Cell-Type Heterogeneity in Adipose Tissue Is Associated with Complex Traits and Reveals Disease-Relevant Cell-Specific eQTLs

Adipose-derived mesenchymal stromal cells in clinical trials: Insights from single-cell studies

Mesenchymal Stromal Cells (MSCs) offer tremendous potential for the treatment of various diseases and their healing properties have been explored in hundreds of clinical trials. These trails primarily focus on immunological and neurological disorders, as well as regenerative medicine. Adipose tissue is a rich source of mesenchymal stromal cells and methods to obtain and culture adipose-derived MSCs (AD-MSCs) have been well established. Promising results from pre-clinical testing of AD-MSCs activity prompted clinical trials that further led to the approval of AD-MSCs for the treatment of complex perianal fistulas in Crohn's disease and subcutaneous tissue defects. However, AD-MSC heterogeneity along with various manufacturing protocols or different strategies to boost their activity create the need for standardized quality control procedures and safety assessment of the intended cell product. High-resolution transcriptomic methods have been recently gaining attention, as they deliver insight into gene expression profiles of individual cells, helping to deconstruct cellular hierarchy and differentiation trajectories, and to understand cell-cell interactions within tissues. This article presents a comprehensive overview of completed clinical trials evaluating the safety and efficacy of AD-MSC treatment, together with current single-cell studies of human AD-MSC. Furthermore, our work emphasizes the increasing significance of single-cell research in elucidating the mechanisms of cellular action and predicting their therapeutic effects.

sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human adipose tissues

Deconvolution algorithms mostly rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to estimate tissues' cell-type composition, with performance accuracy validated on deposited databases. Adipose tissues' cellular composition is highly variable, and adipocytes can only be captured by single-nucleus RNA-sequencing (snRNA-seq). Here we report the development of sNucConv, a Scaden deep-learning-based deconvolution tool, trained using 5 hSAT and 7 hVAT snRNA-seq-based data corrected by (i) snRNA-seq/bulk RNA-seq highly correlated genes and (ii) individual cell-type regression models. Applying sNucConv on our bulk RNA-seq data resulted in cell-type proportion estimation of 15 and 13 cell types, with accuracy of R = 0.93 (range: 0.76-0.97) and R = 0.95 (range: 0.92-0.98) for hVAT and hSAT, respectively. This performance level was further validated on an independent set of samples (5 hSAT; 5 hVAT). The resulting model was depot specific, reflecting depot differences in gene expression patterns. Jointly, sNucConv provides proof-of-concept for producing validated deconvolution models for tissues un-amenable to scRNA-seq.

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types

As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, we can automatically segment tissues into their constituent tissue substructures and pathology proportions across thousands of whole slide images, outperforming other self-supervised methods (43% increase in silhouette score). Additionally, we can detect and quantify histological pathologies present, such as arterial calcification (AUROC = 0.93) and identify missing calcification diagnoses. Finally, to link gene expression to tissue morphology, we introduce RNAPath, a set of models trained on 23 tissue types that can predict and spatially localise individual RNA expression levels directly from H&E histology (mean genes significantly regressed = 5156, FDR 1%). We validate RNAPath spatial predictions with matched ground truth immunohistochemistry for several well characterised control genes, recapitulating their known spatial specificity. Together, these results demonstrate how self-supervised machine learning when applied to vast histological archives allows researchers to answer questions about tissue pathology, its spatial organisation and the interplay between morphological tissue variability and gene expression.

White adipocytes in subcutaneous fat depots require KLF15 for maintenance in preclinical models

Healthy adipose tissue is essential for normal physiology. There are 2 broad types of adipose tissue depots: brown adipose tissue (BAT), which contains adipocytes poised to burn energy through thermogenesis, and white adipose tissue (WAT), which contains adipocytes that store lipids. However, within those types of adipose, adipocytes possess depot and cell-specific properties that have important implications. For example, the subcutaneous and visceral WAT confers divergent risk for metabolic disease. Further, within a depot, different adipocytes can have distinct properties; subcutaneous WAT can contain adipocytes with either white or brown-like (beige) adipocyte properties. However, the pathways that regulate and maintain this cell and depot-specificity are incompletely understood. Here, we found that the transcription factor KLF15 is required for maintaining white adipocyte properties selectively within the subcutaneous WAT. We revealed that deletion of Klf15 is sufficient to induce beige adipocyte properties and that KLF15's direct regulation of Adrb1 is a critical molecular mechanism for this process. We uncovered that this activity is cell autonomous but has systemic implications in mouse models and is conserved in primary human adipose cells. Our results elucidate a pathway for depot-specific maintenance of white adipocyte properties that could enable the development of therapies for obesity and associated diseases.

