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International audience; Infectious diseases are among the strongest selective pressures driving human evolution1,2. This includes the single greatest mortality event in recorded history, the first outbreak of the second pandemic of plague, commonly called the Black Death, which was caused by the bacterium Yersinia pestis3. This pandemic devastated Afro-Eurasia, killing up to 30-50% of the population4. To identify loci that may have been under selection during the Black Death, we characterized genetic variation around immune-related genes from 206 ancient DNA extracts, stemming from two different European populations before, during and after the Black Death. Immune loci are strongly enriched for highly differentiated sites relative to a set of non-immune loci, suggesting positive selection. We identify 245 variants that are highly differentiated within the London dataset, four of which were replicated in an independent cohort from Denmark, and represent the strongest candidates for positive selection. The selected allele for one of these variants, rs2549794, is associated with the production of a full-length (versus truncated) ERAP2 transcript, variation in cytokine response to Y. pestis and increased ability to control intracellular Y. pestis in macrophages. Finally, we show that protective variants overlap with alleles that are today associated with increased susceptibility to autoimmune diseases, providing empirical evidence for the role played by past pandemics in shaping present-day susceptibility to disease.

We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well established within the context of supervised classification – using the gradients of target outputs with respect to the inputs – on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence data set. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets.

Bartonet al.1raise several statistical concerns regarding our original analyses2that highlight the challenge of inferring natural selection using ancient genomic data. We show here that these concerns have limited impact on our original conclusions. Specifically, we recover the same signature of enrichment for high FSTvalues at the immune loci relative to putatively neutral sites after switching the allele frequency estimation method to a maximum likelihood approach, filtering to only consider known human variants, and down-sampling our data to the same mean coverage across sites. Furthermore, using permutations, we show that the rs2549794 variant nearERAP2continues to emerge as the strongest candidate for selection (p = 1.2×10−5), falling below the Bonferroni-corrected significance threshold recommended by Bartonet al. Importantly, the evidence for selection onERAP2is further supported by functional data demonstrating the impact of theERAP2genotype on the immune response toY. pestisand by epidemiological data from an independent group showing that the putatively selected allele during the Black Death protects against severe respiratory infection in contemporary populations.

1AbstractCytochromes P450 (CYP450) are hemoproteins generally involved in the detoxification of the body of xenobiotic molecules. They participate in the metabolism of many drugs and genetic polymorphisms in humans have been found to impact drugs responses and metabolic functions. In this study, we investigate the genetic diversity forCYP450genes. We found that two clusters,CYP3AandCYP4F, are notably differentiated across human populations with evidence for selective pressures acting on both clusters: we found signals of recent positive selection inCYP3AandCYP4Fgenes and signals of balancing selection inCYP4Fgenes. Furthermore, unusual linkage disequilibrium pattern is detected in both clusters, suggesting co-evolution of genes within clusters. Several of these selective signals co-localize with expression quantitative trait loci, which suggest co-regulation and epistasis within these highly important gene families. We also found that SNPs under selection in Africans within theCYP3Acluster are associated toCYP3A5expression levels which are causally associated with reticulocytes count, as established by mendelian randomization. Furthermore, as theCYP3AandCYP4Fsubfamilies are involved in the metabolism of nutrients and drugs, our findings linking natural selection and gene expression in these gene clusters are of importance in understanding population differences in human health.

As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16(hi)CD66b(lo) neutrophil and IFN-γ(+) granzyme B(+) Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.

Throughout the SARS-CoV-2 pandemic, several variants of concern (VOC) have been identified, many of which share recurrent mutations in the spike protein’s receptor binding domain (RBD). This region coincides with known epitopes and can therefore have an impact on immune escape. Protracted infections in immunosuppressed patients have been hypothesized to lead to an enrichment of such mutations and therefore drive evolution towards VOCs. Here, we show that immunosuppressed patients with hematologic cancers develop distinct populations with immune escape mutations throughout the course of their infection. Notably, by investigating the co-occurrence of substitutions on individual sequencing reads in the RBD, we found quasispecies harboring mutations that confer resistance to known monoclonal antibodies (mAbs) such as S:E484K and S:E484A. Furthermore, we provide the first evidence for a viral reservoir based on intra-host phylogenetics. Our results on viral reservoirs can shed light on protracted infections interspersed with periods where the virus is undetectable as well as an alternative explanation for some long-COVID cases. Our findings also highlight that protracted infections should be treated with combination therapies rather than by a single mAbs to clear pre-existing resistant mutations.

