability of the immune synapse, we thought it plausible that persistent differences in CD2 signalling could elicit more profound phenotypic changes. Proteomic and flow cytometric analysis of CD4\n \n +\n \n T cells treated with an anti-CD2 mAb resulted in a selective up-regulation of the immune inhibitory marker LAG-3 (Fig.\n \n 6\n \n h). Thus, our data suggests that CD2 signalling modulates T cell activation not only by stabilizing the immune-synapse, but also by regulating the expression of the inhibitory marker LAG-3.\n
\n\n \n CD2\n \n \n associates with rheumatoid arthritis (RA) and is regulated by E2 in humans\n \n
\n\n Our results in mice suggested a regulatory role for\n \n CD2\n \n on T cell-dependent autoimmunity, which is genetically determined in a sex-linked manner. We therefore explored the relevance of our findings in humans in the context of RA. In a genetic association study, we found a significant association between\n \n CD2\n \n polymorphisms and RA (Fig.\n \n 7\n \n a). While this association was more often found in females than in males, this was likely due to higher prevalence of RA in females (female to male ratio 3:1). Interestingly, several of the SNPs associated with RA (p\u2009<\u20090.05) can enhance expression of\n \n CD2\n \n (Fig.\n \n 7\n \n b), as we determined from the GTEx database\n \n \n 27\n \n \n . Further analysis of available microarray datasets\n \n \n 28\n \n \n revealed a mild yet significant correlation between\n \n CD2\n \n expression in RA synovia and disease activity (Fig.\n \n 7\n \n c). Moreover,\n \n CD2\n \n is strongly up-regulated in the synovial tissue from RA patients when compared to osteoarthritis or healthy synovium (Fig.\n \n 7\n \n d). Thus, it is likely that CD2 is involved in joint inflammation, and that\n \n CD2\n \n polymorphisms affecting its expression contribute to the development or perpetuation of joint autoimmunity.\n
\n\n Importantly, women expressed higher levels of\n \n CD2\n \n than men, both in RA synovium and healthy PBMCs (Fig.\n \n 7\n \n c and\n \n 7\n \n e, respectively), suggesting the E2-mediated regulation of\n \n CD2\n \n observed in mice is conserved in humans as well. To corroborate our findings, we stimulated CD4\n \n +\n \n T cells from healthy human donors with increasing amounts of E2. Firstly, we noticed a strong up-regulation of CD2 in antigen experienced CD45RO\n \n +\n \n T cells compared to their na\u00efve CD45RA\n \n +\n \n counterparts (Fig.\n \n 7\n \n f). But more importantly, expression of CD2 could be enhanced in CD45RO\n \n +\n \n T cells by incubation with E2 in a concentration-dependent manner (Fig.\n \n 7\n \n g). Indeed, analysis of available ChIP-seq data\n \n \n 29\n \n \n revealed that ER\u03b1 robustly binds the human\n \n CD2\n \n gene locus (Fig.\n \n 7\n \n h). Thus, these data demonstrate the evolutionary conserved nature of E2-mediated regulation of CD2.\n
\n\n We reasoned that hormonal regulation of CD2 expression could have implications for anti-CD2-mediated therapy, as previous research suggests that anti-CD2 (Alefacept) preferentially targets CD2\n \n hi\n \n T cells\n \n \n 30\n \n \n . To test this, we compared the\n \n in vivo\n \n effects of anti-CD2 mAb administration on circulating T cells from male and female mice (Fig.\n \n 7\n \n i). Anti-CD2 mAb treatment partially depleted circulating T cells, and resulted in the relative enrichment of remaining effector CD44\n \n +\n \n T cells, skewing the na\u00efve CD62L\n \n +\n \n /effector CD44\n \n +\n \n T cell ratio. This effect was significantly more pronounced in females, which, like in humans, expressed higher levels of CD2 in circulating T cells. Taken together, these data demonstrate that sex-dependent differences in CD2 expression determine the response to anti-CD2 mAb.\n
\n\n Using forward genetics, we have positionally identified a polymorphic ERBS regulating T cell-dependent autoimmunity. This site orchestrates expression of surrounding genes in a sex-specific manner, including expression of the T cell co-stimulatory molecule, CD2. We find that E2-mediated regulation of CD2 is a conserved mechanism that influences T cell activation in a sex-specific manner, contributing to the sexual dimorphism in autoimmune diseases.\n
\n\n Understanding the sexual dimorphic immune responses is fundamental for personalized medicine but is methodologically challenging. Common approaches to study this phenomenon rely on intricate manipulation of gonadal or hormonal systems\n \n \n 31\n \n \n , which has yielded valuable insights but with limited physiological relevance. Our study provides a more physiological perspective by the identification of a naturally occurring polymorphism in an ERBS, which enables studies on sex-associated differences in T cell-mediated autoimmunity. Since we used a hypothesis-free approach, our findings strongly suggest E2-mediated regulation of CD2 as a key physiological mechanism contributing to sex differences in the T cell responses and susceptibility to autoimmunity.\n
\n\n Our results also highlight the importance of genotype-sex interactions for the sexual dimorphism in autoimmune diseases. While much attention has been devoted to the contribution of sex chromosomes, epigenetic mechanisms or direct actions of hormones to the sexually dimorphic immune responses, the interactions between sex and predisposing autosomal polymorphisms have remained elusive. Isolated studies have demonstrated sex-dependency of expression (e) QTLs\n \n \n 32\n \n \n and sex differences in the genetic associations to inflammatory diseases\n \n \n 33\n \n \u2013\n \n 35\n \n \n , but evidence is limited. Using a hypothesis-free approach, we for the first time conclusively identify a sex biased QTL with direct consequences for the development of autoimmunity. Polymorphisms in the identified ERBS modulate E2-driven CD2 expression, leading to sex-specific differences in T cell autoimmunity. Our results demonstrate not only that genetic polymorphisms influence hormonal regulation of gene expression, but also that genotype-sex interactions shape the sexually dimorphic immune response.\n
\n\n Independent of our sex-related findings, this study provides valuable insights into CD2 immunobiology. Polymorphisms in the\n \n CD2\n \n locus have been previously associated with several autoimmune diseases\n \n \n 4\n \n ,\n \n 26\n \n \n , but not much attention was given to the mechanism of action of these polymorphisms. Similarly, CD2 has been explored as a therapeutic target, but its mechanism of action beyond depletion of circulating T cell is poorly characterised\n \n \n 36\n \n \n , and complex adverse effects (including malignancies\n \n \n 37\n \n \n ) warrant further research. CD2 in isolation affects the formation of the immune synapse\n \n 3839\n \n and T cell activation\n \n 4041\n \n , but the relevance of these findings\n \n in vivo\n \n are less clear. For example, targeting CD2 in mice does not seem to affect immune system development\n \n \n 42\n \n \n or thymic T cells\n \n \n 43\n \n \n , unless TCR transgenic systems are used\n \n 4445\n \n . Thus, there is a need to study this therapeutically promising pathway in a physiologically relevant context. The D3-31 mice used in this study exhibit discrete changes in CD2 expression mediated by E2, thus enabling us to study the effect of CD2 on T cell mediated autoimmunity in a physiological setting.\n
\n\n We show for the first time that changes in CD2 expression, caused by natural polymorphisms, affect the T cell responses. Reduced CD2 expression protected mice from T cell-dependent inflammation and autoimmunity by reducing the activation and proliferation of antigen-specific T cells. This is consistent with studies demonstrating that CD2 membrane density is proportional to TCR signalling strength\n \n \n 38\n \n \n , and that peptide-based blocking of CD2 signalling reduces CIA severity\n \n \n 46\n \n \n . Our results also implicate CD2 in the generation of Th17 and Treg- type T cell responses. Mice with reduced CD2 expression had a diminished T cell response characterized by a reduced expansion of Th17 and Treg cells. Accordingly, blocking CD2 resulted in the suppression of both cell types\n \n in vitro\n \n . Indeed, CD2 has been linked to Treg\n \n \n 47\n \n ,\n \n 48\n \n \n and Th17 phenotypes\n \n \n 39\n \n \n before, and targeting CD2 is effective in the treatment of Th17-mediated inflammatory diseases like psoriatic arthritis\n \n \n 49\n \n \n . In summary, this suggests a key role for CD2-mediated activation in the induction of Th17 and Treg cells.\n
\n\n Mechanistically, CD2 seems to play a role in T cell activation beyond its ability to stabilize the immune synapse, as blocking CD2 results in selective up-regulation of the exhaustion marker, LAG-3. This finding is supported by studies showing an inverse correlation between CD2 expression and exhaustion of T cells\n \n \n 38\n \n 50\n \n , and up-regulation of LAG-3 in human CD8 T cells after treatment with Alefacept (anti-CD2)\n \n \n 51\n \n \n . Thus, together with other studies, our data suggests that CD2 signalling maintains T cell autoreactivity by reducing the expression of inhibitory LAG-3 molecule.\n
\n\n Our findings in mice are likely relevant to the sexual dimorphism observed in human autoimmune conditions.\n \n CD2\n \n associates with RA and E2 regulation of CD2 expression is highly conserved in human T cells. Women, who are generally more prone to autoimmunity, express higher levels of\n \n CD2\n \n than men. In mice, we demonstrate that these type of discrete and sex-specific differences in CD2 expression result in sexually dimorphic T cell responses and autoimmune phenotypes. Thus, subtle, physiological changes in CD2 expression caused by natural polymorphisms likely modify the risk of T cell-dependent autoimmunity in humans. E2-mediated regulation of CD2 probably contributes to sex differences in the immune responses, both in homeostasis as well as autoimmune conditions.\n
\n\n Sex-dependent differences in CD2 expression have implications for several sexually dimorphic immune processes involving T or other CD2 expressing cells. Hormonal regulation of CD2 could contribute to more vigorous humoral immune responses in women\n \n \n 2\n \n \n , helping to protect their off-spring from infections\n \n \n 52\n \n \n at the cost of an enhanced risk to autoimmunity post-partum\n \n \n 53\n \n \n . Alternatively, an enhanced CD2 expression in women might facilitate the induction of regulatory T cell phenotypes (as we observed in mice) to facilitate foetal-maternal immune tolerance. A hormonal regulation of CD2 expression could have wide ranging implications for the personalized therapy of T cell-mediated inflammatory diseases, as Alefacept was shown to preferentially target CD2\n \n hi\n \n T cells\n \n \n 30\n \n \n . Indeed, we demonstrate strong effects of anti-CD2 mAb administration on the na\u00efve/effector T cell ratio in female, but not male mice. As such, sex-specific differences in T cell CD2 expression may offer a useful biomarker for stratification of patients in the context of CD2 targeted therapies.\n
\n\n In conclusion, our results highlight the importance of genotype-sex interactions for the sexual dimorphism in autoimmunity, demonstrating that sex can determine the penetrance of predisposing genetic factors. Our findings show that CD2 is a sex-sensitive regulator of T cell-mediated autoimmunity. Hormonal regulation of CD2 is a conserved mechanism that has implications for the sexual dimorphism in the susceptibility to -and treatment of- autoimmune diseases like RA.\n
\n\n \n Animals\n \n . The BR.Cia21.D3-31 congenic founder mice were obtained from a partial advanced intercross (PAI) described elsewhere and they were subsequently back crossed for four additional generations\n \n \n 9\n \n \n . In order to ensure strain purity, BR.Cia21.D3-31 mice were screened with a custom designed 8k Illumina chip at genome wide level\n \n \n 54\n \n \n and the mice were found to be devoid of any contaminating RIIIS/J alleles. No SNPs were present between the congenic and the B10.RIII background strain. Mice were kept under specific pathogen free (SPF) conditions in the animal house of the Section for Medical Inflammation Research, Karolinska Institute in Stockholm. Animals were housed in individually ventilated cages containing wood shavings in a climate-controlled environment with a 14 h light-dark cycle, fed with standard chow and water\n \n ad libitum\n \n . All the experiments were performed with age-, sex- and cage-matched mice and all the genetic experiments were performed with littermate controls. All the experimental procedures were approved by the ethical committees in Stockholm, Sweden with ethical permit numbers; 12923/18 and N134/13 (genotyping and serotyping), N35/16 (CIA) and N83/13 (EAE).\n
\n\n \n Preparation of mouse single cell suspensions\n \n . Briefly, spleen or lymph nodes were harvested and mechanically dissociated on a 40 \u00b5M cell strainer (Falcon) using a 1ml syringe plunger (Codan). Cells were counted on a Sysmex KX-21 cell counter. All centrifugation steps throughout the study were carried out a 350 x g for 5 min at RT. For spleen samples, red blood cells were lysed in RBC buffer (155 mM NH4Cl, 12 mM NaHCO3, 0.1 mM EDTA) before counting.\n
\n\n \n Preparation of human peripheral blood mononuclear cells (PBMC).\n \n Human PBMCs were prepared from 8 ml whole blood of healthy donors using SepMate (Stemcell Technologies) tubes and Ficoll density gradient medium (Sigma) according to the manufacturer. Ethical permit number: Dnr 2020\u201305001.\n
\n\n \n Cell culture\n \n . 10\n \n 6\n \n splenocytes, 5\u00d710\n \n 5\n \n lymph node cells, or 10\n \n 5\n \n PBMCs were cultured in 200 \u00b5l of complete RPMI per well in Nunclon U-shaped bottom 96-well plates (Thermo Scientific). Cells were incubated at 37\u00b0C and 5% CO\n \n 2\n \n . Complete RPMI: RPMI 1640 with GlutaMAX\u2122 (Thermo Scientific); 10% heat inactivated FBS (Thermo Scientific); 10 \u00b5M HEPES (Sigma); 50 \u00b5g/ml streptomycin sulfate (Sigma); 60 \u00b5g/ml penicillin C (Sigma); 50 \u00b5M \u03b2-Mercaptoethanol (Thermo Scientific). FBS was heat-inactivated for 30 min at 56\u00b0C. To assess the effect of 17-\u03b2-estradiol (Sigma) on CD2 expression, the medium was supplemented with charcoal-stripped FBS instead (Thermo Scientific). 17-\u03b2-estradiol was solved in ethanol.\n
\n\n \n ELISA\n \n . 10\n \n 6\n \n lymph node cells from CIA mice were plated per well and stimulated with 100 \u00b5g/ml bovine collagen type II (bCII) in complete RPMI for 48 h as described in\n \n cell culture\n \n . Supernatants were used for cytokine analysis. Flat 96-well plates (Maxisorp, Nunc) were coated overnight at 4\u00b0C with the capture antibody (Ab, listed below) in PBS. After removing the coating solution, supernatant from cell cultures were added. Plates were incubated for 3 h at RT before washing (0.05% Tween PBS) and adding the biotinylated detection Ab (listed below) in PBS (1 h at RT). Plates were washed and incubated 30 min at RT with Eu-labelled streptavidin (PerkinElmer, 1:1000) in 50 mM Tris-HCl, 0.9% (w/v) NaCl, 0.5% (w/v) BSA and 0.1% Tween 20, 20 \u00b5M EDTA. After washing, DELFIA Enhancement Solution (PerkinElmer) was added and fluorescence read at 620 nm (Synergy 2, BioTek). Monoclonal antibodies (mAbs) to IL-2 (capture Ab 5 \u00b5g/ml JES6-IA12; detection Ab 2 \u00b5g/ml biotinylated-JES6-5H4, in-house produced), IL-17A (capture Ab 5 \u00b5g/ml TC11-18H10.1; detection Ab 2,5 \u00b5g/ml TC11-8H4, Biolgend), IFN-\u03b3 (capture Ab 5 \u00b5g/ml AN18; detection Ab 2,5 \u00b5g/ml biotinylated R46A2, in-house produced).\n
\n\n \n Analysis of mRNA expression\n \n . 10\n \n 6\n \n lymph node cells per well were stimulated for 24 h using mAb LEAF hamster anti-mouse CD3 (1 \u00b5g/ml, 500A2, BD Pharmingen) and LEAF hamster anti-mouse CD28 (1 \u00b5g/ml, 37.51, BD Pharmingen) as described in\n \n cell culture\n \n . Cells were washed in PBS and RNA was extracted using Qiagen RNeasy columns according to the manufacturer without DNAse digestion. RNA concentration was determined using a NanoDrop 2000 (Thermo Scientific). Sample concentrations were normalized before proceeding with reverse transcription. Samples were stored at -20\u00b0C for short-term storage. cDNA synthesis was carried out using the iSrcipt cDNA synthesis kit (Bio-Rad) according to the manufacturer. qRT-PCR primers covered an exon-exon junction to minimize amplification of genomic DNA and were used at a final concentration of 300 nM. The qPCR reaction was carried out using the iQSYBR Green Mix (Bio-Rad) in white 96-well plates (Bio-Rad) using a CFX96 real-time PCR detection system (Bio-Rad).\n \n ACTB\n \n or\n \n GAPDH\n \n were used as an internal control. Primer sequences are listed in supplementary table 2. Data were analysed according to the \u2206\u2206Ct method\n \n \n 55\n \n \n , assuming equal efficiency for all the primer pairs.\n
\n\n \n ChIP-qPCR.\n \n 10x10\n \n 6\n \n spleen cells/ml were fixed for 10 min in 1% formaldehyde PBS at RT. The reaction was stopped by adding 125 mM glycine and cells were washed twice in ice-cold PBS. Complete protease inhibitor cocktail (Roche) was added in all the following steps. 2x10\n \n 6\n \n cells were lysed in 1 ml cell lysis buffer\n \n \n 56\n \n \n on ice for 15 min, and the extracted nuclei lysed in 1 ml nuclear lysis buffer\n \n \n 56\n \n \n on ice for 15 min. Lysates were sonicated for 15 cycles (on high settings, 30\u2019\u2019ON-30\u2019\u2019OFF) using a Diagenode Bioruptor. The water bath was cooled to 4\u00b0C before beginning sonication. Average DNA length after sonication was 500 bp. 450 \u00b5l of the lysates were incubated with 10 \u00b5g/ml rabbit anti-mouse ER\u03b1 Ab (clone E115, Abcam) or polyclonal rabbit IgG isotype control (Abcam) on a shaker at 4\u00b0C over night. Next day, DNA-Ab complexes were precipitated using protein G magnetic beads (Thermos Scientific). Beads were washed twice for 5 min at RT in buffers of increasing salt concentration according to\n \n \n 56\n \n \n . DNA was eluted by incubating beads in 100 \u00b5l elution buffer\n \n \n 56\n \n \n at 65\u00b0C for 30 min with occasional vortex. Beads were pelleted and fixation was reversed by incubation of supernatants for 8 h at 65\u00b0C in the presence of 0.3 M NaCl in 96-well plates. On the third day, 10 \u00b5g/ml RNAse A (Thermo Scientific) was added for 30 min (37\u00b0C) before incubation with 10 \u00b5g/ml of Proteinase K (Thermo Scientific) at 55\u00b0C for 30 min. DNA was purified using GeneJET PCR purification kit (Thermo Scientific) and used for qPCR. Primers used for amplification of recovered DNA are listed in supplementary table 3. Data was analysed according to\n \n \n 56\n \n \n , but briefly, results are presented as fold change over their respective mock IP controls.\n
\n\n \n Flow cytometry\n \n . 10\n \n 6\n \n cells were blocked in 20 \u00b5l of PBS containing 5 \u00b5g in-house produced 2.4G2 in 96-well plates for 10 min at RT. Samples were washed with 150 \u00b5l of PBS and subsequently stained with the indicated antibodies in 20\u00b5l of PBS diluted 1:100 or 1:200 at 4\u00b0C for 20 min in the dark (Ab list follows). Cells were washed once, fixed and permeabilized for intracellular staining using BD Cytofix/Cytoperm\u2122 (BD) according to the manufacturer. Cells were stained intracellularly with 20 \u00b5l of permeabilization buffer (BD), using the antibodies at a 1:100 final dilution, for 20 min at RT. FOXP3 staining required nuclear permeabilization and was carried out using Bioscience\u2122 FOXP3/Transcription Factor Staining Buffer. For intracellular cytokine staining, cells were stimulated\n \n in vitro\n \n with phorbol 12-myristate 13-acetate (PMA) 10 ng/ml, ionomycin 1 \u00b5g/ml, and BFA 10 \u00b5g/ml for 4\u20136 h at 37\u00b0C prior to fixation, permeabilization and staining.\n
\n\n Flow cytometry anti-mouse antibodies (BD Pharmingen): CD3 (clone: 145-2C11); TCRB (H57-597); CD4 (RM4-5); CD8 (53\u2009\u2212\u20096.7); CD19 (1D3, 6D5); CD11B (M1/70); CD11C (HL3, N418); FOXP3 (FJK-16s); CD25 (7D4); CD44 (IM7); CD62L (MEL-14); CD2 (RM2-5); LY6C (AL-21); LAG-3 (C9B7W); CD40L (MR1); IFN-\u03b3 (R46A2); IL-17A (TC11-18H10.1). CD16/CD32 (2.4G2, in house).\n
\n\n Flow cytometry anti-human antibodies (BD Pharmingen): CD45 (clone: HI30); CD2 (RPA-2,10); TCRB (IP26); CD4 (OKT4); CD45RA (Hl100); CD45RO (UCHL1).\n
\n\n \n Proliferation assay\n \n . 10\n \n 7\n \n lymph node cells were labelled using CellTrace\u2122 Violet Cell Proliferation Kit (ThermoFisher Scientific) according to the manufacturer. 5x10\n \n 5\n \n na\u00efve lymph node cells were cultured per well in U 96-well plates as described under\n \n cell culture\n \n in the presence of hamster anti-mouse CD3 (1 \u00b5g/ml, 500A2, BD Pharmingen) and hamster anti-mouse CD28 (1 \u00b5g/ml, 37.51, BD) for 72\u201396 h. Proliferation by dilution of CTV was assessed using flow cytometry. Complementary antibody staining was done as described under\n \n flow cytometry\n \n . Proliferation parameters were analysed and calculated using FlowJo 8.8.7.\n
\n\n \n Collagen-induced arthritis (CIA)\n \n . 12-week-old mice were immunized with 100 \u00b5g of bovine collagen type II (bCII) in 100 \u00b5l of a 1:1 emulsion with CFA (BD ) and PBS intradermally at the base of the tail. Mice were challenged at day 35 with 50 \u00b5g of bCII in 50 \u00b5l of IFA (BD) emulsion. Mice were monitored for arthritis development as described in\n \n \n 57\n \n \n . In short, each visibly inflamed (i.e. swollen and red) ankle or wrist was given 5 points, whereas each inflamed knuckle and toe joint was given 1 point each, resulting in a total of 60 possible points per mouse and day.\n
\n\n \n Collagen antibody-induced arthritis (CAIA)\n \n . CII-specific antibodies (M2139, CIIC1, CIIC2 and UL1) were generated and purified as previously described\n \n \n 15\n \n \n . The sterile cocktail of M2139, CIIC1, CIIC2 and UL1 mAbs (4 mg per mouse) was injected intravenously. On day 7, lipopolysaccharide (O55:B5 LPS from Merck; 25 \u00b5g in 200 \u00b5l per mouse) was injected intraperitoneally to all mice to increase severity of the disease. Mice were scored as described for CIA.\n
\n\n \n Experimental induced autoimmune encephalomyelitis (EAE)\n \n . 12-week-old mice were immunized with a 100 \u00b5l emulsion of 250 \u00b5g myelin basic protein peptide (MBP) 89\u2013101 peptide in PBS and 50 \u00b5l IFA (incomplete Freud\u2019s adjuvant) containing 50 \u00b5g\n \n Mycobacterium tuberculosis\n \n H37RA (BD). Animals were boosted with 200 ng of\n \n Bordetella pertussis\n \n toxin (Sigma Aldrich, St. Louis, MO, USA) i.p. on day 0 and 48 h post initial immunization. EAE severity was evaluated as described in\n \n \n 58\n \n \n . Briefly, mice were scored as follows: 0, no clinical signs of disease; 1, tail weakness; 2, tail paralysis; 3, tail paralysis and mild waddle; 4, tail paralysis and severe waddle; 5, tail paralysis and paralysis of one limb; 6, tail paralysis and paralysis of two limbs; 7, tetraparesis; 8, moribund or deceased.\n
\n\n \n Delayed type hypersensitivity (DTH)\n \n . Hypersensitivity reaction was elicited by initially immunizing mice with 100 \u00b5g bCII emulsified in 50 \u00b5l CFA (Difco, Detroit, MI, USA). Ten days later mice were challenged with an injection of 10 \u00b5g bCII in 10 mM acetic acid into the dorsal part of the right ear and vehicle control in the left one. Ear swelling was assessed 48 and 72 h later using a calliper.\n
\n\n \n Ovariectomy\n \n . In brief, ovaries of female mice were removed after a single incision through the back skin and bilateral flank incision through the peritoneum. Thereafter, mice were rested for a minimum of 14 days prior to immunization for EAE or CIA as described elsewhere.\n
\n\n \n Luciferase reporter assay\n \n . 2x10\n \n 4\n \n MCF-7 cells were seeded into flat 96-well flat bottom plates (Thermo Scientific) and left to adhere overnight. Then cells were transfected with pGL4.17 (Promega) luciferase reporter construct containing the BR or R3 allele of the candidate ERBS (pGL4.17.BR and pGL4.17.R3, respectively). ERBS cloning primers 5\u2019-3\u2019, Fw: AGATCTCGAGGGGGAAAGCTCTGACTTGGG; Rv: GTCAAGCTTGAGAAAGAATTTTGCTTATTTAGTCC. Cells were transfected in OPTIMEM medium (Thermo Scientific) using lipofectamine 3000 (Thermo Scientific) according to the manufacturer. The transfection mix (per well) contained 400 ng plasmid, 0.3 \u00b5l lipofectamine, and 0.2 \u00b5l P3000 reagent. Respective stimuli (20 ng/ml PMA, 10\u2013100 nM E2) were added after 24 h, and cells were further incubated overnight before lysis. Luciferase activity was measured using Pierce Firefly Luc One-Step Glow Assay Kit (Thermo Scientific) in a Synergy-2 plate reader (BioTek).\n
\n\n \n Genetic association study\n \n . Data for genetic variations within CD2-CD58 locus was extracted from previous Immunochip data published elsewhere (PMID: 23143596). After filtering these data correspond to 263 SNPs in 1940 healthy controls (M/F 524/1416) and 2762 RA patients (M/F 817/1945) from the Swedish EIRA study. Association was analysed by PLINK separately for female and male individuals.\n
\n\n \n Analysis of public microarray expression data\n \n . Microarray data was extracted from NCBI GEO Database\n \n \n 28\n \n \n and analysed using Shiny GEO\n \n \n 59\n \n \n . GEO accession number is cited wherever NCBI GEO data has been used.\n
\n\n \n Statistical analysis\n \n . Statistical analysis was performed using GraphPad Prism v6.0 or higher. Statistical comparison of two unpaired groups was carried out using Mann-Whitney U non-parametric test. CIA and EAE disease curves were compared using two-way ANOVA multiple comparisons test. P-values under 0.05 were considered statistically significant and are denoted with the symbol *. P-values under 0.01 are denoted **.\n
\n\n \n Proteomic analysis of enriched CD4\n \n \n \n +\n \n \n \n T cells\n \n . CD4\n \n +\n \n T cells were enriched from na\u00efve spleens using untouched CD4\n \n +\n \n T cell mouse kit (Dynabeads, Life Technologies). 96-well U bottom plates were pre-coated with 1 \u00b5g/ml of anti-CD3 and 1 \u00b5g/ml of anti-CD2 in PBS for 3 h at 37\u00b0C. 2.5x10\n \n 5\n \n CD4\n \n +\n \n T cells were plated on the pre-coated plates and cultured for 48 h.\n
\n\n Cell pellets were lysed in a buffer consisting of 1% SDS, 8 M urea and 20 mM EPPS pH 8.5 and sonicated using a Branson probe sonicator (3 s on, 3 s off pulses, 45 s, 30% amplitude). Protein concentration was measured using BCA assay and subsequently 50 \u00b5g of protein from each sample were reduced with 5 mM DTT at RT for 45 min followed by alkylation with 15 mM IAA in the dark at RT for 45 min. The reaction was quenched by adding 10 mM DTT and the samples were precipitated using methanol-chloroform mixture. Dried protein pellets were dissolved into 8 M urea, 20 mM EPPS pH 8.5. EPPS (20 mM, pH 8.5) was added to lower the urea concentration to 4 M and LysC digestion was done at a 1:100 ratio (LysC/protein, w/w) overnight at RT. Then urea concentration was lowered to 1 M and trypsin digestion was conducted at a 1:100 ratio (Trypsin/protein, w/w) at RT for 5 h. TMTpro plex (Thermo Fischer Scientific) reagents were dissolved into dry acetonitrile (ACN) to a concentration of 20 \u00b5g/\u00b5l and 200 \u00b5g were added to each sample. The ACN concentration in the samples was adjusted to 20% and the labelling was conducted at RT for 2 h and quenched with 0.5% hydroxylamine (ThermoFischer Scientific) for 15 min at RT. The samples were then combined and dried using Speedvac to eliminate the ACN. Then samples were acidified to pH\u2009<\u20093 using TFA and desalted using SepPack (Waters). Lastly, peptide samples were dissolved into 20 mM NH4OH and 150 \u00b5g of each sample was used for off-line fractionation.\n
\n\n Samples were fractionated off-line in a high-pH reversed-phase manner using an UltimateTM 3000 RSLCnano System (Dionex) equipped with a XBridge Peptide BEH 25 cm column of 2.1 mm internal diameter, packed with 3.5 \u00b5m C18 beads having 300 \u00c5 pores (Waters). The mobile phase consisted of buffer A (20 mM NH\n \n 4\n \n OH) and buffer B (100% ACN). The gradient started from 1% B to 23.5% in 42 min, then to 54% B in 9 min, 63% B in 2 min and stayed at 63% B for 5 min and finally back to 1% B and stayed at 1% B for 7 min. This resulted in 96 fractions that were concatenated into 24 fractions. Samples were then dried using Speedvac and re-suspended into 2% ACN and 0.1% FA prior to LC-MS/MS analysis.\n
\n\n Peptides were separated on a 50 cm EASY-spray column, with a 75 \u00b5m internal diameter, packed with 2 \u00b5m PepMap C18 beads, having 100 \u00c5 pores (Thermo Fischer Scientific). An UltiMate\u2122 3000 RSLCnano System (Thermo Fischer Scientific) was used that was programmed to a 91 min optimized LC gradient. The two mobile phases consisted of buffer A (98% milliQ water, 2% ACN and 0.1% FA) and buffer B (98% ACN, 2% milliQ water and 0.1% FA). The gradient was started with 4% B for 5 min and increased to 26% B in 91 min, 95% B in 9 min, stayed at 95% B for 4 min and finally decreased to 4% B in 3 min and stayed at 4% B for 8 more min. The injection was set to 5 \u00b5L corresponding to approximately 1 \u00b5g of peptides.\n
\n\n Mass spectra were acquired on a Q Exactive HF mass spectrometer (Thermo Fischer Scientific). The Q Exactive HF acquisition was performed in a data dependent manner with automatic switching between MS and MS/MS modes using a top-17 method. MS spectra were acquired at a resolution of 120,000 with a target value of 3.10\n \n 6\n \n or maximum integration time of 100 ms. The m/z range was from 375 to 1500. Peptide fragmentation was performed using higher-energy collision dissociation (HCD), and the normalized collision energy was set at 33. The MS/MS spectra were acquired at a resolution of 60,000 with the target value of 2.10\n \n 5\n \n ions and a maximum integration time of 120 ms. The isolation window and first fixed mass were set at 1.6 m/z units and m/z 100, respectively.\n
\n\n Protein identification and quantification were performed with MaxQuant software (version 1.6.2.3). MS2 was selected as the quantification mode and masses of TMTpro labels were added manually and selected as peptide modification. Acetylation of N-terminal, oxidation of methionine and deamidation of asparagine and glutamine were selected as variable modifications while carbamidomethylation of the cysteine was selected as fixed modification. The Andromeda search engine was using the UP000000589_Mus musculus database (22129 entries) with the precursor mass tolerance for the first searches and the main search set to 20 and 4.5 ppm, respectively. Trypsin was selected as the enzyme, with up to two missed cleavages allowed; the peptide minimal length was set to seven amino acid. Default parameters were used for the instrument settings. The FDR was set to 0.01 for peptides and proteins. \u201cMatch between runs\u201d option was selected with a time window of 0.7 min and an alignment time window of 20 min.\n
\n\n Reporting Summary\n
\n \n\n A limitation of standard brightfield microscopy is its low contrast images, especially for thin specimens of weak absorption, and biological species with refractive indices very close in value to that of their surroundings. Here, we demonstrate, using a planar photonic chip with tailored angular transmission as the sample substrate, a standard brightfield microscopy can provide both darkfield and total internal reflection (TIR) microscopy images with one experimental configuration. The image contrast is enhanced without altering the specimens and the microscope configurations. This planar chip consists of several multilayer sections with designed photonic band gaps and a central region with dielectric nanoparticles, which does not require top-down nanofabrication and can be fabricated in a large scale. The photonic chip eliminates the need for a bulky condenser or special objective to realize darkfield or TIR illumination. Thus, it can work as a miniaturized high-contrast-imaging device for the developments of versatile and compact microscopes.\n
\n\n \n \n Optics/Lasers\n \n \n
\n\n \n microscopy\n \n
\n\n \n total internal reflection\n \n
\n\n \n photonics\n \n
\n\n Modern microscopes can produce images of high resolution and high magnifications\n \n 1, 2, 3\n \n . Enough contrast of the image is also essential to clearly reveal the details of the specimens\n \n 4, 5\n \n , resulting in the widespread use of fluorescent probes. A variety of techniques have been developed to improve image contrast without modification of the samples (label-free imaging). One approach is darkfield illumination, which is particularly suitable for specimens that display little or no absorption and/or weakly absorbing biological samples. Darkfield microscopy (DFM) has been widely used in many fields of science and engineering, such as biological imaging, nanoparticle characterization and inspection of semiconductor devices\n \n 6, 7, 8, 9\n \n . However, it cannot be a simple and inexpensive imaging system. In a typical DFM, firstly, the specimen is illuminated at oblique angles far from the direction normal to the sample, then a bulky darkfield condenser is needed. Secondly, only light that is scattered by the specimen into a cone of apex angle cantered around the microscope\u2019s optical axis should be collected. To meet this requirement, the objective is chosen such that it collects rays over a small range of angles which are far from the normal axis, so no light directly from the darkfield condenser contributes to the image. The regions on the specimen where there are no small features to scatter light are almost completely dark, often resulting in high-contrast images and giving \u2018darkfield\u2019 microscopy its name. Thirdly, the specialized condenser, objective and additional components are prone to misalignment and add cost and complexity to the microscope\n \n 10, 11\n \n . The use of a bulky condenser also results in the very small illumination area (of micrometer scale).\n
\n\n In recent years, there has been interesting in developments of new imaging instruments with the nanophotonic devices to downsize or simplify the microscope setup and improve the imaging performances. For example, two multifunctional and compact metasurface layers were used to develop a compact phase gradient microscope, which can generate a quantitative phase gradient image with increased image contrast\n \n 12\n \n . The combination of ptychographic coherent diffractive imaging with sub-surface nanoaperture arrays was shown to yield an enhancement of both the reconstructed phase and amplitude\n \n 13\n \n . A luminescent photonic substrate with a controlled angular fluorescence emission profile was used in a conventional microscopy to replace the bulk condenser for miniaturized lab-on-chip darkfield imaging devices\n \n 14, 15\n \n .\n
\n\n Here, we demonstrate that after the attaching of a planar photonic chip to the substrate of a standard brightfield microscopy (BFM), both darkfield and total internal reflection (TIR) imaging can be realized in one experimental setup without the use of a bulky darkfield condenser and other specialized components. The new microscopes can be named as chip-based darkfield microscopy (C-DFM) and chip-based total internal reflection microscopy (C-TIRM). The C-DFM and C-TIRM have the merits of large illumination area, high imaging contrast, simple configuration and easy for optical-alignment. Both DFM and TIRM emphasize the high-spatial-frequency components associated with small features in the specimen morphology and in some imaging scenarios, it can even provide resolution beyond the diffraction limit\n \n 16, 17\n \n . Different from the DFM that uses far-field propagating light as the illumination source, the TIRM uses pure evanescent waves on the surface as the illumination source, which will have higher spatial frequency and are more sensitive to the changes on the surface. It is ideally suited to analyze the localization and dynamics of molecules and events occurring near the interface, such as the plasma membrane.\n
\n\n \n Configuration of the planar photonic chip.\n \n The proposed chip is designed to provide evanescent wave excitation (or TIR) at 640 nm wavelength and darkfield conditions at 750 nm wavelength, using a standard brightfield microscope. The photonic chip consists of three parts (Figure 1a). The middle is a dielectric layer (thickness about 2 \u03bcm) doped with TiO2 nanoparticles (diameter at 60 nm). The bottom and top are the dielectric multilayers with different PBGs\n \n 18, 19\n \n . The multilayers are made of alternating SiO\n \n 2\n \n and SiN\n \n x\n \n layers.\u00a0Details of the structural parameters are given in Figure S1. The color-scale encoded reflectivity (Figure 1b and 1c) of the bottom and top multilayer was calculated by using transfer matrix method (TMM)\n \n 20\n \n .\n
\n\n For the bottom multilayer, when the incident beams are of transverse-magnetic (TM) or transverse-electric (TE) polarization, there are reflection valleys at nearly 0\n \n o\n \n (Figure 1d), meaning that only a near-normal incident beam can transmit through this multilayer (inset of Figure 1d). The unusual transmission of the bottom multilayer layer is the result of two periodical dielectric structures, separated by a thicker layer of SiO\n \n 2\n \n (Figure S1). Surface normal transmission occurs at designed wavelengths (640 and 750 nm here) and no transmission happens at other angles for the designed wavelengths (Figure S2). This restricted surface-normal transmission prevents the scattered light from leaving the bottom layer.\n
\n\n When the transmitted beam reaches the middle layer containing TiO\n \n 2\n \n nanoparticles, its propagating direction will be changed due to the scattering at the nanoparticles. A portion of the scattering lights return to the bottom multilayer, but they will bounce back to the scattering layer due to the PBG of the bottom multilayer, and only scattering light at near normal angle can escape through the bottom multilayer. Some of the scattered light will reach the top multilayer. The PBG of top multilayer was designed (Figure 1c) so that the scattering light at 750 nm wavelength can transmit at designed polar angles (from 27\n \n o\n \n to 42\n \n o\n \n in glass, within N.A < 0.7, the reflection valleys on Figure 1e, TM-polarization). For the scattering light with propagating directions within N.A <0.7, it will be reflected to the scattering layer, and then reflected by the bottom multilayer. When the N. A of the imaging objective is less than 0.7, darkfield imaging can be realized\n \n 1, 21\n \n . For the scattering light at 640 nm wavelength, it lies in the forbidden band (Figure 1c). When the scattering angle is larger than the critical angle, TIR and evanescent waves will happen at the multilayer/air interface, resulting in TIR imaging\n \n 11\n \n .\n
\n\n In brief, the scattering layer is to provide light of various propagating directions. The role of the top multilayer is to select the transmitting angles of the scattered light, either larger than a designed polar angle or induced evanescent waves on the top-surface. The bottom multilayer only allows the transmittance of near-normal incident beam, so it can recycle scattering light into propagation angle ranges that are transmitted by the top multilayer, and then amplify the light intensity on or out of the top multilayer. Through properly design of the PBGs, the desired angular transmission of the photonic chip (bottom and top multilayer) can be realized, which is benefit for high contrast imaging.\n
\n\n \n Fabrication and characterization of the planar photonic chip.\n \n
\n\n The multilayers were fabricated via plasma-enhanced chemical vapour deposition (PECVD) and characterized with a scanning electron microscope (SEM, Figure 2a and 2b). The manufacturing procedures are described in Figure S3 and section methods. The color-scale encoded reflectivity of the bottom and top dielectric multilayer were measured with a reflection back focal plane (R-BFP) imaging setup\n \n 22, 23\n \n (Figure S4-S6) respectively, as shown in Figure 2c and 2d, which are nearly consistent with numerical calculations (Figure 1b and 1c) and experimentally verify the desired roles of two multilayers.\n
\n\n In the imaging experiments, two low-coherent light-emitting diodes (LED) were used to generate the scattering lights from the non-fluorescent TiO\n \n 2\n \n nanoparticles, which will work as the illumination source for the darkfield image at 750 nm wavelength or TIR images at 640 nm wavelength. Compared to the use of a laser light source for label-free imaging, the LED light will greatly reduce the speckles or interference noise on the optical images. On the other hand, a renewed interest in transmitted darkfield microscopy has arisen due to its advantage when used in combination with fluorescence microscopy. Compared to fluorescence emission from quantum dots (QDs) for the illumination source in luminescent-surface-based darkfield imaging\n \n 14\n \n , the scattered light originating from the high-intensity LED light can excite fluorophores more efficiently than the fluorescence-light from QDs. Also, the LED light source will not encounter the problem of photobleaching or blinking that is typical for QDs and other fluorophores, then the proposed C-DFM and C-TIRM will be more stable.\n
\n\n The transmission BFP images of whole photonic chip were measured with the experimental setup (Figure 3a), which presents the transmission directions of the scattered light from the TiO2 nanoparticles. Figure 3 demonstrates three predicted phenomena. Firstly, each point on the BFP image represents one emitting angle (both polar and azimuthal angle) of the scattering light, so the intensity distribution on the BFP images can represent the propagating directions of the scattered light passing through the top multilayer\n \n 23\n \n . Secondly, comparisons between the top and bottom BPF images on Figure 3d and 3f, show that the use of the bottom multilayer can highly amplify the intensity of the scattered light that reaches the top multilayer. Thus, it results in much more intense light exiting from the top multilayer at the designed polar angles, and more intense evanescent waves, which benefit both darkfield and TIR imaging. Thirdly, the intensity of the scattered light within N. A< 0.70 and that with N.A from 0.70 to 1.49 were derived from Figure 3d and Figure 3f, as shown in Figure 3e and Figure 3g. It is clearly show that this chip can efficiently prevent the direct transmission at lower N.A (corresponding to small polar angle), and most of transmission energy (about 98.5%) was localized inside the large N.A regions (0.7-1.49), which is favorable for the contrast of DFM or TIRM images.\n
\n\n \n The planar photonic chip working as a high-contrast imaging device.\n \n In the following experiments, a polymer nanowire and polymer microwire were used as the specimens, although they can certainly be replaced with real cells. The nanowire is approximately 70 nm in diameter, and the microwire is approximately 3 to 4 \u03bcm in diameter (Figure S7b). The single polymeric nanowire was located on a polymeric microwire that are both placed on a coverslip. A regular air objective (60 X, N.A 0.70) was used for the standard brightfield imaging under the normal illumination of LED light at 640 and 750 nm wavelengths, and the brightfield images are shown in Figure 4b and 4f. Secondly, When the photonic chip was attached below this coverslip with index-matched oil, C-DFM and C-TIRM images of the polymer wires are much more defined. When the illumination wavelength was 750 nm, the scattered light from the chip will be out of the N.A of the objective. The images (Figure 4c and 4d) show typical darkfield characters, a bright image of the specimens superimposed onto a dark background. The darkfield image contrast (CR), calculated as the difference between the maximum and minimum image intensity values divided by their sum (Figure 4e and 4i), was significantly improved when comparing with that of the brightfield image (Figure 4e). The polymer wires on Figure 4d is brighter than those on Figure 4c, verifying that the bottom multilayer can recycle the scattering light and amplify the intensity of the scattered light that transmit through the top multilayer at the designed polar angles, which are consistent with the transmission BFP images (Figure 3d and 3f).\n
\n\n Except for the differences in contrasts, the darkfield images of the nanowire appear discontinuous when it crosses the microwire. This phenomenon can be explained as following. When the illumination wavelength is 750 nm, the specimen will be illuminated not only by the transmitting light at oblique polar angles (0. 70 <N.A <1.0) and at all azimuths, but also evanescent waves (N. A >1.0) on the coverslip-air interface induced by the TIR, as shown in Figure 3d. In this crossing area, the nanowire is on the microwire and is far away from the coverslip. Due to the longitudinal decay of evanescent waves\n \n 24\n \n , this crossed section will not be imaged. On the contrary, this longitudinal air gap cannot be discovered from brightfield images (Figure 4b and 4f). This phenomenon will be more obviously, as that the nanowire is nearly invisible in the crossing area, when the wavelength was changed to 640 nm (Figure 4g and 4h). In this case, the specimens are illuminated only by pure evanescent waves, and the BFM was transformed to a C-TIRM with the aid of this photonic chip as an add-on.\n
\n\n When the polymer wires were replaced with polymer particles, the images enhancement induced by the photonic chip is also obvious (Figure S8). The edge of the microparticles is shaper on the TIR image (Figure S7c) than that on the darkfield image (Figure S7b), because that the TIR imaging uses higher-spatial-frequency component of the illumination source and can provide higher spatial resolution. It should be noted that the photonic chip still works when the specimens were immersed in water solution (Figure S9). The phenomena on Figure 4f-4i verify that, using this high compatible photonic chip below the substrate, the TIRM images can be captured by a standard BFM, which can focus on the targets within the evanescent field (100 to 200 nm) only, rather than those contained in the entire sample.\n
\n\n \n Excitation of large-scale surface plasmons with the planar photonic chip.\n \n Furthermore, it can be anticipated that this photonic chip has the potentials to excite other kinds of surface waves under normal incidence, such as surface plasmons (SPs) and Bloch surface waves (BSWs) that are more sensitive to environmental changes than the evanescent waves used in TIR fluorescence microscopy\n \n 25\n \n . Typically, either a bulk prism, or a nanofabricated structure, or an oil-immersed objective is required for the excitation of SP or BSWs, which result in either bulk and complicated system or limited excitation areas\n \n 26\n \n . However, these limitations can be removed by using the photonic chip. To verify this point, the top multilayer was replaced with a coverslip coated with a 55-nm-thick Ag film (Figure 5a). The images (Figure 5b) show a typical character of surface-sensitive patterns with enhanced imaging contrast (Figure 5d), where the nanowire appears discontinuous in the cross-region with the microwire. The phenomena verify the excitation of the SPs. The advantage of images illuminated by SPs over the brightfield images will be obviously when the diameter of the polymer nanoparticles changes from 200 nm to 50 nm (from Figure 5e to 5f). When the diameter of the polymer nanoparticles is about 50 nm, these nanoparticles are nearly invisible on the brightfield image (Figure 5f), but are visible on the image illuminated by the SPs (Figure 5e). The excitation of the SPs with the normal incident light will make the optical path easily aligned and simplify the configuration. The use of a planar chip means that the excitation areas of the SPs can be much larger than that excited with in-plane nanostructures (such as the inscribed gratings), which is favorable for high throughput imaging, sensing and tracking of the specimens.\n
\n\n In summary, through placing a photonic chip below the specimens, a standard BFM can be easily transformed to both a C-DFM and C-TIRM without modifying the configuration or the specimens. The principle lies on that, under normal incidence, both the inverted hollow cones of light and various evanescent waves can be generated with this photonic chip due to its tailored angular transmission. The roles of this photonic chip working as a high contrast imaging device, are confirmed by both theoretical and experimental results. Thus, it demonstrates the potential of the proposed imaging device for a novel type of versatile and compact microscopy. The working wavelengths of the photonic chip can be tuned through changing thickness and refractive index of the dielectric layers; thus, the devices enable multispectral darkfield and TIR imaging using simple brightfield microscopes\n \n 14\n \n . If the specimens are fluorescent, fluorescence imaging also can be realized, similar as the TIRF\n \n 27\n \n . Then, the setup can perform simultaneous C-DFM/C-TIRM and fluorescence microscopy of the same specimen, and thus make it possible to combine the strengths of both labelled and label-free detection and fluorescent imaging technologies in one integrated set-up\n \n 28, 29, 30\n \n . When combining with stochastic optical reconstruction technique, the C-TIRM will has the potential to be a chip-based wide field-of-view nanoscopy\n \n 2\n \n . The imaging device can be washed, sterilized and used multiple times. It has proved affordable and easy for users to launch as an add-on to a regular brightfield microscope, thus, it will make full use of the BFM that can be found in many academic and industrial labs.\n
\n\n When comparing with bulk Abbe condensers used in a conventional DFM, and prism or oil-immersed objective used in conventional TIR imaging, this imaging device made of planar chip is more compact, low cost and easy aligned. It can be fabricated on an extremely large substrate with standard deposition and spin-coating method, without any top-down nanofabrication procedures, thus open new avenues towards the design of a fully integrated on-chip microscopy. Different from the condenser or objective that has limited illumination area (of micrometer scale), this imaging device enables extremely large illumination area up to centimeter-scale or even larger. In the future, the combination of photonic chip with micro lens arrays for light collection have the potential for extraordinarily high throughput, with illumination and collection done in parallel over large areas, completely removing the dependency on a bulky objective lens\n \n 3\n \n .\n
\n\n \n Fabrication of the planar phonic chips working as the compact imaging-devices.\n \n The top and bottom dielectric multilayers were fabricated via PECVD (Oxford System 100) of SiO\n \n 2\n \n and SiN\n \n x\n \n on a coverslip (0.17 mm thickness) at a vacuum 0.1 m torr and temperature of 300\n \n o\n \n C. Before the PECVD of a dielectric multilayer, the coverslip was cleaned with piranha solution and then with nanopure deionized water and dried with an N\n \n 2\n \n stream. The process of PECVD depends on the chemical reaction of SiH4 with N\n \n 2\n \n O and NH\n \n 3\n \n at high temperature. The refractive index of SiN\n \n x\n \n can be adjusted from 1.9 to 2.4 by changing the ratio of SiH\n \n 4\n \n to NH3. The SiO\n \n 2\n \n layer is of the low (L) refractive index dielectric and the SiN\n \n 4\n \n layer is the high (H) one. Thickness of each layer was presented in details in Figure S1. There are 18 pairs of SiN\n \n x\n \n (2) + SiN\n \n x\n \n (3) and 11 pairs of SiO\n \n 2\n \n + SiNx (1) in total for the bottom multilayer. There are 10 pairs of SiO\n \n 2\n \n + SiN\n \n x\n \n (1) in total for the top multilayer. The top and bottom multilayer were fabricated on two independent coverslips for the BFP imaging experiments to measure the PBG of the multilayer (Figure 2 and Figure 3).\n
\n\n The spacer layer between the top and bottom multilayer is made of Intermediate Coating IC1-200, which is a polysiloxane-based spin-on dielectric material. The IC1-200 solution doped with TiO\n \n 2\n \n nanoparticles (diameter at about 60 nm) was then spin-coated on to the bottom multilayer, which will work as the scattering layer to generate scattering light of various propagating directions. Thickness of the spacer layer is about 2 \u03bcm and its refractive index is about 1.41. The SEM image of the scattering nanoparticle TiO\n \n 2\n \n was shown in Figure S7a.\n
\n\n For the darkfield and TIR imaging experiments, the three parts of the planar photonic chip will be assembled together with the refractive index matched oil and then form the unit substrate (Figure 4a). The silver film was deposited on the bare coverslip with the thermal evaporation method, whose thickness is about 55 nm. For the surface-imaging with SPs (Figure 5), this coverslip coated with Ag film was attached onto the spacer (scattering) layer with refractive index matched oil.\n
\n\n It should be noted, this photonic chip still can be used to realize the darkfield and TIR imaging if the bottom multilayer was removed, as shown Figure 4c and Figure 4g. However, the obtained darkfield and TIR images will be much weaker than those (Figure 4d and Figure 4h) obtained with the whole photonic chip (Top multilayer+ scattering layer + bottom multilayer). Or in other word, the bottom multilayer can recycle scattering light into propagation angle ranges that are transmitted by the top multilayer, and then amplify the light intensity on or out of the top multilayer, which was verified by the comparisons between the top and bottom images on Figure 3d and Figure 3f.\n
\n\n \n Preparation of the polymer wires and nanoparticles as the specimens to be imaged.\n \n The typical procedure for the fabrication of electro spun micro and nano fibers is given below. A 2 ml formic solution (solvent for the Nylon) containing 1.6 g Nylon 6 was ejected at a continuous rate using a syringe pump through a stainless-steel needle. A voltage of 10 kV was applied to the needle with a high voltage power supply and a feed rate of 0.2 mm per minute was maintained with a syringe pump. A collector (the glass substrate with the fabricated dielectric multilayer) was placed at 10 cm from the needle tip to collect the polymer nanowires. By replacing the solution in the syringe pump into a tetrahydrofuran solution containing 0.25g hydrogel, the hydrogel microwire can be produced with the same procedure. The SEM images of the polymer wires are shown in Figure S7.\n
\n\n The polystyrene nanoparticles (Figure 5, Figure S8) were purchased from Thermo Fisher Scientific (USA). The certified mean diameters of the nanoparticles supplied were about 20 nm, 50 nm, 100 nm, 200 nm and 2\u03bcm. The SEM images of these nanoparticles are shown in Figure S7. The nanoparticles dispersed in water are spin coated on the substrate. After dried by hot plate, the nanoparticles are fixed on the surface of the substrate.\n
\n\n In the darkfield and TIR imaging (Figure 4, Figure S8 and S9), the polymer wires and polystyrene nanoparticles were placed on a clean coverslip, which was then attached to the top surface of the photonic chip (Figure 1a, Figure 4a) with refractive index matched oil between them. However, the specimens (wires and particles) can also be put on the top surface of the photonic chip directly for darkfield and TIR imaging, then this planar photonic chip both holds and illuminates the specimen. In the brightfield imaging used for comparisons, this photonic chip was removed. For the surface-imaging with SPs (Figure 5), the wires and nanoparticles were placed on the silver film directly.\n
\n\n \n Optical characterization set-up.\n \n All optical measurements were performed on modified optical microscope (Nikon Ti2-U). For the reflection BFP imaging setup (Figure S4), an oil immersion objective (CFI Apochromat TIRF 100X, N.A. 1.49, W.D. 0.12mm) from Nikon, Japan was used to fully measure the reflecting angular distribution, which corresponds to the polar angle ranging from -80\n \n o\n \n to 80\n \n o\n \n in the oil medium. To minimize the interference fringes on the BFP images, we used a noncoherent light (tungsten bromine lamp combined with a serials of band-pass filters) as the illumination source. The center wavelengths of the band pass filter ranges from 600 nm to 790 nm (20 filters in-total), with a full width at half maximum (FWHM) of 10\u00b12nm. The Neo sCMOS detector for recording the BFP images was from Andor Oxford Instruments (UK). By properly tuning the distance between the tube lens and the detector, BFP image of the objective can be recorded. At each incident wavelength, one BFP image can be obtained. From all these BFP images (Figure S4, Figure S5 and Figure S6), PBGs of the top and bottom multilayers can be derived.\n
\n\n For the transmitted BFP images (Figure 3a), the illumination source was changed to LED with center wavelength at 640 and 750 nm. Two bandpass filters (full width at half maximum (FWHM) of 10 \u00b12 nm, center wavelengths of 640 and 750 nm, Thorlabs Inc.) were used to select the required emission wavelengths from the LED sources. Under normal incidence, the transmitting angular distribution of the scattering light escaping out of the photonic chip was measured with the same objective with N.A at 1.49. The detector and tube lens are the same as those used in reflection BFP imaging setup. It should be noted, in the BFP imaging of the photonic chip and independent multilayer, no specimens (polymer wires and polystyrene particles) were put on the chip.\n
\n\n In the darkfield and TIR imaging of the specimens, a regular air objective (CFI Super Plan Fluor ELWD 60X, N.A. 0.70, W.D. 2.61-1.79mm) was used. The distance between the tube lens and the detector was changed so that the front focal plane (FFP) of the objective can be imaged. The LED with bandpass filter was used as the illumination source.\n
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\n\n Supplementary information for the main text\n
\n \n\n Growing evidence suggests that long-term air pollution exposure is a risk factor for cardiovascular mortality and morbidity. However, few studies have investigated air pollution below current regulatory limits, and causal evidence is limited. We used a double negative control approach to examine the association between long-term exposure to air pollution at low concentrations and three major cardiovascular events among Medicare beneficiaries aged\u2009\u2265\u200965 years across the contiguous United States between 2000 and 2016. We derived ZIP code-level estimates of ambient fine particulate matter (PM\n \n 2.5\n \n ), nitrogen dioxide (NO\n \n 2\n \n ), and warm-season ozone (O\n \n 3\n \n ) from high-resolution spatiotemporal models. The outcomes of interest were hospitalizations for stroke, heart failure (HF), and atrial fibrillation and flutter (AF). The analyses were restricted to areas with consistently low pollutant levels on an annual basis (PM\n \n 2.5\n \n <10 \u00b5g/m\u00b3, NO\n \n 2\n \n <\u200945 or 40 ppb, warm-season O\n \n 3\n \n <\u200945 or 40 ppb). For each 1 \u00b5g/m\n \n 3\n \n increase in PM\n \n 2.5\n \n , the hospitalization rates increased by 2.25% (95% confidence interval (CI): 1.96%, 2.54%) for stroke and 3.14% (95% CI: 2.80%, 3.94%) for HF. Each ppb increase in NO\n \n 2\n \n increased hospitalization rates for stroke, HF, and AF by 0.28% (95% CI: 0.25%, 0.31%), 0.56% (95% CI: 0.52%, 0.60%), and 0.45% (95% CI: 0.41%, 0.49%), respectively. For each ppb increase in warm-season O\n \n 3\n \n , there was a 0.32% (95% CI: 0.21%, 0.44%) increase in hospitalization rate for stroke. The associations for NO\n \n 2\n \n and warm-season O\n \n 3\n \n became stronger under a more restrictive upper threshold. Using an approach robust to omitted confounders, we concluded that long-term exposure to low-level PM\n \n 2.5\n \n , NO\n \n 2\n \n , and warm-season O\n \n 3\n \n was associated with increased risks of cardiovascular diseases in the US elderly. Stricter national air quality standards should be considered.\n
\n\n \n Air Pollution\n \n
\n\n \n Double Negative Control\n \n
\n\n \n Stroke\n \n
\n\n \n Heart Failure\n \n
\n\n \n Atrial Fibrillation\n \n
\n\n Long-term exposure to air pollution has been recognized as an important modifiable risk factor for cardiovascular diseases\n \n 1,2\n \n . An increasing number of epidemiological studies support positive associations between long-term air pollution and the occurrence of cardiovascular events, although specific cardiovascular outcomes have been less investigated relative to overall cardiovascular mortality and morbidity. Stroke, which is characterized by high incidence and mortality, is the second leading cause of death worldwide\n \n 3\n \n . Researchers have reported that long-term exposure to air pollution, particularly fine particulate matter with an aerodynamic diameter less than 2.5 \u00b5m (PM\n \n 2.5\n \n ), could be associated with an increased risk of hospitalization, incidence, and mortality due to stroke\n \n 4\n \n . Heart failure (HF) and atrial fibrillation (AF) are other two major cardiovascular diseases. They are important risk factors for stroke onset\n \n 5\n \n . Several studies demonstrated the adverse effect of long-term air pollution on the risk of HF\n \n 6\u20138\n \n and AF\n \n 9,10\n \n , although these two endpoints have been understudied as primary outcomes of interest. Overall, the evidence for the hypothesized association, especially with HF and AF, remains scarce and inconsistent. In addition, with the predominant focus on PM\n \n 2.5\n \n , the potential cardiovascular effects of long-term exposure to other major air pollutants such as nitrogen dioxide (NO\n \n 2\n \n ) and ozone (O\n \n 3\n \n ) have been under-examined, and the correlations between different pollutants have also been overlooked. To conclude, the potential causal relationships between multiple air pollutants and specific cardiovascular events need to be further elucidated.\n
\n\n Most of the existing studies linking long-term exposure to air pollution to cardiovascular events examined the entire range of exposure. The average pollution levels may differ substantially by region and therefore partially account for the geographical differences in the estimated associations. There is a dearth in our understanding of the health impacts of air pollution at concentrations below regulatory standards, which has important implications for air pollution regulations in regions such as the United States (US) where populations experience generally low air pollutant exposures. Previous studies found the shape of the exposure-response curves for long-term PM\n \n 2.5\n \n and all-cause and cardiovascular mortality to be curvilinear with no evidence of a threshold\n \n 11,12\n \n . According to several studies of large cohorts in the US\n \n 9,13,14\n \n and Europe\n \n 15,16\n \n , the risk of cardiovascular diseases could persist and even become stronger at lower exposure levels below the annual limit values set by the US Environmental Protection Agency (EPA) and European Union (EU). The suggested higher incremental risk in relation to a lower air pollutant level raises the question of whether the national and international air quality guidelines are protective enough. Further research specifically at lower concentrations can help elucidate this.\n
\n\n Furthermore, many observational studies fail to utilize causal modeling methods to identify confounding and eliminate non-causal associations, therefore, they may yield estimates that lack validity to some extent. Propensity scores are the most widely adopted approach to simulate counterfactuals in randomized trials by balancing measured covariates between the exposed group and unexposed group or across different levels of continuous exposure in air pollution research. However, this method is weakened by its stringent requirement for precisely specified regression of exposure on measured covariates and its inability to control for unmeasured covariates. Negative controls have been suggested as a useful tool to enhance causal inference independently of covariate distributions and to tackle unmeasured confounding bias\n \n 17\n \n . The negative exposure control is a variable known not to be causally related to the outcome of interest, while the negative outcome control is a variable known not to be caused by the exposure of interest. Both of them may share a common confounding mechanism with the exposure and outcome\n \n 18\n \n . Therefore, they can serve as instruments for reducing bias by unmeasured confounders. In prior air pollution and health studies, researchers have used future air pollution as a negative control exposure\n \n 19\u201322\n \n , or a negative outcome due to causes other than primary exposure as a negative control outcome\n \n 23,24\n \n . More recently, double negative control adjustment has been employed to strengthen causal inference in studies examining short- and long-term effects of air pollution\n \n 25\u201327\n \n .\n
\n\n To address the research gaps, the present study used a double negative control approach to analyze the relationships between long-term exposure to PM\n \n 2.5\n \n , NO\n \n 2\n \n , and warm-season O\n \n 3\n \n at low concentrations with risk of hospitalizations for three major cardiovascular diseases (stroke, HF, and AF) in the Medicare population aged\u2009\u2265\u200965 years across the contiguous US from 2000 to 2016. We focused on the areas where populations were consistently exposed to low pollutant concentration levels (PM\n \n 2.5\n \n <10 \u00b5g/m\u00b3, NO\u2082<40 or 20 ppb, warm-season O\n \n 3\n \n <\u200945 or 40 ppb). Furthermore, we conducted stratified analyses to investigate potential susceptible demographic subpopulations.\n
\n\n We used data from a national cohort of fee-for-service (FFS) Medicare beneficiaries aged 65 years and older across the contiguous US from January 1st, 2000 to December 31st, 2016. The beneficiaries were followed up from January 1st of the year after their Medicare enrollment until the development of the outcome of interest, death, censoring, or the end of the follow-up time. In this study, we restricted the analyses to the individuals who were consistently exposed to low-level annual air pollution for the entire period (2000\u20132016) with certain thresholds (PM\n \n 2.5\n \n <10 \u00b5g/m\n \n 3\n \n , NO\n \n 2\n \n <\u200940 or 20 ppb, warm-season O\n \n 3\n \n <\u200945 or 40 ppb). Therefore, three datasets were created for each pollutant according to its specified threshold. We further restricted the datasets to ZIP code areas with at least 100 beneficiaries.\n
\n\n Beneficiary records were provided by the Medicare denominator file from the Centers for Medicare and Medicaid Services, which contained information on age, self-reported sex, self-reported race, Medicaid eligibility, date of death, and residential ZIP code for each beneficiary. Information on age, Medicaid eligibility, and residential ZIP code are updated each year. We obtained the hospital discharge claims of Medicare enrollees from the Medicare Provider Analysis and Review (MEDPAR) file. The International Classification of Diseases (ICD) codes were used to identify the primary discharge diagnosis for each of our three cardiovascular outcomes of interest: stroke (ICD-9 codes: 430\u2013438, ICD-10 codes: I60-I69), heart failure (ICD-9 code: 428, ICD-10 code: I50; hereafter referred to as HF), and atrial fibrillation and flutter (ICD-9 code: 427.3, ICD-10 code: I48; hereafter referred to as AF). For each cardiovascular outcome, we computed the ZIP code-level annual counts based on the beneficiaries\u2019 residential addresses.\n
\n\n This study was approved by the institutional review board at Harvard T. H. Chan School of Public Health. It was exempt from informed consent requirements as a study of previously collected administrative data.\n
\n\n We obtained the daily concentrations of ambient PM\n \n 2.5\n \n , NO\n \n 2\n \n , and O\n \n 3\n \n at 1 km\u00d71 km spatial resolution across the contiguous US from three ensemble prediction models that combined multiple machine learning algorithms\n \n 28\u201330\n \n . The exposure models incorporated meteorological variables, chemical transport model simulations, land-use features, and satellite remote sensing data. They were well validated using 10-fold cross-validation. We aggregated the daily predictions of PM\n \n 2.5\n \n and NO\n \n 2\n \n to annual averages. For long-term O\n \n 3\n \n , we calculated its warm-season levels based on the daily predictions from April 1st through September 30th, since the health impacts of O\n \n 3\n \n are suggested to be more observable during warm seasons compared to throughout the year\n \n 13,31,32\n \n . We then computed the ZIP code-level exposures by averaging the 1 km\u00d71 km grid cell predictions whose centroids were within the boundary of ZIP code polygons or assigning the nearest grid cell predictions for the ZIP codes that do not have polygon representations. Annual average exposures were then linked to Medicare beneficiaries based on their residential ZIP codes for each calendar year over the study period.\n
\n\n For each exposure, we limited our dataset to the ZIP code areas where the populations were always exposed to low-concentration air pollution below thresholds we set over the study period of 2000\u20132016. We chose 10 \u00b5g/m\n \n 3\n \n as the threshold for annual average PM\n \n 2.5\n \n concentration, because this value has been proposed by the US EPA\u2019s Clean Air Scientific Advisory Committee to substitute the current National Ambient Air Quality Standards (NAAQS) of 12 \u00b5g/m\n \n 3 33\n \n . For NO\n \n 2\n \n , we chose an annual limit of 40 ppb and an even lower limit of 20 ppb for our analysis, well below the NAAQS standard of 53 ppb, as the annual NO\n \n 2\n \n concentrations in the US rarely exceeded this standard. Although there is no formal annual regulatory standard for long-term O\n \n 3\n \n , we selected 45 and 40 ppb as the threshold values to define low-level O\n \n 3\n \n , which has been chosen as a plausible pollution target in previous studies to evaluate its effectiveness in reducing health risk\n \n 34,35\n \n .\n
\n\n We considered a variety of SES covariates at the ZIP code tabulation-area (ZCTA) level, including percent of the population self-reporting as Black, percent of the population self-reporting as Hispanic, percent of the population\u2009\u2265\u200965 years of age living in poverty, population density, percent of the population\u2009\u2265\u200965 years of age who had not graduated from high school, median home value, median household income, and percent of owner-occupied housing unit. These data were obtained from the U.S. Census Bureau 2000 and 2010 Census Summary File 3 and the American Community Survey from 2011 through 2016. To account for long-term smoking behaviors, we included lung cancer hospitalization rates as a surrogate measure for each ZIP code from the MEDPAR file. We also accessed county-level data on the yearly percentage of residents who ever smoked and mean body mass index (BMI) from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS)\n \n 36\n \n . These county-level lifestyle data were assigned to ZIP codes. Additionally, from the Dartmouth Atlas of Health Data\n \n 37\n \n , we obtained several access-to-care covariates in each hospital service area, and further assigned them to ZIP codes: proportion of Medicare beneficiaries with at least 1 hemoglobinA1c test per year, proportion of diabetic beneficiaries who had a lipid panel test in a year, proportion of beneficiaries who had an eye examination in a year, proportion of beneficiaries with at least 1 ambulatory doctor visits in a year, and proportion of female beneficiaries who had a mammogram during a 2-year period. We also calculated the distance from the centroid of each ZIP code to the nearest hospital, a proxy for healthcare accessibility, using data on hospital locations derived from an ESRI dataset\n \n 38\n \n . Given that seasonal meteorological conditions have been known to impact cardiovascular health\n \n 39,40\n \n , we assessed the average temperature and relative humidity (RH) during the summer (June-August) and the winter (December-February) for each ZIP code and each year based on the 4 km Gridded Surface Meteorological (gridMET) dataset\n \n 41\n \n .\n
\n\n Missing values for all area-level risk factors were filled in using linear interpolation and extrapolation. Any other missingness accounting for <\u20091% of the observations was assumed to be random and was excluded from our analyses.\n
\n\n In this study, we analyzed the association between long-term exposure to low-level air pollution and hospitalization rate of major cardiovascular diseases among the US Medicare population. As aforementioned, the analysis was restricted to the low pollution ZIP code areas with at least 100 Medicare beneficiaries. We used a double negative control strategy, which has been recommended to address unmeasured confounding and other bias issues in observational settings\n \n 17,42\n \n , to enhance the causal evidence of a potential relationship. The detailed descriptions of this double negative control approach can be found elsewhere\n \n 27\n \n . A summary of the principles is given below. First, we consider a quasi-Poisson regression model to obtain the unbiased association between the exposure (\n \n A\n \n ) and the outcome (\n \n Y\n \n ), adjusting for unmeasured confounders (\n \n U\n \n ):\n
\n\n
\n\n The negative exposure control (\n \n Z\n \n ) and negative outcome control (\n \n W\n \n ) are designed to capture confounding bias introduced by\n \n U\n \n . In this study, we chose the exposure to air pollution in the year after cause-specific hospitalizations as\n \n Z\n \n . It cannot lead to the hospitalization outcome in the concurrent year, however, it could be influenced by unmeasured or measured confounders that are correlated with air pollution level in the year of the hospitalization outcome. Similarly, we defined the count of cause-specific hospitalizations in the year before exposure as\n \n W\n \n , as it is by no means affected by the exposure in the concurrent year but may be correlated to omitted confounders. Given the hypothesized correlations of\n \n U\n \n with\n \n A\n \n and\n \n Z\n \n , and non-causality between\n \n A\n \n and\n \n W\n \n , the formulas (\n \n 2\n \n ) and (\n \n 3\n \n ) can be derived:\n
\n\n
\n\n If we substitute\n \n U\n \n with its expected value regressed on\n \n A\n \n and\n \n Z\n \n from the formula (\n \n 2\n \n ), the formula (\n \n 1\n \n ) can be interpreted into:\n
\n\n
\n\n where\n \n \n \\({\\beta }_{YU}{\\beta }_{UA}\\)\n \n \n is exactly equal to the bias due to unmeasured confounders. Thus, if the equation\n \n \n \\({\\beta }_{Uz}\\)\n \n \n =\n \n \n \\({\\beta }_{UA}\\)\n \n \n holds, the subtraction between the coefficient of\n \n A\n \n and the coefficient of\n \n Z\n \n will yield a causal effect of\n \n A\n \n on\n \n Y\n \n .\n
\n\n If we substitute\n \n U\n \n with its expected value again in the formula (\n \n 3\n \n ),\n \n \n \\(W\\)\n \n \n as a surrogate for\n \n U\n \n can be predicted by\n \n A\n \n and\n \n Z\n \n based on:\n
\n\n
\n\n Alternatively, assuming the linear correlations of\n \n U\n \n with\n \n A\n \n and\n \n Z\n \n , which renders the formulas (\n \n 2\n \n ) and (\n \n 5\n \n ) valid, we can mitigate the confounding effect of\n \n U\n \n by including the predicted\n \n W\n \n in the outcome regression model.\n
\n\n In the models, we adjusted for a variety of area-level risk factors for cardiovascular diseases selected prior, including SES, behavioral, and meteorological covariates which are described in the\n \n covariates\n \n section, to relax our assumptions and to reduce any uneliminated confounding bias. We also included the admission year as a categorical indicator in the models to control for the time trends of omitted confounders that might drive an association. We analyzed the effect of each air pollutant separately using both a single-pollutant model and a three-pollutant model. As a secondary analysis, we repeated the main analyses using generalized linear models (GLM) without the negative controls.\n
\n\n We examined the potential effect measure modification by individual demographic characteristics, namely, age (65\u201374 years, 75\u201384 years, 85\u2009+\u2009years), sex (male or female), race (White or Black), and Medicaid eligibility (yes or no), using stratified analyses. We conducted pairwise comparisons of coefficients within the strata of each factor to detect any statistically significant differences, assuming the difference between the coefficients to follow a normal distribution with a mean of zero and a variance of the sum of the strata variances.\n
\n\n In the above analyses, we reported the effect as the percent change in hospitalization rate and its 95% confidence intervals (CIs) for each cardiovascular outcome per \u00b5g/m\n \n 3\n \n increase in annual exposure to PM\n \n 2.5\n \n and per ppb increase in annual exposure to NO\n \n 2\n \n and O\n \n 3\n \n . All analyses were performed using R software version 4.2.3 on the Research Computing Environment as part of Research Computer at Harvard University Faculty of Arts and Sciences. A two-sided\n \n P\n \n value\u2009<\u20090.05 was considered statistically significant.\n
\n\n Table\n \n 1\n \n shows the summary statistics of ZIP code-level air pollution and covariates in the low-pollution areas from 2000 through 2016. In low PM\n \n 2.5\n \n areas, the annual average concentrations of PM\n \n 2.5\n \n , NO\n \n 2\n \n , and warm-season O\n \n 3\n \n were 5.9\u2009\u00b1\u20091.8 \u00b5g/m\n \n 3\n \n , 12.8\u2009\u00b1\u20097.6 ppb, and 44.6\u2009\u00b1\u20097.3 ppb, respectively. In the areas with either NO\n \n 2\n \n or O\n \n 3\n \n deemed low in our analyses, the mean annual PM\n \n 2.5\n \n concentration was higher and closer to the typical range. The Pearson correlation coefficients (r) for three air pollutants are presented in\n \n Supplementary Table\u00a01\n \n . We observed a moderate-to-low positive correlation between annual PM\n \n 2.5\n \n and NO\n \n 2\n \n in low NO\n \n 2\n \n areas (r\u2009=\u20090.38 and 0.23 at the thresholds of 40 and 20 ppb, respectively) and in low PM\n \n 2.5\n \n areas (r\u2009=\u20090.17). In contrast, there was a strong correlation between annual PM\n \n 2.5\n \n and NO\n \n 2\n \n in areas with low warm-season O\n \n 3\n \n , with r values of 0.66 and 0.64 at the thresholds of 45 and 40 ppb, respectively. Warm-season O\n \n 3\n \n exhibited a moderate-to-low correlation with both annual PM\n \n 2.5\n \n and NO\n \n 2\n \n in areas with low levels of PM\n \n 2.5\n \n and NO\n \n 2\n \n , while in areas with lower warm-season O\n \n 3\n \n , the correlations were negligible.\n
\n| \n \n Covariates\n \n | \n \n \n PM\n \n 2.5\n \n <10 \u00b5g/m\n \n 3\n \n \n | \n \n \n NO\n \n 2\n \n <\u200940 ppb\n \n | \n \n \n NO\n \n 2\n \n <\u200920 ppb\n \n | \n
|---|---|---|---|
| \n \n N (ZIP code)\n \n | \n \n \n 5,848\n \n | \n \n \n 26,583\n \n | \n \n \n 12,281\n \n | \n
| \n \n N (beneficiaries)\n \n | \n \n \n 12,667,627\n \n | \n \n \n 63,657,996\n \n | \n \n \n 212,454,83\n \n | \n
| \n \n \n Air pollution concentration\n \n \n | \n \n | \n\n | \n\n | \n
| \n \n PM\n \n 2.5\n \n (\u00b5g/m\n \n 3\n \n )\n \n | \n \n \n 5.9 (1.8)\n \n | \n \n \n 9.6 (3.0)\n \n | \n \n \n 9.3 (2.8)\n \n | \n
| \n \n NO\n \n 2\n \n (ppb)\n \n | \n \n \n 12.8 (7.6)\n \n | \n \n \n 14.7 (7.1)\n \n | \n \n \n 9.9 (3.5)\n \n | \n
| \n \n Warm-season O\n \n 3\n \n (ppb)\n \n | \n \n \n 44.6 (7.3)\n \n | \n \n \n 45.0 (5.4)\n \n | \n \n \n 44.5 (4.6)\n \n | \n
| \n \n \n Meteorological covariates\n \n \n | \n \n | \n\n | \n\n | \n
| \n \n Summer average temperature\u00a0(\u00b0C)\n \n | \n \n \n 17.6 (4.3)\n \n | \n \n \n 20.2 (3.7)\n \n | \n \n \n 20.2 (3.8)\n \n | \n
| \n \n Winter average temperature\u00a0(\u00b0C)\n \n | \n \n \n 3.8 (6.8)\n \n | \n \n \n 6.2 (5.8)\n \n | \n \n \n 5.6 (5.9)\n \n | \n
| \n \n Summer average RH (%)\n \n | \n \n \n 58.8 (14.4)\n \n | \n \n \n 66.7 (9.4)\n \n | \n \n \n 67.8 (7.8)\n \n | \n
| \n \n Winter average RH (%)\n \n | \n \n \n 66.8 (10.4)\n \n | \n \n \n 66.9 (7.1)\n \n | \n \n \n 67.4 (6.3)\n \n | \n
| \n \n \n SES covariates\n \n \n | \n \n | \n\n | \n\n | \n
| \n \n Percent Black (%)\n \n | \n \n \n 1.8 (5.4)\n \n | \n \n \n 8.5 (16)\n \n | \n \n \n 7.2 (14.7)\n \n | \n
| \n \n Percent Hispanic (%)\n \n | \n \n \n 10.3 (16.2)\n \n | \n \n \n 7.8 (14.4)\n \n | \n \n \n 4.9 (10.8)\n \n | \n
| \n \n Median household income ($)\n \n | \n \n \n 47,911 (17,976)\n \n | \n \n \n 48,420 (20,205)\n \n | \n \n \n 43,105 (14,352)\n \n | \n
| \n \n Median house value ($)\n \n | \n \n \n 173,484 (135,217)\n \n | \n \n \n 149,977 (124,612)\n \n | \n \n \n 117,697 (89,262)\n \n | \n
| \n \n Percent owner occupied (%)\n \n | \n \n \n 75.5 (11.6)\n \n | \n \n \n 74.2 (14.0)\n \n | \n \n \n 77.9 (9.7)\n \n | \n
| \n \n Percent education\u2009<\u2009high school (%)\n \n | \n \n \n 22.2 (14.3)\n \n | \n \n \n 28.1 (16.1)\n \n | \n \n \n 30.6 (16.1)\n \n | \n
| \n \n Population density\u00a0(persons/mi\n \n 2\n \n )\n \n | \n \n \n 435 (1,684)\n \n | \n \n \n 841 (2,095)\n \n | \n \n \n 144 (446)\n \n | \n
| \n \n Percent\u2009\u2265\u200965 below poverty (%)\n \n | \n \n \n 9.3 (7.4)\n \n | \n \n \n 10.1 (7.7)\n \n | \n \n \n 11.2 (7.6)\n \n | \n
| \n \n Percent annual HbA1c test (%)\n \n | \n \n \n 82.8 (8.6)\n \n | \n \n \n 83.5 (5.9)\n \n | \n \n \n 83.8 (6.1)\n \n | \n
| \n \n Percent ambulatory visit (%)\n \n | \n \n \n 78.3 (7.4)\n \n | \n \n \n 79.8 (6.0)\n \n | \n \n \n 80.9 (6.5)\n \n | \n
| \n \n Percent eye exam (%)\n \n | \n \n \n 69.2 (7.5)\n \n | \n \n \n 67.3 (6.7)\n \n | \n \n \n 67.5 (7.5)\n \n | \n
| \n \n Percent LDL test (%)\n \n | \n \n \n 76.7 (9.9)\n \n | \n \n \n 78.6 (7.3)\n \n | \n \n \n 78.1 (7.6)\n \n | \n
| \n \n Percent mammogram (%)\n \n | \n \n \n 65.2 (8.7)\n \n | \n \n \n 64.1 (7.3)\n \n | \n \n \n 64.3 (8.0)\n \n | \n
| \n \n Distance to nearest hospital (km)\n \n | \n \n \n 16.3 (14.9)\n \n | \n \n \n 12.2 (10.6)\n \n | \n \n \n 15.4 (10.8)\n \n | \n
| \n \n Lung cancer rate (\u2030)\n \n | \n \n \n 0.4 (4.8)\n \n | \n \n \n 0.4 (2.2)\n \n | \n \n \n 0.5 (2.2)\n \n | \n
| \n \n Ever smokers (%)\n \n | \n \n \n 47.8 (7.6)\n \n | \n \n \n 47.3 (7.4)\n \n | \n \n \n 47.9 (7.9)\n \n | \n
| \n \n Mean BMI (kg/m\n \n 2\n \n )\n \n | \n \n \n 28.1 (3.2)\n \n | \n \n \n 28.1 (2.5)\n \n | \n \n \n 28.7 (3.0)\n \n | \n
| \n \n \n Covariates\n \n \n | \n \n \n \n Warm-season O\n \n \n \n 3\n \n \n \n <\u200945 ppb\n \n \n | \n \n \n \n Warm-season O\n \n \n \n 3\n \n \n \n <\u200940 ppb\n \n \n | \n \n | \n
| \n \n N (ZIP code)\n \n | \n \n \n 3,740\n \n | \n \n \n 1,120\n \n | \n \n | \n
| \n \n N (beneficiaries)\n \n | \n \n \n 12,995,867\n \n | \n \n \n 5,262,809\n \n | \n \n | \n
| \n \n \n Air pollution concentration\n \n \n | \n \n | \n\n | \n\n | \n
| \n \n PM\n \n 2.5\n \n (\u00b5g/m\n \n 3\n \n )\n \n | \n \n \n 7.8 (2.8)\n \n | \n \n \n 7.9 (2.5)\n \n | \n \n | \n
| \n \n NO\n \n 2\n \n (ppb)\n \n | \n \n \n 15.8 (9.4)\n \n | \n \n \n 17.2 (7.9)\n \n | \n \n | \n
| \n \n Warm-season O\n \n 3\n \n (ppb)\n \n | \n \n \n 37.3 (3.9)\n \n | \n \n \n 33.2 (2.9)\n \n | \n \n | \n
| \n \n \n Meteorological covariates\n \n \n | \n \n | \n\n | \n\n | \n
| \n \n Summer average temperature\u00a0(\u00b0C)\n \n | \n \n \n 19.1 (5.1)\n \n | \n \n \n 20.8 (5.7)\n \n | \n \n | \n
| \n \n Winter average temperature\u00a0(\u00b0C)\n \n | \n \n \n 7.6 (8.7)\n \n | \n \n \n 13.2 (7.5)\n \n | \n \n | \n
| \n \n Summer average RH (%)\n \n | \n \n \n 68.9 (7.1)\n \n | \n \n \n 70.4 (6.7)\n \n | \n \n | \n
| \n \n Winter average RH (%)\n \n | \n \n \n 70.0 (7.2)\n \n | \n \n \n 71.5 (6.7)\n \n | \n \n | \n
| \n \n \n SES covariates\n \n \n | \n \n | \n\n | \n\n | \n
| \n \n Percent Black (%)\n \n | \n \n \n 6.5 (13.8)\n \n | \n \n \n 7.8 (13.8)\n \n | \n \n | \n
| \n \n Percent Hispanic (%)\n \n | \n \n \n 14.8 (22.2)\n \n | \n \n \n 24.8 (28.0)\n \n | \n \n | \n
| \n \n Median household income ($)\n \n | \n \n \n 52,236 (23,004)\n \n | \n \n \n 55,294 (26,806)\n \n | \n \n | \n
| \n \n Median house value ($)\n \n | \n \n \n 230,706 (191,275)\n \n | \n \n \n 289,056 (239,879)\n \n | \n \n | \n
| \n \n Percent owner occupied (%)\n \n | \n \n \n 69.1 (18.5)\n \n | \n \n \n 64.2 (18.0)\n \n | \n \n | \n
| \n \n Percent education\u2009<\u2009high school (%)\n \n | \n \n \n 24.4 (16.4)\n \n | \n \n \n 25.8 (19.3)\n \n | \n \n | \n
| \n \n Population density\u00a0(persons/mi\n \n 2\n \n )\n \n | \n \n \n 3,446 (10,154)\n \n | \n \n \n 3,874 (5,701)\n \n | \n \n | \n
| \n \n Percent\u2009\u2265\u200965 below poverty (%)\n \n | \n \n \n 10.3 (8.4)\n \n | \n \n \n 11.8 (10.1)\n \n | \n \n | \n
| \n \n Percent annual HbA1c test (%)\n \n | \n \n \n 84.6 (4.8)\n \n | \n \n \n 83.7 (3.8)\n \n | \n \n | \n
| \n \n Percent ambulatory visit (%)\n \n | \n \n \n 76.6 (7.2)\n \n | \n \n \n 75.6 (5.8)\n \n | \n \n | \n
| \n \n Percent eye exam (%)\n \n | \n \n \n 70.5 (6.3)\n \n | \n \n \n 69.3 (4.9)\n \n | \n \n | \n
| \n \n Percent LDL test (%)\n \n | \n \n \n 80.6 (5.5)\n \n | \n \n \n 81.8 (4.9)\n \n | \n \n | \n
| \n \n Percent mammogram (%)\n \n | \n \n \n 66.1 (7.3)\n \n | \n \n \n 63.9 (6.9)\n \n | \n \n | \n
| \n \n Distance to nearest hospital (km)\n \n | \n \n \n 10.2 (10.6)\n \n | \n \n \n 7.2 (9.5)\n \n | \n \n | \n
| \n \n Lung cancer rate (\u2030)\n \n | \n \n \n 0.4 (6.0)\n \n | \n \n \n 0.4 (7.6)\n \n | \n \n | \n
| \n \n Ever smokers (%)\n \n | \n \n \n 47.6 (7.7)\n \n | \n \n \n 45.0 (7.9)\n \n | \n \n | \n
| \n \n Mean BMI (kg/m\n \n 2\n \n )\n \n | \n \n \n 27.4 (1.7)\n \n | \n \n \n 27.1 (1.4)\n \n | \n \n | \n
| \n Note: Numbers in the table are presented as Mean (SD) for ZIP code-level covariates.\n | \n
\n \n Supplementary Table\u00a02\n \n presents the total number of hospitalizations and the annual rate for stroke, HF, and AF in the low pollution areas during the study period. The annual hospitalization rate for stroke, HF, and AF among the Medicare participants were 0.97%, 0.96%, and 0.46%, respectively, in low PM\n \n 2.5\n \n areas where low NO\n \n 2\n \n and O\n \n 3\n \n exposures concurrently occurred. The corresponding hospitalization rates were similar in low O\n \n 3\n \n areas with both thresholds. However, the hospitalization rates were higher in low NO\n \n 2\n \n areas where people experienced more normal PM\n \n 2.5\n \n exposures. Nevertheless, the pattern of the hospitalization rates for each cardiovascular outcome within demographic groups was generally similar across all the defined low pollution areas. Overall, we observed higher annual hospitalization rates for stroke and HF among those aged 85 years and older and eligible for Medicaid. However, there were some inconsistencies in the pattern by sex and race across specific outcomes. While the annual hospitalization rate for stroke and HF was higher in males and black individuals, more AF hospitalizations occurred in females and white individuals.\n
\n\n Figure\n \n 1\n \n shows the associations of long-term exposures to PM\n \n 2.5\n \n , NO\n \n 2\n \n , and O\n \n 3\n \n at low concentrations with the rates of hospitalizations for stroke, HF, and AF as determined from three-pollutant double negative control models and GLM. The estimated associations from single-pollutant models are illustrated in\n \n Supplementary Fig.\u00a01\n \n . Overall, the adjustments for co-pollutants resulted in stronger estimates for PM\n \n 2.5\n \n , while those for NO\n \n 2\n \n and warm-season O\n \n 3\n \n remained similar. When examining the associations between PM\n \n 2.5\n \n and all three outcomes, we found that the GLM yielded estimates that were modestly comparable but lower than those derived from the double negative control models. While both modeling approaches produced relatively similar estimates for the associations of NO\n \n 2\n \n and warm-season O\n \n 3\n \n with AF, there were slight differences in the estimates for stroke and HF. All the numeric results of the overall analyses can be found in\n \n Supplementary Table\u00a03\n \n .\n
\n\n In this study, we focused on the results adjusted for co-pollutants using double negative control adjustment. For long-term PM\n \n 2.5\n \n exposure below 10 \u00b5g/m\n \n 3\n \n , we found that each 1-\u00b5g/m\n \n 3\n \n increase in annual PM\n \n 2.5\n \n concentration was associated with the percent increases of 2.25% (95% CI: 1.96%, 2.54%) and 3.14% (95% CI: 2.80%, 3.49%) in the hospitalization rates for stroke and HF, respectively. However, the association with AF was merely marginally significant with an estimate of 0.28% (95% CI: -0.10%, 0.67%). We observed adverse effects on all three outcomes associated with long-term exposure to NO\n \n 2\n \n at concentrations below 40 and 20 ppb. Specifically, for each 1-ppb increase in annual NO\n \n 2\n \n below 40 ppb, we estimated the percent increases in the hospitalization rates for stroke, HF, and AF to be 0.28% (95% CI: 0.25%, 0.31%), 0.56% (95% CI: 0.52%, 0.60%), and 0.45% (95% CI: 0.41%, 0.49%), respectively. At a lower threshold of 20 ppb, these estimates increased to 0.62% (95% CI: 0.54%, 0.71%), 1.04% (95% CI: 0.94%, 1.14%), and 0.59% (95% CI: 0.47%, 0.70%), respectively. Regarding the health effects of long-term exposure to warm-season O\n \n 3\n \n below 45 ppb, we found an adverse effect on stroke only with a 0.32% (95% CI: 0.21%, 0.44%) percent increase in the hospitalization rate per ppb increase in warm-season O\n \n 3\n \n . In areas with even lower warm-season O\n \n 3\n \n levels, specifically below 20 ppb, the percent changes in hospitalization rates for stroke, HF, and AF per ppb increase in warm-season O\n \n 3\n \n were 0.79% (95% CI: 0.58%, 1.01%), 0.70% (95% CI: 0.46%, 0.95%), 0.71% (95% CI: 0.40%, 1.02%), respectively.\n
\n\n We conducted stratified analyses by individual demographic characteristics to identify the subgroups vulnerable to the harmful effects of PM\n \n 2.5\n \n , NO\n \n 2\n \n , and warm-season O\n \n 3\n \n . The results of the stratified analyses for stroke, HF, and AF from three-pollutant models are shown in Figs.\n \n 2\n \n ,\n \n 3\n \n , and\n \n 4\n \n , respectively. We found that the observed positive associations in the overall analyses generally persisted in demographic subgroups. In general, the patterns of the potential effect modification by demographics were similar in models with and without adjustment for co-pollutants, despite some changes in the magnitude and statistical significance of the subgroup-specific effect estimates (\n \n Supplementary Figs.\u00a01, 2, and 3\n \n ). All the detailed numeric results of the stratified analyses are presented in\n \n Supplementary Tables\u00a04, 5, and 6\n \n .\n
\n\n In the association of long-term PM\n \n 2.5\n \n exposure with stroke and AF, we identified Medicaid eligibility as a significant modifier, with a higher risk seen in individuals who were eligible for Medicaid than those who were not. We also found a larger effect of PM\n \n 2.5\n \n on all three outcomes for black people compared to white people, although the difference did not reach statistical significance for stroke and AF. In addition, age modified the PM\n \n 2.5\n \n association for HF with a stronger effect in the younger group (aged 64\u201375 years), but this modification pattern was not observed for stroke or AF. In contrast, we found no evidence of any effect modification by sex on the association of all outcomes in relation to PM\n \n 2.5\n \n .\n
\n\n For long-term exposure to NO\n \n 2\n \n below 40 ppb, individuals aged over 84 years and those who were not Medicaid-eligible were at greater risk of stroke. We observed similar effect modification patterns by age and Medicaid eligibility in the associations of HF and AF with NO\n \n 2\n \n . Regarding the modification by sex, males were at greater NO\n \n 2\n \n -associated risk of HF compared to females. At the same time, white people exhibited a significantly higher NO\n \n 2\n \n -associated risk of HF and AF compared to black people. However, the modification analyses for NO\n \n 2\n \n below 20 ppb were not apparent, with only stronger estimates observed for white individuals in relation to HF and for the oldest age group in relation to AF.\n
\n\n In terms of long-term exposure to warm-season O\n \n 3\n \n below 45 ppb, individuals aged 64\u201375 years, black individuals, and Medicaid-eligible individuals were found to be more susceptible to stroke and HF. Additionally, we observed positive associations between warm-season O\n \n 3\n \n and AF for individuals aged 65\u201374 years and those eligible for Medicaid, whereas other subgroups showed non-significant associations. For warm-season O\n \n 3\n \n below 40 ppb, we saw larger effects among Medicaid-eligible individuals across all outcomes. Furthermore, females were at greater risk of AF due to exposure to warm-season O\n \n 3\n \n .\n
\n\n Among US Medicare participants, we found that long-term exposure to low-level PM\n \n 2.5\n \n (<\u200910 \u00b5g/m\n \n 3\n \n ), NO\n \n 2\n \n (<\u200940 or 20 ppb), and warm-season O\n \n 3\n \n (<\u200945 or 40 ppb) could significantly increase the rate of hospitalizations for stroke in three-pollutant models that accounted for correlations between co-existing air pollutants and controlled for unmeasured confounders using negative controls. We also observed positive associations between PM\n \n 2.5\n \n and NO\n \n 2\n \n with HF and AF, although the effect of PM\n \n 2.5\n \n on AF was non-significant. When applying a more restrictive threshold to NO\n \n 2\n \n and warm-season O\n \n 3\n \n , the estimates became even stronger. Black people and Medicaid-eligible people appeared to be more vulnerable to the risk attributable to PM\n \n 2.5\n \n and warm-season O\n \n 3\n \n . For the NO\n \n 2\n \n -related risk, very elderly people and those who were not Medicare-eligible may be more susceptible to all outcomes, and white people may be more susceptible to HF and AF. We designed a pair of negative control exposure and outcome variables to capture any uncontrolled confounding. If the assumption of linearity between unmeasured covariates with exposure and negative exposure control holds, the double negative control adjustment can strengthen the causal interpretation of our observed associations. The GLM method yielded comparable results with the double negative control approach, exhibiting only slight differences in the effect size estimates. Such discrepancies may be attributable to unadjusted confounding bias. The consistent findings derived from these two statistical methods demonstrate the robustness of our results to different model adjustments, suggesting that any omitted confounding bias is small, and, in the case of PM\n \n 2.5\n \n and NO\n \n 2\n \n , negative. A previous study reported that greater control for SES resulted in increased effect sizes for PM\n \n 2.5\n \n \n 43\n \n .\n
\n\n Our study has a special emphasis on long-term exposure to low-level air pollution below the annual US EPA limits. While a growing number of prior studies have revealed increased health risks at lower levels of air pollution exposure under regulatory standards, most have focused on all-cause and cardiovascular mortality\n \n 11,12,44,45\n \n . However, the available evidence concerning cardiovascular disease risk at these lower pollution levels remains limited. For instance, in a large population-based Canadian cohort, Bai et al.\n \n 7\n \n found the concentration-response curves for congestive HF with long-term exposure to PM\n \n 2.5\n \n and NO\n \n 2\n \n to be supralinear with no discernable threshold values. They also observed a sublinear relationship for O\n \n 3\n \n with an indicative threshold. Similarly, Brunekreef et al.\n \n 15\n \n observed steeper slopes at low exposures to PM\n \n 2.5\n \n below 15 \u00b5g/m\n \n 3\n \n and NO\n \n 2\n \n below 40 \u00b5g/m\n \n 3\n \n in the supralinear associations for stroke incidence, based on data from 22 European cohorts in the European Study of Cohorts for Air Pollution Effects Project. Several previous studies of the Medicare population have found a greater risk of a range of cardiovascular outcomes when restricted to lower exposures\n \n 9,13,14,27\n \n . Our main finding adds to epidemiologic evidence of potential population-level health concerns at pollution levels conventionally considered safe and provides some assurance that the associations are not biased by unmeasured confounding. More importantly, this highlights the need to reassess the current air quality guidelines and tighten pollution control policies and measures.\n
\n\n This study also supplemented the limited epidemiologic evidence regarding the long-term effects of multiple air pollutants on cause-specific cardiovascular morbidity. We concluded that long-term exposure to PM\n \n 2.5\n \n , NO\n \n 2\n \n , and warm-season O\n \n 3\n \n even at low concentrations could be associated with an increase in the rate of hospitalizations for major cardiovascular diseases. The adverse association was more pronounced for stroke and HF than for AF. Our findings are in accordance with some of the existing literature. Prior studies of the Medicare population using diverse methodologies and different ranges of exposure have reported significant positive associations of all our studied outcomes with PM\n \n 2.5\n \n , NO\n \n 2\n \n , and warm-season O\n \n 3\n \n \n 13,31\n \n . A review and meta-analysis identified five studies of long-term exposure to PM\n \n 2.5\n \n and stroke incidence from North America and Europe and found a 6.4% (95% CI: 2.1%, 10.9%) increase in the hazard for each 5-\u00b5g/m\n \n 3\n \n increase in PM\n \n 2.5\n \n \n 46\n \n . A more recent review article reported that each 10-\u00b5g/m\n \n 3\n \n increase in long-term PM\n \n 2.5\n \n exposure could be associated with an increased risk of 13% (95% CI: 11%, 15%) for incident stroke, synthesizing the results of fourteen studies across the globe\n \n 47\n \n . In a large population-based study of about 5.1\u00a0million adults living in Ontario, Canada, annual PM\n \n 2.5\n \n , NO\n \n 2\n \n , and O\n \n 3\n \n were found to elevate the risk of HF with HRs of 1.05 (95% CI: 1.04, 1.05), 1.02 (95% CI: 1.01, 1.04), and 1.03 (95% CI: 1.02, 1.03) per each interquartile range increase in exposure, respectively. Similarly, a prospective study in the UK reported positive associations of incident HF with long-term PM\n \n 2.5\n \n and NO\n \n 2\n \n \n 8\n \n . Yue et al.\n \n 10\n \n conducted a systematic review and meta-analysis to quantify the association between air pollutants and AF based on eighteen studies. They indicated that exposure to all air pollutants including PM\n \n 2.5\n \n and NO\n \n 2\n \n had a deleterious impact on AF onset in the general population. By contrast, several other studies reported null relationships between air pollution and the risk of these outcomes\n \n 48\u201351,51\n \n . It is worth noting that direct comparisons across these studies might be challenging because of potentially heterogeneous air pollution ranges and diverse demographic characteristics of study populations.\n
\n\n Multiple pathophysiological mechanisms have been proposed to explain the detrimental cardiovascular effects of air pollution. It is widely accepted that air pollution can trigger systemic inflammation, oxidative stress reactions, and dysfunction of the autonomic nervous system\n \n 1\n \n . The autonomic imbalance can further result in increases in cardiac frequency and arterial pressure, and a reduction in heart rate variability\n \n 52\n \n . Numerous experimental studies have demonstrated that these responses may further instigate endothelial dysfunction, atherosclerosis, and vascular dysfunction\n \n 52,53\n \n . Another plausible mechanism underlying the onset of cardiovascular diseases is that inhaled irritants can traverse the pulmonary epithelium and directly enter the blood circulation and cardiac organs, which may alter blood coagulability and contribute to thrombus formation\n \n 54\n \n . The heart failure hospitalization was the most vulnerable outcome possibly because it was the common consequence of most cardiovascular diseases, especially for elderly people.\n
\n\n Environmental justice is an increasing concern and we found evidence that independent of differences in exposure, some disadvantaged groups had worse responses to any given level of air pollution. Specifically, we identified Medicaid eligibility as a positive modifier of the association of low-level PM\n \n 2.5\n \n and warm-season O\n \n 3\n \n with both stroke and AF. This suggests a greater vulnerability for lower-SES individuals even when residing in low-pollution regions, as Medicaid coverage is provided for low-income elderly beneficiaries to expand their healthcare access\n \n 55\n \n . Low SES has been determined as a significant risk factor for cardiovascular diseases because socio-economically disadvantaged individuals tend to have poorer health, higher psychosocial stress, and a propensity for unhealthy behaviors and lifestyles\n \n 56\n \n . In addition to Medicaid eligibility, we found that the effect sizes for effects of PM\n \n 2.5\n \n and warm-season O\n \n 3\n \n on all outcomes were more pronounced for Black individuals compared to white individuals. The tendency of a higher susceptibility among Blacks is consistent with much of the existing evidence\n \n 13,57\n \n . Black populations have been disproportionately affected by the detrimental health impacts of historic discrimination and ongoing racial segregation, and this study demonstrates additional susceptibility to air pollution. Additionally, while we observed increased susceptibility to warm-season O\n \n 3\n \n in individuals aged 65\u201374 years, the specific underlying reasons for this pattern remain unclear. It is likely that a lower baseline risk in this age group may influence these findings.\n
\n\n In terms of the adverse effects of NO\n \n 2\n \n , our results indicated that people aged\u2009\u2265\u200985 years, males, white people, and those who were not Medicaid-eligible may be more vulnerable to at least one cardiovascular disease we studied. First, an increased risk in the oldest group is understandable, given that advanced age significantly drives the deterioration of cardiovascular functionality in older people\n \n 58\n \n . Relative to age differences, sex as a potential modifier of cardiovascular risk in relation to air pollution as well as the relevant biological mechanisms has been more underappreciated. While some researchers found a more prominent NO\n \n 2\n \n -attributed cardiovascular risk among males\n \n 59,60\n \n , which is comparable to our finding for HF, there is no consensus on this question\n \n 32,61\n \n . Our findings of a higher susceptibility among the very elderly and males are not conclusive, but we think that paying more attention to these questions can be meaningful to inform more scientific appointments of preventive medical care in the future. Interestingly, when we looked at the modification by race and Medicaid eligibility, the greater susceptibility for NO\n \n 2\n \n seen in white individuals and non-Medicaid eligible individuals contrasts with our findings for PM\n \n 2.5\n \n . Such inconsistent results in the modifying roles of demographics and SES exist in the literature examining the association between air pollution and cardiovascular health, which may have to do with different air pollutants, specific outcomes, and neighborhood samples\n \n 9,62,63\n \n . In fact, the specious modification patterns we found for NO\n \n 2\n \n are unlikely but still possible. As a pollutant predominantly coming from urban origins and often transported on a local scale, NO\n \n 2\n \n can vary by urbanicity level\n \n 64\n \n . It is reasonable to assume that NO\n \n 2\n \n might be more of a proxy for commercial activities, since its emissions from other major sources (e.g., diesel traffic, fuel combustion, power plants) have been reduced in recent years\n \n 65,66\n \n . Therefore, the observed higher vulnerability in white and Medicaid-eligible individuals might be partially accounted for by their higher access to urbanization or commercial activities. In addition, we should also note that our estimate is a measure relative to the baseline risk and does not necessarily represent the magnitude of its absolute attributable risk. For example, the lower baseline risk of HF hospitalization rate in white beneficiaries might have exaggerated the magnitude of relative risk.\n
\n\n Our study has multiple strengths. Foremost is the use of a double negative control approach. This methodology provides an alternative tool to instrumental variables to control for omitted confounding and thus enhance the credibility of the estimated associations. We also thoroughly considered a variety of cardiovascular risk factors to reinforce the confounding adjustment. Another notable strength is that we leveraged the data from the Medicare population. The data that we used was from a very large nationwide cohort, which ensured sufficient statistical power and increased the generalizability of our results to the population that suffers over three quarters of the deaths in the US. Furthermore, the exposure data were derived from high-quality models with a fine resolution and satisfactory predictive accuracy, further assuring the reliability of our analyses. Moreover, compared to restricting the analyses to low exposures in ZIP code-year combinations in prior Medicare studies\n \n 13,67\n \n , the selection criteria applied in this study are somewhat more rigorous by imposing low-exposure constraints over the 17-year study duration. Hence, the possibility of mistakenly including the individuals impacted by past higher exposures was reduced. Last, we attempted to address the correlations among air pollutants and more accurately estimate the independent effect of each exposure by constructing both single- and three-pollutant models.\n
\n\n Some limitations of this study should also be cautioned. First, we may not generalize the conclusions to younger populations or highly polluted regions. Second, there could be residual or unmeasured confounding because the assumptions for the double negative control method might be violated. However, we considered a series of major confounders, ranging from possible meteorological conditions, and health behavioral factors, to socioeconomic measures, which should have captured most of the confounding associations. It is noteworthy that we controlled for co-exposures of other air pollutants using the three-pollutant models as well. Admittedly, the moderate correlation between annual PM\n \n 2.5\n \n and NO\n \n 2\n \n concentrations may indicate potential collinearity and the risk of over-controlling issues. Third, the ZIP code-level air pollution data derived from exposure models may not fully represent true personal exposures. Specifically, our exposure metrics did not account for the exposures occurring distant from the participants\u2019 residences. However, the National Human Activity Pattern Survey reported that US adults spent 69% of their time at home and 8% of the time immediately outside their home\n \n 68\n \n . Older people may spend even more time at home, implying that the exposure misclassification would be relatively minor. Another concern is that the variations in personal exposures caused by different indoor activity patterns and building features might not be captured by the neighborhood metrics. Nevertheless, the resulting error is likely a Berksonian exposure error and may cause little bias\n \n 69\n \n . Some residual prediction errors of exposure models may be present, but they should be minimal because we studied low air pollutant concentrations. Last, we accessed hospital discharge diagnoses from the administrative Medicare database as the morbidity measure, which may not capture some cases with milder symptoms. However, since the potential outcome classification is not expected to relate to air pollution, it will introduce a non-differential bias towards the null.\n
\n\n Using a double negative control approach, we found positive associations of long-term exposure to PM\n \n 2.5\n \n , NO\n \n 2\n \n , warm-season O\n \n 3\n \n at low concentrations with the hospitalization rate of stroke, HF, and AF in US Medicare older adults. Black and Medicaid-eligible people may be more susceptible to the risk attributed to PM\n \n 2.5\n \n and warm-season O\n \n 3\n \n , whereas those who are very elderly, white, and non-Medicaid-eligible may be at greater risk attributed to NO\n \n 2\n \n . Our findings suggest that the current NAAQS for annual PM\n \n 2.5\n \n and NO\n \n 2\n \n may not be adequate to minimize the cardiovascular disease burden. Future guidelines for warm-season O\n \n 3\n \n could be warranted.\n
\n\n Supplementary materials\n
\n \n| \n \nCovariates\n \n | \n\n \nPM2.5 <10 \u00b5g/m3\n \n | \n\n \nNO2 <\u200940 ppb\n \n | \n\n \nNO2 <\u200920 ppb\n \n | \n
|---|---|---|---|
| \n \nN (ZIP code)\n \n | \n\n \n5,848\n \n | \n\n \n26,583\n \n | \n\n \n12,281\n \n | \n
| \n \nN (beneficiaries)\n \n | \n\n \n12,667,627\n \n | \n\n \n63,657,996\n \n | \n\n \n212,454,83\n \n | \n
| \n \n\nAir pollution concentration\n\n \n | \n\n | \n\n | \n\n | \n
| \n \nPM2.5 (\u00b5g/m3)\n \n | \n\n \n5.9 (1.8)\n \n | \n\n \n9.6 (3.0)\n \n | \n\n \n9.3 (2.8)\n \n | \n
| \n \nNO2 (ppb)\n \n | \n\n \n12.8 (7.6)\n \n | \n\n \n14.7 (7.1)\n \n | \n\n \n9.9 (3.5)\n \n | \n
| \n \nWarm-season O3 (ppb)\n \n | \n\n \n44.6 (7.3)\n \n | \n\n \n45.0 (5.4)\n \n | \n\n \n44.5 (4.6)\n \n | \n
| \n \n\nMeteorological covariates\n\n \n | \n\n | \n\n | \n\n | \n
| \n \nSummer average temperature\u202f(\u00b0C)\n \n | \n\n \n17.6 (4.3)\n \n | \n\n \n20.2 (3.7)\n \n | \n\n \n20.2 (3.8)\n \n | \n
| \n \nWinter average temperature\u202f(\u00b0C)\n \n | \n\n \n3.8 (6.8)\n \n | \n\n \n6.2 (5.8)\n \n | \n\n \n5.6 (5.9)\n \n | \n
| \n \nSummer average RH (%)\n \n | \n\n \n58.8 (14.4)\n \n | \n\n \n66.7 (9.4)\n \n | \n\n \n67.8 (7.8)\n \n | \n
| \n \nWinter average RH (%)\n \n | \n\n \n66.8 (10.4)\n \n | \n\n \n66.9 (7.1)\n \n | \n\n \n67.4 (6.3)\n \n | \n
| \n \n\nSES covariates\n\n \n | \n\n | \n\n | \n\n | \n
| \n \nPercent Black (%)\n \n | \n\n \n1.8 (5.4)\n \n | \n\n \n8.5 (16)\n \n | \n\n \n7.2 (14.7)\n \n | \n
| \n \nPercent Hispanic (%)\n \n | \n\n \n10.3 (16.2)\n \n | \n\n \n7.8 (14.4)\n \n | \n\n \n4.9 (10.8)\n \n | \n
| \n \nMedian household income ($)\n \n | \n\n \n47,911 (17,976)\n \n | \n\n \n48,420 (20,205)\n \n | \n\n \n43,105 (14,352)\n \n | \n
| \n \nMedian house value ($)\n \n | \n\n \n173,484 (135,217)\n \n | \n\n \n149,977 (124,612)\n \n | \n\n \n117,697 (89,262)\n \n | \n
| \n \nPercent owner occupied (%)\n \n | \n\n \n75.5 (11.6)\n \n | \n\n \n74.2 (14.0)\n \n | \n\n \n77.9 (9.7)\n \n | \n
| \n \nPercent education\u202f<\u202fhigh school (%)\n \n | \n\n \n22.2 (14.3)\n \n | \n\n \n28.1 (16.1)\n \n | \n\n \n30.6 (16.1)\n \n | \n
| \n \nPopulation density\u202f(persons/mi2)\n \n | \n\n \n435 (1,684)\n \n | \n\n \n841 (2,095)\n \n | \n\n \n144 (446)\n \n | \n
| \n \nPercent\u202f\u2265\u202f65 below poverty (%)\n \n | \n\n \n9.3 (7.4)\n \n | \n\n \n10.1 (7.7)\n \n | \n\n \n11.2 (7.6)\n \n | \n
| \n \nPercent annual HbA1c test (%)\n \n | \n\n \n82.8 (8.6)\n \n | \n\n \n83.5 (5.9)\n \n | \n\n \n83.8 (6.1)\n \n | \n
| \n \nPercent ambulatory visit (%)\n \n | \n\n \n78.3 (7.4)\n \n | \n\n \n79.8 (6.0)\n \n | \n\n \n80.9 (6.5)\n \n | \n
| \n \nPercent eye exam (%)\n \n | \n\n \n69.2 (7.5)\n \n | \n\n \n67.3 (6.7)\n \n | \n\n \n67.5 (7.5)\n \n | \n
| \n \nPercent LDL test (%)\n \n | \n\n \n76.7 (9.9)\n \n | \n\n \n78.6 (7.3)\n \n | \n\n \n78.1 (7.6)\n \n | \n
| \n \nPercent mammogram (%)\n \n | \n\n \n65.2 (8.7)\n \n | \n\n \n64.1 (7.3)\n \n | \n\n \n64.3 (8.0)\n \n | \n
| \n \nDistance to nearest hospital (km)\n \n | \n\n \n16.3 (14.9)\n \n | \n\n \n12.2 (10.6)\n \n | \n\n \n15.4 (10.8)\n \n | \n
| \n \nLung cancer rate (\u2030)\n \n | \n\n \n0.4 (4.8)\n \n | \n\n \n0.4 (2.2)\n \n | \n\n \n0.5 (2.2)\n \n | \n
| \n \nEver smokers (%)\n \n | \n\n \n47.8 (7.6)\n \n | \n\n \n47.3 (7.4)\n \n | \n\n \n47.9 (7.9)\n \n | \n
| \n \nMean BMI (kg/m2)\n \n | \n\n \n28.1 (3.2)\n \n | \n\n \n28.1 (2.5)\n \n | \n\n \n28.7 (3.0)\n \n | \n
| \n \n\nCovariates\n\n \n | \n\n \n\nWarm-season O\n\n\n\n3\n\n\n\n<\u200945 ppb\n\n \n | \n\n \n\nWarm-season O\n\n\n\n3\n\n\n\n<\u200940 ppb\n\n \n | \n\n | \n
| \n \nN (ZIP code)\n \n | \n\n \n3,740\n \n | \n\n \n1,120\n \n | \n\n | \n
| \n \nN (beneficiaries)\n \n | \n\n \n12,995,867\n \n | \n\n \n5,262,809\n \n | \n\n | \n
| \n \n\nAir pollution concentration\n\n \n | \n\n | \n\n | \n\n | \n
| \n \nPM2.5 (\u00b5g/m3)\n \n | \n\n \n7.8 (2.8)\n \n | \n\n \n7.9 (2.5)\n \n | \n\n | \n
| \n \nNO2 (ppb)\n \n | \n\n \n15.8 (9.4)\n \n | \n\n \n17.2 (7.9)\n \n | \n\n | \n
| \n \nWarm-season O3 (ppb)\n \n | \n\n \n37.3 (3.9)\n \n | \n\n \n33.2 (2.9)\n \n | \n\n | \n
| \n \n\nMeteorological covariates\n\n \n | \n\n | \n\n | \n\n | \n
| \n \nSummer average temperature\u202f(\u00b0C)\n \n | \n\n \n19.1 (5.1)\n \n | \n\n \n20.8 (5.7)\n \n | \n\n | \n
| \n \nWinter average temperature\u202f(\u00b0C)\n \n | \n\n \n7.6 (8.7)\n \n | \n\n \n13.2 (7.5)\n \n | \n\n | \n
| \n \nSummer average RH (%)\n \n | \n\n \n68.9 (7.1)\n \n | \n\n \n70.4 (6.7)\n \n | \n\n | \n
| \n \nWinter average RH (%)\n \n | \n\n \n70.0 (7.2)\n \n | \n\n \n71.5 (6.7)\n \n | \n\n | \n
| \n \n\nSES covariates\n\n \n | \n\n | \n\n | \n\n | \n
| \n \nPercent Black (%)\n \n | \n\n \n6.5 (13.8)\n \n | \n\n \n7.8 (13.8)\n \n | \n\n | \n
| \n \nPercent Hispanic (%)\n \n | \n\n \n14.8 (22.2)\n \n | \n\n \n24.8 (28.0)\n \n | \n\n | \n
| \n \nMedian household income ($)\n \n | \n\n \n52,236 (23,004)\n \n | \n\n \n55,294 (26,806)\n \n | \n\n | \n
| \n \nMedian house value ($)\n \n | \n\n \n230,706 (191,275)\n \n | \n\n \n289,056 (239,879)\n \n | \n\n | \n
| \n \nPercent owner occupied (%)\n \n | \n\n \n69.1 (18.5)\n \n | \n\n \n64.2 (18.0)\n \n | \n\n | \n
| \n \nPercent education\u202f<\u202fhigh school (%)\n \n | \n\n \n24.4 (16.4)\n \n | \n\n \n25.8 (19.3)\n \n | \n\n | \n
| \n \nPopulation density\u202f(persons/mi2)\n \n | \n\n \n3,446 (10,154)\n \n | \n\n \n3,874 (5,701)\n \n | \n\n | \n
| \n \nPercent\u202f\u2265\u202f65 below poverty (%)\n \n | \n\n \n10.3 (8.4)\n \n | \n\n \n11.8 (10.1)\n \n | \n\n | \n
| \n \nPercent annual HbA1c test (%)\n \n | \n\n \n84.6 (4.8)\n \n | \n\n \n83.7 (3.8)\n \n | \n\n | \n
| \n \nPercent ambulatory visit (%)\n \n | \n\n \n76.6 (7.2)\n \n | \n\n \n75.6 (5.8)\n \n | \n\n | \n
| \n \nPercent eye exam (%)\n \n | \n\n \n70.5 (6.3)\n \n | \n\n \n69.3 (4.9)\n \n | \n\n | \n
| \n \nPercent LDL test (%)\n \n | \n\n \n80.6 (5.5)\n \n | \n\n \n81.8 (4.9)\n \n | \n\n | \n
| \n \nPercent mammogram (%)\n \n | \n\n \n66.1 (7.3)\n \n | \n\n \n63.9 (6.9)\n \n | \n\n | \n
| \n \nDistance to nearest hospital (km)\n \n | \n\n \n10.2 (10.6)\n \n | \n\n \n7.2 (9.5)\n \n | \n\n | \n
| \n \nLung cancer rate (\u2030)\n \n | \n\n \n0.4 (6.0)\n \n | \n\n \n0.4 (7.6)\n \n | \n\n | \n
| \n \nEver smokers (%)\n \n | \n\n \n47.6 (7.7)\n \n | \n\n \n45.0 (7.9)\n \n | \n\n | \n
| \n \nMean BMI (kg/m2)\n \n | \n\n \n27.4 (1.7)\n \n | \n\n \n27.1 (1.4)\n \n | \n\n | \n
| \nNote: Numbers in the table are presented as Mean (SD) for ZIP code-level covariates.\n | \n
|
+ +Covariates + + |
+
+ +PM2.5 <10 µg/m3 + + |
+
+ +NO2 < 40 ppb + + |
+
+ +NO2 < 20 ppb + + |
+
|---|---|---|---|
|
+ +N (ZIP code) + + |
+
+ +5,848 + + |
+
+ +26,583 + + |
+
+ +12,281 + + |
+
|
+ +N (beneficiaries) + + |
+
+ +12,667,627 + + |
+
+ +63,657,996 + + |
+
+ +212,454,83 + + |
+
|
+ + +Air pollution concentration + + + |
++ | ++ | ++ | +
|
+ +PM2.5 (µg/m3) + + |
+
+ +5.9 (1.8) + + |
+
+ +9.6 (3.0) + + |
+
+ +9.3 (2.8) + + |
+
|
+ +NO2 (ppb) + + |
+
+ +12.8 (7.6) + + |
+
+ +14.7 (7.1) + + |
+
+ +9.9 (3.5) + + |
+
|
+ +Warm-season O3 (ppb) + + |
+
+ +44.6 (7.3) + + |
+
+ +45.0 (5.4) + + |
+
+ +44.5 (4.6) + + |
+
|
+ + +Meteorological covariates + + + |
++ | ++ | ++ | +
|
+ +Summer average temperature (°C) + + |
+
+ +17.6 (4.3) + + |
+
+ +20.2 (3.7) + + |
+
+ +20.2 (3.8) + + |
+
|
+ +Winter average temperature (°C) + + |
+
+ +3.8 (6.8) + + |
+
+ +6.2 (5.8) + + |
+
+ +5.6 (5.9) + + |
+
|
+ +Summer average RH (%) + + |
+
+ +58.8 (14.4) + + |
+
+ +66.7 (9.4) + + |
+
+ +67.8 (7.8) + + |
+
|
+ +Winter average RH (%) + + |
+
+ +66.8 (10.4) + + |
+
+ +66.9 (7.1) + + |
+
+ +67.4 (6.3) + + |
+
|
+ + +SES covariates + + + |
++ | ++ | ++ | +
|
+ +Percent Black (%) + + |
+
+ +1.8 (5.4) + + |
+
+ +8.5 (16) + + |
+
+ +7.2 (14.7) + + |
+
|
+ +Percent Hispanic (%) + + |
+
+ +10.3 (16.2) + + |
+
+ +7.8 (14.4) + + |
+
+ +4.9 (10.8) + + |
+
|
+ +Median household income ($) + + |
+
+ +47,911 (17,976) + + |
+
+ +48,420 (20,205) + + |
+
+ +43,105 (14,352) + + |
+
|
+ +Median house value ($) + + |
+
+ +173,484 (135,217) + + |
+
+ +149,977 (124,612) + + |
+
+ +117,697 (89,262) + + |
+
|
+ +Percent owner occupied (%) + + |
+
+ +75.5 (11.6) + + |
+
+ +74.2 (14.0) + + |
+
+ +77.9 (9.7) + + |
+
|
+ +Percent education < high school (%) + + |
+
+ +22.2 (14.3) + + |
+
+ +28.1 (16.1) + + |
+
+ +30.6 (16.1) + + |
+
|
+ +Population density (persons/mi2) + + |
+
+ +435 (1,684) + + |
+
+ +841 (2,095) + + |
+
+ +144 (446) + + |
+
|
+ +Percent ≥ 65 below poverty (%) + + |
+
+ +9.3 (7.4) + + |
+
+ +10.1 (7.7) + + |
+
+ +11.2 (7.6) + + |
+
|
+ +Percent annual HbA1c test (%) + + |
+
+ +82.8 (8.6) + + |
+
+ +83.5 (5.9) + + |
+
+ +83.8 (6.1) + + |
+
|
+ +Percent ambulatory visit (%) + + |
+
+ +78.3 (7.4) + + |
+
+ +79.8 (6.0) + + |
+
+ +80.9 (6.5) + + |
+
|
+ +Percent eye exam (%) + + |
+
+ +69.2 (7.5) + + |
+
+ +67.3 (6.7) + + |
+
+ +67.5 (7.5) + + |
+
|
+ +Percent LDL test (%) + + |
+
+ +76.7 (9.9) + + |
+
+ +78.6 (7.3) + + |
+
+ +78.1 (7.6) + + |
+
|
+ +Percent mammogram (%) + + |
+
+ +65.2 (8.7) + + |
+
+ +64.1 (7.3) + + |
+
+ +64.3 (8.0) + + |
+
|
+ +Distance to nearest hospital (km) + + |
+
+ +16.3 (14.9) + + |
+
+ +12.2 (10.6) + + |
+
+ +15.4 (10.8) + + |
+
|
+ +Lung cancer rate (‰) + + |
+
+ +0.4 (4.8) + + |
+
+ +0.4 (2.2) + + |
+
+ +0.5 (2.2) + + |
+
|
+ +Ever smokers (%) + + |
+
+ +47.8 (7.6) + + |
+
+ +47.3 (7.4) + + |
+
+ +47.9 (7.9) + + |
+
|
+ +Mean BMI (kg/m2) + + |
+
+ +28.1 (3.2) + + |
+
+ +28.1 (2.5) + + |
+
+ +28.7 (3.0) + + |
+
|
+ + +Covariates + + + |
+
+ + +Warm-season O + + + +3 + + + +< 45 ppb + + + |
+
+ + +Warm-season O + + + +3 + + + +< 40 ppb + + + |
++ | +
|
+ +N (ZIP code) + + |
+
+ +3,740 + + |
+
+ +1,120 + + |
++ | +
|
+ +N (beneficiaries) + + |
+
+ +12,995,867 + + |
+
+ +5,262,809 + + |
++ | +
|
+ + +Air pollution concentration + + + |
++ | ++ | ++ | +
|
+ +PM2.5 (µg/m3) + + |
+
+ +7.8 (2.8) + + |
+
+ +7.9 (2.5) + + |
++ | +
|
+ +NO2 (ppb) + + |
+
+ +15.8 (9.4) + + |
+
+ +17.2 (7.9) + + |
++ | +
|
+ +Warm-season O3 (ppb) + + |
+
+ +37.3 (3.9) + + |
+
+ +33.2 (2.9) + + |
++ | +
|
+ + +Meteorological covariates + + + |
++ | ++ | ++ | +
|
+ +Summer average temperature (°C) + + |
+
+ +19.1 (5.1) + + |
+
+ +20.8 (5.7) + + |
++ | +
|
+ +Winter average temperature (°C) + + |
+
+ +7.6 (8.7) + + |
+
+ +13.2 (7.5) + + |
++ | +
|
+ +Summer average RH (%) + + |
+
+ +68.9 (7.1) + + |
+
+ +70.4 (6.7) + + |
++ | +
|
+ +Winter average RH (%) + + |
+
+ +70.0 (7.2) + + |
+
+ +71.5 (6.7) + + |
++ | +
|
+ + +SES covariates + + + |
++ | ++ | ++ | +
|
+ +Percent Black (%) + + |
+
+ +6.5 (13.8) + + |
+
+ +7.8 (13.8) + + |
++ | +
|
+ +Percent Hispanic (%) + + |
+
+ +14.8 (22.2) + + |
+
+ +24.8 (28.0) + + |
++ | +
|
+ +Median household income ($) + + |
+
+ +52,236 (23,004) + + |
+
+ +55,294 (26,806) + + |
++ | +
|
+ +Median house value ($) + + |
+
+ +230,706 (191,275) + + |
+
+ +289,056 (239,879) + + |
++ | +
|
+ +Percent owner occupied (%) + + |
+
+ +69.1 (18.5) + + |
+
+ +64.2 (18.0) + + |
++ | +
|
+ +Percent education < high school (%) + + |
+
+ +24.4 (16.4) + + |
+
+ +25.8 (19.3) + + |
++ | +
|
+ +Population density (persons/mi2) + + |
+
+ +3,446 (10,154) + + |
+
+ +3,874 (5,701) + + |
++ | +
|
+ +Percent ≥ 65 below poverty (%) + + |
+
+ +10.3 (8.4) + + |
+
+ +11.8 (10.1) + + |
++ | +
|
+ +Percent annual HbA1c test (%) + + |
+
+ +84.6 (4.8) + + |
+
+ +83.7 (3.8) + + |
++ | +
|
+ +Percent ambulatory visit (%) + + |
+
+ +76.6 (7.2) + + |
+
+ +75.6 (5.8) + + |
++ | +
|
+ +Percent eye exam (%) + + |
+
+ +70.5 (6.3) + + |
+
+ +69.3 (4.9) + + |
++ | +
|
+ +Percent LDL test (%) + + |
+
+ +80.6 (5.5) + + |
+
+ +81.8 (4.9) + + |
++ | +
|
+ +Percent mammogram (%) + + |
+
+ +66.1 (7.3) + + |
+
+ +63.9 (6.9) + + |
++ | +
|
+ +Distance to nearest hospital (km) + + |
+
+ +10.2 (10.6) + + |
+
+ +7.2 (9.5) + + |
++ | +
|
+ +Lung cancer rate (‰) + + |
+
+ +0.4 (6.0) + + |
+
+ +0.4 (7.6) + + |
++ | +
|
+ +Ever smokers (%) + + |
+
+ +47.6 (7.7) + + |
+
+ +45.0 (7.9) + + |
++ | +
|
+ +Mean BMI (kg/m2) + + |
+
+ +27.4 (1.7) + + |
+
+ +27.1 (1.4) + + |
++ | +
| +Note: Numbers in the table are presented as Mean (SD) for ZIP code-level covariates. + | +|||
\n Functionalization of carboranes, icosahedral boron\u2009\u2212\u2009carbon molecular clusters, is of great interest as they have wide applications in medicinal and materials chemistry. Thus, site- and enantioselective synthesis of carboranes requires complete control of the reaction. Herein, we describe the first asymmetric Rh(II)-catalyzed insertion reactions of carbenes into cage B\u2009\u2212\u2009H bond of carboranes. This reaction thereby generates carboranes possessing a carbon-stereocenter adjacent to cage boron of the carborane, in excellent site- and enantioselectivity under mild reaction conditions. The first fully computed transition structures of Rh(II)-catalyzed carbene insertion process through density functional theory are reported. These B\u2009\u2212\u2009H insertion transition structures, in conjunction with newly employed topographical proximity surfaces analyses, visually reveal the region between the carborane and the phthalimide ligands responsible for the selectivities of this reaction.\n
\n\n Control of selectivity in reactions is of utmost importance in chemistry and the ultimate driving force for developing new reactions. Asymmetric catalytic reactions to control the stereoselectivity with chiral organic molecules, chiral auxiliaries, chiral reagents, and chiral metal complexes have been recognized as the most solid method in asymmetric synthesis\n \n 1\u20134\n \n . Their syntheses involve breaking the point, axial, planar, and helical symmetry elements of symmetric molecules. Among these, transition metal-catalyzed enantioselective reactions have emerged as one of the most powerful approaches to get optically pure compounds\n \n 2,4\n \n . To date, a huge myriad of chiral structures including central, axial, planar, and helical chirality have been achieved by catalytic asymmetric synthesis\n \n 5,6\n \n . However, site-selective reactions to break the symmetry in hypersymmetric three-dimensional cluster compounds, such as the icosahedral carboranes, with exohedral stereocontrol is a formidable challenge due to various classes of chirality such as plane chirality, cage chirality, and carbon chirality adjacent to the cage carbon or boron.\n
\n\n Carboranes are icosahedral boron\u2009\u2212\u2009carbon molecular clusters, attractive building blocks combining properties such as chemical and biological stability, lipophilicity and hydrophobicity solubility properties, hydridic B\u2013H bonds, spherical geometry, and three dimensional \u03c3-aromaticity\n \n 7,8\n \n . Carboranes have been utilized in boron neutron capture therapy agents in medicine\n \n 9\n \n , as unique pharmacophores\n \n 10\n \n , as ligands in transition metal catalysis\n \n 11\u201313\n \n , and even confer unique and powerful structural and photooptical properties in supramolecular design and materials (Fig.\n \n 1\n \n a)\n \n 14\u201317\n \n . However, despite these distinctive function that carboranes can confer, we are crippled in our ability to access the full potential of carboranes because we do not have means to site-selectively react on carboranes and build complexity rapidly.\n
\n\n
\n\n Accordingly, a variety of research and applications based on these facts have increased the interest in the site- and enantioselective functionalization on ten boron vertexes of carborane (Fig.\n \n 1\n \n b)\n \n 18,19\n \n . However, significant advances in the synthesis of chiral molecules possessing carborane moieties have only recently achieved despite of recent progress for the functionalization of carboranes\n \n 20\u201324\n \n . For the first time, Kalinin and co-workers carried out the direct asymmetric synthesis (up to 32% e.e.) for\n \n o\n \n -carborane derivatives bearing chirality on C-proximity by Pd-catalyzed allylation reaction in the presence of chiral ligand (Fig.\n \n 1\n \n c)\n \n \n 25\n \n \n . If the substituents on the cage carbon atoms are different, substitution at B(\n \n 3\n \n ) position provides a pair of enantiomer possessing chiral center on the plane. In this regard, Krasnov and co-workers were the first to obtain planar-chiral 3-amino-1-methyl-1,2-dicarba-\n \n closo\n \n -dodecaborane in enantiomerically moderate form through a chiral resolution\n \n 26\n \n . On the other hand, Xie, Qiu, and co-workers developed for the first time an enantioselective synthesis of chiral-at-cage\n \n o\n \n -carboranes through Pd-catalyzed intramolecular cross-coupling reactions with (\n \n R\n \n )-BI-DIME as a chiral ligand\n \n 27\n \n . Furthermore, they reported Ir-catalyzed enantioselective B\u2009\u2212\u2009H alkenylation with chiral phosphoramidite ligand, affording chiral-at-cage B(\n \n 4\n \n )-alkenyl\n \n o\n \n -carboranes\n \n 28\n \n . In these seminal reports, the functionalization was achieved either intramolecularly or made use of a directing group to control the site-selectivity. In contrast, there has been no success in achieving stereoselective introduction of an exohedral chiral center, which is a carbon-stereocenter adjacent to cage boron of the carborane, through direct B\u2009\u2212\u2009H functionalization that is highly enantioselective and in the absence of any directing group. Rudimentary control of enantioselectivity using carboranes as the steric element or control of regioselectivity in B\u2013H bond activation have been reported. However, true complexity building synthetic processes which can activate B\u2013H bonds on carboranes in a regiocontrolled manner while simultaneously creating exohedral chiral centers are unknown at this time.\n
\n\n Although some reactions of carbenes with unsubstituted carboranes have been reported, the yield and site-selectivity were lackluster without stereoselectivity issue (see the Supplementary Information, Fig.\n \n S1\n \n ). For example, Jones reported the reaction of\n \n o\n \n -carborane with ethyl diazoacetate under irradiation, providing inseparable four regioisomers in combined 10% yield (12:7.2:4.8:1)\n \n 29\n \n . Moreover, he found that methylene carbene could insert into B\u2009\u2212\u2009H bonds of\n \n m\n \n -carborane under irradiation, producing inseparable regioisomeric mixture\n \n 30\n \n . Reaction of\n \n p\n \n -carborane with methylene carbene under irradiation afforded\n \n B\n \n -alkylated product in low yield even giving single product due to identical ten B\u2009\u2212\u2009H bonds. Therefore, the development of concise and efficient method that controls site- and enantioselectivity in functionalization of carborane is extremely attractive and a significant challenge.\n
\n\n We describe herein the first effective site- and enantioselective B\u2009\u2212\u2009H functionalization, thereby generating\n \n o\n \n -,\n \n m\n \n -, and\n \n p\n \n -carboranes possessing an exohedral carbon-stereocenter, which is a carbon-stereocenter adjacent to cage boron of the carborane, in excellent enantioselectivity (99% e.e.), site-selectivity (>\u200950:1 r.r.) and in excellent yields with broad substrate scope under mild reaction conditions (Fig.\n \n 1\n \n d). We are also pleased to report the first fully quantum mechanically computed transition structures of this chiral dirhodium carbenoid insertion process into an icosahedral cage B\u2013H bond of carboranes. Our density functional theory (DFT) results reproduce experimentally observed site- and enantioselectivity of the B\u2013H functionalization. We employed a topographical tool to visualize and highlight the structurally subtle, but energetically critical, steric close contacts to elucidate the specific structural elements involved. This topographical proximity surfaces (TPS) analysis visually revealed the specific steric interactions between the carborane and the phthalimide ligand responsible for the observed selectivities.\n
\n\n To develop a site- and enantioselective carbene insertion reaction into B\u2009\u2212\u2009H bond of\n \n o\n \n -carboranes, reaction conditions were extensively explored (see Supplementary Information Table\n \n S1\n \n for details). Our investigation began with the reaction of 1,2-(DMPS)\n \n 2\n \n -\n \n o\n \n -C\n \n 2\n \n B\n \n 10\n \n H\n \n 10\n \n (\n \n 1a\n \n , DMPS\u2009=\u2009dimethylphenylsilyl) with methyl 2-diazo-2-phenylacetate (\n \n 2a\n \n ) in dichloromethane (DCM) at 40\n \n o\n \n C using various dirhodium tetracarboxylate catalysts. Upon detailed examination of these reactions, we found that two regioisomers [\n \n 3aa-B\n \n (\n \n 9\n \n ) and\n \n 4aa-B\n \n (\n \n 8\n \n )] were obtained as an inseparable mixture, while no other regioisomers were observed. The regioisomeric ratio (r.r.) of\n \n 3aa\n \n and\n \n 4aa\n \n and the enantiomeric excess (e.e.) of\n \n 3aa\n \n were determined through\n \n 1\n \n H NMR and chiral HPLC analysis, respectively. Among the achiral catalysts, Rh\n \n 2\n \n (OAc)\n \n 4\n \n gave the mixture of regioisomers in 87% yield with 2.6:1 r.r. as racemate (entry 1). On the other hand, Rh\n \n 2\n \n (oct)\n \n 4\n \n provided a relatively low yield (71%) but high site-selectivity (14:1) (entry 2). Furthermore, we investigated various chiral catalysts to achieve the enantioselective reaction (entries 3\u201311)\n \n 31\n \n . As a result of examining chiral Rh(II) catalysts, it was revealed that Rh\n \n 2\n \n (\n \n S\n \n -TCPTTL)\n \n 4\n \n showed quantitative yield, high site-selectivity (29:1), and enantioselectivity (25% e.e.) (entry 10). Rh\n \n 2\n \n (\n \n R\n \n -BTPCP)\n \n 4\n \n , the most sterically encumbering catalyst, gave trace amount of conversion of\n \n 1a\n \n , probably due to the steric effect of carborane cluster (entry 11)\n \n 32\n \n . The enantioselectivity of the present reaction was affected by the solvents such as dichloroethane (DCE), cyclohexane, benzene, and trifluorotoluene (PhCF\n \n 3\n \n ). Especially, benzene and PhCF\n \n 3\n \n enhanced the enantioselectivity to 44% e.e. and 42% e.e., respectively, and then PhCF\n \n 3\n \n was chosen as an optimum solvent (entry 15)\n \n 33\n \n . When the reaction temperature was lowered from 40\u00b0C to 0\u00b0C, the enantiomeric excess increased from 42\u201351% (entry 17). We were pleased to observe that quantitative yield was obtained even with 1.0 mol % catalyst loading (entry 19). When\n \n 2a\n \n (1.5 equiv) was used, the yield was reduced to 79% (entry 21).\n
\n\n Based on these results, the substrate scope of\n \n o\n \n -carboranes was next investigated (Fig.\n \n 2\n \n ). When unsubstituted, silyl- or benzyl-disubstituted\n \n o\n \n -carboranes (\n \n 1a-1d\n \n ) were treated with methyl 2-diazo-2-phenylacetate (\n \n 2a\n \n ), the yields of the desired products (\n \n 3aa-3da\n \n ) were all quantitative, but the selectivity was affected by the substituents of\n \n o\n \n -carborane. We found that 1,2-(TMS)\n \n 2\n \n -\n \n o\n \n -C\n \n 2\n \n B\n \n 10\n \n H\n \n 10\n \n (\n \n 1b\n \n ; TMS\u2009=\u2009trimethylsilyl) gave rise to\n \n 3ba\n \n in high site- and enantioselectivity (22:1 r.r. and 55% e.e.). After close examination of diazo substrate scope in the reaction with\n \n 1b\n \n , it is disclosed that substituents on aryl and ester group play an important role in enantioselectivity (\n \n 3bb-3bo\n \n ). As a result, 2,2,2-trichloroethyl 2-(4-bromophenyl)-2-diazoacetate (\n \n 2d\n \n ) underwent the B\u2009\u2212\u2009H insertion reaction to produce the desired product\n \n 3bd\n \n in 96% yield with high site- and enantioselectivity (25:1 r.r. and 99% e.e.). This result indicates that trichloroethyl (TCE) group is very effective to the B\u2009\u2212\u2009H insertion\n \n 34\n \n . When there was no substituent on the aryl group, the desired\n \n 3be\n \n was obtained in 98% yield with 25:1 r.r. and 89% e.e., suggesting that\n \n para\n \n -substituents on aryl group are essential for excellent enantioselectivity. Then, we evaluated various electron-withdrawing groups on\n \n para\n \n -position of the aryl ring. TCE aryl diazoacetates possessing chloro, iodo, trifluoromethyl, ketone, and ester groups provided the corresponding B\u2009\u2212\u2009H insertion products (\n \n 3bf\n \n -\n \n 3bj\n \n ) in high yields ranging from 89\u201399% with excellent site- and enantioselectivity (up to >\u200950:1 r.r. and 99% e.e.).\n \n p\n \n -Nitro-substituted diazoacetate (\n \n 2k\n \n ) was reacted with\n \n 1b\n \n at 40\u00b0C, resulting in the formation of the desired product (\n \n 3bk\n \n ) in 69% yield with >\u200950:1 r.r. and 93% e.e.. In addition, a variety of electron-donating groups such as methyl,\n \n tert\n \n -butyl, phenyl, and methoxy group on\n \n para\n \n -position were tolerable, affording the desired carboranes (\n \n 3bl\n \n -\n \n 3bo\n \n ) in high yields with site- and enantioselectivities. Diazo compounds possessing bromo, methyl, and methoxy groups on\n \n meta\n \n -position (\n \n 2p\n \n -\n \n 2r\n \n ) and fluoro, bromo, and methyl groups on\n \n ortho\n \n -position (\n \n 2s\n \n -\n \n 2u\n \n ) gave the corresponding products (\n \n 3bp\n \n -\n \n 3bu\n \n ) in good to excellent site-selectivities, enantioselectivities (up to 97% e.e.), and yields (up to 99%). TCE aryl diazoacetates that possess 3,4-dichlorophenyl, 3,5-dimethylphenyl, and 2-naphthyl groups were also compatible with the present reaction conditions (\n \n 3bv\n \n -\n \n 3bx\n \n ). TCE heteroaryl diazoacetates including thiophene and pyridine (\n \n 2y\n \n and\n \n 2z\n \n ) successfully applied to the present reaction. The enantiomeric excesses of\n \n 3bk\n \n ,\n \n 3bq\n \n ,\n \n 3bt\n \n , and\n \n 3by\n \n were determined after desilylation because of the difficulty in separation of enantiomers.\n
\n\n Encouraged by these results, a wide range of carboranes were investigated in the reaction with 2,2,2-trichloroethyl 2-(4-bromophenyl)-2-diazoacetate (\n \n 2d\n \n ) to verify if the excellent site- and enantiooselectivity would be maintained. When\n \n o\n \n -C\n \n 2\n \n B\n \n 10\n \n H\n \n 12\n \n , 1,2-dibenzyl- and 1,2-dimethyl-\n \n o\n \n -C\n \n 2\n \n B\n \n 10\n \n H\n \n 10\n \n (\n \n 1c\n \n -\n \n 1e\n \n ) were treated with\n \n 2d\n \n , the desired products (\n \n 3cd\n \n -\n \n 3ed\n \n ) were obtained in high yield with 99% e.e.. However, these substrates exhibited inferior site-selectivity (4.9:1\u2009~\u20096.3:1 r.r.), suggesting that silyl groups on the cage carbon of the carborane play a critical role. To demonstrate the versatility of these Rh-catalyzed cage B\u2009\u2212\u2009H insertion reactions, we examined whether the substrates possessing substituent on the cage boron could be employed. It is noteworthy that both\n \n 1f\n \n and\n \n 1g\n \n were smoothly converted to the desired products (\n \n 3fd\n \n and\n \n 3gd\n \n ) in 89% and 97% yields, respectively, without any regioisomers. Although 3,6-diphenyl-\n \n o\n \n -C\n \n 2\n \n B\n \n 10\n \n H\n \n 10\n \n (\n \n 1f\n \n ) showed 35% enantioselectivity, 1-methyl-7,11-diphenyl-\n \n o\n \n -C\n \n 2\n \n B\n \n 10\n \n H\n \n 9\n \n (\n \n 1g\n \n ) exhibited excellent enantioselectivity (99% e.e.). The structures of (\n \n R\n \n )-\n \n 3bd\n \n and (\n \n R\n \n )-\n \n 3gd\n \n were confirmed by X-ray crystallography (see Supplementary Information). Crystal structure of (\n \n R\n \n )-\n \n 3gd\n \n was obtained after transformation of ester to carboxylic acid because of difficulty in crystal formation.\n
\n\n Next, we applied the present method to\n \n m\n \n - and\n \n p\n \n -carboranes (Fig.\n \n 2\n \n ). Gratifyingly, the carbenes on phthalimido Rh catalyst smoothly underwent B\u2009\u2212\u2009H insertion reactions with\n \n m\n \n - and\n \n p\n \n -carboranes. When\n \n m\n \n -C\n \n 2\n \n B\n \n 10\n \n H\n \n 12\n \n (\n \n 1h\n \n ) was treated with\n \n 2d\n \n under optimum reaction conditions, the corresponding product\n \n 3hd\n \n was obtained in 76% yield with excellent enantioselectivity (99% e.e.). 1,7-(TMS)\n \n 2\n \n -\n \n m\n \n -C\n \n 2\n \n B\n \n 10\n \n H\n \n 10\n \n (\n \n 1i\n \n ) was transformed to the desired product (\n \n 3id\n \n ) in 92% yield with 95% e.e. with 3.0 equivalents of\n \n 2d\n \n .\n \n p\n \n -Carborane (\n \n 1j\n \n ) having equivalent ten B\u2009\u2212\u2009H bonds can react with two or more carbenes to give multialkylated products. To suppress repetitive B\u2009\u2212\u2009H insertion reaction, steric influence of the rhodium catalyst was enhanced, and it was revealed that Rh\n \n 2\n \n (\n \n S\n \n -TPPTTL)\n \n 4\n \n is suitable for mono-selective B\u2009\u2212\u2009H insertion reactions of\n \n p\n \n -carboranes, affording\n \n 3jb\n \n -\n \n 3jd\n \n in good yield with high enantioselectivity (up to 97% e.e.). The structures of (\n \n R\n \n )-\n \n 3hd\n \n and (\n \n R\n \n )-\n \n 3jd\n \n were confirmed by X-ray crystallography (see Supplementary Information).\n
\n\n To prove the practicability of the present catalytic procedure, the B\u2009\u2212\u2009H insertion reaction of\n \n o\n \n -carboranes was examined on a large scale using 1.01 g (3.50 mmol) of\n \n 1b\n \n . After completion of Rh-catalyzed B\u2009\u2212\u2009H insertion reaction, the one-pot desilylation reaction was successfully carried out, leading to the desilylated products\n \n 5\n \n and\n \n 6\n \n in high yields (85% and 91%, each) with excellent enantioselectivity (99% e.e.).\n
\n\n Circular dichroism (CD) spectra of\n \n (\n \n \n R\n \n \n )-3bd\n \n and\n \n (\n \n \n S\n \n \n )-3bd\n \n obtained with Rh\n \n 2\n \n (\n \n S\n \n -TCPTTL)\n \n 4\n \n and Rh\n \n 2\n \n (\n \n R\n \n -TCPTTL)\n \n 4\n \n catalyst exhibited unambiguously mirror images to each other, indicating a pair of enantiomers (Fig.\n \n 3\n \n a). Furthermore, the absolute configuration of\n \n (\n \n \n R\n \n \n )-3bd\n \n and\n \n (\n \n \n S\n \n \n )-3bd\n \n was confirmed by X-ray crystallography.\n
\n\n To examine the reactivity of carboranes with rhodium carbenoids, competition experiments were conducted with various carbenophiles using 1.0 equivalent of\n \n 2d\n \n under the optimum reaction conditions (Fig.\n \n 3\n \n b). First, we initiated competition experiment with 1,4-cyclohexadiene (1,4-CHD) that rapidly undergoes allylic C\u2009\u2212\u2009H insertion reactions with rhodium carbenoids\n \n 35\n \n . As a result,\n \n 7\n \n was obtained in 70% yield without the formation of\n \n 3bd\n \n , suggesting that reactivity of 1,4-CHD is strong compared to\n \n 1b\n \n . Next, competition reaction of\n \n 1b\n \n with dioxolane furnished\n \n 3bd\n \n (21%) and\n \n 8\n \n (47%). This result implies that reactivity of dioxolane has slightly better than that of\n \n 1b\n \n . Finally, since\n \n 3bd\n \n was only produced from competition experiment of\n \n 1b\n \n and tetrahydrofuran (THF), relative reactivity order of these carbenophiles could be listed as follows 1,4-CHD\u2009>\u2009>\u2009dioxolane\u2009>\u2009TMS-carborane (\n \n 1b\n \n )\u2009>\u2009>\u2009THF.\n
\n\n To explore the application of these reactions, further functionalization of\n \n 5\n \n and\n \n 6\n \n was attempted. When\n \n 5\n \n was treated with DIBAL-H, the corresponding alcohol\n \n 10\n \n was obtained in 75% yield without erosion of the stereochemical fidelity. Trichloroethyl ester was successfully transformed to carboxylic acid\n \n 11\n \n in 89% yield using zinc and acetic acid also with no erosion of enantiomeric excess. Enantiomeric excess of\n \n 6\n \n was slightly deteriorated under coupling reaction conditions. As a result of Sonogashira and Buchwald-Hartwig cross-coupling reactions with\n \n 6\n \n , desired internal alkyne\n \n 12\n \n and diaryl amine\n \n 13\n \n were produced in 86% (89% e.e.) and 68% yields (86% e.e.), respectively.\n
\n\n A deuterium labeling experiment revealed that B\u2009\u2212\u2009H insertion reaction occurs through concerted mechanism because the deuterium atom substituted at B(\n \n 9\n \n )-position of\n \n o\n \n -carborane\n \n 1b-[D]\n \n \n \n n\n \n \n was transferred to the\n \n \u03b1\n \n -carbon adjacent to cage boron of the product\n \n 3bd-[D\n \n \n \n n\n \n \n \n ]\n \n without a change in the H/D ratio. When\n \n 1b-[D]\n \n \n \n n\n \n \n or\n \n 1b\n \n was treated with H\n \n 2\n \n O or D\n \n 2\n \n O under the optimum reaction conditions, deuterium scrambling was not observed at all (Fig.\n \n 4\n \n a).\n
\n\n In addition to the experimental data, computations were also conducted to understand the site- and enantioselectivity of this dirhodium-catalyzed carbenoid B\u2013H insertion reaction into icosahedral cage\n \n o\n \n -carborane using density functional theory (DFT). The applicability of DFT for studying dirhodium-catalyzed reactions and C\u2013H bond insertions have been explored by others\n \n 36\u201346\n \n . It is noteworthy that despite significant efforts by multiple research groups, these pioneering efforts reveal the enormous challenges and complexities involved with computing transition structures of large and conformationally flexible systems. Most computational studies of dirhodium carbenoid insertion processes have been rationalizations from ground state structures. To date, there are only two computed transition state studies involving the full dirhodium-catalyzed carbenoid insertion for C\u2013H bonds, and none for B\u2013H bond insertions using carboranes. Houk, Davies, and co-workers reported an enantioselective functionalization of a non-activated primary C\u2013H bond using an alkyl substrate, but this study involved a relatively conformationally rigid catalyst and a judicious choice of QM/MM methodology to deal with the cost of computing such large structures\n \n 40\n \n . Tantillo and co-workers reported\n \n ab initio\n \n molecular dynamics simulations to rationalize the origins of selectivity in a C\u2013H functionalization involving an intramolecular 1,4-shift\n \n 46\n \n . Herein, we are pleased to report the first fully quantum mechanically computed transition structures of a chiral dirhodium-catalyzed carbenoid B\u2013H insertion reaction of carboranes involving the complete experimentally used ligands and substrates with no structural simplifications. Our DFT results reproduce experimentally observed site- and enantioselectivity. In addition, we reveal a tool to visualize and highlight the structurally subtle, but energetically critical, steric close contacts in a topographical view to elucidate the specific functional groups and moieties. All computations and structures presented in this paper were performed at the PBE-D3BJ level of theory in conjunction with the LANL2DZ(Rh, Br, Cl) & 6-31G* (for all other atoms) basis sets as implemented in Gaussian 16. CPCM(C\n \n 6\n \n H\n \n 6\n \n ) solvation corrections were also used at 0\u00b0C. Single point energy refinements were performed at the PBE-D3BJ level of theory with the Ahlrich def2-TZVP basis set (see Supplementary Information for details).\n
\n\n The proposed catalytic cycle for the synthesis of product (\n \n R\n \n )-\n \n 3bd\n \n begins with the decomposition of the diazo compound\n \n 2d\n \n by the dirhodium catalyst Rh\n \n 2\n \n (\n \n S\n \n -TCPTTL)\n \n 4\n \n to afford the dirhodium carbenoid intermediate\n \n I\n \n (\u2206G\u2009=\u20090.0 kcal/mol) with the release of molecular nitrogen gas (Fig.\n \n 4\n \n b). The highly reactive dirhodium carbenoid\n \n I\n \n undergoes B\u2009\u2212\u2009H insertion with the incoming\n \n o\n \n -carborane\n \n 1b\n \n , forming the major three-member transition state (TS) (\n \n II-TS\n \n \n \n (\n \n \n R\n \n \n )\u2212B(9)\n \n \n , \u2206G\n \n \u2021\n \n = 6.92 kcal/mol), which gives the site selective at B(\n \n 9\n \n )-position and enantioselective preference (\n \n R\n \n )-enantiomer at the exohedral carbon-stereocenter, which is a carbon-sterocenter adjacent to cage boron of the carborane. This major\n \n II-TS\n \n \n \n (\n \n \n R\n \n \n )\u2212B(9)\n \n \n leads to the following ground state product complex\n \n III\n \n (\u2206G = \u2212\u200937.5 kcal/mol) wherein the desired product is embedded in the dirhodium catalyst pocket. A second diazo compound\n \n 2d\n \n releases the major product\n \n (\n \n \n R\n \n \n )-3bd\n \n (\u2206G = \u2212\u200951.1 kcal/mol), as well as molecular nitrogen gas, resulting in regeneration of the dirhodium carbenoid\n \n I\n \n for the next catalytic cycle. The complete reaction coordinate diagram for this proposed mechanism and the energies are shown in the Supplementary Information (Fig. S8).\n
\n\n Previous reports by Houk and Davies hypothesized that the helical arrangement of the phthalimide ligands of the chiral dirhodium catalyst observed in the ground state as important in determining the selectivities of the C\u2009\u2212\u2009H insertion process in their studies. The conformational complexity and substantial molecular size of this chiral dirhodium-catalyzed carbenoid B\u2013H insertion of carboranes that were challenging to DFT compute also posed significant difficulties to discover and explain where the origins of selectivity arose within the large transition structure complexes. To address these challenges, we employed a Topographical Proximity Surfaces (TPS) visualization to analyze the steric repulsions that exist in the TSs (Fig.\n \n 4\n \n c). First, taking the DFT optimized\n \n II-TS\n \n , the electron isodensity surface of the dirhodium carbenoid complex\n \n I\n \n was rendered. Then the surface was color-coded to reflect the close steric contact between it and TMS groups of the\n \n o\n \n -carborane\n \n 1b\n \n . In this manner, the intensity of the color represents the severity of steric interactions. Hence, this TPS approach can reveal close steric contacts in large, complex transition structures and aid in the rationalization of reaction selectivities.\n
\n\n In the favored major (\n \n R\n \n )-B(\n \n 9\n \n )-insertion TS (\n \n II-TS\n \n \n \n (\n \n \n R\n \n \n )\u2212B(9)\n \n \n , \u2206G\n \n \u2021\n \n = 6.92 kcal/mol), the dirhodium carbenoid insertion occurs at B(\n \n 9\n \n ) position of\n \n o\n \n -carborane\n \n 1b\n \n to give the (\n \n R\n \n )-configuration product at the exohedral carbon-stereocenter adjacent to cage boron of the carborane. The unfavored epimeric dirhodium carbenoid insertion results in the minor (\n \n S\n \n )-B(\n \n 9\n \n )-insertion TS (\n \n II-TS\n \n \n \n (\n \n \n S\n \n \n )\u2212B(9)\n \n \n , \u2206G\n \n \u2021\n \n = 8.96 kcal/mol, i.e. stereoselectivity of 2.04 kcal/mol), and the unfavored regioisomeric insertion results in the minor (\n \n R\n \n )-B(\n \n 8\n \n )-insertion TS (\n \n II-TS\n \n \n \n (\n \n \n R\n \n \n )\u2212B(8)\n \n \n , \u2206G\n \n \u2021\n \n = 9.00 kcal/mol, i.e. site-selectivity of 2.08 kcal/mol). These DFT results agree with the experimental site- and enantioselectivity of 2.00 kcal/mol and 2.87 kcal/mol, respectively. The TPS visualization of the major (\n \n R\n \n )-B(\n \n 9\n \n )-insertion TS (\n \n II-TS\n \n \n \n (\n \n \n R\n \n \n )\u2212B(9)\n \n \n ) reveals a comparatively diminished steric repulsion between TMS groups of the\n \n o\n \n -carborane\n \n 1b\n \n and the dirhodium carbenoid complex\n \n I\n \n (Fig.\n \n 4\n \n c). This is a result of the\n \n o\n \n -carborane angle and positioning of the TMS groups into the phthalimide ligand cavity (movie S1). In contrast, the TPSs of the epimeric (\n \n S\n \n )-B(\n \n 9\n \n )-insertion TS (\n \n II-TS\n \n \n \n (\n \n \n S\n \n \n )\u2212B(9)\n \n \n ) and the regioisomeric (\n \n R\n \n )-B(\n \n 8\n \n )-insertion TS (\n \n II-TS\n \n \n \n (\n \n \n R\n \n \n )\u2212B(8)\n \n \n ) both show greater steric repulsion of the TMS groups against the phthalimide ligands of the dirhodium carbenoid complex\n \n I\n \n . In the former, in order to achieve the epimeric insertion of the minor (\n \n S\n \n )-configuration product, it necessitates the angle and positioning of the\n \n o\n \n -carborane such that the TMS groups clash into the wall of the phthalimide cavity (\n \n II-TS\n \n \n \n (\n \n \n S\n \n \n )\u2212B(9)\n \n \n , movie S2). Similarly, in the regioisomeric (\n \n R\n \n )-B(\n \n 8\n \n )-insertion TS (\n \n II-TS\n \n \n \n (\n \n \n R\n \n \n )\u2212B(8)\n \n \n ), the rotation of the\n \n o\n \n -carborane to achieve insertion at the B(\n \n 8\n \n ) position not only results in steric interactions between the TMS groups with the phthalimide ligands, but also with the aryl substituent of the carbene substrate itself (movie S3). These results visually reveal the extent and severity of steric interactions that govern the preference for the favored B\u2013H insertion process by this large and conformationally flexible dirhodium catalyst, Rh\n \n 2\n \n (\n \n S\n \n -TCPTTL)\n \n 4\n \n .\n
\n\n In summary, effective site- and enantioselective B\u2009\u2212\u2009H insertion reactions have been developed for the first time from the reaction of donor/acceptor carbenes into cage B\u2009\u2212\u2009H bond of carboranes with chiral rhodium(II) catalyst. This selective B\u2013H functionalization thereby constructs\n \n o\n \n -,\n \n m\n \n -, and\n \n p\n \n -carboranes possessing exohedral carbon-stereocenter, which is adjacent to cage boron of the carborane in excellent site-selectivity (>\u200950:1 r.r.) and enantioselectivity (99% e.e.) in high yields with broad substrate scope under mild reaction conditions. We also report the first fully quantum mechanically computed transition structures of the B\u2013H insertion process of carboranes involving the complete large and conformationally flexible chiral dirhodium catalyst carbenoids. Gratifyingly, the computed site-selectivity and enantioselectivity (2.08 kcal/mol and 2.04 kcal/mol, respectively) were in good agreement with the experiments (2.00 kcal/mol and 2.87 kcal/mol, respectively). Furthermore, we reveal a tool to visually highlight the structurally subtle, but energetically critical, distribution of the close steric contact in a topographical fashion. This clearly shows the overall topographical shape created by the large and flexible dirhodium catalyst to which the substrate must bind to undergo reaction. This tool may play a significant role in future computational studies involving large catalyst systems. Ultimately, we discovered that the chiral dirhodium carbenoid is capable of this unique and impressive site- and enantioselectivity on an icosahedral cage substrate because the favored (\n \n R\n \n )-B(\n \n 9\n \n )-insertion TS is able to angle the di-TMS substituted\n \n o\n \n -carborane into the phthalimide ligand cavity with minimal steric repulsion. This work opens a new way for true site selective transformations of icosahedral complexes and enantioselective functionalization, affording exohedral chirality through the formation of a single, new B\u2013C bond involved in a concerted B\u2013H insertion.\n
\n\n Site- and enantioselective B\u2212H functionalization of carboranes\n
\n \n\n Animation of transition states\n
\n \n\n CheckCif.zip\n
\n \n\n Supplementary Information\n
\n \n\n Butyrophilin (BTN)-3A and BTN2A1 molecules control TCR-mediated activation of human V\u03b39V\u03b42 T-cells triggered by phosphoantigens (PAg) from microbes and tumors, but the molecular rules governing antigen sensing are unknown. Here we establish three mechanistic principles of PAg-action. Firstly, in humans, following PAg binding to the BTN3A1-B30.2 domain, V\u03b39V\u03b42 TCR triggering involves the V-domain of BTN3A2/BTN3A3. Moreover, PAg/B30.2 interaction, and the critical \u03b3\u03b4-T-cell-activating V-domain, localize to different molecules. Secondly, this distinct topology as well as intracellular trafficking and conformation of BTN3A heteromers or ancestral-like BTN3A homomers are controlled by molecular interactions of the BTN3 juxtamembrane region. Finally, the ability of PAg not simply to bind BTN3A-B30.2, but to promote its subsequent interaction with the BTN2A1-B30.2 domain, is essential for T-cell activation. Defining these determinants of cooperation and division of labor in BTN proteins deepens understanding of PAg sensing and elucidates a mode of action potentially applicable to other BTN/BTNL family members.\n
\n\n \n \n Immunology\n \n \n
\n\n \n Butyrophilin\n \n
\n\n \n phosphoantigens\n \n
\n\n \n BTN3A1\n \n
\n\n \n BTN2A1\n \n
\n\n \n V\u03b39V\u03b42 T cells\n \n
\n\n \n \u03b3\u03b4 T cells\n \n
\n\n \n TCR\n \n
\n\n \n juxtamembrane\n \n
\n\n V\u03b39V\u03b42 T cells comprise 1\u20135% of human peripheral blood T cells. They are massively expanded in some infections and exert multiple effector functions such as perforin-mediated cell lysis, help for other immune cells and peptide antigen-presentation. These functions are instrumental in the control of infection and tumors. Consequently, they have become the subject of an increasing number of preclinical and clinical studies\n \n \n 1\n \n \u2013\n \n 3\n \n \n
\n\n V\u03b39V\u03b42 TCRs contain a semi-invariant \u03b3 chain with a V\u03b39JP (alternatively termed V\u03b32J\u03b31.2) rearrangement and highly diverse V\u03b42-bearing \u03b4 chains\n \n \n 4\n \n \n and are activated by diphosphorylated isoprenoid metabolites (phosphoantigens, or PAgs) such as host-derived isopentenyl diphosphate (IPP) and microbially derived (\n \n E\n \n )-4-hydroxy-3-methyl-but-2-enyl diphosphate (HMBPP). In some tumors and infected cells, IPP levels reach a level sufficient to activate V\u03b39V\u03b42 T cells\n \n \n 5\n \n \u2013\n \n 8\n \n \n . This activation can also be achieved pharmacologically by aminobisphosphonates (e.g. zoledronate), which inhibit the IPP-catabolizing farnesyl disphosphate synthase\n \n \n 5\n \n ,\n \n 9\n \n \n or by farnesyl diphosphate synthase specific inhibitory RNA\n \n \n 10\n \n \n . HMBPP is the immediate precursor of IPP in the non-mevalonate pathway of IPP synthesis in many eubacteria, in apicomplexan parasites such as\n \n Plasmodium spp.\n \n , and in chloroplasts. PAg-activity of HMBPP is several orders of magnitude higher than that of IPP\n \n \n 11\n \n ,\n \n 12\n \n \n .\n
\n\n PAg-mediated activation of V\u03b39V\u03b42 T cells requires expression of butyrophilin 2A1 (BTN2A1)\n \n \n 13\n \n ,\n \n 14\n \n \n and butyrophilin 3A1 (BTN3A1)\n \n \n 15\n \n \n by the stimulator or target cell. Both molecules are single membrane-spanning type I proteins composed of a B7-like extracellular region comprising an N-terminal IgV-like (V) and a membrane-proximal IgC-like (C) domain, a transmembrane domain, and a cytoplasmic region comprising a juxtamembrane (JM) region and a B30.2 domain\n \n \n 16\n \n ,\n \n 17\n \n \n . BTN2A1 binds with its V-domain to germ-line encoded regions in the CDR2 and HV4 regions of the V\u03b39-domain of the TCR\u03b3 chain\n \n \n 13\n \n ,\n \n 14\n \n \n and to the V domain of BTN3A1. The BTN3A1-B30.2 domain binds to PAg\n \n \n 18\n \n 19\n \n . Furthermore, we and others showed that the binding of PAg to the B30.2 domain of BTN3A1 induces binding of the latter to the B30.2 domain of BTN2A1\n \n 20 21\n \n , a process in which the JM regions of both molecules play a pivotal role. How these events finally translate into TCR-mediated V\u03b39V\u03b42 T cell activation is not yet understood\n \n \n 22\n \n \n but evidence suggests that multiple CDRs of both the TCR-\u03b3 and -\u03b4 chains are involved as evidenced by site-directed mutagenesis\n \n \n 23\n \n \n and demonstration of interdependence of CDR3s from both chains in PAg-reactivity\n \n \n 24\n \n \n .\n
\n\n \n BTN3A\n \n genes emerged with placental mammals but became defunct in many species, including mice and rats, similar to the co-evolving homologs of human V\u03b39 (\n \n TRGV9\n \n ) and V\u03b42 (\n \n TRDV2\n \n ) TCR genes\n \n \n 25\n \n \n . The human\n \n BTN3A\n \n gene family comprises\n \n BTN3A1, BTN3A2\n \n , and\n \n BTN3A3\n \n and was generated by gene duplication events during primate evolution\n \n \n 26\n \n ,\n \n 27\n \n \n . The gene products are expressed by most cell types including \u03b1\u03b2 and \u03b3\u03b4 T cells. The PAg-binding site of BTN3A1 is a highly conserved positively charged pocket formed by 6 amino acids of the intracellular B30.2 domain\n \n \n 18\n \n ,\n \n 28\n \n \n . Upon PAg-binding, this domain and the adjacent JM region undergo conformational changes\n \n 19,29\u221231\n \n mandatory for mediating PAg-induced activation of V\u03b39\u03b42 T cells.\n
\n\n Since their emergence in primates, BTN3A family members have diversified structurally and most likely functionally. Relative to BTN3A1, BTN3A2 lacks the entire B30.2 domain and parts of the JM region, while BTN3A3 bears an H381R substitution which abrogates PAg-binding to the pocket (numbering of amino acids as in Supplementary Fig.\u00a01a)\n \n \n 18\n \n \n . The amino acid sequence identity of C-domains of the human BTN3As is about 90%, while the V domains of BTN3A1 and BTN3A2 are identical and that of BTN3A3 differs by a single conservative substitution (K66R) (Supplementary Fig.\u00a01a)\n \n \n 22\n \n \n .\n
\n\n The contribution of BTN3A2 and BTN3A3 to PAg-mediated activation has been reported based on BTN3A family member knockdown studies in HeLa cells\n \n \n 32\n \n \n and BTN3A knockout of 293T cells and various other cell lines\n \n \n 33\n \n \u2013\n \n 35\n \n \n ; consistent with this, we have observed superior PAg responses when BTN3A1 was re-expressed in BTN3A1KO (\n \n BTN3A1\n \n gene inactivated) cells than in BTN3KO cells in which all three\n \n BTN3A\n \n genes are inactivated\n \n \n 34\n \n \n , suggesting that BTN3A1 needs the support of other BTN3A members. Moreover, association between BTN3A1 and BTN3A2, which occurs via their membrane-proximal IgC-like domains, was previously analysed, and retention motif-dependent ER sequestration of BTN3A1 was shown to be rescued by coexpression of BTN3A1 with BTN3A2 and resulting BTN3A1-3A2 heteromer formation\n \n \n 33\n \n \n . Nevertheless, how this relates to increased or altered PAg sensing functionality remains unclear. Furthermore, the exchange of the JM of BTN3A1 for that of BTN3A3 increases this activation\n \n \n 36\n \n \n . Nevertheless, how the BTN3A3JM contributes for enhanced function remains unknown.\n
\n\n In order to define minimal requirements of the different BTN3A molecules for PAg-induced activation of V\u03b39V\u03b42 T cells, we expressed combinations of wild-type and mutated BTN3A molecules in BTN3A-deficient 293T (BTN3KO) cells and demonstrated that the functional features of various BTN3A molecules can be merged in \u201csuper-BTN3\u201d molecule, similar to a hypothesized primordial BTN3A present in species that encode single BTN3A isoforms such as Alpaca\n \n \n 28\n \n ,\n \n 34\n \n ,\n \n 37\n \n \n . We describe the BTN3A molecules as complexes in which for optimal function a division of labor takes place, whereby PAg-sensing is initiated by the B30.2 domain of one BTN3A chain and requires an intact IgV domain present within the paired BTN3A chain of each dimer. Our results show that the BTN3 JM region controls both trafficking and conformation of homomeric and heteromeric BTN3A complexes. In these complexes, the PAg-bound state is accompanied by binding of the BTN3A1-B30.2-PAg complex to the B30.2 domain of BTN2A1. These results not only clarify the molecular mechanism underlying PAg-mediated activation of V\u03b39V\u03b42 T cells but also have implications for \u03b3\u03b4 T cell activation by butyrophilin-related molecules such as BTNL or SKINT family members\n \n \n 38\n \n \n .\n
\n\n At first, we validated the necessity of all three isoforms for an optimal PAg response by testing inactivation of different BTN3A genes in 293T cells (\u2013 Fig.\n \n 1\n \n a - d)\n \n \n 33\n \n ,\n \n 34\n \n \n .To this end, we employed the murine reporter TCR-transductant MOP 53/4 r/mCD28 cell line (TCR-MOP), which shows no cross or self-presentation as is observed for human \u03b3\u03b4 T cells\n \n \n 15\n \n ,\n \n 24\n \n ,\n \n 39\n \n \n . The stimulation of the reporter TCR transductants is abrogated by BTN3A1 deficiency alone, or by knockout of both BTN3A2 and BTN3A3, and strong reduction of stimulation was observed for BTN3A2- than for BTN3A3-deficiency. A similar outcome was observed with primary human V\u03b39V\u03b42 T cells as responders, except that the loss of BTN3A3 alone was not as impactful as seen with TCR transductants. We also demonstrated the cooperation of BTN3A isoforms by transduction with 3A1 alone or in combination with 3A2, 3A3 or 3A2 plus 3A3 in 293T cells with all three BTN3A genes inactivated (BTN3KO cell line or 3KO). Additionally, 3KO cells that expressed 3A2 or 3A3 in the absence of 3A1 did not result in activation. Subsequently, all the experiments were performed in the 293T BTN3KO (3KO)\n \n \n 34\n \n \n background and recombinant BTN3A derivatives were designated as 3A. A schematic overview of the constructs used in the study is provided in Fig.\n \n 1\n \n i.\n
\n\n
\n\n Binding of BTN3A-V to the V\u03b39V\u03b42 TCR has been claimed\n \n \n 40\n \n \n but could not be confirmed by surface plasmon resonance\n \n \n 18\n \n \n , isothermal titration calorimetry\n \n \n 18\n \n \n or by staining of BTN3A1 transductants with V\u03b39V\u03b42 TCR-tetramers\n \n \n 13\n \n \n . To test the function of the human BTN3A family member V-domains, we generated recombinant BTN3A V-domain deletion mutants (V\u0394) in which V domains were replaced by a FLAG-sequence preceded by a BTN3A1 leader sequence. If not explicitly stated, 293T BTN3KO cells (3KO)\n \n \n 34\n \n \n were used as recipients for gene transduction. V\u03943A1 or V\u03943A2 were transduced alone or together with 3A1, 3A1mC, 3A2 or 3A3 (a schematic overview of the constructs is provided in Fig.\n \n 1\n \n i). V\u03943A3 was not tested since expression in 3KO cells failed. A sequence alignment of BTN3A molecules with relevant domains and regions marked is shown in Supplementary Fig.\u00a01a. The transductants were sorted for similar BTN3A expression with the V-specific 103.2 mAb (Supplementary Fig.\u00a01d and e) and stained for total expression (intracellular\u2009+\u2009surface expression of permeabilized and fixed cells) and surface expression (live cells) of the FLAG tag (Fig.\n \n 1\n \n e and g). Flow cytometry revealed that the V\u03943A1 transductant displayed no surface staining of the FLAG-tag unless a heterologous 3A-molecule was co-expressed (3A2 or 3A3 but not 3A1), and this result was confirmed with confocal microscopy (Supplementary Fig.\u00a01f). Cell surface FLAG-staining of V\u03943A2 also required co-transduction of intact 3A-molecules. In this case, the reconstitution of FLAG-epitope surface expression by homologous 3A2 was weak but efficient for the heterologous 3A1 and 3A3. In conclusion, lack of the V-domain disrupts the BTN3A trafficking to cell surface and staining of such V\u0394-domain constructs (FLAG-V\u03943A) required co-expression of appropriate full-length BTN3A molecules.\n
\n\n Next, we tested for HMBPP-induced stimulation of the MOP TCR-transductant cell line\n \n \n 15\n \n ,\n \n 24\n \n ,\n \n 39\n \n \n . 3KO cells transduced with V\u03943A1 and 3A2, or V\u03943A1 and 3A3 stimulated better than wild-type 293T cells, while cells co-expressing V\u03943A2 and 3A1 stimulated even worse than cells expressing only 3A1 (Fig.\n \n 1\n \n f and h). This reduced efficacy was not an effect of the FLAG-tag (Supplementary Fig.\u00a01c). Notably, protein domains contained in the complexes of V\u03943A1 and 3A2, or V\u03943A2 and 3A1, are identical (Fig.\n \n 1\n \n a and Fig.\n \n 3\n \n g), indicating that functional differences of the complexes result from the different localization of domains within the complexes, as will be discussed later.\n
\n\n
\n\n A major difference when comparing BTN3A1 relative to both BTN3A2 and BTN3A3 is their JM region (Supplementary Fig.\u00a01a). To address its role in BTN3A isoform interaction and function, FLAG-V\u03943A1 was coexpressed with HA-tagged 3A1 or 3A1 containing the JM of 3A3 (3A1_A3JM). In cells with similar total levels (intracellular and cell surface) of FLAG-V\u03943A1, its surface expression was detected by flow cytometry only when co-transduced with 3A1_A3JM but not native 3A1 (Fig.\n \n 2\n \n a). This finding suggests that the BTN3A1 JM region might hinder formation of fully functional BTN3A complexes while the heterologous BTN3A3 JM region may support such complexes. The ratio of cell surface to total expression was also considerably higher for HA-3A1_A3JM compared to wild-type HA-3A1 (Fig.\n \n 2\n \n a). This demonstrates the capacity of 3A3JM to alter the pattern of cellular distribution of 3A1_A3JM as well as the associated FLAG-V\u03943A1. Similar observations were made using confocal microscopic examination of immuno-stained live 3KO cells expressing FLAG-V\u03943A1 and HA-3A1 or HA-3A1_A3JM (Fig.\n \n 2\n \n b). Immuno-staining with anti-FLAG antibody detected the FLAG-V\u03943A1 (red) at the cell surface under live conditions only when co-transduced with HA-3A1_A3JM (right) but not with HA-3A1 (center). Furthermore, HA-3A1 or HA-3A1_A3JM (blue) proteins were clearly detected at the cell surface by anti-HA antibody, validating the presence of full-length proteins at the cell surface. Under fixed-permeabilized conditions (right hand panels) FLAG-V\u22063A1 was detectable at the cell surface only if colocalizing with HA-3A1_A3JM (violet, right). In contrast to live conditions, clear colocalization of FLAG-V\u22063A1 and HA-3A1 was observed in cytoplasmic vesicles. Notably, when FLAG-V\u22063A1 was coexpressed with HA-3A1_A3JM, HA-tag was detected largely at the membrane, with hardly any detectable in cytoplasmic vesicles. Similar observations were made with FLAG-V\u22063A1-CFP coexpressed with 3A1-YFP or 3A1_A3JM-YFP (Supplementary Fig.\u00a02b). Finally, microscopic examination of these cells revealed the altered trafficking of 3A1_A3JM attributed to 3A3JM.\n
\n\n
\n\n We performed immunoprecipitations (IP) using the cells mentioned above to extend our findings to biochemical interactions. Cell lysates were subjected to anti-FLAG IP and subsequent anti-HA western blot (Fig.\n \n 2\n \n c). In line with the colocalization of FLAG-V\u22063A1 with HA-3A1 under fixed-permeabilized conditions and at the cell surface for FLAG-V\u22063A1 with HA-3A1_3A3M, IP demonstrated potential interactions between FLAG-V\u22063A1 with HA-3A1 or HA-3A1_A3JM but did not show any differences in the quantities of co-precipitated HA-proteins. The differential size of HA-3A1_A3JM and HA-3A1 in the immunoblot coincided with their differential localization and trafficking.\n
\n\n Although V\u22063A1 association was observed with both HA-3A1 and HA-3A1_A3JM constructs in IP, the differential surface expression of V\u22063A1 led us to postulate that the resulting heteromeric 3A complexes adopted different conformations. FRET analysis was used to test the interaction between fluorescent fusion proteins and to infer the conformation or mode of association between 3A-molecules within homomers or heteromers. For FRET assays, 3KO co-transductants of FLAG-V\u22063A1-CFP or FLAG-3A1-CFP and 3A1-YFP or 3A1_A3JM-YFP were generated (Fig.\n \n 2\n \n d). FRET ratio was measured as stipulated in the methods section and acquired images are presented as ratiometric images (Fig.\n \n 2\n \n f).\n
\n\n The setup was optimized with 3KO single transductants of FLAG-3A1-CFP, and 3A1-YFP/3A1_A3JM-YFP constructs; the intensity 480/30 and 535/40 filters were similar with CFP constructs, and no image was visualized with YFP constructs as YFP was not excited by a 440nM CoolLED (Supplementary Fig.\u00a02c).\n
\n\n The full-length 3A1-CFP/V\u22063A1-CFP coexpressed with 3A1-YFP displayed no FRET (Fig.\n \n 2\n \n f, left panel) and yielded images with similar intensities with both the filters, suggesting no interaction between CFP and YFP either on the cell membrane or in the cytoplasmic compartments (Supplementary Fig.\u00a02c). However, 3A1-CFP co-expressed with 3A1_A3JM-YFP revealed high FRET predominantly at the membrane (Fig.\n \n 2\n \n f, upper right), and with the increased intensity with the 530nM-filter (Supplementary Fig.\u00a02c).\n
\n\n Even stronger FRET was observed at the membrane when FLAG-V\u22063A1-CFP was co-expressed with 3A1_A3JM-YFP (Fig.\n \n 2\n \n f, lower right). This was consistent with observations from immune staining and confocal microscopy (Fig.\n \n 2\n \n b and Supplementary Fig.\u00a02b), where 3A1_A3JM was overwhelmingly detected at the cell membrane but not in cytoplasmic organelles, and in spite of the predominant cellular retention of the V\u22063A1 protein, detectable levels of FLAG-tagged protein managed to reach the cell membrane when cotransduced with 3A1_A3JM.\n
\n\n Collectively, these data suggest that expression of FLAG-V\u22063A1-CFP or 3A1-CFP with 3A1-YFP led to3A-complexes where B30.2 domains are distantly spaced. On the contrary, co-expression of FLAG-V\u22063A1-CFP or 3A1-CFP with 3A1_A3JM suggests the formation of heteromers in which their respective B30.2 domains are in FRET-able distance as predicted (Fig.\n \n 2\n \n e). We hypothesized that an equivalent type of association occurs for the intracellular domains of V\u22063A1 or 3A1 when co-expressed with 3A2 but could not address this using the same methodology due to the different lengths of the intracellular domains and consequently of the adjacent fluorophores, which would confound FRET efficiency.\n
