Cell type-specific genetic regulation of gene expression across human tissues

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.

Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues

Tissue gene expression studies are impacted by biological and technical sources of variation, which can be broadly classified into wanted and unwanted variation. The latter, if not addressed, results in misleading biological conclusions. Methods have been proposed to reduce unwanted variation, such as normalization and batch correction. A more accurate understanding of all causes of variation could significantly improve the ability of these methods to remove unwanted variation while retaining variation corresponding to the biological question of interest. We used 17,282 samples from 49 human tissues in the Genotype-Tissue Expression data set (v8) to investigate patterns and causes of expression variation. Transcript expression was transformed to z-scores, and only the most variable 2% of transcripts were evaluated and clustered based on coexpression patterns. Clustered gene sets were assigned to different biological or technical causes based on histologic appearances and metadata elements. We identified 522 variable transcript clusters (median: 11 per tissue) among the samples. Of these, 63% were confidently explained, 16% were likely explained, 7% were low confidence explanations, and 14% had no clear cause. Histologic analysis annotated 46 clusters. Other common causes of variability included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), disease status, and age. Technical causes included blood draw timing and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens data set of single-cell expression. This is among the largest explorations of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression and demonstrated the utility of matched histologic specimens. It further demonstrated the value of acquiring meaningful tissue harvesting metadata elements to use for improved normalization, batch correction, and analysis of both bulk and single-cell RNA-seq data.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain

Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Disentangling cell-intrinsic and extrinsic factors underlying gene expression evolution

Chimeras have played a foundational role in biology, for example by enabling the classification of developmental processes into those driven intrinsically by individual cells versus those driven extrinsically by their extracellular environment. Here, we extend this framework to decompose evolutionary divergence in gene expression and other quantitative traits into cell-intrinsic, extrinsic, and intrinsic-extrinsic interaction components. Applying this framework to reciprocal rat-mouse chimeras, we found that the majority of gene expression divergence is attributable to cell-intrinsic factors, though extrinsic factors also play an integral role. For example, a rat-like extracellular environment extrinsically up-regulates the expression of a key transcriptional regulator of the endoplasmic reticulum (ER) stress response in some but not all cell types, which in turn strongly predicts extrinsic up-regulation of its target genes and of the ER stress response pathway as a whole. This effect is also seen at the protein level, suggesting propagation through multiple regulatory levels. We also demonstrate that our framework is applicable to a cellular trait, neuronal differentiation, and estimated the intrinsic and extrinsic contributions to its divergence. Finally, we show that imprinted genes are dramatically mis-expressed in species-mismatched environments, suggesting that mismatch between rapidly evolving intrinsic and extrinsic mechanisms controlling gene imprinting may contribute to barriers to interspecies chimerism. Overall, our conceptual framework opens new avenues to investigate the mechanistic basis of evolutionary divergence in gene expression and other quantitative traits in any multicellular organism.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Genetic variation and pesticide exposure influence blood DNA methylation signatures in females with early-stage Parkinson's disease

Although sex, genetics, and exposures can individually influence risk for sporadic Parkinson's disease (PD), the joint contributions of these factors to the epigenetic etiology of PD have not been comprehensively assessed. Here, we profiled sex-stratified genome-wide blood DNAm patterns, SNP genotype, and pesticide exposure in agricultural workers (71 early-stage PD cases, 147 controls) and explored replication in three independent samples of varying demographics (n = 218, 222, and 872). Using a region-based approach, we found more associations of blood DNAm with PD in females (69 regions) than in males (2 regions, Δβadj| ≥0.03, padj ≤ 0.05). For 48 regions in females, models including genotype or genotype and pesticide exposure substantially improved in explaining interindividual variation in DNAm (padj ≤ 0.05), and accounting for these variables decreased the estimated effect of PD on DNAm. The results suggested that genotype, and to a lesser degree, genotype-exposure interactions contributed to variation in PD-associated DNAm. Our findings should be further explored in larger study populations and in experimental systems, preferably with precise measures of exposure.

Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle

BACKGROUND

Characterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22-150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle.

RESULTS

We identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits.

CONCLUSIONS

This study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.

