In spite of extensive research, cancer remains a major health problem worldwide. As cancer progresses, cells acquire traits that allow them to disperse and disseminate to distant locations in the body – a process known as metastasis. While in the vasculature, these cells are referred to as circulating tumour cells (CTCs) and can manifest either as single cells or clusters of cells , with the latter being the most aggressive. The increased metastatic potential of CTC clusters is generally associated with cooperative group benefits in terms of survival, including increased resistance to shear stress, anoikis, immune attacks and drugs. However, the adoption of a group phenotype poses a challenge when exiting the vasculature (extravasation) as the large size can hinder the passage through vessel walls. Despite their significant role in the metastatic process, the mechanisms through which CTC clusters extravasate remain largely unknown. Based on the observed in vivo association between CTC clusters and platelets, we hypothesized that cancer cells take advantage of the platelet-derived Transforming Growth Factor Beta 1 (TGF-β1) – a signalling factor that has been widely implicated in many aspects of cancer, to facilitate their own dissemination. To address this possibility, we evaluated the effect of exogenous TGF-β1 on an experimentally evolved non-small cell lung cancer cell line that we previously developed and used to investigate the biology of CTC clusters.We found that exogenous TGF-β1 induced the dissociation of clusters in suspension into adherent single cells. Once adhered, cells released their own TGF-β1 and were able to individually migrate and invade in the absence of exogenous TGF-β1. Based on these findings we developed a model that involves a TGF-β1-mediated plastic switch between a cooperative phenotype and a single-celled stage that enables the extravasation of CTC clusters.This model allows for the possibility that therapies can be developed against TGF-β1 signalling components and/or TGF-β1 target genes to suppress the metastatic potential of CTC clusters. Considering the negative impact that metastasis has on cancer prognosis and the lack of therapies against this process, interfering with the ability of CTC clusters to switch between cooperative and individual behaviours could provide new strategies to improve patient survival. In spite of extensive research efforts, cancer remains a major health problem and the second leading cause of death worldwide . While dThe notion of cancer cells cooperating while in the circulatory system is based on the more recent findings of multicellular assemblages of CTCs – referred to as CTC clusters, in the bloodstream of cancer patients with a wide range of malignancies  as the large size would hamper passing through the narrow spaces between the endothelial cells that make up the vessel walls . One waThe extravasation of single CTCs is thought to require the formation of stable attachments with the endothelial wall . Some stCTC clusters typically consist of 2-100 cells organized in grape-like morphologies  and ofteBased on the observed in vivo association between CTC clusters and platelets, we hypothesized that CTC clusters recruit platelets, and the platelet-derived TGF-β1 can facilitate the extravasation and dissemination of CTC clusters into new tissues. To address this possibility, we employed an in vitro model-system that we developed and previously used to investigate the biology of CTC clusters , 45. SpeTo address whether TGF-β1 released by recruited platelets could facilitate the extravasation of CTC clusters, we first exposed the H2122 SS cells to TGF-β1 (10 ng/ml) for 48 h, and observed changes in their phenotypic state and morphology. In its native state, the H2122 SS cell line grows as clusters of cells in suspension , indicating that most cells responded to the exogenous TGF-β1  and transitioned to a single-cell adherent state. Interestingly, the treated cultures also showed an overall lower number of live cells . Overall, these data indicate that TGF-β1 can also induce the migration of the H2122 SS cells that adhered.To determine whether the morphology of TGF-β1-treated cells reflects an ability to migrate, we used the Transwell assay and assessed the migration of H2122 SS in the presence and absence of TGF-β1, at 24 and 48 h  indicating that TGF-β1 increases the invasive abilities of the H2122 SS line.We also used the Transwell assay to evaluate the invasive abilities of the H2122 SS line treated with TGF-β1, after 48 and 72 h Fig. . CompareTo evaluate whether the TGF-β1-induced adherent phenotype is stable in the absence of exogenous TGF-β1, we first treated H2122 SS cells with 10 ng/ml TGF-β1 for 48 h to induce the adherent phenotype. We then removed the cells that were still in suspension and assessed the number and proportion of adherent and suspension cells. Next, we washed the adherent cells with PBS and maintained them for another 7 days in RPMI media without TGF-β1 (with media being refreshed every 2 days). After 7 days in the absence of exogenous TGF-β1, we recorded the numbers and proportions of adherent and suspended cells and compared them with those in the population initially treated with TGF-β1 for 48-hours   and restored cell proliferation. Collectively, the data suggest that the ability of the conditioned medium to induce the transition of the naïve SS cells to an adherent phenotype is due to the secretion of TGF-β1 by the adherent cells.To test if the stability of the TGF-β1-induced phenotype and the lack of proliferation might be due to the ability of TGF-β1-induced adherent cells to release and respond to their own TGF-β1 , we collected conditioned media from TGF-β1-induced adherent cells and added it to naïve H2122 SS cells in the presence or absence of a TGFβ1 receptor 1 (TGFβRI) inhibitor. After 48 h, the suspension and adherent cell populations were counted. The addition of conditioned medium (1:1 ratio with fresh medium) resulted in a significant increase in the proportion of adherent cells compared to the control . Reported TGF-β1 levels in human plasma vary greatly (from 0.1 ng/ml to more than 25 ng/ml), with ca. 4 ± 2 ng/ml being the most common , 48. HowUsing our in vitro model-system, we first observed that TGF-β1 induced a switch from a suspension cell-cluster phenotype to an adherent mostly single-cell state. Importantly, once adhered, these cells do not require TGF-β1 (from either an exogeneous source or self-produced) to maintain their adherent state. In addition, while cell clusters have almost no migration and invasion potential in the absence of TGF-β1, the TGF-β1-treated cells have greatly enhanced abilities to migrate and invade relative to the naïve cell clusters. Furthermore, once adherent, the cells produce and release their own TGF-β1 that can induce a migratory behaviour in the absence of exogenous TGF-β1. Based on these findings, below we propose and discuss a step-by-step model for the extravasation and dissemination of CTC clusters in which platelet-derived TGF-β1 induces the dissociation of clusters into individual adherent cells that secrete their own TGF-β1, which allows them to extravasate and disseminate into nearby tissues Fig. .Based on our finding that upon treatment with TGF-β1, the H2122 SS cell clusters dissociate and develop an adherent phenotype, we suggest that in the bloodstream, crosstalk between platelets and CTC clusters may cause a TGF-β1-mediated transition from a cooperative phenotype to an individual adherent stage Fig. . Since TAdhesion to the endothelial wall is an important step in CTCs dissemination; in fact, it was suggested that such cell-cell interactions, and not mechanical entrapment, are responsible for the arrest of CTCs in microvessels . NeverthSlug, a gene coding for a prominent EMT-inducing transcription factor [Although the attached cells might initially remain in contact with platelets and the associated TGF-β1, we found that the adherent phenotype can be stable in the absence of any source (exogenous or self-produced) of TGF-β1. One possibility is that the stability of the phenotype is the result of trans-generational epigenetic changes. In fact, it has been suggested that TGF-β1-induced EMT can involve epigenetic changes. For example, TGF-β1 treatment of certain lung cancer cell lines results in EMT being induced in part by demethylation of n factor . Alternan factor . Therefon factor  and thron factor  active TBased on the TGF-β1-induced migratory and invasive abilities seen in vitro, we suggest that, in response to TGF-β1, the cells that adhered to the endothelial wall can migrate/invade as single cells through the endothelium into new tissues Fig. . TGF-β1 The dissociation of clusters into single cells would allow for easier passage through the endothelial wall and prevent the need for very large openings in the blood vessel. In the single cell state, cells could extravasate using any of the known mechanisms proposed for single CTCs, such as through diapedesis  by inducThe initial steps in the migration and invasion of single cells during extravasation might be mediated by platelet-derived TGF-β1 if the association with platelets is maintained after adherence. However, once cells exit the bloodstream, the limited availability of TGF-β1 in tissues  can resTGF-β1 autocrine signalling is known to be important in cancer. For instance, in certain types of breast cancer, invasiveness and tumour progression are promoted by autocrine TGF-β1 signalling , 69. SimInterestingly, we also found that adherent cells in the presence of either exogenous or self-produced TGF-β1 have a low proliferation potential. This is consistent with known anti-proliferative effects of TGF-β1  and the Although cancer progression is predicated on selection acting at the cell level, the possibility that cancer cells can engage in cooperative interactions/behaviours that can increase their fitness should also be addressed . Such phHowever, as with other multicellular phenotypes, benefits can be counteracted by costs in some specific contexts or conditions. For instance, a large size can be beneficial in terms of avoiding predation but can be costly in terms of growth or reproduction. In order to fully realize the benefits of cooperation, various additional strategies have to evolve, some of which involve transitory phenotypic changes as part of the life cycle. In the case of CTC clusters, the dissociation of multicellular aggregates into single cells with migratory capabilities can be understood as such a change that allows cancer cells to take advantage of the benefits of being in a group during the passive dispersal through the vasculature but still be able to actively extravasate as single cells.In this framework, the overall dispersal/dissemination strategy adopted by cancer cells is rather similar to the life cycle of dictyostelid social amoebae . SpecifiSimilarly, in advanced primary tumours distinct clones and/or phenotypes  with different migration/invasion abilities can engage in cooperative behaviours during the early metastatic stages (collective migration and invasion) , 80 resuGiven the heterogenous nature of the clusters in the H2122 SS line  it willDespite increasing research efforts, the five-year survival rate for most patients with metastatic cancer remains distressingly low . Our inaOur model allows for the possibility that new therapeutic strategies can be developed against TGF-β1 signalling components and/or TGF-β1 target genes to slow the development of metastatic tumours by specifically inhibiting steps in the extravasation and dissemination of CTC clusters. Of interest are recent reports that metformin  can interfere with both the secretion of and the response to TGF-β1 as well as the TGF-β1 itself –86. AddiOur study provides in vitro experimental evidence for a new model for the extravasation and dissemination of CTC clusters involving cluster dissociation and adherence mediated by crosstalk between platelets and cancer cells, followed by single-cell extravasation and migration facilitated by autocrine TGF-β1 signalling Fig. . Current2 in RPMI-1640 media  supplemented with 10% FBS and 1% Penicillin/Streptomycin mix. The concentrations of