\n\n Alpaca-like species demonstrating single BTN3-dependent PAg responses led us to postulate a single BTN3 molecule as a primordial requirement, and it was of interest to generate such a BTN3 protein, which encompasses requisite domains for the PAg-dependent response. To this end, 3KO cells transduced with mCherry (mC) fused to 3A1 (3KO_3A1mC), 3A3 gain of function mutant R381H (3KO_3A3-R381H-mC), 3A1 with the JM of BTN3A3 (3KO_3A1_A3JM-mC) and finally with a gain of function 3A3 mutant possessing JM of 3A1 (3A3_A1JM_R381H-mC) were analyzed (Fig.\n \n 3\n \n a-d). In the functional assay (Fig.\n \n 2\n \n a), cells expressing a 3A-proteins with a functional PAg sensing B30.2 domain and the 3A3 JM region were indistinguishable from 293T cells, whereas cells co-expressing 3A1-mC and 3A3_A1JM_R381H-mC that possess the 3A1 JM region were very poor stimulators, and as expected 3A3 expressing cells did not stimulate at all. Analysis of recombinant BTN3A protein distribution in these cells revealed that despite a similar degree of mCherry fusion protein expression (Fig.\n \n 3\n \n b) the cells exhibited pronounced differences in intracellular localization and in the formation of mCherry aggregates (Fig.\n \n 3\n \n c). In all cases, cells expressing 3A-molecules bearing exclusively 3A1JM displayed a higher degree of intracellular retention of fluorescent complexes than their 3A3JM expressing counterparts, which displayed enhanced expression at the plasma membrane (Fig.\n \n 3\n \n c). Finally, we tested the effects of the aminobisphosphonate pamidronate, and the agonistic mAb 20.1, on the cell surface immobility of 3A-molecules by FRAP (Fluorescence Recovery after Photobleaching)\n \n \n 15\n \n \n . Constructs with a 3A1JM displayed no increased immobilization whereas those with a 3A3JM did (Fig.\n \n 3\n \n d). Notably, medium controls of the cells expressing the 3A3JM-containing constructs also displayed a higher degree of immobilization than that of the transductants with 3A1JM-containing constructs (3A1mC and 3A3-A1JM-R381H-mC), which is consistent with the reported higher background stimulation for activation of short term V\u03b39V\u03b42 T cell lines by 293T transfected with 3A1_A3JM\n \n \n 36\n \n \n or 3A3_A1_B30.2 and 3A3_R381H\n \n \n 18\n \n \n . Likewise, cells expressing 3A1-mC plus 3A2-3A3 (Supplementary Fig.\u00a03a) behaved analogously to cells expressing the 3A3 JM-containing constructs in terms of intracellular trafficking and aggregate formation. Furthermore, native gel electrophoresis of solubilized membrane extracts revealed very large 3A1-mC complexes when prepared with detergent Brij 96 and Triton X100 (Supplementary Fig.\u00a03b). In contrast, membranes solubilized with digitonin, which binds to cholesterol, massively reduced the size of 3A1mC molecular complexes. In the presence of 3A2 and 3A3 these complexes were dissociated into two complexes of less than 440 kDa apparent MW\n \n \n 18\n \n \n . Altogether the 3A3-JM-containing constructs can substitute for \u201chelp\u201d for 3A1 JM by 3A2 or 3A3 in terms of stimulation capacity, cellular trafficking of 3A proteins, and formation of molecular clusters.\n
\n\n So far, we showed that functional impairment of 3A-heteromer formation coincides with reduced stimulatory capacity. Surprisingly, V\u03943A1\u2009+\u20093A2 and V\u03943A2\u2009+\u20093A1 complexes stimulated quite differently, although the surface expression of each heteromer was similar (Fig.\n \n 1\n \n f-h). Moreover, as depicted in Fig.\n \n 3\n \n g, both complexes possess sequence-identical protein domains and differ only in the relative arrangement of the V domains. In one case the IgV domain is located on the PAg-binding protein (3A1), in the other on the pairing chain (3A2). This feature relates back to a previous report on V-domain mutants (K136A) affecting PAg-mediated stimulation\n \n \n 41\n \n \n where heteromers of 3A2_K136A and 3A1 lost stimulatory potential while heteromers of 3A1_K136A and 3A2 did not. To test whether similar effects were also observed for a homomeric \u201csuper\u201d BTN3A\u201d (3A3_R381H), a mutant with a substitution at position 136 was generated (3A3_R381H_K136A-mC). 3A3_R381H_K136A-mC was co-expressed with one of two different PAg-binding-insufficient BTN3A-IRES-GFP reporter constructs (3A3 (GFP) or 3A1_H381R(GFP)) (Fig.\n \n 3\n \n e). Stimulation was successfully detected with both the co-transductants where the PAg-binding site and wild type V-domain were located on different molecules (Fig.\n \n 3\n \n f-g), which is consistent with the differential stimulatory capacity of V\u03943A1\u2009+\u20093A2 vs V\u03943A2\u2009+\u20093A1 transduced cells. Altogether, these results suggest PAg-binds to one BTN3A molecule that via the JM region is connected to a paired BTN3A molecule whose intact V-domain is essential for PAg sensing mediated via the V\u03b39V\u03b42 TCR.\n
\n\n To probe the differential impact of the JM region on BTN3A function, we compared the sequence of BTN3A1JM to that of other BTN3A molecules (Supplementary Fig.\u00a01a and Fig.\n \n 4\n \n a). We noted that the JM of BTN3A1 contains a positively charged lysine-triplet (KKK) (position 283\u2013285) while BTN3A2, BTN3A3, and alpaca BTN3A possess two negatively charged glutamic acid residues (ExE) at this position (Fig.\n \n 4\n \n a). Moreover, the substitution of the BTN3A3 ETE motif by KKK (3A3-KKK) abolished the rescue of surface expression of FLAG-V\u22063A1 and reduced the stimulatory activity to that of 3A3-R381H-KKK-mC (Fig.\n \n 4\n \n b and c). This suggested that this triplet motif is essential for the JM-mediated interaction of 3A1 and 3A3 molecules. Interestingly, replacement of KKK of BTN3A1 by ETE (3A1_ETE) did not rescue FLAG-V\u22063A1 cell surface expression and did not change the stimulatory capacity of the 3A1, suggesting other regions of the JM may also be involved in controlling cooperation and trafficking of associated 3A-molecules (Supplementary Fig.\u00a04a and b).\n
\n\n
\n\n To probe the molecular basis of these effects, we carried out molecular modeling of the coiled-coil region of the BTN3A isoforms. We restricted these efforts to the 273\u2013312 region that was previously strongly predicted to form a coiled-coil domain by mediating BTN3A dimer interactions\n \n \n 42\n \n \n , within which the BTN3A1 KKK \u2018triplet region\u2019 is located (283\u2013285), and employed a parametric \u03b1-helical coiled coil prediction methodology (CCBuilder 2.0)\n \n \n 43\n \n \n .\n
\n\n These efforts first highlighted the potential of human BTN3A1, BTN3A2, BTN3A3, and also the single alpaca isoform VpBTN3, to each form biophysically plausible homodimers via intermolecular coiled-coil interactions, stabilized in each case by numerous polar and non-polar interactions at the inter-helical molecular interface. Of note, these models predicted inter-helical interactions mediated by the 283\u2013285 triplet residues that could partly account for differential stability and conformation (Fig.\n \n 4\n \n d), and therefore surface expression and functionality (Fig.\n \n 4\n \n b-c). In the BTN3A3 homodimer, E283 and T284 were predicted to form stabilizing hydrogen-bonding interactions to equivalent residues of the opposing helix, with the involvement of R288 from each monomer; in contrast E285 was solvent exposed and not involved in interhelical contacts (Fig.\n \n 4\n \n d I). In BTN3A2, I284 was the sole mediator of interhelical triplet region interactions comprised of non-polar interface contacts with the corresponding residue of the opposing helix (Fig.\n \n 4\n \n d II); unlike BTN3A3, E283 and E285 were solvent exposed and uninvolved in intermolecular contacts. While biophysically feasible, the relative stability of this arrangement was unclear. Nevertheless, it is consistent with the weaker surface expression of V\u22063A2 when coexpressed with 3A2 compared to that of coexpression with 3A1 and 3A3. Similar to human BTN3A3, modelling of the single alpaca-encoded \u2018superagonist\u2019 isoform, VpBTN3, indicated involvement at the inter-helical interface of E283 and K284, which mediated reciprocal salt bridge interactions with the same pair of residues from the opposing monomer (Fig.\n \n 4\n \n d III). Notably, for the BTN3A1 model the indicated \u2018KKK\u2019 at 283\u2013285 region was arranged differently, with 284 and 285 positioned at the inter-helical interface and 283 solvent exposed and uninvolved (Fig.\n \n 4\n \n d IV). Most importantly, this model predicted the positively charged K284 and K285 were directly facing the same residues from the opposing monomer at the interface (Fig.\n \n 4\n \n d IV). This arrangement is likely to be energetically highly unfavorable and destabilize the BTN3A1 homodimer via electrostatic repulsion; moreover, consistent with results from FRET analyses (Fig.\n \n 2\n \n ), it may favor a weaker inter-molecular association. Therefore, while biophysically feasible, BTN3A1 modeling highlights the KKK motif of BTN3A1 is likely to disfavor homodimer formation in a way that is not predicted to occur with other isoforms.\n
\n\n Modelling approaches also shed light on heteromeric interactions. BTN3A1/3A2 (Fig.\n \n 4\n \n d V) and BTN3A1/3A3 (Fig.\n \n 4\n \n d VI) coiled-coil models highlighted not only a loss of the interhelical electrostatic repulsion evident from the 283\u2013285 region of BTN3A1 homodimers (Fig.\n \n 4\n \n d IV), but also predicted a favorable salt-bridge interaction from K285 of 3A1 to E283 of BTN3A2/3A3. This was consistent with more stable coiled-coil heterodimers relative to the BTN3A1 homodimer, including a potential for closer intermolecular association between the two BTN3A chains in this context, consistent with the results of the FRET analyses. Of note, modelling of BTN3A3 mutated to incorporate the KKK motif of BTN3A1 at 283\u2013285 (Fig.\n \n 4\n \n d VII) indicated that close opposition of K283 and K284 to identical residues across the inter-helical interface. Although this differed from the predicted native BTN3A1 dimer interface, where K284 and K285 are localized to the dimer interface, it was nevertheless likely to substantially destabilize the BTN3A3-KKK dimer and was entirely consistent with the pronounced deleterious effect of the BTN3A1 JM region (Figs.\n \n 1\n \n and\n \n 3\n \n ) and KKK motif (Fig.\n \n 4\n \n ) on both surface expression, conformation, and functionality.\n
\n\n Finally, inspection of the models strongly indicated extra-triplet effects contribute to differential homodimer and heterodimer stability (Supplementary Fig.\u00a04, Supplementary Material). In particular, the 276\u2013278 region appeared particularly significant (Supplementary Fig.\u00a04c I-VI), as it was predicted to form stabilizing non-polar (BTN3A2 homodimers) (Supplementary Fig.\u00a04c II), or salt bridge interactions (BTN3A3 homodimer, alpaca BTN3 homodimer, BTN3A1/A2 heterodimer, BTN3A1/A3 heterodimer) (Supplementary Fig.\u00a04c III-VI), whereas in BTN3A1 the presence of K277 and K278 introduced electrostatic repulsion at the dimer interface (Supplementary Fig.\u00a04c I). Moreover, the intermolecular packing interactions mediated by L280 in all other isoforms were lost in BTN3A1 homodimers (Supplementary Fig.\u00a04c VII-X), in which the polar residue (Q) at this position was predicted to be solvent-exposed (Supplementary Fig.\u00a04c VII). In summary, interhelical interactions outside of the 283\u2013285 region clearly also preferentially destabilize BTN3A1 homomers relative to both BTN3A2/3 homomers, and also relative to heteromers involving BTN3A1 and BTN3A2/A3. This provides a molecular explanation for the observation that introduction of the 283\u2013285 ETE sequence of BTN3A3 into 3A1 is insufficient to confer substantially increased expression and functionality (Supplementary Fig.\u00a04a-b).\n
\n\n We next compared HMBPP and 4-M-HMBPP, a HMBPP derivative incorporating a bulky head group that permits HMBPP-like binding to the BTN3A1-B30.2 domain with reduced stimulatory capacity that has been suggested to result from an \u201caberrant\u201d BTN3A1-B30.2 homodimer\n \n \n 44\n \n \n . We previously demonstrated that the intracellular domains of BTN2A1 and BTN3A1 interact, but only in the presence of a potent PAg such as HMBPP\n \n \n 20\n \n \n . Here we examined the ability of 4-M-HMBPP to support this interaction. We confirmed a robust binding interaction between 4-M-HMBPP and the BTN3A1 full intracellular domain (BFI) (Fig.\n \n 5\n \n b), albeit with a somewhat lower binding affinity of 2.9 \u00b5M that may result from different 3A1 constructs or compound purities. Next, we titrated BTN2A1 intracellular domain (ID271) into 3A1 BFI. In agreement with our prior study, no interaction was observed in the absence of PAg (Fig.\n \n 5\n \n c) while in the presence of HMBPP, a strong interaction was observed (K\n \n D\n \n , 0.8 \u00b5M) (Fig.\n \n 5\n \n d) which coincides with the finding reported in a recent preprint by the Zhang group\n \n \n 21\n \n \n . However, in the presence of 4-M-HMBPP, no binding occurred between BTN2A1 ID271 and BTN3A1 BFI (Fig.\n \n 5\n \n e) as shown in Table\n \n 1\n \n . Therefore, we can conclude that while 4-M-HMBPP binds to BTN3A1, yet it does not allow it to engage subsequently with BTN2A1. Together, binding of PAg to BTN3A1 in the BTN3A heteromer allows it to interact with BTN2A1 homodimer to promote T cell activation.\n
\n\n
\n\n This study addresses the contribution of BTN3A protein domains and their binding partners to PAg-induced V\u03b39V\u03b42 T cell activation. Firstly, it demonstrates a crucial role for the V-domain for cell surface expression of BTN3A molecules. Secondly, the impaired trafficking of BTN3 lacking its membrane distal IgV-domain could be rescued by partnering preferentially BTN3 molecules possessing the equivalent domain. Thirdly, the functional contribution of the BTN3A membrane distal IgV domain to PAg stimulation can be compensated by the paired BTN3A molecule. Such compensation of loss of function BTN3A1-V constructs by residual levels of BTN3A2/BTN3A3 isoforms could explain the observation that BTN3A1 V-domain mutants expressed in BTN3A-knockdown 293T cells did not display any phenotype\n \n \n 43\n \n \n . It may also explain why a human V\u03b39V\u03b42 TCR transductant (TCR-MOP) that does not react to HMBPP-pulsed 3KO cells transduced with an alpaca BTN3(V-C)-human intracellular domain chimera but gains responsiveness when the same construct was transduced into BTN3A1KO cells, suggesting that chimera comprising heteromers involving V domains of endogenous BTN3A2 and/or BTN3A3 may engage with the human TCR or permit its ligation by an associated ligand\n \n \n 34\n \n ,\n \n 37\n \n \n .\n
\n\n BTN3A2 as well as BTN3A3 reconstituted surface expression of V\u22063A1 and the resulting complexes permitted PAg-induced V\u03b39V\u03b42 TCR-mediated activation as efficiently as naturally occurring BTN3A heteromers or \u201csuper\u201d BTN3As. In striking contrast simultaneous expression of V\u22063A2 with BTN3A1, despite rescuing V\u22063A2 cell surface expression, failed to increase BTN3A1 mediated stimulation. Since the protein domains of surface-expressed 3A1-V\u22063A2 complexes and of 3A2-V\u22063A1 are identical we conclude that localization of the V-domain within the complex is crucial for HMBPP-mediated stimulation. Such a topological effect could also explain the differential stimulation by 3KO cells co-expressing V-domain mutated BTN3A1 and wild-type BTN3A2\n \n versus\n \n cells expressing wild-type BTN3A1 and mutated BTN3A2\n \n 41\n \n and unpublished data from the Morita group (personal communication) coming to the same conclusion by testing the response of a \u03b3\u03b4 T cell clone to Zoledronate-pulsed BTN3A knock out cells expressing V- and C-domain mutants of BTN3A1 and BTN3A2. It is further supported by the HMBPP-induced stimulation by 3KO cells expressing homomer-like BTN3A3-derivatives consisting of 3A3 and V-domain mutated super BTN3 (3A3\u2009+\u20093A3_K136A_R381H) whose possible mechanistic basis will be discussed later.\n
\n\n Several aspects of the contribution of the JM of BTN3A to PAg stimulation were analyzed in previous studies. Firstly, PAg binding to the B30.2 domain was described and changes in the JM were found to be linked to PAg-induced stimulation\n \n \n 29\n \n ,\n \n 30\n \n \n . Vantourout and colleagues noted the importance of association of BTN3A1 and BTN3A2 molecules as well as the superiority of BTN3A1-BTN3A2 heteromers over BTN3A1 homomers in stimulation. Also identified were ER retention motifs in the JM of both molecules, which control intracellular trafficking and cell surface expression and are crucial for PAg-induced stimulation but could not explain the superiority of BTN3A heteromers over homomers\n \n \n 33\n \n \n . Finally, the Scotet group showed an increase in stimulation after replacing the JM of BTN3A1 with 3A3JM\n \n \n 36\n \n \n . Importantly, the current study can discriminate BTN3A complexes efficiently mediating PAg-stimulation from weak or non-stimulatory forms. It defines JM-controlled features: firstly, the rescue of surface expression of a paired V-deleted BTN3A molecule and secondly, in the case of BTN3A complexes, adaptation of a conformation that supports FRET between C-terminal fluorochromes. Notably, both cell surface rescue and efficient C-terminal FRET were not achieved for exclusively 3A1_JM containing molecules unless they were co-expressed with other BTN3A2 or BTN3A3 or 3A3_JM containing constructs. The high efficacy of heteromers that contain only a single PAg-binding site or in the case of BTN3A1-BTN3A2 dimers even only a single B30.2 domain over BTN3A1 homodimers is of special importance when discussing models postulating certain conformers of the extracellular domains (e.g. head to tail\n \n versus\n \n V-shaped dimers) or B30.2 domain dimers (symmetric\n \n versus\n \n asymmetric)\n \n 29,44\u221247\n \n as being crucial for PAg-induced activation. Intriguingly, rescue of surface expression of V\u0394BTN3A1 as an indicator for successful formation of BTN3A-complexes coincided very well with molecular modeling results on forces determining stabilization of coiled coil structures formed by JM \u03b1-helices, which are reduced for 3A1 JM and favor interaction between BTN3A3 JM or alpaca BTN3JM, and heteromeric BTN3A1 JM interactions with BTN3A2 or BTN3A3JM. The residual activation seen with (overexpressed) BTN3A1 or 3A1JM containing constructs (Fig.\n \n 1\n \n a-d and\n \n 3\n \n a) might result from a small number of molecules still adopting a suitable extracellular BTN3A1-BTN2A1 topology despite unfavorable JM association\n \n \n 29\n \n ,\n \n 33\n \n ,\n \n 34\n \n \n
\n\n Our phylogeny informed approach to assign functions to certain BTN3A-regions allowed the identification of the 3A3_R381H mutant and a 3A1_3A3JM chimera as \u201csuper\u201d BTN3A, merging the functions of heteromeric human BTN3A complexes in single, homomer-forming BTN3A molecules naturally occurring in the alpaca. The primordial BTN3A has been predicted to be a BTN3A3-like molecule with a functional PAg-binding site that emerged with placental mammals\n \n \n 34\n \n ,\n \n 48\n \n ,\n \n 49\n \n \n . This raises the question of what might have favored the evolution of BTN3A heteromers in primates\n \n \n 27\n \n \n despite the efficacy of BTN3A homomers as witnessed in alpaca\n \n \n 34\n \n \n . Duplication of functional genes directly allows acquisition of new features even if these might have negative effects on the original function. This appears the case in humans, whereby the partnering BTN3A2 and BTN3A3 even lost PAg-binding function, which is compensated by formation of new functional units via heteromerization with BTN3A1, thereby preserving the\n \n BTN3A-TRGV9-TRDV\n \n triad mandatory for PAg-sensing. One possibility is that devolving from a single BTN3A molecule a substantial element of control of intracellular trafficking and IgV-related functionality may enable local fine-tuning of the strength of PAg-sensing via regulation of BTN3A2 and BTN3A3 expression. It will also be of interest to determine whether BTN3A1-JM might contribute to V\u03b39V\u03b42 T cell independent features of BTN3A1, including ligation of CD45\n \n 50\n \n or control of induction of type I interferon production by cytosolic TLR ligands\n \n \n 51\n \n \n .\n
\n\n Furthermore, it would be interesting to determine whether functional fusion proteins of different BTN relatives can also be achieved for the naturally occurring heteromers of Btn1/Btnl6, BTNL3/BTNL8, and Skint1/Skint2. Of note, such a fusion product is a frequently occurring copy number variation of\n \n BTNL3\n \n and\n \n BTNL8\n \n , resulting in fusion of intracellular BTNL3 with the BTNL8 extracellular domain\n \n \n 52\n \n \n which would be expected not to bind V\u03b34-TCR\n \n \n 41\n \n \n . This experiment of nature will allow testing of the physiological significance of the crosstalk, or the lack of it, between BTN(L) molecules, and to resolve the importance of TCR-BTNL3/8 binding for intestinal V\u03b34 T-cell function, and gut homeostasis and pathophysiology\n \n \n 53\n \n \n . In addition, synthetic or natural \u201csuper\u201d BTN3As such as that of alpaca might also be utilized as probes in the search for other factors involved in PAg-mediated V\u03b39V\u03b42 T cell activation.\n
\n\n A fourth key finding from our study was that we confirmed that HMBPP-binding to the BTN3A1 B30.2 domain promotes binding to the intracellular B30.2 domain of BTN2A1, and is consistent with our prior study\n \n \n 20\n \n \n highlighting this interaction only occurs in the presence of a BTN3A1-B30.2-bound PAg such as HMBPP. Zhang group recently reported this interaction by size exclusion chromatography and an HMBPP coordinated complex consisting of an HMBPP-bound single BTN3A1-B30.2 domain and a dimer of BTN2A1 B30.2 domains\n \n \n 21\n \n \n . Notably, our ITC data are consistent with that model because we observe an n value near 1, which may be expected if a dimer of BTN2A1 is interacting with a monomeric PAg-ligand-bound form of BTN3A1-B30.2. The importance of PAg-induced interaction between BTN3A1-ID and-BTN2A1-ID for PAg-induced activation is also in line with that BTN3A1-B30.2 complexes with 4-M-HMBPP being a very poorly stimulatory analog of HMBPP\n \n \n 44\n \n \n , as it does not support this interaction.\n
\n\n Based on these findings we formulate the following working hypothesis as a model (Fig.\n \n 6\n \n ). PAg-binding to the BTN3A1-B30.2 domain renders the BTN3A1-HMBPP complex into a ligand for the BTN2A1 intracellular domain. The function of the BTN2A1-V domain would be to recruit the TCR by binding to the CDR2 and HV4 regions of the TCR\u03b3 chain, and that of BTN2A1 intracellular domain to recruit the HMBPP-bound BTN3A1-V. In the new complex, binding of TCR\u03b3 (CDR2 and HV4) chain to the C-F-G surface of BTN2A1-V domain would be retained, while other CDRs might additionally interact with the newly formed BTN2A1-BTN3A complex that is in line with the findings of Willcox research group. A direct interaction of the V\u03b39V\u03b42 TCR with V-domains of BTN2A1-BTN3A complexes would also be compatible with a most recent report that shows direct stimulation of V\u03b39V\u03b42 T cells by recombinant BTN3A1-BTN2A1 heteromers in the presence of a co-stimulus\n \n \n 54\n \n \n . However, it is yet to be proven whether BTN2A1 and BTN3A1 can form a functional heterodimer. In conclusion, our composite ligand model would allow inside-out signaling induced by conformational changes of the intracellular domains of BTN3A and BTN2A1 molecules without direct induction of conformational changes of their extracellular domains and predicts the formation of a new BTN2A1/BTN3A-TCR complex or BTN2A1/BTN3A plus hypothetical TCR-ligand - TCR complex in which both germline-encoded and somatically recombined CDRs of TCR chains are engaged. Such interactions are likely to surpass the requirements to initiate TCR signaling (Fig.\n \n 6\n \n ).\n
\n\n
\n\n The scenario discussed above is hypothetical and final clarification of the exact nature of the ligand recognized by the V\u03b39V\u03b42 TCR during PAg-activation has still to be elucidated. Nevertheless, the data we present and the molecular ground rules they formulate will be instrumental in guiding future studies to resolve this problem.\n
\n\n
\n| \n \n Titrant\n \n | \n \n \n Titrand\n \n | \n \n \n K\n \n D\n \n (\u00b5M)\n \n | \n \n \n n\n \n | \n \n \n \u0394H (kJ/mol)\n \n | \n \n \n \u0394S (J/mol*K)\n \n | \n
|---|---|---|---|---|---|
| \n \n BTN2A1 ID271\n \n | \n \n \n BTN3A1 BFI +\n \n\n HMBPP\n \n | \n \n \n 0.78\u2009\u00b1\u20090.28\n \n | \n \n \n 0.94\u2009\u00b1\u20090.08\n \n | \n \n \n -48.66\u2009\u00b1\u20092.11\n \n | \n \n \n -45.62\u2009\u00b1\u20097.54\n \n | \n
| \n \n BTN2A1\n \n\n ID271\n \n | \n \n \n BTN3A1 BFI\u2009+\u20094-M-HMBPP\n \n | \n \n \n 189.9\u2009\u00b1\u2009174.8\n \n | \n \n \n 0.08\u2009\u00b1\u20090.06\n \n | \n \n \n -100\u2009\u00b1\u20090\n \n | \n \n \n -260.4\u2009\u00b1\u20097.92\n \n | \n
| \n \n \n a\n \n The binding parameters are obtained by independent fit using NanoAnalyze. Dates represent the mean\u2009\u00b1\u2009SEM. (n\u2009=\u20093 independent experiments).\n \n | \n
\n
\n\n Contact for Reagents and Resource Sharing\n
\n\n For further information and requests for reagents please contact the lead author (\n \n [email\u00a0protected]\n \n )\n
\n\n Experimental models and cell lines\n
\n\n 53/4 hybridoma TCR transductants were cultured with RPMI (Gibco) supplemented with heat inactivated 10% FCS, 1 mM sodium pyruvate, 2.05 mM glutamine, 0.1 mM nonessential amino acids, 5 mM \u03b2-mercaptoethanol, penicillin (100 U/mL) and streptomycin (100 U/mL). Peripheral blood mononuclear cells were isolated from healthy volunteers. They were also maintained with the above-mentioned medium with or without rhIL-2 (Novartis Pharma). 293T cells were maintained in DMEM (Gibco) supplemented with 10% FCS.\n
\n\n Generation of 293T BTN3AKO cells\n
\n\n 293T BTN3KO (3KO) and BTN3A1KO (A1KO) cells used were mentioned in our previous study. The BTN3A2KO (A2KO), BTN3A3KO (A3KO) and BTN3A2 & BTN3A3KO (A2A3KO) cells were also generated as previously reported\n \n \n 34\n \n \n . The CRISPR sequences and the primers used for the validation of KO with genomic DNA are mentioned in the Supplementary Table\u00a01.\n
\n\n Generation of BTN3A, tagged BTN3A and BTN3A-fluorescent protein constructs\n
\n\n The full-length BTN3A1 and BTN3A1-mCherry fusion construct were generated as mentioned previously\n \n \n 34\n \n \n . The full-length BTN3A2 and BTN3A3 were subcloned from previously reported pIRES1hyg vectors\n \n \n 15\n \n \n . For the generation of pIH-FLAG, pIH vector\n \n \n 55\n \n \n was digested with EcoRI and BamHI. Sequentially, the insert with Mfe1 and BglII restriction sequences as 5` and 3\u00b4overhangs that comprises BTN3A1 leader sequence followed by FLAG sequence, linker sequence, and restriction sites for BamHI and EcoRI was digested with MfeI and BglII and cloned to EcoRI-BamHI digested pIH vector. This vector was further digested with BamHI and EcoRI and used to clone the desired BTN3A sequence from IgV to stop codon or IgC to stop (V\u22063A1 or V\u22063A2) sequence. pIZ-HA tagged BTN3A1 or BTN3A1_A3JM was generated with EcoRI and BamHI digested pIZ vector\n \n \n 55\n \n \n . Two PCR products with overlapping overhang sequences in which product 1, BTN3A1 leader sequence followed by HA tag and linker sequence (used above) and product 2, BTN3A1-IgV-domain till stop codon were cloned into above-digested pIZ vector using In-Fusion HD cloning (TAKARA) as per manufacturer\u2019s instruction. The BTN3A1_A3JM chimera was subcloned from below mentioned pCDNA 3.1 vector. The multiple cloning site sequences pIH-FLAG and pZ-HA are provided in Table S1. GeneArt gene synthesis (ThermoFischer Scientific) synthesized the full-length BTN3A_JM chimeras by swapping the nucleic acids encoding for the JM region (272\u2013340 amino acid\n \n \n 36\n \n \n between BTN3A1 and BTN3A3. The JM chimeras cloned in pCDNA 3.1 vector were provided by the manufacturer and JM chimeras were further subcloned into phNGFR linker mCherry vector. phNGFR linker mCherry was used as the backbone to generate phNGFR linker CFP and phNGFR linker YFP, to which FLAG-3A1 or FLAG-V\u22063A1 and BTN3A1 or BTN3A1_A3JM chimera was subcloned, respectively. NEB 5-alpha (NEB) was used as transformant of the above-mentioned plasmids. The plasmids cloned with wild type BTN3A proteins or mutant BTN3A were expressed in 293T 3KO via retroviral transduction\n \n \n 56\n \n \n . All the restriction enzymes were purchased from Thermo Fischer Scientific. All the plasmids and cloned corresponding constructs were mentioned in Supplementary Table\u00a02\n
\n\n In vitro stimulation of human V\u03b39V\u03b42 TCR transductants\n
\n\n 1*10\n \n 4\n \n 293T (DSMZ, ACC 635) or KO and their BTN3A transductants were seeded in 50 \u00b5L DMEM medium in 96 well flat-bottom tissue culture plate on day 1 and incubated overnight. On day 2, 50 \u00b5L of 53/4 r/mCD28 human V\u03b39V\u03b42 TCR transductants (MOP)\n \n \n 24\n \n \n at 1*10\n \n 6\n \n cells/mL density and 100 \u00b5L of HMBPP (SIGMA, 95058) at mentioned concentrations were added to the culture and incubated for 22 hours at 37\u00b0C. Post 22 hours, the activation of TCR reporter cells was measured by analyzing the supernatants of cocultures for mouse IL-2 via ELISA (Invitrogen, 88-7024-88) as per the manufacturer\u2019s protocol.\n
\n\n Expansion of primary polyclonal human V\u03b39V\u03b42 T cells\n
\n\n Fresh peripheral blood mononuclear cells (PBMCs) were obtained from healthy volunteers with informed consent according to the University of Wuerzburg institutional review board (Gz. 20220927 01). Tubes preloaded with Histopaque-1077 (SIGMA, 10711) were layered with whole blood and centrifuged at 400*g for 20 mins at room temperature with no acceleration or brakes. The opaque interface containing PBMCs was aspirated after centrifugation and was washed twice at 461*g for 5 mins. PBMCs were cultivated with RPMI containing heat inactivated 10% FCS, 100 IU/mL recombinant human IL-2 (Novartis Pharma) and 10 nM BrHBPP in 10\n \n 6\n \n cells/mL density in a 96 well plate round bottom plate. After 10 days, cells were pooled and washed twice, and cultured in a 6 well plate in 10\n \n 6\n \n cells/mL for 3 days without rhIL-2. Such rested cells were subjected to further experiments.\n
\n\n Human polyclonal V\u03b39V\u03b42 T cell activation assay\n
\n\n 293T cells at 2*10\n \n 4\n \n cells/100 \u00b5L (DMEM, 10% FCS) per well were cultured in triplicates in 96 well-plate flat bottom with or without 25 \u00b5M zoledronate (SIGMA) overnight. The next day, cells were washed twice with PBS, and V\u03b39V\u03b42 T cells expanded from PBMCs at 2*10\n \n 4\n \n cells/100 \u00b5L per well were added and cultured for 4 hours. After 4 hours, supernatants were frozen at -20\u00b0C until human INF\u03b3 assay ELISA (Invitrogen, EHIFNG) could be performed as per the manufacturer\u2019s instructions. For the CD107a assay, 293T cells were seeded as above-mentioned. V\u03b39V\u03b42 T cells expanded from PBMCs were also added as above-mentioned but along with anti-CD107a-PE (BD Pharmingen) conjugated antibody and cultured for 4 hours. After 4 hours, the cells were collected from the wells as triplicates and washed once with PBS. After which cells were treated with anti-human V\u03b42-FITC (Beckman Coulter) conjugated antibody for 20 mins and washed once, followed by analysis at FACSCalibur (BD) for the percentage of V\u03b42-FITC and CD107a-PE population.\n
\n\n Flow cytometry for surface and total expression of BTN3As\n
\n\n 293T and 3KO transductants of BTN3As (WT and Chimaeras) were acquired by FACScalibur (BD) and analyzed with FlowJo. For total staining, cells were fixed with fixation buffer for 30 mins at RT, followed by wash and incubated for 30 mins with permeabilization buffer at RT. Then cells were stained with antibodies that were prediluted in permeabilization for 30 mins at 4\u00b0C, as per the manufacturer\u2019s instructions (eBiosciences, eBiosciences\u2122 Intracellular Fixation & Permeabilization buffer set). For surface staining, cells were directly stained with antibodies of interest for 30 minutes at 4\u00b0C. The BTN3As were detected by unconjugated mAb 103.2 (gift from Daniel Olive). If tagged, unconjugated anti-FLAG (M2, SIGMA) and anti-HA (F-7, Santa Cruz) antibodies were used. The primary antibodies were detected by Fab Donkey anti mouse IgG (H\u2009+\u2009L)-APC (Jackson Immunoresearch, 115-136-146). mIgG1k and mIgG2a k (eBiosciences) were used as isotype controls.\n
\n\n Immunoprecipitation\n
\n\n 3*10\n \n 6\n \n cells of 3KO and BTN3A-transductants were seeded in a 10 cm tissue culture plate on day 1. On day 3, the cells were lysed with 400 \u00b5L of lysis buffer\n \n \n 33\n \n \n [(50 mM Tris\u00b7HCl at pH 7.4, 150 mM KCl, 10 mM MgCl\n \n 2\n \n , 1 mM CaCl\n \n 2\n \n , 0.5% Nonidet P-40, 0.1% digitonin, 5% glycerol, Complete Protease inhibitor(Roche)]. The lysate was rigorously vortexed for 15 mins at 4\u00b0C and was centrifuged at 14,000 rpm for 15 mins at 4\u00b0C. After centrifugation, 50 \u00b5L lysate was kept aside as input. The remaining lysate was incubated for 4 hours at 4\u00b0C with 50 \u00b5L of protein-G Sepharose\u2122 (GE, 1706180) beads complexed with anti-FLAG (M2 clone, SIGMA) and washed thrice with lysis buffer. Proteins were eluted with 80 \u00b5L of Laemmli and analyzed by SDS-PAGE and Western Blotting. The blots were treated with anti-Vinculin (SIGMA), anti-FLAG and anti-HA (CST) as primary antibodies overnight at 4\u00b0C. The following day, the blots were washed thrice and treated with protein-A-HRP (SIGMA) conjugate for an hour at RT and washed and developed with Pierce SuperSignal\u2122 West Femto Maximum Sensitivity Substrate (Thermo Fischer Scientific). The blots were visualized with LI-COR Odyssey imaging system.\n
\n\n Blue native gel electrophoresis\n
\n\n Blue native gel electrophoresis was performed as described in\n \n \n 57\n \n \n .\n
\n\n Immunofluorescent staining\n
\n\n 293T, 3KO and 3KO-BTN3A transductants were seeded in 5*10\n \n 4\n \n /200 \u00b5L in Ibidi 8 well \u00b5Slides on day 1. On day 2, for live-cell imaging, cells were washed twice with PBS and treated with anti-FLAG (M2) or anti-HA for 20 mins, followed by three washes and treated with anti-mouse AF648 (Invitrogen) or anti-Rabbit AF565 (Invitrogen) for 30 mins. After 30 mins, cells were washed thrice and visualized with confocal microscope Zeiss LSM 780 under 63x (NA 1.4) oil immersion lens with 514 and 633 lasers. Acquired images were further analyzed using ImageJ. For fixed cell imaging, the cells were fixed with 4% paraformaldehyde for 30 mins and either treated with 0.1% TritonX-100 for permeabilization or treated with anti-FLAG or anti-HA antibodies overnight. The following day, cells were washed and treated with anti-mouse AF648 or anti-rabbit AF565 for 1 hour and washed thrice before acquiring images under the microscope as above.\n
\n\n Fluorescence recovery after photobleaching\n
\n\n 293T and 3KO transduced with BTN3A1-mCherry fusion construct were seeded in Ibidi 8well \u00b5Slides at 5*10\n \n 4\n \n /200 \u00b5L per well on day 1. On day 2 cells were analyzed with confocal microscope Zeiss LSM 780 under a 63x (NA 1.4) oil immersion lens with a 560 laser. The rectangular regions were marked on the cells of interest, the marked regions were photobleached with 100% laser energy for 5 seconds (>\u200990% loss of fluorescence). Images were collected after every 5 seconds after photobleaching for 100 seconds. The percentage of the immobile fraction was derived from the below-mentioned formula\n
\n\n Mobile fraction F\n \n m\n \n = (I\n \n E\n \n - I\n \n 0\n \n ) / (I\n \n I\n \n - I\n \n 0\n \n ); Immobile fraction F\n \n i\n \n = 1 \u2013 Fm; where: I\n \n E\n \n : Endvalue of the recovered fluorescence intensity, I\n \n 0\n \n : first postbleach fluorescence intensity, I\n \n I\n \n : Initial (prebleach) fluorescence intensity.\n
\n\n Fluorescence resonance energy transfer\n
\n\n 3KO transduced with FLAG-BTN3A1-CFP or FLAG-V\u22063A1-CFP and BTN3A1-YFP or BTN3A1_A3JM YFP constructs were plated over the glass coverslips. Before imaging, cells were incubated in the imaging medium (144 mM NaCl, 5.4 mM KCl, 1 mM MgCl2, 1 mM CaCl2, 10 mM HEPES; pH\u2009=\u20097.4) and mounted on Leica DMI 3000 B microscope fitted with a 63x/1.40 objective. The cells were excited with CoolLED (440 nm) and the emission light was split into donor and acceptor channels using the DV2 QuadView (Photometrics) equipped with the 505dcxr dichroic mirror and D480/30m and D535/40m emission filters. When CFP and YFP are in FRETable distance, the emitted light detected by 535 filters (YFP) would be greater than 480 filters which can be presented as pseudo-colored ratio images with a reference FRET ratio (FR) chart.Images were acquired using CMOS camera (OptiMOS, QImaging) and MicroManager 1.4. software was used for data analysis\n \n \n 58\n \n ,\n \n 59\n \n \n .\n
\n\n Synthesis of 4-M-HMBPP\n
\n\n Binding of 4-hydroxy-3-(4-methylbenzyl)but-2-en-1-yl diphosphate (4-M-HMBPP) to BTN3A1 was previously described by Yang et al.\n \n \n 44\n \n \n but the synthetic route has not yet been reported. We adapted the method of Yang et al. (Yonghui Zhang, personal communication to TH) to obtain 4-M-HMBPP as detailed in the supplemental for use in these studies.\n
\n\n Isothermal titration calorimetry (ITC):\n
\n\n ITC was performed as described\n \n \n 20\n \n \n using a nanoITC (TA Instruments). The concentrations of the titrant and titrand are indicated in the figure legend.\n
\n\n Modelling BTN3 juxtamembrane coiled-coil dimers\n
\n\n Models of the juxtamembrane (JM) coiled-coil dimers were generated using the CCBuilder2 server (\n \n \n http://coiledcoils.chm.bris.ac.uk/ccbuilder2/builder\n \n \n \n \n )\n \n \n 43\n \n \n . Models were generated using default settings assuming a parallel homo/hetero dimeric structure, encompassing residues Q273\u2013L312 for human BTN3A1, BTN3A2, and BTN3A3 and alpaca BTN3A3. BTN3A1 was modelled with Q273 at the \u201cc\u201d position of the heptad repeat, whereas all other BTN3 molecules were modelled with Q273 at the \u201cd\u201d position. Models of human BTN3 proteins were further refined using the \u201cOptimize\u201d function of the CCBuilder2 program. JM coiled-coil dimer interface contacts were determined using the program NCONT as part of the CCP4 suite\n \n \n 60\n \n \n . Structural figures were generated using PyMol\n \n \n 61\n \n \n .\n
\n\n Statistics\n
\n\n Statistical analysis of stimulations was performed with GraphPad Prism using two-way ANOVA and statistical significance in terms of P-value adjusted as per GraphPad Prism tool are presented in asterisk (*) (**** <0.0001; *** <0.001; ** <0.01; * <0.05; ns\u2009>\u20090.05). Similarly, samples analyzed for FRAP were subjected to multiple t-tests and statistical significance was determined using the Bonferroni-Dunn method. The representation of statistical significance in P-value as asterisks (*) or non-significant (ns) as above was adjusted in GraphPad Prism.\n
\n\n Cities are drivers of the global economy, containing products and industries that emit many chemicals. We used the Multimedia Urban Model (MUM) to estimate atmospheric emissions and fate of organophosphate esters (OPEs) from 19 global \u201cmega or major cities,\u201d finding that they collectively emitted\u2009~\u200981,000 kg yr\n \n \u2212\u20091\n \n of \u2211\n \n 10\n \n OPEs in 2018. Typically, polar \"mobile\" compounds tend to partition to and be advected by water, while non-polar \"bioaccumulative\" chemicals do not. Depending on the built environment and climate of the city considered, the same compound behaved like either a \"mobile\" or a \"bioaccumulative\" chemical. Cities with large impervious surface areas, such as Kolkata, mobilized even \u201cbioaccumulative\u201d contaminants to aquatic ecosystems. By contrast, cities with large areas of vegetation fixed and transformed contaminants, reducing loadings to aquatic ecosystems. Our results therefore suggest that urban design choices could support policies aimed at reducing sources of emissions to reduce chemical releases to the broader environment without increasing exposure for urban residents.\n
\n\n Cities are hotspots of human dynamism, culture, and industry, containing more than half of the world\u2019s population and generating over 80% of global GDP.\n \n 1\n \n This concentration of people, products, and activities means that cities act as emissions sources for many chemicals, exposing urban residents, surrounding communities, and ecosystems to high levels of many chemical pollutants.\n \n 2\n \n Understanding the dynamics of chemicals emissions and fate in cities is therefore essential for reducing chemicals exposure, and helping us build \u201cSustainable Cities and Communities\u201d (United Nations Sustainable Development Goal 11).\n
\n\n The control of Persistent Organic Pollutants (POPs), for example through the Stockholm Convention,\n \n 3\n \n has focused on chemicals with persistent, bioaccumulative, and toxic (PBT) properties.\n \n 4\n \n More recent work has recognized that although persistent, mobile, and toxic (PMT) organic chemicals do not bioaccumulate, they also pose a hazard, as they are not easily removed from water through traditional sorptive treatment processes and are therefore able to contaminate surface, ground, and drinking water resources.\n \n 5,6\n \n By definition, a less bioaccumulative substance will be more hydrophilic and mobile in water. Regulations aimed at controlling the use and release of PBT substances are therefore much less effective for PMT substances.\n \n 5\n \n This can be one cause of \u201cregrettable substitution,\u201d whereby chemicals manufacturers respond to regulations around PBT substances by using chemicals that are less bioaccumulative, yet have PMT characteristics. One example of this phenomenon was the replacement of the flame retardant polybrominated diphenyl ethers (PBDEs) after their listing by the Stockholm Convention in 2009 and 2017.\n \n 7\n \n Organophosphate esters (OPEs) were used as drop-in replacements for PBDEs in many commercial products, including the more soluble chlorinated OPEs, some of which are PMT substances.\n \n 5,8\u201310\n \n OPEs have been found to undergo long-range transport, to be persistent in the environment, and to have serious health impacts on exposed populations, leading them to be called \u201cregrettable substitutes\u201d for PBDEs.\n \n 11\n \n
\n\n OPEs are ubiquitous contaminants found in cities across the world at high levels in urban air\n \n 12\u201314\n \n and water,\n \n 15,16\n \n with large (1\u20132 orders of magnitude) variations in air concentrations observed between cities.\n \n 12\n \n These large concentration differences arise from differences in both emissions and in the fate of the compounds within cities. Chemical emissions in cities come from a wide variety of sources. Point-source emissions originate from industrial and manufacturing processes, while diffuse emissions originate from OPEs used in products. Depending on the chemical and the location, either point-source or diffuse emissions can dominate.\n \n 17,18\n \n This wider variety of sources makes estimating OPE emissions difficult, with uncertainties that span orders of magnitude.\n \n 18\u201320\n \n In places with large manufacturing bases such as Beijing, China, a combination of emissions from OPE production and manufacturing may be responsible for the majority of emissions.\n \n 21\n \n In other areas where manufacturing plays a smaller role, such as Toronto, Canada, diffuse sources may dominate.\n \n 8\n \n
\n\n Urban environments tend to increase chemical mobility through water. Urbanization is typified by large areas of impervious surfaces, which reduces the ability of natural sorptive processes (such as infiltration through a riverbank) that would otherwise capture contaminants.\n \n 22\n \n The large area of impervious surfaces in urban environments accumulates an organic surface film\n \n 23\n \n that further enhances the transport of semi-volatile organic compounds (SVOCs) from the atmosphere to surface compartments.\n \n 2,24\n \n The films capture gas- and particle-phase SVOCs that are transferred by rainwater to soils and into urban waterways.\n \n 25\n \n Thus, cities are important starting points for the global long-range transport of chemicals through air and water.\n \n 8,26\n \n A changing climate is also affecting how chemicals move through the environment,\n \n 27,28\n \n by promoting more release to warmer air, more water-borne transport in locations experiencing greater precipitation, and more atmospheric transport in locations experiencing drought.\n
\n\n Despite the importance of cities as sources of many chemicals, differences in chemicals fate between urban environments have not been well-studied. Here, we address this gap by combining a unique dataset from the Global Atmospheric Passive Sampling (GAPS)-Megacities network\n \n 12\n \n with the Multimedia Urban Model (MUM)\n \n 8,24\n \n to investigate the emissions and fate of OPEs in 19 \u201cmega or major cities\u201d around the world. The goals of this study were: 1) Estimate the emissions of OPEs in the 19 GAPS-Megacities locations, 2) Investigate the sources of those emissions, 3) Investigate how built-environment, physicochemical properties, and climatic factors influence the fate of chemicals in different urban environments, and 4) Provide recommendations for policy or engineering solutions that could reduce chemicals emissions from cities.\n
\n\n We estimated aggregate air emissions by back-calculating the emissions required to maintain the reported air concentrations from the 19 cities under GAPS-Megacities study,\n \n 12\n \n using an instantiation of MUM parameterized for each city across the ~\u2009three-month sampler deployment period (Fig.\n \n 1\n \n ).\n
\n\n Overall, we estimated that the 19 cities in our study emitted 81,000 kg yr\n \n 1\n \n \u2211\n \n 10\n \n OPEs (Fig.\n \n 2\n \n ) to air in 2018. Estimated emissions varied by nearly 40-fold between cities. London had the largest emissions at ~\u200939,000 kg yr\n \n \u2212\u20091\n \n , followed by Bogot\u00e1 at ~\u200913,000 kg yr\n \n \u2212\u20091\n \n , while Sydney, Kolkata and Istanbul all had\u2009<\u2009100 kg yr\n \n \u2212\u20091\n \n of \u2211\n \n 10\n \n OPE emissions.\n
\n\n On a compound-specific basis (Table S2 contains the names and identifiers for all compounds modeled in this study), emissions of tris (1-chloro-2-propyl) phosphate (TCIPP) were the largest, at ~\u200953,000 kg yr\n \n \u2212\u20091\n \n , followed by TCEP, at ~\u200915,000. Together, these two compounds accounted for ~\u200985% of all estimated \u2211\n \n 10\n \n OPE emissions. In every city, either TCIPP or TCEP had the largest emissions, and combined they comprised 48\u201391% of emissions in each city.\n
\n\n Based on the comparisons presented here and the full MUM uncertainty analysis of Rodgers et al.\n \n 8\n \n , our emissions estimates have ~\u2009an order of magnitude uncertainty in either direction for each city. The 2018 \u2211\n \n 10\n \n OPEs predicted emissions were similar to previous estimates, which were available for Toronto and for Beijing on a provincial level. In Toronto, the 2018 emissions were ~\u200945% lower than the emissions predicted by Rodgers et al.\n \n 8\n \n for 2010 using the same model, with most of the difference caused by the lower air concentrations used here. In Beijing, our estimates for the municipal area were ~\u200950% lower on an area-normalized basis than the provincial estimates of He et al.\n \n 21\n \n for 2018. Their estimated air concentrations were close to those input here to back-calculate the emissions, meaning that the difference in emissions intensity was likely caused by different estimations of chemical fate within the modeled domain. Further, our predicted concentrations in media other than air were generally within a factor of 100 of published measurements in those same media (Extended Data Figure ED 1, SI Section S1), comparable to the accuracy of predictions of remote air concentrations made using the BETR-Global model for PBDEs\n \n 19\n \n and to the agreement between predicted and measured soil concentration across China for OPEs.\n \n 21\n \n
\n\n One of our central goals was to assess whether we could identify the sources or sectors that contribute to OPE emissions, and if we could use our results to develop proxies for OPE emissions in the absence of measured inventories. We therefore correlated the log\n \n 10\n \n -transformed emissions flux (log\n \n 10\n \n kg m\n \n \u2212\u20092\n \n yr\n \n \u2212\u20091\n \n ) with several proxies for emission sources (Figure ED 2, Table S3). For instance, we used gross domestic product (GDP, 2015\n \n $\n \n at purchasing power parity)\n \n 31\n \n and population\n \n 32\n \n to estimate broad-based emissions from in-use products, and we used sector-specific estimates of anthropogenic greenhouse gas emissions\n \n 33\n \n to estimate contributions from various industrial sectors.\n
\n\n Our results suggested that at a global scale, most OPE emissions originate from numerous complex, diffuse sources, rather than from specific manufacturing or production processes. The strongest single correlation was with \u2211GDP in the modeled area, which explained 36% of variation (measured by r\u00b2) for the log\n \n 10\n \n \u2211\n \n 10\n \n OPEs, driven by an r\u00b2 of 0.31 for TCEP and correlations for TnBP, TCIPP, TDCIPP, TPhP and TmCP that had regression probabilities\u2009<\u20090.05 (Figure ED 2). Most individual correlations between emissions and sector-specific proxies were weak (p\u2009<\u20090.05, Table S3). For TCIPP, which is used extensively in building insulation,\n \n 34,35\n \n diffuse emissions from building materials appeared to be a major source, with log\n \n 10\n \n emissions moderately correlated with the percentage of greenhouse gas emissions (% of CO\n \n 2\n \n equivalent kg m\n \n \u2212\u20092\n \n s\n \n \u2212\u20091\n \n ) from the \u201cenergy for buildings\u201d and the \u201csolvents and other products use\u201d (a broad-based measure of in-use products) categories (r\u00b2 = 0.28 and 0.35, respectively). SI Section S4.1 contains additional information on the correlations, including Table S3 with all regression statistics.\n
\n\n Our results showed that contaminant fate processes had a large impact on environmental concentrations, and therefore both the magnitude and the pathways for human and ecosystem exposures. Our sensitivity analysis (Figure ED 4, SI Section S2) indicated that there were three groups of parameters which collectively controlled contaminant fate in outdoor urban environments: those representing the built environment, physicochemical properties, and climate. We investigated the relationships between these groups of parameters by running the model for several scenarios across a \u201ccity-space\u201d which represented different cities by their \u201csparsity index\u201d and \u201cfilm-vegetation index\u201d. As described in the Methods, we built these two indices to represent three critical built-environment drivers of chemicals fate: the city\u2019s footprint (A\n \n city\n \n , m\u00b2), the area-factor of the vegetation compartment (AF\n \n veg,\n \n A\n \n V\n \n /A\n \n city\n \n , m\u00b2 m\n \n \u2212\u20092\n \n ), and the area-factor of the urban film compartment (AF\n \n film,\n \n A\n \n F\n \n /A\n \n city\n \n , m\u00b2 m\n \n \u2212\u20092\n \n ). We defined this \u201cSparsity Index\u201d (m\u00b2 m\n \n \u2212\n \n \u00b2) with Eq.\u00a0(\n \n 1\n \n ):\n
\n\n Where A\n \n j\n \n represents the area of compartment j in m\u00b2. The second index explored the nature of those surfaces. We defined this \u201cFilm-vegetation Index\u201d with Eq.\u00a0(\n \n 2\n \n ):\n
\n\n First, we looked at the influence of the built-environment alone by running our model using a synthetic \u201caverage\u201d city, with the model parameters (outside of those in the sparsity and film-vegetation indices) and the input concentrations for each of the OPEs set to their mean values across the 19 cities (Fig.\n \n 3\n \n A). Next, we looked at the influence of physicochemical properties by contrasting the fate of a polar PMT-like compound, TCEP, with a non-polar PBT-like compound, TPhP, across the same \u201caverage\u201d city-space and for scenarios exploring transformation half-lives. Finally, we probed the influence of climate by running the model across the city-space with the \u201caverage\u201d city climate replaced by composite \u201clow-deposition\u201d and \u201chigh-deposition\u201d climates for our PMT-like and our PBT-like compound.\n
\n\n Across our city-space diagram (Fig.\n \n 3\n \n A), cities with a high \u201csparsity index\u201d have fewer depositional surfaces, while cities with a low sparsity index have more surfaces. The \u201cfilm-vegetation index\u201d describes the nature of those depositional surfaces. For cities with a film-vegetation index\u2009>\u20090, the area of urban film is greater than the area of vegetation, and vice-versa. We note that TCIPP and TCEP therefore contributed most to total back-calculated OPE emissions in the \u201caverage\u201d city.\n
\n\n \u2211\n \n 10\n \n OPE fate varied substantially between three city archetypes: \u201cSparse,\u201d \u201cDensely vegetated,\u201d and \u201cDensely urbanized,\u201d represented by Cairo, Bogot\u00e1, and Kolkata, respectively (Fig.\n \n 3\n \n b, Figure ED 3 shows the \u2211\n \n 10\n \n OPE fate diagrams for all 19 cities and SI Figures S2-S11 show the fate diagrams for each compound across all 19 cities). In \u201csparse\u201d cities with fewer depositional surfaces (Fig.\n \n 3\n \n A, blue-shaded contours), such as Cairo (Fig.\n \n 3\n \n b), \u2211\n \n 10\n \n OPE fate was dominated by air advection from the city to its surrounding region. In our dataset, Cairo was the only city where the area of film and vegetation surfaces was lower than the area of the city\u2019s footprint, due to the large area of bare ground, and this led to ~\u200994% of the \u2211\n \n 10\n \n OPEs emissions remaining in the air compartment and either undergoing primary transformation or being blown down-wind.\n
\n\n In cities with many surfaces (low sparsity index) like Bogot\u00e1 (vegetation) and Kolkata (urban film), deposition played a much more significant role, with up to 93% of emitted chemicals deposited to surfaces within Bogot\u00e1\u2019s city limits. The fate of compounds deposited was then determined by the nature of the depositional surfaces. In \u201cdensely vegetated\u201d cities (Fig.\n \n 3\n \n A, green-shaded contours), represented by Bogot\u00e1 (Fig.\n \n 3\n \n b), deposition to and subsequent transformation in the vegetation compartment dominated chemical fate. Plants are able to take-up and metabolize some OPEs,\n \n 36\u201338\n \n so the vegetation compartment here acted to fix the compounds in place and transform them. In densely-vegetated Bogot\u00e1, 39% of overall OPE mass was transformed in the vegetation compartment, while 14% was predicted to be washed-off into the soil. Further, we predicted that 24% of overall atmospheric OPE emissions would either be buried in the soil or infiltrate into groundwater, highlighting an important risk with PMT chemicals.\n
\n\n In \u201cdensely urbanized\u201d cities (Fig.\n \n 3\n \n A, purple-shaded contours) with very high impervious surface coverage, like Kolkata (Fig.\n \n 3\n \n b), OPE fate was dominated by deposition to film followed by wash-off through stormwater and subsequent advection from the city to the surrounding aquatic ecosystem. Thus, water advection accounted for the fate of ~\u200944% of emissions, with 42% lost via wind advection. This is less than the >\u200956% water advection that would be predicted using the characteristics of the \u201caverage\u201d city, due in part to the physicochemical properties of the OPEs released in Kolkata and in part to its climate, as will be explained below.\n
\n\n We compared the fates of individual OPEs to assess the influence of physicochemical properties, using TCIPP and TPhP as chemicals with representative \u201cmobile\u201d and \u201cbioaccumulative\u201d behavior, respectively (Fig.\n \n 4\n \n a and b, SI Fig S2-S11 show the base-case fate of each compound). Low K\n \n OW\n \n , Soluble PMT-like compounds such as TCEP required fewer surfaces for deposition to dominate due to their higher solubilities leading to more atmospheric wash-out. Conversely, for the K\n \n OW\n \n , lower solubility PBT-like compounds, represented here by TPhP, less efficient scavenging from precipitation meant that more surfaces were required for atmospheric deposition to take place. Thus, air advection dominated across almost all cities, with water-advection being considerably less important than for the PMT-like compounds, as expected.\n
\n\n For OPEs and other compounds with shorter transformation half-lives in vegetation (i.e. that were susceptible to phyto-transformation), plants acted as fixing and transforming surfaces, reducing the concentration of OPE parent compounds that either remained in the air compartment or were exported to aquatic ecosystems. Although atmospheric transformation products of OPEs can be more persistent and toxic than the parent compounds,\n \n 39\n \n Wan et al.\n \n 3\n \n showed that plants continued to metabolize the primary diester transformation products of several OPEs in an experiment involving wheat plants in a controlled hydroponic environment. This continued metabolization suggests that plant transformation may be able to reduce the overall persistence of OPEs and their transformation products, thereby lowering the overall hazard posed from OPEs deposited to plants. For two cities (Bogot\u00e1 and Mexico City), reaction in the soil dominated overall fate of TPhP, following chemical deposition to vegetation and subsequent wash-off to soil, as TPhP is less susceptible to transformation in vegetation than in soil.\n
\n\n The amount of transformation in the vegetation compartment was sensitive to the modeled transformation half-life, meaning that compounds that are only slightly less susceptible to phytotransformation are unlikely to be transformed by plants, and for those compounds, plants will be less effective at fixing and transforming contaminants rather than mobilizing them. Slowing the vegetation transformation half-life (T\n \n 1/2,V\n \n ) by a factor of 10 (to represent hypothetical compounds more resistant to or slower at transformation) removed plant transformation as a dominant process (Fig.\n \n 4\n \n C shows the city-space diagram for TCIPP under these conditions), with most of the mass deposited to plant surfaces either re-volatilizing to air and leaving the city through air advection, or washing through to soil to the water compartment and then advecting downstream; for some compounds, this also increased transfer to groundwater.\n
\n\n By contrast, the urban film mobilized OPEs by enhancing their transfer to the water compartment and increasing loadings to aquatic ecosystems. Urban film consists of a mixture of organic matter, soot, and deposited atmospheric particles that accumulate over time, thus giving it complex chemical characteristics.\n \n 25,40,41\n \n Surface-mediated chemical reactions on urban films or particles can be important for some chemicals,\n \n 41,42\n \n but OPEs are generally believed to have up to order-of-magnitude lower reaction rates when particle-bound due to the ability of particles or atmospheric water to shield OPEs from hydrolysis.\n \n 39,43,44\n \n
\n\n Fate in the film compartment was less sensitive to the transformation half-life (T\n \n 1/2,F\n \n ) than fate in the vegetation compartment, as a similar 10x decrease in T\n \n 1/2,F\n \n did not change the dominant fate processes across the city-space diagrams. However, increasing T\n \n 1/2,F\n \n by 100x (likely a maximum rate, although the reaction rate in urban film is poorly constrained) led to transformation in the film compartment dominating (Fig.\n \n 4\n \n d). Thus, the film compartment was likely to transfer chemicals to water rather than fix and transform them.\n
\n\n Inter-city climatic variability was mainly responsible for the differences seen between the \u201caverage\u201d cities (contour lines) and the fate in individual cities (filled-in circles) in Fig.\n \n 3\n \n and Fig.\n \n 4\n \n . Across the city-space, a \u201clow-deposition\u201d climate was warmer, drier, and windier, with a higher \u201cceiling\u201d (planetary boundary layer height), and cities with this climate tended to be dominated by air advection (Fig.\n \n 5\n \n A & B). A \u201chigh-deposition\u201d climate was cooler, wetter, and calmer, with a lower ceiling, and cities with this climate tended to be dominated by vegetation reaction and water advection (Fig.\n \n 5\n \n C & D). The low-deposition climate was parameterized using the 5th percentile lowest precipitation rate observed across the 19 megacities and the 95th percentile highest windspeed, temperature, and planetary boundary layer height, while the high-deposition climate was parameterized with the inverse.\n
\n\n The fate of even the same chemical in the same built environment was substantially different between the low-deposition and the high-deposition climates (Fig.\n \n 5\n \n ). This meant that, depending on the climate, traditionally waterborne PMT-like chemicals such as TCEP could be advected via air rather than water, and traditionally sorptive PBT-like chemicals such as TPhP could become water-borne contaminants. Under the warmer, windier, and drier low-deposition climate advection via air would dominate across almost all of our cities (Fig.\n \n 5\n \n A & B) for both our soluble PMT-like compound TCEP and for our sorptive PBT-like compound TPhP. By contrast, in the cooler, wetter and calmer high-deposition climate, water advection and vegetation reaction were predicted to dominate across all our cities for TCEP, and water advection dominated for most (~\u200912/19) of the megacities for TPhP (Fig.\n \n 5\n \n C & D).\n
\n\n The \u201cSSP3-7.0\u201d projected 2100 climatic differences between a more aggressive climate-change mitigation pathway with low emissions (SSP1-2.6) and a less aggressive mitigation pathway with higher emissions (SSP3-7.0) did not substantially change projected chemical fate across the conceptual \u201ccity-space\u201d using the \u201caverage\u201d city. Localized changes did, however, change the dominant fate processes for individual cities (colored circles with a white border, Fig.\n \n 5\n \n ).\n
\n\n First, our results confirm that cities are important sources of OPE emissions. Further, we found that emissions of OPEs likely dwarfed emissions of the PBDEs that they replaced. The population of the 19 megacities presented here represents\u2009~\u200913% of the global population in cities with a population larger than 500,000.\n \n 1\n \n We estimated \u2211\n \n 10\n \n OPE emissions of between 3.8 -7,000 mg capita\n \n \u2212\u20091\n \n . Extrapolating these values to the global urban population implies that cities with a population of >\u2009500,000 could emit 0.88\u2013140 (mean of 16) kt yr\n \n \u2212\u20091\n \n of \u2211\n \n 10\n \n OPEs. This compares with a total of 9.3\u201325 (mean of 16) kt of PBDEs estimated to be emitted since production began in the 1970s.\n \n 19\n \n
\n\n Second, emissions across cities appeared to be driven more by diffuse, economy-wide processes than individual manufacturing sectors represented by proxies. We identified that a city\u2019s total GDP was the overall best proxy for OPE emissions. This indicates that OPE emissions come from a profusion of complex, distributed sources, making engineered solutions on manufacturing facilities unlikely to have much impact on overall OPE emissions.\n
\n\n Third, our results showed that both the built environment and climate strongly influenced chemical fate. Strikingly, the difference in the fate of a single chemical between cities with different climate and built environment factors was of a similar magnitude to the difference between a PMT-like and a PBT-like chemical in the same environment. Chemicals management tools and regulatory approaches generally screen chemicals for hazard traits (such as bioaccumulation or mobility) using their physicochemical properties, and the tools used to support chemicals management regulations often consider a single evaluative environment, such as is the case for the OECD Tool\n \n 45\n \n or the evaluative multimedia environment in the Estimations Program Interface (EPI) Suite of software tools.\n \n 46\n \n Our results indicate that in order to take a precautionary approach, regulatory support tools should consider that in different plausible emissions environments the same chemical may appear to be \u201cmobile\u201d or \u201cbioaccumulative\u201d. To account for this influence of climate and the built environment on chemicals fate, more weight could be placed on persistence and toxicity as hazard traits than on mobility and bioaccumulation.\n
\n\n Fourth, our results indicate that densely urbanized, sparsely vegetated cities in non-arid environments are extremely efficient at mobilizing chemicals to water through stormwater, and this means that more chemicals are likely to be found in stormwater than might be expected based on physicochemical properties alone. Recent work has highlighted the need for more \u201cgreen infrastructure\u201d to treat a wide variety of pollutants.\n \n 22\n \n Our results suggest that diverting stormwater runoff from directly entering receiving bodies could significantly reduce aquatic loadings. Depending on the local context, this \u201cgreen infrastructure\u201d could range from engineered systems like bioretention cells to simpler redirection of stormwater from rooves to, for example, gardens or other vegetated areas. Encouragingly, sorption-based green infrastructure technologies are effective for compounds with log K\n \n OW\n \n >\u2009~\u20093.8,\n \n 47\n \n meaning that for many of the more hydrophobic chemicals mobilized by cities (that would not be released to water in non-urban environments), green infrastructure should be an effective way to decrease loadings to aquatic ecosystems. One additional note of optimism is that our results suggest that increasing the amount of green space in a city can increase a city\u2019s \u201curban metabolism\u201d, directly removing chemical contaminants from the air and prevent them from being washed into water, at least for those compounds that phytotransform into less toxic products.\n
\n\n Finally, the processes governing OPE emissions and fate in urban areas have significant implications for human and ecosystem exposure. Both emissions and urban design levers could therefore affect these exposures, though further research is needed on the impacts of different interventions. People are exposed to OPEs mainly via diet, dust ingestion and dermal absorption (for toddlers); and via diet, indoor air inhalation, and dermal absorption (for adults); with drinking water a less studied but potentially significant pathway for the mobile chlorinated OPEs.\n \n 48\n \n Designing our built environments to favor certain processes over others will therefore involve complicated tradeoffs between exposures to different groups, and will therefore require further investigation. For instance, as most food production occurs outside of cities, processes which act to retain OPEs in urban areas are likely to reduce human exposure via diet. However, if these processes simply mobilize OPEs to surface water, they will increase human exposure through drinking water, especially for the chlorinated OPEs, which are poorly removed by water treatment systems\n \n 47,49,50\n \n and therefore may accumulate in water cycles.\n \n 5\n \n Aquatic ecosystems are believed to be sensitive to certain OPEs,\n \n 51\n \n so moving OPEs from the atmosphere to water would also increase environmental damages. Further research to better understand these tradeoffs will allow us to design cities to better \u201cmetabolize\u201d OPEs and other contaminants, preventing exposure for people and ecosystems within and outside of urban areas. Ultimately, our results suggest that supplementing policies that reduce sources of emissions with careful urban design to fix and transform (or \u201cmetabolize\u201d) those chemicals we cannot eliminate provides the best pathway towards building healthier, more sustainable cities.\n
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\n\n The \u201cMultimedia Urban Model\u201d\n \n 24\n \n (MUM) is a multimedia fugacity-modeling tool that accounts for urban contaminant dynamics in a steady-state, city-scale modeling domain (Figure 1). It has been used to estimate levels of PAHs, PCBs and PBDEs.\n \n 52,53\n \n We used a version of the model that was parameterized for PMTs and used to estimate the emissions of OPEs from Toronto.\n \n 8\n \n
\n\n We parameterized the model for each of the 19 cities in the GAPS-Megacities network using a combination of remotely-sensed and locally available data. Datasets were processed using a combination of the numpy,\n \n 54\n \n xarray\n \n 55\n \n and rioxarray\n \n 56\n \n python packages, QGIS,\n \n 57\n \n and Google Earth Engine\n \n 58\n \n ; all of the code used in this analysis is available from the lead author\u2019s GitHub and our Data Repository. Our data repository\n \n 59\n \n contains the values that were used as inputs to the model, the processed geospatial datasets that were used in this paper, or the code that can be used to obtain them. All continuous variables were clipped to the required city\u2019s model boundary using QGIS, taking either the mean value or the sum as appropriate.\n
\n\n Full details of the model parameterization have been provided elsewhere.\n \n 8,24,52\n \n Briefly, we used the Copernicus Global Land Services 100m Epoch 2018 land cover\n \n 29\n \n as a basis to parameterize the dimensions of the model compartments. We estimated the area of surfaces covered in urban film using the \u201cimpervious surface index\u201d (ISI), defined as the ratio of the total surface area of impervious surfaces (e.g. building walls, roofs, roads, etc.) to the total built up area, which was obtained from the land-use data. We were able to find detailed building footprints and heights for 8 cities: Buenos Aires,\n \n 60\n \n Sydney,\n \n 61\n \n Toronto,\n \n 62\n \n Warsaw,\n \n 63\n \n Madrid,\n \n 63\n \n New York,\n \n 64\n \n S\u00e3o Paulo,\n \n 65\n \n and London.\n \n 63\n \n For each of these cities, we calculated the impervious surface area for each building as the perimeter multiplied by the average building height plus the building footprint area. \u00a0For datasets that were provided in raster format, we first converted the building footprints to a vector format with one vector object per building. The processed dataset with all 8 cities is available in vector form from our data repository. We calculated the ISI for eight city administrative areas, five 5km buffer areas and two 15km buffer areas where we could find detailed information on building heights and footprints. For the other city boundaries, we predicted the ISI using a linear regression (r\u00b2 = 0.78) with the \u201cbuilt-up area density\u201d (number of people per m\u00b2 built-up area), a common metric of urban density\n \n 66\n \n that we found provided the most stable predictions of ISI (Figure S1).\n
\n\n We obtained data on the leaf area index, relative humidity (estimated from the dewpoint and surface temperature), windspeed (used to calculate the advective flow rate in the upper and lower air compartments), precipitation rate, and temperature from the Copernicus ERA5 Land ECMWF reanalysis dataset.\n \n 67\n \n The height of the planetary boundary layer was used as the top of the \u201cupper air\u201d compartment, and was obtained from the Copernicus ERA5 ECMWF dataset.\n \n 68\n \n We used a fixed height of 50m for the height of the \u201clower air\u201d as in Rodgers et al.\n \n 8\n \n We obtained river flow rates from the GLOFAS ERA5 reanalysis (choosing the pixel or sum of pixels that appeared to accumulate each city\u2019s flow),\n \n 69\n \n and river depths from Andreadis et al.\n \n 70\n \n These were used to parameterize the flow rate and depth of the water compartment, with the area taken from the land cover dataset. In the air compartment, total suspended particle concentrations were obtained from a variety of sources depending on the city. Generally, TSP was not available so we used empirical relationships\n \n 71\n \n to derive TSP from PM\n \n 10\n \n or PM\n \n 2.5\n \n , using the largest size-fraction for which data were available. Some notable sources include the SPARTAN network\n \n 72\n \n and the AirNow platform from US Embassies.\n \n 73\n \n If no other data were available, we used a global PM\n \n 2.5\n \n dataset by van Donkelaar et al.\n \n 74\n \n All of the particulate matter data used is available in the Data Repository.\n
\n\n For chemical-specific parameters, where available, we used the recommended Final Adjusted Values (FAVs) from Rodgers et al.\n \n 75\n \n that incorporated measured and\n \n in silico\n \n estimations. We also calculated new FAVs for TEP, TPrP and TnBP. Several of the OPE FAVs from Rodgers et al.\n \n 75\n \n included\n \n K\n \n \n OA\n \n measurements made using an indirect technique that may show bias for more polar compounds.\n \n 76\u201378\n \n As the FAV method adjusts the parameters of all of a compound\u2019s physicochemical properties based on their agreement, this bias in one property could propagate to all of the property values for a compound. An advantage of the Bayesian FAV method is that the prior distributions can be parameterized to incorporate our understanding of the uncertainty around the inputs in a transparent, reproducible manner. Since the indirect method is thought to produce\n \n K\n \n \n OA\n \n values that are biased low, we re-calculated the FAVs for these compounds with a skew-normal distribution on the log\n \n K\n \n \n OA\n \n prior, increasing the probability that the model would adjust the\n \n K\n \n \n OA\n \n values upwards. As in Rodgers et al.\n \n 8\n \n ,we also used polyparameter linear free energy relationships (ppLFERs) to estimate some partition coefficients. We parameterized these using Abraham\u2019s solvation parameters from the UFZ-LSER Database.\n \n 79\n \n
\n\n To reflect differences between anthropogenically-driven shared socioeconomic pathways (SSP) and their influence on OPE fate in urban areas, we ran the model for an \u201cSSP3-7.0\u201d scenario using the back-calculated base-case emissions along with the projected difference in the temperature, wind speed and precipitation between the SSP1-2.6 and SSP3-7.0 scenarios in 2100. Data were obtained from the curated, quality-controlled CMIP6 projections available on the Copernicus Data Store.\n \n 80\n \n We calculated ensemble-average decadal averages for 2041-2050 and 2091-2100 from all available model runs for each variable.\n
\n\n We parameterized and applied the model in several different manners, depending on the intended purpose. First, we back-calculated the emissions from the measured air concentrations. For this, we parameterized the model using the averaged values of the leaf-area index, relative humidity, rain rate, windspeed, planetary boundary layer height, and temperature across the ~3-month sampler deployment period at each location and annual-average values for 2018 for all other values. A key assumption of the model was that the air concentrations measured by the passive air samplers were representative of the urban areas across the sampling period. To test the applicability of this assumption, we ran the model using three different model boundaries (using the administrative boundary,\n \n 30\n \n and with a radius of 5 or 15km from the sampling location), and compared the results for the emissions flux (kg m\n \n -2\n \n ) of each boundary. The modeled emissions for each of the boundary areas were within \u00b1 2x of each other (Table S1), well within our \u00b1 order-of-magnitude overall uncertainty, indicating that the fate processes within the city remained similar at different scales, and providing confidence that the model results could be extrapolated over a larger domain. Our estimates of total emissions used the cities\u2019 administrative boundaries under the assumption that those boundaries represented a cohesive unit across which emissions sources and fate were similar, while regressions with emissions proxies used the emissions flux (kg m\n \n -2\n \n yr\n \n -1\n \n ) from the 15km buffer radius.\n
\n\n Second, to compare contaminant fate between cities we ran the model using annual-average values for the sampler deployment year of 2018 with the estimated annual emissions described above to remove the influence of seasonality and show average differences between cities. We justify this because although air concentrations are known to vary in the course of a year,\n \n 81,82\n \n emissions of OPEs are thought to be driven more by the intensity of local sources than by seasonal effects, such as increases in vapor pressure at higher temperatures.\n \n 81,83\n \n As discussed in SI Section S4.1, we generally found that the factors indicated by our sensitivity analysis to control contaminant fate were poorly correlated with our estimated emissions, supporting the assumption that local sources controlled emissions was valid.\n
\n\n Third, to reflect differences between anthropogenically-driven shared socioeconomic pathways (SSP) and their influence on OPE fate in urban areas, we ran the model for an \u201cSSP3-7.0\u201d scenario using the back-calculated base-case emissions along with the projected difference in the temperature, wind speed and precipitation between the SSP1-2.6 and SSP3-7.0 scenarios in 2100. Data were obtained from the curated, quality-controlled CMIP6 projections available on the Copernicus Data Store.\n \n 80\n \n We calculated ensemble-average decadal averages for 2041-2050 and 2091-2100 from all available model runs for each variable.\n
\n\n Fourth, we also explored the influence of different parameters on the fate of chemicals across the \u201ccity-space\u201d represented by different urban environments. For this, we defined two indices based on the area of urban film and of vegetation within a city. The first index defines how the built-environment impacts chemical deposition within a city. We defined this \u201cSparsity Index\u201d (SI, m\u00b2 m\n \n -\n \n \u00b2) with Equation (1):\n
\n\n Where A\n \n j\n \n represents the area of compartment j in m\u00b2. The second index explored the nature of those surfaces. We defined this \u201cFilm-vegetation Index\u201d (FVI) as Eq. (2):\n
\n\n For these scenarios we back-calculated emissions to a composite \u201caverage\u201d city, consisting of the mean values for the city-specific variables not included in the SI and the FVI, targeting the mean concentration of each OPE across the 19 cities. We also conducted limited scenario analyses to test the response of the model to certain parameter sets. First, we tested the influence of the half lives in the film and vegetation by multiplying the default value by 10 and 0.01, respectively. Then, we tested the influence of climate by constructing a \u201clow-deposition\u201d scenario where the windspeed, temperature, and planetary boundary layer values were set to the 95th percentile across the 19 megacities, and the precipitation rate was set to the 5th percentile; and a \u201chigh-deposition\u201d environment where the windspeed, temperature, and planetary boundary layer values were set to the 5th percentile across the 19 megacities, and the precipitation rate was set to the 95th percentile.\n
\n\n Finally, we performed a model sensitivity analysis focused on the differences between cities to elucidate trends that control the fate of OPEs in different urban environments. We used the Elemental Effects\n \n 84\n \n method to characterize MUM\u2019s sensitivity as it can identify non-monotonic, discontinuous interactions between variables. Although MUM is based on a system of linear equations, the parameters used to calculate the fugacity capacity can be non-linear and non-monotonic. We parameterized the range of values explored in the sensitivity analysis using the \u201caverage\u201d location-specific values across cities and the observed inter-city ranges plus 10% on each side, a hypothetical chemical with \u201caverage\u201d physical-chemical properties and the observed ranges between chemicals plus 10% on each side, and the input probability distribution functions presented in Rodgers et al.\n \n 8\n \n The Data Repository contains the parameterization for each input variable that was tested.\n
\n\n To investigate the sources driving OPE emissions, we correlated the log\n \n 10\n \n transformed emissions flux using the 15 km\u00b2 boundary area with various potential emissions proxies (such as GDP), and controls (such as temperature). Following initial correlations to reduce the number of variables, we correlated the emissions against the percentage of built up-area, bare area and vegetated area for each city,\n \n 29\n \n the average temperature in each city across the sampler deployment period,\n \n 67\n \n the total population in each city,\n \n 32\n \n global GDP and GDP per capita,\n \n 31,85\n \n and against total and sector-specific CO\n \n 2\n \n emissions from the Emission Database for Global Atmospheric Research (EDGAR),\n \n 33\n \n which we used as proxies for emissions of OPEs from specific economic sectors. The extracted proxy and control values we used for each city are available in our Data Repository.\n
\n\n The data, model, and code used in this study are available from this article\u2019s Data Repository (\n \n https://doi.org/10.5683/SP3/KT1DG5\n \n )\n \n 59\n \n or from a repository on the lead author\u2019s Github (\n \n https://github.com/tfmrodge/FugModel\n \n ), which also contains a tutorial for running the model.\n
\n\n Supplementary Information For: Where do they come from, where do they go? Emissions and fate of OPEs in global megacities\n
\n \n\n Extended Data Figures\n
\n \n\n Colorectal cancer (CRC) is the third most common cancer worldwide. The\n \n TP53\n \n gene is mutated in approximately 60% of all CRC cases. Sporadic CRC is characterized by high prevalence of\n \n TP53\n \n hotspot missense mutations. In particular, over 20 percent of all\n \n TP53\n \n -mutated CRC tumors carry either the p53\n \n R175H\n \n structural mutant or the p53\n \n R273H\n \n DNA contact mutant. Importantly, clinical data analysis suggests that CRC tumors harboring p53 R273 mutations are more prone to progress to metastatic disease than those with R175 mutations, in association with decreased survival. By combining in vitro CRC cell line models and human CRC data mining, we identified a unique transcriptional signature orchestrated by p53\n \n R273H\n \n , implicating activation of oncogenic signaling pathways and predicting worse patient outcome. Concordantly, p53\n \n R273H\n \n selectively promotes rapid CRC cell spreading, migration and invasion in vitro and metastasis in vivo. Mechanistically, the transcriptional output of p53\n \n R273H\n \n is associated with, and presumably driven by, its preferential binding to regulatory elements of R273 signature genes. Together, this demonstrates that different\n \n TP53\n \n missense mutations contribute differently to cancer progression, and that p53\n \n R273H\n \n possesses distinct gain-of-function activities in CRC that bear on disease course and possibly on patient management strategy. Given that practically all current analytical cancer gene panels include\n \n TP53\n \n , elucidation of the differential impact of distinct\n \n TP53\n \n mutations on disease features is expected to make information on\n \n TP53\n \n mutations more actionable and holds potential for better precision-based medicine.\n
\n\n \n \n Oncology\n \n \n
\n\n \n colorectal cancer (CRC)\n \n
\n\n \n TP53\n \n
\n\n \n genetics\n \n
\n\n \n mutation\n \n
\n\n The\n \n TP53\n \n gene, encoding the p53 tumor suppressor protein, is frequently mutated in many types of human cancer (1,2). The most common type of\n \n TP53\n \n mutations are missense mutations, leading to a single amino acid substitution in an otherwise intact p53 protein. In addition,\n \n TP53\n \n nonsense and frameshift mutations, usually resulting in production of truncated p53 proteins, are also fairly common in cancer (3). The common and arguably most important consequence of all these different types of mutations is the partial or complete loss of the tumor suppressor effects of the wild type (wt) p53 protein. Yet, there is growing evidence that missense\n \n TP53\n \n mutations may often also confer upon the mutant p53 (mutp53) proteins oncogenic gain-of-function (GOF) properties, which can actively contribute to cancer-related processes (4,5,6,7,8).\n
\n\n The spectrum of\n \n TP53\n \n missense mutations in human cancer comprises hundreds of different variants, although a small number of hotspot mutations are observed more frequently (9). Broadly speaking, cancer-associated p53 missense mutant proteins can be divided into two main classes: (A) structural mutants, where the mutation causes misfolding of the protein and leads to a significant conformational alterations within p53\u2019s DNA binding domain (DBD), and (B) DNA contact mutants, where the overall structure of the DBD is only minimally perturbed, but the mutant protein loses its ability to engage in high-affinity sequence-specific interactions with p53 binding sites within the DNA (10). Both mutp53 classes fail to activate canonical wtp53 target genes, but can modify the cell transcriptome through protein-protein interactions that involve a multitude of transcription factors and other DNA binding proteins (5,7).\n
\n\n While most of the studies on mutp53 have addressed features shared by all common mutants, there also is evidence for mutant-specific effects (5,11,12). Notably, knock-in mice harboring different p53 mutations exhibit non-identical tumor phenotypes: p53\n \n R270H/+\n \n mice, corresponding to the human p53\n \n R273H\n \n DNA contact hotspot mutation, show increased incidence of carcinomas and B cell lymphomas compared to p53+/\u2212 mice, while p53\n \n R172H/+\n \n mice, corresponding to the human p53\n \n R175H\n \n structural hotspot mutation, develop mainly osteosarcomas (13). However, the clinical implications of such mutant-specific differences remain largely unknown.\n
\n\n Colorectal cancer (CRC) is the 2nd most common cause of cancer-related deaths worldwide (14). The malignant progression of CRC is driven largely by the sequential accumulation of genetic alterations, affecting both oncogenes and tumor suppressor genes (15). Like other cancer types, CRC displays a wide spectrum of\n \n TP53\n \n mutations, which are observed in approximately 60% of all CRC tumors and are usually associated with the transition from large adenoma to invasive carcinoma (15).\n
\n\n In the present study, we set out to compare the impact of the two most common hotspot\n \n TP53\n \n mutations in CRC, p53\n \n R273H\n \n and p53\n \n R175H\n \n . Interestingly, we found marked differences between the effects of these two mutants. Specifically, p53\n \n R273H\n \n but not p53\n \n R175H\n \n can orchestrate a unique transcriptional program, which drives oncogenic signaling pathways, leads to more aggressive disease, and is associated with significant differences in patient survival. Better understanding of the distinct contributions of different\n \n TP53\n \n mutants might guide better CRC patient management and treatment decisions.\n
\n\n \n \n p53 R273 mutants are associated with more aggressive colorectal tumors relative to R175 mutants\n \n \n
\n\n Compared to most other cancers, in colorectal cancer (CRC) the relative representation of \"hotspot\" missense mutations among carriers of\n \n TP53\n \n mutations is particularly high. Specifically, missense mutations in the four most commonly mutated p53 residues (R175, R248, R273 and R282) comprise approximately 37% of all\n \n TP53\n \n mutations in this type of cancer (Fig. S1a). In contrast, mutations in these four residues encompass only 17% of all\n \n TP53\n \n mutations in all other cancer types together. Although this might be simply due to the mutational signature of particular carcinogens, it might also suggest a more significant GOF effect of such missense mutations in CRC.\n
\n\n One obvious question is whether different hotspot mutations may exert different effects on disease features and patient outcome. To address this question, we set out to compare R175 structural mutations to R273 DNA contact mutations. Notably, these mutations together represent over 20% of all CRC tumors harboring\n \n TP53\n \n mutations, as compared to only approximately 10% in all other cancers (Fig 1A). We analyzed clinical data from several patient cohorts, using the TCGA and ICGC open-source platforms as well as additional published datasets (16,17,18) (Supplementary Table 1). Remarkably, while R175 mutations are significantly more frequent than R273 mutations in early disease stages, the predominance of R175 mutations is abolished at later stages (Fig. 1b). This suggests that, relative to R175 mutations, R273 mutations might accelerate disease progression from early stages to advanced stages, involving cancer cell spreading to nearby lymph nodes (stage 3) and metastases to distant organs (stage 4).\n
\n\n Interestingly, when we analyzed the MSKCC CRC dataset, comprising 1134 cases of which ~90% were metastatic (19), we found that while both R175 and R273 mutants exhibited a similar percentage of liver, lung and lymph node first site metastases (data not shown), R273 mutants were significantly more associated with tumors that metastasize first to less common sites such as brain, bone, pelvis, peritoneum and gynecological sites (Fig. 1c). Importantly, unlike liver and lung metastases, metastatic lesions in these sites are usually considered unresectable, and thus incurable. Indeed, many studies have linked the presence of metastases at those sites to worse survival (20, 21, 22). Furthermore, R273 mutants were found to be significantly associated with multiple metastatic sites at the time of diagnosis of metastatic disease (Fig. 1d), further supporting the notion that R273 mutants selectively augment the metastatic capacity of CRC cancer cells. Importantly, R273 mutants were associated with significantly shorter disease-specific overall survival than R175 mutants (Fig 1e), regardless of patient age, sex, tumor location or presence of\n \n KRAS\n \n mutations (Fig 1f and Supplementary Table 2). Interestingly, while the impact of R273 mutations on overall survival was prominent in CRC patients presenting at stages 1-3 (Fig. S1b), it was not seen anymore when the patients presented with stage 4 disease (Fig. S1c); this is consistent with the notion that the main effect of R273 mutations is on the rate of progression from early stage CRC to advanced disease .\n
\n\n To explore the possibility that R273 mutant tumors might be associated with a particular mutational landscape, which may account for the observed clinical effects, we compared the co-occurrence of the most common gene mutations in CRC with either R175 or R273 mutations. Notably, other than SMAD4 mutations which showed a mild co-occurrence with R273 mutations (\n \n P\n \n =0.02), all other gene mutations were not differentially enriched in R273 mutated vs R175 mutated tumors (Fig S1d).\n
\n\n In sum, compared to R175 mutations, R273 mutations are preferentially associated with more advanced disease, higher rate of multiple and uncommon metastases, and shorter patient survival.\n
\n\n \n \n p53\n \n R273H\n \n orchestrates a distinct transcriptional signature\n \n \n
\n\n We next wished to elucidate the molecular mechanisms underpinning the differential impact of R273 vs R175 mutants in CRC, and to assess whether R273 mutations confer a true GOF. To that end, we utilized CRC-derived SW480 cells. SW480 is a microsatellite stable cell line, harboring APC and KRAS mutations; hence, it properly represents sporadic CRC. SW480 cells endogenously express two p53 mutants: p53\n \n R273H\n \n , and the less common p53\n \n P309S\n \n (23). SW480 cells depleted of their endogenous mutp53 by CRISPR/Cas9-mediated knockout (p53KO) were stably transduced with either p53\n \n R273H\n \n or p53\n \n R175H\n \n (Fig. 2a). Western blot analysis confirmed comparable overexpression of both mutants (Fig. 2b). As mutp53 GOF often involves changes in the cell transcriptome, we next subjected the different SW480 cell pools to RNA sequencing (RNA-seq) analysis, using the MARS-seq protocol (24). Clustering analysis revealed substantial differences between the transcriptome of the R273H cells and the parental p53KO cells (Fig. 2c). Surprisingly, overexpression of p53\n \n R175H\n \n had rather limited impact on the transcriptome of these cells (Fig. 2c). By comparing the observed transcriptional profiles, we generated a gene signature comprising 140 genes upregulated by p53\n \n R273H\n \n relative to both p53\n \n R175H\n \n and p53KO cells. This gene signature was defined as the \u201cR273 signature\u201d (Fig. 2d).\n
\n\n To further validate our conclusions, we adopted an alternative approach wherein SW480 cells were stably transduced with shRNA directed against the 3' UTR of the\n \n TP53\n \n gene (shp53), followed by stable overexpression of shRNA-resistant p53\n \n R175H\n \n or p53\n \n R273H\n \n (Fig. 2e). The resultant cell pools were subjected to MARS-seq analysis as above. Clustering analysis of the data confirmed that, also by this approach, p53\n \n R273H\n \n had a stronger effect on the SW480 cell transcriptome than p53\n \n R175H\n \n (Fig. S2a). Importantly, the \u201cR273 signature\u201d, deduced from the reconstituted p53KO cells, was strongly correlated with the differences in gene expression between the R273H-reconstituted shp53 cells and the control (Fig. 2f) or R175H-reconstituted (Fig. 2g) cells, as determined by gene set enrichment analysis (GSEA).\n
\n\n Lastly, since the above RNA-seq analyses were done with ectopically overexpressed p53 mutants, we quantified the relative expression of representative R273 signature genes by RT-qPCR analysis in control (expressing endogenous mutp53) and p53KO SW480 cells (Western blot in Fig. S2b). As seen in Fig S2c, all tested genes were significantly downregulated in the knockout cells, consistent with their being positively regulated by p53\n \n R273H\n \n . Moreover, comparison by GSEA of our R273 signature to published RNA-seq data of SW480 cells before and after shRNA-mediated p53 knockdown (25) confirmed significantly higher expression of the R273 signature in the control cells, which harbor endogenous p53\n \n R273H\n \n (Fig. S2d). Thus, p53\n \n R273H\n \n drives a distinct transcriptional program in SW480 cells.\n
\n\n \n \n The R273 signature is upregulated in multiple CRC cell lines and tumors and is associated with poor survival\n \n \n
\n\n To assess the generality of the R273 signature we interrogated experimentally three additional CRC-derived cell lines, by expressing p53\n \n R273H\n \n and p53\n \n R175H\n \n ectopically in HCT116 and RKO cells depleted of their endogenous wtp53 (KO), and COLO-205 cells endogenously expressing truncated p53 (Fig. 3a and Fig. S3a,c). Reassuringly, RT-qPCR analysis of representative R273 signature genes confirmed that, in all three cell lines, p53\n \n R273H\n \n selectively upregulated these genes, albeit to varying extents (Fig. 3b, Fig. S3b,d). Moreover, using the cancer cell line encyclopedia (CCLE) database, we found that the R273 signature is significantly upregulated in CRC cell lines harboring R273 mutations, compared to CRC lines carrying protein-truncating\n \n TP53\n \n mutations (Fig. 3c). The CCLE includes only three R175-mutated CRC lines; while their analysis indicated a similar trend as above, statistical significance could not be reached (p=0.067; data not shown).\n
\n\n We next wished to extend these findings to human CRC tumors. Importantly, GSEA analysis of the TCGA CRC cohort revealed that tumors harboring R273 mutations displayed significantly higher expression of the R273 signature than those with R175 mutations (Fig. 3d). Comparison of the R273-mutated tumors to tumors carrying truncating\n \n TP53\n \n mutations yielded a similar trend, but the difference did not reach statistical significance (data not shown). Of note, the truncating mutations group is very heterogeneous, and not all cases may resemble a p53-null state. Yet, tumors with extremely low p53 mRNA levels, presumably owing to nonsense-mediated decay (3), are more likely to approximate true nulls. Indeed, when we included only truncating mutation cases displaying greatly reduced steady-state p53 mRNA, unequivocal association of R273-mutated tumors with the R273 signature was clearly evident (Fig. 3e). Interestingly, analysis of the entire set of CRC tumors revealed a remarkable degree of positive correlations between the expression levels of the genes comprising the R273 signature, which was not observed in three independent control signatures (Fig S3e,f). This suggests that many of the genes comprising the R273 signature may be subject to common transcriptional or post-transcriptional regulatory mechanisms.\n
\n\n Guinney et al. have recently employed comprehensive data analysis to define four consensus molecular subtypes (CMS) for colorectal cancer (26). Remarkably, when we compared our R273 signature with the cell-intrinsic transcriptional signatures of the four CMS subtypes, as determined by Sveen et al. (27), the R273 signature displayed a strong (R=0.66) and significant (p<2.2e-16) correlation with the CMS4 signature (Fig S4a). Furthermore, GSEA analysis confirmed that CRC tumors harboring R273 mutations are significantly associated with the CMS4 gene signature compared to tumors harboring R175 mutations or truncating mutation (Fig S4b). Interestingly, the GSEA analysis revealed that tumors harboring R175 mutations are significantly associated with the CMS2 gene signature, when compared to tumors harboring either R273 or truncating mutations (Fig S4c). Hence, R273 mutations and R175 mutations are differentially associated with distinct CRC molecular subtypes, implicating markedly different cancer-promoting biological processes (26\n \n ).\n \n
\n\n Importantly, comparison of TCGA CRC tumors displaying high (upper quartile) R273 signature vs those with low (bottom quartile) signature revealed that high R273 signature was significantly associated with late-stage disease (Fig. 3f) and shorter patient survival (Fig. 3g). Furthermore, multivariate Cox regression analysis for overall survival, including age, sex, tumor location and the presence of KRAS mutations, demonstrated that high expression of the R273 signature is an independent prognostic factor (multivariate hazard ratio 2.314; 95% confidence interval 1.344\u20133.977; P=0.002; Supplementary Table 3).\n
\n\n In sum, the R273 gene signature is broadly enriched in CRC cells and tumors harboring R273 mutations, and is correlated with shorter patient survival. This further supports the hypothesis that the transcriptional output directed by R273 mutants endows CRC tumors with more aggressive features, which adversely affect patient outcome.\n
\n\n \n \n R273 mutants selectively promote cell spreading, migration and invasion\n \n \n
\n\n To elucidate oncogenic pathways that may contribute to the clinical impact of R273 mutations, we subjected the R273 signature to Gene Ontology analysis by METASCAPE (28). Interestingly, many observed pathways were directly or indirectly related to cytoskeleton dynamics (Fig. 4a), which is often associated with cancer-related properties such as cell adhesion, spreading, migration and invasion (29,30,31,32). Specifically, the Rho signaling pathway, ranking high in this analysis, can promote cancer by driving actin cytoskeleton remodeling and augmenting cell migration, survival, polarity, and more (33,34).\n
\n\n Phenotypically, the morphology of SW480 cells expressing p53\n \n R273H\n \n differed visibly from that of parental knockout cells or p53\n \n R175H\n \n expressors. This was evident as accelerated spreading, confirmed by time-lapse microscopy (Fig. 4b and Supplementary movies 1-3). Similar observations were made with RKO cells, depleted of their endogenous wtp53 and reconstituted with either p53\n \n R175H\n \n or p53\n \n R273H\n \n (Fig S5a). Importantly, RNA-seq analysis six hours after plating (Fig. S5b) showed that already at this early time point the R273 signature was upregulated in the p53\n \n R273H\n \n expressors to a similar extent as after 24 hours. This supports the notion that the inherent gene expression pattern dictated by p53\n \n R273H\n \n drives cell spreading, rather than being secondary to it.\n
\n\n Cell cycle analysis did not reveal differences between the effects of p53\n \n R273H\n \n and p53\n \n R175H\n \n (Fig. S5c). However, the p53\n \n R273H\n \n expressors displayed a significant increase in cell migration (Fig. 4c,d) and invasion (Fig. 4e,f), relative to p53\n \n R175H\n \n expressors or knockout cells. Moreover, while both p53\n \n R273H\n \n and p53\n \n R175H\n \n augmented the migration of p53-depleted RKO cells, the effect of p53\n \n R273H\n \n was significantly greater (Fig S5d,e). Thus, p53\n \n R273H\n \n preferentially promotes cell spreading, migration and invasion.\n
\n\n Rho signaling is one of the top enriched pathways in the R273 signature (Fig. 4a). In agreement, a Rho proteins GTPase activation assay confirmed that overexpression p53\n \n R273H\n \n of in SW480 cells augmented the activation of both Cdc42 and Rac1, relative to p53\n \n R175H\n \n overexpressors (Fig. 4g). Interestingly, RhoA activation was not differentially affected. Importantly, the migratory phenotype of p53\n \n R273H\n \n overexpressors was completely abolished by treatment with the Rac1/Cdc42 inhibitor MBQ-167 (Fig. 4h). Hence, p53\n \n R273H\n \n selectively drives Rac1/Cdc42-dependent cancer cell migration.\n
\n\n \n \n p53\n \n R273H\n \n preferentially\n \n promotes metastasis\n \n \n \n
\n\n We next wished to assess whether the differential impact of p53\n \n R273H\n \n in vitro is also reflected in a more aggressive phenotype in vivo. To that end, SW480 cells ectopically expressing either p53\n \n R175H\n \n or p53\n \n R273H\n \n were injected into the tail vein of NSG mice (Fig. 5a). Remarkably, 9 weeks after injection, the lungs of the mice injected with p53\n \n R273H\n \n -overexpressing cells displayed a significantly larger area of lung metastases than in mice injected with p53\n \n R175H\n \n overexpressors (Fig. 5b,c). Moreover, to better recapitulate CRC biology, we orthotopically injected SW480 cells harboring the two p53 mutants into the cecal wall of NSG mice (Fig 5d). Seven weeks later, mice were sacrificed and evaluated for distant organ metastases. Notably, four out of five mice in the R273H group developed both lung and liver metastases, while no metastases were observed in any of the mice injected with p53\n \n R175H\n \n overexpressors (Fig 5e,f). Thus, p53\n \n R273H\n \n not only confers increased migration and invasion in vitro, but also preferentially promotes metastatic behavior in vivo.\n
\n\n \n \n p53\n \n R273H\n \n is recruited to R273 signature genes and activates them via its transactivation domain\n \n \n
\n\n To explore the molecular mechanisms driving the transcriptional upregulation of R273 signature genes by p53\n \n R273H\n \n , we interrogated published p53 CHIP-seq data of SW480 cells (25), which express endogenous p53\n \n R273H\n \n (along with p53\n \n P309S\n \n ). Remarkably, analysis of all mutp53 peaks using GREAT (35), revealed that the most significantly enriched cellular components associated with those peaks were related to cytoskeleton structure and function (Fig. 6a). Moreover, the mutp53 chromatin binding peaks were significantly correlated with the genes upregulated upon p53\n \n R273H\n \n overexpression in our SW480 RNA-seq (Fig. 6b), suggesting that regulation of their expression by p53\n \n R273H\n \n is mediated, at least in part, via the recruitment of p53\n \n R273H\n \n to the corresponding chromatin regions. To query experimentally this notion, we compared by ChIP-qPCR the binding of p53\n \n R273H\n \n and p53\n \n R175H\n \n to regulatory elements of representative R273 signature genes, in SW480 cells ectopically expressing either mutant. As seen in Fig. 6c, p53\n \n R273H\n \n indeed displayed significantly stronger binding than p53\n \n R175H\n \n to those regulatory regions.\n
\n\n Previous work has demonstrated that p53\n \n R273H\n \n can act as a potent transcriptional activator when recruited to DNA, e.g. as a GAL4 fusion protein (36,37,38). The N-terminal transactivation domain (TAD) is essential for this activity (36). In agreement, while transiently-transfected p53\n \n R273H\n \n augmented the expression of endogenous R273 signature genes in p53KO SW480 cells, this effect was lost when the cells were transfected with a TAD-mutated version of p53\n \n R273H\n \n (Fig. 6d and Fig. S6a), despite being expressed at comparable amounts in the transfected cells (Fig. 6e,f). In addition to p53\n \n R273H\n \n , the p53\n \n R273C\n \n mutation is also fairly common in human cancer, including CRC. As seen in Fig. S6b,c, p53\n \n R273C\n \n was also capable of transactivating endogenous R273 signature genes in transiently transfected p53KO SW480 cells.\n
\n\n Collectively, these observations support the notion that recruitment of R273-mutated p53 proteins to specific chromatin regions alters the expression of associated genes, in a TAD-dependent manner. These transcriptional alterations may underpin the observed biological effects of the R273 mutants, leading to enhanced tumor progression and worse patient outcome.\n
\n\n The abundance of\n \n TP53\n \n mutations and the increasing amount of clinical and genomic data derived from cancer patient tumors represent an opportunity to better understand the impact of different\n \n TP53\n \n mutants on the features of the tumors that harbor them. Such understanding may potentially help in translating\n \n TP53\n \n status information into better individualized treatment decisions. This is particularly relevant for CRC, where the frequency of\n \n TP53\n \n missense mutations, and especially hotspot mutations, is very remarkable.\n
\n\n In the present study, we compared the effects in CRC of two prevalent\n \n TP53\n \n mutations, representing distinct types of mutp53 proteins. We show that R273 mutations direct a unique transcriptional program, which is not expressed in p53-null CRC cells or in tumors harboring truncating\n \n TP53\n \n mutations, and thus constitutes a GOF activity of R273 mutants. Importantly, this program, which entails activation of critical cancer-related pathways associated with cytoskeleton function, cell invasion and metastatic properties, is not shared with R175 mutants. This corresponds to clinical data from multiple CRC cohorts, suggesting that R273 mutants are associated with accelerated cancer progression and overall more aggressive disease. Mechanistically, this appears to entail differential recruitment of R273 mutants to specific regulatory elements on the DNA. Most probably, such recruitment is not direct, relying on the preferential association of R273-mutated p53 with sequence-specific DNA binding proteins (7,39,40).\n
\n\n Although many of the published studies on mutp53 GOF have focused on common features shared by multiple mutants (41,42,43,44,45,46,47), differential effects of different hotspot mutants have also been described (11,48,49,50), including quantitative differences in their interaction with critical partner proteins (51,52). Of note, a recent study employing HCT116 CRC cells showed that p53\n \n R273H\n \n is a more potent enhancer of cancer cell stemness than other p53 hotspot mutants, owing to selective regulation of a subset of long noncoding RNAs (53). We now show that the differences between mutants go beyond molecular features and may actually dictate different patient survival. Moreover, we show that selective mutp53 GOF effects can be abolished by a specific pathway inhibitor, suggesting that patients whose tumors harbor different p53 mutants might react differently to the same treatment protocol. Hence the particular\n \n TP53\n \n mutation, not just the presence or absence of\n \n TP53\n \n mutations, may be of future value when devising individualized treatment strategies for CRC, and most probably also for other cancer types.\n
\n\n Surprisingly, in our study p53\n \n R175H\n \n did not exert measurable effects on the transcriptional landscape and biological features of SW480 cells. This was unexpected, given that R175 mutations are very frequent in CRC: if they have no contribution to this type of cancer, why are they seen so often? A trivial explanation might be that they merely occur at high frequency because of particular mutation signatures inherent to CRC, without any acquired GOF (9). Yet, a more appealing possibility is offered by the fact that R175 mutations are strongly associated with the CMS2 transcriptional signature (Fig. S4c). CMS2 tumors are characterized by WNT and MYC signaling activation (26). If p53 R175 mutants facilitate such activation, they are expected to promote CRC initiation and rapid primary tumor growth. Indeed, R175 mutations are more prevalent than R273 mutations in early stages of the disease, but become less prevalent at late stages, when invasive and metastatic capacities take the lead role (Fig. 1b). Furthermore, CMS2 tumors tend to be more \u201cimmune cold\u201d, displaying minimal expression of immune-related transcripts and low infiltration of immune cells (54,55). It is conceivable that this may be partly due to GOF effects of mutp53, as suggested recently for pancreatic cancer (44). In such scenario, one might propose that R175 mutants may be particularly potent facilitators of immune evasion at early stages of CRC development, favoring their high abundance at those stages.\n
\n\n Still, it is surprising p53\n \n R175H\n \n hardly affected at all the SW480 transcriptome, despite being abundantly expressed. The most plausible explanation is that the effects of distinct p53 mutants are highly context-dependent. SW480 cells carry endogenous p53\n \n R273H\n \n , and their transcriptional profile is consistent with the R273 signature and hence with the CMS4 program. Presumably, their intrinsic signaling context has been evolutionarily optimized to support the transcriptional and biological GOF effects of their endogenous p53\n \n R273H\n \n , while concomitantly becoming non-supportive of alternative programs driven by other mutants such as p53\n \n R175H\n \n , which are characteristic of CMS2 tumors. This conjecture is in line with broader evidence for context-dependent GOF effects of missense mutp53 proteins. For example, whereas a particular subset of p53 mutants is selectively enriched in vivo, consistent with GOF, these mutants are not enriched and do not reveal any GOF properties when the same cells are grown in vitro (56,57\n \n ).\n \n A striking example of the context dependency of p53 mutations in CRC has recently been described by showing that the gut microbiome can dictate whether mutp53 proteins enhance tumor growth or, conversely, even restrict it, displaying surprising tumor suppressor features (58). Intriguingly, even the R273 mutant, which we show here to exert distinct GOF effects, did not exhibit measurable GOF effects in a genetically modified mouse model of CRC (59), further demonstrating that the contribution of a particular p53 mutation to cancer progression is highly context-dependent.\n
\n\n The role of adjuvant therapy for colon cancer patient with stage 2 tumors remains unclear. Today, decisions regarding adjuvant therapy include estimation of recurrence risk assasment through high-risk clinicopathologic features (60). Our data suggest that CRC pateints with the R273 mutation are prone to advance to late stage disease and therfore might benefit from adjuvant therapy in this stage. Given that\n \n TP53\n \n mutations are the most frequent single gene mutations in human cancer and that practically all current analytical cancer gene panels include\n \n TP53\n \n , provide an opportunity for a better treatment decision making for stage 2 colorectal patients.\n
\n\n Altogether, our findings argue that different p53 mutants may impart non-identical features on tumors, eventually impacting patient management and treatment decisions. Better understanding of such differential contributions of distinct p53 mutants and their context dependency is bound to make information on\n \n TP53\n \n mutations more valuable in the future and hold great potential for better precision-based medicine in the future.\n
\n\n \n \n Data Acquisition and Processing\n \n \n
\n\n \n TP53\n \n somatic mutation status and clinical attributes from the DFCI, CPTAC-2 and MSKCC cohorts (16, 17,19) were retrieved from the CBioPortal open Platform.\n \n TP53\n \n somatic mutation status and clinical attributes from the TCGA and ICGC (CRC cohorts) were downloaded from UCSC Xena Browser\n \n http://xena.ucsc.edu/\n \n .\n \n TP53\n \n somatic mutation status and clinical attributes from GECCO and CCFR were taken from published data (18). All patients were grouped according to their\n \n TP53\n \n status.\n
\n\n TCGA RNA-Seq expression profiles ((HT-Seq count, log2(fpkm-uq+1) for normalization)), were downloaded from UCSC Xena Browser\n \n http://xena.ucsc.edu/\n \n . TCGA colon adenocarcinoma (TCGA-COAD) and rectal adenocarcinoma (TCGA-READ) samples were filtered for primary tumour samples and divided according to their\n \n TP53\n \n status into R175 mutant tumours, R273 mutant tumours and Truncated tumours (comprised of tumours with frameshift, nonsense and splice site mutations).\n
\n\n RNA-seq data and gene somatic mutations data from cancer cell lines was downloaded from Xena Browser (CCLE dataset, RPKM), filtered for large intestine cell lines and divided into groups according to their\n \n TP53\n \n status.\n
\n\n \n \n Cell Lines, transfections and viral infections\n \n \n
\n\n Cells were maintained at 37\u00b0C with 5% CO\n \n 2\n \n . SW480 and RKO cells were cultured in DMEM (Biological Industries, BI), COLO-205 cells were grown in RPMI (BI) and HCT116 cells were grown in McCoy's 5A (Sigma). All culture media were supplemented with 10% FBS (BI) and 1% penicillin\u2013streptomycin (BI). All cell lines were tested negative for Mycoplasma. SW480\n \n TP53\n \n knockout cells and RKO\n \n TP53\n \n knockout cells were a kind gift from Varda Rotter (Weizmann Institute of Science). HCT116\n \n TP53\n \n knockout cells were a kind gift from Keren Vousden (Francis Crick Institute).\n
\n\n Plasmid transfection was done with the jetPEI DNA transfection reagent (Polyplus Transfection). The final DNA amount was 2 \u03bcg per well in a 6-well plate, and the transfection medium was replaced after 24 hours. Cells were collected 48 hours after transfection for gene expression profiling by RT-qPCR. pCB6, pCB6-R273H and pCB6-R273H with substitutions of residues 22 and 23 (L22Q/W23S; R273H TAD mutant), were a generous gift from Keren Vousden.\n
\n\n For lentivirus infections, SW480, HCT116, and RKO\n \n TP53\n \n knockout cells and CACO-205 parental cells were infected with recombinant lentiviruses (pEF1alpha-p53 R273H IRES-EGFP and pEF1alpha-p53R175H IRES-EGFP) to express the corresponding mutant proteins. Lentiviral packaging was performed by jetPEI-mediated transfection of Phoenix cells with the indicated plasmid DNAs, together with a plasmid encoding the VSVG envelope protein and packaging plasmids.\u00a0Virus-containing supernatants were collected 48h and 72h after transfection, filtered, and supplemented with 8\u00b5g/ml polybrene (Sigma). One week post infection, cells were subjected to FACS sorting for GFP positive cells. Alternatively, SW480 cells were infected with recombinant lentiviruses (pLKO.1-puro-shp53, TRCN0000010814 (Sigma) to produce shRNA directed against the 3' UTR of the endogenous mutant p53 mRNA, together with recombinant lentiviruses (pEF1alpha-p53 R273H IRES-EGFP and pEF1alpha-p53R175H IRES-EGFP) to express the corresponding mutant proteins. 48h after infection, p53 knockdown cells were selected with puromycin, and one week later were subjected to FACS sorting for GFP positive cells.\n
\n\n p53 knockdown and mutant protein expression were verified by RT-qPCR and Western blot analysis.\n
\n\n \n \n Immunoblotting\n \n \n
\n\n Cell pellets were resuspended in RIPA (Radioimmunoprecipitation assay) buffer, and protein sample buffer was added after centrifugation. Samples were boiled and resolved by SDS-PAGE. The following antibodies were used: GAPDH (Cell signaling, 14C10), p53 (mixture of monoclonal antibodies DO1\u00a0+ PAb1801). Imaging and quantification were performed using ChemiDoc MP Imager with Image Lab 4.1 software (Bio-Rad).\n
\n\n \n \n Time-lapse microscopy\n \n \n
\n\n Cells were plated in 6 well plastic bottom dishes and monitored by time-lapse imaging using a Celldiscoverer 7 microscope (Carl Zeiss Ltd.) Imaging was performed using the oblique contrast method through a Plan- Apochromat 20X/0.7 and a 0.5x Tubelens (effective magnification of 5X and 0.35NA). Illumination was done with a white-light LED set to 10% and detection by a 14bit Axiocam 506 CCD camera (Carl Zeiss Ltd.) with 10ms exposure time. Pixel size was 0.462m X 0.462m. Image tiling was used in order to cover a large area. Images were taken at 1 hour intervals, for total of 24 hours.\n
\n\n To quantify the cell shape, we segmented the cells using the ilastik Boundary based segmentation with Multicut workflow (61). We trained in ilastik 1) auto-context pixel classifier for 3 classes: boundary/cell/background and 2) multi-cut edge classifier. These were then applied sequentially to all the images in batch. We wrote a Fiji (62) macro to select cells from the multi-cut objects based on their size (between minimum and maximum values) and their average probability of belonging to the \u201ccell\u201d class of the ilastik auto-context pixel classifier. We discarded cells touching the border of the image. For each cell, we measured the aspect ratio (AR) \u2013 the ratio between the major and minor axis of the best-fitted ellipse. Spread cells were defined as those with AR > 1.8. For each time point, the percentage of spread cells out of the total number of detected cells was calculated.\n
\n\n \n \n RNA-seq\n \n \n
\n\n SW480\n \n TP53\n \n knockout cells and SW480 cells stably expressing R175H and R273H mutant protein were seeded at a density of 1.5 million per 10 centimeter dish, and RNA was extracted either 6 hours or 24 hours post seeding, using a NucleoSpin kit (Macherey Nagel). RNA of SW480 cells with stable p53 knockdown or overexpression of shRNA -resistant p53\n \n R175H\n \n or p53\n \n R273H\n \n was extracted similarly.\n
\n\n RNA-seq libraries were prepared at the Crown Genomics Institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science. A bulk adaptation of the MARS-Seq protocol (24) was used to generate RNA-seq libraries for expression profiling. Briefly, 30 ng of input RNA from each sample was barcoded during reverse transcription and pooled. Following Agencourct Ampure XP beads cleanup (Beckman Coulter), the pooled samples underwent second strand synthesis and were linearly amplified by T7 in vitro transcription. The resulting RNA was fragmented and converted into a sequencing-ready library by tagging the samples with Illumina sequences during ligation, RT and PCR. Libraries were quantified by Qubit and TapeStation as well as by qPCR for GAPDH as previously described (24). Sequencing was done with a Nextseq 75 cycles high output kit (Illumina).\n
\n\n Heatmaps were generated with Partek Genomics Suite 7.0 (Partek Inc.), using log normalized values (rld), with row standardization and Euclidean clustering\n
\n\n \n \n Gene Set Enrichment Analysis\n \n \n
\n\n Gene Set Enrichment Analysis (GSEA) (63), was employed to determine whether the R273 gene signature exhibits a statistically significant bias in its distribution within a ranked gene list. We followed the standard procedure as described in the GSEA user guide (\n \n http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html\n \n ) to create the ranked gene list for RNA-seq profiling of our data/published data/TCGA data, and tested the R273 signature for significant differences in distribution. The FDR for GSEA is the estimated probability that a gene set with a given NES (normalized enrichment score) represents a false-positive finding.\n
\n\n \n \n RT-qPCR\n \n \n
\n\n RNA was isolated using the NucleoSpin kit (Macherey Nagel). 1 \u03bcg of each RNA sample was reverse transcribed using Luna\u00ae Universal qPCR Master Mix (New England Biolabs). Real-time qPCR was performed using SYBR Green PCR Supermix (Invitrogen) with a StepOne real-time PCR instrument (Applied Biosystems). For each gene, values for the standard curve were measured and the relative quantity was normalized to\n \n GAPDH\n \n mRNA. Primers are listed in Supplementary Table 4.\n
\n\n \n \n RhoGTPase activity assay\n \n \n
\n\n Endogenous activity of RhoA, Rac1 and Cdc42 levels was determined by using an enzyme-linked immunosorbent assay (ELISA)-based G-LISA kit (Cytoskeleton, Inc #BK135) strictly following the manufacturer\u2019s instructions. Briefly,\u00a0Briefly, SW480 cells stably expressing p53\n \n R175H\n \n or p53\n \n R273H\n \n were plated and allowed to grow to ~ 70% confluence before being washed with PBS and lysed in 100 \u03bcl of ice-cold lysis buffer in the presence of protease and phosphatase inhibitors. The lysate was clarified by centrifugation at 10,000 \u00d7 g for 1 min, and snap-frozen in liquid nitrogen. After normalizing protein concentration using PrecisionRed (Cytoskeleton, Inc), samples were added in triplicate to wells coated with a respective GTP-binding protein. After washing, bound GTPases levels were determined by subsequent incubations with a respective antibody and a secondary HRP-conjugated antibody, followed by addition to an HRP detection reagent. Background was determined by a negative control well.\u00a0Absorbance was measured at a wavelength of 490 nm using a microplate reader (Thermo Fisher Scientific). Values are expressed as mean \u00b1 SEM of Three technical replicates.\n
\n\n \n \n Migration assays\n \n \n
\n\n Migration assays were performed using the transwell system (8 \u03bcm pore size; Costar). In brief, 60,000\u00a0 cells in either serum-free medium (RKO) or medium containing 1% FBS (SW480) were seeded in the upper chamber, while the lower chamber was filled with 600 microliter of culture medium supplemented with 10% FBS. Cells were allowed to migrate for 24 hours (SW480) or 30 hours (RKO). Cells on the lower surface of the chamber were fixed with 4% PFA and stained with crystal violet. Cells on the upper surface were removed with cotton plugs. Stained cells were imaged with a Nikon Eclipse Ti-E microscope at \u00d74 magnification, capturing at least three fields for each condition, and crystal violet stained areas were quantified with an ImageJ macro. Coverage by migrating cells was calculated as percentage of stained area relative to total area.\n
\n\n For MBQ-167 migration assay, SW480 cells were treated for 4 hours with either MBQ (750nM) or DMSO. After 4 hours, cells were trypsinized and placed in the upper transwell as above. 600 microliter of culture medium containing 10% FBS and either MBQ-167 (750nM) or DMSO were added to the bottom chamber. 24 hours post seeding, cells were fixed and stained. Stained area was quantified as above.\n
\n\n \n \n Invasion assays\n \n \n
\n\n For invasion assays, 200,000 cells were seeded in transwell chambers pre-coated with Matrigel (Corning). 600 microliter of culture medium containing 10% FBS and supplemented with EGF (100ng/ml) were added to the bottom chamber. After 24 hours cells were fixed and stained. Stained area was quantified as above.\n
\n\n \n \n In vivo experiments\n \n \n
\n\n All animal experiments and methods were approved by the Weizmann Institutional Animal Care and Use Committee. For tail vein injection, 2.5^10\n \n 6\n \n cells were resuspended in 100 microliter PBS before being injected through the tail vein. Tumours were harvested 9 weeks post-injection, as indicated in the corresponding figure legends. For orthotopic injection, 1^10\n \n 7\n \n cells were resuspended in 50 microliter PBS, diluted in Matrigel (1:1), and injected into the cecal wall.\u00a0 Tumours were harvested 7 weeks post-injection.\n
\n\n \n \n Chromatin Immunoprecipitation (ChIP) analysis\n \n \n
\n\n Chromatin immunoprecipitation was performed as previously described (39). SW480-p53\n \n R175H\n \n and SW480-p53\n \n R273H\n \n cells at 70% confluence were subjected to crosslinking by adding 1/10 volume of fresh 11% formaldehyde solution (50 mM HEPES-KOH pH7.5, 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 11% formaldehyde) for 10 min, followed by incubation in 0.125M glycine for 5 min. DNA was sheared to a range of 100-600 bp by subjecting the chromatin to sonication in a Bioruptor sonicator (Diagenode). 1/10 of the chromatin sample was set aside as input. Mouse anti-p53 (Santa Cruz, DO1, sc-126) and normal mouse IgG (Santa Cruz, sc-2025) were used for immunoprecipitation. Immune complexes were collected using Dynabeads protein G (Thermo Fisher Scientific). After reverse crosslinking and Proteinase K digestion, DNA was recovered using ChIP DNA Clean & Concentrator columns (Zymo Research). qPCR was performed using Luna\u00ae Universal qPCR Master Mix (New England Biolabs) on a 7500 Fast Real-Time PCR System (Thermo Fisher Scientific). Data was normalized by the \u0394\u0394Ct method over Input (1:20 dilution) and IgG samples. Sequences of the primers used for ChIP analysis are listed in Supplementary Table 4.\n
\n\n For Genomic Regions Enrichment of Annotations Tool (GREAT), we used published ChIP-seq data (GEO Series Accession Number GSE102796). 17,980 peaks identified in two replicates were analyzed for GO cellular component enrichment using GREAT (35).\n
\n\n \n \n Cell cycle profiling\n \n \n
\n\n Cells were grown in 6 cm dishes for 24 hours, trypsinized, and subjected to cell cycle analysis with a Phase-Flow BrdU Cell Proliferation Kit (BioLegend). Briefly, cells were incubated with BrdU for 75 minutes and labeled with Alexa Fluor-647-conjugated anti-BrdU antibody. Total DNA was stained with DAPI. Then, 50,000 cells were collected and analyzed by multispectral imaging flow cytometry. The percentage of cells in each cell cycle phase was manually determined on the basis of BrdU intensity and total DNA content, using FlowJo (Becton, Dickinson and Company).\n
\n\n \n \n Statistical data analysis\n \n \n
\n\n Independent biological replicates were performed and group comparisons were done as detailed in the figure legends.\n \n P\n \n -values below 0.05 were considered significant. Statistical analysis was performed using the Graph-Pad Prism 9.1.0 software. Statistical significance between two experimental groups is indicated by asterisks; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.\n
\n\n Supplementary movie legends\n
\n \n\n Supplementary movie 1\n
\n \n\n Supplementary movie 2\n
\n \n\n Supplementary movie 3\n
\n \n\n Supplementary Tables 1-4\n
\n \n\n Supplementary figures S1-S6\n
\n \n\n Ambient noise polarizes inside low-velocity fault zones, yet the spatial and temporal resolution of polarized noise on gas-bearing fluids migrating through stressed volcanic systems is unknown. Pressurized fluids increase stress and lead to volcanic earthquakes; imaging their location in real time would be a giant leap toward forecasting eruptions and monitoring volcanic unrest. Here, we show that depolarized noise detects fluid injections and migrations leading to earthquakes inside the laterally-stressed hydrothermal systems of Campi Flegrei caldera (Southern Italy). A polarized transfer structure connects the deforming centre of the caldera to open hydrothermal vents and extensional caldera-bounding faults during periods of low seismic release. Fluids depolarize the transfer structure and pressurize the hydrothermal system, building up stress before earthquakes and migrating after seismic sequences. During sequences, fluid migration pathways connect the location of the last eruption (Monte Nuovo, 1538AD) with the part of the eastern caldera trapped between transfer and extensional structures. After recent intense seismicity (December 2019-April 2020), the transfer structure appears sealed while fluids stored in the east caldera have moved further east. Depolarized noise has the potential to monitor fluid migrations and earthquakes at stressed volcanoes quasi-instantaneously and with minimum processing.\n
\n \n\n \n \n Seismology\n \n \n
\n\n \n \n Volcanology\n \n \n
\n\n \n ambient noise\n \n
\n\n \n volcano\n \n
\n\n \n volcanic earthquakes\n \n
\n\n We have learned how to use noise produced by humans, ocean swell, and atmosphere solid-Earth interactions\n \n 12,13\n \n to illuminate the interior of magmatic and hydrothermal systems\n \n 14-17\n \n . Noise data from expanding seismic networks are analyzed with novel array\n \n 20\n \n and interferometric\n \n 21,22\n \n techniques, allowing detection of volcanic processes and forecasting hazards without having to wait for earthquakes\n \n 18,19\n \n .\n \n Noise polarization\n \n across dip-angle normal faults has been related to stress and variations in stiffness anisotropy\n \n 1,2\n \n . However, the potential of noise polarization to illuminate pressurized fluids in volcanic systems is yet to be explored. Campi Flegrei (Southern Italy, Fig. 1a, small lower panel) is an inhabited volcanic caldera bordering Naples (the third most populous city of Italy) and the ideal location to discover this potential. The caldera is a capped\n \n 11,23-26\n \n geothermal system, where hazardous CO\n \n 2\n \n -bearing fluids propagate from the primary deformation source (Figs. 1-3, black dot) to fumaroles (S) at least since 1984\n \n 5-11,23-26\n \n . Heating of the hydrothermal system, volcanic gas emissions at the surface\n \n 5-8\n \n and seismic release\n \n 5,7,8,27,28\n \n result from consecutive episodes of unrest, promoting a long-term accumulation of lateral stress and expanding reservoirs\n \n 4,5\n \n . Accumulated stress and fluid migrations left marks across extensional faults and feeding systems at the caldera. Polarized noise can see through the overlying rocks to catch these marks. The azimuth of the horizontal polarization vector derived from ambient noise and the resultant length of its distribution (\n \n R\n \n )\n \n 1,2,\n \n \n 29\n \n \n ,3\n \n \n 0\n \n are used here for the first time as both imaging and diagnostic tools (Methods, 0.2-1 Hz). During periods of low seismic release\n \n 31,32\n \n , they detect the hypothesized link between deep extensional and caldera-bounding faults (\n \n extensional\n \n \n structures\n \n ) that bear regional stress\n \n 3\n \n \n 3\n \n \n -3\n \n \n 7\n \n north and east of the caldera (Fig. 1a, white dotted line), and a dynamic\n \n transfer structure\n \n \n 34\n \n that crosses its deforming centre and vents outgassing at the surface\n \n 6-10\n \n (Fig. 1a, black dotted line). At higher frequencies (1-5 Hz, Methods, Extended Data Fig. 1), regional and caldera-bounding faults disappear due to the sensitivity of noise to shallower and smaller structures\n \n 13,29\n \n . High resultant lengths and polarized azimuths mark NW-SE-trending extensional faults\n \n 34-37\n \n with exceptional stability between 2009 and 2020 (Figs. 1, Methods, Extended Data Figs. 1-5). The transfer structure develops instead SW-NE (Fig. 1a), the direction of the volcanic ridge under the caldera\n \n 34\n \n . When hydrothermal pressure, gas emission and seismicity increase (2018)\n \n 31,32\n \n , the transfer structure depolarizes, allowing to monitor fluid migrations leading to high-duration-magnitude (Md>3) earthquakes (Figs. 1b-d).\n
\n\n The polarized extensional and transfer structures are a direct consequence of processes that have been consistently imaged and monitored during the last thirty-six years. The high-attenuation\n \n 24\n \n signature of the repeated injections\n \n 25\n \n that caused the strongest volcano-tectonic event recorded at the caldera (Md=4.1) appears as an unpolarized anomaly after more than three decades (Fig. 2a). The central hydrothermal system opened in the WSW-ENE direction on April 1st, 1984 (black diamonds, Fig. 2a) due to a NW-directed injection of magma\n \n 9\n \n , magmatic or supercritical fluids\n \n 23-25\n \n . After thirty years (2011-13), a low-velocity aseismic reservoir\n \n 17,38\n \n had expanded from the injection point (Fig. 2b, black cross). Expansion toward west and north continued until fluids had reached the western caldera-bounding faults, producing high magnitude earthquakes in 2012\n \n 4,5,17\n \n (Fig 2b, western black diamonds). However, no apparent lateral expansion was visible east and south of the injection point (black cross, Fig. 2a,b). Fluids stopped at a barrier delineated by high velocities and high stresses, as shown by combined seismic and InSAR interferometric analyses\n \n 10\n \n . This barrier coincides with the transfer structure that crosses the eastern sector of the Solfatara crater (Fig. 2b). Here, shear-wave-splitting anisotropy\n \n 39\n \n , InSAR\n \n 11\n \n , and gravity gradiometry\n \n 33\n \n identify a SN anomaly that accumulates the highest lateral stress during unrest\n \n 10\n \n , producing small-magnitude earthquakes\n \n 31\n \n (Fig 2b, diamonds).\n
\n\n \n Imaging stressed fluid-filled structures\n \n
\n\n In the eastern caldera, the highest resultant lengths show azimuths consistently parallel to the NW-SE high-velocity extensional faults\n \n 11,35-37\n \n (Fig. 1). The area is wide enough to become a high-velocity waveguide for horizontally-polarized isotropic S waves generated either in the centre of the Tyrrhenian Sea\n \n 12\n \n or across the near coastline (Extended Data Fig. 7a,b). This waveguide explains azimuths parallel to the trend for both source configurations at stations with\n \n R>0.25\n \n (Methods). Still, a far-field source\n \n 12\n \n better fits azimuths observed across the entire caldera (Extended Data Fig. 7a,b, Residuals). Far-field sources cannot explain azimuths perpendicular to the primary direction (SW-NE) of the transfer structure between 2009 and 2017 (Fig. 1a). These azimuths could be a consequence of seismic anisotropy, which tracks permanent directional signatures from the deep Earth mantle\n \n 40\n \n to hydrated subducting slabs\n \n 41\n \n . If low-velocity faults are wide enough, stiffness anisotropy\n \n 1,2\n \n and trapping and reverberations\n \n 42\n \n on high-dip fault walls can polarize noise perpendicular to fault walls. Across the transfer structure, azimuths indeed develop perpendicular to high-dip fault walls (Fig. 2c) and crack anisotropy at least at Solfatara\n \n 39\n \n . Yet, the transfer structure is a small\n \n high-velocity structure\n \n (Fig. 2b)\n \n 17\n \n consequence of lateral stress accumulated in the crust\n \n 4,5,10\n \n . Azimuths across this structure better fit those obtained for sources generated at the near coastline\n \n 12\n \n (Extended Data Fig. 7a,b right). Near-field sources\n \n 7\n \n seem a more likely controller of azimuths than anisotropy, yet anisotropy increases polarization across similarly compressed structures\n \n 21,22\n \n .\n
\n\n \n Depolarization\n \n of the 2009-2017 transfer structure is central to explain stress release and structural changes in the volcano. While the extensional trend appears consistent over time, the transfer structure only polarizes during periods of lower seismic and geochemical release\n \n 31,32\n \n , when deep injections and hydrothermal recharge are sparse and rarely coupled\n \n 6-8,27,31,32\n \n (Fig. 1a). The structure is in contact with the high-attenuation\n \n 24\n \n and deforming\n \n 9,10\n \n location of deep injections (Fig. 2a, black cross). It runs along:\n
\n\n The dynamics associated with these geophysical responses and maps are linked to the sub-caprock migration of over-saturated CO\n \n 2\n \n -bearing fluids\n \n 5-11,17,19,23\n \n , adding persistent low-frequency noise and long-period events\n \n 30\n \n . The high-scattering fluids rising and migrating from deep injections pervade fractures, producing local noise that progressively intensifies\n \n 7\n \n and depolarizes the transfer structure (Fig. 1b,c). In the presence of high-velocity contrasts, stations within one wavelength from such extended sources lose polarization in the heterogeneous medium (Extended Data Fig. 7a,b, right,\n \n R\n \n decrease at station ACL2). This behaviour is apparent at Solfatara in 2018, when the central and eastern unpolarized reservoirs connect (Figs. 1b). Fluids eventually outflow on metasediments\n \n 11\n \n between transfer and extensional structures. These high-attenuation\n \n 24,25\n \n sediments reduce ambient noise directionality between 0.2 and 1 Hz\n \n 45\n \n and are the most consistent unpolarized anomaly during the decade (Fig. 1).\n
\n\n The pre-seismic (Fig. 1c) and post-seismic (Fig. 1d) patterns show the progressive depolarization induced by fluids migrating from the injection location to: (1) the eastern sector of Solfatara and the Pisciarelli vent (S, Fig. 1), where the geochemical unrests of the last fifteen years have been monitored\n \n 6,11\n \n ; (2) Monte Nuovo, the location of the last eruption at Campi Flegrei (M, 1538AD). The Solfatara-Pisciarelli vents emit from 2000 to 3000 tons/day of CO\n \n 2\n \n in the atmosphere\n \n 7,8\n \n . They have been consistently deforming toward the east in the last 20 years\n \n 46\n \n , moving along with seismicity from the injection location\n \n 25,38\n \n . Joint interpretations of resistivity, geochemistry and field data\n \n 11,36\n \n detect the plume that feeds these vents, the surrounding metasediments, and the eastern extensional faults that bind low-density metasomatized rocks\n \n 11\n \n (Fig. 2c). In the western portion of Fig. 2c, the transfer structure crosses the capped resistive plume that stores steam and gas, feeding fumaroles. Here, injections of fluids from depth\n \n 6-11,23-26,47\n \n coupled with meteoric recharge\n \n 27,28,43\n \n produce stress\n \n 10\n \n and outflow eastern liquid-bearing sediments\n \n 11\n \n . Gas-bearing fluids over-pressurized the eastern caldera between 2011 and 2013\n \n 19\n \n due to concurrent lateral expansion\n \n 10\n \n of the source region and saturation of the reservoirs\n \n 6-8\n \n . The depolarization of the transfer structure that started in 2018 (Fig. 1b) led to the highest seismic release in thirty-six years at the caldera\n \n 31,32\n \n . The area east of the Solfatara feeder was already suffering the highest horizontal stress in 2011-13 (white diamond\n \n 10\n \n , Fig. 2b,c). Here, fluid injections from depth coupled with progressive permeability increases from heavy rains\n \n 27,28,43\n \n started the seismic sequence in December 2019\n \n 31\n \n . Stresses on the high-dip fault east of Solfatara (Figs. 2c and 3) generated two high-magnitude volcano-tectonic events\n \n 7,\n \n \n 31\n \n after minor earthquake swarms\n \n 47\n \n : a Md3.1 on December 6\n \n th\n \n , 2019 and a Md3.3 on April 26th, 2020 (white circles\n \n 1\n \n and\n \n 2\n \n , Figs. 1c,d, 2c and 3a,b).\n
\n\n \n Monitoring stress and fluid migrations\n \n
\n\n This seismic sequence is the effect of pressurization of the hydrothermal system\n \n 32\n \n induced by lateral stress and fluid migrations, which horizontal noise polarization can monitor. The mechanical weakening of the crust\n \n 15\n \n and the corresponding depolarization of ambient noise\n \n 22\n \n after the Tohoku earthquake detect the release of stress and upward fluid migration at volcanoes hundreds of kilometres afar. In a stressed geothermal environment\n \n 23-26\n \n like Campi Flegrei, these surges appear at sharp lateral discontinuities, as caldera-bounding faults. In September 2012, fluid injections activated western caldera faults near Monte Nuovo (Fig. 2b, M, western black diamonds)\n \n 9,10\n \n . The resultant lengths measured over months at the nearest station detect the permanent depolarization following the earthquakes, in analogy to interferometric analyses\n \n 21,22\n \n (Methods, Extended Data Fig. 8). Fluid migrations between western and eastern caldera were the mechanism that released stress at the end of the 1984 unrest\n \n 25\n \n . Months after the 2012 swarm, it is the part of the eastern caldera compressed between transfer and extensional structures (Fig. 1a) that suffered the highest long-lasting velocity reductions (>0.1%)\n \n 19\n \n . These reductions are symptomatic of the area bearing the highest concentration of pressurized fluids\n \n 15,19\n \n , most likely to erupt, form new hydrothermal vents, and nucleate earthquakes\n \n 48,49\n \n . The temporal patterns (Figs. 1a-d, 3a,b, Extended Data Figs. 5, 6) clarify that fluid migrations connecting western and eastern caldera coexist and possibly drive stress build-up and release through the seismic sequence. Fluids migrate under the Campi Flegrei caprock\n \n 23-25\n \n , which forbids surges directly above the primary source of deformation\n \n 23\n \n . After each earthquake in 2019-20 (Extended Data Fig. 9), the change in polarization is similar to that observed after the earthquakes in 2012 (Extended Data Fig. 8). It is analogue to the decrease in ambient noise polarization caused by hydrothermal fluid surges at Mount Fuji after the Tohoku earthquake\n \n 22\n \n . Unlike Mount Fuji, horizontal stress was already in a critical state at Campi Flegrei due to magma degassing\n \n 5-8\n \n and supercritical fluids, pressurized under the caprock\n \n 11,23\n \n .\n
\n\n During the pre-seismic period (Fig. 1c, 3a), after minor swarms stroke the eastern caldera\n \n 7,41\n \n , the unpolarized anomaly under the Solfatara and Pisciarelli vents develops from north to south. After the Md3.1 earthquake, this anomaly expanded toward the eastern flank of the Solfatara and the Pisciarelli vents (Fig. 3b), matching the hypothesized low-gravity fluid-ascension path between the two vents\n \n 33,36\n \n . During the inter-seismic period, the anomalies in the western and eastern caldera connected across the seismic pathways that released stress and closed the 1984 unrest\n \n 25\n \n (Fig. 3a, diamonds). These maps track fluids generated by the deformation source\n \n 6-8\n \n and over-pressurized in the capped system\n \n 12,23-26\n \n . The fluids migrated both seismically\n \n 31,32\n \n and aseismically in 2020, pressurizing the eastern hydrothermal system until the Md3.3 released stress\n \n 7\n \n . The Md3.3 sealed migration by polarizing noise across the transfer structure (Fig. 3a, rightmost panel). By May-June 2020, the eastern unpolarized anomaly was one km east of its original location. It comprised the earthquake location (compare Fig. 3a, left to right) and an area that was polarized before the sequence (Fig. 1a-c). This dislocation is the seismic signature of the persistent lateral stress leading to fluid migrations toward the eastern caldera\n \n 38\n \n .\n
\n\n \n Toward monitoring with depolarized noise\n \n
\n\n Heat increase and critical degassing pressure from depth\n \n 6\n \n coupled with hydrothermal recharge\n \n 27,28,30,32,43\n \n make the area between regional extension and transfer structure (Fig. 1a,d) most likely to break in the future\n \n 48,49\n \n . Once informed by thermo-hydro-mechanical simulations\n \n 41\n \n , polarization parameters show a quasi-real-time monitoring potential. Recent thermo-hydro-mechanical modelling\n \n 47\n \n shows that fluids are injected at the base of faults in the east caldera between three and five days before the Md3.1, depending on injection volumes. Fig 3b and Extended Data Fig. 6 show polarization parameters measured using three hours of noise each day in these periods. After a consistent depolarization five days before the earthquake (Extended Data Fig. 6, 01/12/2019), the\n \n R\n \n increases at all the stations around the location of the Md3.1 (Fig. 3b, pre-seismic), in a manner that is consistent with an increase in compression preceding earthquakes\n \n 47\n \n . After the Md3.1, the unpolarized anomaly east of Solfatara expands toward the east (Fig. 3b) with significant statistical variations at stations in the eastern caldera (Extended Data Fig. 9). Similar maps are obtained in a shorter time interval (one to three days) around the Md3.3 to account for the increase in pore pressure following the inter-seismic period\n \n 47\n \n (Fig. 3b, Extended Data Fig. 6). Two days before the Md3.3, the eastern unpolarized anomaly had focused on the earthquake location. Two days after the Md3.3, fluids had outflown the area east of the Md3.3\n \n 11\n \n , depolarizing the eastern extensional trend like after the Md3.1 (Fig. 3b, from left to right). These spatial and temporal relations confirm that depolarized noise can monitor deep sub-caprock\n \n 23-27\n \n migrations of fluids preceding and following higher-magnitude earthquakes.\n
\n\n Ambient noise polarization answers the long-standing question of how this stressed volcano feeds its hydrothermal vents and builds and releases stress. A transfer structure connects the central deforming caldera to regional extensional faults\n \n 33,34\n \n , running under a caprock whose characteristics allow over-pressurization, lateral fluid migration and strong lateral deformation\n \n 23\n \n . The area of major volcanic and seismic hazard\n \n 48,49\n \n is compressed between transfer and extensional systems. The opening of the transfer structure detects deep fluid migrations toward the surface. These fluids trigger changes in polarization patterns\n \n 30\n \n , allowing mapping of stress build-up and release through further eastern fluid migrations. Temporal scanning of depolarized noise represents a substantial step toward instantaneous imaging of hydrothermal expansion, leading to earthquakes in stressed calderas. Polarization measurements from ambient noise interferometry\n \n 21,22\n \n require yearly recordings for stable imaging, several days of monitoring measurements, and high amounts of processing. As previously hypothesized\n \n 1,2\n \n , horizontal noise polarization can achieve similar results using hours of noise and minimal processing.\n
\n\n \n Data processing and estimates\n \n \n of horizontal polarization values\n \n
\n\n The seismic noise recordings used in this study are obtained across eleven years from broadband stations\n \n 7,17,19,27\n \n (Fig. 1a). They comprise:\n
\n\n The seismic noise samples were filtered by applying an a-causal Butterworth filter in the bands 0.2-1 Hz and 1\u20135 Hz. Resultant lengths (\n \n R\n \n ) and azimuths of the seismic wavefield were obtained by applying the covariance matrix method\n \n 1,2,29\n \n to three-component seismograms at each station, using contiguous sliding windows containing three wave cycles of the maximum period.\n \n R\n \n ranges in the interval [0,1]. The closer it is to one, the more concentrated the values around the mean polarization direction are. Data for which the rectilinearity\n \n 1,2\n \n was less than 0.5 were discarded, as the angular parameters are associated with seismic wave propagation only if above this threshold\n \n 30\n \n . We focused on horizontal ground motion polarization as it is strongly controlled by the medium properties (e.g., presence of faults and cracks)\n \n 1,2\n \n . We thus selected the azimuth values associated with a high horizontal polarization degree, fixing an incidence angle < 45\u00b0 as threshold\n \n 1\n \n . Extended Data Figure 1a,b shows\n \n R\n \n and azimuths measured at each station for 2009 and 2017. Panels c and d show the corresponding interpolated mapping. Compared to 0.2-1 Hz, the 1-5 Hz patterns (Extended Data Figure 1b,d) are more affected by anthropic noise\n \n 13\n \n . They identify a high-polarization SN structure compatible with a connection between Solfatara and the crater north of it, part of the low-frequency extensional trend. A second high-polarization region characterizes the area north of Monte Nuovo (M, panel d).\n
\n\n \n Stability of the polarization values\n \n \n between 2009 and 2017\n \n \n \n .\n \n \n
\n\n We compared the results evaluated at five stations (ASBG, CELG, CMSA, CSOB, OMN2 OVDG) of the permanent and mobile networks that were operative in 2009 and 2017. In none of these cases, variations of the polarization features were observed (Extended Data Fig. 2a). A bootstrap test calculated 1000 means of random samples drawn from the R distribution. The subtraction of the average\n \n R\n \n of the real distribution and the bootstrap mean (Extended Data Fig. 2b) shows that, over 47 stations recording in these periods, 41 present minimal changes in\n \n R\n \n (<0.1).\n
\n\n \n Stability of the polarization\n \n \n patterns measured during 2017 and 2018\n \n \n \n .\n \n \n
\n\n We assess the stability of our results when using data recorded over six months for 2017 and 2018 (Extended Data Fig. 3, blue and orange lines, respectively). A total of 22 stations recorded noise in both periods. The parameters are compared with one hour of signal recorded simultaneously at all stations in 2018 (Extended Data Fig. 3, green, this was possible only for 20 stations). In the figure, there is a 180\u00b0 periodicity so that apparent changes in azimuths like that at station RENG are uninfluential. Azimuths show minimal differences for\n \n R>0.3\n \n and always within uncertainties, while\n \n R\n \n values are most stable across the extensional trend (red labelled, Extended Data Fig. 3). The comparison between patterns computed over 6 months and 1 hr in 2018 is reported in Extended Data Fig. 4. When considering a single hour, minimal variations are observed across extensional trend.\n
\n\n \n Monthly and daily variations during seismic unrest\n \n
\n Extended Data Fig. 5 shows the monthly variation in the polarization patterns between September 2019 and June 2020. Monthly variations of\n \n R\n \n and azimuths mean values for the pre- seismic period, during swarms (September-November 2019) show a progressive increase of\n \n R\n \n at all stations. After the Md3.1 earthquake (circled number\n \n 1\n \n , December 2019- January 2020) the eastern unpolarized anomaly moves to comprise the earthquake location, while the western caldera polarizes. In the inter-seismic period (January-March 2019) western and eastern unpolarized anomalies connect north of the deformation source while the eastern unpolarized anomaly moves back to its original location. The Md3.3 post-seismic maps (circled number\n \n 2\n \n , May-June 2020) show polarization increases in the sealed central migration system (June 2020), while the eastern unpolarized anomaly moves to the earthquake location. Extended Data Fig. 6 shows the daily variations, where the intervals have been interpreted using the results of thermo-hydro-mechanical modelling\n \n 47\n \n .\n
\n \n Simulation of isotropic homogeneous horizontal noise polarization\n \n
\n\n We model noise polarization from an extended line of noise sources located in the central Tyrrhenian basin and from a circle representing noise sources offshore\n \n 12\n \n . As sources, we use Morlet wavelets of dominant frequency 0.7 Hz, repeating every 8 s in an isotropic simulation of the wave-equation. The staggered stress-displacement description of SH propagation incorporates viscoelasticity\n \n 25\n \n by using memory variables assuming constant-Q Zener model\n \n 50\n \n . To obtain seismic velocities from displacements, we apply a finite-impulse-differentiator filter of order 24. The propagation grid extends to the area shown in Fig. 1a (Extended Data Table 1). The strains are obtained from their relationship with displacements, using a spatial derivative operator of fourth order. The discretization of the memory-variable equations is performed using the central differences operator for the time derivative and the mean value operator for the memory variable. Two sponges attenuate boundary propagation.\n
\n\n The finite-difference simulations are most unstable if polarization azimuths are either 0\u00b0 or 90\u00b0\n \n 50\n \n and near the center of the caldera for circular polarization. We used grid spacings of 40 m for the two source settings, obtaining\n \n R\n \n varying in the intervals shown in Extended Data Fig. 7a,b. Thus, the simulation grid comprises 750 nodes regularly spaced at 40 m; of these, 150 nodes on each side are allocated for the absorption boundary conditions. The lowest/highest velocities\n \n 17\n \n used are 0.5 km/s and 1.5 km/s. For S waves of velocity\n \n v\n \n S\n \n \n and a grid step\n \n Dl\n \n , stability is given at times of at least\n
\n\n [IMAGE_METHODS_1]\n
\n\n in an isotropic medium\n \n 49\n \n . To take into account the variations induced by anelasticity and grid dispersion we reduced the time step to\n \n D\n \n \n t=\n \n \n 1\n \n ms. We modelled noise signals lasting 100 s.\n
\n\n We simulated seismograms at all stations recording noise in 2009-2017 and having a minimum\n \n R\n \n =0.25 in the results (Extended Data Fig. 7a,b). The decrease in the homogeneous cases (Extended Data Fig. 7a) is due to numerical instability and boundary conditions. It is lowest in the far field case (Extended Data Fig. 7a) but remains below 1% at all stations for both source configurations. The polarization parameters are retrieved with a blind test. L.D.S. ran simulations while S.P. processed synthetic seismograms without inputs on the original source polarization. The results for the homogeneous cases are shown in Fig. 7a and are compared with real azimuths in Fig. 7c. The square residuals between azimuths in the two source configurations indicate that a far field source is on average more likely to reproduce results (a line residual of 208 against 294).\n
\n\n \n Simulation of isotropic heterogeneous horizontal noise polarization\n \n
\n\n The results of the polarization analysis (Fig. 1a) are inserted in the propagation matrix with a 50% increase in shear modulus, a value derived by ambient noise tomography\n \n 17\n \n and fixing constant density values\n \n 4\n \n . The change is applied only to nodes where\n \n R>0.31\n \n (Extended Data Fig. 7b). For the extensional path, we restricted the area of change to within the extensional faults. The results of the blind tests show a strong reduction of\n \n R\n \n at station ACL2 (\n \n R\n \n ~0.5), the only station both inside the waveguide and within one wavelength from noise sources. Without waveguide and with the same source configuration, no near-field trapped wave responsible for decreasing polarization can develop. This explains lower\n \n R\n \n values as due to a combination of medium heterogeneity and extended near-field sources. The azimuths rotate parallel to the extensional trend (NW-SE) in the eastern caldera independently of the starting source polarization (Extended Data Fig. 7b); yet only near-field coastline sources reproduce azimuths perpendicular to the primary direction of the transfer structure. However, the lowest residuals are produced by the heterogeneous case with far line sources (residuals of 202 against 295).\n
\n\n \n Changes of horizontal noise polarization with swarms - 2012\n \n
\n\n High frequency (1-5 Hz) horizontal noise loses polarization (\n \n R\n \n ) permanently near the location of the last eruption of the volcano (Monte Nuovo, 1538 AD)\n \n 8\n \n after the strongest swarm recorded at the caldera between 1984 and 2019 (Extended Data Fig. 8, right). An unequal variance t-test confirmed (\n \n p\n \n <0.05) the hypothesis that the two sample populations (before and after September 2012) have different means. The permanent decrease of\n \n R\n \n is the likely consequence of fluids that permeated the area, saturating and isotropizing the system\n \n 22\n \n . Between 0.2 Hz and 1 Hz (left) the hypothesis of the unequal mean is confirmed only considering data between June 2012 and January 2013. The cause is a small swarm in April 2012 that decreases\n \n R\n \n temporarily. After this swarm we observe a progressive low-frequency increase, indicative of pressurization of the deeper systems.\n
\n\n The SpaceX Inspiration4 mission provided a unique opportunity to study the impact of spaceflight on the human body. Biospecimen samples were collected from the crew at different stages of the mission, including before (L-92, L-44, L-3 days), during (FD1, FD2, FD3), and after (R\u2009+\u20091, R\u2009+\u200945, R\u2009+\u200982, R\u2009+\u2009194 days) spaceflight, creating a longitudinal sample set. The collection process included samples such as venous blood, capillary dried blood spot cards, saliva, urine, stool, body swabs, capsule swabs, SpaceX Dragon capsule HEPA filter, and skin biopsies, which were processed to obtain aliquots of serum, plasma, extracellular vesicles, and peripheral blood mononuclear cells. All samples were then processed in clinical and research laboratories for optimal isolation and testing of DNA, RNA, proteins, metabolites, and other biomolecules. This paper describes the complete set of collected biospecimens, their processing steps, and long-term biobanking methods, which enable future molecular assays and testing. As such, this study details a robust framework for obtaining and preserving high-quality human, microbial, and environmental samples for aerospace medicine in the Space Omics and Medical Atlas (SOMA) initiative, which can also aid future experiments in human spaceflight and space biology.\n
\n\n Our human space exploration efforts are at a unique transition point in history, with more crewed launches and human presence in space than ever before\n \n 1\n \n . We can attribute this to the commercial spaceflight sector entering an industrial renaissance, with multiple companies forming collaboration and competition networks to send commercial astronauts into space. This recent evolution of human space exploration endeavors presents a valuable opportunity to accumulate more biological research specimens and improve our understanding of the impact of spaceflight on human health. This is critical since there is still much to learn about the varied biological responses to the spaceflight environment, characterized by microgravity and space radiation landscape\n \n 2\n \n . The impact of spaceflight on human health includes musculoskeletal deconditioning\n \n 3\n \n , cardiovascular adaptations\n \n 4\n \n , vision changes\n \n 5\n \n , space motion sickness\n \n 6\n \n , neurovestibular changes\n \n 7\n \n , immune dysfunction\n \n 8\n \n , and increased risk of rare cancers\n \n 9\n \n , among other changes\n \n 2\n \n . However, we are still at the very beginning of the work to catalog biological responses to spaceflight exposure at the molecular resolution.\n
\n\n Prior work has characterized molecular changes that occur during spaceflight in astronauts. These include changes in cytokine profiles\n \n 8,10,11\n \n , urinary albumin abundance\n \n 12\n \n , and hemolysis\n \n 13\n \n . Furthermore, multi-omic assays have provided genomic maps of structural changes in DNA\n \n 14\u201316\n \n , RNA expression profiles\n \n 11,17,18\n \n , sample-wide protein measurements\n \n 17,19,20\n \n , and metabolomic status\n \n 17\n \n . Additionally, International Space Station (ISS) surfaces have been studied with longitudinal microbial profiles to track microbial pathogenicity and evolution to assess their potential influence on crew health\n \n 21,22\n \n . To better improve our understanding of both human and microbial biology in space, it is critical that these analyses continue and expand as more spacecraft and stations are built and flown.\n
\n\n Combining and comparing work from prior missions in these new spacecraft and stations is especially important to overcome the small sample sizes and highlights a need for standardization between missions. In addition, recruiting large cohorts of astronauts is difficult, as the ISS typically can only house up to six astronauts at a time. As of the time of writing, only 647 humans have been to space, starting with the launch of Yuri Gagarin in 1961. Studies have spanned the Vostok program, Project Mercury, the Voskhod program, Project Gemini, Project Apollo, the Soyuz program, the Salyut space stations, MIR, the Space Shuttle Program, SkyLab, Tiangong Space Station, and the ISS. From the breadth of experiments that have been performed on the ISS, only a minority have specifically been human research-oriented\n \n 23\n \n , and just a subset involve omics studies. The NASA Twin Study created the most in-depth multi-omic study of astronauts prior to Inspiration4, but was limited to one astronaut and one ground control\n \n 17\n \n . All of these factors have limited the statistical power of astronaut omic experiments and increase the difficulty of providing robust scientific conclusions. Standardizing biospecimen collections across multiple missions will create larger sample-sets needed to draw these conclusions.\n
\n\n Here, we establish the standard biospecimen sample collection and banking procedures for the Space Omics and Medical Atlas (SOMA). A key goal of SOMA is to standardize biospecimen collection and processing for spaceflight, to generate high-quality multi-omics data across spaceflight investigations. This paper provides sample collection methods built for standardized collections across different crews and missions. These can generate harmonized datasets with greater statistical power and thus increase our scientific return yields from spaceflight investigations. We also present metrics on sample collection yields, instances of prior astronaut sample collection in scientific literature, and considerations for improvement of sample collection on future missions based on crew feedback. In its inaugural use case, these samples were collected from the Inspiration4 (I4) astronaut cohort and are currently in use for several other missions (Polaris Dawn, Axiom-2), which will enable continued utilization for future crewed space missions.\n
\n\n We formulated and executed a sampling plan that spans a wide range of biospecimen samples: venous blood, capillary dried blood spots (DBSs), saliva, urine, stool, skin swabs, skin biopsies, and environmental swabs (Fig.\n \n 1\n \n a). The collection of various types of samples covered the scope of previous assays on astronaut samples (Table\n \n 1\n \n ), but also enabled newer omics technologies, such as spatially resolved, single-molecule, and single-cell assays.\n
\n\n
\n\n
\n| \n \n Sample(s)\n \n | \n \n \n Measure(s)\n \n | \n \n \n Number of Subjects (n)\n \n | \n \n \n Duration Range (days)\n \n | \n \n \n Collection Time points\n \n | \n \n \n Study (citation)\n \n | \n
|---|---|---|---|---|---|
| \n \n Plasma\n \n | \n \n \n mtDNA, Long Non-coding RNA, Exosomes\n \n | \n \n \n 3\u201314\n \n | \n \n \n 5\u201313\n \n | \n \n \n L-10, R-0, R\u2009+\u20093\n \n | \n \n \n \n 24,25\n \n \n | \n
| \n \n Plasma, Saliva\n \n | \n \n \n Cytokines\n \n | \n \n \n 13\n \n | \n \n \n 140\u2013290\n \n | \n \n \n L-180, L-45, L-10, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, and R\u2009+\u200930\n \n | \n \n \n \n 10\n \n \n | \n
| \n \n Plasma\n \n | \n \n \n Cytokines\n \n | \n \n \n 28\n \n | \n \n \n ~\u2009180\n \n | \n \n \n L-180, L-45, L-10, FD15,30,60,120,180; R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 26\n \n \n | \n
| \n \n Plasma\n \n | \n \n \n Proteomics\n \n | \n \n \n 13\u201318\n \n | \n \n \n 169\u2013199\n \n | \n \n \n L-30, R\u2009+\u20090, R\u2009+\u20097\n \n | \n \n \n \n 20,27\u201329\n \n \n | \n
| \n \n Plasma\n \n | \n \n \n sRNAseq (miRNA from sEV)\n \n | \n \n \n 14\n \n | \n \n \n 12 (median)\n \n | \n \n \n L-10, R\u2009+\u20090, R\u2009+\u20093\n \n | \n \n \n \n 30\n \n \n | \n
| \n \n PBMCs\n \n | \n \n \n Peripheral Leukocyte Distribution, T-cell Function, Virus-specific Immunity, and Mitogen-stimulated Cytokine Production profiles\n \n | \n \n \n 23\n \n | \n \n \n <\u200960 days (n\u2009=\u20092), >\u2009100 days (n\u2009=\u20095), 6 months (n\u2009=\u200916)\n \n | \n \n \n L-180, L-45, FD14, FD 2\u20134 mn, FD6 mn, R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 31\n \n \n | \n
| \n \n plasma, PBMCs\n \n | \n \n \n snoRNA Expression Levels\n \n | \n \n \n n\u2009=\u20095 (plasma), n\u2009=\u20096 (PBMCs)\n \n | \n \n \n 14 (median)\n \n | \n \n \n L-10, R\u2009+\u20093\n \n | \n \n \n \n 32\n \n \n | \n
| \n \n Whole blood, serum\n \n | \n \n \n Hematology\n \n | \n \n \n 14\n \n | \n \n \n 167\u2009\u00b1\u200931 days (mean\u2009\u00b1\u2009sd)\n \n | \n \n \n L-100, FD5, FD11, FD64, FD157, R\u2009+\u20094, R\u2009+\u200914, R\u2009+\u200941, R\u2009+\u2009184\u2009<\u2009R\u2009+\u2009365\n \n | \n \n \n \n 13\n \n \n | \n
| \n \n Whole blood\n \n | \n \n \n Transcriptome\n \n | \n \n \n 6\n \n | \n \n \n 10\u201313\n \n | \n \n \n L-10, R\u2009+\u20090 (2\u20133 hour after return)\n \n | \n \n \n \n 33\n \n \n | \n
| \n \n Whole blood\n \n | \n \n \n Hematology\n \n | \n \n \n 31\n \n | \n \n \n Up to 180\n \n | \n \n \n L-180, L-45; FD-14, FD60-FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 34\n \n \n | \n
| \n \n Whole blood, Saliva\n \n | \n \n \n Immune Cell Counts, Cortisol\n \n | \n \n \n 9\n \n | \n \n \n 162\n \n | \n \n \n L-25, FD90, FD150, R\u2009+\u20091, R\u2009+\u20097, R\u2009+\u200930\n \n | \n \n \n \n 35\n \n \n | \n
| \n \n body swabs, saliva\n \n | \n \n \n Metagenomics\n \n | \n \n \n 4\n \n | \n \n | \n\n \n L-180, L-45; FD-14, FD60-FD120, FD180, R\u2009+\u20090, R\u2009+\u200930, R\u2009+\u2009180\n \n | \n \n \n \n 36\n \n \n | \n
| \n \n ISS section swab\n \n | \n \n \n Metagenomics, Physiological Characterization of Microbes\n \n | \n \n \n Locations: Columbus (air, light cover, SSC laptop, handrails, RGSH); Node2 (sleeping unit, panel outside, ATU); Cupola (air, surface), Node3 (ARED, treadmill, WHC); Node1 (panel inside, dining table)\n \n | \n \n | \n\n \n 3 timepoints (session A, B, and C)\n \n | \n \n \n \n 37\n \n \n | \n
| \n \n Saliva, Swab: mouth, ear, nostril, pooled skin\n \n\n 8 environmental locations\n \n | \n \n \n Microbiome\n \n | \n \n \n 1; node1, node2, node3, US laboratory module, permanent multipurpose module\n \n | \n \n \n 135 days\n \n | \n \n \n Before, During, After Spaceflight (L-180, L-90; FD60, FD97, FD126, R\u2009+\u20091, R\u2009+\u200930, R\u2009+\u2009180)\n \n | \n \n \n \n 38\n \n \n | \n
| \n \n microbiome swabs, stool, saliva, plasma, environmental swabs\n \n | \n \n \n Metagenomics, Cytokine\n \n | \n \n \n 9\n \n | \n \n \n 180 (n\u2009=\u20098) to 360 (n\u2009=\u20091)\n \n | \n \n \n L-240, L-160, L-90, L-60, FD7, FD90, FD126, R\u2009+\u20090/3, R\u2009+\u200930, R\u2009+\u200960, R\u2009+\u2009180\n \n | \n \n \n \n 39\n \n \n | \n
| \n \n Blood, urine, saliva\n \n | \n \n \n Antiviral antibodies and viral load (DNA) were measured for Epstein-Barr virus (EBV), varicella-zoster virus (VZV), and cytomegalovirus (CM)\n \n | \n \n \n 17\n \n | \n \n \n 12\u201316 days\n \n | \n \n \n Saliva: L-180, L-10, every other day during flight, and every other day post flight until R\u2009+\u200914\n \n\n Blood/Urine: L-180, L-10, R\u2009+\u20090, R\u2009+\u200914\n \n | \n \n \n \n 40\n \n \n | \n
| \n \n Whole Blood, Plasma\n \n | \n \n \n Immunophenotyping, NK Cell cytotoxicity and conjugation, Degranulation,\n \n\n Plasma stimulation\n \n | \n \n \n 9\n \n | \n \n \n 6 mn to 340 days\n \n | \n \n \n L-180, L-60\u2009<\u2009FD90, FD180 (n\u2009=\u20091), R-1, R\u2009+\u20090, R\u2009+\u200918, R\u2009+\u200933, R\u2009+\u200966\n \n | \n \n \n \n 41\n \n \n | \n
| \n \n Whole Blood, Plasma\n \n | \n \n \n Leukocyte distribution, T cell Blastogenesis, and cytokine production profiles\n \n | \n \n \n 19\n \n | \n \n \n 10\u201315 days\n \n | \n \n \n L-180, L-10, in-flight (R-1), R\u2009+\u20090, R\u2009+\u200914\n \n | \n \n \n \n 42\n \n \n | \n
| \n \n Plasma, whole blood, saliva\n \n | \n \n \n B Cell Phenotyping\n \n\n Ig Analyses\n \n | \n \n \n Integral Immune Study (n\u2009=\u200915)\n \n\n Salivary Markers Study (n\u2009=\u20098)\n \n | \n \n \n 6 months\n \n | \n \n \n Salivary:\n \n\n Plasma: L-180, L-45, FD10, FD90, FD180/R-1, R\u2009+\u20090, R\u2009+\u200930\n \n\n Salivary Marker Study: L-180, L-60, FD-10, FD-90, FD-180/R-1, R\u2009+\u20090, R\u2009+\u200918, R\u2009+\u200933, and R\u2009+\u200966\n \n | \n \n \n \n 43\n \n \n | \n
| \n \n Saliva, Blood, Urine\n \n | \n \n \n Salivary Biomarkers, Stress biomarkers\n \n | \n \n \n 8 ISS Crew, 7 control\n \n | \n \n \n 6 months\n \n | \n \n \n L-180, L-60, FD10, FD90, R-1, R\u2009+\u20090, R\u2009+\u200918, R\u2009+\u200933, R\u2009+\u200966\n \n | \n \n \n \n 44\n \n \n | \n
| \n \n Saliva\n \n | \n \n \n Salivary Microbiome\n \n | \n \n \n 10 (male)\n \n | \n \n \n 2\u20139 months\n \n | \n \n \n L-180, L-90\n \n\n FD 1\u20132 months\n \n\n FD 2\u20134 months\n \n\n FD (R-10)\n \n\n R\u2009+\u20090, R\u2009+\u200930, R\u2009+\u200960, R\u2009+\u2009180\n \n | \n \n \n \n 45\n \n \n | \n
| \n \n Blood, Urine, Saliva\n \n | \n \n \n Antiviral Antibodies and Viral Load\n \n | \n \n \n 17 (16 male, 1 female)\n \n | \n \n \n 12\u201316 days\n \n | \n \n \n Blood, Urine: L-180, L-10, R\u2009+\u20090, R\u2009+\u200914\n \n\n Saliva Dry: L-180, L-10, FD1, FD11, R\u2009+\u20091, R\u2009+\u200914\n \n\n Saliva Liquid: L-180, L-10, FD1,FD3,FD5,FD7,FD9,FD11 R\u2009+\u20090, R\u2009+\u20092, R\u2009+\u20094, R\u2009+\u20096, R\u2009+\u20098, R\u2009+\u200910, R\u2009+\u200912, R\u2009+\u200914\n \n | \n \n \n \n 46\n \n \n | \n
| \n \n Plasma, PBMCs, Urine\n \n | \n \n \n Thymopoiesis\n \n | \n \n \n 16 (14 male, 2 female)\n \n | \n \n \n Median: 184 days\n \n | \n \n \n Regular Intervals (preflight, return, postflight)\n \n | \n \n \n \n 47\n \n \n | \n
| \n \n Core Body Temperature,\n \n\n Whole Blood\n \n | \n \n \n Core Body Temperature, IL-1ra\n \n | \n \n \n 11 (7 male, 4 female)\n \n | \n \n \n 180 days\n \n | \n \n \n CBT: L-90, FD15, FD45, FD75, FD105, FD135, FD165, R\u2009+\u20091, R\u2009+\u200910, R\u2009+\u200930\n \n\n Blood: L-180, L-45, L-10, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 48\n \n \n | \n
| \n \n Plasma, Serum, Urine\n \n | \n \n \n Iron Status\n \n | \n \n \n 23 (16 male, 7 female)\n \n | \n \n \n 50\u2013247 days (mean: 157\n \n | \n \n \n L-180, L-45, L-10, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 49\n \n \n | \n
| \n \n Blood, Urine\n \n | \n \n \n Bone Loss and Kidney Stone Risk\n \n | \n \n \n 42\n \n | \n \n \n 49\u2013215 days\n \n | \n \n \n 10\u2013131 days before flight and after flight ( R\u2009+\u20090, R\u2009+\u20090 amd R\u2009+\u20092)\n \n | \n \n \n \n 50\n \n \n | \n
| \n \n Blood, Urine\n \n | \n \n \n Bone Metabolism and Renal Stone Risk\n \n | \n \n \n 23\n \n | \n \n \n 4\u20136 months\n \n | \n \n \n L-180, L-45, L-10, FD15, FD30, FD60, FD120, FD180\n \n | \n \n \n \n 51\n \n \n | \n
| \n \n Serum, Urine, Epithelial cells (sublingual mucosa)\n \n | \n \n \n Magnesium\n \n | \n \n \n 43\n \n | \n \n \n 4\u20136 months\n \n | \n \n \n Serum/Urine: L-180, L-45, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n\n Tissue: L-180, L-45, R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 52\n \n \n | \n
| \n \n Serum, Urine\n \n | \n \n \n Bone Metabolism\n \n | \n \n \n 17 (13 male, 4 female)\n \n | \n \n \n 160 +/-20 days\n \n | \n \n \n L-180, L-45, FD15, FD30, FD60, FD120, FD180\n \n | \n \n \n \n 53\n \n \n | \n
| \n \n Blood\n \n | \n \n \n Natriuretic Peptide, Creatinine, Aldosterone, Sodium\n \n | \n \n \n 8\n \n | \n \n \n Long Duration\n \n | \n \n \n Not specified\n \n | \n \n \n \n 54\n \n \n | \n
| \n \n Blood, Urine, Ultrasound\n \n | \n \n \n Arterial Structure and Function\n \n | \n \n \n 13 (10 male, 3 female)\n \n | \n \n \n 126\u2013340 days\n \n | \n \n \n L-180, L-60, FD15, FD60, FD160, R\u2009+\u20095\n \n | \n \n \n \n 55\n \n \n | \n