Mammalian Diaphanous1 signalling in neurovascular complications of diabetes

Over the past few decades, diabetes gradually has become one of the top non-communicable disorders, affecting 476.0 million in 2017 and is predicted to reach 570.9 million people in 2025. It is estimated that 70 to 100% of all diabetic patients will develop some if not all, diabetic complications over the course of the disease. Despite different symptoms, mechanisms underlying the development of diabetic complications are similar, likely stemming from deficits in both neuronal and vascular components supplying hyperglycaemia-susceptible tissues and organs. Diaph1, protein diaphanous homolog 1, although mainly known for its regulatory role in structural modification of actin and related cytoskeleton proteins, in recent years attracted research attention as a cytoplasmic partner of the receptor of advanced glycation end-products (RAGE) a signal transduction receptor, whose activation triggers an increase in proinflammatory molecules, oxidative stressors and cytokines in diabetes and its related complications. Both Diaph1 and RAGE are also a part of the RhoA signalling cascade, playing a significant role in the development of neurovascular disturbances underlying diabetes-related complications. In this review, based on the existing knowledge as well as compelling findings from our past and present studies, we address the role of Diaph1 signalling in metabolic stress and neurovascular degeneration in diabetic complications. In light of the most recent developments in biochemical, genomic and transcriptomic research, we describe current theories on the aetiology of diabetes complications, highlighting the function of the Diaph1 signalling system and its role in diabetes pathophysiology.

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks

Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins - namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD-were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

Placental expression quantitative trait loci in an East Asian population

Expression quantitative trait loci (eQTL) analysis measures the contribution of genetic variation in gene expression on complex traits. Although this methodology has been used to examine gene regulation in numerous human tissues, eQTL research in solid tissues is relatively lacking. We conducted eQTL analysis on placentas collected from an East Asian population in an effort to identify gene regulatory mechanisms in this tissue. Placentas (n = 102) were collected at the time of cesarean delivery. mRNA was extracted, sequenced with NGS, and compared with matched maternal and fetal DNA arrays performed using maternal and neonatal cord blood. Linear regression modeling was performed using tensorQTL. Fine-mapping along with epigenomic annotation was used to select putative functional variants. We identified 2,703 coding genes that contained at least one eQTL with statistical significance (false discovery rate <0.05). After fine-mapping, we found 108 previously unreported eQTL variants with posterior inclusion probability >0.1. Of these, 19% were located in genomic regions with evidence from public placental epigenome suggesting that they may be functionally relevant. For example, variant rs28379289 located in the placenta-specific regulatory region changes the binding affinity of transcription factor leading to higher expression of LGALS3, which is known to affect placental function. This study expands the knowledge base of regulatory elements within the human placenta and identifies 108 previously unreported placenta eQTL signals, which are listed in our publicly available GMI eQTL database. Further studies are needed to identify and characterize genetic regulatory mechanisms that affect placental function in normal pregnancy and placenta-related diseases.

The association of cigarette smoking with DNA methylation and gene expression in human tissue samples

Cigarette smoking adversely affects many aspects of human health, and epigenetic responses to smoking may reflect mechanisms that mediate or defend against these effects. Prior studies of smoking and DNA methylation (DNAm), typically measured in leukocytes, have identified numerous smoking-associated regions (e.g., AHRR). To identify smoking-associated DNAm features in typically inaccessible tissues, we generated array-based DNAm data for 916 tissue samples from the GTEx (Genotype-Tissue Expression) project representing 9 tissue types (lung, colon, ovary, prostate, blood, breast, testis, kidney, and muscle). We identified 6,350 smoking-associated CpGs in lung tissue (n = 212) and 2,735 in colon tissue (n = 210), most not reported previously. For all 7 other tissue types (sample sizes 38-153), no clear associations were observed (false discovery rate 0.05), but some tissues showed enrichment for smoking-associated CpGs reported previously. For 1,646 loci (in lung) and 22 (in colon), smoking was associated with both DNAm and local gene expression. For loci detected in both lung and colon (e.g., AHRR, CYP1B1, CYP1A1), top CpGs often differed between tissues, but similar clusters of hyper- or hypomethylated CpGs were observed, with hypomethylation at regulatory elements corresponding to increased expression. For lung tissue, 17 hallmark gene sets were enriched for smoking-associated CpGs, including xenobiotic- and cancer-related gene sets. At least four smoking-associated regions in lung were impacted by lung methylation quantitative trait loci (QTLs) that co-localize with genome-wide association study (GWAS) signals for lung function (FEV1/FVC), suggesting epigenetic alterations can mediate the effects of smoking on lung health. Our multi-tissue approach has identified smoking-associated regions in disease-relevant tissues, including effects that are shared across tissue types.