glucose and glutamine in the media were adjusted to 5 mM and 0.5 mM respectively, to simulate physiological levels. Cells were passaged every 2–3 days at a 1:3, 1:4 or 1:5 ratio depending on the required cell density.We used an experimentally evolved cancer cell line derived from a non-small cell lung cancer cell line obtained from the American Type Culture Collection – ATCC . This original cell line (NCI-H2122) was established from the pleural effusion of a 46-year-old female with stage-4 adenocarcinoma and grows as a mixture of two distinct phenotypes: clusters of cells growing in suspension and adherent cells that grow as a monolayer. By selectively passaging only the adherent or suspension cell populations, two cell lines were experimentally evolved: one that grows as cell clusters in suspension (referred to as the H2122 Suspension-Selected or SS line) and one that grows as adherent cells . The SS 5 cells/ml for all experiments . Several independent trials were conducted for each experiment.Cultures were seeded (in triplicates) in tissue-treated 12-well plates (Sarstedt) at a density of 1 × 10Human recombinant TGF-β1 (R&D Systems − 7754-BH-005) was reconstituted in 4 mM HCl at 10 mg/ml and was added directly to the culture medium at 10 ng/ml. A TGF-β receptor 1 (TGFβRI) inhibitor (EMD Millipore − 616464) was used at a concentration of 10 µM.Cells were stained with Syto-9  and Propidium Iodide  at a final concentration of 10 µM and 60 µM, respectively. Numbers of live and dead cells were assessed  using the Countess™ II FL Automated Cell Counter (Invitrogen). The suspension and adherent cell populations for each biological replicate were counted separately.5 cells/ml and treated with TGF-β1 for 48 h. The media was removed, the adherent cells were washed with PBS, and fresh media containing 1% FBS was added to the cells. After 24 h, this conditioned medium was collected, centrifuged at 1000xg for 10 min, and the supernatant was filter sterilized before being used in experiments at 1:1 ratio with fresh medium.To collect conditioned media from TGF-β1-treated cells, naïve SS cells were placed in a 12-well plate at 1 × 106 cells/ml, and aliquots of 100 µl were added to 6.5 mm Transwell inserts (Corning). For invasion, 100 µl of Matrigel (Corning − 354277) (1.5 mg/ml) was first added to the inserts and allowed to solidify for 8 h.Cell migration and invasion were assessed using the Transwell assay , 88. CelThe inserts were each placed into individual wells of 24-well plates containing RPMI-1640 media with FBS, which was used as chemoattractant. Cells were then allowed to migrate/invade through the membrane towards the chemoattractant media for 48 or 72 h and the cells trapped on the membrane were fixed and stained (see below). Stained membranes were then visualized at 100x magnification, and 10 random fields of view were photographed. The number of cells for each photo was assessed using the ImageJ cell counter tool  and the TGF-β1-induced adherent cells and cells attached to Transwell inserts were initially fixed to their substrate by coating them with 70% ethanol for 5 min after which the ethanol was removed, and the cells were dried for 15 min. They were then stained with a 0.2% crystal violet solution (in 20% ethanol) for 10 min, washed with PBS five times and imaged , 91.Photos were taken with an OMAX USB digital microscope camera. Background noise was removed using the Calculator plus tool in FIJI  and the p < 0.05.Statistical analyses were done using GraphPad Prism 8.0.2. The average of each group was calculated from three replicates and expressed as mean ± SE. Welch’s two sample t-test was used to assess statistical significance for differences between two groups, while ANOVA was used for 3 or more groups. A difference between groups was considered statistically significant at


A) that quantifies how ocean temperature, dissolved oxygen and organismal mass interact to constrain the total oxygen budget an organism can use to fuel sustainable levels of aerobic metabolism. We calibrate species-specific parameters of ΦA with physiological measurements for red abalone  and purple urchin (Strongylocentrotus purpuratus). ΦA models highlight that the temperature where oxygen supply is greatest shifts cooler when water loses oxygen or organisms grow larger, providing a mechanistic explanation for observed thermal preference patterns. Viable habitat forecasts are disproportionally deleterious for red abalone, revealing how species-specific physiologies modulate the intensity of a common climate signal, captured in the newly developed ΦA framework.In an ocean that is rapidly warming and losing oxygen, accurate forecasting of species’ responses must consider how this environmental change affects fundamental aspects of their physiology. Here, we develop an absolute metabolic index (Φ This study presents an absolute metabolic index that quantifies how ocean temperature, dissolved oxygen and organismal mass interact to constrain the oxygen budget an organism can use to fuel aerobic metabolism. The index is calibrated with physiological measurements from purple sea urchin and red abalone and the authors test if the index can delimit the distribution of these two species. Ectothermic marine organisms may respond to this environmental change by shifting their distributions7, maximum size and growth rates8, and/or phenology9, which ultimately influence population persistence and productivity and the livelihoods of ocean resource users10. Proactive management and adaptation approaches aimed at mitigating impacts of climate-induced resource declines are therefore critically needed12, but are hampered by our inability to forecast the direction and magnitude of species-specific responses14. Because organismal responses to ocean warming and deoxygenation are mediated through their physiology, frameworks that quantify effects of this environmental change on vital physiological processes can serve as a gauge for how future environmental conditions will be processed, and thereby improve forecasting accuracy on focal species17.The ocean’s physical and chemical environment is currently in a period of accelerated change. Ocean average temperatures19. The amount of energy that can be allocated to these activities is therefore fundamentally constrained by an organism’s capacity to supply (S) oxygen to its aerobic metabolic pathways above that level required to sustain its basal oxygen demand (D)21. Two of the most pervasive extrinsic drivers on an ectothermic organism’s oxygen demand (D) and capacity for oxygen supply (S) are ocean temperature and dissolved oxygen bioavailability, respectively23. Species have therefore evolved such that their physiological machinery is designed to supply sufficient oxygen to fuel sustainable levels of aerobic metabolism, given the prevailing environmental conditions they are exposed to24. As the ocean continues to warm and lose oxygen, species responses must be partly determined by their capacity to maintain a sustainable balance between oxygen supply (S) and demand (D), which is captured by a metabolic index (Φ)25  representing the oxygen level required to fuel sustainable levels of aerobic metabolism25. Because the parameters of Φ are species-specific, calibrated with experimental physiological measurements, it is a general framework that can account for varied species responses to ocean warming and deoxygenation. Indeed, Φ has proved useful for explaining the spatial variability in extinction rates across the Permian Triassic boundary26, contemporary species biogeography patterns27 and the interannual productivity variation of marine fish28 to date.The metabolic index (Φ) is the ratio between oxygen supply and demand nts Fig. . For an 25). In such cases Φ will always increase towards either warm or cool sides of a species distribution provided oxygen remains saturated, as is the case in surface waters  because of its factorial form, when oxygen supply is governed by a single monotonic exponential function . To test the explanatory power of ΦA we calibrate species-specific parameters with physiological measurements following acute thermal exposure for the purple sea urchin (Strongylocentrotus purpuratus) and red abalone , two sedentary benthic marine invertebrates that experience dynamic environmental temperature and oxygen variability throughout the California Current Large Marine Ecosystem (CCLME). Haliotis rufescens is a culturally important and once economically valuable fishery species whose numbers have recently collapsed, while at the same time numbers of S. purpuratus have exploded and decimated kelp forests in the region34. We compare ΦA predictions for optimal temperatures (Topt ΦA), considering oxygen availability and organismal mass, with experimental and aquaculture evidence of both species’ optimal temperatures (Topt). We further test if ΦA can delimit the core biogeographic distribution of these two species. Finally, we use ΦA to make predictions of how future climate change scenarios in the CCLME may drive distributional changes of both species, which can ultimately inform climate adaptation for fisheries management and marine conservation.Here we build on the foundations of Φ and recent experimental evidence of the environmental oxygen and thermal dependence of aquatic species’ oxygen supply and demand to develop an absolute metabolic index (ΦS) oxygen to an organism’s tissue is the product of the oxygen available in the environment (PO2) and its oxygen supply capacity (αS – a rate constant for gas exchange between water and organism), up towards a ceiling (PO2crit max) where additional oxygen provides no increases and the maximum metabolic rate (MMR) is reached  required to sustain basic functioning is quantified as an organism’s basal or standard metabolic rate (SMR)  equals maximum oxygen supply (S) the critical oxygen partial pressure (PO2crit) is reached, below which there is insufficient oxygen obtainable to fuel SMR and conditions become unsuitable for long-term survival  is sensitive to temperature because the biophysical mechanisms governing it  are themselves dependent on temperature as well as organismal body mass 35.The effect of environmental oxygen availability in constraining an organism’s maximum rate of oxygen consumption is depicted by a line of oxygen limitation  per unit body mass (B) and its scaling exponent (δ) at a given the temperature (T), while supply is the product between the amount of oxygen available in the environment (PO2) and oxygen supply capacity (αS) given the temperature (T) per unit body mass (B) and its scaling exponent (σ).The effect of temperature, environmental oxygen and body mass on the ratio of oxygen supply (S) to oxygen demand (D) is quantified by a metabolic index (Φ) following Eq. 1A) is therefore oxygen supply minus oxygen demand following Eq. A will equal zero when the amount of oxygen in the environment (PO2) is equivalent to an organism’s PO2crit and a functional relationship for oxygen supply capacity (αS) at any temperature is derived as D) and PO2crit are experimentally measured permitting supply capacity (αS) to be quantified. The mass scaling exponent for supply (σ) is therefore captured as the difference between the mass scaling coefficients for demand (δ) and for PO2crit (ε).An absolute version of the metabolic index  and is not temperature sensitive, which is supported by recent experimental evidence35, and that oxygen supply (S) declines linearly (constant αS) with decreasing oxygen availability between PO2crit and PO2crit max  to between  following Eq. A’ range from zero (ΦA <= zero) to one - the temperature where ΦA is greatest for each level of oxygen availability organisms are exposed to in the ocean.The major assumptions of ΦPO2crit) increased with temperature for both S. purpuratus and H. rufescens  by Arrhenius models for both species, especially for S. purpuratus where temperature had little influence on PO2crit between 5 and 10 °C. The temperature effect on PO2crit was well approximated by a quadratic polynomial for S. purpuratus and a general exponential model for H. rufescens. Wet mass had little effect on PO2crit for both species, with weak allometric mass exponents estimated as 0.08 