| \n \n Blood, Urine, quantitative CT\n \n | \n \n \n Bone Metabolism, Bone Density, Bone Strength\n \n | \n \n \n 17 (14 male, 3 female)\n \n | \n \n \n 3.5-7 months (mean: 170 days)\n \n | \n \n \n Blood/Urine: L-180, L-45, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090\n \n | \n \n \n \n 56\n \n \n | \n
| \n \n Stool, Saliva, Skin, Urine, Blood, Plasma, PBMCs\n \n | \n \n \n Metabolomics, Proteomics, Cognition, Microbiome, Telomeres, Epigenomics, Biochemical Profile, Gene Expression, Integrative Omics, Immunome\n \n | \n \n \n 2\n \n | \n \n \n 1-Year (340 days)\n \n | \n \n \n Before, during, and after spaceflight\n \n | \n \n \n \n 17\n \n \n | \n
| \n \n Blood, Urine\n \n | \n \n \n Multi-omics\n \n | \n \n \n 59\n \n | \n \n \n 4\u20136 months\n \n | \n \n \n L-180, L-45, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 57\n \n \n | \n
| \n \n Plasma\n \n | \n \n \n Cell-free DNA, Exosome\n \n | \n \n \n 2\n \n | \n \n \n 340 days\n \n | \n \n \n Before, during, and after spaceflight (12 timepoints from twin on earth and 11 from twin in space)\n \n | \n \n \n \n 58\n \n \n | \n
| \n \n Plasma, Urine\n \n | \n \n \n Multi-omic, Single-Cell, Biochemical Measures\n \n | \n \n \n 2\n \n | \n \n \n 340 days\n \n | \n \n \n Before, during, and after spaceflight\n \n | \n \n \n \n 11\n \n \n | \n
| \n \n Blood, Urine\n \n | \n \n \n Telomere Length\n \n | \n \n \n 3\n \n | \n \n \n 1 Year (n\u2009=\u20091), 6 months (n\u2009=\u20092)\n \n | \n \n \n Blood: L-270, L-180, L-60, FD45, FD90, FD140, FD260, R\u2009+\u20091, R\u2009+\u2009180, R\u2009+\u2009270\n \n\n Urine: L-180, L-45, FD15, FD240, FD330, R\u2009+\u20091, R\u2009+\u200960\n \n\n Biochemistry:L-80, L-45, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 59\n \n \n | \n
| \n \n PBMCs, Lymphocyte-depleted Cells\n \n | \n \n \n Circulating miRNA\n \n | \n \n \n 2\n \n | \n \n \n 340 days\n \n | \n \n \n Before, during, and after flight\n \n | \n \n \n \n 18\n \n \n | \n
| \n \n Blood\n \n | \n \n \n Clonal Hematopoiesis Panel, Whole Genome Sequencing, RNA-seq\n \n | \n \n \n Astronauts: n\u2009=\u20092\n \n | \n \n \n 340 days\n \n | \n \n \n Before, during, and after spaceflight\n \n | \n \n \n \n 15\n \n \n | \n
| \n \n Blood\n \n | \n \n \n Multi-omic, Untargeted RNA-seq\n \n | \n \n \n 2\n \n | \n \n \n 340 days\n \n | \n \n \n Before, During, and After Spaceflight\n \n | \n \n \n \n 60\n \n \n | \n
| \n \n Blood\n \n | \n \n \n Uremic Toxin\n \n p\n \n -Cresol\n \n | \n \n \n 2\n \n | \n \n \n 340 days\n \n | \n \n \n Before, During, and After Spaceflight\n \n | \n \n \n \n 61\n \n \n | \n
| \n \n Blood\n \n | \n \n \n Metabolic Profile\n \n | \n \n \n 51\n \n | \n \n \n 4\u20136 months\n \n | \n \n \n L-45, L-10, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n \n \n \n 62\n \n \n | \n
\n
\n\n For the Inspiration4 mission, sample collection spanned three time points pre-launch (L-92, L-44, L-3 days), three time points during flight (Flight Day 1 (FD1), FD2, FD3), and four time points post-return (R\u2009+\u20091, R\u2009+\u200945, R\u2009+\u200982, R\u2009+\u2009194 days). Venous blood, urine, stool, and skin biopsies were collected during ground timepoints only, while capillary DBSs, saliva, and skin swabs were collected both on the ground and during flight (Fig.\n \n 1\n \n b). Environmental swabs of the Dragon capsule were collected pre-flight in the crew training capsule and during flight in the spacecraft launched from Cape Canaveral (Fig.\n \n 1\n \n b).\n
\n\n Samples were collected across a variety of locations based on the crew\u2019s training and travel schedule. L-92 and L-44 were collected in Hawthorne, CA at SpaceX Headquarters, L-3 and R\u2009+\u20091 were collected at Cape Canaveral, FL at a facility near the launch-site. FD1, FD2, and FD3 were collected inside the Dragon capsule while in orbit. R\u2009+\u200945 was collected at the crew members' individual locations (which spanned the US States NY, NJ, TN, and WA), R\u2009+\u200982 was collected at Weill Cornell Medicine, NY and R\u2009+\u2009194 was collected at Baylor College of Medicine, TX (Fig.\n \n 1\n \n c).\n
\n\n Blood was collected using a combination of venipuncture tubes to collect venous blood and contact-activated lancets to collect capillary blood from the fingertip. Each crew member provided blood samples, collected into one blood RNA tube (bRNA), four K2 EDTA tubes, two cell preparation tubes (CPTs), one cell-free DNA tube (cfDNA BCT), one serum separator tube (SST), and one dried blood spot (DBS) card per time point. From these tubes, whole blood, plasma, PBMCs, serum, and cell pellet samples were collected (Table\n \n 2\n \n ). Sample yields are reported below. Samples were aliquoted for long-term storage and biobanking (Table\n \n 3\n \n ).\n
\n\n
\n| \n \n Sample Type\n \n | \n \n \n Tube Source\n \n | \n \n \n Assay Allocation(s)\n \n | \n
|---|---|---|
| \n \n Whole Blood\n \n | \n \n \n bRNA\n \n | \n \n \n Total RNA Extraction\n \n | \n
| \n \n Plasma\n \n | \n \n \n CPT\n \n | \n \n \n Proteomics, Metabolomics; Biobanking\n \n | \n
| \n \n PBMCs\n \n | \n \n \n CPT\n \n | \n \n \n Biobanking\n \n | \n
| \n \n Red Blood Cell Pellet\n \n | \n \n \n CPT\n \n | \n \n \n gDNA; Biobanking\n \n | \n
| \n \n Serum\n \n | \n \n \n SST\n \n | \n \n \n Immune and Cardiovascular Disease Panel, Metabolic Panel; Biobanking\n \n | \n
| \n \n Red Blood Cell Pellet\n \n | \n \n \n SST\n \n | \n \n \n gDNA; Biobanking\n \n | \n
| \n \n Plasma\n \n | \n \n \n cfDNA BCT\n \n | \n \n \n cfDNA; Biobanking\n \n | \n
| \n \n Red Blood Cell Pellet\n \n | \n \n \n cfDNA BCT\n \n | \n \n \n gDNA; Biobanking\n \n | \n
| \n \n PBMCs\n \n | \n \n \n K2 EDTA\n \n | \n \n \n Single-Cell Multiome GEX\u2009+\u2009ATAC and BCR/TCR Immune Repertoire Profiling\n \n | \n
| \n \n Plasma\n \n | \n \n \n K2 EDTA\n \n | \n \n \n EVPs\n \n | \n
| \n \n Whole Blood\n \n | \n \n \n K2 EDTA\n \n | \n \n \n Complete Blood Count\n \n | \n
\n
\n\n
\n| \n \n Sample Type\n \n | \n \n \n Tube Source\n \n | \n \n \n Aliquot Sizes\n \n | \n \n \n Freezing Condition\n \n | \n
|---|---|---|---|
| \n \n Plasma\n \n | \n \n \n cfDNA BCT\n \n | \n \n \n 500 uL\n \n | \n \n \n -80\u00b0C Freezer\n \n | \n
| \n \n Plasma\n \n | \n \n \n CPT\n \n | \n \n \n 500 uL\n \n | \n \n \n -80\u00b0C Freezer\n \n | \n
| \n \n Serum\n \n | \n \n \n SST\n \n | \n \n \n 500 uL\n \n | \n \n \n -80\u00b0C Freezer\n \n | \n
| \n \n PBMCs\n \n | \n \n \n CPT\n \n | \n \n \n \u2159 tube yield\n \n | \n \n \n -196\u00b0C Liquid Nitrogen\n \n | \n
\n
\n\n bRNA tubes were collected in order to isolate total RNA using the PAXgene blood RNA kit (Fig.\n \n 2\n \n a\n \n )\n \n . Yield ranged from 3.04\u201314.04 \u00b5g/tube of total RNA across all samples and the RNA integrity number (RIN) ranged from 3.2\u20138.5 (mean: 6.95) (Fig.\n \n 2\n \n b). RNA was stored at -80\u00b0C after extraction. The collection of total RNA enables a variety of downstream RNA profiling methods. It will allow comparative studies to prior RNA-sequencing performed on astronauts, particularly snoRNA & lncRNA biomarkers analyzed from Space Shuttle era blood\n \n 25,32\n \n , mRNA & miRNA measured during the NASA Twin Study\n \n 17,18\n \n , and whole blood RNA arrays from the ISS\n \n 33\n \n . Additionally, RNA yields are more than sufficient to perform direct-RNA sequencing using Oxford Nanopore Technologies (ONT) platforms, which require 500 ng of total RNA per library (Manufacturer\u2019s protocol, ONT kit SQK-RNA002). This enables the study of RNA modification changes during spaceflight to create epitranscriptomic profiles for the first time in astronauts.\n
\n\n
\n\n Four K2 EDTA tubes were drawn at each timepoint from each crew member (Fig.\n \n 2\n \n c). One K2 EDTA tube was submitted to Quest Diagnostics to perform a complete blood count (CBC, Quest Test Code: 6399). One tube was used to isolate extracellular vesicles and particles (EVPs) for proteomic quantification (Fig.\n \n 3\n \n a). Total EVP quantities varied from 2.71\u201328.27 ug (Fig.\n \n 2\n \n d). Two K2 EDTA tubes were used to isolate PBMCs for single-cell sequencing (10X Chromium Single Cell Multiome ATAC\u2009+\u2009Gene Expression and Chromium Single Cell Immune Profiling workflows). After collection, a Ficoll separation was performed to isolate PBMCs, which ranged from 340,000-975,000 cells per mL of blood (Fig.\n \n 2\n \n e). One prior single-cell gene expression experiment, NASA Twin study, was performed on astronauts, which found immune cell population specific gene expression changes and a correlation with microRNA signatures\n \n 11,18\n \n .\n
\n\n
\n\n Additional PBMCs, plasma, and serum were collected from CPTs (Fig.\n \n 4\n \n a), cfDNA BCTs (Fig.\n \n 4\n \n d), SSTs (Fig.\n \n 4\n \n c), as well as red blood cell pellets. CPTs were spun and aliquoted according to the manufacturer\u2019s instructions (Fig.\n \n 3\n \n b). Plasma volume per tube ranged from 3000-14,000 uL per tube (Fig.\n \n 4\n \n d). There were a few instances were CPT tubes shattered in the centrifuge and plasma could not be salvaged. Plasma can be used to validate or refute previous studies, including cytokine panel\n \n 10,26\n \n , exosomal RNA-seq\n \n 25,32\n \n , extracellular vesicle microRNA\n \n 30\n \n , and proteomic\n \n 20,27\u201329\n \n results. PBMCs were also collected, aliquoted into 6 cryovials per CPT, and stored in liquid nitrogen after slowly cooled in a Mr. Frosty to -80\u00b0C. These can be used to follow-up on previous studies on adaptive immunity, cell function, and immune dysregulation\n \n 8,31,41\u201343\n \n . The remaining red blood cell pellet mixtures from below the gel plug in each CPT Tube were stored at -20\u00b0C.\n
\n\n
\n\n cfDNA BCT tubes were collected to isolate high-quality cfDNA from plasma. cfDNA BCTs were spun and aliquoted according to the manufacturer\u2019s instructions (Fig.\n \n 3\n \n c). The remaining cell pellet mixture was frozen at -20\u00b0C. Plasma volume per timepoint ranged from 1500\u20135000 uL (Fig.\n \n 4\n \n e). 500 uL aliquots were frozen at -80\u00b0C. cfDNA extracted from these tubes can be analyzed for fragment length, mitochondrial or nuclear origin, and cell type or tissue of origin\n \n 24,58\n \n .\n
\n\n The SST was spun and aliquoted according to the manufacturer\u2019s instructions (Fig.\n \n 3\n \n d). Serum volume ranged from 2000\u20138000 uL per timepoint (Fig.\n \n 4\n \n f). Similar to plasma, serum can be allocated for cytokine analysis and can also be used to perform comprehensive metabolic panels, including one we used at Quest (CMP, Quest Test Code: 10231) for metrics on alkaline phosphatase, calcium, glucose, potassium, and sodium, among other metabolic markers. The remaining cell pellet mixture from each SST tube was stored at -20\u00b0C.\n
\n\n In addition to venous blood, capillary blood was collected onto a DBS card using a contact-activated lancet pressed against the fingertip (Fig.\n \n 5\n \n a). Capillary blood was collected onto a dried blood spot (DBS) card to preserve nucleic acids and proteins. The amount of capillary blood collected across timepoints varied (Fig.\n \n 5\n \n b,\n \n 5\n \n c) according to how much blood could be collected before the puncture wound closed.\n
\n\n
\n\n Saliva was collected at the L-92, L-44, L-3, FD1, FD2, FD3, R\u2009+\u20091, R\u2009+\u200945, and R\u2009+\u200982 timepoints using two methods. First, saliva was collected using the OMNIgene Oral Kit, which preserves nucleic acids (Fig.\n \n 6\n \n a) during the ground timepoints. From these samples, DNA, RNA, and protein were extracted. DNA yield ranged from 28.1 to 3,187.8 ng, RNA yield from 396.0 to 3544.2 ng (less the two samples had concentrations too low for measurement), and protein concentration from 92.97\u201393.15 ng.\n
\n\n
\n\n Second, crude saliva (i.e. saliva with no preservative added) was collected into a 5mL DNase/RNase-free screw top tube during the ground and flight timepoints. Saliva volume varied from 150\u20134,000 uL per tube (Fig.\n \n 6\n \n b). Crude saliva was also collected during flight (FD2 and FD3), in addition to the ground timepoints.\n
\n\n Saliva collections have been conducted throughout spaceflight studies for assessing the immune state, particularly in the context of viral reactivation. Previously identified viruses that reactivate during spaceflight include Epstein\u2013Barr, varicella-zoster, and cytomegalovirus\n \n 46\n \n . Responses to reactivation of these viruses can be asymptomatic, debilitating, or even life-threatening, thus assessing these adaptations is beneficial in understanding viral spaceflight activity as well as crew health. In addition to viral nucleic acid quantification, numerous biochemical assays can also be performed, including measurements of C-reactive protein (CRP), cortisol, dehydroepiandrosterone (DHEA), and cytokines, among others\n \n 10,35,44,46\n \n .\n
\n\n Urine was collected in sterile specimen cups at the L-92, L-44, L-3, R\u2009+\u20091, R\u2009+\u200945, and R\u2009+\u200982 timepoints. Specimen cups were collected 1\u20132 times per day. For preservation, urine was aliquoted and stored at -80\u00b0C. Half the urine had Zymo Urine Conditioning Buffer (UCB) added before freezing, to preserve nucleic acids. Samples yielded 23\u2013155.5 mL of crude urine and 21\u2013112 mL of UCB urine per specimen cup (Fig.\n \n 7\n \n a). Urine was split into 1 mL \u2212\u200915 mL aliquots before freezing at -80\u00b0C.\n
\n\n
\n\n A wide variety of assays can be performed on urine samples. Previous studies have included viral reactivation\n \n 40,44,46\n \n , urinary cortisol\n \n 47,55\n \n , iron and magnesium measurements\n \n 49,52\n \n , bone status\n \n 50,51,53,56\n \n , kidney stones\n \n 50,51\n \n , proteomics\n \n 11\n \n , telomere measurements\n \n 59\n \n , and various biomarkers and metabolites\n \n 17,55\n \n .\n
\n\n Stool was collected at the L-92, L-44, R\u2009+\u20091, R\u2009+\u200945, and R\u2009+\u200982 timepoints. Stool samples were stored into two collection containers at each timepoint, one DNA Genotek OMNIgene Gut (OMR-200) kit with a preservative for metagenomics and another (ME-200) with a preservative for metabolomics (Fig.\n \n 7\n \n b). Stool was the least consistent sample collected due to the limited windows available for sampling during collection timeframes. DNA and RNA were extracted from aliquots of the OMNIgene Gut (OMR-200) tubes for downstream microbiome analysis. DNA yield ranged from 358.5\u201316,660 ng, RNA from 690\u20132010 ng (Fig.\n \n 7\n \n c). Large variations in yield are attributable to variable stool mass collected between kits.\n
\n\n Stool samples enable various biochemical, immune, and microbiome changes studies. Previous metagenomic assays have found that shannon alpha diversity and richness during long duration missions to the ISS\n \n 39\n \n .\n
\n\n Body swabs were collected at all timepoints. Samples were collected by swabbing the body region of interest for 30 seconds, then placing the swab in a sterile 2D matrix tube (Thermo Scientific #3710) with Zymo DNA/RNA shield preservative. For the first two swab locations, the oral and nasal cavity, the swab was placed directly on the body after removal from its sterile packaging (dry-swab method; Fig.\n \n 8\n \n a). For the remaining body locations, the swab was briefly dipped in nuclease-free, DNA/RNA-free water before proceeding (wet-swab method). Eight distinct sites were swabbed with the wet-swab method: post-auricular, axillary vault, volar forearm, occiput, umbilicus, gluteal crease, glabella, and the toe-web space (Fig.\n \n 8\n \n b). The astronaut microbiome has previously been studied in the forehead, forearm, nasal, armpit, navel, postauricular, and tongue body locations, and changes have been documented during flight. Changes in alpha diversity and beta diversity were documented, as well as shifts in microbial genera\n \n 39\n \n . However, the impact of these changes on skin health and immunological health are not well understood.\n
\n\n
\n\n Acquiring extensive swab samples from the crew skin allows for characterization of the habitat environment, crew skin microbiome adaptations, and interactions with potential human health adaptations resulting from spaceflight exposure. This is very relevant for crew health, considering astronauts become more susceptible to infections during spaceflight missions\n \n 63\n \n , with the relationship between microbe-host interactions from spaceflight exposure, which may be a causative factor of astronauts immune dysfunction, which is still not well understood.\n
\n\n A skin biopsy on the deltoid was obtained from the L-44 and R\u2009+\u20091 timepoint. Biopsies were also collected in advance of a flight to ensure the biopsy site is fully healed before the flight so there is no risk of complication.The wet-swab method was used to collect the skin microbiome before the skin biopsy. The skin biopsies were three millimeters in diameter and were collected for histology and spatially resolved transcriptomics (SRT) (Fig.\n \n 8\n \n c). One-third of the sample was stored in formalin and kept at room temperature to perform histology. The remaining two-thirds of the sample was stored in a cryovial and placed at -80\u00b0C for SRT (Fig.\n \n 8\n \n c). This is the first sample collected from astronauts for spatially resolved transcriptomics. The skin is of high interest due to the inflammation-related cytokine markers such as IL-12p40, IL-10, IL-17A, and IL-18\n \n 10,17\n \n and skin rash\u2019s status as the most frequent clinical symptom reported during spaceflight\n \n 64\n \n .\n
\n\n Environmental swabs were collected in flight during the F1 and F2 timepoint. Additionally, environmental swabs were collected from the flight simulation capsule at SpaceX headquarters after days of crew training during the L-92 and L-44 timepoints. Environmental swabs were collected using the wet-swab method. Ten environmental swabs were collected per time point at the following locations in the capsule: an ambient air/control swab, the execute button, the viewing dome, the side hatch mobility aid, the lid of the waste locker, the head section of one of the seats, the commode panel, the right and left sides of the control screen, and the g-meter button (Fig.\n \n 9\n \n a-d). Additionally, the spacecraft\u2019s high-efficiency particulate absorbing (HEPA) filter was acquired post-flight (Fig.\n \n 10\n \n a). This filter was cut into 127 rectangular pieces (1.2\u201d x 1.6\u201d x 4\u201d) and stored at -20\u00b0C (Fig.\n \n 10\n \n b, Fig.\n \n 10\n \n c).\n
\n\n
\n\n
\n\n Previous microbial profiling of spacecraft environments has revealed that equipment sterilized on the ground becomes coated in microbial life in space due to interactions with crew and the introduction of equipment that has not undergone sterilization\n \n 65\n \n . Subsequent microbial monitoring assays performed on the ISS have detected novel, spaceflight-specific species on the ISS\n \n 66\n \n . Once in space, surface microbes are subject to the unique microgravity and radiation environment of flight, which will influence evolutionary trajectory. The potential impact of this influence on pathogenesis is a concern for long-duration space missions, especially given that changes in host-pathogen interactions may also be affected during spaceflight\n \n 67\n \n .\n
\n\n We report here on biospecimen samples collected from the SpaceX I4 Mission, the most comprehensive human biological specimen collection effort performed on an astronaut cohort to date. The extensive archive of biospecimens included venous blood, dried blood spot cards, saliva, urine, stool, microbiome body swabs, skin biopsies, and environmental capsule swabs. The study objective was to establish a foundational set of methods for biospecimen collection and banking on commercial spaceflight missions suitable for multi-omic and molecular analysis. Biospecimens were collected to enable comprehensive, multi-omic profiles, which can then be used to develop molecular catalogs with higher resolution of human responses to spaceflight. Select, targeted measures in clinical labs (CLIA) were also performed immediately after sample collection (CBC, CMP), and samples and viable cells were preserved in a long-term Cornell Aerospace Medicine Biobank, such that additional assays and measures can be conducted in the future.\n
\n\n There are several reasons why rigorous biospecimen collection methods for commercial and private spaceflight missions must be developed, which are scalable and translational across populations, missions, and mission parameters. First, little is known about the biological and clinical responses that occur in civilians during and after space travel. While professional astronauts are generally young, healthy, and extensively trained, civilian astronauts have been, and likely will be, far more heterogeneous. They will possess a variety of phenotypes, including older ages, different health backgrounds, and greater medication use, and may experience different medical conditions, risks, and comorbidities. Careful molecular characterization will be beneficial for the development of appropriate baseline metrics and countermeasures and, therefore, beneficial for the individual spaceflight experience. In the future, such analyses may enable precision medicine applications aimed at optimizing countermeasures for each individual astronaut who enters and returns safely from space\n \n 68,69\n \n .\n
\n\n Second, multi-omic studies inherently present a large number of measurements within a small set of subjects. These high-dimensional datasets present numerous potential challenges with regard to amplification of noise, risk of overfitting, and false discoveries\n \n 70\n \n . At all times, scientists engaged in multi-omic analyses must take special care that true biological variance is what has been measured. The introduction of experimental variance through the progression from sample collection, transport, storage, to sequencing and analysis can introduce artifacts of variance that render the detection of true biological variance and interpretation of results more difficult. For this reason, tight adherence to experimental controls or annotation at every step of the experimental condition is crucial. Careful annotation allows for the assignment of class variables in post hoc analysis. Among such applications are the attempt to detect batch effects or determine the impact of variations in temperature (collection, storage, or transport)\n \n 71\n \n .\n
\n\n The necessary means to address experimental variance are longitudinal sampling and specimen aliquoting. Longitudinal sampling (i.e. collecting numerous serial samples from each test condition) from pre-flight, in-flight, and post-flight allows for greater statistical power when assessing changes attributable to spaceflight. In addition, each sample collected should be divided upon collection into multiple aliquots. This better assures that freeze-thaw cycles can be avoided in the analysis stage, as freeze-thaw events can introduce considerable experimental variance depending on the molecular class being measured. Maintaining all samples at their optimal storage temperature at all times, typically \u2212\u200980\u00b0C or lower, is crucial\n \n 72\n \n . Special attention must be given to how the collection and storage methods in-flight vary in relation to the conditions on Earth. Spaceflight presents considerable differences in the operating environment, where ground conditions are far easier to control than flight. In practice, this may limit the types of samples that can be collected during flight.\n
\n\n Third, rigorous methods must be developed and followed to pursue comparisons across missions with varying design parameters. In this consideration, there is an argument for the development of specimen collection, transport, storage, processing, analysis, and reporting standards. At the same time, this must be balanced with the flexibility required for innovation since standards can sometimes limit advancement in methodology. In the present study, common methods were used for the Inspiration4 and the forthcoming Polaris Dawn and Axiom missions. However, selected methods may require optimization for Polaris Dawn to increase the yields during sample processing and adapt to unique parameters imposed by the anticipated spacewalk (extravehicular activity; EVA). Moreover, within standards or best practices, unique research for each mission may require alteration of previously successful methods. With these considerations in mind, we must balance methodology standardization with advances in methodology options and mission-specific objectives.\n
\n\n As the commercial spaceflight sector gains momentum and more astronauts with different health profiles and backgrounds have access to space, comprehensive data on the biological impact of short-duration spaceflight is of paramount importance. Such data will further expand our understanding and knowledge of how spaceflight affects human physiology, microbial adaptations, and environmental biology. The use of integrative omics technologies for civilian astronauts will unveil novel data on genomics, proteomics, metabolomics, and transcriptomics. Creating multi-omic datasets from spaceflight studies on astronaut cohorts will further advance our understanding, inform future mission planning, and help discover what appropriate countermeasures can be developed to minimize future risk and enhance performance.\n
\n\n Validating sample collection methodologies initially in short-duration commercial spaceflight is a key step for future human health research in long-duration and exploration-class missions to the Moon and beyond. To help meet these challenges, we have established the SOMA protocols, which detail standard multi-omic measures of astronaut health and protocols for sample collection from astronaut cohorts. Although the all-civilian Inspiration4 crew pioneered the first use of the SOMA protocols, the methodology outlined here is robust and generalizable, making it applicable to future astronaut crews from any commercial mission provider (e.g., SpaceX, Axiom Space, Sierra Space, Blue Origin) or space agencies (NASA, ESA, JAXA, ROSCOSMOS). Furthermore, the SOMA banking, sequencing, and processing methods are a springboard for continuing biospecimen analysis and expanding our knowledge of multi-omic dynamics before, during, and after human spaceflight missions, providing a molecular roadmap for crew health, medical biometrics, and possible countermeasures.\n
\n\n \n Venous Blood Draw\n \n
\n\n Venipuncture was performed on each subject using a BD Vacutainer\u00ae Safety-Lok\u2122 blood collection set (BD Biosciences, #367281) and a Vacutainer one-use holder (BD Biosciences, 364815). The puncture site was located near the cubital fossa and was sterilized with a BZK antiseptic towelette (Dynarex, Reorder No. 1303). Blood was collected into 1 serum separator tube (SST, BD Biosciences: #367987, Lot: #1158449, #1034773), 2 cell processing tubes (CPT, BD Biosciences: #362753, Lot: #1133477, #1012161), 1 blood RNA tube (bRNA, PAXgene: #762165, Lot: #1021333), 1 cell-free DNA BCT (cfDNA BCT, Streck: #230470, Lot: #11530331), and 4 K2 EDTA blood collection tubes (BD Biosciences, #367844, Lot: #0345756) per crew member per time point. For samples collected in Hawthorne, blood was drawn at SpaceX headquarters, then immediately transported to USC for processing. Samples collected at Cape Canaveral were processed on-site.\n
\n\n \n Blood Tube Processing\n \n
\n\n For processing, serum separator tubes (SST) were centrifuged at 1300xg for 10 minutes. 500uL aliquots of serum were aliquoted into 1mL Matrix 2D Screw Tubes (ThermoFisher, 3741-WP1D-BR) and stored at -80\u00b0C. SST tubes were recapped and stored at -20\u00b0C to preserve the red blood cell pellet.\n
\n\n Cell processing tubes were centrifuged at 1800xg for 30 minutes. Plasma was aliquoted into 1mL Matrix 2D Screw Tubes and stored at -80\u00b0C. 5mL of 2% FBS (ThermoFisher, #26140079) in PBS (ThermoFisher, #10010023) was added to the CPT tube to resuspend PBMCs. PBMC suspension was transferred to a clean 15mL conical tube. The total volume was brought to 15mL with 2% FBS in PBS. The tube was centrifuged for 15 minutes at 300xg. Supernatant was discarded. PBMCs were resuspended 6mL of 10% DMSO (Millipore Sigma, #D4540-500mL) in FBS. 1mL of PBMCs were moved to 6 cryogenic vials (Corning, #8672). Cryovials were placed in a Mr. Frosty\n \n TM\n \n (ThermoFisher, #5100-0001) and stored at -80\u00b0C. CPTs were recapped and stored at -20\u00b0C to preserve the red blood cell pellet.\n
\n\n Cell-free DNA blood collection tubes (cfDNA BCTs) were centrifuged at 300xg for 20 minutes. Plasma was transferred to a 15mL conical tube. Plasma was centrifuged 5000xg for 10 minutes. 500uL aliquots of plasma were aliquoted into 1mL Matrix 2D Screw Tubes and stored at -80\u00b0C. cfDNA BCTs were recapped and stored at -20\u00b0C to preserve the red blood cell pellet.\n
\n\n PAXgene blood RNA tubes were processed according to the manufacturer's instructions. Briefly, tubes were left upright for a minimum of 2 hours before freezing at -20\u00b0C. For RNA extraction, tubes were thawed and processed with the PAXgene blood RNA kit (Qiagen, #762164).\n
\n\n \n Extracellular Vesicles and Particles (EVPs) Isolation\n \n
\n\n One 4mL K2 EDTA tube was shipped on ice overnight to WCM for processing. Blood was centrifuged at 500 x g for 10 minutes, then plasma was transferred to a new tube and centrifuged at 3000 x g for 20 minutes, and the supernatant was collected and stored at -80\u00b0C for EVP isolation. Plasma volumes ranged between 0.6 - 1.7 ml. Plasma was later thawed for downstream processing, when concentrations were measured. Plasma samples were thawed on ice and EVPs were isolated by sequential ultracentrifugation, as previously described (Hoshino et al., 2020). Briefly, samples were centrifuged at 12,000 x g for 20 minutes to remove microvesicles, then EVPs were collected by ultracentrifugation in a Beckman Coulter Optima XE or XPE ultracentrifuge at 100,000 x g for 70 minutes. EVPs were then washed in PBS and pelleted again by ultracentrifugation at 100,000 x g for 70 minutes. The final EVP pellet was resuspended in PBS.\n
\n\n \n Dried Blood Spot (DBS)\n \n
\n\n Crew members warmed their hands and massaged their finger towards the fingertip to enrich blood flow towards the puncture site. The puncture site was sterilized using a BZK antiseptic towelette (Dynarex, Reorder No. 1303). Skin was punctured using a contact-activated lancet (BD Biosciences, #366593) or a 21-gauge needle (BD Biosciences, #305167), depending on crew member preference. Capillary blood was collected onto the Whatman 903 Protein Saver DBS cards (Cytiva, #10534612). Blood was transferred by touching only the blood droplet to the surface of the DBS card. DBS cards were stored at room temperature with a desiccant pack (Cytiva, #10548239).\n
\n\n \n Saliva\n \n
\n\n To collect crude saliva, crew members uncapped and spit into a sterile, PCR-clean, 5mL screw-cap tube (Eppendorf, 30122330). Crew spit repeatedly until at least 1mL was collected. Saliva was transported to a sterile flow hood and separated into 500uL aliquots. Aliquots were frozen at -80\u00b0C. To collect preserved saliva, crew members used the OMNIgene ORAL kit (DNA Genotek, OME-505). Crew members spit into the kit\u2019s tube until they reached the fill line. The tube was re-capped, which released the preservative liquid. Tubes were inverted to mix the saliva and preservative before being placed at -20\u00b0C for storage. After all timepoints were collected, DNA, RNA, and protein were extracted using the AllPrep DNA/RNA/Protein kit (Qiagen, #47054). Sample concentrations were measured with Qubit high sensitivity dsDNA and RNA platform. Proteins were quantified with the Pierce\u2122 Rapid Gold BCA Protein Assay Kit (Thermo Scientific, #A53225) on Promega GloMax Plate Reader.\n
\n\n \n Urine\n \n
\n\n Crew members urinated into sterile specimen containers (Thermo Scientific, #13-711-56). The container was stored at 4C until it was prepared for long-term storage. To prepare urine samples for long-term storage, urine was aliquoted into 1mL, 15mL, and 50mL tubes. Half of the urine was immediately placed at -80\u00b0C. The other half had urine conditioning buffer (Zymo, #D3061-1-140) added to the sample before placing in the -80\u00b0C freezer.\n
\n\n \n Stool Collection\n \n
\n\n Crew members isolated a stool sample using a paper toilet accessory (DNA Genotek, OM-AC1). Stool was transferred into and OMNIgene\u2022GUT tube (DNAgenotek, OMR-200) and an OMNImet\u2022GUT tube (DNA Genotek, ME-200). Tubes were placed at -80\u00b0C for long-term storage. For nucleic acid extraction, 200uL of each tube was allocated for DNA extraction with the QIAGEN PowerFecal Pro kit and 200uL was allocated to RNA extraction with the QIAGEN PowerViral\u00a0kit. The remaining sample was split into 500uL aliquots and re-stored at -80\u00b0C.\n
\n\n \n Swab Collection\n \n
\n\n Crew members put on gloves and remove a sterile swab from its packaging. For collection of the postauricular, axillary vault, volar forearm, occiput, umbilicus, gluteal crease, glabella, toe web space, and capsule environment regions, swabs were dipped in nuclease-free water (this step was skipped for oral and nasal swabs) for ground collections. For in-flight collections, HFactor hydrogen infused water was used in place of nuclease-free water. Each body location was swabbed for 30 seconds, using both sides of the swab. Swabs were then placed in 1mL Matrix 2D Screw Tubes containing 400uL of DNA/RNA Shield (Zymo). The tip of the swab was broken off so that only the swab tip was stored in the Matrix 2D Screw Tube. Tubes were stored at 4C.\n
\n\n \n Skin Biopsies\n \n
\n\n Skin biopsies were performed on the deltoid region of the arm. Each site was prepared by application of ChloraPrep and anesthesia was induced with administration of 1% lidocaine with 1:100,000 epinephrine. A trephine punch was used to remove a 3- or 4-mm diameter piece of skin. The resected piece was cut into approximately \u2153 and \u2154 sections. The smaller piece was added to a formalin-filled specimen jar. The larger piece was placed in a cryovial and stored at -80\u00b0C. Surgical defects were closed with 1 or 2 5-0 or 4-0 nylon sutures.\n
\n\n \n HEPA Filter\n \n
\n\n HEPA Filter was taken apart and sectioned under a chemical hood to avoid contamination. The filter contained two parts, an activated carbon component and a HEPA filter. The activated carbon component was discarded and the filter was sectioned using a sterile blade. Sections were placed in individual specimen containers and stored at -20\u00b0C.\n
\n\n \n Human Subjects Research\n \n
\n\n All subjects were consented and samples were collected and processed under the approval of the IRB at Weill Cornell Medicine, under Protocol 21-05023569.\n
\n\n \n Manuscript Preparation\n \n
\n\n Figures were generated using Adobe Illustrator and Biorender. Plots were generated in R using ggplot2. SpaceX Dragon capsule images are from the SpaceX Flickr Account (https://www.flickr.com/people/spacex/).\n
\n| \n \nSample(s)\n \n | \n\n \nMeasure(s)\n \n | \n\n \nNumber of Subjects (n)\n \n | \n\n \nDuration Range (days)\n \n | \n\n \nCollection Time points\n \n | \n\n \nStudy (citation)\n \n | \n
|---|---|---|---|---|---|
| \n \nPlasma\n \n | \n\n \nmtDNA, Long Non-coding RNA, Exosomes\n \n | \n\n \n3\u201314\n \n | \n\n \n5\u201313\n \n | \n\n \nL-10, R-0, R\u2009+\u20093\n \n | \n\n \n\n24,25\n\n \n | \n
| \n \nPlasma, Saliva\n \n | \n\n \nCytokines\n \n | \n\n \n13\n \n | \n\n \n140\u2013290\n \n | \n\n \nL-180, L-45, L-10, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, and R\u2009+\u200930\n \n | \n\n \n\n10\n\n \n | \n
| \n \nPlasma\n \n | \n\n \nCytokines\n \n | \n\n \n28\n \n | \n\n \n~\u2009180\n \n | \n\n \nL-180, L-45, L-10, FD15,30,60,120,180; R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n26\n\n \n | \n
| \n \nPlasma\n \n | \n\n \nProteomics\n \n | \n\n \n13\u201318\n \n | \n\n \n169\u2013199\n \n | \n\n \nL-30, R\u2009+\u20090, R\u2009+\u20097\n \n | \n\n \n\n20,27\u201329\n\n \n | \n
| \n \nPlasma\n \n | \n\n \nsRNAseq (miRNA from sEV)\n \n | \n\n \n14\n \n | \n\n \n12 (median)\n \n | \n\n \nL-10, R\u2009+\u20090, R\u2009+\u20093\n \n | \n\n \n\n30\n\n \n | \n
| \n \nPBMCs\n \n | \n\n \nPeripheral Leukocyte Distribution, T-cell Function, Virus-specific Immunity, and Mitogen-stimulated Cytokine Production profiles\n \n | \n\n \n23\n \n | \n\n \n<\u200960 days (n\u2009=\u20092), >\u2009100 days (n\u2009=\u20095), 6 months (n\u2009=\u200916)\n \n | \n\n \nL-180, L-45, FD14, FD 2\u20134 mn, FD6 mn, R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n31\n\n \n | \n
| \n \nplasma, PBMCs\n \n | \n\n \nsnoRNA Expression Levels\n \n | \n\n \nn\u2009=\u20095 (plasma), n\u2009=\u20096 (PBMCs)\n \n | \n\n \n14 (median)\n \n | \n\n \nL-10, R\u2009+\u20093\n \n | \n\n \n\n32\n\n \n | \n
| \n \nWhole blood, serum\n \n | \n\n \nHematology\n \n | \n\n \n14\n \n | \n\n \n167\u2009\u00b1\u200931 days (mean\u2009\u00b1\u2009sd)\n \n | \n\n \nL-100, FD5, FD11, FD64, FD157, R\u2009+\u20094, R\u2009+\u200914, R\u2009+\u200941, R\u2009+\u2009184\u2009<\u2009R\u2009+\u2009365\n \n | \n\n \n\n13\n\n \n | \n
| \n \nWhole blood\n \n | \n\n \nTranscriptome\n \n | \n\n \n6\n \n | \n\n \n10\u201313\n \n | \n\n \nL-10, R\u2009+\u20090 (2\u20133 hour after return)\n \n | \n\n \n\n33\n\n \n | \n
| \n \nWhole blood\n \n | \n\n \nHematology\n \n | \n\n \n31\n \n | \n\n \nUp to 180\n \n | \n\n \nL-180, L-45; FD-14, FD60-FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n34\n\n \n | \n
| \n \nWhole blood, Saliva\n \n | \n\n \nImmune Cell Counts, Cortisol\n \n | \n\n \n9\n \n | \n\n \n162\n \n | \n\n \nL-25, FD90, FD150, R\u2009+\u20091, R\u2009+\u20097, R\u2009+\u200930\n \n | \n\n \n\n35\n\n \n | \n
| \n \nbody swabs, saliva\n \n | \n\n \nMetagenomics\n \n | \n\n \n4\n \n | \n\n | \n\n \nL-180, L-45; FD-14, FD60-FD120, FD180, R\u2009+\u20090, R\u2009+\u200930, R\u2009+\u2009180\n \n | \n\n \n\n36\n\n \n | \n
| \n \nISS section swab\n \n | \n\n \nMetagenomics, Physiological Characterization of Microbes\n \n | \n\n \nLocations: Columbus (air, light cover, SSC laptop, handrails, RGSH); Node2 (sleeping unit, panel outside, ATU); Cupola (air, surface), Node3 (ARED, treadmill, WHC); Node1 (panel inside, dining table)\n \n | \n\n | \n\n \n3 timepoints (session A, B, and C)\n \n | \n\n \n\n37\n\n \n | \n
| \n \nSaliva, Swab: mouth, ear, nostril, pooled skin\n \n\n8 environmental locations\n \n | \n\n \nMicrobiome\n \n | \n\n \n1; node1, node2, node3, US laboratory module, permanent multipurpose module\n \n | \n\n \n135 days\n \n | \n\n \nBefore, During, After Spaceflight (L-180, L-90; FD60, FD97, FD126, R\u2009+\u20091, R\u2009+\u200930, R\u2009+\u2009180)\n \n | \n\n \n\n38\n\n \n | \n
| \n \nmicrobiome swabs, stool, saliva, plasma, environmental swabs\n \n | \n\n \nMetagenomics, Cytokine\n \n | \n\n \n9\n \n | \n\n \n180 (n\u2009=\u20098) to 360 (n\u2009=\u20091)\n \n | \n\n \nL-240, L-160, L-90, L-60, FD7, FD90, FD126, R\u2009+\u20090/3, R\u2009+\u200930, R\u2009+\u200960, R\u2009+\u2009180\n \n | \n\n \n\n39\n\n \n | \n
| \n \nBlood, urine, saliva\n \n | \n\n \nAntiviral antibodies and viral load (DNA) were measured for Epstein-Barr virus (EBV), varicella-zoster virus (VZV), and cytomegalovirus (CM)\n \n | \n\n \n17\n \n | \n\n \n12\u201316 days\n \n | \n\n \nSaliva: L-180, L-10, every other day during flight, and every other day post flight until R\u2009+\u200914\n \n\nBlood/Urine: L-180, L-10, R\u2009+\u20090, R\u2009+\u200914\n \n | \n\n \n\n40\n\n \n | \n
| \n \nWhole Blood, Plasma\n \n | \n\n \nImmunophenotyping, NK Cell cytotoxicity and conjugation, Degranulation,\n \n\nPlasma stimulation\n \n | \n\n \n9\n \n | \n\n \n6 mn to 340 days\n \n | \n\n \nL-180, L-60\u2009<\u2009FD90, FD180 (n\u2009=\u20091), R-1, R\u2009+\u20090, R\u2009+\u200918, R\u2009+\u200933, R\u2009+\u200966\n \n | \n\n \n\n41\n\n \n | \n
| \n \nWhole Blood, Plasma\n \n | \n\n \nLeukocyte distribution, T cell Blastogenesis, and cytokine production profiles\n \n | \n\n \n19\n \n | \n\n \n10\u201315 days\n \n | \n\n \nL-180, L-10, in-flight (R-1), R\u2009+\u20090, R\u2009+\u200914\n \n | \n\n \n\n42\n\n \n | \n
| \n \nPlasma, whole blood, saliva\n \n | \n\n \nB Cell Phenotyping\n \n\nIg Analyses\n \n | \n\n \nIntegral Immune Study (n\u2009=\u200915)\n \n\nSalivary Markers Study (n\u2009=\u20098)\n \n | \n\n \n6 months\n \n | \n\n \nSalivary:\n \n\nPlasma: L-180, L-45, FD10, FD90, FD180/R-1, R\u2009+\u20090, R\u2009+\u200930\n \n\nSalivary Marker Study: L-180, L-60, FD-10, FD-90, FD-180/R-1, R\u2009+\u20090, R\u2009+\u200918, R\u2009+\u200933, and R\u2009+\u200966\n \n | \n\n \n\n43\n\n \n | \n
| \n \nSaliva, Blood, Urine\n \n | \n\n \nSalivary Biomarkers, Stress biomarkers\n \n | \n\n \n8 ISS Crew, 7 control\n \n | \n\n \n6 months\n \n | \n\n \nL-180, L-60, FD10, FD90, R-1, R\u2009+\u20090, R\u2009+\u200918, R\u2009+\u200933, R\u2009+\u200966\n \n | \n\n \n\n44\n\n \n | \n
| \n \nSaliva\n \n | \n\n \nSalivary Microbiome\n \n | \n\n \n10 (male)\n \n | \n\n \n2\u20139 months\n \n | \n\n \nL-180, L-90\n \n\nFD 1\u20132 months\n \n\nFD 2\u20134 months\n \n\nFD (R-10)\n \n\nR\u2009+\u20090, R\u2009+\u200930, R\u2009+\u200960, R\u2009+\u2009180\n \n | \n\n \n\n45\n\n \n | \n
| \n \nBlood, Urine, Saliva\n \n | \n\n \nAntiviral Antibodies and Viral Load\n \n | \n\n \n17 (16 male, 1 female)\n \n | \n\n \n12\u201316 days\n \n | \n\n \nBlood, Urine: L-180, L-10, R\u2009+\u20090, R\u2009+\u200914\n \n\nSaliva Dry: L-180, L-10, FD1, FD11, R\u2009+\u20091, R\u2009+\u200914\n \n\nSaliva Liquid: L-180, L-10, FD1,FD3,FD5,FD7,FD9,FD11 R\u2009+\u20090, R\u2009+\u20092, R\u2009+\u20094, R\u2009+\u20096, R\u2009+\u20098, R\u2009+\u200910, R\u2009+\u200912, R\u2009+\u200914\n \n | \n\n \n\n46\n\n \n | \n
| \n \nPlasma, PBMCs, Urine\n \n | \n\n \nThymopoiesis\n \n | \n\n \n16 (14 male, 2 female)\n \n | \n\n \nMedian: 184 days\n \n | \n\n \nRegular Intervals (preflight, return, postflight)\n \n | \n\n \n\n47\n\n \n | \n
| \n \nCore Body Temperature,\n \n\nWhole Blood\n \n | \n\n \nCore Body Temperature, IL-1ra\n \n | \n\n \n11 (7 male, 4 female)\n \n | \n\n \n180 days\n \n | \n\n \nCBT: L-90, FD15, FD45, FD75, FD105, FD135, FD165, R\u2009+\u20091, R\u2009+\u200910, R\u2009+\u200930\n \n\nBlood: L-180, L-45, L-10, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n48\n\n \n | \n
| \n \nPlasma, Serum, Urine\n \n | \n\n \nIron Status\n \n | \n\n \n23 (16 male, 7 female)\n \n | \n\n \n50\u2013247 days (mean: 157\n \n | \n\n \nL-180, L-45, L-10, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n49\n\n \n | \n
| \n \nBlood, Urine\n \n | \n\n \nBone Loss and Kidney Stone Risk\n \n | \n\n \n42\n \n | \n\n \n49\u2013215 days\n \n | \n\n \n10\u2013131 days before flight and after flight ( R\u2009+\u20090, R\u2009+\u20090 amd R\u2009+\u20092)\n \n | \n\n \n\n50\n\n \n | \n
| \n \nBlood, Urine\n \n | \n\n \nBone Metabolism and Renal Stone Risk\n \n | \n\n \n23\n \n | \n\n \n4\u20136 months\n \n | \n\n \nL-180, L-45, L-10, FD15, FD30, FD60, FD120, FD180\n \n | \n\n \n\n51\n\n \n | \n
| \n \nSerum, Urine, Epithelial cells (sublingual mucosa)\n \n | \n\n \nMagnesium\n \n | \n\n \n43\n \n | \n\n \n4\u20136 months\n \n | \n\n \nSerum/Urine: L-180, L-45, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n\nTissue: L-180, L-45, R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n52\n\n \n | \n
| \n \nSerum, Urine\n \n | \n\n \nBone Metabolism\n \n | \n\n \n17 (13 male, 4 female)\n \n | \n\n \n160 +/-20 days\n \n | \n\n \nL-180, L-45, FD15, FD30, FD60, FD120, FD180\n \n | \n\n \n\n53\n\n \n | \n
| \n \nBlood\n \n | \n\n \nNatriuretic Peptide, Creatinine, Aldosterone, Sodium\n \n | \n\n \n8\n \n | \n\n \nLong Duration\n \n | \n\n \nNot specified\n \n | \n\n \n\n54\n\n \n | \n
| \n \nBlood, Urine, Ultrasound\n \n | \n\n \nArterial Structure and Function\n \n | \n\n \n13 (10 male, 3 female)\n \n | \n\n \n126\u2013340 days\n \n | \n\n \nL-180, L-60, FD15, FD60, FD160, R\u2009+\u20095\n \n | \n\n \n\n55\n\n \n | \n
| \n \nBlood, Urine, quantitative CT\n \n | \n\n \nBone Metabolism, Bone Density, Bone Strength\n \n | \n\n \n17 (14 male, 3 female)\n \n | \n\n \n3.5-7 months (mean: 170 days)\n \n | \n\n \nBlood/Urine: L-180, L-45, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090\n \n | \n\n \n\n56\n\n \n | \n
| \n \nStool, Saliva, Skin, Urine, Blood, Plasma, PBMCs\n \n | \n\n \nMetabolomics, Proteomics, Cognition, Microbiome, Telomeres, Epigenomics, Biochemical Profile, Gene Expression, Integrative Omics, Immunome\n \n | \n\n \n2\n \n | \n\n \n1-Year (340 days)\n \n | \n\n \nBefore, during, and after spaceflight\n \n | \n\n \n\n17\n\n \n | \n
| \n \nBlood, Urine\n \n | \n\n \nMulti-omics\n \n | \n\n \n59\n \n | \n\n \n4\u20136 months\n \n | \n\n \nL-180, L-45, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n57\n\n \n | \n
| \n \nPlasma\n \n | \n\n \nCell-free DNA, Exosome\n \n | \n\n \n2\n \n | \n\n \n340 days\n \n | \n\n \nBefore, during, and after spaceflight (12 timepoints from twin on earth and 11 from twin in space)\n \n | \n\n \n\n58\n\n \n | \n
| \n \nPlasma, Urine\n \n | \n\n \nMulti-omic, Single-Cell, Biochemical Measures\n \n | \n\n \n2\n \n | \n\n \n340 days\n \n | \n\n \nBefore, during, and after spaceflight\n \n | \n\n \n\n11\n\n \n | \n
| \n \nBlood, Urine\n \n | \n\n \nTelomere Length\n \n | \n\n \n3\n \n | \n\n \n1 Year (n\u2009=\u20091), 6 months (n\u2009=\u20092)\n \n | \n\n \nBlood: L-270, L-180, L-60, FD45, FD90, FD140, FD260, R\u2009+\u20091, R\u2009+\u2009180, R\u2009+\u2009270\n \n\nUrine: L-180, L-45, FD15, FD240, FD330, R\u2009+\u20091, R\u2009+\u200960\n \n\nBiochemistry:L-80, L-45, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n59\n\n \n | \n
| \n \nPBMCs, Lymphocyte-depleted Cells\n \n | \n\n \nCirculating miRNA\n \n | \n\n \n2\n \n | \n\n \n340 days\n \n | \n\n \nBefore, during, and after flight\n \n | \n\n \n\n18\n\n \n | \n
| \n \nBlood\n \n | \n\n \nClonal Hematopoiesis Panel, Whole Genome Sequencing, RNA-seq\n \n | \n\n \nAstronauts: n\u2009=\u20092\n \n | \n\n \n340 days\n \n | \n\n \nBefore, during, and after spaceflight\n \n | \n\n \n\n15\n\n \n | \n
| \n \nBlood\n \n | \n\n \nMulti-omic, Untargeted RNA-seq\n \n | \n\n \n2\n \n | \n\n \n340 days\n \n | \n\n \nBefore, During, and After Spaceflight\n \n | \n\n \n\n60\n\n \n | \n
| \n \nBlood\n \n | \n\n \nUremic Toxin\n\np\n\n-Cresol\n \n | \n\n \n2\n \n | \n\n \n340 days\n \n | \n\n \nBefore, During, and After Spaceflight\n \n | \n\n \n\n61\n\n \n | \n
| \n \nBlood\n \n | \n\n \nMetabolic Profile\n \n | \n\n \n51\n \n | \n\n \n4\u20136 months\n \n | \n\n \nL-45, L-10, FD15, FD30, FD60, FD120, FD180, R\u2009+\u20090, R\u2009+\u200930\n \n | \n\n \n\n62\n\n \n | \n
| \n \nSample Type\n \n | \n\n \nTube Source\n \n | \n\n \nAssay Allocation(s)\n \n | \n
|---|---|---|
| \n \nWhole Blood\n \n | \n\n \nbRNA\n \n | \n\n \nTotal RNA Extraction\n \n | \n
| \n \nPlasma\n \n | \n\n \nCPT\n \n | \n\n \nProteomics, Metabolomics; Biobanking\n \n | \n
| \n \nPBMCs\n \n | \n\n \nCPT\n \n | \n\n \nBiobanking\n \n | \n
| \n \nRed Blood Cell Pellet\n \n | \n\n \nCPT\n \n | \n\n \ngDNA; Biobanking\n \n | \n
| \n \nSerum\n \n | \n\n \nSST\n \n | \n\n \nImmune and Cardiovascular Disease Panel, Metabolic Panel; Biobanking\n \n | \n
| \n \nRed Blood Cell Pellet\n \n | \n\n \nSST\n \n | \n\n \ngDNA; Biobanking\n \n | \n
| \n \nPlasma\n \n | \n\n \ncfDNA BCT\n \n | \n\n \ncfDNA; Biobanking\n \n | \n
| \n \nRed Blood Cell Pellet\n \n | \n\n \ncfDNA BCT\n \n | \n\n \ngDNA; Biobanking\n \n | \n
| \n \nPBMCs\n \n | \n\n \nK2 EDTA\n \n | \n\n \nSingle-Cell Multiome GEX\u2009+\u2009ATAC and BCR/TCR Immune Repertoire Profiling\n \n | \n
| \n \nPlasma\n \n | \n\n \nK2 EDTA\n \n | \n\n \nEVPs\n \n | \n
| \n \nWhole Blood\n \n | \n\n \nK2 EDTA\n \n | \n\n \nComplete Blood Count\n \n | \n
| \n \nSample Type\n \n | \n\n \nTube Source\n \n | \n\n \nAliquot Sizes\n \n | \n\n \nFreezing Condition\n \n | \n
|---|---|---|---|
| \n \nPlasma\n \n | \n\n \ncfDNA BCT\n \n | \n\n \n500 uL\n \n | \n\n \n-80\u00b0C Freezer\n \n | \n
| \n \nPlasma\n \n | \n\n \nCPT\n \n | \n\n \n500 uL\n \n | \n\n \n-80\u00b0C Freezer\n \n | \n
| \n \nSerum\n \n | \n\n \nSST\n \n | \n\n \n500 uL\n \n | \n\n \n-80\u00b0C Freezer\n \n | \n
| \n \nPBMCs\n \n | \n\n \nCPT\n \n | \n\n \n\u2159 tube yield\n \n | \n\n \n-196\u00b0C Liquid Nitrogen\n \n | \n
\n Epilepsy is a chronic and heterogenous disease characterized by recurrent unprovoked seizures, that are commonly resistant to antiseizure medications. This study is the first to apply a transcriptome network-based approach across epilepsies aiming to improve understanding of molecular disease pathobiology, recognize affected biological mechanisms and apply causal reasoning to identify novel therapeutic hypotheses. This study included the most common drug-resistant epilepsies (DREs), such as temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), and mTOR pathway-related malformations of cortical development (mTORopathies). This systematic comparison characterized the global molecular signature of epilepsies, elucidating the key underlying mechanisms of disease pathology including neurotransmission and synaptic plasticity, brain extracellular matrix and energy metabolism. In addition, specific dysregulations in neuroinflammation and oligodendrocyte function were observed in TLE-HS and mTORopathies, respectively. The aforementioned mechanisms are proposed as molecular hallmarks of DRE with the identified upstream regulators offering novel opportunities for drug-target discovery and development.\n
\n\n \n \n Biological sciences/Computational biology and bioinformatics/Gene regulatory networks\n \n \n
\n\n \n refractory epilepsy\n \n
\n\n \n transcriptomics\n \n
\n\n \n gene coexpression modules\n \n
\n\n \n biological mechanism\n \n
\n\n \n causal reasoning\n \n
\n\n Epilepsy is typically defined as a chronic disease characterized by recurrent unprovoked seizures\n \n \n 1\n \n \n . However, the concept of epilepsy is evolving and it is recognized that besides seizures patients are also affected by cognitive, psychological and social impairments\n \n \n 2\n \n ,\n \n 3\n \n \n , as well as increased mortality\n \n \n 4\n \n \n . The heterogeneity in causes and clinical expression of the disease leads us to more commonly use the term \u2018epilepsies\u2019. There is an urgent need to identify new therapeutic targets and develop novel tailored medications that go beyond the current antiseizure medications (ASMs)\n \n \n 5\n \n \n , both in efficacy and in addressing the disease starting from the pathobiology. Discriminating the factors contributing to different subtypes of drug-resistant epilepsy (DRE) would shed light on the pathobiological mechanisms that are shared or specific across disease types, and enable hypotheses to be established for developing precision medicines to ensure better patient care.\n
\n\n Here, we focused on some of the most common forms of DREs, temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) and malformations of cortical development, including focal cortical dysplasia type IIa and type IIb (FCD IIa and FCD IIb) and cortical tubers in tuberous sclerosis complex (TSC). TLE-HS is characterized by selective neuronal cell loss with concomitant astrogliosis in the hippocampus\n \n \n 6\n \n \n . FCD type II and TSC cortical tubers are characterized by hyperactivation of the mTOR-signaling pathway and collectively termed mTORopathies\n \n \n 7\n \n \n . Furthermore, both pathologies are characterized by common histopathological hallmarks such as cortical dyslamination, dysmorphic neurons and large immature cells called balloon cells in FCD IIb (absent in FCD IIa) or giant cells in TSC cortical tubers\n \n \n 8\n \n ,\n \n 9\n \n \n . Despite the large research efforts to elucidate the molecular mechanisms underlying epilepsies, the molecular profile contributing to the epileptogenicity in TLE-HS and the mTORopathies is not completely understood.\n
\n\n Discovering novel disease pathways has the potential to reveal new druggable targets that could restore impaired gene expression back to homeostasis. The network-based system analysis \u2018Causal Reasoning Analytical Framework for Target discovery\u2019 (CRAFT) previously identified epilepsy-specific gene coexpression modules (i.e. sets of coexpressed genes) in a pilocarpine mouse model, allowing the identification of novel therapeutic candidates\n \n \n 10\n \n \n . Here, gene coexpression modules allowed for the assembly of an unbiased, global model of the pathobiology based on the assumption that biological pathways are dysregulated in the disease state. CRAFT identifies potential upstream regulators by predicting the interaction between cell membrane receptor proteins (CMPs), transcription factors (TFs) and downstream target genes\n \n \n 10\n \n \n .\n
\n\n To our knowledge, available transcriptomics datasets for epilepsy are often limited to one pathology, lacking comparison across epilepsies, and are low in sample number\n \n \n 11\n \n \u2013\n \n 15\n \n \n . Therefore, further investigation of a larger cohort involving different pathologies can extend our understanding of the pathobiological mechanisms that underly epilepsy.\n
\n\n This study enabled the construction of the global molecular signature of epilepsies by comparing disease transcriptional profiles, and identified key underlying mechanisms shared across epilepsies that are involved in neurotransmission and synaptic plasticity, immune response, brain extracellular matrix (ECM) and energy metabolism. In addition, specific dysregulations in neuroinflammation and neuronal support and myelination were identified in TLE-HS and mTORopathies, respectively. We propose that these mechanisms are the putative molecular hallmarks of DRE and may be active players in disease progression. The upstream regulators identified here by causal reasoning offer hypotheses to test their effect on disease and, potentially, generate novel opportunities for drug-target discovery.\n
\n\n This study was the first to provide a data-driven framework for the systematic identification of dysregulated biological pathways in the disease state and to categorize global epilepsy mechanisms across DREs. The identification of impaired transcriptional coregulations in and across different epilepsy pathologies combined with predicted mechanistic regulatory hypotheses can be leveraged experimentally to test their therapeutic potential.\n
\n\n Transcriptional differentiation between cohorts by tissue type and disease\n
\n\n In total, 28,366 expressed genes (mapped reads\u2009\u2265\u20096 counts in at least 20% of samples within each cohort) were detected across the cohorts. First, to obtain a global understanding of the transcriptional landscape and assess potential differentiation between clinical cohorts, sample clustering was explored using both unsupervised hierarchical clustering and supervised discriminant analysis on principal components to identify discriminatory features between cohorts.\n
\n\n The unsupervised hierarchical clustering showed that the TLE-HS cohort could be distinguished from the mTORopathies cohort, and further, there was no clear separation within the latter (Fig.\n \n 1\n \n a). Discriminant features associated with tissue on the first component (cortex vs hippocampus) and disease status on the second component (epilepsy vs healthy) were identified (Fig.\n \n 1\n \n b,c). However, as the epilepsy condition is partly defined by the brain area of seizures origin, the effect of tissue and disease could not be assessed independently. Figure\n \n 1\n \n d shows the prior and posterior assignment of individuals to the cohorts which indicated a good reassignment rate for TLE-HS. A lower reassignment rate for the mTORopathies, specifically for FCD IIa patient samples, where only half of the individuals were reassigned to their cohort (Fig.\n \n 1\n \n d), indicated difficulty in discriminating between these populations when taking all six cohorts together.\n
\n\n A focused analysis was performed on the three mTORopathies cohorts to explore their transcriptional similarity\n \n \n 16\n \n ,\n \n 17\n \n \n . The first discriminant component and reassignment proportion suggest a gradual change in gene expression profile in individuals diagnosed with FCD IIa that were reassigned to FCD IIb but not TSC (Fig.\n \n 1\n \n e). Similarly, more overlap was found between TSC and FCD IIb than with FCD IIa (Fig.\n \n 1\n \n e). Based on these results, all three pathologies will be considered as an additional meta-cohort to explore potential shared regulations between mTORopathies.\n
\n\n Identification of gene coexpression modules within epilepsy pathologies\n
\n\n It is hypothesized that gene coexpression modules (\u2018gene modules\u2019) can build an unbiased, global model of epilepsy pathobiology based on the assumption that some biological pathways may be differentially regulated in the disease state due to perturbations of gene expression control. The workflow to annotate the identified gene modules is described in the Materials and methods section. Briefly, the pathway and cell type annotation aimed to capture the potential underlying biology. The differential coexpression between disease and healthy control samples identified gene modules affected in the disease state. Finally, the correlation of each gene within each module is assumed to be the consequence of a common (set of) upstream transcriptional regulator(s) activity. The causal reasoning framework, CRAFT, predicts upstream regulators (transcriptional regulators, TFs and miRNA, as well as CMPs) that, based on current knowledge, could affect the modules to form an actionable regulatory hypothesis.\n
\n\n This workflow was applied to all cohorts (TLE-HS, FCD IIa, FCD IIb and TSC) except the FCD IIa cohort due to insufficient sample numbers. Figure\n \n 2\n \n shows the change in gene coexpression (R\u00b2) highlighting the annotated biology for the affected modules related to multiple brain functions such as neurotransmission and synaptic plasticity, immune response and energy metabolism among others. No association to phenotype was identified for the modules in any cohort. A summary of the results of the identified gene modules per cohort is described in Table\n \n 1\n \n . The next paragraphs describe the most affected gene modules and there are further details in Supplementary Tables\u00a01\u20134.\n
\n\n For TLE-HS, 37 gene modules were identified with eight modules presenting a significant change in coexpression as measured by R\u00b2 between disease and healthy control patient samples, indicating that these modules were significantly affected in TLE-HS (Fig.\n \n 2\n \n a, panel TLE-HS). For example, TLE.13.o, TLE.7.o and TLE.12.u were the most perturbed modules with more than 50 genes per module with an \u0394R\u00b2 ranging between 0.24 and 0.32. These modules highlighted different biological function as affected in epilepsy (immune response/neuroinflammation, extracellular matrix function and mRNA/protein processing). Multiple upstream regulators were identified using the causal reasoning framework. For TLE.13.o up to 26 module regulators were predicted, including miRNAs (2), TF (14) and CMPs (328). For TLE.7.o up to 366 regulators were predicted, including TF (4) and CMP (275) with no candidate regulators for TLE.12.u. Overall, out of the nine gene modules identified to be affected in epilepsy, transcriptional regulators and CMPs were available for six and four gene modules, respectively.\n
\n\n FCD IIb\n
\n\n The analysis of FCD IIb identified 28 gene modules with 22 gene modules significantly differentially coexpressed (Fig.\n \n 2\n \n a, panel FCD IIb). Gene modules that showed significant differential coexpression were involved in immune response, oligodendrocyte function, oxidative phosphorylation among others (Supplementary Tables\u00a03 and 4). The most affected modules FCD2b.7.o and FCD2b.14.u (\u0394R\u00b2 ranging between 0.49 and 0.54) captured less than 20 genes, limiting their relevance. Modules FCD2b.5.o, FCD2b.6.o and FCD2b.6.u contained between 240 and 330 genes with functions related to mRNA translation (FCD2b.5.o), oxidative phosphorylation (FCD2b.6.o) and endosome function (FCD2b.6.u) (Supplementary Table\u00a04). Overall, six of the 28 identified gene modules lacked functional annotation. The causal reasoning identified multiple regulatory hypotheses. For FCD2b.5.o, one TF (SAFB) and 19 upstream CMPs were predicted. For FCD2b.6.o, 62 transcriptional regulators (60 miRNA/2 TF) and 33 upstream CMPs were predicted. No upstream regulator could be identified for FCD2b.6.u.\n
\n\n In TSC, 31 gene modules were identified with 23 gene modules significantly differentially coexpressed (Fig.\n \n 2\n \n a, panel TSC). The strongest differential coexpression resulted for modules TSC.11.u, TSC.13.o, TSC.13.u and TSC.14.o containing 120\u2013290 genes in the modules with a \u0394R\u00b2 ranging from 0.48 and 0.52. These four modules were enriched for a broad spectrum of different functions, such as modulation of chemical synaptic transmission, positive regulation of cytokine production, postsynaptic density and interferon signaling. Like FCD IIb, not all affected modules could be biologically annotated despite utilizing different pathway resources (Supplementary Table\u00a04). CRAFT identified two TFs as well as 12 CMPs for TSC.11.u. For TSC.13.o, 21 transcriptional regulators (3 miRNA / 18 TF) and 380 upstream CMPs were found. Although no upstream regulators were identified for TSC.13.u, 68 transcriptional regulators were predicted for TSC.14.o (2 miRNA / 66 TF) as well as 392 upstream CMPs.\n
\n\n mTORopathies\n
\n\n In the mTOR cohort (all FCD IIa, FCD IIb and TSC samples), 27 gene modules were identified but only nine gene modules were found differentially coexpressed (Fig.\n \n 2\n \n a, panel mTORopathy). The strongest significant differential coexpression could be identified for gene modules mTOR.1.o (393 genes), mTOR.10.o (293 genes), mTOR.10.u (257 genes) and mTOR.1.o (3 genes) with R\u00b2 ranging from 0.33 to 0.35. Due to the limited size of mTOR.1.u, only the remaining three modules will be described further here. Functional annotation of these modules related to RNA splicing, response to topologically incorrect protein folding and extracellular matrix organization. CRAFT could not identify any upstream regulators for gene module mTOR.10.o, whereas for mTOR.1.o it identified 41 potential transcriptional regulators (4 miRNA / 37 TF) and 384 upstream CMPs. Similarly for mTOR.10.u, 51 transcriptional regulators (49 miRNA / 2 TF) and 25 upstream CMPs were identified.\n
\n\n Identified affected gene module and regulators may provide novel opportunities to modulate these networks and restore their homeostatic gene expression profile. Figure\n \n 2\n \n a shows the identification of neurotransmission and synaptic plasticity, immune response, brain ECM, energy metabolism and neuronal support and myelination affected in epilepsy. To enable a global understanding of the regulation of pathobiology of epilepsy, the next section discusses the overall comparison of these identified modules and their regulators.\n
\n\n Connecting gene modules across epilepsy cohorts identifies shared biology\n
\n\n The gene coexpression module analysis identified modules related to similar biological functions across the different epilepsy patient cohorts. Here, systematic comparison based on all identified modules was performed to enable a global and objective understanding of conserved or disease-specific modules. Unsupervised clustering of gene modules based on the inclusion index identified clusters of gene modules that were functionally annotated to infer their potential shared biology. These clusters are termed \u2018regulomes\u2019 to better capture the functional role of cluster of gene modules as global regulatory pathways in the epilepsy pathobiology. In this context, a regulome refers to the transcriptional regulation that may depend on the pathological state of the tissue\n \n \n 18\n \n \n . Finally, the shared predicted TFs by the individual CRAFT analyses were listed as candidate regulators with potential to act across epilepsies.\n
\n\n Differential coexpression and conservation was used to measure activity states across the different pathologies enabling the regulomes to be separated into four different categories: constitutive, enhanced, activated, and pathology-specific regulomes. \u2018Constitutive\u2019 regulomes show no change between the control and epilepsy patient samples. \u2018Enhanced\u2019 regulomes are conserved in cohorts but showed significant increased activity in epilepsy. \u2018Activated\u2019 regulomes are only present and active in epilepsy. Finally, some gene modules did not present a strong overlap with gene modules from any other epilepsy conditions; however, as these modules were differentially coexpressed in a specific epilepsy cohort, these were referred to as \u2018pathology-specific\u2019 regulomes.\n
\n\n The analysis revealed 28 regulomes varying in size from two to 10 gene modules (Fig.\n \n 2\n \n b, Supplementary Table\u00a05) as not all gene modules could be grouped (\n \n n\n \n =\u200910). Here, regulomes (\n \n n\n \n =\u200912) with a consistent functional annotation across multiple pathway databases and effect in epilepsy were selected. Based on the classification described above, regulomes related to neurotransmission and synaptic plasticity, immune response, brain ECM, energy metabolism and oligodendrocyte function are highlighted.\n
\n\n Immune response and neuroinflammation\n
\n\n The discrimination between clusters enriched for immune response pathways and neuroinflammation relies on the pathway annotations. Neuroinflammation concerns the process mediated by resident central nervous system glia (microglia and astrocytes) and endothelial cells\n \n \n 19\n \n \n , whereas immune response is defined as the reaction of the body against the impaired homeostasis involving the recruitment of immune cells leading to a systemic response\n \n \n 19\n \n \n . Although regulomes can show a stronger association to one or another, differentiation between immune response and neuroinflammation regulomes is not absolute and they are presented here together.\n
\n\n The first regulome enriched for immune response and neuroinflammation belongs to the \u2018enhanced\u2019 regulomes capturing modules TLE.10.o, TLE.19.o, TSC.3.o, TSC.13.o and mTOR.13.o. The enrichment for the intersecting genes showed enrichment for \u2018immune response_Antigen presentation by MHC class I: cross-presentation\u2019 (MetaBase), \u2018Neutrophil degranulation\u2019 (Reactome), \u2018positive regulation of cell activation\u2019 and \u2018immunoglobulin binding\u2019 (GO). Cell type marker enrichment from PanglaoDB identified significant overlap with markers from macrophages and microglia. These immune response-related gene modules showed a differentiated effect across the different cohorts, with significant increase in gene coexpression detected in TLE (TLE.10.o) and TSC (TSC.3.o and TSC.13.o). In contrast, module TLE.19.o and mTOR.13.o showed no activation in the TLE-HS and mTORopathy cohorts (Supplementary Fig.\u00a01a). Conservation statistics also differed between the cohorts. For TLE-HS the regulome was conserved in hippocampus controls but not in cortex controls. Similarly, module TLE.19.o was not conserved in FCD IIb whereas module TLE.10.o was not conserved in either FCD IIa or IIb. The TSC modules showed no conservation of coexpression in control cortex indicating the activated status of this particular regulome in the disease state, in alignment with the strong observed differential coexpression. mTOR.13.o showed conservation in control and all epilepsy cohorts but, similarly, no change in coexpression comparing disease and control cohorts (Supplementary Table\u00a03). Finally, several common transcriptional regulators, such as PU.1, ETS1, STAT1, IRF8 and NF-kB were consistently predicted to activate their downstream genes, with the single exception of STAT3 which showed inhibition of module TSC.3.o and mTOR.13.o while activating modules mTLE.10.o, mTLE.19.o and TSC.13.o (Supplementary Fig.\u00a01b).\n
\n\n A pathology-specific regulome (module TLE.20.o) was identified related to \u2018immune response_IL-1 signaling pathway\u2019 and \u2018innate immune response to contact allergens\u2019 (MetaBase), \u2018interleukin-4 and Interleukin-13 signaling\u2019 and \u2018interleukin-10 signaling\u2019 (Reactome) and \u2018inflammatory response\u2019 (GO). In addition, this gene module was enriched for cell type markers related to microglia (PanglaoDB). Although several gene modules across different cohorts were related to microglia function, TLE.20.o has a limited gene overlap with any of the other identified gene modules in the FCD IIb, mTOR or TSC cohorts (Supplementary Table\u00a01). This specific module showed a stronger and significant coregulation in brain tissues from TLE patients versus control post-mortem samples (Supplementary Fig.\u00a01c).\n
\n\n Neuronal support and myelination\n
\n\n The neuronal support and myelination regulome includes FCD2b.4.o, FCD2b.14.o, mTOR.2.o, TSC.4.o, TLE.4.o and TLE.17.o. However, only mTORopathies gene modules FCD2b.14.o, mTOR.2.o and TSC.4.o were significantly perturbed, except FCD2b.4.o and TLE-HS modules, TLE.4.o and TLE.17.o (Fig.\n \n 3\n \n a). Therefore, this neuronal support and myelination regulome was assigned as \u2018pathology-specific\u2019. The following annotations \u2018triacylglycerol metabolism p.2\u2019 (MetaBase), \u2018G alpha (i) signaling events\u2019 (Reactome), \u2018ensheathment of neurons\u2019 and \u2018actin binding\u2019 (GO) were identified as enriched in each module. In addition, the intersecting genes showed significant overlap with oligodendrocyte cell type markers (PanglaoDB). The regulations of all gene modules were conserved in both control and disease samples but enhanced in the disease state. The two most common upstream transcriptional regulators identified by CRAFT were SOX10 which activated the modules and miR-488-5p which inhibited the expression of genes belonging to the gene modules (Fig.\n \n 3\n \n b).\n
\n\n
\n\n Brain extracellular matrix\n
\n\n Modules FCD2b.1.o, mTOR.1.o, mTLE.5.o and mTLE.7.o were identified in brain ECM \u2018activated\u2019 regulome. Significant enrichment was found for \u2018cytoskeleton remodeling\u2019 (MetaBase), \u2018extracellular matrix organization\u2019 (Reactome), \u2018supramolecular fiber organization\u2019 and \u2018extracellular matrix structural constituent\u2019 (GO), as well as enrichment for markers of Bergmann glia, the highly specialized radial astrocytes of the cerebellar cortex (PanglaoDB) (Supplementary Table\u00a04). Among the gene modules involved in this regulation, mTOR.1.o, mTLE.7.o and FCD2b.1.o showed a significant increase in coexpression (Fig.\n \n 3\n \n c). This regulome was not conserved in control patient samples but became activated in the disease cohorts (Fig.\n \n 3\n \n c). Finally, a common transcriptional regulator was identified to activate regulation of modules, namely SP1 (Fig.\n \n 3\n \n d). The cellular expression pattern of SP1 immunoreactivity (IR) was confirmed in astroglial cells in TLE-HS samples, whereas control hippocampus only showed low expression of SP1 in neuronal cells (Fig.\n \n 3\n \n e). Similarly, in control cortex the expression of SP1 was low in neuronal cells and sporadic in astrocytes within the white matter. In FCD IIb and TSC, SP1 IR was observed in dysplastic neurons, astrocytes and balloon cells/giant cells, whereas microglia/macrophages showed absence of SP1 expression.\n
\n\n Energy metabolism\n
\n\n The regulome capturing energy metabolism consists of FCD2b.6.o, mTOR.5.u, TSC.7.u, FCD2b.12.u and mTLE.11.o. As this regulome was affected in the epilepsy cohort only, it was classified as \u2018activated\u2019. Functional annotation associated with this module included \u2018oxidative phosphorylation\u2019 (MetaBase), \u2018respiratory electron transport\u2019 (Reactome), and \u2018generation of precursor metabolites and energy\u2019 (GO). However, no annotation with cell type markers from PanglaoDB could be identified (Supplementary Table\u00a04). All gene modules showed an increase in coexpression but significance was only reached for gene modules FCD2b.6.o, mTOR.5.u, TSC.7.u and FCD2b.12.u (Fig.\n \n 3\n \n f). None of these gene modules were conserved in the control cohorts (Fig.\n \n 3\n \n f). The most common transcriptional regulator KMD1A/LSD1 was predicted to activate gene modules FCD2b.12.u, TSC.7.u and mTOR.5.u (Fig.\n \n 3\n \n g). Cellular expression patterns of LSD1 IR in TLE-HS, FCD IIb and TSC (Fig.\n \n 3\n \n h) showed restricted neuronal expression in control hippocampus, contrary to nuclear expression in both neurons and astrocytes in TLE-HS resected hippocampus. Similarly, in control cortex and white matter, the expression of LSD1 was restricted to neuronal cells, whereas FCD IIb and TSC showed LSD1 expression in dysplastic neurons, astrocytes and balloon cells/giant cells.\n
\n\n Neurotransmission and synaptic plasticity\n
\n\n A second \u2018enhanced\u2019 regulome captured neurotransmission and synaptic plasticity showing enrichment for \u2018nicotine signaling\u2019 (MetaBase), \u2018transmission across chemical synapse\u2019 (Reactome) and \u2018chemical synaptic transmission\u2019 (GO). Cell type marker enrichment from PanglaoDB identified significant overlap with markers from interneurons and neurons (Supplementary Table\u00a04). These neurotransmission and synaptic plasticity-related modules showed a differentiated effect across the different pathologies with a significant increase in gene coexpression in FCD IIb (FCD2b.7.u) and TSC (TSC.10.u) (Supplementary Fig.\u00a02a). However, the modules are conserved in both control and epilepsy cohorts. Common upstream regulators NRSF and CoREST have been identified as having an inhibitory effect (Supplementary Fig.\u00a02b).\n
\n\n Chronic DREs are highly heterogeneous but despite differences in etiology and clinical presentations, TLE-HS and mTORopathies (FCD II and TSC) potentially share downstream molecular mechanisms underlying drug-resistance. To our knowledge, this is the first study to apply a network-based approach across human epilepsies and independently identify multiple dysregulated biological processes. Upstream regulators identified by CRAFT open up the possibility of assessing their ability to restore gene expression towards the healthy state.\n
\n\n In this study, a global comparison of the transcriptional profile of 162 human brain samples showed separation according to disease and tissue of origin. However, as the epilepsy condition is partly defined by the brain region of seizure origin, the effect of tissue type and disease could not be assessed independently. A more detailed assessment of mTORopathies aligned with well-described histopathological evidence indicates a spectrum from FCD IIa to FCD IIb to TSC cortical tubers. The only discriminator between FCD IIa and FCD IIb is the presence of balloon cells in FCD IIb, which appear to act as crucial drivers of inflammation in this FCD subtype\n \n \n 20\n \n \n . The low reassignment rate of FCD IIb and TSC cortical tubers may reflect their similar histopathology (balloon cells closely resemble giant cells in TSC) and cell signaling abnormalities\n \n \n 13\n \n ,\n \n 20\n \n \n . The molecular resemblance between FCD IIa, FCD IIb and TSC patient samples supported the creation of an additional meta-cohort in order to identify transcriptional similarities in the downstream analyses.\n
\n\n To build a regulatory molecular model of the pathobiology, gene modules were identified per cohort. The application of this network-based system analysis, developed by Srivastava et al.\n \n \n 10\n \n \n , revealed different numbers of affected gene modules across the cohorts, in line with the underlying heterogenicity and structure of the population. No association to seizure frequency could be identified in any of the cohorts, suggesting that regulomes may capture the current regulatory networks mostly involved in the pathobiology but not directly affected by seizure frequency. Finally, functional annotation is missing for some modules due to absence of cell type and pathway enrichment, limiting our current understanding of these pathologies.\n
\n\n Connecting these identified mechanisms across the DREs enabled a global understanding of disease dysregulations captured by 28 regulomes. Using different metrics, their link to disease biology was established, classifying them as \u2018constitutive\u2019 if present in healthy controls and patients, \u2018enhanced\u2019 regulomes if showing an increased activity in epilepsy, \u2018activated\u2019 regulomes when only present in epilepsy, and finally \u2018pathology-specific\u2019 regulomes. The annotation of these impaired mechanisms identified a diverse array of function related to immune response, neurotransmission and synaptic plasticity, brain ECM, neuroinflammation, neuronal support and myelination and energy metabolism, among others. Here, we have focused on more novel mechanisms identified in the disease state only.\n
\n\n In the TLE-HS patient population, a specific regulome enriched for microglial cell type markers and associated with immune response and neuroinflammation was identified in module TLE.20.o. Although the relevance of these pathways is not only limited to TLE-HS, this particular gene set was found only to be coregulated in TLE-HS. The activation and function of microglia in combination with upregulation of pro-inflammatory cytokines and innate immune response receptors are described in TLE-HS patients and status epilepticus (SE)\n \n \n 21\n \n \n . Srivastava et al.\n \n \n 10\n \n \n highlighted the dysregulated neuroinflammatory modules in pilocarpine mouse model, describing the association to seizure frequency, the conservation in human TLE brain and the therapeutic efficacy of targeting the predicted regulator, Csf1r. TLE.20.o was shown to correspond to the microglial modules identified in the pilocarpine mouse model (MmPIL.16.o, MmPIL.18.o, MmPil.24.o) based human/mouse gene orthologs\n \n \n 10\n \n \n . Finally, Csf1R is also predicted as a regulator for TLE.20.o, supporting the robustness and importance of this impaired mechanism in TLE-HS disease pathobiology. The gene modules and correspondence across patient data and animal models enable the construction of a translational disease framework and identification of relevant animal models for subsequent validation.\n
\n\n The mTORopathies presented a specific activated regulome associated with neuronal support and myelination. Multiple studies have shown a link between hyperactivation of mTOR pathway and myelin deficiency, impairment of proliferation and differentiation of oligodendrocytes progenitor cells as well as oligodendroglial turnover\n \n \n 22\n \n ,\n \n 23\n \n \n . Our transcriptomic data corroborate for the first time the reported literature findings. CRAFT identified SOX10, a TF essential for the differentiation of myelinating Schwann cells and oligodendrocytes\n \n \n 24\n \n \n , implicated in demyelinating diseases\n \n \n 25\n \n \n . In addition, miR-488-5p was predicted to inhibit oligodendrocyte dysregulated modules, however, limited literature is available on the role of this microRNA in the brain\n \n \n 26\n \n ,\n \n 27\n \n \n .\n
\n\n The overall comparison of gene modules across epilepsies highlighted the activated regulome related to brain ECM organization and enriched for astrocytes cell type markers. The brain ECM provides structural and functional support to glia and neurons. Several studies have reported the involvement of astrocytes in different epilepsy models showing SE-induced glial cell death and subsequent enhanced proliferation of immature astrocytes. Modified expression of multiple ECM components affect neurotransmission, synaptic plasticity and remyelination in the epileptic zone\n \n \n 28\n \n \n . Seizure activity has been associated with degradation of ECM components and regulators\n \n \n 29\n \n \n while targeting specific matrix metalloproteinases (MMPs) can reduce seizure severity and frequency in a rat model of TLE\n \n \n 30\n \n \n . The activity of SP1, the CRAFT predicted regulator, was linked to MMPs in oncology and it was also associated to multiple cellular processes via ECM degradation\n \n \n 31\n \n ,\n \n 32\n \n \n . Recent molecular studies showed that SP1 plays a role in epilepsy, neuronal injury and maintenance of spontaneous seizure activity\n \n \n 33\n \n \n . The cellular expression pattern of SP1 IR was confirmed in astroglial cells in TLE-HS as well as dysplastic neurons, astrocytes and balloon/giant cells across mTORopathy cohorts. The IR in control tissues was sporadic, further supporting SP1 potential role in ECM in epilepsy.\n
\n\n Another activated regulome was identified related to energy metabolism. Different studies observed deficiencies in key components of the glycolytic metabolism and oxidative phosphorylation (OXPHOS), potentially due to oxidative stress, slowing the tricarboxylic acid cycle in epilepsy\n \n \n 34\n \n \n , leading to neuronal hyperexcitability\n \n \n 35\n \n \n and generation of reactive oxygen species and/or NOX\n \n \n 35\n \n \n . Our results showed that the (dys)regulation(s) of energy metabolism was not conserved in healthy tissue, but only became activated in epileptic conditions. CRAFT identified LSD1 (KDM1A), which has been reported to modulate OXPHOS in metabolic tissues by genome-wide binding and transcriptome analyses. In addition, an imbalance in LSD1/neuroLSD1, a neuron-specific alternative splicing of exon 8a, has been identified to affect neurotransmission, synaptic plasticity\n \n \n 36\n \n ,\n \n 37\n \n \n and hyperexcitability in the pilocarpine mouse model\n \n \n 38\n \n \n . The cellular expression pattern of LSD1 IR in TLE-HS, FCD IIb and TSC corroborated these findings and supports further investigation into the role of LSD1 in the pathobiology of DRE to determine its therapeutic potential.\n
\n\n In this study, gene modules were used to establish a computational framework of the epilepsy pathobiology. We summarize these impaired biological mechanisms as the molecular hallmarks of epilepsy derived from transcriptional profiles and supported by our current understanding of epilepsy pathobiology (Fig.\n \n 4\n \n ). This overview captures the immune response and neuroinflammation regulome enhanced in all epilepsy cohorts and is pathology-specific in TLE-HS as well as the mTORopathy pathology-specific regulome involved in neuronal support and myelination. The brain ECM and energy metabolism regulomes activated across all epilepsy cohorts and the neurotransmission and synaptic plasticity regulome were enhanced in all epilepsy cohorts.\n
\n\n
\n\n In this study, gene modules were used to describe the molecular heterogenicity of DREs. This network-based system analysis revealed multiple dysregulated coexpression modules in the disease state. Employing the CRAFT framework allowed identification of multiple biological regulators that can be used to assess the therapeutic effect of a module\u2019s activity. The systematic comparison across TLE-HS, FCD IIa, FCD IIb and TSC allowed the identification of impaired mechanisms related to neurotransmission and synaptic plasticity, immune response and neuroinflammation, brain ECM, energy metabolism and neuronal support and myelination. We propose that these impaired pathways may affect epilepsy development across the studied pathologies, becoming the potential hallmarks of DREs, with the identified upstream protein offering novel opportunities for drug-target discovery and development.\n
\n\n \n Patients\n \n
\n\n Four distinct epilepsy pathologies were considered in this study, namely TLE-HS, FCD IIa, FCD IIb and TSC cortical tubers. In addition, age- and tissue-matched control tissue samples were collected (control cortex\n \n n\n \n =\u200914; control hippocampus\n \n n\n \n =\u200913). Brain tissues included in this study were obtained from the archives of the Departments of Neuropathology of the Amsterdam UMC (Amsterdam, The Netherlands) and the UMC Utrecht (Utrecht, The Netherlands) (Supplementary Table\u00a06). Cortical and hippocampal brain samples were obtained from patients undergoing surgery for intractable epilepsy and diagnosed with FCD type II (\n \n n\n \n =\u200917 FCD IIa,\n \n n\n \n =\u200933 FCD IIb), TSC cortical tubers (\n \n n\n \n =\u200921) and TLE-HS (\n \n n\n \n =\u200964), respectively (Table\n \n 2\n \n ; more details in Supplementary Table\u00a06).\n
\n\n All cases were reviewed independently by two neuropathologists (A.E. and A.M.). Patients who underwent implantation of strip and/or grid electrodes for chronic subdural invasive monitoring before resection and patients who underwent previous resective epilepsy surgery were excluded from this study. The classification of hippocampal sclerosis (HS) was based on analysis of microscopic examination as described by the International League Against Epilepsy\n \n \n 6\n \n \n . The diagnosis of FCD was confirmed according to the international consensus classification system proposed for grading FCD\n \n \n 9\n \n \n . All patients with cortical tubers fulfilled the diagnostic criteria for TSC cortical tubers (including genetic analysis for the detection of germline mutations)\n \n 39\n \n . All FCD type II samples underwent deep sequencing using DNA extracted from snap-frozen surgical brain tissue targeting 13 genes (FCD panel SoVarGen, South Korea); analysis for replicated data was performed in accordance with a previous study\n \n 40\n \n (Supplementary Table 7).\n
\n\n Control material was obtained at autopsy from age- and brain area-matched control samples that were obtained at autopsy from individuals without a history of seizures or other neurological disease (Table\n \n 2\n \n ; more details in Supplementary Table\u00a06). Brain tissue was frozen and kept at \u2212\u200980\u00b0C (for molecular analysis) or fixed in 4% paraformaldehyde and embedded in paraffin (FFPE) for histological analysis. All procedures received prior approval by the local ethics committee of the contributing medical centers, and were conducted in accordance with the guidelines for good laboratory practice of the European Commission.\n
\n\n \n RNA isolation\n \n
\n\n For RNA isolation, human tissue was homogenized in 700 \u00b5l Qiazol Lysis Reagent (Qiagen Benelux, Venlo, The Netherlands). Total RNA including the microRNA (miRNA) fraction was isolated using the miRNeasy Mini Kit (Qiagen Benelux, Venlo, The Netherlands) according to the manufacturer\u2019s instructions. The concentration and purity of RNA was determined at 260/280 nm using a Nanodrop spectrophotometer (Ocean Optics, Dunedin, FL, USA) and RNA integrity was assessed using a Bioanalyser 2100 (Agilent Technologies, Santa Clara, CA, USA).\n
\n\n \n RNA-Seq library preparation and sequencing\n \n
\n\n All library preparation and sequencing were performed by GenomeScan (Leiden, The Netherlands). The NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA) was used to process the samples. Sample preparation was performed according to the protocol \u2018NEBNext Ultra II Directional RNA Library prep Kit for Illumina\u2019 (NEB #E7760S/L). Briefly, mRNA was isolated from total RNA using oligo-dT magnetic beads. After fragmentation of mRNA, cDNA synthesis was performed. Next, sequencing adapters were ligated to the cDNA fragments followed by polymerase chain reaction amplification. Clustering and DNA-sequencing was performed using the NovaSeq6000 (Illumina, Foster City, CA, USA) in accordance with manufacturers\u2019 guidelines. All samples underwent paired-end sequencing of 150 nucleotides in length; the mean read depth per sample was 47\u00a0million reads.\n
\n\n The Decontamination Using Kmers (BBDuk) tool from the BBTools suite was used for adapter removal, quality trimming and removal of contaminant sequences (ribosomal or bacterial)\n \n 41\n \n . A phred33 score of 20 was used to assess the quality of the read, with any read shorter than 31 nucleotides in length excluded from the downstream analysis.\n
\n\n Reads were aligned directly to the human GRCh38 reference transcriptome (Gencode version 33)\n \n 42\n \n using Salmon v0.11.3\n \n 43\n \n . Transcript counts were summarized to the gene level and scaled using library size and average transcript length using the R package tximport\n \n 44\n \n . Genes detected in less than 20% of the samples in any diagnosis cohort and with counts less than six across all samples were filtered out, resulting in 28,366 genes for downstream analysis. The gene counts were then normalized using the weighted trimmed mean of M-values method with the R package edgeR\n \n 45\n \n . The normalized counts were then log\n \n 2\n \n transformed using the voom function from the R package limma\n \n 46\n \n .\n
\n\n \n Unsupervised hierarchical clustering and discriminant analysis on principal components\n \n
\n\n Unsupervised hierarchical clustering based on principal components was used to identify underlying structure in the gene expression matrix using the\n \n stats\n \n R package\n \n \n 16\n \n \n . Next, a discriminant analysis of principal components (DAPC) was performed using\n \n optim.a.scor\n \n e to identify the optimal number of principal components to retain as implemented by the\n \n adegenet\n \n R package\n \n \n 16\n \n ,\n \n 17\n \n \n .\n
\n\n \n Identification of gene coexpression modules\n \n
\n\n The details of the module identification workflow are described by Srivastava et al.\n \n \n 10\n \n \n . Briefly, coexpression networks were constructed per epilepsy cohort using hierarchical clustering of normalized gene expression. First, as healthy matching control samples were age-matched across the general sample set, the age distribution was assessed per cohort before applying the workflow. In addition, any outliers due to area of resection or library preparation were removed. Next, only genes showing high variability across samples were retained (median absolute deviation [MAD]\u2009\u2265\u20090.25). For all remaining genes, the 1-Spearman rank correlation was computed for all gene pairs\n \n 47\u201349\n \n and used to construct the adjacency matrix (soft-thresholding power\u2009=\u20096)\n \n 50\n \n . Unsupervised hierarchical clustering using Ward\u2019s method identified the clusters of genes\n \n 51\n \n (from K\u2009=\u20091\u2013200). The optimal number (K\n \n x\n \n ) was defined based on the second derivative of percentage of the variance explained (R\u00b2) per K\n \n 52\n \n . Next, a leave-one-out bootstrapping procedure was implemented to assess the effect of samples on the stability and robustness of gene coregulation modules. For each permutation, gene coexpression modules were identified using the above-mentioned workflow and records of gene module membership. Cluster membership was used to construct the similarity matrix to identify genes assigned to the junk module based on an arbitrary threshold (50% assigned to junk module). The remaining genes were clustered based on the similarity matrix to obtain the coexpression modules. Finally, the modules were divided using (anti-)correlation of genes within the module. Based on the relative over- or underexpression of the module\u2019s genes compared with healthy control samples, each submodule was assigned an \u2018o\u2019 or \u2018u\u2019 suffix, respectively.\n
\n\n To ensure the robustness of the identified modules, coexpression modules were only assembled in epilepsy cohorts with greater than 20 samples. An additional joint analysis was performed across all mTORopathies (FCD IIa, FCD IIb and TSC cortical tubers). The presence of outliers related to technical covariates was assessed using principal component analysis regression and removed from further analyses.\n
\n\n \n Differential coexpression\n \n
\n\n For each module the correlation between gene expression was calculated in both healthy controls and epilepsy patients to obtain the difference in median R\u00b2. The empirical\n \n P\n \n value was estimated for each module by comparing the difference in median R\u00b2 to the null distribution generated by performing 10,000 permutations of samples across cohorts\n \n \n 10\n \n ,53\n \n .\n
\n\n \n Phenotype association to module eigenGene\n \n
\n\n The relationship between module expression and the different reported phenotypes was explored using a linear model between each module\u2019s eigenGene and the covariate: HS subtype, log\n \n 10\n \n of self-reported seizure frequency, sex, age, duration, sequencing group and library preparation batch. As duration also depends on the age of the patients, age was made an additional covariate when assessing association to duration.\n
\n\n \n Functional annotation using enrichment analysis\n \n
\n\n The modules were functionally annotated using multiple pathway resources (MetaBase, Reactome and GO) as well as cell type enrichment based on marker gene signatures derived from PanglaoDB\n \n 54\n \n . A hypergeometric test was used to assess the significance of enriched pathway terms or marker gene signatures using a false discovery rate (FDR) correction to rectify for multiple testing using all expressed genes as a background\n \n 55\n \n .\n
\n\n \n CRAFT framework: in silico causal reasoning\n \n
\n\n Candidate upstream regulators for the identified gene coexpression modules were predicted using the CRAFT framework. Srivastava et al.\n \n \n 10\n \n \n defined a causal reasoning framework that utilizes the direction of effects between the three components of the system, namely CMPs, TFs and target genes. The interactions between these three components and the direction of these interactions were obtained from the Clarivate Analytics MetaBase\u00ae (version 6.15.62452,\n \n \n https://clarivate.com/products/metacore/\n \n \n ), a meta-database of manually curated literature-based contextual biological interactions. Details of the module identification workflow have been described by Srivastava et al.\n \n \n 10\n \n \n . Briefly, all expressed membrane receptors, TFs and target genes from MetaBase were identified. Next, for each TF the set of target genes was retrieved as well as its activity (activation, inhibition, unspecified) and upstream membrane receptors affecting a TF and their effect were obtained using MetaBase\u00ae defined canonical linear pathways. The overall effect of the membrane receptor on the underlying module was defined by combining the separate effects of CMP-TF and TF-gene. The significance of effect of a regulator (TF or CMP) on a module was subsequently assessed by testing the overlap between genes under the control of the regulator and the genes belonging to a module (hypergeometric test), taking all expressed genes as the universe. FDR was calculated using Benjamini-Hochberg correction of enrichment\n \n P\n \n values, taking into account the total number of enrichment tests performed in testing\n \n 55\n \n .\n
\n\n \n Identification of shared epilepsy regulations based on gene coexpression modules\n \n
\n\n The subsequent paragraph details the identification of specific epilepsy regulations as captured by gene coexpression modules in the independent epilepsy cohorts. Although different structural epilepsies are studied, similar pathways or mechanisms may still be dysregulated. To identify shared epilepsy regulations, the amount of gene content overlap between the gene coexpression modules from each epilepsy cohorts was identified using the inclusion index:\n
\n\n \n \n \\(inclusion index = \\frac{length\\left(intersect\\right(x,y\\left)\\right)}{min\\left(length\\right(x),length(y\\left)\\right)}\\)\n \n \n
\n\n with\n \n x\n \n and\n \n y\n \n as two gene coexpression modules. Next, unsupervised hierarchical clustering based on Ward\u2019s method was used to identify modules that showed overlap in gene content\n \n 51\n \n using the silhouette method to identify the optimal number of clusters. The analyses were performed with the\n \n stats\n \n and\n \n factoextra\n \n R packages\n \n 56\n \n . By design, within an epilepsy cohort, a gene can only belong to one coexpression module. Therefore, the intersect between gene coexpression modules across epilepsy cohorts was defined as those genes occurring in at least one module per epilepsy cohort. This gene intersection was subsequently submitted to a hypergeometric test to obtain functional annotation with pathway resources (MetaBase, Reactome, GO) as well as cell type enrichment based on marker gene signatures derived from PanglaoDB\n \n 54\n \n . Finally, the conservation of gene coexpression in other epilepsy cohorts and healthy control tissue was assessed with the same permutation approach as for differential coexpression analysis.\n
\n\n \n Immunohistochemistry\n \n
\n\n Human brain tissue was fixed in 10% buffered formalin and embedded in paraffin. Paraffin embedded tissue was sectioned at 6 \u00b5m, mounted on pre-coated glass slides (Star Frost, Waldemar Knittel Glasbearbeitungs, Braunschweig, Germany) and processed for immunohistochemical staining. Immunohistochemistry was carried out as previously described\n \n \n 20\n \n \n on samples from patients as reported in Supplementary Table 6. The following antibodies and dilutions were applied: SP-1 (SP-1, rabbit monoclonal, Abcam, ab124804, 1:200) and lysine-specific demethylase 1 (LSD-1) (LSD-1, rabbit polyclonal, Cell Signaling Technology, Cat#2139S, 1:200) incubated overnight at 4\u00b0C. For double labeling of SP-1 and LSD-1, sections were incubated with NeuN (mouse monoclonal, clone MAB377; Chemicon, Temecula, CA, USA; 1:2,000), glial fibrillary acidic protein (GFAP; mouse monoclonal, clone GA5, Sigma-Aldrich, St. Louis, MO, USA; 1:4,000) and HLA-DP/DR/DQ (HLA-II, mouse monoclonal, clone CR3/43, Agilent Technologies, Santa Clara, CA, USA; 1:100) antibodies, after incubation with the primary antibodies overnight at 4\u00b0C. For detection, sections were first incubated with Brightvision poly-alkaline phosphatase-anti-rabbit (DVPR55AP, Immunologic, Duiven, The Netherlands) for 30 min at room temperature, and washed with phosphate-buffered saline and then with Tris\u2013HCl buffer (0.1 M, pH 8.2) to adjust the pH. Alkaline phosphatase activity was visualized with the alkaline phosphatase substrate kit III Vector Blue (SK-5300, Vector Laboratories Inc., CA, USA). After washing in phosphate-buffered saline, sections were secondly incubated with Brightvision poly-horseradish peroxidase-anti-mouse (DPVM55HRP, Immunologic, Duiven, The Netherlands) for 30 min at room temperature. Signal was detected using the chromogen 3-amino-9-ethylcarbazole (AEC, Sigma- Aldrich, St. Louis, MO, USA) in 0.05 M acetate buffer filtered substrate solution. Sections incubated without the primary antibodies or with the primary antibodies followed by heating treatment were essentially blank.\n
\n\n \n References\n \n
\n\n 39. Northrup, H.\n \n , et al.\n \n Updated international tuberous sclerosis complex diagnostic criteria and surveillance and management recommendations.\n \n Pediatr Neurol\n \n \n 123\n \n , 50\u201366 (2021).\n
\n\n 40. Sim, N.S.\n \n , et al.\n \n Precise detection of low-level somatic mutation in resected epilepsy brain tissue.\n \n Acta Neuropathol\n \n \n 138\n \n , 901\u2013912 (2019).\n
\n\n 41. Bushnell, B., Rood, J. & Singer, E. BBMerge - Accurate paired shotgun read merging via overlap.\n \n PLoS One\n \n \n 12\n \n , e0185056 (2017).\n
\n\n 42. Harrow, J.\n \n , et al.\n \n GENCODE: the reference human genome annotation for The ENCODE Project.\n \n Genome Res\n \n \n 22\n \n , 1760\u20131774 (2012).\n
\n\n 43. Patro, R., Duggal, G., Love, M.I., Irizarry, R.A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression.\n \n Nat Methods\n \n \n 14\n \n , 417\u2013419 (2017).\n
\n\n 44. Soneson, C., Love, M.I. & Robinson, M.D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.\n \n F1000Res\n \n \n 4\n \n , 1521 (2015).\n
\n\n 45. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data.\n \n Bioinformatics\n \n \n 26\n \n , 139\u2013140 (2010).\n
\n\n 46. Ritchie, M.E.\n \n , et al.\n \n limma powers differential expression analyses for RNA-sequencing and microarray studies.\n \n Nucleic Acids Res\n \n \n 43\n \n , e47 (2015).\n
\n\n 47. Peterson, L.E. CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles.\n \n Genome Biol\n \n \n 3\n \n , SOFTWARE0002 (2002).\n
\n\n 48. van Houte, B.P. & Heringa, J. Accurate confidence aware clustering of array CGH tumor profiles.\n \n Bioinformatics\n \n \n 26\n \n , 6\u201314 (2010).\n
\n\n 49. Otto, B.\n \n , et al.\n \n Transcription factors link mouse WAP-T mammary tumors with human breast cancer.\n \n Int J Cancer\n \n \n 132\n \n , 1311\u20131322 (2013).\n
\n\n 50. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis.\n \n Stat Appl Genet Mol Biol\n \n \n 4\n \n , Article17 (2005).\n
\n\n 51. Ward, J.H. Hierarchical grouping to optimize an objective function.\n \n J Am Stat Assoc\n \n \n 58\n \n , 236\u2013244 (1963).\n
\n\n 52. Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic.\n \n J R Stat Soc Ser B Stat Methodol\n \n \n 63\n \n , 411\u2013423 (2001).\n
\n\n 53. Choi, Y. & Kendziorski, C. Statistical methods for gene set co-expression analysis.\n \n Bioinformatics\n \n \n 25\n \n , 2780\u20132786 (2009).\n
\n\n 54. Franz\u00e9n, O., Gan, L.M. & Bj\u00f6rkegren, J.L.M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data.\n \n Database (Oxford)\n \n \n 2019\n \n , baz046 (2019).\n
\n\n 55. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.\n \n J R Stat Soc Ser B Methodol\n \n \n 57\n \n , 289\u2013300 (1995).\n
\n\n 56. Kassambara, A. & Mundt, F. Factoextra: extract and visualize the results of multivariate data analyses. R Package Version 1.0.7. (2020).\n
\n\n
\n| \n \n Pathology\n \n | \n \n \n Module genes\n \n \n 1\n \n \n \n | \n \n \n Modules\n \n \n 2\n \n \n \n | \n \n \n DC\n \n \n 3\n \n \n \n | \n \n \n Functional annotation\n \n \n 4\n \n \n \n | \n \n \n CRAFT (TF/CMP)\n \n 5\n \n \n | \n \n \n TF/miRNA\n \n 6\n \n \n | \n \n \n CMP\n \n \n 7\n \n \n \n | \n
|---|---|---|---|---|---|---|---|
| \n \n TLE-HS\n \n | \n \n \n 4,481\n \n | \n \n \n 37\n \n | \n \n \n 9\n \n | \n \n \n 28\n \n | \n \n \n 20/17\n \n | \n \n \n 1,581\n \n | \n \n \n 508\n \n | \n
| \n \n FCD IIb\n \n | \n \n \n 9,928\n \n | \n \n \n 28\n \n | \n \n \n 22\n \n | \n \n \n 24\n \n | \n \n \n 21/17\n \n | \n \n \n 918\n \n | \n \n \n 456\n \n | \n
| \n \n TSC\n \n | \n \n \n 9,453\n \n | \n \n \n 30\n \n | \n \n \n 23\n \n | \n \n \n 26\n \n | \n \n \n 17/17\n \n | \n \n \n 1,051\n \n | \n \n \n 489\n \n | \n
| \n \n mTOR\n \n | \n \n \n 7,466\n \n | \n \n \n 26\n \n | \n \n \n 9\n \n | \n \n \n 23\n \n | \n \n \n 16/14\n \n | \n \n \n 1,069\n \n | \n \n \n 463\n \n | \n
| \n CMP, cell membrane receptor protein; FCD, focal cortical dysplasia; miRNA, microRNA; mTOR pathway-related malformations of cortical development; TF, transcription factor; TLE-HS, temporal lobe epilepsy with hippocampal sclerosis; TSC, tuberous sclerosis complex.\n | \n|||||||
| \n \n 1\n \n Number of genes assigned to modules.\n | \n|||||||
| \n \n 2\n \n Number of identified modules.\n | \n|||||||
| \n \n 3\n \n Number of significantly differentially coexpressed modules per analysis.\n | \n|||||||
| \n \n 4\n \n Number of modules for which functional annotation is available.\n | \n|||||||
| \n \n 5\n \n Number of modules for which a direct TF or indirect CMP is available.\n | \n|||||||
| \n \n 6\n \n Number of predicted transcriptional regulators, including both TFs and miRNA.\n | \n|||||||
| \n \n 7\n \n Number of predicted CMPs.\n | \n
\n
\n\n
\n| \n | \n\n | \n\n \n Mean age at onset of epilepsy (years)\n \n | \n \n \n Mean age surgery (years)\n \n | \n \n \n Average seizure frequency (months)\n \n | \n \n \n Mutation\n \n | \n \n \n Medications\n \n | \n ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| \n | \n\n | \n\n \n \n DEPDC5\n \n \n | \n \n \n \n AKT3\n \n \n | \n \n \n \n MTOR\n \n \n | \n \n \n \n NLPR2/NLPR3\n \n \n | \n \n \n \n TSC1\n \n \n | \n \n \n \n TSC2\n \n \n | \n \n \n 1\n \n | \n \n \n 2\n \n | \n \n \n \u2265\u20093\n \n | \n ||
| \n \n \n Control\n \n \n\n \n Cortex\n \n \n\n \n (\n \n \n n\n \n \n =\u200914)\n \n \n | \n \n | \n\n \n 21\n \n\n (0\u201361)\n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n
| \n \n \n Control Hippocampus (\n \n \n n\n \n \n =\u200913)\n \n \n | \n \n | \n\n \n 47\n \n\n (0\u201382)\n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n
| \n \n \n TLE-HS\n \n \n\n \n (\n \n \n n\n \n \n =\u200964)\n \n \n | \n \n \n 12\n \n\n (0\u201348)\n \n | \n \n \n 35\n \n\n (2\u201362)\n \n | \n \n \n 24\n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n \n 13\n \n | \n \n \n 32\n \n | \n \n \n 19\n \n | \n
| \n \n \n FCD IIa\n \n \n\n \n (\n \n \n n\n \n \n =\u200917)\n \n \n | \n \n \n 5\n \n\n (0\u201322)\n \n | \n \n \n 11\n \n\n (0\u201334)\n \n | \n \n \n 356\n \n | \n \n \n 4\n \n | \n \n \n 3\n \n | \n \n \n 4\n \n | \n \n \n 2\n \n | \n \n | \n\n | \n\n \n 1\n \n | \n \n \n 3\n \n | \n \n \n 13\n \n | \n
| \n \n \n FCD IIb\n \n \n\n \n (\n \n \n n\n \n \n =\u200933)\n \n \n | \n \n \n 4\n \n\n (0\u201321)\n \n | \n \n \n 15\n \n\n (2\u201346)\n \n | \n \n \n 208\n \n | \n \n | \n\n | \n\n \n 10\n \n | \n \n | \n\n \n 1\n \n | \n \n | \n\n \n 4\n \n | \n \n \n 11\n \n | \n \n \n 18\n \n | \n
| \n \n \n TSC cortical tubers\n \n \n\n \n (\n \n \n n\n \n \n =\u200921)\n \n \n | \n \n \n 3\n \n\n (0\u201326)\n \n | \n \n \n 7\n \n\n (0\u201330)\n \n | \n \n \n 148\n \n | \n \n | \n\n | \n\n | \n\n | \n\n \n 6\n \n | \n \n \n 15\n \n | \n \n \n 3\n \n | \n \n \n 6\n \n | \n \n \n 12\n \n | \n
| \n FCD, focal cortical dysplasia; TLE-HS, temporal lobe epilepsy with hippocampal sclerosis; TSC, tuberous sclerosis complex.\n | \n
\n
\n\n Two-photon absorption (TPA) has been widely applied for 3D imaging and nanoprinting; however, the efficiency of TPA imaging and nanoprinting using laser scanning techniques is extremely low due to the trade-off shackle between resolution and efficiency. In this work, we unveil a novel concept, few-photon irradiated TPA (\n \n fp\n \n TPA), supported by a spatiotemporal model that describes the precise time-dependent mechanism of TPA under ultralow photon irradiance with a tightly focused femtosecond laser pulse. We demonstrate that a feature size of 26 nm (1/20 \u03bb) and a pattern period of 0.41 \u03bb with a laser wavelength of 517 nm can be achieved by performing two-photon digital optical projection nanolithography (TPDOPL) under few-photon irradiation using the in-situ digital multiple exposure (\n \n i\n \n DME) technique, improving printing efficiency by 5 orders of magnitude. Our work offers deeper insights into the TPA mechanism and encourages the exploration of new potential applications for TPA in nanoprinting and nanoimaging.\n
\n\n Few-photon irradiated TPA achieves nanoprinting with unprecedented efficiency and resolution.\n
\n\n The two-photon absorption (TPA) process, which involves two quantum transitions originally studied by Maria G\u00f6ppert-Mayer\n \n 1\n \n based on Dirac\u2019s dispersion theory\n \n 2\n \n , has found widespread application in various fields, including nonlinear spectroscopy\n \n 3\n \n , fluorescence microscopy\n \n 4\n \n , optical memory\n \n 5\n \n , lithography\n \n 6-8\n \n . In general, the TPA rate of photoactive materials irradiated by a focused laser beam is proportional to the squared light intensity\n \n I\n \n \n 2\n \n . It is typically assumed that the two photons are simultaneously absorbed via a virtual state\n \n 9\n \n . Since the absorption cross-section of TPA is extremely low, a tightly focused femtosecond laser beam is generally used in TPA fluorescence microscopy and lithography.\n
\n\n As a well-established nanoprinting technique, two-photon lithography (TPL) utilizing a femtosecond laser direct writing (LDW) can fabricate arbitrary two-dimensional (2D) and three-dimensional (3D) structures with feature sizes ranging from nanometers to micrometers.\n \n 10-12\n \n . Leveraging the quadratic nonlinearity of TPA and precise control over processing parameters\n \n 13\n \n , TPL finds wide-ranging applications in microelectronics\n \n 14\n \n , optics\n \n 15,16\n \n , mechanical electric microsystems\n \n 17\n \n , biomedicine\n \n 18-20\n \n . However, with diffraction-limited focusing, the laser peak intensities can reach values as high as\n \n I\n \n = 10\n \n 12\n \n W cm\n \n \u20132\n \n ,\n \n 21-22\n \n accompanied by corresponding photon irradiance of 3 \u00d7 10\n \n 31\n \n s\n \n \u20131\n \n cm\n \n \u20132\n \n that is sufficient to enable appreciably effective TPA. Such high photon irradiance can easily trigger high-order nonlinear optical processes, leading to photobleaching, micro-explosions and a narrowed process window\n \n 23\n \n . Furthermore, TPA only occurs in the tiny area of the focused laser spot, resulting in extremely low throughput. Although multi-focus techniques\n \n 24-26\n \n can partially enhance fabrication efficiency, the serial point-by-point writing protocol of LDW remains inadequate for efficiently fabricating structures with multiscale components, ranging from nanoscale to macroscale.\n
\n\n To improve the manufacturing efficiency of TPL, two-photon digital optical projection nanolithography (TPDOPL) technology has been developed\n \n 27\n \n . This method utilizes a digital micromirror device (DMD) as a digital mask\n \n 28\n \n which can be easily changed by replacing the data of the digital mask with millions of pixels. The throughput of TPDOPL significantly exceeds that of LDW by several orders of magnitude\n \n 27,29\n \n .\u00a0Meanwhile, a resolution of 32 nm, equivalent to 1/12 of the laser wavelength, was achieved by inducing TPA under irradiation with a laser peak intensity of only 1.40 \u00d7 10\n \n 5\n \n W/cm\u00b2. This corresponds to a photon irradiance of 2.8 \u00d7 10\n \n 23\n \n s\u207b\u00b9 cm\u207b\u00b2,\n \n 30\n \n which is almost 8 orders of magnitude lower than that required for LDW. Notably, the number of irradiated photons per pulse per pixel (\n \n N\n \n \n spp\n \n ) for a single DMD pixel was as low as 5, demonstrating that TPA can be effectively triggered under conditions of ultralow-photon irradiance, which we define here as few-photon irradiation.\n
\n\n Here, we introduce a novel concept, few-photon irradiated TPA (\n \n fp\n \n TPA), offering a new perspective on the TPA process and its probability distribution under ultralow photon irradiance with a tightly focused femtosecond laser pulse. We developed a spatiotemporal model based on the wave-particle duality of light and photon distribution to describe the precise time-dependent mechanism of TPA. Through simulations using this model, we determined the probability and distribution of effective TPA (\n \n e\n \n TPA) as a function of varying photon irradiance. \u00a0To validate the\n \n fp\n \n TPA concept and spatiotemporal model, we conducted TPDOPL experiments, which achieved a feature size of 26 nm, equivalent to 1/20 of the wavelength (\u03bb), and improved patterning efficiency by 5 orders of magnitude, effectively breaking the trade-off shackle between resolution and efficiency in TPL. Additionally, we proposed and developed the in-situ digital multiple exposures (\n \n i\n \n DME) method, enabling extremely fine, dense, and complex patterning with TPDOPL. We describe the underlying physics of the\n \n fp\n \n TPA and demonstrate TPDOPL as a versatile and powerful tool for fabricating devices with high resolution, efficiency and accuracy in the fields of microelectronic integrated circuits, optical waveguides, and biological microfluidics.\n
\n\n TPA is widely recognized as the simultaneous absorption of two photons via a virtual state\n \n 1\n \n . From the perspective of quantum electrodynamics\n \n 31\n \n , the process begins with the absorption of a photon, transitioning the system from the initial state (\n \n g\n \n ) to an intermediate state (\n \n i\n \n ). The absorption occurring at the intermediate stage without energy conservation is referred to as virtual absorption, and the intermediate state is known as the virtual state. Subsequently, a second photon is absorbed, completing the transition to the final excited state (\n \n e\n \n ). The total energy is conserved, resulting in\n \n \n \\(\\:{E}_{TPA}\\approx\\:2\\hslash\\:\\nu\\:\\)\n \n \n . To illustrate this, we use a simplified photon diagram, where photons with energy\n \n \n \\(\\:\\hslash\\:\\nu\\:\\)\n \n \n and polarization\n \n k\n \n are depicted as point-like particles. The interaction between the photon (with energy\n \n \n \\(\\:\\hslash\\:\\nu\\:\\)\n \n \n ) and the molecule can be represented by the time-ordered graph for TPA, as shown in the inset of Fig.\n \n 1\n \n A. (detail shown in S1)\n
\n\n The virtual state does exist; however, it does not remain populated long enough for the second photon to interact with the molecule before the virtual state \u201cdecays\u201d. Therefore, the molecule can absorb two photons arriving at different times simultaneously when the time interval\n \n t\n \n \n 0\n \n between the arrival of the two photons is less than the virtual state lifetime,\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n , as illustrated in Fig.\n \n 1\n \n A. An estimation of the intrinsic lifetime of the virtual state,\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n , can be obtained using Heisenberg\u2019s uncertainty principle and the single intermediate state approximation\n \n 32\n \n ,\n
\n\n where\n \n h\n \n is Planck\u2019s constant and\n \n \n \\(\\:{\\Delta\\:}{\\stackrel{\\sim}{\\nu\\:}}_{ie}\\)\n \n \n is the energy difference between the photon energy of\n \n \n \\(\\:\\hslash\\:\\nu\\:\\)\n \n \n and the stationary energy of the lowest excited state of the molecule (\n \n E\n \n \n 1\n \n ). The values of\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n , calculated using Eq.\u00a0(\n \n 1\n \n ) with the absorption cut-off wavelength of the photoresist AR-N-7520 utilized in this study, were confirmed to be 0.8 fs and 0.3 fs for incident laser wavelengths of 400 nm and 517 nm, respectively, as depicted in Fig.\n \n 1\n \n B. The relationship between\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n and\n \n \n \\(\\:{\\Delta\\:}{\\stackrel{\\sim}{\\nu\\:}}_{ie}\\)\n \n \n is illustrated in Fig.\n \n 1\n \n C, clearly demonstrating that the values of\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n are significantly dependent on\n \n \n \\(\\:{\\Delta\\:}{\\stackrel{\\sim}{\\nu\\:}}_{ie}\\)\n \n \n .\n
\n\n The probability of TPA is critically influenced by the spatial and temporal distribution of incident photons from a tightly focused femtosecond laser pulse. Each photon can be treated as a discrete event, with its position adhering to a Poisson distribution within the focal spot. Figure\n \n 2\n \n A illustrates a model depicting the interaction between a tightly focused photon stream, characterized by a repetition frequency\n \n R\n \n , pulse width\n \n \n \\(\\:\\tau\\:\\)\n \n \n , and photosensitive molecules within the photoresist. Considering the time-dependent mechanism of TPA, we hypothesize that two photons, each with energy \u210f\u03bd where\n \n \n \\(\\:\\hslash\\:\\nu\\:\\)\n \n \n <\n \n E\n \n \n 1\n \n \u2264\n \n \n \\(\\:2\\hslash\\:\\nu\\:\\)\n \n \n , continuously interact with the same molecule within the time interval\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n (Fig.\n \n 1\n \n A). This interaction facilitates the effective TPA (\n \n e\n \n TPA) of the molecule. The time-averaged number of\n \n e\n \n TPA at position\n \n r\n \n and time\n \n t\n \n is given by\n
\n\n where\n \n \n \\(\\:{\\delta\\:}_{i,j}\\)\n \n \n and\n \n \n \\(\\:{\\delta\\:}_{j,k}\\)\n \n \n are the single-photon absorption coefficients from\n \n i\n \n to\n \n j\n \n states and\n \n j\n \n to\n \n k\n \n states, respectively.\n \n \n \\(\\:I(r,t)\\)\n \n \n and\n \n \n \\(\\:I(r,t-{t}_{0})\\)\n \n \n represent the energy flow density separated by time\n \n t\n \n \n 0\n \n at position\n \n r\n \n .\n \n \n \\(\\:I(r,t)/(hc/\\lambda\\:)=n(r,t)\\)\n \n \n is defined as the photon flow density. The precise spatial position of a photon as an individual particle colliding on the focal plane is uncertain. However, the distribution of incident photons can be represented by the light intensity distribution of a tightly focused laser spot, as calculated by the point spread function (PSF) (for details on the light intensity distribution, see Supplementary Information S2 and figure\n \n S1\n \n ). Similarly, the exact timing of an individual photon reaching the focal plane within the pulse duration \ud835\udee4 is also uncertain, though the hyperbolic secant function (HSF) can describe the temporal profile of the femtosecond laser pulse. Consequently, the potential distribution of\n \n e\n \n TPA under few-photon irradiation with a femtosecond laser pulse must incorporate both spatial and temporal uncertainties associated with the photons in the pulse.\n
\n\n We employ the Monte Carlo method to simulate the spatial and temporal stochastic processes of photons within a femtosecond pulse, coupled to a focusing system with a numerical aperture (NA) of 1.49 (for detailed quantum model, see Supplementary Information S3 and figure S2). Each focused photon beam originates from a pixel in the graphics generator, such as DMD, and the sampling area on the focal plane is defined as 3 nm \u00d7 3 nm in the simulation. Subsequently, the number of\n \n e\n \n TPA (\n \n N\n \n \n \n e\n \n TPA\n \n ) is calculated by integrating the spatial and temporal methods as described above.\n
\n\n The triggerable\n \n N\n \n \n \n e\n \n TPA\n \n increases quadratically with the\n \n N\n \n \n spp\n \n at wavelengths of 400 nm and 517 nm (Fig.\n \n 2\n \n B), consistent with the\n \n I\n \n \u00b2 relationship. The shorter pulse width of the femtosecond laser enhances\n \n N\n \n \n \n e\n \n TPA\n \n (Fig.\n \n 2\n \n C) by temporally increasing the probability of effective second photon absorption. Our simulations indicate that achieving reliable\n \n e\n \n TPA a single pulse requires approximately 1000 and 1800 photons (\n \n N\n \n \n spp\n \n ) at wavelengths of 400 nm and 517 nm, respectively, with a pulse width of 238 fs. When the pulse width narrows to 100 fs, the required\n \n N\n \n \n spp\n \n decreases to 400 and 1000, aligning with the temporal distribution of photons in an ultrashort pulse laser. The\n \n N\n \n \n \n e\n \n TPA\n \n at wavelengths of 400 nm is nearly seven times greater than at that of 517 nm when using the same pulse width (refer to fig. S3 and Table S2). Reducing the pulse width from 238 fs to 100 fs results in a 2.4-fold increase in\n \n N\n \n \n \n e\n \n TPA\n \n . This suggests that shorter pulse widths significantly enhance the probability of triggering\n \n e\n \n TPA. The efficiency of photon conversion to\n \n N\n \n \n \n e\n \n TPA\n \n depends on\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n ; longer\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n values yield higher\n \n N\n \n \n \n e\n \n TPA\n \n . This correlation with\n \n \n \\(\\:{\\Delta\\:}{\\stackrel{\\sim}{\\nu\\:}}_{ie}\\)\n \n \n (Fig.\n \n 1\n \n C) implies that incident photon energy closer to\n \n E\n \n \n lowest\n \n likely increases\n \n N\n \n \n \n e\n \n TPA\n \n due to extended\n \n \n \\(\\:{\\tau\\:}_{i}\\)\n \n \n .\n
\n\n The spatial and temporal stochasticity of photons at the focal spot determines the potential distribution of\n \n N\n \n \n \n e\n \n TPA\n \n . Under few-photon irradiation, the randomness in the distribution of\n \n e\n \n TPA decreases as\n \n N\n \n \n spp\n \n increases, as illustrated in fig. S4. A lower\n \n N\n \n \n spp\n \n results in a higher variance coefficient for the probability of\n \n e\n \n TPA occurrence (Table\n \n S1\n \n ), attributable to quantum random noise (fig. S4). Nonetheless, while pulse accumulation increases\n \n N\n \n \n \n e\n \n TPA\n \n , it does not affect the efficiency of triggerable\n \n e\n \n TPA (fig. S5). Next, we focus on the spatial distribution of\n \n e\n \n TPA. Typical examples with an\n \n N\n \n \n spp\n \n of 6000 and an irradiated pulse number (\n \n N\n \n \n pluse\n \n ) of 700 are shown in Figs.\n \n 2\n \n D and\n \n 2\n \n E (and fig. S6) for wavelengths of 400 nm and 517 nm, respectively. The statistical densities of\n \n N\n \n \n \n e\n \n TPA\n \n (\n \n d\n \n \n \n e\n \n TPA\n \n ) are presented in Fig.\n \n 2\n \n F. We calculated the\n \n N\n \n \n \n e\n \n TPA\n \n within a circular belt of 4 nm width and divided it by the area of the belt. The\n \n d\n \n \n \n e\n \n TPA\n \n sharply decreases from the center of the focal spot, reaching approximately half its value at a radius of 8 nm, independent of wavelength (Figs.\n \n 2\n \n F-G). This result indicates that the resolution of TPA under few-photon irradiation can significantly can significantly surpass the diffraction limit of the employed wavelength.\n
\n\n To evaluate the effectiveness of our proposal concept and spatiotemporal model, we conducted TPDOPL using a femtosecond pulse laser and a DMD (Fig.\n \n 3\n \n A, fig. S7). The DMD, with a megapixel-resolution projection layout of arbitrary features, is irradiated by a flat-top beam and focused onto a photoresist film on a cover glass using an oil-immersion objective lens (Nikon, 100\u00d7, NA 1.49). We chose a commercially available non-chemically amplified (non-CA) negative photoresist (AR-N 7520) because its degree of polymerization, driven by the stepwise photopolymerization mechanism, is easily controllable and quantifiable for\n \n N\n \n \n \n e\n \n TPA\n \n under few-photon irradiation. This resist has an absorption peak at 323 nm and an absorption cut-off wavelength of 353 nm (fig. S8), ensuring that only TPA occurs when using femtosecond pulse lasers at both 400 nm and 517 nm wavelengths. Utilizing this system, a uniform exposure of 80 \u00d7 100 \u00b5m\n \n 2\n \n can be achieved in a single exposure field, as demonstrated in fig. S9. Note that the number of excitable molecules in the photoresist by TPA should be less than the calculated\n \n N\n \n \n \n e\n \n TPA\n \n from the proposed model. The calculated\n \n N\n \n \n \n e\n \n TPA\n \n predicts the possible opportunity and distribution of\n \n e\n \n TPA under photon irradiation from the viewpoint of incident photons, but it ignores the molecular concentration, distribution, and quantum yield of TPA in the photoresist. Furthermore, the conversion efficiency from excited molecules by TPA to the practically initiated coupling reaction between molecules should be considered. The number of reaction sites of the photosensitive molecules is ultimately limited by the final absorbed\n \n N\n \n \n \n e\n \n TPA\n \n and their quantum yield to initiate the reaction.\n
\n\n We utilized incident light with an average power of 1 mW after passing through the objective lens as an illustrative example, specifically with parameters\n \n N\n \n \n spp\n \n = 1250 (0.48 fJ/pulse pixel) and\n \n N\n \n \n pulse\n \n = 1 \u00d7 10\n \n 7\n \n . The distribution of\n \n N\n \n \n \n e\n \n TPA\n \n for a line composed of one pixel demonstrates a notable 24% reduction in full width at half maximum (FWHM) compared to the photon distribution (Fig.\n \n 3\n \n A). According to photopolymerization theory\n \n 33,34\n \n , the relationship between the concentration of photosensitive molecules (M) excited by TPA and the photon distribution is nonlinear. As the reaction step is repeated, the molecular weight at the center of the exposure field increases exponentially due to the chemical cross-linking reaction. The concentration distribution of photosensitive molecules involved in the reaction is illustrated in Fig.\n \n 3\n \n B (Supplementary Information S3 for more details). The exponential increase in molecular weight leads to faster gelation at the exposure field's center, forming insoluble polymer networks more quickly than in the surroundings. Consequently, the superposition and coordination of optical and chemical nonlinearity can effectively reduce the feature size in TPDOPL under few-photon irradiation.\n
\n\n To investigate the effectiveness of the proposed spatiotemporal model for TPDOPL under few-photon irradiation, we fabricated separate lines using our TPDOPL system with a femtosecond laser wavelength (\n \n \u03bb\n \n ) of 517 nm and pulse width of 238 fs. Using a single-pixel DMD layout, a line with an average width of 41 nm and a minimum feature size of 28 nm (Fig.\n \n 3\n \n C) was achieved under the irradiation of a total incident photon number per pixel of 4.37 \u00d7 10\n \n 11\n \n (0.167 \u00b5J) with accumulation\n \n N\n \n \n pulse\n \n of 8.5 \u00d7 10\n \n 7\n \n pulses containing\n \n N\n \n \n spp\n \n of 5.14 \u00d7 10\n \n 3\n \n (1.97 fJ/pulse pixel). Correspondingly, we calculated the\n \n e\n \n TPA distribution using the same photon flux as the experimental result in Fig.\n \n 3\n \n C but only performed 8.5 \u00d7 10\n \n 3\n \n pulses in simulation. The simulation result shown in Fig.\n \n 3\n \n D indicates that the\n \n e\n \n TPA distribution is concentrated in a central area of about 30 nm. This validates the effectiveness of our spatiotemporal model for predicting the feature size of TPDOPL under few-photon irradiation.\n
\n\n Photon irradiance density and the accumulated pulse numbers critically influence the line width of TPDOPL. By decreasing\n \n N\n \n \n spp\n \n from 1.12\u00d710\n \n 4\n \n (4.30 fJ/pulse pixel) to 6.52 \u00d7 10\n \n 3\n \n (2.51 fJ/pulse pixel), the average line width of the polymer line was reduced from 164 nm to 43 nm under the accumulation of 6 \u00d7 10\n \n 7\n \n pulses, achieving a minimum feature size of 26 nm (1/20 \u03bb), as shown in Fig.\n \n 3\n \n E. The\n \n N\n \n \n \n e\n \n TPA\n \n under different\n \n N\n \n \n spp\n \n irradiations can be observed in fig. S10. The relationship between the polymer line width and photon irradiance density is depicted in Fig.\n \n 3\n \n F, indicating that the feature size can be reduced by decreasing the photon irradiance density. However, lower photon irradiance density may increase line roughness due to quantum noise, which can increase edge roughness for extremely fine lines (fig. S11). On the other hand, increasing the accumulation of pulses with a fixed photon flux density leads to a widening of the line width, as shown in Fig.\n \n 3\n \n G.\n
\n\n Another significant aspect pertains to periodic lines in photolithography, which determine the potential feature density achievable in device applications. Generally, the minimum distinguishable period between adjacent lines is dependent on the wavelength and determined by the equation\n \n HP\n \n (half pitch)\u2009=\u20090.5\n \n \u03bb\n \n /NA, following the Sparrow criterion\n \n 35\n \n . When the design pattern period is less than the minimum resolvable distance between two lines, double patterning lithography (DPL) can overcome this problem\n \n 36\n \n . For instance, at \u03bb\u2009=\u2009517 nm and NA\u2009=\u20091.45, this criterion yields an approximate value of 217 nm. We designed a line array using the DMD pixel period of 7.56 \u00b5m combined with 2 pixels on and 1 pixel off periodically (fig. S12A), corresponding to a period of 226.8 nm. Using irradiation conditions with\n \n N\n \n \n pulse\n \n = 6 \u00d7 10\n \n 7\n \n and\n \n N\n \n \n spp\n \n = 1.52 \u00d7 10\n \n 4\n \n (5.84 fJ/pulse pixel), the lines were indistinguishable (fig. S12C). We efficiently utilized the flexibility of TPDOPL by using a DMD as a digital mask, enabling in-situ digital multiple exposures (\n \n i\n \n DME) to print dense features without being constrained by the diffraction limit. Exploiting DMD characteristics, two split layouts with a period of 2\u2018p\u2019 are sequentially loaded in situ for double exposure, achieving an exposure result with a period of \u2018p\u2019, as depicted in Fig.\n \n 4\n \n A. Under twice alternating exposure of\n \n N\n \n \n pulse\n \n = 6 \u00d7 10\n \n 7\n \n and\n \n N\n \n \n spp\n \n = 8.53 \u00d7 10\n \n 3\n \n (3.28 fJ/pulse pixel), we successfully achieved a dense line array with a period of 210 nm (\n \n HP\n \n ~\u20090.3 \u03bb/NA), a linewidth of 150 nm, and a gap spacing of 60 nm, as shown in Fig.\n \n 4\n \n B, surpassing the diffraction limit.\n
\n\n Taking advantage of TPDOPL-\n \n i\n \n DME, we can achieve distinguishable dense structure patterning. When the pitch is less than 5 pixels, a single exposure cannot meet the resolution consistent with the design pattern (fig. S13). Figure\n \n 4\n \n C shows a typical circuit layout selected from a commercial chip, including isolated and dense lines with a width of 3 or 7 pixels and intervals of 1 and 2 pixels between lines (fig. S14). We employ algorithms\n \n 37\n \n to strategically distribute polygons with interspacing distances below 2 pixels across distinct sub-masks, optimizing their arrangement for TPDOPL-\n \n i\n \n DME. SEM images show that direct single exposure causes indistinguishable results in dense line areas (Fig.\n \n 4\n \n D). By splitting this layout into two (Fig.\n \n 4\n \n E) and performing our TPDOPL-\n \n i\n \n DME approach, we successfully achieved the expected circuit patterning (Fig.\n \n 4\n \n F). The dense lines are clearly distinguished, and the periods agree well with the design. Furthermore, by optimizing exposure parameters and layout design for TPDOPL-\n \n i\n \n DME, line width, period, and gap distance can be controlled for finer and denser feature patterning.\n
\n\n Optical devices with curved and circular microstructures have been fabricated using TPDOPL, such as patterns including arrayed waveguide gratings and micro-ring resonators\n \n 38\n \n . The radius of the ring affects the value of the free spectral range, and the gap or spacing between the guide and the ring affects the coupling ratio between the waveguide and the ring\n \n 39\n \n . Through layout design and the TPDOPL-\n \n i\n \n DME method, we can fabricate micro-ring filters with varied radius pitches. The widths of the circular rings can be adjusted from 220 nm to 346 nm by increasing\n \n N\n \n \n pulse\n \n under the irradiation of\n \n N\n \n \n spp\n \n = 8.53\u00d710\n \n 3\n \n (3.28 fJ/pulse pixel) (fig. S15 A). We patterned the line waveguides first, then fabricated circular rings with different diameters (Fig.\n \n 5\n \n A), leveraging TPDOPL-\n \n i\n \n DME. The gap distances between the line and circular rings can be finely adjusted from 66 nm to 480 nm (fig. S15B), optimizing the structures and improving the properties of photonic resonance devices.\n
\n\n The flexibility of TPDOPL-\n \n i\n \n DME allows us to create arbitrary patterns with various sizes, shapes, and densities, applicable not only in microelectronics and microphotonics but also in microfluidics\n \n 40,41\n \n . Microfluidics in microbiology offer an in vitro platform for interactions among diverse cell types, enabling real-time observation and assessment of reaction processes\n \n 42\n \n . We designed a rectangular module to substitute the cell chamber and a circular module to replace the cell secretion chamber, with channels of varied sizes to facilitate the addition and observation of multiple cell types and their reactions\n \n 43\n \n . Figure\n \n 5\n \n B shows complex patterns of biological microfluidics fabricated by TPDOPL-\n \n i\n \n DME, where square cell incubators (3 \u00d7 3 \u00b5m\u00b2), rectangular cell chambers (2.8 \u00d7 6 \u00b5m\u00b2), and circular cell collectors with micrometer and sub-micrometer scales are connected by different channels with widths from 70 nm to 800 nm (Fig.\n \n 5\n \n B iii), effectively carrying and separating viruses of different sizes. Most biomolecular analytes are below microns in size\n \n 44\n \n , especially foreign objects such as viruses\n \n 45\n \n , which are usually 20\u2013300 nm in size. Cross-scale biological microfluidics, from micrometer to nanometer, hold promise for providing research platforms for new diagnostic and therapeutic methods for viruses like the new coronavirus.\n
\n\n In this study, we introduced a novel concept, few-photon irradiated TPA (\n \n fp\n \n TPA), offering a new perspective on understanding the TPA process and its probability distribution under the few-photon irradiation from a tightly focused femtosecond laser pulse. The concept of\n \n fp\n \n TPA is based on the principles of wave-particle duality and the spatiotemporal uncertainty of photons inherent to such laser pulses. Furthermore, we have developed a spatiotemporal model to accurately describe the definite time-dependent mechanism of TPA. The simulated results using this model clearly indicate that the probability of TPA is strongly dependent on the lifetime of the molecule's virtual state under few-photon irradiation. Notably, the distribution of TPA under few-photon irradiation is significantly narrower compared to the diffraction limit of the tightly focused light spot. The results obtained from the TPA spatiotemporal model and simulations challenge existing understandings of TPA, offering a deeper insight into the TPA mechanism under few-photon irradiation and encouraging the exploration of new potential applications for TPA in such conditions.\n
\n\n As validation, the results of TPDOPL experiments show good agreement with the simulations. Notably, by optimizing\n \n N\n \n \n spp\n \n and\n \n N\n \n \n pulse\n \n in TPDOPL, we achieved a smaller feature size of 26 nm (1/20 \u03bb) with a laser wavelength of 517 nm, compared to 32 nm (1/12 \u03bb) with a laser wavelength of 400 nm. Furthermore, the structure period of 210 nm (0.41 \u03bb) and a gap distance of 37 nm were significantly decreased by performing\n \n i\n \n DME. This technique has proven powerful for creating dense structures when we finely control the line width. Additionally, digital projection lithography with a DMD as the digital mask is equivalent to possessing millions of individual laser focus spots, improving the patterning efficiency for multiscale structures by approximately 5 orders of magnitude. Consequently, TPDOPL under few-photon irradiation effectively breaks through the trade-off shackle between resolution and efficiency.\n
\n\n The\n \n i\n \n DME technique in TPDOPL is suitable not only for nanoprinting but also for nanoimaging. Although TPA microscopy has been widely applied for 3D bioimaging, its resolution has not reached the nanoscale with femtosecond laser scanning. By employing the concept of\n \n fp\n \n TPA and\n \n i\n \n DME technique, it is possible to achieve rapid imaging with nanoscale resolution. The thousands of focused spots generated by the discrete multiple focuses with DMD pattern design can simultaneously trigger TPA in thousands of molecules with minimal photon irradiation. The positions of TPA fluorescence at each focus spot can be distinctly imaged. By rapidly changing the designed discrete multiple focuses with DMD, a TPA fluorescence image with nanoscale resolution can be obtained in a short time.\n
\n\n Finally, it is noteworthy that the distribution of TPA induced by few-photon irradiation has been narrowed down to the nanometer scale, independent of the light wavelength. Theoretically, the linewidth fabricated by TPDOPL could be reduced to nearly 10 nm or less by selecting compatible photoresist molecules and optimizing processing parameters. Additionally, the minimal period would be limited only by the pixel size of the DMD with the\n \n i\n \n DME method. By combining TPA under few-photon irradiation with\n \n i\n \n DME, it is promising to achieve single-molecule imaging and nanoprinting at the sub-10 nanometer scale.\n
\n\n Experimental system and fabrication method\n
\n\n Using a fiber laser with a fundamental wavelength of 1035 nm, a femtosecond pulse at 517 nm was generated via a frequency-doubling crystal. The pulse repetition rate was 1 MHz, with each pulse lasting 238 fs. Single-color bitmap images of 1920\n \n \n \\(\\:\\times\\:\\)\n \n \n 1080 pixels were created using Photoshop to meet the loading requirements of the DLP6500 1080p DMD. The images were projected onto the photoresist sample using a Nikon oil-immersion objective with 100\u00d7 magnification and a NA of 1.49 (see Supplementary S8 for details).\n
\n\n Printed photoresist samples were prepared on clean glass slides (size: 24 mm\n \n \n \\(\\:\\times\\:\\)\n \n \n 40 mm, thickness: 0.13\u20130.16 mm). HMDS was applied for adhesion enhancement, followed by spin-coating undiluted AR-N-7520 commercial resist at 7000 r.p.m. for 60 minutes to achieve a uniform thin film. Subsequently, soft-baking was performed on a hotplate at 85\u00b0C for 1 minute. After exposure, development was carried out using AR 300\u2009\u2212\u200947 developer for 1 minute at 22\u00b0C.\n
\n\n Computational simulation methods\n
\n\n To better explore the principles of two-photon absorption under few-photon and experimentally verify them, we utilized MATLAB to establish a vector optical field distribution model based on the theory of vector optics. Subsequently, we integrated Monte Carlo random distribution algorithms to statistically distribute photons within a single pulse randomly. The statistical distribution of the reaction quantity of two-photon absorption conforms to the square of the intensity, rendering the optical field distribution particle-like. The virtual state lifetime was obtained through the Heisenberg\u2019s uncertainty principle. The number of photons per pixel within a single pulse was calculated based on the average power behind the objective lens, while the number of pulses was determined according to the exposure time (see Supplementary S1-S3 for details).\n
\n\n Due to the complexity of the catalytic FeMo cofactor site in nitrogenases that mediates the reduction of molecular nitrogen to ammonium, mechanistic details of this reaction remain under debate. In this study, selenium- and sulfur-incorporated FeMo cofactors of the catalytic MoFe protein component from\n \n Azotobacter vinelandii\n \n were prepared under turnover conditions and investigated by using different EPR methods. Complex signal patterns were observed in the continuous wave EPR spectra of selenium-incorporated samples, which were analyzed by Tikhonov regularization, a method that has not yet been applied to high spin systems of transition metal cofactors, and by an already established grid-of-error approach. Both methods yielded similar probability distributions that revealed the presence of at least four other species with different electronic structures in addition to the ground state E\n \n 0\n \n . Some of these species were preliminary assigned to hydrogenated E\n \n 2\n \n states. In addition, advanced pulsed-EPR experiments were utilized to verify the incorporation of sulfur and selenium into the FeMo cofactor, and to assign hyperfine couplings of\n \n 33\n \n S and\n \n 77\n \n Se that directly couple to the FeMo cluster. With this analysis, we report selenium incorporation under turnover conditions as a straightforward approach to stabilize and analyze early intermediate states of the FeMo cofactor.\n
\n\n \n Nitrogenase\n \n
\n\n \n FeMo cofactor\n \n
\n\n \n stable isotope labeling\n \n
\n\n \n EPR spectroscopy\n \n
\n\n \n reaction intermediates\n \n
\n\n \n regularization\n \n
\n\n The conversion of the largely inert N\n \n 2\n \n molecule to bioavailable ammonia is essential for life on earth and is a critical step in the biological nitrogen cycle. Biological nitrogen fixation is catalyzed by enzymes of the nitrogenase family that are widespread in bacteria and archaea, but absent in eukaryotes\n \n \n 1\n \n \n . Three isoforms of nitrogenases are distinguished based on the composition of their catalytic cofactor: the Mo-dependent, V-dependent, and Fe-only nitrogenases\n \n \n 2\n \n ,\n \n 3\n \n \n . All nitrogenases are two-component proteins consisting of (i) the [4Fe:4S] cluster-containing homodimeric Fe-protein (component Av2 in\n \n Azotobacter vinelandii\n \n ) that serves as reductase and site of ATP hydrolysis, and (ii) the catalytic, \u03b1\n \n 2\n \n \u03b2\n \n 2\n \n -heterotetrameric (or heterohexameric in case of V and Fe) MoFe protein (component Av1 in\n \n Azotobacter vinelandii\n \n ) with two metal cofactors, the [8Fe:7S] P-cluster and the catalytic cofactor. The latter is designated as FeMo cofactor in Mo-dependent nitrogenases and is the most complex bioinorganic metal cluster known to date. The FeMo cofactor consists of seven Fe atoms, nine S atoms, one Mo atom, a central C (carbide) atom, and an organic\n \n R\n \n -homocitrate moiety (Fig.\n \n 1\n \n , inset), and is accordingly complex in its electronic and magnetic properties\n \n \n 4\n \n \u2013\n \n 7\n \n \n .\n
\n\n Important traits of the molecular mechanism of nitrogen reduction remain under discussion. It is known that the FeMo cofactor binds the natural substrate N\n \n 2\n \n (alternatively also a variety of other small molecules such as CO) during catalysis, and strictly sequentially accepts electrons from the [4Fe:4S] cluster of the Fe protein. This transfer is coupled to the hydrolysis of 2 ATP/e\n \n \u2013\n \n by the Fe protein, whereby one electron is first transferred from the reduced P cluster to the FeMo cofactor, and the electron deficit at the P cluster is subsequently replenished by the Fe protein\n \n \n 8\n \n \n . The reductase component then dissociates from the MoFe protein for reduction and nucleotide exchange before the next 1-electron transfer can take place\n \n \n 9\n \n \n . Largely due to the complexity of this process, Fe protein is the only known reductant to sustain productive N\n \n 2\n \n reduction by MoFe protein, although recent electrochemical approaches have been reported to achieve similar results\n \n \n 10\n \n \n . The reduction of N\n \n 2\n \n follows a minimal stoichiometry of\n
\n\n N\n \n 2\n \n +\u20098 e\n \n \u2013\n \n + 8 H\n \n +\n \n + 16 ATP \u08e7\u2192 2 NH\n \n 3\n \n +\u2009H\n \n 2\n \n +\u200916 [ADP\u2009+\u2009P\n \n i\n \n ],\n
\n\n including the obligatory release of H\n \n 2\n \n with a limiting stoichiometry of 1 H\n \n 2\n \n /N\n \n 2\n \n . The kinetics of the reaction are comprehensively outlined in a scheme proposed by Lowe and Thorneley (LT)\n \n \n 11\n \n \n , in which the system cycles through eight distinct states, E\n \n 0\n \n to E\n \n 7\n \n , each representing the addition of a single electron (Fig.\n \n 1\n \n ). Under reductive conditions the FeMo cofactor is commonly isolated in the resting state E\n \n 0\n \n \n 11\n \n , and then successively receives electrons (and protons for charge balance) through states E\n \n 1\n \n to E\n \n 7\n \n . Importantly, the binding and activation of N\n \n 2\n \n requires the enzyme to reach state E\n \n 3\n \n or E\n \n 4\n \n , which is complicated by the risk of an unproductive loss of 2 electrons as additional H\n \n 2\n \n \n 2,12\n \n . This finding indicated that an essential aspect of electron accumulation on the FeMo cofactor is the formation of surface hydrides that can be lost as H\n \n 2\n \n by accidental protonation\n \n \n 3\n \n ,\n \n 13\n \n \n . Stabilization of these surface-associated hydride adducts may be achieved by a bridging binding mode\n \n \n 14\n \n \n ; this type of electron storage is crucial for the cluster to accumulate four electrons at isopotential (i.e., from the Fe protein) and allows for a mechanistic twist upon reaching the E\n \n 3\n \n or E\n \n 4\n \n state. Triggered by the presence or binding of the substrate N\n \n 2\n \n , the two adjacent hydrides present in the E\n \n 4\n \n state can reductively eliminate H\n \n 2\n \n , leaving the enzyme in a 2-electron-reduced state that cannot be achieved by electron transfer from the Fe protein alone and that is sufficiently reactive to break the N\n \n 2\n \n triple bond\n \n \n 15\n \n \n . From states E\n \n 5\n \n -E\n \n 7\n \n , the reaction then proceeds to the release of the product NH\n \n 3\n \n , but different mechanistic routes remain under debate\n \n \n 2\n \n ,\n \n 16\n \n ,\n \n 17\n \n \n .\n
\n\n --- Please insert Fig.\n \n 1\n \n here ---\n
\n\n The E\n \n 0\n \n state of the FeMo cofactor has a total spin of\n \n S\n \n =\u20093/2\n \n 18,19\n \n and the oxidation state of the FeMo cofactor changes by 1 with each reaction step; so that the total spin of the cofactor is half-integer for any even state and integer for any odd state. The odd states are thus either diamagnetic (\n \n S\n \n =\u20090) or have \"non-Kramers\" spin states\n \n \n 2\n \n \n with high zero-field splitting and hence the absence of EPR transitions at common EPR frequencies\n \n \n 20\n \n ,\n \n 21\n \n \n . EPR spectroscopy provides access to the characterization of the ground state as well as the\n \n S\n \n \n \n \\(\\ne\\)\n \n \n 0 reaction intermediates, and supports the drawing of mechanistic and \u2013 within limits \u2013 also structural conclusions. In particular, freeze-quenched samples with different substrates, some of them stable-isotope-labeled, have been studied\n \n \n 22\n \n \u2013\n \n 24\n \n \n . Several of these studies showed complex continuous-wave (cw)-EPR spectra with well-resolved anisotropy of the\n \n g\n \n -tensor, indicating that the substrate directly couples with at least one Fe atom of the FeMo cofactor\n \n \n 24\n \n ,\n \n 25\n \n \n . However, an unambiguous assignment of the binding position was not possible.\n
\n\n The substrate binding site of the CO-inhibited FeMo cofactor in its resting state was identified by crystallography\n \n \n 26\n \n ,\n \n 27\n \n \n . CO displaces the S at position S2B and a CO bond in an end-on \u00b5\n \n 2\n \n -bridging mode to Fe2 and Fe6 is formed at this position\n \n \n 26\n \n ,\n \n 27\n \n \n . In a subsequent study, KSeCN was found to be both a substrate and an inhibitor of nitrogenase activity, and crystal structures from freeze-quenched nitrogenase samples generated during turnover with KSeCN revealed that S2B was replaced by Se\n \n \n 28\n \n \n . When KSeCN was removed from the reaction mixture and the reaction was allowed to proceed, further Se exchange first occurred at positions 3A and 5A, the other two \u00b5\n \n 2\n \n -bridging S that form the equatorial \u2018belt\u2019 of the cofactor (Fig.\n \n 1\n \n , inset). Only after several thousand more reaction cycles, the incorporated Se was again replaced by S. Starting from the exclusive Se2B labeling, the approximately equal labeling distribution of the other two positions (3A and 5A) was reached after about 1000 turnover cycles\n \n \n 28\n \n \n . Comparable S-to-S exchange experiments within the sulfur belt were carried out with the VFe cofactor of V-dependent nitrogenase\n \n \n 29\n \n \n . A subsequent study examining Se incorporation into the FeMo cofactor of a Mo-dependent nitrogenase at high and low KSeCN concentrations established that both conditions lead to a similar Se distribution within the cofactor\n \n \n 30\n \n \n . Furthermore, it could be demonstrated that Se labeling is also possible at positions 3A and 5A by gassing the 2B-Se-labeled protein with CO during catalysis. In this process, the Se2B is exchanged by CO, while the two S atoms at the 3A/5A positions are replaced by Se. The use of such a Se-labeled FeMo cofactor allowed its electronic structure to be analyzed by various methods like X-ray spectroscopy\n \n \n 30\n \n \n .\n
\n\n Based on these studies, the goal of this work was to determine whether and to what extent Se is incorporated into the FeMo cofactor and what geometric or electronic changes result from this manipulation. We use high-resolution EPR spectroscopy for this purpose, as structure-determination methods can identify the labeling positions of individual isotopes within the FeMo cofactor, but the various electronic structures or redox states of the cluster are difficult to be distinguished other than with complex approaches like spatially resolved anomalous dispersion refinement\n \n \n 31\n \n \n . Tikhonov regularization, commonly applied to analyze complex magnetic resonance datasets, e. g., from PELDOR/DEER spectroscopy\n \n \n 32\n \n \u2013\n \n 36\n \n \n , was employed for the first time on cw-EPR spectra of the high-spin FeMo cofactor to assign individual species formed by Se incorporation. The resulting probability distributions revealed several species with different electronic structures in each sample, making an assignment to specific intermediates and/or redox states possible. The quality of our analyses was compared to those obtained from a grid-of-error approach\n \n \n 37\n \n \n .\n
\n\n Together, these studies establish that Se incorporation into the FeMo cofactor provides access to other states in the kinetic LT scheme that will help to better understand the molecular mechanism of the FeMo cofactor in the nitrogenase reaction.\n
\n\n \n Sample preparation\n \n . The MoFe-protein and Fe-protein from\n \n Azotobacter vinelandii\n \n (designated as Av1 and Av2, respectively) were isolated under anoxic conditions as described previously\n \n \n 28\n \n \n .\n
\n\n \n Enzyme assays\n \n . Turnover assays for Av1 and Av2 were prepared in a buffer containing 50 mM Tris-HCl (pH 7.5), 200 mM NaCl, 5 mM Na\n \n 2\n \n S\n \n 2\n \n O\n \n 4\n \n and supplemented with 20 mM creatine phosphate, 5 mM ATP, 5 mM MgCl\n \n 2\n \n , 25 units/mL phosphocreatine kinase and 25 mM Na\n \n 2\n \n S\n \n 2\n \n O\n \n 4\n \n (in 50 mM Tris-HCl, pH 7.5 and 200 mM NaCl)\n \n \n 28\n \n \n . All samples except for the Av1-Se-C\n \n 2\n \n H\n \n 2\n \n sample were kept under an argon/H\n \n 2\n \n atmosphere and the indicated amounts of KSCN, K\n \n 33\n \n SCN, KSeCN, or K\n \n 77\n \n SeCN were added to the reaction (see Table\n \n 1\n \n ). C\n \n 2\n \n H\n \n 2\n \n was used as substrate in the Av1-Se-C\n \n 2\n \n H\n \n 2\n \n sample. Afterward, the Av2 protein and remaining SCN\n \n \u2013\n \n or SeCN\n \n \u2013\n \n were removed by three rounds of sample concentration and dilution with a 100-kDa molecular weight cut-off ultrafiltration device (Vivaspin, Sartorius). An additional desalting step (Sephadex G25, GE Healthcare) was applied with samples Av1-WT, Av1-\n \n 33\n \n S, Av1-Se2B-1, Av1-Se-C\n \n 2\n \n H\n \n 2\n \n , Av1-Se-low and Av1-\n \n 77\n \n Se2B. Sample concentrations were determined by absorbance at 410 nm\n \n \n 28\n \n \n ; relative EPR signal intensities were determined by double-integration of the respective X-band cw-EPR spectra.\n
\n\n \n Cw-EPR experiments\n \n . X-band cw-EPR experiments were performed using Bruker E500 or E580 spectrometers in combination with Bruker resonators (4122SHQE or 4119HS-W1) combined with an Oxford ESR900 helium gas flow cryostat. Power-sweep experiments were done at 5 K, a microwave frequency of 9.39 GHz, a modulation amplitude of 0.6 mT, and a conversion time of 165.25 ms. For testing the relaxation behavior of the individual samples, cw-EPR spectra at different microwave powers (from 0.025 to 39.4 mW at the E500, or from 0.377 to 37.7 mW at the E580) were recorded. Temperature-dependent experiments were recorded at 6, 9, or 12 K using a microwave power of 0.095 mW, a modulation amplitude of 0.6 mT, and a conversion time of 165.25 ms.\n
\n\n \n Light induced cw-EPR experiments.\n \n Similar to the protocol described in\n \n \n 14\n \n \n , two samples, Av1-Se2B-1 and Av1-Se-low, were illuminated inside the cooled cavity (Bruker 4119HS-W1) in combination with the cryostat (Oxford ESR900) for about 10 min using a blue-light LED (100 mW, Schott KL 2500). The cw-EPR experiments were performed at 6 K at 9.38 GHz by using microwave power of 3.77 mW, a conversion time of 160 ms and a modulation amplitude of 0.6 mT. The cryogen annealing was done by keeping the samples in a cryogen-solution (isopropanol-liquid nitrogen) at about 150 K for some hours. Additionally, the samples were stored for 16 h in liquid nitrogen.\n
\n\n \n Pulse EPR experiments\n \n . Pulse Q-Band EPR experiments were performed using a Bruker E580 spectrometer in combination with a Bruker EN 5107D2-flexline resonator immersed in an Oxford CF935 helium gas-flow cryostat. All experiments were carried out at a microwave frequency of 33.8 GHz at 4.5 K. Unless noted otherwise, a video gain setting of 200 MHz was used.\n
\n\n \n Longitudinal transient nutation experiments.\n \n Experimental conditions: pulse length \u03c0/2\u2009=\u200910 ns, nutation step width 4 ns, \u03c4\u2009=\u2009110 ns,\n \n T\n \n =\u2009600 ns, and a shot repetition time of 51 \u00b5s. A 4-step phase cycle was used. The spectra were measured in steps of 10 mT. As the nutation frequency depends on the local microwave magnetic field strength\n \n B\n \n \n 1\n \n , all frequency axes were normalized to the nutation frequency measured with a coal reference sample (Bruker). This standardization makes the frequency axis essentially independent of spectrometer-specific settings such as microwave power or the resonator quality (Q-factor). The nutation signals were processed as follows: After subtraction of a polynomial baseline, a Hamming window function and a zero filling with a fill factor of 4 were applied. Finally, an FFT was performed.\n
\n\n \n Inversion recovery experiments.\n \n Experimental conditions: pulse lengths \u03c0/2\u2009=\u200912 ns, \u03c4\u2009=\u2009100 ns,\n \n T\n \n \n start\n \n = 400 ns,\n \n T\n \n -steps\u2009=\u200980 ns, and a shot repetition time of 100 \u00b5s. The video gain was set to 20 MHz. The spectra were measured in steps of 3 mT. From each spectrum the resonator background was subtracted. Exponential fit functions were used to determine\n \n \n \\({T}_{1}^{\\text{e}\\text{f}\\text{f}}\\)\n \n \n .\n
\n\n \n 2-pulse ESEEM versus\n \n \n B\n \n \n \n 0\n \n \n \n experiments.\n \n Experimental conditions: pulse length \u03c0/2\u2009=\u200912 ns, \u03c4\n \n start\n \n =\u2009100 ns, \u03c4-steps\u2009=\u20094 ns with 40 steps and a shot repetition time of 20 \u00b5s. The spectra were measured in steps of 0.3253 mT. The resonator background was subtracted from each spectrum. Pseudo modulation was performed using a modulation amplitude of 1.0 mT and a binominal smoothing with 4 smoothing points.\n
\n\n For determining\n \n \n \\({T}_{\\text{M}}^{\\text{e}\\text{f}\\text{f}}\\)\n \n \n , modified experimental conditions were used: pulse length \u03c0/2\u2009=\u200912 ns, \u03c4\n \n start\n \n =\u2009100 ns, \u03c4-steps\u2009=\u20094 ns with 500 steps and a shot repetition time of 50 \u00b5s. A 16-step phase cycle was used. The spectra were measured in steps of 3.0 mT. Exponential fit functions were used for analysis.\n
\n\n \n 3-pulse ESEEM experiments\n \n . Experimental conditions: pulse lengths \u03c0/2\u2009=\u200910 ns, \u03c4\u2009=\u200990 ns,\n \n T\n \n \n start\n \n = 100 ns,\n \n T\n \n -steps\u2009=\u20098 ns with 750 steps, shot repetition time 70 \u00b5s. The spectra were measured in steps of 10 mT. A 4-step phase cycling was used. Spectra have been processed as follows: The phase of the time domains have been optimized, a mono- or bi-exponential background function has been subtracted, a Hamming window function has been applied, a zero-filling factor of 4 has been used, and finally, a cross-term-averaged FFT was applied.\n
\n\n \n Data Analysis\n \n . Spectral simulations of cw-EPR spectra were carried out using the Matlab (The MathWorks, Natick, MA) package EasySpin\n \n \n 38\n \n \n with its \u201cpepper\u201d simulation routine; spectral analysis was done using self-written Matlab scripts. The regularization and the grid-of-errors method were implemented as Matlab scripts (for details see Supporting Information, part A). The regularization results were analyzed using a multi-Gaussian approach described in the Supporting Information, section B11.\n
\n\n 3P-ESEEM simulations were carried out using the EasySpin algorithm \u201csaffron\u201d\n \n \n 39\n \n \n . Pseudo-nuclear and effective hyperfine couplings were included by calculating with a total electron spin quantum number of\n \n S\n \n =\u20093/2 and zero-field coupling\n \n D\u2009=\n \n 180 GHz. ESEEM signals of the two nitrogen atoms were simulated using literature parameters:\n \n A\n \n (N1) = [1.02 0.98 1.14] MHz, Q(N1)\u2009=\u20092.17 MHz/\u03b7(N1)\u2009=\u20090.6, and\n \n A\n \n (N2) = [0.5 0.4 0.4] MHz, Q(N2)\u2009=\u20093.5 MHz/\u03b7(N2)\u2009=\u20090.35; Euler angles of 60\u00b0, 20\u00b0, 0\u00b0 between the\n \n g\n \n and quadrupolar tensor for the second nucleus axis were used\n \n \n 40\n \n \n .\n
\n\n Spectral simulations of ESEEM signals of sample Av1-\n \n 77\n \n Se2B were performed as follows: Using the determined\n \n 14\n \n N hyperfine couplings and assuming one\n \n 77\n \n Se nucleus, the analysis of the spectral pattern in the Av1-\n \n 77\n \n Se2B sample was done by manual optimization. For a one-to-one simulation, different rhombicity (\n \n \n \\(\\lambda\\)\n \n \n ) values that affect both the effective hyperfine couplings and the pseudonuclear\n \n g\n \n -factors were taken into account. These effects scale with the magnitude of the hyperfine couplings and thus, alter the effective nuclear Larmor frequency and the effective hyperfine couplings. For this reason, simulations were performed for each\n \n \n \\(\\lambda\\)\n \n \n value individually. The simulations were done between 0 \u2264\n \n \n \\(\\lambda\\)\n \n \n \u2264 1/3 in 167 steps. For each\n \n \n \\(\\lambda\\)\n \n \n value the 3P-ESEEM spectra\n \n S\n \n (\n \n \n \\(\\lambda\\)\n \n \n ) were calculated and weighted by the probability-distribution\n \n P\n \n (\n \n \n \\(\\lambda\\)\n \n \n ) obtained from regularization. The total spectrum was obtained by:\n \n \n \\(S={\\sum }_{\\lambda }S\\left(\\lambda \\right)P\\left(\\lambda \\right)\\)\n \n \n . Only the lower Kramers doublet was considered.\n
\n\n \n Runtime estimation of the regularization and grid-of-errors methods\n \n . Using a standard desktop PC (Intel Core i5-4590 CPU @ 3.3 GHz) with Matlab 2019a and EasySpin 5.2.25, the calculation of the kernels (667\n \n \n \\(\\lambda\\)\n \n \n -steps with\n \n \n \\(0\\le \\lambda \\le 1/3\\)\n \n \n , 18 intrinsic lineshape-steps with 0.5 mT steps, and an angle step width of 0.5\u00b0 for calculation of a powder spectrum) required about 12 hours. The regularization itself, using 27 \u03b1-values and 18\n \n \n \\(lwpp\\)\n \n \n points required a compute time of approximately 150 seconds. It is therefore time-saving to calculate the kernel once per series of spectra. On the other hand, the calculation of the grid (223\n \n \n \\(\\lambda\\)\n \n \n -steps with\n \n \n \\(0\\le \\lambda \\le 1/3\\)\n \n \n , 249\n \n \n \\(lwpp\\)\n \n \n -steps\n \n \n \\(0 \\text{m}\\text{T}\\le lwpp\\le 25 \\text{m}\\text{T}\\)\n \n \n , and an angle step width of 0.5\u00b0 for calculation of a powder spectrum) required about 56 hours. The grid-of-errors optimization itself required only about 300 seconds. The comparison of the compute times clearly shows that the regularization method requires less computing time and should therefore be preferred over the grid-of-errors method if the prerequisites for regularization are fulfilled (see below). The reduction of computation time is mainly due to the lower required number of steps in the second parameter dimension (here:\n \n \n \\(lwpp\\)\n \n \n ). By choosing identical number of steps for\n \n \n \\(\\lambda\\)\n \n \n and\n \n \n \\(lwpp\\)\n \n \n , the compute times for both methods are very similar.\n
\n\n \n Regularization of cw-EPR spectra.\n \n To spectroscopically follow the changes of the FeMo cofactor after incorporation of Se, different nitrogenase Av1 samples were produced under various turnover conditions in the absence of N\n \n 2\n \n (see Table\n \n 1\n \n ); these samples exhibited different labeling positions (position 2B and/or positions 2B, 3A, and 5A) or labeling yields. For this purpose, different KSeCN and KSCN concentrations (samples Av1-Se2B-1, Av1-Se-low, Av1-Se2B- lowflux), different Av1/Av2 ratios (samples Av1-Se2B-lowflux and Av1-S) and different reaction cycles (samples Av1-Se-C\n \n 2\n \n H\n \n 2\n \n and Av1-S-remigration) were applied. Two S-incorporated samples, one with\n \n 33\n \n S (Av1-\n \n 33\n \n S) and one with natural abundance\n \n 32\n \n S (Av1-S) were prepared under turnover conditions and analyzed in comparison. All samples were frozen after the defined number of reaction cycles, but not under freeze-quench conditions. Therefore, no short-lived intermediate states are expected to be trapped. Figure\n \n 2\n \n depicts the cw-EPR spectra of all Av1 samples under investigation covering a magnetic field range of 50\u2013283 mT.\n
\n\n --- Please insert Fig.\n \n 2\n \n here ---\n
\n\n The Av1-WT sample in its resting state exhibits the well-known\n \n S\n \n =\u20093/2 spin state EPR spectrum of the lower Kramer\u2019s doublet (panel A). The two EPR spectra of the S-incorporated samples, Av1-S and Avl-\n \n 33\n \n S (panels B and C) are virtually identical compared to the unmodified protein; therefore, incorporation of S and in particular\n \n 33\n \n S (with a nuclear spin of\n \n I\n \n =\u20093/2) into the FeMo cofactor is not detectable by cw-EPR spectroscopy. All Se-exchanged samples, however, exhibit a complex signal shape with at least five peaks spanning the 120\u2013260 mT magnetic field range. Unexpectedly, the \u201cSe-patterns\u201d of samples Av1-Se2B-1, Av1-Se2B-lowflux, Av1-Se-C\n \n 2\n \n H\n \n 2\n \n , Av1-Se-low, and even of Av1-\n \n 77\n \n Se2B (panels D-H) are similar, only differences in individual peaks intensities can be observed. It is important to note that\n \n 77\n \n Se has a nuclear spin of\n \n I\n \n = \u00bd, which is different to the\n \n I\n \n =\u20090 of the naturally most abundant isotopes\n \n 78\n \n Se and\n \n 80\n \n Se. As those samples show very similar spectral patterns, hyperfine couplings of\n \n 77\n \n Se and the FeMo cofactor can be excluded as the origin of the Se-pattern. The cw-EPR spectrum of the Av1-S-remigration sample (panel I) again exhibits the Se-pattern, but with decreased intensity. Qualitatively, the observed signal pattern can be described as a mixture of signals from unlabeled and Se-incorporated samples.\n
\n\n For a more quantitative evaluation of S-, Se- and unlabeled samples, the intensity differences of the respective cw-EPR spectra were compared using spin counting via double integration. Samples Av1-WT, Av1Se2B-1, Av1-\n \n 77\n \n Se2B, Av1-Se-low, Av1-Se-C\n \n 2\n \n H\n \n 2,\n \n and Av1-\n \n 33\n \n S were compared, as all were prepared from the same enzyme batch and under identical electron flux. The analysis shows that the signal intensity of sample Av1-\n \n 33\n \n S is comparable to the intensity of the Av1-WT sample, but all Se-incorporated samples have only\u2009\u2248\u200960% of the resting-state intensity (Supporting Figure B2). Consequently, Se incorporation leads to \u2248\u200940% EPR-inactive (\n \n S\n \n =\u20090) and/or non-Kramers states (\n \n S\n \n =\u20091, 2, 3, ...).\n
\n\n It is essential to know the origin of the complex Se-pattern to perform correct spectral simulations of the experimental data. Hyperfine couplings have already been ruled out as the source, geometric distortions due to Se incorporation are also unlikely as the only explanation, as there is no evidence for such in the crystal structures\n \n \n 28\n \n \n , assuming that the Se incorporation in crystals is representative of that in solutions. Moreover, the EPR signal pattern of sample Av1-Se-C\n \n 2\n \n H\n \n 2\n \n , in which Se should be incorporated over the entire sulfur belt, is almost identical to those of the other Se-incorporated samples labeled mainly at the 2B position (see also below). Therefore, different states of the FeMo cofactor that manifest in different zero-field splitting parameters are the most plausible assumption. In this case, the cw-EPR spectra of all samples are dominated only by the rhombicity parameter (\n \n \n \\(\\lambda )\\)\n \n \n of the zero-field splitting as the effective\n \n g\n \n -factors\n \n \n \\({g}_{\\left\\{x,y,z\\right\\}}^{1/2}\\)\n \n \n of the lower Kramer doublet of an\n \n S\n \n =\u20093/2 system are functions of\n \n \n \\(\\lambda =|E/D|\\)\n \n \n (see Supporting Information Part A, Theory).\n
\n\n Exact\n \n \n \\(|E/D|\\)\n \n \n values are thus desired for a precise simulation of pulsed EPR data as the zero-field Hamiltonian\n \n \n \\({H}_{\\text{Z}\\text{F}\\text{S}}\\)\n \n \n depends on\n \n \n \\(\\left|D\\right|\\)\n \n \n and\n \n \n \\(\\lambda =|E/D|\\)\n \n \n .\n \n \n \\(\\left|D\\right|\\)\n \n \n can be estimated experimentally by temperature-dependent measurements of the intensity ratios of the lower and upper Kramers doublet at\n \n g\u2009\u2248\n \n 6\n \n 41\n \n . These measurements were conducted on samples Av1-WT and Av1-Se2B-1 at 6 K and 15 K (Supporting Figure B3), and small differences were observed: The signal of the latter sample is slightly shifted to\n \n \u2248\n \n 115 mT and shows a more complex signal pattern compared to the single signal at 111 mT in the Av1-WT sample. However, quantitative extraction of signal intensities was not possible due to the substantial overlap of the signals from the lower and upper Kramer doublet (Supporting Figure B3). Nevertheless, the analysis demonstrates that\n \n \n \\(\\left|D\\right|\\)\n \n \n is of the same magnitude in the Se-incorporated samples and hence, using the WT value of\n \n \n \\(D=180 \\text{M}\\text{H}\\text{z}\\)\n \n \n is a valid approximation. Please note that the effective\n \n \n \\(g\\)\n \n \n -values are independent of\n \n \n \\(D\\)\n \n \n , if the energy of the Zeeman interaction is small compared to zero-field energy.\n
\n\n Inhomogeneous broadening of the magnetic parameters of protein-bound (metal) cofactors is usually approximated by a random distribution of the EPR parameters, in particular the\n \n \n \\(\\varvec{D}\\)\n \n \n tensor and the\n \n \n \\(\\varvec{g}\\)\n \n \n matrix, using Gaussian distributions, so-called strain models\n \n \n 42\n \n \u2013\n \n 45\n \n \n . These distribution models are valid as long as the width of the distribution is small compared to its magnitude. However, the experimental spectra of the high-spin Se-FeMo cofactor exhibit a large splitting compared to their size (Fig.\n \n 2\n \n ), so that such simple strain models cannot correctly reproduce these data sets, and thus other approaches are required.\n
\n\n Having only the parameter\n \n \n \\(\\lambda\\)\n \n \n that dominates the cw-EPR spectrum, a regularization method was applied to disentangle the complex signal pattern in the Se-incorporated samples (see Supporting Information Part A for theoretical details). Briefly, ill-posed problems can be solved by Tikhonov regularization, and although this method is commonly used in the analysis of DEER datasets\n \n \n 33\n \n ,\n \n 34\n \n \n , its application to cw-EPR spectra of high-spin transition metal clusters is yet not established. First, the potential and robustness of the regularization method was thoroughly tested using three calculated model datasets (Supporting Table\n \n 1\n \n ). After the optimal regularization parameter\n \n \n \\({\\alpha }_{\\text{O}\\text{p}\\text{t}}\\)\n \n \n was determined by different methods, the distribution function was obtained. From this, the respective cw-EPR spectrum was calculated (Supporting Figures A3\u201312). The regularization reproduced the calculated model spectra very well (Supporting Figure A9\u201312), and therefore, the method was used to analyze all experimental Av1 cw-EPR spectra. As the regularization allows only one free parameter (\n \n \n \\(\\lambda\\)\n \n \n ), an intrinsic linewidth (\n \n lwpp\n \n ) analysis of all samples was first performed, and optimal intrinsic Lorentzian peak-to-peak line shapes of 2.5\u20133 mT, 3.0\u20133.5 mT, and 3.5\u20134.0 mT were obtained for spectra recorded at 5\u20136 K, 9 K and 12 K, respectively (Supporting Information Part A and Supporting Figures B4\u20138). The distribution functions obtained from regularization are shown in Fig.\n \n 3\n \n and the individual\n \n \n \\(\\lambda\\)\n \n \n values of all species are summarized in Table\n \n 2\n \n . A multi-Gaussian fit was applied to quantify the individual distributions (Supporting Figure B11 and Table B1).\n
\n\n It is observed that samples Av1-WT, Av1-S, and Av1-\n \n 33\n \n S (panels A\u2013C) contain only one spin species with an average value of\n \n \n \\({\\lambda }_{2}\\)\n \n \n = 0.054. In contrast, samples Av1-Se2B-1, Av1-Se2B-lowflux, Av1-Se-C\n \n 2\n \n H\n \n 2\n \n , Av1-Se-low and Av1-\n \n 77\n \n Se2B (panels D-H) contain five species with average\n \n \n \\(\\lambda\\)\n \n \n values of\n \n \n \\({\\lambda }_{1}\\)\n \n \n = 0.033,\n \n \n \\({\\lambda }_{2}\\)\n \n \n = 0.057,\n \n \n \\({\\lambda }_{3}\\)\n \n \n = 0.082,\n \n \n \\({\\lambda }_{4}\\)\n \n \n = 0.116 and\n \n \n \\({\\lambda }_{5}\\)\n \n \n \n \u2248\n \n 0.19. The second value,\n \n \n \\({\\lambda }_{2}\\)\n \n \n , matches that of the Av1-WT and accordingly was assigned to the electronic resting state of the FeMo cofactor. Even though the other four \"Se-species\" are present in all Se-incorporated samples, noticeable population differences between samples can be detected. In Av1-Se2B-1 and Av1-\n \n 77\n \n Se2B, all four Se-species are populated, with\n \n \n \\({\\lambda }_{4}\\)\n \n \n being the largest fraction (~\u200936%). In Av1-Se-low, on the other hand, the fraction of species\n \n \n \\({\\lambda }_{2}\\)\n \n \n is below 10%, the Se-species are more highly populated, in particular\n \n \n \\({\\lambda }_{4}\\)\n \n \n . It is worth noting that the\n \n \n \\(\\lambda\\)\n \n \n populations of samples Av1-Se2B-1 and Av1-Se2B-lowflux differ; In contrast to Av1-Se2B-1, sample Av1-Se2B-lowflux shows predominantly\n \n \n \\({\\lambda }_{2}\\)\n \n \n and only small amounts of any of the Se-species. This can be rationalized by a lower electron flux in sample Av1-Se2B-lowflux due to the lower Av2/Av1 ratio, which in turn might result in a decreased formation rate of Se-species per time. The largest\n \n \n \\({\\lambda }_{5}\\)\n \n \n value of \u2248\u20090.19 has a very broad\n \n \n \\(\\lambda\\)\n \n \n distribution and in most cases only a low (<\u200910%) population. Sample Av1-S-remigration (panel I), in which the Se is expected to be re-replaced by S, shows a different distribution than any of the other Se-incorporated samples: Species\n \n \n \\({\\lambda }_{1}\\)\n \n \n and\n \n \n \\({\\lambda }_{4}\\)\n \n \n are depopulated, and in addition to the resting state, only the\n \n \n \\({\\lambda }_{3}\\)\n \n \n state is populated.\n
\n\n To evaluate the relaxation behavior of the individual spin species, cw-EPR spectra were recorded at different microwave powers of 0.377 mW, 3.77 mW, and 37.7 mW for analysis by regularization (Fig.\n \n 3\n \n , red and blue lines, additional microwave powers are shown as Supporting Figure B9). The relaxation behavior of all Se-species is similar, but different from that of the resting-state FeMo cofactor (\n \n \n \\({\\lambda }_{2}\\)\n \n \n ). Temperature-dependent measurements at 6, 9, and 12 K produced similar results (Supporting Figure B5\u20138).\n
\n\n
\n\n --- Please insert Fig.\n \n 3\n \n here ---\n