Ex vivo Anti-Senescence Activity of N-Acetylcysteine in Visceral Adipose Tissue of Obese Volunteers

INTRODUCTION

Excessive visceral adiposity is known to drive the onset of metabolic derangements, mostly involving oxidative stress, prolonged inflammation, and cellular senescence. N-acetylcysteine (NAC) is a synthetic form of l-cysteine with potential antioxidant, anti-inflammatory, and anti-senescence properties. This ex-vivo study aimed to determine the effect of NAC on some markers of senescence including β-galactosidase activity and p16, p53, p21, IL-6, and TNF-α gene expressions in visceral adipose tissue in obese adults.

METHODS

This ex-vivo experimental study involved 10 obese participants who were candidates for bariatric surgery. Duplicate biopsies from the abdominal visceral adipose tissue were obtained from the omentum. The biopsies were treated with or without NAC (5 and 10 mm). To evaluate adipose tissue senescence, beta-galactosidase (β-gal) activity and the expression of P16, P21, P53, IL-6, and TNF-α were determined. ANOVA test was employed to analyze the varying markers of cellular senescence and inflammation between treatment groups.

RESULTS

The NAC at concentrations of 5 mm and 10 mm resulted in a noteworthy reduction β-gal activity compared to the control group (p < 0.001). Additionally, the expression of P16, P21, and IL-6 was significantly reduced following treatment with NAC (5 mm) and NAC (10 mm) compared to the control group (All p < 0.001).

DISCUSSION/CONCLUSION

Taken together, these data suggest the senotherapeutic effect of NAC, as it effectively reduces the activity of SA-β-gal and the expression of IL-6, P16, and P21 genes in the visceral adipose tissue of obese individuals.

The Metabolic Syndrome, a Human Disease

This review focuses on the question of metabolic syndrome (MS) being a complex, but essentially monophyletic, galaxy of associated diseases/disorders, or just a syndrome of related but rather independent pathologies. The human nature of MS (its exceptionality in Nature and its close interdependence with human action and evolution) is presented and discussed. The text also describes the close interdependence of its components, with special emphasis on the description of their interrelations (including their syndromic development and recruitment), as well as their consequences upon energy handling and partition. The main theories on MS's origin and development are presented in relation to hepatic steatosis, type 2 diabetes, and obesity, but encompass most of the MS components described so far. The differential effects of sex and its biological consequences are considered under the light of human social needs and evolution, which are also directly related to MS epidemiology, severity, and relations with senescence. The triggering and maintenance factors of MS are discussed, with especial emphasis on inflammation, a complex process affecting different levels of organization and which is a critical element for MS development. Inflammation is also related to the operation of connective tissue (including the adipose organ) and the widely studied and acknowledged influence of diet. The role of diet composition, including the transcendence of the anaplerotic maintenance of the Krebs cycle from dietary amino acid supply (and its timing), is developed in the context of testosterone and β-estradiol control of the insulin-glycaemia hepatic core system of carbohydrate-triacylglycerol energy handling. The high probability of MS acting as a unique complex biological control system (essentially monophyletic) is presented, together with additional perspectives/considerations on the treatment of this 'very' human disease.

Age-dependent genes in adipose stem and precursor cells affect regulation of fat cell differentiation and link aging to obesity via cellular and genetic interactions

BACKGROUND

Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner.

METHODS

We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI).

RESULTS

We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level.

CONCLUSIONS

Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.

