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Common genetic polymorphisms associated with COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Given the importance of immune cells in the pathogenesis of COVID-19 illness, here we assessed the effects of COVID-19-risk variants on gene expression in a wide range of immune cell types. Transcriptome-wide association study and colocalization analysis revealed putative causal genes and the specific immune cell types where gene expression is most influenced by COVID-19-risk variants. Notable examples include OAS1 in non-classical monocytes, DTX1 in B cells, IL10RB in NK cells, CXCR6 in follicular helper T cells, CCR9 in regulatory T cells and ARL17A in T H 2 cells. By analysis of transposase accessible chromatin and H3K27ac-based chromatin-interaction maps of immune cell types, we prioritized potentially functional COVID-19-risk variants. Our study highlights the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations. Immune cells are important in the pathogenesis of COVID-19. Here the authors assessed the effects of COVID-19-risk variants on gene expression in a range of immune cell types, highlighting their potential to impact the function of diverse immune cell types and influence severe disease.

Expression quantitative trait loci (eQTLs) studies provide associations of genetic variants with gene expression but fall short of pinpointing functionally important eQTLs. Here, using H3K27ac HiChIP assays, we mapped eQTLs overlapping active cis -regulatory elements that interact with their target gene promoters (promoter-interacting eQTLs, pieQTLs) in five common immune cell types (Database of Immune Cell Expression, Expression quantitative trait loci and Epigenomics (DICE) cis -interactome project). This approach allowed us to identify functionally important eQTLs and show mechanisms that explain their cell-type restriction. We also devised an approach to eQTL discovery that relies on HiChIP-based promoter interaction maps as a structural framework for deciding which SNPs to test for association with gene expression, and observe ultra-long-distance pieQTLs (>1 megabase away), including several disease-risk variants. We validated the functional role of pieQTLs using reporter assays, CRISPRi, dCas9-tiling guides and Cas9-mediated base-pair editing. In this article we present a method for functional eQTL discovery and provide insights into relevance of noncoding variants for cell-specific gene regulation and for disease association beyond conventional eQTL mapping. H3K27ac HiChIP analysis helps to identify promoter-interacting expression quantitative trait loci (pieQTLs) in five common immune cell types. Some pieQTLs overlap with nontranscribed promoters that act as enhancers.

Immune-checkpoint blockade (ICB) has shown remarkable clinical success in boosting antitumor immunity. However, the breadth of its cellular targets and specific mode of action remain elusive. We find that tumor-infiltrating follicular regulatory T (T FR ) cells are prevalent in tumor tissues of several cancer types. They are primarily located within tertiary lymphoid structures and exhibit superior suppressive capacity and in vivo persistence as compared with regulatory T cells, with which they share a clonal and developmental relationship. In syngeneic tumor models, anti-PD-1 treatment increases the number of tumor-infiltrating T FR cells. Both T FR cell deficiency and the depletion of T FR cells with anti-CTLA-4 before anti-PD-1 treatment improve tumor control in mice. Notably, in a cohort of 271 patients with melanoma, treatment with anti-CTLA-4 followed by anti-PD-1 at progression was associated with better a survival outcome than monotherapy with anti-PD-1 or anti-CTLA-4, anti-PD-1 followed by anti-CTLA-4 at progression or concomitant combination therapy. Vijayanand and colleagues show highly suppressive CD4 + CTLA-4 + PD-1 + follicular regulatory T (T FR ) cells reside within tumor microenvironments. Depleting T FR cells or blocking their activity with CTLA-4-depleting antibodies before anti-PD-1 checkpoint blockade therapy improved the efficacy of anti-PD-1 treatment in mouse tumor models and was also associated with better survival outcomes in a large cohort of patients with melanoma.

Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells. Single-cell analysis of multi-condition cohorts requires modelling the interaction between sample variables and cell states. Here, authors develop GEDI to enable integration, cluster-free differential expression analysis and regulon analysis for both gene expression and alternative splicing modalities.

Analysis of single cells across multiple samples and/or conditions encompasses a series of interrelated tasks, which range from normalization and inter-sample harmonization to identification of cell state shifts associated with experimental conditions. Other downstream analyses are further needed to annotate cell states, extract pathway-level activity metrics, and/or nominate gene regulatory drivers of cell-to-cell variability or cell state shifts. Existing methods address these analytical requirements sequentially, lacking a cohesive framework to unify them. Moreover, these analyses are currently confined to specific modalities where the biological quantity of interest gives rise to a singular measurement. However, other modalities require joint consideration of dual measurements; for example, modeling the latent space of alternative splicing involves joint analysis of exon inclusion and exclusion reads. Here, we introduce a generative model, called GEDI, to identify latent space variations in multi-sample, multi-condition single cell datasets and attribute them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with the state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states in the form of transcriptomic vector fields, and machine learning-based prediction of sample characteristics from single-cell data. By incorporating gene-level prior knowledge, it can further project pathway and regulatory network activities onto the cellular state space, enabling the computation of the gradient fields of transcription factor activities and their association with the transcriptomic vector fields of sample covariates. Finally, we demonstrate that GEDI surpasses the gene-centric approach by extending all these concepts to the study of alternative cassette exon splicing and mRNA stability landscapes in single cells.