\n\n --- Please insert Table\n \n 2\n \n here ---\n
\n\n
\n\n From the normalized population distributions (Fig.\n \n 3\n \n ), cw-EPR spectra were calculated (red lines in Fig.\n \n 2\n \n ). The agreement between experiment and regularization is remarkably good in all samples and demonstrates the potential of the regularization method. Slight differences, e.g., in the signals at 145 mT and 200 mT (panels D-I), are only intensity differences and are most likely caused by small baseline artifacts.\n
\n\n \n Analysis of cw-EPR spectra using the grid-of-error method\n \n . The question remains whether the cw-EPR spectra are dominated only by the\n \n \n \\(\\lambda\\)\n \n \n parameter or whether the intrinsic line shape\n \n lwpp\n \n is a second important parameter that differs between samples and/or between individual spin species. Therefore, the established grid-of-error approach\n \n \n 37\n \n \n was used as a second method to re-evaluate all Av1 cw-EPR spectra. The results are depicted in Fig.\n \n 4\n \n and demonstrate that this method yields similar distribution functions compared to the regularization method. It is noteworthy that the\n \n P\n \n (\n \n \n \\(\\lambda\\)\n \n \n ) functions are significantly narrower than those obtained by regularization. This is not surprising, as the width of the distribution is partially compensated by a distribution of the intrinsic spectral linewidths. Again, samples Av1-WT Av1-S and Av1-\n \n 33\n \n S (panel A\u2013C) contain only one species with a\n \n \n \\(\\lambda\\)\n \n \n = 0.054 value, and samples Av1-Se2B-1, Av1-Se2B-lowflux, Av1-Se-C\n \n 2\n \n H\n \n 2\n \n , Av1-Se-low and Av1-\n \n 77\n \n Se2B (panels D\u2013H) contain four Se-species with\n \n \n \\(\\lambda\\)\n \n \n values of\n \n \n \\({\\lambda }_{1}\\)\n \n \n = 0.035,\n \n \n \\({\\lambda }_{2}\\)\n \n \n = 0.058,\n \n \n \\({\\lambda }_{3}\\)\n \n \n = 0.085,\n \n \n \\({\\lambda }_{4}\\)\n \n \n = 0.12. A fifth species with a\n \n \n \\(\\lambda\\)\n \n \n value of around \u2248\u20090.19 can be detected in samples Av1-Se2B-1, Av1-Se-C\n \n 2\n \n H\n \n 2\n \n , Av1-Se-low, and Av1-\n \n 77\n \n Se2B. Sample Av1-S-remigration (panel I) shows only three species with\n \n \n \\(\\lambda\\)\n \n \n values of 0.058, 0.085, and 0.12. These\n \n \n \\(\\lambda\\)\n \n \n values are very similar to those obtained by regularization.\n
\n\n Qualitatively, both methods yield similar population trends for all Se-incorporated samples. However, the individual populations differ depending on the method of analysis, and as we believe that the regularization provides more reliable populations, only for this method, a quantitative evaluation was carried out (Table\n \n 2\n \n ). One major advantage of the grid-of-error method is that two (or even more) parameters can be optimized simultaneously so that linewidths are obtained for all species analyzed. A 2-dimensional representation (\n \n \n \\(\\lambda\\)\n \n \n and\n \n lwpp\n \n ) shows that the non-Se-incorporated cofactors exhibit a\n \n lwpp\n \n between 1 mT and 3 mT (Supporting Figure B10), consistent with the result of 2.5 mT from regularization. The analysis of the Se-incorporated samples confirms that the\n \n lwpp\n \n of\n \n \n \\({\\lambda }_{1}\\)\n \n \n ,\n \n \n \\({\\lambda }_{2}\\)\n \n \n and\n \n \n \\({\\lambda }_{3}\\)\n \n \n are between 1\u20133 mT, and only the\n \n lwpp\n \n of\n \n \n \\({\\lambda }_{4}\\)\n \n \n is significantly larger than 5 mT. This result is unexpected, as the analyses of the relaxation times led to similar values for all Se-incorporated samples (see below). One explanation could be that the bandwidth of the individual\n \n \n \\({\\lambda }_{4}\\)\n \n \n values is significantly broader than\n \n \n \\({\\lambda }_{1-3}\\)\n \n \n , mainly because the grid-of-error method tends to overrate the parameter\n \n lwpp\n \n (see also section \u201cRegularization versus grid-of-error approach\u201d).\n
\n\n --- Please insert Fig.\n \n 4\n \n here ---\n
\n\n \n Light excited experiments.\n \n
\n\n Hoffman and coworkers\n \n \n 14\n \n \n have used intra-EPR cavity photolysis at 450 nm to characterize hydride containing states of the FeMo cofactor; by irradiating nitrogenase samples with blue light and subsequent annealing at 150 K, a conversion of two E\n \n 2\n \n (2H) isomers (denoted as 1b and 1c) could be demonstrated. Following these studies, samples Av1-Se2B-1 and Av1-Se-low were used to perform such experiments. The respective cw-EPR spectra (Supporting Figure B12) were analyzed by regularization and are shown in Fig.\n \n 5\n \n . It is evident that both samples respond to light irradiation and subsequent cryo-annealing, i.e. the probability distributions of the species change, but the changes are more pronounced in sample Av1-Se-low. This may be due to the fact that this sample contains a higher Se concentration.\n
\n\n In contrast to the results presented in reference\n \n \n 14\n \n \n , no species appear upon light illumination, but rather only reduction of signal intensities can be detected (blue arrows in Fig.\n \n 5\n \n ). A one-to-one correspondence to the published results cannot be expected, however, as the FeMo cofactor used in reference\n \n \n 14\n \n \n and the Se-FeMo cofactors and accompanying intermediates studied in our experiments do have slightly different properties such as binding strengths and absorption coefficients. The regularization clearly shows that the population probabilities of the individual species are different: while\n \n \n \\({\\lambda }_{2}\\)\n \n \n and\n \n \n \\({\\lambda }_{3}\\)\n \n \n do not change, the population probabilities of\n \n \n \\({\\lambda }_{1}\\)\n \n \n and\n \n \n \\({\\lambda }_{4}\\)\n \n \n decrease significantly, and similarly. As the ground state\n \n \n \\({\\lambda }_{2}\\)\n \n \n is not supposed to change, we can identify two distinct responses: The population probabilities of\n \n \n \\({\\lambda }_{1}\\)\n \n \n and\n \n \n \\({\\lambda }_{4}\\)\n \n \n change with light, those of\n \n \n \\({\\lambda }_{3}\\)\n \n \n do not.\n
\n\n --- Please insert Fig.\n \n 5\n \n here ---\n
\n\n \n Pulse EPR experiments.\n \n Prior analyses of hyperfine couplings, transient nutation, inversion recovery, and 2-pulse ESEEM experiments were conducted at Q-band microwave frequencies to determine the relaxation times and spin states of all samples. The transient nutation experiments revealed that unlabeled and Se-incorporated samples contain the same nutation frequencies, and only the intensities and linewidths of individual signals differ to a small extent (Supporting Figure C1). Therefore, all \u201cSe-species\u201d must possess the same total spin as the FeMo cofactor in its resting state (\n \n S\n \n =\u20093/2). Analysis of 2-pulse ESEEM and inversion recovery spectra yielded the relaxation times\n \n \n \\({T}_{\\text{M}}^{\\text{e}\\text{f}\\text{f}}\\)\n \n \n and\n \n \n \\({T}_{1}^{\\text{e}\\text{f}\\text{f}}\\)\n \n \n , which are in the range of 200\u2013400 ns and 1\u20133 \u00b5s, respectively (Supporting Figures C2 and C3). The relaxation times of all samples are similar, and are too short to conduct certain pulse experiments like ENDOR spectroscopy under our experimental conditions.\n
\n\n Representative Q-Band\n \n \n \\(\\tau\\)\n \n \n -averaged 2-pulse ESEEM experiments of samples Av1-WT and Av1-Se2B-1 are depicted as upper panels of Fig.\n \n 6\n \n . Additionally, the pseudo-modulated spectra are shown for a direct comparison with the cw-EPR spectra shown in Fig.\n \n 2\n \n A/D. Both spectra are quite similar to the ones obtained from X-band microwave frequencies: the Av1-WT sample shows the typical spectrum of the FeMo cofactor in its resting state (Fig.\n \n 6\n \n , left), and the Av1-Se2B-1 sample shows the already described complex Se-pattern (Fig.\n \n 6\n \n , right). However, the signal-to-noise ratio (S/N) of the pulse EPR spectrum is significantly lower, which is mainly due to the lock-in detection of the cw-EPR spectra, and the intensities of the individual signals differ slightly due to the incomplete compensation of different ESEEM modulation depths at different magnetic field positions by\n \n \n \\(\\tau\\)\n \n \n -averaging.\n
\n\n 3P-ESEEM spectra (Fig.\n \n 6\n \n , lower panels) of Av1-WT (black traces), Av1-Se2B-1 (red traces), and Av1-S (dark blue traces) are depicted at four different magnetic-field positions (580, 660, 740, and 880 mT), these spectra show nearly identical hyperfine couplings close to the proton Larmor frequency and in the range between 0\u20135 MHz; the latter signals have been assigned to two nitrogen atoms of the surrounding amino acids\n \n \n 40\n \n ,\n \n 46\n \n \n . Using literature values\n \n \n 40\n \n ,\n \n 46\n \n \n , the ESEEM signals of the three samples can be simulated with good agreement. This result confirms that the direct protein environment of the FeMo cofactor remains structurally intact after turnover with KSeCN, and that no other ligand such as SeCN\n \n \u2013\n \n or CN\n \n \u2013\n \n is attached to the cluster. In addition, it is reconfirmed that the overall spin of the cluster remains the same, otherwise, additional nitrogen hyperfine couplings would be expected.\n
\n\n On the other hand, samples Av1-\n \n 77\n \n Se2B (orange traces) and Av1-\n \n 33\n \n S (light blue traces) show additional resonances (shaded orange and light blue areas in Fig.\n \n 6\n \n ), which originate from hyperfine couplings of the respective EPR-active nuclei (\n \n 33\n \n S and\n \n 77\n \n Se) and the FeMo cofactor. Differences in the frequencies and signal patterns are due to different Larmor frequencies of the two nuclei and additional quadrupole couplings in the case of\n \n 33\n \n S. Sample Av1-Se2B-1 does not show any Se hyperfine couplings as the natural abundance of\n \n 77\n \n Se is below 8%. Spectral simulations of these additional hyperfine couplings are required for a quantitative analysis. However, such simulations are complex because at almost all magnetic positions the EPR spectra of the Se-species overlap, and therefore the observed\n \n 33\n \n S and\n \n 77\n \n Se hyperfine couplings are the weighted sum of each species\u2019 contribution.\n
\n\n Additional difficulties arise when simulating the\n \n 33\n \n S hyperfine couplings in sample Av1-\n \n 33\n \n S, as the quadrupole coupling of the\n \n 33\n \n S nucleus overlaps strongly with the resonances of the two\n \n 14\n \n N nuclei. Moreover, depending on the magnetic-field position, different ESEEM resonances are suppressed due to cross-suppression effects, and the 3P-ESEEM spectrum of two\n \n 14\n \n N nuclei and one\n \n 33\n \n S nucleus shows a large number of peaks due to the product rule. Therefore, no unequivocal spectral simulation could be achieved. Qualitatively, the few signals in the 580 mT and 660 mT spectra indicate that a single\n \n 33\n \n S nucleus with hyperfine and quadrupole couplings of a few MHz can generate such a pattern.\n
\n\n Using published\n \n 14\n \n N hyperfine couplings and assuming one\n \n 77\n \n Se nucleus, the analysis of the spectral pattern in the Av1-\n \n 77\n \n Se2B sample was done by manual optimization (see Methods section for details) and yielded principal\n \n 77\n \n Se hyperfine coupling values of\n \n A\n \n \n x\n \n = 3 MHz,\n \n A\n \n \n y\n \n = 10.5 MHz and\n \n A\n \n \n z\n \n = 0 MHz (\n \n a\n \n \n iso\n \n (\n \n 77\n \n Se)\u2009~\u20094 MHz) (grey shaded dotted traces in Fig.\n \n 5\n \n ). Of these values, only\n \n A\n \n \n y\n \n can be trusted, as\n \n B\n \n \n 0\n \n =\u2009560 mT corresponds to the effective\n \n g\n \n \n y\n \n principal value of the\n \n \n \\({\\lambda }_{2}\\)\n \n \n species. Variations of\n \n A\n \n \n x\n \n and\n \n A\n \n \n z\n \n , especially at higher magnetic fields, do not affect the quality of the simulations, so both values are undefined.\n
\n\n --- Please insert Fig.\n \n 6\n \n here ---\n
\n\n \n Regularization versus grid-of-error approach\n \n . For the analysis of the complex cw-EPR spectra, two model-free methods, the grid-of-errors method\n \n \n 37\n \n \n and the regularization method, were chosen to identify and analyze the individual spin species. The former method has been successfully applied to a high-spin Fe-EDTA complex\n \n \n 37\n \n ,\n \n 47\n \n \n . An accurate\n \n \n \\(|E/D|\\)\n \n \n value is necessary for both methods, as only then the computed rhombicity values can be converted into a correct effective\n \n g\n \n -matrix (see Supporting Information Part Theory). In the Fe-EDTA system,\n \n \n \\(\\left|D\\right|\\)\n \n \n is not significantly larger than the electron-Zeeman splitting in X-band, therefore measurements at several magnetic field strengths and simultaneous evaluation of all spectra with the grid-of-errors approach lead to accurate\n \n \n \\(\\left|D\\right|\\)\n \n \n values\n \n \n 47\n \n \n . In the FeMo cofactor,\n \n \n \\(\\left|D\\right|\\)\n \n \n (\n \n \n \\(\\approx\\)\n \n \n 180 GHz\n \n \n 19\n \n \n ) is much larger than the electron Zeeman splitting in X-band (\n \n \n \\(\\approx 10 \\text{G}\\text{H}\\text{z})\\)\n \n \n and therefore,\n \n \n \\(\\left|D\\right|\\)\n \n \n can only be precisely determined at frequencies above\n \n \n \\(\\left|2D\\right|\\)\n \n \n , or by performing a frequency sweep experiment at different magnetic fields\n \n \n 48\n \n \n . Such experiments are quite difficult to perform in terms of sample size and experimental conditions; however, the qualitative analysis performed in this study showed that\n \n \n \\(\\left|D\\right|\\)\n \n \n can be safely assumed unchanged in all samples (Supporting Figure B3).\n
\n\n As regularization has never been applied to statistical distributions of the zero-field parameter in high-spin systems, both analytical methods were first tested and compared using three model systems. A fixed intrinsic lineshape\n \n lwpp\n \n of 1 mT was used, thus the only variable parameter in these simulations was the rhombicity\n \n \n \\(\\lambda\\)\n \n \n . Comparison of the calculated and simulated spectra showed that the grid-of-errors approach, in particular in the case of low S/N, gave inferior results in comparison to the regularization method, which consistently performed exceedingly well (Supporting Information Part A).\n
\n\n For analysis of the experimental FeMo cofactor spectra, a\n \n lwpp\n \n of 2.5 mT was determined for all samples at 5 K from a\n \n lwpp\n \n analysis (determination of the minimum in a\n \n \n \\(\\rho \\left(lwpp\\right)\\)\n \n \n versus\n \n \n \\(lwpp\\)\n \n \n plot) and was used in the regularization method (Supporting Figure B4). The\n \n lwpp\n \n was used as a second independent parameter in the grid-of-error approach; this may be advantageous if the\n \n lwpp\n \n differs from species to species. In the Se-incorporated samples only\n \n \n \\({\\lambda }_{4}\\)\n \n \n showed a\n \n lwpp\n \n of more than 5 mT, while the linewidths of the other species matched the value of 2.5 mT quite well (Supporting Figure B10).\n
\n\n To best analyze the quality and robustness of both methods, the X-band cw-EPR spectrum and the pseudo-modulated and \u03c4-averaged Q-band pulse EPR spectrum of sample Av1-Se2B-1 (Fig.\n \n 6\n \n ) were analyzed by both methods, and the results were compared (Fig.\n \n 7\n \n ). Because\n \n lwpp\n \n is a second independent parameter in the grid-of-errors approach, slightly better results are obtained in the simulation of X-band cw-EPR spectra than using the regularization (Fig.\n \n 7\n \n , upper panels). On the other hand, the\n \n lwpp\n \n parameter is slightly overestimated by the grid-of-errors method in Q-band (Fig.\n \n 7\n \n , lower panels), which lowers the quality of these results. Overall, spectral simulations obtained from either method are of excellent quality and show only minor deviations from the experimental data.\n
\n\n This detailed analysis hence demonstrates that regularization is a powerful and fast approach to simulate EPR spectra that are either dominated by only one statistically-distributed parameter (in this case,\n \n \n \\(\\lambda\\)\n \n \n ), or depend only on a second, non-dominant parameter (in this case,\n \n lwpp\n \n ). To further improve the accuracy of the distribution\n \n P\n \n (\n \n \n \\(\\lambda\\)\n \n \n ), the samples could be measured in several frequency bands\n \n \n 37\n \n \n , and evaluated using a global regularization analogous to the analysis of DEER data sets\n \n \n 34\n \n \n . In summary, we believe that this method is more applicable to a high-spin EPR system than any simple strain models, since it provides faster, better, and model-free results for systems with many states and therefore many parameters.\n
\n\n --- Please insert Fig.\n \n 7\n \n here ---\n
\n\n \n EPR analysis and assignment of Se-incorporated samples.\n \n
\n\n Pulsed- and cw-EPR experiments revealed that all species contain a total spin of 3/2, and all Se-species (\n \n \n \\({\\lambda }_{\\text{1,3}-5}\\)\n \n \n ) relax faster than the FeMo resting state (\n \n \n \\({\\lambda }_{2}\\)\n \n \n ). 3P-ESEEM experiments of sample Av1-Se\n \n 77\n \n Se, which is labeled only at position 2B, confirms that Se is incorporated into the cofactor as its presence leads to additional hyperfine couplings. The same interpretation can be assumed for sample Av1-\n \n 33\n \n Se, although only spectroscopic, but no crystallographic confirmation is available for this sample\n \n \n 28\n \n \n . Spectral simulation revealed that the\n \n A\n \n \n y\n \n value of the Se hyperfine coupling is 10.5 MHz, while the other two principal values\n \n A\n \n \n x\n \n and\n \n A\n \n \n z\n \n have to be treated with caution as their values only moderately influence the quality of the simulations. In addition to dead-time artifacts and cross suppression, the different hyperfine couplings of the individual spin species also impede unambiguous simulation results. The two S-labeled samples (Av1-S and Av1-\n \n 33\n \n S) were generated under turnover conditions in the presence of KSCN without N\n \n 2\n \n . Sample AvI-\n \n 33\n \n S exhibits additional hyperfine and quadrupole couplings with a strength of only a few MHz, which originate from one\n \n 33\n \n S, and demonstrate that S exchange occurs even in the absence of N\n \n 2\n \n . We note that ENDOR experiments using uniformly\n \n 33\n \n S-labeled nitrogenase have already been conducted.\n \n 33\n \n S hyperfine couplings between \u2212\u200910 MHz and \u2212\u200916 MHz, including a quadrupole coupling of ~\u20091 MHz, have been reported, but no specific S atom could be assigned\n \n \n 19\n \n \n . In summary, additional hyperfine couplings in the 3P-ESEEEM spectra can be simulated by only one additional isotope (\n \n 33\n \n S or\n \n 77\n \n Se).\n
\n\n All Se-incorporated samples contain four additional spin species (\n \n \n \\({\\lambda }_{\\text{1,3}-5}\\)\n \n \n ), indicating that Se-exchange is possible under all experimental conditions studied (Table\n \n 1\n \n ), most likely with yields above 90% at position 2B\n \n 2\n \n 8\n \n ,\n \n 30\n \n \n . Results from regularization (and from the grid-of-errors approach) show that regardless of the expected distribution of Se within the sulfur belt, the cw-EPR spectra always show similar rhombicity distributions and vary only in their probability intensities (Fig.\n \n 3\n \n D-H and Table\n \n 2\n \n ). As crystallographic studies confirm different labeling pattern\n \n \n 28\n \n \n , it is possible that only the exchange at position 2B is detected spectroscopically and that additional Se exchange at positions 3A and 5A does not involve further changes in the electronic structure of the cluster. Note that Henthorn and colleagues carried out cw-EPR measurements with similarly prepared samples and detected no relevant changes in the EPR signals (Figure S2 in Ref.\n \n \n 30\n \n \n ). This does not contradict our results, as a closer look at their cw-EPR spectra reveals some additional low-intensity signals. Besides slightly different sample preparations, the reason could be the increased temperature of their measurements (10 K versus 5 K). Comparable cw-EPR measurements at 12 K support this interpretation: due to the short relaxation times of the FeMo cofactor, only a significantly broadened Se-pattern of low intensity can be detected (Supporting Figure B7).\n
\n\n To gain first insights into the nature of the four Se species, published EPR parameters of freeze-quenched reaction intermediates of the FeMo cofactor were extracted and compare with our values\n \n \n 14\n \n ,\n \n 22\n \n ,\n \n 23\n \n ,\n \n 49\n \n \n . Two identified\n \n \n \\(S=3/2\\)\n \n \n spin states (\"1b\u201d and \"1c\u201d)\n \n \n 22\n \n ,\n \n 49\n \n \n have been previously assigned to hydride isomers of state E\n \n 2\n \n (2H)\n \n \n 14\n \n \n . The effective\n \n \n \\(g{\\prime }\\)\n \n \n factors of these species were extracted, and by using equations\n \n \n \\(\\lambda =\\frac{2\\left({\\Delta }g\\right)}{3{\\left({\\Delta }g\\right)}^{2}-1}\\)\n \n \n ,\n \n \n \\({\\Delta }g=\\frac{{{g}_{\\text{y}}^{{\\prime }}}^{1/2}+{{g}_{\\text{x}}^{{\\prime }}}^{1/2}}{{{g}_{\\text{y}}^{{\\prime }}}^{1/2}-{{g}_{\\text{x}}^{{\\prime }}}^{1/2}}\\)\n \n \n and assuming\n \n \n \\({g}_{\\text{x}}={g}_{\\text{y}}\\)\n \n \n , the rhombicity values of these states may be calculated as\n \n \n \\({\\lambda }_{1\\text{b}}\\)\n \n \n \u2248 0.04 and\n \n \n \\({\\lambda }_{1\\text{c}}\\)\n \n \n \u2248 0.114, respectively. Other studies assigned a\n \n \n \\(S=3/2\\)\n \n \n spin state with a\n \n \n \\(\\lambda\\)\n \n \n value of about 0.12 to the protonated resting state E\n \n 0\n \n (H\n \n +\n \n )\n \n \n 50\n \n ,\n \n 51\n \n \n , and a photoinduced state with a\n \n \n \\(\\lambda\\)\n \n \n value of about 0.08 was very recently assigned to the (protonated) E\n \n 2\n \n state\n \n \n 51\n \n \n . Moreover, in freeze-quench experiments during turnover using an \u03b1-70Ile variant of Av1, a rhomboid signal (\n \n \n \\(\\lambda\\)\n \n \n = 0.24) was assigned to state E\n \n 2\n \n \n 52\n \n . This state can be excluded to be present in any of our samples as\n \n \n \\(\\lambda\\)\n \n \n = 0.24 is well above the\n \n \n \\(\\lambda\\)\n \n \n values observed in our spectra. The fact that a variant was used could explain the different\n \n \n \\(\\lambda\\)\n \n \n values of the study and the one by Chica and coworkers\n \n \n 51\n \n \n . An\n \n \n \\(S\\)\n \n \n = 1/2 signal with a\n \n \n \\(g\\)\n \n \n -factor of \u2248\u20092.00 that was assigned to state E\n \n 4\n \n \n 15,52\n \n is again not observed in any of our Se-incorporated Av1 spectra.\n
\n\n These literature values and the \u03bb values determined in this study are summarized in Table\n \n 3\n \n and allow a first comparison: Species\n \n \n \\({\\lambda }_{1}\\)\n \n \n has very similar values to the assigned state E\n \n 2\n \n (2H), species\n \n \n \\({\\lambda }_{3}\\)\n \n \n to the assigned (hydrogenated) state E\n \n 2\n \n , and species\n \n \n \\({\\lambda }_{4}\\)\n \n \n to the assigned states E\n \n 2\n \n (2H) or E\n \n 0\n \n (H\n \n +\n \n ). Species\n \n \n \\({\\lambda }_{5}\\)\n \n \n has never been observed in any other EPR experiment yet. Despite geometric distortions,\n \n \n \\({\\lambda }_{5}\\)\n \n \n could represent another hydride isomer of E\n \n 2\n \n or of any other higher state, as long as the total spin is\n \n S\n \n =\u20093/2.\n
\n\n The combination of the literature comparison, the analysis of the species distribution of sample Av1-S-remigration, and the results of the experiments with blue-light irradiation together support a more definite assignment of the different Se-species: Exchange of Se back to S, which was the rationale behind preparing sample Av1-S-remigration, does lead to a reduction of the states\n \n \n \\({\\lambda }_{1}\\)\n \n \n and\n \n \n \\({\\lambda }_{4}\\)\n \n \n , but besides the ground state (\n \n \n \\({\\lambda }_{2}\\)\n \n \n ), state\n \n \n \\({\\lambda }_{3}\\)\n \n \n persist even after prolonged reaction cycles. The light-irradiation experiments show very similar results: The probability of state\n \n \n \\({\\lambda }_{3}\\)\n \n \n (and\n \n \n \\({\\lambda }_{2}\\)\n \n \n ) does not change in response to light. On the other hand,\n \n \n \\({\\lambda }_{1}\\)\n \n \n and\n \n \n \\({\\lambda }_{4}\\)\n \n \n respond reversibly to blue light, but whether a conversion of the hydrides upon light illumination really takes place, or only partial photolysis, needs to be clarified by further experiments.\n
\n\n Therefore, states\n \n \n \\({\\lambda }_{1}\\)\n \n \n and\n \n \n \\({\\lambda }_{4}\\)\n \n \n , whose\n \n \n \\(g\\)\n \n \n -factors are very similar as those reported by\n \n \n 14\n \n \n , and were referred to as states 1b and 1c, are most likely two different hydride isomers of state E\n \n 2\n \n . State\n \n \n \\({\\lambda }_{3}\\)\n \n \n , on the other hand, is irreversibly formed, representing a non-productive state that cannot be re-exchanged. It could either arise from a geometrical distortion due to the Se incorporation, or it could be a \"stable\" protonated E\n \n 0\n \n state, which is irreversible due to the different p\n \n K\n \n \n a\n \n value of the Se-FeMo cofactor (see below).\n
\n\n If the published assignments of the intermediate stages were not to be trusted, could in principle all Se species originate from geometric distortions? A number of findings speak against such an interpretation: First, it is highly unlikely that the\n \n \n \\(g\\)\n \n \n -values of unlabeled FeMo intermediates and of geometrically distorted Se-FeMo cofactors are very similar (Table\n \n 3\n \n ). Second, if the individual Se-species would result from a simple geometric distortion of the FeMo cofactor by incorporation of the larger Se atom, either none or all of the Se-species would be re-exchanged by S in the Av1-S-remigration sample. Third, geometric distortions would lead to different\n \n \n \\(\\lambda\\)\n \n \n -distributions of samples with Se-exchange at position 2B (samples Av1-Se2B-1 and Av1-Se2B-lowflux) compared to samples with an equal labeling of the sulfur belt (sample Av1-Se-C\n \n 2\n \n H\n \n 2\n \n ), yet, our distributions do not show differences between the samples. In this context, it must be noted that the ground state values of the Se-FeMo and FeMo cofactors differ slightly (green dashed lines in Fig.\n \n 3\n \n ), and hence potential differences in geometry may have a minor influence on the\n \n \n \\(\\lambda\\)\n \n \n values.\n
\n\n A further important aspect in the discussion of the individual Se species is the decreased signal intensity of the Se-labeled samples compared to the S- or unlabeled samples: 40% of the FeMo cofactors are in an EPR silent state, which confirms that EPR inactive intermediate states of the FeMo cluster like E\n \n 1\n \n or E\n \n 3\n \n are also stabilized by the Se-incorporation method. As the S-labeled FeMo cofactors have the same cw-EPR intensity as the unlabeled cofactor, S-to-S exchange does not stabilize any intermediate states.\n
\n\n --- Please insert Table\n \n 3\n \n here ---\n
\n\n \n Mechanistic insights.\n \n The question remains why intermediate states are stabilized by incorporation of Se. Basically, Se has a higher polarizability compared to S, and the Se-H group has a lower p\n \n K\n \n \n a\n \n value compared to the S-H group\n \n \n 53\n \n \n , while serving as a structural surrogate for S in iron-sulfur clusters\n \n \n 54\n \n \n . Moreover, calculations on Se (or S) metal model complexes discovered that the substitution of S with Se leads to a reduction of the ligand field strength and can additionally affect the energy of the electronic states\n \n \n 55\n \n \n . These differences could lead to an equilibrium shift of the overall reaction upon Se substitution within the FeMo cofactor, and favor side reactions to early intermediate states (E\n \n 4\n \n , E\n \n 3\n \n , E\n \n 2\n \n , and E\n \n 1\n \n ) accompanied by the release of H\n \n 2\n \n . No states higher than E\n \n 2\n \n are observed, suggesting that the incorporation of Se into the FeMo cluster has to occur very early in the reaction scheme. The incorporation of Se into the 2B position of the FeMo cofactor could be accomplished via different reaction pathways\n \n \n 56\n \n ,\n \n 57\n \n \n . Our results support mechanisms that include protonation steps, as direct Se labeling would likely not result in as many different hydride isomers.\n
\n\n Earlier experiments with Se-modified samples showed that remigration of S results in a delayed enzyme activity\n \n \n 26\n \n \n , which lead to the assumption that the Se incorporated cofactor has a different activity and only regains its full enzymatic activity after S remigration. Our results demonstrate that Se incorporation leads to stabilization of different intermediate states containing different electronic structures. These differences could be due to changes in the effective oxidation states of the Fe atoms in the FeMo cofactor, whereby the total spin of\n \n S\n \n =\u20093/2 must be maintained. X-ray spectroscopy with a Se-labeled FeMo cofactor showed that position 2B and positions 3A/5A are electronically different\n \n \n 30\n \n \n . It was observed that the two iron atoms (Fe2/Fe6) that bind the Se at the 2B position show a \"local oxidized character\", whereas the iron nuclei which bind to the Se atoms at positions 3A/5A are rather reduced. It was also noted that both the incorporation of Se and hydrogen bonds affect the effective oxidation state and the electronic structure\n \n \n 30\n \n \n .\n
\n\n Can the additional protons of the E\n \n 2\n \n (2H) intermediate states be detected and characterized by EPR spectroscopy? Basically, additional protons show up in 3P-ESEEM spectra as additional signals around the proton Larmor frequency. Insets in Fig.\n \n 6\n \n B show these regions magnified for samples Av1-WT and Av1-Se2B-1. The proton hyperfine couplings of the Se-labeled sample (red) show a broadening compared to the unlabeled sample (black), and a weak splitting can be observed in the spectrum at the magnetic-field position 740 mT (see also Supporting Figure C4). Both of these indicate additional proton hyperfine couplings. ENDOR studies on the resting-state FeMo cofactor as well as on the CO-labeled cofactor have shown that the hyperfine couplings of the surrounding protons have only the strength of only a few MHz\n \n \n 19\n \n ,\n \n 58\n \n \n . It is therefore likely that any additional hyperfine couplings are only hardly visible in the 3P-ESEEM spectra due to fast relaxation times, low modulation depth, cross-suppression effects and are masked by the linewidth. It can still be concluded that the incorporation of Se leads to a broadened proton hyperfine signal pattern that most likely originate from additional protons attached to the Se-FeMo cofactor. Again, ENDOR spectroscopy at about 2 K could be helpful to further characterize these additional protons, in particular as the signals from species\n \n \n \\({\\lambda }_{1-5}\\)\n \n \n are at least partially spectrally separated; a combination of blue-light illumination and orientation selection can further reduce the number of Se-species and enable unequivocal assignment.\n
\n\n In this study, various Se incorporation experiments into the catalytically active FeMo cofactor of a nitrogenase were investigated by EPR spectroscopy, as the property of such labels, e.g., their different reactivity, are far from being fully understood\n \n \n 28\n \n \n . Cw-EPR spectra of Se-incorporated samples showed complex signal patterns compared to unlabeled samples. Using Tikhonov regularization, applied for the first time in such a problem, it was possible to assign four different electronic states, each with a total spin of 3/2, differing only in their rhombicity. An independent second analysis using a grid-of-errors approach confirmed these results.\n
\n\n Using EPR parameters from already assigned intermediate states of the FeMo cofactor and irradiation experiments with blue light, one of the states could be assigned to the ground state (E\n \n 0\n \n ) of the cofactor, and the other to (protonated) intermediate states (E\n \n 0\n \n (H\n \n +\n \n ) and E\n \n 2\n \n (2H), see Table\n \n 3\n \n ). Only one state\n \n \n \\({\\lambda }_{3}\\)\n \n \n , could potentially be stabilized by geometric distortions of the cofactor. By pulsed-EPR spectroscopy, small hyperfine couplings of\n \n 77\n \n Se (and of\n \n 33\n \n S) could be detected and spectrally simulated. These experiments confirmed the incorporation of at least one Se (or S) atom under turnover conditions.\n
\n\n The reason for the accumulation of the different reaction intermediates by Se incorporation presumably arises from the stabilization of these states due to the differences in polarizability and p\n \n K\n \n \n a\n \n values between Se and S. As only \"early\" intermediates of the LT scheme were detected, the opening and incorporation of Se (and presumably also of other substrates) is very likely to proceed in the first steps of the reaction. It is also important to mention that 40% of the FeMo cofactors are in an EPR-silent state after Se-incorporation. Even if state E\n \n 1\n \n is most probable of these states, higher odd states are also in principle possible.\n
\n\n The results presented here demonstrate that cw-EPR spectroscopy combined with Tikhonov regularization can analyze complex spectra with multiple species; this approach may be applied to other systems that contain paramagnetic transition metal centers. The result that under the selected experimental conditions a defined incorporation of Se or S takes place and reaction intermediates can be stabilized without effort offers great potential with respect to further investigations using different molecular spectroscopy methods.\n
\n\n continuous-wave (cw), Lowe and Thorneley (LT), MoFe-protein and Fe-protein from\n \n Azotobacter vinelandii\n \n (designated as Av1 and Av2), intrinsic peak-to-peak Lorentzian linewidth (lwpp), signal-to-noise ratio (S/N).\n
\n| \n \n Sample\n \n | \n \n \n Abbreviation\n \n | \n \n \n Sample condition\n \n | \n \n \n Labeling (based on\n \n \n 28\n \n ,\n \n 30\n \n \n )\n \n | \n \n \n Concentration\n \n | \n
|---|---|---|---|---|
| \n \n \n Azotobacter vinelandii\n \n MoFe protein (Av1) wild type \u2013 resting state\n \n | \n \n \n Av1-WT\n \n | \n \n \n 100% Av1\n \n | \n \n \n --\n \n | \n \n \n \u2248\u200948\u00a0mg/mL\n \n | \n
| \n \n Av1 wild type\n \n\n (turnover)\n \n | \n \n \n Av1-Se2B-1\n \n | \n \n \n 10\u00a0mM KSeCN was added to the turnover assay (Av2/Av1 ratio\u2009=\u20092).\n \n | \n \n \n Se (position 2B)\n \n | \n \n \n \u2248\u200973\u00a0mg/mL\n \n | \n
| \n \n Av1 wild type\n \n\n (turnover)\n \n | \n \n \n Av1-\n \n 77\n \n Se2B\n \n | \n \n \n 10\u00a0mM K\n \n 77\n \n SeCN was added to the turnover assay (Av2/Av1 ratio\u2009=\u20092).\n \n | \n \n \n \n 77\n \n Se (position 2B)\n \n | \n \n \n \u2248\u200974\u00a0mg/mL\n \n | \n
| \n \n Av1 wild type\n \n\n (turnover)\n \n | \n \n \n Av1-Se-low\n \n | \n \n \n 0.25\u00a0mM KSeCN was added to the turnover assay (Av2/Av1 ratio\u2009=\u20092).\n \n | \n \n \n Se (predominantly at position 2B)\n \n | \n \n \n \u2248\u200970\u00a0mg/mL\n \n | \n
| \n \n Av1 wild type\n \n\n (turnover)\n \n | \n \n \n Av1-\n \n 33\n \n S\n \n | \n \n \n 10\u00a0mM K\n \n 33\n \n SCN was added to the turnover assay (Av2/Av1 ratio\u2009=\u20092).\n \n | \n \n \n \n 33\n \n S (position 2B)\n \n | \n \n \n \u2248\u200940\u00a0mg/mL\n \n | \n
| \n \n Av1 wild type\n \n\n (turnover)\n \n | \n \n \n Av1-Se-C\n \n 2\n \n H\n \n 2\n \n \n | \n \n \n sample Av1-Se2B-1 was used for a second turnover assay, which was quenched with 10\u00a0mM KSeCN after a reaction time of 5\u00a0min.\n \n | \n \n \n Se (positions 2B, 3A and 5A)\n \n | \n \n \n \u2248\u200970\u00a0mg/mL\n \n | \n
| \n \n Av1 wild type\n \n\n (turnover)\n \n | \n \n \n Av1-Se2B-lowflux\n \n | \n \n \n 15\u00a0mM KSeCN was added to the turnover assay (Av2/Av1 ratio\u2009=\u20091.5).\n \n | \n \n \n Se (position 2B)\n \n | \n \n \n \u2248\u200957\u00a0mg/mL\n \n | \n
| \n \n Av1 wild type\n \n\n (turnover)\n \n | \n \n \n Av1-S\n \n | \n \n \n 22.5\u00a0mM KSCN was added to the turnover assay (Av2/Av1 ratio\u2009=\u20091.5).\n \n | \n \n \n S (position 2B)\n \n | \n \n \n \u2248\u200940\u00a0mg/mL\n \n | \n
| \n \n Av1 wild type (prolonged turnover)\n \n | \n \n \n Av1-S-remigration\n \n | \n \n \n sample Av1-Se2B-lowflux was used. A second turnover assay was started with an Av2/Av1 ratio\u2009=\u20094. The assay proceeded for \u2248\u20091\u00a0h.\n \n | \n \n \n Se is expected to be replaced by S again.\n \n | \n \n \n \u2248\u200946\u00a0mg/mL\n \n | \n
| \n | \n\n \n \n \n \\({{\\lambda }}_{1}\\)\n \n \n (fraction %)\n \n | \n \n \n \n \n \\({{\\lambda }}_{2}\\)\n \n \n (fraction %)\n \n | \n \n \n \n \n \\({{\\lambda }}_{3}\\)\n \n \n (fraction %)\n \n | \n \n \n \n \n \\({{\\lambda }}_{4}\\)\n \n \n (fraction %)\n \n | \n \n \n \n \n \\({{\\lambda }}_{5}\\)\n \n \n (fraction %)\n \n | \n |
|---|---|---|---|---|---|---|
| \n \n Avl-WT\n \n | \n \n | \n\n \n 0.055\n \n | \n \n | \n\n | \n\n | \n|
| \n \n Av1-S\n \n | \n \n | \n\n \n 0.053\n \n | \n \n | \n\n | \n\n | \n|
| \n \n Av1-\n \n 33\n \n S\n \n | \n \n | \n\n \n 0.053\n \n | \n \n | \n\n | \n\n | \n|
| \n \n Av1-Se2B-1\n \n | \n \n \n 0.033 (11.5%)\n \n | \n \n \n 0.056 (17.2%)\n \n | \n \n \n 0.080 (23.3%)\n \n | \n \n \n 0.113 (\n \n 35.9%\n \n )\n \n | \n \n \n 0.166\u20130.190 (12.1%)\n \n | \n |
| \n \n Av1-Se2B-lowflux\n \n | \n \n \n 0.033 (14.4%)\n \n | \n \n \n 0.058 (\n \n 34.3%)\n \n \n | \n \n \n 0.086 (18.4%)\n \n | \n \n \n 0.118 (30%)\n \n | \n \n \n \u2248\u20090.189 (2.9%)\n \n | \n |
| \n \n Av1-\n \n 77\n \n Se2B\n \n | \n \n \n 0.032 (14.1%)\n \n | \n \n \n 0.056 (20.2%)\n \n | \n \n \n 0.080 (10.6%)\n \n | \n \n \n 0.115 (\n \n 37.6%\n \n )\n \n | \n \n \n 0.163\u20130.220 (4.3%)\n \n | \n |
| \n \n Av1-Se-low\n \n | \n \n \n 0.033 (24.2%)\n \n | \n \n \n 0.058 (8.4%)\n \n | \n \n \n 0.078 (22%)\n \n | \n \n \n 0.120 (\n \n 38.9%\n \n )\n \n | \n \n \n 0.171\u20130.230 (4.3%)\n \n | \n |
| \n \n Av1-Se-C\n \n 2\n \n H\n \n 2\n \n \n | \n \n \n 0.034 (19.2%)\n \n | \n \n \n 0.058 (20.8%)\n \n | \n \n \n 0.084 (10.6%)\n \n | \n \n \n 0.120 (\n \n 33.4%\n \n )\n \n | \n \n \n 0.175\u20130.217 (6.5%)\n \n | \n |
| \n \n Av1-S-remigration\n \n | \n \n \n 0.033 (4.1%)\n \n | \n \n \n 0.057 (30.5%)\n \n | \n \n \n 0.086 (\n \n 39.4%\n \n )\n \n | \n \n \n 0.112 (16.1%)\n \n | \n \n \n 0.180 (9.9%)\n \n | \n |
| \n \n \n Average value\n \n \n | \n \n \n \n 0.033\n \n \n | \n \n \n \n 0.056\n \n \n | \n \n \n \n 0.082\n \n \n | \n \n \n \n 0.116\n \n \n | \n \n \n \n \u2248\u20090.19\n \n \n | \n |
| \n \n Effective\n \n g\n \n -values*\n \n | \n \n \n \n \n \\({{g}^{{\\prime }}}_{\\text{x}}^{1/2}\\)\n \n \n \n\n \n \n \\({g{\\prime }}_{\\text{y}}^{1/2}\\)\n \n \n \n\n \n \n \\({g{\\prime }}_{\\text{z}}^{1/2}\\)\n \n \n \n | \n \n \n 3.80\n \n\n 4.20\n \n\n 2.03\n \n | \n \n \n 3.66\n \n\n 4.33\n \n\n 2.02\n \n | \n \n \n 3.50\n \n\n 4.48\n \n\n 2.02\n \n | \n \n \n 3.30\n \n\n 4.68\n \n\n 2.00\n \n | \n \n \n \u2248\u20092.92\n \n\n \u2248\u20094.98\n \n\n \u2248\u20091.82\n \n | \n
| \n \n *\n \n Calculated from Supporting Information Eq. 1 and\n \n \n \\({g}_{x}={g}_{y}=2.00\\)\n \n \n and\n \n \n \\({g}_{z}=2.03\\)\n \n \n | \n
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\n TOC figure\n
\n \n\n While pregnancy has been associated with an altered immune response and distinct clinical manifestations of COVID-19, the influence of pregnancy on the persistence and severity of post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID, remains uncertain. This study investigated PASC risk in individuals with SARS-CoV-2 infection during pregnancy and compared it with that in reproductive-age females with SARS-CoV-2 infection outside of pregnancy. This retrospective analysis identified 72,151 individuals who contracted SARS-CoV-2 during pregnancy and 1,439,354 females who contracted SARS-CoV-2 outside of pregnancy, aged 18 to 50 years old, from March 2020 to June 2023 in the National Patient-Centered Clinical Research Network (PCORnet) and the National COVID Cohort Collaborative (N3C). A comprehensive list of PASC outcomes was investigated, including a PCORnet rule-based PASC definition, an N3C PASC machine learning (ML) Phenotype, unspecified PASC ICD-10 diagnoses (ICD10 codes U09.9 or B94.8), and a cluster of cognitive, fatigue, and respiratory conditions. Overall, the estimated risk of PASC at 180 days of follow-up for those infected during pregnancy was 16.47 events per 100 persons (95% CI, 16.00 to 16.95) in the PCORnet cohort, based on the PCORnet rule-based PASC definition, and 4.37 events per 100 persons (95% CI, 4.18 to 4.57) in the N3C cohort based on the ML model. The risks of unspecified PASC diagnoses were 0.19 events per 100 persons (95% CI, 0.14 to 0.25) in PCORnet, and 0.23 events per 100 persons (95% CI, 0.19 to 0.28) in N3C; and the risks of any post-acute cognitive, fatigue, and respiratory condition were 4.86 events per 100 persons (95% CI, 4.59 to 5.14) in PCORnet, and 6.83 events per 100 persons (95% CI, 6.59 to 7.08) in N3C. The PASC risk varied across different subpopulations within pregnant females. The observed risk factors for PASC included self-reported Black race, advanced maternal age, infection during the first two trimesters, obesity, and the presence of baseline comorbid conditions. While the findings suggest a high incidence of PASC in individuals following SARS-CoV-2 infection during pregnancy, the risk of PASC in pregnant females was lower than in matched non-pregnant females.\n
\n\n Many individuals who contract SARS-CoV-2 infection\n \n \n 1\n \n \n experience new, persistent, or exacerbated symptoms for months, or even years, afterward, often referred to as post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID.\n \n \n 2\n \n \n Existing knowledge on PASC, including its incidence, risk factors, subtypes, treatment, and pathophysiology were mostly developed from non-pregnant, adult populations.\n \n \n 2\n \n \u2013\n \n 5\n \n \n Little is known about PASC after SARS-CoV-2 infection during pregnancy.\n
\n\n The SARS-CoV-2 infection in pregnancy presents a unique set of challenges, intertwining aspects of virology, obstetrics, pediatrics, and public health.\n \n \n 6\n \n ,\n \n 7\n \n \n Acquiring SARS-CoV-2 infection during pregnancy is associated with an increased risk of mortality and obstetric complications.\n \n \n 6\n \n ,\n \n 8\n \n \u2013\n \n 10\n \n \n These adverse pregnancy outcomes can extend beyond maternal health to affect the short- and long-term quality of life of the offspring.\n \n \n 11\n \n \u2013\n \n 13\n \n \n The immune response and proteomic changes during pregnancy in the context of COVID-19 exhibit distinct characteristics compared to non-pregnant individuals, indicating a nuanced relationship between maternal protection of the fetus and susceptibility to severe disease manifestations.\n \n \n 7\n \n \n While SARS-CoV-2 infection acquired in pregnancy is associated with worse perinatal outcomes, infection during pregnancy has been described as protective against PASC.\n \n \n 12\n \n \n However, prior studies have been conducted on relatively small pregnancy cohorts,\n \n \n 12\n \n \n limiting the generalizability of the results. Further, knowledge gaps still exist for patient counseling including further consideration of gestational age at the time of SARS-CoV-2 infection in pregnancy and interval PASC risk, as well as the influence of pre-existing co-morbid health conditions.\n
\n\n In this study, within the National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER) initiative,\n \n \n 14\n \n \n electronic health records (EHR) data from 29 sites from the National Patient-Centered Clinical Research Networks (PCORnet) and 65 sites from the National COVID Cohort Collaborative (N3C) were analyzed to build one of the largest retrospective cohorts of females with SARS-CoV-2 infection during pregnancy. The objective of this study was to estimate PASC risk in individuals acquiring SARS-CoV-2 infection during pregnancy compared with a similar cohort of reproductive-age females who acquired SARS-CoV-2 outside of pregnancy. The secondary aim was to evaluate the influence of other variables such as race, infection by pregnancy trimester, SARS-CoV-2 variants, body mass index, baseline co-morbid health conditions, and vaccination status on the risk of developing PASC.\n
\n\n A total of 1,511,505 eligible reproductive-age females, with documented SARS-CoV-2 infection between March 1, 2020, and October 31, 2022, and follow-up to June 1, 2023, who were connected to the healthcare network before infection, were identified. Of those, 72,151 were pregnant when they acquired a SARS-CoV-2 infection (29,975 in the PCORnet cohort and 42,176 in the N3C cohort). For each pregnant individual, non-pregnant females were selected for comparison by exactly matching on region, age, infection time, acute severity, and baseline comorbidities (Method) with a ratio of 1:3, resulting in a total of 207,859 individuals in the comparison group (87,127 in the PCORnet cohort and 120,732 in the N3C cohort). The patient selection flow and the population characteristics are presented in Fig.\n \n 1\n \n and Table\n \n 1\n \n , respectively. See the population characteristics before matching in Extended Table\n \n 1\n \n .\n
\n\n
\n\n
\n| \n | \n\n \n PCORnet\n \n | \n \n \n N3C\n \n | \n ||||
|---|---|---|---|---|---|---|
| \n | \n\n \n COVID Positive Pregnant\n \n\n females\n \n | \n \n \n COVID Positive Non-Pregnant\n \n\n females\n \n | \n \n \n SMD\n \n b\n \n \n | \n \n \n COVID Positive Pregnant\n \n\n Females\n \n | \n \n \n COVID Positive Non-Pregnant\n \n\n females\n \n | \n \n \n SMD\n \n | \n
| \n \n \n N\n \n \n | \n \n \n 29,975\n \n | \n \n \n 87,127\n \n | \n \n | \n\n \n 42,176\n \n | \n \n \n 120,732\n \n | \n \n | \n
| \n \n \n Age (years) \u2014 Median (IQR)\n \n \n | \n \n \n 30 (26\u201434)\n \n | \n \n \n 30 (26\u201435)\n \n | \n \n \n 0.00\n \n | \n \n \n 30 (26\u201334)\n \n | \n \n \n 30(26\u201334)\n \n | \n \n \n -0.04\n \n | \n
| \n \n \n Age group \u2014 N (%)\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n|
| \n \n \n 18-<25 years\n \n \n | \n \n \n 5,858 (19.5)\n \n | \n \n \n 17,065 (19.6)\n \n | \n \n \n 0.00\n \n | \n \n \n 7,932 (18.8)\n \n | \n \n \n 23,057 (19.1)\n \n | \n \n \n -0.01\n \n | \n
| \n \n \n 25-<30 years\n \n \n | \n \n \n 8,212 (27.4)\n \n | \n \n \n 23,784 (27.3)\n \n | \n \n \n 0.00\n \n | \n \n \n 11,622 (27.6)\n \n | \n \n \n 32,855 (27.2)\n \n | \n \n \n 0.01\n \n | \n
| \n \n \n 30-<35 years\n \n \n | \n \n \n 9,303 (31.0)\n \n | \n \n \n 26,956 (30.9)\n \n | \n \n \n 0.00\n \n | \n \n \n 13,668 (32.4)\n \n | \n \n \n 38,855 (32.2)\n \n | \n \n \n 0.00\n \n | \n
| \n \n \n 35-<40 years\n \n \n | \n \n \n 5,305 (17.7)\n \n | \n \n \n 15,553 (17.9)\n \n | \n \n \n 0.00\n \n | \n \n \n 7,293 (17.3)\n \n | \n \n \n 21,059 (17.4)\n \n | \n \n \n 0.00\n \n | \n
| \n \n \n 40-<45 years\n \n \n | \n \n \n 1,188 (4.0)\n \n | \n \n \n 3,460 (4.0)\n \n | \n \n \n 0.00\n \n | \n \n \n 1,572 (3.7)\n \n | \n \n \n 4,648 (3.8)\n \n | \n \n \n -0.01\n \n | \n
| \n \n \n 45\u201350 years\n \n \n | \n \n \n 109 (0.4)\n \n | \n \n \n 309 (0.4)\n \n | \n \n \n 0.00\n \n | \n \n \n 86 (0.2)\n \n | \n \n \n 258 (0.2)\n \n | \n \n \n 0.00\n \n | \n
| \n \n \n Acute severity \u2014 N (%)\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n|
| \n \n \n ICU/Ventilation\n \n \n | \n \n \n 264 (0.9)\n \n | \n \n \n 147 (0.2)\n \n | \n \n \n 0.10\n \n | \n \n \n 25 (0.1)\n \n | \n \n \n 154 (0.1)\n \n | \n \n \n -0.02\n \n | \n
| \n \n \n Race \u2014 N (%)\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n|
| \n \n Asian\n \n | \n \n \n 1,301 (4.3)\n \n | \n \n \n 3,756 (4.3)\n \n | \n \n \n 0.00\n \n | \n \n \n 1,533 (3.6)\n \n | \n \n \n 3,926 (3.3)\n \n | \n \n \n 0.02\n \n | \n
| \n \n Black or African American\n \n | \n \n \n 5,250 (17.5)\n \n | \n \n \n 14,888 (17.1)\n \n | \n \n \n 0.01\n \n | \n \n \n 7,049 (16.7)\n \n | \n \n \n 21,524 (17.8)\n \n | \n \n \n -0.03\n \n | \n
| \n \n White\n \n | \n \n \n 16,874 (56.3)\n \n | \n \n \n 47,946 (55.0)\n \n | \n \n \n 0.03\n \n | \n \n \n 27,250 (64.6)\n \n | \n \n \n 77,663 (64.3)\n \n | \n \n \n 0.01\n \n | \n
| \n \n Other\n \n c\n \n \n | \n \n \n 3,221 (10.7)\n \n | \n \n \n 6,667 (7.7)\n \n | \n \n \n 0.11\n \n | \n \n \n 818 (1.9)\n \n | \n \n \n 4,134 (3.4)\n \n | \n \n \n -0.09\n \n | \n
| \n \n Missing\n \n | \n \n \n 3,329 (11.1)\n \n | \n \n \n 13,870 (15.9)\n \n | \n \n \n -0.14\n \n | \n \n \n 5,526 (13.1)\n \n | \n \n \n 13,484 (11.2)\n \n | \n \n \n 0.06\n \n | \n
| \n \n \n Hispanic Ethnicity \u2014 N (%)\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n|
| \n \n Yes\n \n | \n \n \n 6,970 (23.3)\n \n | \n \n \n 12,580 (14.4)\n \n | \n \n \n 0.23\n \n | \n \n \n 6,476 (15.4)\n \n | \n \n \n 14,821 (12.3)\n \n | \n \n \n 0.09\n \n | \n
| \n \n No\n \n | \n \n \n 21,837 (72.9)\n \n | \n \n \n 64,370 (73.9)\n \n | \n \n \n -0.02\n \n | \n \n \n 32,150 (76.2)\n \n | \n \n \n 94,772 (78.5)\n \n | \n \n \n -0.05\n \n | \n
| \n \n Missing\n \n | \n \n \n 1,168 (3.9)\n \n | \n \n \n 10,177 (11.7)\n \n | \n \n \n -0.29\n \n | \n \n \n 3,539 (8.4)\n \n | \n \n \n 11,129 (9.2)\n \n | \n \n \n -0.03\n \n | \n
| \n \n \n Area Deprivation Index \u2014 Median (IQR)\n \n \n | \n \n \n 45 (24\u201467)\n \n | \n \n \n 42 (19\u201463)\n \n | \n \n \n 0.13\n \n | \n \n \n 45 (15\u201375)\n \n | \n \n \n 45 (25\u201375)\n \n | \n \n \n -0.08\n \n | \n
| \n \n \n BMI\u2014Median(IQR)\n \n \n | \n \n \n 30 (26\u201436)\n \n | \n \n \n 28 (23\u201435)\n \n | \n \n \n -0.02\n \n | \n \n \n 31 (27\u201336)\n \n | \n \n \n 29 (24\u201337)\n \n | \n \n \n 0.07\n \n | \n
| \n \n BMI: <18.5 underweight\n \n | \n \n \n 173 (0.6)\n \n | \n \n \n 1,456 (1.7)\n \n | \n \n \n -0.10\n \n | \n \n \n 102 (0.2)\n \n | \n \n \n 999 (0.8)\n \n | \n \n \n -0.08\n \n | \n
| \n \n BMI: 18.5-<25 normal weight\n \n | \n \n \n 4,801 (16.0)\n \n | \n \n \n 19,631 (22.5)\n \n | \n \n \n -0.17\n \n | \n \n \n 4,402 (10.4)\n \n | \n \n \n 18,072 (15.0)\n \n | \n \n \n -0.14\n \n | \n
| \n \n BMI: 25-<30 overweight\n \n | \n \n \n 8,076 (26.9)\n \n | \n \n \n 13,714 (15.7)\n \n | \n \n \n 0.28\n \n | \n \n \n 9,146 (21.7)\n \n | \n \n \n 17,959 (14.9)\n \n | \n \n \n 0.18\n \n | \n
| \n \n BMI: >=30 obese\n \n | \n \n \n 13,758 (45.9)\n \n | \n \n \n 24,987 (28.7)\n \n | \n \n \n 0.36\n \n | \n \n \n 17,962 (42.6)\n \n | \n \n \n 36,641 (30.3)\n \n | \n \n \n 0.26\n \n | \n
| \n \n BMI: missing\n \n | \n \n \n 3,167 (10.6)\n \n | \n \n \n 27,339 (31.4)\n \n | \n \n \n -0.53\n \n | \n \n \n 10,564 (25.0)\n \n | \n \n \n 47,061 (39.0)\n \n | \n \n \n -0.3\n \n | \n
| \n \n \n Smoking Status\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n|
| \n \n Never\n \n | \n \n \n 11,540 (38.5)\n \n | \n \n \n 31,913 (36.6)\n \n | \n \n \n 0.04\n \n | \n \n | \n\n | \n|
| \n \n Current\n \n | \n \n \n 1,526 (5.1)\n \n | \n \n \n 5,287 (6.1)\n \n | \n \n \n -0.04\n \n | \n \n | \n\n | \n|
| \n \n Former\n \n | \n \n \n 2,094 (7.0)\n \n | \n \n \n 4,201 (4.8)\n \n | \n \n \n 0.09\n \n | \n \n \n 3,402 (8.1)\n \n | \n \n \n 9,719 (8.1)\n \n | \n \n \n 0.00\n \n | \n
| \n \n Missing\n \n | \n \n \n 14,815 (49.4)\n \n | \n \n \n 45,726 (52.5)\n \n | \n \n \n -0.06\n \n | \n \n | \n\n | \n|
| \n \n \n Pre-Infection Vaccination Status\u2014 N(%)\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n|
| \n \n Fully vaccinated\n \n | \n \n \n 2,882 (9.6)\n \n | \n \n \n 13,091 (15.0)\n \n | \n \n \n -0.17\n \n | \n \n \n 6,104 (14.5)\n \n | \n \n \n 22,067 (18.3)\n \n | \n \n \n -0.1\n \n | \n
| \n \n Partially vaccinated\n \n | \n \n \n 1,497 (5.0)\n \n | \n \n \n 5,795 (6.7)\n \n | \n \n \n -0.07\n \n | \n \n \n 1,328 (3.1)\n \n | \n \n \n 3,935 (3.3)\n \n | \n \n \n -0.01\n \n | \n
| \n \n No evidence\n \n | \n \n \n 25,631 (85.5)\n \n | \n \n \n 68,377 (78.5)\n \n | \n \n \n 0.18\n \n | \n \n \n 34,744 (82.4)\n \n | \n \n \n 94,730 (78.5)\n \n | \n \n \n 0.1\n \n | \n
| \n \n \n Index Time of Infection \u2014 N(%)\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n|
| \n \n \n 03/01/20\u2009\u2212\u200906/01/20\n \n \n | \n \n \n 1,849 (6.2)\n \n | \n \n \n 5,245 (6.0)\n \n | \n \n \n 0.01\n \n | \n \n \n 1,738 (4.1)\n \n | \n \n \n 4,375 (3.6)\n \n | \n \n \n 0.03\n \n | \n
| \n \n \n 07/01/20\u2009\u2212\u200910/01/20\n \n \n | \n \n \n 2,768 (9.2)\n \n | \n \n \n 8,035 (9.2)\n \n | \n \n \n 0.00\n \n | \n \n \n 2,715 (6.4)\n \n | \n \n \n 7,678 (6.4)\n \n | \n \n \n 0.00\n \n | \n
| \n \n \n 11/01/20\u2009\u2212\u200902/01/21\n \n \n | \n \n \n 4,531 (15.1)\n \n | \n \n \n 13,296 (15.3)\n \n | \n \n \n 0.00\n \n | \n \n \n 5,669 (13.4)\n \n | \n \n \n 16,644 (13.8)\n \n | \n \n \n -0.01\n \n | \n
| \n \n \n 03/01/21\u2009\u2212\u200906/01/21\n \n \n | \n \n \n 2,090 (7.0)\n \n | \n \n \n 5,589 (6.4)\n \n | \n \n \n 0.02\n \n | \n \n \n 2,530 (6.0)\n \n | \n \n \n 7,080 (5.9)\n \n | \n \n \n 0.01\n \n | \n
| \n \n \n 07/01/21\u2009\u2212\u200910/01/21\n \n \n | \n \n \n 3,401 (11.3)\n \n | \n \n \n 9,859 (11.3)\n \n | \n \n \n 0.00\n \n | \n \n \n 4,717 (11.2)\n \n | \n \n \n 13,655 (11.3)\n \n | \n \n \n 0.00\n \n | \n
| \n \n \n 11/01/21\u2009\u2212\u200902/01/22\n \n \n | \n \n \n 8,368 (27.9)\n \n | \n \n \n 24,832 (28.5)\n \n | \n \n \n -0.01\n \n | \n \n \n 14,047 (33.3)\n \n | \n \n \n 41,253 (34.2)\n \n | \n \n \n -0.02\n \n | \n
| \n \n \n 03/01/22\u2009\u2212\u200906/01/22\n \n \n | \n \n \n 3,332 (11.1)\n \n | \n \n \n 9,691 (11.1)\n \n | \n \n \n 0.00\n \n | \n \n \n 5,041 (12.0)\n \n | \n \n \n 14,191 (11.8)\n \n | \n \n \n 0.01\n \n | \n
| \n \n \n 07/01/22\u2009\u2212\u200910/01/22\n \n \n | \n \n \n 3,636 (12.1)\n \n | \n \n \n 10,580 (12.1)\n \n | \n \n \n 0.00\n \n | \n \n \n 5,719 (13.6)\n \n | \n \n \n 15,856 (13.1)\n \n | \n \n \n 0.01\n \n | \n
| \n \n \n Coexisting Conditions \u2014 N(%)\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n|
| \n \n Anemia\n \n | \n \n \n 3,871 (12.9)\n \n | \n \n \n 6,146 (7.1)\n \n | \n \n \n 0.20\n \n | \n \n \n 7,931 (18.8)\n \n | \n \n \n 9,456 (7.8)\n \n | \n \n \n 0.33\n \n | \n
| \n \n \n Asthma\n \n \n | \n \n \n 3,237 (10.8)\n \n | \n \n \n 8,709 (10.0)\n \n | \n \n \n 0.03\n \n | \n \n \n 4,675 (11.1)\n \n | \n \n \n 12,573 (10.4)\n \n | \n \n \n 0.02\n \n | \n
| \n \n Cancer\n \n | \n \n \n 396 (1.3)\n \n | \n \n \n 1,862 (2.1)\n \n | \n \n \n -0.06\n \n | \n \n \n 524 (1.2)\n \n | \n \n \n 2,299 (1.9)\n \n | \n \n \n -0.05\n \n | \n
| \n \n Chronic Kidney Disease\n \n | \n \n \n 156 (0.5)\n \n | \n \n \n 579 (0.7)\n \n | \n \n \n -0.02\n \n | \n \n \n 406 (1.0)\n \n | \n \n \n 873 (0.7)\n \n | \n \n \n 0.03\n \n | \n
| \n \n Chronic Pulmonary Disorders\n \n | \n \n \n 3,531 (11.8)\n \n | \n \n \n 10,362 (11.9)\n \n | \n \n \n 0.00\n \n | \n \n \n 5,260 (12.5)\n \n | \n \n \n 15,014 (12.4)\n \n | \n \n \n 0.00\n \n | \n
| \n \n Coagulopathy\n \n | \n \n \n 1,244 (4.2)\n \n | \n \n \n 1,481 (1.7)\n \n | \n \n \n 0.15\n \n | \n \n \n 1,671 (4.0)\n \n | \n \n \n 1,997 (1.7)\n \n | \n \n \n 0.14\n \n | \n
| \n \n \n Diabetes (Type 1 or 2)\n \n \n | \n \n \n 794 (2.6)\n \n | \n \n \n 1,636 (1.9)\n \n | \n \n \n 0.05\n \n | \n \n \n 1,374 (3.3)\n \n | \n \n \n 2,971 (2.5)\n \n | \n \n \n 0.05\n \n | \n
| \n \n \n Hypertension\n \n \n | \n \n \n 1,548 (5.2)\n \n | \n \n \n 3,765 (4.3)\n \n | \n \n \n 0.04\n \n | \n \n \n 2,440 (5.8)\n \n | \n \n \n 6,260 (5.2)\n \n | \n \n \n 0.03\n \n | \n
| \n \n \n Mental Health Disorders\n \n \n | \n \n \n 4,223 (14.1)\n \n | \n \n \n 11,768 (13.5)\n \n | \n \n \n 0.02\n \n | \n \n \n 7,815 (18.5)\n \n | \n \n \n 21,891 (18.1)\n \n | \n \n \n 0.01\n \n | \n
| \n \n Substance Abuse\n \n | \n \n \n 2,316 (7.7)\n \n | \n \n \n 6,521 (7.5)\n \n | \n \n \n 0.01\n \n | \n \n \n 1,468 (3.5)\n \n | \n \n \n 3,763 (3.1)\n \n | \n \n \n 0.02\n \n | \n
| \n \n Obstructive sleep apnea\n \n | \n \n \n 407 (1.4)\n \n | \n \n \n 1,830 (2.1)\n \n | \n \n \n -0.06\n \n | \n \n \n 607 (1.4)\n \n | \n \n \n 3,348 (2.8)\n \n | \n \n \n -0.09\n \n | \n
| \n \n Prescription of Corticosteroids\n \n | \n \n \n 2,957 (9.9)\n \n | \n \n \n 10,823 (12.4)\n \n | \n \n \n -0.08\n \n | \n \n \n 3,269 (7.8)\n \n | \n \n \n 9,255 (7.7)\n \n | \n \n \n 0.00\n \n | \n
| \n \n Prescription of Immunosuppressant drug\n \n | \n \n \n 1,748 (5.8)\n \n | \n \n \n 3,326 (3.8)\n \n | \n \n \n 0.09\n \n | \n \n \n 370 (0.9)\n \n | \n \n \n 1,189 (1.0)\n \n | \n \n \n -0.01\n \n | \n
| \n \n \n Autoimmune/ Immune Suppression\n \n \n \n d\n \n \n \n | \n \n \n 4,581 (15.3)\n \n | \n \n \n 13,114 (15.1)\n \n | \n \n \n 0.01\n \n | \n \n \n 3,869 (9.2)\n \n | \n \n \n 10,686 (8.9)\n \n | \n \n \n 0.01\n \n | \n
| \n \n \n Severe Obesity\n \n \n \n e\n \n \n \n | \n \n \n 4,255 (14.2)\n \n | \n \n \n 11,578 (13.3)\n \n | \n \n \n 0.03\n \n | \n \n \n 4,903 (11.6)\n \n | \n \n \n 13,116 (10.9)\n \n | \n \n \n 0.02\n \n | \n
| \n IQR, interquartile range. BMI, Body Mass Index. CCI, Charlson Comorbidity Index. a. The SARS-CoV-2 positive were identified by polymerase chain reaction (PCR) test or antigen test or diagnosis U07.1 or prescription of Paxlovid or Remdesivir. The percentage may not sum up to 100 because of rounding. Each pregnant individual was matched with non-pregnant females on site region, age, infection time, acute severity, and selected baseline comorbidities including diabetes, hypertension, autoimmune or immune suppression, mental health disorders, severe obesity, and asthma with a ratio of 1 to 3. b. A standardized mean difference (SMD) of >\u20090.10 or <-0.10 indicates an important effect size difference between the two populations, otherwise, no significant difference is assumed. c. The other category encompasses American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Multiple race, and other categories in the PCORnet Common Data Model d. Autoimmune/immune suppression denotes any prescription of corticosteroids or immunosuppressant drugs, or any diagnosis of Lupus/Systemic Lupus Erythematosus, rheumatoid arthritis, or inflammatory bowel disorder. e. Severe obesity derived from either BMI\u2009>\u2009=\u200940 kg/m2 or any severe obesity diagnosis.\n | \n
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\n\n Before matching, as shown in Extended Table\n \n 1\n \n , the median age in the pregnant female group was younger than the non-pregnant female group (30 [interquartile range (IQR), 26\u201334] vs 35 [IQR 27\u201343]) in PCORnet and 30 [IQR, 26\u201334 vs 36 [IQR 27\u201344] in N3C). Compared to non-pregnant females, pregnant females were less likely to have cancer, chronic kidney disease, chronic pulmonary disorders, hypertension, mental health disorders, severe obesity, or to be fully vaccinated at baseline. By contrast, pregnant females were more likely to have anemia, coagulopathy, and to be overweight compared with the non-pregnant females in both cohorts. After matching, as shown in Table\n \n 1\n \n , the two comparison groups became more comparable in terms of these baseline covariates. To further adjust for any residual differences, inverse probability of treatment weighting (IPTW) was applied to the matched cohorts (see Methods) for estimating relative risks. All the measured variables were well-balanced between the two comparison groups in PCORI and N3C as summarized in the Extended Table\u00a02.\n
\n\n Four PASC definitions were comprehensively examined: a PCORnet rule-based PASC definition which includes 15 incident conditions across multi-organ systems on the PCORnet cohort,\n \n \n 5\n \n ,\n \n 15\n \n \n an N3C Long COVID ML Phenotype trying to predict miss- or under-diagnosed PASC diagnosis U09.9 on the N3C cohort,\n \n \n 16\n \n ,\n \n 17\n \n \n unspecified PASC ICD-10 diagnosis U09.9/B94.8, and a sub-cluster of cognitive, fatigue, and respiratory diagnoses.\n \n \n 18\n \n \n The latter two were cross-checked among two cohorts as sensitivity analysis.\n
\n\n At 180 days of follow-up, the estimated risk of PASC was 16.47 events per 100 persons (95% confidence interval (CI), 16.00 to 16.95) in the pregnant group, and 18.88 (95% CI, 18.59 to 19.17) in the non-pregnant group (Fig.\n \n 2\n \n ). Compared to non-pregnant females, pregnant females had a lower risk of PASC, with a Hazard Ratio (HR) of 0.86 (95% CI, 0.83 to 0.90) and risk reduction of 2.41 events per 100 persons (95% CI, 1.85 to 2.96).\n
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\n\n Lower risk of incident PASC in the pregnant group was observed across systems as shown in Fig.\n \n 2\n \n , including post-acute neurological conditions (sleep disorders, cognitive problems, encephalopathy), post-acute pulmonary conditions (pulmonary fibrosis, acute pharyngitis, shortness of breath), post-acute circulatory condition (chest pain), and some general conditions in the post-acute phase (e.g., malaise and fatigue, unspecified Post-COVID-19 diagnostic codes U099/B948, smell, and taste). A few exceptions are post-acute metabolic conditions (edema, diabetes, malnutrition), post-acute musculoskeletal conditions (joint pain), pulmonary fibrosis, and fever, which showed no significant difference between the two groups.\n
\n\n Due to a different primary definition of PASC applied in the N3C cohort (ML phenotype), the estimated risk of PASC at 180 days in the N3C cohort was 4.37 events per 100 persons (95% CI, 4.18 to 4.57) in the pregnant group and 6.21 (95% CI, 6.07 to 6.35) in the non-pregnant group. However, the same relatively lower risk of PASC in the pregnant group compared to the non-pregnant group was observed in the N3C cohort (Fig.\n \n 2\n \n ) with HR of 0.70 (95% CI, 0.66 to 0.74) and risk reduction of 1.84 events per 100 persons (95% CI, 1.60 to 2.08).\n
\n\n Regarding absolute risks in the pregnant female group, as shown in Fig.\n \n 3\n \n , we observed higher PASC risk in several subgroups: self-reported Black individuals compared to White individuals, individuals with advanced maternal age (\n \n \n \\(\\:\\ge\\:\\)\n \n \n 35 years compared to those aged <\u200935 years), those infected during the first two trimesters compared to the third trimester, those infected during the Delta and Omicron periods (compared to earlier variants), individuals with obesity compared to those who were overweight or of normal weight, and those with baseline chronic medical conditions compared to those without. Similar absolute risks were observed in subgroups regardless of vaccination status.\n
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\n\n When compared to the non-pregnant group, the same relatively lower risk of PASC in the pregnant group was obtained across different subpopulations stratified by self-reported race (White, Black), age (<\u200935 years,\n \n \n \\(\\:\\ge\\:\\)\n \n \n 35 years), SARS-CoV-2 variants of concern (ancestral, Alpha, Delta, and Omicron), body mass index (normal, overweight, and obese), having baseline chronic medical conditions (yes or no), vaccination status (fully vaccinated, any vaccine records, or no vaccine records), and acquiring SARS-CoV-2 during the 3rd trimester, across two cohorts (Fig.\n \n 3\n \n ). A few exceptions are no significant or moderate higher risk in patients infected during the 1st trimester (HR 1.07 (0.97 to 1.19) in PCORnet, HR 1.17 (1.03, 1.34) in N3C) or 2nd trimester (HR 1.15 (1.08 to 1.23) in PCORnet, HR 0.89 (0.81, 0.97) in N3C.\n
\n\n We further cross-checked the results in both cohorts in terms of unspecified PASC ICD-10 diagnostic codes U099 or B948, and a subcluster of post-acute cognitive, fatigue, and respiratory conditions as shown in Fig.\n \n 4\n \n .\n
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\n\n Regarding the PASC diagnostic codes, the estimated risk at 180 days was 0.19 (95% CI, 0.14 to 0.25) events per 100 persons in the pregnant group and 0.60 (0.55 to 0.66) in the non-pregnant group within the PCORnet cohort. In the N3C cohort, the estimated risk was 0.23 (0.19 to 0.28) events per 100 persons in the pregnant group and 0.44 (0.40 to 0.48) in the non-pregnant group. This indicates that the pregnant group consistently exhibited a relatively lower risk\u2014approximately two to three times lower\u2014compared to the matched non-pregnant group across both cohorts.\n
\n\n For having any post-acute cognitive, fatigue, and respiratory conditions, the estimated risk was 4.86 (4.59 to 5.14) events per 100 persons in the pregnant group and 6.79 (6.60 to 6.97) events per 100 persons in the non-pregnant group within the PCORnet cohort. In the N3C cohort, the estimated risk was 6.83 (6.59 to 7.08) events per 100 persons in the pregnant group and 9.54 (95% CI, 9.37 to 9.71) events per 100 persons in the non-pregnant group.\n
\n\n Consistency was observed in both absolute and relative risks when applying these two PASC definitions across the two cohorts. Regarding different PASC outcomes in various subpopulations (Figs.\n \n 5\n \n and\n \n 6\n \n ), we observed a consistent pattern of lower relative risk in pregnant females compared with non-pregnant females, along with similar gradients of absolute risks across subgroups within the pregnant group. One exception was a higher incidence of unspecified PASC diagnoses in the Delta era among pregnant groups compared to other periods.\n
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\n\n In this retrospective cohort study involving 29 PCORnet sites and 65 N3C sites as part of the RECOVER initiative, we estimated the risk of PASC in pregnant females with SARS-CoV-2 infection during pregnancy. The long-term implications of COVID-19 in pregnancy are significant, as reflected in the different PASC outcomes captured across the two cohorts. In the PCORnet cohort, the estimated risk of PASC at 180 days of follow-up was 16.47 events per 100 persons (95% CI, 16.00 to 16.95) based on a rule-based PASC capture. In the N3C cohort, the estimated risk of PASC was events per 100 persons 4.37 (4.18 to 4.57) using a machine learning-based approach. The risks of unspecified PASC diagnostic codes U099 or B948 were 0.19 events per 100 persons (95% CI, 0.14 to 0.25) in PCORnet and 0.23 events per 100 persons (95% CI, 0.19 to 0.28) in N3C. The risks of post-acute cognitive, fatigue, and respiratory condition were 4.86 events per 100 persons (95% CI, 4.59 to 5.14) in PCORnet and 6.83 events per 100 persons (95% CI, 6.59 to 7.08) in N3C. A higher incidence of PASC was observed in self-reported Black patients, patients with advanced maternal age, those infected during the first two trimesters, individuals with obesity, and those with baseline conditions.\n
\n\n After adjustments, we observed a relatively lower risk of PASC in pregnant females compared to SARS-CoV-2-infected non-pregnant females who were exactly matched on region, age, infection time, acute severity, and baseline comorbidities. When comparing relative risks between corresponding subgroups among pregnant and non-pregnant females, the pattern of relatively lower risk of PASC was largely consistent across different subpopulations, various PASC definitions, and both the PCORnet and N3C cohorts.\n
\n\n Pregnancy reflects a period of physiologic immune tolerance to accommodate fetal development. Differences in regulatory T cells, cytokines, and other immune cells have been described during pregnancy and are thought to prevent maternal immune system rejection of the fetus.\n \n \n 19\n \n \n More severe disease courses from other viruses, such as influenza, have been described during pregnancy and attributed to these immune alterations.\n \n \n 20\n \n \n The observed risk differences in pregnant females compared to non-pregnant females in this analysis also suggest future dedicated pathophysiology studies of PASC in pregnant individuals are warranted. A higher risk of PASC in self-reported Black females draws attention to racial and ethnic disparities both in the acquisition of SARS-CoV-2 infection and the development of PASC, which may be related to factors such as inequitable healthcare access, socioeconomic factors, and structural racism.\n
\n\n This study has several strengths. First, the utilization of two large-scale clinical data networks, consisting of 73 unique hospital systems, allowed for more comprehensive analyses with substantial statistical power, particularly for the pregnant groups. In a prior publication,\n \n \n 12\n \n \n a subset of 5,397 eligible pregnant females acquiring COVID-19 during pregnancy from 19 PCORnet sites was reported. The sample size precluded subgroup analyses with adequate power. Through collaborative efforts from PCORnet, N3C, and the RECOVER-Pregnancy Cohort within RECOVER, for this analysis, 72,151 eligible pregnant females with infection during pregnancy, and 207,859 exactly matched infected non-pregnant females with a ratio of 1:3, were identified. Second, Detailed subgroup analyses were performed, stratified by self-reported race, maternal age, variants of concern, BMI, baseline co-morbid health conditions, and infection by trimester. Third, we characterized and cross-checked the PASC risk in terms of four different definitions including a rule-based definition organized by multi-organ systems in PCORnet,\n \n \n 5\n \n ,\n \n 15\n \n \n a machine-learning Long COVID phenotype in N3C,\n \n \n 16\n \n \n unspecified PASC diagnosis U099/B948, and a sub-cluster of cognitive, fatigue, and respiratory diagnoses.\n \n \n 18\n \n \n The similar patterns and triangulation from different PASC definitions across two different cohorts further strengthen the confidence in these findings.\n
\n\n There are also several limitations. First, this is a retrospective observational study based on electronic health records, which might suffer from potential residual confounding, misclassification of pregnancy, and study variables. Second, due to separate data systems, we did not implement the PCORnet PASC definition for the N3C cohort or the N3C PASC predictive model for the PCORnet cohort. However, un-specific PASC diagnoses and the cognitive, fatigue, and respiratory conditions were cross-checked in both cohorts. Third, the associations between vaccine status and PASC require further dedicated investigation. More than 82% of patients in the pregnant female group showed no vaccine data (Table\n \n 1\n \n ), higher than the nearly 77% no data portion in the infected non-pregnant group. The no-vaccine data could have derived from both poor capture of vaccine data in EHR and the initial low public confidence about COVID-19 vaccination in pregnancy (due to lack of enrollment of pregnant people in the early vaccine trials), and thus low vaccination rates in pregnant individuals. Forth, we did not investigate long-term implications on the child's development, which also requires future investigation. Finally, though adjusting for healthcare utilizations at baseline, pregnant individuals usually have frequent prenatal care visits (particularly for first and second-trimester infections), which may result in higher rates of detection of the PASC outcome variables in those populations.\n
\n\n This study utilized electronic healthcare records (EHR) data from two clinical research networks (CRN), namely the National Patient-Centered CRN (PCORnet) and the National COVID Cohort Collaborative (N3C), within the RECOVER initiative. Analyses were conducted separately for each cohort by following a common protocol and the same statistical analytics.\n
\n\n The PCORnet RECOVER infrastructure leveraged PCORnet to develop a single, unified EHR/RWD repository to study PASC across ~\u200928.25\u00a0million (18.75\u00a0million adult \u2212\u20099.5\u00a0million pediatric) patients from 40 adult and pediatric health systems nationwide who continue to refresh their data at least quarterly. The source data includes patients tested for COVID-19 (regardless of result), those diagnosed with COVID-19, those who received COVID-19 vaccine and therapeutics (e.g., Remdesivir and Paxlovid), and/or those who have received a respiratory diagnosis since 2019. The enclave contains structured EHR data consisting of inpatient and ambulatory encounters, laboratory results, vital signs, medications, diagnoses, procedures, birthdates, gender, and race information. The EHR data is linked to geocoded data to the level of the census tract, block group, and/or 9-digit zip code to allow linkage to exposome information to assess the influence of SDoH and environmental exposures on COVID-19 outcomes. In addition, the data enclave includes clinical notes for NLP, vaccine registries, and death registries.\n
\n\n Individual EHR data is stored in the N3C Data Enclave, which provides access to harmonized EHRs from 84 health sites with data from over 22.8\u00a0million patients (as of August 1st, 2024). For the current investigation, we used N3C data from version 152 (2023-12-07), and our final cohort encompasses contributions from 65 sites that had individuals who met our inclusion criteria. The N3C Data Enclave uses the Palantir Foundry platform (2021, Denver, CO), a secure analytics platform, for data access and analysis. N3C\u2019s methods for patient identification, data acquisition, ingestion, data quality assessment, and harmonization have been described previously.\n \n \n 21\n \n ,\n \n 22\n \n \n The N3C EHR data is structured in a similar way to PCORnet, consisting of inpatient and ambulatory encounters, laboratory results, vital signs, medications, diagnoses, procedures, birthdates, gender, and race information. Data for individuals is geocoded at the 9-digit zip code level, and sites are linked to vaccine registries, as well as a privacy-preserving record linkage to mortality and CMS (Medicare and Medicaid) claims data.\n
\n\n For our base cohort in PCORnet, we included SARS-CoV-2 patients with at least one positive SARS-CoV-2 polymerase-chain-reaction (PCR) or antigen laboratory test, COVID-19 diagnosis code U07.1, or prescription of Paxlovid or Remdesivir, between March 01, 2020, and June 30, 2023. The COVID-19 index date was defined as the date of the first documented positive COVID-19 record if they had (a) positive SARS-CoV-2 polymerase-chain-reaction (PCR) or antigen laboratory tests; (b) the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code U07.1 representing COVIID-19 diagnosis; or (c) Paxlovid (nirmatrelvir/ritonavir) or Remdesivir prescriptions, whichever occurred earlier. We required female patients, aged between 18 to 50 years old, and at least one diagnosis code within three years to seven days before the index date to be included in the cohort. The baseline period was defined as three years before the index date, and the post-acute phase, or the follow-up period, was set as 31 days to 180 days after the index date. We further require the index date before October 31, 2022, to guarantee at least a 180-day follow-up period.\n
\n\n For our base cohort in N3C, we included SARS-CoV-2 patients with at least one positive SARS-CoV-2 polymerase-chain-reaction (PCR) or antigen laboratory test, or COVID-19 diagnosis code U07.1 before October 31, 2022. The COVID-19 index date was defined as the date of the first documented positive COVID-19 lab test or diagnosis. The baseline period included all individual records going back to 2018, and we required at least two visits within one year before the index date. We further required at least one visit more than 100 days after the index date to ensure individuals didn\u2019t leave our data sample.\n
\n\n The primary exposure group included SARS-CoV-2 infection during pregnancy compared with outside of pregnancy. Thus, we identified two comparison groups: females acquiring SARS-CoV-2 during pregnancy versus outside pregnancy, applying additional eligibility criteria requiring infection in the gestational period for the pregnant females. The infection during pregnancy was defined as the first documented SARS-CoV-2 infection occurring between the start of pregnancy and the date of delivery. The delivery event was ascertained by identifying diagnosis codes related to delivery outcomes or delivery-related procedures\n \n \n 23\n \n \n after March 01, 2020. The start of the pregnancy and gestational age were approximated using the Z3A codes associated with the date of the delivery in PCORnet.\n \n \n 24\n \n \n Pregnancies in N3C were identified using a hierarchical rules-based algorithm described in a previous paper, which also uses Z3A codes to define gestational age.\n \n \n 25\n \n \n The gestational period was defined as the start of the pregnancy to the delivery event. In both PCORnet and N3C, we identified the SARS-CoV-2-infected pregnant group as those females with identified delivery events and SARS-CoV-2-infection occurring within the gestational period. The SARS-CoV-2-infected non-pregnant group consisted of individuals without any identified delivery events within the study windows.\n
\n\n The pregnant individuals were compared with exactly matched non-pregnant females on site region, age, infection time, acute severity, and selected baseline comorbidities including diabetes, hypertension, autoimmune or immune suppression, mental health disorders, severe obesity, and asthma with a ratio of 1 to 3. The cohort selection flow is illustrated in Fig.\n \n 1\n \n a.\n
\n\n The definition of Post-acute Sequelae of SARS-CoV-2 (PASC, or Long COVID) used for this study varies between PCORnet and N3C. In PCORnet, the PASC definition for pregnant females is a rules-based computable phenotyping algorithm leveraging International Classification of Diseases (ICD) 10th Version codes for 15 incident conditions, including cognitive problems, encephalopathy, sleep disorders, acute pharyngitis, shortness of breath (dyspnea), pulmonary fibrosis, chest pain, diabetes, edema, malnutrition, joint pain, fever, malaise and fatigue, ICD-10-CM diagnosis codes U099/B948 for unspecified PASC, and smell and taste. These conditions were identified based on previous studies,\n \n \n 5\n \n ,\n \n 15\n \n \n evidence from the literature,\n \n \n 15\n \n ,\n \n 26\n \n ,\n \n 27\n \n \n , and tailored for pregnant females.\n \n \n 12\n \n \n An incident condition was defined as occurring in SARS-CoV-2 infected patients who developed the condition between 31 days and 180 days after the acute infection, provided they did not have the condition three years to seven days before their acute infection. PASC was defined as having any incident condition from the abovementioned list.\n
\n\n In contrast, in the N3C cohort, PASC was defined primarily through a machine learning algorithm, specifically, the PASC Machine Learning 2.0 (LCM 2.0).\n \n \n 17\n \n ,\n \n 28\n \n \n This machine-learning pipeline predicts the presence of PASC using information extracted from the EHR data, creating a computable phenotype for PASC. The model was designed to address challenges such as missing data and idiosyncratic coding practices inherent in EHRs. Unlike its predecessor, LCM 1.0, which relied on the acute COVID-19 date as an anchor point for analysis, LCM 2.0 employs set time windows applicable to all patients, regardless of their COVID-19 index dates. These time windows, progressing through overlapping 100-day periods, enable the model to assess the probability of PASC across diverse patient populations, including those with suspected or untested COVID-19 cases and individuals experiencing multiple SARS-CoV-2 reinfections.\n
\n\n Two alternative definitions for PASC were further cross-checked in both PCORnet and N3C including a) un-specific PASC ICD-10-CM diagnostic codes U099 (Post COVID-19 condition, unspecified) or B948 (Sequelae of other specified infectious and parasitic diseases) and b) cognitive, fatigue, and respiratory diagnoses cluster.\n \n \n 18\n \n \n
\n\n A broad range of potential confounders collected at the time of infection were considered for the adjusted analyses. These covariates included age at infection, self-reported Race and Ethnicity, national-level Area Deprivation Index (ADI),\n \n \n 29\n \n \n healthcare utilization, time of infection, the most recent body mass index (BMI), smoking status, ICU or ventilation in acute infection, COVID-19 vaccine status, and a range of baseline health comorbidities. Age was categorized into 18\u201324 years, 25\u201329 years, 30\u201334 years, 35\u201339 years, 40\u201344 years, and 45\u201350 years). The self-reported Race was categorized as Asian, Black or African American, White, other (by grouping American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Multiple race, and other categories in the PCORnet Common Data Model\n \n \n 30\n \n \n ), missing, and self-reported ethnicity as Hispanic, not Hispanic, and other/missing. The ADI was used to capture the socioeconomic disadvantage of patients\u2019 residential neighborhoods.\n \n \n 29\n \n \n We used geocodes or 9-digit zip codes to link to the national ADI percentiles (ranging from 1 to 100, with higher numbers indicating higher levels of disadvantage. Healthcare utilization was measured as the number of inpatients and emergency encounters (0 visits, 1 or 2 visits, 3 or 4 visits, and 5 or more visits for each encounter type). The infection time was categorized into bins spanning every four months since March 2020 to account for different periods of the pandemic. BMI was categorized into underweight (<\u200918.5 kg/m\n \n 2\n \n ), normal weight (18.5\u201324.9 kg/m\n \n 2\n \n ), overweight (25.0\u201329.9 kg/m\n \n 2\n \n ), and obese (\u2009>\u2009=\u200930.0 kg/m\n \n 2\n \n ), and missing according to the Centers for Disease Control and Prevention guideline for adults.\n \n \n 31\n \n \n The severe acute infection was approximated by the ventilation status and critical care during the infection.\n
\n\n We collected a wide range of baseline co-morbid health conditions based on a tailored list of the Elixhauser comorbidities\n \n \n 32\n \n \n and related drug categories, including alcohol abuse, anemia, arrhythmia, asthma, cancer, chronic kidney disease, chronic pulmonary disorders, cirrhosis, coagulopathy, congestive heart failure, chronic obstructive pulmonary disease, coronary artery disease, dementia, diabetes type 1 or 2, end-stage renal disease on dialysis, hemiplegia, HIV, hypertension, inflammatory bowel disorder, lupus or systemic lupus erythematosus, mental health disorders, multiple sclerosis, Parkinson's disease, peripheral vascular disorders, pulmonary circulation disorder, rheumatoid arthritis, seizure/epilepsy, severe obesity (BMI\u2009>\u2009=\u200940 kg/m\n \n 2\n \n ), weight loss, Down syndrome, other substance abuse, cystic fibrosis, autism, sickle cell, obstructive sleep apnea, Epstein-Barr and Infectious Mononuclesosi, Herpes Zoster, corticosteroid drug prescriptions, and immunosuppressant drug prescriptions. Patients in PCORnet were considered to have a condition if they had at least one corresponding diagnosis or medication documented in the three years before the COVID-19 index date, and in N3C conditions were defined as any corresponding diagnosis or medication in the data (starting in 2018) prior to COVID-19 index date. The N3C used OMOP concept sets to match corresponding variables in PCORnet, but did not include cirrhosis, multiple sclerosis, lupus, Parkinson\u2019s disease, seizure/epilepsy, cystic fibrosis, autism, Epstein-Barr and Infectious Mononucleosis, or Herpes Zoster as health conditions. Corticosteroid and immunosuppressant prescription variables were created using the same drug codes as PCORnet.\n
\n\n We followed each patient from 30 days after their index date until the occurrence of the first target outcome, documented death, loss of follow-up in the database, 180 days after the baseline, or the end of our observational window (June 30, 2023), whichever came first.\n
\n\n For each individual in the pregnant group, the SARS-CoV-2 infected non-pregnant comparators were exactly matched on the site region, age, infection time, acute severity, and selected baseline comorbidities including diabetes, hypertension, autoimmune or immune suppression, mental health disorders, severe obesity, and asthma with a ratio of 1:3. Based on pregnant and matched non-pregnant cohorts, the relative risks were further adjusted via inverse probability of treatment weighting (IPTW) by considering a broader range baseline covariates. The propensity scores for the two groups were calculated with the regularized logistic regression with L2 norm with all the baseline covariates as independent variables.\n \n \n 15\n \n ,\n \n 33\n \n \n The stabilized IPTW was used and extreme weights beyond their 1st or 99th percentiles were further trimmed to reduce variability\n \n \n 34\n \n \n . The balance of covariates was evaluated by comparing standardized mean differences (SMD), with a difference of less than 0.1 considered to be balanced. The cumulative incidence for the two groups was estimated with the Aalen-Johansen model in the matched and reweighted population by considering death as a competing risk.\n \n \n 35\n \n \n The hazard ratios were estimated by the Cox survival model in the matched and reweighted population and two-sided 95% confidence intervals were calculated with the use of a robust variance estimator to account for stabilized IPTW weights. The absolute risk reduction was the difference in cumulative incidences at 180 days of follow-up between pregnant and non-pregnant groups.\n
\n\n The subgroup analysis was conducted by stratifying patients in both pregnant and non-pregnant groups by self-reported race, maternal age, trimesters when acquiring infection, variants by infection time, body mass index, baseline comorbidities (diabetes, hypertension, asthma, class III obesity), and vaccination status. To check the robustness of results in two cohorts, the unspecific PASC diagnostic codes U099 or B948 and the post-acute cognitive, fatigue, and respiratory conditions were cross-checked in both PCORnet and N3C cohorts, in terms of overall population and different sub-populations.\n
\n\n Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of \u201cmetabolically healthy obese\u201d. Using comprehensive lipidomic datasets from two large independent population cohorts in Australia (n\u2009=\u200914,831), we developed models that predicted BMI and calculated a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. We postulated that the mBMI score would be an independent metric for defining obesity and help identify a hidden risk for metabolic disorders regardless of the measured BMI. Based on the difference between mBMI and BMI (mBMI delta; \u201cmBMI\u0394\u201d), we identified individuals with a similar BMI but differing in their metabolic health profiles. Participants in the top quintile of mBMI\u0394 (Q5) were more than four times more likely to be newly diagnosed with T2DM (OR\u2009=\u20094.5; 95% CI\u2009=\u20093.1\u20136.6), more than two times more likely to develop T2DM over a five year follow up period (OR\u2009=\u20092.5; CI\u2009=\u20091.5\u20134.1) and had higher odds of cardiovascular disease (heart attack or stroke) (OR\u2009=\u20092.1; 95% CI\u2009=\u20091.5\u20133.1) relative to those in the bottom quintile (Q1). Exercise and diet were associated with mBMI\u0394 suggesting the ability to modify mBMI with lifestyle intervention. In conclusion, our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify individuals at risk for targeted intervention and monitoring.\n
\n\n The prevalence of obesity and overweight is growing worldwide\n \n \n 1\n \n ,\n \n 2\n \n \n . According to recent estimates, some 30% of men and 35% of women are obese in many countries including in North America, the Middle East, Asia, and Australia\n \n \n 3\n \n \n . Excess body weight is partly explained by high calorie intake coupled with insufficient physical exercise\n \n \n 4\n \n ,\n \n 5\n \n \n . Obesity is strongly associated with an increased risk of cardiometabolic disorders including type 2 diabetes mellitus (T2DM)\n \n \n 6\n \n ,\n \n 7\n \n \n and cardiovascular disease (CVD)\n \n \n 8\n \n ,\n \n 9\n \n \n .\n
\n\n Body mass index (BMI), defined as weight divided by height squared (kg/m\n \n 2\n \n ) is an accessible surrogate measure of obesity. Compared with direct measures of adiposity, such as computed tomography and dual energy x-ray absorptiometry, BMI is an inexpensive, simple and easily interpretable metric. World Health Organization (WHO) provides classifications and standardized cut-off points. Specifically, an individual whose BMI falls between 18.5\u201324.9 is considered a normal weight; 25.0-29.9, overweight; and 30.0 or higher representing obese. Despite not directly measuring body composition and adiposity, BMI strongly associates with cardiometabolic outcomes\n \n \n 10\n \n \n . However, it has been recognized that not all individuals who are obese/overweight - based on measured BMI - present with an increased risk of metabolic complications\n \n \n 11\n \n \n . A specific group of individuals who are obese, but \u201cmetabolically healthy\u201d, have been reported in multiple population cohort studies\n \n \n 12\n \n ,\n \n 13\n \n \n . Conversely, certain individuals, whom are within normal BMI range, are metabolically unhealthy, resulting in an increased risk for cardiometabolic disease\n \n \n 14\n \n ,\n \n 15\n \n \n .\n
\n\n Several studies have identified profound perturbations in circulating lipids associated with obesity\n \n \n 16\n \n \u2013\n \n 18\n \n \n . In addition, we have previously shown that the plasma lipidome is strongly associated with BMI, with several hundred plasma lipid species significantly associated in large population cohorts\n \n \n 19\n \n ,\n \n 20\n \n \n . Of note, positive associations of triacylglycerol, diacylglycerol, deoxyceramide and sphingomyelin, and negative associations of lysophosphatidylcholine and ether-lipid species have been consistently reported with BMI\n \n \n 19\n \n ,\n \n 21\n \n ,\n \n 22\n \n \n highlighting the potential impact of obesity on multiple lipid metabolic pathways. In contrast to some genetic loci stringently associated with BMI which explain less than 3% of phenotypic variation of BMI\n \n \n 23\n \n \n , metabolism, driven by multiple environmental factors (diet, exercise and other exposures), can explain up to 49% of BMI variability\n \n \n 17\n \n ,\n \n 18\n \n \n . Importantly, in several prospective studies, many BMI associated metabolites (including lipids) were also markedly associated with risk of diabetes\n \n \n 23\n \n \u2013\n \n 25\n \n \n and CVD\n \n \n 26\n \n \u2013\n \n 28\n \n \n independent of BMI. These findings convey an important message about the potential of metabolic phenotyping to refine the obesity definition beyond BMI measurements.\n
\n\n The strong associations of lipids and other metabolites with BMI has raised the prospect of developing metabolic scores that better capture the hidden risk of cardiometabolic diseases, i.e. the risk not explained by BMI itself, as in normal weight but metabolically unhealthy individuals. Using the human metabolome, Cirulli\n \n et al.\n \n identified metabolic signatures that distinguish healthy obese and normal weight individuals with abnormal metabolic profile\n \n \n 17\n \n \n . Of note, individuals who were classified as obese based on their metabolome, had 2 to 5 times higher risk of cardiovascular events compared to their counterparts with similar BMI but opposing metabolic signature. Moreover, a recent study has showed that, lean individuals with abnormal metabolism related to obesity had higher risk of developing T2DM and all-cause mortality compared to those individuals with lean BMI and healthy metabolism\n \n \n 29\n \n \n . The human lipidome has also been used to model BMI where it explained up to 47% of BMI variation with just 75 predictors in a LASSO model\n \n \n 18\n \n \n . These findings suggest the potential utility of the human lipidome and or metabolome to characterizing the heterogeneity in obesity and identify individuals at an increased risk of obesity-related diseases.\n
\n\n These early studies have identified mBMI scores that capture residual risk of a range of cardiometabolic outcomes. However, the signal being captured by metabolic BMI scores has not been clearly defined nor has the relationship with disease outcomes been adequately quantified. To address this, we developed models to predict BMI and calculated mBMI scores using plasma lipidomic data in a large Australian cohort - the Australian Diabetes, Obesity and Lifestyle Study (AusDiab; n\u2009=\u200910,339) (Fig.\n \n 1\n \n A, Fig.\n \n 1\n \n B). Metabolic BMI scores were validated in an independent Australian cohort, the Busselton Health Study (BHS, n\u2009=\u20094,492) (Fig.\n \n 1\n \n C). The mBMI score, and a derived score from the difference between mBMI and measured BMI (mBMI\u0394), were examined for their association with metabolic traits, the lipids used to generate the scores and with prevalent-, and incident-cardiometabolic outcomes (Fig.\n \n 1\n \n D). We demonstrate that mBMI\u0394 captures a metabolic signal that is independent of BMI, but closely mirrors the BMI signal. This provides an independent measure of the metabolic dysregulation associated with obesity. The role of such a measure in cardiometabolic risk and personalised health is discussed. Importantly, our work shows a strong association of diet and lifestyle habits with mBMI\u0394; higher intake of \u201chealthier foods\u201d such as fruits and fibre and higher levels of leisure time physical activity (PA) were associated with the lower mBMI\u0394 while prolonged television (TV) viewing time was markedly associated with higher mBMI\u0394. This suggests that lifestyle interventions may improve individuals\u2019 metabolic health through modification of their mBMI, independent of their measured BMI.\n
\n\n
\n\n AusDiab and BHS are longitudinal, Australian, adult population cohorts. As such, they show similar baseline characteristics, including comparable sex composition, age-, and BMI distribution (Table\n \n 1\n \n ). The prevalence of T2DM, CVD, and smoking were also comparable between the two cohorts. The clinical endpoints in the present study include prevalent (newly diagnosed and untreated) and incident (over a 5-year follow up period) T2DM, pre-diabetes (both prevalent and 5-year incident cases) and incident (over a 10-year follow up period) major cardiovascular events (CVE) and ischemic heart disease (IHD) (Supplementary Tables\u00a01 and 2). The definitions for these outcomes are provided in the method section. The AusDiab and BHS cohorts respectively comprise of 55% and 56% female participants. From the 11,247 AusDiab participants who attended both the interview and the biomedical examinations at baseline, 10,339 had fasting plasma samples available for lipidomic analysis. Of the 10,339 participants, 395 (3.8%) and 291 (2.8%) were identified as newly diagnosed T2DM and known diabetes respectively. Participants with the known diabetes at baseline (i.e. those receiving pharmacological treatment for diabetes, and or previously diagnosed with diabetes) were excluded. During, a 5-year follow up time, 218 incident cases of T2DM were also recorded (Fig.\n \n 1\n \n A, Supplementary Table\u00a01). In addition, some, 414 major CVEs and 304 IHD (in the AusDiab cohort) and 284 incident IHD (in the BHS cohort) occurred over 10-year follow up (Fig.\n \n 1\n \n A, Supplementary Tables\u00a01 and 2). We examined at the relationship of the anthropometric, clinical and behavioural data in relation to disease outcomes and controls for both cohorts. Most of the explanatory variables were significantly different between cases and controls (Supplementary Tables\u00a01 and 2).\n
\n| \n \n Characteristic\n \n | \n \n \n AusDiab (n\u2009=\u200910,339)\n \n | \n \n \n BHS\n \n\n (n\u2009=\u20094,492)\n \n | \n
|---|---|---|
| \n \n Age (years)\n \n a\n \n \n | \n \n \n 51.3 (14.3)\n \n | \n \n \n 50.8 (17.4)\n \n | \n
| \n \n Sex, n (%men)\n \n b\n \n \n | \n \n \n 4,654 (45)\n \n | \n \n \n 1976 (44.0)\n \n | \n
| \n \n BMI (kg/m\n \n 2\n \n )\n \n a\n \n \n | \n \n \n 26.9 (4.9)\n \n | \n \n \n 26.2 (4.2)\n \n | \n
| \n \n WC (cm)\n \n a\n \n \n | \n \n \n 90.8 (13.8)\n \n | \n \n \n 86.1 (12.7)\n \n | \n
| \n \n Cholesterol (mmol/L)\n \n a\n \n \n | \n \n \n 5.7 (1.1)\n \n | \n \n \n 5.6 (1.1)\n \n | \n
| \n \n HDL-C (mmol/L)\n \n a\n \n \n | \n \n \n 1.44 (0.4)\n \n | \n \n \n 1.39 (0.39)\n \n | \n
| \n \n Triglycerides (mmol/L)\n \n c\n \n \n | \n \n \n 1.28 (0.9)\n \n | \n \n \n 1.18 (0.90)\n \n | \n
| \n \n SBP (mmHg)\n \n a\n \n \n | \n \n \n 129.2 (18.6)\n \n | \n \n \n 124.0 (17.9)\n \n | \n
| \n \n DBP (mmHg)\n \n a\n \n \n | \n \n \n 70.0 (11.7)\n \n | \n \n \n 74.5 (10.2)\n \n | \n
| \n \n FBG (mmol/L)\n \n a\n \n \n | \n \n \n 5.3 (1.1)\n \n | \n \n \n 5.0 (1.4)\n \n | \n
| \n \n 2h-PLG (mmol/L)\n \n a\n \n \n | \n \n \n 6.3 (2.7)\n \n | \n \n \n -\n \n | \n
| \n \n HbA1C (%)\n \n a\n \n \n | \n \n \n 5.2 (0.6)\n \n | \n \n \n -\n \n | \n
| \n \n HOMA-IR\n \n a\n \n \n | \n \n \n 3.6 (2.4)\n \n | \n \n \n 1.78 ( (2.5)\n \n | \n
| \n \n Current smoking, n (%)\n \n b\n \n \n | \n \n \n 1,623 (15.9)\n \n | \n \n \n 608 (13.5)\n \n | \n
| \n \n BP treatment, n (%)\n \n b\n \n \n | \n \n \n 1,577 (15.3)\n \n | \n \n \n -\n \n | \n
| \n \n Lipid lowering medication, n (%)\n \n b\n \n \n | \n \n \n 871 (8.4)\n \n | \n \n \n 108 (2.4)\n \n | \n
| \n \n Diabetes at baseline, n (%)\n \n b\n \n \n | \n \n \n 686 (6.6)\n \n | \n \n \n 271 (6.0)\n \n | \n
| \n \n Baseline CVD prevalence, n (%)\n \n b\n \n \n | \n \n \n 577 (5.6)\n \n | \n \n \n 238 (5.3)\n \n | \n
\n \n a\n \n Values expressed as mean (\u00b1\u2009SD).\n
\n\n \n b\n \n Values expressed as frequency, n (%) for dichotomous variables.\n
\n\n \n c\n \n Data in Median, (IQR) as Triglyceride distribution was right skewed.\n
\n\n WC, waist circumference; HDL-C, high density cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; 2h-PLG, 2-hour post load glucose; HbA1C%, percent glycated haemoglobin; HOMA-IR, homeostasis model assessment of insulin resistance.\n
\n\n We utilized previously generated lipidomic data from two large Australian population cohorts, AusDiab\n \n \n 20\n \n \n and BHS\n \n \n 30\n \n \n . Targeted lipidomic profiling was performed in each cohort using liquid chromatography coupled to electrospray ionization-tandem mass spectrometry\n \n \n 19\n \n \n , from fasting plasma samples (AusDiab, n\u2009=\u200910,339) and fasting serum samples (BHS, n\u2009=\u20094,492). Lipidomic data encompassing 575 lipid species within 33 lipid classes, from the major glycerophospholipid, sphingolipid, glycerolipid and sterol classes was available on all AusDiab and BHS participants. The coefficient of variation (%CV) of pooled plasma quality control (PQC) samples were calculated for each lipid species to assess the assay performance. In the AusDiab cohort, the median %CV was 9.5% and over 90% of the lipid species were measured with a %CV\u2009<\u200920%\n \n 20\n \n . In the BHS cohort, the median %CV was 8.6% with 570 (95.6%) lipid species showing a %CV less than 20%.\n
\n\n We used ridge regression to create a lipidome based predictive model for BMI including age and sex as covariates. To avoid, overfitting, a 10-fold cross validation was employed in the AusDiab cohort (i.e. models trained on the 9/10th and used to predict BMI in the holdout 1/10th of the cohort; lambda average\u2009=\u20090.094, range\u2009=\u20090.087\u20130.105). This model provided predicted BMI (pBMI) values and was able to explain 60.4% of the variance in BMI as shown in Fig.\n \n 2\n \n A. When the model was validated in the BHS cohort it explained 40% of the BMI variance (Supplementary Fig.\u00a01A, Supplementary Table\u00a03). To standardise the pBMI to the population, the metabolic BMI (mBMI) was then derived from the pBMI scores as follows: mBMI\u2009=\u2009BMI + (pBMI \u2013 pBMI value on the line of best fit between pBMI and BMI). The mBMI\u0394 was then defined as the difference between BMI and mBMI. The correlation between BMI and mBMI was strong: R\n \n 2\n \n =\u20090.811 in the AusDiab cohort (Fig.\n \n 2\n \n B) and R\n \n 2\n \n =\u20090.65 in the BHS cohort (Supplementary Fig.\u00a01B). To further assess the precision in estimating mBMI, we generated mBMI scores for the NIST 1950 QC samples (200 replicates, assuming an average BMI of 26.0) that were analysed throughout the AusDiab cohort. The %CV for mBMI in the NIST 1950 QC samples was 5.5%. When we created models using the clinical lipid measures (total cholesterol, HDL-C and triglycerides) with age and sex, this model explained only 15.6% variation in BMI in the AusDiab cohort and 10.4% of BMI when validated in the BHS cohort (Supplementary Table\u00a03).\n
\n\n To better understand the lipid biology captured by the mBMI, we performed regression analysis of lipid species with BMI and mBMI\u0394. In age and sex adjusted models, we observed a significant association with 505 out of 575 lipid species with BMI. Diacylglycerol, triacylglycerol and ceramide species showed a strong positive association, while most hexosylceramide, lyso and ether phospholipid species were negatively associated (Fig.\n \n 2\n \n C, Supplementary Table\u00a04) (e.g. LPC(18:2)[sn1] decrease by 2.15% per unit increase in BMI, p\u2009=\u20091.56x10\n \n \u2212\u2009245\n \n ). Of the triacylglycerol species, TG(52:1)[NL-18:0] was the strongest predictor (4.94% increase per unit of BMI, p\u2009=\u20094.56x10\n \n \u2212\u2009283\n \n ). We then performed the same regression analysis of lipid species against mBMI\u0394 (Fig.\n \n 2\n \n D, Supplementary Table\u00a05) and compared the lipidomic profile associated with BMI with the profile associated with mBMI\u0394. Interestingly, the association of mBMI\u0394 with lipid species and the association of BMI with lipid species were almost identical with the correlation between effect sizes of each lipid associated with BMI (x-axis) and mBMI\u0394 (y-axis) having a R\n \n 2\n \n =\u20090.999. However, we note the effect sizes were stronger against mBMI\u0394 (Fig.\n \n 2\n \n E, Supplementary Table\u00a05) reflecting that variance in mBMI\u0394 is completely explained by the lipid species whereas variance in BMI is only partially explained by lipid species. For example, the effect size for TG(52:1)[NL-18:0] was 4.94% against BMI (Fig.\n \n 2\n \n C, Supplementary Table\u00a04) and 8.7% for the same species against mBMI\u0394 (Fig.\n \n 2\n \n D, Supplementary Table\u00a05). The statistical explanation why the plot of the beta coefficients of lipids for BMI and mBMI\u0394 are correlated is elaborated in Supplementary material 1. A LASSO model performed nearly the same as the ridge model (Supplementary Fig.\u00a02A and 2B, Supplementary Table\u00a03). Using the LASSO model, associations of BMI with plasma lipid species (Supplementary Fig.\u00a02C) and association of mBMI\u0394 with plasma lipid species (Supplementary Fig.\u00a02D) were identical after adjusting for age and sex. The correlation between effect sizes of each lipid associated with BMI (x-axis) and mBMI\u0394 calculated from the LASSO model (y-axis) provided an R\n \n \n 2\n \n \n close 1.0 (Supplementary Fig.\u00a02E).\n
\n\n To assess the importance of the number of lipid species in the models, we compared regularized linear models (ridge, elastic-net and LASSO), incorporating lipid species, age and sex, for their ability to predict BMI in the AusDiab cohort and validated these in the BHS. Using elastic-net (384 lipid species selected) and LASSO (349 lipid species selected) models, we observed similar performance as for the ridge model for the prediction of BMI, with models explaining 60.8\u201360.9% of BMI variance in the AusDiab. Validation of these models in the BHS dataset explained to 36.8% and 35.9% of the BMI variance, compared to 40.0% with the ridge model. When we utilised clinical lipids, age and sex in the model development, the elastic-net and LASSO models respectively explained only 15.5\u201315.6% BMI variance in AusDiab and only 10.0% and 10.2% BMI variance in the BHS cohort (Supplementary Table\u00a03).\n
\n\n As, LASSO and elastic-net showed very similar performance we focused further analysis on the ridge and LASSO models only. To investigate how a further reduction in the number of lipid species in the model affected model performance, we tuned the regularization parameter, lambda, in the LASSO models and in the ridge models for comparison, with log10 lambda values between \u2212\u20094 and 0.2 (Fig.\n \n 3\n \n A \u2013\n \n 3\n \n C). As lambda was increased, the number of features selected into the LASSO model decreased until only 9 lipids are included in the model with a log10 lambda of 0.\n
\n\n In the LASSO models, as lambda increased, the correlation (R\n \n \n 2\n \n \n ) between BMI and the pBMI decreased, while in the ridge models the R\n \n \n 2\n \n \n remained relatively stable (Fig.\n \n 3\n \n B). The correlation (R\n \n \n 2\n \n \n ) between BMI and mBMI increased in the LASSO models reaching a R\n \n \n 2\n \n \n of 1.0 as the number of features incorporated into the LASSO models decreased to 0, but again showed little variation in the ridge models (Fig.\n \n 3\n \n B). Optimization of the lambda parameter by minimizing the mean-squared error (MSE) using\n \n cv.glmnet\n \n showed the cross-validated MSE increasing in the LASSO models but again relatively stable in the ridge models (Fig.\n \n 3\n \n C). The optimum lambda used to model BMI for the ridge and LASSO models was defined by the lowest MSE. We then extracted the beta-coefficients of the optimum ridge and LASSO models: the lipid species showing the strongest contribution in the ridge and LASSO models were similar. SM(d18:2/14:0), displayed the strongest positive effect size in both models, \u03b2\u2009=\u20091.677 (ridge) and \u03b2\u2009=\u20093.172 (LASSO). Figure\n \n 3\n \n D and Fig.\n \n 3\n \n E show the beta coefficients from the ridge model and the LASSO model respectively.\n
\n\n While the ridge and LASSO models showed comparable performances, when lambda was optimised, the ridge model was more stable across all the possible lambda values and showed better validation in the BHS cohort (Supplementary Table\u00a03) and so was used for further analyses.\n
\n\n We hypothesized that the difference between the mBMI the BMI; the mBMI\u0394 captures cardiometabolic health/risk and this potentially offers clinically relevant information to identify high risk individuals. To assess the relationship between mBMI\u0394 and cardiometabolic risk factors and explore whether mBMI\u0394 identifies metabolic subtypes, we grouped the AusDiab participants into quintiles of the mBMI\u0394, with just over 2,000 participants in each (Fig.\n \n 4\n \n A). The distributions of BMI and mBMI for the 5 groups are shown in Fig.\n \n 4\n \n B and\n \n 4\n \n C respectively. We performed linear regression analysis between cardiometabolic traits (outcome) and the quintiles of mBMI\u0394 (predictor) to assess the overall association. Quintiles 1 to 5 (Q1-Q5), as expected, have comparable BMI values, but substantially different mBMIs. The two most discordant groups (Q1) and (Q5) had similar mean BMI and mean age, while their mBMI scores were significantly different (Fig.\n \n 4\n \n B, C, and D). The median (IQR) mBMI values were 30.6 (5.5) and 22.7 (6.9) for the Q5 and Q1 respectively. Individuals in Q5 were characterized by unfavourable lipoprotein profiles (higher total cholesterol, higher triglycerides, and lower HDL-C; Fig.\n \n 4\n \n D), as well as being more insulin resistant, having higher 2-hour post-load glucose (2h-PLG), glycated haemoglobin C (HBA1C) and higher blood pressure compared to individuals in Q1 (Fig.\n \n 4\n \n E), despite Q5 and Q1 having similar mean BMI.\n
\n\n To validate these findings, we statistically tested whether the profile of cardiometabolic traits differ between the two most discordant groups in the AusDiab cohort and validated this in the BHS cohort. We performed linear regression analyses (with cardiometabolic traits as outcomes and the discordant groups as the predictor, using Q1 as the reference group), adjusting for age, sex and BMI or for age, sex, BMI, and clinical lipids (excluding the outcome). All the metabolic traits, except FBG, differed between the discordant groups before and after adjusting for clinical lipids despite these groups having a similar BMI in both cohorts (Fig.\n \n 5\n \n A, Fig.\n \n 5\n \n B, and Supplementary Table\u00a06). Individuals in Q5 relative to those in Q1 had statistically significantly elevated levels of triglycerides (fold difference 95% CI\u2009=\u20091.52, 1.45\u2013 1.59), HOMA-IR (fold difference 95% CI\u2009=\u20091.59, 1.50\u20131.68) and 2h-PLG (fold difference 95% CI\u2009=\u20091.17, 1.15\u20131.19). These associations remained significant after further adjustment for clinical lipids, although the effect size was reduced in most cases (Fig.\n \n 5\n \n B, Supplementary Table\u00a06). The findings observed in the AusDiab cohort were validated on the BHS cohort (note, the 2h-PLG and the HbA1c measures were not available in the BHS cohort). Individuals in the top quintile (Q5) had a significantly elevated level of triglycerides (fold difference 95% CI\u2009=\u20091.44, 1.40\u2013 1.49), HOMA-IR (fold difference 95% CI\u2009=\u20091.45, 1.41\u20131.50) and lower HDL-C (fold difference 95% CI\u2009=\u20090.86, 0.85\u20130.87) relative to those in the bottom quintile (Q1) (Fig.\n \n 5\n \n A). These associations remained significant after adjustment with clinical lipids (Fig.\n \n 5\n \n B).\n
\n\n We assessed the odds of T2DM and pre-diabetes across the quintiles of the mBMI\u0394 with Q1 as a reference. Individuals with T2DM had higher BMI (mean\u2009\u00b1\u2009SD\u2009=\u200929.9\u2009\u00b1\u20096.1) (Fig.\n \n 6\n \n A) and mBMI (mean\u2009\u00b1\u2009SD\u2009=\u200931.0\u2009\u00b1\u20096.0) (Fig.\n \n 6\n \n B) relative to NGT (mean\u2009\u00b1\u2009SD BMI\u2009=\u200926.2\u2009\u00b1\u20094.5 and mBMI\u2009=\u200926.1\u2009\u00b1\u20095.1). Based on the quintile analyses, there was a progressive increase in the odds ratio of T2DM from the lowest mBMI\u0394 range (Q1) to the highest (Q5) (Fig.\n \n 6\n \n C). Individuals in Q5 relative to Q1 had more than four-fold higher odds for prevalent T2DM (OR 95% CI\u2009=\u20094.5, 3.1\u20136.6, p-value\u2009=\u20091.48x10\n \n \u2212\u200915\n \n ) (Fig.\n \n 6\n \n C, Supplementary Table\u00a07) and 2.5-fold higher odds for incident T2DM (Fig.\n \n 6\n \n C, p\u2009=\u20092.45 x10\n \n \u2212\u20094\n \n ) after adjusting for age, sex, and BMI. These associations were only slightly attenuated but remained significant after adjusting for circulating total cholesterol level, HDL-C, triglycerides, and smoking status. Further details of these associations are provided in Supplementary Tables\u00a07.\n
\n\n Next, we investigated whether the strong associations of mBMI\u0394 with T2DM observed above also exist in the pre-diabetic state. We performed a logistic regression between mBMI\u0394 quintiles and prevalent pre-diabetes (n\u2009=\u20091,920) versus NGT (n\u2009=\u20097,733) or 5-year incident pre-diabetes (n\u2009=\u2009417) versus NGT controls (n\u2009=\u20094023, those who remained NGT over the follow up period). With an increase in mBMI\u0394, the odds of pre-diabetes at baseline and risk of future pre-diabetes increased in a progressive manner; subjects in the top quintile of mBMI\u0394 (Q5), despite having a BMI similar to those in the Q1, had a three-fold higher odds of prevalent pre-diabetes (OR 95% CI\u2009=\u20093.0, 2.5\u20133.5, p\u2009=\u20091.54x10\n \n \u2212\u200933\n \n ) compared to those belonging to the lowest quintile of mBMI\u0394. In addition, subjects in Q5 with NGT at baseline had more than two-fold higher odds of progressing to pre-diabetes prospectively compared to those in the Q1 (OR 95% CI\u2009=\u20092.5, 1.8\u20133.5, p-value\u2009=\u20093.67x10\n \n \u2212\u20098\n \n ) (Fig.\n \n 7\n \n , Supplementary Table\u00a08). This association remained significant (although attenuated) upon adjusting for total cholesterol, HDL-C, and triglycerides. The details of the odds ratios and p-values before and after adjusting for clinical lipids across the full quintile range are provided in Supplementary Table\u00a08. Prevalent pre-diabetes constitutes two distinct pre-diabetic states: isolated impaired fasting glucose (IFG) and impaired glucose tolerant (IGT) and the composite of these two. The association of mBMI\u0394 with isolated IGT was stronger than the association with IFG, although, in both cases a strong and progressive increase in the odds ratio was observed as one moves from Q1 to Q5 of mBMI\u0394 (Supplementary Fig.\u00a03). A significant association exists between the mBMI\u0394 and the isolated IFG versus NGT, despite the weak association of mBMI\u0394 with FBG itself. We identified that, the later finding (i.e. weak associations of mBMI\u0394 with FBG) resulted from the presence of subjects with very high FBG levels and known diabetes mellitus (KDM) in the whole cohort (Supplementary Fig.\u00a04). Of note individuals with KDM has a lower mBMI\u0394 than those with IFG, IGT and NGT (Supplementary Fig.\u00a04). The associations of mBMI\u0394 with IFG were independent of 2h-PLG and associations with IGT were independent of FBG (Supplementary Table\u00a09).\n
\n\n We assessed whether the mBMI\u0394 was associated with prevalent CVD and risk of future CVE independent of the measured BMI. Individuals in the top mBMI\u0394 quintile, Q5 were twice as likely to have prevalent CVD relative to those in the lowest quintile, Q1 (OR 95% CI\u2009=\u20092.1, 1.5\u20133.1, p\u2009=\u20096.43x10\n \n \u2212\u20095\n \n ) (Table\n \n 2\n \n ). Additional adjustment for total cholesterol, HDL-C, triglycerides, smoking status and family history of diabetes did not attenuate mBMI\u0394/mBMI\u0394 quintile \u2013 prevalent CVD associations (Supplementary Table\u00a010). The mBMI\u0394 was only marginally associated with 10-year major incident CVE (HR 95% CI\u2009=\u20091.11, 1.01\u20131.22, p\u2009=\u20094.3 x10\n \n \u2212\u20092\n \n ) (Table\n \n 2\n \n ) and IHD event (HR 95% CI\u2009=\u20091.13, 1.01\u20131.27, p\u2009=\u20093.6 x10\n \n \u2212\u20092\n \n ) (Table\n \n 2\n \n ). Only the, IHD events in the AusDiab were defined in the same way in the BHS. Consequently, we validated the mBMI\u0394 \u2013 IHD associations in the BHS cohort showing similar results as in the AusDiab (Supplementary Table\u00a011).\n
\n| \n \n mBMI\u0394\n \n | \n \n \n Prevalent CVD (n\u2009=\u2009577 cases versus 9690 controls)\n \n | \n \n \n 10 year incident CVE (n\u2009=\u2009414 events versus 7936 non-events)\n \n | \n \n \n 10 year incident IHD (n\u2009=\u2009304 events versus 8046 non-events)\n \n | \n |||
|---|---|---|---|---|---|---|
| \n \n Odds ratio\n \n\n (95% CI)\n \n a\n \n \n | \n \n \n p-value\n \n | \n \n \n Hazard ratio\n \n\n (95% CI)\n \n b\n \n \n | \n \n \n p-value\n \n | \n \n \n Hazard ratio\n \n\n (95% CI)\n \n c\n \n \n | \n \n \n p-value\n \n | \n |
| \n \n mBMI\u0394 (continuous scale)\n \n | \n \n \n 1.3 (1.1, 1.4)\n \n | \n \n \n \n 3.40x10\n \n \n \n \u2212\u20095\n \n \n \n | \n \n \n 1.11 (1.01, 1.22)\n \n | \n \n \n \n 4.30x10\n \n \n \n \u2212\u20092\n \n \n \n | \n \n \n 1.13 (1.01, 1.2)\n \n | \n \n \n \n 3.6x10\n \n \n \n \u2212\u20092\n \n \n \n | \n
| \n \n Q1 (Ref)\n \n | \n \n \n -\n \n | \n \n \n -\n \n | \n \n \n -\n \n | \n \n \n -\n \n | \n \n \n -\n \n | \n \n \n -\n \n | \n
| \n \n Q2\n \n | \n \n \n 1.4 (1.01, 2.1)\n \n | \n \n \n 7.30x10\n \n \u2212\u20092\n \n \n | \n \n \n 1.1 (0.8, 1.5)\n \n | \n \n \n 7.00x10\n \n \u2212\u20091\n \n \n | \n \n \n 1.0 (0.7, 1.5)\n \n | \n \n \n 9.00x10\n \n \u2212\u20091\n \n \n | \n
| \n \n Q3\n \n | \n \n \n 1.3 (0.9, 2.0)\n \n | \n \n \n 1.70x10\n \n \u2212\u20091\n \n \n | \n \n \n 1.0 (0.7, 1.3)\n \n | \n \n \n 8.00x10\n \n \u2212\u20091\n \n \n | \n \n \n 0.9 (0.6, 1.4)\n \n | \n \n \n 7.00x10\n \n \u2212\u20091\n \n \n | \n
| \n \n Q4\n \n | \n \n \n 1.7 (1.1, 2.4)\n \n | \n \n \n \n 1.10x10\n \n \n \n \u2212\u20092\n \n \n \n | \n \n \n 1.4 (1.02, 1.9)\n \n | \n \n \n \n 4.00x10\n \n \n \n \u2212\u20092\n \n \n \n | \n \n \n 1.4 (1.02, 2.1)\n \n | \n \n \n \n 4.10x10\n \n \n \n \u2212\u20092\n \n \n \n | \n
| \n \n Q5\n \n | \n \n \n 2.1 (1.5, 3.1)\n \n | \n \n \n \n 6.40x10\n \n \n \n \u2212\u20095\n \n \n \n | \n \n \n 1.3 (0.9, 1.7)\n \n | \n \n \n 1.30x10\n \n \u2212\u20091\n \n \n | \n \n \n 1.3 (0.9, 1.8)\n \n | \n \n \n 2.00x10\n \n \u2212\u20091\n \n \n | \n
\n \n a\n \n Logistic regression between the mBMI\u0394 /quintiles of mBMI\u0394 and prevalent CVD adjusting for age, sex, BMI, smoking status and diabetes history.\n
\n\n \n b\n \n Proportional hazard Cox-regression between the mBMI\u0394 /quintiles of mBMI\u0394 and major incident CVE adjusting for age, sex, BMI, smoking status and diabetes history.\n
\n\n \n c\n \n Proportional hazard Cox-regression between the mBMI\u0394 /quintiles of mBMI\u0394 and incident IHD adjusting for age, sex, BMI, smoking status and diabetes history.\n
\n\n Significant p-values (<\u20090.05) are shown in bold.\n
\n\n Using mBMI\u0394 as a continuous outcome, we assessed the relative contribution of BMI and mBMI\u0394 in models containing both BMI and mBMI\u0394 adjusting for age and sex in the AusDiab cohort. We also assessed the association of mBMI against the same outcomes. As expected, BMI was strongly associated with both prevalent and incident T2DM and to the lesser extent with prevalent CVD and incident CVE (Supplementary Table\u00a012). The mBMI itself was also significantly associated with T2DM and prevalent CVD independent of age and sex; these associations were stronger (lower p-values) than either the associations with measured BMI or mBMI\u0394 (Supplementary Table\u00a012). The mBMI\u0394 showed, an independent association with prevalent and incident T2D after correcting for age, sex and BMI (Supplementary Table\u00a012) and with CVD outcomes after adjusting for age, sex, BMI, smoking status and diabetes. To assess the significance of the additional information provided by the mBMI\u0394 to the prediction of T2DM, Akaike\u2019s information criterion (AIC) and Likelihood ratio test (LRT) were calculated to compare the two competing nested models (i.e., one containing mBMI\u0394 the other without mBMI\u0394). Using this approach, we showed that models with mBMI\u0394 showed a better fit in predicting newly diagnosed prevalent T2DM (i.e. models with mBMI\u0394 have smaller AIC (AIC\u2009=\u20092603.1) compared to models without mBMI\u0394 (AIC\u2009=\u20092652.4) and a LRT p-value of 8.02 x10\n \n \u2212\u200913\n \n . In predicting incident T2DM, the model with mBMI\u0394 fit significantly better (AIC\u2009=\u20091733.1) than the model without mBMI\u0394 (AIC\u2009=\u20091742.4) and a LRT p-value\u2009=\u20097.98x10\n \n \u2212\u20094\n \n . The model with mBMI\u0394 also showed a better fit for prevalent CVD relative to a model without mBMI\u0394 (Supplementary Table\u00a013).\n
\n\n Using dietary data in the AusDiab (n\u2009=\u200910, 339), we assessed whether certain dietary habits were associated with mBMI\u0394. Total fruit intake (quintiles) encompassing 10 different types (Supplementary Fig.\u00a05) and total fibre intake (quintiles) were inversely associated with mBMI\u0394. In a model adjusted for age, sex and BMI (model 1), total fruit intake was inversely associated with mBMI\u0394 (Q5 vs Q1, \u03b2\u2009\u2212\u20090.56 [95% CI -0.71 \u2013 -0.41], p\u2009=\u20098.54E-14) (Fig.\n \n 8\n \n A). In the full model, adjusted for smoking, PA time, TV viewing time, SBP, family history of diabetes, history of CVD and other dietary and lifestyle factors (model 2), this association remained significant (\u03b2 -0.25, [95% CI -0.44 \u2013 -0.06], p\u2009=\u20093.90E-03) (Supplementary Table\u00a014). Compared to participants with the lowest intake of total dietary fibre (Q1), participants with the highest intake (Q5) had 0.57 lower mBMI\u0394 (\u03b2, -0.57; 95% CI, -0.72 \u2013 -0.43, p\u2009=\u20094.36E-14) (Fig.\n \n 8\n \n B). In the full model, this association was only slightly attenuated but remained significant (Supplementary Table\u00a014). A strong dose-response relationship between the quintiles of PA time and mBMI\u0394 was observed. Participants in Q5 (average PA time, 2 hrs/day) had 0.64 (\u03b2 -0.64 [95% CI -0.79 \u2013 -0.50], p\u2009=\u20096.31E-18) lower mBMI\u0394 relative to those in Q1 (average PA time\u2009=\u20090 hrs/day) (Fig.\n \n 8\n \n C). In the fully adjusted model PA remained significantly associated with mBMI\u0394 (P\u2009<\u20090.05) (Supplementary Table\u00a014). Prolonged TV viewing time was also significantly associated with mBMI\u0394. Compared to the Q1 reference category (TV viewing time\u2009<\u20091 hr/day), participants in Q5 who spent\u2009\u2265\u20094 hours/day had 0.57 higher mBMI\u0394 (\u03b2, 0.57; 95% CI, 0.39\u20130.76], p\u2009=\u20091.76E-09 (Fig.\n \n 8\n \n D), and remained significant in the fully adjusted model (Supplementary Table\u00a014).\n
\n\n Obesity is a major risk factor for many non-communicable diseases such as T2DM and CVD\n \n \n 7\n \n \u2013\n \n 9\n \n ,\n \n 31\n \n \n . However, the widely used measure of obesity, BMI, does not fully capture the metabolic dysregulation associated with obesity leading to the misclassification of metabolic health and metabolic risk. Characterizing the metabolic consequences of obesity calls for deeper metabolic phenotyping rather than relying on BMI itself. In the present study, we constructed a lipidome-based BMI score, that represents the mBMI of an individual, with a view to understand its biological significance and examine whether the score provides additional information over the measured BMI for the metabolic health and risk assessment of multiple clinical outcomes. We introduced quintiles of mBMI\u0394 and stratified the population based on the disparity between BMI and mBMI. We report key associations of mBMI\u0394 and metabolic discordant groups with cardiometabolic traits, pre-diabetes, T2DM, and CVD after accounting for BMI and other appropriate covariates. In addition, we assessed the relationship of dietary and lifestyle habits with mBMI\u0394. We observed that, higher intakes of fruits and fibre or higher levels of PA time were inversely associated with mBMI\u0394, while prolonged TV viewing time was associated with higher mBMI\u0394.\n
\n\n \n mBMI associates with the same lipid metabolism as BMI but is independent of BMI\n \n
\n\n Lipidomic and metabolomic studies show that BMI is strongly associated with dysregulation in lipid metabolism\n \n \n 17\n \n \u2013\n \n 21\n \n ,\n \n 32\n \n ,\n \n 33\n \n \n . To better understand the biology captured by mBMI, we, examined the relationship of the mBMI\u0394 with the lipidomic profile and compared this with the relationship of BMI with the same lipid species. As previously reported by us and others, most plasma lipid class/subclasses/species were significantly associated with BMI. Glycosphingolipids and phospholipids were generally negatively associated, while most ceramide, diacylglycerol and triacylglycerol species were positively associated. The associations of the same lipid species with mBMI\u0394 were almost identical to the associations with BMI, with the correlation of the coefficients showing a R\n \n \n 2\n \n \n of 0.999. However, the effect size was 1.72-fold greater for the mBMI\u0394 relative to the associations with BMI. This similarity between the associations of lipid species with BMI and mBMI\u0394 demonstrates that the mBMI\u0394 captures the same biology (i.e. dysregulation of lipid metabolism associated with BMI), but captures that portion that is missed (orthogonal to the measured BMI) in the BMI measure. Given the method used to calculate the mBMI\u0394, it is not surprising that the correlation between coefficients is close to 1.0. A theoretical description of this relationship is given in Supplementary material 1. This has important implication as to how we understand and interpret the mBMI\u0394 and the mBMI itself. It appears that mBMI then, represents the metabolic status of each individual and that this incorporates both the metabolic dysregulation captured by their measured BMI but also the metabolic dysregulation (of the same lipid metabolic pathways) that is not captured by their BMI. It is not surprising then that mBMI provides an improved risk marker compared to BMI itself.\n
\n\n \n Complex models are required to capture metabolic BMI\n \n
\n\n In the present study, our ridge and LASSO models, included 575 lipid species spanning the sphingolipid, phospholipid, glycolipid, and sterol classes along with age and sex as input variables, explained 60.4% and 60.9% of BMI variability respectively (Supplementary Table\u00a03), implying that dysregulation in lipid metabolism is a major consequence of obesity. We included all the measured lipids in the model to determine how well the entire lipidome explains BMI, rather than focusing on only those that were significantly associated with BMI. In previous studies, ridge regression has been used to create mBMI scores using different sets of metabolites\n \n \n 17\n \n ,\n \n 29\n \n \n . A study that used untargeted metabolomic datasets encompassing 650 blood metabolites (47% lipids) and 49 BMI associated metabolites out of the 650 (40% lipids) demonstrated that 49% and 43% of BMI variation was explained by these sets respectively\n \n \n 17\n \n \n . Using three independent clinical cohorts, a ridge model with 108 plasma metabolites explained BMI variation ranging from 19 to 47%\n \n 29\n \n . While with, a LASSO model, a set of 250 randomly selected lipid species were used to model BMI, and these explained 47% of the variation in BMI\n \n \n 18\n \n \n . The difference in the BMI variance explained in these different studies could be related to the population setting, experimental design and modelling approaches. Generally, models based on limited set of metabolites result in a smaller proportion of the variance in BMI being explained compared to models based on more complex metabolite profiles\n \n \n 17\n \n \n . Indeed, although our LASSO model (containing 349 lipid species) performed equal to the ridge model (containing 571 lipid species), when we further decreased the number of lipid species in the LASSO models by increasing lambda, we observed a decrease in the correlation of pBMI and BMI scores (proportion of variance explained). Examination of Fig.\n \n 3\n \n shows that this effect occurs as the number of lipid species in the model drops below 200 with the correlation decreasing more dramatically as the number decreases below 100. This was associated with an increase in the mean square error (MSE) of the models. Increasing lambda did not have the same effect in the ridge models where all lipid species were retained in the models. These results suggest a minimum number of lipid species (100\u2013200) are required to capture the maximum variance in BMI and so provide an optimal mBMI score. We recognise that the number of lipid species will also be dependent on the species themselves, their association with BMI and the quality of the measurements. In this later regard, models based on targeted lipidomic profiling as used here may offer some advantages over models based on untargeted metabolomics\n \n \n 17\n \n \n and shotgun lipidomics\n \n \n 18\n \n \n . Notwithstanding these dependencies, we observe that the coefficients in the optimal ridge and LASSO models were very similar with many of the strongest lipids identical between models and the weighting structure showing similarities across lipid classes (Fig.\n \n 3\n \n D and\n \n 3\n \n E).\n
\n\n \n mBMI adds to BMI in the prediction of metabolic disease\n \n
\n\n Despite its simplicity and convenience, BMI alone does not capture the myriad of obesity related health consequences\n \n \n 36\n \n \n . Prior evidence suggests that, people with the same or similar BMI can display a substantial difference in their metabolic health outcomes\n \n \n 37\n \n ,\n \n 38\n \n \n . A subset of individuals whose BMI was within normal range but showed features of cardiovascular risk such as insulin resistance, high triglycerides and coronary heart disease has been identified\n \n \n 39\n \n ,\n \n 40\n \n \n . There are also overweight or obese individuals based on their BMI who are metabolically healthy\n \n \n 41\n \n \n . As, BMI does not account for ethnic differences, lifestyle factors, and muscle mass, certain populations such as Asians have higher risk of cardiometabolic disease compared to white Europeans at the same BMI\n \n \n 42\n \n \n . Similarly, in professional athletes, high BMI overestimates adiposity due to the increased muscle mass. Thus, relying on BMI alone as a marker for obesity and associated metabolic health consequences leads to unreliable risk assessment for some individuals.\n
\n\n With the large sample size in the discovery cohort (AusDiab, n\u2009=\u200910,339) and validation (BHS, n\u2009=\u20094,492) we stratified individuals into quintiles based on the disparity between mBMI and BMI (mBMI\u0394). Despite having a comparable BMIs, the most discordant mBMI groups (Q5 and Q1), displayed distinct metabolic risk profiles. Participants with a mBMI substantially higher than their actual BMI (Q5) presented with a deleterious metabolic profile (i.e., higher triglyceride, HOMA-IR, 2h-PLG and a significantly lower HDL-C) compared to participants with a mBMI substantially less than their BMI (Q1). This was consistent with previous reports in which individuals with an overestimated BMI had higher levels of triglycerides and lower levels of HDL-C compared to those with underestimated BMI\n \n \n 29\n \n ,\n \n 43\n \n \n . We also observed that the odds of having a newly diagnosed prevalent T2DM was more than four-fold higher in Q5 compared with Q1, despite Q5 having nearly same average BMI as Q1. Similarly, the risk of 5-year incident T2DM was more than twofold higher in Q5 compared to Q1. These findings have important clinical implications. As mBMI was significantly associated with an increased risk of incident T2DM and incident pre-diabetes, 5 years prior to onset, early pharmacological and lifestyle interventions could be implemented to reduce risk and/or prevent disease progression.\n
\n\n Being overweight or obese based on BMI is a strong risk factor for pre-diabetes and diabetes\n \n \n 31\n \n ,\n \n 44\n \n ,\n \n 45\n \n \n . However, recent reports demonstrate varying risk of diabetes across different obesity phenotypes and or metabolic health status\n \n \n 46\n \n \u2013\n \n 48\n \n \n , including a high prevalence of diabetes among normal weight individuals\n \n \n 49\n \n ,\n \n 50\n \n \n . Here we identified that mBMI\u0394 associates with T2DM risk independently of BMI and so may be useful in identifying metabolic disturbances, and T2DM risk, in lean individuals. The precise phenotyping of metabolic obesity and understanding the difference in metabolically distinct groups may lead to new insights for preventing and treating cardiometabolic diseases.\n
\n\n \n mBMI provides new insight into CVD risk\n \n
\n\n In the present study, we observed that, mBMI\u0394 was associated with CVD risk independently of BMI and may explain some of the apparent inconsistencies in associations between BMI and disease outcomes. While BMI is an independent risk factor for CVD\n \n \n 51\n \n ,\n \n 52\n \n \n , not all obese or overweight people show abnormal cardiovascular risk profiles. There is remarkable metabolic heterogeneity in obesity, and hence the risk of CVD\n \n \n 53\n \n \u2013\n \n 55\n \n \n . Thus, BMI has limited value as a marker of CVD risk. This is highlighted by the absence of BMI in the discriminatory features of the Framingham CVD risk scores\n \n \n 56\n \n \n . Moreover, a significant portion of obese individuals (31.7%) have been shown to remain free of CVD for life (i.e., metabolically healthy)\n \n \n 57\n \n \n . Furthermore, a recent debate over the obesity paradox (in which obesity is associated with favourable outcomes and/or improved survival after a CVD event\n \n \n 58\n \n \u2013\n \n 60\n \n \n ) arises partly due to the use of BMI as a single measure to assess CVD risk. The stronger association of mBMI and mBMI\u0394 with T2D compared to CVD likely reflects the stronger involvement of lipid metabolism, and its dysregulation, in the aetiology of insulin resistance and progression to T2D. In contrast CVD risk likely incorporates other metabolic and inflammatory pathways not covered in this mBMI score.\n
\n\n \n mBMI can be modified by dietary and lifestyle factors\n \n
\n\n In this study, we report specific dietary and lifestyle factors independently associated in a strong, dose responsive manner with mBMI\u0394, suggesting that targeting these factors might improve an individual\u2019s metabolic health. As expected, higher total fruit intake, and dietary fibre consumption were independently associated with a lower mBMI\u0394, showing a linear trend across the quintiles of intake. In a recent study, lower fruit and vegetable consumption was reported in participants whose predicted BMI difference (pBMI-BMI) was >\u20095 kg/m2 relative to the normal weight individuals\n \n \n 29\n \n \n . Indeed, several epidemiological studies have reported an inverse relationship between fruit consumption or dietary fibre and risk of T2D and atherosclerosis\n \n \n 61\n \n \u2013\n \n 64\n \n \n . We report an inverse association between the level of PA and mBMI\u0394 but an independent positive association of TV viewing time with mBMI\u0394 implying that lifestyle habits particularly inadequate exercise and or prolonged sitting time contribute to metabolic risk. Our findings are consistent with prior studies in the AusDiab cohort reporting an inverse association between PA time and 2h-PLG level but not FBG\n \n \n 65\n \n \n and deleterious associations between TV viewing time and 2h-PLG, WC, BMI, SBP, fasting triglycerides, and HDL-C, but not FBG\n \n \n 66\n \n ,\n \n 67\n \n \n . Taken together, these findings suggest that diet and exercise/sedentary behaviour impact on our metabolism leading to increased risk of impaired glucose tolerance, a key risk factor for T2DM. Indeed, dietary and lifestyle interventions remain important primary prevention strategies for cardiometabolic health management to delay the onset and progression of T2D and CVD\n \n \n 68\n \n ,\n \n 69\n \n \n . mBMI may be a useful biomarker to monitor how diet and lifestyle impact our metabolic health.\n
\n\n The rich lipidomic data, the large sample size and the inclusion of an independent validation cohort as well as the prospective study design of the study cohorts are the major strengths of the present study. However, there are also limitations: 1) As with all such studies we were limited by breadth of the lipidomic profile captured with our platform, although the high proportion of BMI variance explained suggests this is not a major drawback. 2) The lack of some traits such as the 2h-PLG and HbA1c in the BHS validation cohort, however we were able to validate the BMI model and many of the associations in the BHS cohort. 3) Ethnicity of the present study populations was primarily white/European ancestry, and this may limit the generalizability of the findings to other populations. It is likely that normalisation of mBMI will be required for other ethnicities.\n
\n\n In summary, our results demonstrate that mBMI can accurately capture the dysregulation of the plasma lipidomic profile associated with BMI but which is independent of measured BMI. This places mBMI as an important biomarker of metabolic health and a potential tool to monitor dietary and lifestyle interventions to improve metabolic health and reduce cardiometabolic risk.\n
\n\n \n Australian Diabetes, Obesity and Lifestyle Study (AusDiab)\n \n
\n\n The AusDiab cohort is a national population-based prospective study that was established to study the prevalence and risk factors of diabetes and CVD in an Australian adult population. The baseline survey was conducted in 1999/2000 with 11,247 participants aged\u2009\u2265\u200925 years randomly selected from the six states and the Northern Territory comprising 42 urban and rural areas of Australia using a stratified cluster sampling method. The detailed description of study population, methods, and response rates of the AusDiab study is found elsewhere\n \n \n 70\n \n \n . Measurement techniques for clinical lipids including fasting serum total cholesterol, HDL-C, and triglycerides as well as for height, weight, BMI, and other behavioural risk factors have been described previously\n \n \n 71\n \n \n . We utilized all baseline fasting plasma samples from the AusDiab cohort (n\u2009=\u200910,339) (Table\n \n 1\n \n ) after excluding samples from pregnant women (n\u2009=\u200921), those with missing data (n\u2009=\u2009277), technical reasons (n\u2009=\u200919) or whose fasting plasma samples were unavailable (n\u2009=\u2009591). The mean (SD) age was 51.3 (14.3) years with women comprising 55% of the cohort.\n
\n\n \n The Busselton Health Study (BHS)\n \n
\n\n We utilized the BHS cohort as a validation cohort. The BHS is a community-based study in the town of Busselton, Western Australia; the participants are predominantly of European origin. A total of 4,492 subjects in the 1994/95 survey of the ongoing epidemiological study were included (Table\n \n 1\n \n ). The mean (SD) age was 50.8 (17.4) years with women constituting 56% of the cohort. The details of the study and measurements for HDL-C, LDL-C, triglycerides, total cholesterol, and BMI are described elsewhere\n \n \n 72\n \n ,\n \n 73\n \n \n . The baseline characteristics of study participants are provided in Table\n \n 1\n \n .\n
\n\n \n Clinical, lifestyle and dietary data\n \n
\n\n The demographic and behavioural data collection has been described in detail elsewhere for AusDiab\n \n \n 70\n \n ,\n \n 74\n \n \n and BHS\n \n \n 73\n \n \n . Fasting plasma cholesterol and lipoprotein concentration including total cholesterol, high density cholesterol, (HDL-C), low density lipoprotein cholesterol (LDL-C) and triglycerides, fasting plasma glucose (FPG) and 2 h post load glucose (2h-PLG) were measured using standard protocols\n \n \n 75\n \n \n . Methods for assessment of dietary intake, PA time and TV viewing time are provided in the Supplementary Material 2.\n
\n\n \n Clinical endpoints\n \n
\n\n Diabetes status was ascertained using the American Diabetes Association criteria (FBG\u2009>\u2009=\u20097.0 mmol/L or 2h-PLG\u2009>\u2009=\u200911.1 mmol/L after a 75-g oral glucose load)\n \n \n 76\n \n \n . In the AusDiab cohort, both a newly diagnosed prevalent T2DM (n\u2009=\u2009395/7,733 NGT) and 5 year incident (n\u2009=\u2009218/5,354 controls) were included. Participants with newly diagnosed prevalent T2DM are those not receiving pharmacological treatment for diabetes, nor previously diagnosed with diabetes, and who had FBG or 2h-PLG measurements over the diabetes cut-off range. Participants were classified as having IFG, if FBG was 6.1\u20136.9 mmoL/L and 2h-PLG was <\u20097.8 mmol/L and IGT if FBG\u2009<\u20097 and 2h-PLG is 7.8\u201311.0 mmol/L. The detailed diagnostic criteria for the presence of diabetes and pre-diabetes can be found elsewhere\n \n \n 77\n \n \n . In the AusDiab cohort, some 577 prevalent CVD (history of heart attack and stroke combined) and 414 major CVEs were recorded over 10 years of follow-up. The major CVEs included IHD (angina pectoris, myocardial infarction, coronary artery bypass grafting and percutaneous transluminal coronary angioplasty), cerebrovascular diseases (intracerebral haemorrhage, cerebral infarction and stroke). The CVE outcomes are defined based on the international classification of diseases (ICD) codes and ascertained through linkage to the National Death Index and medical records. The detailed baseline characteristics of the AusDiab participants in the disease and control groups can be found in Supplementary Table\u00a01. In the BHS cohort, there were 238 prevalent CVD cases and 4,254 controls ascertained through health linkage data at baseline and 284 IHD events (including myocardial infarction, angina, coronary artery bypass grafting and percutaneous transluminal coronary angioplasty) recorded over 10 years follow up (Fig.\n \n 1\n \n , Supplementary Table\u00a02). The baseline characteristics of those who had an event and those who hadn\u2019t are summarized in Supplementary Table\u00a02.\n
\n\n \n Lipid extraction\n \n
\n\n A butanol/methanol extraction method described previously\n \n \n 26\n \n \n was used to extract lipids from human plasma. Briefly, 10\u00b5L of plasma was mixed with 100\u00b5L of a 1-butanol and methanol (1:1 v/v) solution containing 5mM ammonium formate and the relevant internal standards (Supplementary Table\u00a015). The resulting mix was vortexed (10 seconds) and sonicated (60 min, 25\u00b0C) in a sonic water bath. Immediately after sonication, the mix was centrifuged (16,000xg, 10 mins, 20\u00b0C). The supernatant was transferred into samples tubes containing 0.2ml glass inserts and Teflon seals. The extracts were stored at -80\n \n o\n \n C until analysed by liquid chromatography tandem mass spectrometry (LC-MS/MS).\n
\n\n \n Liquid chromatography mass spectrometry\n \n
\n\n Targeted lipidomic analysis was performed using liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). An Agilent 6490 triple quadrupole (QQQ) mass spectrometer [(Agilent 1290 series HPLC system and a ZORBAX eclipse plus C18 column (2.1x100mm 1.8\u00b5m, Agilent)] in positive ion mode was used [details of the method and chromatography gradient have been described previously\n \n \n 19\n \n \n ]. Compared to our earlier study, we modified the methodology to enable a dual column setup (while one column runs a sample, the other is equilibrated) to increase throughput\n \n \n 19\n \n \n for the AusDiab. In brief, the temperature was reduced to 45\n \n o\n \n c from 60\n \n o\n \n c with modifications to the chromatography to enable similar level of separation. Starting at 15% solvent B and increasing to 50% B over 2.5 minutes, then quickly ramping to 57% B for 0.1 minutes. For 6.4 minutes, %B was increased to 70%, then increased to 93% over 0.1 minutes and increased to 96% over 1.9 minutes. The gradient was quickly ramped up to 100% B for 0.1 minutes and held at 100% B for a further 0.9 minutes. This is a total run time of 12 minutes. The column is then brought back down to 15% B for 0.2 minutes and held for another 0.7 minutes prior to switching to the alternate column for running the next sample. The column that is being equilibrated is run as follows: 0.9 minutes of 15% B, 0.1 minutes increase to 100% B and held for 5 minutes, decreasing back to 15% B over 0.1 minutes and held until it is switched for the next sample. We used a 1-\u00b5L injection per sample with the following mass spectrometer conditions were used: gas temperature, 150\u02daC; gas flow rate, 17 L/min; nebuliser, 20 psi; sheath gas temperature, 200\u02daC; capillary voltage, 3,500 V; and sheath gas flow, 10 L/min. Given the large sample size, samples were run across several batches, as described above. The LC-MS/MS conditions and settings with the respective MRM transitions for each lipid (n\u2009=\u2009747) can be found in Supplementary Table\u00a015. For the BHS, lipidomic profiling was performed using the standardised methodology as described previously\n \n \n 19\n \n ,\n \n 30\n \n \n . Overall, 596 lipid species were quantified; 575 of which were common to AusDiab cohort.\n
\n\n \n Data pre-processing\n \n
\n\n Integration of the chromatograms for the corresponding lipid species was performed using Agilent Mass Hunter version 8.0. Relative quantification of lipid species was determined by comparing the peak areas of each lipid in each patient sample with the relevant internal standard (Supplementary Table\u00a015). A median centring approach was carried out to correct for batch effect i.e. remove technical batch variation using PQC samples\n \n \n 78\n \n \n in both AusDiab and BHS. Briefly, the lipidomic data in each batch consisting about 485 samples was aligned to the median value in pooled PQC samples included in each run. More than 90% of the lipid species were measured with a coefficient of variation\u2009<\u200920% (based on PQC, samples). Only technical outliers (n\u2009=\u200919 samples) were excluded from the downstream analysis for the AusDiab. In this study, we utilised lipid species (n\u2009=\u2009575) spanning across the sphingolipid, glycerophospholipid and glycerolipid categories that were common in both study cohorts (AusDiab and the BHS). These were used for model development.\n
\n\n \n Predictive modelling\n \n
\n\n Lipidomic data was log10 transformed, mean centred and scaled to unit SD prior to statistical analysis. A ridge regression model including age, sex and the lipidome (comprising 575 lipid species common to the AusDiab and the BHS cohorts) was employed to determine a predicted BMI (pBMI). In addition, Elastic-Net and least absolute shrinkage and selection operator (LASSO) models were also developed to predict BMI. A 10-fold cross validation was employed for the generation pBMI scores in the AusDiab (i.e. models trained on the 9/10th and used to predict BMI in holdout 1/10th of the cohort). The lambda parameter was optimized using\n \n cv.glmnet\n \n R package, minimizing the MSE, lambda range restricted between 0.2 and \u2212\u20094.0 on log10 scale. A metabolic BMI (mBMI) was derived from the pBMI scores as follows: mBMI\u2009=\u2009BMI + (pBMI \u2013 pBMI value on the line of best fit between pBMI and BMI). We then used the 10 ridge regression models developed in the AusDiab (10-fold cross validation) to calculate mBMI scores in the BHS cohort. A final mBMI was calculated as the average of the 10 scores derived from the AusDiab models. The mBMI values were also calculated for the National Institutes of Standards Technology (NIST 1950) QC samples using a value of 26 as the measured BMI. The %CV of the NIST mBMI scores were calculated after excluding technical outliers. Further to the optimized models, we established a LASSO framework to generate an array of models (n\u2009=\u2009120 different models) with the respective lambda value between 0.2 and \u2212\u20094.0 on log10 scale or the number of features selected into the model ranging from all lipid species to null.\n
\n\n The difference between the mBMI and the BMI, termed the \u2018mBMI\u0394\u2019, was used to stratify individuals into quintiles. Z-score values for cardiometabolic traits were calculated as follows [(z\u2009=\u2009x-mean(x))/SD(x)] to allow better comparison across groups. A linear regression analysis was performed between cardiometabolic traits (outcome) and the quintiles of mBMI\u0394 (as a predictor). The association of cardiometabolic risk factors with metabolic discordant groups (Q5 relative to Q1) were evaluated by using logistic regression adjusting for age, sex and BMI and other appropriate covariates. Linear regression models were used to examine the association of mBMI\u0394 or BMI with the plasma lipidomic profile adjusting for the appropriate covariates and correcting p-values for multiple comparison using the Benjamini-Hochberg procedure\n \n \n 79\n \n \n . The Akaike information criteria (AIC) was used to assess the relative quality of individuals models with and without mBMI\u0394.\n
\n\n A logistic regression model was used to assess the relationship between the mBMI\u0394 or quintiles of mBMI\u0394 and pre-diabetes or T2DM (both prevalent and the 5-year incident cases) adjusting for age, sex and BMI or these covariates plus clinical lipids, and smoking status. Further, we examined the association of mBMI\u0394 with the prevalent CVD and incident CVEs adjusted for age, sex, BMI, smoking and diabetes status or these covariates plus clinical lipids. Cox regression models were fitted to compute hazard ratios (HRs) associated with CVEs that occurred during the 10 year follow up using age as the time scale using\n \n coxph()\n \n function in the\n \n survival\n \n package while logistic regression was used for prevalent cases.\n
\n\n Multivariable linear regression was performed to assess the associations between dietary components such as total fruit intake or lifestyle habits such as total leisure PA time and TV viewing time (as predictor variables) and mBMI\u0394 (as a continuous outcome variable). We created two different models: model 1 (age, sex and BMI adjusted) and model 2 additionally adjusted for potential confounders such as intake of daily total energy, total alcohol, total fat, carbohydrate, sugar, processed meat, red meat, tinned fish, total fibre, fruit intake and total protein as continuous variables and smoking, baseline diabetes status and history of cardiovascular disease, and educational level as dichotomous variables. STATA v15 (StataCorp LP, Inc., Texas, USA) or R (version 3.6.1) were used to analyse the data as necessary.\n
\n\n This study used datasets from the AusDiab biobank (project grant APP1101320) approved by the Alfred Human Research Ethics Committee, Melbourne, Australia (project approval number, 41/18) and the BHS cohort (informed consent obtained from all participants, and the study was approved by the University of Western Australia Human Research Ethics Committee [UWAHREC; approval number, 608/15]). Both studies were conducted in accordance with the ethical principles of the Declaration of Helsinki.\n
\n\n Supplementary Figures\n
\n \n\n Supplementary Material 1\n
\n \n\n Supplementary Material 2\n
\n \n\n Supplementary Tables\n
\n \n\n Developing an anti-infective shape-memory hemostatic sponge with ability of guiding\n \n in situ\n \n tissue regeneration for noncompressible hemorrhage in civilian and battlefield settings remains a challenge. Here, hemostatic chitosan sponge with highly interconnective microchannels was engineered by combining 3D printed fiber leaching and freeze-drying methods and then modified with hydrophobic alkyl chains. The microchannelled alkylated chitosan sponge (MACS) exhibited a strong capacity for water/blood absorption and rapid shape recovery. Compared to clinically used gauze, gelatin sponge, CELOX, and CELOX-gauze, the MACS demonstrated higher pro-coagulant and hemostatic capacities in lethally normal/heparinized rat and pig liver perforation models. Also, it exhibited strong anti-infective activity against\n \n S. aureus\n \n and\n \n E. coli\n \n . Additionally, it promoted liver parenchymal cell infiltration, vascularization, and tissue integration in a rat liver defect model. Overall, the MACS demonstrated promising clinical translational potential in cost effectively treating lethal noncompressible hemorrhage and in facilitating wound healing.\n
\n\n \n \n Health Policy\n \n \n
\n\n \n Shape memory chitosan sponge\n \n
\n\n \n microchannel\n \n
\n\n \n noncompressible hemorrhage\n \n
\n\n \n in situ tissue regeneration\n \n
\n\n Hypotension and multi-organ failure caused by massive blood loss often results in high mortality in civilian and military populations\n \n 1, 2\n \n . So, rapid and efficient hemorrhage control is of paramount importance in such scenarios. The body\u2019s natural coagulation cascade process is activated in response to bleeding, but, incapable of timely stopping severe hemorrhage from a deep and noncompressible perforation wound in the absence of shape-memory hemostats\n \n 3,\n \n \n 4\n \n . Thus, the development of shape-memory hemostats is urgently needed. In general, ideal shape-memory hemostats should possess several properties, including a highly interconnected porous structure, active coagulation, strong anti-infection activity, biocompatibility, biodegradability, ready availability, low weight, and low cost\n \n 4, 5, 6, 7\n \n . Notably, an interconnected porous structure permits fluid to flow freely in and out of hemostats, which allows the hemostats to be fixed by draining off the free water and promotes fast recovery to their initial shapes by absorbing the fluid\n \n 6\n \n . Rapid-shape recovery timely exerts pressure on the wound, leading to effective hemorrhage control\n \n 6,\n \n \n 8\n \n . Moreover, hemostats left in the injury site and used in directly guided\n \n in situ\n \n tissue regeneration are more practical for clinical application\n \n 9\n \n .\n
\n\n Until now, several shape-memory hemostats have been developed, and some have been applied in clinical practice\n \n 10, 11, 12, 13\n \n . For instance, the XStat\n \n TM\n \n device composed of multiple compressed cellulose sponges was shown to rapidly expand to fill and exert pressure on a wound to control hemorrhage\n \n 10\n \n . However, it took much more time to take out each sponge from the wound bed due to its nondegradable property, which may cause patient discomfort\n \n 8\n \n . Moreover, such sponge lacking highly interconnected porous structure was incapable of guiding tissue repair. Many shape-memory polymer foams as hemostats have been applied to treat noncompressible hemorrhage and exhibited a certain degree of hemostatic ability\n \n 11, 12, 13\n \n . However, they displayed limited absorption of blood and required decades of seconds to restore their shapes, which may cause the prologation of hemostasis time and more blood loss\n \n 5\n \n . Injectable cryogels with high blood absorbability and rapid-shape recovery capacity have also been developed for treatment of noncompressible hemorrhage\n \n 6, 14, 15\n \n . The hemostatic effect of these materials was achieved by restoring shape and applying mechanical compression on the wound. Shape-recovery property mainly originates from the reversible change of porous structure\n \n 10, 11, 12, 13\n \n . However, the pores inside these hemostats generated by gas foaming or ice crystal removing methods possess low interconnectivity, which might slow down the blood flow into hemostats, resulting in weakened hemostatic efficiency. The effect of pore structure, especially interconnectivity, on hemostatic performance was usually ignored in the design and construction of hemostats\n \n 5, 8, 10, 14\n \n . Besides, some of these hemostats lacked strong active pro-coagulant and anti-infective properties, which may result in their failure to complete the hemostasis in a timely and effective way and in their inability to protect wounds from bacterial infection. Therefore, simultaneously regulating pore structure and active modification is expected to improve the hemostatic and anti-infective effects of these hemostats.\n
\n\n Incorporating a microchannel into three-dimensional (3D) constructs is a simple and controllable architectural feature, and capable of promoting transport of nutrients, oxygen, and metabolites, host cell infiltration, vascularization, and integration with the surrounding tissue\n \n 16, 17, 18, 19\n \n . To create an embedded and hollow microchannel, the sacrificial fibrous template with a well-defined 3D architecture was first enclosed within a matrix material solution and later removed via external stimuli\n \n 20\n \n . Such an approach showed better controllability and interconnectivity in pore structure than conventional pore-forming methods, including gas foaming and ice crystal removing\n \n 18\n \n . Still, developing shape-memory hemostats with a microchannel structure has not been previously investigated.\n
\n\n Chitosan (CS) has been used to prepare hemostats due to its inherent properties, such as biocompatibility, biodegradability, non-toxicity, anti-infection ability, hemostasis, and so forth\n \n 21, 22\n \n . Nevertheless, as mentioned above, its hemostatic and anti-infective properties were limited, especially in cases complicated by severe hemorrhage and bacterial infections\n \n 23\n \n . Previous studies by our group and others have demonstrated that grafting hydrophobic alkyl chains onto a CS backbone could improve its hemostatic and anti-infective abilities, attributed to the strong hydrophobic interactions between the alkyl chains and the membranes of red blood cells (RBCs), platelets, and bacteria\n \n 23, 24, 25, 26\n \n .\n
\n\n Based on these studies, we propose that the shape-memory, pro-coagulant and anti-infective properties of hemostats for noncompressible hemorrhage and\n \n in situ\n \n tissue regeneration can be improved by optimizing the materials pore structure and further active modification. The CS sponges with microchannels were firstly engineered by combining 3D printing polymer microfiber template leaching and freeze-drying methods. To further improve pro-coagulant and anti-infective properties, the microchannelled CS sponges were modified with hydrophobic alkyl chains, named MACSs. They presented a highly interconnective and controllable microchannel structure, high water/blood absorbability, a fast shape-recovery property, a strong coagulation-promoting effect, and anti-infection activity. Notably, they demonstrated better hemostatic performance compared with clinically used gauze, gelatin sponge, CELOX, and CELOX-gauze in lethally normal/heparinized rat and normal pig liver perforation wound models. Moreover, they enabled liver cell infiltration, vascularization, and tissue/sponge integration. These results suggest that the MACSs may be beneficial for treating noncompressible hemorrhage and for promoting\n \n in situ\n \n penetrating wound healing, and thus, have convincing potential for clinical and translational applications.\n
\n\n \n Fabrication and characterization of the MACSs\n \n
\n\n According to our design criteria, the MACSs were fabricated by the procedure illustrated in Fig. 1A. First, the sacrificial PLA microfiber templates were printed by a 3D printer (Fig. 1B and Supplementary Fig. 1). Then, the templates were lyophilized after filling with a 4% (w/v) CS solution. A CS sponge with a uniform microchannel structure was obtained following complete removal of the PLA templates, which was confirmed by FTIR measurement (Supplementary Fig. 2). The resultant CS sponge was further grafted with hydrophobic alkyl chains to improve its pro-coagulant and anti-infective properties. The grafting was carried out via a highly efficient Schiff-base reaction between the amine group of CS and aldehyde group of DA (Fig. 2A). The unstable imine bonds (C=N) were converted into stable alkylamine (C-N) linkages using a reductant (NaCNBH\n \n 3\n \n ). Compared to the N1s spectrum of the CS sponge, the appearance of C-N\n \n *\n \n H-C with a peak area of 39.84% and reduction of the peak area of C-N\n \n *\n \n H\n \n 2\n \n in the N1s spectrum of the alkylated CS sponge indicated the successful reaction of the amine and aldehyde groups (Fig. 2B-D). Moreover, the modified CS sponge showed increased Atom Conc % and Mass Conc % of C1s, further demonstrating the successful grafting of hydrophobic alkyl chains (Fig. 2E).\n
\n\n Interconnected pores of the hemostatic sponge could endow itself with the ability to concentrate blood clotting factors and rapidly recover initial shape\n \n 5, 10, 29\n \n . Moreover, they were able to provide a comfortable niche to support host cell infiltration, vascularization, and tissue ingrowth\n \n 30\n \n . Micro-CT images showed that the alkylated CS sponges with different porosity (MACS-1/2/3) fabricated by a combination of the template leaching method and freeze-drying possessed a uniform microchannel structure with an increased microchannel density (Fig. 1C). The alkylated CS sponge (ACS) prepared by direct freeze-drying presented dense structure. Furthermore, SEM images displayed a hierarchical porous structure including microchannel (138 \u00b1 4.3\u03bcm) and micropores (8.7 \u00b1 1.5\u03bcm) in the MACS-1/2/3 (Fig. 1D-F), while only micropores (8.4 \u00b1 0.9\u03bcm) randomly distributed throughout the ACS. The microchannel structure was highly interconnected and tunable, and distributed uniformly across the MACS-1/2/3 (Supplementary Movies 1-3). However, the micropores distributed in the ACS showed a dense structure and low interconnectivity (Supplementary Movie 4). The interconnectivity of the porous structure played a key role in accelerating hemostasis and guiding tissue regeneration, which usually was ignored in most previous studies\n \n 5, 6, 10, 13\n \n . The MACSs were expected to exhibit an obvious advantage in the treatment of noncompressible hemorrhage and\n \n in situ\n \n tissue regeneration in comparison with reported porous hemostats\n \n 5, 6, 10, 14\n \n . Accordingly, the porosity of the MACSs gradually increased from 70 \u00b1 2.0 to 90 \u00b1 0.6% with an increase in filling ratio of PLA microfiber, which were significantly higher than the 31 \u00b1 0.7% of the ACS (Fig. 1G). Hemostats filled into the wound cavity should possess desirable mechanical strength to prevent their shape deformation caused by external stress from surrounding tissues, thereby providing durable compression on the bleeding site. We first examined the effect of CS concentration on the compressive stress of the MACSs. As the CS concentration increased from 1 to 4% (w/v), the compressive stress was enhanced from 0.6 \u00b1 0.2 to 23 \u00b1 1.5kPa (Fig. 1H, I). When the CS concentration was lower than 4%, the sponges could not maintain their shapes (Supplementary Fig. 3A). The CS solution with concentration higher than 4% possessed higher viscosity (Supplementary Fig. 3B, C), and was difficult to be sucked into the gap of the PLA microfiber template under negative pressure. So, the 4% CS solution was selected to fabricate the MACSs. Next, we investigated the effect of the filling ratio of the PLA microfiber template on the compressive stress. The compressive stress decreased from 46.2 \u00b1 8.0 to 8.1 \u00b1 0.9kPa by increasing the filling ratio of the PLA microfiber template from 20 to 60% (Fig. 1J, K). Indeed, the compressive stress of the MACSs was significantly lower than the 138.0 \u00b1 16.3kPa of the ACS due to the incorporation of the microchannel structure. To better approach practical application, we further detected the compression stress of the sponges after absorbing blood. All the sponges exhibited reinforced mechanical strength (Fig. 1L, M), attributing to the formation of blood clots within the sponges. Both the CS and hydrophobic alkyl chain have been proven to facilitate blood clotting by promoting the adhesion and activation of platelets and the aggregation of RBCs. The MACSs had a higher mechanically reinforced fold than the ACS (Fig. 1N)\n \n 1, 9\n \n . Also, the mechanically reinforced fold of the MACSs gradually enhanced with the increase in porosity (Fig. 1N). The MACSs with high porosity and large surface area could absorb more blood and facilitate the blood to fully contact with the matrix to form more blood clots. Also, the alkylated CS sponge (MACS-2) displayed an improved mechanically reinforced fold compared to the unmodified CS sponge (MCS-2) due to the introduction of hydrophobic alkyl chains (Fig. 1N).\n
\n\n \n Fig\n \n \n .\n \n \n 1\n \n \n Fabrication and characterization of the MACSs with different porosity.\n \n (A) Schematic illustration of the fabrication process of the MACSs. (B) Stereomicroscopic images of the PLA microfiber template, CS/PLA composite, microchannelled CS sponge, and microchannelled alkylated CS sponge. (C, D) Micro-CT and SEM images showing the macro and microstructure of the ACS and MACS-1/2/3. (E, F) The pore size of the ACS and MACS-1/2/3 in cross-section and longitudinal-section. (G) The porosity of the ACS and MACS-1/2/3. (H, I) Compressive stress-strain curves and compressive stress of the MACSs with different CS concentrations (1, 2, and 4% (w/v)). (J, K, L, M) Compressive stress-strain curves and compressive stress of the ACS, MCS-2, and MACS-1/2/3 before and after absorbing blood. (N) Mechanically reinforced folds of the ACS, MCS-2, and MACS-1/2/3 before and after absorbing blood.\n \n n\n \n =3, Data are means \u00b1 SD. ns indicated no significant difference,\n \n *P\n \n <0.05, *\n \n *P\n \n <0.01,\n \n ***P\n \n <0.001.\n
\n\n \n Fig\n \n \n . 2\n \n \n Chemical characterization of the MACSs.\n \n (A) Modification of the CS sponge with DA in the presence of NaCNBH\n \n 3\n \n as a reducing agent. (B, C) XPS spectra showing N1s peak of the CS and alkylated CS sponges. (D, E) The area of N1s peak and the calibrate value of C1s in the CS and alkylated CS sponges.\n
\n\n \n Water/blood absorbability of the MACSs\n \n
\n\n The main hemostatic mechanism of expandable hemostats was mechanical compression on the bleeding site, which mainly resulted from water/blood-triggered shape recovery and volume expansion\n \n 1, 5, 14, 27, 29\n \n . Thus, strong water/blood absorbability was indispensable for expandable hemostats. After absorbing water and blood, the MACSs rapidly sank to the bottom of the container, while the ACS suspended in water and blood (Fig. 3A, B), revealing that the MACSs could absorb a higher volume of water and blood compared with the ACS. The maximum water and blood absorption capacity of the MACSs was significantly higher than that of the ACS and gradually improved with an increase in the porosity (Fig. 3C-F). Notably, the MACSs took much less time to achieve saturated water/blood absorption than that of the ACS (Fig. 3C, D). The water and blood absorption rate of the MACSs was higher than that of the ACS (Fig. 3G, H), which resulted from the increased number of microchannels. The more microchannels present, the higher the water/blood absorption rate. We further stimulated the fluid absorption behavior of the sponges, whose pore size originated from the statistical analysis of SEM images, as shown in Fig. 3I. We found that the fluid speed in the microchannels of the alkylated sponges (MACS-1/2/3) was higher than that in micropores of the ACS. The higher number of microchannels resulted in a larger area of distribution of the high fluid speed. The total fluid speed of the MACSs was notably higher than that of the ACS and gradually improved as the number of microchannels increased.\n
\n\n \n Fig\n \n \n .\n \n \n 3\n \n \n The water/blood absorbability of the ACS and MACSs.\n \n (A, B) Photographs of the ACS and MACS-1/2/3 after absorbing water and blood. (C, D) Water and blood absorption capacity-time dynamic curves of the ACS and MACS-1/2/3. (E, F) Maximum water and blood absorption capacity of the ACS and MACS-1/2/3. (G, H) Water and blood absorption rate of the ACS and MACS-1/2/3 within 2s. (I) Fluid simulation images of water absorption behaviors of the ACS and MACS-1/2/3. (J) Total fluid speed of the ACS and MACS-1/2/3.\n \n n\n \n =3, Data are means \u00b1 SD. ns indicated no significant difference,\n \n *P\n \n <0.05, *\n \n *P\n \n <0.01,\n \n ***P\n \n <0.001.\n
\n\n \n Shape-memory property of the MACSs\n \n
\n\n We further evaluated the water- and blood-triggered shape-memory property of the MACSs and ACS. All sponges could be compressed and shape-fixed after squeezing out the free water (Fig. 4A, B). Upon absorbing the water, they could recover to their original shapes (Fig. 4A, C), giving a 100% recovery ratio. The recovery time (3.3 \u00b1 0.6s, 2.0 \u00b1 0.1s, 1.7 \u00b1 0.6s) of the MACSs was significantly shorter than the 41 \u00b1 3.6s of the ACS (Fig. 4D and Supplementary Movies 5, 6). After absorbing blood, the shape-fixed MACSs could achieve full shape recovery (4.0 \u00b1 1.0s, 2.5 \u00b1 0.5s, 2.0 \u00b1 0.1s) (Supplementary Movie 7); however, the ACS kept a compressed shape and could not recover any further (Fig. 4B, D, F and Supplementary Movie 8).\n
\n\n The microstructure of the compressed sponges after absorbing water and blood was further observed by SEM (Fig. 4G). In their original state, homogeneous and circular microchannels with gradient numbers distributed throughout the MACSs. The circle microchannels changed to flat channels under compression stress. After absorbing water/blood, the deformed microchannels recovered to their original shapes, and the size of the microchannels had no obvious change before and after absorbing water and blood (Supplementary Fig. 4A-C). Furthermore, a large number of RBCs aggregated on the surface of the microchannels. The deformed micropores of the ACS recovered to their original state after absorbing water; however, they did not recover to their original shape after absorbing blood, and almost no RBCs were observed within the ACS (Fig. 4G). In addition, the shape-recovery time of the MACSs was significantly shorter (especially absorbing blood) than that of reported shape-memory hemostats (Fig. 4H). Indeed, a large number of studies have demonstrated that, compared to water, blood is more likely to prolong the shape recovery time of hemostats due to its higher viscosity\n \n 14, 15\n \n . In contrast, there was no significant difference in shape recovery time for the MACSs after the absorption of water and blood. This was attributed to the highly interconnected microchannel structure, which allowed the blood to freely penetrate into the sponges. The pore structure inside the ACS and reported shape-memory hemostats generated by the removal of ice crystals and by gas foaming methods exhibited low interconnectivity, which slowed down the flow speed of the blood.\n \n 6, 9, 10, 11, 15, 16\n \n .\n
\n\n \n Fig\n \n \n .\n \n \n 4 The shape-memory property of the ACS and MACSs after absorbing water and blood.\n \n (A, B) Photographs of the water- and blood-triggered shape recovery of the ACS and MACS-1/2/3. (C, D, E, F) The shape-recovery ratio and time of the compressed sponges. (G) SEM images showing the microstructure of the compressed sponges before and after absorbing water and blood. Red arrows represented the flat channels. (H) Comparison of shape-recovery time between the MACS-2/3 and reported shape-memory hemostats.\n \n n\n \n =3, Data are means \u00b1 SD. ns indicated no significant difference,\n \n *P\n \n <0.05, *\n \n *P\n \n <0.01,\n \n ***P\n \n <0.001.\n
\n\n \n \n In vitro\n \n \n \n pro-coagulant ability of the MACSs\n \n
\n\n We also assessed the pro-coagulant ability of the gauze, GS, CELOX, CELOX-G, ACS, MCS-2, and MACSs by the BCI test, in which the lower the BCI value, the stronger the pro-coagulant ability. The BCI values of the MACSs decreased as the porosity increased at 5 and 10min (Fig. 5A), indicating a positive correlation between the promotion coagulation ability and porosity. The BCI values of the MACSs were significantly lower than that of the ACS (Fig. 5A). Also, the alkylated CS sponge (MACS-2) exhibited stronger pro-coagulant ability than the unmodified CS sponge (MCS-2) due to the introduction of alkyl chains\n \n 24, 25, 26\n \n . Notably, the MACSs demonstrated better pro-coagulant performance compared with clinically used gauze, GS, CELOX, and CELOX-G due to the synergistic effects of the microchannel structure, CS itself, and hydrophobic modification.\n
\n\n The active coagulation cascade mainly relied on the aggregation of RBCs and adhesion and activation of platelets\n \n 5\n \n . Thus, we further evaluated the blood coagulation effect of various samples using RBCs and platelets adhesion assays. The number of adhered RBCs and platelets to the MACSs was remarkably higher than that on the gauze, GS, CELOX, CELOX-G, ACS, and MCS-2 (Fig. 5B, C). Additionally, the higher porosity resulted in a higher number of adhered RBCs and platelets. Consistently, as observed in SEM images, more RBCs and platelets adhered to the MACSs than on other samples (Fig. 5D, E). A higher number of aggregated RBCs and activated platelets were detected in the MACSs than that in other samples (Fig. 5D, E), which accelerated blood coagulation\n \n 31\n \n . CS has been proven to accelerate platelet adhesion and activation, and the aggregation of RBCs through electrostatic interactions\n \n 32, 33\n \n . The microchannel structure was able to promote penetration of the blood and aggregation of RBCs and platelets. The hydrophobic alkyl chains could insert into membranes of the RBCs and platelets, further promoting active capture and aggregation\n \n 24, 25, 34\n \n . We concluded that the CS, microchannel structure, and hydrophobic alkyl chains synergistically contributed to the strong pro-coagulant ability of the MACSs (Fig. 5F).\n