Network-based screening identifies sitagliptin as an antitumor drug targeting dendritic cells

BACKGROUND

Dendritic cell (DC)-mediated antigen presentation is essential for the priming and activation of tumor-specific T cells. However, few drugs that specifically manipulate DC functions are available. The identification of drugs targeting DC holds great promise for cancer immunotherapy.

METHODS

We observed that type 1 conventional DCs (cDC1s) initiated a distinct transcriptional program during antigen presentation. We used a network-based approach to screen for cDC1-targeting therapeutics. The antitumor potency and underlying mechanisms of the candidate drug were investigated in vitro and in vivo.

RESULTS

Sitagliptin, an oral gliptin widely used for type 2 diabetes, was identified as a drug that targets DCs. In mouse models, sitagliptin inhibited tumor growth by enhancing cDC1-mediated antigen presentation, leading to better T-cell activation. Mechanistically, inhibition of dipeptidyl peptidase 4 (DPP4) by sitagliptin prevented the truncation and degradation of chemokines/cytokines that are important for DC activation. Sitagliptin enhanced cancer immunotherapy by facilitating the priming of antigen-specific T cells by DCs. In humans, the use of sitagliptin correlated with a lower risk of tumor recurrence in patients with colorectal cancer undergoing curative surgery.

CONCLUSIONS

Our findings indicate that sitagliptin-mediated DPP4 inhibition promotes antitumor immune response by augmenting cDC1 functions. These data suggest that sitagliptin can be repurposed as an antitumor drug targeting DC, which provides a potential strategy for cancer immunotherapy.

QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration

DNA methylation provides a crucial epigenetic mark linking genetic variations to environmental influence. We have analyzed array-based DNA methylation profiles of 160 human retinas with co-measured RNA-seq and >8 million genetic variants, uncovering sites of genetic regulation in cis (37,453 methylation quantitative trait loci and 12,505 expression quantitative trait loci) and 13,747 DNA methylation loci affecting gene expression, with over one-third specific to the retina. Methylation and expression quantitative trait loci show non-random distribution and enrichment of biological processes related to synapse, mitochondria, and catabolism. Summary data-based Mendelian randomization and colocalization analyses identify 87 target genes where methylation and gene-expression changes likely mediate the genotype effect on age-related macular degeneration. Integrated pathway analysis reveals epigenetic regulation of immune response and metabolism including the glutathione pathway and glycolysis. Our study thus defines key roles of genetic variations driving methylation changes, prioritizes epigenetic control of gene expression, and suggests frameworks for regulation of macular degeneration pathology by genotype-environment interaction in retina.

The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease

The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.

Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders

Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.

Evaluation of noninvasive biospecimens for transcriptome studies

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

eQTL studies: from bulk tissues to single cells

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Single-cell multiomics of the human retina reveals hierarchical transcription factor collaboration in mediating cell type-specific effects of genetic variants on gene regulation

BACKGROUND

Systematic characterization of how  genetic variation modulates gene regulation in a cell type-specific context is essential for understanding complex traits. To address this question, we profile gene expression and chromatin accessibility in cells from healthy retinae of 20 human donors through single-cell multiomics and genomic sequencing.

RESULTS

We map eQTL, caQTL, allelic-specific expression, and allelic-specific chromatin accessibility in major retinal cell types. By integrating these results, we identify and characterize regulatory elements and genetic variants effective on gene regulation in individual cell types. The majority of identified sc-eQTLs and sc-caQTLs display cell type-specific effects, while the cis-elements containing genetic variants with cell type-specific effects are often accessible in multiple cell types. Furthermore, the transcription factors whose binding sites are perturbed by genetic variants tend to have higher expression levels in the cell types where the variants exert their effects, compared to the cell types where the variants have no impact. We further validate our findings with high-throughput reporter assays. Lastly, we identify the enriched cell types, candidate causal variants and genes, and cell type-specific regulatory mechanism underlying GWAS loci.