Baboons inform on human gut microbiota Commensal bacteria are found throughout an organism, but it is not known whether associations between gut bacteria and their host are heritable. Grieneisen et al. examined changes in the microbiomes of 585 wild baboons from fecal samples collected over 14 years (see the Perspective by Cortes-Ortiz and Amato). Almost all microbiome traits tested demonstrated some level of statistically significant heritability. Most heritability values were low but varied over time correlating with the age of the host. Baboons live in an environment similar to that postulated for early humans and have a microbiome similar to that of humans. Thus, this heritability of the microbiome may reflect similar genetic determinants in humans, for which similar datasets are not available. Science , aba5483, this issue p. 181 ; see also abj5287, p. 159

A deeper understanding of the molecular determinants that drive humoral responses to coronaviruses, and in particular severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is critical for improving and developing diagnostics, therapies and vaccines. Moreover, viral mutations can change key antigens in a manner that alters the ability of the immune system to detect and clear infections. In this study, we exploit a deep serological profiling strategy coupled with an integrated, computational framework for the analysis of SARS-CoV-2 humoral immune responses of asymptomatic or recovered COVID-19-positive patients relative to COVID-19-negative patients. We made use of a novel high-density peptide array (HDPA) spanning the entire proteomes of SARS-CoV-2 and endemic human coronaviruses to rapidly identify B cell epitopes recognized by distinct antibody isotypes in patients’ blood sera. Using our integrated computational pipeline, we then evaluated the fine immunological properties of detected SARS-CoV-2 epitopes and relate them to their evolutionary and structural properties. While some epitopes are common across all CoVs, others are private to specific hCoVs. We also highlight the existence of hotspots of pre-existing immunity and identify a subset of cross-reactive epitopes that contributes to increasing the overall humoral immune response to SARS-CoV-2. Using a public dataset of over 38,000 viral genomes from the early phase of the pandemic, capturing both inter- and within-host genetic viral diversity, we determined the evolutionary profile of epitopes and the differences across proteins, waves and SARS-CoV-2 variants, which have important implications for genomic surveillance and vaccine design. Lastly, we show that mutations in Spike and Nucleocapsid epitopes are under stronger selection between than within patients, suggesting that most of the selective pressure for immune evasion occurs upon transmission between hosts.

Coeliac disease is a complex, polygenic inflammatory enteropathy caused by exposure to dietary gluten that occurs in a subset of genetically susceptible individuals who express either the HLA-DQ8 or HLA-DQ2 haplotypes1,2. The need to develop non-dietary treatments is now widely recognized3, but no pathophysiologically relevant gluten- and HLA-dependent preclinical model exists. Furthermore, although studies in humans have led to major advances in our understanding of the pathogenesis of coeliac disease4, the respective roles of disease-predisposing HLA molecules, and of adaptive and innate immunity in the development of tissue damage, have not been directly demonstrated. Here we describe a mouse model that reproduces the overexpression of interleukin-15 (IL-15) in the gut epithelium and lamina propria that is characteristic of active coeliac disease, expresses the predisposing HLA-DQ8 molecule, and develops villous atrophy after ingestion of gluten. Overexpression of IL-15 in both the epithelium and the lamina propria is required for the development of villous atrophy, which demonstrates the location-dependent central role of IL-15 in the pathogenesis of coeliac disease. In addition, CD4+ T cells and HLA-DQ8 have a crucial role in the licensing of cytotoxic T cells to mediate intestinal epithelial cell lysis. We also demonstrate a role for the cytokine interferon-γ (IFNγ) and the enzyme transglutaminase 2 (TG2) in tissue destruction. By reflecting the complex interaction between gluten, genetics and IL-15-driven tissue inflammation, this mouse model provides the opportunity to both increase our understanding of coeliac disease, and develop new therapeutic strategies. An HLA- and gluten-dependent mouse model of coeliac disease with villous atrophy provides evidence for the cooperative role of IL-15 and gluten-specific CD4+ T cells in licensing the full activation of cytotoxic T cells that are necessary for inducing epithelial damage.