ABSTRACT Common genetic polymorphisms associated with severity of COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Here, we assessed the effects of 679 COVID-19-risk variants on gene expression in a wide-range of immune cell types. Severe COVID-19-risk variants were significantly associated with the expression of 11 protein-coding genes, and overlapped with either target gene promoter or cis-regulatory regions that interact with target promoters in the cell types where their effects are most prominent. For example, we identified that the association between variants in the 3p21.31 risk locus and the expression of CCR2 in classical monocytes is likely mediated through an active cis-regulatory region that interacted with CCR2 promoter specifically in monocytes. The expression of several other genes showed prominent genotype-dependent effects in non-classical monocytes, NK cells, B cells, or specific T cell subtypes, highlighting the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵6 Joint senior authors.

Summary Brain tumour stem cells (BTSCs) and intratumoural heterogeneity represent major challenges in glioblastoma therapy. Here, we report that the LGALS1 gene, encoding the carbohydrate binding protein, galectin1, is a key regulator of BTSCs and glioblastoma resistance to therapy. Genetic deletion of LGALS1 alters BTSC gene expression profiles and results in downregulation of gene sets associated with mesenchymal subtype of glioblastoma. Using a combination of pharmacological and genetic approaches, we establish that inhibition of LGALS1 signalling in BTSCs impairs self-renewal, suppresses tumourigenesis, prolongs lifespan, and improves glioblastoma response to ionizing radiation in preclinical animal models. Mechanistically, we show that LGALS1 is a direct transcriptional target of STAT3 with its expression robustly regulated by the ligand OSM. Importantly, we establish that galectin1 forms a complex with the transcription factor HOXA5 to reprogram BTSC transcriptional landscape. Our data unravel an oncogenic signalling pathway by which galectin1/HOXA5 complex maintains BTSCs and promotes glioblastoma. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵7 Lead contact

To determine the nature of CD4+ T cells that provide 'help' for generating robust anti-tumor CD8+ cytotoxic T cell (CTL) responses, we profiled the transcriptomes of patient-matched CD4+ and CD8+ T cells present in the tumor micro-environment (TME) and analyzed them jointly using integrated weighted gene correlation network analysis. We found the follicular helper T cell (TFH) program in CD4+ T cells was strongly associated with proliferation and tissue-residency in CD8+ CTLs. Single-cell analysis demonstrated the presence of TFH-like cells and features linked to cytotoxic function and their provision of CD8+ T cell 'help'. Tumor-infiltrating TFH-like cells expressed PD-1 and were enriched in tumors following checkpoint blockade, suggesting that they may respond to anti-PD-1 therapy. Adoptive transfer or induction of TFH cells in mouse models resulted in augmented CD8+ CTL responses and impairment of tumor growth, indicating an important role of TFH-like CD4+ T cells in anti-tumor immunity.

Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-individual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR); (2) standardised mean differences (SMD) using Hedges g; (3) modal distribution of individual-level glutamate data (Hartigan’s unimodality dip test). MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx in schizophrenia. 123 studies reporting on 8256 patients and 7532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex (MFC, glutamate: CVR = 0.15, p  < 0.001; glutamine: CVR = 0.15, p  = 0.003; Glx: CVR = 0.11, p  = 0.002), dorsolateral prefrontal cortex (glutamine: CVR = 0.14, p  = 0.05; Glx: CVR = 0.25, p  < 0.001) and thalamus (glutamate: CVR = 0.16, p  = 0.008; Glx: CVR = 0.19, p  = 0.008). Studies in younger, more symptomatic patients were associated with greater variability in the basal ganglia (BG glutamate with age: z  = −0.03, p  = 0.003, symptoms: z  = 0.007, p  = 0.02) and temporal lobe (glutamate with age: z  = −0.03, p  = 0.02), while studies with older, more symptomatic patients associated with greater variability in MFC (glutamate with age: z  = 0.01, p  = 0.02, glutamine with symptoms: z  = 0.01, p  = 0.02). For individual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower MFC glutamate ( g  = −0.15, p  = 0.03), higher thalamic glutamine ( g  = 0.53, p  < 0.001) and higher BG Glx in patients relative to controls ( g  = 0.28, p  < 0.001). Proportion of males was negatively associated with MFC glutamate ( z  = −0.02, p  < 0.001) and frontal white matter Glx ( z  = −0.03, p  = 0.02) in patients relative to controls. Patient PANSS total score was positively associated with glutamate SMD in BG ( z  = 0.01, p  = 0.01) and temporal lobe ( z  = 0.05, p  = 0.008). Further research into the mechanisms underlying greater glutamatergic metabolite variability in schizophrenia and their clinical consequences may inform the identification of patient subgroups for future treatment strategies.