CONCLUSIONS

Overall, genetic effects on gene regulation are highly context dependent. Our results suggest that cell type-dependent genetic effect is driven by precise modulation of both trans-factor expression and chromatin accessibility of cis-elements. Our findings indicate hierarchical collaboration among transcription factors plays a crucial role in mediating cell type-specific effects of genetic variants on gene regulation.

Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits

The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.

Trans -eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae . We discovered thousands of genetic correlations between gene expression and growth, suggesting functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans -acting regulatory loci. Trans -acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans -acting regulatory hotspots shape complex traits by altering cellular states.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Sex differences in the neuronal transcriptome and synaptic mitochondrial function in the cerebral cortex of a multiple sclerosis model

Introduction

Multiple sclerosis (MS) affects the cerebral cortex, inducing cortical atrophy and neuronal and synaptic pathology. Despite the fact that women are more susceptible to getting MS, men with MS have worse disability progression. Here, sex differences in neurodegenerative mechanisms are determined in the cerebral cortex using the MS model, chronic experimental autoimmune encephalomyelitis (EAE).

Methods

Neurons from cerebral cortex tissues of chronic EAE, as well as age-matched healthy control, male and female mice underwent RNA sequencing and gene expression analyses using RiboTag technology. The morphology of mitochondria in neurons of cerebral cortex was assessed using Thy1-CFP-MitoS mice. Oxygen consumption rates were determined using mitochondrial respirometry assays from intact as well as permeabilized synaptosomes.

Results

RNA sequencing of neurons in cerebral cortex during chronic EAE in C57BL/6 mice showed robust differential gene expression in male EAE compared to male healthy controls. In contrast, there were few differences in female EAE compared to female healthy controls. The most enriched differential gene expression pathways in male mice during EAE were mitochondrial dysfunction and oxidative phosphorylation. Mitochondrial morphology in neurons showed significant abnormalities in the cerebral cortex of EAE males, but not EAE females. Regarding function, synaptosomes isolated from cerebral cortex of male, but not female, EAE mice demonstrated significantly decreased oxygen consumption rates during respirometry assays.

Discussion

Cortical neuronal transcriptomics, mitochondrial morphology, and functional respirometry assays in synaptosomes revealed worse neurodegeneration in male EAE mice. This is consistent with worse neurodegeneration in MS men and reveals a model and a target to develop treatments to prevent cortical neurodegeneration and mitigate disability progression in MS men.

RET enhancer haplotype-dependent remodeling of the human fetal gut development program

Hirschsprung disease (HSCR) is associated with deficiency of the receptor tyrosine kinase RET, resulting in loss of cells of the enteric nervous system (ENS) during fetal gut development. The major contribution to HSCR risk is from common sequence variants in RET enhancers with additional risk from rare coding variants in many genes. Here, we demonstrate that these RET enhancer variants specifically alter the human fetal gut development program through significant decreases in gene expression of RET, members of the RET-EDNRB gene regulatory network (GRN), other HSCR genes, with an altered transcriptome of 2,382 differentially expressed genes across diverse neuronal and mesenchymal functions. A parsimonious hypothesis for these results is that beyond RET's direct effect on its GRN, it also has a major role in enteric neural crest-derived cell (ENCDC) precursor proliferation, its deficiency reducing ENCDCs with relative expansion of non-ENCDC cells. Thus, genes reducing RET proliferative activity can potentially cause HSCR. One such class is the 23 RET-dependent transcription factors enriched in early gut development. We show that their knockdown in human neuroblastoma SK-N-SH cells reduces RET and/or EDNRB gene expression, expanding the RET-EDNRB GRN. The human embryos we studied had major remodeling of the gut transcriptome but were unlikely to have had HSCR: thus, genetic or epigenetic changes in addition to those in RET are required for aganglionosis.