and 0.07 for S. purpuratus and H. rufescens, respectively  and critical oxygen partial pressure  for both S. purpuratus and H. rufescens . Predicted temperature dependent PO2crit corresponds to experimentally quantified PO2crit  are consistent with optimal temperature estimates from published experimental studies for both species. ΦA models predict that in oxygen-saturated seawater (21 kPa) and for a median sized specimen used in this study , the optimum temperature (Topt ΦA) is 11.5 °C for S. purpuratus and 18 °C for H. rufescens  was beneficial as it mitigated the negative effects of low pH on fertilization success. Some studies have however found deleterious effects of warmer temperatures on H. rufescens, manifested via increased susceptibility to abalone withering syndrome47. However, studies comparing growth and survival both with and without exposure to the agent of the withering syndrome suggests this is not a result of direct thermal stress or physiological impairment but rather increased disease infection efficacy in warmer water48.Optimal temperature predictions for Φens Fig. . These TA models vary with both oxygen and organismal mass, consistent with results of empirical studies. Specifically, Topt ΦA decreases as oxygen availability decreases  held in a flow-through sea-water system with natural oxygen variability showed better growth, survival and performance at ~13.5 °C  compared to temperatures ~18.5 °C (2.5 °C above)45, while substantially larger specimens (~180 mm shell length) showed compromised fecundity and the onset of withering syndrome when exposed to 18 °C water46. H. rufescens is predicted to be particularly sensitive to low oxygen availability, with a 50% drop in oxygen corresponding to a 9 °C cooler shift in Topt ΦA and complete collapse in ΦA at temperatures just above 20 °C .Optimal temperatures predicted by Φses Fig.  or organses Fig. , and thi °C Fig. . In contgen Fig. , and TopA (ΦA’) predictions across a temperature and oxygen space approximate the conditions where S. purpuratus and H. rufescens naturally occur  experimental respirometry data, 2) modeled or measured seafloor oxygen and temperature conditions, and 3) biogeographic occurrence data.Normalized Φcur Fig. . For botA (ΦA’) delimits the core contemporary spatial distribution of both species. We estimate an occurrence threshold  of 0.849 for S. purpuratus and 0.928 for H. rufescens  around Vancouver Island, which remarkably match seven documented occurrences of the species, believed to have been transported via drift kelp53. Isolated patches of predicted viable habitat for H. rufescens further north are likely unviable and a result of spatial over-projection due to poorer fit with occurrences.Spatial models predict a continuous contemporary range for H. rufescens compared to S. purpuratus in their core distribution range. Forecasts up to 2100 predict that the viable habitat distribution of S. purpuratus will shift north but will remain relatively unaffected throughout its core range  accurately depicts how environmental temperature, oxygen availability, and organismal mass interact to constrain the viable habitat for two common marine invertebrates, S. purpuratus and H. rufescens, across the California Current Large Marine Ecosystem (CCLME). A key advantage of ΦA demonstrated here is its ability to discern a thermal optimum, and how this changes with body mass and deoxygenation, permitting the delimitation of a species’ entire biogeographic envelope – which is not typically achieved with the metabolic index (Φ) in its factorial form54. Congruency between ΦA predictions of thermal optima and experimental studies further corroborates this framework and supports the theory that the balance between oxygen supply and demand has an important role in regulating thermal responses, even if oxygen limitation may not be the proximate cause or set critical thermal limits57. Rather, the ΦA framework highlights that the temperature where excess oxygen supply is maximized varies depending on organismal mass and environmental oxygen level. Thus, aquatic organisms can exhibit ecological thermal responses even in the absence of environmental temperature change  or in a direction not explained by temperature alone as they seek conditions that maximize oxygen provision.We find that the absolute metabolic index  than demand (δ) causing ΦA to decrease per unit body mass as organisms get bigger. The effect of this decrease is amplified at warmer temperatures because the thermal sensitivity of demand is greater than supply, such that even if PO2crit scales close to zero with mass62 the peak ΦA will exhibit a cool temperature shift. While we cannot pinpoint the specific mechanism it is notable that this pattern is consistent with observations of temperature preference with size in aquatic ectotherms63.ΦA predicts deoxygenation will drive organisms to cooler temperatures, providing a mechanistic explanation for observed thermal responses to hypoxia. Indeed, experimental evidence for cooler temperature preference in less oxygenated water is reported for the Atlantic cod (Gadus morhua)64, and the Carmine shiner (Notropis percobromus)65. This pattern is driven, in ΦA models, because oxygen supply (S) is the product of oxygen supply capacity (αS), which increases with temperature, and environmental oxygen availability (PO2), resulting in a smaller relative increase in oxygen supply with temperature when environmental oxygen is low. Furthermore, deoxygenation itself is directly detrimental to organismal functioning with small decreases making habitat inhospitable and triggering distribution shifts67. Organisms in ocean regions that are warming and losing oxygen – the climate change signal in the CCLME and other upwelling ecosystems – are thus particularly vulnerable as not only will deoxygenation shift the Topt ΦA cooler, but ocean warming will concurrently move temperatures further away from Topt ΦA. If temperature and oxygen change are heterogenous across space , such as in the CCLME, viable habitat can become fractured, limiting potential latitudinal range shifts28. The synergistic negative effects of concurrent deoxygenation and warming highlighted by the ΦA framework provides further evidence that temperature and oxygen availability must be considered in tandem when assessing climate resilience of marine organisms.ΦTopt ΦA in oxygen-saturated water and temperatures where physiological processes such as growth are maximized for both species. While we do not explicitly measure maximum metabolic rate (MMR) the ΦA in oxygen saturated sea water should theoretically provide a similar value to absolute aerobic scope (MMR - SMR) if modelled oxygen supply (S) is similar to MMR, for which some evidence exists34. Numerous studies have however found mismatches between the temperatures where absolute aerobic scope is maximized and preferred temperatures68 or temperatures where growth efficiency is maximized69 for fishes. The ΦA framework shows that the temperature where excess oxygen supply is greatest decreases with deoxygenation  for H. rufescens and hence modelled a more monotonic thermal relationship for PO2crit data, resulting in broader ΦA’ curves per oxygen level and potentially erroneous suitable ΦA’ predictions at cold temperatures (<7 °C). We also found geographic bias for this species in the OBIS occurrence data we used, with few documented occurrences of H. rufescens recorded in the warmer Mexican ocean despite the species occurring there52. In fact, H. rufescens had less than half the geo-referenced occurrence points in OBIS as S. purpuratus, and both species showed geographic areas where the species currently exists but is simply not recorded in OBIS. Like all modeling approaches, ΦA’ is sensitive to data inputs, and the predictive power for a given species will continue to improve as the three main data inputs—biogeographic occurrences, species-specific physiological parameters, and measured or modeled oceanographic conditions—continue to be better documented. A final plausible explanation is that the contemporary distribution of H. rufescens is more constrained by additional factors other than temperature and oxygen availability, such as resource competition, fishing pressure, predation, or life history and habitat specialization73. Forecasted future distribution changes based solely on environmental variables therefore need to be considered in the context of over predicting contemporary biogeographic distribution and predicted future range contractions.While the ΦH. rufescens’ distribution through constraints on ΦA. This is in addition to a multitude of other interacting stressors – e.g., overfishing, kelp deforestation and associated loss of food and habitat, and disease - that have already contributed to collapsing the once productive H. rufescens fishery74. In contrast, climate-driven reduction in ΦA is predicted to have relatively little effect on S. purpuratus, whose numbers have recently exploded along the California margin, shifting kelp forests to barren states38. Caution must be exercised when considering such forecasts as they do not include higher spatial or temporal levels of environmental variability or mechanisms that confer resilience such as generational adaptation or acclimation following chronic exposure, where rates return to baseline levels after the initial increase during acute exposure (which are only captured in this study)75. Nonetheless, despite the nature of future predictions being inherently imprecise they can serve as a comparative tool to identify what species and areas are relatively vulnerable to warming and deoxygenation and focus improving management regulations. Currently implemented management approaches to maintain or rebuild wild abalone stocks and kelp forest ecosystems include the implementation of marine reserves to re-establish top-down, predatory control of urchin populations, urchin culling76, fishing moratoria, and kelp restoration74, but the efficacy and scalability of these approaches is debated77. Ensuring the persistence of abalone populations, as well as other ecologically, economically, and culturally important species and whole kelp forest ecosystems, will require prioritizing these management approaches in areas predicted to provide relative refugia under future climate scenarios, such as, for abalone, northern Baja  models are used to predict species specific values of ΦA given the prevailing temperature (T), environmental oxygen (PO2) and organismal mass (B) following Eq. δ is the allometric mass scaler for SMR and ε is the allometric mass scaler for PO2crit (equation presented in the introduction).Parameters from A values at various temperature (T) and oxygen levels (O) accounting for plasticity in the shape of the limiting oxygen level line, we normalize ΦA by rescaling between zero and one for each oxygen level (whole kPa unit) following Eq. A’ thus ranges from zero (lowest ΦA) to one (highest ΦA) for each kPa level (22–0 kPa) where ΦA has a positive value, permitting an easier comparison of Topt ΦA across the oxygen scape and accounting for potential non-linear limiting oxygen level curves, assuming the thermal dependance of α still holds35.To compare ΦPO2crit and SMR across a range of temperatures and body masses are therefore first required.Species-specific experimental physiological measurements for Strongylocentrotus purpuratus  and red abalone Haliotis rufescens  were obtained from Monterey Abalone Company prior to each experimental trial, where they had been held in underwater cages exposed to natural ocean temperatures and fed a natural kelp diet. Respirometry techniques were used to quantify standard metabolic rate (SMR) and critical oxygen partial pressure (PO2crit) for each specimen at a specific test temperature. A single trial involved placing freshly obtained specimens directly into respirometry chambers  at 12.7 °C  and subsequently adjusting the water temperature at a rate of ~1 °C per hour until a test temperature of 5, 7, 10, 13, 16, 19 or 22 °C was reached and held constant (+/−0.4 °C). Specimens were given ~24 h to adjust to novel respirometry chamber and temperature conditions and purge any digesting food, after which intermittent