Cell-Type Composition Affects Adipose Gene Expression Associations With Cardiometabolic Traits

Understanding differences in adipose gene expression between individuals with different levels of clinical traits may reveal the genes and mechanisms leading to cardiometabolic diseases. However, adipose is a heterogeneous tissue. To account for cell-type heterogeneity, we estimated cell-type proportions in 859 subcutaneous adipose tissue samples with bulk RNA sequencing (RNA-seq) using a reference single-nuclear RNA-seq data set. Cell-type proportions were associated with cardiometabolic traits; for example, higher macrophage and adipocyte proportions were associated with higher and lower BMI, respectively. We evaluated cell-type proportions and BMI as covariates in tests of association between >25,000 gene expression levels and 22 cardiometabolic traits. For >95% of genes, the optimal, or best-fit, models included BMI as a covariate, and for 79% of associations, the optimal models also included cell type. After adjusting for the optimal covariates, we identified 2,664 significant associations (P ≤ 2e-6) for 1,252 genes and 14 traits. Among genes proposed to affect cardiometabolic traits based on colocalized genome-wide association study and adipose expression quantitative trait locus signals, 25 showed a corresponding association between trait and gene expression levels. Overall, these results suggest the importance of modeling cell-type proportion when identifying gene expression associations with cardiometabolic traits.

ARTICLE HIGHLIGHTS

Time-varying and tissue-dependent effects of adiposity on leptin levels: A Mendelian randomization study

Background

Findings from Mendelian randomization (MR) studies are conventionally interpreted as lifelong effects, which typically do not provide insight into the molecular mechanisms underlying the effect of an exposure on an outcome. In this study, we apply two recently developed MR approaches (known as 'lifecourse' and 'tissue-partitioned' MR) to investigate lifestage-specific effects and tissues of action in the relationship between adiposity and circulating leptin levels.

Methods

Genetic instruments for childhood and adult adiposity were incorporated into a multivariable MR (MVMR) framework to estimate lifestage-specific effects on leptin levels measured during early life (mean age: 10 y) in the Avon Longitudinal Study of Parents and Children and in adulthood (mean age: 55 y) using summary-level data from the deCODE Health study. This was followed by partitioning body mass index (BMI) instruments into those whose effects are putatively mediated by gene expression in either subcutaneous adipose or brain tissues, followed by using MVMR to simultaneously estimate their separate effects on childhood and adult leptin levels.

Results

There was strong evidence that childhood adiposity has a direct effect on leptin levels at age 10 y in the lifecourse (β = 1.10 SD change in leptin levels, 95% CI = 0.90-1.30, p=6 × 10-28), whereas evidence of an indirect effect was found on adulthood leptin along the causal pathway involving adulthood body size (β = 0.74, 95% CI = 0.62-0.86, p=1 × 10-33). Tissue-partitioned MR analyses provided evidence to suggest that BMI exerts its effect on leptin levels during both childhood and adulthood via brain tissue-mediated pathways (β = 0.79, 95% CI = 0.22-1.36, p=6 × 10-3 and β = 0.51, 95% CI = 0.32-0.69, p=1 × 10-7, respectively).

Conclusions

Our findings demonstrate the use of lifecourse MR to disentangle direct and indirect effects of early-life exposures on time-varying complex outcomes. Furthermore, by integrating tissue-specific data, we highlight the etiological importance of appetite regulation in the effect of adiposity on leptin levels.

Funding

This work was supported by the Integrative Epidemiology Unit, which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/1).

Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome

BACKGROUND

B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants.

RESULTS

Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals.

CONCLUSION

Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome.

Gene coexpression network analysis reveals perirenal adipose tissue as an important target of prenatal malnutrition in sheep

We have previously demonstrated that pre- and early postnatal malnutrition in sheep induced depot- and sex-specific changes in adipose morphological features, metabolic outcomes, and transcriptome in adulthood, with perirenal (PER) as the major target followed by subcutaneous (SUB) adipose tissue. We aimed to identify coexpressed and hub genes in SUB and PER to identify the underlying molecular mechanisms contributing to the early nutritional programming of adipose-related phenotypic outcomes. Transcriptomes of SUB and PER of male and female adult sheep with different pre- and early postnatal nutrition histories were used to construct networks of coexpressed genes likely to be functionally associated with pre- and early postnatal nutrition histories and phenotypic traits using weighted gene coexpression network analysis. The modules from PER showed enrichment of cell cycle regulation, gene expression, transmembrane transport, and metabolic processes associated with both sexes' prenatal nutrition. In SUB (only males), a module of enriched adenosine diphosphate metabolism and development correlated with prenatal nutrition. Sex-specific module enrichments were found in PER, such as chromatin modification in the male network but histone modification and mitochondria- and oxidative phosphorylation-related functions in the female network. These sex-specific modules correlated with prenatal nutrition and adipocyte size distribution patterns. Our results point to PER as a primary target of prenatal malnutrition compared to SUB, which played only a minor role. The prenatal programming of gene expression and cell cycle, potentially through epigenetic modifications, might be underlying mechanisms responsible for observed changes in PER expandability and adipocyte-size distribution patterns in adulthood in both sexes.

DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues

Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome architecture. By applying deCOOC to in-house-generated bulk Hi-C data from visceral and subcutaneous adipose tissues, it is found that the characteristic chromatin features of M2 cells in the two anatomical loci are distinctively bound to different physiological functionalities. Taken together, deCOOC is both a reliable Hi-C data deconvolution method and a powerful tool for functional extraction of 3D genome architecture.

Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy

Cystatin C (CyC), a secreted cysteine protease inhibitor, has unclear biological functions. Many patients exhibit elevated plasma CyC levels, particularly during glucocorticoid (GC) treatment. This study links GCs with CyC's systemic regulation by utilizing genome-wide association and structural equation modeling to determine CyC production genetics in the UK Biobank. Both CyC production and a polygenic score (PGS) capturing predisposition to CyC production were associated with increased all-cause and cancer-specific mortality. We found that the GC receptor directly targets CyC, leading to GC-responsive CyC secretion in macrophages and cancer cells. CyC-knockout tumors displayed significantly reduced growth and diminished recruitment of TREM2+ macrophages, which have been connected to cancer immunotherapy failure. Furthermore, the CyC-production PGS predicted checkpoint immunotherapy failure in 685 patients with metastatic cancer from combined clinical trial cohorts. In conclusion, CyC may act as a GC effector pathway via TREM2+ macrophage recruitment and may be a potential target for combination cancer immunotherapy.

Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population

BACKGROUND

Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource.

RESULTS

We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue.

CONCLUSIONS

By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.

Single-cell genomics meets human genetics

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

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.

Dynamic chromatin architecture of the porcine adipose tissues with weight gain and loss

Using an adult female miniature pig model with diet-induced weight gain/weight loss, we investigated the regulatory mechanisms of three-dimensional (3D) genome architecture in adipose tissues (ATs) associated with obesity. We generated 249 high-resolution in situ Hi-C chromatin contact maps of subcutaneous AT and three visceral ATs, analyzing transcriptomic and chromatin architectural changes under different nutritional treatments. We find that chromatin architecture remodeling underpins transcriptomic divergence in ATs, potentially linked to metabolic risks in obesity development. Analysis of chromatin architecture among subcutaneous ATs of different mammals suggests the presence of transcriptional regulatory divergence that could explain phenotypic, physiological, and functional differences in ATs. Regulatory element conservation analysis in pigs and humans reveals similarities in the regulatory circuitry of genes responsible for the obesity phenotype and identified non-conserved elements in species-specific gene sets that underpin AT specialization. This work provides a data-rich tool for discovering obesity-related regulatory elements in humans and pigs.

A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data

Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.

Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes

DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions.

Prioritization of genes associated with type 2 diabetes mellitus for functional studies

Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell 'village' cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.

Blue LED light induces cytotoxicity via ROS production and mitochondrial damage in bovine subcutaneous preadipocytes

The purpose of this study was to investigate the effect and mechanism of blue light irradiation on bovine subcutaneous preadipocytes. In this study, preadipocytes were divided into dark group (control) and blue light group. Results show that blue light exposure time-dependently reduced the viability of preadipocytes and induced mitochondrial damage, in accompaniment with the accumulation of intracellular reactive oxygen species (ROS). Meanwhile, blue light caused oxidative stress, as evidenced by the increased MDA level, the reduced T-AOC contents, as well as the decreased activities of antioxidant enzymes. Additionally, blue light treatment induced apoptosis and G2/M phase arrest via Bcl-2/Bax/cleaved caspase-3 pathway and P53/GADD45 pathway, respectively. Protein expressions of LC3-II/LC3-I and P62 were up-regulated under blue light exposure, indicating blue light initiated autophagy but impeded autophagic degradation. Moreover, blue light caused an increase in the secretion of pro-inflammatory factors (TNF-α, IL-1β, and IL-6). Pretreatment with N-acetylcysteine (NAC), a potent ROS scavenger, restored the loss of mitochondrial membrane potential (Δψ) and reduced excess ROS. Additionally, the above negative effects of blue light on cells were alleviated after NAC administration. In conclusion, this study demonstrates blue light induces cellular ROS overproduction and Δψ depolarization, resulting in the decrease of cell viability and the activation of apoptosis, autophagy, and inflammation, providing a reference for the application of blue light in the regulation of fat cells in the future.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