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

Human gut microbial dynamics are highly individualized, making it challenging to link microbiota to health and to design universal microbiome therapies. This individuality is typically attributed to variation in host genetics, diets, environments and medications but it could also emerge from fundamental ecological forces that shape microbiota more generally. Here, we leverage extensive gut microbial time series from wild baboons-hosts who experience little interindividual dietary and environmental heterogeneity-to test whether gut microbial dynamics are synchronized across hosts or largely idiosyncratic. Despite their shared lifestyles, baboon microbiota were only weakly synchronized. The strongest synchrony occurred among baboons living in the same social group, probably because group members range over the same habitat and simultaneously encounter the same sources of food and water. However, this synchrony was modest compared to each host's personalized dynamics. In support, host-specific factors, especially host identity, explained, on average, more than three times the deviance in longitudinal dynamics compared to factors shared with social group members and ten times the deviance of factors shared across the host population. These results contribute to mounting evidence that highly idiosyncratic gut microbiomes are not an artefact of modern human environments and that synchronizing forces in the gut microbiome (for example, shared environments, diets and microbial dispersal) are not strong enough to overwhelm key drivers of microbiome personalization, such as host genetics, priority effects, horizontal gene transfer and functional redundancy.

Genetic variants in drug targets can be used to predict the effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of a genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes. We found sex and BMI to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers in UK Biobank participants. Female sex and lower BMI were associated with higher HDL-cholesterol and lower LDL-cholesterol for a same genetically predicted reduction in CETP concentration. We found that sex also modulated the effect of genetically lower CETP on cholesterol efflux capacity in samples from the Montreal Heart Institute Biobank. However, these modifying effects did not extend to sex-differences in cardiovascular outcomes in our data. Our results provide insight on the clinical effects of CETP inhibitors in the presence of effect modification based on observational genetic data. The approach can support precision medicine applications and help assess the external validity of clinical trials.

Pharmacogenomic studies have revealed associations between rs1967309 in the adenylyl cyclase type 9 (ADCY9) gene and clinical responses to the cholesteryl ester transfer protein (CETP) modulator dalcetrapib, however, the mechanism behind this interaction is still unknown. Here, we characterized selective signals at the locus associated with the pharmacogenomic response in human populations and we show that rs1967309 region exhibits signatures of positive selection in several human populations. Furthermore, we identified a variant in CETP, rs158477, which is in long-range linkage disequilibrium with rs1967309 in the Peruvian population. The signal is mainly seen in males, a sex-specific result that is replicated in the LIMAA cohort of over 3400 Peruvians. Analyses of RNA-seq data further suggest an epistatic interaction on CETP expression levels between the two SNPs in multiple tissues, which also differs between males and females. We also detected interaction effects of the two SNPs with sex on cardiovascular phenotypes in the UK Biobank, in line with the sex-specific genotype associations found in Peruvians at these loci. We propose that ADCY9 and CETP coevolved during recent human evolution due to sex-specific selection, which points toward a biological link between dalcetrapib’s pharmacogene ADCY9 and its therapeutic target CETP.

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima’s D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

Genetic variants in drug targets can be used to predict the effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of a genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes.We found sex and BMI to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers in UK Biobank participants. Female sex and lower BMI were associated with higher HDL-cholesterol and lower LDL-cholesterol for a same genetically predicted reduction in CETP concentration. We found that sex also modulated the effect of genetically lower CETP on cholesterol efflux capacity in samples from the Montreal Heart Institute Biobank. However, these modifying effects did not extend to sex-differences in cardiovascular outcomes in our data.Our results provide insight on the clinical effects of CETP inhibitors in the presence of effect modification based on observational genetic data. The approach can support precision medicine applications and help assess the external validity of clinical trials.

We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show that ImputeCoVNet leads to accurate results at minor allele frequencies as low as 0.0001. When compared with an approach based on Hamming distance, ImputeCoVNet achieved comparable results with significantly less computation time. We also present the provision of geographical metadata (e.g., exposed country) to decoder increases the imputation accuracy. Additionally, by visualizing the embedding results of SARS-CoV-2 variants, we show that the trained encoder of ImputeCoVNet, or the embedded results from it, recapitulates viral clade’s information, which means it could be used for predictive tasks using virus sequence analysis.