flow respirometry (510 min measure – 10 min flush) was run for a further ~24 h to quantify SMR. Oxygen levels in chambers were continuously recorded with a FireStingO2 fiber optic oxygen meter  connected to oxygen sensor spots (Pyroscience OXSP5) positioned in a circulation loop with bare optical fibers  and logged with Pyro oxygen logger software. After ~24 h of intermittent flow respirometry the PO2crit phase of the trial began by switching off the flush pump so that oxygen could be depleted within respirometer chambers via the specimen’s aerobic metabolism until no oxygen remained. Each specimen was used once to obtain SMR and PO2crit measurements at a single temperature. The trial was then terminated, test specimens frozen and later thawed to weigh to the nearest hundredth of gram (wet mass). We also dried test specimens at 50 °C for ~24 h, measured their dry mass, subsequently ashed them at 450 °C for three hours in a muffle furnace, and measured their ashed mass to calculate a metabolic mass (dry minus ashed mass) which is recorded in the metadata for completeness. A blank was run in an empty respirometry chamber in parallel for each trial.Ethical approval for experiments was not required as the study organisms are classified as invertebrates and were not obtained from the wild. All other research procedures comply with relevant ethical regulations. Purple sea urchin MO2, mg O2. min−1. g−1) of each measurement period was calculated using Eq. 481 where Vre is the total volume of the respirometer in ml; M is the mass of the specimen in g expressed as ml; W is mass of the specimen in g, The mass specific oxygen consumption rate  and removing measurements associated with background respiration rates outside the 95th and 5th quantiles of all measurements, which may have occurred during temperature adjustments in the respirometry system. SMR was estimated as the 0.1 quantile of all remaining oxygen consumption measurements22.Oxygen consumption rates of each measurement period were calculated using the calc_rate function in the respR packagePO2crit phase of the experiment was split into five-minute segments with mass-specific oxygen consumption rates calculated for each segment as per SMR protocol. All MO2 measurements  were paired with corresponding average oxygen concentration values converted to percent saturation using the conv_o2 function from the respirometry package83. We define PO2crit as the the lowest level of oxygen (percent saturation) at which an animal can maintain SMR84 and estimated it with the calcO2crit function22. The maximum number of points (max.nb.MO2.for.reg) to fit the regression below PO2crit was taken as the number of points that had the best fit (R2) and ensured the intercept was negative. We corroborated the robustness of our PO2crit approach by comparing results with a method where PO2crit is taken as the oxygen partial pressure where oxygen supply capacity (αS) is maximized, following Seibel et al.24  quantified as standard metabolic rate (SMR) is described by Eq. 519 where B is mass in grams and δ is its allometric scaling exponent, E is activation energy, k is Boltzmann’s constant (eV/K) and T is absolute temperature in Kelvin85.The effect of mass and temperature on a species’ oxygen demand . We took the constants of the SMR model; E as the back transformed intercept and slope respectively from the linear relationship between the natural logarithm of mass-corrected SMR k.T (product of Boltzmann constant and temperature in Kelvin).We first divided SMR data with its Arrhenius functionPO2crit data for each species but did not fit Arrhenius models across the whole temperature range as the relationship can break down at colder temperatures where PO2crit may be higher than predicted29, possibly due to reduced oxygen diffusivity in colder water86. We first converted PO2crit data from percent saturation to oxygen partial pressure (kPa) using the conv_o2 function from the respirometry package83 including the experiment temperature, salinity = 35 PSU, and standard atmospheric pressure (1013.2) as arguments. For both species, PO2crit data fit Arrhenius relationships at temperatures above 7 °C. We therefore estimate the allometric mass scaling exponent ε from the power relationship between temperature standardized PO2crit (PO2crit divided by the Arrhenius function for temperatures above 7 °C) and organismal mass (B). We then fit more flexible models through the relationship between mass corrected PO2critPO2crit data for S. purpuratus was well approximated by a quadratic polynomial and H. rufescens by a general exponential model.We followed the same approach as SMR for modeling A values in the CCLME we used high-resolution (12 km) oceanic simulations made with the Regional Oceanic Modeling System (ROMS)87. The ocean model domain extends from 15° N to 60° N, 4000 km offshore from the coast of America and has 21 depth bins spaced ten meters apart88. Model simulations are conducted for a hindcast period from 1995 to 2010 as well as a future climate projection for the end of this century (2071–2100) under the RCP 8.5 emissions scenario. The future climate projection applies surface and open boundary condition anomalies averaged across multiple models in the CMIP5 archive, following procedures outlined by Howard et al.88. Mean monthly temperature (°C), oxygen (uM) and salinity (PSU) are interpolated from the native s-coordinate to regular 10-meter intervals for analysis. Gridded bathymetry data were obtained from the General Bathymetric Chart of the Oceans (GEBCO) website at a 15 arc second (450 m) resolution. For each month we extracted the layer of environmental variables closest to the ocean floor by overlapping grid cells of the ocean model with bathymetry cells greater than −150 metres (~maximum depth of occurrence for both species) and extracting values from the depth bin of the ocean model that most closely matched the depth of bathymetry layer. We calculated the water pressure at depth in mbar using the swPressure function in the oce package89 and added atmospheric pressure (1013.25 mbar). We then calculated the oxygen bioavailability in partial pressure units (kPa) using the conv_o2 function including corresponding temperature, salinity, and pressure as arguments.To map the spatial and temporal distribution of species-specific ΦS. purpuratus and H. rufescens from the Ocean Biodiversity Information System (OBIS) using the robis robis,90. Overall, we retrieved 4595 S. purpuratus and 1582 H. rufescens occurrences. We matched these occurrences to the same grid structure as the ocean model and removed duplicates resulting in 230 unique geo-referenced occurrence points for S. purpuratus and 91 for H. rufescens.We obtained geo-referenced occurrence data for A value. We identified habitat suitability thresholds for ΦA’ as the value where 99% of all occurrence points fell above. To map each species distribution, we produced a gridded layer of the minimum monthly ΦA’ value for each species and applied their corresponding habitat suitability thresholds . We followed the same process for the future ocean model forecasts to predict how each species’ distribution may change.We paired each geo-referenced occurrence point with corresponding monthly temperature and oxygen availability from the ocean models and estimated the corresponding Φ91 with the following packages; ggspatial92, raster93, marmap94, rnaturalearth95, sf96, viridis97 and tidyverse98 in addition to those cited in text.All analyses were carried out in R version 4.0.3Further information on research design is available in the Supplementary informationPeer Review FileReporting Summary


S, showinga range of −51 to −101 cal mol–1 K–1 (−2.2 to −4.4 mV K–1) depending on ligand and metal vertex modifications, demonstratingthe potential for rational design of high-entropy electrolytes anda new strategy to overcome theoretical limits on ion solvation reorganizationentropy.An aqueous electrochemicallycontrolled host–guest encapsulationsystem demonstrates a large and synthetically tunable redox entropychange. Electrochemical entropy is the basis for thermally regenerativeelectrochemical cycles (TRECs), which utilize reversible electrochemicalprocesses with large molar entropy changes for thermogalvanic waste-heatharvesting and electrochemical cooling, among other potential applications.A supramolecular host–guest system demonstrates a molar entropychange of 4 times that of the state-of-the-art aqueous TREC electrolytepotassium ferricyanide. Upon encapsulation of a guest, water moleculesthat structurally resemble amorphous ice are displaced from the hostcavity, leveraging a change in the degrees of freedom and orderingof the solvent rather than the solvation of the redox-active speciesto increase entropy. The synthetic tunability of the host allows rationaloptimization of the system’s Δ The magnitude ofthis entropic gain is sensitive to the solvent conditions and thestructure of the guest molecule. The modular nature of these metal–ligandcoordination cage hosts enables systematic investigation of structure–activityrelationships,16 with the potential to gain fundamental insights into water organizationand solvation entropy.Liquid water demonstrates many uniqueproperties due to hydrogen-bondingnetworks and strong directional interactions. Confinement of waterwithin hydrophobic molecular cavities has been observed to disruptthe structure and preferential ordering of water molecules.G = ΔH – TΔS, nonzero reaction entropy formsthe basis for conversion between heat (T) and freeenergy (ΔG). Numerous applications in energyefficiency and green technologies can be derived from systems withlarge and reversible reaction entropy. Phase change materials areone example of a field where a rational molecular design approachis taken to enhancing entropy, with uses in energy storage, heat harvesting,and cooling.18 Thermally regenerative electrochemicalcycles (TRECs) utilize a reversible redox entropy change to convertbetween heat and electricity, presenting a potential application ofa solution-state high-entropy supramolecular electrolyte system. Engineershave employed TRECs for electrochemical heat engines and refrigerators.20 The equilibrium potential of an electrochemical system has a dependenceon temperature, provided that the entropy of the system is not zero (S) of the system: a larger ΔS ofthe redox couple leads to a larger temperature change per mole ofelectrons transferred. These electrochemical heat engines can be usedto harvest waste heat from thermal energy generation,23 and electrochemical refrigerators provide a cooling method thatcan avoid the use of volatile coolants,26 which can contributeto atmospheric pollution .28 To improve the efficiency of TRECs, an electrolyte with a largeand reversible entropic change is required.29 So far, most TRECs have used simple salts as electrolytes, relyingon electrochemical changes in solvation entropy, with ferri/ferrocyanide(FCN4–/3–) being the most promising choice.Optimization experiments have been conducted,31 usually involving changes in the counterion, solvent, or concentrationof the electrolyte, but these salts are not amenable to syntheticdesign and modification. Solvation entropy is also bounded by inherentlimitations, as described by the Born expression for reaction entropy,32 leading to a fundamental bottleneck in thistechnology.33There are few existing ways to rationallytune the reaction entropydespite this property’s great utility. Because of the relationshipΔ effect) 1a. In it4L6 host would result ina system with a large and redox-controlled entropic change, whichwould be synthetically tunable through the structure of the host andguest. This host–guest system would represent the first ofits kind in the TREC literature. The entropic change in the M4L6 supramolecular system reported herein reliesnot only on solvent reorganization but also more closely resemblesa solvent phase change, resembling materials that undergo phase changes.38 The entropy change arises from the large gain indegrees of freedom of the solvent molecules when released from molecularconfinement rather than solely from a change in solvent organizationaround the redox-active species. Deriving the entropic change