\n\n \n Fig\n \n \n .\n \n \n 5 The pro-coagulant ability of the gauze, GS, CELOX, CELOX-G, ACS, MCS-2, and MACSs.\n \n (A) The BCI-time curves of various samples. (B, C) The number of adhered RBCs and platelets on various samples. (D, E) SEM images showing adhesion of RBCs and platelets on various samples. (F) Schematic diagram illustrating the pro-coagulant mechanism of the MACSs.\n \n n\n \n =3, Data are means \u00b1 SD. ns indicated no significant difference,\n \n *P\n \n <0.01, *\n \n *P\n \n <0.01,\n \n ***P\n \n <0.001.\n
\n\n \n \n In vivo\n \n \n \n hemostatic effect of the MACSs\n \n
\n\n The MACS-2 was selected and used for\n \n in vivo\n \n hemostasis based on its mechanical strength, water/blood absorbability, blood-triggered shape-memory property, and pro-coagulant capacity (Supplementary Fig. 5). The hemostatic effect was explored in the normal rat liver perforation wound model, as illustrated in Fig. 6A. After treating the wound with the MACS-2, a small area of bloodstain was observed on the surface of the filter paper beneath the liver, while a large area of bloodstain was sighted in the gauze, GS, CELOX-G, CELOX, ACS, and MCS-2 groups (Fig. 6B and Supplementary Movie 9). Quantitatively, the total blood loss of the MACS-2 group was significantly lower than that of other groups (Fig. 6C). Also, the hemostatic time was significantly shorter than that of other groups (Fig. 6D).\n
\n\n \n Fig\n \n \n .\n \n \n 6\n \n \n Hemostasis in the normal rat liver perforation\n \n \n wound model.\n \n (A) Schematic illustration of the hemostatic process of hemostats in a rat liver perforation wound model. (B) Photographs of the hemostatic effect of the gauze, GS, CELOX-G, CELOX, ACS, MCS-2, and MACS-2. The yellow arrow represents the bleeding site. The yellow dotted line represents the boundary of the liver. (C, D) Total blood loss and hemostatic time in the gauze, GS, CELOX-G, CELOX, ACS, MCS-2, and MACS-2 groups.\n \n n\n \n =3, Data are means \u00b1 SD. ns indicated no significant difference,\n \n *P\n \n <0.01, *\n \n *P\n \n <0.01,\n \n ***P\n \n <0.001.\n
\n\n Hemorrhage control of anti-coagulated patients remains a challenge in the clinical setting\n \n 35\n \n . To simulate clinical application, a heparinized-rat liver perforation wound model was used to evaluate the hemostatic capacity of various samples (Supplementary Fig. 6A). After applying the MACS-2, only a small area of bloodstain distributed on the surface of the filter paper under the liver (Supplementary Fig. 6B and Supplementary Movie 10). In contrast, a large area of bloodstain was observed after applying other hemostats. Statistical analysis showed that the hemostatic time of the MACS-2 group was much shorter than that of other groups (Supplementary Fig. 6C). Also, the MACS-2 was superior in reducing the total blood loss when compared with the other hemostats (Supplementary Fig. 6D).\n
\n\n To further explore the clinical translation potential of the MACSs, a lethal pig liver perforation wound model was used to evaluate its hemostatic capacity (Fig. 7A). Commercial CELOX as a control is a commonly used hemostat in prehospital and hospital scenarios in military and civilian settings\n \n 1, 36\n \n . As the shape-fixed MACS-2 was filled into the wound cavity (diameter of 1.5cm), it rapidly recovered its initial cyclical shape by absorbing blood, and then filled the cavity and exerted pressure on the wound wall, achieving hemostasis within 2.0 \u00b1 0.5min (Fig. 7B, C and Supplementary Movie 11). However, the untreated wound continued to bleed at least 10min (Supplementary Movie 12), and the CELOX-treated wound stopped bleeding at 9.0 \u00b1 0.3min (Supplementary Movie 13). The MACS-2 was fixed on the bleeding cavity by its shape recovery. In contrast, the CELOX was prone to being washed away by the blood without external compression. In fact, manual pressing is very inconvenient in emergencies and it is difficult for the wounded to complete self-rescue on the battlefield\n \n 31\n \n . We further quantified the total blood loss by determining the sum of the weight of the blood absorbed by the filter paper and hemostat. The total blood loss (17.6 \u00b1 4.5g) in the MACS-2 group was much lower than that in untreated (153.0 \u00b1 15.5g) and CELOX (143.0 \u00b1 6.6g) groups (Fig. 7D). The MACS-2 demonstrated superior\n \n in vivo\n \n hemostatic ability for lethal noncompressible hemorrhage compared to clinically used gauze, GS, CELOX, and CELOX-G, which was due to the synergistic effect of CS itself, microchannel structure, and hydrophobic modification (Fig. 7E). The highly interconnected and controllable microchannel structure enhanced the blood adsorption capacity of the sponge, allowed the blood to perfuse into the interior of the sponge quickly, and then facilitated the recovery of its original shape, which pressed the wound and achieved rapid hemostasis. CS and alkyl chains actively captured RBCs and platelets via electrostatic and hydrophobic interactions, and also promoted aggregation of the RBCs and platelet activation. This action triggered the coagulation cascade reaction by fibrinogen-mediated interaction with the activated platelet integrin glycoprotein IIb/IIIa, which further improved hemostasis efficiency\n \n 1, 9, 23\n \n . For clinic application, the MACSs could be customized into different shapes to meet special requirements in practical applications (Supplementary Fig. 7).\n
\n\n \n Fig\n \n \n .\n \n \n 7 Hemostasis in a lethal pig liver perforation wound model.\n \n (A) Schematic illustration of the hemostatic process of hemostats. (B) Photographs of the hemostatic effect of the blank, CELOX, and MACS-2 groups. The yellow dotted line represents the boundary of the liver. (C, D) Hemostatic time and total blood loss in the blank, CELOX, and MACS-2 groups. (E) Schematic diagram of hemostatic procedure and mechanism of the MACS-2.\n \n n\n \n =3, Data are means \u00b1 SD.\u00a0 ns indicated no significant difference, *\n \n P\n \n <0.01, **\n \n P\n \n <0.01, ***\n \n P\n \n <0.001.\n
\n\n \n Comparison of\n \n \n \n in vitro\n \n \n \n anti-infective property of the MACS-2 with other hemostats\n \n
\n\n Severe bacterial infection, similar to massive blood loss, is also responsible for trauma-associated deaths\n \n 37\n \n . Thus, ideal hemostats should possess robust anti-infection property. The anti-infective capacity of the MACS-2 against\n \n S. aureus\n \n and\n \n E. coli\n \n was evaluated by a contact-killing assay and compared with the gauze, GS, CELOX-G, CELOX, ACS, and MCS-2 (Fig. 8). Qualitative and quantitative analysis showed that, after contacting the MACS-2, the CFUs number of\n \n S. aureus\n \n was significantly lower than that of the gauze, GS, and ACS groups. There was no obvious difference in the CFUs number between the MACS-2 and CELOX-G, CELOX, as well as MCS-2 (Fig. 8A, C), because the hydrophobic alkyl chains could not interact with the membranes of\n \n S. aureu\n \n s\n \n 38\n \n . After contacting the MACS-2, the CFUs number of\n \n E. coli\n \n was remarkably lower than that of the gauze, GS, CELOX-G, CELOX, ACS, and MCS-2 (Fig. 8B, D). This enhanced anti-infective activity was ascribed to the synergistic effects of the microchannel structure, grafted hydrophobic alkyl chains, and CS itself\n \n 24, 26\n \n .\n
\n\n \n Fig\n \n \n .\n \n \n 8\n \n In vitro\n \n anti-infective property of the MACS-2 and other hemostats.\n \n (A, B) Photographs of CFUs of\n \n S. aureus\n \n and\n \n E. coli\n \n grown on LB agar plates after contacting with TCP, gauze, GS, CELOX-G, CELOX, ACS, MCS-2, and MACS-2, respectively. (C, D) Corresponding statistical results of the CFUs of\n \n S. aureus\n \n and\n \n E. coli. n\n \n =3, Data are means \u00b1 SD. ns indicated no significant difference,\n \n *P\n \n <0.05, *\n \n *P\n \n <0.01,\n \n ***P\n \n <0.001.\n
\n\n \n MACS-2 guided\n \n in situ\n \n liver regeneration\n \n
\n\n The removal of hemostats may result in secondary bleeding and cause great distress to patients. If hemostats could be left in the injury site and directly guide\n \n in situ\n \n tissue regeneration, this would be favorable to patients and surgeons\n \n 9\n \n .\n \n In situ\n \n liver regeneration as a representative model was used to evaluate the pro-regenerative ability of the MACS-2 and ACS. Rapid host cell infiltration was the first and crucial step for endogenous tissue regeneration\n \n 39, 40\n \n . DAPI and H&E staining showed that the host cells migrated into the interior of the MACS-2, but were mainly distributed around the edge of the ACS due to its dense structure (Fig. 9A). Accordingly, the cell number inside the MACS-2 was significantly higher than that of the ACS (Fig. 9B). Infiltrated cells secreted a large amount of extracellular matrix and formed neotissue. The tissue ingrowth area within the MACS-2 was much larger than that of the ACS. However, almost no neotissue grew inside the ACS (Fig. 9A, C). A rich capillary network capable of delivering adequate oxygen and nutrients is indispensable for newly formed tissue survival. Thus, vascularization was assessed by immunostaining for von Willebrand Factor (vWF). A high density of capillaries distributed inside the MACS-2 (Fig. 9D); in contrast, almost no capillary was observed within the ACS. A large number of ALB positive cells were observed in the interior of the MACS-2, indicating ingrowth of liver parenchymal cells and liver tissue regeneration. In comparison, almost no liver parenchymal cells infiltrated into the ACS (Fig. 9A, E)\n \n 41\n \n . The improved ability of cellularization, vascularization, and tissue ingrowth of the MACS-2 attributed to the highly interconnected microchannels, high porosity, and good biocompatibility (Fig. 9F)\n \n 39\n \n . To our knowledge, there has not been any report to date regarding the use of a shape-memory hemostatic sponge for internal penetrating wound repair. Our MACS-2 simultaneously achieved hemostasis and\n \n in situ\n \n tissue regeneration, which broadens the application of hemostats and opens up an opportunity for the design and construction of clinically beneficial hemostats. Specifically, the application of our hemostatic sponge will reduce patient discomfort, simplify treatment procedures, and potentially decrease healthcare costs.\n
\n\n \n Fig. 9 Liver regeneration in rat models after implantation of the ACS and MACS-2.\n \n (A) DAPI staining showing cell infiltration within the ACS and MACS-2. H&E staining showing tissue ingrowth. Yellow asterisk (*) represents the alkylated CS. Images of immunofluorescent staining for vWF (red) and ALB (red) indicating capillary and liver parenchymal cell (LPC) infiltration within the ACS and MACS-2. Yellow pound key (#) and arrow represent capillary and LPC, respectively. (B, C, D, E) Quantification of cell number, tissue ingrowth area, capillary number, and LPCs per view within the ACS and MACS-2. (F) Schematic illustration of\n \n in situ\n \n liver regeneration, including the host cell infiltration and vascularization.\n \n n\n \n =3, Data are means \u00b1 SD. ns indicated no significant difference,\n \n *P\n \n <0.05, *\n \n *P\n \n <0.01,\n \n ***P\n \n <0.001.\n
\n\n In this study, we incorporated a microchannel structure into a CS sponge and further modified it with hydrophobic alkyl chains. The MACSs achieved rapid shape recovery by absorption of water and blood. Compared with clinically used gauze, GS, CELOX, and CELOX-G, the MACSs demonstrated stronger pro-coagulant ability\n \n in vitro\n \n and hemostatic capacity in lethally normal/heparinized rat and normal pig liver perforation wound models. Moreover, they exhibited enhanced anti-infective activity against\n \n S. aureus\n \n and\n \n E. coli\n \n . Notably, the MACSs could be left in the wound bed and guided\n \n in situ\n \n liver regeneration. All results in this study indicate that MACSs have the clinical translational capacity to provide effective treatment of potentially lethal noncompressible hemorrhage and to facilitate tissue repair.\n
\n\n \n Materials\n \n
\n\n Chitosan (CS, molecular mass of ~100 kDa) was from Jinan Haidebei Biotech Co., Ltd., China. Dodecyl aldehyde (DA, 99.5%) and sodium cyanoborohydride (NaCNBH3, 95%) were from Shanghai Aladin Co., Ltd., China. Polylactic acid (PLA) filament was from Jinluotuo Biotech Co., Ltd., China. Acetic acid, dichloromethane, and ethyl alcohol were from Tianjin Reagent Co., Ltd., China. All chemicals were of analytical grade.\n
\n\n \n Fabrication of the MACSs\n \n
\n\n The fabrication of the MACSs was as follows: First, the PLA microfiber templates with filling ratios of 20, 40, and 60% were printed using a 3D printer (Shenzhen Creality 3D Tech Co., LTD., China). Second, the templates were filled with CS solution (1, 2, and 4%, w/v) dissolved in acetic acid aqueous solution (2%, v/v), followed by freezing in liquid nitrogen and lyophilization. Third, the CS sponges with microchannel structure were obtained by leaching out the templates with dichloromethane. Residual acetic acid was neutralized with a mixed solution of ethyl alcohol/NaOH (9/1, v/v). The resultant CS sponges were further modified with DA in the presence of NaCNBH3. Unreacted DA and NaCNBH3 were removed by rinsing with ethyl alcohol and deionized water (DIW) in turn. The MACSs generated from PLA microfiber templates with filling ratios of 20, 40, and 60% were named as the MACS-1, MACS-2, and MACS-3, respectively. An unmodified microchannelled CS sponge generated from a PLA microfiber template with a ratio of 40% was abbreviated as the MCS-2. The alkylated CS sponge prepared by direct freeze-drying was named ACS.\n
\n\n \n FTIR spectrum test\n \n
\n\n The spectra of CS powder, PLA microfiber template, PLA/CS composite, and microchannelled CS sponge were recorded in the range of 4000-500cm-1 by using a fourier transform infrared spectrometer (FTIR, TENSOR II, Germany).\n
\n\n \n XPS analysis\n \n
\n\n The superficial chemical structure and element content of the CS sponges with or without modification were detected using an X-ray photoelectron spectrometer (XPS, Axis Ultra DLD, England).\n
\n\n \n Characterization of macro/microstructure and porosity\n \n
\n\n The macro and microstructure of the MACSs and ACS were characterized by the micro-computed tomography (Micro-CT, Germany) and scanning electron microscopy (SEM, MERLIN Compact, Germany)\n \n 19\n \n . The average pore size was measured using Image-J software (ImageJ 1.44p). The porosity was calculated using Micro-CT.\n
\n\n \n Mechanical test\n \n
\n\n The mechanical strength of the MACSs generated with different CS concentrations (1, 2, and 4%, w/v) and PLA microfiber filling ratios (20, 40, and 60%) were prepared into cylindrical shapes (5mm in height and 8mm in diameter) and tested in a universal mechanical tester (Instron, 3345). The compression strain and speed were fixed at 70% and 1mm/min, respectively. The maximum compressive stress was obtained from a stress-strain curve. The compressive stress of the MACSs (5mm in height and 8mm in diameter) after absorbing the blood was also measured.\n
\n\n \n Water/blood absorption behavior\n \n
\n\n After squeezing out water, the compressed MACSs and ACS contacted the water and blood. Their positions in water and blood were recorded by a digital camera. To quantitatively evaluate absorption behavior, the compressed MACSs and ACS were weighed (W\n \n d\n \n ) and soaked into water and blood from rats. At different time intervals, they were taken out and weighted (W\n \n w\n \n ). The water/blood absorption capacity was calculated according to the following equation:\n
\n\n Water/blood absorption capacity (g/g) = (W\n \n w\n \n \ufe63W\n \n d\n \n ) / W\n \n d\n \n
\n\n The water/blood absorption rate was calculated by measuring the slope of the water/blood absorption capacity-time curve within 2s. Moreover, the absorption behavior was further measured by digital fluid simulation. The MACSs and ACS were modeled by using the software Solidworks Flow Simulation. The flow orientation of water with a dynamic viscosity of 1.7912\u00d710-3Pa. s was parallel to the axial direction of the sponges. The working temperature and pressure were set as 273.2K and 101325Pa, respectively. The mass flow at the inlet was 0.001m/s. The stimulated pore size was consistent with statistical results from SEM images. To simplify the simulation, the micropore with low interconnectivity in the sponge was replaced by microchannel with comparable size to the micropore.\n
\n\n \n Shape-memory property\n \n
\n\n We assessed the shape-memory property of the MACSs and ACS using the reported method\n \n 6\n \n . The MACSs and ACS were first compressed to squeeze out the free water. Then, both water and blood were dropped onto their top surfaces. The shape-recovery process was recorded using a digital camera. The shape-recovery ratio and time were measured. Also, the microstructure recovery of the MACSs and ACS before and after absorbing water and blood was further observed by SEM.\n
\n\n \n Blood clotting index test\n \n
\n\n The pro-coagulant ability of the MACSs was evaluated by measuring the blood clotting index (BCI)\n \n 27, 28\n \n . The gauze, gelatin sponge (GS), CELOX, CELOX-gauze (CELOX-G), ACS, and MCS-2 were used as controls. The MACSs were compressed to squeeze out water and placed in EP tubes. After warming for 10min at 37\u2103, 50\u03bcL of the citrated whole blood (CWB) from rats was dropped onto their top surfaces. After incubation for 5 and 10min at 37\u2103, 3mL of DIW was added into each EP tube, and optical density (OD) value at 540nm of the supernatant was determined using a microplate reader (BIO-RAD, iMARKTM). The mixed DIW/CWB (3mL/50\u03bcL) solution was used as a negative control and its OD\n \n 540nm\n \n value represented as 100%. The BCI was calculated based on the following equation:\n
\n\n BCI (%) = OD\n \n material\n \n / OD\n \n reference\n \n value \u00d7 100%\n
\n\n \n RBCs and platelets adhesion assays\n \n
\n\n The interactions between the MACSs and RBCs were investigated with the previously reported method with some modification\n \n 27\n \n . The gauze, GS, CELOX, CELOX-G, ACS, and MCS-2 were used as controls. Before testing, RBCs suspension was obtained by centrifuging the CWB for 10min under 400G. The MACSs were compressed to drain off water and placed in a 24-well microplate. Next, 100\u03bcL of RBCs suspension was dropped onto their top surfaces. After incubation for 30min at 37\u2103, they were rinsed with a phosphate buffer solution (PBS, pH=7.4) to remove nonadherent RBCs, and then transferred into DIW (4mL) to lyse adhered RBCs to release hemoglobin. After 1h, 100\u03bcL of the supernatant was taken out and placed into a 96-well microplate followed by measuring its OD\n \n 540nm\n \n value. The OD\n \n 540nm\n \n value of a solution composed of 100\u03bcL of RBCs suspension and 4 mL of DIW was used as a reference value. The percentage of adhered RBCs was calculated by the following equation:\n
\n\n RBCs adhesion (%) = OD\n \n material\n \n / OD\n \n reference\n \n value \u00d7 100%\n
\n\n The interactions between various hemostats and platelets were further evaluated by a platelet adhesion assay\n \n 27\n \n . Before measurement, the platelet-rich plasma (PRP) was obtained by centrifuging the CWB for 10min under 400G. The MACSs were compressed to squeeze out water and placed into a 24-well microplate. Then, 100\u03bcL of PRP was dropped on their top surfaces followed by incubation for 30min at 37\u2103. Next, they were washed with PBS to remove nonadherent platelets and soaked into a 1% Triton X-100 solution to lyse platelets to release the lactate dehydrogenase (LDH) enzyme. The LDH was determined with an LDH kit (Biyuntian, China) according to its instruction. Finally, the OD\n \n 490nm\n \n value of the supernatant was measured. The OD\n \n 490nm\n \n value of a solution composed of 100\u03bcL of PRP unexposed with hemostats was measured and used as a reference value. The percentage of adhered platelets was calculated by the following equation:\n
\n\n Adhered platelets (%) = OD\n \n material\n \n / OD\n \n reference\n \n value \u00d7 100%\n
\n\n The adherence of RBCs and platelets on the various hemostats was observed by SEM. Briefly, hemostats were placed into each well in a 24-well microplate and contacted with 100\u03bcL of RBCs and PRP suspensions. After 30min at 37\u2103, they were rinsed with PBS, and then fixed with 2.5% glutaraldehyde and dehydrated using a series of graded alcohol solutions. After drying, they were cut, and the longitudinal sections were sputtered with gold and observed by SEM.\n
\n\n \n Hemostasis\n \n in vivo\n \n \n
\n\n The hemostatic ability of the MACS-2 was evaluated by lethally normal/heparinized rat and normal pig liver perforation wound models, and compared with gauze, GS, CELOX, CELOX-G, ACS, and MCS-2. All animal experiments were performed with the approval of the Animal Experimental Ethics Committee of Nankai University.\n
\n\n Normal and heparinized rat liver perforation wound models: A rat (male, weight of 250~300g) was anesthetized by injecting 10wt% chloral hydrate in a dose of 1mL/300g. Then, the rat\u2019s abdomen was incised, and the liver was lifted and placed onto the surface of the pre-weighted filter paper. Next, a circular perforation wound (diameter of 6mm) was created on the liver to induce hemorrhaging. Finally, the cylindrical MACS-2 (diameter of 8mm) was compressed to squeeze out water and filled into the wound cavity. The hemostatic process was recorded with a digital camera. The blood loss was measured by determining the total weight of the blood absorbed by the filter paper and hemostats. The hemostatic time was recorded with a timer. The heparin solution (50UI) was injected into the rat (male, weight of 250~300g) through a vein at a dose of 2mL/kg and used for the construction of the heparinized rat liver perforation wound model. Other procedures were similar to the method mentioned above.\n
\n\n Lethal pig liver perforation wound model: Bama miniature pig (3 months, weight of 15kg) was anesthetized by injecting a mixed solution of midazolam and xylazine hydrochloride (2/1, v/v) into its muscle at a dose of 0.14mL/1kg. Then, the abdomen of the pig was incised, and its liver was taken out and placed onto the surface of the filter paper. Next, a 15mm-diameter circular perforation wound was made on the liver. After bleeding, the cylindrical MACS-2 (diameter of 18mm) was compressed to squeeze out the free water and filled into the wound cavity. The hemostatic process was recorded with a digital camera. The total blood loss from each liver was weighed and the hemostatic time were recorded.\n
\n\n \n \n In situ\n \n \n \n liver regeneration\n \n
\n\n The\n \n in situ\n \n pro-regenerative ability of the MACS-2 and ACS was evaluated using a representative rat liver defect model. A rat (male, weight of 200~300g) was anesthetized with 10wt% chloral hydrate, and its abdomen was incised. Then, a 6mm-diameter circular perforation wound was created on the liver. Next, the cylindrical MACS-2 was compressed and filled into the wound. As a comparison, uncompressed ACS was also filled into the wound. After hemostasis, the abdomen was sutured, and the rat was feed normally. After one-month post-surgery, the rat was paralyzed, and the liver was taken out for histological and immunofluorescence staining. H&E staining was used to assess tissue ingrowth. DAPI staining was used to evaluate the host cell infiltration. Immunofluorescence staining for von Willebrand factor (vWF) and albumin (ALB) was performed to evaluate vascularization and liver parenchymal cell infiltration.\n
\n\n \n \n In vitro\n \n \n \n anti-infective activity\n \n
\n\n The\n \n in vitro\n \n anti-infective activity of the MACS-2 against\n \n S. aureus\n \n (ATCC6538) and\n \n E. coli\n \n (ATCC25922) was tested by a contact-killing assay\n \n 24\n \n . Tissue culture plate (TCP), gauze, GS, CELOX, CELOX-G, ACS, and MCS-2 were used as controls. Before the test, the MACS-2 was compressed to squeeze out water and placed into each well in a 48-well microplate. After sterilization for 1h under UV irradiation, 10\u03bcL of the bacterial suspension with a concentration of 10\n \n 8\n \n colony forming units/milliliter (CFUs/mL) was dropped onto their top surface. After 2h at 37\u2103, the survival bacteria were resuspended by adding 200\u03bcL of sterilized PBS into each well. Next, 20\u03bcL of resuspended bacterial suspension was taken out and diluted five times by ten-fold dilution to obtain a final diluting bacterial suspension (FDBS). Subsequently, 20\u03bcL of FDBS was spread onto the surface of the LB agar plate and incubated at 37\u00b0C. After incubation overnight, the formed CFUs on each LB agar plate were counted.\n
\n\n \n Statistical analysis\n \n
\n\n All tests were processed in triplicate. Each group has at least three parallel samples. Statistical analyses were performed using GraphPad Prism 5 software. Values are expressed as the means \u00b1 standard deviation (SD). The One-way ANOVA with Newman-keuls multiple comparison test was used to evaluate the statistical differences between groups. *\n \n P\n \n <0.05 was considered to be statistically significant.\n
\n\n Video of microCT dynamic scanning of the MACS-1\n
\n \n\n Video of microCT dynamic scanning of the MACS-1\n
\n \n\n Video of microCT dynamic scanning of the MACS-2\n
\n \n\n Video of microCT dynamic scanning of the MACS-2\n
\n \n\n Video of microCT dynamic scanning of the MACS-3\n
\n \n\n Video of microCT dynamic scanning of the MACS-3\n
\n \n\n Video of microCT dynamic scanning of the ACS\n
\n \n\n Video of microCT dynamic scanning of the ACS\n
\n \n\n Video of water-triggered shape recovery of the MACS-2\n
\n \n\n Video of water-triggered shape recovery of the MACS-2\n
\n \n\n Video of water-triggered shape recovery of the ACS\n
\n \n\n Video of water-triggered shape recovery of the ACS\n
\n \n\n Video of blood-triggered shape recovery of the MACS-2\n
\n \n\n Video of blood-triggered shape recovery of the MACS-2\n
\n \n\n Video of blood-triggered shape recovery of the ACS\n
\n \n\n Video of blood-triggered shape recovery of the ACS\n
\n \n\n Video of hemostasis of the MACS-2 in normal rat liver perforation wound model\n
\n \n\n Video of hemostasis of the MACS-2 in normal rat liver perforation wound model\n
\n \n\n Video of hemostasis of the MACS-2 in heparinized rat liver perforation wound model\n
\n \n\n Video of hemostasis of the MACS-2 in heparinized rat liver perforation wound model\n
\n \n\n Video of hemostasis of the MACS-2 in lethal pig liver perforation wound model\n
\n \n\n Video of hemostasis of the MACS-2 in lethal pig liver perforation wound model\n
\n \n\n Video of hemostasis of the blank group in lethal pig liver perforation wound model\n
\n \n\n Video of hemostasis of the blank group in lethal pig liver perforation wound model\n
\n \n\n Video of hemostasis of the CELOX in lethal pig liver perforation wound model\n
\n \n\n Video of hemostasis of the CELOX in lethal pig liver perforation wound model\n
\n \n\n Fig. S1 Macro photographs of 3D printed PLA microfiber templates with filling ratios of 20, 40, and 60%.\n
\n \n\n Fig. S1 Macro photographs of 3D printed PLA microfiber templates with filling ratios of 20, 40, and 60%.\n
\n \n\n Fig. S2 FTIR spectra of the CS powder, PLA microfiber template, PLA/CS composite, and microchannelled CS sponge. In the spectrum of CS powder, the strong peak at 3354cm-1 was attributed to the stretching vibration of -NH2. In the spectrum of the PLA microfiber template, the strong peak at 1747cm-1 was ascribed to the stretching vibration of -O-C=O-. In the spectrum of PLA/CS composite, two absorption peaks at 3354cm-1 and 1747cm-1 were observed, indicating the composition of the CS and PLA. In the spectrum of microchannelled CS sponge, no absorption peak of -O-C=O- was sighted, revealing no residue of PLA microfiber in the sponge.\n
\n \n\n Fig. S2 FTIR spectra of the CS powder, PLA microfiber template, PLA/CS composite, and microchannelled CS sponge. In the spectrum of CS powder, the strong peak at 3354cm-1 was attributed to the stretching vibration of -NH2. In the spectrum of the PLA microfiber template, the strong peak at 1747cm-1 was ascribed to the stretching vibration of -O-C=O-. In the spectrum of PLA/CS composite, two absorption peaks at 3354cm-1 and 1747cm-1 were observed, indicating the composition of the CS and PLA. In the spectrum of microchannelled CS sponge, no absorption peak of -O-C=O- was sighted, revealing no residue of PLA microfiber in the sponge.\n
\n \n\n Fig. S3 (A) Photographs of the MACSs with CS concentrations of 1%, 2%, and 4% (w/v) in water and dry environment. (B) Photographs of the position of the 4% and 6% (w/v) CS solutions on sloping glass surface within 10s. (C) Rheological property of 2%, 4%, and 6% (w/v) CS solutions.\n
\n \n\n Fig. S3 (A) Photographs of the MACSs with CS concentrations of 1%, 2%, and 4% (w/v) in water and dry environment. (B) Photographs of the position of the 4% and 6% (w/v) CS solutions on sloping glass surface within 10s. (C) Rheological property of 2%, 4%, and 6% (w/v) CS solutions.\n
\n \n\n Fig. S4 (A, B, C) Pore size of the MACS-1, MACS-2, and MACS-3 before and after absorbing water and blood.\n
\n \n\n Fig. S4 (A, B, C) Pore size of the MACS-1, MACS-2, and MACS-3 before and after absorbing water and blood.\n
\n \n\n Fig. S5 The reason for selecting and using the MACS-2 to conduct in vivo hemostasis, anti-infection, and in situ tissue regeneration experiments.\n
\n \n\n Fig. S5 The reason for selecting and using the MACS-2 to conduct in vivo hemostasis, anti-infection, and in situ tissue regeneration experiments.\n
\n \n\n Fig. S6 Hemostasis of the gauze, GS, CELOX-G, CELOX, and MACS-2 in heparinized rat liver perforation wound model. (A) Schematic illustration of the hemostatic process of hemostats in a heparinized rat liver perforation wound model. (B) Photographs of hemostatic effect of various samples. Yellow arrow represents the bleeding site. Yellow dotted line represents the boundary of the liver. (C, D) Total blood loss and hemostatic time in various groups. n=3, Data are means \u00b1 SD. ns indicated no significant difference, *P<0.01, **P<0.01, ***P<0.001. ***P<0.001.\n
\n \n\n Fig. S6 Hemostasis of the gauze, GS, CELOX-G, CELOX, and MACS-2 in heparinized rat liver perforation wound model. (A) Schematic illustration of the hemostatic process of hemostats in a heparinized rat liver perforation wound model. (B) Photographs of hemostatic effect of various samples. Yellow arrow represents the bleeding site. Yellow dotted line represents the boundary of the liver. (C, D) Total blood loss and hemostatic time in various groups. n=3, Data are means \u00b1 SD. ns indicated no significant difference, *P<0.01, **P<0.01, ***P<0.001. ***P<0.001.\n
\n \n\n Fig. S7 (A) 3D printed PLA microfiber templates with different shapes. (B) Photograph of the MACSs with different shapes.\n
\n \n\n Fig. S7 (A) 3D printed PLA microfiber templates with different shapes. (B) Photograph of the MACSs with different shapes.\n
\n \n\n Individual dietary specialization, where individuals occupy a subset of a population\u2019s wider dietary niche, is of key importance for species\u2019 resilience against environmental change. However, the ontogeny of individual specialization, as well as associated underlying social learning, genetic, and environmental drivers remain poorly understood. Using a multigenerational dataset of female European brown bears (\n \n Ursus arctos\n \n ) followed since birth, we discerned the relative contributions of social learning, genetic predisposition, environmental forcings, and maternal effects to individual dietary specialization. Individual specialization varied from omnivorous to carnivorous diets spanning half a trophic position. The main determinants of this dietary specialization were maternal learning during rearing (13%), environmental similarity (12%), maternal effects (11%), and permanent individual effects (8%), whereas the contribution of genetic heritability was negligible. Importantly, the offspring\u2019s trophic position closely resembled the trophic position of their mothers during the first 3-4 years after separation from the mother, but this relationship ceased with increasing time since separation. Our study reveals that social learning and maternal effects are as important for individual dietary specialization as environmental forcings. We propose a tighter integration of social effects into future studies of range expansion and habitat selection under global change that, to date, are mostly explained by environmental drivers.\n
\n\n \n Dietary specialization\n \n
\n\n \n heritability\n \n
\n\n \n maternal effects\n \n
\n\n \n maternal learning\n \n
\n\n \n trophic position\n \n
\n\n \n trophic niche\n \n
\n\n \n omnivore\n \n
\n\n \n stable isotopes\n \n
\n\n \n nitrogen-15\n \n
\n\n \n Ursus arctos\n \n
\n\n Among individuals of the same species, niche variation is common and may occur when availability of food resources or habitat structure change across the species\u2019 range. Ecological generalists, species with a wide niche, also seem to exhibit more individual specialization\n \n 1\n \n and are hence particularly well adapted to persist under shifts in resource availability or composition enabling them to occupy larger distributional ranges than ecological specialists\n \n 2\n \n . Individual variation is key for making species resilient towards changing resource availabilities in a rapidly changing world and may ultimately determine local persistence or extinction of species\n \n 3\n \n .\n
\n\n Inter- and intraspecific competition, predation and ecological opportunity, alter resource availability and have been identified as the main ecological drivers explaining variation in the degree of individual specialization among populations\n \n 4\n \n . Yet, how individual variation emerges and is maintained within populations has been rarely quantified in the wild. In principle, four potential sources of variation exist: social and individual learning, genetic inheritance, the environment, and maternal effects. Individual differences in resource preference or competence to secure a resource may therefore be determined during early ontogeny through social (e.g., maternal) learning via imitation\n \n 5, 6, 7, 8, 9\n \n leading to similarities between the offspring\u2019s and their mother\u2019s dietary phenotype. Effects of maternal learning can be lifelong or are subsequently modified through individual experiential learning\n \n 10\n \n . Resource preferences have also been suggested to be genetically determined through genes inherited from both mother and father, where closely related individuals have more similar diets than distantly related individuals\n \n 11\n \n . In addition, maternal effects account for lifelong similarities in dietary phenotype among offspring of the same mother\n \n 12\n \n . Such similarities can arise from social interactions, maternal genotype, or maternal environment. Statistically, maternal effects are quantified as the similarity of repeated samples from siblings of the same mother but not as the similarity of behavioral expression between mother and offspring (i.e., \u201cmaternal learning\u201d). In range resident species, where individuals occupy a subset of a population\u2019s range, the environment, in terms of habitat composition or availability of particular food resources, may differ among home ranges and lead to individual specialization\n \n 11\n \n . Accounting for the environmental heterogeneity when studying the drivers of individual specialization is therefore essential in range resident species\n \n 13, 14\n \n .\n
\n\n Attributing variation in diet to the individual level, to isolate its sources and to identify developmental drivers of diet preferences requires multigenerational datasets of repeated measures of the diet of individuals throughout their life. We used a 30-year longitudinal dataset of 72 female Scandinavian brown bears (\n \n Ursus arctos\n \n ) of known mothers with repeated annual isotopic estimates of trophic position to assess whether individual dietary specialization occurs. Using information about their mother\u2019s diet, a genetic pedigree, and individual movement data we then attributed individual variation in diet to its sources: maternal learning, genetic heritability, environment, and maternal effects.\n
\n\n Brown bears are ecological generalists with a species range spanning the northern hemisphere from tundra to deserts, paralleled by extensive variation in diet: from populations tracking food resource pulses, such as spawning fish\n \n 15\n \n , scavenging on ungulate carcasses or preying on ungulates neonates\n \n 16\n \n , or feeding extensively on invertebrates, to populations using primarily fruiting plant based diets\n \n 17, 18\n \n . Given this extreme dietary plasticity, it is not surprising that great dietary variation has been found within populations\n \n 19, 20\n \n , however, the determinants and ontogeny of this variation at the individual level remain largely unknown. In ecology, differences in diet are often primarily attributed to differences in resource availability and abundance. Even within populations inhabiting a continuous biome, home range scale variation in habitat composition\n \n 21\n \n can lead to variation in resource availability. The most parsimonious source of variation in diet are, therefore, differences in the environment. Brown bears maintain non-territorial home ranges but live a solitary lifestyle except for the period of offspring rearing involving up to three years\n \n 22\n \n of maternal care, after which female offspring often settle close to their mother\u2019s home range\n \n 23\n \n . In their first years of life, bear cubs accompany their mother and it is therefore reasonable to assume that brown bear offspring learn behaviors such as habitat, den site, or diet selection from their mothers and hence show similar behavior to them. If mothers differ in their dietary selection, these differences may hence be maintained in the population through learning by imitation of the mother (hereafter \"maternal learning\u201d), even after offspring gain independence, however, such similarities may wane over time\n \n 24\n \n . On the other hand, genetic heritability or maternal effects can have lifelong effects on offspring phenotype. Body size has been shown to be genetically heritable in our study population\n \n 25\n \n suggesting greater similarity among closely related individuals also in other linked traits, such as trophic position. Alternatively, maternal effects (i.e., maternal genotype or maternal environment) alone can shape the phenotype of offspring. For example, milk quantity or quality\n \n 26\n \n can vary among females either due to genetic differences or differences in the environments, leading to greater similarity among all offspring from the same mother (e.g. being smaller or larger in body size), which in turn could cause similarities in trophic position among siblings. To assess individual specialization along a continuum from a more plant-based to a more meat- or insect-based diet, we analyzed annual trophic positions from stable- nitrogen isotopes (\n \n \u03b4\n \n \n 15\n \n N) in bear hair keratin\n \n 27\n \n . Stable isotopes reflect cumulative diet intake and are deposited into the hair during growth with, a delay of approximately one month (i.e. a growing hair in June reflects the diet intake in May,\n \n 28\n \n ). Bear hair is regularly renewed through molting in June, regrows over the summer and fall and stops growing during winter hibernation (\n \n Fig.\u00a01A\n \n ,\n \n \n 29, 30\n \n \n ). Guard hair samples collected in spring and early summer (April - June) therefore reflect an individual\u2019s diet during the previous active season prior to hibernation\n \n 29\n \n . Using repeated samples of known mother-daughter pairs, we fit a spatially explicit Bayesian hierarchical model (i.e. \u00b4animal model\u00b4)\n \n 31, 32, 33\n \n to disentangle the relative contributions of maternal learning, genetic relatedness, the environment, and maternal effects as determinants of individual specialization. Specifically, the model accounted for genetic relatedness with a pedigree and for environmental similarity of bear home ranges with pairwise habitat similarity encompassing the proportion of mature habitat such as old and mid-successional forests, disturbed habitat such as clearcuts and regenerating young forest, and habitat diversity (measured as Simpson\u2019s diversity index) in a bear\u2019s home range. The model also accounted for maternal effects by incorporating the mother\u2019s ID as a random effect (i.e. if daughters from the same mother behaved in a similar fashion throughout life), and for maternal learning as the fixed effect of a mother\u2019s trophic positions on her daughter\u2019s trophic position. To this end, we determined maternal trophic positions from a population-wide model accounting for sexual dimorphism, age, and individual consistency in diet (\n \n Supplement 3\n \n ). Because bears may alter diet selection over time through individual learning, we allowed the effect of maternal learning to shift with time since the offspring gained independence. Last, we also accounted for permanent individual effects that could not be attributed to any of the aforementioned sources, by including a random effect for bear ID. We focused on the effect of maternal trophic position on female offspring trophic position, because male offspring were only monitored for a short period after family breakup. In the supplementary material we provide an additional analysis of the relationship between maternal and both female and male offspring trophic position in the first 4 years after family breakup and of the relationship between paternal trophic position and offspring trophic position. We also provide an alternative analysis accounting for spatial correlation via a spatial distance instead of a habitat similarity matrix, as well as a reduced model excluding the effect of environmental similarity to test whether spatial and genetic effects were confounded in philopatric female bears. Last, we validated our effect of maternal learning by refitting the model to a reduced dataset with observed maternal trophic positions during rearing, instead of modeled-averaged maternal trophic positions.\n
\n\n We analyzed annual trophic positions in 213 hair samples collected from 71 female brown bears born to 33 unique mothers (1\u20137 daughters per mother; median 2 daughters). Repeated sampling (median 3 years; range 1\u201311 years) revealed that female trophic position was unaffected by age (explained variance\u2009=\u20091% [0\u20134%]) and that individuals showed long-term individual specialization, accounting for 48% [31\u201361%] (median [89% equal tails credible interval]) of the total variance in trophic position (Fig.\n \n 2\n \n ,\n \n Basic model\n \n ). Individual specialization spanned half a trophic position ranging from 2.7 to 3.1 for individual females (Fig.\n \n 1\n \n B), which is equivalent to the difference between an omnivore feeding on a mix of plants and animal prey and a carnivore feeding predominantly on animal prey.\n
\n\n Individual specialization was primarily driven by initial maternal learning, the environment, and maternal effects. Maternal trophic position dynamic over the time since separation accounted for 13% [5% \u2212\u200923%] of variation in trophic position, while environmental similarity accounted for 9% [0.1\u20135%] of the total phenotypic variation in trophic position. Additionally, maternal effects accounted for 11% [0.5% \u2212\u200930%] of variation in trophic position, indicating that siblings (full and half) of the same mother were more similar in trophic position throughout life as compared to non-siblings. A remaining 8% [0.3\u201326%] of variance in trophic position was attributed to permanent individual effects (Fig.\n \n 2\n \n ). Genetically more closely related individuals did not share a more similar trophic position (3% [<\u20090.1% \u2013 17%] of variance explained) providing no evidence that dietary specialization could be heritable in this population (Fig.\n \n 2\n \n ).\n
\n\n After separating from their mother, female offspring initially maintained a similar trophic position as their mother (Pearson\u2019s\n \n r\n \n =\u20090.66 in the first two years after separation), which gradually became more dissimilar over time (Pearson\u2019s\n \n r\n \n =\u20090.31 in year 3\u20134 after separation, Fig.\n \n 3\n \n ). In the first years, offspring of more carnivorous mothers also had a high trophic position while offspring of less carnivorous mothers had a lower trophic position. About five years after the separation of the mother, this correlation ceased to exist. Bears inhabiting home ranges with a similar composition of mature and disturbed forest, as well as a similar habitat diversity in the home range, also had more similar trophic positions. The distance between pairwise home range centroids ranged from 0.7 to 172 km with a median pairwise distance of 48 km and individuals living in closer proximity had a more similar trophic position than individuals living farther apart (\n \n Supplementary material S6\n \n ). Spatial distance and maternal effects seemed to be confounded in this female philopatric species: After excluding spatial distance, maternal learning and maternal effects but not heritability explained more variance in trophic position, corroborating that spatial proximity is confounded with philopatric females forming clusters of mothers and daughter in space, so called matrilines (\n \n Supplementary material S7\n \n ). In a separate analysis (\n \n Supplementary material S8\n \n ) we could also show that the relationship between maternal and offspring trophic position in the first years after family breakup was not sex-specific. Both male (n\u2009=\u200931, Pearson correlation coefficient\u2009=\u20090.4) and female (n\u2009=\u200969, Pearson correlation coefficient\u2009=\u20090.45) offspring\u2019s trophic positions resembled their mother\u2019s trophic position in the first 4 years of independence, corroborating our findings that initial maternal learning determines foraging behavior in the early years after family breakup. Conversely, paternal trophic position had no effect on offspring trophic position in the first 4 years of independence (Pearson correlation coefficient\u2009=\u20090.13,\n \n Supplementary material S9\n \n ). While the modelled maternal trophic position correlated strongly with the observed trophic position in any given year (\n \n Supplementary material S3\n \n ), maternal learning explained even more of the phenotypic variance in daughter trophic position (22% [8% \u2212\u200927%] instead of 13%,\n \n Supplementary material S10\n \n ) when fitting the observed maternal trophic position during rearing, instead of the modelled posterior average maternal trophic position to a reduced dataset (62 hair samples collected from 38 daughters). Our estimates of maternal learning are therefore likely conservative and may underestimate the true effect of maternal learning on dietary specialization.\n
\n\n
\n\n
\n\n
\n\n Our multigenerational dataset reveals unique insights into the ontogeny of individual dietary specialization along a continuum from a more herbivorous to a more carnivorous diet in a long-lived omnivore. Specifically, the foraging strategy of sons and daughters was intimately tied to the foraging strategy of their mother, a relationship that lasted up to four years after independence. We interpret this relationship as evidence that maternal learning plays an important role in shaping an individual\u2019s dietary specialization. Five years into independence, the similarity between the mothers\u2019 and their daughters\u2019 trophic position slowly faded, likely due to individual learning and experience. In addition, siblings of the same mother also shared lifelong similarities in their trophic position, potentially mediated through maternal genetic or environmental effects on body size\n \n 25\n \n . In general, previous ecological studies have mainly concentrated on resource availability as the main driver of resource selection\n \n 34\n \n and individual specialization\n \n 4\n \n , however our results show that, within populations, the environment is only one of several components shaping individual dietary variation. We conclude that early-life imitation of maternal dietary preferences and maternal effects (i.e., maternal genotype and environment), which together explained about 24% of the variation in trophic position, play a pivotal role in spreading and maintaining feeding strategies within populations, even in species with otherwise solitary lifestyles. In addition, variation solely linked to individual variation (in our study 8 %) demonstrates potential for behavioral innovation and the potential to adapt to changing conditions.\n
\n\n Our findings are particularly relevant for species in which dietary specialization impacts individual fitness\n \n 7, 35, 36\n \n . For example, protein-rich diets may promote greater offspring survival or mass gain\n \n 37\n \n . Maternal and social learning in general therefore present an important, yet understudied pathway by which alternative behavioral strategies can establish and spread more rapidly within populations than by genetic evolution alone\n \n 38\n \n . Species more adept in social learning of dietary strategies may therefore show greater behavioral variability at the population level, which could give them an advantage when adapting to changing environments due to landscape modification or urbanization, climatic variations or global change in general. Moreover, there is evidence that the strength of social learning in shaping individual phenotypes is not only species-specific, but can also vary among populations or individuals of the same species\n \n 39\n \n .\n
\n\n Our research also points to several aspects of maternal learning that warrant future research. First, there is little information on whether maternal care and maternal learning tend to be more prevalent in species or populations with greater dietary specialization. There is some evidence that within populations, dietary generalists (i.e. those with a wider dietary niche) seem to provide more intense parental care\n \n 40\n \n , than their conspecific dietary specialists (i.e. ones with a narrower dietary niche), but the links to parental learning of foraging preferences remains unclear. Second, while generalist species with a wide ecological niche have been frequently shown to be more successful under changing environmental conditions, such as urban environments or fragmented landscapes, than specialist species\n \n 41, 42, 43\n \n , it is currently unknown whether this success could be partially mediated by social or maternal learning. Last, social learning could alternatively limit behavioral innovation and adaptation due to adherence to social traditions\n \n 44\n \n . We therefore suggest that alternative hypotheses should be evaluated that consider how social learning impacts individual specialization and in turn the adaptability of species under global change.\n
\n\n Our findings that dietary specialization can be socially learned and transmitted are particularly relevant for species where specialization is related to human-wildlife conflict\n \n 45\n \n . For example, the removal of single individuals which are known to cause conflict is an effective strategy to halt the spread of problematic behavior, increase societal acceptance by effectively mitigating the conflict, while minimizing the impact for species conservation goals\n \n 45\n \n . Foraging behavior that causes conflict has also been shown to change in ursids across life time, remarking the crucial role of individuality and plasticity in behavior\n \n 46\n \n . Maternal learning of behavior\n \n 47\n \n , including dietary specialization and foraging on anthropogenic food resources is commonly observed in ursids\n \n 48, 49, 50, 51\n \n . However, none of these studies tracked offspring diet over their lifetimes or were able to simultaneously account for the mother\u2019s diet, genetics, the environment, and other maternal effects, that could explain similar patterns of dietary specialization. While some of the aforementioned studies suggest either the environment or maternal learning as primary drivers of individual specialization, we suggest using caution in assigning causality in dietary specialization, when potentially confounding alternative sources cannot be accounted for. Specifically, in female-biased philopatric species, spatial proximity does not only encode for spatial variation in resource abundance but is also conflated with relatedness and, in particular, with maternal effects. In brown bears,\u00a0some daughters settle close to their mother\u2019s home range\n \n 23\n \n creating spatial clusters of closely related females, so called matrilinear assemblages\n \n 52\n \n .\u00a0Due to spatial dependence of\u00a0these assemblages, it can therefore be difficult to disentangle maternal learning from other maternal effects (i.e., maternal genotype or maternal environment) or the ambient environment. Our study population spanned over 170 km with spatial proximity explaining 59% of the total phenotypic variation in trophic position of female bears: individuals further apart tended to have more different diets. However, when replacing spatial proximity with environmental similarity among home ranges, the explanatory power was attributed to maternal learning and maternal effects along with the environment. Our results therefore demonstrate that individual dietary specialization is not caused by a single driver in isolation but the product of many factors, namely maternal learning, maternal effects, and the environment.\n
\n\n Our finding that maternal learning has a similar impact on resource selection as the environment provides important insights for a range of studies on habitat selection, dispersal, and range expansion. For example, a popular theory known as \u201cnatal habitat preference induction\u201d \u00a0suggests that dispersing animals select areas for settlement that resemble their natal habitat, even at fine habitat scales\n \n 21\n \n . Our results challenge the notion that habitat similarity alone drives natal settlement strategies and rather suggest that maternally induced diet preferences, and hence the selection for food resources themselves, could play an important role in producing similar patterns of settlement selection like induced natal habitat preferences. Recent studies of migration and short stopover behavior in whooping cranes (\n \n Grus americana\n \n ) have also observed that social learning rather than environmental conditions\n \n 53\n \n or genetic inheritance\n \n 54\n \n led to the emergence and establishment of alternative migratory behavior. Similar to what our study shows with respect to dietary specialization, social learning of migration strategies primarily determined behavior in early life whereas individual-experiential learning shaped behavior later in life\n \n 55\n \n .\n
\n\n Drivers of dietary specialization are well documented among populations of the same species, however, systematic studies delineating the sources of individual specialization within populations are lacking, likely because suitable datasets including multigenerational, genetic, environmental, and life-history information are rare. We show here that in addition to the environment, maternal learning and (other) maternal effects can be important sources of dietary specialization.\n
\n\n \n Bear sample collection\n \n
\n\n We collected brown bear hair samples in south-central Sweden (~\u2009N61\u00b0, E15\u00b0) as part of a long-term, individual-based monitoring project (Scandinavian Brown Bear Research Project;\n \n \n www.bearproject.info\n \n \n ). Hair samples were collected from known individuals and their offspring during bear captures in spring (April - June) 1993\u20132016 after bears emerged from hibernation. Bears were immobilized from a helicopter (Arnemo & Fahlman, 2011). A vestigial premolar tooth was collected from all bears not captured as a yearling to estimate age based on the cementum annuli in the root\n \n 56\n \n . Bears were weighed in a stretcher suspended beneath a spring scale. Tissue samples (stored in 95% alcohol) were taken for DNA extraction to assign parentage and construct a genetic pedigree\n \n 52\n \n . Guard hairs and follicles were plucked with pliers from a standardized spot between the shoulder blades and archived at the Swedish National Veterinary Institute. All animal captures and handling were performed in accordance with relevant guidelines and regulations and were approved by the Swedish authorities and ethical committee (Uppsala Djurf\u00f6rs\u00f6ksetiska N\u00e4mnd: C40/3, C212/9, C47/9, C210/10, C7/12, C268/12, C18/ 15. Statens Veterin\u00e4rmediciniska Anstalt, Jordbruksverket, Naturv\u00e5rdsverket: Dnr 35\u2013846/03, Dnr 412-7093-08 NV, Dnr 412-7327-\n
\n\n 09 Nv, Dnr 31-11102/12, NV-01758-14). We used data of adult bears (solitary or with offspring) and of offspring after separation from their mother. Bear cubs are born in January or February during winter hibernation and are typically first captured together with their mother as yearlings at the age of ~\u200915 months. Cubs in this population separate from their mother during the mating season in May or June after 1.5 or 2.5 years\n \n 57\n \n . Only hair samples of solitary, independent offspring taken in spring and early summer at least 10 months after separation from the mother were included in this study. A hair sample taken in spring reflects the summer-fall diet of the bear in the previous active season (Fig.\n \n 1\n \n A).\n
\n\n \n Food sample collection\n \n
\n\n We collected samples of the natural foods most important for brown bear in the study area, including 21 samples of moose hair (\n \n Alces alces\n \n ), the most common meat source in the brown bears\u2019 diet in our study area\n \n 58\n \n , in the spring-autumn field season of 2014 (\n \n Fig\n \n S1\n \n \n ). Samples were placed in a paper envelope and dried at ambient temperature.\n
\n\n \n Stable isotope analyses\n \n
\n\n Hair samples were rinsed with a 2:1 mixture of chloroform:methanol or washed with pure methanol to remove surface oils\n \n 59\n \n . Dried samples were ground with a ball grinder (Retsch model MM-301, Haan, Germany). We weighed 1 mg of ground hair into pre-combusted tin capsules and combusted at 1030\u00b0C in a Carlo Erba NA1500 elemental analyser. N\n \n 2\n \n and CO\n \n 2\n \n were separated chromatographically and introduced to an Elementar Isoprime isotope ratio mass spectrometer (Langenselbold, Germany). Two reference materials were used to normalize the results to VPDB and AIR: BWB III keratin (\n \n \u03b4\n \n \n 13\n \n C =- 20.18\u2030,\n \n \u03b4\n \n \n 15\n \n N = 14.31\u2030, respectively) and PRC gel (\u03b4\n \n 13\n \n C =-13.64\u2030,\n \n \u03b4\n \n \n 15\n \n N = 5.07\u2030, respectively). Measurement precisions as determined from both reference and sample duplicate analyses were \u00b1\u20090.1\u2030 for both\n \n \u03b4\n \n \n 13\n \n C and\n \n \u03b4\n \n \n 15\n \n N.\n
\n\n \n Bear trophic position\n \n
\n\n We calculated the trophic position of each bear hair sample relative to the average \u03b4\n \n 15\n \n N value of moose (mean\u2009\u00b1\u2009sd\u2009=\u20091.8\u2009\u00b1\u20091.26\u2030, n\u2009=\u200921,\n \n Fig\n \n S1\n \n \n ). Trophic position is calculated as the discrepancy of\n \n \u03b4\n \n \n 15\n \n N in a secondary consumer and its food source divided by the enrichment of\n \n \u03b4\n \n \n 15\n \n N per trophic level, plus lambda, the trophic position of the food source (e.g. 1 for primary producers, 2 for primary consumers, 3 for secondary consumer, 4 for tertiary consumers)\n \n 60\n \n . We used an average trophic enrichment factor of 3.4\u2030\n \n 60\n \n and added a lambda of 2 given that the moose baseline trophic position as a strict herbivore.\n
\n\n Bear trophic position = (\n \n \u03b4\n \n \n 15\n \n N\n \n \n Ursus arctos\n \n \n \u2013 average(\n \n \u03b4\n \n \n 15\n \n N\n \n \n Alces alces\n \n \n )) / 3.4\u2009+\u20092\n
\n\n Under an omnivorous diet including the consumption of herbivores (in particular moose but also ants such as\n \n Formica\n \n spp.,\n \n Camponotus herculeanus\n \n with average\n \n \u03b4\n \n \n 15\n \n N indistinguishable from moose), bear trophic position values were expected to fall between 2 and 3. Values approaching 4 indicate a trophic enrichment through consumption of other omnivorous or carnivorous animals.\n
\n\n \n Genetic pedigree and parentage assignment\n \n
\n\n A genetic pedigree based on 16 microsatellite loci was available for the population including 1614 individual genotypes\n \n 61\n \n . Genotyping followed the protocols of Waits, Taberlet\n \n 62\n \n , Taberlet, Camarra\n \n 63\n \n , and Andreassen, Schregel\n \n 64\n \n . All female offspring in this study were genotyped and included in the population\u2019s genetic pedigree. All females included in this study had a known mother that was also captured and followed. We used Cervus 3.0\n \n 65\n \n for assignment of fathers and COLONY\n \n 66\n \n for creating putative unknown mother or father genotypes and sibship reconstruction (see\n \n 61\n \n for details).\n
\n\n \n Maternal trophic position\n \n
\n\n Based on repeated hair samples of 115 female (n\n \n female\n \n = 335) and 98 male (n\n \n male\n \n = 219) bears, we fitted a\n \n basic\n \n linear mixed effects model for female and male bears respectively, to estimate sex-specific among individual variation in trophic position (\n \n Supplementary analysis 3\n \n ). We modelled trophic position as a function of a quadratic relationship with age and we controlled for individual random intercepts. Female trophic position did not vary with age but was highly repeatable over multiple years. For all daughters, we extracted their mother\u2019s (and father\u2019s) trophic position as the median of the posterior distribution of their respective random intercept. The modelled posterior trophic position and the observed trophic position in a given sampling year were highly positively correlated (Pearson correlation coefficient r\u2009=\u20090.78, t\u2009=\u200922.63, df\u2009=\u2009336, p\u2009<\u20090.001).\n
\n\n \n Environmental similarity\n \n
\n\n Resources may not be distributed evenly in space. For moose, population density and hunting quotas (which determine availability of slaughter remains) vary across the study area. For ants, the availability of old forests and clearcuts determine their abundance\n \n 67\n \n . Further, brown bear daughters are often philopatric with limited dispersal and settle close to their mother\u2019s home range\n \n 23\n \n . Genetic, spatial, and maternal learning effects may therefore be confounded with related bears occupying adjacent ranges with similar environments and resource availability. Elsewhere, accounting for environmental similarity through spatial autocorrelation in animal models has revealed that a major portion of variance may be attributed to environmental similarity rather than genetic heritability\n \n 31, 32, 68,\n \n but see also\n \n 69\n \n . Here, we accounted for environmental similarity by extracting habitat composition in each bear\u2019s lifetime home range. For individuals with sufficient locations (>\u20091000 GPS locations or VHF locations on at least 25 days) we constructed home ranges using a 95% kernel density estimator. We used a Corine landcover map (25 m resolution) which we updated annually with polygons of newly emerged clearcuts (data obtained from the Swedish Forest Agency). We extracted home range composition in the year when diet was assessed. When individuals were monitored for multiple years, we extracted the home range composition for the median year. We calculated the proportion of mid-aged and old forest and proportion of disturbed forest (clearcuts and regenerating young forest) within the 95% utilization distribution. Additionally, we calculated habitat diversity using the Simpson diversity index from the R package landscapemetrics\n \n 70\n \n . Following Thomson et al.\n \n 31\n \n we calculated the Euclidean distance between scaled and centered habitat composition and habitat diversity in multivariate space, assuming equal importance of each component. Pairwise distances were scaled between 0 and 1, where increasing values indicated more similar habitat composition. In the supplementary material we provide an alternative analysis accounting for spatial autocorrelation in dietary specialization with a pairwise spatial distance matrix (S matrix; Supplementary analysis 5,\n \n Fig S5\n \n ).\n
\n\n \n Statistical analysis\n \n
\n\n We applied a two-step modelling approach. First, we fitted a\n \n basic\n \n linear mixed effects model to estimate individual specialization as among individual variation in annual trophic position. We accounted for a nonlinear effect of age (second order polynomial) and for repeated measures of the same individual with individual random intercepts. We extracted the variance in fitted values (variance explained by fixed effects), among-individual, and residual variance and estimated the proportional contribution of fixed and random effects on the total phenotypic variance through variance standardization (i.e. repeatability\n \n 71\n \n , marginal and conditional R\n \n 2\n \n -values\n \n 72\n \n ). Second, we used a spatially explicit Bayesian hierarchical model (i.e. \u2018animal model\u2019)\n \n 31, 33\n \n to partition among-individual variance in trophic position into environmental similarity (\u03c3\n \n 2\n \n \n env\n \n ), additive genetic (\u03c3\n \n 2\n \n \n a\n \n ), permanent among-individual (\u03c3\n \n 2\n \n \n ind\n \n ), maternal (\u03c32\n \n mat\n \n ), and residual within-individual effects (\u03c3\n \n 2\n \n \n r\n \n ). Similar to the basic model, we accounted for a nonlinear effect of age on trophic position (fitted as time since separation of mother and daughter scaled by the standard deviation, true age and time since separation were perfectly correlated: Pearson correlation coefficient\u2009>\u20090.99). We tested for maternal effects on offspring trophic position by incorporating the mother\u2019s trophic position as a covariate into the model. To account for a potential decrease of the maternal effect over time, we let maternal trophic position interact with the time since separation of mother and daughter (both scaled by their standard deviation and centered). We partitioned the variance explained by the two components of the fixed effect, the effect of maternal learning over time (i.e. maternal trophic position and the interaction between maternal trophic position and time since separation) and age (i.e. the main effect of time since separation), respectively, by calculating the independent contribution of each component to the total variance explained by the fixed effects, following the approach by Stoffel, Nakagawa\n \n 73\n \n adapted to a Bayesian framework (see code under\n \n 74\n \n ).\n
\n\n All models were fit using the R package \u201cbrms\u201d\n \n 75\n \n based on the Bayesian software Stan\n \n 76, 77\n \n . We ran four chains to evaluate convergence which were run for 6,000 iterations, with a warmup of 3,000 iterations and a thinning interval of 10. All estimated model coefficients and credible intervals were therefore based on 1200 posterior samples and had satisfactory convergence diagnostics with\n \n \n \\(\\widehat{R}\\)\n \n \n < 1.01, and effective sample sizes > 400\n \n 78\n \n . Posterior predictive checks recreated the underlying Gaussian distribution of trophic position well. For all parameters, we report the median and 89% credible intervals, calculated as equal tail intervals, as measure of centrality and uncertainty\n \n 79\n \n . We deemed explained variance proportions as inconclusive when the lower credible interval limit was < 0.001 (i.e., < 0.1%)\n \n 80\n \n . All statistical analyses were performed in R 4.0.0\n \n 81\n \n . Primary data and code to reproduce all analyses are provided under (\n \n \n https://doi.org/10.17605/OSF.IO/68B9U\n \n \n ,\n \n 74\n \n ).\n
\n\n Reporting Summary\n
\n \n\n Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive lung tumor marked by significant molecular heterogeneity. In a study of 590 patients across two independent cohorts, we observed comparable overall survival across treatment regimens (chemotherapy, chemoimmunotherapy, immunotherapy) without unexpected adverse events. Genomic analysis identified distinct NSCLC-like (\n \n KEAP1\n \n ,\n \n KRAS\n \n ,\n \n STK11\n \n mutations) and SCLC-like (\n \n RB1\n \n ,\n \n TP53\n \n mutations) LCNEC subtypes, with 80% aligning with SCLC transcriptional profiles. Serial sampling revealed stable mutational but shifting transcriptomic landscapes over time. NSCLC-like LCNECs showed elevated FGL-1 (a LAG-3 ligand) and SPINK1 expression, while SCLC-like subtypes expressed higher levels of DLL3. Immunofluorescence confirmed FGL-1 in NSCLC-like LCNECs, and H&E slide analyses indicated fewer tumor-infiltrating lymphocytes in LCNECs versus other lung cancers. These findings highlight LCNEC\u2019s distinct immunogenomic profile, supporting future investigations into LAG-3, SPINK1, and DLL3-targeted therapies.\n
\n\n Under the 2015 World Health Organization (WHO) guidelines, pulmonary large cell neuroendocrine carcinoma (LCNEC) is classified as a high-grade neuroendocrine tumor\n \n 1\n \n . For patients with advanced LCNEC, median survival is typically between 7 and 12 months\n \n 2\n \n . However, optimal systemic treatment strategies for this aggressive disease remain undefined due to limited data. Compounding the challenge is the scarcity of clinical studies and the relative rarity of LCNEC, which accounts for only 3% of all lung carcinomas\n \n 3\n \n . At the core of this issue lies the unresolved biological relationship between LCNEC and other lung neoplasms. \u00a0Gene expression and limited genomic studies have produced inconsistent findings on the connection between LCNEC and SCLC, with certain reports indicating highly similar biology\n \n 4\n \n while others have suggested distinct gene expression and mutational profiles\n \n 5, 6\n \n . Additionally, molecular alterations typical of adenocarcinoma, such as\n \n EGFR\n \n mutations\n \n 7, 8\n \n ,\n \n ALK\n \n rearrangements\n \n 9\n \n , and\n \n KRAS\n \n mutations\n \n 10\n \n , have been identified in LCNEC without adenocarcinoma components, sharply contrasting with classic de novo SCLC.\n
\n\n \n Previous integrative genomic and transcriptomic analyses of 75 LCNECs delineated two distinct molecular subtypes\u2014Type I, characterized by co-occurring TP53 and STK11/KEAP1 alterations, and Type II, defined by bi-allelic inactivation of TP53 and RB1.\n \n \n \n 11\n \n \n \n Despite overlapping genomic landscapes, these subtypes demonstrated divergent transcriptional programs: Type I LCNECs display a neuroendocrine-enriched phenotype marked by ASCL1 and DLL3 expression with attenuated NOTCH signaling, whereas Type II LCNECs demonstrate diminished neuroendocrine differentiation, heightened NOTCH pathway activity, and enrichment of immune-related signatures. This discordance between mutational architecture and transcriptional identity underscores the biological heterogeneity of LCNEC and challenges reductionist models that rely solely on genomic alterations for subtype classification.\n \n
\n\n Recent genomic analyses have indicated that LCNEC can be divided into non-small cell lung cancer (NSCLC)-like (characterized by lack of\n \n RB1\n \n genomicalterations and presence of mutations in the\n \n KRAS\n \n ,\n \n STK11\n \n , and\n \n KEAP1\n \n genes) and small cell lung cancer (SCLC)-like genomic subtypes (characterized by concurrent\n \n TP53\n \n and\n \n RB1\n \n mutations or loss).\n \n 3, 11-13\n \n Unfortunately, patients with advanced LCNEC consistently exhibit poor outcomes regardless of the molecular subtype, underscoring the urgent need for new treatment paradigms\n \n 14\n \n .\n
\n\n Immune checkpoint inhibitors (ICIs) have markedly revolutionized the treatment landscape for various cancers, including both NSCLC and\u00a0SCLC\n \n 15-26\n \n \n ,\n \n \n 27\n \n . However, the clinical efficacy data of ICIs in advanced LCNEC predominantly stems from case reports and small retrospective studies\n \n 23, 28-31\n \n . A recent analysis of 125 patients with advanced LCNEC suggested a potential survival\u00a0benefit from\u00a0immunotherapy-based regimens.\n \n 32\n \n However, all patients received ICIs\u00a0after\u00a0frontline\u00a0therapy\u2014a treatment sequence\u00a0no longer standard\u00a0in\u00a0NSCLC and SCLC.\u00a0Prospective evaluation of ICIs in LCNEC is in its infancy, with only a small number of patients enrolled across several ongoing clinical trials (NCT03352934, NCT03190213, NCT03136055, NCT03290079, NCT03728361\n \n 33\n \n , NCT0283401), and biomarker data remain sparse. Given the paucity of effective systemic therapies for LCNEC, there is an urgent need for novel strategies to improve outcomes. In this study, we analyze two independent cohorts comprising 590 patients with advanced LCNEC to define survival outcomes by frontline treatment regimen, including those incorporating ICIs. Through integrative analyses\u2014spanning targeted and whole exome sequencing (WES), digital pathology with machine learning, and whole-transcriptome sequencing (WTS)\u2014we identify therapeutic targets and molecular vulnerabilities, informing future clinical trial development.\n
\n\n \n Patient Cohorts\n \n
\n\n To provide a broad description of treatment patterns in patients with LCNEC, we gathered data from two large historical cohorts: Cohort 1 is a multicenter study of 217 patients with LCENC treated with 1\n \n st\n \n line systemic treatment between 1/2014 and 12/2023. Clinical information was gathered from 26 participating institutions in Belgium, Germany, Italy, Spain, United Kingdom, and the United States\n \n (Supplementary Table 1\n \n This study was conducted in accordance with the principles of the Declaration of Helsinki and received approval from the Yale New Haven Hospital Institutional Review Board (IRB) as well as the IRBs of the respective participating institutions. Although the study relied exclusively on de-identified data, we acknowledge that genetic data, while de-identified, retains inherent identifiability due to its unique nature. In compliance with HIPAA guidelines and considering GDPR classifications of genetic data as personal data, stringent safeguards were implemented to protect patient confidentiality. No direct identifiers were accessible to study investigators, and data were managed within secure, access-controlled environments. Based on the use of de-identified data and the minimal risk posed to participants, written informed consent was waived by the IRBs. For Cohort 1, the pathologic diagnosis of LCNEC was reviewed at the local treating institution and confirmed by pulmonary pathologists according to the 5th edition of the WHO Classification of Lung Tumors\n \n 34\n \n . The diagnosis of pulmonary LCNEC required the presence of neuroendocrine morphology (organoid nesting, palisading, rosettes, or trabeculae) and expression of at least one neuroendocrine marker (chromogranin A, synaptophysin, INSM1, CD56) by immunohistochemistry. High mitotic activity (>10 mitoses per 2 mm\u00b2) and/or extensive necrosis were also required for classification. Tumor specimens with mixed histologic components (adenocarcinoma, squamous cell carcinoma, or SCLC) other than LCNEC were excluded to enrich for LCNECs.\n
\n\n Cohort 2 represents a historical cohort collected by Caris Life Sciences (Phoenix, AZ, USA) between 1/2015 and 11/2023. This included 373 patients diagnosed with LCNEC and underwent tissue-based genomic profiling by a commercial laboratory (Caris Life Sciences). The specimens were primarily composed of diagnostic biopsy or surgical tumor samples. Of these, a subset of 146 patients met the inclusion criteria for clinical outcome analyses, consistent with Cohort 1, defined as having advanced LCNEC treated with first-line systemic therapies. This focus was driven by the study's objective to investigate first-line treatment outcomes in advanced LCNEC\u2014a critical and understudied area in the field. The remaining patients, who either did not have advanced LCNEC or were not treated with first-line systemic therapies, were excluded from the clinical outcome analyses but included in genomic and transcriptomic correlates. This approach ensured alignment with the study's objectives to investigate the treatment landscape and outcomes for advanced LCNEC. Clinical data were acquired from insurance claims, and the selection of systemic therapies was at the discretion of the treating physician. The sex and age of patients were determined from medical forms. For Cohort 2, pathologic diagnosis was initially confirmed at local institutions and later reviewed centrally at Caris Life Sciences for accuracy in a subset of 142 tumors with a diagnostic accuracy rate of 94.3%. Systemic treatments for both cohorts included chemotherapy alone, chemoimmunotherapy, and immunotherapy alone. An independent cohort of 1704 SCLC from Caris Life Sciences was utilized for comparison with LCNECs.\n
\n\n \n Genetic analysis\n \n
\n\n In Cohort 1, local institutions utilized standard-of-care genomic sequencing platforms to identify mutations and copy number alterations in key oncogenic drivers, including\n \n \n ALK\n \n \n \n ,\n \n \n \n EGFR\n \n \n \n ,\n \n \n \n KEAP1\n \n \n \n ,\n \n \n \n KRAS\n \n \n \n ,\n \n \n \n MET\n \n \n \n ,\n \n \n \n RB1\n \n \n \n ,\n \n \n \n SMARCA4\n \n \n \n ,\n \n \n \n STK11\n \n \n \n ,\n \n and\n \n \n TP53\n \n \n . The use of institution-specific platforms introduced variability in gene coverage and analytical methodologies but reflects the diversity inherent in clinical practice.\n
\n\n In Cohort 2, a more standardized approach was employed. Tumor samples underwent microdissection prior to nucleic acid isolation to enrich for tumor content. Next-generation sequencing (NGS) was then conducted on genomic DNA using either the NextSeq platform (Illumina, Inc., San Diego, CA, USA) for a targeted panel of 592 cancer-relevant genes (n=84 samples) or the Illumina NovaSeq 6000 platform (Illumina, Inc., San Diego, CA, USA) for whole-exome sequencing (n=289 samples). For NextSeq-sequenced tumors, a custom-designed SureSelect XT assay (Agilent Technologies, Santa Clara, CA, USA) was employed to enrich for the 592 target genes. For NovaSeq-sequenced tumors, a hybrid pull-down panel of baits was used to achieve high coverage and read depth for >700 clinically relevant genes (average 500x), with additional enrichment for >20,000 genes at an average depth of 200x. Genetic variants were detected with >99% confidence and classified by board-certified molecular geneticists using previously established criteria.\n \n 35\n \n
\n\n These methodological differences between Cohorts 1 and 2 highlight the real-world heterogeneity in clinical and genomic data acquisition. To ensure scientific rigor, analyses were conducted separately where appropriate, accounting for the inherent differences in data generation and processing between the two cohorts.\n
\n\n \n Variant assessment\n \n
\n\n For cohort 1, variants assumed to be oncogenic or likely oncogenic on OncoKB were considered pathogenic\n \n 36, 37\n \n . For cohort 2, genomic alterations were reviewed by board-certified clinical geneticists according to criteria established by the American College of Medical Genetics and Genomics\n \n 38\n \n .\n
\n\n \n RNA sequencing\n \n
\n\n We obtained publicly available RNA WTS data from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data collection (n=515 tumors)\n \n 39\n \n . For each specimen, the normalized transcripts-per-million (TPM) counts were calculated, and the data was log2 transformed. Gene set enrichment analysis (GSEA http://software.broadinstitute.org/gsea/index.jsp) was performed using the clusterProfiler package (version 4.12.2) in R (version 4.4.1), with hallmark gene sets from the Molecular Signatures Database (MSigDB v2023.2).\n
\n\n For cohort 2, RNA WTS was conducted using a hybrid-capture approach from formalin fixed paraffin-embedded (FFPE) tumor samples (n=373) with the Agilent SureSelect Human All Exon V7 bait panel (Agilent Technologies; RRID) and the Illumina NovaSeq platform (Illumina, Inc.). Pathology review of FFPE specimens was performed to determine the percent tumor content and tumor size, requiring at least 20% tumor content in the area for microdissection to allow for enrichment and extraction of tumor-specific RNA. Extraction was carried out using a Qiagen RNA FFPE Tissue Extraction Kit, and the RNA quality and quantity were assessed with the Agilent TapeStation. Biotinylated RNA baits were hybridized to the synthesized and purified cDNA targets, followed by a post-capture PCR amplification of the bait-target complexes. The resulting libraries were quantified, normalized, pooled, denatured, diluted, and sequenced. Raw data were demultiplexed using the Illumina DRAGEN FFPE accelerator. Briefly, FASTQ files were aligned with STAR aligner (Alex Dobin, release 2.7.4a GitHub, https://github.com/alexdobin/STAR/releases/tag/2.7.4a). A complete 22,948-gene dataset of expression data was generated by Salmon, which offers fast and bias-aware quantification of transcript expression\n \n 40\n \n . BAM files from the STAR aligner (RRID: SCR_004463) were further processed for RNA variants using a proprietary custom detection pipeline. The reference genome used was GRCh37/hg19, and analytical validation of this test showed \u226597% positive percent agreement, \u226599% negative percent agreement, and \u226599% overall percent agreement with a validated comparator method.\n
\n\n
\n Immune cell fractions within the tumor microenvironments (TME) were estimated by deconvoluting RNA expression profiles using quanTIseq (RRID:SCR_022993)\n \n 41\n \n . QuanTIseq is a computational tool that quantifies the abundance of ten immune cell populations from WTS. The algorithm is validated against flow cytometry and immunohistochemistry for determining the absolute fractions of myeloid dendritic cells (DCs), regulatory T cells (Tregs), CD8+ and CD4+ T cells, natural killer (NK) cells, neutrophils, monocytes, M1 and M2 macrophages, and B cells.\n
\n \n Immunohistochemistry for PD-L1 status and immunofluorescence for FGL-1\n \n
\n\n For cohort 1, PD-L1 status was determined using one of the following anti-PD-L1 antibodies: 22c3, 28-8 (Agilent, Dako),and SP263 (Ventana). For cohort 2, PD-L1 status was determined using the 22c3 anti\u2013PD-L1 antibody (Dako) on FFPE sections. The evaluation involved calculating the percentage of positively stained tumor cells to obtain a tumor proportion score\n \n 42\n \n .\n
\n\n For FGL-1 immunofluorescence, tumor regions from paraffin-embedded sections were delineated by a board-certified pathologist using corresponding H&E-stained slides. Unstained FFPE slides from NSCLC-like LCNEC (n=2), SCLC-like LCNEC (n=1), NSCLC (n=3), and SCLC (n=4) were immersed in Xylene I/II, absolute ethyl alcohol, 95% and 85% alcohol to deparaffinize the tissue sections. The slides were then subjected to antigen retrieval using Tris-EDTA buffer (pH = 8.0) at 98\u00b0C for 20 min. Slides were blocked with 1% BSA, 4% Horse Serum, 0.4% Triton-X100 in PBS for 30 min, then incubated overnight at 4 \u00b0C with an anti-FGL1 rabbit polyclonal primary antibody (Proteintech, 16000-1-AP) mouse monoclonal primary antibody (Proteintech, 66483-1-Ig) at 1:200. An anti-rabbit corresponding secondary antibody was used at a 1:1,000 dilution, for 2 h at room temperature. Sections were then mounted with Fluoroshield histology medium containing DAPI (Sigma, F6057).Confocal imaging was acquired with LSM880 microscope with airyscan and data were analysed by using ImageJ.\n
\n\n \n Digital pathology assessment of tumor-infiltrating lymphocytes\n \n
\n\n For DFCI lung tumor samples, hematoxylin and eosin (H&E) slides were digitized using the Aperio AT at a resolution of 0.49 microns per pixel. The detailed method is reported previously\n \n 43\n \n . Briefly, the images were processed in QuPath (v.4.0) using built-in functions. This involved color deconvolution to estimate stain vectors and normalize the RGB channels for each image. For cell detection, watershed segmentation was employed to identify cells based on size, shape, and the optical density of nuclei in the hematoxylin channel. Additional features were calculated by adding intensity and smoothed object features, computing Haralick texture features, and determining gaussian-weighted averages per object/cell. A random forest algorithm was used to train an object classifier to identify tumor-infiltrating lymphocytes (TILs), tumor cells, and stromal cells. TILs were defined as mononuclear immune cells, including lymphocytes and plasma cells.\n
\n\n \n Mismatch repair status\n \n
\n\n Multiple test platforms were used to determine the MSI or MMR status. These included fragment analysis (MSI Analysis System kit; Promega, Madison, WI, USA), immunohistochemistry staining (MLH1, M1 antibody; MSH2, G2191129 antibody; MSH6, 44 antibody; and PMS2, EPR3947 antibody; Ventana Medical Systems, Tucson, AZ, USA), and next-generation sequencing (examining 7000 target microsatellite loci and comparing them to the reference genome hg19 from the University of California Santa Cruz (UCSC) Genome Browser database). The results from these three platforms were highly concordant. In rare cases of discordant results, the microsatellite stability or MMR status of the tumor was determined in the order of immunohistochemistry, fragment analysis, and next-generation sequencing\n \n 44\n \n .\n
\n\n \n Tumor mutational burden\n \n
\n\n In cohort 2, tumor mutational burden (TMB) was assessed by counting all nonsynonymous missense, nonsense, in-frame insertion/deletion, and frameshift mutations in each tumor that were not previously identified as germline alterations in dbSNP151, the Genome Aggregation Database (gnomAD), or as benign variants by Caris\u2019s geneticists. TMB-High was defined as having >19mutations per megabase (muts/Mb), in accordance with the KEYNOTE-158 pembrolizumab trial\n \n 45\n \n .\n
\n\n For cohorts 1 and 2, no statistical method was used to predetermine the sample size. To ensure robust analyses and minimize confounding due to cohort-specific biases, clinical outcomes were analyzed separately for Cohorts 1 and 2. Overall survival (OS) in the ICI cohort was calculated from the time of first anti-PD-1/L1 drug treatment (pembrolizumab, nivolumab, atezolizumab, durvalumab, avelumab, or cemiplimab) to death or last follow-up. OS in the chemotherapy and chemoimmunotherapy cohorts was calculated from the time of the first systemic treatment to death or last follow-up. Real-world progression-free survival (rwPFS) was calculated from the date of initiation of first-line systemic therapy to the date of progression or death. Disease progression was determined based on available clinical records, imaging studies, or treating physician assessments, as documented in patient charts or claims data. Alive patients were censored at the date of last follow-up. Time on treatment (ToT) was calculated from the start date of first-line systemic therapy to the end date. Patients who were still alive and receiving ongoing treatment were censored at the date of their last follow-up. Survival functions were estimated using the Kaplan-Meier method, and survival distributions were compared using a two-sided log-rank test. P values less than 0.05 were considered significant. Multivariable Cox proportional hazards regression models for rwPFS and OS were performed and adjusted for variables selected\n \n a priori\n \n : Sex, ECOG performance status, age at time of systemic treatment, and M stage (M1a, M1b, M1c). For the analysis of tumor microenvironment (TME) biomarkers and GSEA, a false discovery rate of 0.05, determined by the Benjamini\u2013Hochberg procedure, was used to define statistical significance. Median follow-up time was determined by the reverse Kaplan-Meier method. Analyses were conducted using Python 3.12.5 and RStudio 2024.04.2+764.pro1\n
\n\n \n Characteristics of clinical cohorts\n \n
\n\n Cohort 1 consisted of 217 patients with LCNEC treated with first-line systemic treatments. Cohort 2 comprised 373 patients diagnosed with LCNEC, of whom a subset had available data on first-line systemic treatment (n=146;\n \n Table 1, Supplementary\n \n \n Figure 1\n \n ,\n \n Supplementary Tables S2,S3)\n \n . Median age was 66 years (range: 18-88) and 67 (range: 38-89) for cohorts 1 and 2, respectively,\n \n Table 1, Supplementary Tables S2,S3\n \n ). The median follow-up time for cohorts 1 and 2 was 48.6 months (95% CI 38-62) and 29.5 months (95% CI 25.3 - 36.7), respectively. The majority of patients identified as white in both cohorts (Cohort 1: n=168, 81%, Cohort 2: n=238, 64%\n \n Table 1\n \n ). For patients with available systemic treatment data, treatment regimens included chemotherapy (n=121 (56%) for cohort 1, n=46 (32%) for cohort 2), chemoimmunotherapy (n=82 (38%) for cohort 1, n=88 (60%) for cohort 2), and immunotherapy (n=14 (6.4%) for cohort 1, n=12 (8.2%) for cohort 2). There were no differences in baseline characteristics across the 3 systemic treatments (\n \n Table 1\n \n ).\n
\n\n \n Clinical Outcomes to First-line Systemic Therapy\n \n
\n\n \n \n rwOS\n \n \n
\n\n There was no significant difference in median OS across the 3 treatment groups in both cohorts (\n \n Figure 1A, 1B\n \n ). In cohort 1, median OS was 15 months (95% CI 8.1-17.4) in the chemotherapy group, 12 months (95% CI 7.4-18.3) in the chemoimmunotherapy group, and 13.6 months (95% CI 6.8-25.2) in the immunotherapy group. In cohort 2, median OS was 14.9 months (95% CI 9.3-26.1) in the chemotherapy group, 17.6 months (95% CI 13.2-21.2) in the chemoimmunotherapy group, and 21.7 months (95% CI 6.0-NR) in the immunotherapy group. To evaluate the potential influence of treatment year on clinical outcomes, we first performed an analysis of overall survival (OS) within the chemotherapy-treated cohort. The analysis revealed no significant difference in OS between patients treated prior to January 1, 2019 (n=71), and those treated thereafter (n=48;\n \n p\n \n =0.57). Subsequently, we compared OS among patients treated with chemotherapy alone (n=32) versus immunotherapy alone (n=8) versus those treated with chemoimmunotherapy (n=74) after March 1st, 2019, and similarly observed no significant difference (\n \n p\n \n =0.3). Among patients who received chemotherapy as first-line systemic treatment, 61 went on to receive a subsequent line of therapy (28 non-ICI based, 33 ICI-based). Within this group, there was no significant difference between patients who received subsequent ICI-based therapy and those who received non-ICI-based therapy (p=0.2). In Cohort 2, there was no significant difference in overall survival between patients with NSCLC-like LCNECs who received NSCLC-based chemotherapy regimens and those with SCLC-like LCNECs treated with SCLC-based chemotherapy regimens (HR = 1.20, 95% CI: 0.59\u20132.31,\n \n p\n \n = 0.65,\n \n Supplementary Figure 2\n \n ). In Cohort 1, this analysis was limited by small sample size (n=5 per group), and thus underpowered to detect meaningful differences.\n
\n\n \n \n Enrichment of pro-Inflammatory signatures in ICI long-term survivors with no association between TMB, PD-L1, and survival in chemoimmunotherapy\n \n \n
\n\n In the ICI-treated group from cohort 2, six patients exhibited a real-world overall survival (rwOS) exceeding 20 months. Among these, 33% (2 out of 6) demonstrated high tumor mutational burden (TMB), and 50% (3 out of 6) were positive for programmed death-ligand 1 (PD-L1) expression. In patients receiving ICI-based therapies, GSEA revealed a significant enrichment of pro-inflammatory immune pathways in those with a rwOS exceeding 20 months compared to those with an rwOS of less than 20 months (\n \n Supplementary Figure 3\n \n ). Expanding the biomarker analysis to include the chemo-immunotherapy group in cohort 2, where the sample size permitted more robust comparisons, the median rwOS was not significantly different between TMB-high (>19) versus TMB-low tumors (\u226419;\u00a0p=0.7,\n \n Supplementary Figure 4A\n \n ). Furthermore, within the chemoimmunotherapy group, rwOS did not significantly differ based on PD-L1 status (p=0.5,\n \n Supplementary Figure 4B\n \n ).\n
\n\n \n \n rwPFS\n \n \n
\n\n Among the 216 evaluable patients in cohort 1, median rwPFS was 5.1 months (95% CI, 3.4-5.5) in the chemotherapy group, 5.4 months (95% CI, 4.4 to 6.1) in the chemoimmunotherapy group, and 3.9 months in the immunotherapy group (95% CI, 2 to 6.5). After adjusting for ECOG, M stage, sex, and age, the chemotherapy group had a statistically significantly lower rwPFS compared to the chemoimmunotherapy group (\n \n p\n \n =0.03; HR: 1.43 [95% CI: 1.04-1.99]). In contrast, the immunotherapy group did not show a significant difference in rwPFS (HR: 1.3 [95% CI: 0.69-2.58]) (\n \n Figure 1C\n \n ). In cohort 2, rwPFS was not available, so ToT was used as a surrogate endpoint. Median ToT was 2.4 months \u00a0(95% CI 2.1-3.6) in the chemotherapy group, 7.5 months (95% CI 5.2-10.4) in the chemoimmunotherapy group, and 6.3 months (95% CI 1.3-18.0) in the immunotherapy group. Patients treated with chemotherapy had significantly worse ToT compared to those receiving chemoimmunotherapy (HR: 1.44,\n \n p\n \n =0.05,\n \n Supplementary Figure\n \n \n 5\n \n ).\n
\n\n \n \n Toxicity Profiles in Cohort 1\n \n \n
\n\n Overall, 112 (52%) patients developed treatment-related adverse events (trAE) of any grade (\n \n Figure 1D\n \n ) with similar frequencies across treatment groups (chemotherapy: n=61, 50%; chemoimmunotherapy: n=45, 55%; immunotherapy: n=6, 43%). Grade\u22653 trAE occurred in\u00a022% (95% CI, 16 to 31), 26% (95% CI, 17 to 36), and 0% (95% CI, 0 to 22) patients in the chemotherapy, chemoimmunotherapy, and immunotherapy groups, respectively (\n \n Supplementary Figure 6\n \n ). Toxicity led to discontinuation of systemic treatment in 10% (95% CI, 5.8 to 17), 15% (95% CI, 8.6 to 24), and 14% (95% CI, 2.5 to 40) patients in the chemotherapy, chemoimmunotherapy, and immunotherapy groups, respectively (\n \n Supplementary Table S4\n \n ).\n
\n\n \n Genomic Map and Clinical Outcomes of LCNEC molecular subtypes\n \n
\n\n Prior genomic mapping of LCNEC has delineated these tumors into SCLC-like and NSCLC-like categories\n \n 11\n \n . Utilizing a similar stratification approach, we classified 217 tumors in cohort 1 into SCLC-like (characterized by concurrent\n \n TP53\n \n and\n \n RB1\n \n mutations) and NSCLC-like (characterized by mutations in either\n \n STK11\n \n ,\n \n KRAS\n \n , or\n \n KEAP1\n \n mutations and wild-type\n \n RB1\n \n status). Tumors that did not conform to either of these subtypes were designated as unclassified. In cohort 1, 85 patients had genomic data that allowed molecular classification. \u00a0Of these, 25 (29%) were classified as NSCLC-like, 19 (22%) were SCLC-like, and 41 (48%) were unclassified (\n \n Figure 2A, Supplementary Figure 1, Supplementary Table S5\n \n ). The remainder of tumors (n=132) did not have full mutation profiling of the genes of interest (\n \n KEAP1\n \n ,\n \n KRAS\n \n ,\n \n STK11\n \n ,\n \n TP53\n \n , and\n \n RB1\n \n ) and thus were labeled \u201cunknown\u201d. In cohort 2, 89 (23.9%) tumors were genomically NSCLC-like, 136 (36.5%)were SCLC-like, and 148 (39.7%) were unclassified (\n \n Figure 2B\n \n ). In addition to the previously mentioned genes, commonly altered genes included other drivers such as\n \n SMARCA4\n \n ,\n \n KMT2D\n \n ,\n \n CDKN2A\n \n ,\n \n PTEN\n \n ,\n \n ARID1A\n \n , and\n \n NF1\n \n (\n \n Figure 2B\n \n ). Targetable alterations were detected in 22 of 373 (5.9%) LCNECs and included\n \n KRAS\n \n \n G12C\n \n (n=13),\n \n EGFR\n \n activating mutations (n=5),\n \n ERBB2\n \n mutation (n=1), and fusions (\n \n EML4\n \n ::\n \n ALK\n \n , n=3;\n \n ETV6\n \n ::\n \n NTRK2\n \n , n=1).\n
\n\n To refine molecular classification of unclassified LCNECs, we developed a support vector machine (SVM) classifier trained on transcriptomic profiles from NSCLC-like and SCLC-like LCNEC subtypes (see methods). Gene selection was guided by both high inter-sample variance and differential expression (adjusted\n \n p\n \n < 0.01), yielding 2,168 gene transcripts as input features. The model, trained on 80% of labeled samples (n=174) and validated on the remaining 20% (n = 44), demonstrated high discriminatory performance (AUC = 0.98; accuracy = 90.1%) (\n \n Figure 3A,B\n \n ). Applying the trained classifier to the 143 previously unclassified tumors, 101 (70.6%) were reclassified as SCLC-like and 46 (32.2%) as NSCLC-like. Dimensionality reduction using UMAP revealed three distinct transcriptomic clusters, with strong concordance between classifier-predicted subtypes and spatial clustering (\n \n Figure 3 C,D\n \n ). Notably, reclassified samples localized proximally to their respective subtype clusters, supporting the biological plausibility of the predictions. With the refined classification, we next evaluated overall survival and found no significant difference across the four LCNEC subtypes (log-rank\n \n P\n \n = 0.23,\n \n Supplementary Figure 7\n \n ).\n
\n\n To assess whether LCNECs maintain their genomic subtype over time, we analyzed data in cohorts 1 and 2 from nine patients with two temporally distinct tumor specimens each. The median time between serial samples was 9.5 months (range 1.6-63 months) in Cohort 1 and 13 months (range 11-15 months) in Cohort 2. Our analysis revealed that the genomic drivers were consistently retained across the specimens, with no acquisition of additional genomic alterations that would reclassify the tumors. In comparison, the transcriptional subtypes exhibited greater fluidity over time, with 4 out of 5 tumor pairs demonstrating a shift in their transcriptional profiles(\n \n Figure 2C\n \n ).\n
\n\n In comparison to NSCLC-like LCNECs,\n \n KMT2D\n \n genomic alterations were predominantly observed in SCLC-like LCNECs, whereas\n \n SMARCA4\n \n alterations were more prevalent in NSCLC-like LCNECs (\n \n Figure 2D\n \n ). Tumors with high tumor mutational burden (TMB-high, defined as at least 10 mutations per megabase) were found in 56.3% (n = 49) of NSCLC-like LCNECs and \u00a049.6% (n = 67) of SCLC-like LCNECs. PD-L1 positivity (at least 1%) exhibited similar rates across the three treatment groups. Mismatch repair deficiency, determined by immunohistochemistry, was identified in 2 (1.47%) \u00a0SCLC-like LCNECs (\n \n Figure 2E\n \n ) and was absent in both NSCLC-like and unclassified LCNECs. There was no difference in rwPFS and OS outcomes to front-line therapy among NSCLC-like, SCLC-like, and unclassified LCNECs (data not shown). Mutation analyses of key driver genes, including\n \n EGFR\n \n ,\n \n KRAS\n \n ,\n \n KEAP1\n \n ,\n \n RB1\n \n ,\n \n SMARCA4\n \n , and\n \n STK11\n \n , revealed that in Cohort 1, tumors harboring mutations in\n \n TP53\n \n or\n \n STK11\n \n were significantly associated with inferior overall survival compared to their wild-type counterparts (\n \n Supplementary Figure 8\n \n ). In contrast, no other genomic alterations demonstrated a statistically significant association with survival in this cohort. Similarly, in Cohort 2, none of the evaluated genomic alterations were significantly correlated with overall survival.\n
\n\n \n LCNEC tumors are enriched for the ASCL1 and YAP1 transcriptomic subtypes\n \n
\n\n SCLC have been classified into one of four transcriptional subtypes: ASCL1, NEUROD1, POU2F3, and YAP1 based on transcription factor (TF) expression levels\n \n 46, 47\n \n . We leveraged an independent cohort of 1704 SCLC from Caris Life Sciences for comparisons between SCLC and LCNECs (\n \n Supplementary Table S6\n \n ). Of the 1704, 1643 SCLC had WTS data. Hierarchical clustering of 1643 SCLC and 361 LCNECs showed enrichment of ASCL1 in SCLC-like LCNEC compared with both NSCLC-like (36.56% versus 23.81%, p=0.04) and unclassified (36.56% versus 11.12%, p<0.001,\n \n Figure 4A\n \n ). The YAP1 subtype was prevalent in about 26.19% of NSCLC-like LCNECs compared to 14.18% and 31.76% of SCLC-like and unclassified LCNECs, respectively. YAP1 LCNECs were characterized by enriched CD8 infiltration as previously described for YAP1-enriched SCLC tumors\n \n 48\n \n (\n \n Figure 4B\n \n ,\n \n Supplementary Figure 9\n \n ). SCLC-like LCNECs exhibit were enriched for\n \n STK11\n \n and\n \n KEAP1\n \n mutations and had a significantly higher TMB compared to SCLC (\n \n Figure 4C-D\n \n ). SCLC and SCLC-like LCNEC had significantly higher expression of DLL3 compared to unclassified LCNEC (SCLC vs unclassified LCNEC: median TPM=8.3 vs 3.9, p<0.0001; SCLC-like LCNEC vs unclassified LCNEC: median TPM=6.3 vs 3.9, p<0.05,\n \n Figure 4E\n \n ). There was no significant difference in DLL3 expression between NSCLC-like and SCLC-like LCNECs. However, DLL3 expression was significantly higher in SCLC compared to NSCLC-like LCNECs (median TPM=8.3 vs 5.7, p<0.05,\n \n Figure 4E\n \n \n )\n \n .\n
\n\n \n Fibrinogen-like protein 1 (FGL-1) and Serine peptidase inhibitor, Kazal type 1 (SPINK1) overexpression in NSCLC-like LCNECs suggest potential therapeutic vulnerabilities\n \n
\n\n De novo differential gene expression analysis between NSCLC-like and SCLC-like LCNECs in cohort 2 revealed substantial differences in the expression of 1061 genes (p<0.05, fold change>2,\n \n Figure 5A\n \n ). Among these, FGL1 and SPINK1 were markedly enriched in NSCLC-like LCNECs relative to SCLC-like LCNECs. This enrichment was characterized by ubiquitous overexpression in NSCLC-like LCNECs, in contrast to the low expression observed in other LCNEC subtypes and SCLC molecular subtypes (\n \n Figure 5B\n \n ). Notably, SFTPB, a hallmark gene of type II alveolar cells, exhibited elevated expression in both NSCLC-like and unclassified LCNECs, suggesting a potentially distinct cellular origin compared to SCLC-like tumors.\n
\n\n Unsupervised clustering analysis of all LCNECs, irrespective of their mutational status, delineated four distinct clusters (\n \n Supplementary Figure 10A\n \n ). Using the top differentially expressed genes between the two largest clusters (B and D,\n \n Supplementary Figure 10B\n \n ), hierarchical clustering of LCNEC samples, irrespective of molecular subtype, showed enrichment of FGL-1 and SPINK1 in cluster A\u00a0whereas FGL-1 expression was minimal in the other three LCNEC clusters (\n \n Supplementary Figure 10C\n \n ).\n
\n\n Given the prior identification of FGL1 as an MHC II-independent ligand for LAG3\n \n 49\n \n , we conducted further in-depth analysis to further explore this relationship within our dataset. Analysis of TCGA LUAD data\n \n 39\n \n indicated that FGL-1 expression was significantly elevated in NSCLC-like LCNEC (n=6) compared to NSCLC tumors (n=503,\n \n Supplementary Figure 11\n \n ). Additionally, RNA expression data from a previously published dataset of 75 LCNECs\n \n 11\n \n demonstrated significant enrichment of FGL-1 in NSCLC-like LCNECs (n=19) compared to LCNEC SCLC-like tumors (n=16,\n \n Figure 5C\n \n ).\n
\n\n Examination of the DepMap dataset, encompassing 54 cell lines from various cancer types, revealed the highest protein expression of FGL-1 in the LCNEC cell line NCIH1155 (\n \n Figure 5D, Supplementary Table S7\n \n ). Furthermore, WTS data from Caris Life Sciences, spanning 125,632 tumor samples across 20 cancer types, indicated that median FGL-1 expression in NSCLC-like LCNECs was the third highest, following intrahepatic cholangiocarcinoma and hepatocellular carcinoma(\n \n Figure\n \n \n 5E\n \n ). SPINK1 shares 50% sequence homology with epidermal growth factor expression and has been shown to engage both EGFR and MAPK pathways\n \n 50, 51\n \n . As these are potentially targetable pathways, we leveraged the study by George et al.\n \n 11\n \n and showed enrichment of SPINK1 expression in NSCLC-like LCNECs compared to SCLC-like LCNECs (\n \n Figure 5C\n \n ). This observation suggests promising therapeutic strategies targeting NSCLC-like LCNECs through LAG3 and/or SPINK1 inhibition.\n
\n\n GSEA of Hallmark gene sets, a collection of genes curated to provide a comprehensive summary of key cellular pathways and functions\n \n 52\n \n , was performed on FGL-1 high versus low NSCLC-like LCNECs. GSEA revealed, among other pathways, significant enrichment of the KRAS signaling pathway in FGL-1 high NSCLC-like tumors compared to FGL-1 low ones, suggesting a potential cross-talk between KRAS signaling and FGL-1 (\n \n Figure\n \n \n 5F\n \n ). FGL-1 immunofluorescence staining was positive in 1 out of 2 (50%) NSCLC-like LCNEC, 0 out of 1 (0%) SCLC-like, 3 out of 3 (100%) NSCLC, and 0 out of 4 (0%) SCLC respectively(\n \n Figure\n \n \n 5G\n \n ).\n
\n\n \n Depletion of tumor-infiltrating lymphocytes in LCNECs compared to other lung cancer cohorts\n \n
\n\n Clinical evidence suggests that the blockade of immune checkpoint pathways, such as PD-1, is most efficacious in tumors that have already initiated an endogenous T-cell response. However, the observed therapeutic response in certain PD-L1\u2013negative tumors implies that the induction of tumor rejection via PD-1 blockade does not necessarily depend on the preexistence of an immune response, as conventionally indicated by the presence of tumor-infiltrating T cells\n \n 53\n \n . Given the potential for targeting alternative immune pathways through LAG-3 inhibition in NSCLC-like LCNECs, we investigated the level of immune infiltration in LCNEC tumors in comparison to SCLC and NSCLC. Employing computational pathology analysis, we quantified tumor-infiltrating lymphocytes (TILs) on H&E slides, following the methodology previously established by our group\n \n 43\n \n . Our analysis revealed that LCNECs (n=16) exhibited significantly lower TIL counts compared to lung adenocarcinomas (n=353), lung squamous cell carcinomas (n=63), and SCLC (n=122) (\n \n Figure 4H, Supplementary Table\n \n \n S8\n \n ). However, we were underpowered to perform analyses stratified by LCNEC molecular subtypes as there were 6 NSCLC-like, 4 SCLC-like, and 6 unclassified LCNECs with TIL assessments.\n
\n\n Integrating mutational subtype classification and RNA expression data leads us to propose a model that may be associated with unique response to therapies and can be prospectively tested in clinical trials (\n \n Figure\n \n \n 6\n \n )\n
\n\n Currently, there is no consensus on the optimal systemic treatment for LCNEC. The advent of immunotherapy has created new treatment paradigms, but comprehensive comparative analyses of first-line treatment regimens in pulmonary LCNEC \u00a0are limited, particularly due to the scarcity of clinical trial data for this patient population. This gap underscores the importance of real-world studies. Our study represents the most comprehensive characterization of LCNEC to date, encompassing detailed clinical cohorts, tumor DNA sequencing, WTS, and an evaluation of the tumor microenvironment. Our findings reveal comparable efficacy and toxicity among patients treated with chemotherapy, chemoimmunotherapy, and immunotherapy alone. Building on existing LCNEC subtyping research, we identify novel therapeutic targets that have the potential to expand the treatment landscape for this aggressive malignancy and we propose a framework to reclassify unclassified LCNECs.\n
\n\n Recent studies in the post frontline setting indicate that immunotherapy-based strategies may hold promise for patients with LCNEC. For instance, a retrospective study involving 23 patients treated with immunotherapy in advanced LCNEC reported a median PFS of 4.2 months\n \n 31\n \n . Another study including 17 patients treated with nivolumab in the second-line setting reported a median OS of 12.1 months and an overall response rate (ORR) of 29.4%, with a median PFS of 3.9 months\n \n 54\n \n . Our analysis did not reveal significant differences in overall survival outcomes across various treatment groups including immunotherapy-based regimens. There was a statistically significantly lower rwPFS for patients treated with chemotherapy compared to chemoimmunotherapy, although the difference was not clinically significant (median rwPFS difference of 0.3 months). In general, patients exhibited typically poor outcomes regardless of the systemic treatment regimen employed.\n
\n\n Genomic analysis from our study revealed that close to 6% of LCNEC possess targetable genomic alterations amenable to existing FDA-approved therapies for lung cancer, corroborating previous findings, and supporting the use of WES in this patient population at the time of diagnosis\n \n 7, 8\n \n . Previous studies have classified LCNEC into genomic subtypes paralleling either SCLC or NSCLC\n \n 3, 11, 14\n \n . In the vast majority of patients lacking targetable driver mutations, our results demonstrate that current systemic treatments do not significantly enhance clinical outcomes across these genomic subtypes. Notably, our data indicate that patients with NSCLC-like LCNECs exhibit elevated expression of FGL-1 and SPINK1 at the RNA level with variable protein expression of FGL-1, suggesting potential therapeutic benefits from targeting LAG-3 or SPINK1 pathways. This emphasizes the critical need for clinical trials investigating LAG-3 inhibitors or FGL-1 antibody-drug conjugates in this context. Furthermore, SPINK1-positive cancers could potentially benefit from interventions targeting downstream effectors such as the MAPK pathway\n \n 55-58\n \n . While our study primarily focuses on the molecular and clinical characterization of LCNEC, the functional significance of FGL-1 and SPINK1 remains unresolved. Future in vitro and in vivo studies are warranted to elucidate its role in tumor progression and immune evasion, which may further support its development as a therapeutic target.\n
\n\n SCLC-like and NSCLC-like LCNECs exhibit elevated DLL3 expression, suggesting that DLL3 antibody-drug conjugates or bispecific antibodies or T cell engagers may provide a promising therapeutic approach for targeting these tumors in a manner analogous to SCLC\n \n 59\n \n . \u00a0Ongoing clinical trials (NCT05882058 and NCT05619744) are actively investigating DLL3-targeted therapies in patients with LCNEC. We also utilized digital assessment of TILs to show a significant reduction of TILs in LCNECs compared to other lung cancer types. The low absolute levels of TILs in LCNECs could suggest that these tumors are either altered or cold immune tumors, potentially explaining the modest efficacy of immunotherapy-based approaches observed so far. Overall, these findings underscore the urgent requirement for innovative clinical trials and the exploration of novel therapeutic strategies to improve outcomes for patients with LCNEC.\n
\n\n A key contribution of our study is the resolution of previously unclassified LCNECs through integrative transcriptomic modeling. Utilizing a support vector machine (SVM) classifier trained on NSCLC-like and SCLC-like subtypes, we reclassified the majority of unclassified tumors into biologically coherent groups with high discriminatory performance (AUC = 0.98). This refined molecular taxonomy offers a critical framework for aligning LCNEC subtypes with targeted therapeutic strategies. Nonetheless, prospective validation in independent cohorts is warranted to confirm the robustness and clinical applicability of this reclassification schema.\n
\n\n Recent studies in SCLC have questioned the existence of a YAP1-defined subtype, as immunohistochemical and molecular profiling analyses failed to confirm its distinction within SCLC\n \n 60, 61\n \n . However, emerging evidence suggests that YAP1 plays a biologically significant role in pulmonary LCNEC. In our cohort, YAP1 subtypes were found in more than a quarter of NSCLC-like, SCLC-like, and unclassified LCNECs. A recent study also demonstrated that YAP1 expression defines two intrinsic subtypes of LCNEC with distinct molecular characteristics and therapeutic vulnerabilities.\n \n 62\n \n The\n \n YAP1-high\n \n subtype is associated with a\n \n mesenchymal and inflamed phenotype\n \n , frequent\n \n \n SMARCA4\n \n \n \n and\n \n CDKN2A/B\n \n genomic alterations\n \n , and vulnerability to\n \n MEK and AXL-targeted therapies\n \n . In contrast, the\n \n YAP1-low\n \n subtype shares genomic and transcriptomic similarities with SCLC, including\n \n \n RB1\n \n \n \n and\n \n TP53\n \n co-mutations\n \n , a\n \n neuroendocrine phenotype\n \n , and potential susceptibility to\n \n SCLC-directed therapies, such as DLL3 and CD56-targeting CAR-T therapies\n \n . These findings underscore the biological significance of YAP1 in LCNEC and highlight its potential role in guiding therapeutic strategies. Future research should further investigate whether YAP1 expression influences tumor plasticity, immune microenvironment interactions, and treatment response, particularly in the context of emerging therapies for LCNEC.\n
\n\n Our study has several limitations that warrant consideration. First, the retrospective design inherently introduces biases and limits the ability to draw causal inferences Second, the clinical data were incomplete, and follow-up intervals were not standardized, potentially introducing variability in the calculation of rwPFS. Moreover the retrospective nature of the study introduces variability in treatment decisions based on evolving clinical guidelines and physician discretion. While PD-L1 expression and TMB were assessed where available, additional factors such as histologic subtype, prior treatment history, and disease burden also influenced therapy initiation. However, due to the lack of standardized prospective selection criteria, we cannot fully account for all variables that may have guided immunotherapy decisions. Overall, these limitations reflect the inherent heterogeneity of real-world data collection and may affect the robustness of rwPFS estimates. As such, we emphasize the need for prospective studies to validate and build upon our findings, thereby enhancing their translational potential. Third, in Cohort 1, the use of variable targeted sequencing platforms to identify mutations and copy number alterations posed a challenge. Differences in gene composition and baitset coverage across these platforms limited the comprehensiveness of genomic analyses. To overcome this limitation, we included Cohort 2, which underwent systematic and uniform genomic and transcriptomic characterization, thereby providing a more consistent and robust dataset of equivalent size. Fourth, matched germline testing was not uniformly available across sequencing platforms, and this limitation was further compounded by variability in germline filtering algorithms.\n \n These factors may influence the interpretation of mutational drivers and TMB estimates.\n \n While this may have led to occasional false-positive somatic calls, it reflects current practice across CLIA-certified platforms, which largely rely on tumor-only sequencing and population databases for germline exclusion. Fifth, the study lacked detailed information on the specific biopsy methods used for diagnosing LCNEC. This limitation may impact the interpretation of diagnostic challenges associated with small biopsy specimens; however, all cases were reviewed and confirmed by board-certified thoracic pathologists. Sixth, our study is limited by the under-representation of non-White populations, which reduces the generalizability of our findings and limits the statistical power to identify genomic and survival associations within these subgroups. This highlights the critical need for more inclusive research to ensure findings are applicable across diverse patient populations.\u00a0\u00a0Moreover, in Cohort 1,\u00a0LCNEC\u00a0diagnoses were made\u00a0by local pathologists without centralized pathological review, raising the possibility of case\u00a0overestimation\u00a0and inadvertent inclusion\u00a0of\u00a0tumors with mixed histologic features. However, a validation study conducted by Caris Life Sciences on a subset of samples initially classified as LCNEC revealed that 95% of these cases were confirmed upon central pathological review, supporting the accuracy of the classifications.\n \n Additionally, the use of FFPE material introduces the potential for sequencing artifacts, although standardized quality control measures were employed to minimize this risk.\n \n Finally, given the rarity of LCNEC, we extended the study period to accumulate a sufficiently large sample size. This approach, while necessary, may have introduced variability in the reliability of estimates when comparing treatment strategies due to temporal trends. To account for this, sensitivity analyses stratified by treatment year were conducted to evaluate potential temporal influences.\n
\n\n Despite these limitations, our analyses consistently revealed similar clinical outcomes across the two distinct cohorts, underscoring the robustness of our findings. The complementary nature of these datasets allowed us to capture a broader spectrum of clinical and molecular characteristics of LCNEC, leveraging the unique strengths of each cohort to provide a more comprehensive understanding of this rare malignancy. By analyzing the cohorts independently for most outcomes, we effectively mitigated the confounding effects of methodological differences, ensuring the integrity of our results. Collectively, the two cohorts represent the most extensive and integrative analysis of LCNEC to date, offering critical insights into its genomic landscapes and clinical behavior, and paving the way for future research and therapeutic innovations.\n
\n\n In conclusion, while the systemic treatment of LCNEC remains an area of unmet clinical need, our study advances the field by offering the most extensive and integrative analysis of this malignancy to date. Through meticulous examination of clinical outcomes, genomic landscapes, and the tumor microenvironment, we illuminate the complexity of LCNEC and highlight critical avenues for therapeutic intervention. Our findings challenge the efficacy of current systemic therapies across LCNEC subtypes, underscoring the urgent need for novel treatment strategies tailored to the molecular underpinnings of this aggressive cancer. The identification of actionable targets such as FGL-1, SPINK1, and DLL3 opens new frontiers in LCNEC therapy, with ongoing clinical trials poised to transform the treatment landscape. However, the modest responses to immunotherapy observed in our study and the paucity of TILs in LCNEC tumors suggest that future efforts must also focus on overcoming immune evasion mechanisms. To truly shift the paradigm in LCNEC treatment, it will be imperative to conduct robust, prospective clinical trials that not only evaluate the efficacy of emerging therapies but also ensure inclusivity across diverse patient populations.\n
\n\n Table 1 is available in the Supplementary Files section\n
\n\n Supplementary Figure 1. CONSORT diagram for cohorts 1 and 2. WES: whole exome sequencing\n
\n \n\n Supplementary Figure 2: \u00a0Overall survival of LCNEC subtypes treated with respective NSCLC or SCLC regimens\n
\n \n\n Supplementary Figure 3: Gene set enrichment analysis highlighting the top pathways enriched in patients treated with ICI-based regimens, comparing those with real-world overall survival (rwOS) greater than 20 months to those with rwOS less than 20 months. * (\n \n p\n \n <0.05) and *** (\n \n p\n \n <0.001)\n
\n \n\n Supplementary Figure 4: Kaplan Meier plots comparing overall survival probability across (A) TMB high (>19muts/Mb) and low (\u226419 muts/Mb) and (B) PD-L1 positive (IHC-22c3\u22651) and negative (IHC-22c3<1) groups in cohort 2.\n
\n \n\n Supplementary Figure 5: Kaplan-Meier plots comparing time on treatment across 1\n \n st\n \n -line systemic therapies (chemotherapy, chemoimmunotherapy, and immunotherapy) in cohort 2.\n
\n \n\n Supplementary Figure 6: Tornado plot depicting treatment-related adverse events (trAEs) for patients treated with first-line systemic therapies. (A) Chemotherapy, (B) chemoimmunotherapy, (C) immunotherapy. Any grade and \u00b3grade 3 trAEs are shown on right and left, respectively.\n
\n \n\n Supplementary Figure 7: Kaplan-Meier curves depicting overall survival by molecular subtype (NSCLC-like, SCLC-like, and Unclassified) among patients receiving first-line systemic therapy in cohort 2.\n
\n \n\n Supplementary Figure 8: Association of Driver Mutations with Overall Survival in LCNEC Patients from Cohort 1\n
\n \n\n Supplementary Figure 9: Gene expression of immune cell populations in LCNEC samples across different SCLC transcriptional subtypes. *** (\n \n p\n \n <0.001) and **** (\n \n p\n \n <0.0001) represent significant associations when comparing expression level of an immune cell population between \u201cA\u201d and \u201cY\u201d subtypes. A: ASCL1; N: NEUROD1; P: POU2F3; Y: YAP1; TF neg: TF negative.\n
\n \n\n Supplementary Figure 10: Transcriptomic clustering of LCNEC samples. (A) UMAP plot demonstrating unsupervised clustering of all LCNEC subgroups (Cohort 2) using whole transcriptomic data. (B) Volcano plot showing differentially expressed genes between the predominant clusters B and D. (C) Heatmap of the top differentially expressed genes across the four identified clusters (A-D).\n
\n \n\n Supplementary Figure 11: Comparison of FGL-1 log-transformed gene expression across NSCLC (n=503), NSCLC-like LCNEC (n=6), and other LCNECs (n=8) from the Cancer Genomic Atlas lung adenocarcinoma cohort (TCGA-LUAD).\n
\n \n\n Supplementary Table 1: Contributing Institutions for Clinico-Genomic Data in Cohort 1.\nSupplementary Table 2: Clinical and Pathologic Patient-Level Data from Cohort 1\nSupplementary Table 3: Summary of Clinical and Pathologic Data from Cohort 1\nSupplementary Table 4: Treatment-Related Adverse Events Across Different First-Line Treatment Options for Patients in Cohort 1\nSupplementary Table 5: Genomic Data Involving Main Driver Genes in Cohort 1\nSupplementary Table 6: Demographic Data of Different SCLC Transcriptional Subtypes and SCLC-Like LCNECs\nSupplementary Table 7: Protein expression of FGL-1 across different cell lines from the DepMap project.\nSupplementary Table 8: Tumor-Infiltrating Lymphocyte Counts (per mm\u00b2) for Samples Analyzed in the DFCI Cohort\n
\n \n\n Efficiently fabricating a cavity that can achieve strong interactions between terahertz waves and matter would allow researchers to exploit the intrinsic properties due to the long wavelength in the terahertz waveband. This paper presents a terahertz detector embedded in a hybrid Tamm cavity with an extremely narrow response bandwidth and an adjustable resonant frequency. A new record has been reached: a\n \n Q\n \n value of 1017 and a bandwidth of only 469 MHz for terahertz direct detection. The hybrid Tamm-cavity detector consists of an Si/air distributed Bragg reflector (DBR), an Nb\n \n 5\n \n N\n \n 6\n \n microbolometer detector on the substrate, and a metal reflector. This device enables very strong light\u2013matter coupling by the detector with an extremely confined photonic mode compared to a Fabry\u2013P\u00e9rot resonator detector at terahertz frequencies. Ingeniously, the substrate of the detector is used as the defect layer of the hybrid cavity. The resonant frequency can then be controlled by adjusting the thickness of the substrate cavity. The detector and DBR cavity are fabricated separately, and a large pixel-array detector can be realized by a very simple assembly process. This versatile structure can be used as a platform for preparing high-performance terahertz devices and is a breakthrough in the study of the strong interactions between terahertz waves and matter.\n
\n\n In recent years, thanks to the development of terahertz sources [1\u20134], detectors [5\u201310], modulators [11\u201313], and other devices [14, 15], many remarkable results in imaging [16\u201319], molecular gas detection [20, 21], and communication [22\u201325] in terahertz science and technology [26] have been achieved. One of the most important scientific issues for the development of these devices is how to enhance the strong interactions between these devices and terahertz signals to achieve efficient coupled input or output [27\u201334]. Optical resonators and nanocavities, such as Fabry\u2013P\u00e9rot (FP) interferometers [35, 36], microcavities [37\u201340], photonic crystals [41, 42], and planar resonators [43, 44], are powerful tools for producing strong interactions [45]. In particular, enhanced structures based on a Tamm cavity [46\u201350] with a built-in distributed Bragg reflector (DBR) are commonly used in photodetectors [51\u201354] and lasers [55\u201357]. Since Tamm cavities in the optical band based on a metal DBR were first proposed by Kaliteevski et al. in 2007 [47], they have had an important role in enhancing the interaction between material and light to realize high Q and tunable devices [58\u201360]. These excellent properties are necessary for terahertz-band devices. However, the functional components integrated with the DBR in the terahertz spectral range have rarely been reported in the literature. The main difficulty is that the smallest planar features are of the order of \u03bb/4nr\u2009\u2248\u200910 \u00b5m, where nr is the refractive index of the dielectric. Terahertz wavelengths are in the range 10 to 1000 \u00b5m, so depositing thin films of an optical dielectric, which is commonly used for optical devices, cannot be used to construct microcavities, which is necessary for the DBR structures used in terahertz devices.\n
\n\n Note that the features of terahertz microcavities are of the order of the thickness of the substrates of the terahertz devices, so researchers have also begun to use the substrates as FP cavities when constructing electromagnetic confinement devices [61\u201363]. To obtain a tunable terahertz device, the length of the cavity can be changed with an electronically controlled displacement platform or a microelectromechanical system (MEMS) with micrometer precision [64, 65]. These FP cavities have greatly facilitated the development of terahertz components that are continuously frequency adjustable and have a wideband response [66, 67]. Obviously, the performance of these devices can be further improved if the Tamm cavity is used in optical wavebands [68, 69]. An optical DBR cavity composed of multiple layers of Si and air was fabricated by an ingenious and complex process. It has a very high refractive index contrast and very high Q value. This device has been used in various high-performance lasers [70\u201372]. Obviously, the difficulties in depositing dielectric thin films and lateral etching are hard to address at the micro-scale in the terahertz band. Therefore, a detector or source integrated with a Tamm cavity in the terahertz band has not been reported experimentally.\n
\n\n Here we propose a terahertz detector with a Tamm cavity consisting of a DBR with silicon/air layers, a microbolometer detector, and a reflective mirror. The air and silicon dielectric layers are formed by deep silicon etching in the same high-resistivity float-zone (HRFZ) Si wafer chip. The silicon chip containing the air cavity is stacked and bonded with a photoresist to form a DBR with multiple Si/air layers. This is bonded to a detector chip containing a FP substrate cavity to form a Tamm cavity. The resonant modes of the detector can be tuned by controlling the thickness of the substrate of the microbolometer detector chip. The DBR cavity and detector are fabricated separately, which reduces the complexity of design and fabrication. Large-scale fabrication can be achieved by simple MEMS process and bonding assembly, which is also compatible with other terahertz devices. This approach overcomes the drawbacks that millimeter-scale multilayers are hard to control precisely and integrate with terahertz devices [73].\n
\n\n We demonstrated experimentally that the Tamm-cavity coupled detector has high Q and very narrowband optical responsivity in the terahertz band. It provides a general operating platform for other devices that need enhanced interactions between matter and a terahertz wave. In particular, it can be used to study the electronics and optoelectronics of 2D materials [74\u201377] or to fabricate terahertz lasers, terahertz detectors, and other high-performance functional devices.\n
\n\n As shown in Fig.\n \n 1\n \n (a), a Tamm-cavity detector contains a DBR with three Si/air layers, a terahertz detector, and a silicon substrate with a reflective mirror. The detector is between the Si/air DBR cavity and the optical FP cavity formed by the HRFZ-Si substrate of the detector. The detector was a Nb\n \n 5\n \n N\n \n 6\n \n microbolometer. The substrate of the detector acts as a defect layer in the entire one-dimensional photonic crystal cavity and controls the electric field intensity at the detector. The detector can, in principle, be any electric field detector. It acts as a near-field terahertz probe [\n \n 78\n \n ]. Here, we used a Nb\n \n 5\n \n N\n \n 6\n \n microbolometer whose voltage responsivity is proportional to the electric field intensity. The key to designing a Tamm-cavity detector is realizing the DBR cavity in the terahertz band and ensuring it is compatible with the detector integration process. A significant advantage of the hybrid cavity is that the detector and the DBR can be prepared separately, making the design and fabrication simple. The HRFZ-Si is, undoubtedly, the best choice for constructing this DBR cavity due to the small losses in the terahertz band [\n \n 79\n \n ,\n \n 80\n \n ] and its compatibility with standard silicon MEMS processes. The air layer can be obtained by Si etching using a square opening in the same chip. More importantly, the DBR cavity is made of HRFZ-Si and air because of the large refractive index contrast between silicon and air (\n \n n\n \n \n si\n \n = 3.4 and\n \n n\n \n \n air\n \n = 1). The thickness of the substrate of the detector chip was determined primarily according to the desired detection band, after which the thicknesses of the silicon and air layers in the DBR were optimized. To detect electromagnetic waves around 0.65 THz, according to the resonant conditions in the FP cavity, the thickness of the detector chip was 510 \u00b5m, which can support this resonant mode. According to design theory for a DBR, we let\n \n n\n \n \n H\n \n \n d\n \n \n H\n \n =\n \n n\n \n \n L\n \n \n d\n \n \n L\n \n =\u2009\u03bb/4, where \u03bb is the target resonant wavelength,\n \n n\n \n \n H\n \n = 3.4147 is the refractive index of silicon,\n \n d\n \n \n H\n \n = 33 \u00b5m is the thickness of the silicon layer,\n \n n\n \n \n L\n \n = 1 is the refractive index of air, and\n \n d\n \n \n L\n \n = 115 \u00b5m is the thickness of the air layer.\n
\n\n By using the electromagnetic wave transfer-matrix method (TMM) of multilayer media (Supplementary note S1), the reflection spectrum of a DBR cavity with three Si/air layers was calculated, as shown by the gray solid line in Fig.\n \n 1\n \n (b). The DBR cavity reflects up to 100% in the band from 0.5 to 0.8 THz. A spectrally wide stop band filter was realized just by three Si/air layers, which benefits from the high refractive index contrast between HRFZ-Si and air. The reduced period number also reduces fabrication and micro-assembly errors.\n
\n\n Figure\n \n 1\n \n (c) shows the spatial distribution of the refractive index of each layer of material of the final Tamm cavity shown in Fig.\n \n 1\n \n (a). The optical Tamm states [\n \n 46\n \n ,\n \n 47\n \n ] occur in this three-layer DBR\u2013substrate\u2013Au hybrid cavity under certain conditions for a one-dimensional photonic crystal. The detector substrate acts as a defect layer in this photonic crystal cavity, and the phase changes by\n \n \n \\({\\text{4}}\\pi {n_{{\\text{Si}}}}{d_{{\\text{cavity}}}}/\\lambda\\)\n \n \n in a round trip. The thickness of the substrate should satisfy the following condition if Tamm plasmon (TP) resonance occurs in the entire hybrid cavity [\n \n 47\n \n ,\n \n 56\n \n ]:\n
\n\n
\n\n The phase condition at the resonant point of the hybrid cavity fully conforms to the conditions for optical Tamm states (Supplementary note S2), indicating that there is also an \u201coptical band-like\u201d TP cavity in the terahertz band. This is special and different because it not only satisfies the conditions for a TP but also maximizes the electric field at the position of the detector [i.e.,\n \n E\n \n \n d\n \n in Fig.\n \n 1\n \n (a)]. The thickness of the detector substrate in the cavity must also satisfy the following condition:\n
\n\n
\n\n where\n \n N\n \n is the resonant mode of the cavity. This is exactly the condition for enhancement of the coherence of the electric field in the detector substrate cavity only, that is, the thickness of the detector substrate determines the resonant frequency of the entire hybrid cavity. Thus, the resonant modes of this TP hybrid cavity are mainly determined by the defect layer (i.e.,\n \n d\n \n \n cavity\n \n , the thickness of the microbolometer substrate here) [\n \n 56\n \n ]. This finding is verified by the following calculated results.\n
\n\n The blue line in Fig.\n \n 1\n \n (b) is the reflection coefficient of the entire Tamm cavity with\n \n d\n \n \n cavity\n \n = 510 \u00b5m calculated by the TMM (Supplementary note S1). Multiple resonant extremum points were formed due to the attachment of the detector chip to the substrate FP cavity.\n \n N\n \n is the resonant mode, as determined by Eq.\u00a0(\n \n 2\n \n ). At 0.4792 THz, 91% of the energy was confined to the cavity and the\n \n Q\n \n value was up to 4649. The full width at half maximum (FWHM) was only 102 MHz. The electric field distributions were calculated at the center of the entire Tamm cavity [the purple dashed line in the direction of the\n \n y\n \n -axis in Fig.\n \n 1\n \n (a)] for points\n \n A\n \n (0.4002 THz),\n \n B\n \n (0.4500 THz), and\n \n C\n \n (0.4792 THz), as shown in Figs.\n \n 1\n \n (d), 1(e), and 1(f). The electric field intensity at the detector |\n \n E\n \n \n d\n \n |\n \n 2\n \n is shown as the black dashed line. In the calculation, the electric field intensity of the incident terahertz plane wave |\n \n E\n \n \n i\n \n | was set as 1, and |\n \n E\n \n |\n \n 2\n \n /|\n \n E\n \n \n i\n \n |\n \n 2\n \n represents the enhancement factor of the electric field intensity at the purple dashed line in Fig.\n \n 1\n \n (a). At point\n \n A\n \n , since the reflection coefficient was 0.5 and the electric field at the detector was not at a node of the standing wave, the enhancement factor was only 37.5. At point\n \n B\n \n , the electric field intensity at the detector was 0 due to total reflection of the incident terahertz wave. At point\n \n C\n \n , the reflection coefficient was only 0.3 and the electric field at the detector was at a node of a standing wave in the entire cavity, so the enhancement factor was a maximum of up to 500. The electromagnetic field oscillated in the substrate FP cavity, and the energy was confined to the cavity and eventually absorbed by the detector, greatly enhancing the response sensitivity of the detector. This kind of hybrid cavity significantly enhances the interaction between terahertz waves and the sensor. There was a significant difference in the electric field intensity at different locations, so it is important to precisely control the thickness of each layer of the media during device preparation. Fortunately, controlling the thickness at the micron level in deep silicon etching of MEMS is no longer a problem.\n
\n\n Figure\n \n 1\n \n (g) shows the calculated enhancement of the electric field intensity at the terahertz detector with\n \n d\n \n \n \n cavity\n \n \n = 510 \u00b5m in the DBR cavity for three cases: (1) only the substrate FP cavity, (2) the substrate cavity with one Si/air layer in the DBR cavity, and (3) the substrate cavity with three Si/air layers in the DBR cavity. At 0.479 THz with no DBR cavity, |\n \n E\n \n \n d\n \n |\n \n 2\n \n /|\n \n E\n \n \n i\n \n |\n \n 2\n \n was only 4 and the FWHM was 15,767 MHz. When the DBR cavity had one Si/air layer, |\n \n E\n \n \n d\n \n |\n \n 2\n \n /|\n \n E\n \n \n i\n \n |\n \n 2\n \n increased to 25 and the FWHM was 2124 MHz. With three Si/air layers, |\n \n E\n \n \n d\n \n |\n \n 2\n \n /|\n \n E\n \n \n i\n \n |\n \n 2\n \n was 484 and the FWHM was only 77 MHz. Notably, the resonant frequencies were consistent with the resonant frequencies of the structure with only the substrate FP cavity. That is, the thickness of the detector chip determines the resonant frequencies of the entire cavity, and this is consistent with the previous analysis. The corresponding reflectance of these three cavities was also calculated, and to further illustrate these characteristics, we also calculated the reflection for a three-layer Si/air DBR Tamm cavity for different substrate thicknesses (Supplementary note S1). The resonant modes shifted to lower frequencies as the substrate thickness was increased. This is the so-called redshift, which is consistent with the case with the substrate cavity only.\n
\n\n The electric field at the position of the detector (\n \n E\n \n \n d\n \n ) is the best figure of merit. Figure\n \n 1\n \n (h) shows the calculated\n \n E\n \n \n d\n \n in the Tamm cavity for different thicknesses of the detector substrate (\n \n d\n \n \n cavity\n \n ) for frequencies from 0.25 to 0.60 THz.\n \n E\n \n \n d\n \n increased significantly at the resonance point, and the resonance was strong in accordance with Eq.\u00a0(\n \n 2\n \n ). Prominently, there are five cavity modes, corresponding to\n \n N\n \n =\u20092, 3, 4, 5, and 6 in Eq.\u00a0(\n \n 2\n \n ), as indicated by the colored dashed lines. The horizontal white dashed line indicates\n \n E\n \n \n d\n \n for the cavity at\n \n d\n \n \n cavity\n \n = 510 \u00b5m, and the white dots are\n \n E\n \n \n d\n \n at\n \n A\n \n ,\n \n B\n \n , and\n \n C\n \n . Clearly, the resonant modes of the cavity can be adjusted by changing the substrate thickness. The resonance shifted to a higher frequency when\n \n d\n \n \n cavity\n \n was decreased, which is a blueshift, and the resonance frequencies overlapped, which means that the low resonance mode of a thin substrate overlapped with the high resonance mode of a thick substrate, as shown in Fig.\n \n 1\n \n (i).\n
\n\n When designing this kind of Tamm cavity, the target resonance points can first be calculated directly from the corresponding resonant modes of the detector chip only, and then the DBR cavity can be designed to enhance the electric field at the detector without changing the resonant frequency points of the entire structure. Moreover, the resonant bandwidth can be narrowed. The detector chip and dielectric DBR were designed separately and then assembled together, which is convenient for design and fabrication. This all-silicon hybrid cavity can be used as a general platform for terahertz sources, detectors, and other functional devices. It is, possibly, the ultimate solution for achieving strong interactions between terahertz electromagnetic waves and matter.\n
\n\n
\n\n To realize the Tamm-cavity detector designed above, a 6-inch HRFZ-Si wafer (\u03c1\u2009>\u200910,000 \u2126.cm) was thinned to 148 \u00b5m. An array of air cavities with a 33-\u00b5m top layer of silicon and a 115-\u00b5m bottom layer of air was formed by deep silicon etching of the same wafer. The unit size was 9 mm \u00d7 9 mm. Considering the spot size of an incident terahertz wave, the area of the opening in a silicon pixel was 5 mm \u00d7 5 mm, as shown in Fig.\n \n 2\n \n (a). We cut the wafer into many single-pixel Si/air layer blocks, which were then stacked to form a multilayer DBR cavity using a photoresist, as shown in Fig.\n \n 2\n \n (a). The thickness of the photoresist distributed around the silicon support leg was about 1 \u00b5m, which has little influence on the entire Tamm cavity. For the detector chip, we used a 510-\u00b5m HRFZ-Si substrate. The Nb\n \n 5\n \n N\n \n 6\n \n microbolometer was micro-fabricated through magnetron sputtering, lithography, air-bridge etching, and other micro-processing techniques. Figure\n \n 2\n \n (d) is an optical photograph of the finished detector chip. The DBR chip and the detector chip were micro-assembled together by a photoresist to form the Tamm cavity. Figure\n \n 2\n \n (b) is a side view of the Tamm-cavity detector. To read out the response voltage of the detector, the entire package was fixed to a printed circuit board [Fig.\n \n 2\n \n (c)]. The preparation of the Tamm-cavity detector is illustrated in detail in Supplementary note S3.\n
\n\n Preparing the detector was simple. The multilayer DBR was obtained by stacking Si/air layer blocks, which were from the same wafer, by deep silicon etching as a MEMS process. Furthermore, the detector chips and the DBR chips were prepared separately and can be assembled or disassembled. Fabricating this kind of Tamm cavity is compatible with the fabrication of other terahertz functional devices, and thus, it provides an excellent platform for enhancing the interactions between terahertz waves and matter. In particular, there are many potential applications due to the strong electromagnetic coupling between the terahertz waves and the two-dimensional material.\n
\n\n
\n\n To verify the design of the proposed Tamm-cavity terahertz detector, three cavities coupled to a Nb\n \n 5\n \n N\n \n 6\n \n microbolometer detector were prepared: (1) only the FP substrate cavity (without a DBR), (2) a one-layer Si/air DBR, and (3) a three-layer Si/air DBR. Figure\n \n 3\n \n shows the measured optical voltage responsivity of these three detectors. The measurement setup and method are described in Section VI.\n
\n\n Figure\n \n 3\n \n (a) shows the optical voltage responsivity of the detector without a DBR. As discussed above, due to the FP effect in the substrate cavity, the response had two resonant peaks at 0.40 and 0.48 THz. The FWHM at 0.48 THz was 20.8 GHz, and the\n \n Q\n \n value was 23, as calculated by Lorentz fitting. Figure\n \n 3\n \n (b) shows the optical voltage responsivity when the detector had a one-layer DBR. It also resonated at about 0.40 and 0.48 THz. There was a twofold increase in the optical voltage responsivity at 0.40 THz and a 1.5- fold increase at 0.48 THz. The FWHM was 3.89 GHz, and the\n \n Q\n \n value was 121 at 0.48 THz, both of which had improved by 5.3 times compared with the substrate FP cavity only. Figure\n \n 3\n \n (c) shows the optical voltage responsivity when the detector had a three-layer DBR. This detector also resonated at about 0.40 and 0.48 THz. Based on Lorentz fitting, the response bandwidth and\n \n Q\n \n value reached 532 MHz and 1017, respectively, as shown in the inset. Note that the optical voltage responsivity of the detector was only a little higher than that of the one-layer DBR. This is because the dielectric loss increased with the number of DBR layers. The voltage responses were almost zero at non-resonant frequencies, which verifies the perfect filtering characteristics of the cavity. Table\n \n 1\n \n compares the measured and calculated\n \n Q\n \n values and FWHM values at the resonant modes for the three detectors. The positions of the measured resonance peaks are almost the same as the calculated peaks. There was a slight deviation in frequency, mainly caused by errors when tuning the thickness of the DBR layers. The calculated results are for an ideal situation that neglects absorption by the layers. As shown in Fig.\n \n 4\n \n , the deviation of the\n \n Q\n \n values increased as the number of layers increased. The theoretical\n \n Q\n \n value reached 6215, but the measured value was only 1017 for the detector with a three-layer DBR. Obviously, the measured results did not achieve the quality of the theoretical values, mainly because the dielectric losses were not taken into consideration in the calculations. To analyze the effects of dielectric losses, the reflectance of the Tamm cavity was calculated with dielectric constants of the HRFZ-Si with different imaginary parts (Supplementary note 4). The calculations showed that the Tamm cavity is extremely sensitive to the refractive index, and the resonant frequency and reflectance have a strong dependence on the permittivities of the HRFZ-Si and the metal. Moreover, the terahertz source was tuned to a resolution of 0.18 GHz in the experiment and the frequency interval used in the simulation was 0.1 GHz, which may also be why the measured\n \n Q\n \n value is not as high as the calculated value.\n
\n\n The few Si/air photonic crystal layers in this Tamm cavity significantly increased the interaction between an incident terahertz wave and the sensor, but hardly changed the resonant modes of the detector. These results are consistent with our previous simulation analysis. The findings are one of the subtleties of a Tamm cavity. To the best of our knowledge, this is the narrowest bandwidth for a terahertz detector that has been reported.\n
\n\n
\n\n
\n\n
\n| \n \n DBR\n \n\n pair\n \n | \n \n \n Resonant frequency (THz)\n \n | \n \n \n FWHM\n \n\n (MHz)\n \n | \n ||
|---|---|---|---|---|
| \n \n Cal.\n \n | \n \n \n Mea.\n \n | \n \n \n Cal.\n \n | \n \n \n Mea.\n \n | \n |
| \n \n 0\n \n | \n \n \n 0.473\n \n | \n \n \n 0.474\n \n | \n \n \n 15767\n \n | \n \n \n 20609\n \n | \n
| \n \n \n 1\n \n \n | \n \n \n \n 0.478\n \n \n | \n \n \n \n 0.472\n \n \n | \n \n \n \n 2124\n \n \n | \n \n \n \n 3901\n \n \n | \n
| \n \n \n 3\n \n \n | \n \n \n \n 0.479\n \n \n | \n \n \n \n 0.476\n \n \n | \n \n \n \n 77\n \n \n | \n \n \n \n 469\n \n \n | \n
\n
\n\n To illustrate that the resonant modes of the detectors with a Tamm cavity can be tuned by controlling the substrate thickness (\n \n d\n \n \n cavity\n \n ) of the detector chip, the substrate was mechanically thinned from 510 to 470 \u00b5m and then to 420 \u00b5m, and assembled with the same three Si/air layers. The measured optical voltage responsivities of these detectors are shown in Fig.\n \n 5\n \n (a). As\n \n d\n \n \n cavity\n \n decreased [black dashed arrow in Fig.\n \n 5\n \n (a)], the resonant frequency of the detector became higher, which is consistent with our calculated results [Fig.\n \n 1\n \n (i)]. Within the range of measured frequencies, the resonant frequency with a substrate thickness of 510 \u00b5m corresponds to the resonant modes\n \n N\n \n =\u20094 and 5 in the substrate FP cavity. The resonant frequency with a substrate thickness of 470 \u00b5m corresponds to the resonant mode\n \n N\n \n =\u20094 in the cavity. The resonant frequency with a substrate thickness of 420 \u00b5m corresponds to the resonant modes\n \n N\n \n =\u20093 and 4. The high resonant mode in the Tamm cavity with a thick substrate overlaps with the low resonant mode with a thin substrate.\n
\n\n To verify the accuracy of the above design and analysis, in particular to demonstrate the tunability and overlap of the cavity modes, the measured resonant frequencies of the Tamm-cavity detector with different values of\n \n d\n \n \n cavity\n \n [red circled crosses] and the calculated resonant frequencies [extracted from Fig.\n \n 1\n \n (h) and shown as blue lines] are plotted in Fig.\n \n 5\n \n (b). The measured and calculated values match very well. The black dashed arrow indicates the blueshifts, and the cyan region is where the cavity modes overlap.\n
\n\n Tunable detection can be realized with this Tamm cavity just by mechanically thinning the substrate and assembling the DBR. Moreover, the signals from other bands can be filtered out by the cavity detection system. Due to the non-negligible dielectric loss and absorption in the cavity, the\n \n Q\n \n value and bandwidth have both significantly deteriorated compared to the theoretical values [Fig.\n \n 1\n \n (i)]. This is the first report of a Tamm cavity in the terahertz band experimentally integrated with a terahertz detector. Thus, this is the first such device to achieve an ultra-high resonant\n \n Q\n \n value and extremely narrow response bandwidth.\n
\n\n
\n\n For the first time, we demonstrate a terahertz detector integrated with a Tamm cavity. The detector chip was embedded in a multilayer Si/air DBR. The interaction between the sensor film and the terahertz signals is greatly strengthened in this Tamm cavity, so that the detector has a high\n \n Q\n \n value (\n \n Q\n \n =\u2009895) and a very narrow bandwidth (FWHM\u2009=\u2009532 MHz). We can tune the frequency for a narrow bandwidth by adjusting\n \n d\n \n \n cavity\n \n . This is one of the best approaches for realizing a terahertz spectrometer. This kind of TP composite structure can be obtained by simple MEMS processing, stacking, and assembling without changing the original resonant frequency of the device, simplifying the design and use. This Tamm-cavity terahertz detector can be applied to improve the performance of terahertz devices, especially high-power sources, high-sensitivity detectors, and high-performance functional devices. It may also lead to a breakthrough in investigating the strong coupling between 2D materials and terahertz waves.\n
\n\n For the optical responsivity measurements, the detector under test was biased by a dc current (0.4 mA). The radiation was focused by two off-axis parabolic mirrors\u00a0to yield the largest possible signal from the detector. For the\u00a0alignment, a laser beam was used for rough adjustment, and then the detector was moved until its response voltage was a maximum. The photovoltage data were collected by a lock-in amplifier (SR830). The terahertz radiation source from 0.34 to 0.50 THz was obtained using multipliers in series (Agilent E8257D microwave source + VDI-AMC-336 + WR4.3X2 + WR2.2X2). The output power of the terahertz source was about 50 \u03bcW, which was varied with the\u00a0signal frequency. It was modulated using a 4-kHz TTL signal. A thermal sensor (3A-P-THz, Ophir) was used to calibrate the optical responsivity as\n \n R\n \n \n O\n \n =\n \n V\n \n \n \n /\n \n P\n \n , where\n \n P\n \n is the total incident power\u00a0and\n \n V\n \n is the output voltage of the detector. To make it easier to compare and explain the responses of detectors with different cavity structures, the entire power incident on the detector was simply assumed to be\u00a0effectively absorbed by the microbolometer. All measurements were performed in air at room temperature.\n
\n\n TMM and electromagnetic simulation software (FDTD) were applied to calculate the reflectivity spectra associated with the profiles of the intensity enhancement of the electric field. In the simulations, the permittivity of metal Au is described using the Drude model:\n
\n\n \n \n \\(\\varepsilon (\\omega )={\\varepsilon _\\infty }+\\frac{{\\omega _{p}^{2}}}{{i\\omega \\gamma - {\\omega ^2}}}\\)\n \n \n
\n\n where\n \n \n \\({\\varepsilon _\\infty }=4.8952\\)\n \n \n ,\n \n \n \\({\\omega _P}/2\\pi =2126.4\\)\n \n \n THz,\n \n \n \\(\\gamma /2\\pi =19.6\\)\n \n \n THz, and\n \n \n \\({n_M}=\\sqrt {\\varepsilon (\\omega )}\\)\n \n \n .\n
\n\n In the simulation, the permittivity of Au was from Ref. 28. The refractive indices of the other materials (e.g., HRFZ-Si) were also from Ref. 28.\n
\n\n Up to 50% of patients with non-small cell lung cancer (NSCLC) develop brain metastasis (BM), yet the study of BM genomics has been limited by tissue access, incomplete clinical data, and a lack of comparison with paired extracranial specimens. Here we report a cohort of 233 patients with resected and sequenced (MSK-IMPACT) NSCLC BM and comprehensive clinical data. With matched samples (47 primary tumor, 42 extracranial metastatic), we showed\n \n CDKN2A/B\n \n deletions and cell cycle pathway alterations to be enriched in the BM samples. Meaningful clinico-genomic correlations were noted, namely\n \n EGFR\n \n alterations in leptomeningeal disease (LMD) and\n \n MYC\n \n amplifications in multifocal regional brain progression. Patients who developed early LMD frequently had uncommon, multiple, and persistently detectable\n \n EGFR\n \n driver mutations. The distinct mutational patterns identified in BM specimens compared to other tissue sites suggest specific biologic underpinnings of intracranial progression.\n
\n\n Lung cancer is a devastating disease that remains a leading cause of cancer-associated death worldwide\n \n \n 1\n \n \n . Nearly 50% of non-small cell lung cancer (NSCLC) patients will eventually develop brain metastasis (BM)\n \n \n 2\n \n \n , which can be a significant cause of morbidity and mortality. The standard treatment approach for limited BM is resection or stereotactic radiosurgery (SRS), although some targeted agents showed promising activity in the central nervous system (CNS). Patients with BMs, however, are often excluded from clinical trials of novel targeted agents given the unpredictable relationship between systemic and CNS responses.\n
\n\n The paucity of high-quality BM samples has limited efforts to understand the fundamental biology of BM, tropism, and biomarkers of CNS progression. Prior studies have sought to understand the molecular characteristics of BM\n \n \n 3\n \n ,\n \n 4\n \n \n . Whole exome sequencing (WES) of a heterogeneous cohort of 86 BMs, including tumors from breast, lung, and other primary histologic types\n \n \n 5\n \n \n demonstrated branched evolution from the primary tumor to matched BMs while finding genetic homogeneity among spatially and temporally separated BMs. A more focused analysis of BM specimens from 73 NSCLC patients\n \n \n 6\n \n \n , revealed more frequent copy number alterations in\n \n CDKN2A/B, MYC, YAP1\n \n , and\n \n MMP13\n \n in BM specimens, as compared to a matched TCGA cohort. A recent larger-scale study evaluating 3,035 NSCLC patients (67 of whom had paired BM and primary tumor samples) using a hybrid capture-based comprehensive genomic profiling assay\n \n \n 7\n \n \n . They reported alterations in\n \n TP53\n \n ,\n \n KRAS\n \n ,\n \n CDKN2A\n \n ,\n \n STK11\n \n ,\n \n CDKN2B\n \n ,\n \n EGFR\n \n ,\n \n NKX2-1\n \n ,\n \n RB1\n \n ,\n \n MYC\n \n , and\n \n KEAP1\n \n enriched in the BM cohort compared to unmatched primary sites. Unfortunately, sparse clinical outcomes were reported.\n
\n\n In the current analysis, we expanded on this prior work through molecular profiling and detailed clinical annotation on a large, homogenous cohort of NSCLC BM specimens with both matched primary tumor (PT) and extracranial metastasis (EM) samples. The main objectives were to 1) describe the unique molecular features of NSCLC BM and 2) identify genomic biomarkers associated with intracranial disease progression.\n
\n\n The cohort consisted of 233 patients with a history of NSCLC BM who underwent therapeutic craniotomy at a single center from January 2010 until April 2021 (Fig.\n \n 1\n \n , A). Complete clinical information was collected for all patients, including baseline characteristics, prior systemic therapy, radiotherapy (RT), and intracranial-specific clinical outcomes. In addition to the NSCLC BM samples, 47 PT samples and 42 EM samples from the same patients were analyzed. EM samples included extracranial metastatic tissue and/or cerebrospinal fluid (CSF) samples. Sub-cohort analyses were performed on patients with lung adenocarcinoma patients (LUAD) only to remove histology as a potential confounding variable.\n
\n\n
\n\n To evaluate the temporal relationship between metastases, paired samples with BMs were grouped by the timing of collection: 1) Synchronous specimens with contemporaneous collection of both BM and EM/PT (within 60 days), 2) Intracranial progressors who had initial EM or PT collection followed by a craniotomy (>\u200960 days later), and 3) Intracranial presenters who had a therapeutic craniotomy at diagnosis followed by systemic progression and re-biopsy of an EM or PT specimen (>\u200960 days after craniotomy). We also identified patients who had both BM and CSF collected, and those who had multiple BM specimens (either multiple independent specimens or locally recurrent disease).\n
\n\n Brain-specific clinical outcomes were defined based on standard approaches clinical practice. Five distinct intracranial disease progression outcomes included: 1) no evidence of intracranial progression (POD) for at least 6 months of clinical follow up, 2) local progression (i.e., clear evidence of regrowth of the initially resected lesion with orthogonal imaging in the form of PET brain or perfusion to confirm active disease, as opposed to radionecrosis/treatment effect), 3) regional progression with a single new lesion (i.e., a new solitary lesion outside of the resected intracranial cavity), 4) multifocal regional progression (i.e., more than one lesion outside of the resected intracranial cavity) and 5) leptomeningeal disease (LMD) development (clear evidence confirmed by contrast-enhanced MRI brain with corroborating neurologic symptoms and/or positive cerebrospinal fluid (CSF) cytology). In cases of mixed POD patterns, patients with regional POD with possible synchronous local progression were considered regional POD, and patients with LMD with simultaneous concern for local or regional POD were considered to have LMD. Radiographic POD was called per the above clinical criteria by a board-certified neuroradiologist, often reviewed at a multidisciplinary tumor board, with the use of orthogonal imaging (contrast-enhanced MRI brain combined with perfusion, PET, delayed contrast, or spectroscopy) and pathologic data, and verified by documentation of a change in clinical management in subsequent medical or radiation oncology notes.\n
\n\n To assess whether underlying genomic profiles of PT in patients with LUAD are associated with BM development, LUAD PT samples from the paired analysis were compared to two distinct institutional LUAD PT cohorts\n \n \n 8\n \n \n . In this manner, three distinct cohorts of LUAD PT samples were formed: 1) patients who developed metastatic disease with intracranial involvement (PT LUAD BM+), 2) patients who developed only extracranial metastatic disease (PT LUAD BM-, EM+), and 3) patients who never developed metastatic disease of any sort (PT LUAD BM-, EM-).\n
\n\n All samples were evaluated using Memorial Sloan Kettering-Integrated Molecular Profiling of Actionable Cancer Targets (MSK-IMPACT) assay\n \n \n 9\n \n \n . This is a custom FDA-authorized next-generation sequencing (NGS)-based assay that uses a paired-sample analysis pipeline to identify somatic variants in the targeted exons with an average coverage depth of 700x. Tumor DNA was sequenced using one of four versions of MSK-IMPACT (IMPACT 341, IMPACT 410, IMPACT 468, or IMPACT 505). A matched normal sample (blood) was used in all cases. Genomic alterations were filtered for oncogenic events using OncoKB\n \n \n 10\n \n \n . Genes were consolidated into pathways using curated templates from the TCGA\n \n \n 11\n \n \n . Germline alterations were excluded from this analysis. Tumor mutational burden (TMB) was defined as the number of nonsynonymous mutations per megabase covered by the IMPACT panel. The fraction genome altered (FGA) was defined as the length of the sequenced genome with a log2 copy number variation (gain or loss)\u2009>\u20090.2 divided by the total size of the genome profiled for copy number. The FACETS (Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing) algorithm\n \n \n 12\n \n \n and the FACETS-suite package (\n \n \n https://github.com/mskcc/facets-suite\n \n \n \n \n ) were used to generate purity-corrected fraction of genome altered estimates and assess whole-genome duplication (WGD). Tumors were considered to have undergone WGD if at least 50% of their autosomal genome had a major copy number of 2 or more\n \n \n 13\n \n \n .\n
\n\n Baseline clinical characteristics and genomic alteration frequencies were compared using a two-sided Fisher\u2019s exact test. Continuous variables were compared using a Wilcoxon test. Kaplan-Meier curves were generated using overall survival (OS) and intracranial progression-free survival data (iPFS). Multiple testing correction was performed using the Benjamini-Hochberg method (q-value cutoff of 0.1). All analyses were performed using R v3.6.1.\n
\n\n \n Patient Cohort\n \n
\n Of 233 patients, 133 (57%) were female, and the median age was 67 (Table 1). Number of current and former smokers were\u00a057 (25%) and 129 (55%), respectively. At the time of BM presentation, the median Karnofsky Performance Status (KPS) was 80 (range 40-100), and 212 (91%) had neurological symptoms, the most common of which were altered\u00a0mental status, ataxia, and motor weakness. Many (122, 52%) patients were treatment-na\u00efve prior to BM resection; 110 (47%) received systemic therapy prior to craniotomy (median number of systemic therapy lines, 1 [range 1-8]). Of patients who received prior systemic therapy,\u00a013 (12%) had tyrosine-kinase inhibitor (TKI) treatment as their last line of therapy before BM resection. Few (16,\u00a07%) patients had brain-directed radiotherapy before BM resection.\n
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\n\n \n Comparison of Genomic Differences Between BM and non-BM Specimens\n \n
\n\n The TMB was significantly higher in the BM specimens compared to other extracranial metastases (BM median: 8.8, extracranial median: 5.8; p = 0.00766; Fig. 1, B). The FGA was also significantly higher in the BM samples compared to either extracranial metastases or the primary site tissue sample (BM vs. extracranial metastases: p = 2.765e-06; BM vs. primary: p = 2.273e-07; Fig. 1, B).\n
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\n\n When comparing mutations, copy-number alterations (CNAs, i.e., amplifications and deletions), and structural variants (i.e., rearrangement and fusions) between the BM, EM, and PT specimens,\n \n CDKN2A/B\n \n alterations were more common in the BM samples (34%) compared to PT (13%p = 0.003, q = 0.04; Fig. 1, C). A similar representation of alterations was identified in other cancer-related genes (e.g.,\n \n TP53\n \n ,\n \n KRAS\n \n , and\n \n EGFR\n \n )\n \n \n in the BM specimens as in the EM and PT.\n \n MYC\n \n alterations were not enriched in the BM specimens compared to the other two groups.\n
\n\n
\n\n At the pathway-level, cell cycle pathway alterations were more common in the BM specimens compared to the PT specimens (56% vs. 32%, p = 0.004, q = 0.041; Fig. 1, D). This effect was driven by differences in\n \n CDKN2A/B\n \n alterations\n \n 10\n \n . When genome-wide CNAs were examined among the three groups, a higher amount of chromosomal instability was observed in the BM samples compared to the other groups (Fig. 1, E).\n
\n\n
\n\n \n Stratified Analyses by Histologic Subtype\n \n
\n When we compared gene and pathway alterations seen in the BM specimens, stratified by histology (LUAD, squamous cell carcinoma [SCC], and other NSCLC) we noted more frequent\n \n KRAS\n \n and\n \n STK11\n \n alterations (\n \n KRAS\n \n : 35% vs 9%, p = 0. 009, q = 0.049;\n \n STK11\n \n : 22% vs 0%, p = 0.01, q = 0.049), as well as RTK-Ras pathway alterations in LUAD BM samples as compared to the SCC BM samples (86% vs 57%, p = 0.002, q = 0.022) (Suppl. Fig. 1, A).\n \n CDKN2A\n \n deletions were more frequent in SCC group as compared to LUAD group. Examination of genome-wide CNAs across histologies revealed markedly varying CNA profiles (Suppl. Fig. 1, B), consistent with previously reported results\n \n 14\n \n .\n
\n
\n\n Thus, to mitigate potential confounding from primary tumor histology, further analyses were performed exclusively in the LUAD cohort (180 of 233, 77%). One other sample was excluded from further genomic analyses due to a high degree of microsatellite instability (MSI). Therefore, 179 BM, 37 PT, and 34 EM samples were included in subsequent analyses. The overall makeup of this sub-cohort was like that of the entire cohort (Suppl. Table 1). Most (97, 54%) patients in the LUAD group were treatment-na\u00efve before BM resection. Similarly, FGA was significantly higher in LUAD BM compared to EM or PT (Supp. Fig. 1, C). Analogous to the total NSCLC cohort,\n \n CDKN2A/B\n \n alterations and cell cycle pathway alterations remained enriched in the BM LUAD group compared to PT and EM (\n \n CDKN2A/B\n \n : 31% vs 18, p = 0.004, q = 0.14; cell cycle pathway: 52% vs 27%, p = 0.007, q =0.072) (Suppl. Fig. 1, D).\n
\n\n
\n\n \n Genomic Biomarkers of CNS Tropism\n \n
\n\n To assess associations between PT genomic profiles and development of BM or EM, three distinct cohorts of LUAD PT samples were compared as outlined above: 1)\u00a0PT LUAD BM+ (N=32), 2)\u00a0PT LUAD BM-, EM+ (N=1549), and 3)\u00a0PT LUAD BM-, EM- (N=582)\n \n 8\n \n . Alterations in\n \n TP53\n \n ,\n \n MYC, SMARCA4, RB1\n \n ,\n \n ARID1A\n \n , and\n \n FOXA1\n \n were significantly enriched in PT specimens from patients who developed BM compared to those who did not have BM (Suppl. Fig. 1, E).\n \n NKX2-1\n \n alterations were also enhanced in both BM and EM cohorts compared to patients without metastatic disease. Additionally, we found MYC pathway alterations were enriched in patients with BM development compared to patients without metastatic disease, and TP53 and DNA damage repair pathway alterations were significantly enriched in those with BM and EM compared to patients without metastatic disease (Suppl. Fig. 1, E).\n
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\n\n \n Genomic Correlates of Paired Analysis\n \n
\n\n We next performed detailed pairwise comparisons of matched specimens, collected asynchronously or synchronously as described above. Interestingly, patients who had BM resection followed by EM or PT biopsy, and patients who had an initial tissue collected from EM/PT, and subsequently developed BM demonstrated many alterations unique to the BM specimens (Fig. 2, A, B).\n \n TP53\n \n (34%)\n \n and EGFR\n \n (27%), alterations were commonly identified alterations shared between BM and later PT/EM samples (Fig. 2, A). In contrast, alterations in\n \n TP53\n \n and\n \n KRAS\n \n were often present at diagnosis and retained in the PT/EM and BM specimens of patients who developed BM later in their clinical course (Suppl. Fig. 2, B). We likewise identified a subset of patients whose BM specimens had acquired private mutations in\n \n HLA-B\n \n (Fig. 2, B).\n
\n\n
\n\n When we compared matched pairs of BM and subsequently acquired CSF specimens, we noted that some BM specimens had unique alterations in\n \n TP53\n \n and\n \n KRAS\n \n , but there were notably very few unique mutations in the CSF specimens (Fig. 2, C). Among patients with simultaneous collection of BM and PT, most alterations were unique to BM or PT (Fig. 2, D); however, this finding is limited by sample size (N=2). We were able to identify a subset of nine patients in whom we had multiple BM specimens. Seven of these patients had two independent lesions resected. Interestingly and in contrast to the synchronous BM/PT specimens, we found high concordance in the genomic profiles in these BM-BM pairs (Fig. 2, E).\n
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\n\n Finally, we identified two patients with three specimens collected through their illness. Remarkably, in one patient who had a PT followed by a BM and then a separate PT sequenced, we identified numerous driver mutations, none of which were shared; by contrast, in another patient who had an EM, then BM, and then a PT biopsied, we noted shared driver mutations in\n \n EGFR\n \n and\n \n TP53\n \n (Fig. 2, F). \u00a0In this patient, there was evidence of acquired resistance in the BM specimen, identifying an\n \n EGFR\n \n T790M mutation in the BM specimen that was retained in the subsequent PT specimen.\n
\n\n \n \n
\n\n \n Genomic Correlates with Clinical Presentation and Prior Therapy\n \n
\n\n We next sought to compare the genomic profiles of BM from patients who: presented with BM as a progression event vs. at diagnosis; had multiple lesions vs. a single lesion; who had received prior chemotherapy vs. those that did not; and lastly, those that received TKI vs. those that did not. As expected,\n \n EGFR\n \n alterations were more common and\n \n KRAS\n \n mutations were less common among patients who received prior TKI treatment, but we did not identify any other statistically significant differences in driver mutations between groups (Fig. 3, A).\n
\n\n
\n\n \n Genomic Biomarkers of Intracranial Disease Progression\n \n
\n\n Most (101, 56%) LUAD patients with BM experienced intracranial POD following initial craniotomy and RT, most frequently as regional progression (54, 30%), followed by local progression (25, 14%), and LMD (20, 11%). Two patients had unclear intracranial disease progression patterns and were excluded from the cohort. The median OS and iPFS from BM diagnosis was 2.7 years (95%CI 2.3-4.0) and 1.2 years (95%CI 1.0-1.5), respectively (Fig. 3, B, C).\n
\n\n
\n\n To evaluate genomic biomarkers of intracranial disease progression, we grouped patients by pattern of progression and looked for differences in driver mutation frequency (Fig. 3, D). We found that patients in the LMD cohort were more likely to have\n \n EGFR\n \n alterations as compared to the non-progressor group (45% vs 21%, p=0.044, q = 0.789). By contrast, patients with local progression had more frequent\n \n RB1\n \n loss (24% vs. 6%, p=0.022, q = 0.573) or\n \n NKX3-1\n \n alterations (16% vs. 3%, p=0.044, q = 0.573) as compared to the non-progressor group. Likewise,\n \n MYC\n \n amplifications were more common in patients who later suffered multifocal regional progression, compared to those with local progression, where no\n \n MYC\n \n amplifications were detected (22% vs 0%, p=0.023, q = 0.790). There was no statistically significant difference in\n \n CDKN2A/B\n \n alterations across the five cohorts (Fig. 3, D).\n \n NKX2-1\n \n had a higher amplification frequency (22%) in patients without intracranial disease progression than those with local or LMD progression (4% and 10 %, respectively). We also noted more frequent alterations in\n \n NF1\n \n in patients who developed LMD (15%) as compared to other groups (Suppl. Fig. 2, D).\n
\n\n
\n\n Upon assessing frequencies of oncogenic pathway alterations, MYC pathway alterations were significantly enriched in the patients with LMD (p = 0.013, q = 0.14) and regional progression (both single: p = 0.023, q = 0.255, and multifocal: p = 0.023, q = 0.255) compared to local progression (Fig. 2, E).\u00a0Alteration frequencies within the RTK and RAS pathways were assessed across progression patterns to identify concurrent events.\n \n EGFR\n \n and\n \n KRAS\n \n were the most frequently altered genes (Suppl. Fig. 2, D). Assessment of WGD events across the progression groups revealed that patients with LMD had the numerically highest WGD frequency (Suppl. Fig. 2, E)\n \n .\n \n