The rapid, global dispersion of SARS-CoV-2 has led to the emergence of a diverse range of variants. Here, we describe how the mutational landscape of SARS-CoV-2 has shaped HLA-restricted T cell immunity at the population level during the first year of the pandemic. We analyzed a total of 330,246 high-quality SARS-CoV-2 genome assemblies, sampled across 143 countries and all major continents from December 2019 to December 2020 before mass vaccination or the rise of the Delta variant. We observed that proline residues are preferentially removed from the proteome of prevalent mutants, leading to a predicted global loss of SARS-CoV-2 T cell epitopes in individuals expressing HLA-B alleles of the B7 supertype family; this is largely driven by a dominant C-to-U mutation type at the RNA level. These results indicate that B7-supertype-associated epitopes, including the most immunodominant ones, were more likely to escape CD8+ T cell immunosurveillance during the first year of the pandemic., Graphical abstract, Hamelin et al. investigated the global mutation landscape of SARS-CoV-2 by interrogating 330,246 SARS-CoV-2 sequences from GISAID. The dominant C→U mutation type was found to diversify the repertoire of experimentally validated SARS-CoV-2 CD8+ T cell epitopes in an HLA-supertype-dependent manner. Notably, the prevalent removal of proline was predicted to preferentially abrogate epitopes presented by the B7 HLA supertype. This model lays a foundation for testing the impact of SARS-COV-2 mutants on T cell escape in an HLA-dependent manner.

The first confirmed case of COVID-19 in Quebec, Canada, occurred at Verdun Hospital on February 25, 2020. A month later, a localized outbreak was observed at this hospital. We performed tiled amplicon whole genome nanopore sequencing on nasopharyngeal swabs from all SARS-CoV-2 positive samples from 31 March to 17 April 2020 in 2 local hospitals to assess the viral diversity of the outbreak. We report 264 viral genomes from 242 individuals (both staff and patients) with associated clinical features and outcomes, as well as longitudinal samples, technical replicates and the first publicly disseminated SARS-CoV-2 genomes in Quebec. Viral lineage assessment identified multiple subclades in both hospitals, with a predominant subclade in the Verdun outbreak, indicative of hospital-acquired transmission. Dimensionality reduction identified two subclades that evaded supervised lineage assignment methods, including Pangolin, and identified certain symptoms (headache, myalgia and sore throat) that are significantly associated with favorable patient outcomes. We also address certain limitations of standard SARS-CoV-2 bioinformatics procedures, notably when presented with multiple viral haplotypes.

Significance Immune response to infection is accompanied by active demethylation of thousands of CpG sites. Yet, the causal relationship between changes in DNA methylation and gene expression during infection remains to be elucidated. Here, we investigated the role of DNA methylation in the regulation of innate immune responses to bacterial infections. We found that virtually all changes in gene expression in response to infection occur prior to detectable alterations in the methylome. We also found that the binding of most infection-induced transcription factors precedes loss of methylation. Collectively, our results show that changes in methylation are a downstream consequence of transcription factor binding, and not essential for the establishment of the core regulatory program engaged upon infection., DNA methylation is considered to be a relatively stable epigenetic mark. However, a growing body of evidence indicates that DNA methylation levels can change rapidly; for example, in innate immune cells facing an infectious agent. Nevertheless, the causal relationship between changes in DNA methylation and gene expression during infection remains to be elucidated. Here, we generated time-course data on DNA methylation, gene expression, and chromatin accessibility patterns during infection of human dendritic cells with Mycobacterium tuberculosis. We found that the immune response to infection is accompanied by active demethylation of thousands of CpG sites overlapping distal enhancer elements. However, virtually all changes in gene expression in response to infection occur before detectable changes in DNA methylation, indicating that the observed losses in methylation are a downstream consequence of transcriptional activation. Footprinting analysis revealed that immune-related transcription factors (TFs), such as NF-κB/Rel, are recruited to enhancer elements before the observed losses in methylation, suggesting that DNA demethylation is mediated by TF binding to cis-acting elements. Collectively, our results show that DNA demethylation plays a limited role to the establishment of the core regulatory program engaged upon infection.