fromsolvent ordering through confinement bypasses solvation entropy limitationsof nonsupramolecular electrolytes, and we hypothesize that the entropicchange of this system would be limited instead by the entropy of thephase transition of the solvent, which is much greater.39 Furthermore, the M4L6 hosthas proven to be amenable to structural modifications at the metalvertices and of the ligand at both its linker and its end groups  to allowmore synthetic flexibility. The redox potential of Cc/Cc+ is over 1 V more negative than that of the catecholate ligands;therefore, structural modifications do not risk shifting the guestoxidation potential to overlap with that of the host, and the systemcan operate without nearing the oxidation potential of the host. Hydrocarboxycobaltoceniumhexafluorophosphate (CcCO2HPF6) was selected,due to its improved solubility and slightly less negative reductionpotential compared to the parent cobaltocenium  ) is overall neutrally chargedand encapsulates due to hydrophobic effects. Upon addition of an excessof strongly binding guest, tetraethylphosphonium (PEt4+), which effectively displaces CcCO2 from the hostcavity , the redox behavior of CcCO2 returns. Thesedata suggest the redox inhibition of CcCO2 by the host.In 100 mM potassium phosphatebuffer in water at pH = 12, charge-neutralCcCOCN4–/3–) 2b. When44 Increasing the driving force alsoincreases the repulsion; therefore, the effect cannot be overcomeby changing the electrode potential. Modifying conditions that affectthe organization of the double layer, such as temperature, electrodematerial, electrolyte concentration, and solvent, can allow the redoxspecies to reside within the electron transfer distance of the electrode.No change was observed when switching from a glassy carbon workingelectrode to a platinum working electrode . Increasing the buffer concentration to 1 M, however, theredox behavior of CcCO2 can be observed in the presenceof the host, although the peak current is smaller  × 10–7 cm2 s–1, compared to 5.71(4) × 10–7 cm2 s–1 in the absence of the host, implying associationof CcCO2 with the host, whether internally or externally,and thus moving together as a larger unit.45 With the addition of 1.2 equiv of PEt4+, thediffusion coefficient is 5.09(5) × 10–7 cm2 s–1, implying that the CcCO2 is now more freely diffusing, displaced by PEt4+ from its association with the host.The Frumkin effect was investigated as an explanation for thisbehavior. This effect, whereby the charge repulsion between the redoxspecies and the ordering of ions in the double layer hinders electrontransfer, has been observed for highly charged species, such as persulfatesalts.lectrode S9. Incre2 inside or outside the host cavity, the experimentwas conducted with a guest with an arbitrarily slow exchange ratefrom the host cavity. Decamethylcobaltocenium (Cp*2Co+) is too large to exchange from the host cavity (the guestexchange rate is on the order of days),46 and [Cp*2Co ⊂ Ga4L6]11– must be synthesized by templation around the Cp*2Co+ guest. We can thus assume that Cp*2Co+ resides exclusively within the [Ga4L6]12– cavity. No redox behavior was observedfor [Cp*2Co ⊂ Ga4L6]11– . In contrast, the titanium-basedhost [Ti4L6]8– has an accessibleTi(III/IV) redox couple, and its reversible redox behavior has beenmeasured . The four verticesof Ti4L6 are essentially electronically independent;therefore, four electrons are transferred simultaneously, and thecharge of the host changes from −8 to −12. We reasonedthat the host itself can access the electrode and undergo electrontransfer in the double layer but only prevents electron transfer fromthe electrode directly to the guest, possibly due to a prohibitivelyhigh reorganization energy in the constricted cavity.To determine whether theobserved redox peaks in 1 M buffer correspondedto CcCO2 in the presence of [Ga4L6]12– at 1 M buffer is likely that of CcCO2 associated through ion-pairing interactions to the exterior of thehost. The overall neutral CcCO2 binds to [Ga4L6]12– through hydrophobic effects inthe naphthalene-walled cavity. It is a weakly binding guest and isobserved both inside and outside the host cavity, as assessed by 1H NMR spectroscopy .In solution, there is an equilibrium between interiorly and exteriorlyassociated CcCO2 to [Ga4L6]12–, and the concentration of CcCO2 completely unassociatedwith the host is relatively low. At a lower ionic strength, the negativecharge excess in the electrode is more poorly screened than at thehigher 1 M buffer concentration, inhibiting electron transfer to thehost–guest complex. As the CcCO2 is associated withthe host, the redox behavior observed by CV reflects a low concentrationof CcCO2 within electron transfer distance . The broadened signals in the NMR of CcCO2 preclude measurement of its entropy of encapsulation by NMR,as these signals could not be reliably integrated. Using an electrochemicalvan’t Hoff technique (see the Supporting Information for Method) enabled measurement of the entropyof the CcCO2 Co(II/III) redox couple, as well as the entropyof the host–guest system, including both the encapsulationand redox processes  was found to be −3.62(4)mV K–1, which corresponds to a reaction entropy(ΔS) of 83.5(9) cal mol–1 K–1 for potassium ferri/ferrocyanide). Control experiments support thatencapsulation in the host is the reason for the increased ΔS, as CcCO2 alone and CcCO2 with thehost cavity blocked by a strongly binding guest (PEt4+) all show significantly lower ΔS (−1.87(9)mV K–1 or −43(2) cal mol–1 K–1 and −2.00(4) mV K–1 or −46.0(9) cal mol–1 K–1, respectively). Despite the increase in ΔS of the system in the presence of the host, a shift in E1/2  is notobserved in the CV (11 The absence of a shift in the voltametric peakin 4L6]12– implies weakhost–guest binding (Ka ∼1) and would be consistent with guest ion pairing to the outside ofthe host cavity. The large entropic value is most likely attributedto displacement of the solvent from within the host cavity, so theguest is likely to reside both inside and outside of the host cavityin equilibrium. This entropic value represents a 4-fold increase versusthe state-of-the-art TREC electrolyte, potassium ferricyanide.29 Furthermore, synthetic modifications of thehost system demonstrated large variations in the entropy (S was Ga-3 (47 which canaccommodate more water molecules that are then released upon encapsulationof the guest. The hosts with 8– charge ([Si4L6]8– and [Ge4L6]8–) showed significantly lower ΔS. Switching from potassium phosphate buffer to sodium phosphatebuffer also raised ΔS, possibly due to themixture of cations, which has been shown to have an effect on solventorganization.31 Taking the conditions andhost structural features that gave the highest ΔS, we synthesized and measured the ΔS of In-3 in sodium phosphate buffer to be −4.38(6) mV K–1 or −101(1) cal mol–1 K–1.The Seebeck temperature coefficient (α) of thissystem ,but it does not significantly outperform ferricyanide.50 A supramolecular system that uses α-cyclodextrin and iodide/triiodidehas also been reported,51 achieving α= 2.6 mV K–1. Another system has been reported inwhich the ferri/ferrocyanide electrolyte switches between crystallineprecipitate and solution state,52 achievingup to α = 3.73 mV K–1. Though these systemscontain supramolecular host–guest chemistry and a phase change,respectively, both of these systems modulate the local concentrationof the redox species at the hot and cold electrodes (either throughsupramolecular encapsulation or precipitation out of solution) andthereby increase the Seebeck coefficient through the relationshipbetween potential and molar quotient described by the Nernst equation.These systems cannot generate power continuously, however, as theelectrolyte must be regenerated batchwise, and generating a precipitatealso risks passivating the electrode. The M4L6 system instead derives entropy primarily from the ordering of solventin confinement. The M4L6 host–guest systemrepresents the first system to use solvent encapsulation and releaseto generate an electrochemical entropic change, with the advantageof being entirely solution-state and compatible with continuouslyrun thermogalvanic devices.A few electrolyte systems that bear superficialsimilarities tothe M–1) 6 and 0.3 M K4Fe(CN)6). At saturation, the host–guest system demonstrated a Seebecktemperature coefficient of α = −2.35(8) mV K–1 (ΔS = −54(2) cal mol–1 K–1), compared to α = −0.92(1) mVK–1 for potassium ferri/ferrocyanide.A simple thermogalvanic heat harvestingengine was constructedand tested with the host–guest system and potassium ferri/ferrocyanide,respectively, both paired with iodide/triiodide, which has a positiveSeebeck coefficient (α = 0.5 mV K–1) 4a.23,24 –2 at 4% of Carnot efficiency, matchingthe reported results.23 The host–guestsystem achieves a higher efficiency value of 6% of Carnot efficiencyat a low flow rate, though with a lower power density of 2.5 μWcm–2  to a functional device. The power density and efficiencyof the host–guest system can be improved through further syntheticmodifications to improve the solubility of the host. Assuming thatcurrent density scales linearly with concentration, the host–guestsystem can be expected to outperform potassium ferri/ferrocyanidewhen it achieves a concentration of over approximately 0.2 M, doubleits present solubility . In future work and scale-ups of this system, syntheticdesign can be employed to improve other aspects as well, by replacingcobalt, gallium, and indium, for example, as the mining of these metalsis known to be ethically concerning,55 or by making operatingconditions more benign by designing for a system closer to neutralpH to reduce corrosivity. The structural tunability of the supramolecularsystem engenders the possibility of engineering improvements to itsproperties through chemical synthesis and demonstrates the potentialcontribution of an approach to such projects through physical organicchemistry.The peak power for the potassium ferri/ferrocyanidesystemis 9μW cm μWcm–2 4b. At a 4L6 tetrahedronhas long beenknown to induce a large entropic increase upon encapsulation of aguest, this feature was until now understood as an interesting propertyof a supramolecular system. With this work, however, it has been shownthat fundamental properties can still find new practical applications.While chemists frequently consider the energy and enthalpy of systems,we less often use molecular design to address entropic properties,and we have fewer strategies to do so. Supramolecular chemists havelong performed studies to understand the fundamental properties andrules underlying noncovalent interactions, which contribute to systemicentropy changes. Supramolecular assemblies have the advantage of beingadaptable to homogeneous solution-state systems, and the systems reportedin this paper leverage the molecular confinement of water to demonstratesome of the largest Seebeck coefficients observed. Therefore, we identifythis as a topic of interest in which synthetic chemists, materialsscientists, and engineers may find fruitful collaborations. Many aspectsof thermogalvanic device development cannot be addressed through chemistryresearch alone , so we urge collaborationand critical evaluation from adjacent fields, including but not limitedto engineering, environmental policy and health, and energy and resourcesmanagement.While the M