Systematic differences in discovery of genetic effects on gene expression and complex traits

Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.

eQTL mapping in fetal-like pancreatic progenitor cells reveals early developmental insights into diabetes risk

The impact of genetic regulatory variation active in early pancreatic development on adult pancreatic disease and traits is not well understood. Here, we generate a panel of 107 fetal-like iPSC-derived pancreatic progenitor cells (iPSC-PPCs) from whole genome-sequenced individuals and identify 4065 genes and 4016 isoforms whose expression and/or alternative splicing are affected by regulatory variation. We integrate eQTLs identified in adult islets and whole pancreas samples, which reveal 1805 eQTL associations that are unique to the fetal-like iPSC-PPCs and 1043 eQTLs that exhibit regulatory plasticity across the fetal-like and adult pancreas tissues. Colocalization with GWAS risk loci for pancreatic diseases and traits show that some putative causal regulatory variants are active only in the fetal-like iPSC-PPCs and likely influence disease by modulating expression of disease-associated genes in early development, while others with regulatory plasticity likely exert their effects in both the fetal and adult pancreas by modulating expression of different disease genes in the two developmental stages.

Recent positive selection signatures reveal phenotypic evolution in the Han Chinese population

Characterizing natural selection signatures and relationships with phenotype spectra is important for understanding human evolution and both biological and pathological mechanisms. Here, we identified 24 genetic loci under recent selection by analyzing rare singletons in 3946 high-depth whole-genome sequencing data of Han Chinese. The loci include immune-related gene regions (MHC cluster, IGH cluster, STING1, and PSG), alcohol metabolism-related gene regions (ADH1B, ALDH2, and ALDH3B2), and the olfactory perception gene OR4C16, in which the MHC cluster, ADH1B, and ALDH2 were also identified by TOPMed and WestLake Biobank. Among the signals, the IGH cluster is particularly interesting, in which the favored allele of variant 14_105737776_C_T (rs117518546, IgG1-G396R) promotes immune response, but also increases the risk of an autoimmune disease systemic lupus erythematosus (SLE). It is also surprising that our newly discovered ALDH3B2 evolved in the opposite direction to ALDH2 for alcohol metabolism. Besides monogenic traits, we found that multiple complex traits experienced polygenic adaptation. Particularly, multi-methods consistently revealed that lower blood pressure was favored in natural selection. Finally, we built a database named RePoS (recent positive selection, http://bigdata.ibp.ac.cn/RePoS/) to integrate and display multi-population selection signals. Our study extended our understanding of natural evolution and phenotype adaptation in Han Chinese as well as other populations.

Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression

Genetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between cis-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.

Global impact of aberrant splicing on human gene expression levels

Alternative splicing (AS) is pervasive in human genes, yet the specific function of most AS events remains unknown. It is widely assumed that the primary function of AS is to diversify the proteome, however AS can also influence gene expression levels by producing transcripts rapidly degraded by nonsense-mediated decay (NMD). Currently, there are no precise estimates for how often the coupling of AS and NMD (AS-NMD) impacts gene expression levels because rapidly degraded NMD transcripts are challenging to capture. To better understand the impact of AS on gene expression levels, we analyzed population-scale genomic data in lymphoblastoid cell lines across eight molecular assays that capture gene regulation before, during, and after transcription and cytoplasmic decay. Sequencing nascent mRNA transcripts revealed frequent aberrant splicing of human introns, which results in remarkably high levels of mRNA transcripts subject to NMD. We estimate that ~15% of all protein-coding transcripts are degraded by NMD, and this estimate increases to nearly half of all transcripts for lowly-expressed genes with many introns. Leveraging genetic variation across cell lines, we find that GWAS trait-associated loci explained by AS are similarly likely to associate with NMD-induced expression level differences as with differences in protein isoform usage. Additionally, we used the splice-switching drug risdiplam to perturb AS at hundreds of genes, finding that ~3/4 of the splicing perturbations induce NMD. Thus, we conclude that AS-NMD substantially impacts the expression levels of most human genes. Our work further suggests that much of the molecular impact of AS is mediated by changes in protein expression levels rather than diversification of the proteome.

Interindividual variation in human cortical cell type abundance and expression

Single-cell transcriptomic studies have identified a conserved set of neocortical cell types from small postmortem cohorts. We extended these efforts by assessing cell type variation across 75 adult individuals undergoing epilepsy and tumor surgeries. Nearly all nuclei map to one of 125 robust cell types identified in the middle temporal gyrus. However, we found interindividual variance in abundances and gene expression signatures, particularly in deep-layer glutamatergic neurons and microglia. A minority of donor variance is explainable by age, sex, ancestry, disease state, and cell state. Genomic variation was associated with expression of 150 to 250 genes for most cell types. This characterization of cellular variation provides a baseline for cell typing in health and disease.