\n\n \n \n
\n\n \n \n EGFR\n \n \n \n Alterations in Patients with LMD\n \n
\n\n Given the clear enrichment in\n \n EGFR\n \n alterations in patients with LMD, this finding was further investigated. Patients who suffered from LMD frequently exhibited less common\n \n EGFR\n \n mutations (45%), such as L861Q, G719A/S, A755G, or N771_H773dup (Fig. 4, A).\n
\n\n
\n\n We next identified patients\u00a0with LMD as an initial form of disease progression\u00a0who had multiple tissue samples collected throughout their disease course for more in-depth evaluation. We identified that above-described uncommon\n \n EGFR\n \n mutations were persistent in various tissue samples despite brain-directed and systemic therapies.\u00a0For example, first patient presented with BM at the time of initial lung cancer diagnosis and underwent craniotomy (Fig. 4, B). This BM specimen contained\n \n EGFR\n \n L861Q and\n \n \n G719S driver mutations. After definitive local therapy (surgical BM resection and postoperative RT) the patient received erlotinib and eventually developed systemic progression, with repeat lung biopsy revealing a known gatekeeper mutation (\n \n EGFR\n \n T790M) with persistence of the less common\n \n EGFR\n \n mutations L861Q and\n \n \n G719S. Systemic therapy was switched to osimertinib, and eventually, the patient had further systemic progression with contemporaneous LMD; additional biopsy specimens demonstrated clearance of the T790M mutation but ongoing presence of the L861Q and G719S mutations.\n
\n\n
\n\n In another example (Fig. 4, C), a patient presented with BM at initial lung cancer diagnosis and underwent craniotomy for BM resection. The BM specimen contained an\n \n EGFR\n \n exon-19 deletion\n \n (\n \n E746_A750del)\n \n .\n \n The patient received postoperative RT followed by osimertinib and chemotherapy but still developed early LMD progression. CSF sampling showed elevated circulating tumor cells (CTCs) that were cleared after proton craniospinal irradiation, but multiple serial CSF samples showed persistence of the\n \n EGFR\n \n exon-19 deletion and a\n \n TP53\n \n R273L mutation until the patient succumbed to neurologic disease.\n
\n\n In this work, we present a detailed analysis of the genomic features and clinical correlates of a large cohort of NSCLC patients with molecularly profiled brain metastases and matched extracranial and serially collected samples. We demonstrate that NSCLC BM are markedly altered compared to extracranial disease, irrespective of stage and temporality, with higher TMB, FGA, WGD seen in BM specimens. We confirm deep deletions in\n \n CDKN2A/B\n \n as a common molecular feature of BM. With matched pairs analyses, we show relatively high genomic concordance between EM/PT and BM specimens, and in independent BM specimens from the same patient. Provocatively, we identify several genomic features correlated with brain-specific outcomes of clinical relevance, most notably a high rate of\n \n EGFR\n \n mutations in the BM specimens of patients who go on to develop LMD. These mutations were frequently uncommon, and persistent\u2014despite maximal therapy\u2014and could be detected on serial analyses of tissue and CSF.\n
\n\n Prior reports of BM genomics have noted copy number deletions in\n \n CDKN2A/B\n \n \n 5,6\n \n . In current study we confirmed that approximately one-third of NSCLC BM had deep copy number deletions in\n \n CDKN2A/B\n \n and concordant cell cycle pathway alterations. The globally increased CNAs in BM compared to EM and PT specimens, as well as increased FGA and TMB in BM specimens, also align with previously reported findings\n \n \n 14\n \n ,\n \n 15\n \n \n , including published reports of divergent and branched evolution of BMs\n \n \n 5\n \n \n . Interestingly, other common cancer-related genes such as\n \n TP53\n \n ,\n \n KRAS\n \n , or\n \n EGFR\n \n did not significantly differ between BM and extracranial samples, suggesting that these genes are pervasive. Likewise, cell cycle alterations may offer opportunities for BM-specific targeted therapies such as CDK4/6 inhibitors\n \n \n 16\n \n \n .In sum, the findings presented here suggest that even synchronously diagnosed BM are more genetically aberrant than matched extracranial metastasis or primary tumor specimens, and the greater TMB, and cell cycle changes might promote survival in the brain tumor microenvironment (TME).\n
\n\n We performed a detailed analysis of patients matched BM-PT/EM pairs. Most mutations were present in both BM and matched PT/EM samples, irrespective of the order in which specimens were collected (BM at diagnosis vs. as a form of progression). Although underpowered to explore fully, it is possible that alterations in\n \n TP53, KRAS\n \n , and\n \n NF1\n \n seen in BM at initial diagnosis but not in later extracranial specimens might promote cancer cell survival in the brain-TME. Intriguingly, in those patients who developed BM as a form of progression later in their disease course, we noted that several acquired unique driver alterations in\n \n HLA-B.\n \n Homozygous deletions in\n \n HLA-B\n \n have previously been reported to confer acquired resistance to immune checkpoint inhibitors (ICIs) in LUAD\n \n \n 17\n \n \n and other work has suggested\n \n HLA-B\n \n downregulation as a means by which metastatic clones escape T-lymphocyte and NK cell-mediated cytotoxicity\n \n \n 18\n \n \n . In the context of recent work showing that the brain TME is characterized by reduced antigen presentation and B/T-cell function and increased M2-type macrophage activity\n \n \n 19\n \n \n ,\n \n HLA-B\n \n alterations in LUAD cells may be permissive for cancer cell growth in the brain TME.\n
\n\n To our knowledge, this work is the first to investigate genomic correlates of intracranial progression in patients with LUAD BM. Genomic profiles of brain-specific disease progression patterns were identified: we found\n \n MYC\n \n amplification to be associated with multifocal regional failure, whereas\n \n RB1\n \n deletions and\n \n NKX3-1\n \n alterations were associated with local disease progression. Prior reports have demonstrated that overexpression of\n \n MYC\n \n promotes tumor cell dissemination throughout the brain parenchyma via translation of antioxidant enzymes that promotes the survival of cancer cells in harsh conditions of oxidative stress\n \n \n 20\n \n \n . The association of\n \n RB1\n \n with local failure is puzzling since one might expect\n \n RB1\n \n loss to sensitize residual microscopic disease to adjuvant radiation therapy\n \n \n 21\n \n \n ; however, co-occurrence of\n \n RB1\n \n loss with other mutations might promote RT resistance.\n \n NKX3-1\n \n is less well understood within the context of NSCLC but is associated with metastatic disease in prostate cancer\n \n \n 22\n \n \n . These findings represent potential predictive biomarkers that could inform personalized therapeutic selection, particularly when planning local therapy targets (e.g., focal RT vs. craniospinal irradiation [CSI]).\n
\n\n Finally, patients who suffered LMD as their first form of intracranial failure were far more likely to have\n \n EGFR\n \n alterations in BM specimens; many of these alterations constituted uncommon drivers, and these mutations were persistent in serial samples despite maximal therapy with\n \n EGFR\n \n -directed TKIs and RT. Recent reports have suggested that the\n \n EGFR\n \n -directed TKI osimertinib has excellent CNS penetrance, and post-hoc analyses of the FLAURA study and single-arm phase II data in patients with T790M mutations have demonstrated promising intracranial responses in intact BM\n \n \n 23\n \n ,\n \n 24\n \n \n . Likewise, the BLOOM study evaluated patients with treatment-na\u00efve\n \n EGFR\n \n mutant NSCLC, establishing that LMD showed excellent initial responses and durability to osimertinib\n \n \n 25\n \n \n . NSCLC patients with\n \n EGFR\n \n mutations in primary lung tissue are at higher risk of developing LMD\n \n \n 26\n \n \n . The finding that patients with\n \n EGFR\n \n mutations in their resected BM specimens were more likely to fail with LMD rather than other forms of intracranial failure may be reflective of both improved control of intact BM through maximal local therapy and\n \n EGFR-\n \n directed TKIs, as well as an inherent propensity of\n \n EGFR\n \n -mutant disease to seed the leptomeningeal compartment\n \n \n 27\n \n \n . The metabolic and microenvironmental features of CSF are markedly different from brain parenchyma\n \n \n 28\n \n \n ; thus,\n \n EGFR\n \n mutations may offer a means of spreading to and surviving in this otherwise nutrient poor environment.\n
\n\n The major limitation of our study is that this is a retrospective analysis of highly selected group of NSCLC patients with BM that were large and symptomatic requiring surgical resection, and thus the genomic profiles and clinical outcomes for such patients may differ significantly from those with more extensive disease at diagnosis. Likewise, molecular data from routinely obtained clinical NGS assay (MSK-IMPACT), and thus only known cancer-associated genes were interrogated, although to high depth. As such, future work will necessitate the use of more expansive approaches such as whole genome DNA sequencing, and whole transcriptome RNA sequencing to identify potentially relevant non-coding elements, lesser-known somatic alterations, and transcriptional programs that are critical the development and progression of brain metastasis.\n
\n\n Considering the growing need to understand the biology of NSCLC BM, we report the largest yet presented cohort of genomically profiled BM samples with detailed clinical annotation and matched extracranial samples. This study further characterizes the genomic landscape of NSCLC BM and identifies potentially relevant biomarkers of intracranial disease progression.\n
\n\n \n \n Table 1: Patient\u00a0and Treatment\u00a0Characteristics\n \n \n
\n| \n \n \n \n Patient Characteristic\n \n \n \n \n \n | \n \n \n \n \n Total 233, N (%)\n \n \n \n | \n
| \n \n \n \n Sex\n \n \n \n ,\u00a0No. (%)\n \n \n\n \n \n \n \n \n Female\n \n \n\n \n Male\n \n \n | \n \n \n \n \n 133 (57)\n \n \n\n \n 100 (43)\n \n \n | \n
| \n \n \n \n Smoking Status\n \n \n \n ,\u00a0No. (%)\n \n \n\n \n Current\n \n \n\n \n Former\n \n \n\n \n Never\n \n \n | \n \n \n \n \n 57 (25)\n \n \n\n \n 129 (55)\n \n \n\n \n 47 (20)\n \n \n | \n
| \n \n \n \n Primary Histology\n \n \n \n ,\u00a0No. (%)\n \n \n\n \n \n \n \n \n Adenocarcinoma\n \n \n\n \n Squamous cell carcinoma\n \n \n\n \n Non-small cell, other\n \n \n | \n \n \n \n \n 180 (77)\n \n \n\n \n 23 (10)\n \n \n\n \n 30 (13)\n \n \n | \n
| \n \n \n \n Age\n \n \n \n , Median (range)\n \n \n | \n \n \n \n 67 (31-91)\n \n \n | \n
| \n \n \n \n KPS\n \n \n \n , Median (range)\n \n \n | \n \n \n \n 80 (40-100)\n \n \n | \n
| \n \n \n \n Number of BM at Resection\n \n \n \n , No. (%)\n \n \n\n \n 1\n \n \n\n \n 2-5\n \n \n\n \n 6-15\n \n \n\n \n >15\n \n \n | \n \n \n \n \n 117 (50)\n \n \n\n \n 84 (36)\n \n \n\n \n 30 (13)\n \n \n\n \n 2 (1)\n \n \n | \n
| \n \n \n \n Diameter of Largest Brain Metastasis\n \n \n \n ,\n \n cm\n \n Median (range)\n \n \n | \n \n \n \n 3.0 (0.9 - 7.6)\n \n \n | \n
| \n \n \n \n Neurologic Symptoms at Resection,\n \n \n \n No. (%)\n \n \n\n \n Yes\n \n \n\n \n No\n \n \n | \n \n \n \n \n 212 (91)\n \n \n\n \n 21 (9)\n \n \n | \n
| \n \n \n \n Treatment Prior to Resection\n \n \n \n \n \n | \n \n \n | \n
| \n \n \n \n None\n \n \n \n , No. (%)\n \n \n | \n \n \n \n 122 (53)\n \n \n | \n
| \n \n \n \n Systemic therapy*, No. (%)\n \n \n \n\n \n Cytotoxic chemotherapy\n \n \n\n \n Immunotherapy\n \n \n\n \n Tyrosine Kinase Inhibitor\n \n \n\n \n VEGF Inhibitor\n \n \n\n \n Other\n \n \n | \n \n \n \n 110 (47)\n \n \n\n \n 71 (65)\n \n \n\n \n 20 (18)\n \n \n\n \n 13 (12)\n \n \n\n \n 3 (3)\n \n \n\n \n 3 (3)\n \n \n | \n
| \n \n \n \n Radiation Therapy,\n \n \n \n No. (%)\n \n \n\n \n Stereotactic Radiosurgery\n \n \n\n \n Whole-brain Radiotherapy\n \n \n\n \n Prophylactic Cranial Irradiation\n \n \n | \n \n \n \n 16 (7)\n \n \n\n \n 11 (69)\n \n \n\n \n 4 (25)\n \n \n\n \n 1 (6)\n \n \n | \n
\n
\n\n \n *Received either monotherapy or combination therapy as the most recent therapy prior to resection\n \n
\n\n Table 1, Supplemental Table 1\n
\n \n\n Figures 1-5, Supplemental Figures 1-2\n
\n \n\n Figure Legends\n
\n \n\n The complex architecture of the endoplasmic reticulum (ER) comprises distinct dynamic features, many at the nanoscale, that enable the coexistence of the nuclear envelope, regions of dense sheets and a branched tubular network that spans the cytoplasm. A key player in the formation of ER sheets is cytoskeleton-linking membrane protein 63 (CLIMP-63). The mechanisms by which CLIMP-63 coordinates ER structure remain elusive. Here, we addressed the impact of S-acylation, a reversible post-translational lipid modification, on CLIMP-63 cellular distribution and function. Combining native mass-spectrometry, with kinetic analysis of acylation and deacylation, and data-driven mathematical modelling, we obtained in depth understanding of the CLIMP-63 life-cycle. In the ER, it assembles into trimeric units. These occasionally exit the ER to reach the plasma membrane. However, the majority undergoes S-acylation by ZDHHC6 in the ER where they further assemble into highly stable super-complexes. Using super resolution microscopy and focused ion beam electron microscopy, we show that CLIMP-63 acylation-deacylation controls the abundance and fenestration of ER sheets. Overall, this study led to the discovery that dynamic lipid post-translational modifications can regulate ER architecture.\n
\n\n \n Endoplasmic reticulum\n \n
\n\n \n cellular compartment\n \n
\n\n \n S-palmitoylation\n \n
\n\n \n S-acylation\n \n
\n\n \n ZDHHC6\n \n
\n\n \n CLIMP-63/CKAP4\n \n
\n\n \n enzymatic reaction\n \n
\n\n \n mathematical modelling\n \n
\n\n The endoplasmic reticulum (ER) is a complex multifunctional organelle that extends from the nuclear envelope to the cell periphery\n \n \n 1\n \n \u2013\n \n 3\n \n \n . Based on morphological features, it is classically separated into three sub-compartments: the nuclear envelope, the rough ER, and the smooth ER. The rough ER consists of packed membrane sheets studded with ribosomes, concentrated in the perinuclear region. The smooth ER is formed by narrow tubular membranes arranged as a tentacular meshwork, of heterogenous density, that occupies the entire cytoplasm with a highly dynamic organization. Pioneering observations established that the relative abundance of ribosome-studded sheets and tubules varies between cell types and correlates with their function\n \n \n 5\n \n ,\n \n 6\n \n \n . Sheets are the major site of synthesis of proteins destined to the secretory pathway and endomembrane system, and are very abundant in secretory cells\n \n \n 6\n \n ,\n \n 7\n \n \n , while tubules are thought to be involved in lipid biogenesis, calcium ion storage, and detoxification\n \n \n 4\n \n \n . Over the past 25 years, the complex architecture of the ER has been shown to be orchestrated by specific membrane shaping proteins\n \n \n 7\n \n \u2013\n \n 14\n \n \n , by proteins that coordinate contact with other cellular organelles\n \n \n 15\n \n \u2013\n \n 17\n \n \n , by proteins that control membrane fusion or fission\n \n \n 18\n \n ,\n \n 19\n \n \n as well as by dynamic interactions with the cytoskeleton\n \n \n 20\n \n \u2013\n \n 24\n \n \n . The local concentration of different shaping proteins correlates with specific architectures and may theoretically explain the interconversion of the different ER morphologies, in a model that is reminiscent of phase diagrams\n \n \n 14\n \n \n . A recent computational study suggested a primary role for the intrinsic curvature of membranes in controlling the formation of the tubular network as well as nanoholes within ER sheets\n \n \n 25\n \n \n . A full mechanistic understanding of the formation and interconversion of sheets and tubules and the regulation thereof is however still lacking.\n
\n\n A key player in sheet formation is CLIMP-63 (cytoskeleton-linking membrane protein 63)\n \n \n 7\n \n \n . CLIMP-63 is a type II membrane protein, with a short N-terminal cytosolic tail and a large C-terminal luminal domain\n \n \n 26\n \n \n . The cytosolic tail has the ability to bind microtubules, thereby linking the ER to the cytoskeleton\n \n \n 27\n \n \n , and more specifically to centrosome microtubules\n \n \n 23\n \n \n . The luminal domain has the capacity to multimerize through coiled-coil interactions\n \n \n 13\n \n ,\n \n 28\n \n \n . It has been proposed that assembly occurs in\n \n trans\n \n , i.e. between CLIMP-63 molecules present in opposing membrane patches \u201cacross\u201d the ER lumen, providing a mechanism to control the width of ER-sheets\n \n \n 7\n \n ,\n \n 29\n \n \n . More recently, CLIMP-63 was found to coordinate the formation and dynamics of ER nanoholes by yet undetermined mechanisms\n \n \n 30\n \n ,\n \n 31\n \n \n . A variety of studies have also reported that CLIMP-63 can act as a receptor for various ligands in a tissue-dependent manner, with significant clinical relevance\n \n 26,32\u221234\n \n . Here we sought to better understand which mechanisms control the relative distribution of CLIMP-63 between the ER and the plasma membrane, and how, within the ER, CLIMP-63 is regulated to tune ER architecture.\n
\n\n We focused on the role of a specific post-translational lipid modification, S-acylation, which consists in the addition of a medium-length acyl chain to cytosolic cysteines, through the action of acyltransferases\n \n \n 35\n \n \n . CLIMP-63 was found to be modified by the acyltransferases ZDHHC2\n \n 36\n \n and ZDHHC5\n \n 33\n \n , which mostly localize to the plasma membrane and endosomal system\n \n \n 33\n \n ,\n \n 37\n \n \n . Acylation was reported to control CLIMP-63 localization to specific plasma membrane domains and enhance its signalling capacity. Here, focused on acylation of CLIMP-63 in the ER, where the bulk of the protein resides.\n
\n\n We combined various experimental methods (biochemistry, kinetic analysis, microscopy) with mathematical modelling of the enzymatic reactions, trafficking and degradation. We found that following synthesis in the ER, CLIMP-63 assembles into parallel homotrimeric units that rapidly undergo S-acylation by the ER-localized acyltransferase ZDHHC6. CLIMP-63 can then either be deacylated, by the Acyl Protein Thioesterase APT2, or assemble into higher order complexes which become insensitive to APT2 action. Higher order CLIMP-63 complexes are retained in the ER, whereas non-acylated CLIMP-63 trimeric units can exit the ER for transport to the plasma membrane. In the ER, acylated CLIMP-63 complexes lead to the generation of ER sheets, with hyperacylation causing an increase in CLIMP-63 abundance, loss of ER fenestration and a massive sheet expansion. Our results reveal that dynamic ZDHHC6/APT2-mediated acylation/deacylation of the ER-shaping protein CLIMP-63 controls it cellular distribution and ER morphology.\n
\n\n \n CLIMP-63 is present mainly in an acylated state in cells and\n \n \n in vivo\n \n
\n\n CLIMP-63 has been shown to undergo S-acylation on its sole cytosolic cysteine residue, Cys-100\n \n 4\n \n . It has only one other cysteine, Cys-126, which is located on the luminal membrane boundary of the transmembrane domain. To study CLIMP-63 S-acylation in depth, we generated a HeLa cell line stably depleted of the endogenous protein using shRNA (shCLIMP-63). We then optimised the expression of HA-tagged CLIMP-63, wild-type (WT) or mutant, in these cells by determining the amount of plasmid DNA required to reach near-endogenous protein expression levels and ensuring that the N-terminal tag did not affect WT CLIMP-63 subcellular distribution (Supplementary Fig. 1a, b). Using this system, we confirmed that CLIMP-63 can undergo S-acylation by monitoring the incorporation of radioactive\n \n 3\n \n H-palmitate in WT CLIMP-63, but not in the C100A mutant (Fig.\u00a01a).\n
\n\n In S-acylation, the lipid is linked to the protein via a thioester bond that can be broken\n \n in vitro\n \n using hydroxylamine. S-acylated proteins, such as CLIMP-63 and calnexin, can be captured after hydroxylamine treatment using a method that has been termed Acyl-Rac (Supplementary Fig. 1c). A variant of this method was used to estimate the proportion of S-acylated CLIMP-63. After cleavage with hydroxylamine the acyl chain is replaced with maleimide polyethylene glycol (mPEG - PEGylation) leading to a mass shift in SDS-PAGE gels. Following PEGylation, we found that the majority of WT CLIMP-63, but not the C100A mutant protein, migrated with a detectable mass change in a western blot analysis (Fig. 1b). Calnexin migrated as three bands, corresponding to S-acylation or not of its two cytoplasmic cysteines\n \n \n 38\n \n \n . The mass of TRAP\u03b1 was unaltered, as expected due to its lack of cytosolic cysteines (Fig.\u00a01b).\n
\n\n For a more accurate quantification of CLIMP-63 S-acylation, we developed another variant of the Acyl-Rac assay, which involves an alkylation step with fluorescent iodoacetamide. This enables detection of free, i.e. non-acylated, cysteines (Supplementary Fig. 1d). Cys-126 was mutated to Alanine to specifically quantify labelling of Cys-100. Only 12.7\u2009\u00b1\u20090.05% of CLIMP-63-C126A could be labelled (Fig. 1c), revealing that, in our system, more than 87% of CLIMP-63 is S-acylated at steady state. Such extensive S-acylation was not restricted to cell lines (HeLa and retinal pigmented epithelial cells-Rpe1) as PEGylation performed on extracts of various mouse tissues indicated that CLIMP-63 is indeed mostly lipid-modified\n \n in vivo\n \n (Fig. 1d).\n
\n\n As its name indicates \u2013 cytoskeleton-linking membrane protein\u2013, CLIMP-63 interacts with microtubules\n \n \n 20\n \n ,\n \n 23\n \n ,\n \n 27\n \n \n via its N-terminal cytosolic tail. We investigated whether this interaction would influence S-acylation, which also occurs on the cytosolic domain. Incorporation of\n \n 3\n \n H-palmitate was not affected by microtubule-altering drugs, nor by mutations of the serine phosphorylation sites involved in microtubule binding (Fig.\u00a01e, f). Consistently, the microtubule stabilizing drug paclitaxel/taxol had comparable effects on the distribution of CLIMP-63 WT and C100A mutant (Fig.\u00a01g). Thus, S-acylation of CLIMP-63 occurs independently of its interactions with microtubules.\n
\n\n Altogether these observations confirm that CLIMP-63 can be acylated on Cys-100 and show that in culture cells and in various mouse organs, the majority of CLIMP-63 molecules are lipid-modified, independently of their microtubule binding.\n
\n\n
\n
\n
\n
\n \n ZDHHC6 S-acylates CLIMP-63 and controls its subcellular distribution\n \n
\n\n Two acyltransferases, ZDHHC2 and ZDHHC5, have been reported to modify CLIMP-63 and influence its cell surface distribution\n \n \n 33\n \n ,\n \n 36\n \n \n . These enzymes localize primarily to the Golgi and plasma membrane\n \n \n 33\n \n ,\n \n 37\n \n \n . However, as CLIMP-63 localizes predominantly to the ER\n \n \n 7\n \n \n , additional, ER-localized ZDHHC enzymes must be involved. ZDHHC6 has been reported to modify various key ER proteins\n \n \n 38\n \n \u2013\n \n 40\n \n \n , prompting us to test its ability to modify CLIMP-63. In ZDHHC6 knockout (KO) cells generated using the CRISPR-Cas9 system (Supplementary Fig. 2a, b),\n \n 3\n \n H-palmitate incorporation into endogenous CLIMP-63 was almost undetectable (Fig. 2a). Quantification of\n \n 3\n \n H-palmitate incorporation showed that silencing ZDHHC6, produced a more pronounced decrease (~\u200980%), than silencing ZDHHC2 (~\u200930%) or ZDHHC5 (~\u200940%) (Fig. 2b, c). Silencing ZDHHC3, which localizes to the Golgi, was used as a negative control. Thus, ZDHHC6 constitutes the major acyltransferase modifying CLIMP-63. Of note, overexpressing each of these ZDHHC enzymes had no significant increment on a 2 h\n \n 3\n \n H-palmitate incorporation pulse into CLIMP-63 (Supplementary Fig.\u00a02c, d).\n
\n\n Next we monitored the interaction between the ZDHHC enzymes and CLIMP-63 using both co-immunoprecipitation experiments (Co-IP) and a proximity ligation assay, which allows quantification of protein-protein interactions in the cellular environment\n \n \n 41\n \n \n . CLIMP-63 co-precipitated with both ZDHHC2 and ZDHHC6, upon co-overexpression (Supplementary Fig.\u00a02e). Proximity ligation however indicated a stronger association between CLIMP-63 and ZDHHC6, compared to ZDHHC2 (Fig.\u00a02d, e), in line with the predominant ER-localization of CLIMP-63.\n
\n\n We next investigated whether S-acylation of CLIMP-63 in the ER by ZDHHC6 could affect its abundance at the plasma membrane. Using a surface biotinylation assay, we confirmed that a small proportion of CLIMP-63 is detected at the plasma membrane (Fig.\u00a02f, g). This population increased three-fold upon ZDHHC6 silencing (Fig.\u00a02f, g), indicating that ZDHHC6 controls CLIMP-63 surface expression, presumably by trapping it in the ER. Consistent with an increased surface expression, the interaction between CLIMP-63 and ZDHHC2 was higher in ZDHHC6 KO than in control cells, monitored by proximity ligation (Fig.\u00a02d, e).\n
\n\n At the cell surface, CLIMP-63 was shown to distribute to lipid raft-like domains in a S-acylation dependent-manner\n \n \n 33\n \n \n . Association with detergent resistant membranes (DRMs) was used as a biochemical readout for raft association\n \n \n 42\n \n \n . Membrane nanodomains resistant to solubilization with cold detergent float in Optiprep\u2122-density gradients, along with established markers of such domains (e.g. caveolin-1). We could confirm that a minor population of endogenous CLIMP-63 (~\u200914%) associated with DRMs (Fig.\u00a02h, i). In combination with surface biotinylation, we demonstrated that CLIMP-63 present within DRMs is indeed at the cell surface (Fig.\u00a02h). Silencing ZDHHC6 increased CLIMP-63 plasma membrane localization (as in Fig.\u00a02f, g), and presence in DRMs (Fig.\u00a02i) further supporting a role of ZDHHC6 in controlling plasma membrane CLIMP-63.\n
\n\n The above observations suggest that ZDHHC6 can S-acylate CLIMP-63 in the ER, but if non-acylated CLIMP-63 exits the ER, it is a substrate for ZDHHC2 or 5 in the plasma membrane-endosomal system. The CLIMP-63 C100A mutant was however barely detectable at the cell surface (Fig.\u00a02j) and mostly excluded from DRMs fractions (Fig.\u00a02k, l), in agreement with previous findings\n \n \n 36\n \n \n .\n
\n\n Altogether, these observations show that S-acylation by multiple ZDHHC enzymes controlles the subcellular distribution of CLIMP-63: modification of the majority of CLIMP-63 by ZDHHC6 leads to ER retention, a small proportion of non-acylated CLIMP-63 exits the ER and undergoes acylation by ZDHHC2/5 later in the secretory pathway or in the plasma membrane - endosomal system. This acylation is important for its sustained presence at the cell surface, within lipid nanodomains.\n
\n\n
\n
\n
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\n We next examined the kinetics of CLIMP-63 S-palmitoylation and depalmitoylation.\n \n 3\n \n H-palmitate incorporation increased gradually over 6 h (Fig. 3a, Supplementary Fig. 3a).\n \n 3\n \n H-Palmitate turnover was monitored by pulse-chase approach, where a 2 h pulse was followed by different periods of chase in label free medium. Approximately 50% of CLIMP-63-bound\n \n 3\n \n H-palmitate was released within 30 min (Fig.\u00a03b, Supplementary Fig.\u00a03b), indicative of rapid depalmitoylation. However, approximately 20% of CLIMP-63 remained radioactively-labelled even after a 5 h chase (Fig.\u00a03b), indicating the presence of longer-lived palmitoylated-CLIMP-63 species. Silencing ZDHHC2 had no significant effect on palmitate turnover, whereas silencing ZDHHC6, despite drastically reducing CLIMP-63 palmitoylation (Fig.\u00a02b, c), allowed the detection of a minor population of palmitoylated-CLIMP-63 with a slow depalmitoylation rate (Fig.\u00a03c). This may correspond to surface CLIMP-63 (Fig.\u00a02f) modified by ZDHHC2/5.\n
\n\n Deacylation is mediated by Protein Acyl Thioesterases (APTs)\n \n \n 35\n \n \n . We tested the involvement of APT1 or APT2. The\n \n 3\n \n H-palmitate turnover was insensitive to APT1 silencing, but significantly delayed upon APT2 siRNA (Fig.\u00a03d, Supplementary Fig.\u00a03c). The same observations were true when using ML348 and ML349, specific inhibitors of APT1 and APT2 respectively (Fig.\u00a03d, Supplementary Fig.\u00a03d). Consistent with these results, ectopically expressed APT2 and the catalytic inactive mutant S122A could be co-immunoprecipitated with endogenous CLIMP-63 (Fig.\u00a03e). We also found that ML349 treatment led to an increase of plasma membrane CLIMP-63 (Fig.\u00a03f, g). While this is consistent with S-acylation stabilizing CLIMP-63 at the surface, we cannot exclude an indirect effect of ML349 on ZDHHC6, since APT2 is essential for its stability\n \n \n 2\n \n \n .\n
\n\n S-acylation has been reported to impact the turnover rate of various proteins\n \n \n 35\n \n ,\n \n 38\n \n ,\n \n 39\n \n ,\n \n 43\n \n ,\n \n 44\n \n \n . Here, we studied CLIMP-63 stability using\n \n 35\n \n S Cys/Met metabolic pulse-chase experiments (Fig.\u00a03h, j). After a 20 min labelling pulse, endogenous CLIMP-63 displayed an apparent half-life (\ua787\n \n 1/2\n \n ) of 25 h (Fig.\u00a03h, j). Silencing ZDHHC6 accelerated the decay (\ua787\n \n 1/2\n \n = 22 h), whereas ZDHHC2 depletion had very little effect (\ua787\n \n 1/2\n \n = 24 h) (Fig. 3h, j). Silencing both enzymes however had a pronounced effect (\ua787\n \n 1/2\n \n = 15 h) (Fig. 3h, j), confirming that ZDHHC6 acts upstream from ZDHHC2. We also monitored the turnover of the S-acylation deficient C100A mutant. This mutant was dramatically less stable (\ua787\n \n 1/2\n \n = 4 h) than WT-CLIMP-63 (Fig. 3i, j). The mutation of Cys-100 had a stronger effect than silencing both ZDHHC6 and ZDHHC2, indicating that either ZDHHC5 (found during this study to modify CLIMP-63 at the plasma membrane\n \n \n 33\n \n \n ) or residual ZDHHC2/6 palmitoylating activity still stabilised CLIMP-63 in our setting. Finally, ZDHHC6 overexpression resulted in a strong stabilization of CLIMP-63 (\ua787\n \n 1/2\n \n = 65 h) (Fig. 3i, j). Thus ZDHHC6-mediated S-acylation of CLIMP-63 leads to a major increase in the life-time of the protein.\n
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\n To better understand how the complex trafficking and turnover of CLIMP-63 is controlled by cycles of acylation and deacylation, we generated a conceptual computational representation of the system using mathematical modelling. Our first model was simply composed of five CLIMP-63 species: acylated or non-acylated monomers in the ER (M\n \n 0\n \n \n ER\n \n , M\n \n 1\n \n \n ER\n \n \u2013 where 0 and 1 superscripts indicate whether the S-acylation site is free or modified), and at the plasma membrane (PM) (M\n \n 0\n \n \n PM\n \n , M\n \n 1\n \n \n PM\n \n ) and a non-acylated transport intermediate. This model properly captured our pulse-chase experiments (Supplementary Fig.\u00a04a), but predicted and equal distribution of CLIMP-63 between the ER and the plasma membrane, with a complete relocation to the plasma membrane upon ZDHHC6 depletion (Fig.\u00a04a). This was inconsistent with the experimental observations, where the bulk of CLIMP-63 resides in the ER, even in the absence of ZDHHC6. The inability of the model to adequately capture the system highlighted the absence of a key mechanistic element to understand CLIMP-63 distribution.\n
\n\n We hypothesized that it could be multimerization of CLIMP-63\n \n 13,28,29\n \n . Information on CLIMP-63 oligomerization is limited, prompting us to further analyse it. First, we verified that CLIMP-63 can self-assemble by performing Co-IP experiments using shCLIMP-63 cells co-expressing HA-CLIMP-63 and RFP-CLIMP-63 (Supplementary Fig. 4b & Fig. 4b). Co-IP in combination with\n \n 35\n \n S Cys/Met metabolic labelling showed that CLIMP-63 monomers interact and assemble rapidly following synthesis (Fig.\u00a04b), irrespective of S-acylation. Blue-NATIVE PAGE revealed 2 prominent CLIMP-63 bands, with apparent molecular weights of approximately 480 and 1048 kDa (Fig.\u00a04c), and no band corresponding to monomers.\n
\n\n To study the stoichiometry of CLIMP-63 complexes, we generated a construct to express a soluble ER luminal domain (with a predicted mass of ~\u200958 Kda) with a N-terminal signal peptide sequence for targeting to the ER lumen and a His-FLAG tag, either at the C-terminus or at the N-terminus, for purification. The protein could be purified from the culture medium and Blue-NATIVE PAGE showed that the CLIMP-63 luminal domain migrates predominantly as a single species, just below the 480 kDa marker (Fig.\u00a04d, e). The C-terminal-tagged luminal CLIMP-63 domain was further analysed by Intact Protein Liquid Chromatography Mass Spectrometry (LC-MS). We almost exclusively detected a complex of approximate 173.4-173.8 kDa (Fig.\u00a04f, g), which would correspond to trimers of the luminal domain, and very small amounts of a\u2009~\u200957.8 kDa protein (Fig.\u00a04h), likely corresponding to monomers. Exact molecular mass determination under denaturing conditions and shotgun proteomics (Supplementary Fig.\u00a04c, d) confirmed that our samples contained solely the luminal domain of CLIMP-63. Altogether these observations indicate that full length CLIMP-63 assembles into elementary trimeric units, which can further assemble into higher ordered assemblies, based on the migration in Blue Native PAGE, possibly dimers of trimers or trimers with other proteins. Since the vast majority of CLIMP.63 is in the ER, the similar abundance of the 480 and 1048 kDa units in Blue Native gels indicate that both complexes exist in the ER.\n
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\n A more complex model could be generated based on CLIMP-63 oligomerization. In the ER, CLIMP-63 can be present either as elementary (E) units, the trimer, or a higher (H) order CLIMP-63 assemblies (Fig.\u00a04i). Different sizes of assemblies did not changed the behaviour of our system, therefore H was modelled as a dimer of elementary units, consistent with the Blue Native analysis. For simplicity, all the S-acylation reactions of E were grouped into one, leading to 5 possible species in the ER: E\n \n 0\n \n , E\n \n \n 1\n \n \n , H\n \n 0\n \n , H\n \n \n 1\n \n \n (in which only one E is acylated) and H\n \n \n 2\n \n \n (both Es acylated). Only E\n \n 0\n \n can be transported to the plasma membrane, based on our observation that only non-acylated CLIMP-63 exits the ER. At the cell surface, E\n \n 0\n \n can undergo S-acylation to yield E\n \n \n 1\n \n \n . All species can undergo degradation, with their own specific kinetics.\n
\n\n A subset of the data from our pulse-chase experiments was used to calibrate the model (Fig.\u00a04j & Supplementary Fig.\u00a04e). A heuristic optimization method generated a population of models that satisfactorily fitted all the calibration experiments. The 100 sets of parameters with the best fits were subsequently used to predict a second set of experiments. All the predictions fitted the experimental data (Fig.\u00a04k & Supplementary Fig.\u00a04f). The introduction of higher order complexes, H, in the ER now led to the correct prediction of the subcellular distribution: the vast majority of CLIMP-63 resides in the ER, both in control and ZDHHC6 siRNA conditions (Fig.\u00a04l). The model allowed to calculate the distribution of the different CLIMP-63 species, indicating that H\n \n 2\n \n \n ER\n \n is by far the most abundant WT form (Fig. 4l). Since silencing is not a knock out, even after ZDHHC6 siRNA, H\n \n 2\n \n \n ER\n \n was still the most abundant species, although E\n \n 1\n \n \n PM\n \n was increased comparing to control conditions. This analysis indicates that higher order assembly of CLIMP-63 elementary units leads to ER retention.\n
\n\n The model was highly consistent with a variety of experimental observations. For example, the model indicated that ZDHHC6 activity promotes ER accumulation of long lived higher ordered complexes (H\n \n 2\n \n \n ER\n \n ) and reduces the surface population of CLIMP-63 (Supplementary Fig.\u00a05a), in agreement with the findings in Fig.\u00a02f, and Fig.\u00a03h-i. It also predicted that ZDHHC6 depletion by siRNA (set to 10% residual activity in the model) does not prevent CLIMP-63 oligomerization, in line with the rapid self-assembly of the C100A mutant (Fig.\u00a04b), but enhances exit of CLIMP-63 from the ER, leading to an increased presence at the plasma membrane, as observed in Fig.\u00a02f-g. Finally, palmitoylation of CLIMP-63 at the plasma membrane was predicted to increase its surface residence time and thus accumulation (Supplementary Fig.\u00a05a), as shown experimentally (Fig.\u00a02f-i).\n
\n\n A final global sensitivity analysis enabled us to determine the parameters that contribute the most to the accurate calibration of the model. These, in turn, reflect the actual biological constraints that govern CLIMP-63 levels and cellular distribution (Supplementary Fig.\u00a05b). Three parameters emerged that highlight the major role of ZDHHC6, in controlling ZDHHC6 life-cycle: the efficiency of ZDHHC6 to modify the CLIMP-63 elementary units (the catalytic rate of ZDHHC6: kcat6); the Michaelis\u2013Menten constant (KM) for such reaction (acylation of CLIMP-63 units by ZDHHC6: KM6) and the rate at which CLIMP-63 exits the ER (knpER_CP) (Supplementary Fig.\u00a05b). In addition, although to a lesser extent, the kinetics of formation of H (kdim) and degradation of E\n \n 0\n \n \n ER\n \n (kdC0ER) also significantly impacted the model, suggesting an important role for CLIMP-63 higher order assembly and ER-associated degradation pathways.\n
\n\n A powerful aspect of mathematical modelling is the possibility of interrogating it to obtain information that may not be readily accessible experimentally. For instance,\n \n 35\n \n S Cyst/Met metabolic pulse-chase kinetics can be deconvoluted to determine the evolution of the individual CLIMP-63 species over time (Fig.\u00a05a). Following synthesis, CLIMP-63 elementary units (E\n \n 0\n \n \n ER\n \n ) are generated. These are rapidly S-acylated (E\n \n 1\n \n \n ER\n \n ) and subsequently assembled into higher ordered complexes (H\n \n 2\n \n \n ER\n \n ), which is the significant species after 20 h of chase (Fig.\u00a05a, WT). A minor population of E\n \n 0\n \n \n ER\n \n exits the ER to reach the PM, where it is exclusively accumulated in the acylated form E\n \n 1\n \n \n PM\n \n . For the S-acylation deficient C100A mutant, E\n \n 0\n \n \n ER\n \n levels also rapidly decay due to a faster degradation rate and the rapid conversion into higher ordered H\n \n 0\n \n complexes (Fig. 5a, C100A).\n
\n\n The model also allowed the extraction of palmitoylation and depalmitoylation rates of the various CLIMP-63 species (Fig.\u00a05b). Elementary units, i.e. trimers, in the ER (E\n \n ER\n \n ) were found to undergo rapid palmitoylation as well as depalmitoylation (Fig.\u00a05b). In contrast, the higher order complexes (H\n \n ER\n \n ) displayed minimal acylation and deacylation (Fig. 5b). These predictions suggest that the\n \n 3\n \n H-palmitate pulse chase experiments (Fig.\u00a03b) were capturing the depalmitoylation of elementary units, and thus, that only E\n \n ER\n \n were undergoing significant palmitoylation during the 2 h pulse. Indeed, the model predicts that after two hours labelling, the\n \n 3\n \n H-palmitate-labelled population is 78% E\n \n 1\n \n \n ER\n \n and only 15% H\n \n 2\n \n \n ER\n \n (Fig. 5c). These proportions could be shifted by increasing the pulse period. After a 20 h pulse, 65% of the labelled population was predicted to be H\n \n 2\n \n \n ER\n \n (Fig. 5c). As the percentage of H\n \n 2\n \n \n ER\n \n at the end of the pulse period increased,\n \n 3\n \n H-palmitate decay was predicted to be less pronounced (Fig.\u00a05d), as could be validated experimentally (Fig.\u00a05e). Thus, our mathematical model supported by the experimental data show that higher-order assembly of CLIMP-63 protects the protein from deacylation.\n
\n\n The model indicates that higher-order assembly acts as an ER retention mechanism, prevents deacylation, leading to the accumulation of H\n \n 2\n \n \n ER\n \n , which becomes the dominant species. We next used the model to infer the half-lives of the different CLIMP-63 species, parameters that are not easily ascertained experimentally. Most forms were predicted to have very similar half-lives of approximately 5 h. One notable exception was H\n \n 2\n \n \n ER\n \n , at above 80 hours (Fig.\u00a05f). H\n \n 2\n \n \n ER\n \n is the most abundant CLIMP-63 species in the cell (Fig. 4l) and thus we sought to estimate its half-life. We generated a fusion protein of CLIMP-63 with an N-terminal SNAP tag to fluorescently label fully folded proteins and monitor their decay with time\n \n \n 43\n \n \n . Consistent with the prediction, SNAP-CLIMP-63 did not undergo significant degradation over 24 h (Fig.\u00a05g). Analysis of the half-lives of CLIMP-63 species indicates that individually, S-acylation or higher order assembly do not stabilize CLIMP-63 in the ER (E\n \n 0\n \n ER and H\n \n 0\n \n ER both have half-lives of \u2248\u20095h), but together they result in more than 15-fold increase in the protein\u2019s half-life.\n
\n\n Of note, the analysis also indicated that S-acylation significantly affected the turnover rate of CLIMP-63 at the cell surface since E\n \n 1\n \n \n PM,\n \n had an approximately 4 times longer predicted half-life than that of E\n \n 0\n \n \n PM\n \n , consistent with the purposed CLIMP-63 surface stabilisation within lipid microdomains\n \n \n 33\n \n \n (Fig. 2h,i)\n
\n\n We next examined the steady state species distribution of the CLIMP-63 C100A mutant. Consistent with ZDHHC6 siRNA (Fig.\u00a04l), higher order complexes were also the most abundant species for this mutant, indicating that accumulation of this species does not require S-acylation (Fig.\u00a05h). Total protein level was predicted to be sensitive to the abundance of ZDHHC6: overexpression of ZDHHC6 was predicted to increase total CLIMP-63 levels by 30% (Fig.\u00a05i), whereas silencing ZDHHC6 decreased CLIMP-63 levels by 32% (Fig.\u00a05i). Again, these predictions were verified experimentally. CLIMP-63 levels were 30% lower in ZDHHC6 KO cells and 20% higher in ZDHHC6 overexpressing cells (Fig.\u00a05j).\n
\n\n Altogether the model and its validation show that following synthesis and folding of CLIMP-63 into trimers, these elementary units rapidly undergo S-acylation by ZDHHC6 and subsequently assemble into higher order complexes, presumably dimers of trimers. S-acylation is not required for this assembly, but since E\n \n 1\n \n \n ER\n \n is predicted to be 1.6 times more abundant than E\n \n 0\n \n \n ER\n \n , formation of H\n \n 2\n \n \n ER\n \n is more likely to occur than that of H\n \n 0\n \n \n ER\n \n . Jointly, but not individually, S-acylation and higher order assembly dramatically stabilize CLIMP-63, and therefore H\n \n 2\n \n \n ER\n \n becomes the most abundant CLIMP-63 species in the cell. Exit of CLIMP-63 trimers from the ER can occur but is in tight kinetic competition with both S-acylation and higher order assembly. The CLIMP-63 trimers that do exit the ER can reach the plasma membrane where they become substrates of acyltransferases ZDHHC2 and 5\n \n 33,36\n \n . This acylation event increases the surface residence time of CLIMP-63, probably delaying its endocytosis and transport to lysosomes for degradation.\n
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\n In addition to its role in connecting the ER to the microtubule network\n \n \n 20\n \n ,\n \n 27\n \n \n , CLIMP-63 has been proposed to control the structure and abundance of ER sheets\n \n \n 7\n \n \n . Our finding that ZDHHC6 expression modulates the cellular levels and distribution of CLIMP-63 raises the possibility that this acyltransferase may regulate ER morphology. In support of this hypothesis, ZDHHC6 KO cells showed a reduced perinuclear ER density (Supplementary Fig.\u00a06a, b) whereas overexpression of ZDHHC6 caused drastic ER-expansion (Fig.\u00a06a, b), and dot formation (explained in section below). This phenotype was dependent on CLIMP-63 acylation since it was absent in cells expressing the acylation-deficient C100A mutant (Fig.\u00a06c, Supplementary Fig.\u00a06c). ER expansion could be observed in different cell types, such as U2OS cells (Supplementary Fig.\u00a06d), specifically upon overexpression of ZDHHC6 but not ZDHHC2 or other unrelated, ER-localized ZDHHC enzymes (Supplementary Fig.\u00a06e). ER expansion was not a consequence of ER-stress since the mRNA levels of major ER stress mediators such as Bip, Ire1, PERK and ATF6 remain unaltered (Supplementary Fig.\u00a06f). Thus, modulating CLIMP-63 S-acylation by varying the cellular levels of ZDHHC6 leads to alterations in ER morphology.\n
\n\n To confirm the importance of CLIMP-63 acylation in the control of ER morphology, we searched for a means to accelerate the formation of acylated higher order complexes (H\n \n 2\n \n \n ER\n \n ). The model suggested that this could be achieved by slowing down the acyl chain turnover rate (Fig.\u00a06d). Accelerated formation of H\n \n 2\n \n \n ER\n \n (Fig. 6d) led to a slower decay of\n \n in silico\n \n metabolically labelled CLIMP-63 (Fig. 6d). We have previously found that dual acylation of calnexin in the vicinity of the transmembrane domain slowed down deacylation\n \n \n 43\n \n \n . We therefore introduced a second cysteine adjacent to Cys-100 i.e. CLIMP-63-CC. The cysteine insertion is unlikely to have structural consequences since the cytosolic tail of CLIMP-63 is predicted to be disordered (\n \n \n https://iupred2a.elte.hu/\n \n \n \n ).\n \n CLIMP-63-CC was properly expressed in cells and showed a Blue NATIVE profile equivalent to WT or C100A CLIMP-63 (Supplementary Fig. 6g).\n \n 3\n \n H-palmitate pulse-chase experiments demonstrated that the rate of depalmitoylation of CLIMP-63-CC was drastically slower than that of WT, with an almost 10-fold increase in the apparent half-life of bound palmitate (Fig. 6e). Consistent with the predictions, metabolic\n \n 35\n \n S Cys/Met-labelled CLIMP-63-CC was also more stable than WT CLIMP-63 (Fig.\u00a06f). Correspondently CLIMP-63-CC showed low abundance at the plasma membrane, and was apparently absent from DRMs (Fig.\u00a06g, h). Thus CLIMP-63-CC has reduced ER depalmitoylation, which in turn increases its ER retention, diminishing its surface expression and association into plasma membrane lipid microdomains.\n
\n\n We evaluated the consequences of CLIMP-63-CC on ER morphology. Confocal analysis of shCLIMP-63 cells overexpressing CLIMP-63-CC showed a striking densification of perinuclear ER-sheets (Fig.\u00a06i) and the number of cells with expanded ER was drastically increased when compared to those expressing WT CLIMP-63 (Fig.\u00a06i). Such CLIMP-63-CC induced expanded ER remained well-structured as observed by super resolution, structured illumination microscopy (SIM) (Supplementary Fig.\u00a06h). In contrast, overexpression of acylation deficient CLIMP-63-C100A, often led to a disorganised ER network (Supplementary Fig.\u00a06h). Altogether, these observations show that altering the dynamics of acylation and deacylation of CLIMP-63 influence the morphology of the ER.\n
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\n Correlative electron microscopy (EM) was performed to gain further insight into the changes in ER-architecture caused by CLIMP-63 acylation by ZDHHC6. The expression of RFP-tagged variants of CLIMP-63 enable the identification of transfected cells (Fig.\u00a07a). Expectedly, shCLIMP-63 cells expressing WT CLIMP-63 displayed well-organised ER-sheets whereas cells expressing the C100S acylation deficient mutant presented a general decreased ER density, as well as a disorganisation of the ER network (Fig.\u00a07a), as observed by SIM (Supplementary Fig.\u00a06h). Expression of CLIMP-63-CC led to a strong densification of ER sheet-like compartments (Fig.\u00a07a), in agreement with the confocal microscopy analysis (Fig.\u00a06i). A similar ER densification was observed in cells co-expressing WT RFP-CLIMP-63 and ZDHHC6-myc (Fig.\u00a07a). High ZDHHC6 expressing cells could clearly be identified by the presence of bright ER clusters, detected in our initial confocal microscopy analysis (Fig.\u00a06c). They appear as highly organised ER structures, known as OSERs (Organised Smooth ER)\n \n \n 45\n \n \n (Supplementary Fig. 7), which differ from ER stress-induced ER whorls\n \n \n 46\n \n \n .\n
\n\n Such ZDHHC6-myc highly overexpressing cells were specifically selected to perform focused ion beam scanning electron microscopy (FIBSEM). This technique provides serial images with near isotropic voxels from which a reconstruction of an ER volume can be generated (Fig.\u00a07b, Supplementary Movie 1\u20133). In control conditions, i.e. endogenous ZDHHC6 expression, the ER sheets formed a stratified matrix with multiple clustered and complex fenestrations between layers. In cells with high ZDHHC6 expression the pattern of ER sheet layers was strikingly more dense, with reduced fenestrations, and abundant membrane convolutions (Fig.\u00a07b, Supplementary Movie 3). The FIBSEM images and their 3D reconstruction confirmed that overexpression of ZDHHC6 strongly increased continuity and densification of the ER sheets.\n
\n\n Quantifying alterations of the ER morphology remains a major challenge for cell biology image analysis. To accurately measure the ER densification phenotype induced by ZDHHC6, we employed persistent homology, a mathematical tool in applied algebraic topology (for background and mathematical introductions please refer to the extended methods and previous studies)\n \n \n 47\n \n \u2013\n \n 49\n \n \n . Persistent homology tracks appearance or disappearance of features \u2013 such as spherical cavities (in degree-2) and loops (in degree-1) \u2013 in data-sets across a range of distance scales (Supplementary Fig. 8). Data is shown as a persistence diagram, which tracks all membrane features throughout the ER-3D reconstructions analysed. Each point refers to a feature, where the horizontal coordinate encodes its appearance, and the vertical, the disappearance. Therefore, abundance in small and noisier features (e.g. resulting from small fenestrations, nanoholes) will correspond to values closer to the diagonal of the diagram, whereas larger, more significant features (e.g. expanded membrane sheets) will have higher persistence values and be farther from the diagonal (Fig. 7c, d and Supplementary Fig. 8). Persistent homology analysis, particularly in degree-2, confirmed the prominent change in ER topology caused by ZDHHC6 overexpression, which promotes the expansion of large and dense ER-sheets and reduces the amount and complexity of ER fenestrations (Fig. 7c, d).\n
\n\n CLIMP-63 is an enigmatic protein, about which there are many open questions. Here we addressed the impact of S-acylation and its dynamics. We used a variety of experimental approaches \u2013biochemistry, microscopy, metabolic labelling\u2013 to describe some behavioural aspects of CLIMP-63 and mathematical modelling to understand their complexity and interconnectedness. Altogether the work leads us to propose the following scenario. CLIMP-63, synthesized by ribosomes on the ER membrane, is co-translationally inserted into the membrane with its large C-terminal domain in the lumen, where it rapidly folds and assembles into trimeric elementary units (E\n \n 0\n \n \n ER\n \n ) (Fig.\u00a04b). The lack of classical ER retention signals within CLIMP-63 allows a minor population of folded E\n \n 0\n \n \n ER\n \n to exit the ER and reach the plasma membrane. The majority, however, is retained in the ER through two independent mechanisms: S-acylation on a single cysteine and higher-order assembly, most likely dimers of trimers (H\n \n ER\n \n ). E\n \n 0\n \n \n ER\n \n are highly susceptible to S-acylation by ZDHHC6, rapidly generating acylated trimers. E\n \n 1\n \n \n ER\n \n can promptly be de-acylated by APT2, in a cycle that sustains limited exit from the ER. The majority of E\n \n ER\n \n however assembles into S-acylated complexes (H\n \n 2\n \n \n ER\n \n ). These are somehow protected from de-acylation and have ~\u200916-times longer half-life than any other ER-localized CLIMP-63 species, leading it to be the major species at steady state. In the absence of S-acylation, higher order assembly still occurs, retaining CLIMP-63 in the ER. H\n \n 0\n \n \n ER\n \n is however less stable, less abundant and altered in its ability to shape the ER. The non-acylated elementary units E\n \n 0\n \n \n ER\n \n that exit the ER are substrates for two other acyltransferases that localize to the late secretory-endosomal system, ZDHHC 2 and 5\n \n 33,36,37\n \n . S-acylation is essential for the maintenance of CLIMP-63 at the cell surface, within raft-like nanodomains\n \n \n 33\n \n \n presumably controlling its residence time there and thus influencing its signalling capacity.\n
\n\n The acylation of CLIMP-63 not only affects its intracellular trafficking and cellular stability, but also its ability to shape the ER. We analysed the ER morphology under conditions where the CLIMP-63 acylation-deacylation kinetics were modified, i.e. accelerated acylation by ZDHHC6 overexpression, delayed deacylation using the double cysteine CC mutant or no acylation with the C100A mutant. WT, C100A and CC all formed similar higher order structures (Supplementary Fig. 6g), yet their effects on ER morphology were different indicating that adequate acylation levels and dynamics of CLIMP-63 are necessary for adapted morphology. When acylation was excessive, we observed a loss of fenestration and a massive expansion of ER sheets. These findings are consistent with a recent studies using live-cell stimulated depletion (STED) microscopy showing that CLIMP-63 coordinates the formation of dynamic nanoholes within ER sheets and luminal ER nanodomain heterogeneity\n \n \n 30\n \n ,\n \n 31\n \n \n . The present work suggests that the control of nanohole formation is tunned through the acylation of CLIMP-63. The addition of medium chain fatty acids to CLIMP-63 trimers and higher order structures is likely to modify the lipid composition and/or physical chemical properties of the surrounding membranes, and possibly thereby the intrinsic membrane curvature. A recent computational analysis indeed proposes that membrane tension and curvature\n \n \n 25\n \n \n , both of which could well be influence by CLIMP-63 acylation and lateral lipid organisation, are the key elements that drive nanohole formation.\n
\n\n How CLIMP-63 acylation cycles are controlled remains to be established. The metabolic state of cells and tissues is likely to play a role. It was indeed recently observed that the ER organization was disrupted in hepatocytes from obese mice, due to an imbalance between the levels of CLIMP-63 and ER tubule-associated proteins, which could be rescued by the exogenous overexpression of CLIMP-63\n \n 50\n \n . Another recent study found that excess fatty acid synthesis leads to the densification of ER membranes causing downstream mitotic complications\n \n \n 51\n \n \n . A link between lipid metabolism and protein acylation, although expected, remains to be explored and mechanistically understood. Future studies should also address the structural features that enable acylated CLIMP-63 to control ER fenestration, whether this property cross-talks to its microtubule binding ability and finally the exact mechanism by which the still mysterious CLIMP-63 luminal domain influence ER sheet formation.\n
\n\n For western blotting and immunofluorescence, myc (RRID:AB_2537024) and Bap31 (RRID:AB_325095) antibodies were from Thermo Fisher (US). Anti-CLIMP-63 were either from Alexis/ENZO (G1/296, CH, RRID:AB_2051140) or Bethyl Laboratories (A302, RRID:AB_1731083). Anti-atlastin-2 (RRID:AB_10971492) and anti-LRP6 were also from Bethyl Laboratories (US), (RRID:AB_21393299). Anti-calreticulin (RRID:AB_1267911), anti-Spastin (RRID:AB_2042945) and anti-BiP (RRID:AB_880312) were from Abcam (UK). Anti-atlastin-3 (RRID:AB_2290228) were from Protein Tech (US). Anti-tubulin ( RRID:AB_477579), anti-GAPDH (RRID:AB_2533438), anti-ZDHHC6 (RRID:AB_2304658), anti-FLAG (RRID:AB_439685), anti-LPXN ( RRID:AB_1853250), anti-Caveolin1(RRID:AB_476842) and anti-transferrin receptor (RRID:AB_86623) were from Sigma (US). Anti-KTN1 (RRID:AB_1852652) and anti-RRBP1 (RRID:AB_1856476) were from Sigma (Atlas, US). Anti-actin was from Millipore (US) (RRID:AB_2223041). Anti-HA was from BioLegend (US) (RRID:AB_2563418). Anti-GFP (RRID:AB_2336883) and anti-RFP (RRID:AB_ 2336063) were from Roche (CH) and anti-V5 was from Invitrogen (US) (RRID:AB_2556565). Anti-calnexin was previously described 1 and provided by Dr. M. Molinari. Anti-TRAP\u03b1 was provided by Dr. R. Hegde. Anti-HA-HRP conjugated was from Roche (CH) (RRID:AB_390918). For immunoprecipitation, sepharose G-beads were from GE Healthcare (US), anti-myc-beads were from Thermo Fisher (US) and anti-HA-beads were from Roche (CH).\n
\n\n The siRNAs for ZDHHC2 (TAGCTACTGCTAGAAGTCTTA), ZDHHC3 (TCCGTTCTCATGAATGTTTAA), ZDHHC5 (ACCACCATTGCCAGACTACAA) and ZDHHC6 (GAGGTTTACGATACTGGTTAT) were from Qiagen, D. As control siRNA, we used either the AllStars negative control siRNA (Qiagen, D) or targeted the viral glycoprotein VSV-G (sequence: ATTGAACAAACGAAACAAGGA).\n
\n\n Point mutations were generated using QuikChange II XL kit from Agilent Tech (US). ZDHHC6-GFP was obtained by inserting the PCR amplified product of ZDHHC6 in a peGFP-C3 vector using XhoI and BamHI sites. CLIMP-63-HA was generated by inserting CLIMP-63-HA cDNA in place of the RFP in a pTagRFP vector. The following constructs were kind gifts: CLIMP-63-YFP from Dr. Hans-Peter Hauri and Dr. Hesso Farhan; ZDHHC2-myc, ZDHHC6-myc and ZDHHC16-FLAG from Dr. Masaki Fukata.\n
\n\n All HeLa cells were cultured in MEM Eagle (Sigma, US) complemented with 10% FCS (PAN Biotech, D), 1% Pen/Strep, 1% L-Glutamine, and 1% MEM NEAA (all Gibco, US). They were mycoplasma negative as tested on a trimestral basis using the MycoProbe Mycoplasma Detection Kit CUL001B. RPE-1cells were grown in complete Dulbeccos MEM (DMEM, Sigma) at 37\u00b0C supplemented with 10% foetal bovine serum (FBS), 2 mM L-Glutamine, penicillin and streptomycin. For transfection, cells were dissociated using trypsin and plated in tissue culture dishes (Falcon, US). After 24 h, the medium was changed and the cells were transfected using Fugene for plasmids (Promega, US) or INTERFERin (Polyplus, F) for silencing with siRNA. The cells were incubated for 24 h to 48 h (for plasmids) or 72 h (for siRNA) before performing experiments. Drug treatments were used at: nocodazole (2 h at 10 \u00b5g/mL), or Taxol (4 h at 5 \u00b5g/mL) both in IM medium (described in\n \n 3\n \n H-labelling). Taxol treatments for IF were done in complete medium. ML348 and ML349 were used at 10 \u00b5M in complete medium for 4 h of pre-treatment followed by the indicated time before harvest.\n
\n\n The stable HeLa cell lines transduced with shRNA were generated as described elsewhere\n \n \n 53\n \n \n . To summarize, the shRNA of interest was inserted in a pRRLsincPPT-hPGK-mcs-WPRE vector. HEK293T cells were co-transfected with pMD2g and pSPAX2, which encode the envelope and packaging proteins, respectively. Lentiviral particles were harvested and titrated by qPCR. Finally, low passage HeLa cells were transduced with a range of viral loads and tested by qPCR and by Western blot to quantify the silencing efficiency of the targeted protein. Cells were maintained\n
\n\n in 8ug/mL puromycin. The sh-control consisted of the parent vector with non-targeting sequence. The shRNA sequence against ZDHHC6 was GATCcccCCTAGTGCCATGATTTAAAttcaagagaTTTAAATCATGGCACTAGGtttttC and against CLIMP-63 was GATCcccGAGGTAACTATGCAAAGCAttcaagagaTGCTTTGCATAGTTACCTCtttttC. Real-time quantitative PCR was performed as described previously\n \n \n 38\n \n \n .\n
\n\n CRISPR/Cas9 KO of ZDHHC6 was obtained following previously published protocols\n \n \n 54\n \n \n using the following guide RNA sequence targeting exon 2 of ZDHHC6: TGGGGTCCCATCATAGCCCT. Cells were selected using 10 \u00b5g/ml of puromycin and blasticidin.\n
\n\n For immunoprecipitation and Western blot, cells were lysed on ice for 30min with lysis buffer (500 mM Tris\u2013HCl pH 7.4, 2 mM benzamidine, 10 mM NaF, 20 mM EDTA, 0.5% NP40 and a protease inhibitor cocktail (Roche, CH)). The lysate was then clarified by centrifugation at 4\u00b0C for 3 min at 5000 rpm. Lysates were pre-cleared using Sepharose G-beads only for 30 min at 4\u00b0C before immunoprecipitation (G-beads plus antibody) turning on a wheel overnight at 4\u00b0C. The beads were then washed 3x with lysis buffer before adding 4x Sample Buffer including beta-mercaptoethanol. The samples were boiled 5 min at 95\u00b0C and vortexed before loading and migrating on 4\u201312% or 4\u201320% Tris-glycine SDS-PAGE gels. Blots were revealed using a Fusion Solo (Vilber Lourmat, CH) and quantified with ImageJ or Bio1D (Vilber Lourmat, CH).\n
\n\n Acyl-RAC was performed according to\n \n \n 55\n \n \n . In brief, a post nuclear supernatant was retrieved and the proteins were blocked in a buffer with 0.5% TX100, a protease inhibitor cocktail and 1.5% MMTS for 4 h at 40\u00b0C vortexing every 15 min. The proteins were then precipitated using cold acetone at -20\u00b0C for 20 min and centrifuged at 4\u00b0C for 10 min at 7500 rpm. The pellet was washed 5x with 70% acetone. After drying, the samples were resuspended in an SDS buffer. 10% of the sample was reserved as input and the rest was separated into two tubes. The first tube was treated with hydroxylamine 0.5 M (final, in Tris pH 7.4) and 10% thiopropyl sepharose beads (Sigma). The second tube (negative control) had only Tris-HCl pH 7.4 with 10% thiopropyl sepharose beads. The samples were incubated at RT overnight. Finally, the beads were washed 3x in SDS-buffer, before adding sample buffer (4x) w/beta-mercaptoethanol and performing SDS-PAGE followed by a western blot as described above.\n
\n\n \n APEGS (PEGylation)\n \n
\n\n The stoichiometry of protein S-Palmitoylation was assessed by APEG. The assay was followed as described elsewhere\n \n \n 56\n \n \n , with minor modifications. Hela cells were lysed in 4% SDS, 5 mM EDTA, in PBS with complete Protease Inhibitor Cocktail (Roche). Supernatant proteins were retrieved after centrifugation at 100\u2019000 g for 15 min. The proteins were reduced with 25 mM TCEP for 1 h at 25\u00b0C, and free cysteine residues were blocked with 20 mM NEM for 3 h at 25\u00b0C. After chloroform/methanol precipitation, the proteins were resuspended in PBS with 4% SDS and 5 mM EDTA and incubated in 1% SDS, 5 mM EDTA, 1 M NH\n \n 2\n \n OH, pH 7.0 for 1 h at 37\u00b0C. As a negative control, 1 M Tris-HCl, pH 7.0, was used. After precipitation, the proteins were resuspended in PBS with 4% SDS and PEGylated with 20 mM mPEGs for 1 h at 25\u00b0C to label newly exposed cysteinyl thiols. As a negative control, 20 mM NEM was used instead of mPEG (5kDa-PEG). After precipitation, proteins were resuspended in SDS-sample buffer and boiled at 95\u00b0C for 5 min. The proteins were separated by SDS-PAGE, transferred and western blotted. Protein concentration was measured by BCA protein assay.\n
\n\n Approximately 1 \u00d7 10\n \n 7\n \n cells were re-suspended in 0.5 ml cold TNE buffer (25 mMTris-HCl, pH 7.5, 150 mM NaCl, 5 mM EDTA, and 1% Triton X-100) with a tablet of protease inhibitors (Roche). Membranes were solubilized in a rotating wheel at 4\u00b0C for 30 minutes. DRMs were isolated using an Optiprep\u2122 gradient: the cell lysate was adjusted to 40% Optiprep\u2122, loaded at the bottom of a TLS.55 Beckman tube, overlaid with 600 \u00b5l of 30% Optiprep\u2122 and 600 \u00b5l of TNE, and centrifuged for 2 hours at 55,000 rpm at 4\u00b0C for cells. Six fractions of 400 \u00b5l were collected from top to bottom. DRMs were found in fractions 1 and 2. Equal volumes from each fraction were analyzed by SDS-PAGE and western blot analysis using anti-CLIMP-63, HRP-conjugated anti-HA, caveolin1 and transferrin receptor antibodies.\n
\n\n Surface biotinylation was performed on transfected cells. Cells were allowed to cool down shaking at 4\u00b0C for 15 minutes to arrest endocytosis. Cells were then washed three times with cold PBS and treated with EZ-Link Sulfo-NHS-SS-Biotin No weight for 30 minutes shaking at 4\u00b0C. Cells were then washed 3 times for 5 minutes with 100mM NH4Cl and lysed in 1% Tx-100 to do DRMs or in IP Buffer for 1h at 4\u00b0C. Lysate were then centrifuged for 5 minutes at 5000rpm and the supernatant incubated with streptavidin agarose beads overnight on a wheel at 4\u00b0C. Beads were washed with IP buffer 5 times and the proteins were eluted from the beads by incubation in SDS sample buffer with \u00df-mercaptoethanol for 5 minutes at 95\u00b0 buffer prior to performing SDS-PAGE and western blotting.\n
\n\n \n \n 3\n \n \n \n H-metabolic labelling\n \n
\n\n Cells were seeded in tissue culture dishes as described above. For labelling, the cells were starved using IM medium (Glasgow minimal essential medium buffered with 10 mM Hepes, pH 7.4). After 1 h, the medium was replaced by IM with 3H-palmitate at 200 \u00b5Ci/mL (American Radiolabeled Chemicals, US) for 2 h at 37\u00b0C. Cell lysis, immunoprecipitation and SDS-PAGE were performed as above. The gels were fixed for 30 min with 10% acetic acid, 25% isopropanol in water and the signal was amplified for 30 min with NAMP100 (GE Healthcare, US). The gels were then dried and applied to an Amersham Hyperfilm MP (GE Healthcare, US). The radioactivity was visualized and quantify using a Typhoon TRIO (GE Healthcare, US).\n
\n\n \n \n 35\n \n \n \n S Pulse chase metabolic labelling\n \n
\n\n The cells were plated in tissue culture dishes as described above. 48 h post-transfection, the cells were starved 30 min at 37\u00b0C in DMEM-HG medium (devoid of Cys/Met). The pulse consisted of 70 \u00b5Ci/mL 35S (American Radiolabeled Chemicals, US) in the same starvation medium for 20 min at 37\u00b0C. Cells were then washed 2 x and incubated in complete MEM medium containing Cys/Met in excess. Finally, cells were lysed and harvested. Proteins of interest were immuno-precipitated and prepared for western blotting as previously described.\n
\n\n Suspension-adapted HEK293E cells transiently transfected with construct expressing CLIMP-63 Luminal Domain with N-terminal signal recognition peptide and either N- or C-terminal His6-FLAG tag using PEI MAX (Polysciences) in RPMI-1640 (Gibco) supplemented with 0.1% Pluronic-F68. After 1.5 hours, cells were diluted into Excell293 medium (Sigma) supplemented with 4 mM glutamine and 3.75 mM valproic acid and agitated for 37\u00b0C. Following a 7-day incubation the cell culture medium was harvested by centrifugation and clarified using a 0.22 \u00b5m filter. The conditioned medium was purified by Ni-NTA affinity chromatography via CLIMP63\u2019s His-tag followed by gel-filtration chromatography in 500 mM NaCl, 50 mM HEPES pH 7.5.\n
\n\n To preserve non-covalent interactions, intact mass measurements were performed under native-like conditions by injecting the samples into MAbPac SEC-1 column (300 \u00c5, 5 \u00b5m, 4 x 150 mm, Thermo Fisher Scientific, Sunnyvale, CA, USA) using a Dionex Ultimate 3000 analytical RSLC system (Dionex, Germering, Germany) coupled to a HESI source (Thermo Fisher Scientific, Bremen, Germany). The isocratic separation was performed within 7 min at flow rate of 300 \u00b5l/min and 50 mM ammonium acetate, pH 7.5 as mobile phase. Eluting fractions were analyzed on high resolution QExactive HF-HT-Orbitrap-FT-MS benchtop instrument (Thermo Fisher Scientific, Bremen, Germany). High-mass-range (HMR) mode was activated with resolution of 15 000, in-source CID of 50 eV and AGC (automatic gain control) target of 5e6. The scan range was set to 1900\u20138000 m/z. Data analysis was performed with Protein Deconvolution 4.0 (Thermo Fischer Scientific, Sunnyvale, CA, USA) using Respect algorithm.\n
\n\n To assess the mass of the monomeric form, intact mass measurements were performed under denaturing conditions by injecting the samples into Acquity UPLC Protein column BEH C4 (300 \u00c5, 1.7 \u00b5m, 1 x 150 mm, Waters, Milford, MA, U.S.A.) using a Dionex Ultimate 3000 analytical RSLC system (Dionex, Germering, Germany) coupled to a HESI source (Thermo Fisher Scientific, Bremen, Germany). The separation was performed with a flow rate of 90 \u00b5l/min by applying a gradient of solvent B from 15 to 20% in 2 min, then from 20 to 45% within 10 min, followed by column washing and re-equilibration steps. Solvent A was composed of MilliQ water with 0.1% formic acid, while solvent B consisted of acetonitrile with 0.1% formic acid. Eluting fractions were analyzed on high resolution QExactive HF-HT-Orbitrap-FT-MS benchtop instrument (Thermo Fisher Scientific, Bremen, Germany). Protein mode was activated with resolution of 15 000, in-source CID of 25 eV, AGC target of 3e6 and averaging 10 \u00b5scans. The scan range was set to 600\u20132000 m/z. Data analysis was performed with Protein Deconvolution 4.0 (Thermo Fischer Scientific, Sunnyvale, CA, USA) using Respect algorithm.\n
\n\n Protein content of the samples was further verified with shotgun/bottom-up proteomic LC-MS/MS analysis. 5 \u00b5g of protein in 25 mM ammonium bicarbonate buffer (pH 7.8) were boiled at 95\u00b0C for 2 min, reduced with TCEP solution of 5 mM final concentration at 55\u00b0C for 30 min, followed by alkylation with IAA solution of 5 mM final concentration in the dark for 30 min at room temperature and digestion with trypsin (enzyme/protein ratio of 1:30 w/w) at 37\u00b0C overnight. Reaction was quenched by acidification using formic acid to a final acid concentration of 0.1%. mObtained proteolytic peptide mixture was separated on column ZORBAX Eclipse Plus C18 column (2.1 x 150 mm, 5 \u00b5m, Agilent, Waldbronn, Germany) using Dionex Ultimate 3000 analytical RSLC system (Dionex, Germering, Germany) coupled to a HESI source (Thermo Fisher Scientific, Bremen, Germany. The separation was performed with flow rate of 250 \u00b5l/min by applying a gradient of solvent B from 5 to 35% within 60 min, followed by column washing and re-equilibration steps. Solvent A was composed of MilliQ water with 0.1% formic acid, while solvent B consisted of acetonitrile with 0.1% formic acid. Eluting peptides were analyzed on QExactive HF-HT-Orbitrap-FT-MS benchtop instrument (Thermo Fisher Scientific, Bremen, Germany). MS1 scan was performed with 60 000 resolution, AGC of 1e6 and maximum injection time of 100 ms. MS2 scan was performed in Top10 mode with 1.6 m/z isolation window, AGC of 1e5, 15 000 resolution, maximum injection time of 50 ms and averaging 2 \u00b5scans. HCD was used as fragmentation method with normalized collision energy of 27%.\n
\n\n Data analysis was performed using Trans-Proteomic Pipeline software (TPP, Institute for Systems Biology, Seattle Proteome Center) using Tandem pipeline with X-Tandem search engine. The cleavage specificity for trypsin was set with two allowed missed cleavages, precursor and product ion mass tolerances of 10 ppm and 0.02 Da, respectively. Cysteine carbamidomethylation and methionine oxidation were chosen as constant and variable modifications, respectively. The false discovery rate (FDR) was set to 1% with minimal peptide length of seven amino acids.\n
\n\n Cells were seeded on glass coverslips (N1.5, Marienfeld, D) for at least 48 h. Fixation and permeabilization were optimised to preserve either i) the secretory pathway (cells were washed 3x with PBS, fixed with 3% paraformaldehyde for 20 min at 37\u00b0C, washed 3x with PBS, quenched with 50 mM of NH4Cl for 10 min at RT, washed 3x with PBS, permeabilized with 0.1% Triton X-100 for 5 min at RT and finally washed 3x with PBS) or ii) the cytoskeleton and ER membranes (cells were washed 3x with PBS, fixed with precooled methanol for 4 min at -20\u00b0C, and washed 3x with PBS). In both cases, the cells were then blocked overnight in PBS\u2009+\u20090.5% BSA (GE Healthcare, US). The coverslips were incubated with primary antibody for 30 min at RT, washed 3x for 5 min with PBS \u2212\u20090.5% BSA and incubated for 30 min at RT with secondary fluorescent antibodies (Alexa 488, 568 or 647, Invitrogen, US), and finally washed again 3x with PBS \u2212\u20090.5% BSA prior to mounting in Mowiol. The coverslips were imaged by confocal microscopy using a LSM710 microscope (Zeiss, D) with a 63x oil immersion objective (NA 1.4).\n
\n\n Cells seeded on glass cover slips (Source? 170\u2009\u00b1\u20095 \ud835\udf07m thickness and between 18 mm and 24 mm in diameter) were processed as for immunofluorescence. Coverslips were imaged using an inverted Nikon Eclipse Ti Motorized microscope, with Andor iXon3 897 detector using a APO TIRF 100x (NA 1.49) oil immersion objective (working distance of 0.12 mm).\n
\n\n Cells were plated and transfected with ZDHHC6-GFP plasmids on glass coverslips coated with a 5-nm layer of carbon outlining a numbered grid reference pattern. After 24 h, the cells were fixed for 60 min in a buffered solution of 2% paraformaldehyde and 2.5% glutaraldehyde at 25\u00b0C, and then washed 3x with cacodylate buffer. The coverslips were then mounted in a holder for fluorescence microscopy and the cells imaged by confocal microscopy (LSM700, Zeiss, 63x objective, NA 1.4). The cells of interest were imaged at a range of magnifications and their location recorded according to the carbon grid pattern. The coverslips were then post-fixed with 1% osmium tetroxide and 1.5% potassium ferrocyanide in cacodylate buffer (0.1 M, pH 7.4) for 40 min at 25\u00b0C. After washing in distilled water and further staining with osmium alone followed by 1% uranyl acetate, they were dehydrated in a series of increasing concentrations of alcohol, then embedded in Durcupan resin, which was hardened overnight at 65\u00b0C. The next day, the resin containing the cells of interest was separated from the coverslips and mounted onto a blank resin block for ultrathin sectioning. Serial ultrathin sections were cut at 50 nm thickness and collected onto a formvar support film on single slot copper grids. Images were acquired at 80 kV using a transmission electron microscope (Tecnai Spirit, FEI Company, US).\n
\n\n Cells of interest, recorded with fluorescent microscopy and prepared for electron microscopy (see above), were serially imaged using FIBSEM. Resin blocks were trimmed using an ultramicrotome so that the cell was located within 5 \u00b5m of the edge. This block was then glued to aluminium stub, coated with a 20-nm layer of gold in a plasma coater, and placed inside the microscope (Zeiss NVision 40, Zeiss NTS). An ion beam of 1.3 nAmps was used to sequentially mill away 10-nm layers of resin from the block surface to enable the cell to be serially imaged. Images were collected using the backscatter detector with the electron beam at 1.6 kV and grid tension set at 1.3 kV to collect only the highest energy electrons.\n
\n\n The final images were precisely aligned using the StackReg algorithm (56) in ImageJ, and the ER, mitochondria, nuclear membrane, and cell membrane were segmented using the Microscopy Image Browser software (57). The mesh models were then exported to the Blender software (\n \n \n \n www.blender.org\n \n \n \n ) for final rendering and visualization.\n
\n\n Statistical analyses were carried using Prism software. Data representation and statistical details can be found in the figure legends. Unless otherwise indicated, an unpaired two-tailed Student\u2019s t-test was used for direct comparison of means between two groups, whereas ANOVA was used to compare the means among three or more groups. For ANOVA analyses p values were obtained by post hoc tests used to compare every mean or pair of means (Tukey\u2019s & Sidak\u2019s) or to compare every mean to a control sample (Dunnet\u2019s). Data are represented as means\u2009\u00b1\u2009standard deviations. ns: not significant, *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****< 0.0001.\n
\n\n The authors declare that all data supporting the findings of this study are available within the paper and in the Supplementary Information.\n
\n\n Further details on materials and methods can be found in Supplementary Information.\n
\n\n The COVID-19 pandemic has been associated with increased neuropsychiatric conditions in children and youths, with evidence suggesting that SARS-CoV-2 infection may contribute additional risks beyond pandemic stressors. This study aimed to assess the full spectrum of neuropsychiatric conditions in COVID-19 positive children (ages 5\u201312) and youths (ages 12\u201320) compared to a matched COVID-19 negative cohort, accounting for factors influencing infection risk. Using EHR data from 25 institutions in the RECOVER program, we conducted a retrospective analysis of 326,074 COVID-19 positive and 887,314 negative participants matched for risk factors and stratified by age. Neuropsychiatric outcomes were examined 28 to 179 days post-infection or negative test between March 2020 and December 2022. SARS-CoV-2 positivity was confirmed via PCR, serology, or antigen tests, while negativity required negative test results and no related diagnoses. Risk differences revealed higher frequencies of neuropsychiatric conditions in the COVID-19 positive cohort. Children faced increased risks for anxiety, OCD, ADHD, autism, and other conditions, while youths exhibited elevated risks for anxiety, suicidality, depression, and related symptoms. These findings highlight SARS-CoV-2 infection as a potential contributor to neuropsychiatric risks, emphasizing the importance of research into tailored treatments and preventive strategies for affected individuals.\n
\n\n Increased neuropsychiatric sequelae associated with the COVID-19 pandemic has been reported worldwide.\n \n 1,2\n \n However, there remains uncertainty whether these can be directly attributed to SARS-CoV-2 infection or the broader pandemic stressors and mitigation strategies.\n \n 2\u20134\n \n Similar to adults, children and youths are also susceptible to experiencing enduring neuropsychiatric and related conditions after an acute COVID-19 infection.\n \n 5,6\n \n Although significant research has been conducted on PASC in the adult population, there remains a notable gap in studies pertaining to pediatric cases.\n \n 7\u20139\n \n Children and youths often exhibit distinct symptoms compared to adults and typically experience a milder acute disease trajectory, with a reduced risk of hospitalization or mortality, especially in cases where pre-existing conditions are absent.\n \n 10,11\n \n Given these variations in acute infection profiles and prevalence in children and youths as compared with adults, it is imperative to separately investigate the characteristics of PASC in the pediatric population in well-controlled studies.\n
\n\n There are existing studies with large pediatric samples investigating neuropsychiatric conditions in pediatric populations with and without COVID-19 infection.\n \n 12\u201315\n \n However, the results remain inconclusive due to limitations such as the reliance solely on diagnoses to identify COVID-19 positive and negative cohorts, with only a subset being confirmed with testing.\n \n 13,14\n \n Given that COVID-19 symptoms are often mild or absent in children, some infected individuals may have been misclassified.\n \n 12\u201314\n \n These studies likely underestimated the prevalence of mental health conditions, as many DSM-5-based diagnoses used by clinicians cannot be fully matched to ICD-10-CM codes.\n \n 12\u201315\n \n
\n\n In our study, the large EHR data set allowed COVID-19 negative cohorts of sufficient size matched for risk factors and stratified by age.\n \n 15\n \n We used both diagnosis and PCR, antigen, or serology tests to reliably identify COVID-19 positive and negative groups. Neuropsychiatric and related conditions were identified by a typology developed to query EHR data for the full spectrum of DSM-5 disorders.\n \n 16\n \n The primary objective of this retrospective cohort study was to ascertain the risk of developing neuropsychiatric and related conditions after the pandemic in children and youths who had tested positive for COVID-19 compared to those who tested negative and never had a positive test at the same time interval. To achieve this, we utilized EHR data collected from twenty-five children's hospitals and healthcare institutions across the United States from the RECOVER program. Initially, we calculated the raw frequency of any neuropsychiatric and related conditions, both before and after the onset of the pandemic. Subsequently, we conducted an interrupted time series analysis to determine whether contracting SARS-CoV-2 increased the risk of being diagnosed with neuropsychiatric and related conditions, compared to the SARS-19 negative group, both groups being exposed to the pandemic psychosocial stressors.\n
\n\n As shown in Tables\n \n 2\n \n and\n \n 3\n \n , there were small increases in frequency of any neuropsychiatric and related condition in the post-COVID phase (compared to pre-COVID) for both COVID-19 positive and COVID-19 negative groups in the children (COVID 19 positive cohort:12\u00b745% to 14\u00b701%; COVID 19-negative cohort: 11\u00b76% to 12\u00b748%) as well as for youths (COVID-19 positive cohort: 16\u00b70% to 17\u00b786%; COVID 19 negative cohorts: 15\u00b755% to 16\u00b776%).\n
\n| \n | \n\n \n Children\n \n | \n \n \n Youths\n \n | \n ||
|---|---|---|---|---|
| \n | \n\n \n COVID-19 positive cohort (N\u2009=\u2009141,349)\n \n | \n \n \n COVID-19 Negative cohort (N\u2009=\u2009441,790)\n \n | \n \n \n COVID-19 positive cohort\n \n\n (N\u2009=\u2009184,725)\n \n | \n \n \n COVID-19 negative cohort\n \n\n (N\u2009=\u2009445,524)\n \n | \n
| \n \n \n Mean (SD) age (years)\n \n \n | \n \n \n 8\u00b702 (2\u00b703)\n \n | \n \n \n 7\u00b768 (2\u00b702)\n \n | \n \n \n 15\u00b785 (2\u00b748)\n \n | \n \n \n 15\u00b772 (2\u00b746)\n \n | \n
| \n \n \n Sex\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n female\n \n | \n \n \n 67200(47\u00b754%)\n \n | \n \n \n 208647(47\u00b723%)\n \n | \n \n \n 102352(55\u00b741%)\n \n | \n \n \n 243311(54\u00b761%)\n \n | \n
| \n \n male\n \n | \n \n \n 74147(52\u00b746%)\n \n | \n \n \n 233128(52\u00b777%)\n \n | \n \n \n 82340(44\u00b757%)\n \n | \n \n \n 202124(45\u00b737%)\n \n | \n
| \n \n other/unknown\n \n | \n \n \n 2(0\u00b700%)\n \n | \n \n \n 15(0\u00b700%)\n \n | \n \n \n 33(0\u00b702%)\n \n | \n \n \n 89(0\u00b702%)\n \n | \n
| \n \n \n Race\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Asian/PI\n \n | \n \n \n 7147(5\u00b706%)\n \n | \n \n \n 22394(5\u00b707%)\n \n | \n \n \n 7103(3\u00b785%)\n \n | \n \n \n 19679(4\u00b742%)\n \n | \n
| \n \n Black/AA\n \n | \n \n \n 26028(18\u00b741%)\n \n | \n \n \n 77109(17\u00b745%)\n \n | \n \n \n 32121(17\u00b739%)\n \n | \n \n \n 71179(15\u00b798%)\n \n | \n
| \n \n Hispanic\n \n | \n \n \n 33470(23\u00b768%)\n \n | \n \n \n 99570(22\u00b754%)\n \n | \n \n \n 40774(22\u00b707%)\n \n | \n \n \n 87388(19\u00b761%)\n \n | \n
| \n \n White\n \n | \n \n \n 58446(41\u00b735%)\n \n | \n \n \n 190066(43\u00b702%)\n \n | \n \n \n 88475(47\u00b790%)\n \n | \n \n \n 223170(50\u00b709%)\n \n | \n
| \n \n Multiple\n \n | \n \n \n 2964(2\u00b710%)\n \n | \n \n \n 11100(2\u00b751%)\n \n | \n \n \n 2528(1\u00b737%)\n \n | \n \n \n 7739(1\u00b774%)\n \n | \n
| \n \n other/unknown\n \n | \n \n \n 13294(9\u00b741%)\n \n | \n \n \n 41551(9\u00b741%)\n \n | \n \n \n 13724(7\u00b743%)\n \n | \n \n \n 36369(8\u00b716%)\n \n | \n
| \n \n \n Hospital\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n A\n \n | \n \n \n 10267(7\u00b726%)\n \n | \n \n \n 37518(8\u00b749%)\n \n | \n \n \n 12060(6\u00b753%)\n \n | \n \n \n 30577(6\u00b786%)\n \n | \n
| \n \n B\n \n | \n \n \n 17983(12\u00b772%)\n \n | \n \n \n 50147(11\u00b735%)\n \n | \n \n \n 17151(9\u00b728%)\n \n | \n \n \n 39349(8\u00b783%)\n \n | \n
| \n \n C\n \n | \n \n \n 5102(3\u00b761%)\n \n | \n \n \n 26782(6\u00b706%)\n \n | \n \n \n 5533(3\u00b700%)\n \n | \n \n \n 23306(5\u00b723%)\n \n | \n
| \n \n D\n \n | \n \n \n 4693(3\u00b732%)\n \n | \n \n \n 13587(3\u00b708%)\n \n | \n \n \n 6934(3\u00b775%)\n \n | \n \n \n 17011(3\u00b782%)\n \n | \n
| \n \n E\n \n | \n \n \n 4154(2\u00b794%)\n \n | \n \n \n 8044(1\u00b782%)\n \n | \n \n \n 7091(3\u00b784%)\n \n | \n \n \n 13052(2\u00b793%)\n \n | \n
| \n \n F\n \n | \n \n \n 2316(1\u00b764%)\n \n | \n \n \n 12643(2\u00b786%)\n \n | \n \n \n 2147(1\u00b716%)\n \n | \n \n \n 10349(2\u00b732%)\n \n | \n
| \n \n G\n \n | \n \n \n 1971(1\u00b739%)\n \n | \n \n \n 4023(0\u00b791%)\n \n | \n \n \n 4222(2\u00b729%)\n \n | \n \n \n 7944(1\u00b778%)\n \n | \n
| \n \n H\n \n | \n \n \n 3199(2\u00b726%)\n \n | \n \n \n 14347(3\u00b725%)\n \n | \n \n \n 8144(4\u00b741%)\n \n | \n \n \n 29957(6\u00b772%)\n \n | \n
| \n \n I\n \n | \n \n \n 1918(1\u00b736%)\n \n | \n \n \n 4976(1\u00b713%)\n \n | \n \n \n 5159(2\u00b779%)\n \n | \n \n \n 9732(2\u00b718%)\n \n | \n
| \n \n J\n \n | \n \n \n 2823(2\u00b700%)\n \n | \n \n \n 12582(2\u00b785%)\n \n | \n \n \n 3572(1\u00b793%)\n \n | \n \n \n 12225(2\u00b774%)\n \n | \n
| \n \n K\n \n | \n \n \n 2065(1\u00b746%)\n \n | \n \n \n 5921(1\u00b734%)\n \n | \n \n \n 2885(1\u00b756%)\n \n | \n \n \n 7978(1\u00b779%)\n \n | \n
| \n \n L\n \n | \n \n \n 13633(9\u00b764%)\n \n | \n \n \n 35395(8\u00b701%)\n \n | \n \n \n 12489(6\u00b776%)\n \n | \n \n \n 26245(5\u00b789%)\n \n | \n
| \n \n M\n \n | \n \n \n 7800(5\u00b752%)\n \n | \n \n \n 39868(9\u00b702%)\n \n | \n \n \n 9978(5\u00b740%)\n \n | \n \n \n 34533(7\u00b775%)\n \n | \n
| \n \n N\n \n | \n \n \n 449(0\u00b732%)\n \n | \n \n \n 1224(0\u00b728%)\n \n | \n \n \n 2084(1\u00b713%)\n \n | \n \n \n 3444(0\u00b777%)\n \n | \n
| \n \n O\n \n | \n \n \n 8372(5\u00b792%)\n \n | \n \n \n 32867(7\u00b744%)\n \n | \n \n \n 7897(4\u00b728%)\n \n | \n \n \n 24043(5\u00b740%)\n \n | \n
| \n \n P\n \n | \n \n \n 5565(3\u00b794%)\n \n | \n \n \n 15017(3\u00b740%)\n \n | \n \n \n 9188(4\u00b797%)\n \n | \n \n \n 22894(5\u00b714%)\n \n | \n
| \n \n Q\n \n | \n \n \n 2970(2\u00b710%)\n \n | \n \n \n 11581(2\u00b762%)\n \n | \n \n \n 4149(2\u00b725%)\n \n | \n \n \n 13249(2\u00b797%)\n \n | \n
| \n \n R\n \n | \n \n \n 20044(14\u00b718%)\n \n | \n \n \n 48775(11\u00b704%)\n \n | \n \n \n 28030(15\u00b717%)\n \n | \n \n \n 47454(10\u00b765%)\n \n | \n
| \n \n S\n \n | \n \n \n 7534(5\u00b733%)\n \n | \n \n \n 3709(0\u00b784%)\n \n | \n \n \n 11109(6\u00b701%)\n \n | \n \n \n 3666(0\u00b782%)\n \n | \n
| \n \n T\n \n | \n \n \n 1253(0\u00b789%)\n \n | \n \n \n 8849(2\u00b700%)\n \n | \n \n \n 1298(0\u00b770%)\n \n | \n \n \n 8494(1\u00b791%)\n \n | \n
| \n \n U\n \n | \n \n \n 3978(2\u00b781%)\n \n | \n \n \n 13696(3\u00b710%)\n \n | \n \n \n 4729(2\u00b756%)\n \n | \n \n \n 15025(3\u00b737%)\n \n | \n
| \n \n V\n \n | \n \n \n 3936(2\u00b778%)\n \n | \n \n \n 13460(3\u00b705%)\n \n | \n \n \n 4514(2\u00b744%)\n \n | \n \n \n 16603(3\u00b773%)\n \n | \n
| \n \n W\n \n | \n \n \n 4010(2\u00b784%)\n \n | \n \n \n 12124(2\u00b774%)\n \n | \n \n \n 6674(3\u00b761%)\n \n | \n \n \n 11213(2\u00b752%)\n \n | \n
| \n \n X\n \n | \n \n \n 3726(2\u00b764%)\n \n | \n \n \n 10243(2\u00b732%)\n \n | \n \n \n 5953(3\u00b722%)\n \n | \n \n \n 11553(2\u00b759%)\n \n | \n
| \n \n Y\n \n | \n \n \n 1588(1\u00b712%)\n \n | \n \n \n 4412(1\u00b700%)\n \n | \n \n \n 1735(0\u00b794%)\n \n | \n \n \n 5628(1\u00b726%)\n \n | \n
| \n \n \n BMI category\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Non-obese\n \n | \n \n \n 55731(39\u00b743%)\n \n | \n \n \n 208023(47\u00b709%)\n \n | \n \n \n 68050(36\u00b784%)\n \n | \n \n \n 203291(45\u00b763%)\n \n | \n
| \n \n obese\n \n | \n \n \n 72423(51\u00b724%)\n \n | \n \n \n 190405(43\u00b710%)\n \n | \n \n \n 96663(52\u00b733%)\n \n | \n \n \n 192282(43\u00b716%)\n \n | \n
| \n \n Unknown\n \n | \n \n \n 13195(9\u00b734%)\n \n | \n \n \n 43362(9\u00b782%)\n \n | \n \n \n 20012(10\u00b783%)\n \n | \n \n \n 49951(11\u00b721%)\n \n | \n
| \n \n \n Clinical characteristics\n \n \n | \n ||||
| \n \n \n ED visits\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n 0\n \n | \n \n \n 105627(74\u00b773%)\n \n | \n \n \n 328426(74\u00b734%)\n \n | \n \n \n 141487(76\u00b759%)\n \n | \n \n \n 342382(76\u00b785%)\n \n | \n
| \n \n 1\n \n | \n \n \n 20116(14\u00b723%)\n \n | \n \n \n 67784(15\u00b734%)\n \n | \n \n \n 24140(13\u00b707%)\n \n | \n \n \n 62116(13\u00b794%)\n \n | \n
| \n \n 2+\n \n | \n \n \n 15606(11\u00b704%)\n \n | \n \n \n 45580(10\u00b732%)\n \n | \n \n \n 19098(10\u00b734%)\n \n | \n \n \n 41026(9\u00b721%)\n \n | \n
| \n \n \n Inpatient visits\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n 0\n \n | \n \n \n 132890(94\u00b702%)\n \n | \n \n \n 413667(93\u00b763%)\n \n | \n \n \n 170628(92\u00b737%)\n \n | \n \n \n 405541(91\u00b703%)\n \n | \n
| \n \n 1\n \n | \n \n \n 4998(3\u00b754%)\n \n | \n \n \n 19330(4\u00b738%)\n \n | \n \n \n 8518(4\u00b761%)\n \n | \n \n \n 26133(5\u00b787%)\n \n | \n
| \n \n 2+\n \n | \n \n \n 3461(2\u00b745%)\n \n | \n \n \n 8793(1\u00b799%)\n \n | \n \n \n 5579(3\u00b702%)\n \n | \n \n \n 13850(3\u00b711%)\n \n | \n
| \n \n \n Outpatient visits\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n 0\n \n | \n \n \n 19623(13\u00b788%)\n \n | \n \n \n 72984(16\u00b752%)\n \n | \n \n \n 26547(14\u00b737%)\n \n | \n \n \n 72841(16\u00b735%)\n \n | \n
| \n \n 1\n \n | \n \n \n 17936(12\u00b769%)\n \n | \n \n \n 71995(16\u00b730%)\n \n | \n \n \n 25188(13\u00b764%)\n \n | \n \n \n 70350(15\u00b779%)\n \n | \n
| \n \n 2+\n \n | \n \n \n 103790(73\u00b743%)\n \n | \n \n \n 296811(67\u00b718%)\n \n | \n \n \n 132990(71\u00b799%)\n \n | \n \n \n 302333(67\u00b786%)\n \n | \n
| \n \n \n PMCA index\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n 0\n \n | \n \n \n 106350(75\u00b724%)\n \n | \n \n \n 326474(73\u00b790%)\n \n | \n \n \n 139008(75\u00b725%)\n \n | \n \n \n 322992(72\u00b750%)\n \n | \n
| \n \n 1\n \n | \n \n \n 22402(15\u00b785%)\n \n | \n \n \n 71193(16\u00b711%)\n \n | \n \n \n 27216(14\u00b773%)\n \n | \n \n \n 71026(15\u00b794%)\n \n | \n
| \n \n 2\n \n | \n \n \n 12597(8\u00b791%)\n \n | \n \n \n 44123(9\u00b799%)\n \n | \n \n \n 18501(10\u00b702%)\n \n | \n \n \n 51506(11\u00b756%)\n \n | \n
| \n \n \n Negative tests prior entry\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n 0\n \n | \n \n \n 84429(59\u00b773%)\n \n | \n \n \n 337684(76\u00b744%)\n \n | \n \n \n 121218(65\u00b762%)\n \n | \n \n \n 348636(78\u00b725%)\n \n | \n
| \n \n 1\n \n | \n \n \n 31703(22\u00b743%)\n \n | \n \n \n 69760(15\u00b779%)\n \n | \n \n \n 36881(19\u00b797%)\n \n | \n \n \n 65033(14\u00b760%)\n \n | \n
| \n \n 2+\n \n | \n \n \n 25217(17\u00b784%)\n \n | \n \n \n 34346(7\u00b777%)\n \n | \n \n \n 26626(14\u00b741%)\n \n | \n \n \n 31855(7\u00b715%)\n \n | \n
\n
\n| \n | \n\n \n COVID-19 Positive cohort\n \n | \n \n \n COVID-19 Negative cohort\n \n | \n ||
|---|---|---|---|---|
| \n \n Pre-COVID*\n \n | \n \n \n Post-COVID**\n \n | \n \n \n Pre-COVID\n \n | \n \n \n Post-COVID\n \n | \n |
| \n \n \n Any mental health disorder\n \n \n | \n \n \n 12\u00b745%\n \n | \n \n \n 14\u00b701%\n \n | \n \n \n 11\u00b760%\n \n | \n \n \n 12\u00b748%\n \n | \n
| \n \n \n Adverse Childhood Experiences\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Emotional Abuse\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b704%\n \n | \n
| \n \n Neglect\n \n | \n \n \n 0\u00b701%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b701%\n \n | \n
| \n \n Physical Abuse\n \n | \n \n \n 0\u00b710%\n \n | \n \n \n 0\u00b710%\n \n | \n \n \n 0\u00b711%\n \n | \n \n \n 0\u00b710%\n \n | \n
| \n \n Sexual Abuse\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b706%\n \n | \n \n \n 0\u00b706%\n \n | \n \n \n 0\u00b706%\n \n | \n
| \n \n \n Anxiety Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Anxiety Disorder\n \n | \n \n \n 2\u00b722%\n \n | \n \n \n 2\u00b793%\n \n | \n \n \n 1\u00b781%\n \n | \n \n \n 2\u00b723%\n \n | \n
| \n \n OCD\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b719%\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b712%\n \n | \n
| \n \n Somatoform Disorder\n \n | \n \n \n 0\u00b723%\n \n | \n \n \n 0\u00b731%\n \n | \n \n \n 0\u00b719%\n \n | \n \n \n 0\u00b723%\n \n | \n
| \n \n Stress Disorder\n \n | \n \n \n 1\u00b745%\n \n | \n \n \n 1\u00b773%\n \n | \n \n \n 1\u00b724%\n \n | \n \n \n 1\u00b742%\n \n | \n
| \n \n \n Disruptive Behavior Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Conduct Disorder\n \n | \n \n \n 0\u00b747%\n \n | \n \n \n 0\u00b746%\n \n | \n \n \n 0\u00b749%\n \n | \n \n \n 0\u00b752%\n \n | \n
| \n \n Impulse Control Disorder\n \n | \n \n \n 0\u00b740%\n \n | \n \n \n 0\u00b743%\n \n | \n \n \n 0\u00b743%\n \n | \n \n \n 0\u00b748%\n \n | \n
| \n \n Oppositional Defiant Disorder\n \n | \n \n \n 0\u00b730%\n \n | \n \n \n 0\u00b736%\n \n | \n \n \n 0\u00b730%\n \n | \n \n \n 0\u00b732%\n \n | \n
| \n \n \n Eating and Feeding Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Avoidant/Restrictive Food Intake\n \n | \n \n \n 0\u00b733%\n \n | \n \n \n 0\u00b739%\n \n | \n \n \n 0\u00b729%\n \n | \n \n \n 0\u00b732%\n \n | \n
| \n \n Other Eating and Feeding Disorder\n \n | \n \n \n 0\u00b709%\n \n | \n \n \n 0\u00b710%\n \n | \n \n \n 0\u00b708%\n \n | \n \n \n 0\u00b710%\n \n | \n
| \n \n \n Elimination Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Encopresis\n \n | \n \n \n 0\u00b714%\n \n | \n \n \n 0\u00b716%\n \n | \n \n \n 0\u00b721%\n \n | \n \n \n 0\u00b720%\n \n | \n
| \n \n Enuresis\n \n | \n \n \n 0\u00b764%\n \n | \n \n \n 0\u00b765%\n \n | \n \n \n 0\u00b762%\n \n | \n \n \n 0\u00b761%\n \n | \n
| \n \n \n Gender Dysphoria/Sexual Dysfunction\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Gender Dysphoria\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b705%\n \n | \n
| \n \n Paraphilia\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n Sexual Dysfunction\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n \n Intentional Self-Harm/Suicidality\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Parasuicidality\n \n | \n \n \n 0\u00b707%\n \n | \n \n \n 0\u00b709%\n \n | \n \n \n 0\u00b707%\n \n | \n \n \n 0\u00b709%\n \n | \n
| \n \n Suicidality\n \n | \n \n \n 0\u00b714%\n \n | \n \n \n 0\u00b718%\n \n | \n \n \n 0\u00b713%\n \n | \n \n \n 0\u00b716%\n \n | \n
| \n \n \n Mood Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Bipolar Disorder\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b707%\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b705%\n \n | \n
| \n \n Major Depression\n \n | \n \n \n 0\u00b721%\n \n | \n \n \n 0\u00b727%\n \n | \n \n \n 0\u00b719%\n \n | \n \n \n 0\u00b725%\n \n | \n
| \n \n Minor Depression\n \n | \n \n \n 0\u00b731%\n \n | \n \n \n 0\u00b747%\n \n | \n \n \n 0\u00b727%\n \n | \n \n \n 0\u00b739%\n \n | \n
| \n \n \n Neurocognitive Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Catatonia\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n Delirium\n \n | \n \n \n 0\u00b709%\n \n | \n \n \n 0\u00b713%\n \n | \n \n \n 0\u00b709%\n \n | \n \n \n 0\u00b709%\n \n | \n
| \n \n Encephalopathy\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b706%\n \n | \n
| \n \n \n Neurodevelopmental Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Academic Developmental Disorder\n \n | \n \n \n 0\u00b768%\n \n | \n \n \n 0\u00b777%\n \n | \n \n \n 0\u00b762%\n \n | \n \n \n 0\u00b772%\n \n | \n
| \n \n ADHD\n \n | \n \n \n 4\u00b725%\n \n | \n \n \n 5\u00b708%\n \n | \n \n \n 3\u00b758%\n \n | \n \n \n 4\u00b728%\n \n | \n
| \n \n Autism Spectrum Disorder\n \n | \n \n \n 2\u00b712%\n \n | \n \n \n 2\u00b732%\n \n | \n \n \n 2\u00b717%\n \n | \n \n \n 2\u00b729%\n \n | \n
| \n \n Communication/Motor Disorder\n \n | \n \n \n 2\u00b757%\n \n | \n \n \n 2\u00b741%\n \n | \n \n \n 2\u00b778%\n \n | \n \n \n 2\u00b753%\n \n | \n
| \n \n Intellectual Disability\n \n | \n \n \n 1\u00b720%\n \n | \n \n \n 1\u00b728%\n \n | \n \n \n 1\u00b734%\n \n | \n \n \n 1\u00b733%\n \n | \n
| \n \n \n Personality Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Personality Disorder\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n
| \n \n \n Psychotic Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Psychotic Disorder\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b703%\n \n | \n
| \n \n Schizoaffective Disorder\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n Schizophrenia\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n \n Sleep-Wake Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Hypersomnia\n \n | \n \n \n 0\u00b706%\n \n | \n \n \n 0\u00b706%\n \n | \n \n \n 0\u00b706%\n \n | \n \n \n 0\u00b706%\n \n | \n
| \n \n Insomnia\n \n | \n \n \n 0\u00b747%\n \n | \n \n \n 0\u00b751%\n \n | \n \n \n 0\u00b746%\n \n | \n \n \n 0\u00b748%\n \n | \n
| \n \n Parasomnias\n \n | \n \n \n 0\u00b722%\n \n | \n \n \n 0\u00b725%\n \n | \n \n \n 0\u00b725%\n \n | \n \n \n 0\u00b724%\n \n | \n
| \n \n \n Standalone Symptoms\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Anger/Aggression\n \n | \n \n \n 0\u00b727%\n \n | \n \n \n 0\u00b732%\n \n | \n \n \n 0\u00b728%\n \n | \n \n \n 0\u00b732%\n \n | \n
| \n \n Anxiety Symptoms\n \n | \n \n \n 0\u00b728%\n \n | \n \n \n 0\u00b734%\n \n | \n \n \n 0\u00b725%\n \n | \n \n \n 0\u00b727%\n \n | \n
| \n \n Attention Symptoms\n \n | \n \n \n 0\u00b747%\n \n | \n \n \n 0\u00b756%\n \n | \n \n \n 0\u00b743%\n \n | \n \n \n 0\u00b749%\n \n | \n
| \n \n Depressive Symptoms\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n
| \n \n Hallucinations\n \n | \n \n \n 0\u00b713%\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b709%\n \n | \n \n \n 0\u00b704%\n \n | \n
| \n \n \n Substance Use and Dependence\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Alcohol\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n Opioid Related\n \n | \n \n \n 0\u00b701%\n \n | \n \n \n 0\u00b701%\n \n | \n \n \n 0\u00b701%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n Other Substances\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b701%\n \n | \n
| \n \n THC\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n Tobacco\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b700%\n \n | \n
| \n \n \n Tic Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Tic Disorder\n \n | \n \n \n 0\u00b732%\n \n | \n \n \n 0\u00b737%\n \n | \n \n \n 0\u00b729%\n \n | \n \n \n 0\u00b730%\n \n | \n
| \n \n *Pre-COVID: Visit dates are between 24 months to 7 days before the index date\n \n\n **Post-COVID: Visit dates are between 28\u2013179 days after the index date\n \n | \n
\n
\n\n
\n| \n | \n\n \n COVID-19 Positive cohort\n \n | \n \n \n COVID-19 Negative cohort\n \n | \n ||
|---|---|---|---|---|
| \n \n Pre-COVID*\n \n | \n \n \n Post-COVID**\n \n | \n \n \n Pre-COVID\n \n | \n \n \n Post-COVID\n \n | \n |
| \n \n \n Any mental health disorder\n \n \n | \n \n \n 16\u00b700%\n \n | \n \n \n 17\u00b786%\n \n | \n \n \n 15\u00b755%\n \n | \n \n \n 16\u00b776%\n \n | \n
| \n \n \n Adverse Childhood Experiences\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Emotional Abuse\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b702%\n \n | \n
| \n \n Neglect\n \n | \n \n \n 0\u00b701%\n \n | \n \n \n 0\u00b701%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n
| \n \n Physical Abuse\n \n | \n \n \n 0\u00b713%\n \n | \n \n \n 0\u00b713%\n \n | \n \n \n 0\u00b716%\n \n | \n \n \n 0\u00b714%\n \n | \n
| \n \n Sexual Abuse\n \n | \n \n \n 0\u00b709%\n \n | \n \n \n 0\u00b710%\n \n | \n \n \n 0\u00b711%\n \n | \n \n \n 0\u00b710%\n \n | \n
| \n \n \n Anxiety Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Anxiety Disorder\n \n | \n \n \n 6\u00b788%\n \n | \n \n \n 7\u00b798%\n \n | \n \n \n 6\u00b719%\n \n | \n \n \n 7\u00b704%\n \n | \n
| \n \n OCD\n \n | \n \n \n 0\u00b710%\n \n | \n \n \n 0\u00b747%\n \n | \n \n \n 0\u00b712%\n \n | \n \n \n 0\u00b746%\n \n | \n
| \n \n Somatoform Disorder\n \n | \n \n \n 0\u00b749%\n \n | \n \n \n 0\u00b755%\n \n | \n \n \n 0\u00b743%\n \n | \n \n \n 0\u00b754%\n \n | \n
| \n \n Stress Disorder\n \n | \n \n \n 2\u00b760%\n \n | \n \n \n 2\u00b790%\n \n | \n \n \n 2\u00b733%\n \n | \n \n \n 2\u00b760%\n \n | \n
| \n \n \n Disruptive Behavior Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Conduct Disorder\n \n | \n \n \n 0\u00b724%\n \n | \n \n \n 0\u00b723%\n \n | \n \n \n 0\u00b726%\n \n | \n \n \n 0\u00b727%\n \n | \n
| \n \n Impulse Control Disorder\n \n | \n \n \n 0\u00b740%\n \n | \n \n \n 0\u00b742%\n \n | \n \n \n 0\u00b744%\n \n | \n \n \n 0\u00b746%\n \n | \n
| \n \n Oppositional Defiant Disorder\n \n | \n \n \n 0\u00b733%\n \n | \n \n \n 0\u00b733%\n \n | \n \n \n 0\u00b739%\n \n | \n \n \n 0\u00b736%\n \n | \n
| \n \n \n Eating and Feeding Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Avoidant/Restrictive Food Intake\n \n | \n \n \n 0\u00b790%\n \n | \n \n \n 1\u00b704%\n \n | \n \n \n 0\u00b797%\n \n | \n \n \n 1\u00b721%\n \n | \n
| \n \n Other Eating and Feeding Disorder\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b706%\n \n | \n \n \n 0\u00b707%\n \n | \n \n \n 0\u00b707%\n \n | \n
| \n \n \n Elimination Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Encopresis\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b704%\n \n | \n
| \n \n Enuresis\n \n | \n \n \n 0\u00b719%\n \n | \n \n \n 0\u00b718%\n \n | \n \n \n 0\u00b721%\n \n | \n \n \n 0\u00b718%\n \n | \n
| \n \n \n Gender Dysphoria/Sexual Dysfunction\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Gender Dysphoria\n \n | \n \n \n 0\u00b730%\n \n | \n \n \n 0\u00b736%\n \n | \n \n \n 0\u00b758%\n \n | \n \n \n 0\u00b764%\n \n | \n
| \n \n Paraphilia\n \n | \n \n \n 0\u00b701%\n \n | \n \n \n 0\u00b700%\n \n | \n \n \n 0\u00b701%\n \n | \n \n \n 0\u00b701%\n \n | \n
| \n \n Sexual Dysfunction\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b702%\n \n | \n
| \n \n \n Intentional Self-Harm/Suicidality\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Parasuicidality\n \n | \n \n \n 0\u00b725%\n \n | \n \n \n 0\u00b730%\n \n | \n \n \n 0\u00b731%\n \n | \n \n \n 0\u00b735%\n \n | \n
| \n \n Suicidality\n \n | \n \n \n 0\u00b787%\n \n | \n \n \n 0\u00b799%\n \n | \n \n \n 1\u00b709%\n \n | \n \n \n 1\u00b713%\n \n | \n
| \n \n \n Mood Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Bipolar Disorder\n \n | \n \n \n 0\u00b716%\n \n | \n \n \n 0\u00b717%\n \n | \n \n \n 0\u00b715%\n \n | \n \n \n 0\u00b716%\n \n | \n
| \n \n Major Depression\n \n | \n \n \n 3\u00b755%\n \n | \n \n \n 3\u00b784%\n \n | \n \n \n 3\u00b758%\n \n | \n \n \n 3\u00b795%\n \n | \n
| \n \n Minor Depression\n \n | \n \n \n 3\u00b744%\n \n | \n \n \n 4\u00b725%\n \n | \n \n \n 3\u00b719%\n \n | \n \n \n 3\u00b776%\n \n | \n
| \n \n \n Neurocognitive Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Catatonia\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b703%\n \n | \n
| \n \n Delirium\n \n | \n \n \n 0\u00b736%\n \n | \n \n \n 0\u00b748%\n \n | \n \n \n 0\u00b739%\n \n | \n \n \n 0\u00b742%\n \n | \n
| \n \n Encephalopathy\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b707%\n \n | \n \n \n 0\u00b707%\n \n | \n \n \n 0\u00b708%\n \n | \n
| \n \n \n Neurodevelopmental Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Academic Developmental Disorder\n \n | \n \n \n 0\u00b741%\n \n | \n \n \n 0\u00b741%\n \n | \n \n \n 0\u00b746%\n \n | \n \n \n 0\u00b743%\n \n | \n
| \n \n ADHD\n \n | \n \n \n 4\u00b748%\n \n | \n \n \n 4\u00b779%\n \n | \n \n \n 4\u00b723%\n \n | \n \n \n 4\u00b730%\n \n | \n
| \n \n Autism Spectrum Disorder\n \n | \n \n \n 1\u00b722%\n \n | \n \n \n 1\u00b728%\n \n | \n \n \n 1\u00b750%\n \n | \n \n \n 1\u00b751%\n \n | \n
| \n \n Communication/Motor Disorder\n \n | \n \n \n 0\u00b751%\n \n | \n \n \n 0\u00b753%\n \n | \n \n \n 0\u00b765%\n \n | \n \n \n 0\u00b763%\n \n | \n
| \n \n Intellectual Disability\n \n | \n \n \n 0\u00b783%\n \n | \n \n \n 0\u00b787%\n \n | \n \n \n 1\u00b709%\n \n | \n \n \n 1\u00b706%\n \n | \n
| \n \n \n Personality Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Personality Disorder\n \n | \n \n \n 0\u00b713%\n \n | \n \n \n 0\u00b716%\n \n | \n \n \n 0\u00b713%\n \n | \n \n \n 0\u00b718%\n \n | \n
| \n \n \n Psychotic Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Psychotic Disorder\n \n | \n \n \n 0\u00b712%\n \n | \n \n \n 0\u00b715%\n \n | \n \n \n 0\u00b717%\n \n | \n \n \n 0\u00b720%\n \n | \n
| \n \n Schizoaffective Disorder\n \n | \n \n \n 0\u00b702%\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b704%\n \n | \n
| \n \n Schizophrenia\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b707%\n \n | \n \n \n 0\u00b706%\n \n | \n \n \n 0\u00b708%\n \n | \n
| \n \n \n Sleep-Wake Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Hypersomnia\n \n | \n \n \n 0\u00b713%\n \n | \n \n \n 0\u00b715%\n \n | \n \n \n 0\u00b716%\n \n | \n \n \n 0\u00b716%\n \n | \n
| \n \n Insomnia\n \n | \n \n \n 0\u00b775%\n \n | \n \n \n 0\u00b790%\n \n | \n \n \n 0\u00b780%\n \n | \n \n \n 0\u00b784%\n \n | \n
| \n \n Parasomnias\n \n | \n \n \n 0\u00b712%\n \n | \n \n \n 0\u00b714%\n \n | \n \n \n 0\u00b717%\n \n | \n \n \n 0\u00b716%\n \n | \n
| \n \n \n Standalone Symptoms\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Anger/Aggression\n \n | \n \n \n 0\u00b728%\n \n | \n \n \n 0\u00b729%\n \n | \n \n \n 0\u00b735%\n \n | \n \n \n 0\u00b733%\n \n | \n
| \n \n Anxiety Symptoms\n \n | \n \n \n 0\u00b731%\n \n | \n \n \n 0\u00b738%\n \n | \n \n \n 0\u00b732%\n \n | \n \n \n 0\u00b732%\n \n | \n
| \n \n Attention Symptoms\n \n | \n \n \n 0\u00b735%\n \n | \n \n \n 0\u00b744%\n \n | \n \n \n 0\u00b733%\n \n | \n \n \n 0\u00b735%\n \n | \n
| \n \n Depressive Symptoms\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b705%\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b704%\n \n | \n
| \n \n Hallucinations\n \n | \n \n \n 0\u00b740%\n \n | \n \n \n 0\u00b711%\n \n | \n \n \n 0\u00b741%\n \n | \n \n \n 0\u00b712%\n \n | \n
| \n \n \n Substance Use and Dependence\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Alcohol\n \n | \n \n \n 0\u00b709%\n \n | \n \n \n 0\u00b710%\n \n | \n \n \n 0\u00b708%\n \n | \n \n \n 0\u00b709%\n \n | \n
| \n \n Opioid Related\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b704%\n \n | \n \n \n 0\u00b703%\n \n | \n \n \n 0\u00b704%\n \n | \n
| \n \n Other Substances\n \n | \n \n \n 0\u00b714%\n \n | \n \n \n 0\u00b717%\n \n | \n \n \n 0\u00b716%\n \n | \n \n \n 0\u00b719%\n \n | \n
| \n \n THC\n \n | \n \n \n 0\u00b720%\n \n | \n \n \n 0\u00b725%\n \n | \n \n \n 0\u00b723%\n \n | \n \n \n 0\u00b728%\n \n | \n
| \n \n Tobacco\n \n | \n \n \n 0\u00b742%\n \n | \n \n \n 0\u00b751%\n \n | \n \n \n 0\u00b733%\n \n | \n \n \n 0\u00b741%\n \n | \n
| \n \n \n Tic Disorders\n \n \n | \n \n | \n\n | \n\n | \n\n | \n
| \n \n Tic Disorder\n \n | \n \n \n 0\u00b728%\n \n | \n \n \n 0\u00b730%\n \n | \n \n \n 0\u00b732%\n \n | \n \n \n 0\u00b731%\n \n | \n
| \n \n *Pre-COVID: Visit dates are between 24 months to 7 days before the index date\n \n\n **Post-COVID: Visit dates are between 28\u2013179 days after the index date\n \n | \n
\n
\n\n
\n
\n As shown in Figs.\n \n 2\n \n and\n \n 3\n \n , after propensity score matching and interrupted time analysis, both the children and youths COVID-19 positive groups retained significant risk differences compared to their respective negative groups in the composite outcome (children: 0\u00b796%, 95% CI [0\u00b775%, 1.16%]; the youth: 0\u00b784%, [0\u00b753%, 1.15%]). The children COVID-19 positive group also exhibited significant risk differences for anxiety disorder (0\u00b726%, [0\u00b719%, 0\u00b733%]), OCD (0\u00b702%, [0\u00b700%, 0\u00b704%]), somatoform disorder (0\u00b703%, [0\u00b700%, 0\u00b705%]), stress disorder (0\u00b708%, [0\u00b702%, 0\u00b714%]), avoidant/restrictive food intake (0\u00b707%, [0\u00b703%, 0\u00b711%]), bipolar disorder (0\u00b701%, [0\u00b700%, 0\u00b702%]), delirium (0\u00b704%, [0\u00b702%, 0\u00b706%]), ADHD (0\u00b711%, [0\u00b702%, 0\u00b721%]), autism spectrum disorder (0\u00b710%, [0\u00b702%, 0\u00b718%]), communication/motor disorder (0\u00b738%, [0\u00b725%, 0\u00b752%]), and intellectual disability (0\u00b712%, [0\u00b705%, 0\u00b720%]), and tic disorder (0\u00b705%, [0\u00b702%, 0\u00b708%]).\n
\n\n For the youth cohorts, the COVID-19 positive group had significantly higher risk difference compared to the COVID-19 negative cohort in anxiety disorder (0\u00b726%, [0\u00b705%, 0\u00b748%]), suicidality (0\u00b711%, [0\u00b702%, 0\u00b719%]), minor depression (0\u00b721%, [0\u00b705%, 0\u00b737%]), delirium (0\u00b708%, [0\u00b703%, 0\u00b714%]), ADHD (0\u00b733% [0\u00b716%, 0\u00b750%]), intellectual disability (0\u00b709%, [0\u00b701%, 0\u00b717%]), insomnia (0\u00b713%, [0\u00b706%, 0\u00b721%]), and anxiety standalone symptoms (0\u00b705%, [0\u00b700%, 0\u00b710%]), attention standalone symptoms (0\u00b708%, [0\u00b703%, 0\u00b714%]), depressive standalone symptoms (0\u00b702%, [0\u00b700%, 0\u00b704%]).\n
\n\n Selective psychotropic medications with the potential to decrease susceptibility to SARS-CoV-2 infection were used by 0\u00b768% of COVID-19 positive children and 0\u00b775% of negative children aged 5\u201312 years. Among youths, these medications were used by 5\u00b709% of COVID-19 positive patients and 5\u00b736% of negative patients. Detailed results can be found in Supplementary Materials Section 3.\n
\n\n Infections have long been linked to neuropsychiatric disorders, as evidenced by reports from the 1890 influenza epidemic, the 1918 Spanish flu, and more recently, a Danish nationwide study.\n \n 17\n \n This study found that children and adolescents who were hospitalized for infections faced an increased risk of subsequent diagnoses of neuropsychiatric disorders and higher rates of psychotropic medication prescriptions. The highest risks following infections were associated with conditions such as schizophrenia, OCD, personality and behavioral disorders, intellectual disability, autism, ADHD, ODD, conduct disorders, and tic disorders.\n \n 17\n \n In this study, the primary objective was to investigate the impact of COVID-19 infection on the potential risk of post-acute sequelae neuropsychiatric and related conditions for both children and youths. Using the real-world EHR data from twenty-five health institutions in the RECOVER program, we conducted the retrospective cohort study of patients 5 to 20 years of age with documented SARS-CoV-2 infection compared to those with a negative test. Our findings, which demonstrate increased rates of neuropsychiatric and related conditions in both COVID-19 positive and negative cohorts during the post-COVID phase, align with global reports highlighting the combined effects of SARS-CoV-2 infection and broader pandemic stressors.\n \n 18\n \n Similarly, the higher frequency rates observed in older age groups in both COVID-19 positive and negative cohorts (1\u00b756% and 0\u00b788%, respectively, for ages 5\u201311, and 1\u00b786% and 1\u00b721%, respectively, for ages 12\u201320) echo prior studies suggesting that adolescents and young adults may be disproportionately affected by both the viral infection and pandemic stress compared to younger children.\n \n 18\n \n Recent large-scale studies using EHR data further support this, reporting a higher likelihood of developing new mental health disorders in both COVID-19 positive and negative adolescents compared to younger children.\n \n 14\n \n
\n\n The key findings from our study show that both children and youth in the COVID-19 positive groups retained significant risk differences compared to their respective negative groups for the composite neuropsychiatric outcome (as shown in Table\n \n 3\n \n and Fig.\n \n 2\n \n ). The risk difference was slightly higher in children than in youths. Additionally, differences across diagnostic categories were observed between the two age groups. Among children with infection, the highest risk difference was seen for communication/motor disorders, followed by anxiety, intellectual disability, ADHD, and autism spectrum disorder. Other conditions, such as stress-related disorders, avoidant/restrictive food intake, tics, delirium, somatoform disorders, OCD, and bipolar disorder, had risk differences ranging from 0\u00b708% to 0\u00b701%. In youth with infection, the highest significant risk difference was for anxiety disorders, followed by minor depression, standalone attention symptoms, insomnia, and suicidality. Intellectual disability and standalone symptoms of anxiety and depression had risk differences ranging from 0\u00b709% to 0\u00b702%. The small increases in risk found in our study support studies indicating that infections may account for only a small proportion of the risk for mental disorders.\n \n 19\n \n That same study also showed that polygenic risk scores for infections were associated with modest increase in risk for ADHD, major depression, and schizophrenia. In our study, increased risk for ADHD and minor depression were found in the COVID-19 positive child and youth cohorts respectively while risks for disorders that are more common in the older age ranges would be less likely to be detected.\n
\n\n Our study has several notable strengths. Firstly, by leveraging EHR data from over twenty clinical institutions nationwide as part of the RECOVER program, our research presents the most comprehensive investigation on U.S. children and youths to date, exploring the impact of SARS-CoV-2 infection on the neuropsychiatric and related conditions. Secondly, our approach included a more extended follow-up period than most existing studies. Specifically, our follow-up extended until December 2022, encompassing the period that included the emergence of the Omicron variant. Thirdly, we accounted for pre-infection differences in neuropsychiatric and related condition risks by employing the difference-in-differences method. This approach allowed us to examine the effects directly attributable to SARS-CoV-2 infection while controlling for any baseline disparities in neuropsychiatric and related conditions. Additionally, we enhanced our analysis by adjusting for over 200 potential confounders through propensity score stratification. This method ensured a balanced comparison between the SARS-CoV-2-infected and non-infected groups. Lastly, our study's comprehensive scope, examining 50 neuropsychiatric and related outcomes at both individual disorder and category levels, facilitated a comprehensive exploration of the patterns and impacts of SARS-CoV-2 infection on neuropsychiatric and related conditions, whereby studies using limited ICD codes for anxiety and depression did not detect a pandemic effect.\n \n 20\n \n This approach offers a better understanding of the association and effects of various factors on neuropsychiatric dysfunction in the context of the pandemic.\n
\n\n Our study is subject to several limitations that can be considered for future studies. Firstly, identifying a high-quality COVID-19 negative group presents a significant challenge. To mitigate potential misclassification of negative status, we have utilized multiple tests, including PCR, antigen, and serology test results, in addition to diagnosis codes for COVID-19 and long COVID, to refine our definition of the COVID-19 negative group. Despite these efforts, the rapid and dynamic developmental changes experienced by children and youths, such as the physical growth and changes in physiological, cognitive, emotional, and social domains, suggest that further enhancements in control selection methods could improve the reliability of our findings. Secondly, although we implemented rigorous methods to ensure comprehensive data collection, certain biases may be intrinsic to our study. For example, in youths with more severe symptoms, parents may have been more likely to disclose additional health-related information, potentially leading to reporting biases. Differential access to clinicians with the appropriate expertise to evaluate neuropsychiatric issues could also have contributed to the underascertainment of such conditions. Thirdly, while our analysis incorporated an extensive list of potential confounders available within the EHR database, the inherent limitations of EHR data completeness may still introduce potential confounding bias. Moreover, our analysis did not account for participants who may have been infected several times during the study period, a factor that could become increasingly relevant in the later stages of the pandemic.\n
\n\n In summary, in both COVID positive and negative cohorts, we found small increases in frequency in composite neuropsychiatric and related outcomes, slightly higher in the COVID positive group and in the older age groups. These small increases are similar to those reported in other studies and attributed to the combined COVID-19 viral infection and broad pandemic stressors.\n \n 18,21\n \n
\n\n While the frequency attributed to the combined viral infection and pandemic stress, and the risk attributed to the viral infection may be small, these raise concern in a pediatric population given that childhood conditions often have lifelong consequences.\n \n 22,23\n \n
\n\n Our results, therefore, indicate an urgent need for well-controlled studies that investigate not only COVID-19 but other infections, known to affect the CNS. Pediatric studies also require cohorts with narrower age stratification, cohorts that also include the prenatal period, and adequate follow-up to control for the rapid neurodevelopmental changes.\n
\n\n The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript.\n
\n\n We conducted a retrospective cohort study using the pediatric EHR cohort of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, which seeks to understand, treat, and prevent long COVID (more information on RECOVER\n \n \n https://recoverCOVID.org/\n \n \n \n \n ). The pediatric RECOVER EHR network spans 38 health systems across the United States, of which 25 were included in the study. The Institutional Review Board (IRB) obtained approval under Biomedical Research Alliance of New York (BRANY) protocol #21-08-508, with a waiver of consent and HIPAA authorization. The participating institutions in this study include Ann & Robert H. Lurie Children\u2019s Hospital of Chicago, Children\u2019s Hospital Colorado, Children\u2019s Hospital of Philadelphia, Children\u2019s National Medical Center, Cincinnati Children\u2019s Hospital Medical Center, Duke University, Medical College of Wisconsin, Medical University of South Carolina (MUSC), Montefiore, Nationwide Children\u2019s Hospital, Nemours Children\u2019s Health System (inclusive of the Delaware and Florida health system), New York University School of Medicine, Northwestern University, OCHIN, Seattle Children\u2019s Hospital, Stanford Children\u2019s Health, University of California, San Francisco, University of Iowa Healthcare, University of Michigan, University of Missouri, University of Nebraska Medical Center, University of Pittsburgh, Vanderbilt University Medical Center, Wake Forest Baptist Health, and Weill Cornell Medical College. Detailed data description can be found in Supplementary Materials Section 1.\n
\n\n In the construction of our COVID-19 positive cohort, we began by identifying individuals who received their first positive COVID-19 PCR, antigen, or serology test and a diagnosis of COVID-19/PASC within the study period from March 1st, 2020, to December 3rd, 2022 (N\u2009=\u20091,017,542). From this initial group, we subsequently filtered for those with at least one medical visit occurring between 28 and 179 days after the index date (follow-up interval)\n \n 24\u201327\n \n (N\u2009=\u2009787,370) and at least one visit within the 7 days to 24 months leading up to the index date (baseline interval) (N\u2009=\u2009676,582). We included only the patients with complete variable records (n\u2009=\u2009488,606), and we refined the positive cohort with age constraints between five and twenty when the study period starts and complete records (N\u2009=\u2009326,074). Among these individuals, we identified a child cohort with ages 5\u201311 years (N\u2009=\u2009141,349) and a youth cohort with ages 12\u201320 (N\u2009=\u2009184,725).\n
\n\n We then constructed a COVID-19 negative group composed of individuals who were not part of the COVID-19 positive cohort, had at least one negative COVID-19 PCR, antigen, or serology test within the same study period, and no diagnoses of COVID-19 or PASC (N\u2009=\u20093,030,550). For this COVID-19 negative group, we imputed index dates randomly from the distribution of index dates observed in the COVID-19 cohort, ensuring that both cohorts shared a similar distribution of follow-up times. We further required that patients in the COVID-19 negative cohort must have had at least one visit between 28 and 179 days after the imputed index date as the follow up period (N\u2009=\u20092,172,217) and at least one visit occurring between 7 days to 24 months before the imputed index date as the baseline period (N\u2009=\u20091,766,033). Similar to the COVID-19 positive cohort, we only included patients with complete variable records (N\u2009=\u20091,416,069) and satisfying age constraints between five and twenty at the start of the study period (N\u2009=\u2009887,314). We further stratified the children cohort with ages from five to eleven (N\u2009=\u2009441,790) and the youth cohort with ages from twelve to twenty (N\u2009=\u2009445,524). Figure\n \n 1\n \n displays attrition tables for both COVID-19 positive and negative cohorts.\n
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\n\n In this research, we utilized covariates assessed before the index date. The predefined covariates were determined based on prior knowledge.\n \n 28,29\n \n The predefined covariates included age, race (Asian/PI, black/AA, Hispanic, white, multiple, and other), gender (male, female, and other), hospital, body mass index, and hospital utilization including number of ED visits, number of inpatient and outpatient encounters, PMCA index, number of negative tests prior to the entry of cohorts, and medical history. The baseline description of covariates in both cohorts is presented in Table\n \n 1\n \n .\n
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\n\n We also evaluated the use of selective psychotropic medications, reported to be activators of Sigma 1-receptor ligand, of varying affinity, as some prior data suggested their potential capacity to decrease susceptibility to SARS-CoV-2 infection. These included SSRIs (fluvoxamine, fluoxetine, citalopram, and escitalopram) and antipsychotics (haloperidol, chlorpromazine, and fluphenazine).\n \n 30,31\n \n We evaluated the prevalence of usage of the above medications in both COVID-19 positive patients and the negative cohort to ensure that SSRI usage did not introduce imbalance or bias into our study results.\n
\n\n The outcomes were predetermined based on our prior research on systematically characterizing the post-acute effects of SARS-CoV-2 infection.\n \n 32\n \n We specify our outcomes based on Systematized Nomenclature of Medicine (SNOMED),\n \n 33\n \n and a typology developed to query aggregated, standardized EHR data for the full spectrum of neuropsychiatric and related conditions. This typology included the pediatric DSM-5 disorder categories including anxiety, OCD, somatic, stress, disruptive behavior, feeding and eating, elimination, gender dysphoria/sexual dysfunction, mood, neurocognitive, neurodevelopmental, personality, psychotic, sleep-wake, substance use, and dependence disorders.\n \n 34\n \n Expansion beyond DSM-5 disorders included intentional self-harm, catatonia, encephalopathies, standalone symptoms, tic disorders, and adverse childhood experiences.\n \n 16\n \n
\n\n We also specified a composite outcome of any neuropsychiatric and related condition. Supp Table\n \n 1\n \n in Supplementary Materials Section 2 details the definition of the outcomes. Frequencies of each outcome were assessed 24 months to 7 days before and 28 days to 179 days after the index date for children and youths, respectively (Table\n \n 2\n \n ,\n \n 3\n \n ).\n
\n\n We defined the pre-COVID period as the span from 24 months to 7 days before the index date and the post-COVID period as the period from 28 to 179 days after the index date (the post-acute phase). For each neuropsychiatric and related condition, we calculated its frequency by dividing the number of patients who were diagnosed during each of the defined periods.\n
\n\n To assess differences in the risk of neuropsychiatric and related conditions between COVID-19 positive and negative patients, we conducted an interrupted time-series analysis using a two-sample proportion test with stratified cohorts of children and youths. To mitigate the potential impact of measured confounding factors, we employed a propensity score matching method with the covariates outlined in the Covariates section. After matching, we assessed the standardized mean difference (SMD) for each covariate, employing a cutoff value of 0\u00b71. Subsequently, we compared the risk difference in neuropsychiatric and related conditions between the COVID-19 positive and the COVID-19 negative cohort. The characteristic balance results before and after propensity score matching are presented in Supplementary Materials Section 4.\n
\n\n We performed comprehensive sensitivity analyses to assess the robustness of our findings. Initially, we conducted an analysis without age stratification and documented the results in Section 5 of the Supplementary Materials. We also performed an analysis with a different control group, which was defined as patients with at least one negative test and one non-COVID respiratory disease diagnosis within 30 days of the negative test. Details of the study design and results are documented in Section 6 of the Supplementary Materials. Furthermore, our sensitivity analysis included subgroup analyses in Sections 7\u201312 of the Supplementary Materials based on gender (male and female), race/ethnicity (Asian/Pacific Islander (PI), Black/African-American(AA), Hispanic, and White), obesity, hospitalization status (non-hospitalized, hospitalized, and admitted to ICU), severity of symptoms (asymptomatic, mild, moderate, and severe), and time frames corresponding to predominant virus variants (pre-Delta, Delta, and Omicron).\n
\n\n Supplementary Materials\n
\n \n\n Connectome maps region-to-region connectivities but does not inform which white matter pathways form the connections. Here we constructed the first population-based\n \n tract-to-region\n \n connectome to fill this information gap. The constructed connectome quantifies the population probability of a white matter tract innervating a cortical region. The results show that ~85% of the tract-to-region connectome entries are consistent across individuals, whereas the remaining (~15%) have substantial individual differences requiring individualized mapping. Further hierarchical clustering on cortical regions revealed their parcellations into dorsal, ventral, and limbic networks based on the tract-to-region connective patterns. The clustering results on white matter bundles revealed the connectome-based categorization of fiber bundle systems in the association pathways. This new tract-to-region connectome provides insights into the connective topology between cortical regions and white matter bundles. The derived hierarchical relation further offers a connectome-based categorization of gray matter and white matter structures.\n
\n\n \n connectome\n \n
\n\n \n tractography\n \n
\n\n \n hierarchical clustering\n \n
\n\n Mapping the human connectome is the key to understanding how brain structure gives rise to functions and how brain diseases cause dysfunctions\n \n \n 1\n \n ,\n \n 2\n \n \n . Studies have used structural or functional connectivity to quantify the\n \n region-to-region\n \n connectivity as the connectome\n \n \n 3\n \n ,\n \n 4\n \n \n and delineate the network topology of the nervous system. The network topology revealed by the brain connectome further informed the functional implications of cortical regions and enabled graphical theoretical analysis\n \n \n 5\n \n \n . However, the conventional region-to-region connectome is agnostic of the role played by white matter pathways and does not indicate which pathways form the cortical connections. Consequently, for many neuroscience studies investigating region-to-region connectivity, the white matter is still a black box with much unknown that needs further exploration.\n
\n\n Here we mapped the first tract-to-region connectome to address this information gap. The connection probability between white matter pathways and cortical regions was evaluated on 1065 young adult subjects. For\n \n m\n \n brain regions and\n \n n\n \n white matter bundles, the tract-to-region connectome can be quantified by an\n \n m\n \n -by-\n \n n\n \n matrix, where each matrix entry records the corresponding population probability of a white matter pathway innervating a cortical region. We leveraged several technical advances to construct the tract-to-region connectome (Fig.\n \n 1\n \n ). The white matter bundles of the 1065 young adults were mapped using the recent advance in automated tractography\n \n \n 6\n \n \u2013\n \n 10\n \n \n . The recognition of white matter bundles was accomplished by comparing the similarity of trajectories with an expert-vetted tractography\n \n \n 11\n \n \n , and the irrelevant connections were removed to achieve high test-retest reliability\n \n \n 12\n \n \n . Three tract-to-region connectome matrices were quantified using the Brodmann parcellation, Kleist parcellation\n \n \n 13\n \n \n , and the Human Connectome Project's multimodal parcellation (HCP-MMP)\n \n \n 14\n \n \n atlases, respectively. The tract-to-region information provided by this novel connectome could complete the circuit diagram for many structure-function models and inform the likelihood of a white matter lesion causing a functional deficit in the dysconnectome studies. Based on the tract-to-region connectome, we further applied hierarchical clustering to carry out connectome-based hierarchical clustering. The clustering results revealed the hierarchical relation of cortical regions and white matter pathways that informed their connectome-based categorization.\n
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\n\n We first examined the tractography of 1065 subjects in the ICBM152 space. Fig.\n \n 2\n \n a shows the voxel-wise probability of the association pathways, whereas Fig.\n \n 2\n \n b shows the projection pathways. Each white matter tract is visualized by a population probability of 20%, 40%, 60%, and 80%, respectively. The probability was quantified by the percentage of subjects with the white matter bundle passing the ICBM152 space voxels. We detail our definition and abbreviations of white matter bundles in Suppl. Table 1. The tractography results shown in Fig.\n \n 2\n \n are consistent with known neuroanatomy\n \n \n 15\n \n \n and existing tractography results\n \n \n 10\n \n ,\n \n 16\n \n ,\n \n 17\n \n \n . The lateralization of left AF can be readily observed by its substantially larger volume.\n
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\n\n Figure\n \n 3\n \n a visualizes all white matter bundles rendered by an iso-surface of 20% voxel-wise probability in the ICBM152 space. The AF and superior longitudinal fasciculus (SLF) in Fig.\n \n 3\n \n show relatively broader coverage than those of the projection pathways such as corticospinal tract (CST), corticobulbar tract (CBT), optic radiation (OR), and fornix (F). This result can be explained by higher between-subject differences in AF and SLF\n \n \n 12\n \n \n . Fig.\n \n 3\n \n b further shows coronal sections of tract probability. The maximum color saturation corresponds to 100% probability, whereas white color corresponds to 0% probability. The probabilities of association pathways are visualized to illustrate their anatomical relation and relative location. The results are consistent with a recent population-based tractography atlas\n \n \n 17\n \n \n .\n
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\n\n The tract-to-region connectome based on Brodmann areas and Kleist parcellations are shown in Fig.\n \n 4\n \n a and Fig.\n \n 4\n \n b, respectively, whereas the one based on HCP-MMP parcellations is shown in Fig.\n \n 5\n \n . The Brodmann, Kleist, and HCP-MMP parcellations have 39, 49, and 180 cortical regions. As shown in Fig.\n \n 4\n \n , the resulting connectivity matrices have 39-by-52 and 49-by-52 entries for Brodmann and Kleist atlases, whereas, in Fig.\n \n 5\n \n , there are 180-by-52 entries. Each row corresponds to a cortical region, and each column corresponds to a white matter tract. The population probability was quantified by checking the corresponding cortical region and white matter tract intersection in the ICBM152 space. The left half of the matrix is the connection probabilities in the left hemisphere, whereas the right half is the those in the right hemisphere. The probabilities are color-coded by red colors: the highest saturation corresponds to the highest probability (100%), while the white color corresponds to the lowest probability (0%). The entries with less than 5% connection probability are left blank to facilitate visualization. Most of the matrix entries in Brodmann (1733 out of 2028 entries, 85.45%) and Kleist atlases (2164 out of 2548 entries, 84.93%) have probability values greater than 95% or smaller than 5%, meaning that these tract-to-region pairs are either connected or not connected in the majority of the study population. Around 15% of the matrix entries in Brodmann and Kleist atlases show probabilities between 5% and 95% due to substantial individual variation. Interestingly, although HCP-MMP parcellations have more parcellation regions (180 regions) than Brodmann and Kleist atlases (39 and 49 regions), it gives a remarkably similar figure. 86.50% of its matrix entries (8096 out of 9360 entries) also have probability values greater than 95% or smaller than 5%. The tract-to-region connectome showed that the young adult population shares a similar connective pattern in ~85% of the tract-to-region entries. The remaining ~15% entries have substantial individual variations with population probability between 5% and 95%, thus warranting individualized mapping.\n
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\n\n We further examine arcuate fasciculus (AF) connections in the tract-to-region connectome using the Sankey flow diagrams shown in Fig.\n \n 6\n \n . The diagrams use the HCP-MMP parcellation, and the color saturation scales with the population probability. The connective pattern shown in Fig.\n \n 6\n \n is consistent with the conventional view that the left AF connects Wernicke's area in the superior temporal regions (red) and Broca's area in the inferior frontal cortex (orange). Furthermore, the diagrams also show more detailed connections of left AF to the caudal dorsolateral prefrontal cortex and the inferior parietal lobule\n \n \n 18\n \n \u2013\n \n 22\n \n \n as well as premotor/motor regions\n \n \n 23\n \n \n . The lateralization of AF to frontoparietal (yellow), angular (green), and superior temporal regions (red) can also be seen by comparing Fig.\n \n 6\n \n a and\n \n 6\n \n b.\n
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\n\n Figure\n \n 7\n \n shows the similarity matrices and the derived hierarchical relations of the cortical regions defined by Brodmann (Fig.\n \n 7\n \n a) and Kleist atlases (Fig.\n \n 7\n \n b), whereas the results for HCP-MMP is shown in Fig.\n \n 8\n \n . The column and row orders of the matrices are reordered based on clustering results to facilitate inspection. The dendrograms on the top of Fig.\n \n 7\n \n and\n \n 8\n \n show the hierarchical relation of the cortical regions and the vertical distance scales by the cost for merging. Overall, Fig.\n \n 7\n \n and\n \n 8\n \n show a consistent result, with cortical regions categorized into dorsal, ventral, and limbic networks. Although differences can be observed at each atlas, the dorsal network includes most frontal (excluding prefrontal) and parietal regions, whereas the ventral network includes temporal and occipital regions. The limbic network is constituted of prefrontal, insula, and upper cingulum regions. In Brodmann atlas (Fig.\n \n 7\n \n a), its dorsal network further includes the superior temporal gyrus. In contrast, the dorsal network in Kleist atlas (Fig.\n \n 7\n \n b) and HCP-MMP atlases (Fig.\n \n 8\n \n ) only include a small posterior section of the superior temporal gyrus. The discrepancy is likely due to more detailed parcellation in Kleist and HCP-MMP at areas 22, 39, and 40. This suggests the necessity of using a detailed parcellation to avoid\n \n under-clustering\n \n . The detailed parcellation in the HCP-MMP atlas allows for revealing the subnetworks under the dorsal network, including frontal (orange colored), inferior parietal (yellowish and light green), and superior parietal (cyan) subnetworks (Suppl. Fig.\u00a01). Similarly, the HCP-MMP shows structures under the ventral networks, including occipital (purple and light blue), inferior temporal (magenta), superior temporal (light red) subnetworks (Suppl. Fig.\u00a02). The limbic networks are primarily consistent across Brodmann, Kleist, and HCP-MMP atlases. The including regions cover prefrontal regions and cingulum that bridges between dorsal and ventral networks. More detailed subnetworks based on HCP-MMP are shown in Suppl. Fig.\u00a03.\n
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\n\n Figure\n \n 9\n \n further shows the similarity matrix between the association pathways (Fig.\n \n 9\n \n a), and the dendrogram illustrates the hierarchical clustering results (Fig.\n \n 9\n \n b) based on the HCP-MMP tract-to-region connectome. The left and right hemispheres show highly similar hierarchical relations that groups association pathways into four systems, including the arcuate system (purple), anterior ventral system (red), posterior ventral system (cyan), and cingulum system (green). The first system includes AF, SLF II, SLF III, and FAT. These pathways all connect to Broca's area are known to be associated with language functions. Fig.\n \n 3\n \n b also shows supporting results that SLF II and III are closely neighboring the AF (Y=-11, Y=-19, and Y=-27). The second system includes MdLF, TPAT, VOF, and ILF. TPAP is also commonly known as the posterior AF. The close relation between VOF and ILF suggests that VOF can be viewed as a component of ILF. The third system includes UF and IFOF, and both are characterized by their frontal connection from the temporal and occipital lobes, respectively. The fourth system includes all cingulum pathways and SLF I, likely due to the fact that SLF I is closely adjacent to cingulum at (Y=-3 and Y=-11) and entirely separated from SLF II and III by FAT (Fig.\n \n 3\n \n b). This result suggests that the SLF I could be considered as part of the cingulum system.\n
\n\n
\n\n Here we quantify the tract-to-region connectome in the young adult population. The constructed matrices provide a resource for both neuroscience and clinical studies to evaluate the probability of a white matter tract connecting to a cortical region. From this tract-to-region connectome, we further derived hierarchical relations between cortical regions to understand the topological relations. The overall result of tract-to-region hierarchical relation revealed dorsal, ventral, and limbic systems, especially using more detailed parcellation atlases such as the HCP-MMP. The revealed dorsal, ventral, and limbic networks can be applied to many existing functional models. The dorsal system includes most frontal lobe (excluding prefrontal) and parietal lobe, whereas the ventral system includes the temporal lobe and occipital lobe. The dorsal and ventral subnetworks shared many similarities with the existing dual-stream model in language and visual functions\n \n \n 24\n \n \u2013\n \n 27\n \n \n . The topological relation shown in this study disclosed the role of white matter pathways to supplement existing fMRI-based localization of cortical regions.\n
\n\n We also derived the hierarchical relation between white matter bundles to show the categorical relation of the association pathways. While many studies have been conducted to cluster white matter tracts\n \n 7,28\u221232\n \n , the clustering methods were based on tract trajectories and did not consider the connective pattern with the cortical regions. The clustering in this study did not use tract trajectories. The results were derived from their connective pattern with cortical regions to offer a different view toward the recent dispute of neuroanatomy nomenclature, particularly in the naming and segmentation of arcuate fasciculus and superior longitudinal fasciculus\n \n \n 33\n \n \n . Our hierarchical clustering results suggest that SLF II and SLF III are closely related to AF, whereas SLF I is closely related to the cingulum. These clustering results support the naming convention by Catani et al.\n \n \n 19\n \n \n that categorized SLF II and III as the subcomponent of the AF. The relation between the SLF I and the cingulum has also been proposed previously: Wang et al.\n \n \n 34\n \n \n suggested that the SLF I should be viewed as part of the cingulum system. Based on our tract-to-region connectome, it is likely that damaging SLF I may not lead to the same function deficit as SLF II and III. Therefore, the SLF I could be reasonably renamed as the frontal-parietal component of the cingulum. It is noteworthy that the current SLF I, II, III definitions could be sourced back to non-human primates studies\n \n \n 35\n \n \n . Our tract-to-region connectome shows a different perspective: the SLF II and III could be viewed as subcomponents of AF, whereas SLF I could be viewed as a subcomponent of the cingulum. Further functional or lesion-based studies are needed to support or refute this categorization conjecture.\n
\n\n In comparison with the tract-to-region connectome, the existing region-to-region connectome predominately focused on region-to-region connections. The graph models based on connectome often simply the role of the white matter bundles as a single \"edge\" in the network model, although neuroanatomical evidence has shown that brain regions in both human and non-human primates can be connected through more than one route\n \n \n 36\n \n \n . As a result, the clustering results are substantially different between the tract-to-region and region-to-region connectome: the clustering based on region-to-region connectome concerns whether the cortical regions are closely connected\n \n \n 37\n \n ,\n \n 38\n \n \n , whereas the clustering based on tract-to-region connectome concerns whether the cortical regions share similar white matter connections. One of the notable differences is that region-to-region connectome tends to group frontal and prefrontal regions due to their strong connections through short association pathways\n \n \n 38\n \n ,\n \n 39\n \n \n , but the results from tract-to-region connectome shown in this stud separated prefrontal and frontal regions due to their distinctly different connections with the limbic system. The difference in clustering context will lead to entirely different results and application scenarios that answer different neuroscience questions.\n
\n\n In addition to differences in clustering results, the region-to-region connectome falls short of illustrating the association between cortical regions and white matter pathways. This limitation becomes obvious in lesion-symptom mapping studies of aphasia: damaging Broca's area does not necessarily lead to Broca's aphasia\n \n \n 40\n \n ,\n \n 41\n \n \n , and in contrast, lesions involving the anterior segment of the left arcuate fasciculus is a strong symptom predictor\n \n \n 42\n \n \n . Thus, the functional role of white matter connections cannot be ignored, and robust prediction of brain dysfunction requires tract-to-region information. Studies have utilized fiber tracking to probe into the effect of white matter lesions to bridge the gap between the connective information between white matter bundles and cortical regions\n \n \n 43\n \n \u2013\n \n 45\n \n \n . For those studies, the tract-to-region connectome can provide a population-based reference to understand the relation between cortical regions and white matter bundles. The rich information between the white matter tract and cortical regions are critical for clinical study of \"dysconnectome,\" as recent studies suggested that cortical regions themselves are not sufficient enough to explain functional deficits\n \n \n 46\n \n \n and the role of white matter pathways should be considered\n \n \n 40\n \n ,\n \n 43\n \n \n . Our tract-to-region connectome can be a stepping stone to explore the lesion-symptom relation and understand how the connective pattern alters after a brain injury.\n
\n\n \n Diffusion MRI Acquisition\n \n
\n\n The diffusion MRI data of 1065 subjects (Fig.\n \n 1\n \n a) were acquired from the Human Connectome Project database (WashU consortium)\n \n \n 2\n \n \n . The age range was 22- to 37-year-old, and the average age was 28.75. The data were acquired using a multishell diffusion scheme with three b-values at 1000, 2000, and 3000 s/mm\n \n 2\n \n with 90 sampling directions for each shell. The spatial resolution was 1.25 mm isotropic. The detailed acquisition parameters are listed in the consortium paper\n \n \n 2\n \n \n . The preprocessed data were used and further corrected for gradient nonlinearity.\n
\n\n The diffusion data were linearly rotated to align with the ac-pc line of the ICBM152 space and simultaneously interpolated at 1mm using a cubic spline. The b-table was also rotated accordingly. The rotated data were then reconstructed using generalized q-sampling imaging\n \n \n 47\n \n \n with a diffusion sampling length ratio of 1.7. An automatic quality control routine was adopted to check the b-table orientation and ensure its accuracy\n \n \n 48\n \n \n . The reconstruction results (Fig.\n \n 1\n \n b) then guided further automated tractography.\n
\n\n For each subject, 52 white matter bundles were mapped using automated tractography that combined deterministic fiber tracking algorithm\n \n \n 49\n \n \n with parameter saturation and randomized parameters\n \n \n 12\n \n \n and 20 iterations of topology-informed pruning\n \n \n 50\n \n \n . Trajectory recognition was based on an updated population-averaged tractography atlas\n \n \n 11\n \n \n , and the maximum allowed Hausdorff distance (tolerance distance) was assigned by 16 and increased to 18 and 20 mm if it yielded no result. The white matter bundles of each subject were then output to the ICBM152 2009 nonlinear space (Fig.\n \n 1\n \n c). The analysis was conducted on the Pittsburgh Supercomputing Center provided through the Extreme Science and Engineering Discovery Environment (XSEDE) resource\n \n \n 51\n \n \n . The tractography result for each subject and each white matter tract are shared on\n \n \n http://brain.labsolver.org\n \n \n .\n
\n\n We examined whether a white matter bundle is connected to a region in each of the 1065 subjects. The trajectories of white matter bundles in the ICBM152 space were examined with the Brodmann, Kleist, and HCP-MMP atlases to derive the population-based tract-to-region connectome (Fig.\n \n 1\n \n d). We used the ICBM152 space version of newly reconstructed Brodmann and Kleist atlases\n \n \n 13\n \n \n . On the other hand, the ICBM152 space version of the HCP-MMP atlas was obtained from\n \n \n https://neurovault.org/collections/1549/\n \n \n (asymmetric, improved reconstruction) and further inspected for each cortical region to manually remove the cross-sulci leakage using DSI Studio. The revised version of the HCP-MMP atlas was shared with the DSI Studio package and is publicly available at\n \n \n http://dsi-studio.labsolver.org\n \n \n . For each subject, a binary tract-to-region connection matrix was obtained with each of the three parcellation atlases by calculating the intersection between the voxel-wise mapping of white matter bundles and cortical regions (Fig.\n \n 1\n \n e). The matrices of 1065 subjects were then aggregated to compute the population probability of the tract-to-region connection. Three matrices were generated for Brodmann, Kleist, and HCP-MMP atlases, respectively. The tract-to-region connectome can be downloaded from\n \n \n http://brain.labsolver.org\n \n \n
\n\n We used row vectors of the tract-to-region matrices as the feature vectors to derive the hierarchical relation between cortical regions (Fig.\n \n 1\n \n f). The similarity matrices between each region pair were calculated using nonparametric Spearman's rank correlation. The hierarchical clustering was conducted using weighted average distance\n \n \n 52\n \n \n provided by the\n \n linkage\n \n function in MATLAB to avoid the high variability drawback of simple single linkage clustering. For each cortical parcellation atlas (Brodmann, Kleist, HCP-MMP), a dendrogram was generated to reveal the hierarchical relation of the cortical regions. The hierarchical clustering for white matter bundles was conducted using column vector of the HCP-MMP connectome matrices (Fig.\n \n 1\n \n g). The clustering routine also used weighted average distance to generate the dendrogram to reveal the hierarchical relation of white matter bundles.\n
\n\n The analysis tool DSI Studio and its source code are available at\n \n \n http://dsi-studio.labsolver.org\n \n \n . The population-based tractography and tract-to-region connectome are publicly available at\n \n \n http://brain.labsolver.org\n \n \n .\n
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\n \n\n Suppl Fig 1\n
\n \n\n Suppl Fig 2\n
\n \n\n Suppl Fig 3\n
\n \n\n Bacteria continually evolve, as postulated in Darwin\u2019s theory of evolution\n \n 1-3\n \n . Previous studies have evaluated bacterial evolution in retrospect, but this approach is based on only speculation\n \n 4-6\n \n . Cohort studies are reliable but require a long duration\n \n 7-11\n \n . Here, using hypermutable strains, we established a rapid and integrated bacterial evolution analysis, RIBEA, based on serial passaging experiments, whole-genome and transposon-directed sequencing, and in vivo evaluation to monitor bacterial evolution for one month in a cohort. RIBEA enabled the elucidation of the mechanism of clinical progression and the potential clinical risk associated with the development of invasive ability and antimicrobial resistance in the major respiratory pathogen\n \n Klebsiella pneumoniae\n \n \n 12\n \n . RIBEA also revealed novel bacterial factors (via the identification of gene mutations that occurred during evolution) contributing to serum and antimicrobial resistance. Our results demonstrate that RIBEA enables the prediction of bacterial evolution and identification of clinically high-risk bacterial strains, clarifying the associated mechanisms of pathogenicity and antimicrobial resistance development.\n
\n \n\n Bacteria emerged approximately 3.5 billion years ago and have continued to evolve according to the theory of evolution described in Charles Darwin's \"Origin of Species\", similar to human evolution\n \n 1-3\n \n . This means that the history of human-bacterial coexistence and bacterial infections is an evolutionary battle between bacteria and humans\n \n 1\n \n . Many clinicians are trying to overcome bacterial infections, but there is no sign of convergence on a universal approach.\n
\n\n For pathogenic and opportunistic bacteria, the evolution of pathogenicity, such as the acquisition of virulence factors and toxins and enhancing gene mutations, influences human health. In addition, bacteria have also evolved antimicrobial resistance (AMR), which has become a major concern worldwide due to the acquisition of antimicrobial resistance genes and resistance-conferring gene mutations\n \n 13,14\n \n . Scientists have tried to retrospectively uncover the evolutionary mechanism of bacterial pathogenesis and the development of AMR by collecting clinical isolates\n \n 4-6\n \n . Additionally, some researchers have tried to monitor pathogen evolution by cohort studies\n \n in vitro\n \n and/or\n \n in vivo\n \n \n 7-11\n \n . Although these studies have succeeded in uncovering the parts of the mechanism, they have not achieved a comprehensive understanding because retrospective analysis yields only speculative results, and cohort studies are reliable but time-consuming. Therefore, innovations must be developed to overcome these problems and uncover the bacterial evolution mechanism to benefit human health. One of the best solutions is constructing a rapid analytical system to observe the details of bacterial evolution.\n
\n\n \n Klebsiella pneumoniae\n \n (Kp) is the main bacterium that causes lower respiratory tract infections, urinary tract infections, and bloodstream infections\n \n 12\n \n . In 2019, more than 0.6 million deaths were caused by AMR-associated Kp infections, the third most prevalent bacterial species among the cases of AMR-associated deaths\n \n 14\n \n . Based on clinical progression, Kp can be divided into two variants, classical and hypervirulent\n \n 15\n \n . Hypervirulent Kp generally exhibits a hypermucoviscous (HMV) phenotype\n \n 15\n \n that is well known\n \n \n as an clinical important phenotype for Kp causing invasive syndromes such as liver abscess, meningitis, pleural empyema, or endophthalmitis\n \n 16,17\n \n . In contrast, previous studies reported that the majority (67.9%) of bacteraemias are caused by non-HMV-Kp infections, which are prevalent in hospital-acquired bacteraemias\n \n 17\n \n . In addition, in the latest meta-analysis, no significant difference was observed in mortality between HMV- and non-HMV-Kp cases\n \n 18\n \n . These observations suggest the clinical impact of non-HMV-Kp. Although the characteristics (K1 and K2 serotypes) and pathogenicity (possession of virulence factors such as\n \n rmpA\n \n ,\n \n rmpA2\n \n ,\n \n iutA\n \n ,\n \n iroN\n \n , and the virulence IncHIB plasmid)\n \n 16,19,20\n \n of HMV-Kp are well understood, evaluations of the actual impact and potential risk of clinical progression have never been established for non-HMV-Kp infections. Therefore, non-HMV-Kp is a logical target for assessing the associated potential risk.\n
\n\n Accordingly, we previously reported a non-HMV-Kp bloodstream infection that rapidly developed multidrug resistance during the infection\n \n 21\n \n . By bacteriological analysis, we revealed that the null mutation in\n \n mutS\n \n accompanied this development. MutS is a DNA mismatch repair enzyme that immediately corrects erroneous nucleotide sequences and facilitates faithful DNA replication with MutL and MutH\n \n 22,23\n \n . This observation implies that we it is possible to predict bacterial evolution according to accelerated gene mutation frequency by the MutS functional disruption.\n
\n\n This study aims to establish a rapid and integrated bacterial evolution analysis (RIBEA) that enables us to monitor the long-term evolution of bacterial pathogenicity and antimicrobial resistance within one month by constructing and utilizing hypermutable bacteria. RIBEA comprises serial passaging experiments, whole-genome sequencing (WGS), transposon-directed sequencing (TraDIS), and\n \n in vivo\n \n evaluation. This approach revealed the potential risk of non-HMV-Kp infections by revealing the clinical progression and antimicrobial resistance mechanisms. RIBEA also enabled the identification of novel serum and antimicrobial resistance factors (via detection of gene mutations that actually occurred during evolution) and revealed that some factors are more critical than those factors previously well known and believed to play a significant role\n \n 4,24\n \n . Thus, we propose that RIBEA is a beneficial tool for the observation of bacterial evolution in front of our eyes.\n
\n\n \n \n High risk of bloodstream infection by non-HMV-Kp\n \n \n
\n\n To evaluate clinical impact of non-HMV-Kp, we first aimed to determine the clinical risk of total Kp infections. Our 5-year retrospective study of Kp\n \n \n infectious cases in a university hospital revealed that these infections were associated with more severe clinical signs in terms of hospitalization, antimicrobial use, and 60-day mortality in immunosuppressed patients and those with bacteraemia due to Kp infections (Fig. 1a and Supplementary Table 1). Therefore, immunosuppressant use and bloodstream infections are key risk factors for Kp infections.\n
\n\n Next, we evaluated the proportion of Kp clinical isolates derived from bloodstream infections. To conduct this analysis, we first performed the string test to distinguish HMV- and non-HMV-Kp isolates from among 277 clinical isolates. Among the 277 isolates, 29 (10.5%) were string test (HMV) positive. The prevalence of HMV isolates for each isolation site ranged from 0 to 17% (Fig. 1b). Notably, none of the HMV isolates were collected from blood samples. Serum susceptibility was determined according to the minimum inhibitory concentration (MIC) of human serum to estimate Kp survival ability in blood. Kp clinical isolates exhibited varied serum MICs, ranging from \u226416 to >64%, and more than 40% of the total isolates were serum resistant (Fig. 1c). In the comparison of serum resistance between HMV and non-HMV populations, there was no significant difference (Fig. 1d). These observations indicate that the potential risk of causal bloodstream infections and serum resistance is associated with non-HMV-Kp rather than HMV Kp.\n
\n\n Next, we evaluated the gene mutation frequency of Kp clinical isolates (Fig. 1e). We found that the gene mutation frequencies of\n \n \n Kp clinical isolates were diverse, ranging from 5.5 \u00d7 10\n \n -10\n \n to 4.4 \u00d7 10\n \n -6\n \n across the sites of infection. The mutation frequency was significantly higher in the non-HMV group than in the HMV group (Fig. 1f). These observations indicate that Kp clinical isolates consisted of a genetically heterogeneous population, with a higher propensity for the non-HMV phenotype. We focused on Kp clinical isolates from respiratory specimens for further analysis due to the majority of isolates being derived from these samples and the fact that respiratory Kp infections are the source of bloodstream Kp infections (Extended Data Fig. 1). We found no specific associations between gene mutation frequency and HMV phenotype, sequence type (ST), antimicrobial resistance, or serum susceptibility, suggesting that gene mutation frequency is an independent and nonfocused factor in respiratory\n \n \n Kp\n \n \n infection risk.\n
\n\n \n \n Construction of rapid-evolution bacteria\n \n \n
\n\n To uncover the pathogenesis of non-HMV-Kp, we constructed hypermutable bacteria by\n \n mutS\n \n deletion to establish RIBEA. We selected a non-HMV strain, namely, SMKP838, derived from a patient with pneumonia, which belongs to a major clone, ST 45, causing respiratory non-HMV-Kp infections\n \n 25\n \n . As anticipated, the\n \n mutS\n \n -deletion SMKP838 mutant accerelated a mutation frequency up to 824-fold compared with that of the parent SMKP838, and this frequency (7.7 x 10\n \n -6\n \n ) was defined as hypermutable (Extended Data Fig. 2a). We used this mutant for serial passaging experiments in the presence of human serum or antimicrobial agents to observe the adaptive evolution in the blood or during antimicrobial treatment.\n
\n\n The hypermutable\n \n mutS\n \n -deletion mutant rapidly acquired serum resistance (on day 6) and continued developing higher serum resistance, which reached a plateau at a serum MIC of 70% after 13 days (Extended Data Fig. 2c). In contrast, the parent (wild-type) strain did not exceed the breakpoint of serum resistance for 20 days. A time-killing assay demonstrated that the\n \n mutS\n \n deletion retained greater survival ability in the presence of human serum than the wild type by accumulating gene mutations (Extended Data Fig. 2d and e). This rapid bacterial evolution was also seen in the serial passaging experiments for ciprofloxacin, amikacin, and meropenem, clinically important antimicrobial agents against Kp infections (Extended Data Fig. 3a). The\n \n mutS\n \n -deletion mutant rapidly acquired antimicrobial resistance within 5 days, whereas wild-type SMKP838 did not exceed the breakpoints during passage for 20 days. A drastic increase in gene mutations occurred in the\n \n mutS\n \n -deletion mutant during serial passaging (Extended Data Fig. 3b). Therefore, these observations suggest that the ability to acquire serum and antimicrobial resistance in non-HMV-Kp relies on impaired DNA repair ability, and this rapid bacterial evolution approach is useful to determine the influence of non-HMV-Kp evolution on the ability to cause infection in different sites and the outcomes of antimicrobial treatment.\n
\n\n Interestingly, the number of gene mutations and genes that had mutations varied depending on the selective pressures (Extended Data Fig. 3b and c). Although the development of serum resistance did not influence antimicrobial susceptibility, the development of antimicrobial resistance decreased serum resistance (Extended Data Fig. 3d and e). Thus, this approach enabled us to identify distinct bacterial evolution depending on the environment.\n
\n\n \n \n Integrated analysis of serum resistance\n \n \n
\n\n We hypothesized that the bacterial factors contributing to serum resistance in non-HMV-Kp could be extrapolated from among the gene mutations occurring during serial passaging in the presence of human serum. However, we could not readily identify serum resistance genes due to the numerous accumulated gene mutations. Thus, we performed transposon-directed sequencing (TraDIS) because TraDIS can comprehensively detect the bacterial factors contributing to survival in different environments (Extended Data Fig. 4a). We successfully identified the difference in the abundances of detected transposon-inserted genes depending on the medium conditions by TraDIS (Extended Data Fig. 4b and c). The numbers of significantly enriched or depleted transposon-inserted genes in 4% and 8% serum (620 and 794 genes, respectively, vs. plain medium) were much higher than that in the presence of surfactant protein A (SPA) (only 3 genes), which is a large multimeric antimicrobial protein found in the airways and alveoli of the lung\n \n 26\n \n (Extended Data Fig. 4d and e). This result suggests that human serum exerts a stronger selective pressure than lung antimicrobial substances. Among the genes detected by TraDIS, the decreased abundance of genes in serum suggests putative serum resistance genes in non-HMV-Kp.\n
\n\n Next, we merged the data for genes that accumulated nonsynonymous mutations in serum-resistant-\n \n mutS-\n \n deletion SMKP838 mutants after serial passaging in the presence of human serum and the data for genes detected by TraDIS (Fig. 2a). Thus, we identified a total of 22 genes that were shared between the serial passaging and TraDIS data (Supplementary Table 2). Next, we constructed specific-gene deletion SMKP838 mutants and measured their serum MIC to determine the change in serum susceptibility. Among the genes, we observed gene-deletion mutants that enhanced serum resistance (from a serum MIC of 14% to 20 or 22%) compared with that of the parent SMKP838 strain (Fig. 2b). Therefore, we finally identified four genes,\n \n ramA\n \n (encodes a DNA-binding transcriptional regulator),\n \n LOCUS_10060\n \n (encodes a putative sugar transferase),\n \n LOCUS_14270\n \n (encodes a pyruvate kinase), and\n \n LOCUS_16740\n \n (encodes a gamma-glutamylcyclotransferase), that are bacterial factors that contribute to serum resistance in non-HMV-Kp.\n \n \n These observations indicated that the integration of serial passaging experiments using rapidly evolving bacteria and TraDIS could be used to identify the contributing gene mutations that actually occurred during bacterial evolution.\n
\n\n \n \n RIBEA in non-HMV-Kp clinical isolates\n \n \n
\n\n To evaluate whether RIBEA reveals the actual bacterial evolution that occurs in clinical isolates, we next performed a serial passaging experiment in the presence of human serum for 20 days for randomly selected serum-sensitive HMV-Kp clinical isolates with extremely high, high and low mutation frequencies (Fig. 3a). Similar to the laboratory-derived\n \n mutS\n \n -deletion mutant, a hypermutable clinical isolate, SMKP590, acquired serum resistance the earliest, after 3 days of passaging. The acquisition of serum resistance was also seen in five Kp (including one\n \n K. quasipneumoniae\n \n ) highly mutable isolates. In contrast, poorly mutable isolates did not develop serum resistance during 20-day passaging (\n \n p\n \n <0.05).\n
\n\n By WGS, we found that SMKP590 gradually accumulated gene mutations along with the increase in serum resistance, and we finally detected 74 gene mutations after 20 days of passaging (Fig. 3b). Interestingly, the number of novel and accumulated mutations in genes increased or decreased, and the number of nonsynonymous mutations was also uniform throughout the passaging (Fig. 3c and d). When we integrated and compared these data with the TraDIS data for SMKP838, we identified that 24 of 103 nonsynonymous mutations that occurred during passaging were associated with serum resistance (Extended Data Fig. 5 and Supplementary Table 3). Taken together, these observations suggest that bacterial adaptative evolution of clinical isolates is also associated with mutation frequency, and the current integrated approach is useful for prediction and/or identification of currently high-risk clones.\n
\n\n \n \n Evaluation of\n \n \n \n \n rapidly\n \n \n \n \n evolved non-HMV-Kp\n \n \n \n in vivo\n \n
\n\n We established a mouse pneumonia model to evaluate the pathogenicity of rapidly evolved non-HMV-Kp by serial passaging experiments. First, we used SMEK838 and the\n \n mutS\n \n -deletion mutant to establish intrabronchial infection. We found that the infection was not established without immunosuppression, as the mice eradicated these strains from their lungs without the development of any symptoms (Fig. 4a), suggesting that immunocompetent mice are protected; this was not unexpected, as non-HMV-Kp\n \n \n is an opportunistic pathogen\n \n 27\n \n . Thus, we established immunosuppressed mice. This immunosuppression drastically enhanced the bacterial load in the lungs (Fig. 4a) and blood 32 h after infection (Fig. 4b). Thus, non-HMV-Kp can cause pneumonia and invade the bloodstream in immunosuppressed hosts. We used this immunosuppression pneumonia model and compared the efficacy of ciprofloxacin treatments between mice infected with the wild-type and hypermutable mutant strains (Fig. 4c).\n
\n\n In contrast with the loads prior to ciprofloxacin treatment, bacterial loads of the wild-type strain and the\n \n mutS-\n \n deletion mutant (day 0) were drastically reduced after ciprofloxacin treatment in the lung (Fig. 4d), and no viable colonies were observed from the blood of infected mice after treatment (Fig. 4e). In contrast, the ciprofloxacin-resistant SMKP838 mutant derived from serial passaging in the presence of ciprofloxacin on day 19 [\u0394\n \n mutS\n \n _CIP\n \n R\n \n (day 19)] (Extended Data Fig. 6) maintained its bacterial load in both the lung and blood after ciprofloxacin treatment. Notably, we observed spontaneous development of serum-resistant clones only from\n \n mutS\n \n -deletion SMKP838 mutants (Fig. 4f and g). These observations indicate that enhancement of the mutation frequency in non-HMV-Kp\n \n \n results in the production of antimicrobial- and serum-resistant mutants\n \n in vivo\n \n and affects clinical outcomes.\n
\n\n In support of this hypothesis, both serum-sensitive and serum-resistant\n \n mutS\n \n -deletion mutants caused enhanced mortality compared with wild-type SMKP838 (\n \n p\n \n = 0.0246) (Fig. 4h). Moreover, the mortality caused by the serum-resistant\n \n mutS\n \n -deletion mutant was higher than that caused by the serum-sensitive\n \n mutS\n \n -deletion SMKP838 (\n \n p\n \n = 0.0012), and a higher bacterial load of the serum-resistant\n \n mutS\n \n -deletion mutant was observed in the blood (Fig. 4i). Severe bacterial masses were present in the livers and kidneys of mice infected with the\n \n mutS\n \n -deletion mutant (Fig. 4j). Collectively, these results suggest that this\n \n in vivo\n \n model is suitable for the evaluation of the clinical risk of rapidly evolving non-HMV-Kp.\n
\n\n \n \n Evaluation of RIBEA for internationally spreading high-risk non-HMV-Kp\n \n \n
\n\n Finally, we tried to evaluate the utility of RIBEA for currently important bacterial clones in clinical settings. In recent decades, high-risk non-HMV-Kp clones such as ST11 and ST258, which exhibit multidrug resistance, have spread worldwide and become a major clinical problem\n \n 28\n \n . We previously reported the presence of\n \n mutS\n \n mutations in ST11 and ST258\n \n 21\n \n . This finding suggests that the worst-case scenario is that these international high-risk non-HMV-Kp clones develop pathogenicity by accumulating gene mutations\n \n 4\n \n . We constructed a multidrug-resistant ST258 mutant that contained a stop codon in\n \n mutS\n \n in the BIDMC1 strain (Fig. 5a). The BIMDC1\n \n mutS\n \n mutant exhibited a drastically enhanced mutation frequency (Fig. 5b) and rapidly acquired serum resistance after 3 days of passaging (Fig. 5c). In contrast, its bacterial growth kinetics were decreased (Fig. 5d). During serial passaging, the BIDMC1\n \n mutS\n \n mutant accumulated more gene mutations than the wild type (Fig. 5e). Finally, we observed that the\n \n mutS\n \n mutant killed mice significantly more rapidly than the wild type (Fig. 5f). Taken together, these observations suggest that RIBEA enables the prediction of the clinical risk of internationally distributed high-risk multidrug-resistant bacteria.\n
\n\n Due to the difficulty of observing bacterial evolution closely and for a long enough time, elucidating the mechanisms of pathogenesis and the potential risk in the field of infectious diseases remains an important focus\n \n 7-11\n \n . Heterogeneity is associated with bacterial colonization\n \n 11\n \n , pathogenesis\n \n 29\n \n , and antimicrobial resistance\n \n 30,31\n \n . In particular, it is well known that gene mutation frequency is associated with the progression of cystic fibrosis in\n \n P. aeruginosa\n \n and\n \n H. influenzae\n \n , and highly mutable strains and hypermutators have fitness and pathogenesis advantages\n \n 32-36\n \n . However, controversial observations have also been reported indicating that hypermutator strains are less virulent than wild-type\n \n P. aeruginosa\n \n \n 37,38\n \n . Therefore, it is necessary to establish a method that allows us to observe bacterial evolution in cohorts over a specific study period to gain a better understanding of the role of mutation frequency in bacterial infection\n \n 39,40\n \n .\n