Our daily endeavors occur in a complex visual environment, whose intrinsic variability challenges the way we integrate information to make decisions. By processing myriads of parallel sensory inputs, our brain is theoretically able to compute the variance of its environment, a cue known to guide our behavior. Yet, the neurobiological and computational basis of such variance computations are still poorly understood. Here, we quantify the dynamics of sensory variance modulations of cat primary visual cortex neurons. We report two archetypal neuronal responses, one of which is resilient to changes in variance and co-encodes the sensory feature and its variance, improving the population encoding of orientation. The existence of these variance-specific responses can be accounted for by a model of intracortical recurrent connectivity. We thus propose that local recurrent circuits process uncertainty as a generic computation, advancing our understanding of how the brain handles naturalistic inputs. Two populations of V1 neurons are described that are either vulnerable or resilient to variance of oriented inputs and these features can be reproduced in a well-established model of intracortical V1 recurrence and interpreted within a Bayesian framework. While this approach offers a clear hypothesis as to what neurons are responding to, it only probes for neural selectivity to individual input parameters, such as orientation or spatial frequency. Natural vision, however, involves rich cortical dynamics3 integrating a mixture of multiple local parameters and global contextual information4. Hence, a majority of our understanding of V1 relies on neural responses to single inputs in orientation space, rather than naturalistic responses to multiple orientations.Selectivity to the orientation of visual stimuli is an archetypal feature of the neurons in the mammalian primary visual cortex (V1)6 in order to drive behavior7. According to Bayesian inference rules, low-variance inputs are processed through fast feedforward pathways, whereas higher sensory variance elicits a slower, recurrent integration8. How the brain performs computations on variance is not yet fully understood. In V1, it has been shown that single neurons undergo nonlinear tuning modulations as a function of their input’s variance9 which can serve as a functional encoding scheme11. These recent results align with earlier models of recurrent cortical activity of V113 and also match psychophysical measurements in humans16. While it seems that local interactions within V1 are sufficient to encode orientation variance17, the quantification of single neuron responses, their dynamics and their link to a functional population encoding of variance remains to be established.This knowledge gap is not trivial, as the variance of distributions of sensory inputs is a fundamental cue on which our brain relies to produce coherent integration of sensory inputs and prior knowledge of the world18. We present a quantitative analysis of single neurons’ variance-tuning functions, as well as their dynamics, reporting heterogeneous modulations. Two archetypal response types emerge in V1, one of which relies on predominantly supragranular neurons that maintain robust orientation tuning despite high sensory variance, allowing them to co-encode orientation and variance, and enhancing V1’s orientation distribution encoding. A well-established V1 intracortical recurrence model accounts for these resilient neurons, aligning with canonical Bayesian frameworks6 and suggesting uncertainty computations as a new generic function for local recurrent cortical connectivity.Here, we investigate the neural basis of variance processes in V1 using stimuli matching the orientation content of natural images18. These stimuli are band-pass filtered white noise textures and offer three advantages over both simple grating-like stimuli and complex natural images. First, they enable fine control of mean θ and variance, controlled by Bθ, of orientation distributions through a generative model, thereby reproducing natural images’ oriented content  units maintained their preferred orientation when variance Bθ increased, while the peak amplitude of the tuning curve diminished significantly . Only 28.5% of the recorded units were still tuned for Bθ = 35.0° stimuli . Thus, increasing input variance reduces single neuron tuning, which manifests heterogeneously across neurons, as evidenced by two representative single units shown in Fig. Bθ reaches 35° , unlike neuron B  which exemplifies the aforementioned 28.5% variance-resilient units. These response types are characterized by functions relating Bθ to the goodness of tuning , named here variance-tuning functions  compared to neuron A . In summary, orientation variance induces changes in both tuning and dynamics of V1 neurons, revealing two archetypal types of response: either fast in time and nonlinear with respect to variance (neuron A) or slow in time and linear with respect to variance (neuron B).Orientation variance impacts not only orientation tuning but also the dynamics of the response of V1 neurons Fig. . Interesuts Fig. a, also h23 in the Within-Clusters-Sum-of-Squares (WCSS) curve , higher changepoints  and better tuning to low-variance inputs , which endows them with the ability to respond to an orientation on a broader range of input variances27. No significant differences in the variance-HWHH and variance-firing rate functions were observed, except for the non-linearity of the latter metric . However, both groups of neurons had a similar circular variance for Bθ = 35° , but both groups were on par for inputs of Bθ = 35° . Interestingly, resilient neurons had significantly lower time to the maximum amplitude of the tuning curve for Bθ = 0° , which opposes the early/late ratio of spikes. Neither group showed variance-dependent modulation of the delay to maximum spike count , and both groups showed similar delay for Bθ = 35° .The K-means clustering resulted in a significant difference between the two groups’ VTF parameters Fig. b: resiliU = 7031.5, p = 0.910). Instead, the location of the recorded units (unused in the clustering process) predominantly positioned the resilient neurons in supragranular layers, offering a mechanistic basis for their existence . This classifier received the firing rate of neurons in a sliding time window (100 ms) and learned, for each neuron, a coefficient that best predicts the class  of the stimulus.As the neuronal population has been separated into well-characterized groups, we wish to understand the functional role played by resilient and vulnerable neurons. To that end, we used a neuronal decoder that probes for population codes in V1, enabling us to seek what parameters of the stimuli each neuron group was encoding. We trained a multinomial logistic regression classifierθ in the population activity. For this purpose, the dataset of trials was separated for each variance, such that eight independent, Bθ-specific, orientation decoders were learned, with optimal parametrization  from the entire population recordings. The temporal evolution of these decoders’ accuracy  is a population tuning curve, which displays the likelihood of decoding all possible input classes , which influences the accuracy of the decoder  from the population activity , which is more than an order of magnitude smaller than the quantity of neurons a single V1 biological decoder can access32. Thus, neither the decoding of variance Bθ nor the decoding of orientation θ accounts for a different role between resilient and vulnerable neurons.Given that orientation encoding did not reveal a fundamental difference in the respective contributions of resilient and vulnerable neurons, we then investigated the encoding of the stimulus’ variance 33 based on a probabilistic computational principle34. Here, this implies that the naturalistic form of processing for a V1 neuron would be co-encoding both the mean feature (θ) and its associated variance (Bθ) to access the entire probability distribution.The decoding methods used so far have assumed that V1 encodes independently single input parameters. However, a more realistic assumption is to consider the visual system’s natural inputs as distributions of information  is shown as the angle of each bin from the upper vertical and the variance Bθ is represented as the eccentricity of each bin from the center. Visualizing the coefficients of the whole population decoder  axis .A notable difference between vulnerable and resilient neurons is their different location within the cortical layers Fig. h. This tons Fig. a. Here, 9, we first ran our model without recurrent synapses. We reproduced the heterogeneous selectivity by convolving the input with tuning curves of varying bandwidths  to be found mostly around the Kexc; Kinh identity line, thus produced by balanced recurrent connectivity. Vulnerable VTFs  were, on the contrary, mostly found above the identity line, where the configuration of the network is dominated by excitation over inhibition. This is consistent with the range of parameters that yielded higher response latency , which has a limited visible effect on V147 and is less likely to cause modulations in this area compared to higher-order areas51.Here, we restricted our approach to orientation space, rather than investigating the full extent of spatial relationships which are present in natural images. Thus, full-field stimuli without second-order correlation were used, which compared to a purely ecological environment, have likely excluded end-stopped cellsdel Fig. . This is10. For instance, Goris et al.9 reported that heterogeneously tuned V1 populations help encode the orientation distributions found in natural images and that this functional diversity could be accounted for by a linear-nonlinear (L-NL) model. While this could explain the diversity of tuning in our data  variance weighting of cortical activity to supragranular recurrent connectivity8, without the need for extrastriate computations. This is an interesting perspective that opens up a general interpretation of our results into the broader context of processing variance/precision/uncertainty at different scales of investigations. Extending the present results to other cortical areas or other sensory modalities would be a simple process, given the generative stimulus framework used here18, which could yield pivotal new insights into our understanding of predictive processes in the brain.This leads to an interesting tie to Bayesian inference, namely under the specific case of predictive coding18 that allow for fine parameterized control over naturalistic stimuli54, which is a desirable trait when probing sensory systems under realistic conditions21. They are mathematically defined as band-pass filtered white noise stimuli, whose filters in Fourier space are defined as a parameterized distribution in a given perceptual axis . Thus, the Motion Clouds presently used are fully characterized by their mean orientation and their orientation variance, such that a given stimulus S can be defined as:O the orientation envelope, characterized by its mean orientation θ and its orientation bandwidth Bθ. For κ is the concentration parameter of a von Mises distribution, and hence approximates the standard deviation57. It thus serves as a measure of the orientation variability in the pattern, and as such, we used the term variance to describe it throughout the text. A total of 96 different stimuli were generated, with 12 mean orientations θ ranging from 0 to π in even steps, and eight orientation variance Bθ ranging from ≈0 to π/5 in even steps. The orientation envelope is a von Mises distribution:θf is the angle of the frequency components of the envelope in the Fourier plane, which controls the spatial frequency parameters of the stimuli, set here at 0.9 cycle per degree. The stimuli were drifting orthogonally in either direction with respect to the mean orientation θ at a speed of 10°/s, which is optimal to drive V1 neurons58. For the range of values of Bθ considered here, the orientation envelope approximates a Gaussian distribution and Bθ is thus a measure of the variance of the orientation content of the stimuli.Motion Clouds are generative model-based stimuli59. Monocular stimuli were projected with a ProPixx projector (VPixx Technologies Inc.) onto an isoluminant screen (Da-Liteⓒ) covering 104° × 79° of visual angle. All stimuli were displayed for 300 ms, interleaved with a mean luminance screen (25 cd/m2) shown for 150 ms between each trial. Trials were fully randomized, and each stimulus (a unique combination of θ × Bθ × drift direction) was presented 15 times. Stimuli were shown at 100% contrast, meaning that as Bθ increased, the amount of orientation energy at median orientation θ decreased, and conversely for off-median orientations  and displayed using Psychopy2:N2O (v/v). Following tracheotomy, animals underwent artificial ventilation as muscle relaxation was achieved and maintained with an intravenous injection of 2% gallamine triethiodide (10 