Transcriptome-wide association studies: recent advances in methods, applications and available databases

Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.

Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis

The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk post-mortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA-editing and alternative polyadenylation (APA), within a cell-type-specific population of human neural progenitors and neurons. More RNA-editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting genetically mediated post-transcriptional regulation during brain development lead to differences in brain function.

Prospective Blood Transcriptomics Study in a Motor Vehicle Collision Cohort Identified a Protective Function of the SAMD15 Gene Against Chronic Pain

Traumatic brain injuries following motor vehicle collisions (MVCs) are ubiquitous. Surprisingly, there are no correlates between concussion impact force and long-term pain outcomes. To study the molecular underpinnings of chronic pain after MVC, we assembled a prospective cohort of 36 subjects that experienced MVC and suffered documented mild traumatic brain injuries. For each participant, a first blood sample was drawn within 72 hours of the collision, then a second one at the 6-month mark. Pain was also assessed at the second blood draw to determine if pain became chronic or resolved. Blood samples enabled transcriptomics analyses for immune cells. At the transcriptome-wide level, we found that Sterile Alpha Motif Domain Containing 15 (SAMD15) mRNA was significantly upregulated with time in subjects who resolved their pain whereas unregulated in those with persistent pain. Using several large publicly available datasets, such as the UK Biobank and the GTeX portal, we then linked elevated SAMD15 gene expression, elevated neutrophils cell counts, and decreased risk for chronic pain to increased dosage of the T allele at SNP rs4903580, situated within SAMD15's gene locus. The causality between the components of our model was established and supported by Mendelian randomization. Overall, our results support the role of SAMD15 as a potential gene effector for neutrophil-dependent chronic pain development. PERSPECTIVE: This article highlights the potential protective role of the SAMD15 gene against chronic pain following a mild traumatic brain injury. The expression of the gene is associated with a SNP rs4903580, which is itself associated with neutrophils counts as well as chronic pain in large genetic studies.

Integration of eQTL and GWAS analysis uncovers a genetic regulation of natural ionomic variation in Arabidopsis

KEY MESSAGE

This study provided important insights into the genetic architecture of variations in A. thaliana leaf ionome in a cell-type-specific manner. The functional interpretation of traits associated variants by expression quantitative trait loci (eQTL) analysis is usually performed in bulk tissue samples. While the regulation of gene expression is context-dependent, such as cell-type-specific manner. In this study, we estimated cell-type abundances from 728 bulk tissue samples using single-cell RNA-sequencing dataset, and performed cis-eQTL mapping to identify cell-type-interaction eQTL (cis-eQTLs(ci)) in A. thaliana. Also, we performed Genome-wide association studies (GWAS) analyses for 999 accessions to identify the genetic basis of variations in A. thaliana leaf ionome. As a result, a total of 5,664 unique eQTL genes and 15,038 unique cis-eQTLs(ci) were significant. The majority (62.83%) of cis-eQTLs(ci) were cell-type-specific eQTLs. Using colocalization, we uncovered one interested gene AT2G25590 in Phloem cell, encoding a kind of plant Tudor-like protein with possible chromatin-associated functions, which colocalized with the most significant cis-eQTL(ci) of a Mo-related locus (Chr2:10,908,806:A:C; P = 3.27 × 10-27). Furthermore, we prioritized eight target genes associated with AT2G25590, which were previously reported in regulating the concentration of Mo element in A. thaliana. This study revealed the genetic regulation of ionomic variations and provided a foundation for further studies on molecular mechanisms of genetic variants controlling the A. thaliana ionome.

Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models

In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles: https://rna-gan.stanford.edu/, and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.

Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions

Comprehensive enhancer discovery is challenging because most enhancers, especially those contributing to complex diseases, have weak effects on gene expression. Our gene regulatory network modeling identified that nonlinear enhancer gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Using human embryonic stem cell definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. We discovered a comprehensive set of enhancers for each of the core endoderm-specifying transcription factors. Many enhancers had strong effects mid-transition but weak effects post-transition, consistent with the nonlinear temporal responses to enhancer perturbation predicted by the modeling. Integrating three-dimensional genomic information, we were able to develop a CTCF-loop-constrained Interaction Activity model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Our study provides generalizable strategies for sensitive and systematic enhancer discovery in both normal and pathological cell state transitions.