The genetic and evolutionary basis of gene expression variation in East Africans

BACKGROUND

Mapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases, though most studies have focused on individuals of European descent. While important progress has been made to study a greater diversity of human populations, many groups remain unstudied, particularly among indigenous populations within Africa. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations.

RESULTS

We find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine-mapping of causal variation. Integrating our QTL scans with signatures of natural selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation.

CONCLUSION

Extending QTL mapping studies beyond European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.

Mediation analysis of multiple mediators with incomplete omics data

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing for some subjects, due to limitations such as cost and sample quality. In this article, we propose a powerful approach for mediation analysis that accommodates missing data among multiple mediators and allows for various interaction effects. We formulate the relationships among genetic variants, other omics measurements, and phenotypes through linear regression models. We derive the joint likelihood for models with two mediators, accounting for arbitrary patterns of missing values. Utilizing computationally efficient and stable algorithms, we conduct maximum likelihood estimation. Our methods produce unbiased and statistically efficient estimators. We demonstrate the usefulness of our methods through simulation studies and an application to the Metabolic Syndrome in Men study.

Clinical traits impacting human tissue transcriptomes

In this issue of Cell Genomics, Garcia-Perez et al.¹ report a comprehensive and careful association analysis between gene expression and splicing measured by the GTEx Consortium² in 46 human tissues and 21 demographic and clinical traits.

The missing link between genetic association and regulatory function

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'

Intraepithelial lymphocytes in the pig intestine: T cell and innate lymphoid cell contributions to intestinal barrier immunity

Intraepithelial lymphocytes (IELs) include T cells and innate lymphoid cells that are important mediators of intestinal immunity and barrier defense, yet most knowledge of IELs is derived from the study of humans and rodent models. Pigs are an important global food source and promising biomedical model, yet relatively little is known about IELs in the porcine intestine, especially during formative ages of intestinal development. Due to the biological significance of IELs, global importance of pig health, and potential of early life events to influence IELs, we collate current knowledge of porcine IEL functional and phenotypic maturation in the context of the developing intestinal tract and outline areas where further research is needed. Based on available findings, we formulate probable implications of IELs on intestinal and overall health outcomes and highlight key findings in relation to human IELs to emphasize potential applicability of pigs as a biomedical model for intestinal IEL research. Review of current literature suggests the study of porcine intestinal IELs as an exciting research frontier with dual application for betterment of animal and human health.

Combined genetic deletion of GDF15 and FGF21 has modest effects on body weight, hepatic steatosis and insulin resistance in high fat fed mice

OBJECTIVES

Obesity in humans and mice is associated with elevated levels of two hormones responsive to cellular stress, namely GDF15 and FGF21. Over-expression of each of these is associated with weight loss and beneficial metabolic changes but where they are secreted from and what they are required for physiologically in the context of overfeeding remains unclear.

METHODS

Here we used tissue selective knockout mouse models and human transcriptomics to determine the source of circulating GDF15 in obesity. We then generated and characterized the metabolic phenotypes of GDF15/FGF21 double knockout mice.

RESULTS

Circulating GDF15 and FGF21 are both largely derived from the liver, rather than adipose tissue or skeletal muscle, in obese states. Combined whole body deletion of FGF21 and GDF15 does not result in any additional weight gain in response to high fat feeding but it does result in significantly greater hepatic steatosis and insulin resistance than that seen in GDF15 single knockout mice.