mg/kg/h) diluted in a 1:1 (v/v) solution of 5% dextrose lactated Ringer solution. Through the experiment, the expired level of CO2 was maintained between 35 and 40 mmHg by adjusting the tidal volume and respiratory rate. Heart rate was monitored and body temperature was maintained at 37 °C by means of a feedback-controlled heated blanket. Lidocaine hydrochlorine (2%) was applied locally at all incisions and pressure points and a craniotomy was performed over area 17 . Dexamethasone (4 mg) was administered intramuscularly every 12 h to reduce cortical swelling. Eye lubricant was regularly applied to avoid corneal dehydration.Experiments were conducted on three adult cats . All surgical and experimental procedures were carried out in compliance with the guidelines of the Canadian Council on Animal Care and were approved by the Ethics Committee of the University of Montreal (CDEA #20-006). Animals were initially sedated using acepromazine  supplemented by atropine (0.1 mg/kg). Anesthesia was induced with 3.5% isoflurane in a 50:50 mixture of O47. Finally, small durectomies were performed before each electrode insertion and a 2% agar solution in saline was applied over the exposed cortical surface to stabilize recordings. Linear probes  were lowered in the cortical tissue perpendicularly to the pia, and extracellular activity was acquired at 30KHz using an Open Ephys acquisition board60. Single units were isolated using Kilosort 261 and manually curated using Phy62. Clusters with low amplitude templates or ill-defined margins were excluded from further analysis. The additional exclusion was performed if a cluster was unstable (firing rate below 5 spikes.s−1 for more than 30 s), or if the neuron was not deemed sufficiently orientation selective (R2 < 0.75 when fitted with a von Mises distribution). Passing that exclusion step, all remaining neurons responded to Motion Clouds. Laminar positions were determined by the depth of the recording site with respect to the pia, which was then cross-validated by the evoked Local Field Potential (LFP) using sink/source analysis64.During each recording session, pupils were dilated using atropine (Mydriacyl) while nictitating membranes were retracted using phenylephrine (Mydfrin). Rigid contact lenses of appropriate power were used to correct the eyes’ refraction. Anesthesia was changed to 0.5–1% halothane to avoid anesthesia-induced modulation of visual responses65. The firing rate was averaged across drift directions and a von Mises distribution57 was fitted to the data:θk is the orientation of the stimuli, θpref, R0 the response at the orientation orthogonal to θpref and κ a measure of concentration. To control for direction selectivity when averaging tuning curves across drift direction, we computed a direction selectivity index:Rpref is the firing rate at the preferred direction (baseline subtracted) and Rnull is the firing rate at the preferred direction plus π. The quality of each tuning curve was assessed by computing a global metric, the circular variance (CV) of the unfitted data, which varies from 0 for perfectly orientation-selective neurons to 1 for orientation-untuned neurons29. It is defined as:R(θk) is the response of a neuron (baseline subtracted) to a stimulus of angle θk. The changes of CV as a function of Bθ were fitted with a Naka-Rushton function22:f0 is the base value of the function, Bθ50 the stimulus’ variance at half n a strictly positive exponent of the function.Orientation tuning curves were computed by selecting a 300 ms window maximizing spike-count varianceBθ which yielded a significant result was designed as θpref between trials where Bθ = 0° and Bθ = 0° and The significance of the tuning to orientation was measured by comparing the unfitted firing rate at the preferred and orthogonal orientations across trials, using a Wilcoxon signed-rank test correct for continuity, and the maximum value of 30. For a given stimulus, the activity of all the recorded neurons was a vector Xi(t) is the spike count of neuron i in a time window . The onset of this window t was slid from −200 to 400 ms (relative to the stimulation time) in steps of 10 ms while ΔT was kept constant at 100 ms. It should be noted that merging neural activity across electrodes or experiments is a common procedure67, which we validated in our data by verifying that the electrode or experiment which yielded the data could not be decoded from the neural activity  between K classes. The probability of any such vector belonging to a given class is:k = 1, …, K is the class out of K possible values and βk are the coefficients learned during the training procedure of the classifier. Several decoders were trained with classification tasks: decoding orientation θ  to 1 (most vulnerable neuron). This score-based decoding was performed on groups of 100 neurons sorted by descending score and repeated a total of seven times on increasingly more vulnerable neurons (thus with an overlap of 20 neurons).As the neurons were clustered into two populations for comparison purposes Fig. , we alsoBθ. The model presently used was first used to account for the intracortical activity in the cat primary visual cortex36, although it was presently simplified as a center-surround filter in the orientation domain29. Notably, this network has been able to account for numerous experimental findings, including learning and adaptation of cortical neurons38, whose implementations are similar to ours.We used a recurrent network of orientation-tuned neurons to model responses to increasing orientation variance N orientation-tuned neurons, evenly tiling the orientation space between −π and π. Each neuron is modeled as a single passive unit whose membrane potential obeys the equation:τ is the membrane time constant and Vff, Vexc, Vinh are the synaptic potentials coming from the feedforward input, recurrent excitatory and recurrent inhibitory connectivity, respectively. The firing rate R at time t of each neuron is computed as an instantaneous quantity modulated by a gain α:V was measured relative to the firing threshold. Each neuron could send mixed excitatory and inhibitory synaptic potentials to its neighbor, although this specific model has been reported to achieve similar behavior with separate units38. For each stimulus of main orientation θ, the input to a cell with preferred orientation θpref is:Jff is the strength of the input and I0 is the modified Bessel function of order 0. The right-hand side of the equation describes a von Mises with mean θpref and concentration κff. This latter parameter is related to the orientation variance Bθ, which was varied to yield a model’s TVF Bθ/CV curves:Bθ spanning the same range used in the experiments were used, each with 32 different θ tiling a  orientation space. The recurrent connectivity profile for excitatory (Cexc) and inhibitory (Cinh) synapses was controlled by separate von Mises distributions over the orientation space Θ:69. The computational procedure to match experimental data was entirely done in a previous publication38. Briefly, it consisted in scanning a range of possible values for each parameter, then finding all possible combinations using a metric of likeliness to single grating response, time-to-peak, peak response and tuning width. The parameters yielded by this procedure were τ = 10.8 ms; α = 10.6 Hz/mV; Jff = 9.57 mv/Hz; Jexc = 1.71 Hz/mV; Jinh = 2.0178 Hz/mV. For the feedforward mode of the model , with a solid line representing mean values. For boxplots, the box extends from the lower to upper quartile values, with a solid white line at the median value. The upper and lower whiskers extend to respectively Q1 − 1.5*IQR and Q3 + 1.5*IQR, where Q1 and Q3 are the lower and upper quartiles and IQR is the interquartile range.All data were analyzed using custom Python code. Statistical analysis was performed using non-parametric tests. Wilcoxon signed-rank test with discarding of zero-differences was used for paired samples and Mann–Whitney Further information on research design is available in the Peer Review FileSupplementary InformationReporting Summary


In the published article, there was an error. The wrong number of amino acids.A correction has been made to Structural features of DDX5 and DDX17, [2]. This sentence previously stated:“The transcription and translation of the DDX5 gene produces a stable protein with a molecular weight of 68 kDa, also known as p68, comprising 621 amino acids.”“these proteins are also known as p72 and p82, respectively, and comprise 729 and 731 amino acids.”The corrected sentence appears below:“The transcription and translation of the DDX5 gene produces a stable protein with a molecular weight of 68 kDa, also known as p68, comprising 614 amino acids.”“these proteins are also known as p72 and p82, respectively, and comprise 650 and 729 amino acids.”The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


NOTCH3 and results in a phenotype characterised by recurrent strokes, vascular dementia and migraines. Whilst a genetic basis for the disease is known, the molecular mechanisms underpinning the pathology of CADASIL are still yet to be determined. Studies conducted at the Genomics Research Centre (GRC) have also identified that only 15–23% of individuals clinically suspected of CADASIL have mutations in NOTCH3. Based on this, whole exome sequencing was used to identify novel genetic variants for CADASIL-like cerebral small-vessel disease (CSVD). Analysis of functionally important variants in 50 individuals was investigated using overrepresentation tests in Gene ontology software to identify biological processes that are potentially affected in this group of patients. Further investigation of the genes in these processes was completed using the TRAPD software to identify if there is an increased number (burden) of mutations that are associated with CADASIL-like pathology. Results from this study identified that cell–cell adhesion genes were positively overrepresented in the PANTHER GO-slim database. TRAPD burden testing identified n = 15 genes that had a higher number of rare (MAF < 0.001) and predicted functionally relevant  mutations compared to the gnomAD v2.1.1 exome control dataset. Furthermore, these results identified ARVCF, GPR17, PTPRS, and CELSR1 as novel candidate genes in CADASIL-related pathology. This study identified a novel process that may be playing a role in the vascular damage related to CADASIL-related CSVD and implicated n = 15 genes in playing a role in the disease.Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a condition caused by mutations in  NOTCH3, or through the presence of granular osmiophilic material (GOM) identified by a tissue biopsy .CADASIL is the most common monogenic cause of stroke and vascular dementia in the world and was originally estimated to have a prevalence of ~ 2–4 per 100,000. However, more recent work completed in the Exome Aggregation Consortium (ExAC) and Genome Aggregation Database (gnomAD) browsers has theorised that the prevalence could be up to 100-fold higher (~ 1:300) . Clinical manifestations of CADASIL can be heterogeneous and include subcortical ischaemic events (60–80%); cognitive impairment including dementia and apathy (40–60%); migraine with/without aura (20–40%); mood disturbances including severe depression and manic episodes (~ 20%); motor disturbances, such as gait disturbances (90%), urinary incontinence (80–90%) and pseudobulbar palsy (50%); and other neurological manifestations such as epilepsy/seizures (~ 10%) . Despite the heterogeneous nature of the phenotype, a definitive diagnosis of CADASIL is completed through either the identification of characteristic cysteine altering mutations between exons 2 and 24 in NOTCH3  has been conducting diagnostic genetic testing for CADASIL since the late 1990s. In this time, only between 16 and 23% of CADASIL referred patients have a detected disease-causing mutation in NOTCH3 negative CADASIL population. We hypothesised that there may be specific damaging genetic variants within biological processes which may be contributing to the CSVD phenotype  with no pathogenic NOTCH3 mutations. All patients had approved diagnostic testing for CADASIL with their doctors and ethical approval for this study was obtained through the QUT HREC along with appropriate consents for the patient cohort . Diagnostic testing was performed using the Genomics Research Centre (GRC) custom 5-gene panel where only the notch receptor 3 (NOTCH3) gene was analysed. Further screening of calcium voltage-gated channel subunit alpha 1A (CACNA1A), ATPase Na+/K+ Transporting subunit Alpha 2 (ATP1A2), sodium voltage-gated channel alpha subunit 1 (SCN1A), and Potassium two pore domain channel subfamily K member 18 (KCNK18)  whole exome sequencing population was used in this study to match the case generate population. This included alignment to Hg19 to match the CSVD population. The gnomAD WES dataset comprised 125,748 unrelated individuals that were sequenced as part of various disease-specific and population genetic studies  that had a SIFT, PolyPhen2, and MutationTaster scores marked as “D” for deleterious and a MAF < 0.001 based on population databases, such as 1000 Genomes, ExAC, and GnomAD  were deemed to be not sequencing error. The remaining mutations were kept for each sample and combined into a master list where information pertaining to the genes where each variant was included. Duplicate genes were removed and a reduced list of individual genes were then analysed using gene ontology software (http://geneontology.org/) that utilised the Protein Analysis Through Evolutionary Relationships (PANTHER) classification system to identify overrepresented pathways and processes  calculation based on the allele count data and the following calculation:https://github.com/mhguo1/TRAPD). But in brief, TRAPD requires the WES data to be pre-processed using bcftools and mpileup to separate out (multinucleotide variants) MNVs and left-align the variants obtained from the Ion Torrent sequencing data. This was completed individually for each sample vcf which was then merged using vcf-merge and annotated using variant effect predictor (VEP) from ensembl. VEP also has a filter function which was used on the merged vcf file to only include variants from the genes in the pathways/processes of the top hits obtained from the burden test. Running TRAPD focussed only on SNVs, coverage depth (CD) > 10, SIFT ≤ 0.05, PolyPhen ≥ 0.8, and a MAF < 0.01 to filter variants based on being rare and functionally affecting the proteins. A SNP file was generated from the case cohort. Variants in individual genes were counted in the CSVD and gnomAD datasets and compared using Fisher’s exact test, and raw p values were extracted for a dominant and recessive model of inheritance. FDR calculations were then completed using the base statistics package in R, and samples with an FDR p value < 0.05 were considered as significant and investigate further for gene ontology, expression data and potential function as a cause of CADASIL-related CSVD.The top hits identified to have the most biological significance were then used and tested using TRAPD Burden testing software  and predicted to have a functional effect. The over-representation test found that cell–cell adhesion processes (GO: 0098609) were the most significant PANTHER GO-Slim biological processes identified with an adjusted (FDR) p value = 1.52 × 10–5  and Ca2+ ion transmembrane transporter activity   p value 7.47 × 10–5 within our cohort that have rare and functional mutations. The number of ECM proteins as mutations in COL4A1 and COL4A2 has been identified to cause CSVD related to CADASIL. In conjunction with the biological processes and molecular function, there is also an overrepresentation of cell adhesion molecules (PC00069) p value 0.0232, Cadherin (PC00057) p value 0.00324 and microtubule-binding proteins (PC00156) p value 0.00283 which are involved in cell adhesion.PANTHER Go-slim protein class output shows an increased number of genes involved in ECM proteins (PC00102) with a –5) and confirmation analysis using the different TOPPGene algorithm also found it highly significant (FDR 4.84 × 10–5). Cell–cell adhesion processes comprised 135 genes which was used in the TRAPD burden test from which 37 genes were identified in this cohort. Based on the results from the TRAPD burden test, 49 genes were identified as significant after multiple testing (FDR < 0.05) under the dominant inheritance model (Table TENM3 (FDR = 3.36 × 10–9), CNTN4 (FDR = 8.63 × 10–8), ARVCF (FDR = 1.85 × 10–7), LRRC7 (FDR = 3.22 × 10–7) and SCRIB (FDR = 3.22 × 10–7). For the recessive model, the top 5 hits included ARVCF (FDR = 8.39 × 10–8), IGSF9 (FDR = 8.39 × 10–8), PKP4 (FDR = 1.75 × 10–7), ROBO4 (FDR = 1.75 × 10–7) and LRFN3 (FDR = 6.80 × 10–7). From the top hits identified, only ARVCF and PKP4 were in the initial candidate gene list. This is most likely due to the conservative nature of the TRAPD burden test which only accounted for MAF, SIFT and PolyPhen2 functional scores, whereas the initial list also incorporated further functional annotations from MutationTaster and PredictSNP2.Based on these results, cell–cell adhesion was chosen as it was the top hit for the GO-PANTHER slim biological processes FDR 1.52 × 10 × 10–5 aCNTN2, PKP4, DLG1, DLG2, CELSR2, ARVCF, OBSL1 and PTPRS as highly expressed across all brain tissues , indicating a potential overlap in the phenotypic spectrum of the disease. No other mutations in genes with high CNS expression had mutations identified in ClinVar.The combined gene list of significant hits identified from both models included a total of 52 genes which was used for targeted WES analysis for novel causative mutations Table . From thNOTCH3 negative CSVD patients, showed an overrepresentation of genes involved in cell–cell adhesion. This indicates that there may be an increased role of cell–cell adhesion as part of the molecular mechanism of CADASIL-related disorders. This particularly matches previously literature which has shown that leaks in the tight junctions between the VSMCs are the cause of haemorrhagic strokes in some CADASIL patients (Dziewulska and Nycz NOTCH3 c.3226C > T p.R1076C), where cell–cell adhesion genes were shown to be over-expressed  has previously been investigated as a pathological feature of CADASIL and has been identified to show that VSMC adhesion to each other as well as the ECM is often impaired in some way . All genes were also identified as associated using the TRAPD autosomal dominant model of inheritance which fits in with an initial diagnosis of CADASIL. Interestingly, only two genes (OBSL1 and PTPRS) from the CNS highly expressed subset were not identified as significant via the recessive model as well.Investigation of the 138 cell–cell adhesion genes identified through from PANTHER through the TRAPD burden test identified 52 individual genes (49 under the dominant model and 28 under the recessive model) with significantly more mutations in this cohort compared to the gnomAD v2.1.1 exome controls. This large number of genes allowed further investigation of the rare and potentially functionally affected genes identified in our cohort as significant may be novel causes of CADASIL-related CSVD pathology. Through the combination of the rare variant-association strategy with targeted mutation analysis of the WES data, the number of mutations identified could be decreased from 1928 to 38. Further stratification based on mRNA expression data gives us some insight into potential biological relevance in brain tissue, which allowed us to focus on 17 variants across 8 genes , and CNTN2 c.5488G > A rs144056952 was likely benign (VCV000474475.4) for causing familial adult-onset myoclonic epilepsy (FAME 5). The other mutation was a novel CNTN2 c.1316_1318delinsTC p.Pro439LeufsTer27) which would result in a truncated protein.CNTN2 and more generalised neurodegeneration has been theorising based on the reduction of CNTN2 expression. It is thought that this reduced expression may predispose neurons to cell death, induced through the binding of TGFβ2 to APP  found evidence of cognitive impairments based on the Morris water maze and novel object recognition tests, as well as reduced spontaneous motor activity, abnormal gait coordination and increased response latency to noxious stimulation  and DLG2 (c.1264dup p.Thr422AsnfsTer21) across two samples. These genes encode for Discs large MAGUK scaffold proteins 1 and 2 and may interact at post-synaptic sites to form multimeric scaffold for the clustering of receptors. To date, neither gene is known to be causative of any disease; however, DLG1 has been associated with cleft-lip/palate and depression, and DLG2 has been associated with schizophrenia and renal oncocytoma.There were two mutations identified in Dlg1 knockout ameliorates depression-like behaviour. However, other studies found that heterozygous Dlg1 ± mice did not exhibit the behavioural deficits seen in mice harbouring the full 3q29 deletion . Both mutations affect the extracellular domain of the CELSR2 protein, where only the p.Gly1752_Gly1753insPro is within a functional region, the Laminin G-like 1 domain. A role of CELSR2 has been linked to axonal guidance with implications of brain wiring in normal development and regeneration through many functional studies, including mouse knockout models, as well as playing a role in ependymal ciliogenesis  and c.1040 T > C p.Leu347Pro (rs1222148229). Whilst both mutations have previously been seen before, neither have been classified according to ClinVar. Interestingly, OBSL1 has been identified as a critical regulator of Cullen-7, which is involved in the regulation of protein abundance , c.4667C > T p.Pro1556Leu (rs1196839847) and c.2615G > C p.Arg872Pro (rs374591804). Whilst all mutations have been identified previously, there is no evidence to show they have been identified as causative of any disease by ClinVar. The PTPRS p.Pro1556Leu is one amino acid away from a binding site (position 1557) and is found in the Tyrosine protein phosphatase 1 domain of the protein. The p.Val1649Met mutation is not in a domain or repeat region of the gene, whereas the p.Arg872Pro change is within the Fibronectin type-III 6 section of the protein. It is unclear what effect these changes have on the protein and for CSVD-related symptoms as there is currently no evidence to suggest a contribution to pathology in humans. Mouse model Ptprs knockouts have shown severe neurological defects which overlap with some inherited CSVD conditions, including spastic movements, tremor, ataxic gait, abnormal limb flexion and defective proprioception  compared to the control dataset  highlights that there was bias in the statistical calculations. It would be recommended for that this study be replicated in a larger CADASIL/CSVD cohort to validate any findings. Furthermore, the gene enrichment pathways that were identified only focussed on the top hit of cell–cell adhesion, as such, other pathways including as immune responses and response to stimulus  were not investigated. Finally, the findings within this work should also be investigated further through segregation or functional studies using either cell lines (CRISPR techniques or patient derived lines) or animal studies (e.g. Zebrafish or C. elegans) to further elucidate a role for these genes in CADASIL-related disorders.There were 20 genes which were identified as candidate causal mutations after the TRAPD burden test and subsequent targeted in silico prediction analysis. From these genes, 8 genes had high mRNA expression across the brain tissue. Investigations of CNTN2, CELRS2 and ARVCF) which may be novel causes of, or contributors to, CSVD pathology.Overall, this study identified cell–cell adhesion as the most significant overrepresented group in CADASIL-related disorders, according to the number of genes with rare and predicted disease-causing mutations. This system makes biological sense in CADASIL and related CSVD as adhesion of the VSMC and epithelial cells which comprise the vascular walls in the small vessels have often been identified to be disrupted in CADASIL-related CSVD. The higher number of mutations in cell–cell adhesion processes support to a role that a disruption caused by functional and rare gene mutations within this system may be a novel factor in CADASIL and CSVD pathology. Further statistical and candidate gene approaches also identified three genes (