Contains fulltext : 215586.pdf (Publisher’s version ) (Open Access) Tissue-resident lymphocytes play a key role in immune surveillance, but it remains unclear how these inherently stable cell populations respond to chronic inflammation. In the setting of celiac disease (CeD), where exposure to dietary antigen can be controlled, gluten-induced inflammation triggered a profound depletion of naturally occurring Vgamma4(+)/Vdelta1(+) intraepithelial lymphocytes (IELs) with innate cytolytic properties and specificity for the butyrophilin-like (BTNL) molecules BTNL3/BTNL8. Creation of a new niche with reduced expression of BTNL8 and loss of Vgamma4(+)/Vdelta1(+) IELs was accompanied by the expansion of gluten-sensitive, interferon-gamma-producing Vdelta1(+) IELs bearing T cell receptors (TCRs) with a shared non-germline-encoded motif that failed to recognize BTNL3/BTNL8. Exclusion of dietary gluten restored BTNL8 expression but was insufficient to reconstitute the physiological Vgamma4(+)/Vdelta1(+) subset among TCRgammadelta(+) IELs. Collectively, these data show that chronic inflammation permanently reconfigures the tissue-resident TCRgammadelta(+) IEL compartment in CeD. VIDEO ABSTRACT.

Despite their close genetic relatedness, apes and African and Asian monkeys (AAMs) differ in their susceptibility to severe bacterial and viral infections that are important causes of human disease. Such differences between humans and other primates are thought to be a result, at least in part, of interspecies differences in immune response to infection. However, because of the lack of comparative functional data across species, it remains unclear in what ways the immune systems of humans and other primates differ. Here, we report the whole-genome transcriptomic responses of ape species (human and chimpanzee) and AAMs (rhesus macaque and baboon) to bacterial and viral stimulation. We find stark differences in the responsiveness of these groups, with apes mounting a markedly stronger early transcriptional response to both viral and bacterial stimulation, altering the transcription of ~40% more genes than AAMs. Additionally, we find that genes involved in the regulation of inflammatory and interferon responses show the most divergent early transcriptional responses across primates and that this divergence is attenuated over time. Finally, we find that relative to AAMs, apes engage a much less specific immune response to different classes of pathogens during the early hours of infection, up-regulating genes typical of anti-viral and anti-bacterial responses regardless of the nature of the stimulus. Overall, these findings suggest apes exhibit increased sensitivity to bacterial and viral immune stimulation, activating a broader array of defense molecules that may be beneficial for early pathogen killing at the potential cost of increased energy expenditure and tissue damage. Copyright

Early-life adversity (ELA) is a major predictor of psychopathology, and is thought to increase lifetime risk by epigenetically regulating the genome. Here, focusing on the lateral amygdala, a major brain site for emotional homeostasis, we described molecular cross-talk among multiple epigenetic mechanisms, including 6 histone marks, DNA methylation and the transcriptome, in subjects with a history of ELA and controls. We first uncovered, in the healthy brain, previously unknown interactions among epigenetic layers, in particular related to non-CG methylation in the CAC context. We then showed that ELA associates with methylomic changes that are as frequent in the CAC as in the canonical CG context, while these two forms of plasticity occur in sharply distinct genomic regions, features, and chromatin states. Combining these multiple data indicated that immune-related and small GTPase signaling pathways are most consistently impaired in the amygdala of ELA individuals. Overall, this work provides new insights into epigenetic brain regulation as a function of early-life experience.

Humans vary in their susceptibility to infectious disease, partly due to variation in the immune response following infection. Here, we used single-cell RNA-sequencing to quantify genetic contributions to this variation in peripheral blood mononuclear cells, focusing specifically on the transcriptional response to influenza infection. We find that monocytes are the most responsive to influenza infection, but that all cell types mount a conserved interferon response, which is stronger in individuals with increased European ancestry. By comparing European American and African American individuals, we show that genetic ancestry effects on expression are common, influencing 29% of genes, but highly cell type-specific. Further, we demonstrate that much of this population-associated expression variation is explained by cis expression quantitative trait loci, which are enriched for signatures of recent positive selection. Our findings establish common cis-regulatory variants—including those that are differentiated by genetic ancestry—as important determinants of the antiviral immune response.