CONCLUSIONS

Collectively the data suggest that overfeeding activates a stress response in the liver which is the major source of systemic rises in GDF15 and FGF21. These hormones then activate pathways which reduce this metabolic stress.

Identification of novel genes whose expression in adipose tissue affects body fat mass and distribution: an RNA-Seq and Mendelian Randomization study

Many studies have shown that abdominal adiposity is more strongly related to health risks than peripheral adiposity. However, the underlying pathways are still poorly understood. In this cross-sectional study using data from RNA-sequencing experiments and whole-body MRI scans of 200 participants in the EPIC-Potsdam cohort, our aim was to identify novel genes whose gene expression in subcutaneous adipose tissue has an effect on body fat mass (BFM) and body fat distribution (BFD). The analysis identified 625 genes associated with adiposity, of which 531 encode a known protein and 487 are novel candidate genes for obesity. Enrichment analyses indicated that BFM-associated genes were characterized by their higher than expected involvement in cellular, regulatory and immune system processes, and BFD-associated genes by their involvement in cellular, metabolic, and regulatory processes. Mendelian Randomization analyses suggested that the gene expression of 69 genes was causally related to BFM and BFD. Six genes were replicated in UK Biobank. In this study, we identified novel genes for BFM and BFD that are BFM- and BFD-specific, involved in different molecular processes, and whose up-/downregulated gene expression may causally contribute to obesity.

ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19

BACKGROUND

COVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain.

SUBJECTS/METHODS

In a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry.

RESULTS

Lower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 × 10-6), obesity status (P = 4.81 × 10-5), higher serum fasting insulin (P = 5.32 × 10-4), BMI (P = 3.94 × 10-4), and lower serum HDL levels (P = 1.92 × 10-7). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 × 10-4) and higher proportion of macrophages (P = 2.74 × 10-5). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression.

CONCLUSIONS

Our results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19.

Adipose methylome integrative-omic analyses reveal genetic and dietary metabolic health drivers and insulin resistance classifiers

BACKGROUND

There is considerable evidence for the importance of the DNA methylome in metabolic health, for example, a robust methylation signature has been associated with body mass index (BMI). However, visceral fat (VF) mass accumulation is a greater risk factor for metabolic disease than BMI alone. In this study, we dissect the subcutaneous adipose tissue (SAT) methylome signature relevant to metabolic health by focusing on VF as the major risk factor of metabolic disease. We integrate results with genetic, blood methylation, SAT gene expression, blood metabolomic, dietary intake and metabolic phenotype data to assess and quantify genetic and environmental drivers of the identified signals, as well as their potential functional roles.

METHODS

Epigenome-wide association analyses were carried out to determine visceral fat mass-associated differentially methylated positions (VF-DMPs) in SAT samples from 538 TwinsUK participants. Validation and replication were performed in 333 individuals from 3 independent cohorts. To assess functional impacts of the VF-DMPs, the association between VF and gene expression was determined at the genes annotated to the VF-DMPs and an association analysis was carried out to determine whether methylation at the VF-DMPs is associated with gene expression. Further epigenetic analyses were carried out to compare methylation levels at the VF-DMPs as the response variables and a range of different metabolic health phenotypes including android:gynoid fat ratio (AGR), lipids, blood metabolomic profiles, insulin resistance, T2D and dietary intake variables. The results from all analyses were integrated to identify signals that exhibit altered SAT function and have strong relevance to metabolic health.

RESULTS

We identified 1181 CpG positions in 788 genes to be differentially methylated with VF (VF-DMPs) with significant enrichment in the insulin signalling pathway. Follow-up cross-omic analysis of VF-DMPs integrating genetics, gene expression, metabolomics, diet, and metabolic traits highlighted VF-DMPs located in 9 genes with strong relevance to metabolic disease mechanisms, with replication of signals in FASN, SREBF1, TAGLN2, PC and CFAP410. PC methylation showed evidence for mediating effects of diet on VF. FASN DNA methylation exhibited putative causal effects on VF that were also strongly associated with insulin resistance and methylation levels in FASN better classified insulin resistance (AUC=0.91) than BMI or VF alone.

CONCLUSIONS

Our findings help characterise the adiposity-associated methylation signature of SAT, with insights for metabolic disease risk.