\n\n Bacterial evolution assays using hypermutable strains have already been established\n \n 21,39\n \n . However, the identification of novel bacterial factors among evolved bacteria is very challenging due to the numerous accumulated gene mutations. Therefore, no previous studies covering all the genetic variations has been performed during the evolution period for the identification of bacterial factors, and previous studies have always had a limited focus on inferable genes, resulting in the focus on genes that have not received any association or contribution in the past among the numerous detected genes being out of scope or lower priority\n \n 4,21,31,36,39\n \n . These limitations are bottlenecks in the comprehensive elucidation of bacterial ecology. RIBEA can solve this problem for a one-month period (Fig. 6a). By this approach, we successfully covered all gene mutations occurring during bacterial evolution and identified novel bacterial factors that contribute to pathogenesis. Importantly, this rapid and integrated approach can be used to select and identify the genes that are actually required for bacterial evolution.\n
\n\n Using the rapid-bacterial evolution method, we succeeded in dramatically accelerating the speed of the adaptive evolution of bacteria (more than 800-fold higher frequency than the wild-type strain in one day) and caused selection pressure-dependent evolution with an increase in gene mutations. Thus, we were able to observe the details of bacterial evolution, which sometimes takes decades or centuries, within only two weeks (the time to reach a plateau in phenotype during serial passaging experiments).\n
\n\n A previous study using\n \n Escherichia coli\n \n reported that the selection pressure provided by an environment is more essential for the evolution of novel traits than the mutational supply experienced by wild-type and mutator strains\n \n 41\n \n . Consistent with this finding, evaluating non-HMV-Kp as the bacterial evolution model showed that the presence of human serum was more impactful than surfactant protein A. This is explained by antibacterial components within serum, including complement (forms the membrane attack complex) and antimicrobial peptides\n \n 42\n \n . Thus, the environment (infection site) greatly affects evolution speed. Antimicrobial pressure is also a harsh environment for bacterial survival, but we revealed that non-HMV-Kp can overcome growth restriction by clinically important antimicrobial agents via the accumulation of gene mutations. Therefore, a rapid serial passaging experiment is also helpful in identifying the environments that promote bacterial evolution.\n
\n\n By the construction of the rapid bacterial evolution method, we also determined that although serum-resistant mutants exhibited decreased bacterial growth, they did not exhibit antimicrobial susceptibility, contrary to the development of antimicrobial resistance enhancing serum sensitivity. Thus, RIBEA supports the notion that a given combination of gene mutations during bacterial evolution does not affect bacterial fitness but significantly increases virulence, as shown in mouse models. Overall, RIBEA can reveal the changes in bacterial characteristics during bacterial evolution within a cohort.\n
\n\n Previous studies have reported a high mortality rate of lower respiratory tract infection and subsequent bacteraemia in non-HMV-Kp\n \n \n infections\n \n 14,43,44\n \n , suggesting the importance of understanding the mechanisms of clinical progression in non-HMV-Kp infections. RIBEA showed that non-HMV-Kp can adapt to the pressure of human serum and antimicrobial agents during dissemination from the lung to the blood; specifically, gene mutations contributed to serum and antimicrobial resistance. This finding indicates that non-HMV-Kp causes more severe conditions during infection and the loss of antimicrobial therapy efficacy. Importantly, this phenomenon was robustly observed for hypermutators and in immunosuppressed hosts, which was demonstrated in a previous clinical case report\n \n 21\n \n . Therefore, RIBEA is also useful for clarifying the mechanisms of clinical progression of bacterial infection, as it was revealed that non-HMV-Kp has more risk to immunosuppressant-using and/or immunodeficient hosts and that gene mutations of non-HMV-Kp affect the infection outcome.\n
\n\n In addition, we revealed that these identified gene mutations, such as those in\n \n wecC\n \n ,\n \n wzc\n \n \n gnd,\n \n and\n \n wbaP\n \n , are already harboured in non-HMV-Kp clinical isolates to a certain degree, as well-known components of serum resistomes\n \n 4,8,24,45,46\n \n . Another important observation is that the non-HMV-Kp clinical isolates consist of a more heterogeneous population in terms of gene mutation frequency than HMV-Kp isolates. This finding indicates that some isolates with high hypermutation frequencies can evolve serum and antimicrobial resistance, consistent with RIBEA results derived using\n \n mutS\n \n -engineered mutants. Collectively, we conclude that RIBEA can mirror the present and/or future of clinical bacterial isolates and estimate the potential risk, suggesting that non-HMV-Kp cannot be underestimated in clinical settings (Fig. 6b).\n
\n\n The limitations of this study are that this integrated approach does not consider the influence of the acquisition of exogenous factors such as virulence plasmids and horizontally transferred antimicrobial resistance genes\n \n 47\n \n . In addition, the accumulation of gene mutations is not only a survival strategy for bacteria, as shown by enhanced persistence\n \n 48\n \n . Evaluation of these persistent cohorts in the short term is also needed. A comprehensive evaluation of these systems will bring us closer to understanding bacterial evolution and survival strategies.\n
\n\n In conclusion, in this study, the adaptive evolution of bacteria according to Darwin's theory of evolution was demonstrated in a short time, and prediction of bacterial adaptation while identifying causal factors was made possible. This prediction is also helpful for assessing the bacterial clones we should be aware of today, as shown here regarding the health risk of the internationally distributed high-risk multidrug-resistant non-HMV-Kp clone ST258. Therefore, our established rapid and integrated bacteriological approach represents a beneficial and suitable analysis to elucidate the mechanisms of bacterial survival, adaptation, and infection and for predictions outcomes of infection by various pathogenic bacteria and multidrug-resistant bacteria. Furthermore, this technology is useful for elucidating the ecosystem of nonpathogenic bacteria, such as those in nature and the environment. Thus, RIBEA and its derivatives have the potential to accelerate our understanding of bacterial evolution along with human evolution and to become valuable tools for predicting the future of the Earth\u2019s ecosystem, which is largely responsible for determining human life.\n
\n\n \n \n Clinical epidemiology\n \n \n
\n\n Clinical epidemiology analysis was performed using 695 Kp infections reported from 2017 to 2022 at Sapporo Medical University Hospital, including 393 \u201cColonization\u201d and 302 \u201cInfection\u201d cases. \u201cInfection\u201d cases were analysed by classifying them into the presence or absence of immunosuppression, site of infection, and presence or absence of bacteraemia. Contingency tables were analysed using Fisher\u2019s exact test. A\n \n p\n \n value < 0.05 was considered to indicate significance.\n
\n\n \n \n Bacterial isolation, antimicrobial susceptibility testing, and string test\n \n \n
\n\n A total of 277 Kp strains were isolated from clinical specimens from hospitalized patients at Sapporo Medical University between 2017 and 2021. These clinical specimens comprised 100 urine samples, 113 respiratory samples, 12 blood samples, and 52 other samples (drainage, tongue coating, skin, vaginal lubricant, pus, and bile). Identification of Kp (\n \n K. pneumoniae\n \n subsp.) was performed by MALDI Biotyper (Bruker Corporation, Billerica, USA)\n \n .\n \n BIDMC_1, a carbapenem-resistant Kp strain isolated at the Beth Israel Deaconess Medical Center (BIDMC), was provided by BEI Resources (NIAID, NIH, USA).\n
\n\n The antimicrobial susceptibility of Kp\n \n \n strains was tested by the broth microdilution method, and the results were interpreted according to Clinical and Laboratory Standards Institute (CLSI) recommendations. In this study, the following antimicrobial agents were used: ciprofloxacin (Wako Pure Chemical Industry, Tokyo, Japan), ciprofloxacin hydrochloride monohydrate (Tokyo Chemical Industry, Tokyo, Japan), amikacin (Wako Pure Chemical Industry, Tokyo, Japan), kanamycin (Wako Pure Chemical Industry, Tokyo, Japan), and meropenem (Wako Pure Chemical Industry, Tokyo, Japan).\n
\n\n HMV strains were defined by a positive string test as previously described\n \n 1\n \n . A single colony grown overnight on Mueller-Hinton II agar was fished, and the formation of a string > 5 mm in length was defined as a positive result. For detection of hypervirulence factors (serotypes K1 and K2,\n \n rmpA\n \n ,\n \n rmpA2\n \n ,\n \n iutA\n \n ,\n \n iroN\n \n , and an IncHIB plasmid), multiplex PCR was performed as previously described\n \n 2\n \n .\n
\n\n \n \n Serum susceptibility\n \n \n
\n\n In this study, we used human serum from individual healthy donors (Cedarlane Laboratories Ltd, Burlington, Canada). The serum MIC was defined as the minimum % serum concentration that prevented the visible growth of microorganisms. We set the resistance breakpoint at 32%, and isolates that exhibited more than 48% of serum MIC were defined as serum-resistant isolates because this concentration is the composition of the total blood in humans. Kp strains were grown in 0.5 ml of tryptic soy broth from an overnight culture. The strains were diluted 10\n \n -4\n \n -fold (10\n \n 5\n \n CFU/mL) and incubated in plates with different serum concentrations for each well. After 20 h, colonies were visually confirmed because wells with high serum concentrations had high optical density (OD600 nm).\n
\n\n \n \n Measurement of mutation frequency\n \n \n
\n\n Mutation frequency was measured by rifampicin assay\n \n 3\n \n . The Kp isolates were cultured overnight in tryptic soy broth. The solution was concentrated 10-fold and plated onto plain or 100 mg/L rifampicin-containing Mueller Hinton II agar plates, and the plates were cultured at 37\u00b0C for 24 h. After cultivation, the number of colony-forming units (CFU) that grew on the agar plates was counted. The gene mutation frequency was calculated as [CFU on the rifampicin-containing MH agar plate]/[CFU on the plain MH agar plate]. We defined mutator types as hyper (> 10\n \n -7\n \n ), high (from 10\n \n -8\n \n to 10\n \n -7\n \n ), moderate (from 10\n \n -9\n \n to 10\n \n -8\n \n ), and low (<10\n \n -9\n \n ). Student\u2019s t test was used for the statistical analysis. A\n \n p\n \n value < 0.05 was considered to indicate significance.\n
\n\n \n \n Serial passaging experiments\n \n \n
\n\n Serial passaging experiments were performed by incubating Kp isolates (SMKP838, SMKP590, and BIDMC) in 96-well plates with MHBII containing certain concentrations (serial dilutions from original concentrations) of human serum or antimicrobial agents (ciprofloxacin, amikacin, and meropenem), as previously described\n \n 4\n \n . For the experiments using other Kp clinical isolates, we selected 19 serum-susceptible Kp isolates (serum MICs were from 8 to 16%) from among the hyper- (n = 1), high- (n = 9; contains one\n \n K. quasipneumoniae\n \n ), and low-mutators (n= 9) in the serial passaging experiment in the presence of human serum. In the ciprofloxacin assay, we selected 22 ciprofloxacin-susceptible Kp clinical isolates (ciprofloxacin MICs were from 0.03 to 0.25 mg/L) from among the hyper- (n = 1), high- (n = 11; contains one\n \n K. quasipneumoniae\n \n ), and low-mutators (n = 10). We picked the well with the highest concentration (sub-MIC) of human serum or antimicrobial agent in which the bacteria grew and diluted the bacterial culture 100-fold with 0.85% NaCl. Then, 1 \u00b5L of the dilution was inoculated in 96-well plates containing 100 \u00b5L of MHBII with various concentrations of human serum or antimicrobial agents and cultivated at 37\u00b0C for 24 h. This serial passaging was repeated for 20 days in triplicate.\n
\n\n \n \n Time-killing assay\n \n \n
\n\n Single colonies of SMKP838 and the\n \n mutS\n \n mutant strains were grown overnight in TSB medium. The culture solution was adjusted to a final concentration of 1 \u00d7 10\n \n 5\n \n CFU/mL and incubated for 0-24 h with each serum-containing solution (1/4 \u00d7 MIC, 1 \u00d7 MIC, or 2 \u00d7 MIC) or in solution without serum (Ctl) at 37\u00b0C without shaking. The assay result was determined at 0 min, 30 min, 1 h, 3 h, 6 h, and 24 h.\n
\n\n \n \n WGS\n \n \n
\n\n Genomic DNA was isolated by a DNeasy Blood & Tissue Kit (Qiagen, Hulsterweg, The Netherlands). The DNA library was prepared by a Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA) for sequencing 300 bp paired-end reads according to the manufacturer\u2019s protocol. An Illumina MiSeq was used for WGS. CTX-M genes were identified using assembled genome data by Resfinder (https://www.genomicepidemiology.org). MLST was performed using the Institute Pasteur MLST database and software (https://bigsdb.pasteur.fr/klebsiella/). Fast average nucleotide identification (FastANI) against the type strain genome was utilized for species identification. Core-genome single-nucleotide polymorphism (SNP)-based phylogenetic analysis was conducted: the Kp ATCC 35657 genome (accession number: CP015134.1) was used as a mapping reference. Mapping and core-genome extraction were performed using BWA version 0.7.17 with the \u201cbwasw\u201d option, SAMtools version 1.6 with the \u201cmpileup\u201d option, and VerScan version 2.3.9 with the \u201cmpileupcns\u201d option. The exclusion of estimated homologous recombination regions was performed using ClonalFrameML version v1.11-2. Snp-dists was used to count the pairwise SNP distance. A phylogenetic tree was generated using FastTree version 2.1.11 and FigTree version 1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/). The number of accumulated gene mutations during serial passaging experiments was analysed by mapping the genome reads to the reference genome (wild-type strain on day 0) obtained from WGS, followed by basic variant detection using CLC Genomics Workbench 21 (QIAGEN).\n
\n\n \n \n Bacterial growth determination\n \n \n
\n\n Bacterial growth was monitored by measuring the turbidity (that is, the optical density at 600 nm [OD600]) using an Infinite M200 PRO multimode microplate reader (Tecan, Kawasaki, Japan). Strains were grown in 0.5 ml of TSB (Becton Dickinson) overnight at 37\u00b0C, and 1 \u00d7 10\n \n 5\n \n CFU/ml bacteria were cultured in 0.1 ml of MHBII broth (Becton Dickinson) in a 96-well plate at 37\u00b0C with shaking at 140 rpm for 16 h. Bacterial growth curves were created based on measurements every 10 min for 16 h.\n
\n\n \n \n Transposon-directed insertion site sequencing (TraDIS)\n \n \n
\n\n The SMKP838 transposon library was constructed using the EZ-Tn5\u2122 <KAN-2> Tnp Transposome\u2122 Kit (Epicentre, Wisconsin, USA). The bacteria with transposase introduced by electroporation (2.5 kV/cm, 200 \u03a9, and 25 \u03bcF) were selected by the formation of colonies on MHII agar containing 50 mg/l kanamycin. Over 100,000 colonies were collected, pooled, and frozen at -80\u00b0C in TSB with 10% glycerol as stock solutions until use. The transposon mutant library (10\n \n 6\n \n cfu/mL) was inoculated into 1 mL of plain MHBII, MHBII containing 4% or 8% serum, or MHBII containing 40 mg/L surfactant protein A (SPA) and cultured at 37\u00b0C for 20 h. Total DNA was isolated using a Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA). Total DNA (500 ng) was used to prepare the DNA library for TraDIS using an NEBNext Ultra II FS DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA). After fragmentation, end repair, 5' phosphorylation, dA-tailing, and adaptor ligation, and size selection (275-475 bp) according to the manufacturer\u2019s protocol, the transposon-inserted genes were amplified by PCR using NEBNext Ultra II Q5 Master Mix (New England Biolabs), 20 nM NEBTnF2fas (5'-TCGACCTGCAGGCATGCAAGCTTCAGGGTTGAGATGTG-3') and NEBTn5-700 (5'-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATC-3') primers, and 20 ng of fragmented DNA as the template with following condition: initial denature at 98\u00b0C for 30 sec, 22 cycles of 98\u00b0C for 10 sec and 72\u00b0C for 1 min 15 sec, and final extension at 72\u00b0C for 2 min. After the purification of the PCR product using AMPure XP beads (Beckman Coulter, Brea, CA, USA), enrichment PCR was performed by using a KAPA HiFi HotStart Library Amplification Kit (Roche, Basel, Switzerland), 20 nM NEBNext i700 primers including NEBNext Multiplex Oligos for Illumina (New England Biolabs) and NEBTn5-501-3 (5'- AATGATACGGCGACCACCGAGATCTACACTATAGCCTACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGACCTGCAGGCATGCAAGCTTC-3'), and 20 ng of the purified DNA as the template with following condition: initial denature at 98\u00b0C for 45 sec, 10 cycles of 98\u00b0C for 15 sec, 60\u00b0C for 30 sec, and 72\u00b0C for 10 sec, and final extension at 72\u00b0C for 30 sec. The PCR products were purified and size selected (average: 650 bp) using AMPure XP beads. These products were pooled, and a NovaSeq600 was used for TraDIS. TraDIS analysis was performed according to a previous study\n \n 5\n \n , and a false discovery rate-adjusted\n \n p\n \n value (FDR\n \n p\n \n ) < 0.05 (vs. plain medium) was defined as significant.\n
\n\n Genes with significantly lower detected levels in serum or SPA samples (> 2-fold vs. plain medium) were considered putative serum or SPA resistance genes.\n
\n\n \n \n Construction of gene deletion mutants\n \n \n
\n\n The\n \n mutS\n \n -deletion SMKP838 mutant and each serum resistance gene were generated by the \u03bb-Red recombinase system, as previously described, using pKD46-hyg\n \n 6,7\n \n . Each gene was replaced with Mini genes containing kanamycin resistance cassettes (Gene Bridges, Heidelberg, Germany) and 50 bases corresponding to the upstream and downstream regions of the target genes. The gene deletions were confirmed by PCR using specific primers.\n
\n\n \n \n Mouse models of lung and bloodstream infection\n \n \n
\n\n Ten- to 12-week-old female BALB/c mice were anaesthetized and infected transbronchially with a microsprayer (TORAY PRECISION, Tokyo, Japan) with 50 \u03bcl of a 1\u00d710\n \n 8\n \n CFU/ml solution. The mice were immunosuppressed by intraperitoneal injection of 250 mg/kg five days prior to infection and 125 mg/kg one day prior to infection with cyclophosphamide monohydrate (lot No. SKE6784; FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan). In the treatment group, mice were injected subcutaneously with 100 mg/kg ciprofloxacin monohydrochloride (TOKYO CHEMICAL INDUSTRY, Tokyo, Japan) 1 and 24 hours after infection. After 32 hours in the nontreated group and 48 hours in the treated group or when a \u2018prelethal critical endpoint\u2019 had been reached, mice were euthanized by cervical dislocation. Lungs were washed with sterile PBS and homogenized with a gentleMACS Dissociator (Miltenyi Biotec). Homogenates were plated for determination of the number of CFU per lung. Blood was collected by puncturing the jugular vein. A drop of blood was added to 1 ml of PBS, vortexed, and then cultured on an appropriate plate. In the experiments, wild type-derived strains were selected by seeding on MHII agar containing 100 mg/l ampicillin sodium and \u0394\n \n mutS\n \n -derived strains on MHII agar containing 50 mg/l kanamycin to eliminate the effects of other indigenous and environmental bacteria. Fifty colonies from each specimen were randomly selected, and their serum MIC was measured.\n
\n\n \n \n Construction of mutS-mutated BIDMC_1\n \n \n
\n\n Since the \u03bb-Red recombination system was unable to generate\n \n mutS\n \n -deficient strains of BIDMC1, we used pORTMAGE and constructed\n \n mutS-\n \n mutated BIDMC1 (BIDMC1 MutS_Tyr37STOP)\n \n 8\n \n . Hygromycin-integrated pORTMAGE was generated as previously described\n \n 9\n \n . Transformants were produced by electroporation. Oligonucleotides (90 bp) for\n \n mutS\n \n containing the C111T mutation were designed using the MEGA Oligo Design Tool: MegamutS, CGCAACATCCTGACATTCTGCTGTTTTACCGGATGGGGGATTTT\n \n TA\n \n a\n \n \n GAGCTATTTTATGACGATGCGAAACGCGCCTCGCAGCTGCTCG; bold \u201ca\u201d indicates an introduced base. Gene replacement was confirmed by direct DNA sequencing.\n
\n\n \n Ethics statement\n \n
\n\n This study was approved by the Sapporo Medical University Hospital Institutional Review Board (IRB No. 272-70) and Sapporo Medical University Animal Care and Use Committee (Nos. 17-137, 18-083, and 20-006).\n
\n\n \n \n Statistical analysis\n \n \n
\n\n We used Prism 9 to calculate the significance of differences. Unpaired, two-tailed Student\u2019s\n \n t\n \n test or a two-tailed Mann\u2012Whitney U test was used to compare two groups, and Dunn\u2019s comparison test followed by the Kruskal\u2013Wallis test was used to compare three or more groups. In addition, the log-rank test was used for survival data analysis. Statistical methods and\n \n P\n \n values are described in each figure. A\n \n P\n \n value < 0.05 was considered to indicate significance. In addition, Fisher\u2019s exact test and Student\u2019s\n \n t\n \n test were used in the clinical analysis to compare two groups.\n
\n\n \n Methods references\n \n
\n\n 1. Vila, A. et al. Appearance of\n \n Klebsiella pneumoniae\n \n liver abscess syndrome in Argentina: case report and review of molecular mechanisms of pathogenesis.\n \n Open Microbiol. J.\n \n \n 5\n \n , 107-113 (2011).\n
\n\n 2 Yu, F.\n \n et al.\n \n Multiplex PCR Analysis for Rapid Detection of\n \n Klebsiella pneumoniae\n \n Carbapenem-Resistant (Sequence Type 258 [ST258] and ST11) and Hypervirulent (ST23, ST65, ST86, and ST375) Strains.\n \n J Clin Microbiol\n \n \n 56\n \n , e00731-18 (2018).\n
\n\n 3. Zhou, H. et al. The mismatch repair system (mutS and mutL) in\n \n Acinetobacter baylyi\n \n ADP1.\n \n BMC Microbiol.\n \n \n 20\n \n , 40 (2020).\n
\n\n 4. Khil, P. P. et al. Dynamic emergence of mismatch repair deficiency facilitates rapid evolution of ceftazidime-avibactam resistance in\n \n Pseudomonas aeruginosa\n \n acute infection.\n \n mBio\n \n \n 10\n \n , e01822-19 (2019).\n
\n\n 5. Dorman, M. J., Feltwell, T., Goulding, D. A., Parkhill, J. & Short, F. L. The Capsule Regulatory Network of\n \n Klebsiella pneumoniae\n \n Defined by density-TraDISort.\n \n mBio\n \n \n 9\n \n , e01863-18, (2018).\n
\n\n 6. Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in\n \n Escherichia coli\n \n K-12 using PCR products.\n \n Proc. Natl. Acad. Sci. U. S. A.\n \n \n 97\n \n , 6640-6645 (2000).\n
\n\n 7. Sato, T. et al. Tigecycline nonsusceptibility occurs exclusively in fluoroquinolone-resistant\n \n Escherichia coli\n \n clinical isolates, including the major multidrug-resistant lineages O25b:H4-ST131-H30R and O1-ST648.\n \n Antimicrob. Agents Chemother.\n \n \n 61\n \n , e01654-16 (2017).\n
\n\n 8. Nyerges, \u00c1. et al. A highly precise and portable genome engineering method allows comparison of mutational effects across bacterial species.\n \n Proc. Natl. Acad. Sci. U. S. A.\n \n \n 113\n \n , 2502-2507 (2016).\n
\n\n 9. Sato, T.\n \n et al.\n \n Emergence of the Novel Aminoglycoside Acetyltransferase Variant\n \n aac(6')-Ib-D179Y\n \n and Acquisition of Colistin Heteroresistance in Carbapenem-Resistant\n \n Klebsiella pneumoniae\n \n Due to a Disrupting Mutation in the DNA Repair Enzyme MutS.\n \n mBio\n \n \n 11\n \n , e01954-20 (2020).\n
\n\n Extended Data 1 to 6, Supplementary Tables 1 to 3\n
\n \n\n SARS-CoV-2 remains a global threat to human health particularly as escape mutants emerge. There is an unmet need for effective treatments against COVID-19 for which neutralizing single domain antibodies (nanobodies) have significant potential.\u00a0Their small size and stability mean that nanobodies are compatible with respiratory administration. We report four nanobodies (C5, H3, C1, F2) engineered as homotrimers with pmolar affinity for the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Crystal structures show C5 and H3 overlap the ACE2 epitope, whilst C1 and F2 bind to a different epitope. Cryo Electron Microscopy shows C5 binding results in an all down arrangement of the \u00a0Spike protein. C1, H3 and C5 all neutralize the Victoria strain, and the highly transmissible Alpha (B.1.1.7 first identified in Kent, UK) strain and C1 also neutralizes the Beta (B.1.35, first identified in South Africa). Administration of C5-trimer via the respiratory route showed potent therapeutic efficacy in the Syrian hamster model of COVID-19 and separately effective prophylaxis. The molecule was similarly potent by intraperitoneal injection.\n
\n\n \n SARS-CoV-2\n \n
\n\n \n COVID-19\n \n
\n\n \n Syrian golden hamster model\n \n
\n\n \n nanobodies\n \n
\n\n There are currently seven known coronaviruses that infect humans of which three (SARS-CoV-1, MERS, SARS-CoV-2) have emerged in the last 20 years and caused severe and even fatal respiratory diseases\n \n 1\n \n . By far the most serious outbreak has been caused by SARS-CoV-2 which is responsible for the current global pandemic currently presently associated with 3.94 million deaths worldwide. Although vaccines are now being administered against SARS-CoV-2, building up immunity in the global population will take time. The imperative to treat SARS-CoV-2 infection has led to the search for agents that neutralize the virus for use in passive immunotherapy. \u00a0Early attention has focused on identifying neutralising monoclonal antibodies from patients who have recovered from COVID-19\n \n 2-6\n \n ; the therapeutic use of antibodies is widespread and draws on existing knowledge and resources. However, nanobodies or VHHs (Variable Heavy-chain domains of Heavy-chain antibodies) derived from the heavy chain-only subset of camelid immunoglobulins offer an alternative with multiple advantages over conventional antibodies. The small molecular size and stability of nanobodies allows them to be formulated for topical delivery directly to the airways of infected patients through aerosolization. This results in improved bioavailability, simpler therapeutic compliance and easier administration. Secondly, while conventional antibodies that comprise two disulphide-linked polypeptides, heavy and light chain, typically require mammalian cells for production, nanobodies can be manufactured using readily available microbial systems. The potency of nanobodies against SARS-CoV-2\n \n 7\n \n infection has been demonstrated in cell-based assays\n \n 8-16\n \n and most recently in animal studies\n \n 17,18\n \n . Several strategies for engineering VHH into a multivalent species are known. These include fusing to an Fc\n \n 17,19-21\n \n and simple N to C fusion of two or more nanobodies to the same epitope\n \n 19,22\n \n . Multivalent presentations increase the binding avidity to the molecular target and thus the biological potency of such agents\n \n 23\n \n . We have isolated four nanobodies that bind different epitopes on the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) glycoprotein with high affinity and potently neutralize the virus\n \n in vitro\n \n with picomolar potency\n \n .\n \n We have explored their binding to and neutralization of two newly emergent variants (B.1.1.7 and B.1.351), identifying a potent cross-reactive agent. We have shown that treatment either systemically (intraperitoneal route) or via the respiratory tract (intranasal route) with a single dose of the most potent nanobody prevented disease progression in the Syrian hamster model of COVID-19.\n
\n\n \n Isolation and binding characterisation of nanobodies that block ACE2 binding to the Spike protein of SARS-CoV-2\n \n
\n\n Antibodies to the RBD of SARS-CoV-2 were raised in a llama by primary immunisation with a combination of purified RBD alone and RBD fused to human IgG1, followed by a single boost with purified S (spike) protein mixed with RBD. The S protein sequence was derived from the original Wuhan or Victoria (B) strain of SARS-CoV-2. A phage display VHH library was constructed from the cDNA of peripheral blood mononuclear cells, and RBD binders selected by two rounds of bio-panning. The phage clones with the highest affinity for RBD were identified by an inhibition ELISA and classified by sequencing of complementary determining region 3 (CDR3) (Supplementary Fig. 1). Four VHHs were selected for production and their RBD binding kinetics measured by surface plasmon resonance (SPR) (Fig.\n \n 1\n \n a-d). The calculated K\n \n D\n \n s were all in the picomolar range (20\u2013615 pM) with the rank order of affinities H3\u2009>\u2009F2\u2009>\u2009C5\u2009>\u2009>\u2009C1 (Table\n \n 1\n \n ).\n
\n| \n \n Analyte\n \n | \n \n \n Ligand\n \n | \n \n \n Ka (1/Ms)\n \n | \n \n \n Kd (1/s)\n \n | \n \n \n K\n \n D\n \n (pM)\n \n | \n \n \n T\n \n 1/2\n \n (min)\n \n | \n
|---|---|---|---|---|---|
| \n \n C1\n \n | \n \n \n RBD\n \n | \n \n \n 9.3E\u2009+\u200905\n \n | \n \n \n 5.7E-04\n \n | \n \n \n 615\n \n | \n \n \n 20\n \n | \n
| \n \n C1\n \n | \n \n \n Alpha RBD\n \n | \n \n \n 7.5E\u2009+\u200905\n \n | \n \n \n 5.4E-04\n \n | \n \n \n 725\n \n | \n \n \n 21\n \n | \n
| \n \n C1\n \n | \n \n \n Beta RBD\n \n | \n \n \n 9.2E\u2009+\u200905\n \n | \n \n \n 6.0E-04\n \n | \n \n \n 648\n \n | \n \n \n 19\n \n | \n
| \n \n C5\n \n | \n \n \n RBD\n \n | \n \n \n 9.8E\u2009+\u200906\n \n | \n \n \n 9.8E-04\n \n | \n \n \n 99\n \n | \n \n \n 12\n \n | \n
| \n \n C5\n \n | \n \n \n Alpha RBD\n \n | \n \n \n 6.8E\u2009+\u200906\n \n | \n \n \n 1.7E-02\n \n | \n \n \n 2523\n \n | \n \n \n 1\n \n | \n
| \n \n H3\n \n | \n \n \n RBD\n \n | \n \n \n 1.3E\u2009+\u200907\n \n | \n \n \n 3.3E-04\n \n | \n \n \n 25\n \n | \n \n \n 35\n \n | \n
| \n \n H3\n \n | \n \n \n Alpha RBD\n \n | \n \n \n 1.2E\u2009+\u200907\n \n | \n \n \n 1.2E-03\n \n | \n \n \n 102\n \n | \n \n \n 10\n \n | \n
| \n \n F2\n \n | \n \n \n RBD\n \n | \n \n \n 4.7E\u2009+\u200906\n \n | \n \n \n 1.9E-04\n \n | \n \n \n 40\n \n | \n \n \n 61\n \n | \n
| \n \n F2\n \n | \n \n \n Alpha RBD\n \n | \n \n \n 4.8E\u2009+\u200906\n \n | \n \n \n 2.3E-04\n \n | \n \n \n 47\n \n | \n \n \n 51\n \n | \n
| \n \n F2\n \n | \n \n \n Beta RBD\n \n | \n \n \n 5.9E\u2009+\u200906\n \n | \n \n \n 2.2E-04\n \n | \n \n \n 38\n \n | \n \n \n 52\n \n | \n
| \n \n C5 Fc\n \n | \n \n \n RBD\n \n | \n \n \n 3.1E\u2009+\u200906\n \n | \n \n \n 1.2E-04\n \n | \n \n \n 37\n \n | \n \n \n 99\n \n | \n
| \n \n C5 trimer\n \n | \n \n \n RBD-Fc\n \n | \n \n \n 7.1E\u2009+\u200906\n \n | \n \n \n 1.2E-04\n \n | \n \n \n 18\n \n | \n \n \n 92\n \n | \n
| \n \n C5 trimer\n \n | \n \n \n Alpha RBD-Fc\n \n | \n \n \n 9.9E\u2009+\u200906\n \n | \n \n \n 2.8E-04\n \n | \n \n \n 29\n \n | \n \n \n 41\n \n | \n
| \n \n H3 trimer\n \n | \n \n \n RBD-Fc\n \n | \n \n \n 1.2E\u2009+\u200908\n \n | \n \n \n 3.3E-05\n \n | \n \n \n 0.3\n \n | \n \n \n 349\n \n | \n
| \n \n H3 trimer\n \n | \n \n \n Alpha RBD-Fc\n \n | \n \n \n 1.8E\u2009+\u200907\n \n | \n \n \n 1.2E-04\n \n | \n \n \n 6\n \n | \n \n \n 98\n \n | \n
| \n \n C1 trimer\n \n | \n \n \n RBD-Fc\n \n | \n \n \n 9.0E\u2009+\u200905\n \n | \n \n \n 4.8E-05\n \n | \n \n \n 53\n \n | \n \n \n 242\n \n | \n
| \n \n C1 trimer\n \n | \n \n \n Alpha RBD-Fc\n \n | \n \n \n 1.0E\u2009+\u200906\n \n | \n \n \n 7.4E-05\n \n | \n \n \n 73\n \n | \n \n \n 154\n \n | \n
| \n \n C1 trimer\n \n | \n \n \n Beta RBD-Fc\n \n | \n \n \n 8.2E\u2009+\u200905\n \n | \n \n \n 6.2E-05\n \n | \n \n \n 75\n \n | \n \n \n 186\n \n | \n
\n
\n
\n Competition binding experiments were carried out by SPR to investigate whether the VHHs blocked the binding of RBD to ACE2 and the overlap with the epitope recognized by the human monoclonal antibody CR3022\n \n 24\n \n as well as the nanobody H11-H4\n \n 25\n \n . The results showed that C1, H3 and C5 blocked ACE2 binding whereas F2 did not affect ACE2 binding (Fig.\n \n 1\n \n e). C1 and F2 but not C5 or H3 competed with CR3022 for binding to the RBD (Fig.\n \n 1\n \n f) whereas C5 and H3 but not C1 and F2 competed with H11-H4 binding (Fig.\n \n 1\n \n g). (CR3022 is known to recognize an epitope that does not overlap with ACE2\n \n 25\u201327\n \n or H4-H11\n \n 25\n \n ). C5 and H3 would be expected to target a similar epitope to that of H11-H4, human monoclonal antibodies and other nanobodies that neutralise SARS-CoV-2 by competing directly with the interaction between the spike protein and the ACE2 receptor (cluster 2 antibodies\n \n \n 28\n \n \n ). C1 and F2 belong to the group of antibodies (cluster 1 antibodies\n \n \n 28\n \n \n ) including CR3022\n \n 26\n \n and EY-6A\n \n 2\n \n 9\n \n \n that bind to a region distinct from the ACE2 receptor binding interface. These two antibodies have been reported to destabilize the trimeric spike protein and by this mechanism prevent receptor engagement\n \n \n 26\n \n ,\n \n 29\n \n \n thereby neutralizing the virus.\n
\n\n ITC was used to analyse the binding of C5, F2 and C1 to RBD and spike proteins in solution However, as the agents bind so tightly conventional ITC has large errors. Therefore a displacement assay was devised using the H11 nanobody previously identified\n \n \n 25\n \n \n that weakly binds to RBD with a K\n \n D\n \n of 1\u00b5M measured by ITC (Supplementary Fig.\u00a02a). Combining the H11 titration with viral proteins (Supplementary Fig.\u00a02a,b), C5 titration with viral proteins (Supplementary Fig.\u00a02c,d) and C5 titration with viral proteins pre-incubated with H11 (displacement assay Supplementary Fig.\u00a01e,f), we determined K\n \n D\n \n for C5 to RBD as 210\u2009\u00b1\u200960 pM and to Spike as 350 pM\u2009\u00b1\u20096 pM (Supplementary Fig.\u00a01g,h). The estimated K\n \n D,\n \n confirms sub-nanomolar binding of C5 to the Spike protein in solution and indicates 1:1 stoichiometry. No displacement agent was available for F2 and C1, and therefore the binding K\n \n D\n \n for RBD of 320\u2009\u00b1\u200930 and 600\u2009\u00b1\u200940 pM respectively were estimated by direct binding but are subject to considerable uncertainty (Supplementary Fig.\u00a01i,j). Both C1 and F2 when bound to Spike gave complex traces, suggesting that when engaging the Spike other conformational changes occur (Supplementary Fig.\u00a01i,j ).\n
\n\n The four nanobodies were also assessed for their binding to RBD from the Alpha (B.1.1.7; N501Y originally identified from the UK) and Beta (B.1.351; N501Y, N417K and E484K, originally identified from South Africa). C5 and H3 bound strongly to the Alpha variant albeit with reduced affinity compared to the Victoria strain (Fig.\n \n 1\n \n h,i) however, no binding was detected to the Beta strain. By contrast, C1 and F2 bound with a similar affinity to all three strains (Fig.\n \n 1\n \n ). These results are consistent with the C5 and H3 epitopes overlapping with the mutated regions which are known to be adjacent to and part of the ACE2 binding region.\n
\n\n \n Structural Analysis Of RBD Binding\n
\n \n
\n To further define the epitopes recognized by the nanobodies, crystal structures of the C5-RBD (Victoria), H3-C1-RBD (Victoria) and F2-RBD (Victoria) co-complexes were determined to high resolution (Table\n \n 2\n \n , 1.5, 1.9 and 2.3 \u00c5, respectively), however, the C1-RBD binary complex failed to give high quality crystals. Examination of the three structures confirmed the results of binding experiments that indeed H3 and C5 occlude the RBD binding site for ACE2 (Fig.\n \n 2\n \n a). C1 does not occlude the ACE2 epitope but would sterically prevent ACE2 binding to RBD, F2 would not be predicted to interfere with ACE2 binding (Fig.\n \n 2\n \n a). The C5 epitope has only a small overlap with the H3 epitope or with the H11-H4 epitope that we previously reported\n \n \n 25\n \n \n . The interface between C5 and RBD is extensive and involves all three CDR loops and the fixed sequence loop (FR2) at A75 of the nanobody (Fig.\n \n 2\n \n b and supplementary Fig. 3a).\n
\n| \n | \n\n \n C5 \u2013RBD\n \n\n (7OAO)\n \n | \n \n \n H3- C1-RBD\n \n\n (7OAP)\n \n | \n \n \n F2\u2013RBD\n \n\n (7OAY)\n \n | \n \n \n C5-Alpha RBD\n \n\n (7OAU)\n \n | \n \n \n H3-C1-Alpha RBD\n \n\n (7OAQ)\n \n | \n
|---|---|---|---|---|---|
| \n \n \n Data collection\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n
| \n \n Space group\n \n | \n \n \n \n P\n \n 2\n \n 1\n \n 2\n \n 1\n \n 2\n \n | \n \n \n \n P\n \n 4\n \n 3\n \n 2\n \n 1\n \n 2\n \n | \n \n \n \n P\n \n 3\n \n 1\n \n \n | \n \n \n \n P\n \n 2\n \n 1\n \n \n | \n \n \n \n P\n \n 4\n \n 3\n \n 2\n \n 1\n \n 2\n \n | \n
| \n \n Cell dimensions\n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n
| \n \n \n a\n \n ,\n \n b\n \n ,\n \n c\n \n (\u00c5)\n \n | \n \n \n 71.2, 154.3, 28.1\n \n | \n \n \n 105.7, 105.7, 112.5\n \n | \n \n \n 108.4, 108.4, 165.5\n \n | \n \n \n 28.8, 153.7, 75.9\n \n | \n \n \n 105.9, 105.9, 112.7\n \n | \n
| \n \n \u03b1, \u03b2, \u03b3 (\u00b0)\n \n | \n \n \n 90, 90, 90\n \n | \n \n \n 90, 90, 90\n \n | \n \n \n 90, 90, 120\n \n | \n \n \n 90, 100.3, 90\n \n | \n \n \n 90, 90, 90\n \n | \n
| \n \n Resolution (\u00c5)\n \n a\n \n \n | \n \n \n 51\u20131.50\n \n\n (1.54\u2013 1.50)\n \n | \n \n \n 62\u20131.9\n \n\n (1.95\u20131.90)\n \n | \n \n \n 94\u20132.34\n \n\n (2.40\u20132.34)\n \n | \n \n \n 39\u20131.65\n \n\n (1.69\u20131.65)\n \n | \n \n \n 53\u20131.55\n \n\n (1.59\u20131.55)\n \n | \n
| \n \n \n R\n \n \n merge\n \n \n | \n \n \n 0.045 (0.39)\n \n | \n \n \n 0.124 (1.83)\n \n | \n \n \n 0.156 (1.75)\n \n | \n \n \n 0.104 (1.29)\n \n | \n \n \n 0.100 (3.12)\n \n | \n
| \n \n \n R\n \n \n pim\n \n \n | \n \n \n 0.013 (0.15)\n \n | \n \n \n 0.025 (0.40)\n \n | \n \n \n 0.051 (0.7)\n \n | \n \n \n 0.044 (0.56)\n \n | \n \n \n 0.020 (0.59)\n \n | \n
| \n \n \n I/\n \n \u03c3 (\n \n I\n \n )\n \n | \n \n \n 28.1 (3.7)\n \n | \n \n \n 14.4 (0.7)\n \n | \n \n \n 9.9 (0.8)\n \n | \n \n \n 10.0 (1.2)\n \n | \n \n \n 16.9 (0.6)\n \n | \n
| \n \n \n CC\n \n \n 1/2\n \n \n | \n \n \n 1.0 (0.96)\n \n | \n \n \n 0.99 (0.94)\n \n | \n \n \n 1.0 (0.5)\n \n | \n \n \n 1.0 (0.6)\n \n | \n \n \n 1.0 (0.6)\n \n | \n
| \n \n Completeness (%)\n \n | \n \n \n 99.4 (93.7)\n \n | \n \n \n 100 (100)\n \n | \n \n \n 100(99.6)\n \n | \n \n \n 100 (100)\n \n | \n \n \n 100 (93)\n \n | \n
| \n \n Redundancy\n \n | \n \n \n 11.8 (6.0)\n \n | \n \n \n 25.4 (22.1)\n \n | \n \n \n 10.1 (7.0)\n \n | \n \n \n 6.6 (6.0)\n \n | \n \n \n 26.8 (27.6)\n \n | \n
| \n \n \n Refinement\n \n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n
| \n \n Resolution (\u00c5)\n \n | \n \n \n 46.3\u20131.5\n \n\n (1.54\u20131.50))\n \n | \n \n \n 62\u20131.9\n \n\n (1.95\u20131.90))\n \n | \n \n \n 94\u20132.34\n \n\n (2.40\u20132.34)\n \n | \n \n \n 39\u20131.65\n \n\n (1.69\u20131.65)\n \n | \n \n \n 53\u20131.55\n \n\n (1.59\u20131.55)\n \n | \n
| \n \n No. reflections\n \n | \n \n \n 51782 (3353)\n \n | \n \n \n 50644(3478)\n \n | \n \n \n 91842(4643)\n \n | \n \n \n 77705 (5819)\n \n | \n \n \n 93033(6677)\n \n | \n
| \n \n \n R\n \n \n work\n \n /\n \n R\n \n \n free\n \n \n | \n \n \n 15.2 / 18.6\n \n\n (19.3 / 25.3)\n \n | \n \n \n 18.0 / 20.3\n \n\n (33.0 / 30.8)\n \n | \n \n \n 19.2 / 22.7\n \n\n (33.5 / 29.9)\n \n | \n \n \n 17.8 / 19.9\n \n\n (31.6/ 32.9)\n \n | \n \n \n 15.5 / 17.8\n \n\n (38.9 / 39.6)\n \n | \n
| \n \n No. atoms\n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n
| \n \n Protein\n \n | \n \n \n 2506\n \n | \n \n \n 3550\n \n | \n \n \n 15376\n \n | \n \n \n 5018\n \n | \n \n \n 3604\n \n | \n
| \n \n Ions / buffer\n \n | \n \n \n 4\n \n | \n \n \n 14\n \n | \n \n \n -\n \n | \n \n \n 6\n \n | \n \n \n 14\n \n | \n
| \n \n Water\n \n | \n \n \n 290\n \n | \n \n \n 235\n \n | \n \n \n 323\n \n | \n \n \n 470\n \n | \n \n \n 375\n \n | \n
| \n \n Residual\n \n B\n \n factors\n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n
| \n \n Protein\n \n | \n \n \n 28\n \n | \n \n \n 28\n \n | \n \n \n 36\n \n | \n \n \n 18\n \n | \n \n \n 39\n \n | \n
| \n \n Ligand/ion\n \n | \n \n \n 44\n \n | \n \n \n 71\n \n | \n \n \n -\n \n | \n \n \n 43\n \n | \n \n \n 46\n \n | \n
| \n \n Water\n \n | \n \n \n 38\n \n | \n \n \n 45\n \n | \n \n \n 48\n \n | \n \n \n 37\n \n | \n \n \n 41\n \n | \n
| \n \n R.m.s. deviations\n \n | \n \n | \n\n | \n\n | \n\n | \n\n | \n
| \n \n Bond lengths (\u00c5)\n \n | \n \n \n 0.008\n \n | \n \n \n 0.010\n \n | \n \n \n 0.009\n \n | \n \n \n 0.007\n \n | \n \n \n 0.008\n \n | \n
| \n \n Bond angles (\u00b0)\n \n | \n \n \n 1.4\n \n | \n \n \n 1.52\n \n | \n \n \n 1.72\n \n | \n \n \n 1.34\n \n | \n \n \n 1.40\n \n | \n
| \n Data were collected from a single crystal for each structure.\n | \n|||||
| \n \n a\n \n Values in parentheses are for highest-resolution shell.\n | \n
\n
\n
\n The epitopes recognized by H3 and H11-H4 as we hypothesized do have a significant overlap (Fig.\n \n 3\n \n a). H3 however has 100 fold higher affinity than H11-H4. Since H3 and H11-H4 have quite different sequences and this results from many small changes in loops between the structure. This means that the identification of the atomic features that drive the difference in affinity from simple structural analysis is not straightforward. Comparison of the structures reveals several features that may contribute to the increased affinity The H3 RBD interface buries just under 10 % more surface area and satisfies 4 more hydrogen bonds than in H11-H4 RBD. In addition, in H3 the key R52 E484 salt bridge makes additional hydrophobic interactions with W53 and F59 of H3 (Supplementary Fig. 3b), these contacts are absent in H11-H4. In a future study, we suggest these regions should be probed.\n
\n\n The key binding interaction between C5 and H3 nanobodies and RBD is a combined salt bridge \u03c0-cation interaction involving an arginine from the nanobody (R31 in C5, R52 in H3) with E484 and F490 of RBD. This arrangement of the positively charged guanidine group, phenyl ring and glutamate was previously highlighted in the H11-H4 study\n \n \n 25\n \n \n . In C5, R31 is located in CDR1 and as result the side chain of R31 enters the salt bridge \u03c0-cation interaction from the opposite side to R52 but preserves the interaction (Fig.\n \n 3\n \n b). The E484K mutation found in the recently emergent South African and Brazilian strains will disrupt this interface in both C5 and H3 (as well as H11-H4). The formation of a salt bridge with E484 is a feature of many antibodies isolated from the B cells of COVID-19 convalescent and vaccinated individuals and escape mutants at this position are obviously a major concern for the efficacy of current vaccines\n \n \n 30\n \n ,\n \n 31\n \n \n .\n
\n\n In addition to R31, residues T28 to G30 from CDR1 of C5 are also in contact with residues Y453, L455, Q493 and S494 of RBD (Fig.\n \n 2\n \n b and supplementary Fig. 3a). The aromatic ring of Y449 of the RBD makes extensive hydrophobic contacts with the main chain residues, T53 to G56 from CDR2 of C5. From C5 FR2 the main chain of S72, the side chains of N73 and N74 make hydrogen bonds with the side chains of Q498, N501 and the main chain of S494 respectively. The bidentate hydrogen bonding arrangement of N73 (from C5) with N501 explains why this interaction is sensitive to the N501Y mutation (Alpha variant). FR2 of C5 makes van der Waal interactions with Y449 and Y495 to G496 of the RBD. Finally, CDR3 residues V100, Y109 and F110 in C5 make van der Waals contacts with E484 to F486 of RBD (Fig.\n \n 2\n \n b and supplementary Fig.\u00a03a).\n
\n\n In H3, in addition to the R52 salt bridge, residues in CDR2 (R52 - F59) make either (or both) hydrogen bonds and van der Waals contacts with RBD (residues T470-I472, G482-E484 and F490) (Fig.\n \n 2\n \n c and supplementary Fig.\u00a03a). From CDR3, I101 to Y106 make either (or both) hydrogen bonds and van der Waals contacts with RBD (Y449, L455, F456, E484, Y489, F490, L492-S494). Compared to the H11-H4 interaction, H3 has pivoted around V102 resulting in a shift of 2 \u00c5 at R52. It is this pivot that brings FR2 of H3 into contact with RBD (Fig.\n \n 2\n \n b and supplementary Fig.\u00a03a).\n
\n\n Based on the structure, the H3 interaction would not be expected to be sensitive to the mutation (N501Y) (Fig.\n \n 2\n \n c). The observation of the lower affinity of H3 for Alpha RBD is therefore surprising. In order to investigate this further the crystal structures of both H3 and C5 in complex with the Alpha RBD were determined. In neither the H3-RBD or H3-Alpha RBD complex is there any direct contact with residue 501. The crystal structures of these complexes do not reveal any differences in the nanobody RBD interface that result from the mutation. Molecular dynamics studies have identified that this mutation alters the dynamics of RBD and leads to an increase in affinity for ACE2\n \n 32\n \n . It may be that altered dynamics are responsible for modifying the binding of H3. In the C5-Alpha RBD complex, N73 still makes a hydrogen bond interaction with Y501 but the arrangement is less geometrically ideal than with N501, consistent with the lower binding affinity observed (Fig.\n \n 3\n \n c).\n
\n\n The RBD epitopes recognized by C1 and F2 substantially overlap (Y369-A372, F374-T385 in common) but are not identical (Fig.\n \n 2\n \n a, f, and g and supplementary Fig.\u00a03c,d). The C1 and F2 nanobodies are oriented differently, the relationship can be described as an approximate 40\n \n o\n \n rotation around residues 102 and 103 of CD3 (Fig.\n \n 2\n \n h). Interestingly this is very similar pivot point as we observed between H3 and H11-H4 (Fig.\n \n 3\n \n a). C1 buries more surface area and engages with several residues that are not contacted by F2 (G404-D405, V407, V503-G504, Y508). F2 meanwhile contacts L368, P412-Q414, D427-E429 that are not engaged by C1. C1 relies mainly on CDR3 (R100-W107, S109-S110, D112) with some contact with CDR2 (W50, S52, S54, D55, T57-T59) and one interaction with CDR1 (F31). The same regions are employed by F2 and once again CDR3 dominates (D99-Y105, R108, T110, E11, E113) followed by CDR2 (S52, W53, T56, P57, Y59) and one residue in CDR1 (T28). Comparing the RBD structures in the various complexes shows that Y104 of F2 displaces the helix of RBD at Y369 by 3 \u00c5 (Fig.\n \n 2\n \n i).\n
\n\n Residues T376- T385 of RBD also form part of the binding site of the VH domain of CR3022\n \n 26\n \n . Koenig et al\n \n \n 11\n \n \n very recently reported two anti-RBD nanobodies (VHH_V and VHH_U) that bind in a similar location to C1 (and F2) and target this epitope (residues Y369-K378). On repeated passage of SARS-CoV-2 escape mutations were observed at these interface residues (Y369H, S371P, F377L and K378Q/N)\n \n 11\n \n , however actual variants incorporating these changes have yet to be identified\n \n \n 33\n \n \n .\n
\n\n In the context of the whole virus and from ultrastructural analysis of purified Spike by cryo-EM, RBD exists in an equilibrium of up and down conformations. Interaction between the spike protein and cell-surface ACE2 requires at least one RBD in the up or open conformation\n \n \n 34\n \n ,\n \n 35\n \n \n . The cryo-EM structure of the C5 bound to the spike protein (stabilised in the prefusion state\n \n \n 34\n \n \n ) was determined by single particle cryo-EM (Table\n \n 3\n \n , Supplementary Fig. 4, and 5). C5 nanobodies were observed bound to the \u201c3 down\u201d (inactive)\n \n \n 36\n \n \n form of the spike trimer (Fig.\n \n 4\n \n a). Simple modelling shows that C5 (unlike H11-H4) is unlikely to bind to the \u201c1 up 2 down\u201d active form due to steric clashes (Fig.\n \n 4\n \n b). We conclude that although C5 can only bind to the \u201call down\u201d of the Spike, dynamic equilibrium between Spike conformers, results in the conversion to the \u201call down\u201d complex. Other nanobody bound spike complexes have shown binding to either both up and down RBDs\n \n \n 12\n \n \n or only up conformations\n \n \n 11\n \n \n . Incubation of C1 or F2 with the trimeric spike protein led to ill-defined aggregates on EM grids, indicating they destabilise the trimer, which would disrupt ACE2 engagement (Fig. S4). Similar findings were reported for CR3022\n \n 26\n \n and EY-6A\n \n 2\n \n 9\n \n \n that recognize this epitope and are consistent with the complex ITC traces observed for binding of C1 and F2 to the spike protein in solution (Supplementary Fig. 2) This was attributed to the epitope being in the middle of the molecule and binding of a protein to this epitope is incompatible with the trimeric Spike structure.\n
\n| \n | \n\n \n Spike C5\n \n\n (PDB ID 7OAN, EMD-12777)\n \n | \n
|---|---|
| \n \n \n Data collection and processing\n \n \n | \n \n | \n
| \n \n Magnification\n \n | \n \n \n 81,000\n \n | \n
| \n \n Voltage (kV)\n \n | \n \n \n 300\n \n | \n
| \n \n Electron exposure (e\n \n \u2212\n \n /\u00c5\n \n 2\n \n )\n \n | \n \n \n 50\n \n | \n
| \n \n Defocus range (\u00b5m)\n \n | \n \n \n 1.0\u20133.0\n \n | \n
| \n \n Pixel size (\u00c5/pix) (Super resolution)\n \n | \n \n \n 0.53\n \n | \n
| \n \n Symmetry imposed\n \n | \n \n \n \n C\n \n 3\n \n | \n
| \n \n Initial particle images (no.)\n \n | \n \n \n 1,061,364\n \n | \n
| \n \n Final particle images (no.)\n \n | \n \n \n 227,898\n \n | \n
| \n \n Map resolution (\u00c5)\n \n | \n \n \n 2.9\n \n | \n
| \n \n FSC threshold\n \n | \n \n \n 0.143\n \n | \n
| \n \n Map resolution range (\u00c5)\n \n | \n \n \n 2.7\u20136.7\n \n | \n
| \n \n \n Refinement\n \n \n | \n \n | \n
| \n \n Initial model used\n \n | \n \n \n 6VXX\n \n | \n
| \n \n Model resolution (\u00c5)\n \n | \n \n \n 3.0\n \n | \n
| \n \n FSC threshold\n \n | \n \n \n 0.143\n \n | \n
| \n \n Model resolution range (\u00c5)\n \n | \n \n \n 198.2-3.0\n \n | \n
| \n \n Map sharpening\n \n B\n \n factor (\u00c5\n \n \n 2\n \n \n )\n \n | \n \n \n -118\n \n | \n
| \n \n Model composition\n \n | \n \n | \n
| \n \n Non-hydrogen atoms\n \n | \n \n \n 28218\n \n | \n
| \n \n Protein residues\n \n | \n \n \n 3510\n \n | \n
| \n \n \n B\n \n factors (\u00c5\n \n \n 2\n \n \n )\n \n | \n \n | \n
| \n \n Protein\n \n | \n \n \n 121\n \n | \n
| \n \n R.m.s. deviations\n \n | \n \n | \n
| \n \n Bond lengths (\u00c5)\n \n | \n \n \n 0.011\n \n | \n
| \n \n Bond angles (\u00b0)\n \n | \n \n \n 1.241\n \n | \n
| \n \n \n Validation\n \n \n | \n \n | \n
| \n \n MolProbity score\n \n | \n \n \n 1.84\n \n | \n
| \n \n Clashscore\n \n | \n \n \n 8.13\n \n | \n
| \n \n Poor rotamers (%)\n \n | \n \n \n 1.35\n \n | \n
| \n \n Ramachandran plot\n \n | \n \n | \n
| \n \n Favored (%)\n \n | \n \n \n 95.75\n \n | \n
| \n \n Allowed (%)\n \n | \n \n \n 4.08\n \n | \n
| \n \n Disallowed (%)\n \n | \n \n \n 0.17\n \n | \n
\n
\n
\n \n Potent neutralisation of SARS-CoV2\n \n \n in vitro\n \n \n by trimeric nanobodies\n \n
\n\n Linking more than one nanobody together to create bivalent and trivalent assemblies significantly increases antigen-binding due to avidity\n \n 11,13,23,37\u221239\n \n . Therefore, trivalent versions of the four nanobodies were constructed by joining the VHH domains with a glycine-serine flexible linker, (GS)\n \n 6\n \n . The nanobody homo-trimers (C5, C1 and H3) were produced by transient expression in expi293 cells and purified by metal chelate affinity chromatography and size exclusion. Although the F2 trimer was expressed it proved to be unstable on purification and was not pursued further. Binding of the trimeric nanobodies to the RBD was measured by SPR, and an approximate 10 to 100-fold enhancement in K\n \n D\n \n was observed compared to the monomers ( Table\n \n 1\n \n and Supplementary Fig. 6 ). Notably, the H3 trimer was shown to have a sub-picomolar K\n \n D\n \n for the RBD-Victoria with an off rate of approximately 6 hours. Binding of C5 trimer to RBD-Kent was shown to be only two-fold weaker than to RBD-Victoria, whilst binding of C5 monomer was ~\u200925-fold weaker ( Table\n \n 1\n \n , Fig.\n \n 1\n \n and Supplementary Fig. 6).\n
\n\n Micro-neutralisation assays were carried out to test the effectiveness of the three nanobody trimers to block infection of Vero E6 cells by either Victoria, Alpha or Beta strains of the virus. All nanobodies potently neutralized some if not all the strains (Fig.\n \n 5\n \n ). Although H3 bound more tightly than C5 to the RBDs\n \n in vitro\n \n , it was less potent than C5 against both Victoria and Beta strains (Fig.\n \n 5\n \n b). Crucially, C5 was equipotent in neutralising these strains with IC50s of 18 pM (Victoria - B) and 25 pM (Kent - B1.1.7) (Fig.\n \n 5\n \n b). As anticipated from the\n \n in vitro\n \n binding data, only C1 was active against the Beta (B1.351) strain (Fig.\n \n 5\n \n c).\n
\n\n The neutralization potency of the C5 trimer was confirmed in the Gold Standard Plaque Reduction Neutralisation Test (PRNT) against the Victoria strain which gave an ND50 of 3 pM (Supplementary Fig. 7)). This corresponds to one of the most potent neutralising nanobodies that has been identified to date\n \n \n 10\n \n ,\n \n 13\n \n ,\n \n 39\n \n ,\n \n 40\n \n \n and was therefore chosen to test for efficacy in an animal model of COVID-19.\n
\n\n \n C5-Fc fusion shows therapeutic efficacy\n \n \n \n \n \n \n \n \n in vivo\n \n \n \n
\n\n To probe neutralization\n \n in vivo\n \n , we tested C5 in the Syrian hamster model of COVID-19\n \n 41\u201343\n \n . As first demonstrated with SARS-CoV\n \n \n 44\n \n \n , Syrian hamsters are readily infectable, display both upper and lower respiratory tract viral replication, clinical signs and also pathological changes that are similar those seen in infected humans. Since an anti-MERS-CoV nanobody fused to immunoglobulin Fc fragment has previously shown to extend the half-life of the protein\n \n in vivo\n \n and ameliorate disease in a mouse challenge model\n \n \n 45\n \n \n we first tested C5 as a huIgG1 Fc fusion protein. The RBD binding affinity (K\n \n D\n \n 37 pM) and virus neutralisation potency (ND50 of 2 pM; 180 pg/ml) of C5-Fc was similar to the trivalent C5 protein, confirming the importance of multivalency for effective neutralisation (Table\n \n 1\n \n , Supplementary Fig.\u00a06, 7). Efficacy of a human IgG1 antibody has also been demonstrated in the Syrian hamster model with the isotype matched control showing no therapeutic effect\n \n \n 6\n \n \n .\n
\n\n The study comprised an experimental and a control group each of six animals. All animals in both groups were challenged intranasally (IN) with SARS-CoV-2 Victoria (5 x10\n \n 4\n \n pfu). The experimental group was treated 24 h later with a single dose of C5-Fc (4 mg /kg) administered intraperitoneally (IP) whilst the control group were left untreated (Fig.\n \n 6\n \n a). As a measure of disease progression, the animals were weighed each day over 7 days and nasal washes and oropharyngeal swabs were taken every other day (Fig.\n \n 6\n \n a). On day 7 the animals were culled and viral load in lung, trachea and duodenum measured by sub-genomic (sg)-RT-qPCR. Vital organs were formalin-fixed for histopathology (H&E staining) and ISH RNAScope staining with SARS-CoV-2 S-gene probe to detect presence of virus RNA. SARS-CoV-2 infected animals exhibited progressive mean body weight loss (up to 17%) from day 1 to day 7 post challenge (pc) (Fig.\n \n 6\n \n b). In contrast, by day 7 post challenge (pc), animals in the nanobody treated group had lost significantly (P\u2009<\u20090.005, Mann Whitney) less weight (7%). High levels of nasal shedding of live virus (10\n \n 4\n \n -10\n \n 5\n \n FFU/ml) were detected in 6/6 untreated animals (100%) on day 2 pc, whereas only 3/6 (50%) animals in the nanobody treated group shed virus (Fig.\n \n 6\n \n c). Some live viral shedding was seen in the throats of 3/6 control animals whereas no live virus was detected in the nanobody treated animals (0/6) on any day (Fig.\n \n 6\n \n c). Statistically significant lower levels of viral RNA were detected in throat swabs of treated compared to untreated controls on days 2, 4 and 7 pc (Fig.\n \n 6\n \n e). However no difference in viral RNA was found in the nasal washes taken over the time course of the study or in homogenates of lung, trachea and duodenum following culling of the animals on day 7 (Fig.\n \n 6\n \n e and f). Measurements of sgRNA copies in either nasal washes, throat swabs and tissues showed no significant differences between the number of genomic copies of the virus between control and treated animals (Fig.\n \n 6\n \n d and f).\n
\n\n Histopathology and RNAScope ISH techniques were used to compare the pathological changes and the presence of viral RNA in tissues from nanobody-treated and untreated control hamsters. A semiquantitative scoring system was combined with digital image analysis to calculate the area of lung with pneumonia and the quantity of virus. Viral RNA and lesions consistent with infection with SARS-CoV-2 were observed only in the nasal cavity ( Supplementary Fig. 8 ) and lungs (Supplementary Fig. 9). No lesions were observed in any other organ studied. The lung lesions consisted of a bronchointerstitial pneumonia showing areas of parenchymal consolidation and were characterized by infiltration of macrophages and neutrophils, but also some lymphocytes and plasma cells (Supplementary Fig. 8c). The lesions in the nasal cavity consisted in necrosis of the respiratory and olfactory mucosa and presence of inflammatory exudates and cell debris within the nasal cavity lumen. The area with pneumonia was significantly lower in the nanobody-treated hamsters together with a marked reduction of histopathology scores in the nasal cavity (Supplementary Fig. 9a). Statistically significant differences were also found for the presence of virus RNA in the lung or the nasal cavity (Supplementary Fig. 8b and 9b). Together, these results showed that a single therapeutic dose of C5-Fc administered IP reached the site of action in the lungs and nasal cavity and reduced viral load and associated pathological changes. Therefore, based on these promising results we undertook a larger study to evaluate the C5 trimer in the Syrian hamster model.\n
\n\n \n Trimeric C5 nanobody shows efficacy when administered via the respiratory route.\n \n
\n\n The smaller molecular size of the C5-trimer (40 kDa) compared to the C5-Fc (80 kDa plus 2N-linked glycans) renders the nanobody suitable for respiratory administration directly to the airways\n \n \n 46\n \n \n . Previously an anti-RSV nanobody trimer had been shown to be effective in reducing viral load in a disease model following intranasal delivery\n \n \n 23\n \n \n . Therefore, in the second animal study, the efficacy of the trimeric version of C5 was evaluated in the COVID-19 hamster model by administration using both IP and intranasal routes. The study consisted of five groups of six animals that were challenged with the SARS-CoV-2 strain Liverpool (1 x10\n \n 4\n \n pfu) on day 1 and weight changes followed over 7 days (Fig.\n \n 7\n \n a). To compare to the results obtained with the C5-Fc, the trimer was administered IP at 4 mg/kg; the same dose was delivered directly to the airways via intranasal installation (IN). A tenfold lower intranasal dose of 0.4 mg/kg of C5-trimer was also tested. As in the first study, animals in the untreated group showed a significant and progressive weight loss (20 % by day 7), whereas all animals treated therapeutically, 24 h after viral challenge, showed only a small weight loss and from day 2 had recovered to pre-challenged weights (Fig.\n \n 7\n \n b). The animals pre-treated 2 h before IN virus inoculation with 4 mg/kg C5 via the intranasal route showed no change in weight. The weight loss in all C5-treated groups was significantly different from the control group given PBS alone (p\u2009<\u20090.01; repeated measures two-way ANOVA). Analysis of viral load in the post-mortem lungs at day 7 by qPCR for Nucleoprotein (NP) RNA showed a decrease in the median value in treated compared to the untreated control animals. (Fig.\n \n 7\n \n c). This decrease was significantly different in the IP treated group. While there was a clear trend in the other groups, there were two outliers with higher RNA load in each of the groups treated via the intranasal route. No live virus was detected by plaque assay in day 7 samples of lung homogenates consistent with what was observed in the first animal study (Fig.\n \n 6\n \n c).\n
\n\n The histological and immunohistological examination showed multifocal extensive consolidation of the lung parenchyma in the untreated group, with multifocal patches of cells that expressed viral antigen (mainly type I and II pneumocytes, some cells morphologically consistent with macrophages) (Fig.\n \n 7\n \n d). The consolidated areas contained aggregates of macrophages and some neutrophils and were otherwise comprised of activated type II pneumocytes with occasional syncytial cell formation, and hyperplastic bronchiolar epithelial cells (Supplementary Fig. 10). In all treated groups, the extent of parenchymal consolidation was substantially reduced as quantified by automated morphometric analysis which resulted in a statistically-significantly larger area of ventilated lung parenchyma (Fig.\n \n 7\n \n d). The lungs of treated animals showed very limited viral antigen expression and only in occasional individual macrophages within small infiltrates or in pneumocytes in individual alveoli (Fig.\n \n 7\n \n e).\n
\n\n More detailed assessment of the consolidated areas in untreated animals confirmed that at day 7 post SARS-CoV-2 infection, the pathological processes in the lungs are dominated by regenerative attempts, as shown by type II pneumocyte and bronchiolar epithelial hyperplasia, in combination with macrophage dominated inflammatory infiltration (Supplementary Fig.\u00a010). Animals that had received either C5-trimer (4 mg/kg) 2 h pre-infection or the lower dose (0.4 mg/kg) at 4 h post infection, resulted in substantially less regenerative processes; the observed small, consolidated areas were dominated by infiltrating macrophages (Supplementary Fig.\u00a010). These findings at the late, i.e., regenerative stage of SARS-CoV-2 infection in hamsters\n \n \n 42\n \n \n indirectly confirm that the C5-trimer treatment significantly reduced pulmonary infection and induced a strong macrophage response, likely leading to phagocytosis and thereby sequestration of the virus. Double immunofluorescence for viral N protein and the macrophage marker Iba1 undertaken on the lungs of hamsters that had been pre-treated with C5-trimer 2h prior to virus inoculation confirmed that numerous macrophages in the focal lesions contained viral antigen (Supplementary Fig. 11).\n
\n\n Collectively the animal studies described herein have established that a multivalent nanobody (Fc fusion or trimer) targeted to the RBD of SARS-CoV-2 spike protein delivered either systemically or via the respiratory route has a therapeutic benefit in the hamster disease model of COVID-19. In particular, efficacy was observed with a single IN dose of 0.4 mg/kg (equating to approximately 40 ug/ animal) of the C5-trimer demonstrating the high potency of this biological agent. A further dose ranging study will be required to establish the minimum amount of the nanobody required to be therapeutically effective in the hamster disease model.\n
\n\n The RBD of SARS-CoV-2 is the immuno-dominant region of the virus spike protein and the target for neutralizing antibodies generated either by vaccination or infection. Following immunisation of a llama with a combination of the RBD and stabilised spike trimer\n \n \n 34\n \n \n based on the Victoria strain sequence, we obtained nanobodies designated C5, F2, H3 and C1 that bound one of two orthogonal sites on the RBD. The site recognized by C5 and H3 overlapped with the ACE2 binding site on the top surface of the domain, whilst the second recognized by C1 and F2 corresponded to a location on the side of the RBD originally identified by the SARS-CoV antibody CR3022\n \n 24,26,47\n \n and nanobody VHH72\n \n 48\n \n . Consistent with other recent reports\n \n \n 10\n \n ,\n \n 17\n \n ,\n \n 39\n \n \n nanobodies that bound to both sites showed very potent neutralization activity when configured as multivalent trimers, with the C5 trimer demonstrating complete inhibition of infection of Vero cells at <\u2009100 pM in a PRNT assay. This activity was translated into a marked disease-modifying effect in the Syrian golden hamster model of COVID-19 with treated animals showing minimal weight loss and very limited pulmonary infection and associated changes following a single dose of C5 trimer 24 h post virus challenge. Most importantly, administration of the nanobody agent either directly by nasal administration or systemically (IP) was effective at 4.0 mg/kg. Nasal administration appeared to promote faster recovery than IP perhaps reflecting increased levels of the C5 trimer reaching the sites of infection in the lungs. Recently, mice challenged intranasally with SARS-CoV-2, and then treated prophylactically IP with a nanobody Fc fusion has also been shown to reduce viral load in the lungs\n \n \n 17\n \n \n . More recently, Nebulli et al\n \n \n 18\n \n \n , showed that nasal administration of a nanobody 6 h after viral challenge also reduced viral load and weight change in the Syrian hamster model. Our data are consistent with these results but our treatment with the C5 trimer 24 h after viral challenge when the clinical manifestations of disease first become apparent is a more demanding test of nanobody efficacy and arguably a more realistic model of therapeutic treatment.\n
\n\n The independent emergence of SARS-CoV-2 variants which appear to be more transmissible is now a major concern. Although in this study, animals were challenged with the Victoria and Liverpool (lineage B) strains, the\n \n in vitro\n \n neutralisation data strongly indicates the C5 trimer will be equally effective against the lineage B.1.1.7 or Alpha variant in this COVID-19 disease model. Although, the Alpha variant dominated infections in the UK in early 2021, the new the new Delta virus (B.1.671.2) that first originated in India has become the most recent variant of concern. The epitope recognised by C5 does not include the two residues that are mutated in the RBD of the Delta virus, L452R and T478K. However, F54 in Framework 3 of C5 does make a Van der Waal interaction with L452 that may be disrupted by mutation to R452 (Supplementary Fig.\u00a03). The B.1.351 (Beta variant) and P.1 (Gamma variant) lineages are characterized by three mutations (K417N, E484K and N501Y) in the RBD, which, although less prevalent, are a serious concern as they are associated with immune evasion\n \n \n 30\n \n \n . Structural analysis of the C5-RBD and H3-RBD complexes showed the central importance of E484 in RBD to the interaction and unsurprisingly these nanobodies failed to neutralize the Gamma virus. The C1 nanobody is significantly less potent than C5 against the Victoria strain, NT50 of C1 trimer is 4.9 nM compared to18 pM and binds to a different epitope. However, C1 was equally effective against all three strains of the virus tested for neutralization\n \n in vitro\n \n , thus it has the potential to be a broadly neutralizing agent.\n
\n\n The relative size and stability of nanobody based bio-therapeutics has fueled interest in their use as inhaled drugs for the treatment of respiratory diseases\n \n \n 49\n \n \n , including for COVID-19\n \n 50\n \n . Furthermore, since some of their formulations, for example the trimeric molecule discussed here, do not require mammalian cell culture, they are relatively inexpensive to produce. In laboratory tests, anti-SARS-CoV-2 nanobody trimers, similar to the ones we report here, have already been shown to be stable under aerosolisation\n \n \n 10\n \n ,\n \n 13\n \n \n . Indeed, the trimeric anti-RSV nanobody (ALX-0171)\n \n \n 23\n \n \n , was successfully administered using a nebulizer in a Phase 1 safety study. This provides a useful precedent for developing locally administered products to treat respiratory viral illnesses. L local administration of nanobody therapy may not only treat disease but by reducing viral load, may rapidly and substantially lower infectivity.\n
\n\n In summary, we have identified a set of potent neutralizing SARS-CoV-2 nanobodies from an immunised llama library and mapped these onto the receptor binding domain of the spike protein. The two epitopes correspond to those targeted by human antibodies recovered from convalescent patients pointing to their cross species immunodominance. We show that SARS-CoV-2 infection in a hamster model can be treated with a single dose of the most potent trimeric nanobody delivered either systemically or intranasally. Combinations of nanobodies that target different epitopes may improve resilience in combating new variants of the virus.\n
\n\n \n Immunisation and construction of VHH library\n \n
\n\n The SARS-CoV-2 receptor-binding domain (amino acids 330\u2013532), SARS-CoV-2 receptor-binding domain fused to hIgG1 Fc (RBD-Fc) and trimeric spike protein (amino acids 1-1208) were produced as described by Huo et al 2020\n \n 25\n \n . Antibodies were raised in a llama by intramuscular immunization with 200 \u00b5g of recombinant RBD and 200 \u00b5g of RBD-Fc on day 0, and then 200 \u00b5g RBD and 200 \u00b5g S protein on day 28. The adjuvant used was Gerbu LQ#3000. Blood (150 ml) was collected on day 38. Immunizations and handling of the llama were performed under the authority of the project license PA1FB163A. Peripheral blood mononuclear cells were prepared using Ficoll-Paque PLUS according to the manufacturer\u2019s protocol; total RNA was extracted using TRIzol\u2122; reverse transcription and PCR was carried out with SuperScript IV Reverse Transcriptase using primer CALL_GSP. The pool of VHH encoding sequences were amplified by two rounds of PCR using CALL_001 and CALL_02 (round 1), VHH_For and VHH_Rev_IgG2 plus VHH_Rev_IgG3 (round 2). Following purification by agarose gel electrophoresis, the VHH cDNAs were cloned into the SfiI sites of the phagemid vector pADL-23c. In this vector, the VHH encoding sequence is preceded by a pelB leader sequence followed by a linker, His6 and cMyc tag (GPGGQHHHHHHGAEQKLISEEDLS). Electro-competent\n
\n\n \n E. coli\n \n TG1 cells were transformed with the recombinant pADL-23c vector resulting in a VHH library of about 4 x 10\n \n 9\n \n independent transformants. The resulting TG1 library stock was then infected with M13K07 helper phage to obtain a library of VHH-presenting phages.\n
\n\n \n Isolation of VHHs\n \n
\n\n Phages displaying VHHs specific for the RBD of SARS-CoV-2 were enriched after two rounds of bio-panning on 50 nM and 2 nM of biotinylated RBD respectively, through capturing with Dynabeads\u2122 M-280 (Thermo Fisher Scientific). Enrichment after each round of panning was determined by plating the cell culture with 10-fold serial dilutions. After the second round of panning, 93 individual phagemid clones were picked, VHH displaying phages were recovered by infection with M13K07 helper phage and tested for binding to RBD by a combination of competition and inhibition ELISAs. In these assays, RBD was immobilized on a 96-well plate and binding of phage clones was measured in the presence of excess soluble RBD (inhibition ELISA) or the RBD-binding H11-H4-Fc\n \n \n 25\n \n \n (competition ELISA).\n
\n\n Phage binders were ranked according to the inhibition assay and then classified as either competitive with H11-H4 (i.e., sharing the same epitope) or non-competitive (i.e. binding to a different epitope on RBD). Clones were sequenced and grouped according to CDR3 sequence identity.\n
\n\n \n Construction of trivalent VHHs\n \n
\n\n To generate the trimeric VHHs, the C1, C5, H3 and F2 gene fragments were used as templates to amplify three fragments by PCR with the following pairs of primers: TriNb_Neo_F1 and TriNb_R1; TriNb_F2 and TriNb_R2; TriNb_F3 and TriNb_Neo_R1; the three fragments were then joined together with a PCR reaction using primers TriNb_Neo_F2 and TriNb_Neo_R2. The trimeric gene product was then inserted into the pOPINTTGneo vector by Infusion\u00ae cloning. pOPINTTG contains a mu-phosphatase leader sequence and C-terminal His6 tag\n \n \n 51\n \n \n .\n
\n\n To generate the RBD-Kent, using the RBD-WT as template, the gene was firstly amplified as two fragments with pairs of primers (1) TTGneo_RBD_F and N501Y_R and (2) TTGneo_RBD_R and N501Y_F; the two fragments were then joined together with a PCR reaction using primer TTGneo_RBD_F and TTGneo_RBD_R. The RBD-Kent gene product was then cloned into the pOPINTTGneo vector by Infusion\u00ae cloning.\n
\n\n To generate the RBD-SA, using the RBD-Kent as template, the gene pre-RBD-SA was firstly amplified as two fragments with pairs of primers of (1) TTGneo_RBD_F and E484K_R and (2) TTGneo_RBD_R and E484K_F; the two fragments were then joined together with a PCR reaction using primer TTGneo_RBD_F and TTGneo_RBD_R. The pre-RBD-SA gene product was then cloned into the pOPINTTGneo vector by Infusion\u00ae cloning. Next, using the pre-RBD-SA as template, the gene RBD-SA was firstly amplified as two fragments with pairs of primers of (1) TTGneo_RBD_F and K417V_R and (2) TTGneo_RBD_R and K417V_F; the two fragments were then joined together with a PCR reaction using primer TTGneo_RBD_F and TTGneo_RBD_R. The RBD-SA gene product was then cloned into the pOPINTTGneo vector by Infusion\u00ae cloning.\n
\n\n To generate the huIgG1 Fc-fusion versions of RBDs, the RBD genes from the pOPINTTGneo vector were amplified by a pair of primers TTGneo_RBD_F and RBD_Fc_R, followed by being cloned into the pOPINTTGneo-Fc vector by Infusion\u00ae cloning. The pOPINTTGneo-Fc contains a mu-phosphatase leader sequence, a huIgG1 Fc and C-terminal His6 tag\n \n \n 51\n \n \n
\n\n \n Protein production\n \n
\n\n In general, the monovalent VHHs were cloned into the vector pOPINO\n \n \n 52\n \n \n containing an OmpA leader sequence and C-terminal His6 tag. The C5 and H3 VHH constructs used for the crystallization of C5-Kent RBD and H3-Kent RBD complexes, respectively, were generated through amplification with a pair of primers PelB_F and PelB_R, followed by being cloned into the phagemid vector pADL-23c by Infusion\u00ae cloning. pADL-23c contains a PelB leader sequence and C-terminal His6 tag. The plasmids were transformed into the WK6\n \n E. coli\n \n strain and protein expression induced by 1mM IPTG grown overnight at 20\u00b0C. Periplasmic extracts were prepared by osmotic shock and VHH proteins purified by immobilised metal affinity chromatography (IMAC) using an automated protocol implemented on an \u00c4KTXpress followed by a Hiload 16/60 Superdex 75 or a Superdex 75 10/300GL column, using phosphate-buffered saline (PBS) pH 7.4 buffer. The C5-Fc was produced by transient expression in expi293\u00ae cells and purified by a combination of HiTrap MabSelect SuRe\u2122 (Cytiva) and gel filtration in PBS pH 7.4 buffer. The trimeric versions of the nanobodies were produced by transient expression in expi293\u00ae cells and purified by a combination of IMAC and gel filtration in PBS pH 7.4 buffer. For animal studies, an additional ion exchange chromatography step was introduced after the IMAC (GE, Capto S 1mL column) to lower endotoxin levels which were further reduced to <\u20090.1 EU/ml by passing in the final purified product through two Proteus NoEndo\u2122 clean-up columns (Generon, Slough, UK). Endotoxin levels were quantified using the Pierce\u2122 LAL Chromogenic Endotoxin Quantitation Kit (Thermofisher Scientific). Protein was concentrated to 4mg /ml and flash frozen for storage at -80\u00b0C. The biotinylated and non-biotinylated RBDs, ACE2-Fc and CR3022-Fc were produced as previously described\n \n \n 25\n \n \n .\n
\n\n \n Surface plasmon resonance & ITC\n \n
\n\n The surface plasmon resonance experiments were performed using a Biacore T200 (GE Healthcare). All assays were performed with a running buffer of PBS pH 7.4 supplemented with 0.005% vol/vol surfactant P20 (GE Healthcare) at 25\u00b0C.\n
\n\n The competition assay was performed with a Sensor Chip Protein A (Cytiva). CR3022-Fc, ACE2-Fc or H11-H4-Fc was used as the ligand, ~\u20091,000 RU of CR3022-Fc, ACE2-Fc or H11-H4-Fc was immobilized. The following samples were injected: (1) a mixture of 1 \u00b5M nanobody C1 / C5 / H3/ F2 and 0.1 \u00b5M RBD-WT; (2) a mixture of 1 \u00b5M C2Nb6 (an anti-Caspr2 nanobody) and 0.1 \u00b5M RBD-WT; (3) 1 \u00b5M nanobody C1 / C5 / H3 / F2; (4) 1 \u00b5M C2Nb6; (5) 0.1 \u00b5M RBD-WT. All curves were plotted using GraphPad Prism 8.\n
\n\n To determine the binding kinetics between the SARS-CoV-2 RBD and nanobody C1 / C5 / H3 / F2, a Biotin CAPture Kit (Cytiva) was used. Biotinylated RBDs were immobilized onto the sample flow cell of the sensor chip. The reference flow cell was left blank. Nanobody was injected over the two flow cells at a range of five concentrations prepared by serial two-fold dilutions, at a flow rate of 30 \u00b5l min\n \n \u2212\u20091\n \n using a single-cycle kinetics program. Running buffer was also injected using the same program for background subtraction. All data were fitted to a 1:1 binding model using Biacore T200 Evaluation Software 3.1.\n
\n\n To determine the binding kinetics between the SARS-CoV-2 RBD-WT and C5-Fc, a Biotin CAPture Kit (Cytiva) was used. Biotinylated RBD was immobilized onto the sample flow cell of the sensor chip. The reference flow cell was left blank. C5-Fc was injected over the two flow cells at a single concentration of 10 nM, at a flow rate of 30 \u00b5l min\n \n \u2212\u20091\n \n . Running buffer was also injected using the same program for background subtraction. All data were fitted to a 1:1 binding model using Biacore T200 Evaluation Software 3.1.\n
\n\n To determine the binding kinetics between the SARS-CoV-2 RBD and the trimeric nanobodies C1/C5/H3, a Sensor Chip Protein A (Cytiva) was used. The huIgG1 Fc-fusion versions of RBDs were immobilized onto the sample flow cell of the sensor chip. The reference flow cell was left blank. Trimeric nanobody was injected over the two flow cells at a single concentration of 25 nM for C1 trimer, 10 nM for C5 trimer and 10 nM (RBD-Kent interaction) or 2.5 nM (RBD-WT interaction) for H3 trimer, at a flow rate of 30 \u00b5l min\n \n \u2212\u20091\n \n . Running buffer was also injected using the same program for background subtraction. All data were fitted to a 1:1 binding model using Biacore T200 Evaluation Software 3.1.\n
\n\n Isothermal titration calorimetry (ITC) measurements were carried out using an iTC200 and PEAQ-ITC MicroCalorimeter (GE Healthcare) at 25\u00b0C. RBD and all nanobodies were dialyzed into PBS and titrations into RBD were performed using 150 to 25 \u00b5M of nanobody and 14\u2009\u2212\u20092 \u00b5M RBD with the exception of Nb-H11 (470 \u00b5M) and RBD (47\u00b5M). For spike protein, 80\u2009\u2212\u200960 \u00b5M nanobody were titrated into 8\u2009\u2212\u20096 \u00b5M spike (monomer concentration). Each experiment consisted of an initial injection of 0.4 \u00b5l followed by 16\u201319 injections of 2-2.4 \u00b5l nanobody into the cell containing RBD or spike, while stirring at 750 rpm. For the displacement assays, approximately 200 \u00b5M of C5 nanobody was titrated into a mixture of 20 \u00b5M RBD and 100 \u00b5M H11 and 66 \u00b5M C5 nanobody was titrated into a mixture of 6 \u00b5M spike and 186 \u00b5M H11. Data acquisition and analysis were performed using the Origin scientific graphing and analysis software package (OriginLab) or AFFINImeter for global fitting of the displacement assay. For the fitting of C5 and H11 into spike, the monomeric concentration of spike and a single binding mode have been used. Data analysis was performed by generating a binding isotherm and best fit using the following parameters: N (number of sites), \u0394H (kJmol\n \n \u2212\u20091\n \n ), \u0394S (JK\n \n \u2212\u20091\n \n mol\n \n \u2212\u20091\n \n ), and K (binding constant in molar\n \n \u2212\u20091\n \n ). Following data analysis, K was converted to the dissociation constant (Kd).\n
\n\n \n Determination of the structure of VHH- RBD complexes by X-ray crystallography\n \n
\n\n Purified VHHs were mixed with de-glycosylated RBD at a molar ratio of 1.2:1, and the complex purified by size exclusion chromatography as described\n \n \n 8\n \n \n . The optimal conditions for crystallization of each complex were F2-RBD 0.1M Succinic Acid, Sodium Dihydrogen Phosphate and Glycine (SPG), pH 8, 25 % Polyethylene glycol (PEG) 1500, H3-C1-RBD and H3-C1-Alpha RBD 1.0 M Lithium chloride, 0.1 M Citric acid pH 4, 20 % PEG 6000 and C5-RBD 0.2 M Sodium Acetate, 0.1 M Sodium Cacodylate pH 6.5, 30 % w/v PEG 8000 and the C5-Alpha RBD 0.2 M Ammonium fluoride and 20 % PEG 3350. The protein concentrations for all complexes were 18 mg/ml except for F2-RBD, where 34 mg/ml was used. Crystals were grown at 20\u00b0C by sitting drop vapour diffusion method by mixing 0.1 ul of protein complex (C5-RBD) with 0.1 \u00b5l of reservoir; mixing 0.2 \u00b5l of protein complex (F2-RBD; H3-C1-RBD) with 0.1 \u00b5l of reservoir or 0.1 \u00b5l of protein complex (C5-Alpha RBD; H3-C1-Alpha RBD) and 0.2 \u00b5l of reservoir as stated above. Crystals were cryoprotected with 30 % glycerol, cryocooled in liquid nitrogen, diffraction data collected and processed at the beamlines I03, I04 and I24 of Diamond Light Source, UK. The structures were solved by molecular replacement using the H11-H4 RBD structure as the search model.\n
\n\n \n Cryo-EM structures\n \n
\n\n Preparation of cryo-EM grids, data collection and processing were carried out as previously described\n \n \n 8\n \n \n . Briefly, purified spike protein in 10 mM Hepes, pH 8, 150 mM NaCl, at 1 mg/ml was incubated with nanobody C5, purified in PBS, at a molar ratio of 1:1.2 (Spike monomer:nanobody) at 16\u00b0C overnight. SPT Labtech prototype 300 mesh 1.2/2.0 nanowire grids were glow-discharged on low for 4 min (Plasma Cleaner PDC-002-CE, Harrick Plasma) and used in a Chameleon EP system (SPT Labtech) at 80% relative humidity, ambient temperature. Frozen grids were screened, and data collected using Titan Krios G2 (Thermo Fisher Scientific) equipped with a Bioquantum-K3 detector (Gatan, UK) operated at 300 kV. Data collection statistics are given in Supplementary Table\u00a03. The RELION_IT.py processing pipeline as implemented in eBIC was used for automatic data processing up to 2D classification. The data were first processed as C1 but as the complex showed C3 symmetry, this was later changed to C3. The best 3D class was selected for further refinement, CTF refinement, and particle polishing within Relion. An initial model based on PDB ID 6VXX was created and the RBD-C5 crystal structure placed into density. The final model with correlation coefficient 0.76 was generated by multiple cycles of manual intervention in coot\n \n \n 53\n \n \n followed by jelly body refinement using RefMac5 via CCP-EM GUI\n \n \n 53\n \n ,\n \n 54\n \n \n . Model validation was carried out in PHENIX\n \n \n 54\n \n \u2013\n \n 56\n \n \n . Data processing and refinement statistics are given in Table\n \n 3\n \n .\n
\n\n \n Micro-neutralisation assay\n \n
\n\n VHH trimers were serially diluted into Dulbecco\u2019s Modified Eagles Medium (DMEM) containing 1 % (w/v) foetal bovine serum (FBS) in a 96-well plate. SARS-CoV-2 strains (B VIC01, B1.17 and B1.351) passage 4 (Vero 76) [9x10\n \n 4\n \n pfu/ml] diluted 1:5 in DMEM-FBS were added to each well with media only as negative controls. After incubation for 30 min at 37\u00b0C, Vero cells (100 \u00b5l) were added to each well and the plates incubated for 2 h at 37\u00b0C. Carboxymethyl cellulose (100 \u00b5l of 1.5 % v/v) was then added to each well and the plates incubated for a further 18\u201320 h at 37\u00b0C. Cells were fixed with paraformaldehyde (100 \u00b5l /well 4 % v/v) for 30 min at room temperature and then stained for SARS-CoV-2 nucleoprotein using a human monoclonal antibody (EY2A). Bound antibody was detected by incubation with a goat anti-human IgG HRP conjugate and following substrate addition imaged using an ELISPOT reader. The neutralization titer was defined as the titer of VHH trimer that reduced the Foci forming unit (FFU) by 50% compared to the control wells.\n
\n\n \n PRNT assay\n \n
\n\n Plaque reduction neutralization tests (PRNT) were carried out at Public Health England using SARS-CoV-2 (hCoV-19/Australia/VIC01/2020) (GISAID accession number EPI_ISL_406844) generously provided by The Doherty Institute, Melbourne, Australia at P1 and passaged twice in Vero/hSLAM cells [ECACC 04091501]. Virus was diluted to a concentration of 933 p.f.u. ml\u2009\u2212\u20091 (70 p.f.u./75 \u00b5l) and mixed 50:50 in minimal essential medium (MEM; Life Technologies) containing 1 % FBS (Life Technologies) and 25 mM HEPES buffer (Sigma) with doubling antibody dilutions in a 96-well V-bottomed plate. The plate was incubated at 37\u00b0C in a humidified box for 1 h to allow neutralization to take place. Afterwards, the virus-antibody mixture was transferred into the wells of a twice Dulbecco\u2019s PBS-washed 24-well plate containing confluent monolayers of Vero E6 cells (ECACC 85020206, PHE) that had been cultured in MEM containing 10 % (v/v) FBS. Virus was allowed to adsorb onto cells at 37\u00b0C for a further hour in a humidified box, then the cells were overlaid with MEM containing 1.5 % carboxymethyl cellulose (Sigma), 4 % (v/v) FBS and 25 mM HEPES buffer. After five days incubation at 37\u00b0C in a humidified box, the plates were fixed overnight with 20 % formalin/PBS (v/v), washed with tap water and then stained with 0.2 % crystal violet solution (Sigma) and plaques were counted. A mid-point probit analysis (written in R programming language for statistical computing and graphics) was used to determine the dilution of antibody required to reduce SARS-CoV-2 viral plaques by 50 % (ND50) compared with the virus-only control (n\u2009=\u20095). The script used in R was based on a previously reported source script44. Antibody dilutions were run in duplicate and an internal positive control for the PRNT assay was also run in duplicate using a sample of heat-inactivated (56\u00b0C for 30 min) human MERS convalescent serum pH 7.4, 137 mM NaCl, 1 mM CaCl ) and 1 mg ml\u2009\u2212\u20091 trypsin (Sigma-Aldrich) to neutralize SARS-CoV-2 (National Institute for Biological Standards and Control, UK).\n
\n\n \n Evaluation of C5-Fc efficacy in the Syrian hamster model (Public Health England)\n \n
\n\n Golden Syrian hamsters (\n \n Mesocricetus auratus\n \n ) (males and females) aged between 7\u20139 weeks old, weighing 110-140g, were obtained from Envigo, London, UK. Hamsters were assigned randomly and housed in individual cages with access to food and water ad libitum. All experimental work was conducted under the authority of a UK Home Office approved project license that had been subject to local ethical review at PHE Porton Down by the Animal Welfare and Ethical Review Body (AWERB) as required by the \u2018Home Office Animals (Scientific Procedures) Act 1986\u2019.\n
\n\n Twelve hamsters were briefly anesthetized with 5 % isoflurane (Zoetis, Leatherhead, UK) and 4L/m O2 and inoculated by the intranasal route with 5 x 10\n \n 4\n \n p.f.u/animal of SARS-CoV-2 (hCoV-19/Australia/VIC01/2020) delivered in 100 \u00b5l per nostril (200 \u00b5l in total). At day 1 post-challenge (pc) 6 hamsters were treated with 4 mg/kg of C5 Nanobody via the intraperitoneal route. Control hamsters (n\u2009=\u20096) received no treatment. Temperature (taken using a microchip reader and implanted temperature/ID chip) and clinical signs were monitored twice daily, weight once daily. Clinical signs were scored as follows; healthy\u2009=\u20090, behavioral changes\u2009=\u20091, ruffled fur\u2009=\u20092, wet tail\u2009=\u20092, dehydrated\u2009=\u20092, eyes shut\u2009=\u20093, arched back\u2009=\u20093, wasp waisted\u2009=\u20093, labored breathing\u2009=\u20095. Clinical samples of nasal washes in Dulbecco\u2019s PBS (DPBS, Gibco) (200 \u00b5l) as well as oropharyngeal (throat) swabs (MWE, Corsham, UK) were obtained prior to infection (day \u2212\u20092) and on days 2, 4, 6 and 7 pc; animals were briefly anesthetized for the collection of these samples. On day 7 all the hamsters were euthanized by an overdose of anesthetic (sodium pentobarbitone [Dolelethal, Vetquinol UK Ltd]) via the intraperitoneal route. At necropsy nasal washes and oropharyngeal swabs and tissue samples (lung, trachea and duodenum) were collected in PBS and stored frozen at -80\u00b0C for viral RNA measurement and viral culture. Tissue samples for histopathological examination were fixed in 10% buffered formalin at room temperature (see below).\n
\n\n A micro-plaque assay\n \n \n 57\n \n \n was used to determine the amount of virus in tissue samples. The animal sample was serially diluted in assay diluent (MEM supplemented with L-glutamine (Life Technologies), non-essential amino acids (Life Technologies), 25mM HEPES (Sigma) and 1x antibiotic/antimycotic) and added to confluent monolayers of Vero E6 cells. The virus was adsorbed to the cells for 1 hr at 37\u00b0C. The inoculas were removed from the cell plates and a viscous overlay (1% carboxymethylcellulose, Sigma) was added. The plates were then incubated for 24 hr at 37\u00b0C. The cells were then fixed using 8 % formalin for >\u20098 hrs and an immunostaining protocol was performed on the fixed cells (Bewley et al, 2021). Stained foci [foci forming units (FFU)] were counted using an ELISpot counter (Cellular Technology Limited, USA). The counted foci data was then plotted using Graph Pad version 9. A SARS-CoV-2 positive control at 1x10\n \n 5\n \n PFU/ml was run alongside the animal samples, on each assay plate, with uninfected assay diluent as negative control.\n
\n\n RNA was isolated from nasal washes, oropharyngeal swabs and tissue samples (lung, trachea and duodenum). Weighed tissue samples were homogenized and inactivated in RLT (Qiagen) supplemented with 1% (v/v) beta-mercaptoethanol. Tissue homogenate was then centrifuged through a QIAshredder homogenizer (Qiagen) and supplemented with ethanol as per manufacturer\u2019s instructions. Downstream extraction was then performed using the BioSprint\u212296 One-For-All vet kit (Indical Bioscience) and Kingfisher Flex platform as per manufacturer\u2019s instructions. Non-tissue samples were inactivated in AVL (Qiagen) and ethanol, with final extraction using the BioSprint\u212296 One-For-All vet kit (Indical Bioscience) and Kingfisher Flex platform as per manufacturer\u2019s instructions. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed using TaqPath\u2122 1-Step RT-qPCR Master Mix, CG (Applied Biosystems\u2122), 2019-nCoV CDC RUO Kit (Integrated DNA Technologies) and QuantStudio\u2122 7 Flex Real-Time PCR System. Sequences of the N1 primers and probe were: 2019-nCoV_N1-forward, 5\u2019 GACCCCAAAATCAGCGAAAT 3\u2019; 2019-nCoV_N1-reverse, 5\u2019 TCTGGTTACTGCCAGTTGAATCTG 3\u2019; 2019-nCoV_N1-probe, 5\u2019 FAM-ACCCCGCATTACGTTTGGTGGACC-BHQ1 3\u2019. The cycling conditions were 25\u00b0C for 2 min, 50\u00b0C for 15 min, 95\u00b0C for 2 min, followed by 45 cycles of 95\u00b0C for 3 seconds, 55\u00b0C for 30 seconds. The quantification standard was in vitro transcribed RNA of the SARS-CoV-2 N ORF (accession number NC_045512.2) with quantification between 10 and 1x10\n \n 6\n \n copies/\u00b5l. Positive samples detected below the lower limit of quantification (LLOQ) of 10 copies/\u00b5l were assigned the value of 5 copies/\u00b5l, undetected samples were assigned the value of 2.3 copies/\u00b5l, equivalent to the assays LLOD. For nasal wash and oropharyngeal swab extracted samples this equates to an LLOQ of 1.29 x10\n \n 4\n \n copies/mL and LLOD of 2.96 x10\n \n 3\n \n copies/mL. Samples detected between LLOQ and LLOD were assigned 6.43 x10\n \n 3\n \n copies/mL. For tissue samples this equates to an LLOQ of 1.31x10\n \n 4\n \n copies/g and LLOD of 5.71 x10\n \n 4\n \n copies/g. Samples detected between LLOQ and LLOD were assigned 2.86 x10\n \n 4\n \n copies/g.\n
\n\n Subgenomic RT-qPCR was performed on the QuantStudio\u2122 7 Flex Real-Time PCR System using TaqMan\u2122 Fast Virus 1-Step Master Mix (Thermo Fisher Scientific) and oligonucleotides as specified by Wolfel et al\n \n \n 58\n \n \n ., with forward primer, probe and reverse primer at a final concentration of 250 nM, 125 nM and 500 nM respectively. Sequences of the sgE primers and probe were: 2019-nCoV_sgE-forward, 5\u2019 CGATCTCTTGTAGATCTGTTCTC 3\u2019; 2019-nCoV_sgE-reverse, 5\u2019 ATATTGCAGCAGTACGCACACA 3\u2019; 2019-nCoV_sgE-probe, 5\u2019 FAM- ACACTAGCCATCCTTACTGCGCTTCG-BHQ1 3\u2019. Cycling conditions were 50\u00b0C for 10 minutes, 95\u00b0C for 2 min, followed by 45 cycles of 95\u00b0C for 10 seconds and 60\u00b0C for 30 seconds. RT-qPCR amplicons were quantified against an in vitro transcribed RNA standard of the full-length SARS-CoV-2 E ORF (accession number NC_045512.2) preceded by the UTR leader sequence and putative E gene transcription regulatory sequence described by Wolfel et al. in 202049. Positive samples detected below the lower limit of quantification (LLOQ) were assigned the value of 5 copies/\u00b5l, whilst undetected samples were assigned the value of \u2264\u20090.9 copies/\u00b5l, equivalent to the lower limit of detection of the assay (LLOD). For nasal washes and oropharyngeal swabs extracted samples this equated to an LLOQ of 1.29x10\n \n 4\n \n copies/mL and LLOD of 1.16x10\n \n 3\n \n copies/mL. For tissue samples this equates to an LLOQ of 5.71x10\n \n 4\n \n copies/g and LLOD of 5.14x10\n \n 3\n \n copies/g.\n
\n\n The lung, nasal cavity including olfactory and respiratory mucosa, heart, liver, spleen, pancreas, trachea/larynx brain and small intestine (duodenum) were taken from each animal and were fixed in 10% neutral-buffered formalin, processed, embedded in paraffin wax and 4 \u00b5m thick sections cut and stained with haematoxylin and eosin (H&E). The tissue sections were digitally scanned and reviewed by a qualified veterinary pathologist blinded to treatment and group details and the slides were randomised prior to examination in order to prevent bias (blind evaluation). A scoring system was used to evaluate objectively the histopathological lesions observed in the tissue sections: 0\u2009=\u2009within normal limits; 1\u2009=\u2009minimal; 2\u2009=\u2009mild; 3\u2009=\u2009moderate and 4\u2009=\u2009marked/severe. Moreover, the area of the lung with pneumonia was calculated using digital image analysis (Nikon-NIS-Ar software package).\n
\n\n RNAscope (an in-situ hybridisation method used on formalin-fixed, paraffin-embedded tissues) was used to identify the SARS-CoV-2 virus in all tissues. Briefly, tissues were pre-treated with hydrogen peroxide for 10 mins at room temperature (RT) target retrieval for 15 mins (98\u2013101 \u2070C) and protease plus for 30 mins (40 \u2070C) (all Advanced Cell Diagnostics). A V-nCoV2019-S probe (Advanced Cell Diagnostics) targeting the S-protein gene was incubated on the tissues for 2 hours at 40\u2070C. Amplification of the signal was carried out following the RNAscope protocol (RNAscope 2.5 HD Detection Reagent \u2013 Red) using the RNAscope 2.5 HD red kit (Advanced Cell Diagnostics). Appropriate controls were included in each ISH run. Digital image analysis was carried out with the Nikon NIS-Ar software package in order to calculate the total area of the tissue section positive for viral RNA. The images were scanned digitally using a Hamamatsu NanoZoomer S360 digital slide scanner and examined using Ndp.view2 v2.9.22 software. Nikon NIS-Ar software was used to perform digital image analysis in order to quantify the presence of viral RNA in lung sections. Graph and statistical analysis were performed with Graphpad Prism 9 and Minitab version 16.\n
\n\n \n Evaluation of C5 trimer therapeutic efficacy in the Syrian hamster model (University of Liverpool)\n \n
\n\n Animal work was approved by the local University of Liverpool Animal Welfare and Ethical Review Body and performed under UK Home Office Project Licence PP4715265. Male golden Syrian hamsters ( 8\u201310 weeks old) were purchased from Janvier Labs (France). Animals were maintained under SPF barrier conditions in individually ventilated cages. For virus infection the Liverpool strain was used, a PANGO lineage B strain of SARS-CoV-2 (hCoV-2/human/Liverpool/REMRQ0001/2020)\n \n 59\n \n . Animals were randomly assigned into multiple cohorts of 6 animals. For SARS-CoV-2 infection, hamsters were anaesthetised lightly with isoflurane and inoculated intra-nasally with 100 \u00b5l containing 10\n \n 4\n \n PFU SARS-CoV-2 in PBS. Hamsters were treated with 100 \u00b5l via either the intraperitoneal or intranasal route with C5 trimer contained in PBS. Animals were sacrificed at variable time-points after infection by an overdose of pentabarbitone. Tissues were removed immediately for downstream processing.\n
\n\n From all animals the left lung was fixed in 10% buffered formalin for 48 h and then stored in 70% ethanol until further processing. Two longitudinal sections were prepared and routinely paraffin wax embedded. Consecutive sections (3\u20135 \u00b5m) were prepared and stained with HE for histological examination or subjected to immunohistological staining. Immunohistology was performed to detect SARS-CoV-2 antigen, macrophages (Iba1+), type II pneumocytes (SP-C+) and epithelial cells (pan-cytokeratin+), using the horseradish peroxidase (HRP) method and the following primary antibodies: rabbit anti-SARS-CoV nucleocapsid protein (Rockland, 200-402-A50), rabbit anti-human Iba1/AIF1 (Wako, 019-19741), rabbit anti-human prosurfactant protein-C (SP-C; Abcam, ab40879), and mouse anti-human pan-cytokeratin (clone PCK-26; Novus Biologicals, NB120-6401). Briefly, after de-paraffination, sections underwent antigen retrieval in citrate buffer (pH 6.0; Agilent) (anti-SARS-CoV-2, -Iba1) or Tris-EDTA buffer (pH 9.0) (anti-SP-C, -pan-cytokeratin) for 20 min at 98\u00b0C and for 20 min at 37\u00b0C respectively, followed by incubation with the primary antibody overnight at 4 \u2070C (anti-SARS-CoV, -SP-C) or 60 min at RT (anti-Iba1, -pan-cytokeratin). This was followed by blocking of endogenous peroxidase (peroxidase block, Agilent) for 10 min at room temperature (RT) and incubation with the secondary antibody, EnVision+/HRP, Rabbit and Mouse respectively (Agilent) for 30 min at RT, followed by EnVision FLEX DAB\u2009+\u2009Chromogen in Substrate buffer (Agilent) for 10 min at RT, all in an autostainer (Dako). Sections were subsequently counterstained with haematoxylin. The anti-Iba1, -SP-C and -pan-cytokeratin antibodies were tested for their cross reactivity in hamster tissues, using the lung of an uninfected control hamster as positive control.\n
\n\n For double immunofluorescence, sections underwent antigen retrieval in citrate buffer (pH 6.0) and were then incubated with the first primary antibody (rabbit anti-SARS-CoV), overnight at 4 \u2070C, followed by blocking of the endogenous peroxidase (see above) and 1 h incubation with the red fluorescence labelled antibody (goat anti-rabbit 594; Invitrogen, A11012), incubation with the second primary antibody (goat anti-human Iba1; Abcam, ab 5076), overnight at 4 \u2070C, and 1 h incubation with the green fluorescence labelled antibody (donkey anti-goat 488; Invitrogen, A1105). The final incubation was with DAPI (4\u2032, 6-diamidino-2-phenylindole, Novus Biologicals), for 15 min at RT. After that, sections were washed twice with distilled water, air dried, and a coverslip placed with FluoreGuard mounting medium (Biosystems, Switzerland).\n
\n\n For morphometric analysis, the HE-stained sections were scanned (NanoZoomer-XR C12000; Hamamatsu, Hamamatsu City, Japan) and analysed using the software programme Visiopharm (Visiopharm 2020.08.1.8403; Visiopharm, Hoersholm, Denmark) to quantify the area of non-aerated parenchyma and aerated parenchyma in relation to the total area (=\u2009area occupied by lung parenchyma on two sections prepared from the left lung lobes) in the sections. This was used to compare the amount of air space (as an equivalent for the gas exchange surface) in the lungs between untreated and treated animals. A first app was applied that outlined the entire lung tissue as Region Of Interest (ROI, total area). For this a Decision forest method was used and the software was trained to detect the lung tissue section (total area). Once the lung section was outlined as ROI the large bronchi and vessels were manually excluded from the ROI. Subsequently, a second app with Decision forest method was trained to detect dense parenchyma (non-ventilated) and alveolar spaces (clear spaces; ventilated area) within the ROI.\n
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\n\n Supplementary information\n
\n \n\n \n Bombyx mori\n \n silk is a super-long natural protein fiber with a unique structure and excellent performance. Innovative silk structures with high performance are in great demand, thus resulting in an industrial bottleneck. Herein, a transgenic method was used in which the outer layer sericin SER3 in silk is secreted into the inner fibroin layer, thus generating a new structural fiber with non-fibrous sericin microsomes dispersed in fibroin fibrils. The water-soluble SER3 protein secreted by the posterior silk gland causes P25\u2019s detachment from the fibroin unit of the Fib-H/Fib-L/P25 polymer and accumulation on the fibroin surface. Moreover, the water solubility and stability of the fibroin-colloid in the silk glandular cavity are increased, thus significantly improving the \u03b2-sheet content of fibroin, as well as the mechanical properties, moisture absorption and moisture liberation of the silk fiber. Our silkworm mutant system circumvents the problems of low vitality and abnormal silk gland development, and enables higher production efficiency of cocoon silk than that of the wild type. We describe a silk gland transgenic target protein selection strategy to alter the ancient silk fiber structure and to innovate the properties of silk protein materials. This study thus provides an efficient, green method to produce new silk fibers.\n
\n\n \n Bombyx mori\n \n
\n\n \n silk fiber\n \n
\n\n \n structure\n \n
\n\n \n production efficiency\n \n
\n\n \n transgenic strategy\n \n
\n\n The beautiful silk fibers produced by the silkworm (\n \n Bombyx mori\n \n ) have excellent performance and are an easily available renewable protein material. The toughness of silk fiber and its unusual combination of high strength and expansibility have not been surpassed by synthetic materials to date\n \n \n 1\n \n ,\n \n 2\n \n \n . The silk gland (SG) in silkworm larvae is the most efficient insect organ for protein synthesis and exocrine secretion; it can synthesize 20\u201335% of its own weight in protein in approximately 1 week in 5th instar larvae\n \n \n 3\n \n \n . The concentration of aqueous silk protein solution in the SG cavity is as high as 30%. This fiber processing unit, which maintains a metastable state of ultra-high-level protein, is difficult to recapitulate through modern textile engineering technology\n \n \n 4\n \n ,\n \n 5\n \n \n . Simulating the biological template of SG has emerged as a new research direction for developing high-performance, multifunctional protein fiber materials through green chemical processing. The multifunctional materials processed from silk, such as hydrogels, fibers, sponges, films and other forms, has been used in many applications, such as medical materials, electronic information and fine chemicals, thus demonstrating broad application potential\n \n \n 1\n \n ,\n \n 2\n \n ,\n \n 6\n \n \n .\n
\n\n Although many important insights in the synthesis and self-assembly of silk proteins have been obtained in the past 10 years\n \n \n 7\n \n \u2013\n \n 13\n \n \n , understanding remains lacking regarding the mechanism of the metastability of ultra-high concentration aqueous solutions of Fib-H/Fib-L/P25 polymers in SGs. Many advances and engineering applications have extended the functions of silk fibers, including silk processing by chemical or physical methods, and obtaining biomaterials for many purposes by modulating the self-assembly properties of silk fibroin\n \n \n 14\n \n \u2013\n \n 19\n \n \n . However, these achievements have been based on the reprocessing and transformation of the ancient silk structure. The future advancement of related technologies and achievements will depend on breakthroughs in altering the ancient silk structure.\n
\n\n The germplasm resources for mutant genes associated with silkworm cocoon silk purification have a long history of thousands of years, and the cultivation of hybrid varieties has also been performed for hundreds of years. These efforts have greatly contributed to optimizing the fiber characteristics of silk. However, owing to a bottleneck in the homogenization of silkworm varieties, the silk fibers produced by thousands of silkworm varieties worldwide have nearly the same composition, structure and characteristics\n \n \n 20\n \n ,\n \n 21\n \n \n . Therefore, innovative reprogramming of the genomes of SG cells to alter the structure and characteristics of the silk fiber is highly desirable\n \n \n 21\n \n ,\n \n 22\n \n \n . An attractive method is to directly integrating special functional fiber protein genes into the silkworm genome has been described to aid in achieving high-efficiency expression in SGs, such as the expression of a high-strength spider silk protein gene in silkworm SGs to obtain silk fibers with improved mechanical properties\n \n \n 23\n \n \u2013\n \n 26\n \n \n , and the expression of fusions of optical functional protein and silk protein to obtain photoelectric silk or fluorescent silk\n \n \n 27\n \n ,\n \n 28\n \n \n . However, the efforts to express and secrete exogenous proteins in the SGs of silkworms through transgenic technology to date have generally resulted in low efficiency silk protein synthesis and secretion, at expression levels far below those of normal silk proteins (Supplementary, Table\u00a01). The performance of new structural silk produced by transgenic silkworms is far inferior to that of fibers produced by donor animals. Moreover, the problems of SG deformity and declining silk production efficiency are common\n \n \n 29\n \n ,\n \n 30\n \n \n . The strategy of directly introducing functional silk protein genes from other organisms or similar artificially designed genes into the silkworm genome to produce new silkworm silk in the SG has been problematic. In this study, a new strategy was developed. Through expression of the silkworm's own sericin protein in the PSG, the fibril structure and function of the ancient silk fiber were greatly altered, and a new type of silk fiber was obtained. This method may help address the bottleneck problems of the low survival rate and low silk yield of genetically transgenic silkworms.\n
\n\n \n A genetically modified silkworm mutation system to alter silk fiber structure.\n \n The structure and formation of silkworm silk fiber is shown in Fig.\n \n 1\n \n a. The water-soluble Sericin \u2162 (SER3) protein secreted by the anterior part of the middle silk gland (MSG) and wrapped in the outermost layer of the silk is not present in the fibroin fibrils. The technical strategy of this study involved specifically expressing SER3 recombinant protein in silkworm PSG cells to achieve secretion into the PSG lumen and the incorporation of water-soluble SER3 into the silk fibroin colloidal solution, thus altering the metastable state of silk fibroin protein polymers and further affecting the structure of silk fibers after self-assembly (Fig.\n \n 1\n \n b, c). Using red fluorescence in the eyes and green fluorescence in silk fibers as markers, after six consecutive generations of screening, we obtained the SER (SER3/SER3) mutant system (Supplementary Fig.\u00a01b-d). In the PSG cells of the SER larvae, the mRNA and SER3 protein expressed by the\n \n Ser3\n \n gene were detected, and the results were consistent with those from screening of the RFP/EGFP reporter genes (Supplementary Fig.\u00a01e, f). Tail-PCR detection revealed that a single copy of the piggyBac transposon was inserted into the\n \n Bombyx mori\n \n genome at the non-functional gene sequence at Chr.23 (scaf12: 4699379\u20264699384) (Supplementary Fig.\u00a01g). Many sericin microsomes (SM) were present in the fibroin of silk fibers produced by SER silkworms, and vacuoles were observed in the SM (Fig.\n \n 1\n \n b). Continued investigation for 12 generations revealed that the mutant silkworms showed stable growth and development, and the production efficiency of cocoon silk was significantly higher than that of the wild type (WT). We observed no SG shortening, deformities or decreased individual survival rates, which are common problems in SG transgenic silkworms (Supplementary Fig.\u00a02). From the perspective of sericulture, our findings demonstrate that the transgenic silkworm SGs have superior production performance.\n
\n\n
\n\n \n Mutant silkworms show silk fiber structure rearrangement and performance improvement.\n \n The silk fibers produced by the mutant silkworms exhibited green fluorescence from an EGFP fusion with SER3. As observed from a cross-section of the silk fiber, the fluorescence distribution was uneven, and strong fluorescence appeared on the surface, in both the inner silk fibroin area and the outer sericin layer (Fig.\n \n 2\n \n a). The longitudinal section of the silk fiber was observed by transmission electron microscopy (TEM). Large amounts of SER3-EGFP microsomes (SM) in the SER group were dispersed in the silk fibroin area. The SM shape was rain-thread-like or fusiform, and the shape was altered in the same direction as the movement of silk protein colloid under squeezing during the spinning process in mature larvae. Notably, in SM, vacuoles of low-density silk protein aqueous solutions of different sizes and shapes were observed (Fig.\n \n 2\n \n b). The cross-sectional TEM images further confirmed the presence of SM and vacuoles in the mutant silk fibers (Supplementary Fig.\u00a03). The percentage of sericin in cocoon silk in the SER group was 7.39% higher than that in the WT group (Fig.\n \n 2\n \n c), an increase in 21.8%. Our results indicated that the PSG of the mutant silkworm synthesized the SER3 protein very efficiently and successfully secreted it into the silk fiber.\n
\n\n
\n\n Using immunofluorescence, we observed that the P25 protein in the WT silk fiber was evenly distributed in the silk fibril area, whereas in the silk fibers of SER, almost all P25 had transferred to the outside of the silk fibrils and was unevenly distributed between the silk fibroin layer and the sericin layer, with different micro-body sizes (Fig.\n \n 2\n \n d). Our findings suggested that P25 in SER silk fibers was separate from the silk protein comprising Fib-H/Fib-L/P25 polymers, thus indicating that the ordered fibril structure in the silk protein was greatly altered by the influence of the SER3 protein synthesized in the PSG.\n
\n\n The protein secondary structure of silk fibers was analyzed by FTIR. After deconvolution of the amide I band (Fig.\n \n 2\n \n e) in the silk fibers of mutant SER, we observed that the content of \u03b2-sheets increased significantly, whereas the content of random coil and \u03b1-helices decreased significantly (Fig.\n \n 2\n \n f). These results suggest that the \u03b2-sheet level positively correlated with the rigid composition of silk fibers, and the random coils and \u03b1-helices, which are positively associated with mechanical properties such as silk fiber ductility, might also have been altered accordingly.\n
\n\n The amino acid composition of silk fiber was analyzed. We observed no difference in amino acid composition between the cocoon silk of SER and WT. However, the silk fiber containing sericin protein showed changes in the relative content of a variety of amino acids, such as increased relative content of serine and aspartic acid and decreased relative content of glycine, alanine and tyrosine. In the silk fiber (fibroin) for textile raw materials after removal of the outer sericin, the content of alanine increased by only 1.7% (29.9% in WT versus 30.41% in SER), and the relative content of other amino acids scarcely changed (Supplementary Table\u00a02), because the amino acid composition of Fib-H/Fib-L/P25 polymers of silk fibroin is the same as that of SER3, and the relative content is also similar (Supplementary Table\u00a03).\n
\n\n The mechanical properties of fibroin fibers after removal of the outer sericin were analyzed. The stress and strain curve indicated that the tensile initial modulus of the SER group increased significantly, from 73.48 MPa to 110.93 MPa, a value 1.51 times higher than that in the WT group (Fig.\n \n 3\n \n a). The maximum stress level (Fig.\n \n 3\n \n b) and Young's modulus (Fig.\n \n 3\n \n d) in the SER group were also significantly higher than those in the WT group. Only the maximum elastic modulus had no statistically significant change (Fig.\n \n 3\n \n c). The fibroin fibers produced by mutant silkworms thus had better mechanical properties and better shaping effects for textile materials.\n
\n\n
\n\n The moisture absorption and desorption performance of fibroin showed significant improvements in the SER group. The moisture absorption curve (Fig.\n \n 3\n \n e) and dehumidification curve (Fig.\n \n 3\n \n g) of silk fiber in the SER group were highly similar to those in the WT group. The moisture absorption and regained dehumidification were 22.0% and 8.0% higher, respectively, than those in the WT group. The moisture absorption rate and moisture liberation rate in 0\u20131 min were 142.5\u2013139.4% (Fig.\n \n 3\n \n f) and 165.5\u2013164.1% (Fig.\n \n 3\n \n h) those of the WT group, respectively.\n
\n\n SEM characterization indicated that the adhesion between silk fibers in the SER cocoon silk layer was closer, and the pores were smaller than those in the WT (Fig.\n \n 3\n \n i). After removal of sericin with the alkali method, the surfaces of fibroin fibers in the SER group were smoother, and less fibril damage was observed than that in the WT (Fig.\n \n 3\n \n j). The results showed that the silk fibers in the SER group were more alkali resistant than those in the WT group.\n
\n\n Biocompatibility testing indicated that fibroin fibers showed no adverse effects on the proliferation and growth of mammalian cells. Fibroin and L929 cells were co-cultured for 48 hours. The cell growth state (Fig.\n \n 3\n \n k) and the proportion of dead cells (Fig.\n \n 3\n \n l), as determined by Live-Dead staining, indicated that the fibroin fibers of SER were significantly better than the medical non-absorbable suture (NASS), and no statistical difference was observed relative to WT fibroin and the negative control (null). The MTT test results also indicated that the number of L929 cells in the SER group was significantly higher than that in the NASS group (Fig.\n \n 3\n \n m). The content of the pro-inflammatory factor nitric oxide in the culture medium was found to be significantly lower in the SER group than the NASS group (Fig.\n \n 3\n \n n). SER silk fibroin had good biocompatibility, as compared with classical silk fibroin, although sericin SER3 protein had been introduced.\n
\n\n \n Enhanced water solubility and stability of the silk fibroin colloid in the mutant PSG.\n \n Frozen sections were used to observe the SGs with the most vigorous stage of silk protein synthesis in the 5th instar 3rd day larvae, and the distribution of SER3 was assessed via the EGFP fusion protein (Fig.\n \n 4\n \n a). In the PSG lumen of the mutant larvae, we observed fluorescent particles of different sizes and shapes, with diameters of several micrometers (1\u20135 \u00b5m), scattered in a liquid comprising a grid of bubbles. The water-soluble EGFP-SER3 fusion protein distributed in the silk protein aqueous solution also entered the fibroin mass in an aggregated state. In the MSG lumen, the green fluorescence was distributed in both the fibroin and the sericin, but the fluorescence was stronger in the boundary area between the silk fibroin layer and the sericin layer. Notably, the fluorescent particles increased to tens of micrometers (10\u201350 \u00b5m) in diameter, and the bubble grid-like characteristics of the liquid distribution in the PSG lumen disappeared. The fluorescence distribution pattern in the ASG lumen indicated that the fluorescence in the outer layer of sericin was weak, and the distribution of fluorescent particles in the inner layer of silk fibroin tended to be uniform, but the sizes remained different, and the diameter decreased to 1\u20135 \u00b5m. On the microvilli in the MSG lumen and ASG, droplet-like green fluorescence was observed with a higher intensity than that in the sericin of the middle layer. With the movement of silk protein from the PSG to the ASG via the MSG, the aqueous solution of EGFP-SER3 fusion protein was incorporated into the forming fibroin mass, and the colloidal aggregation state of sericin (SER3) significantly changed, appearing in size and shape different fluid sericin microsomes. The structure and morphology of the fluid SER3 protein microsomes are shown in Fig.\n \n 2\n \n b and Supplementary Fig.\u00a03.\n
\n\n
\n\n TEM was used to observe the substructure of the SG cells and the secretion of silk protein in the 5th instar larvae (Fig.\n \n 4\n \n b). The organelles of the mutant PSG cells were normal, and appeared to be identical to those in the WT, with abundant rough endoplasmic reticulum, Golgi apparatus, mitochondria and other subcellular structures, thus indicating normal protein synthesis. The significant difference was that in the mutant PSG cells, the storage silk protein layer of was thinner than that in the WT cells, and the amount of fibroin secreted into the glandular cavity was much greater. Few spherical aggregates of fibroin mass were observed in the lumen, and the silk protein colloids were more evenly distributed. Thus, the SER3 protein expression in the PSG improved the water solubility of the silk fibroin colloid.\n
\n\n The gene transcription levels of\n \n EGFP\n \n ,\n \n Ser3\n \n , and the silk fibroin components\n \n Fib-H\n \n ,\n \n Fib-L\n \n and\n \n P25\n \n in different parts of the SG cells were measured (Fig.\n \n 4\n \n c-\n \n 4\n \n e). The PSG cells of the 5th instar larvae of the mutant efficiently expressed the\n \n Ser3\n \n gene, which is specifically expressed in the WT silkworm MSG (MA and MM). In PP cells, the transcription level of the\n \n Ser3\n \n gene reached that in MM cells. Notably, the mRNA of the\n \n Ser3\n \n gene was detected in MP cells of SER 5th instar larvae, although the transcription level was only 1\u20135% that of PSG cells (Fig.\n \n 4\n \n c-\n \n 4\n \n e), similarly to the fibroin genes expressed in the MP cells of both WT and SER 5th instars (Fig.\n \n 4\n \n c). Our results indicated that the Fib-H promoter used by the transgenic mutant expressed the SER3 and EGFP genes in MP cells and accounted for the strong green fluorescence observed in the outer sericin in the MSG lumen in Fig.\n \n 4\n \n a.\n
\n\n \n The PSG expresses water-soluble sericin enabling the sustainable production of novel silk fibers with controllable changes in their structure and properties.\n \n Herein, the\n \n Ser3\n \n gene specifically expressed by the silkworm MSG was expressed in the PSG. The amino acid composition of the SER3 protein was the same as that of silk fibroin, and the relative amino acid content was also highly similar, thus avoiding imbalances in amino acid supply in the mutant PSG cells. The ratio of the number of cysteine molecules in the amino acid residues of the SER3 protein (0.50%) was intermediate between that of Fib-H (0.10%) and Fib-L (1.10%), which can form disulfide bonds and combine with Fib-H and other silk fibroin in the PSG. The SER3 protein expressed in the PSGs was present in the long cocoon silk fibers composed of silk fibroin protein polymers. SER3 is a water-soluble protein wrapped in the outer layer of silkworm silk fibers. It does not exist in the fibroin layer, does not contact the fibrils and is completely dissolved by hot water during the silk reeling process. In the silk fiber produced by the mutant (Fig.\n \n 2\n \n ), we observed SER3 protein microsomes dispersed among the silk fibrils in many different droplet sizes, thus indicating that the fibrils were not broken but were incorporated into other silk proteins. In the PSG of the SER silkworm larvae, less fibroin mass was retained in the gland cells, and we observed few spheroid aggregations of fibroin mass in the lumen and silk fibroin colloids, which were more evenly distributed (Fig.\n \n 4\n \n ). Our findings demonstrated that the hydrophilic SER3 protein synthesized and secreted by PSG improves the water solubility and stability of the silk fibroin colloid in the SG cavity, and further affects the polymerization and fibrogenesis of silk fibroin.\n
\n\n Studies have shown that the silk fibroin protein synthesized by silkworm PSG is present as fibroin units of Fib-H/Fib-L/P25 (molecular ratio 6:6:1) in silk fibers\n \n \n 9\n \n \n . Among these proteins, P25 can form intermolecular interactions with Fib-H/Fib-L\n \n 7\n \n and is evenly distributed in fibrils. Our results showed that in the silk fibers produced by the mutant, P25 broke away from the fibroin units and fibrils, and accumulated in the connecting layer between the silk core and the outer sericin (Fig.\n \n 2\n \n ). We demonstrated that P25, a major component of the ancient cocoon silk structure, is substitutable, as also demonstrated by a recent report of knocking out the P25 coding gene in\n \n Bombyx mori\n \n \n \n 8\n \n ,\n \n 31\n \n \n . However, SER's silk fibers exhibit greater advantages in deep processing and alkali corrosion resistance than WT silk fibers (Fig.\n \n 3\n \n ), and can prevent damage to the silk core fibrils. The P25 protein is distributed in the silk fibroin surface layer of the silk core. The silk fiber's textile material advantages remained unchanged, but new characteristics were additionally derived from the changes in the basic silk fibril structure.\n
\n\n Silk fibroin is a hydrophobic fibrous protein whose molecules are connected by disulfide bonds and whose secondary structure mainly comprises \u03b2-sheets\n \n \n 2\n \n ,\n \n 9\n \n ,\n \n 32\n \n \n . SER3 protein is a hydrophilic globular protein whose secondary structure is dominated by random coils\n \n \n 33\n \n \n . In the silk fibers produced by the mutant SER silkworms, the level of \u03b2-sheets significantly increased, and the levels of random coils and \u03b1-helices significantly decreased. Clearly, it is not a direct contribution by \u03b2-sheets of SER3 protein but is caused by changes in the protein secondary structure of the silk fiber. Correspondingly, the mechanical properties such as the maximum stress level and Young's modulus of SER silk fibers were also significantly improved, thereby enabling ultra-thin and ultra-dense fabrics to be woven (Supplementary Fig.\u00a03). Our findings demonstrate the practical value of engineering applications. Moreover, the sericin microsomes dispersed in the fibrils significantly improved the moisture absorption and liberation of the silk fibers, thereby improving the performance of the textile material.\n
\n\n \n Efficiency of transgene-specific expression of foreign proteins in SGs of\n \n \n Bombyx mori\n \n . Since the piggyBac transposon-based expression system was developed in silkworms\n \n \n 34\n \n \n , dozens of transgenic silkworms with SG expression of foreign proteins have been established. However, the output of these foreign proteins is far lower than that of cocoon silk, and the higher the molecular weight of the foreign protein, the lower the output. Subsequently, researchers have made breakthroughs in increasing the expression levels of foreign proteins through continuous optimization. For example, with the TALEN-mediated gene replacement system and transgenic technology, the spider\u2019s Major ampullate spidroin-1 gene (\n \n MaSp1\n \n ) has been used to replace the silkworm\n \n Fib-H\n \n gene; after targeted integration into the PSG for expression, as much as 35.2% of the chimeric protein MaSp1 was obtained from cocoon silk fibers\n \n \n 23\n \n \n . Related explorations have included the introduction of more than three foreign genes into the silkworm genome\n \n \n 35\n \n \n and the use of enhancer combinations (hr3/IE1)\n \n 36\n \n . Our laboratory has designed the artificial coding sequence Hpl, which is similar to Fib-H, and is specifically expressed in the PSG and binds Fib-L more strongly. In the cocoon silk produced by the transgenic silkworms, the content of the foreign protein HPL is 51.9% and 38.93% of the silk fibroin and cocoon silk, respectively\n \n \n 30\n \n \n . Although these studies have significantly improved the expression efficiency of recombinant protein, they remain far from achieving the expression level of endogenous silk protein (Supplementary Table\u00a01).\n
\n\n No ideal solution has been described to address the bottleneck problems that commonly occur in SG target tissue transgenic silkworms, such as reduced viability, abnormal SG development and low silk yield\n \n \n 29\n \n ,\n \n 30\n \n \n . The growth and development of the SGs and individual mutant silkworms in this study were normal. The weight of the cocoon shell, which reflects the protein synthesis and secretion function of the SG, exceeded that of the WT by 16.8%. The cocoon layer rate, which reflects the comprehensive production capacity of mature larvae, was 14.7% higher than that of the control (Supplementary Fig.\u00a02). We demonstrated that while suitable exogenous protein was expressed efficiently, the protein synthesis and secretion ability of by the silkworm SG were further improved.\n
\n\n In conclusion, we report an effective silkworm SG transgenic strategy. By selecting non-fibrous protein targets recombinantly expressed by the PSG, the metastable state of the silk protein aqueous solution in the SG cavity was affected, thus enabling alteration of the composition, structure and performance of the fibril molecules of the ancient silk fiber. The mutant completely overcame bottlenecks such as decreased viability, abnormal SG development and low silk yield. Although the suitable SG transgene target proteins remain unclear, the results of this article provide a biological platform for effective in-depth analysis of efficient specific silk protein synthesis by SG cells in the regulation of the synthesis of other proteins. This initial research may provide new ideas for bottom-up molecular design and biological production of silk protein materials.\n
\n\n \n Experimental animal preparation.\n \n The classic genetic strain N4W was used in this study. Larvae were reared on fresh mulberry leaves. The entire generation was maintained at 25.0 \u2103 \u00b1 2.0\u2103 in a natural light environment, except for special treatment methods. According to the steps described in Supplementary Text 1, the full-length sequence (3120 bp) of the outer sericin SER3 gene specifically expressed in the MSG was cloned (Supplementary Sequence 1). The strategy in Fig.\n \n 1\n \n c and steps in Supplementary Text 1 were used to construct the TALEN transgene vector and perform egg injection. The strategies and effects of mutant screening and genetic purification are shown in Supplementary Fig.\u00a01, and the recombinant SER3 gene insertion site of the mutant was analyzed by tail-PCR sequencing (Supplementary Fig.\u00a01g).\n
\n\n \n Microscopic observation.\n \n The middle cocoon shell and degummed silk fiber of silkworm cocoons were observed by SEM. The spraying current was 20 mA, with platinum vacuum spraying for 3 min. Samples were observed by SEM (S4800, Hitachi, Japan) at room temperature, with repeated observations for three independent samples. The silk fiber was degummed and boiled in 0.2% Na\n \n 2\n \n CO\n \n 3\n \n solution for 30 min.\n
\n\n \n TEM\n \n was used to observe the raw silk samples and SG tissues. The samples were pre-cooled at 4\u00b0C and fixed with electron microscope fixative (G1102, Servicebio, China) for 2 h, then fixed with 1% osmium acid for 2\u20134 h. The fixed samples were dehydrated with an ethanol gradient (50%, 70%, 80%, 90%, 95% and 100%) at 4\u00b0C and then dehydrated with 100% ethanol and 100% acetone two times, with each dehydration lasting 15 min. After embedding and sectioning (thickness 60\u201380 nm), uranium-lead double staining (2% uranyl acetate saturated ethanol solution and lead citrate) was performed for 15 min each, and samples were dried at room temperature, then observed by TEM (HT7700, Hitachi, Japan).\n
\n\n \n P25 immunofluorescence assays.\n \n Paraffin sections of cocoon silk fiber were made according to the conventional method. After dewaxing, sections were soaked in 0.01 M citrate buffer at 96\u00b0 C for 15 min for antigen repair, and the sections were exposed to blocking solution for 40\u201360 min. P25 antibody was added to the tissue surfaces of the sections and incubated at room temperature for 1 h. The sections were washed three times with PBST for 5 min each. Then a TRITC labeled secondary antibody (S0015, Affinity Biosciences, Ohio, USA) was added, incubated at room temperature for 1 h, then washed with PBST in the dark three times for 5 min each. Red fluorescence was observed with a fluorescence microscope (BX51, Olympus, Tokyo, Japan).\n
\n\n \n Secondary structure analysis of silk fibroin.\n \n The infrared absorption spectrogram (wavelength range 4000\u2013800 cm\n \n \u2212\u20091\n \n ) of degummed silk fibers was determined with an infrared spectrometer (Nicolet 5700, Thermo Electron Corporation, USA) with a resolution of 8 cm\n \n \u2212\u20091\n \n . Each sample was scanned 256 times, and three samples were repeatedly analyzed. Spectral data were analyzed in OMNIC 9 software (Thermo Scientific) and PeakFit software (Seasolve, version 4.12). The amide I region deconvolution spectrum fitting method was used, and the peak position was determined by the second derivative peak position of the infrared spectrum.\n
\n\n \n Silk fiber performance measurement.\n \n The mechanical properties were measured with a universal material testing machine (3365, Instron, USA) in a room with constant temperature and humidity (20 \u2103, R.H. 65%). The test conditions were as follows: initial length, 250 mm; tensile speed, 250 mm/min. The sample was a cocoon silk fiber (100\u2013200 meters) without the sericin protein of the outer layer removed (n\u2009=\u200920 cocoons).\n
\n\n \n Moisture absorption testing.\n \n After degumming, the silk fibers (n\u2009=\u20093 samples) were dried to a constant weight at 80\u00b0C and then returned to normal temperature (20\u00b0C) for accurate weighing. Hygroscopic properties were determined in a room with constant temperature and humidity (20 \u2103 \u00b1 2 \u2103, R.H. 65% \u00b1 3%), and the weight was measured every 10 min until the fiber reached moisture absorption balance. When the moisture release performance was measured, the sample was first placed in an R.H. 100% container and sealed for 24 h, so that the fiber achieved moisture absorption balance (W0). Then the fibers were placed in a constant temperature and humidity chamber (20 \u2103 \u00b1 2 \u2103, R.H. 65% \u00b1 3%), and the quality changes were monitored continuously until the fiber reached a moisture balance. Regaining of moisture was expressed as the percentage of the mass of water absorbed or released by a unit mass of silk fiber in different time periods with respect to the original fiber mass. The moisture absorption rate and moisture release rate are expressed as water mass absorbed or released by the silk fiber per unit mass at a certain time, according to a previously described method\n \n \n 37\n \n \n . Origin2018 software was used to calculate the correlation constants of the fitting curve equation of regaining of moisture over time.\n
\n\n \n Biocompatibility assays.\n \n The degummed silk fibers and non-absorbing polyester suture (NASS) were sterilized under high temperature and high pressure (121 \u2103, 30 min). L929 (ZQ0093, ZQXZ Biotech, Shanghai, China) mouse fibroblasts were used for cytotoxicity testing. L929 cells were cultured overnight in 96 well plates with 100 \u00b5L Eagle's minimum essential medium (ZQ301, ZQXZ Biotech, Shanghai, China). The test fiber (1.0 mg /well) was soaked in the medium and gently cultured in the wells, and the normal cultured L929 cells were used as a negative control. After continuous culture for 24 h and 48 h, a Live-Dead Kit (l3224, Thermo Fisher Scientific, USA) was used to distinguish living cells from dead cells, and an MTT Kit (C0009, Beyotime, Nantong, China) was used to detect cell proliferation. RAW264.7 cells (ZQ0098, ZQXZ Biotech, Shanghai, China) were used for cell inflammatory testing. The cells were cultured in 500 \u00b5L Dulbecco's modified Eagle medium (high glucose) (ZQ101, ZQXZ Biotech, Shanghai, China) on a 24 well plate overnight (the number of cells was as high as 3\u00d710\n \n 4\n \n ). The test fiber (10.0 mg/well) was soaked in the culture medium and gently cultured for 24 h and 48 h. The nitrous oxide content in the culture medium was determined with a Nitric Oxide Colorimetric Assay Kit (NO Kit) (S0021, Beyotime, Nantong, China).\n
\n\n \n Gene expression analysis.\n \n An RNAiso Plus (TaKaRa, Dalian, China) was used to extract total RNA from PSG tissues of silkworm larvae on the third day of the 5th instar (5L3d). The cDNA was synthesized with a PrimerScript\u2122 RT reagent kit with gDNA Eraser (Perfect Real Time) (TaKaRa, Dalian, China) according to the manufacturer\u2019s instructions. qRT-PCR was performed in a total reaction volume of 20 \u00b5l with the fluorescent dye SYBR Premix Ex Taq (TaKaRa, Dalian, China), according to the manufacturers\u2019 instructions, and detected with ABI Stepone Plus (Ambion, Foster City, CA, USA). The\n \n BmRp49\n \n gene was selected as the internal control. Primers used in this study are listed in Supplementary Table\u00a04.\n
\n\n \n Western blotting.\n \n Silk protein in PSG tissue of 5L3D silkworm larvae was extracted with RIPA lysis buffer (P0013C, Beyotime, Nantong, China) (containing 1 mmol/L PMSF). The total protein concentration was measured with a BCA Protein Assay Kit (Beyotime, Shanghai, China). Western blotting was performed according to conventional methods\n \n \n 30\n \n \n . A 100 \u00b5g mass of total protein was electrophoresed by 10% SDS-PAGE and then transferred to a PVDF membrane. After blocking at 25 \u2103 for 2 h, HRP-labeled goat anti-rabbit IgG (Bioworld Technology, Minneapolis, MN, USA) was added and incubated. Under dark conditions, 1 mL EZ-ECL chemiluminescence reagent was added to the membrane, and the bands were observed through chemiluminescence detection (1708370, Bio-Rad, USA) after 1 min.\n
\n\n \n Data availability\n \n
\n\n All data generated in this study are available within the Article, Supplementary Information and Source Data files. Source data are provided with this paper.\n
\n\n Supplementary information: Supplementary Text S1- S5, Sequence S1, Figure S1- S3, Table S1- S4.\n
\n \n\n Radio-immunotherapy exploits the immunostimulatory features of ionizing radiation (IR) to enhance antitumor effects and offers emerging opportunities for treating invasive tumor indications such as melanoma. However, insufficient dose deposition and immunosuppressive microenvironment (TME) of solid tumors limit its efficacy. To address these challenges, a cascade-amplification strategy based on multifunctional fusogenic liposomes (Lip@AUR-ACP-aptPD-L1) was reported. The liposomes were loaded with gold-containing Auranofin (AUR) and inserted with multivariate-gated aptamer assemblies (ACP) and PD-L1 aptamers in the lipid membrane, potentiating melanoma-targeted AUR delivery while transferring ACP onto cell surface through selective membrane fusion. AUR amplified IR-induced immunogenic death of melanoma cells to release antigens and damage-associated molecular patterns such as ATP for triggering adaptive antitumor immunity. AUR-sensitized radiotherapy also upregulated MMP-2 expression that combined with released ATP to cause AND-gate activation of ACP, thus triggering the in-situ release of CpG-based immunoadjuvants for stimulating dendritic cell-mediated T cell priming. Furthermore, AUR inhibited tumor-intrinsic ERK1/2-HIF-1\u03b1-VEGF signaling to suppress infiltration of immunosuppressive cells for fostering an anti-tumorigenic TME. This study offers an approach for solid tumor treatment in the clinics.\n
\n\n \n Radio-immunotherapy\n \n
\n\n \n radiosensitization\n \n
\n\n \n AND logic aptamer assembly\n \n
\n\n \n liposomal drug delivery\n \n
\n\n \n tumor microenvironment remodeling\n \n
\n\n Radiotherapy (RT) is an antitumor modality that employs high-energy X ray or subatomic particles to destroy tumor cells, which is commonly used for the treatment of a variety of solid tumor indications due to its good cost-effectiveness, high treatment compliance and curative/palliative benefit\n \n \n 1\n \n \u2013\n \n 3\n \n \n . Recent studies reveal that radiotherapy also has the potential to substantially modify the tumor ecosystem to exert multifaceted immunostimulatory effects including induction of immunogenic tumor cell death, tumor-associated antigen presentation, and activation of tumor-specific effector T cells, thus offering potential synergy with various immunotherapeutic modality for enhanced antitumor efficacy\n \n \n 3\n \n \u2013\n \n 6\n \n \n . Indeed, these emerging radio-immunotherapies have demonstrated unique advantages compared with conventional antitumor therapies including systemic antitumor effects and long-lasting antitumor immune memory, which are highly favorable for treating invasive and refractory solid tumor indications such as melanoma\n \n \n 7\n \n \u2013\n \n 9\n \n \n . However, solid tumors possess multiple intrinsic traits that may undermine the efficacy of radio-immunotherapy\n \n \n 10\n \n \u2013\n \n 12\n \n \n . Typically, the actual deposition of ionizing radiation (IR) in tumor tissues is usually insufficient, which requires dangerously high IR doses to achieve significant tumor inhibition effects and thus elevates the RT-associated side effects\n \n \n 13\n \n \u2013\n \n 16\n \n \n . Furthermore, the immunosuppressive TME will substantially impair the T cell-mediated antitumor immunity despite the RT-triggered immunostimulatory effects\n \n \n 17\n \n \u2013\n \n 19\n \n \n . Therefore, new treatment strategies with cooperative radiosensitization and anti-tumorigenic TME immunomodulatory capabilities are urgently needed to overcome these challenges, which hold promise to augment the therapeutic potency of radio-immunotherapy for robust and persistent tumor inhibition.\n
\n\n The excessive presence of immunosuppressive cell populations such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs) in TME is a major driver of tumor immune escape. Notably, tumor cells frequently express abundant VEGF to recruit MSDCs and Tregs to TME as well as stimulating their proliferation thereafter, which is recognized as a crucial promoter of tumor immunoresistance and a potential target for clinical exploitation\n \n \n 20\n \n \u2013\n \n 22\n \n \n . Auranofin (AUR) is a gold coordination compound that has been long approved by FDA for treating rheumatoid arthritis in the clinics. Interestingly, it has demonstrated multiple therapeutically favorable bioactivities in recent studies and been increasingly repurposed for tumor treatment\n \n \n 23\n \n \u2013\n \n 25\n \n \n . Recent studies reveal that AUR could abolish VEGF-dependent pro-tumorigenic immunosignaling pathways through inhibiting ERK1/2-HIF-1\u03b1 axis in tumor cells for enhancing the tumor-infiltration and cytotoxic potential of antitumor T cells\n \n 23,26\u221229\n \n . Moreover, due to the complexation with high-Z gold (I) species, AUR treatment could significantly enhance the deposition of ionizing radiation doses in tumor cells for effective radiosensitization\n \n \n 30\n \n \u2013\n \n 33\n \n \n . Therefore, tumor-targeted AUR treatment could be a promising strategy for boosting radio-immunotherapy efficacy in the clinical context.\n
\n\n Aptamer is a class of synthetic oligonucleotide ligands with antibody-like binding behavior with designated molecular targets\n \n \n 34\n \n \u2013\n \n 36\n \n \n , which has attracted broad interest for therapeutic applications due to the high binding affinity/specificity and may fulfill a variety of functional roles including signaling mediators and targeting ligands, which are particularly favorable in the field of antitumor immunotherapeutics\n \n \n 37\n \n \u2013\n \n 41\n \n \n . For example, CpG ODN (CpG oligonucleotide) is a clinically tested aptamer-based immune adjuvant that can promote DC activation via triggering toll-like receptor 9 (TLR9) immune signaling to stimulate the downstream adaptive immune reactions\n \n \n 42\n \n \u2013\n \n 44\n \n \n . Alternatively, there is abundance evidence that PD-L1-targeting aptamers could bind with PD-L1-overexpressing tumor cells for efficient PD-L1 antagonization\n \n \n 28\n \n ,\n \n 45\n \n ,\n \n 46\n \n \n . Notably, the versatile aptamer chemistry allows the further modular integration of multiple chemically-tailored aptamer units to introduce logic-gate bioresponsive reactivity without altering their original biological functions\n \n \n 47\n \n \u2013\n \n 49\n \n \n . It is thus anticipated that implementing programmable aptamer assemblies into therapeutic systems could be a practical approach for regulating their biointeractions and potentiating cooperative therapeutic combinations.\n
\n\n In this study, we reported a multivariate-gated aptamer assembly-modified AUR-loaded fusogenic liposome as an adjuvant for melanoma-targeted radio-immunotherapy. We modified the 5' end of commercially available CpG aptamers with a 10-nucleotide long sequence that could complex with the 5' end region of aptATP through complementary binding (engineered CpG, eCpG). Meanwhile, we also prepared synthetic MMP-2-degradable peptide nucleic acid (PmP) sequence with complementary binding affinity with the 3' end region of aptATP, which could combine with the aptATP-eCpG complex to form physiologically-stable duplex assemblies. Notably, the 3' ends of aptATP and aptPD-L1 were both modified with lipophilic cholesterol moieties, thus allowing their insertion into the lipid bilayers of DMPC-based fusogenic liposomes. Meanwhile, the hydrophobic AUR was loaded into the lipid contents through physical dissolution, eventually leading to the spontaneous formation of bioresponsive fusogenic liposomes (Lip@AUR-ACP-aptPD-L1). Taking advantage of aptPD-L1 modification, Lip@AUR-ACP-aptPD-L1 could bind with PD-L1-overexpressing melanoma cells and fuse with the cytoplasmic membrane, thus transferring the ACP assemblies onto melanoma cell surface while releasing AUR into tumor cytoplasm. The liposome-mediated tumor-targeted AUR delivery substantially enhanced the IR dose accumulation in melanoma cells in the context of radiotherapy and induced efficient ICD, releasing abundant tumor-derived antigens and DAMPs such as ATP into TME while also inducing MMP-2 upregulation. Notably, MMP-2 would remove the PmP chain from the ACP assembly through biocatalytic degradation, while tumor-derived ATP would further trigger the detachment of eCpG through competitive binding, leading to AND-gate eCpG release into TME to promoting DC maturation through binding to TLR9, which would substantially enhance DC-mediated cross-priming of antitumor T cells. In addition, AUR would also inhibit the ERK1/2-HIF-1\u03b1-VEGF axis in tumor cells and impair the immunosuppression orchestrated by tumor-infiltrating immunosuppressive cells such as MSDCs and Tregs for boosting the antitumor function of activated T cells. These effects could act in a cooperative manner to substantially abolish melanoma growth and establish robust antitumor immune memory to prevent melanoma metastasis or recurrence (Fig.\u00a01). This work presented a programmable cascading-amplification strategy to enhance the radio-immunotherapeutic efficacy against invasive melanomas, showing significant potential as a generally-applicable antitumor option in the clinics.\n
\n\n To obtain the bioresponsive multi-component aptamer assemblies, we first synthesized eCpG, aptATP, PmP and aptPD-L1 via established procedures as the basic components, of which the complementary binding affinity between aptATP/eCpG and aptATP/PmP pairs provided the mechanistic basis for assembly formation (Fig.\n \n 2\n \n a). Notably, to avoid the potential negative impact of cholesterol modification on the structural and biochemical features of aptATP and aptPD-L1 aptamers, multiple base T units were added at the 3' end of the aptamer sequences as a functional handle. NUPACK simulation of secondary structures of these engineered aptamers showed no changes in the structure and \u25b3G of the aptamers (Fig.\n \n 2\n \n b, c), confirming successful aptamer modification without altering their designated biological functions. To ensure effective eCpG detachment from aptATP/eCpG complexes under ATP competition, we proactively constructed aptamer assemblies with different aptATP/eCpG ratios and tested their responsiveness to ATP treatment. Comparative PAGE analysis under graded ATP concentrations showed that aptamer assemblies at the aptATP/eCpG ratio of 2:1 presented enhanced sensitivity to ATP competition to trigger efficient eCpG release, which was used as the standard condition for subsequent experiment (Fig.\n \n 2\n \n d). The aptATP/eCpG complexes were further integrated with PmP at an aptATP: PmP ratio of 1:1.5, leading to the formation of duplex structures with robust stability under physiological conditions.\n
\n\n Meanwhile, the liposomal nanosubstrates were synthesized through the self-assembly of DMPC, DSPE-PEG\n \n 2000\n \n , DOTAP and AUR, thus endowing cytoplasm membrane fusion and long-circulating stability while also achieving spontaneous AUR loading. Due to the proactive modification of cholesterol on the 3' position of aptATP and aptPD-L1, the multivariate-gated ACP assembly and tumor-targeting aptPD-L1 could be facilely inserted into the lipid bilayers for non-invasive modification (Fig.\n \n 2\n \n a). According to transmission electron microscopic imaging analysis, the bioresponsive liposomes showed uniform spherical morphology and high monodispersity (Fig.\n \n 2\n \n i). Quantitative DLS analysis further suggested that the average diameter of the liposomes was around 130nm (Extended Data Fig.\n \n 2\n \n b), which was within the optimal size range of intravenously administered antitumor nanomedicines. Zeta potential analysis showed that pristine liposomes have an average surface charge of around 38mV, which was attributed to the positively charged status of DOTAP contents (Extended Data Fig.\n \n 2\n \n a). However, the zeta potential of Lip@AUR-ACP-aptPD-L1 dropped significantly to -13.7mV, supporting the successful immobilization of the negatively-charged aptamers. We also found that the Lip@AUR-ACP-aptPD-L1 nanoformulation presented good loading capacity for the therapeutic contents. Specifically, quantitative fluorescence analysis showed that the AUR loading ratio in the final Lip@AUR-ACP-aptPD-L1 was around 5% (Extended Data Fig.\n \n 3\n \n a, b), while the average number of ACP assembly and aptPD-L1 on a single liposome was 109 and 51 based on fluorescence spectroscopy (Extended Data Fig.\n \n 3\n \n c, d and Fig.\n \n 4\n \n ). Due to the spontaneous loading procedures, the loading of ACP assembly and aptPD-L1 was highly efficient, of which the loading efficiency was 86.5% and 81%, respectively.\n
\n\n The multivariate-gated activation mode of the ACP assembly is an essential perquisite for enhancing the radio-immunotherapeutic efficacy of the liposomal nanoformulation, which is crucial for enabling optimal immunostimulation in post-IR melanomas with spatial-temporal precision while minimizing the potential side effects. Here we first profiled the ATP-responsiveness of aptATP/eCpG complex by PAGE assay. Indeed, treating aptATP/eCpG complexes with an ATP concentration of 0.05\u00b5M was sufficient to induce significant eCpG release (Fig.\n \n 2\n \n e). However, the eCpG release from ACP assembly under sole ATP treatment (0.25\u00b5M) was almost negligible, which was only around 5nM after 8 h of incubation. Similarly, treating ACP with only MMP-2 (10nM) also failed to induce obvious eCpG release (Fig.\n \n 2\n \n j). Comparative analysis on eCpG release profiles immediately suggested that PmP complexation inhibited the ATP recognition and binding capability of aptATP while also supporting the necessity of competitive ATP binding to trigger eCpG detachment from aptATP in the absence of PmP. The DNA-PAGE analysis results were also supported by fluorescence spectroscopic analysis using eCpG\n \n Cy\n \n 5\n \n \n (Fig.\n \n 2\n \n f). Consistent with the data above, we observed that the combinational treatment of ATP and MMP-2 caused a substantial increase in the eCpG release rate from ACP assembly, which reached around 80% after 8 h of incubation (Fig.\n \n 2\n \n j). The trends from fluorescence analysis were further validated via gel electrophoresis assay, where the band representing eCpG release in the ATP\u2009+\u2009MMP-2 group showed evidently higher intensity compared to all other groups (Fig.\n \n 2\n \n g). The results above collectively validated the AND-gate eCpG release behavior of the ACP assembly in conditions mimicking IR-modulated melanoma microenvironment, supporting its potential utility for post-RT immunostimulation. Gel electrophoresis results further validated that the AND-gate logic operation of ACPs was still maintained after their insertion into fusogenic liposomes (Fig.\n \n 2\n \n h), again showing the non-invasiveness of the cholesterol-enabled ACP insertion strategy for liposome functionalization.\n
\n\n We employed multiple fluorescence-based characterization techniques to investigate the interaction of Lip@AUR-ACP-aptPD-L1 liposomes with typical cell population in melanoma microenvironment. First, we synthesized aptPD-L1 and eCpG with fluorescent FAM tags for in vitro tracking. Flow cytometric results immediately suggested that the amount of aptPD-L1 bound to B16F10 cell surface was 5-fold higher than splenocytes, which was in line with the elevated PD-L1 expression status of melanoma cells compared with their normal counterparts or immune cells. Alternatively, eCpG showed preferential binding and accumulation in DCs, while its binding with other cell populations was modest at most (Fig.\n \n 3\n \n a). Subsequently, to investigate the melanoma-targeting effect of the aptPD-L1-modified fusogenic liposomes, we developed a co-culture system comprising B16F10 cells and mouse splenocytes and monitored the cellular distribution of fluorescently labeled liposomes after incubation. B16F10 cells showed enhanced uptake capacity for Lip@Dil-aptPD-L1 compared with non-aptPD-L1-containing Lip@Dil samples (Fig.\n \n 3\n \n b), ascribing to the specific aptPD-L1-PD-L1 binding between the fusogenic liposomes and melanoma cells. Notably, most of the Dil fluorescence was enriched in the cytoplasmic membrane of B16F10 cells, immediately suggesting that the aptPD-L1 modification could enhance both the specificity and efficiency of charge-dependent interaction between liposomal and cellular membranes to facilitate the fusion process.\n
\n\n The fusion of Lip@AUR-ACP-aptPD-L1 with cytoplasmic membrane would cause the transference of liposomal ligands onto tumor cell surface, which is crucial for enabling the AND-gate logic operation of ACP in RT-treated melanomas. To monitor the membrane retention kinetics of the fusogenic liposomes, we incubated B16F10 cells with different Dil-labeled nanosamples and comparatively analyzed the fluorescence distribution patterns after incubation for 1/3/6/12/18 h (Fig.\n \n 3\n \n b). Substantially amount of Dil fluorescence still largely overlapped with the cytoplasmic membrane of B16F10 cells in the Lip@Dil-aptPD-L1 group after 12 h of incubation. In contrast, most the Dil fluorescence relocated to the intracellular compartment after 18 h. Based on the data above, the time interval between in vivo liposome administration and IR treatment was set to 12 h to ensure that sufficient ACP assemblies were still anchored on tumor cell surface. It is also noteworthy that Dil fluorescence in Lip@Dil-aptPD-L1-treated NIH3T3 cells generally remained at a relatively low level with no obvious changes throughout the incubation period (Extended Data Fig.\n \n 6\n \n ), ascribing to the overall slow liposome uptake rate due to the lack of aptPD-L1-mediated tumor binding. The tumor-targeted binding and uptake capability of the Lip-AC\n \n Cy5\n \n P-aptPD-L1 liposomes was further validated using tumor spheroid model, evidenced by the strong Cy5 fluorescence in the Lip-AC\n \n Cy5\n \n P-aptPD-L1 group (Fig.\n \n 3\n \n c). Owing to the aptPD-L1-mediated tumor targeting effect above, we employed ICP to monitor cellular AUR abundance after various treatment and found that the AUR levels steadily increased in a time-dependent manner, for which the cellular AUR concentration reached around 2.7\u00b5M after 12 h of incubation (Extended Data Fig.\n \n 7\n \n ). Together, these data showed that the Lip@AUR-ACP-aptPD-L1 liposomes potentiated efficient surface anchoring of the multivariate-gated ACP assemblies and targeted delivery of AUR to melanoma cells.\n
\n\n To test if the liposome-delivered Au-containing AUR could enhance the IR susceptibility of melanoma cells, we incubated B16F10 cells under different conditions of liposomal nanosamples with or without IR treatment. B16F10 cells showed significant resistance to low IR doses that their survival rate was still around 90% under the IR dose of 4Gy (Extended Data Fig.\n \n 8\n \n a). In contrast, the combined treatment of Lip@AUR-aptPD-L1 liposomes and 4Gy IR caused significant melanoma inhibition effect, of which the survival rate dropped to only around 65% at 12 h post treatment, evidently supporting the radiosensitization effect of AUR-containing liposomes (Extended Data Fig.\n \n 8\n \n a). It is also of interest to note that Lip@AUR-aptPD-L1 liposomes induced slight melanoma inhibition effects even without IR treatment, which was ascribed to the intrinsic antitumor activity of AUR and also consistent with the observations in recent reports (Extended Data Fig.\n \n 5\n \n ), although the changes were not therapeutically appreciable due to the low loading amount of AUR\n \n \n 50\n \n \u2013\n \n 52\n \n \n . On the other hand, the IR treatment of melanoma tissues would also inevitably impose negative impact on tumor-infiltrating immune cells and thus impair the immunostimulatory efficacy, and it is thus clinically favorable to limit the IR dose at a minimum necessary level. Indeed, we also monitored the response of mouse splenocytes to different IR doses and found that 4Gy IR did not induce obvious splenocyte inhibition (less than 10%) even in the presence of Lip@AUR-aptPD-L1 liposomes, while the combined treatment of 8Gy IR and Lip@AUR-aptPD-L1 liposomes caused a 22% reduction in splenocyte survival and the changes were statistically significant (Extended Data Fig.\n \n 8\n \n b). Based on a balanced consideration of AUR-enabled radiosensitization and potential risk of immunosuppression, the final IR dose for in vitro and in vivo tests was set to 4 Gy. Next, we measured the total ATP release in B16F10 cells at 0/2/4/12/18/24 h after radiotherapy, which exceeded the threshold concentration for ACP actuation after 2 h and eventually reached a plateau after 18h (Fig.\n \n 3\n \n d). It is also observed that the membrane-fused liposomal contents gradually translocated to the cytoplasm at 4 h post IR treatment, which is crucial for enabling the VEGF-inhibition function of AUR contents (Fig.\n \n 3\n \n e). Based on the kinetic insights described above, the treatment schedule of Lip@AUR-aptPD-L1 in vitro was established and shown in Fig.\n \n 3\n \n h to ensure balanced AUR-mediated IR sensitization/VEGF inhibition and logic operation of ACP. According to the optimized treatment schedule above, Lip@AUR-aptPD-L1 showed significant improvement on the RT efficacy even under the low IR dose of 4Gy according to MTT assay (Extended Data Fig.\u00a09).\n
\n\n The crosstalk between tumor cells and immunosuppressive cells is a major driver of the immunosuppressive TME. There is already clinical evidence that VEGF secreted by melanoma cells could recruit MSDCs and Tregs to TME for suppressing the effector function of CTLs, thus contributing to their immune escape. Interestingly, recent reports reveal that AUR could demonstrate potent VEGF suppressing capability through inhibiting ERK1/2-HIF-1\u03b1 signaling activity in tumor cells\n \n \n 53\n \n \u2013\n \n 55\n \n \n . Indeed, we have carried out transcriptome sequencing on AUR-treated B16F10 cells to screen the treatment-induced impact on various immune-related signaling pathways, and the KEGG enrichment analysis results immediately suggested that AUR treatment pronouncedly inhibited the VEGF signaling pathways (Fig.\n \n 3\n \n f and Extended Data Fig.\u00a010). The VEGF-inhibiting function of AUR-incorporated liposomes was investigated in greater detail via western blot assay. As shown in Fig.\n \n 3\n \n g and Extended Data Fig.\u00a011a, b, sole IR treatment induced significant activation of the ERK1/2-HIF-1\u03b1-VEGF axis, which was attributed to the oxygen-consumption effect of IR and consistent with the clinical data in previous reports\n \n \n 56\n \n \u2013\n \n 59\n \n \n . Similar trends in the activation status of ERK1/2-HIF-1\u03b1-VEGF signaling pathway were also observed in those non-AUR-containing groups including Lip\u2009+\u2009IR, Lip-aptPD-L1\u2009+\u2009IR and Lip-ACP-aptPD-L1\u2009+\u2009IR, suggesting their inability to suppressive VEGF expression in melanoma cells. In contrast, Lip@AUR-aptPD-L1\u2009+\u2009IR and Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR both induced obvious inhibition on ERK1/2, HIF-1\u03b1 and VEGF regardless of the IR treatment condition. The data above collectively confirmed that the AUR component in the Lip@AUR-ACP-aptPD-L1 liposomes could effectively inhibit VEGF expression in IR-treated melanoma cells through inhibiting ERK1/2-HIF-1\u03b1 axis, offering potential opportunities to impede the recruitment of immunosuppressive cells into TME for restoring antitumor immunity. The potential therapeutic benefit of liposome-induced VEGF suppression was evaluated using co-culture system of B16F10 cells and splenocytes. Flow cytometry analysis showed that fewer Tregs and MDSCs migrated to tumor cells after Lip@AUR-aptPD-L1\u2009+\u2009IR treatment, which were as low as 9.39% (Extended Data Fig.\u00a012a) and 1.52% (Extended Data Fig.\u00a012b), respectively, accompanied with increasing DC (Extended Data Fig.\u00a013b) and CD8\u2009+\u2009T cell (Extended Data Fig.\u00a013a) infiltration into tumor cell chamber. The results showed that AUR-mediated VEGF inhibition could reduce Tregs and MDSCs infiltration into tumor niche and potentially establish an anti-tumorigenic microenvironment. We further investigated if the Lip@AUR-aptPD-L1-mediated radiosensitization of melanoma cells could enhance their immunogenic feature and contribute to immunostimulation. Here we first monitored the cellular status of key DAMPs including ATP (Extended Data Fig.\u00a014a), CRT (Extended Data Fig.\u00a014b) and HMGB1 (Extended Data Fig.\u00a014c) using the corresponding assay kits. Notably, untreated B16F10 cells showed negligible CRT expression as well as low levels of ATP and HMGB1 release, which is in accordance with their low immunogenic potential under common conditions. Low dose (4Gy) IR treatment induced significant enhancement in CRT expression (140%) and ATP/HMGB1 release (170%/130%) (Extended Data Fig.\u00a014), which was attributed to the IR-induced ICD of melanoma cells. However, the relative increase for the abundance of typical DAMPs in IR-treated B16F10 cells were modest at most due to ineffective radiotherapeutic effect. Remarkably, melanoma cells in the Lip@AUR-aptPD-L1\u2009+\u2009IR group showed the greatest increase in CRT expression (370%) and ATP/HMGB1 secretion (570%/310%) compared with the control group (Extended Data Fig.\u00a014), which is in line with the pronounced radiosensitization effect of the Lip@AUR-aptPD-L1 liposomes. These observations evidently supported our hypothesis that the radiosensitization effect of the Lip@AUR-aptPD-L1 liposomes could induce pronounced ICD of melanoma cells and thus offer multifaceted therapeutic benefit. On one hand, the released DAMPs and tumor-associated neoantigens could stimulate the adaptive immune system to initiate antitumor immune responses. On the other hand, the enhanced ATP secretion could cooperate with IR-upregulated MMP-2 to trigger the AND-gate activation of the ACP assembly and release eCpG into TME, thus promoting DC maturation and facilitating the cross-priming of antitumor T cells.\n
\n\n Extending from the IR-triggered liposome-augmented ICD of melanoma cells above, we further comprehensively investigated the immunostimulatory impact of liposome-sensitized melanoma radiotherapy in vitro. To start with, we evaluated if the molecular engineering of 5' end of CpG ODN would alter its immunological activities via NUPACK analysis. As shown by the simulation results, the addition of the 10-base aptATP binding sequence caused no alterations in the structure of the stem-loop domain (Fig.\n \n 4\n \n a, b). Subsequently, we employed 3D model-based molecular dock analysis to further profile the complexation of pristine CpG ODN and eCpG with TLR9 proteins. The binding sequence of CpG ODN to TLR9 is base 6\u201311 (GACGTT), which is directly complexed to 337Arg and 338Lys on TLR9 while also presenting indirect interaction with 347Lys, 348Arg and 353His (Fig.\n \n 4\n \n c, d), which was consistent with the structural analysis in previous reports\n \n \n 60\n \n \u2013\n \n 62\n \n \n . Interestingly, eCpG bond to TLR9 through the same GACGTT sequence with identical amine acid interaction, immediately suggesting that the addition of aptATP-binding sequence at the 5' end of CpG induced negligible impact on its TLR9 binding behavior. We further prepared Cy5 labeled eCpG and tested their binding with TLR9-positive DCs (Fig.\n \n 4\n \n e). Notably, eCpG showed comparable TLR9-binding affinity to pristine CpG ODN and showed pronounced promotional effects on DC maturation (51.1%) (Fig.\n \n 4\n \n f), while mutating the CG bases in the GACGTT sequence induced significant reduction in the DC-binding capacity of the aptamers and failed to induce significant changes in DC maturation ratio after co-incubation. Meanwhile, we detected that pretreating eCpG with the complementary sequence (CTGCAA) of the TLR9-binding domain also impaired their complexation with TLR9-positive DCs and abolished their pro-DC maturation function (20.8%) (Fig.\n \n 4\n \n f). These results collectively supported that the molecularly engineered eCpG successfully expanded its nanointegrative functionality without impairing its DC-stimulatory activity.\n
\n\n Next, we investigated if the Lip-ACP-aptPD-L1 liposomes could activate the adaptive antitumor immunity through mediating AND-gate eCpG release in vitro using co-incubation system of B16F10 cells and mouse splenocytes. To monitor the cellular distribution of eCpG in the co-incubation system, it was labeled by Cy5 for fluorescent tracking. Based on the liposome fusion time and DAMP release data shown in Fig.\n \n 3\n \n d and Extended Data Fig.\u00a014, the optimal time interval between liposome administration and IR treatment was determined to be 12 hours to ensure balanced IR exposure and ATP and MMP-2 elevation, while the complexation status of ACP was observed at 4/8/12/18/24 h post IR treatment. Fluorescence imaging results showed that abundant Cy5 fluorescence appeared on the surface of Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1-treated B16F10 cells after 12 h of incubation, suggesting the successful transference of the AC\n \n Cy5\n \n P assemblies to tumor cytoplasmic membrane. Notably, the red fluorescence retention in the Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1 group was evidently higher than the Lip@AUR-AC\n \n Cy\n \n 5\n \n \n -aptPD-L1 under the same dose conditions, immediately supporting our hypothesis that the complementary binding of PmP could stabilize the aptamer assembly to reduce eCpG leakage (Fig.\n \n 4\n \n g, h). We further observed that the both Lip@AUR-AC\n \n Cy\n \n 5\n \n \n -aptPD-L1 and Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1 groups showed significant reduction in the intensity of the membrane-bound Cy5 fluorescence without obvious changes in intracellular fluorescence deposition, suggesting that substantial release of eCpG into the incubation media. Fluorescence analysis of Cy5 on the cell membrane and cell supernatant of B16F10 also showed that significant proportion of eCpG\n \n Cy\n \n 5\n \n \n was released after IR treatment (Extended Data Fig.\u00a015a, b). As a result of the efficient AND-gate eCpG release, DCs in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group showed the highest maturation ratio (CD80\u2009+\u2009CD86+) at 18h post IR (Fig.\n \n 4\n \n i), indicating that the liposome-sensitized RT successfully triggered eCpG release to promote DC maturation (Fig.\n \n 4\n \n j). These observations evidently supported our hypothesis that the AND-gate eCpG release feature of the Lip-ACP-aptPD-L1 liposomes could effectively promote the maturation of DCs and stimulate the adaptive antitumor immune response in IR-treated melanomas.\n
\n\n We further studied whether the liposome-augmented IR-induced ICD of melanoma cells and the cooperative AND-gate eCpG release could evoke adaptive immunity to achieve effective radio-immunotherapy against melanomas. It is well-established that tumor cells undergoing ICD would release tumor-associated immunogenic materials for the processing and recognition by tumor-infiltrating antigen-presenting cells for mediating the downstream immune reactions. Indeed, flow cytometric analysis on the extracted immune cell populations from the co-incubation system showed that the combined Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR treatment substantially improved the maturation and antigen-presentation capacity of DC population, where the frequencies of CD80\u2009+\u2009CD86+ (Fig.\n \n 5\n \n a and Extended Data Fig.\u00a018a) and CD11c\u2009+\u2009MHC-II+ ( Extended Data Fig.\u00a016a and Fig.\u00a018b) DCs have increased by 36.21% and 38.57% compared with the control group and obviously higher than all other groups. As a result of their enhanced maturation status, DCs in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group showed significantly enhanced secretion of pro-inflammatory cytokines including TNF-\u03b1 (Extended Data Fig.\u00a017a) and IL-2 (Extended Data Fig.\u00a017b), which was about 6 and 5 times higher than PBS\u2009+\u2009IR group.\n
\n\n In line with the enhanced activation status of DCs, the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group showed a substantial expansion of the CD4+/CD8\u2009+\u2009T cell populations to 77.66% (Fig.\n \n 5\n \n b and Extended Data Fig.\u00a018c), while the frequency of IFN-\u03b3\u2009+\u2009CD8+( Extended Data Fig.\u00a016b and Fig.\u00a018d) T cells had also increased to 45.81%, suggesting effective DC-mediated priming of antitumor T cells thereof. In addition, the secretion of key immune-related molecular markers in the co-incubation system was analyzed by ELISA assay to indicate the alteration in the immune composition, and the results revealed that the secretion levels of pro-inflammatory cytokines and chemokines including IFN-\u03b3 (Fig.\n \n 5\n \n c), TNF-\u03b1 (Fig.\n \n 5\n \n d), CXCL10 (Fig.\n \n 5\n \n e) and IL-2 (Fig.\n \n 5\n \n f) in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group were the highest among all groups, which have increased to 7-fold, 9-fold, 6.5-fold and 7.5-fold compared to the control group, respectively. Extending from the mechanistic evaluations above, we then systematically evaluated the antitumor efficacy of the liposome-augmented radio-immunotherapy using B16F10/mouse splenocyte co-incubation system. According to the flow cytometric data, the apoptosis rate of B16F10 cells in Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group reached around 76.78%, which was almost 9-fold higher than the PBS\u2009+\u2009IR group (Fig.\n \n 5\n \n g). Consistently, MTT data showed that Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group presented the lowest B16F10 survival rate of only around 18% (Extended Data Fig.\u00a019a, b). It is also of interest to note that B16F10 cells in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group showed significantly elevated \u03b3-H2AX levels, a typical marker of IR-induced DNA damage, according to immunochemical staining and western blotting analysis (Fig.\n \n 5\n \n h and Extended Data Fig.\u00a020), again validating the therapeutic contribution of AUR-mediated radiosensitization. These observations are immediate evidence that the Lip@AUR-ACP-aptPD-L1 liposomes could enhance the radio-immunotherapuetic efficacy against melanoma cells in vitro through a cascade-amplifiable manner.\n
\n\n \n Therapeutic evaluation of Lip@AUR-ACP-aptPD-L1\n \n \n in vivo\n \n