1SummaryThe biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a continuous coarse graining process called diffusion condensation, Multiscale PHATE creates a tree of data granularities that can be cut at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hi CD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Further, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

Despite their close genetic relatedness, apes and African and Asian monkeys (AAMs), strongly differ in their susceptibility to severe bacterial and viral infections that are important causes of human disease. Such differences between humans and other primates are thought to be a result, at least in part, of inter-species differences in immune response to infection. However, due to the lack of comparative functional data across species, it remains unclear in what ways the immune systems of humans and other primates differ. Here, we report the whole genome transcriptomic responses of ape species (human, common chimpanzee) and AAMs (rhesus macaque and olive baboon) to bacterial and viral stimulation. We find stark differences in the responsiveness of these groups, with apes mounting a markedly stronger early transcriptional response to both viral and bacterial stimulation, altering the transcription of ∼40% more genes than AAMs. Additionally, we find that genes involved in the regulation of inflammatory and interferon responses show the most divergent early transcriptional responses across primates and that this divergence is attenuated over time. Finally, we find that relative to AAMs, apes engage a much less specific immune response to different classes of pathogens during the early hours of infection, upregulating genes typical of anti-viral and anti-bacterial responses regardless of the nature of the stimulus. Overall, these findings suggest apes exhibit increased sensitivity to bacterial and viral immune stimulation, activating a broader array of defense molecules that may be beneficial for early pathogen killing at the potential cost of increased energy expenditure and tissue damage.

Early-life adversity (ELA) is a major predictor of psychopathology, and is thought to increase lifetime risk by epigenetically regulating the genome. Here, focusing on the lateral amygdala, a major brain site for emotional homeostasis, we describe molecular cross-talk among multiple mechanisms of genomic regulation, including 6 histone marks and DNA methylation, and the transcriptome, in subjects with a history of ELA and controls. In the healthy brain tissue, we first uncover interactions between different histone marks and non-CG methylation in the CAC context. Additionally, we find that ELA associates with methylomic changes that are as frequent in the CAC as in the canonical CG context, while these two forms of plasticity occur in sharply distinct genomic regions, features, and chromatin states. Combining these multiple data indicates that immune-related and small GTPase signaling pathways are most consistently impaired in the amygdala of ELA individuals. Overall, this work provides insights into genomic brain regulation as a function of early-life experience., Early-life adversity is thought to increase the risk of psychopathology through epigenetic mechanisms. Here, the authors profile 6 histone marks, chromatin states and DNA methylation in the lateral amygdala in subjects with a history of early-life adversity.

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. Here we present Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Our approach creates a tree of data granularities that can be cut at coarse levels for high level summarizations, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Our analysis identifies pathogenic cellular populations, CD16-hiCD66b-lo neutrophils and IFNγ+GranzymeB+ Th17 cells, and shows that cellular groupings discovered by Multiscale PHATE are directly predictive of disease outcome. We use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another on patient clinical features, both associating immune subsets and clinical markers with outcome. Conflict of Interest: Dr. Krishnaswamy is on the scientific advisory board of KovaDx and AI Therapeutics. Dr. Iwasaki a member of the SAB for InProTher. Dr. Iwasaki is a co-founder of RIGImmune. Dr. Wilson is founder of Efference. Dr. Ko is a member of the expert panel of the Reckit Global Hygiene Institute. The remaining authors have no competing interests to declare. Ethical Approval: This study was approved by Yale Human Research Protection Program Institutional Review Boards (FWA00002571, protocol ID 2000027690). Informed consent was obtained from all enrolled patients and healthcare workers.

A copper-based photocatalyst, Cu(tmp)(BINAP)BF4, was found to be active in a photoredox Appel-type conversion of alcohols to bromides. The catalyst was identified from a screening of 50 complexes and promoted the transformation of primary and secondary alcohols to their corresponding bromides and carboxylic acids to their anhydrides. The protocol was also amendable and optimized under continuous flow conditions.

A library of 50 copper-based complexes derived from bisphosphines and diamines was prepared and evaluated in three mechanistically distinct photocatalytic reactions. In all cases, a copper-based catalyst was identified to afford high yields, where new heteroleptic complexes derived from the bisphosphine BINAP displayed high efficiency across all reaction types. Importantly, the evaluation of the library of copper complexes revealed that even when photophysical data is available, it is not always possible to predict which catalyst structure will be efficient or inefficient in a given process, emphasizing the advantages for catalyst structures with high modularity and structural variability.

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual’s response to environmental challenges., Individuals with different genotypes may respond differently to environmental variation. Here, Favé et al. find substantial impacts of different environment exposures on the transcriptome and clinical endophenotypes when controlling for genetic ancestry by analyzing data from ∼1000 individuals from a founder population in Quebec.