Interpreting non-coding disease-associated human variants using single-cell epigenomics

Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.

ADAMTS7: a Novel Therapeutic Target in Atherosclerosis

PURPOSE OF REVIEW

Genome-wide association studies have repeatedly linked the metalloproteinase ADAMTS7 to coronary artery disease. Here we aim to highlight recent findings surrounding the human genetics of ADAMTS7, novel mouse models that investigate ADAMTS7 function, and potential substrates of ADAMTS7 cleavage.

RECENT FINDINGS

Recent genome-wide association studies in coronary artery disease have replicated the GWAS signal for ADAMTS7 and shown that the signal holds true even across different ethnic groups. However, the direction of effect in humans remains unclear. A recent novel mouse model revealed that the proatherogenicity of ADAMTS7 is derived from its catalytic functions, while at the translational level, vaccinating mice against ADAMTS7 reduced atherosclerosis. Finally, in vitro proteomics approaches have identified extracellular matrix proteins as candidate substrates that may be causal for the proatherogenicity of ADAMTS7. ADAMTS7 represents an enticing target for therapeutic intervention. The recent studies highlighted here have replicated prior findings, confirming the genetic link between ADAMTS7 and atherosclerosis, while providing further evidence in mice that ADAMTS7 is a targetable proatherogenic enzyme.

Macrophage-to-endothelial cell crosstalk by the cholesterol metabolite 27HC promotes atherosclerosis in male mice

Hypercholesterolemia and vascular inflammation are key interconnected contributors to the pathogenesis of atherosclerosis. How hypercholesterolemia initiates vascular inflammation is poorly understood. Here we show in male mice that hypercholesterolemia-driven endothelial activation, monocyte recruitment and atherosclerotic lesion formation are promoted by a crosstalk between macrophages and endothelial cells mediated by the cholesterol metabolite 27-hydroxycholesterol (27HC). The pro-atherogenic actions of macrophage-derived 27HC require endothelial estrogen receptor alpha (ERα) and disassociation of the cytoplasmic scaffolding protein septin 11 from ERα, leading to extranuclear ERα- and septin 11-dependent activation of NF-κB. Furthermore, pharmacologic inhibition of cyp27a1, which generates 27HC, affords atheroprotection by reducing endothelial activation and monocyte recruitment. These findings demonstrate cell-to-cell communication by 27HC, and identify a major causal linkage between the hypercholesterolemia and vascular inflammation that partner to promote atherosclerosis. Interventions interrupting this linkage may provide the means to blunt vascular inflammation without impairing host defense to combat the risk of atherosclerotic cardiovascular disease that remains despite lipid-lowering therapies.

Mixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectory

Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but achieving fine-resolution and high-accuracy deconvolution remains a challenge. Here we introduce MeDuSA, a mixed model-based method that leverages single-cell RNA-sequencing data as a reference to estimate cell-state abundances along a one-dimensional trajectory in bulk RNA-sequencing data. The advantage of MeDuSA lies primarily in estimating cell abundance in each state while fitting the remaining cells of the same type individually as random effects. Extensive simulations and real-data benchmark analyses demonstrate that MeDuSA greatly improves the estimation accuracy over existing methods for one-dimensional trajectories. Applying MeDuSA to cohort-level RNA-sequencing datasets reveals associations of cell-state abundances with disease or treatment conditions and cell-state-dependent genetic control of transcription. Our study provides a high-accuracy and fine-resolution method for cell-state deconvolution along a one-dimensional trajectory and demonstrates its utility in characterizing the dynamics of cell states in various biological processes.

An analytical framework for decoding cell type-specific genetic variation of gene regulation

A deeper understanding of genetic regulation and functional mechanisms underlying genetic associations with complex traits and diseases is impeded by cellular heterogeneity and linkage disequilibrium. To address these limits, we introduce Huatuo, a framework to decode genetic variation of gene regulation at cell type and single-nucleotide resolutions by integrating deep-learning-based variant predictions with population-based association analyses. We apply Huatuo to generate a comprehensive cell type-specific genetic variation landscape across human tissues and further evaluate their potential roles in complex diseases and traits. Finally, we show that Huatuo's inferences permit prioritizations of driver cell types associated with complex traits and diseases and allow for systematic insights into the mechanisms of phenotype-causal genetic variation.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.