\n\n The therapeutic activity of Lip@AUR-ACP-aptPD-L1 liposomes was further comprehensively profiled in vivo using B16F10-Luc tumor mouse model. Here we first monitored the pharmacokinetic activity of the liposomes in mice after intravenous injection via HPLC. The Lip@AUR-aptPD-L1 liposomes showed significantly longer blood circulation time compared with AUR, of which the blood half-life has increased by 5-fold and reached around 8 h (Extended Data Fig.\u00a021), attributing to the liposome-mediated stabilization and may facilitate their interaction with PD-L1-overexpressing melanoma cells. Meanwhile, we also profiled the systemic distribution of the liposomes by measuring the AUR abundance in specific organs and tissues via ICP test. The comparative analysis of AUR deposition patterns immediately suggested that the AUR-incorporated liposomes predominantly accumulated in the B16F10 tumors with a relative ratio of around 46% after 24 h of incubation (Extended Data Fig.\u00a022a-d). In contrast, non-targeting Lip@AUR liposomes were mostly detected in mouse kidney, attributing to the nanoparticle clearance capacity of the mononuclear phagocyte system (MPS) therein. The observations above collectively demonstrated that the liposomal formulation could avoid the rapid clearance of the therapeutic components after systemic administration while enabling targeted delivery to melanoma sites. Next, we tested the inhibition effect of the liposome-augmented radio-immunotherapy against B16F10-luc tumors in vivo (Fig.\n \n 6\n \n a). Mice treated with non-drug-loaded liposomes showed rapid tumor growth similar to the PBS-only control group due to the lack of antitumor function, in which the average tumor volume reached around 1750mm\n \n \n 3\n \n \n after 15-day of treatment (Fig.\n \n 6\n \n b, c). Sole RT treatment induced modest inhibition on melanoma growth with a final tumor volume of around 1550mm\n \n \n 3\n \n \n , which was slightly lower than the control group and suggested the innate radiotherapeutic resistance of melanomas (Fig.\n \n 6\n \n b, c). Similarly, treating melanomas with Lip-aptPD-L1 also only induced slight antitumor effect (1490mm\n \n \n 3\n \n \n ), attributing to the low aptPD-L1 dosage as well as the immunosuppressive TME. Remarkably, the combination of Lip@AUR-ACP-aptPD-L1 and 4Gy IR induced the highest melanoma inhibition among all groups, of which the final tumor volume was only around 95mm\n \n \n 3\n \n \n (Fig.\n \n 6\n \n b, c). Analysis of tumor weight revealed the same trend that the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group showed the lowest final tumor weight of around 0.26g (Fig.\n \n 6\n \n d). Resulting from the treatment-ameliorated tumor burdens, mice in Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group presented the longest average survival time with a median survival period of more than 50 days (Fig.\n \n 6\n \n e). H&E and TUNEL-based histological analysis on the extracted tumor tissue slices showed that the combined Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR treatment induced severe apoptosis in melanoma cells (Fig.\n \n 6\n \n h and Extended Data Fig.\u00a023), further substantiating its antitumor potency in vivo. Overall, these observations confirmed that combining Lip@AUR-ACP-aptPD-L1 with low-dose IR treatment enabled efficient elimination of melanoma cells in vivo. The biochemical alterations in the extracted tumor samples were further analyzed to clarify the mechanism underlying the liposome-mediated cascade-amplification of the radio-immunotherapeutic effects. Notably, WB analysis revealed that tumors in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group presented significant enhancement in the expression levels of \u03b3-H2AX and PARP1 (Fig.\n \n 6\n \n f and Extended Data Fig.\u00a024), evidently supporting the AUR-mediated radiosensitization effect by enhancing the IR-dependent DNA damage in melanoma cells. Meanwhile, treating mice with AUR-containing samples such as Lip@AUR-aptPD-L1 and Lip@AUR-ACP-aptPD-L1 inhibited key mediators in the ERK1/2/HIF-1\u03b1/VEGF pathway in melanoma cells at varying degrees (Fig.\n \n 6\n \n f), which was consistent with the trends in vitro. immunofluorescence analysis showed that the Lip@AUR-ACP-aptPD-L1-augmented radio-immunotherapy of melanomas induced evident increases in the tumor abundance of typical DAMPs including CRT (Extended Data Fig.\u00a025a) and HMGB1(Extended Data Fig.\u00a025b), supporting our hypothesis that the liposome-mediated radiosensitization effect could promote IR-induced ICD of melanoma cells in vivo. Quantitative analysis further demonstrated that the liposome-amplified radiotherapeutic effects caused significant upregulation of ATP and MMP-2 by 2.1-fold and 1.9-fold compare with PBS\u2009+\u2009IR group in melanoma tissues (Fig.\n \n 6\n \n g), thus enabling the AND-gate release of eCpG into the tumor tissues for DC stimulation. As the combined result of these immunostimulatory traits, the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR treatment substantially enhanced the overall immune cell infiltration (CD45+) in the melanoma tissues by about 14% (Fig.\n \n 7\n \n a). Specifically, the frequency of mature DCs (CD80\u2009+\u2009CD86+/CD11c\u2009+\u2009MHC-II+) in the melanoma tissues in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group has increased by more than 35% compared with the control group (Fig.\n \n 7\n \n b and Extended Data Fig.\u00a027a). Meanwhile, the Lip@AUR-aptPD-L1\u2009+\u2009IR group also showed drastically lower frequency of tumor-infiltrating immunosuppressive cells including MSDCs (1.78%) (Extended Data Fig.\u00a026b) and Tregs (7.31%) (Extended Data Fig.\u00a026a), which was in line with the VEGF-inhibiting function of AUR incorporated liposomes. Owing to the liposome mediated stimulation of DCs and inhibition of immunosuppressive cell populations, mice in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group showed enhanced tumor infiltration of CD4+/CD8\u2009+\u2009T cells that was 42% higher than the control group (Fig.\n \n 7\n \n c), accompanied with a significant expansion of IFN-\u03b3\u2009+\u2009CD8\u2009+\u2009T cells by 34% (Extended Data Fig.\u00a027b). The flow cytometric results regarding the tumor infiltration status of various immune cell populations were also consistently supported by the immunofluorescence assay based on relevant markers (Fig.\n \n 7\n \n d and Extended Data Fig.\u00a028a, b). Extending from the treatment-induced changes in the immunocomposition of the melanoma tissues, tumors in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group showed the highest enhancement in the secretion levels of pro-inflammatory cytokines and chemokines including IFN-\u03b3 (8-fold), TNF-\u03b1 (9.5-fold), CXCL10 (7.5-fold) and IL-2 (9.5-fold), indicating that the liposome-augmented radio-immunotherapy has significantly boosted the adaptive immune responses for eliminating the melanoma cells in vivo (Fig.\n \n 7\n \n e-h). In addition to the therapeutic evaluations above, we also comprehensively studied the biocompatibility of the liposomes in vivo from a translational perspective. Notably, mice receiving combinational liposome\u2009+\u2009IR treatment showed no significant weight loss compared to the PBS-only control group, which was attributed to the low toxicity of the liposomal formulations and the minimal IR dose (Extended Data Fig.\u00a029). Alternatively, histological inspections on the tissue slices of H&E-stained organs showed that Lip@AUR-ACP-aptPD-L1 did not induce obvious damage to major mouse organs regardless of the IR treatment conditions (Extended Data Fig.\u00a030a-e). These results indicate that Lip@AUR-ACP-aptPD-L1 could be a safe and effective radio-immunotherapeutic option for melanomas.\n
\n\n To investigate if the combinational treatment of Lip@AUR-ACP-aptPD-L1 and low dose IR could induce robust and long-lasting antitumor immunity to offer systemic protection against invading melanomas, we have developed bilateral B16F10-luc-bearing mouse model for evaluating the therapeutic activities. To construct the bilateral melanoma mouse models, B16F10-Luc cells were first inoculated into the right flank of the mice to establish the primary tumors, while B16F10-Luc cells were later injected into the left flank after 15 days of incubation to create the secondary tumors (Fig.\n \n 8\n \n a). Mice in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group showed the smallest tumor sizes for secondary tumors (88mm\n \n \n 3\n \n \n ) (Fig.\n \n 8\n \n b), indicating the pronounced inhibitory effect thereof. Owing to the efficient treatment-induced melanoma inhibition, mice in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group also presented the highest survival time (median survival: 52 days) among all groups (Fig.\n \n 8\n \n c). Flow cytometry analysis of extracted tumor samples showed a significant increase in the frequency of mature DCs in both primary (Extended Data Fig.\u00a031a) and distal (Fig.\n \n 8\n \n d) B16F10-luc tumors in the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group, which has increased by 38% and 36% compared with the control group. Consistent with the immunoregulatory role of DCs as the primary APC populations for activating the CTL-mediated adaptive antitumor immunity, the infiltration status of CD8\u2009+\u2009T cells in the primary (Extended Data Fig.\u00a031b) and secondary tumors (Fig.\n \n 8\n \n e) of the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR group was the highest among all groups, indicating that the combined Lip@AUR-ACP-aptPD-L1 and low-dose IR treatment successfully evoked potent systemic antitumor immune responses to eliminate the distal tumors. In addition, we have detected a significant expansion of CD62L\u2009+\u2009CD44\u2009+\u2009memory T cells in the melanoma tissue samples according to flow cytometric analysis (Fig.\n \n 8\n \n f). The results confirmed that the Lip@AUR-ACP-aptPD-L1\u2009+\u2009IR-augmented radio-immunotherapy could substantially promote the formation of memory T cells to establish robust antitumor immune memory, which is beneficial for preventing melanoma metastasis and post-treatment relapse.\n
\n\n In summary, we have developed melanoma-targeted fusogenic liposomal nanoformulations integrated with AUR and multivariate-gated aptamer assemblies for cascade-amplified radio-immunotherapy against melanomas. The liposomes could efficiently bind with PD-L1-overexpressing melanoma cells for rapid membrane fusion, which would deliver AUR to tumor intracellular compartment while transferring the multivariate-gated ACP assembly to tumor membrane. The gold-containing AUR could sensitize melanoma cells to incoming IR and facilitate their ICD even under a low IR dose of 4 Gy. This strategy allows the effective stimulation of melanoma immunogenicity while avoiding common RT-associated side effects such as collateral tissue damage or impairment of immune systems. Meanwhile, the released AUR contents could also inhibit tumor-intrinsic ERK1/2/HIF-1\u03b1/VEGF pathway to suppress the migration of immunosuppressive cells into post-IR melanoma and thus maintain an anti-tumor tumor microenvironment. The melanoma-specific sensitized radiotherapy would also trigger the release of abundant ATP as well as upregulate MMP-2 expression in the TME, which would induce the AND-gate activation of the ACP assembly to trigger eCpG for stimulating DCs maturation in a sequential manner, further expanding the tumor-infiltrating antitumor T cell populations for mounting potent adaptive immune responses. It is important to note that the nano-enabled cascade-amplification of radio-immunotherapy could not only efficiently abolish melanoma growth but also orchestrate robust antitumor immune memory, which is beneficial for preventing melanoma metastasis or local relapse. This study offers a facile and expandable strategy for the clinical management of a broad spectrum of solid tumor indications.\n
\n\n \n Chemicals and reagents.\n \n 1,2-Dimyristoyl-sn-glycero-3-phosphocholine (DMPC), distearoyl phosphoethanola-mine-PEG\n \n 2000\n \n (DSPE-PEG\n \n 2000\n \n ), 1,2-dioleoyl-3-trimethylam-monium-propane (DOTAP) were purchased from Meryer (Shanghai) Chemical Technology Co., Ltd. Chloroform (CHCl\n \n 3\n \n ) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. Auranofin(AUR) was purchased from Target Molecule Corp. AptATP, eCpG, aptPD-L and deoxyribonucrenase I were all purchased from Sangon Biotech (Shanghai) Co., LTD. Peptide nucleic acid (PmP) was purchased from Tahepna Biotechnologies Co., Ltd. Adenosine triphosphate (ATP) was purchased from Beijing Solarbio Science & Technology Co., Ltd. Recombinant matrix metalloproteinase-2 (MMP-2) was purchased from MedChemExpress(MCE).\n
\n\n \n Cell lines and animal.\n \n B16F10 and NIH3T3 cell lines were bought from Yeze Shanghai Biological Technology Co., LTD. B16F10-luc cell line was bought from Nanjing Wanmuchun Biotechnology Co., LTD. C57BL/6 (female, 6-week-old) were provided by in the Second Affiliated Hospital of the Army Medical University (Xinqiao Hospital) and all mice were kept in the animal house of Xinqiao Hospital. All characterizations were carried out following the Animal Management Rules of the Ministry of Health of the People's Republic of China.\n
\n\n \n Synthesis of Lip@AUR.\n \n 7.255mgDMPC, 1.516mg DSPE-PEG\n \n 2000\n \n , 1.963mgDOTAP, 2mgAUR were added into a clean 500mL single-neck flask and dissolved by adding 10mL chloroform, stirred and ultrasonicated for 5min. Lipid film was obtained by rotary evaporation at 80rpm and 40\u2103 in a water bath overnight. The lipid membranes were rehydrated using 10mL sterile PBS and ultrasonicated for 30min. Impurities or aggregates were removed by centrifugation at 3000rpm for 10min. The liposomes were filtered through 0.22\u00b5m membrane and repeatedly extruded by an extruder for about 10 times, followed by dialysis with an MWCO of 1000 Da for two days to obtain Lip@AUR.\n
\n\n \n Construction of aptATP/eCpG/PmP (ACP) assembly.\n \n Moderate amount of DEPC water was added to solubilize the synthesized aptATP, eCpG, and PmP powder at 100\u00b5M. aptATP, eCpG, PmP solutions were placed in clean 1.5mL EP tubes. aptATP and eCpG samples were heat in 95\u2103 oil bath for 10min and then mixed in the ratio of aptATP:eCpG\u2009=\u20092:1, followed by further incubation in the oven at 42\u2103 for 1h. PmP was added to aptATP/eCpG at the ratio of aptATP:PmP\u2009=\u20091:1.5 and heated in oil bath at 80\u2103-90\u2103 for 10min. ACP assembly was obtained after incubating in oven at 42\u2103 for 1h.\n
\n\n \n Synthesis of Lip@AUR-ACP-aptPD-L1.\n \n Firstly, Lip@AUR was refrigerated at -80\u2103 and then freeze-dried in a freeze dryer to obtain liposome powder. The powder was rehydrated by DEPC water and mixed with ACP assemblies with the molarity ratio of lipid: aptATP\u2009=\u200980:2, and incubated in the oven at 37\u2103 for 4h. aptPD-L1 powder was resuspended with DEPC water at 100\u00b5M, and then aptPD-L1 was added at the molarityratio of lipid: aptATP: aptPD-L1\u2009=\u200980:2:1. AptPD-L1 was incubated with Lip@AUR-ACP overnight in a 37\u2103 oven to obtain Lip@AUR-ACP-aptPD-L1. The product was frozen at -80\u2103 and then freeze-dried to obtain Lip@AUR-ACP-aptPD-L1 powder.\n
\n\n \n DNA-PAGE analysis regarding aptamer binding and release.\n \n The formulation of 20%PAGE solution is as follows: 6.666mL 30% acrylamide, 1mL 10\u00d7TBE buffer, 2.3\u00b5L DEPC water, 50\u00b5L 10%APS, 5\u00b5L TEMED. After solidification, the corresponding samples were added to each hole and then electrophoresis was carried out at 140V constant voltage. After electrophoresis, 0.29g NaCl was dissolved in 50mL deionized water and mixed with 5\u00b5L GelRed. The gel was soaked in GelRed solution for 30min and then taken out for observation with a gel imaging system.\n
\n\n \n Loading and releasing of AUR.\n \n Firstly, 2%Triton X-100 solution was prepared with PBS, while 1mg Lip@AUR-ACP-aptPD-L1 powder was dissolved in 1mL PBS to afford Lip@AUR-ACP-aptPD-L1 solution. 100\u00b5L Lip@AUR-ACP-aptPD-L1 solution was added into 900\u00b5L 2%Triton X-100 solution and incubated at 37\u2103 for 1h to lyse the liposomes and release AUR. The AUR release was detected by fluorescence spectrophotometer and quantified via standard curve calibration.\n
\n\n \n The release of eCpG.\n \n For ease of understanding, Cy5 labeled eCpG was denoted as eCpG\n \n Cy\n \n 5\n \n \n , while the molecular complex of eCpG\n \n Cy\n \n 5\n \n \n and aptATP was denoted as AC\n \n Cy\n \n 5\n \n \n . After the complementary binding with PmP, the aptamer assembly was denoted as AC\n \n Cy5\n \n P. Finally, the aptamer-based ligands were inserted into liposomal membrane to afford Lip@AUR-AC\n \n Cy\n \n 5\n \n \n -aptPD-L1 or Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1. Lip@AUR-AC\n \n Cy\n \n 5\n \n \n -aptPD-L1, Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1, Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1\u2009+\u2009MMP-2 (5nM) and Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1\u2009+\u2009MMP-2(10nM) groups were treated with ATP and then centrifuged under 5000rpm for 10min to extract the supernatant. The release of eCpG\n \n Cy\n \n 5\n \n \n was measured via fluorescence spectroscopy.\n
\n\n \n Loading analysis of eCpG and aptPD-L1.\n \n The synthesis of fluorescently labeled liposomes was generally the same with those unmarked ones except that the original aptamers were replaced by Cy5-labeled eCpG or FAM-labeled aptPD-L1, leading to the formation of Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1 or Lip@AUR-ACP-aptPD-L1\n \n FAM\n \n . 56\u00b5L Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1 (5mg\u00b7mL\n \n \u2212\u20091\n \n ) or Lip@AUR-ACP-aptPD-L1\n \n FAM\n \n (5mg\u00b7mL\n \n \u2212\u20091\n \n ) aqueous solution was added to 1\u00d7DNase 1 buffer solution and then treated with 20U\u00b7mL\n \n \u2212\u20091\n \n DNase 1. They were incubated at 37\u2103 for 15min and transferred to an ultrafiltration tube. After centrifugation at 10,000rpm for 15min, the supernatant was collected and fluorescence intensity of Cy5 or FAM was detected by fluorescence spectrophotometer. ECpG\n \n Cy\n \n 5\n \n \n or aptPD-L1\n \n FAM\n \n solution with different concentrations were configured to establish the standard curve via a fluorescence spectrophotometer. The aptamer concentration in Lip@AUR-AC\n \n Cy5\n \n P-aptPD-L1 or Lip@AUR-ACP-aptPD-L1\n \n FAM\n \n was quantified according to the standard curve, and then the load efficiency of eCpG\n \n Cy\n \n 5\n \n \n or aptPD-L1\n \n FAM\n \n on liposomes was calculated accordingly.\n
\n\n \n Morphological characterization of Lip@AUR-ACP-aptPD-L1.\n \n 5\u00b5L of Lip@AUR-ACP-aptPD-L1 solution was dropped on the carbon support film and dried naturally. Then the film was re-dyed with 4% phosphotungstic acid solution for 3 times (10min each time) to observe its morphology with a transmission electron microscope.\n
\n\n \n Cell culture.\n \n Mouse-derived melanoma cell line B16F10 was cultured in 1640 medium containing 10% fetal bovine serum (Gibco), penicillin (100 \u00b5g\u00b7mL\n \n \u2212\u20091\n \n ), and streptomycin (100 \u00b5g\u00b7mL\n \n \u2212\u20091\n \n ). Mouse embryonic fibroblasts NIH3T3 and B16F10-luc cell lines were cultured in high-glucose DMEM medium containing 10% fetal bovine serum (Gibco), penicillin (100 \u00b5g\u00b7mL\n \n \u2212\u20091\n \n ), and streptomycin (100 \u00b5g\u00b7mL\n \n \u2212\u20091\n \n ). The cells were cultured in a 37\u2103 constant temperature incubator containing 5% carbon dioxide.\n
\n\n For cellular related experiments with MMP-2 pretreatment, the MMP-2 concentration was 10nM and the incubation time was 2h.\n
\n\n \n Extraction of splenocytes from C57BL/6 mice.\n \n Scissors, tweezers, sterile 40\u00b5m cell filter and other utensils were sterilized for 30min by ultraviolet light on ultra-clean workbench. C57BL/6 mice were sacrificed and treated with 75% alcohol for 10min. The spleen of the mice was dissected on a clean table. The cell strainer was placed into a six-well plate containing RPMI1640 medium, and the spleen was placed in the strainer. The spleen was pulverized with the tip of the suction head of a sterile 5mL syringe, and the strainer was removed after grinding until no obvious spleen tissue was found on the filter. The cells collected from the six-well plate were homogenized and transferred to a centrifuge tube, centrifuged at 2000rpm for 5min. The supernatant was discarded, the red blood cell lysate was added and mixed for 10min, and the lysis was terminated by adding 7 times the volume of PBS. After centrifugation at 2000rpm for 5min, cells were collected.\n
\n\n \n Effects of different samples on the activity of B16F10 cells or immune cells.\n \n \n Toxicity analysis of Lip@AUR-aptPD-L1 to B16F10 cells.\n \n B16F10 cells were inoculated into the 96-well plate with a density of 1\u00d710\n \n 4\n \n cells per well. When the cell confluence reached 80%, B16F10 cells were mixed with splenocytes at a ratio of 1:10 for co-culture. Medium containing different concentrations of Lip@AUR or Lip@AUR-aptPD-L1 was added for incubation for 12h, and the fresh medium was used as blank control (TCPS). 100\u00b5L of serum-free fresh medium containing MTT reagent (0.5 mg\u00b7mL\n \n \u2212\u20091)\n \n was added to each well, and MTT agent was discarded after incubation at 37\u2103 for 4h in the dark. Then the absorption intensity of the sample was measured at 490 nm by SpectraMax i3x microplate reader using 100\u00b5L dimethyl sulfoxide (DMSO) to dissolve emerging crystals.\n
\n\n \n Toxicity of Lip@AUR-aptPD-L1 to B16F10 cells or immune cells at different IR doses.\n \n B16F10 cells were inoculated into the 24-well plate with a density of 5\u00d710\n \n 4\n \n cells per well. When the cell confluence reached 80%, cells were incubated with medium containing 40\u00b5g\u00b7mL\n \n \u2212\u20091\n \n Lip@AUR-aptPD-L1 or Lip@AUR-aptPD-L1 for 12h and fresh medium was used as blank control (TCPS). After incubation, 500\u00b5L serum-free fresh medium containing MTT reagent (0.5 mg\u00b7mL\n \n \u2212\u20091\n \n ) was added to each well, and MTT agent was discarded after incubation at 37\u2103 for 4h in the dark. Afterwards, 300\u00b5L DMSO was added into each well and homogenized, 100\u00b5L of the added DMSO was extracted from each well for analysis. The OD values of the sample were measured at the wavelength of 490 nm using SpectraMax i3x microplate reader. After placing splenocytes in the 12-well plate at a concentration of 1\u00d710\n \n 6\n \n per well, the drug was administered in the same way as above for 12h, and then were stained with CCK-8 for 2h and transferred to a clean 96-well plate. The OD values of the samples were measured at the wavelength of 450 nm using a SpectraMax i3x microplate reader.\n
\n\n \n Toxicity of Lip@AUR-aptPD-L1\u2009+\u2009RT on B16F10 cells.\n \n B16F10 cells were inoculated into 24-well plates with a density of 5\u00d710\n \n 4\n \n cells per well. When the cell confluence reached 80%, the co-culture system was constructed with the B16F10: splenocyte ratio of 1:10 and incubated with fresh medium containing different concentrations Lip@AUR-aptPD-L1 for 12h. After incubation, IR groups were treated with 4Gy IR. Splenocytes and tumor cells were separated, and 500\u00b5L serum-free fresh medium containing MTT reagent (0.5 mg\u00b7mL\n \n \u2212\u20091\n \n ) was added to each well of tumor cells, and the rest treatment was kept the same.\n
\n\n \n Concentration-dependent toxicity evaluation.\n \n B16F10 cells were inoculated into the 24-well plate with a density of 5\u00d710\n \n 4\n \n cells per well. When the cell confluence reached 80%, the co-culture system was established with the B16F10: splenocyte ratio of 1:10. I: Lip, II: Lip-aptPD-L1, III: Lip-ACP-aptPD-L1, IV: Lip@AUR-aptPD-L1, V: Lip@AUR-ACP-aptPD-L1 (40\u00b5g\u00b7mL\n \n \u2212\u20091\n \n ) was incubated for 12h with fresh medium as blank control (TCPS). After incubation, IR groups were treated with 4Gy IR. Splenocytes and tumor cells were separated, and 500\u00b5L serum-free fresh medium containing MTT reagent (0.5 mg\u00b7mL\n \n \u2212\u20091\n \n ) was added to each well of tumor cells, and the rest treatment was kept the same.\n
\n\n \n Flow cytometric analysis on the receptor binding effect of aptPD-L1 and eCpG.\n \n B16F10 cells were mixed with splenocytes at a ratio of 1:10 and transferred to an EP tube. 170nM aptPD-L1\n \n FAM\n \n and 360nM eCpG\n \n FAM\n \n were added and incubated for 30min, followed by the addition of 1\u00b5L APC-anti-CD11c and 1\u00b5L PE-anti-MHCII antibody. Flow cytometry was used to detect the binding status between aptPD-L1\n \n FAM\n \n and B16F10 cells or between eCpG\n \n FAM\n \n and DCs.\n
\n\n \n Tumor cell targeting and membrane fusion.\n \n Here orange-red probe Dil was loaded into the liposome instead of AUR for fluorescence tracking, of which the samples were denoted as Lip@Dil and Lip@Dil-aptPD-L1. B16F10 or NIH3T3 cells were inoculated into confocal dishes at a density of 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, the co-culture system was established with the B16F10: splenocyte ratio of 1:10. Subsequently, samples were added and treated for 1, 3, 6, 12, 18 h, respectively. For the IR-incorporated groups of B16F10cells, 4Gy IR was applied 12 h after the addition of nanosamples, and the incubation would continue for 4, 8, 16 h. The cell membrane was stained with Cellmask orange for 10min, while cell nucleus was stained with DAPI for 10min after cleaning with PBS. The cell samples were mounted on the glass slides and sealed with glycerin, and the membrane fusion status was detected by laser confocal microscopy.\n
\n\n \n B16F10 tumor sphere assay for testing targeting effect.\n \n 90mg agarose gel was dissolved in 6mL serum-free 1640 medium and sterilized at 115\u2103 for 30min. 80\u00b5L of the melted gel was added into sterile 96-well plates and cooled down naturally for solidification. The B16F10 cells were homogenized in 1640 medium containing 2.5% matrix gel and added into the wells at 5000 cells per well, of which the volume was 100\u00b5L per well. The cells were cultured for about 7 days until pellets were formed under an optical microscope. AC\n \n Cy5\n \n P, Lip-AC\n \n Cy5\n \n P or Lip-AC\n \n Cy5\n \n P-aptPD-L1 were added and incubated for 12h, then cells were detached, centrifuged at 700rpm for 5min to remove matrix gel, cleaned with PBS for 3 times, and transferred to a confocal laser confocal dish for detection.\n
\n\n \n ICP assay for determining AUR uptake.\n \n B16F10 cells were inoculated into 6-well plates with an initial cell density of 3\u00d710\n \n 5\n \n cells/well. After the cell confluence reached 80%, fresh medium containing Lip@AUR or Lip@AUR-aptPD-L1 was added, and untreated cells were used as control. After incubation for 1, 3, 6, 12 and 18 h, the cells were digested by trypsin and collected by centrifugation. After 24h of lysis, supernatant was extracted by centrifugation at 1500 rpm for 5min, while pure AUR solution with concentration gradient was configured for establishing the standard curve. The volume of the above samples was maintained at 5mL. Finally, inductively coupled plasma emission spectroscopy was used to determine AUR uptake in each group.\n
\n\n \n ATP abundance and MMP-2 expression levels in B16F10 cells or tumors.\n \n B16F10 cells were inoculated into 12-well plates, and the initial cell density was 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, B16F10 cells were treated with Lip@AUR-aptPD-L1 for 12h and irradiated with 4Gy IR. The concentrations of total ATP in 2, 4, 12, 18, and 24h after radiotherapy were detected by ATP assay kit. For the in vivo analysis, mice were treated with PBS, Lip, Lip@AUR or Lip@AUR-aptPD-L1 (2mg\u00b7kg\n \n \u2212\u20091\n \n ), followed by 4Gy IR at 12 h post intravenous injection. The concentrations of ATP and MMP-2 in each tumor were detected by relevant kits.\n
\n\n \n AUR induced secretion of critical DAMPs.\n \n B16F10 cells were inoculated into 12-well plates, and the initial cell density was 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, B16F10 cells were treated with Lip@AUR-aptPD-L1 for 12h and irradiated with 4Gy. The cell supernatant was collected after the whole culture was continued for 18h. Then the concentration of ATP was detected by kit, and the secretion of CRT and HMGB1 in supernatant was detected by ELISA.\n
\n\n \n CLSM and flow cytometry for determining eCpG release\n \n \n in vitro\n \n . B16F10 cells were inoculated into the confocal dish, and the initial cell density was 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, the co-culture system was established with the B16F10: splenocyte ratio of 1:10. B16F10 cells were treated with PBS, Lip-AC\n \n Cy\n \n 5\n \n \n -aptPD-L1 and Lip-AC\n \n Cy5\n \n P-aptPD-L1 for 12h and then treated with 4Gy IR. Confocal and flow cytometry were used to analyze the fluorescence retention on cell membrane under -RT\u2009+\u20094h and RT\u2009+\u20094h conditions.\n
\n\n \n Validation of AND-gate release of eCpG.\n \n B16F10 cells were inoculated into 12-well plates, and the initial cell density was 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, B16F10 cells were treated with Lip-AC\n \n Cy5\n \n P-aptPD-L1 for 12h and irradiated with 4Gy IR. Supernatant was collected after incubating for another 18h. The B16F10 cell membrane was stained with Cellmask orange for 10min, while cell nucleus was stained with DAPI for 10min after cleaning with PBS. The cell samples were mounted on the glass slides and sealed with glycerin, and the Cy5 fluorescence intensity on the membrane was detected by laser confocal microscopy. The fluorescence intensity of Cy5 in supernatant was measured by a fluorescence spectrophotometer.\n
\n\n \n Analysis of eCpG-DC binding and stimulation of DC maturation.\n \n After incubation for 30min with Cy5-labeled sequences, the fluorescence intensity of Cy5 on DCs was verified by flow cytometry for profiling aptamer binding. Subsequently, B16F10 cells were inoculated into 12-well plates, and the initial cell density was 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, the co-culture system was established with the B16F10: splenocyte ratio of 1:10. Lip@AUR-ACP-aptPD-L1 was co-incubated with B16F10 cells and splenocytes for 12h to detect DC maturation without radiation treatment or at 4, 8, 12, 18 and 24h after 4Gy IR treatment. The mutant or blocked sequences were also co-incubated with DCs for 18h, and the stimulation effect of DC maturation was detected by flow cytometry.\n
\n\n \n Transcriptome sequencing and protein expression evaluation.\n \n B16F10 cells were inoculated into a 100mm cell culture dish, and the initial cell density was 2\u00d710\n \n 6\n \n cells/well. When the cell confluence reached 80%, the co-culture system was established with the B16F10: splenocyte ratio of 1:10. After B16F10 cells were treated with PBS, Lip-aptPD-L1, Lip@AUR-aptPD-L1, the tumor cells were extracted and sent to Sangon Biotech (Shanghai) Co., LTD for detection. For the WB assay, B16F10 cells were inoculated into 100mm cell culture dish, and the initial cell density was 1\u00d710\n \n 6\n \n cells/well. When the cell confluence reached 80%, the co-culture system was established with the B16F10: splenocyte ratio of 1:10. The cells were treated with PBS, Lip, Lip-aptPD-L1, Lip ACP-aptPD-L1, Lip@AUR-aptPD-L1, Lip@AUR-ACP-aptPD-L1 for 12h, and then cultured for 18h after 4Gy IR. The cells were collected and treated with RIPA lysis solution on ice for 30min to extract markers of interest, which was then subjected to WB assay kit for imaging and quantitative analysis.\n
\n\n \n Evaluation on the impact of VEGF on anti-tumor immunity.\n \n B16F10 cells were inoculated into the 12-well plate at the concentration of 1\u00d710\n \n 5\n \n per well. When the cell confluence reached 80%, the cells were treated with PBS, Lip, Lip@AUR, Lip@AUR-aptPD-L1, the upper chamber is placed into 12-well plate. Splenocytes were added into the upper chamber with B16F10: splenocyte ratio of 1:10. After 12h of co-incubation, the IR groups were treated with 4Gy IR. The culture continued for 18h, cells in the upper chamber were discarded and the bottom chamber supernatant was collected. After centrifugation at 2000rpm for 5min, 200\u00b5L PBS was added to each tube to resuspend the spleen immune cells. 1\u00b5L APC-anti-CD25/FITC-anti-CTLA-4/PE-anti-CD4 or 1\u00b5L APC-anti-CD45/FITC-anti-CD11b/PE-anti-GR1 were added into each tube. Finally, the infiltration of Tregs or MDSCs in the bottom chamber was detected by flow cytometry.\n
\n\n Alternatively, the recovered cell samples in the bottom chamber were treated with 1\u00b5L APC-anti-CD3/1\u00b5L FITC-anti-CD4/1\u00b5L PE-anti-CD8a or 1\u00b5L APC-anti-CD11c/1\u00b5L FITC-anti-CD80/1\u00b5L PE-anti-CD86 were added into each tube. Finally, the infiltration of effector T cells or DCs was detected by flow cytometry.\n
\n\n The B16F10 tumor-bearing mouse model was constructed and treated with PBS, Lip, Lip@AUR, Lip@AUR-aptPD-L1 (2mg\u00b7kg\n \n \u2212\u20091\n \n ) for 12h and treated with 4Gy IR. Tumors were collected from each group after treatment and pulverized to collect various cell populations. 200\u00b5L PBS was added to each tube to suspend tumor cells. 1\u00b5L APC-anti-CD25/1\u00b5L FITC-anti-CTLA-4/1\u00b5L PE-anti-CD4 or 1\u00b5L APC-anti-CD45/1\u00b5L FITC-anti-CD11b/1\u00b5L PE-anti-GR1 were added into each tube. Finally, the infiltration of Tregs or MDSCs in tumor tissues was detected by flow cytometry.\n
\n\n \n Evaluation of immune activation effect of nanoparticles\n \n \n in vitro\n \n . Splenocytes of C57BL/6 mice were extracted and DCs were sorted out according to the above method. B16F10 cells were inoculated into 12-well plates with the initial cell density of 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, mouse DCs were added into 12-well plates and co-cultured with B16F10 cells at a ratio of B16F10: DC\u2009=\u20091:10. After 12 h treatment with PBS, Lip, Lip-aptPD-L1, Lip-ACP-aptPD-L1, Lip@AUR-aptPD-L1, Lip@AUR-ACP-aptPD-L1, the IR groups were treated with 4Gy IR and incubated for another 18h. DCs were collected via centrifugation and supernatant was recovered for later use. DCs was resuspended with 200\u00b5L PBS and 1\u00b5L APC-anti-CD11c/1\u00b5L FITC-anti-CD80/1\u00b5L PE-anti-CD86 antibodies or 1\u00b5L APC-anti-CD11c/1\u00b5L PE-anti-MHCII antibodies. The treatment-induced stimulation effect on DCs maturation in each group was detected by flow cytometry. The supernatant was used to detect the concentrations of cytokines TNF-\u03b1 and IL-2 by ELISA kit.\n
\n\n After B16F10 cells were inoculated into the 12-well plate in the above way, mouse splenocytes were added into the 12-well plate and co-cultured with B16F10 cells at the B16F10: splenocyte ratio of 1:10. Splenocytes and supernatants were collected after treatment for later use. Here the splenocytes were suspended with 200\u00b5L PBS, 1\u00b5L APC-anti-CD3/1\u00b5L FITC-anti-CD4/1\u00b5L PE-anti-CD8a or 1\u00b5L APC-anti-CD3/1\u00b5L FITC-anti-IFN-\u03b3/1\u00b5L PE-anti-CD8a antibodies were added to each tube. Finally, the activation status of T cells in each group was detected by flow cytometry. TNF-\u03b1, IL-2, IFN-\u03b3 and CXCL10 secretion was detected by ELISA kit.\n
\n\n \n Detection of tumor cell apoptosis\n \n : C57BL/6 mouse splenocytes were extracted by the above method. B16F10 cells were inoculated into the 12-well plate with the initial cell density of 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, mouse splenocytes were added into the 12-well plate at the B16F10: splenocyte ratio of 1:10, and the supernatant was collected after relevant treatment. Tumor cells were digested by trypsin, then suspended with 200\u00b5L FITC bonding solution at 37\u2103 for 30min, followed by PI dye solution for 10min. After extensive staining, apoptosis of tumor cells under different treatments was detected by flow cytometry.\n
\n\n For the imaging analysis of melanoma cell apoptosis, B16F10 cells were inoculated into confocal dishes with the initial cell density of 1\u00d710\n \n 5\n \n cells/well. When the cell confluence reached 80%, mouse splenocytes were added into the 12-well plate at the B16F10: splenocyte ratio of 1:10. After treatment was complete, the cells were washed with PBS for 3 times and splenocytes were immediately drained. The cells were fixed with 4% paraformaldehyde for 30min, blocked with 5% bovine serum albumin solution for 30min after cleaning, and permeabilized with 0.5%Triton X-100 solution for 5min after cleaning with PBS. Then \u03b3-H2AX antibody was added and incubated at 4\u2103 overnight. The primary antibody was removed, and Cy3-labeled fluorescent secondary antibody was added after purification, followed by the incubation at room temperature for another 2h. The secondary antibody was removed and the cell nuclei were stained with DAPI for 10min after washing with PBS. After cleaning, the cell samples were mounted on glass slides with glycerin and the immunofluorescence of \u03b3-H2AX was detected by confocal laser microscopy.\n
\n\n \n Blood circulation stability of different samples.\n \n B16F10-luc tumor cells (1\u00d710\n \n 6\n \n cells) were injected subcutaneously into 6-week-old mice to establish B16F10-luc tumor mouse model. The mice were cultured continuously until the tumor size reached 100mm\n \n \n 3\n \n \n and the body weight of mice was maintained at 17.5\u2009\u00b1\u20090.3g. Three groups of mice were randomly selected and intravenously injected AUR, Lip@AUR, Lip@AUR-aptPD-L1(2mg\u00b7kg\n \n \u2212\u20091\n \n ), respectively. Then tail venous blood was collected according to the scheduled time point, and AUR content in samples of each group was detected by HPLC.\n
\n\n \n ICP-dependent blood distribution analysis.\n \n B16F10-luc tumor cells (1\u00d710\n \n 6\n \n cells) were injected subcutaneously into 6-week-old mice to establish B16F10-luc tumor mouse model. The mice were cultured continuously until the tumor size reached 100mm\n \n \n 3\n \n \n and the body weight of mice was maintained at 17.5\u2009\u00b1\u20090.3g. Three groups of mice were randomly selected and intravenously injected with AUR, Lip@AUR and Lip@AUR-aptPD-L1 (2mg\u00b7kg\n \n \u2212\u20091\n \n ), respectively. The mice in each group were euthanized at predetermined time points to collect major organs and tumors were collected, and the supernatant was collected after grinding and cracking for 24h. The samples were filled to 5mL with deionized water, and the AUR concentration in each tissue was detected by ICP.\n
\n\n \n Antitumor evaluation of the liposomes\n \n \n in vivo\n \n . C57BL/6 mice were used in animal experiments and were kept in the Second Affiliated Hospital of the Army Medical University (Xinqiao Hospital). All animal tests have been reviewed and approved by the Animal Care and Use Committee of Laboratory Animals Administration of Xinqiao Hospital, which strictly followed the national and institutional guidelines. B16F10-luc tumor cells (1\u00d710\n \n 6\n \n cells) were injected subcutaneously into 6-week-old mice to establish B16F10-luc tumor mouse model. The mice were cultured continuously until the tumor size reached 100mm\n \n \n 3\n \n \n and the body weight of mice was maintained at 17.5\u2009\u00b1\u20090.3g(n\u2009=\u20095). They were randomly divided into 12 groups with 5 animals in each group, which were subjected to intravenous injection of PBS (100\u00b5L) containing Lip, Lip-aptPD-L1, Lip-ACP-aptPD-L1, Lip@AUR-aptPD-L1, Lip@AUR-ACP-aptPD-L1 (2mg\u00b7kg\n \n \u2212\u20091\n \n ), and the same volume of fresh PBS was administered as the control group. 12h after injection, the IR groups were treated with 4Gy IR. Treatment was performed once every 5 days for a total of 15 days. Bioluminescence imaging was performed every 5 days, and 20\u00b5L (7.5mg\u00b7mL\n \n \u2212\u20091\n \n ) luciferase was injected into the intraperitoneal cavity of mice. After anesthesia with isoflurane, tumor volume of each group was detected by IVIS imaging system. The tumor volume and body weight of mice were recorded by electronic balance and vernier caliper. The volume and size of the tumor were measured every two days, and the longitudinal and transverse diameters of the tumor were measured. The calculation formula was V\u2009=\u20091/2*A*B\n \n 2\n \n (A was the longitudinal diameter, B was the transverse diameter). After 15 days of treatment, serums of all tumor mice were collected, and tumor tissues and major organs were collected for subsequent analysis. A parallel set of animal models were established, and the survival of mice in each group was observed until the 50th day after the 15-day treatment (n\u2009=\u20096).\n
\n\n At the end of treatment, the tumors in each group were dissected, and the tumors were pulverized after freezing with liquid nitrogen, and then the cells were disintegrated by tip ultrasonication. The grinded tumors were treated with cell lysis solution on ice, and Western blot assay was carried out to detect the expression levels of related proteins in the tumor. Paraffin sections of tumor and heart, liver, spleen, lung and kidney were created for optical imaging after H&E staining. The tumor was dissected and cleaned with PBS, and further cut into thin sections for TUNEL staining, CD4/CD8/IFN-\u03b3 immunofluorescence staining, CRT/HMGB1 immunofluorescence staining and \u03b3-H2AX immunofluorescence staining using related assay kits and observed by CLSM.\n
\n\n The tumor was ground and treated with red cell lysate for 15min, followed by the treatment with 1\u00b5L APC-anti-CD45, 1\u00b5L APC-anti-CD3/1\u00b5L FITC-anti-CD4/1\u00b5L PE-anti-CD8a antibodies, 1\u00b5L APC-anti-CD3/1\u00b5L FITC-anti-IFN-\u03b3 /1\u00b5L PE-anti-CD8a antibodies, 1\u00b5L APC-anti-CD11c/1\u00b5L FITC-anti-CD80/1\u00b5L PE-anti-CD86 antibodies or 1\u00b5L APC-anti-CD11c/1\u00b5LPE-anti-MHCII antibodies. The tumor cells were incubated and detected by flow cytometry. IFN-\u03b3, TNF-\u03b1, CXCL10 and IL-2 levels in collected blood samples were detected using ELISA kits.\n
\n\n \n Establishment and treatment of bilateral tumor model in C57BL/6 mice.\n \n 1\u00d710\n \n 6\n \n B16F10-luc cells were injected subcutaneously into the right flank of C57BL/6 mice to establish B16F10 tumor bearing mice. They were cultured in the same way as above and divided into groups (n\u2009=\u20095), and intravenously injected with PBS, Lip, Lip-aptPD-L1, Lip-ACP-aptPD-L1, Lip@AUR-aptPD-L1, Lip@AUR-ACP-aptPD-L1 (2mg\u00b7mL\n \n \u2212\u20091\n \n ) (100\u00b5L). After 15 days of treatment, Secondary tumors were established by subcutaneous injection of 2\u00d710\n \n 6\n \n B16F10-luc cells on the left flank. The growth of distal tumor was monitored from the 18th day, and the treatment ended on the 28th day. Bilateral tumors were dissected for analysis. In addition, a batch of bilateral tumor models were established. After 15 days of treatment, the survival of mice in each group was observed for up to 50 days(n\u2009=\u20096). The primary and distal tumors were dissected, cleaned with PBS, pulverized and treated with erythrocyte lysate for detection. Cells in the primary tumors in each group were labeled with 1\u00b5L APC-anti-CD11c/1\u00b5L FITC-anti-CD80/1\u00b5L PE-anti-CD86 antibodies or 1\u00b5L APC-anti-CD3/1\u00b5L FITC-anti-CD4/ 1\u00b5L PE-anti-CD8a antibodies or 1\u00b5L APC-anti-CD62L/1\u00b5L FITC-anti-CD44/ 1\u00b5L PE-anti-CD8a antibodies, and the infiltration of immune cells was detected by flow cytometry. Distal tumors were also treated similarly.\n
\n\n Chondrule-like objects and Ca-Al-rich inclusions (CAIs) are discovered in the retuned samples from asteroid Ryugu. Three chondrule-like objects, which are\n \n 16\n \n O-rich and -poor with D\n \n 17\n \n O (=\u2009d\n \n 17\n \n O \u2013 0.52 \u00d7 d\n \n 18\n \n O) values of ~ \u2212\u200923\u2030 and ~ \u2212\u20093\u2030, are dominated by Mg-rich olivine, resembling what proposed as earlier generations of chondrules. The\n \n 16\n \n O-rich objects are likely to be melted amoeboid olivine aggregates that escaped from incorporation into\n \n 16\n \n O-poor chondrule precursor dust. Two CAIs composed of spinel, hibonite, and perovskite are\n \n 16\n \n O-rich with D\n \n 17\n \n O of ~ \u2212\u200923\u2030 and possibly as old as the oldest CAIs. The chondrule-like objects and CAIs (<\u200930 \u00b5m) are as small as those from comets, suggesting radial transport favoring smaller objects from the inner solar nebula to the formation location of the Ryugu original parent body, which is farther from the Sun and scarce in chondrules. The transported objects may have been mostly destroyed during aqueous alteration.\n
\n\n Chondrules, Ca-Al-rich inclusions (CAIs), and fine-grained matrix are the main components of chondritic meteorites (chondrites) coming from undifferentiated asteroids\n \n 1\n \n . Chondrules are igneous spherules composed mainly of olivine, pyroxene, glass, and Fe-Ni metal and considered to have formed by transient heating and rapid cooling\n \n 2\n \n , ~ 2\u20134 Myr after CAIs\n \n 3\n \n . Based on the Mg# (=\u2009molar [MgO]/[MgO\u2009+\u2009FeO]%), chondrules are classified into type I (FeO-poor; Mg# \u2265 90) and type II (FeO-rich; Mg# < 90)\n \n 2\n \n . The Mg# of chondrules are controlled by the oxygen fugacity of the chondrule-forming environment\n \n 4\n \n , and type I chondrules formed under more reducing conditions than type II chondrules\n \n 5\n \n . CAIs, composed of Ca-Al-rich minerals including spinel, melilite, perovskite, hibonite, diopside, and anorthite, are condensation products in a gas of solar composition near the Sun and the oldest solids in our Solar System with the Pb-Pb absolute age of 4567.3 Ma\n \n 6,7\n \n . A subset of CAIs experienced melting processes\n \n 7\n \n . Amoeboid olivine aggregates (AOAs) are also condensates composed of Mg-rich olivine, Fe-Ni metal, and Ca-Al-rich minerals including spinel, diopside, and anorthite and as old as CAIs\n \n 8,9\n \n . Since chondrule-like and CAI-like objects were observed in cometary samples such as particles returned from comet Wild2\n \n 10,11\n \n and anhydrous interplanetary dust particles (IDPs)\n \n 12,13\n \n , it is considered that chondrules and CAIs were widely distributed from the inner Solar System to the Kuiper belt regions. Thus, chondrules and CAIs are essential for understanding of the material evolution in the early Solar System.\n
\n\n Oxygen isotope ratios of chondrules are internally homogeneous, except for relict grains with distinct oxygen isotope ratios\n \n 14\n \n . The homogeneous oxygen isotope ratios represent oxygen isotope ratios of chondrule-forming regions. Chondrules from carbonaceous chondrites have oxygen isotope ratios plotting along the slope 1 line with D\n \n 17\n \n O (=\u2009d\n \n 17\n \n O \u2013 0.52 \u00d7 d\n \n 18\n \n O) ranging from ~ \u2212\u20095\u2030 to +\u20095\u2030 in the oxygen three-isotope diagram, and those from ordinary chondrites have oxygen isotope ratios plotting above the terrestrial fractionation line with D\n \n 17\n \n O of ~\u2009+\u20091\u2030\n \n 5,15\n \n . CAIs and AOAs generally have\n \n 16\n \n O-rich isotope ratios with D\n \n 17\n \n O of ~ \u2212\u200924\u2030, which are nearly as\n \n 16\n \n O-rich as that of the Sun\n \n 7,16\n \n . Relict grains occasionally found in chondrules are generally more\n \n 16\n \n O-rich (D\n \n 17\n \n O down to ~ \u2212\u200924\u2030) than coexisting mineral phases, so that the genetic link to CAIs and AOAs has been suggested\n \n 17\u201319\n \n .\n
\n\n CI (Ivuna-type) carbonaceous chondrites consist mainly of phyllosilicate such as saponite and serpentine, magnetite, Fe-sulfide, and carbonate\n \n 1\n \n . Chondrules and CAIs are extremely rare or absent in the CI chondrites, though isolated olivine and pyroxene grains inferred to be fragments of chondrules are observed\n \n 1,20\n \n . It is not clear if the CI chondrites ever contained chondrules and CAIs and are essentially all matrix component, or if the chondrules and CAIs were consumed and their primary chondrite textures destroyed during extensive aqueous alteration\n \n 21\n \n .\n
\n\n The Hayabusa2 spacecraft returned samples of ~\u20095.4 g from C-type asteroid (162173) Ryugu\n \n 22\n \n . The \u201cstone\u201d team, which is one of the six initial analysis teams, received 16 stone samples from the ISAS curation facility and conducted analyses for elucidation of early evolution of asteroid Ryugu\n \n 23\n \n . The Ryugu samples mineralogically and chemically resemble CI chondrites\n \n 23\u201326\n \n . Remote sensing observations by the Hayabusa2 spacecraft suggested that asteroid Ryugu formed by reaccumulation of rubble ejected by impact from a larger asteroid\n \n 27\n \n . It was suggested that the Ryugu original parent body formed beyond the H\n \n 2\n \n O and CO\n \n 2\n \n snow lines in the solar nebula at 1.8\u20132.9 Myr after CAI formation\n \n 23\n \n , which is as early as formation of major types of carbonaceous chondrite chondrules at 2.2\u20132.7 Myr after CAI formation\n \n 3\n \n . In the present study, chondrule-like objects and CAIs are observed in the Ryugu samples, which are smaller than 30 \u00b5m (Fig.\n \n 1\n \n ). The chondrule-like objects and CAIs are observed with field emission scanning electron microscope (FE-SEM) and analyzed for major elemental compositions with field emission electron probe microanalyzer (FE-EPMA) and oxygen three-isotope ratios with secondary ion mass spectrometer (SIMS). A focused ion beam (FIB) section is taken out from one chondrule-like object and observed with field emission transmission electron microscope (FE-TEM). As a result, the chondrule-like objects and CAIs in the Ryugu samples have similarities and differences with chondrules and CAIs in chondrites. Here, we discuss the significance of the presence of chondrule-like objects and CAIs in asteroid Ryugu and their origins.\n
\n\n
\n\n \n Occurrence of chondrule-like objects and CAIs in the Ryugu samples\n \n
\n\n Chondrules and CAIs with sizes of ~ 100 \u00b5m \u2013 1 cm, which are typical for chondrites\n \n 1\n \n , are not observed from the synchrotron radiation X-ray computed tomography of the bulk Ryugu samples with a resolution of 0.85 \u00b5m/voxel at BL20XU in SPring-8 (a synchrotron facility in Hyogo, Japan)\n \n 23\n \n . A small number of chondrule-like objects and CAIs are found by elemental mapping using FE-EPMA and FE-SEM observation of 42 polished sections from the 13 Ryugu samples (52.6 mm\n \n 2\n \n in total). The chondrule-like objects and CAIs analyzed for oxygen isotopes occur along with isolated olivine, pyroxene and spinel grains in the polished sections of C0040-02 and C0076-10 and in less-altered clasts (clast 1 and 2) in the polished section C0002-P5\n \n 23\n \n . Fractions of the surface areas of all chondrule-like objects and CAIs observed in the Ryugu polished sections including those reported in Nakamura et al.\n \n 23\n \n are estimated as ~ 15 ppm and 20 ppm respectively, which are much smaller than those in carbonaceous chondrites\n \n 1\n \n .\n
\n\n
\n\n \n Mineralogy and chemistry of the chondrule-like objects in the Ryugu samples\n \n
\n\n Chondrule-like objects found in the Ryugu samples have rounded-to-spherical shapes with diameters of 10 \u2013 20 \u00b5m (\n \n Figs. 1a-c\n \n ; see also Nakamura et al.\n \n 23\n \n ), which are as small as chondrule-like Wild2 particles\n \n 11\n \n . Although remote sensing observations found mm-sized inclusions similar to chondrules on the surface of asteroid Ryugu\n \n 28\n \n , sizes of the chondrule-like objects that we found are much smaller. The chondrule-like objects analyzed for oxygen isotopes consist of olivine with Mg# of ~ 99, Fe-Ni metal, sulfide, and diopside free from Al and Ti (En\n \n 56.0\n \n Wo\n \n 43.7\n \n ;\n \n Supplementary Table A1\n \n ). The three chondrule-like objects do not contain glass or glass-altered phase and are not surrounded by fine- or coarse-grained rim, unlike chondrules in chondrites\n \n 1\n \n . In C0002-P5-C1-Chd, one out of three EPMA spots on Mg-rich olivine show a MnO/FeO ratio (wt%) exceeding 1 (\n \n Supplementary Table A1\n \n ), which is characteristic for low-iron, manganese-enriched (LIME) olivine\n \n 29\n \n . TEM analysis of the FIB section from C0040-02-Chd shows sub-\u00b5m-sized mixture of diopside and olivine with straight grain boundaries and well-developed 120\u00b0 triple junctions (\n \n Fig. 2\n \n ), which is evidence of annealing\n \n 30\n \n . The sub-\u00b5m-sized olivine grains are LIME olivine (\n \n Supplementary Table A1\n \n ).\n
\n\n
\n\n \n Mineralogy and chemistry of the CAIs in the Ryugu samples\n \n
\n\n CAIs found in the Ryugu samples are 4 \u2013 30 \u00b5m in size (\n \n Figs. 1d-e\n \n ; see also Nakamura et al.\n \n 23\n \n ), which are as small as CAI-like Wild2 particles\n \n 10\n \n . The two CAIs analyzed for oxygen isotopes consist of spinel and hibonite along with tiny perovskite particles (detected by energy-dispersive X-ray spectrometry of FE-EPMA). Phyllosilicate with low totals of 69 \u2013 93 wt% occurs around the two CAIs and interstitial region of spinel grains in C0040-02-CAI and is free from opaque minerals such as Fe-sulfide and magnetite, unlike phyllosilicate of the surrounding Ryugu matrix (\n \n Figs. 1d-e\n \n ). Phyllosilicate of the two CAIs has Al\n \n 2\n \n O\n \n 3\n \n concentrations of 3.2 \u2013 21.7 wt% (\n \n Supplementary Table A1\n \n ), which are higher than those in the Ryugu matrix phyllosilicate (2.3 wt%)\n \n 23\n \n and as high as those in phyllosilicate of CAIs in a CM carbonaceous chondrite (4.8 \u2013 12.4 wt%)\n \n 31\n \n .\n
\n\n
\n\n \n Oxygen isotope ratios\n \n
\n\n We made a total of 11 spot analyses in the 3 chondrule-like objects and 2 CAIs.\u00a0In each object, 1 to 4 spot analyses were made.\u00a0A summary of the 11 spot analyses is shown in\n \n Table 1\n \n ; a more complete table is given in\n \n Supplementary Table A2\n \n . The oxygen isotope ratios show a bimodal distribution at peaks of ~ \u201343\u2030 and ~ 0\u2030 in\u00a0d\n \n 18\n \n O along the\u00a0Carbonaceous Chondrite Anhydrous Mineral (CCAM) and the primitive chondrule mineral (PCM) lines\n \n 14,32\n \n (\n \n Fig. 3\n \n ). Oxygen isotope ratios of the individual objects are indistinguishable within the uncertainty (see\n \n Supplementary Fig. A1-A5\n \n ). Two out of the three chondrule-like objects are\n \n 16\n \n O-rich with average\u00a0D\n \n 17\n \n O values of \u201323.0 \u00b1 6.0\u2030 (2s; C0002-P5-C2-Chd) and \u201322.9 \u00b1 5.2\u2030 (C0040-02-Chd; single spot), while the other one containing LIME olivine is\n \n 16\n \n O-poor with average\u00a0D\n \n 17\n \n O value of \u20133.4 \u00b1 6.0\u2030 (C0002-P5-C1-Chd). The two CAIs are\n \n 16\n \n O-rich with average\u00a0D\n \n 17\n \n O values of \u201322.5 \u00b1 2.5\u2030 (C0040-02-CAI) and \u201324.2 \u00b1 3.6\u2030 (C0076-10-CAI).\n
\n\n The three chondrule-like objects in the Ryugu samples are rounded-to-spherical objects dominated by olivine, which is characteristic for chondrules in chondrites\n \n 1\n \n . One out of the three chondrule-like objects (C0002-P5-C1-Chd) has Mg# of 98.6, which is within the Mg# range of type I chondrules. The object has\n \n 16\n \n O-poor isotope ratios with D\n \n 17\n \n O of \u2212\u20093.4\u2009\u00b1\u20096.0\u2030 (Fig.\n \n 3\n \n ; Table\n \n 1\n \n ), which is within the D\n \n 17\n \n O range (~ \u2212\u20092\u2030 to \u2212\u20095\u2030) of type I chondrules from carbonaceous chondrites\n \n 5\n \n though with large uncertainty. Other two chondrule-like objects (C0002-P5-C2-Chd and C0040-02-Chd) are dominated by Mg-rich olivine and have\n \n 16\n \n O-rich isotope ratios with D\n \n 17\n \n O of ~ \u2212\u200923\u2030 (Fig.\n \n 3\n \n ; Table\n \n 1\n \n ), which is within the D\n \n 17\n \n O range of CAIs and AOAs\n \n 7\n \n . One of them (C0040-02-Chd) contains sub-\u00b5m-sized diopside and LIME olivine grains and shows an annealed texture with 120\u00b0 triple junctions (Fig.\n \n 2\n \n ), which are characteristic for AOAs\n \n 30,33,34\n \n .\n
\n\n Olivine in AOAs is depleted in refractory elements such as Ca, Al, and Ti compared with that in type I chondrules\n \n 35\n \n , which is evident in Fig.\n \n 4\n \n where CaO and Cr\n \n 2\n \n O\n \n 3\n \n concentrations in olivine from AOAs and type I chondrules in type\u2009<\u20093.0 chondrites are compared. Calcium and Cr are minor in olivine as indicated by the low concentrations of CaO and Cr\n \n 2\n \n O\n \n 3\n \n in type I chondrule-olivine. This is because Ca is incompatible with olivine\n \n 36\n \n and Cr is originally minor in chondrule precursor dust\n \n 5\n \n . The AOA-olivine shows even lower concentrations of CaO and Cr\n \n 2\n \n O\n \n 3\n \n , which is explained by olivine condensation from a residual gas depleted in the refractory elements after condensation of refractory-rich minerals\n \n 37,38\n \n followed by isolation from the gas before condensation of Cr. The CaO and Cr\n \n 2\n \n O\n \n 3\n \n concentrations in olivine in the two\n \n 16\n \n O-rich chondrule-like objects plot in the range of the AOA-olivine, while those in olivine in the\n \n 16\n \n O-poor one plot in the range of type I chondrule-olivine (Fig.\n \n 4\n \n ). Thus, the\n \n 16\n \n O-poor chondrule-like object shares characteristics with type I chondrules in carbonaceous chondrites, and two\n \n 16\n \n O-rich ones share characteristics with AOAs. It is worth mentioning that CaO and Cr\n \n 2\n \n O\n \n 3\n \n concentrations in olivine in the\n \n 16\n \n O-rich chondrule from a CH chondrite\n \n 39\n \n plot in the range of the AOA-olivine (Fig.\n \n 4\n \n ), suggesting a genetic link to AOAs.\n
\n\n
\n\n AOAs are characterized by irregular shapes, numerous pores, and refractory minerals including anorthite, Al-diopside, and spinel, besides Mg-rich olivine\n \n 8,34,40\n \n . The two\n \n 16\n \n O-rich chondrule-like objects are rounded and free from pores and refractory minerals (Figs.\n \n 1\n \n b-c). It is less likely that the two objects are AOA fragments with no pores and refractory minerals, given that\n \n 16\n \n O-rich isolated olivine in the Ryugu samples and CI chondrites which are suggested to be AOA fragments have angular shapes\n \n 20,41\n \n . Alternatively, the two\n \n 16\n \n O-rich chondrule-like objects are likely to have been originally AOAs (or fragments) and melted (and annealed) by a heating event in the\n \n 16\n \n O-rich environment possibly near the Sun.\n
\n\n Chondrules are products of multiple heating events\n \n 5,17\n \n . Remnants of the earlier generations of chondrules are observed in chondrules\n \n 5,17\u201319,42\n \n , of which characteristics are similar to those of the three chondrule-like objects in the Ryugu samples; e.g., Mg-rich olivine-dominated mineralogy and\n \n 16\n \n O-rich isotope ratios. Here we discuss the possibility that the three chondrule-like objects are earlier generations of chondrules.\n
\n\n Chondrules in chondrites are diverse in texture, but they commonly contain glassy mesostasis, except for cryptocrystalline chondrules\n \n 1\n \n . Differently, the three chondrite-like objects are free from glass (or glass-altered phase) and are dominated by Mg-rich olivine along with Fe-Ni metal and sulfide (Figs.\n \n 1\n \n a-c), which are similar to what proposed as earlier generations of chondrules in Libourel and Krot\n \n 42\n \n . Especially, one of the three chondrule-like objects show an annealed texture (Fig.\n \n 2\n \n ), like earlier generations of chondrules proposed in Libourel and Krot\n \n 42\n \n . It is therefore suggested that the three chondrule-like objects are earlier generations of chondrules. The earlier generations of chondrules suggested in Libourel and Krot\n \n 42\n \n are products from differentiated planetesimals, but which cannot provide objects with variable oxygen isotope ratios of\n \n 16\n \n O-rich and -poor observed in the present study. Instead, the three chondrules are nebular products as suggested in Whattham et al.\n \n 43\n \n . Chondrules in chondrites contain relict olivine, which are generally more\n \n 16\n \n O-rich than coexisting mineral phases\n \n 5\n \n . Such\n \n 16\n \n O-rich relict olivine is likely to be a remnant of earlier generations of chondrules or fragments of AOAs\n \n 5,18,19\n \n . The two\n \n 16\n \n O-rich chondrule-like objects may be earlier generations of chondrules that escaped from incorporation into\n \n 16\n \n O-poor chondrule precursor dust. As the\n \n 16\n \n O-poor counterpart, SiO gas is suggested in Marrocchi and Chaussidon\n \n 44\n \n , which results in distinct d\n \n 18\n \n O values between olivine and pyroxene in chondrules. However, the d\n \n 18\n \n O values are consistent between olivine and pyroxene in chondrules within uncertainty\n \n 5\n \n , and therefore SiO gas is unlikely to be the\n \n 16\n \n O-poor counterpart.\n
\n\n If the three chondrule-like objects in the Ryugu samples are earlier generations of chondrules, the two distinct oxygen isotope ratios of\n \n 16\n \n O-rich and -poor (Fig.\n \n 3\n \n ) are evidence for the argument that\n \n 16\n \n O-rich (~ \u2212\u200923\u2030 in D\n \n 17\n \n O) and\n \n 16\n \n O-poor (~\u20090\u2030) isotope reservoirs existed in the early stage of the chondrule formation\n \n 5,18,45\n \n . While\n \n 16\n \n O-poor chondrules are commonly observed in chondrites\n \n 5\n \n ,\n \n 16\n \n O-rich chondrules are extremely rare\n \n 39\n \n . Only\n \n 16\n \n O-rich relict grains are observed as minor constituents in chondrules\n \n 18,19\n \n . A possible explanation is that the\n \n 16\n \n O-rich chondrules were incorporated into\n \n 16\n \n O-poor chondrule precursor dust and reheated, as described above. Even if the\n \n 16\n \n O-rich chondrules escaped from the recycling events, they should have been incorporated into early-formed planetesimals such as parent bodies of differentiated meteorites (0.5\u20131.9 Myr after CAIs)\n \n 46\n \n and destroyed during the differentiation processes. The reason for the presence of\n \n 16\n \n O-rich (and -poor) chondrule-like objects in the Ryugu samples is discussed in the final section.\n
\n\n The two Ryugu CAIs consist of spinel, hibonite, and perovskite and have\n \n 16\n \n O-rich isotope ratios (Figs.\n \n 1\n \n d-e and\n \n 3\n \n ), which are characteristic for CAIs in chondrites\n \n 7\n \n . The two Ryugu CAIs are surrounded by Al-rich phyllosilicate. Unlike phyllosilicate in the Ryugu matrix, Al-rich phyllosilicate is free from opaque minerals such as magnetite and sulfide but contains certain amounts of SO\n \n 3\n \n (0.8\u20135.6 wt%;\n \n Supplementary Table A1\n \n ). Tiny sulfide grains that are unrecognizable under FE-SEM may be present in Al-rich phyllosilicate. It is likely that Al-rich phyllosilicate surrounding the two Ryugu CAIs is originally an Al-rich mineral phase susceptible to aqueous alteration such as melilite or anorthite\n \n 7,31\n \n . In this case, the depletion in Ca in Al-rich phyllosilicate (<\u20090.6 wt%;\n \n Supplementary Table A1\n \n ) is attributed to mobilization of this element to form calcite during aqueous alteration\n \n 47\n \n .\n
\n\n Spinel-hibonite inclusions accompanied by altered phases like C0040-02-CAI and spinel inclusions surrounded by altered phases like C0076-10-CAI are observed in CM chondrites\n \n 31,48\n \n . However, the two Ryugu CAIs are smaller than CAIs in CM chondrites and as small as CAI-like Wild2 particles\n \n 10\n \n . The cometary CAIs are younger than the CM-CAIs, which are as old as the oldest CAIs\n \n 6,48,49\n \n . In addition, the cometary CAIs contain relatively high concentrations of Cr\n \n 2\n \n O\n \n 3\n \n compared with CAIs in chondrites\n \n 50\n \n . It is therefore suggested that the cometary CAIs experienced remelting events with addition of less refractory elements after initial formation\n \n 50\n \n . Spinel is the only common mineral between the two Ryugu CAIs (perovskite is too tiny to analyze elemental compositions precisely) and occurs in the CM-CAIs and cometary CAIs. Here we discuss whether the two Ryugu CAIs resemble CM-CAIs or cometary CAIs based on the Cr\n \n 2\n \n O\n \n 3\n \n concentrations (Fig.\n \n 5\n \n ), which may facilitate estimation of timing of the two Ryugu CAI formation. Spinel in the CM-CAIs contain Cr\n \n 2\n \n O\n \n 3\n \n mostly less than 0.6 wt%, while that in CAI-like Wild2 particles and CAI-like IDP contains more Cr\n \n 2\n \n O\n \n 3\n \n than 1.7 wt%. Matzel et al.\n \n 49\n \n suggested that the CAI-like Wild2 particle, Coki, is classified into type C CAIs, which experienced remelting events\n \n 51\n \n . The high concentrations of Cr\n \n 2\n \n O\n \n 3\n \n in the cometary CAIs and type C CAIs are explained by addition of Cr from Cr-bearing gas or dust during the remelting events\n \n 50,52\n \n . Based on the\n \n 26\n \n Al-\n \n 26\n \n Mg chronometry, CAI-like Wild2 particles are younger (few Myr or more)\n \n 49,53\n \n than CM-CAIs\n \n 48\n \n , which reflects the relatively late remelting events. Spinel in the two Ryugu CAIs contain Cr\n \n 2\n \n O\n \n 3\n \n less than 0.2 wt% (Fig.\n \n 5\n \n ). It is possible that the two Ryugu CAIs escaped from remelting events that supply Cr. If this is the case, the two Ryugu CAIs are possibly as old as the CM-CAIs.\n
\n\n
\n\n We found three chondrule-like objects that are likely to be earlier generations of chondrules (two of them have affinities to AOAs) and two CAIs that are possibly as old as the oldest CAIs based on the mineralogy, chemistry, and oxygen isotope ratios. Additional important observations in the present study are the smallness (<\u200930 \u00b5m) and rarity (~\u200915 ppm and 20 ppm) of chondrule-like objects and CAIs in the Ryugu samples. Isolated olivine, pyroxene, and spinel grains that are likely to be fragments of chondrules and CAIs and AOA-like porous olivine in the Ryugu samples are also small (<\u200930 \u00b5m)\n \n 23,41,54\n \n . The Ryugu original parent body formed beyond the H\n \n 2\n \n O and CO\n \n 2\n \n snow lines in the solar nebula (>\u20093\u20134 au from the Sun)\n \n 23\n \n , while CAIs and AOAs formed near the Sun\n \n 7\n \n . Radial transport of CAIs and AOAs from the innermost regions to the region where the Ryugu original parent body formed is required. The two\n \n 16\n \n O-rich chondrule-like objects formed near the Sun may have been transported along with CAIs. Likewise, it has been suggested from the observations of chondrule-like and CAI-like Wild2 particles that chondrules and CAIs were transported from the inner regions to the Kuiper belt (~\u200930\u201350 au) in the solar nebula\n \n 10,11,55\n \n . Given the smaller sizes of the cometary chondrules and CAIs than those in chondrites, radial transport favoring smaller objects to farther locations may have occurred in the solar nebula; e.g., a combination of advection and turbulent diffusion\n \n 56\n \n or photophoresis\n \n 57\n \n . If this is the case, the occurrence of chondrule-like objects and CAIs in the Ryugu samples as small as those in the Wild2 particles suggests that the Ryugu parent body formed at farther location than any other chondrite parent bodies and acquired\n \n 16\n \n O-rich and -poor chondrule-like objects and CAIs transported from the inner solar nebula.\n
\n\n Chondrules in different chondrite groups have distinct chemical, isotopic, and physical properties, which suggests chondrule formation in local disk regions and subsequent accretion to their respective parent bodies without significant inward/outward migration\n \n 5,58\n \n . It is considered from the rarity of chondrules (and chondrule-like objects) in the Ryugu samples that the Ryugu original parent body formed in a region scarce in chondrules. Instead, small chondrules (and chondrule-like objects) and their fragments may have been transported from the inner solar nebula and accreted along with CAIs onto the Ryugu original parent body. Since the formation age of the Ryugu original parent body (1.8\u20132.9 Myr after CAI formation)\n \n 23\n \n is as early as those of major types of carbonaceous chondrite chondrules (2.2\u20132.7 Myr after CAI formation)\n \n 3\n \n , chondrules typically observed in chondrites (100 \u00b5m \u2013 1 mm)\n \n 1\n \n should have presented in the inner regions of the solar nebula when forming the Ryugu original parent body. Considering radial transport favoring smaller objects to the formation location of the Ryugu original parent body, fragments of the relatively large chondrules may have been provided and observed as isolated olivine and pyroxene grains in the Ryugu samples. Recently, Morin et al.\n \n 20\n \n analyzed oxygen isotope ratios of isolated olivine and low-Ca pyroxene grains in CI chondrites. Although they suggested that\n \n 16\n \n O-poor grains are fragments of chondrules formed in the CI chondrite formation regions, the reason for the limited size range of the isolated grains (<\u200930 \u00b5m) compared with that for other carbonaceous chondrites (up to ~\u2009200 \u00b5m)\n \n 59\n \n is unclear.\n
\n\n CAIs in the Ryugu samples are much less abundant (~\u200920 ppm) than those in the Wild2 particles (~\u20090.5%)\n \n 50\n \n , suggesting destruction of the CAIs and chondrules (and chondrule-like objects) in the Ryugu original parent body during the extensive aqueous alteration. The observed chondrule-like objects and CAIs may have survived along with isolated anhydrous grains in less-altered regions in the Ryugu parent body.\n
\n\n \n Sample preparation\n \n
\n\n Polished sections were prepared from the Ryugu samples C0002, C0040, and C0076 based on the methods dedicated to the Ryugu samples\n \n 60\n \n . C0002-P5 (C0002-Plate5 in Nakamura et al.\n \n 23\n \n ) means 5th plate of six plates from C0002. C0040-02 and C0070-10 mean 2nd polished section from C0040 and 10th polished section from C0076. The polished sections were coated with carbon (20 -30 nm in thickness). C0002-P5 was loaded in the 3-hole disk, and other two polished sections were loaded in the 7-hole disk\n \n 61\n \n for electron microscopy and oxygen isotope analysis with SIMS. The chondrule-like objects and CAIs analyzed for oxygen isotopes are located outside of the 500 \u00b5m and 1 mm radius of the center of holes for 7-hole and 3-hole disks, which allow accurate SIMS analysis within \u00b10.5\u2030 in\u00a0d\n \n 18\n \n O with ~ 10 \u00b5m primary beam (~ 2 nA)\n \n 61\n \n . But the analytical uncertainty of the oxygen isotope analysis of the chondrule-like objects and CAIs is more than \u00b11\u2030 in\u00a0d\n \n 18\n \n O as described later, so that the instrumental mass bias is insignificant.\n
\n\n
\n\n \n Electron microscopy\n \n
\n\n Chondrule-like objects and CAIs in the Ryugu samples were examined using a FE-SEM (JEOL JSM-7001F) at Tohoku University, and BSE images were obtained. Major elemental compositions of the chondrule-like objects and CAIs were measured using a FE-EPMA (JEOL JXA-8530F) equipped with wavelength-dispersive X-ray spectrometers (WDSs) at University of Tokyo. WDS quantitative chemical analyses of olivine in the chondrule-like objects and spinel and hibonite in the CAIs were performed at 12 kV accelerating voltage and 30 nA beam current with a focused beam. For analyses of phyllosilicate of the CAIs, 15 kV accelerating voltage and 12 nA beam current with a defocused beam of 1 \u00b5m were applied. Natural and synthetic standards were chosen based on the compositions of the minerals being analyzed\n \n 23\n \n .\n
\n\n A FIB section from C0040-02 was extracted using a FIB-SEM (Thermo Fischer Scientific Versa 3D) at Tohoku University for TEM observation. The region of interest was coated by platinum deposition to prevent damage during FIB processing. Then, it was cut out as a thick plate (~ 1 \u00b5m in thickness) and mounted on copper grids and thinned to 100 \u2013 200 nm using a Ga\n \n +\n \n ion beam at 30 kV and 0.1 \u2013 2.5 nA. The damaged layers formed on the thin sections during the thinning were removed using a Ga\n \n +\n \n ion beam at 5 kV and 16 \u2013 48 pA.\n
\n\n The thin section was observed with FE-TEM (JEOL JEM-2100F) operating at 200 kV and equipped with an energy-dispersive X-ray spectrometer (EDS) at Tohoku University. TEM images were recorded using a charge-coupled device (CCD) and then processed by the Gatan Digital Micrograph software package. Crystal structures were identified based on analysis of SAED patterns. We also acquired STEM images. X-ray maps and quantitative EDS data were obtained using JEOL JED-2300 EDS detectors and JEOL analysis station software package. Quantifications of EDS spectra were carried out using the Cliff-Lorimer thin film approximation using theoretical k-factors.\n
\n\n
\n\n \n Oxygen isotope analysis\n \n
\n\n Before the oxygen isotope analysis of chondrule-like objects and CAIs in the Ryugu samples, FIB markings were employed at selected locations of each object, which were identified by the\n \n 16\n \n O\n \n \u2013\n \n secondary ion imaging\n \n 62,63\n \n . Accurate aiming using FIB marking and\n \n 16\n \n O\n \n \u2013\n \n ion imaging avoids significant beam overlap with adjacent mineral phases, so that accurate oxygen isotope ratios are obtained. FIB-SEM (Thermo Fischer Scientific Helios NanoLab 600i) equipped with a gallium ion source at Tohoku University was used to remove surface carbon coating from the chondrule-like objects and CAIs. A 30 kV focused Ga\n \n +\n \n ion beam set to 7 pA was rastered within a 1 \u00b5m \u00d7 1 \u00b5m square on the sample surface for 30 sec, so that only the surface coating was removed without significant milling of underlying mineral. This 1 \u00b5m square region was later identified by secondary\n \n 16\n \n O\n \n \u2013\n \n ion imaging in SIMS before oxygen isotope analysis.\n
\n\n Oxygen isotope ratios of three chondrule-like objects and two CAIs in the Ryugu samples were analyzed with the CAMECA IMS 1280 at the University of Wisconsin-Madison. The analytical conditions and measurement procedures were similar to those in Zhang et al.\n \n 63\n \n . A focused Cs\n \n +\n \n primary beam was set to ~ 1 \u00b5m \u00d7 0.8 \u00b5m and intensity of ~ 0.3 pA. The secondary\n \n 16\n \n O\n \n \u2013\n \n ,\n \n 17\n \n O\n \n \u2013\n \n , and\n \n 18\n \n O\n \n \u2013\n \n ions were detected simultaneously by a Faraday Cup (\n \n 16\n \n O\n \n \u2013\n \n ) with 10\n \n 12\n \n ohm feedback resistor and electron multipliers (\n \n 17\n \n O\n \n \u2013\n \n ,\n \n 18\n \n O\n \n \u2013\n \n ) on the multicollection system. Intensities of\n \n 16\n \n O\n \n \u2013\n \n were ~ 2 \u2013 3 \u00d7 10\n \n 5\n \n cps. The contribution of the tailing of\n \n 16\n \n O\n \n 1\n \n H\n \n \u2013\n \n interference to\n \n 17\n \n O\n \n \u2013\n \n signal was corrected by the method described in Heck et al.\n \n 64\n \n , though the contribution was negligibly small (\u2264 0.5%). One to four analyses were performed for each object, bracketed by six analyses (three analyses before and after the unknown sample analyses) on the San Carlos olivine (SC-Ol) grains mounted in the same multiple-hole disks. The external reproducibility of the running standards was 1.3 \u2013 2.4\u2030 for\u00a0d\n \n 18\n \n O, 4.8 \u2013 7.9\u2030 for\u00a0d\n \n 17\n \n O, and 4.1 \u2013 8.5\u2030 for\u00a0D\n \n 17\n \n O (2SD; standard deviation), which were assigned as analytical uncertainties of unknown samples; see Kita et al.\n \n 15\n \n for detailed explanations. We analyzed olivine (Fo\n \n 100\n \n ), spinel, and hibonite standards\n \n 15,65\n \n in the same session for correction of instrumental bias of olivine, spinel, and hibonite. Instrumental biases estimated from above mineral standards (matrix effect) are within a few \u2030 in\u00a0d\n \n 18\n \n O (\n \n Supplementary Table A3\n \n ). After SIMS analyses, all SIMS pits were inspected using a FE-SEM to confirm the analyzed positions (\n \n Fig. 1\n \n ).\n
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\n| \n \n \n Table 1. Oxygen isotope ratios of chondrule-like objects and CAIs in the Ryugu samples.\n \n a\n \n \n \n | \n ||||||||
| \n \n \n Sample name\n \n \n | \n \n \n \n Spot#\n \n \n | \n \n \n \n d\n \n \n \n 18\n \n \n \n O \u00b1\n \n \n | \n \n \n \n 2SD (\u2030)\n \n \n | \n \n \n \n d\n \n \n \n 17\n \n \n \n O \u00b1\n \n \n | \n \n \n \n 2SD (\u2030)\n \n \n | \n \n \n \n D\n \n \n \n 17\n \n \n \n O \u00b1\n \n \n | \n \n \n \n 2SD (\u2030)\n \n \n | \n \n \n \n Target\n \n b\n \n \n \n | \n
| \n \n \n C0002-P5-C1-Chd\n \n \n | \n \n \n \n 1\n \n \n | \n \n \n \n 2.6\n \n \n | \n \n \n \n 2.0\n \n \n | \n \n \n \n -2.5\n \n \n | \n \n \n \n 7.9\n \n \n | \n \n \n \n -3.8\n \n \n | \n \n \n \n 8.5\n \n \n | \n \n \n \n Ol (Fo\n \n 98.6\n \n )\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n 2\n \n \n | \n \n \n \n -1.4\n \n \n | \n \n \n \n 2.0\n \n \n | \n \n \n \n -3.7\n \n \n | \n \n \n \n 7.9\n \n \n | \n \n \n \n -3.0\n \n \n | \n \n \n \n 8.5\n \n \n | \n \n \n \n Ol\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n Average\n \n \n | \n \n \n \n 0.6\n \n \n | \n \n \n \n 3.9\n \n \n | \n \n \n \n -3.1\n \n \n | \n \n \n \n 5.6\n \n \n | \n \n \n \n -3.4\n \n \n | \n \n \n \n 6.0\n \n \n | \n \n \n \n \n \n | \n
| \n \n \n C0002-P5-C2-Chd\n \n \n | \n \n \n \n 1\n \n \n | \n \n \n \n -39.8\n \n \n | \n \n \n \n 2.0\n \n \n | \n \n \n \n -43.6\n \n \n | \n \n \n \n 7.9\n \n \n | \n \n \n \n -22.9\n \n \n | \n \n \n \n 8.5\n \n \n | \n \n \n \n Ol (Fo\n \n 98.9\n \n )\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n 2\n \n \n | \n \n \n \n -47.5\n \n \n | \n \n \n \n 2.0\n \n \n | \n \n \n \n -47.8\n \n \n | \n \n \n \n 7.9\n \n \n | \n \n \n \n -23.1\n \n \n | \n \n \n \n 8.5\n \n \n | \n \n \n \n Ol\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n Average\n \n \n | \n \n \n \n -43.6\n \n \n | \n \n \n \n 7.7\n \n \n | \n \n \n \n -45.7\n \n \n | \n \n \n \n 5.6\n \n \n | \n \n \n \n -23.0\n \n \n | \n \n \n \n 6.0\n \n \n | \n \n \n \n \n \n | \n
| \n \n \n C0040-02-Chd\n \n \n | \n \n \n \n 1\n \n \n | \n \n \n \n -44.4\n \n \n | \n \n \n \n 1.3\n \n \n | \n \n \n \n -46.0\n \n \n | \n \n \n \n 5.4\n \n \n | \n \n \n \n -22.9\n \n \n | \n \n \n \n 5.2\n \n \n | \n \n \n \n Ol (Fo\n \n 99.7\n \n )\n \n \n | \n
| \n \n \n C0040-02-CAI\n \n \n | \n \n \n \n 1\n \n \n | \n \n \n \n -39.1\n \n \n | \n \n \n \n 2.4\n \n \n | \n \n \n \n -46.5\n \n \n | \n \n \n \n 4.8\n \n \n | \n \n \n \n -26.1\n \n \n | \n \n \n \n 4.1\n \n \n | \n \n \n \n Hib\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n 2\n \n \n | \n \n \n \n -43.1\n \n \n | \n \n \n \n 2.4\n \n \n | \n \n \n \n -42.7\n \n \n | \n \n \n \n 4.8\n \n \n | \n \n \n \n -20.2\n \n \n | \n \n \n \n 4.1\n \n \n | \n \n \n \n Sp\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n 3\n \n \n | \n \n \n \n -42.5\n \n \n | \n \n \n \n 2.4\n \n \n | \n \n \n \n -44.0\n \n \n | \n \n \n \n 4.8\n \n \n | \n \n \n \n -21.9\n \n \n | \n \n \n \n 4.1\n \n \n | \n \n \n \n Sp\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n 4\n \n \n | \n \n \n \n -43.1\n \n \n | \n \n \n \n 2.4\n \n \n | \n \n \n \n -44.2\n \n \n | \n \n \n \n 4.8\n \n \n | \n \n \n \n -21.8\n \n \n | \n \n \n \n 4.1\n \n \n | \n \n \n \n Sp\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n Average\n \n \n | \n \n \n \n -42.0\n \n \n | \n \n \n \n 1.9\n \n \n | \n \n \n \n -44.3\n \n \n | \n \n \n \n 2.4\n \n \n | \n \n \n \n -22.5\n \n \n | \n \n \n \n 2.5\n \n \n | \n \n \n \n \n \n | \n
| \n \n \n C00706-10-CAI\n \n \n | \n \n \n \n 1\n \n \n | \n \n \n \n -44.0\n \n \n | \n \n \n \n 1.3\n \n \n | \n \n \n \n -46.3\n \n \n | \n \n \n \n 5.4\n \n \n | \n \n \n \n -23.4\n \n \n | \n \n \n \n 5.2\n \n \n | \n \n \n \n Sp\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n 2\n \n \n | \n \n \n \n -40.3\n \n \n | \n \n \n \n 1.3\n \n \n | \n \n \n \n -46.0\n \n \n | \n \n \n \n 5.4\n \n \n | \n \n \n \n -25.1\n \n \n | \n \n \n \n 5.2\n \n \n | \n \n \n \n Sp\n \n \n | \n
| \n \n \n \n \n | \n \n \n \n Average\n \n \n | \n \n \n \n -42.1\n \n \n | \n \n \n \n 3.7\n \n \n | \n \n \n \n -46.1\n \n \n | \n \n \n \n 3.8\n \n \n | \n \n \n \n -24.2\n \n \n | \n \n \n \n 3.6\n \n \n | \n \n \n \n \n \n | \n
| \n \n \n \n a\n \n \n \n The uncertainties associated with average values are twice the standard error of the mean (2SE).\n \n \n | \n ||||||||
| \n \n \n \n b\n \n \n \n Average (or representative) chemical compositions are shown.\n \n \n | \n
\n Dataset 1\n
\n \n\n Dataset 2\n
\n \n\n Author contributions\n
\n \n\n Postnatal growth failure is often attributed to dysregulated somatotropin action, however marked genetic and phenotypic heterogeneity exist. We report four patients from two families who present with short stature, immune dysfunction, atopic eczema and gut-associated pathology associated with recessive variants in\n \n QSOX2\n \n .\n \n QSOX2\n \n encodes a nuclear membrane protein linked to disulphide isomerase and oxidoreductase activity. Loss of QSOX2 disrupts GH-mediated STAT5B nuclear translocation despite enhanced GH-induced STAT5B phosphorylation. Moreover, patient-derived dermal fibroblasts demonstrate novel GH-induced mitochondriopathy and reduced mitochondrial membrane potential. We describe a definitive role of QSOX2 in modulating human growth likely due to impairment of STAT5B downstream activity and mitochondrial dynamics leading to growth failure, immune dysregulation and gut dysfunction. Located at the nuclear membrane, QSOX2 acts as a gatekeeper for regulating stabilisation and import of p-STAT5B. Furthermore, our work suggests that therapeutic recombinant IGF-1 may circumvent the GH-mediated STAT5B molecular defect and potentially alleviate organ specific disease.\n
\n\n Short stature, a potential indicator of underlying maladies, is defined as height for age more than 2 deviations below the population median (~\u20092% of the population), and is the commonest reason for referral to paediatric endocrinology clinics\n \n 1\n \n . Although adult height is 80\u201390% heritable\n \n 2\n \n , the molecular basis for growth failure in 50\u201390% of patients remains unidentified despite advances in genomic sequencing strategies\n \n 1\n \n .\n
\n\n Defects in growth hormone (GH) action account for a substantial percentage of endocrine causes of growth failure but are frequently unrecognised due to wide clinical and biochemical variability. Marked genetic and phenotypic heterogeneity exist, with heritable defects in genes downstream of the GH receptor (GHR) or interacting pathways accounting for a significant number of \u2018non-classical\u2019 cases\n \n 3\n \n . Homozygous inactivating variants in signal transducer and activator of transcription (\n \n STAT5B\n \n ), a key effector of GH-GHR regulated production of the growth promoting insulin-like growth factor 1 (IGF-1), cause classical GH insensitivity (GHI) with severe postnatal growth failure and IGF-1 deficiency. Additional distinctive phenotypic features include eczema, progressive immunodeficiency and respiratory compromise\n \n 4\u20138\n \n . Milder phenotypes, with variable degrees of GHI and immunodeficiency have been characterised in dominant negative\n \n STAT5B\n \n heterozygotes\n \n 4\n \n . We now report probands with milder phenotypes akin to dominant negative\n \n STAT5B\n \n heterozygotes but associated with a novel regulatory interactor of STAT5B which, when absent, blunts STAT5B-mediated regulation of IGF-1 expression by impairing STAT5B nuclear translocation.\n
\n\n \n QSOX2\n \n (Quiescin sulfhydryl oxidase 2, MIM 612860) belongs to a family of sulfhydryl oxidases best known for catalysing the introduction of disulphide bonds in secreted proteins.\n \n QSOX2\n \n shares a 41.2% sequence homology with\n \n QSOX1\n \n , a well characterized sulfhydryl oxidase shown\n \n in vitro\n \n to be protective against oxidative stress-mediated cell death\n \n 9\u201311\n \n . Contrastingly, the poorly characterized QSOX2, a ubiquitously expressed protein, localises to the nuclear membrane/nucleoplasm and Golgi apparatus. No pathological defects in either\n \n QSOX1\n \n or\n \n QSOX2\n \n have been reported, although two genome-wide association studies have identified\n \n QSOX2\n \n polymorphisms in association with height\n \n 12,13\n \n . A study of 19,633 Japanese subjects, identified the\n \n LHX3-QSOX2\n \n locus as a significant adult height quantitative trait locus (QTL)\n \n 12\n \n . More recently, meta-analyses of large genetic data repositories identified 12,111 height-associated SNPs, of which two\n \n QSOX2\n \n polymorphisms (rs7024579 and rs7038554) were significantly associated with height in European populations\n \n 13\n \n . However, to date, clinically relevant\n \n QSOX2\n \n variants associated with postnatal growth failure or other phenotypes, have not been reported.\n
\n\n We describe the first pathological\n \n QSOX2\n \n variants, discovered by next generation sequencing of four individuals with short stature. We demonstrate a direct interaction between QSOX2 and STAT5B. All variants lead to robust GH-stimulated tyrosine phosphorylation of STAT5B. STAT5B nuclear translocation was attenuated with resultant reduced STAT5B downstream transcriptional activities. Intriguingly, robust GH-induced STAT5B phosphorylation correlated with reorganisation of oxidative phosphorylation complexes and diminished mitochondrial membrane potential in patient-derived dermal fibroblasts. Collectively, QSOX2 deficiency abrogates downstream STAT5B activity causing a unique syndrome with additional features of atopic eczema, feeding difficulties, gastrointestinal dysmotility and recurrent infections.\n
\n\n \n Ethical approval\n \n
\n\n Informed written consents for genetic research and publication of clinical details were obtained from patient\u2019s parents and adult patients. The study was approved by the Health Research Authority, East of England-Cambridge East Research Ethics Committee (REC reference 17/EE/0178).\n
\n\n \n Clinical phenotypes of probands\n \n
\n\n \n Index Family 1\n \n
\n\n Identical male twins, probands P1 (Twin 1) and P2 (Twin 2), from a non-consanguineous British Caucasian/South Asian kindred (\n \n Figure 1A\n \n ), were born at 30 weeks gestation with a birth weight appropriate for gestational age. They presented at age 1.3 years with significant postnatal growth failure (UK-WHO growth reference height and weight standard deviation scores (SDS) of -3.9 and -2.6 and -5.0 and -3.3, respectively) (\n \n Figure 1B, Table 1\n \n ). Bone age was concordant with chronological age. Feeding difficulties associated with reduced gastrointestinal motility and oral aversion, chronic refractory constipation, oesophageal reflux, and recurrent episodes of gastroenteritis were more pronounced in P2, whose oral feeding aversion necessitated insertion of a percutaneous endoscopic gastrostomy (PEG) feeding tube at age 2.8 years. Hindgut dysmotility was confirmed with rectal outlet dysfunction in P1 and a mixed-type of slow colonic transit with rectal outlet dysfunction in P2 (\n \n Figure 1\n \n \n C, D\n \n ), requiring laxative and stimulant treatment as well as repeated injections of botulinum toxin into the anal sphincter. Weight and BMI standard deviation scores remained low but stable with optimised nutritional status as evidenced by normal vitamin and trace element levels, but linear growth remained significantly impaired. Skeletal surveys and developmental milestones were normal.\n
\n\n GH-provocation testing elicited a normal GH response for P2, the disparate peak GH response in P1 may be explained by significant technical difficulties. Basal IGF-1 levels remained consistently low in both probands, with serum IGFBP-3 within normal ranges, consistent with partial GH insensitivity (GHI)\n \n 14,15\n \n . Collectively, the clinical picture was suggestive of primary post-natal growth failure. Both probands exhibited mild dysmorphism with prominent forehead and downward slanting palpebral fissures and mild immune dysregulation characterised by atopic eczema, asthma, recurrent respiratory tract infections and cows\u2019 milk protein, soy and egg allergies. These additional clinical features, in association with postnatal growth failure and persistently low IGF-1 levels, overlapped with those of STAT5B deficiency (MIM 245590), a growth disorder associated with variable degrees of immunodeficiency\n \n 7,16\n \n . Peripheral blood immune profiling revealed persistently low IgM levels, raised gamma delta and double negative T cells denoting immune dysregulation. Basal levels of tyrosine phosphorylated STAT5 (p-STAT5) in peripheral blood mononuclear cells (PBMC) were elevated compared to controls (1.9% in control vs. >7% in probands). CT chest with contrast showed no evidence of lung fibrosis, a well-reported feature in patients with homozygous STAT5B deficiency.\n
\n\n Initial genetic testing (karyotype, microarray-based comparative genomic hybridisation and PTEN sequencing) did not reveal a unifying diagnosis. Targeted genome sequencing revealed no pathogenic variants in\n \n STAT5B\n \n or other common growth-related genes, including the key genes of the GH-IGF-1 axis, whilst methylation analyses of both imprinted domains associated with short stature at 11p15.5, H19DMR and KvDMR (MS-MLPA) were normal. We further undertook whole exome sequencing (WES) which corroborated the targeted gene panel sequencing data. Intriguingly, the top candidate variants were compound heterozygous variants in\n \n QSOX2\n \n , a gene in which no pathological variants have been reported to date. These were a novel paternally-inherited single base deletion (\n \n c.973delG\n \n ) predicted to result in a frameshift and truncated protein (p.V325Wfs*26) and a maternally-inherited missense variant rs61744120, (\n \n c.1055C>T\n \n , p.T352M) with a MAF of 0.008509 (gnomAD), predicted deleterious by several computational platforms (SIFT, PolyPhen-2 and CADD). Despite the subtle predicted conformational change to the QSOX2 protein, thermostability analysis deemed the\n \n c.1055C>T\n \n variant to be destabilizing (\n \n Supplementary Figure 1A, B\n \n ). Both variants are located in exon 8 of\n \n QSOX2\n \n gene and precede the ERV/ALR sulfhydryl oxidase domain which is lost in the frameshift truncation and likely impacted by the thermally unstable p.T352M substitution (Figure 2A).\n
\n\n As recombinant human GH treatment can improve growth in partial GHI\n \n 3,17\n \n , a trial of rhGH therapy was initiated at 4.5 years (dose 0.025mg/kg/day; 0.3mg/day). Following 1.5 years of therapy, modest increases in height and weight SDS (+0.7 and +0.4 in P1, and +0.9 and +0.6 in P2, respectively) were observed with normalization of serum IGF-1.\n
\n\n \n Index Family 2\n \n
\n\n Proband P3, a female from a consanguineous Pakistani kindred (\n \n Figure 1A\n \n ) was enrolled in the U.K. 100,000 Genomes Project during adolescence with intractable eczema and lichen planus associated with hyper IgE levels. She was born appropriate for gestational age and demonstrated early postnatal growth retardation associated with feeding difficulties. Despite exhibiting moderate catch-up growth, the patient presented at the age of 3 years with intractable asthma, extensive eczema, allergy rhinitis, recurrent respiratory tract, bacterial skin infections and gastrointestinal dysmotility (\n \n Table 1\n \n ).\n
\n\n P3 was identified by interrogating the 100,000 Genomes Project rare disease cohort for subjects harbouring potentially causative\n \n QSOX2\n \n variants, utilising HPO terms related to short stature, eczema and immune dysfunction. P3 with relevant phenotypic features, harboured a recessive homozygous\n \n QSOX2\n \n variant. The variant, an in-frame p.F474del deletion with a MAF of 0.00001314 (gnomAD; no homozygotes) was predicted disease-causing by Mutation Taster\n \n 18\n \n .\n
\n\n At age 24 years, P3 had an adult height SDS of -1.9 and continued to experience ongoing recurrent severe eczema and constipation. Genotyping of family members revealed both parents were heterozygous for the p.F474del variant. The patient\u2019s mother was asymptomatic with normal height (-0.4 SDS). The patient\u2019s father (proband 4; P4) had short stature (height -2.2 SDS) and harboured an additional\n \n de novo\n \n missense variant in\n \n QSOX2\n \n (\n \n c.1720G>T\n \n , p.D574Y), absent in other family members tested. This variant was predicted deleterious by SIFT and PolyPhen-2. P3\u2019s two younger siblings, an asymptomatic sister aged 15 (height -0.4 SDS) and brother aged 18 years with short stature (height -2.0 SDS), constipation and chronic bowel inflammation, declined to participate in this study.\n
\n\n Of note, 2 additional probands with recessively inherited variants in QSOX2, short stature and at least one other feature of QSOX2 deficiency were recently identified and are currently under investigation.\n
\n\n \n Protein altering\n \n QSOX2\n \n variants are significantly associated with reduced height\n \n
\n\n In 420,162 individuals of European ancestry in the UK biobank (UKBB), we identified 200 carriers of 39 rare (MAF<0.1%) protein truncating variants (PTVs) in\n \n QSOX2\n \n . In combination, these PTVs were associated with reduced adult height (beta: -1.13cm, 95% CI: -0.45- to -1.8, p=0.001).\n
\n\n A role for\n \n QSOX2\n \n in the regulation of adult height was also supported by a reported genome-wide significant common variant signal within its first intron (rs7038554-G, beta=0.023 standard deviations, 95% CI=0.021-0.024, p=8.82x10\n \n -154\n \n , n= 3,922,710)\n \n 13\n \n . The height-increasing allele also confers increased\n \n QSOX2\n \n mRNA levels in several tissues\n \n 19\n \n , an effect directionally consistent with the above impact of rare PTVs.\n
\n\n We further detected 6371 adults in UKBB (MAF 0.7%) harbouring the\n \n c.1055C>T\n \n variant (p.T352M, rs61744120). Of these, 31 were homozygotes whose adult heights ranged from -1.7 SDS to +2.0 SDS (mean -0.28, SD 1.02;\n \n Supplementary Table 1\n \n ). Across all carriers of\n \n c.1055C>T\n \n , an additive model showed a non-significant association with adult height (p=0.49).\n
\n\n \n SNP rs61744120 is enriched in the Finnish population and has a significant effect on height\n \n
\n\n The\n \n QSOX2\n \n \n c.1055C>T\n \n , p.T352M variant (rs61744120) has a MAF of 0.05197 in the Finnish population\n \n 20\n \n . Cross validation of FinnGen SNP array data with whole genome sequence data in FINRISK identified 16 homozygotes of\n \n c.1055C>T\n \n , of whom 15 had adult height SDS values below the population average (range -0.1 to -2.5 SDS;\n \n Supplementary Tables 2 and 3\n \n ). In contrast to UKBB, across all carriers of\n \n c.1055C>T\n \n in FINRISK, an additive model showed an association with shorter adult height (p=0.0154, adjusted for age and sex).\n
\n\n \n Phenotypic variability associated with\n \n \n p.T352M (SNP rs61744120) may be due to aberrant splicing\n \n
\n\n Given the height variability demonstrated among homozygotes for this variant, we postulated that alternative splicing transcripts may occur\n \n in vivo\n \n despite predictions from\n \n in silico\n \n computational platforms, human splicing finder\n \n 21\n \n and MaxEntScan\n \n 22\n \n which suggested no impact on splicing.\n \n In vitro\n \n splicing assays (\n \n Supplementary\n \n \n Figure 1C\n \n ) revealed the presence of two transcripts (\n \n Supplementary\n \n \n Figure 1D\n \n ) for the homozygous p.T352M variant, one consistent with unaltered splicing (489bp) and a smaller transcript demonstrating exon 8 skipping (359bp)(\n \n Supplementary\n \n \n Figure 1E\n \n ). This aberrantly spliced transcript, which likely occurs due to naturally weak canonical splice sites, is predicted to result in a frameshift p.N319Kfs*51 and undergo degradation by nonsense mediated mRNA decay.\n
\n\n \n Blunted QSOX2 p.T352M and p.V325Wfs*26 expression cause robust phospho-STAT5B responses to GH\n \n
\n\n In GH-mediated post-natal growth, the binding of GH to hepatic GHR, leads to STAT5B recruitment to activated GHR whereupon STAT5B is tyrosine phosphorylated, homodimerized and translocated to the nucleus to function as a transcription factor regulating expression of target genes including\n \n IGF1\n \n and\n \n IGFBP3\n \n . Dysregulation of this pathway can cause partial or atypical GHI, which, in part, explains varying therapeutic efficacy of rhGH or rhIGF-1 treatments. Since the\n \n in vivo\n \n phenotype of our patients was suggestive of partial GHI, the role of QSOX2 in GH-mediated growth was investigated.\n
\n\n The\n \n QSOX2\n \n \n c.1055C>T\n \n variant could result in potential inefficient aberrant splicing events and a missense variant, p.T352M. We, therefore, assessed p.T352M in our established\n \n in vitro\n \n HEK293-hGHR reconstitution system. Expression of QSOX2 p.T352M was markedly reduced when compared to wild-type (WT) QSOX2 (\n \n Figure 2A\n \n ), corroborating\n \n in-silico\n \n thermodestability predictions. A protein of lower mass was visualised for p.V325Wfs*26 consistent with a frameshift truncation (\n \n Figure 2B\n \n ). Immuno-fluorescent analyses of FLAG-tagged constructs, revealed diminished abundance of both variants at the nuclear membrane, when compared to WT-QSOX2 (\n \n Figure 2C\n \n ).\n
\n\n We next treated\n \n QSOX2\n \n variant-transfected cells with recombinant GH and assessed STAT5B signalling. Intriguingly, although tyrosine phosphorylation of STAT5B (p-STAT5) was more robust in the presence of the QSOX2 variants than WT-QSOX2 (\n \n Figure 2D\n \n ), p-STAT5 was not associated with increased nuclear shuttling, confirmed by subcellular fractionation analysis (\n \n Figure 2E\n \n ). Nuclear p-STAT5 levels were markedly and reproducibly reduced in the presence of both variants compared to WT-QSOX2, with p-STAT5 cytoplasmic accumulation observed by immunofluorescent microscopy (\n \n Figure 2F\n \n ). Notably, the impact of QSOX2 deficiency was restricted to STAT5 since GH-induced phosphorylation and nuclear localisation of STAT3 and STAT1 in the presence of both variants were analogous to WT-QSOX2 (\n \n Figure 2E\n \n ). Dimerization of p-STAT5, utilizing our generated\n \n QSOX2\n \n deficient isogenic cell line (\n \n Extended data Figure 2A, B\n \n ), was unimpeded. We conclude that nuclear import of GH-stimulated p-STAT5 requires functional QSOX2.\n
\n\n \n QSOX2 directly interacts with STAT5B, affecting STAT5B transcriptional activities\n \n
\n\n We next investigated possible interactions between QSOX2 and endogenous STAT5B. From HEK 293-hGHR cell lysates overexpressing WT QSOX2, unstimulated endogenous STAT5B and QSOX2 were readily co-immunoprecipitated (co-IP) (\n \n Figure 2G\n \n ). To negate potential co-IP interferences by antibodies, we also evaluated protein-protein interaction by Nanoluc Binary technology (NanoBit). Reporter-tagged target proteins were generated, and, through complementation assays, positive WT reporter fragment interaction was detected as robust luminescent activity. Importantly, this interaction was disrupted when QSOX2 variants were assayed against WT-STAT5B (\n \n Figure 2H\n \n ). The critical role of QSOX2 in binding and facilitating STAT5B nuclear localization was supported by demonstrating a markedly reduced interaction of WT-QSOX2 with a well-expressed dominant-negative STAT5B p.Q177P variant known to be unable to translocate to the nucleus\n \n 4\n \n (\n \n Figure 2I\n \n ).\n
\n\n The consequence of impaired STAT5B nuclear translocation is impaired transcriptional activities as assessed by GHRE dual luciferase reporter assays, with induction of luciferase activity significantly reduced in the presence of both QSOX2 variants (\n \n Figure 2J\n \n ). Collectively, disruption of QSOX2-STAT5B interactions, either through QSOX2 deficiency or STAT5B defects, significantly impairs STAT5B nuclear localization and transcriptional activities.\n
\n\n \n In frame deletion p.F474del similarly disrupts STAT5B nuclear localisation\n \n
\n\n Expression of QSOX2 p.F474del (identified in P3) was markedly reduced when compared to wild-type (WT) QSOX2 both on immunoblotting and immunofluorescence (\n \n Figure 3A, B\n \n ). GH stimulation elicited robust p-STAT5 in the presence of the p.F474del variant (\n \n Figure 3C\n \n ) although nuclear fractions demonstrated reduced levels of p-STAT5 which appeared to localise to the nuclear membrane (\n \n Figure 3D, E\n \n ). Similar to p.T352M and p.V325Wfs*26 variants, NanoBit complementation assays demonstrated disrupted interactions between p.F474del and WT-STAT5B (\n \n Figure 3F\n \n ).\n
\n\n \n Patient-derived fibroblasts demonstrate aberrant STAT5B activity\n \n
\n\n Patient-derived dermal fibroblasts were procured from P2 and parents, with consent. P2 fibroblasts were noted to have negligible full-length QSOX2 expression when compared to parental (M, F) and control (C) fibroblasts (\n \n Supplementary\n \n \n Figure 2C,\n \n \n Figure 4A\n \n ). Similar to\n \n in vitro\n \n reconstitution studies, P2 cells demonstrated enhanced GH-induced STAT5B phosphorylation, nuclear sparing and cytoplasmic accumulation (\n \n Figure 4B, C\n \n ). Interestingly, when cells were treated with IGF-1, IGF-1-induced unequivocal phosphorylation of AKT and ERK in control, P2 and parental fibroblasts confirming the impacts of QSOX2 deficiency precede\n \n IGF1\n \n transcription (\n \n Figure 4D\n \n ).\n
\n\n \n Mitochondrial dysfunction induced by GH in QSOX2 deficient cells\n \n
\n\n Recent studies have implicated STAT5 in mitochondrial gene expression acting as both activator and repressor\n \n 23,24\n \n . We, therefore, investigated the effect of enhanced GH-induced p-STAT5 on mitochondrial architecture. When compared to control and parental fibroblasts, confocal microscopy showed markedly fragmented mitochondria in P2 fibroblasts only following GH, but not when untreated or following IGF-1 stimulation (\n \n Figure 4E\n \n ). A concomitant increase in phospho-Ser616-DRP1 (Dynamin-related protein 1), a pro-fission marker of mitochondrial fragmentation, was observed\n \n (Figure 4F\n \n ). Increased cytoplasmic p-STAT5 in P2 fibroblasts co-localised to the mitochondrial outer membrane suggesting that in the absence of functional QSOX2, p-STAT5 may impact mitochondrial fragmentation via enhanced DRP1-S616 phosphorylation\n \n 25\n \n (\n \n Figure 4G\n \n ). Profiling of electron transport chain complexes revealed remarkable reduction of all complexes, except complex IV (\n \n Figure 4H\n \n ) which correlated with significant reductions in mitochondrial membrane potential (\n \n Figure I\n \n ), solely in P2 fibroblasts and only after GH provocation.\n
\n\n The role of QSOX2 in GH signalling was further supported by targeted\n \n QSOX2\n \n knockout human chondrocyte cell line which recapitulated the GH-mediated impact on STAT5B phosphorylation and mitochondriopathy (\n \n Supplementary Figure 2D-G\n \n ).\n
\n\n We present the first clinical cases of autosomal recessive QSOX2 deficiency, characterised by a distinct phenotypic spectrum including significant postnatal growth restriction, feeding difficulties, eczema, gastrointestinal dysmotility and mild immunodeficiency. The identified\n \n QSOX2\n \n variants were associated with attenuated STAT5B nuclear localisation. Simultaneously, increased GH-induced cytosolic accumulation of p-STAT5 in dermal fibroblasts correlated with disrupted mitochondrial morphology suggesting potential inter-organelle dysfunction. Hence, we uncovered novel, biologically distinct functions for QSOX2, which, when lost, results in a complex disorder.\n
\n\n Population-based data implicate\n \n QSOX2\n \n as a height-associated locus with several polymorphisms (9:136227369_A/G, 9:136220024_G/T and 9:136229894_A/C,T) identified as adult height determinants\n \n 12,26,27\n \n . Interestingly, genetic association analysis of homozygous missense variant SNP rs61744120 (\n \n c.1055C>T\n \n , p.T352M), enriched in the Finnish population (the Finnish THL Biobank), identified a significant inverse association with adult height. Discernible differences, however, were noted amongst the 16 validated homozygotes. We postulated, based on our\n \n in vitro\n \n assays, that the SNP may give rise to a predicted missense variant as well as mis-spliced skipping of exon 8, where this alternate transcript is liable to undergo nonsense mediated mRNA decay (NMD). Altogether, transcript heterogeneity, different genetic backgrounds in\n \n c.1055C>T\n \n homozygotes, variable penetrance and variable expressivity can all account for imperfect phenotype-genotype correlations\n \n 28,29\n \n .\n
\n\n All clinically associated\n \n QSOX2\n \n variants evaluated strikingly attenuated nuclear localisation of STAT5B with preservation of both GH-mediated phosphorylation and dimerization, akin to the translocation defect of the known STAT5B p.Q177P. This variant is located in the CCD, a module critical for nuclear localisation\n \n 4\n \n . The reduced affinity of STAT5B p.Q177P for QSOX2 implies that the CCD module may be involved in QSOX2 binding. Collectively, our data support nuclear membrane QSOX2 as a new player for directing the nuclear import of STAT5B, a process integral for\n \n IGF1\n \n transcriptional regulation. These findings are consistent with low serum IGF-1 in P1 and P2. Both variants in these siblings precede the active ERV/ALR sulfhydryl oxidase domain, while the p.F474del variant in index family 2 is within this domain and p.D574Y, further downstream. How functional loss of ERV/ALR sulfhydryl oxidase domain and/or other regions contribute to disordered growth and phenotypic variability require further analysis.\n
\n\n The striking mitochondrial dysregulation induced by GH has not been previously reported. Mitochondrial disruption was observed in both our\n \n QSOX2\n \n knock-out gene-edited C28/I2 chondrocytes and in patient fibroblasts. The induction of mitochondrial fragmentation, dramatic reduction in detectable oxidative phosphorylation complexes and decreased mitochondrial membrane potential were only noted after GH stimulation. Whether these effects were a direct consequence of increased cytoplasmic p-STAT5 remains to be fully determined. Notably, both tyrosine phosphorylated and un-phosphorylated forms of STAT5A/B have been reported to translocate to the mitochondria and disrupt the pyruvate dehydrogenase complex (PDC) leading to altered mitochondrial function, decreased membrane potential and overall reductions in mitochondrial proteome quality control\n \n 30,31\n \n .\n
\n\n Cytokine activated STAT5 has also been shown to reduce mitochondrial DNA expression by binding to the D-loop leading to attenuation of the electron transport chain\n \n 23,24\n \n . Global reorganisation of oxidative phosphorylation complexes and significantly attenuated mitochondrial membrane potential in our QSOX2-deficient fibroblasts suggest a definitive impact on mitochondrial metabolism. In neurons, Interferon-\u03b2 (IFN-\u03b2) stimulation leads to mitochondrial localisation of phosphorylated STAT5 which induces phospho-S616-DRP1 via upregulation of PGAM5 phosphatase, thereby promoting mitochondrial fission\n \n 25\n \n . In the QSOX2 deficient fibroblasts, the striking detection of phospho-S616-DRP1 is consistent with abundance of GH-stimulated cytoplasmic tyrosine phosphorylated STAT5B. Overall, our findings suggest a definitive impact of QSOX2 deficiency on mitochondrial metabolism, possibly involving STAT5B.\n
\n\n Disorders of mitochondrial DNA are often characterised by altered gastrointestinal sensorimotor kinetics\n \n 32\n \n . Interestingly, all\n \n QSOX2\n \n deficient patients presented with gastrointestinal (GI) manifestations, the cumulative effect of which may be due, in part, to dysregulated STAT5B signalling on mitochondrial dynamics although a distinct role for QSOX2 in GI tract physiology remains to be elucidated. The possibility of GI manifestations contributing to growth impairment in our QSOX2 deficient patients cannot be entirely discounted although optimisation of nutrition/PEG feeding in P1 and P2 did not result in catch-up growth.\n
\n\n Despite functional evidence that GH exerts disruptive effects in QSOX2 deficiency, a 2.0-year regime of recombinant-GH normalized serum IGF-1 and promoted modest increases in growth velocity in both P1 and P2. GI symptoms, however, did not improve with GH therapy. Interestingly, murine studies have demonstrated an intestinotropic effect of IGF-1, independent of GH, which positively regulate intestinal growth and physiology\n \n 33\n \n . We hypothesize that tissue-specific deficiency of IGF-1 may, in part, account for disease pathogenesis and as demonstrated in other partial GHI patients, initiation of rhIGF-1 therapy alone or in combination with rhGH in our patients may be able to induce accelerated/sustainable growth and improve other symptoms\n \n 3,17,34\n \n .\n
\n\n In summary, we describe a novel human disease, QSOX2 deficiency, which should be suspected in individuals with atypical GHI, low IGF-1 and prominent immune/gastrointestinal dysregulation. Therapeutic recombinant IGF-1 may potentially circumvent the GH-mediated STAT5B molecular defect and potentially alleviate organ specific disease. We describe new functions of QSOX2, located at the nuclear membrane, namely acting as a \u201cgatekeeper\u201d for regulating import of p-STAT5B and important for mitochondrial integrity. Ongoing and future work include monitoring the young probands and advance the understanding of cellular mechanisms involved in QSOX2 deficiency.\n
\n\n \n Antibodies and probes\n \n
\n\n Rabbit anti-QSOX2 antibody (ab121376, RRID:AB_11128050), Monoclonal ANTI-FLAG\u00ae M2 antibody (Sigma Aldrich F3165, RRID:AB_259529), Rabbit anti-Phospho-Stat5 antibody (Tyr694) (Cell Signalling Technology D47E7, RRID:AB_10544692), Rabbit anti-phospho-Stat3 antibody (Tyr705) (Cell Signalling Technology D3A7, RRID:AB_2491009), Rabbit anti-phospho-Stat1 antibody (Tyr701) (Cell Signalling Technology Clone 58D6, RRID:AB_561284), Rabbit anti-Tom20 antibody (Cell Signalling Technology D8T4N, RRID:AB_2687663), Rabbit anti-phospho-DRP1 (Ser616) (Cell Signalling Technology D9A1, RRID:AB_11178659), Rabbit anti-GAPDH antibody (ab9485, RRID:AB_307275), Mouse anti-Actin beta monoclonal antibody (ab6276, RRID:AB_2223210), Mouse anti-Histone Deacetylase 1 antibody (Santa-Cruz biotechnology sc-81598, RRID:AB_2118083), Rabbit anti-GFP antibody (ab290, RRID:AB_303395), Fluorescent probe - MitoTracker\u2122 Red (M22425, Thermo Fisher Scientific), Rat anti-Human phospho-STAT5a/b Y694/Y699 (R&D Systems Clone MAB4190), Mouse anti-alpha Tubulin antibody DM1A (ab7291, RRID:AB_2241126), Total OXPHOS Rodent WB Antibody Cocktail (ab110413, RRID:AB_2629281), Rabbit anti-Phospho-Akt (Ser473) antibody (Cell Signalling Technology D9E, RRID:AB_2315049), Rabbit anti-Akt (pan) antibody (Cell Signalling Technology C67E7, RRID:AB_915783), Rabbit anti-MAP Kinase (ERK-1, ERK-2) antibody (Sigma Aldrich M5670, RRID:AB_477216), Monoclonal anti-MAP Kinase, Activated (Diphosphorylated ERK-1&2) antibody (Sigma Aldrich M9692, RRID:AB_260729), Goat anti-Rat IgG (H+L) Highly Cross-Adsorbed Secondary Antibody Alexa Fluor Plus 488 (A48262 RRID:AB_2896330), Goat anti-mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody Alexa Fluor Plus 488 (A32723, RRID:AB_2633275), Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor Plus 647 (A32733, RRID:AB_2633282), IRDye\u00ae 800CW Goat anti-Mouse IgG (RRID:AB_10793856), IRDye\u00ae 800CW Goat anti-Rabbit IgG (RRID:AB_10796098), IRDye\u00ae 680RD Goat anti-Mouse IgG (RRID:AB_2651128), IRDye\u00ae 680RD Goat anti-Rabbit IgG (RRID:AB_2721181), Tetramethylrhodamine, ethyl ester (TMRE, ab113852).\n
\n\n \n \n QSOX2\n \n \n \n Variant Detection and Confirmation\n \n
\n\n Variants in\n \n QSOX2\n \n were found on whole exome/genome sequencing and confirmed by Sanger sequencing using primers amplifying exon 8 (forward: 5\u2032-CCAGGACAGGGAGACTTG-3\u2032 and reverse: 5\u2032-GGTGGAGAGCACCTCAG-3\u2032), exon 10 (forward: 5\u2032-CCCAGTCAAGAAGGCAG-3\u2032 and reverse: 5\u2032-AGTACATGCCTTTGCACAC-3\u2032) and exon 12 (forward: 5\u2032-GAGTGGGAGTCCGGTTG-3\u2032 and reverse: 5\u2032-CATCCGATGTGAAACCAG-3\u2032) of\n \n QSOX2\n \n . Pathogenicity of both variants was evaluated using a combination of predictive tools: Sorting Intolerant from Tolerant, Polymorphism Phenotyping v2, Combined Annotation Dependent Depletion and Mutation taster.\n
\n\n \n Protein Structure Modelling and Thermostability Analysis\n \n
\n\n Protein 3D modelling of the Alpha Fold Protein Structure Database\n \n 35\n \n QSOX2 crystal structure Q6ZRP7 was performed using the tool PyMOL (Schrodinger, LLC. 2010. The PyMOL Molecular Graphics System, Version X.X) with thermostability of the missense mutant protein assessed using computational platforms: DynaMut\n \n 36\n \n , I-Mutant\n \n 37\n \n , SDM\n \n 38\n \n , DUET\n \n 39\n \n , MUpro_SVM\n \n 40\n \n and mCSM\n \n 41\n \n .\n
\n\n \n UK Biobank (UKBB) data analysis\n \n
\n\n We included 420,162 samples of European ancestry in the UKBB for exome-wide association tests. For the 450K release of exome-sequencing data in the UKBB, we performed individual and variant level quality control procedures previously described by Gardner\n \n et al\n \n .\n \n 42\n \n Variants were annotated using ENSEMBL Variant Effect Predictor (VEP) v104\n \n 43\n \n . Protein truncating variants were defined as stop gain, frameshift, splice acceptor and splice donor variants. The burden test assumed the presence or absence of variants of interest in a gene as an indicator variable, which was regressed against the phenotype in a linear mixed model using BOLT-LMM v2.3.6\n \n 44\n \n on the UKBB Research Analysis Platform (RAP). Covariates adjusted in the burden test included age at assessment (UKBB Data-field 21003), age squared, the whole-exome sequencing batches (as a categorical variable, either 50K, 200K, or 450K) and the first 10 genetic principal components (UKBB Data-field 22009.1-10).\n
\n\n \n Quality check for rs61744120 imputation and data analysis\n \n
\n\n To study the quality of the imputed SNP rs61744120, we compared the genotypes between WGS and FinnGen imputed data in FINRISK participants where data was available for both formats. The FINRISK cohorts comprise the respondents of representative, cross-sectional population surveys that are carried out every 5 years since 1972 (to assess the risk factors of chronic diseases and health behaviour in the working age population) in 3-5 large study areas of Finland. THL Biobank host samples were collected in the following survey years: 1992, 1997, 2002, 2007, and 2012. Genome-wide imputation was done as part of the FinnGen project using Sequencing Initiative Suomi (SISu) project data as reference.\n
\n\n Individuals with the minor/minor genotype were identical between WGS and both releases of the imputed data. However, there were variations in minor/major and major/major genotypes in 10 individuals producing an error rate of 0.25%. The additive genetic association model was utilised to estimate the proportional risk of disease i.e. reduction in height associated with this single nucleotide polymorphism. Calculation of height standard deviation scores based on raw height data of minor/minor homozygotes was performed using Finnish population based references for healthy subjects as outlined by Saari\n \n et. al\n \n (2011)\n \n 45\n \n .\n
\n\n \n \n In-vitro\n \n \n \n splicing assay\n \n
\n\n An\n \n in-vitro\n \n splicing assay was designed, as previously described by Maharaj\n \n et al\n \n .\n \n 46\n \n , using the Exontrap vector pET01 (MoBiTec). A designated DNA sequence, including exons 7 and 8 of\n \n QSOX2\n \n as well as intervening introns, was selectively cloned into the multiple cloning site of the exontrap splicing machinery. Clones were selected and verified by sanger sequencing using vector-specific primers ET 06 (forward: 5\u2032-GCGAAGTGGAGGATCCACAAG-3\u2032) and ET 07 (reverse: 5\u2032-ACCCGGATCCAGTTGTGCCA-3\u2032). Site directed mutagenesis to generate the\n \n c.1055C>T\n \n (p.T352M) variant was performed using the QuikChange II XL site-directed mutagenesis kit (Agilent, 200521) according to the manufacturer\u2019s instructions. Empty pET01 vector,\n \n QSOX2\n \n -WT and variant clones were transfected into mammalian HEK293 cells for 24 hours followed by RNA extraction. cDNA synthesis was performed using the vector-specific hexamer GATCCACGATGC and RT-PCR conducted using pET01 primer 02 (forward: 5\u2032-GAGGGATCCGCTTCCTGGCCC-3\u2032) and primer 03 (reverse: 5\u2032-CTCCCGGGCCACCTCCAGTGCC-3\u2032). PCR products were analysed on a 2% agarose gel and bands gel extracted, column purified and confirmed by Sanger sequencing.\n
\n\n \n Site-directed Mutagenesis\n \n
\n\n Site-directed mutagenesis of a QSOX2 (NM_181701.4) Human Tagged ORF Clone (GenScript, ID: OHu07590C) was performed using the QuikChange II XL site-directed mutagenesis kit (Agilent, 200521) according to the manufacturer\u2019s instructions. Primers for generation of\n \n QSOX2\n \n variants were designed using the online tool https://www.agilent.com/store/primerDesignProgram.jsp.\n
\n\n
\n\n \n Primary fibroblast cell culture\n \n
\n\n Fibroblast isolation was performed from skin punch biopsies of proband 2, parents and a healthy control. Immediately after excision, the specimen was incubated in DMEM high glucose supplemented with 10% Fetal Bovine Serum (FBS) and 1% Penicillin/Streptomycin. The skin specimen, chopped into 1mm cubes, was subsequently digested using a mixture of nutrient media (DMEM high glucose supplemented with 10% FBS, 1% penicillin/streptomycin and 1:100 non-essential amino acids), 0.25% collagenase and 0.05%\n \n Dnase\n \n I. The mixture, incubated at 37 \u00b0C in 5% CO\n \n 2\n \n overnight, was centrifuged at 1000rpm for 5min and the pellet resuspended in fibroblast primary culture media (DMEM high glucose with 10 % FBS, 1% penicillin/streptomycin and 1:100 non-essential amino acids). The resuspended mixture was plated in a 0.1% gelatin coated T25 flask and left in an incubator at 37\u00b0C in 5% CO\n \n 2\n \n until fibroblast cultures were established.\n
\n\n \n Cell culture, GH/IGF-1 stimulation and nuclear fractionation\n \n
\n\n Dermal fibroblasts and C28/I2 chondrocytes were cultured in DMEM high glucose supplemented with 10% FBS and 1% penicillin/streptomycin. HEK 293-hGHR cells\n \n 47\n \n were similarly cultured in DMEM high glucose base media with selection antibiotic, G-418 (Sigma Aldrich) at a concentration of 400\u03bcg/ml. Prior to GH treatment, cells were serum deprived for at least 24hours in serum-free media supplemented with 0.1% Bovine serum albumin (BSA). Optimal standardised human GH (Cell Guidance Systems) concentration (500ng/ml) was used for all experiments with a stimulation time of 20minutes at 37 \u00b0C in 5% CO\n \n 2\n \n . For IGF-1 stimulation, cells were similarly serum deprived for 24hours prior to treatment with recombinant human IGF-1 (Peprotech, 100ng/ml) for 30minutes at 37 \u00b0C in 5% CO\n \n 2\n \n . Nuclear and cytoplasmic cell fractions were prepared using the NE-PER\u2122 Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher) according to the manufacturer\u2019s instructions. Cross contamination of cellular fractions was negligible.\n
\n\n \n CRISPR-Cas9 Engineered Knockout of\n \n QSOX2\n \n in C28/I2 Human Chondrocyte Cell Line\n \n
\n\n CRISPR gene editing was achieved utilizing the protocol outlined by Ran et. al\n \n 48\n \n . Guide sequences were designed using the online CRISPR Design Tool (http://tools.genome-engineering.org). The single guide RNA oligos (Forward 5\u2019-GGGACCTGCGCTGAGAG-3\u2019 and Reverse 5\u2019-GCGGTAAGGAAAGAAATACGG-3\u2019) were then cloned into pSpCas9(BB)-2A-GFP (PX458), a gift from Feng Zhang (Addgene plasmid #48138; http://n2t.net/addgene:48138; RRID:Addgene_48138, https://www.addgene.org/48138)\n \n 48\n \n and introduced into immortalized C28/I2 (Sigma Aldrich\u2122, Catalog no. SCC043) human chondrocyte cells via transfection using Lipofectamine\u2122 3000 according to manufacturer\u2019s instructions. After 72 hours, GFP-positive cells were cell sorted by fluorescence-activated cell sorting into prepared 96-well plates, to ensure single cell clonal expansion. Colonies were expanded and genotyped after 4 to 6 weeks.\n
\n\n \n Co-immunoprecipitation\n \n
\n\n In order to probe the interaction between QSOX2 and endogenous STAT5B, 7\u00b5g of QSOX2 cDNA was transfected into 2x10\n \n 6\n \n HEK 293-hGHR cells (10cm dish) using Lipofectamine\u2122 3000 according to manufacturer\u2019s instructions. After 48hours cells were lysed with 0.5% NP-40 buffer (0.5% NP-40, 20 mM Tris\u2013HCl, 150 mM NaCl, 1 mM EDTA, 10% glycerol, 1 mM PMSF). The lysate was added to a micro-centrifuge tube, placed on a rotary mixer for 1 hour at 4\u00b0C, then centrifuged for 20 minutes at 14,000g. Protein concentration was quantified using a Bradford protein assay (Bio-Rad). Lysate was equally divided into three separate micro-centrifuge tubes and Immunoprecipitation carried out at 4\u00b0C overnight following addition of primary antibodies (5\u00b5g anti-STAT5B, 5\u00b5g anti-QSOX2 and 5\u00b5g Goat anti-mouse IgG - H&L - Fab Fragment Polyclonal Antibodies) and Protein G Sepharose beads (Sigma-Aldrich). Bound proteins were extracted from coated beads and analysed by immunoblotting.\n
\n\n \n Pull down assay\n \n
\n\n To assess whether the presence or absence of QSOX2 impacts dimerization of STAT5B,\n \n QSOX2\n \n wild type and knockout C28/I2 cells were transfected in parallel with pCMV6-AC-GFP-STAT5B and pCMV6-AC-STAT5B-FLAG plasmids using Lipofectamine\u2122 3000 according to manufacturer\u2019s instructions. After 12hours, complete media was removed and cells cultured in serum free media supplemented with 0.1% BSA for a further 24hours. Cells were treated with GH 500ng/ml for 20 minutes prior to addition of lysis Buffer (50mM Tris HCl, pH 7.4, with 150mM NaCl, 1mM EDTA, and 1% TritonX-100). Lysates were placed on a rotary mixer for 1hour at 4\u00b0C prior to clarification by centrifugation at 14,000xg for 15minutes. ANTI-FLAG M2-Agarose Affinity Gel beads (Sigma Aldrich) were equilibrated with TBS prior to addition of protein samples and incubated at 4\u00b0C overnight on a rotary mixer. Coated beads were collected and washed with TBS (twice). Samples were eluted using SDS sample buffer, separated by SDS-PAGE gel electrophoresis and probed by immunoblotting using monoclonal anti-FLAG and monoclonal anti-GFP antibodies.\n
\n\n \n Immunoblotting\n \n
\n\n Whole cell lysates were prepared by addition of RIPA buffer (Sigma Aldrich) supplemented with protease and phosphatase inhibitor tablets (Roche). Protein concentrations were quantified using a Bradford protein assay (Bio-Rad) and lysates denatured by addition of SDS sample buffer 6\u00d7 (Sigma Aldrich) and boiled for 5 minutes at 98\u00b0C. A 20-\u00b5g bolus of protein was loaded into the wells of a 4% to 20% sodium dodecyl sulfate-polyacrylamide gel electrophoresis gel (Novex) prior to electrophoretic separation using MOPS buffer. Protein transfer to nitrocellulose membrane was achieved by electroblotting at 15 V for 45 minutes. The membrane was blocked with either 5% fat-free milk or BSA in tris-buffered saline/0.1% Tween-20 (TBST) and left to gently agitate for 1 hour. Primary antibody was added at a concentration of 1:1000 with housekeeping control at a concentration of 1:10,000. Primary antibody incubation was left overnight at 4\u00b0C with gentle agitation. The membrane was then washed for 5 minutes (3 times) with TBST. Secondary antibodies were added at a concentration of 1:5000 to blocking buffer and the membrane incubated at 37\u00b0C for 60 to 90 minutes. The membrane was subsequently washed 3 times (5 minutes each) with TBST and visualized with the LI-COR Image Studio software for immune-fluorescent detection.\n
\n\n \n Mitochondrial Membrane Potential Assay\n \n
\n\n Fibroblasts were seeded in clear bottomed 96 well plates (1x10\n \n 5\n \n cells/well) and cultured at 37\u00b0C in 5% CO\n \n 2\n \n overnight. Culture medium was aspirated, replaced with serum free base media supplemented with 0.1% BSA and cells incubated at 37\u00b0C for a further 8hours. GH (500ng/ml) and depolarisation control carbonilcyanide p-triflouromethoxyphenylhydrazone, FCCP (20\u03bcM) were added to relevant wells and plate incubated at 37\u00b0C in 5% CO\n \n 2\n \n for 10minutes. Tetramethylrhodamine ethyl ester (TMRE) was then added at a concentration of 500nM and cells incubated for a further 20minutes at 37\u00b0C in 5% CO\n \n 2\n \n . Media was aspirated from wells and replaced by 100\u03bcl of PBS/0.2% BSA. This process was repeated prior to fluorescence measurement (Ex/Em = 549/575nm) using the CLARIOstar Multimode Plate Reader (BMG Labtech).\n
\n\n \n GHRE Luciferase reporter assay\n \n
\n\n HEK 293-hGHR cells were seeded in six-well plates and transiently transfected with 2.5\u03bcg DNA per well: 1.0\u03bcg pGL2 8xGHRE (growth hormone response element) luciferase reporter plasmid, 0.5\u00b5g STAT5B WT, 0.5\u00b5g QSOX2 WT/mutant cDNA /empty vector and 0.5\u00b5g pRL-SV40 (\n \n Renilla\n \n luciferase). After overnight incubation, culture medium was replaced with serum free DMEM supplemented with 0.1% BSA and incubated for a further 8hours. Cells were stimulated with GH (500\u2009ng/ml) for 24 hours and lysates collected and assayed using the Dual-Luciferase\u00ae Reporter Assay System (Promega, E1910) on the CLARIOstar Multimode Plate Reader (BMG Labtech).\n
\n\n \n Immunofluorescence\n \n
\n\n Cells seeded on glass coverslips (24 well plate) were fixed with 4% paraformaldehyde for 15minutes. Cells were then washed three times in PBS and permeabilized in ice cold 100% methanol for 10minutes at -20\u00b0C. After three further PBS washes, coverslips were incubated in Blocking buffer (1X PBS / 5% goat serum / 0.3% Triton\u2122 X-100) at room temperature for 60minutes. Primary antibody (rat anti-STAT5B, rabbit anti-QSOX2, rabbit anti-Tom20, rabbit anti-phospho-DRP1, mouse anti-alpha tubulin) reconstituted in dilution Buffer (1X PBS / 1% BSA / 0.3% Triton\u2122 X-100 buffer) was added to cells and left at 4\u00b0C overnight with gentle agitation. Cells were then washed three times with PBS prior to addition of fluorescent secondary antibody and left at room temperature for 90minutes (protected from light). Coverslips were counterstained with DAPI and washed with PBS to mounting on microscope slides.\n
\n\n \n MitoTracker immunostaining\n \n
\n\n For MitoTracker staining of mitochondria, fibroblast and C28/I2 cells were seeded at a density of 2.5 \u00d7 10\n \n 3\n \n per well (24 well plate) on glass coverslips. The MitoTracker lyophilized probe was reconstituted in anhydrous DMSO to a stock concentration of 1mM. A working concentration of 100nM was established by dilution in nutrient media prior to addition to cells and incubated at 37\u00b0C in 5% CO\n \n 2\n \n for 30minutes. After incubation, cells were washed twice with phosphate buffered saline (PBS) and coverslips fixed with 4% paraformaldehyde for 15minutes. Permeabilization was achieved by addition of 0.2% TritonX-100 for 5minutes. Coverslips were counterstained with DAPI and washed with PBS to mounting on microscope slides. Images were obtained using the 63x oil objective of the confocal Laser scanning microscope 710.\n
\n\n \n Generation of Nanoluc SmBiT and LgBiT (STAT5B-N-small BiT and QSOX2-N-Large BiT fusion vectors) by Gibson Assembly\n \n
\n\n Wild type STAT5B and QSOX2 constructs were generated by cloning Nanoluc small BiT (SmBiT) and large BiT (LgBiT) sequences to the N terminus of each receptor using a flexible Glycine-(gly)-Serine-(ser) linker by Gibson assembly. Primers were designed using the Benchling assembly wizard (Benchling Biology Software 2020, https://benchling.com). Constructs were generated following the Gibson assembly methodology according to the manufacturer\u2019s instructions (Gibson Assembly Master Mix, NEB\u00ae). A Phusion High-Fidelity PCR Kit (NEB\u00ae) was used to amplify target sequences. Thermocycling conditions were as follows: Denaturation at 98\u00b0C for 3minutes, amplification 35 x (98\u00b0C for 30 seconds and 72\u00b0C for 20-30seconds/Kb) and elongation at 72\u00b0C for 10minutes. Gel electrophoresis was used to visualise products prior to\n \n Dpn\n \n I digestion. Fragments were ligated using NEBuilder\u00ae HiFi DNA Assembly Master Mix (NEB\u00ae) and transformed using NEB\u00ae competent E. coli cells. Single colonies were selected for mini-preparation, and accurate assembly of constructs verified by Sanger sequencing.\n \n QSOX2\n \n (p.T352M, p.V325Wfs*26, p.F474del) and\n \n STAT5B\n \n (p.Q177P) variant constructs were generated by site directed mutagenesis as outlined above.\n
\n\n \n NanoBiT complementation assays\n \n
\n\n Protein\u2013protein interactions were assessed with NanoBiT complementation assays using the STAT5B WT/mutant and QSOX2 WT/mutant plasmids N terminally fused with NanoBiT fragments (LgBiT and SmBiT). HEK 293-hGHR cells (1x10\n \n 5\n \n cells/well) were seeded in poly-D-lysine coated white bottom 96-well plates and plasmids were reverse-transfected using Lipofectamine\u2122 3000 according to the manufacturer\u2019s instructions. The optimal DNA concentration required for maximum bioluminescence signal was determined to be 200ng per well; 100ng SmBiT-STAT5B and 100ng LgBiT-QSOX2. 24hours post-transfection, cell culture medium was removed and replaced with 100\u00b5L NanoBiT assay buffer (pH 7.4, HBSS 1X, HEPES 24mM, NaHCO\n \n 3\n \n 3.96mM, CaCl\n \n 2\n \n 1.3mM, MgSO\n \n 4\n \n 1mM, BSA 0.1%) per well and equilibrated for 1 hour at 37\u00b0C in 5% CO\n \n 2\n \n . Following equilibration, six (6) baseline luminescence readings were recorded using the CLARIOstar Multimode Plate Reader (BMG Labtech). Furimazine (Nanolight Technology) was prepared in a 1:50 dilution with assay buffer and 25\u00b5l added to each well following baseline measurements and readings continued for 1hour.\n
\n\n \n Statistics\n \n
\n\n Statistical analysis was performed using either a 2-tailed Student\u2019s t test or one-way ANOVA (where three or more data groups were compared) to generate P values. P \u22640.05 was considered statistically significant. Data are presented as mean \u00b1 SD in all figures in which error bars are shown.\n
\n\n \n Table 1. The clinical and biochemical profiles of the probands harbouring bi-allelic\n \n QSOX2\n \n variants.\n \n
\n| \n | \n\n \n \n Proband 1 (P1, Twin 1)\n \n \n | \n \n \n \n Proband 2 (P2, Twin 2)\n \n \n | \n \n \n \n Proband 3 (P3)\n \n \n | \n \n \n \n Proband 4 (P4)\n \n \n | \n
| \n \n Sex\n \n\n Gestational age (weeks)*\n \n\n Birth weight (kg)\n \n\n Birth weight SDS\n \n | \n \n \n Male\n \n\n 30\n \n\n 1.3\n \n\n -0.5\n \n | \n \n \n Male\n \n\n 30\n \n\n 1.0\n \n\n -1.6\n \n | \n \n \n Female\n \n\n 40\n \n\n 3.2\n \n\n -0.5\n \n | \n \n \n Male\n \n\n \n\n \n | \n
| \n \n \n Auxology\n \n (aged 1.3 yrs)\n \n\n Height (cm)\n \n\n Height SDS**\n \n\n Weight (kg)\n \n\n Weight SDS**\n \n\n BMI SDS\n \n\n Target height SDS\n \n\n HC SDS\n \n | \n \n \n \n\n 69.8\n \n\n -3.9\n \n\n 7.9\n \n\n -2.6\n \n\n -0.2\n \n\n 0.04\n \n\n 0.1\n \n | \n \n \n \n\n 67.0\n \n\n -5.0\n \n\n 7.2\n \n\n -3.3\n \n\n -0.3\n \n\n 0.04\n \n\n -0.1\n \n | \n \n \n \n Auxology\n \n (aged 24 yrs)\n \n\n 152.7\n \n\n -1.9\n \n\n 47.6\n \n\n -1.5\n \n\n -0.77\n \n | \n \n \n \n Auxology\n \n (aged 53 yrs)\n \n\n 163.4\n \n\n -2.2\n \n\n 69.2\n \n\n -1.27\n \n\n 0.92\n \n | \n
| \n \n \n Biochemistry\n \n \n\n Basal GH (\u00b5g/L)\n \n\n Post provocation GH (\u00b5g/L)\n \n\n IGF-I (ng/ml) (NR 47-231)\n \n\n IGF-I SDS\n \n\n IGFBP 3 (mg/L) (NR 1.1-5.2)\n \n\n Prolactin (mU/L) (NR 47-438)\n \n\n \n \n \n\n \n Immunology\n \n \n\n IgA IgG\n \n\n IgM (g/L) (NR 0.5-2.2)\n \n\n IgE (kU/L)\u00a0(NR <52)\n \n\n T and B cells\u2020\n \n\n Na\u00efve CD4 and na\u00efve CD8\n \n\n Class switched memory B cells\n \n\n Transitional B cells\n \n\n CD21 low B cells\n \n\n CD4+CD25+FoxP3+\n \n\n Gamma delta T cells (NR 1-5%)\n \n\n Double negative T cells (NR <2%)\n \n\n Vaccine responses to tetanus and pneumococcal protein vaccine\n \n\n Complement levels; C3 and C4\n \n\n STAT5 ptyr (%) (control 1.9%)\n \n\n \n \n \n\n \n Clinical features\n \n \n\n Downslanted palpebral fissures\n \n\n Allergies\n \n\n Recurrent Respiratory infections\n \n\n \n\n Asthma\n \n\n Atopic eczema\n \n\n \n\n Gastrointestinal disturbance\n \n\n \n\n \n\n Other\n \n\n \n \n \n\n \n \n \n\n \n\n \n\n \n\n \n Radiological\n \n \n\n Bone age (yr)\n \n\n Pituitary MRI\n \n\n Skeletal survey\n \n\n Chest CT with contrast\n \n | \n \n \n \n\n 3.6\n \n\n 3.6\n \n \u2021\n \n \n\n 30.1\n \n\n -2.4\n \n\n 2.2\n \n\n 396\n \n\n \n\n \n\n Normal\n \n\n 0.2\n \n\n 2.9\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n\n 12.2%\n \n\n 4.4%\n \n\n Normal\n \n\n \n\n Normal\n \n\n 7.7\n \n\n \n\n \n Yes\n \n\n Egg, soy, milk\n \n\n Yes (prophylactic azithromycin)\n \n\n Yes\n \n\n Yes\n \n\n \n\n Chronic constipation, recurrent gastroenteritis, gastro-oesophageal reflux\n \n\n \n\n \n\n Recurrent fractures on minor trauma\n \n\n \n\n 1.3\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n | \n \n \n \n\n 7.4\n \n\n 9.2\n \n\n 50.5\n \n\n -2.0\n \n\n 2.6\n \n\n 244\n \n\n \n\n \n\n Normal\n \n\n 0.3\n \n\n 7.3\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n\n 12.9%\n \n\n 2.7%\n \n\n Normal\n \n\n \n\n Normal\n \n\n 7.7\n \n\n \n\n \n Yes\n \n\n Egg, soy, milk\n \n\n Yes (prophylactic azithromycin)\n \n\n Yes\n \n\n Yes\n \n\n \n\n Oral feeding aversion requiring PEG, chronic constipation, recurrent gastroenteritis, gastro-oesophageal reflux\n \n\n \n\n \n\n Hypospadias, bilateral inguinal hernias\n \n\n \n\n \n\n 1.3\n \n\n Normal\n \n\n Normal\n \n\n Normal\n \n | \n \n \n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n No\n \n\n No\n \n\n Yes (in childhood)\n \n\n \n\n Yes\n \n\n Yes\n \n\n \n\n Constipation\n \n | \n \n \n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n No\n \n\n No\n \n\n No\n \n\n \n\n No\n \n\n No\n \n\n \n\n Bile acid malabsorption, cholelithiasis\n \n | \n
\n * Placental insufficiency from 16 weeks gestation and born by emergency caesarean section. GH provocative testing undertaken using glucagon stimulus with GH <6.7\u00b5g/L indicative of GH deficiency (UK guidance).\n \n \u2021\n \n Technical difficulties with likely inaccuracy of analysis (nadir glycaemia 3.3 mmol/L). **Height, weight and target height standard deviation scores (SDS) calculated using the sex and age-appropriate UK-WHO references (PCPAL GrowthXP version 2.8). \u2020Immunology tests confirmed normal T cell number with normal proliferation to the mitogen PHA, B cells were normal with normal tetanus and pneumococcus vaccine responses. STAT5 ptyr, STAT5 tyrosine phosphorylation at baseline was increased and there were normal responses to IL-2, 7 and 15. Hypospadias and inguinal hernia repairs in P2 (Twin 2) aged 2.5 yr. Bone age calculated by BoneXpert 3.0 (Visiana) at chronological age 1.3 years. NR, normal range; HV, height velocity; HC, head circumference.\n
\n\n Supplementary data\n
\n \n\n The increased trade in live wildlife for pets and other uses potentially elevates colonization pressure, and hence the risk of invasions. Yet, we have limited knowledge on number of species traded outside their native ranges as aliens. We create the most comprehensive global live terrestrial vertebrate trade database, and use it to investigate the richness of alien species in trade, and correlates of establishment richness, for aliens across countries worldwide. We identify 10,378 terrestrial vertebrate species in the live wildlife trade globally. Approximately 90.1% of these species are aliens, and 9.1% of the aliens establish populations. Large numbers of alien species have been imported to countries with high incomes and large areas. Such countries are also hotspots for establishment, along with some island nations. Colonization pressure and insularity consistently promote establishment richness across countries. Socio-economic and climatic factors are also associated with establishment richness for different taxa. This study identifies daunting challenges to global biosecurity from future invasion risks posed by wildlife trade.\n
\n\n The wildlife trade is a valuable global industry worth US\n \n $\n \n billions annually\n \n \n 1\n \n \n . Traded wildlife includes native species sold within the countries where they naturally occur, and alien species that are traded beyond the borders of their native countries. The latter present major challenges to global biosecurity\n \n \n 2\n \n \n , as they can escape or be released into the wild and establish reproducing populations, posing threats to species persistence\n \n \n 3\n \n \n , and emerging disease risks\n \n \n 4\n \n \n . The trade in live wildlife has grown dramatically over recent decades as increasing human populations and incomes have fostered demand for exotic pets\n \n \n 5\n \n ,\n \n 6\n \n \n , which can be supplied by improved international transport capacity and rapid growing online trade\n \n \n 7\n \n \u2013\n \n 9\n \n \n . The volume and number of alien species in trade has increased concomitantly: millions of wild-caught or captive-bred live animals are traded annually as pets, or for zoos, food, and other uses\n \n \n 5\n \n \n , including many of the most notorious invasive alien species (e.g. Red-eared Slider\n \n Trachemys scripta elegans\n \n , African Clawed Frog\n \n Xenopus laevis\n \n , Burmese Python\n \n Python bivittatus\n \n )\n \n \n 10\n \n ,\n \n 11\n \n \n .\n
\n\n Previous studies have addressed the impacts of the wildlife trade on species persistence and abundance in their native distributions\n \n 5,6,8,9,12\u221214\n \n , but invasion risks from alien species in trade have received comparatively less attention\n \n \n 7\n \n ,\n \n 10\n \n ,\n \n 15\n \n \n . Studies to date have focused on a single taxon (e.g. birds or reptiles), or a specific human use (e.g. pets), and on regional scales\n \n \n 10\n \n ,\n \n 15\n \n ,\n \n 16\n \n \n . The key questions of how many species are involved in the live wildlife trade as aliens outside their native ranges, and to what extent these aliens establish feral populations worldwide, remain to be resolved.\n
\n\n The number of alien species that establish viable populations in an area, here termed\n \n establishment richness\n \n , is determined by three variables: the number of alien species introduced (colonization pressure), the number of individuals of each species introduced (propagule pressure), and the probability that a founding individual leaves a surviving lineage (lineage survival probability)\n \n \n 17\n \n \n . Colonization pressure is the number of species with the potential to establish viable alien populations, while propagule pressure and lineage survival probability determine which, and how many, introduced alien species actually do establish. Socio-economic factors and environmental conditions are likely to affect these variables, and hence numbers of established alien species\n \n \n 10\n \n ,\n \n 11\n \n ,\n \n 18\n \n ,\n \n 19\n \n \n . Lineage survival probability will depend on abiotic and biotic conditions, such as climate match, and native species richness\n \n \n 20\n \n \n . While islands tend to have higher establishment richness of alien species than mainland regions\n \n \n 21\n \n ,\n \n 22\n \n \n , it is currently disputed whether this is due to higher colonization or propagule pressures, or natural features of islands, such as more amenable climates or lower biotic resistance from the relatively impoverished biotas found on islands. Colonization pressure data are key to distinguishing these effects, yet there has to date been no attempt to disentangle the effects of colonization pressure and other factors on global spatial patterns of establishment richness along a specific invasion pathway.\n
\n\n Here, we compile the most comprehensive global live (terrestrial) vertebrate trade database (GLVTD) available to date (see Methods, Supplementary Data 1). The GLVTD catalogues most species in four vertebrate groups \u2013 mammals, birds, reptiles and amphibians \u2013 that are involved in the live wildlife trade (including illegal trade), or kept in zoos, or sold by online trade and physical stores (OTAPS) for pets or other uses, and the countries that imported or exported this wildlife. We define alien species in the GLVTD as those that are traded beyond the borders of their native range countries, regardless of whether they have established feral populations or not. We use this database (i) to demonstrate the geographical distribution of colonization pressure for alien vertebrates in trade across countries and taxa; (ii) to identify hotspots of establishment richness for alien species, and the contributions of colonization pressure, socio-economic factors and climate conditions to establishment richness; (iii) to quantify flows of alien species and established alien species between native regions and received regions.\n
\n\n Based on IUCN taxonomy, we identify 10,378 species involved in the live vertebrate trade worldwide in GLVTD (Fig.\n \n 1\n \n , Fig S1 A-D and Supplementary Data 1), including 2,374 species collected from CITES, 7,729 species from LEMIS, 3,116 species from ISIS, and 5,054 species from OTAPS. Approximately 50.4% (5,233 species) of the species are contained within individual datasets, and 49.6% overlapped between two, three or four datasets. These species account for 30.3% (in blue) of the 34,285 extant species in these groups on the IUCN Red List (Fig.\n \n 2\n \n ), covering 1,772 species from 146 families of mammals, 5,080 species from 227 families of birds, 2,532 species from 85 families of reptiles, and 994 species from 62 families of amphibians (Supplementary Data 1).\n
\n\n After excluding 120 species lacking data on geographical range from the dataset, we aligned the list of countries where a species is traded with its native country for each species (Methods), and identified alien species as those that are traded in a country where they do not naturally occur. These alignments reveal that 9,339 vertebrate species are traded outside native range countries as aliens, including 1,619 species of mammals, 4,402 birds, 2,378 reptiles and 940 amphibians (Supplementary Data 1). Approximately 76.7% of these species (7,162/9,339) are also traded in their native range countries, ranging from 65.2% in amphibians to 83.5% in birds. Alien species comprise 90.1% (9,339/10,258) of vertebrate species with range data in trade globally (Fig.\n \n 2\n \n , in red)), 94.5% of mammals, 86.7% of birds, 96% reptiles, and 94.5% of amphibians. Only 9.9% (919 /10,258) of species are traded solely within native range countries.\n
\n\n Every country has records of alien species in trade (Fig.\n \n 3\n \n ). The number of traded alien species ranges from 9 species in Tuvalu to 7,465 species in the United States, with an average of 483\u2009\u00b1\u2009736 species/country. Particularly high numbers of alien vertebrate species have been imported to countries with high income and large area, including the United States, Western Europe (e.g. Great Britain 3,157, Germany 3,053) and Canada (2,657). Similar patterns are observed among taxonomic groups (Fig S2 A-D), with strong correlations in alien trade richness across countries (Table S1, r\u2009\u2265\u20090.909, p\u2009<\u20090.001 for all pairs).\n
\n\n Figure\n \n 4\n \n shows the proportions of alien species in total species traded across countries. Alien species account for 73.4% of species richness in trade on average for vertebrates within a country, ranging from 71.3% in birds to 78.9% in reptiles. Alien amphibians and reptiles have higher proportions in trade than birds (Wilcoxon-rank-sum tests,\n \n p\n \n <\u20090.001, Table S2). Amphibians also have higher proportion of aliens in trade than mammals and reptiles (\n \n p\n \n \u2264\u20090.002). Furthermore, the proportion of alien richness in trade is 3.1 times higher than native species richness for vertebrates, 3.9 times for mammals, 2.5 times for birds, 6 times for reptiles, and 4.1 times for amphibians (Paired\n \n t\n \n test,\n \n p\n \n <\u20090.001 for all, Table S3).\n
\n\n We identify 1041 vertebrate species with established alien populations, of which 846 are involved in the live wildlife trade: 191 species of mammals, 377 birds, 191 reptiles and 87 amphibians (Supplementary Data 2). Traded species with established populations account for 9.1% (in yellow) of alien vertebrates in trade (Fig.\n \n 2\n \n ; 11.8% of mammals, 8.6% of birds, 8% of reptiles, and 9.3% of amphibians). Furthermore, traded species comprise 81.3% (in green) of established vertebrate species, ranging from 75.9% for amphibians to 85.3% for birds (Fig.\n \n 2\n \n ).\n
\n\n Hotspot countries for the establishment richness of traded alien species are the United States (295 species), Australia (118 species) and Spain (90 species), along with some island nations (New Zealand, 91 species; Japan, 86 species; Great Britain, 80 species) (Fig.\n \n 5\n \n and Fig S3 A-D). Emerging countries in the global economy, like Brazil, South Africa, Mexica, Russia and China, have moderate establishment richness. There are no records of establishment for traded alien vertebrates in South Sudan (Fig.\n \n 5\n \n , in grey). Establishment richness of alien species in trade is again correlated between taxonomic groups across countries (Table S1, r\u2009\u2265\u20090.156,\n \n p\n \n <\u20090.05 for all pairs).\n
\n\n We use multimodel inference and information theory (Akaike\u2019s Information Criterion corrected for small sample sizes, AICc)\n \n \n 23\n \n \n (see Methods) to quantify the relative contributions of colonization pressure (the number of alien species in trade), socio-economic factors, and environmental conditions to established richness of traded alien species across 99 countries with upper middle income or high income, which have more complete records of trade data. Conditional averaging based on linear mixed models showed that establishment richness in a country increases (estimate\u2009>\u20090) with colonization pressure, area, per capita GDP (GDPpc), population density, congeneric richness, insularity, and sampling effort, (Table\n \n 1\n \n ), but decreases (estimate\u2009<\u20090) with temperature and precipitation. Colonization pressure and insularity are consistent predictors of established richness for each group, and GDPpc for all groups except mammals. Area, population density, sampling effort, congeneric richness, and climatic variables each relate to establishment richness for one or two groups.\n
\n| \n \n Predictors\n \n | \n \n \n Mammals\n \n | \n \n \n Birds\n \n | \n \n \n Reptiles\n \n | \n \n \n Amphibians\n \n | \n ||||
|---|---|---|---|---|---|---|---|---|
| \n | \n\n \n Estimates\n \n | \n \n \n Pr(>|z|)\n \n | \n \n \n Estimates\n \n | \n \n \n Pr(>|z|)\n \n | \n \n \n Estimates\n \n | \n \n \n Pr(>|z|)\n \n | \n \n \n Estimates\n \n | \n \n \n Pr(>|z|)\n \n | \n
| \n \n Intercept\n \n | \n \n \n 0.137\n \n | \n \n \n 0.731\n \n | \n \n \n -0.679\n \n | \n \n \n 0.372\n \n | \n \n \n 0.264\n \n | \n \n \n 0.697\n \n | \n \n \n 0.742\n \n | \n \n \n 0.076\n \n | \n
| \n \n Area\n \n | \n \n \n 0.027\n \n | \n \n \n 0.652\n \n | \n \n \n 0.171\n \n | \n \n \n \n 0.021\n \n \n | \n \n \n 0.133\n \n | \n \n \n 0.097\n \n | \n \n \n 0.106\n \n | \n \n \n 0.062\n \n | \n
| \n \n Population density\n \n | \n \n \n 0.058\n \n | \n \n \n 0.356\n \n | \n \n \n 0.207\n \n | \n \n \n \n 0.022\n \n \n | \n \n \n 0.165\n \n | \n \n \n 0.109\n \n | \n \n \n 0.147\n \n | \n \n \n 0.096\n \n | \n
| \n \n GDPpc\n \n | \n \n \n 0.069\n \n | \n \n \n 0.263\n \n | \n \n \n 0.220\n \n | \n \n \n \n 0.043\n \n \n | \n \n \n 0.171\n \n | \n \n \n \n 0.046\n \n \n | \n \n \n 0.127\n \n | \n \n \n \n 0.042\n \n \n | \n
| \n \n Colonization pressure\n \n | \n \n \n 0.248\n \n | \n \n \n \n 0.000\n \n \n | \n \n \n 0.475\n \n | \n \n \n \n 0.001\n \n \n | \n \n \n 0.466\n \n | \n \n \n \n 0.000\n \n \n | \n \n \n 0.135\n \n | \n \n \n \n 0.003\n \n \n | \n
| \n \n Insularity\n \n | \n \n \n 0.285\n \n | \n \n \n \n 0.003\n \n \n | \n \n \n 0.269\n \n | \n \n \n \n 0.000\n \n \n | \n \n \n 0.358\n \n | \n \n \n \n 0.000\n \n \n | \n \n \n 0.240\n \n | \n \n \n \n 0.004\n \n \n | \n
| \n \n Mean temperature\n \n | \n \n \n -0.027\n \n | \n \n \n \n 0.000\n \n \n | \n \n \n 0.001\n \n | \n \n \n 0.746\n \n | \n \n \n 0.006\n \n | \n \n \n 0.209\n \n | \n \n \n -0.028\n \n | \n \n \n \n 0.000\n \n \n | \n
| \n \n Mean precipitation\n \n | \n \n \n 0.018\n \n | \n \n \n 0.839\n \n | \n \n \n -0.072\n \n | \n \n \n 0.384\n \n | \n \n \n -0.225\n \n | \n \n \n \n 0.001\n \n \n | \n \n \n -0.098\n \n | \n \n \n 0.325\n \n | \n
| \n \n Congeneric richness\n \n | \n \n \n 0.315\n \n | \n \n \n \n 0.006\n \n \n | \n \n \n -0.054\n \n | \n \n \n 0.635\n \n | \n \n \n 0.022\n \n | \n \n \n 0.551\n \n | \n \n \n 0.189\n \n | \n \n \n \n 0.006\n \n \n | \n
| \n \n Sampling effort\n \n | \n \n \n 0.546\n \n | \n \n \n \n 0.018\n \n \n | \n \n \n 0.277\n \n | \n \n \n 0.195\n \n | \n \n \n 0.374\n \n | \n \n \n 0.073\n \n | \n \n \n 0.619\n \n | \n \n \n \n 0.006\n \n \n | \n
\n
\n
\n Colonization pressure and insularity are also included in all the highly supported models (i.e. \u0394AICc\u2009\u2264\u20092) for each group (Table S4-7). Fixed factors explained 64.2\u201365.4% of the variation in establishment richness (\n \n R\n \n \n \n 2\n \n \n \n m\n \n ) for mammals (Table S4), 38.9\u201346.5% for birds (Table S5), and 46.7\u201353.3% for reptiles (Table S6), and 55.8\u201356.6% for amphibians (Table S7), respectively.\n
\n\n Every region worldwide imports and exports alien vertebrates from or to other regions, with interregional exchange dominating the flows of species (Fig.\n \n 6\n \n ), reflecting the increasing complexities in global wildlife trade networks. North America, Europe, and South and East Asia import the largest numbers of species, while South and East Asia, Africa and South America are the main export regions. For established species, North America, Europe, South and East and Oceania are the main recipients (Fig.\n \n 7\n \n ), while South and East Asia, Africa, Europe, and North America are the main donors. Intraregional exchange is relatively more frequent for established alien species than for species in trade in general (Figs.\n \n 6\n \n and\n \n 7\n \n ). Patterns are largely similar across taxa (Fig S4 A-D and S5A-D).\n
\n\n Our analyses quantify the numbers of alien species in live wildlife trade at the global scale and country level, and identify drivers of establishment richness for traded alien species globally. We find that, globally, most species (86.7%-96%) in live wildlife trade are traded outside their native range countries for each taxon, and hence are aliens, and that aliens comprise much higher proportions (71.3%-78.9%) of species in trade in each country than do natives. These findings suggest that aliens dominate species richness in the live wildlife trade, reflecting increased globalization of exotic pets in trade\n \n \n 7\n \n \u2013\n \n 9\n \n \n . Differences in proportions of alien species in trade among taxa may be due to different native range sizes. Native range size in terrestrial vertebrates increases from amphibians to reptiles to mammals to birds\n \n \n 24\n \n ,\n \n 25\n \n \n . Traded species with narrower native range size may have more chances to be traded beyond their native range countries and become aliens, resulting in higher proportion of alien richness in trade for amphibians than other taxa.\n
\n\n Colonization pressure is a consistently strong predictor of establishment richness for every vertebrate taxon, at least for upper middle income or high income countries. The positive association between colonization pressure and established alien richness provides evidence for colonization pressure as a fundamental determinant of spatial variation in the establishment richness of aliens in an area\n \n \n 17\n \n ,\n \n 20\n \n \n , a hypothesis that has rarely be examined at a large scale.\n
\n\n Consistently positive influences of insularity on establishment richness across taxonomic groups indicate that island countries are more invasible to alien vertebrates than mainland nations. This is the first study to confirm an island effect while accounting for the confounding effect of colonization pressure. It most likely arises from the effects on lineage survival probability of reduced biodiversity or increased ecological naivet\u00e9 of native insular communities\n \n \n 22\n \n \n . Positive effects of GDPpc on establishment richness may be because GDPpc partly reflects the import volume of alien wildlife and release frequency. With increasing living standards (and GDPpc), the market for exotic pets (e.g., species without a long history of domestication) expands and pet ownership grows\n \n \n 10\n \n ,\n \n 26\n \n ,\n \n 27\n \n \n . This likely increases pet import volume and promotes occasional or intended releases, and hence propagule pressure.\n
\n\n Overall, our results identify huge challenges to global biosecurity from future invasion risks posed by wildlife trade. The large number of alien species in trade represent high colonization pressures for alien vertebrate species as yet lacking established populations. Countries with high or rapidly increasing GDPpc, such as developed or emerging countries, South and East Asian countries, and more invasible island countries, are likely to be future hotspots for alien vertebrate establishment. Fast economic development in emerging countries is driving demands for alien pets\n \n \n 10\n \n \n , and thus increasing establishment risks. Strengthening surveillance of alien species in the live wildlife trade is urgently needed to respond to these challenges, and mitigate their associated future environmental, economic and zoonotic disease impacts. The current CITES Trade Database provides important information on trade-restricted alien species, but it is insufficient to meet the challenges. Many species without trade restrictions are not covered or have incomplete trade records. Future surveillance should focus on collecting key information on traded alien species, including trade volume, flows and direction of trade, and import and export countries.\n
\n\n \n Extracting data on live wildlife trade from databases\n \n . We extracted data on the trade in live wildlife from the CITES Trade Database, International Species Information System (ISIS) and LEMIS metadata. The CITES Trade Database (\n \n \n https://trade.cites.org/\n \n \n \n \n , last visited on 1 August 2022) is developed and maintained by the UNDP World Conservation Monitoring Centre on behalf of the CITES Secretariat. This database includes more than 25\u00a0million entries on records of international trade in CITES\u2013listed species reported by CITES parties (1975\u20132021). The database covers data on both legal trade and illegal trade (seized data). ISIS is a network of 837 zoos and aquaria that shares information about 2.5\u00a0million animals of more than 10,000 species among member institutions\n \n \n 28\n \n \n . The ISIS Database compiled by Conde et al.\n \n \n 28\n \n \n holds the most comprehensive information on animals kept in the zoos across the world in 2011. LEMIS metadata is based on The United States Fish and Wildlife Service\u2019s (USFWS) Law Enforcement Management Information System (LEMIS) data (2000\u20132014) derived from legally mandated reports submitted to USFWS, containing 5,207,420 entries on US imports of both live organisms and wildlife (animal and plant) products. The LEMIS data were curated, cleaned, and compiled as the LEMIS metadata for improving data usability by the EcoHealth Alliance\n \n \n 29\n \n \n . We obtained data on scientific name, class, family, import country, export country, and year for each transaction from the CITES Trade Database (version 2022.1) between 1975 to 2021 (term \u201clive\u201d) and LEMIS between 2000\u20132014 (term \u201cLIV\u201d). Not all animals in zoos are sourced from trade, and threatened species (categorized as vulnerable, endangered, or critically endangered) in zoos are being bred for ex-situ conservation or conservation campaign\n \n \n 28\n \n \n . We therefore collected data on scientific name, class, family of animals kept in zoos and countries from ISIS but excluding threatened species. We performed quality control of data by excluding records with duplicated lines or the same importer and exporter countries, and those with no scientific name, or unidentified and hybrid species.\n
\n\n \n Data on contemporary online trade of wildlife\n \n . We searched the websites of live wildlife trade for pets and other uses, and crawled data on listings (advertisements and posts) in websites. We built keys for species names and extracted information of species names and countries from crawled data.\n
\n\n \n Searching for the websites of live wildlife trade\n \n . We searched for the websites of live wildlife trade on Google using search phases \u201ctaxon (each group of mammals, birds, reptiles and amphibians)\u2009+\u2009for sale\u2009+\u2009country name\u201d for each of 193 countries from March to May 2022 (Table S8-11). We consistently performed the search for all countries using the phases in English. We additionally searched websites in other languages for each country (up to three, by Google Translation) based on widely spoken languages (official or national languages) (quickgs.com). In total, we used 1414 phases in 69 languages for the searches (Table S8-S11). We browsed each website in URLs returned by a search phase (in English) in 10 randomly selected countries in Europe and Asia to choose a cutoff point that balances the quality of search results with search effort\n \n \n 30\n \n \n . These browses revealed that when 20 consecutive websites in a list of returned URLs did not show listings (advertisements or posts) of exotic pets or live wildlife, additional browsing was unlikely to find other relevant websites in the rest of the list. We therefore used this cutoff point in all searches. We browsed 95,965 websites across 193 countries in total, and identified 1463 websites of live wildlife trade in 177 countries. These websites used 47 languages, though approximately 55% (799) were made in English (Table S12), while 44% used other languages, and 1% a mix of two languages.\n
\n\n \n Scraping online data\n \n . We scraped and extracted data on title, contents, scientific name and price of pets, locality (city in a country), date of listings posted, and URLs, for all pages stocked in a website in Jun-August 2022 using Web Scraper on the Chrome browser (\n \n \n https://www.webscraper.io/\n \n \n \n \n ) in Jun-August 2022. The Web Scraper is a web scraping tool with many advanced features to get exact information from websites. It can perform data scraping from multiple pages, multiple data extraction types (text, images, URLs, and more), scraping data from dynamic pages (JavaScript\u2009+\u2009AJAX, infinite scroll), browsing scraped data and other functions. We created a sitemap for each website to be crawled and pasted the URL root (webpage 1) of a website for this sitemap in Start URL. We then created a loop through the web pages by repeatedly going to the next page for the scraper by establishing a new column for this function. We clicked on \u2018Add new selector\u2019; under root window, we input a name for the column in ID box, selecting \u2018Pagination (Beta)\u2019 in the Type box. We clicked on \u2018Select\u2019 in the Selectors box and then on Paging button (Next or 2) in the webpage. We selected both root and name of this column in the Parents selector box. and saved these settings by finishing pagination settings. We gave a name for the column of listings, and selected \u2018element\u2019 in the Type box (for websites with scrolling listings, selected \u2018Element Scroll Down\u2019), and clicked on \u2018Select\u2019 in the Selectors box and then on two listings in the webpage (the scraper could automatically select others with same structure). We checked if all listings are selected (in red) by clicking on the Element preview button. For some listings not selected due to different structures, we additionally clicked on these listings. We then saved the settings by finishing the selection of listings. We performed data craping as following:\n
\n\n Cycle. For websites with pages of listings containing all data to be crawled, we simply input a name of a phase to be crawled in ID box, selected \u2018text\u2019 in the Type box, clicked on \u2018Select\u2019 in the Selectors box, and selected the phase in a listing in the webpage, and saved the settings.\n
\n\n Crawls. For websites with pages (cycle or not) showing parts of information and other information contained in different levels of subordinate linked pages, we selected a name for the phase linked to the information in ID, then selected \u2018link\u201d in the Type box and selected the phase in the webpage. The name for this phrase will show in the Parents box. In root window, we clicked on the name, which show the linked page in the webpage, and we set new name in ID and selected a phase to be crawled. For the deeper links in a website, we used the procedure as above.\n
\n\n We clicked on the sitemap file in the Toolbar after all settings finished, and then on \u201cScrape\u201d to open a configuration table, then on \u2018Start Scraping\u2019 by default setting (Request interval and Page load delay (2000 ms)) to run the program. We downloaded the sitemap in XLSX file once the program was done. We crawled all websites of wildlife trade except websites displaying listings in PDF. In this case, we directly downloaded PDF files and transferred them into the text.\n
\n\n \n Data on keys.\n \n We gathered keys from different databases. We downloaded data on scientific names, synonyms and common names in different languages for mammals, birds, reptiles and amphibians from the IUCN Red List and taxonomic websites (mammaldiversity.org; avibase.bsc-eoc.org/; reptile-database.reptarium.cz/; amphibiaweb.org/, last visited on 17 Sep. 2022). We also obtained trade names of species in English, French and Spanish from the CITES Trade Database (2022V.1), and specific names of species in English from LEMIS metadata. In total, we obtained 484,470 species names, including 47,041 names for mammals, 304,246 names for birds, 93,401 names for reptiles and 39,782 names for amphibians.\n
\n\n \n Extracting species names from crawled data\n \n . We extracted string keys for species names from titles, contents or scientific names in the data crawled using the formula of Lookup function combined Find function in Excel 2016 as follows\n \n \n 31\n \n \n :\n
\n\n Formula\u2009=\u2009LOOKUP(1,0/FIND(\n \n $\n \n X\n \n $\n \n i:\n \n $\n \n X\n \n $\n \n j,Yi),\n \n $\n \n X\n \n $\n \n i:\n \n $\n \n X\n \n $\n \n j)(1)\n
\n\n Where X is the column of the keys that we want to look up, with i and j indicating the range where keys are located in rows. The column X was sorted in ascending order based on the number of characters contained in a string using the Len function. Y identifies the columns including titles, contents or scientific names where we searched for keys. As the Find function is case sensitive, we transformed keys and crawled data (titles, contents or scientific names) into lowercase using the Lower function before extraction. We matched the extracted keys with the scientific names in the key database using the vlookup function:\n
\n\n Formula\u2009=\u2009VLOOKUP(xi, y:z,2,0) (2)\n
\n\n Where X is the column returning keys, Y is columns contained synonyms, or common names, traded names, or specific names, and z is the column with corresponding scientific names.\n
\n\n \n Publications on historical online trade and physical markets.\n \n We intensively searched on Google Scholar or Baidu Scholar for publications using the search phases (taxa name\u2009+\u2009for sale\u2009+\u2009country) based on the method of website searching above. We reviewed the title and abstract of each publication searched and excluded studies solely on data from the CITES Trade Database, LEMIS and ISIS. We downloaded 110 publications in total (Table S13), including studies on online trade, physical stores or markets, zoos, those on both online trade and physical markets, and on databases of wildlife trade\n \n \n 14\n \n \n . These studies included surveys on legal or illegal wildlife trade, or both. We extracted records of the species names and countries involved in live wildlife trade from these publications.\n
\n\n We combined datasets from CITES, LEMIS, ISIS, contemporary online trade and publications on historical online trade and physical stores (shortened as online trade and physical store, OTAPS) into a list of species traded in countries. Different taxonomies were used in different data sources, which would inflate the list of species in trade and bias the delimiting of native ranges for some species. We resolved species names and higher-level taxa according to the taxonomy of the IUCN Red List. We aligned the list of traded species with those of scientific names and synonyms in the IUCN Red List using the vlookup function in Excel. We obtained a final list of matched species in trade (trade data) by removing duplications. This list includes 8,992 species collected from CITES, EMIS and ISIS, 3,204 species from contemporary online trade, and 3,551 species from publications on historical online trade and physical markets. Unmatched names (1874 names) might be due to typing errors, unaccepted names, or different taxonomies used, and were excluded from downstream analysis.\n
\n\n We obtained data on geographic ranges of species for each taxon from the IUCN Red List. We defined native range countries (native countries) for a species as countries having native extant or native possibly extant presence of the species. We obtained the list of native countries in which a species naturally occurs by excluding species without range data and countries with extant introduced presence of species. We matched all combinations (traded species name\u2009+\u2009country name) of a traded species and countries in trade with those combinations of the species and native countries (species name\u2009+\u2009native country name) using the vlookup function. While matched combinations indicate that species were traded in native countries, unmatched ones suggest that they were traded outside their native countries, namely alien species in trade. We transformed unmatched or matched combinations to columns and counted alien richness across countries.\n
\n\n We obtained data on established alien terrestrial vertebrate species and their distributions (established countries) from a number of databases (the Global Invasive Species Database (GISD,\n \n \n http://www.iucngisd.org/gisd/\n \n \n \n \n , last visited on 30 May 2021; mammals:\n \n \n 32\n \n \u2013\n \n 34\n \n \n ; birds:\n \n \n 35\n \n \n ; reptiles and amphibians:\n \n \n 36\n \n \u2013\n \n 39\n \n \n ). We collected additional data by retrieving information on the geographical ranges of species from the IUCN Red List and including the species that have an extant introduced presence in countries. We also reviewed each paper published in the journal\n \n BioInvasions Records\n \n between 2015 and 2022, and extracted records (species and distribution) of established vertebrates (Table S14). We only included established species with distribution data in this study. We checked the species names of established vertebrates against the scientific names and synonyms in the IUCN Red List and excluded repeated names from our list. We included a total of 1041 established vertebrate species (Supplementary data 2). We matched the list of established vertebrates with trade data, and identified established alien species by trade as those that were involved in the live wildlife trade. We mapped the richness of alien species in trade and established alien species richness in ArcGIS.\n
\n\n We obtained data on area, GDP and population size for each country in 2010 from the World Bank (\n \n \n http://data.worldbank.org/\n \n \n \n \n ,\n \n last visited 1 July 2020\n \n ). The per capital GDP (GDPpc) was calculated as GDP divided by population size, and population density as population size divided by area. We identified a country as an island nation (e.g. insularity) based on world atlas (\n \n \n https://www.worldatlas.com/geography/island-countries-of-the-world.html\n \n \n \n \n , last visited on 15 June 2022). Nations with different income are identified according to analytical categories of World Bank based on Gross National Income per capita (GNI per capita) US\n \n $\n \n in 2010 (\n \n \n https://data.worldbank.org/indicator/\n \n \n \n \n , last visited on 24 Dec. 2022). Data on annual mean temperature and precipitation were calculated from the spatial data set for the period 1950 to 2000 at a resolution of 10 arc\u00ad minutes from WorldClim (\n \n \n \n www.worldclim.org\n \n \n \n \n \n ). We used data on undiscovered proportion of vertebrate species for each country as a metric of sampling effort; these data were collected from Moura and Jetz (2021)\n \n \n 40\n \n \n . We obtained data on the congeneric richness of each taxonomic group from each country from the IUCN Red List (\n \n \n https://www.iucnredlist.org/search\n \n \n \n \n , last visited on 15 July 2021).\n
\n\n We identified the effects of predictors on the species richness of established traded alien vertebrates across countries for each taxonomic group separately, using multimodel inference. This approach makes more reliable inference of the relative importance of predictors, compared to any single model, by including a group of models and merging model uncertainty\n \n \n 23\n \n ,\n \n 41\n \n \n . The full model is a linear mixed model (LMM) with established alien species richness (establishment richness) as the response variable, and nine factors as predictors (fixed effects: area, population density, GDPpc, colonization pressure, insularity (binary variable, island country or not), annual mean temperature, annual mean precipitation, congeneric richness and sampling effort (proportion of undiscovered species). Area, population density, GDPpc and mean precipitation were log transformed, and establishment richness of alien species, number of alien species in trade, and congeneric richness were log (1\u2009+\u2009x) transformed to improve their linearity. Biogeographical realms where a country is located (the midpoint of its latitudinal and longitudinal ranges) was included as a random variable to account for geographic autocorrelation. We used biogeographical realms following the definition of Olson et al.\n \n \n 42\n \n \n : Afrotropics (including Madagascar), Australasia, Indo-Malay, Nearctic, Neotropics, Palaearctic and Oceania. We constructed 512 models (2\n \n 9\n \n ) representing all combinations of predictor variables. We calculated standardized estimates for regression coefficients and standard errors for each variable\n \n \n 41\n \n \n . We calculated the statistical significance of the coefficient for each predictor based on a z-score with a 95% upper confidence limit (\u2223\n \n z\n \n \u2223\u22651.96). As bias in data might exist between countries, we here included 99 nations with upper middle income or high income in the models. These countries have a more open economy, invest more heavily on effort to conserve biodiversity\n \n \n 43\n \n \n , and are likely to have more comprehensive data on wildlife trade than those with low middle or low income. All countries with upper middle or high income are CITES parties, having relative complete records of CITES-restricted species.\n
\n\n We also performed model selection by ranking the performance of models based on the Akaike information criterion adjusted for small samples (AICc)\n \n \n 44\n \n \n . We identified those models that were within 2 AICc units of the highest-ranked models (i.e. \u0394AICc\u2009\u2264\u20092) as top models\n
\n\n We performed network analysis to quantify the global flows of traded alien species and traded alien species with established populations (established aliens) from their native and alien countries. Following Sander et al\n \n \n 45\n \n \n , we classified the world into 8 economic regions: South and East Asia, Mideast and Central Asia, Africa, Europe, North America, Central America, South America and Oceania. We identified major donor and recipient regions in terms of number of species.\n
\n\n We performed LMMs using the \u2018lmer\u2019 function in the lme4 package. We ran the model-averaging analysis using \u2018dredge\u2019 and \u2018model.avg\u2019 in the MuMIn package. We carried out network analysis using the Circlize package based on the procedures of Sander et al\n \n \n 45\n \n \n (2014). These analyses were conducted in R Studio 2022 (\n \n \n https://github.com/rstudio/rstudio\n \n \n \n \n ). R scripts used in this study are provided in Table\u00a015.\n
\n\n Reporting Summary\n
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