The first confirmed case of COVID-19 in Quebec, Canada, occurred at Verdun Hospital on February 25, 2020. A month later, a localized outbreak was observed at this hospital. We performed tiled amplicon whole genome nanopore sequencing on nasopharyngeal swabs from all SARS-CoV-2 positive samples from 31 March to 17 April 2020 in 2 local hospitals to assess viral diversity (unknown at the time in Quebec) and potential associations with clinical outcomes. We report 264 viral genomes from 242 individuals–both staff and patients–with associated clinical features and outcomes, as well as longitudinal samples and technical replicates. Viral lineage assessment identified multiple subclades in both hospitals, with a predominant subclade in the Verdun outbreak, indicative of hospital-acquired transmission. Dimensionality reduction identified two subclades with mutations of clinical interest, namely in the Spike protein, that evaded supervised lineage assignment methods–including Pangolin and NextClade supervised lineage assignment tools. We also report that certain symptoms (headache, myalgia and sore throat) are significantly associated with favorable patient outcomes. Our findings demonstrate the strength of unsupervised, data-driven analyses whilst suggesting that caution should be used when employing supervised genomic workflows, particularly during the early stages of a pandemic.

Summary Untargeted metabolomics is used to refine the development of biomarkers for the diagnosis of cardiovascular disease. Myocardial infarction (MI) has major individual and societal consequences for patients, who remain at high risk of secondary events, despite advances in pharmacological therapy. To monitor their differential response to treatment, we performed untargeted plasma metabolomics on 175 patients from the platelet inhibition and patient outcomes (PLATO) trial treated with ticagrelor and clopidogrel, two common P2Y12 inhibitors. We identified a signature that discriminates patients, which involves polyunsaturated fatty acids (PUFAs) and particularly the omega-3 fatty acids docosahexaenoate and eicosapentaenoate. The known cardiovascular benefits of PUFAs could contribute to the efficacy of ticagrelor. Our work, beyond pointing out the high relevance of untargeted metabolomics in evaluating response to treatment, establishes PUFA metabolism as a pathway of clinical interest in the recovery path from MI., Graphical abstract, Highlights We detect an extreme metabolomic signature of myocardial infarction (MI) in plasma Polyunsaturated fatty acids (PUFAs) are upregulated in patients taking ticagrelor PUFA metabolism is a pathway of clinical interest in the recovery path from MI Data science methods detect biologically meaningful patterns in metabolite signals, Samman et al. present a metabolomics study of 350 samples from participants hospitalized for acute coronary syndrome. The analyses reveal that polyunsaturated fatty acids levels are upregulated in patients taking ticagrelor compared to clopidogrel, highlighting a pathway of potential clinical interest in the recovery path from myocardial infarction.

Status alters immune function in macaques Rhesus macaques experience variable levels of stress on the basis of their position in the social hierarchy. To examine how stress affects immune function, Snyder-Mackler et al. manipulated the social status of individual macaques (see the Perspective by Sapolsky). Social status influenced the immune system at multiple levels, from immune cell numbers to gene expression, and altered signaling pathways in a model of response to infection. Macaques possess a plastic and adaptive immune response wherein social subordination promotes antibacterial responses, whereas high social status promotes antiviral responses. Science , this issue p. 1041 ; see also p. 967

The shift from a hunter-gatherer (HG) to an agricultural (AG) mode of subsistence is believed to have been associated with profound changes in the burden and diversity of pathogens across human populations. Yet, the extent to which the advent of agriculture may have impacted the evolution of the human immune system remains unknown. Here we present a comparative study of variation in the transcriptional responses of peripheral blood mononuclear cells to bacterial and viral stimuli between Batwa rainforest hunter-gatherers and Bakiga agriculturalists from Uganda. We observed increased divergence between hunter-gatherers and agriculturalists in the transcriptional response to viruses compared to that for bacterial stimuli. We demonstrate that a significant fraction of these transcriptional differences are under genetic control, and we show that positive natural selection has helped to shape population differences in immune regulation. Across the set of genetic variants underlying inter-population immune response differences, however, the signatures of positive selection were disproportionately observed in the rainforest hunter-gatherers. This result is counter to expectations based on the popularized notion that shifts in pathogen exposure due to the advent of agriculture imposed radically heightened selective pressures in agriculturalist populations.