A human adipose tissue cell-type transcriptome atlas

The importance of defining cell-type-specific genes is well acknowledged. Technological advances facilitate high-resolution sequencing of single cells, but practical challenges remain. Adipose tissue is composed primarily of adipocytes, large buoyant cells requiring extensive, artefact-generating processing for separation and analysis. Thus, adipocyte data are frequently absent from single-cell RNA sequencing (scRNA-seq) datasets, despite being the primary functional cell type. Here, we decipher cell-type-enriched transcriptomes from unfractionated human adipose tissue RNA-seq data. We profile all major constituent cell types, using 527 visceral adipose tissue (VAT) or 646 subcutaneous adipose tissue (SAT) samples, identifying over 2,300 cell-type-enriched transcripts. Sex-subset analysis uncovers a panel of male-only cell-type-enriched genes. By resolving expression profiles of genes differentially expressed between SAT and VAT, we identify mesothelial cells as the primary driver of this variation. This study provides an accessible method to profile cell-type-enriched transcriptomes using bulk RNA-seq, generating a roadmap for adipose tissue biology.

A compound directed against S6K1 hampers fat mass expansion and mitigates diet-induced hepatosteatosis

The ribosomal protein S6 kinase 1 (S6K1) is a relevant effector downstream of the mammalian target of rapamycin complex 1 (mTORC1), best known for its role in the control of lipid homeostasis. Consistent with this, mice lacking the S6k1 gene have a defect in their ability to induce the commitment of fat precursor cells to the adipogenic lineage, which contributes to a significant reduction of fat mass. Here, we assess the therapeutic blockage of S6K1 in diet-induced obese mice challenged with LY2584702 tosylate, a specific oral S6K1 inhibitor initially developed for the treatment of solid tumors. We show that diminished S6K1 activity hampers fat mass expansion and ameliorates dyslipidemia and hepatic steatosis, while modifying transcriptome-wide gene expression programs relevant for adipose and liver function. Accordingly, decreased mTORC1 signaling in fat (but increased in the liver) segregated with defective epithelial-mesenchymal transition and the impaired expression of Cd36 (coding for a fatty acid translocase) and Lgals1 (Galectin 1) in both tissues. All these factors combined align with reduced adipocyte size and improved lipidomic signatures in the liver, while hepatic steatosis and hypertriglyceridemia were improved in treatments lasting either 3 months or 6 weeks.

Comprehensive evaluation of deconvolution methods for human brain gene expression

Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.

Transcriptome deconvolution aims to estimate cellular composition based on gene expression data. Here the authors evaluate deconvolution methods for human brain transcriptome and conclude that partial deconvolution algorithms work best, but that appropriate cell-type signatures are also important.

Harnessing tissue-specific genetic variation to dissect putative causal pathways between body mass index and cardiometabolic phenotypes

Body mass index (BMI) is a complex disease risk factor known to be influenced by genes acting via both metabolic pathways and appetite regulation. In this study, we aimed to gain insight into the phenotypic consequences of BMI-associated genetic variants, which may be mediated by their expression in different tissues. First, we harnessed meta-analyzed gene expression datasets derived from subcutaneous adipose (n = 1257) and brain (n = 1194) tissue to identify 86 and 140 loci, respectively, which provided evidence of genetic colocalization with BMI. These two sets of tissue-partitioned loci had differential effects with respect to waist-to-hip ratio, suggesting that the way they influence fat distribution might vary despite their having very similar average magnitudes of effect on BMI itself (adipose = 0.0148 and brain = 0.0149 standard deviation change in BMI per effect allele). For instance, BMI-associated variants colocalized with TBX15 expression in adipose tissue (posterior probability [PPA] = 0.97), but not when we used TBX15 expression data derived from brain tissue (PPA = 0.04) This gene putatively influences BMI via its role in skeletal development. Conversely, there were loci where BMI-associated variants provided evidence of colocalization with gene expression in brain tissue (e.g., NEGR1, PPA = 0.93), but not when we used data derived from adipose tissue, suggesting that these genes might be more likely to influence BMI via energy balance. Leveraging these tissue-partitioned variant sets through a multivariable Mendelian randomization framework provided strong evidence that the brain-tissue-derived variants are predominantly responsible for driving the genetically predicted effects of BMI on cardiovascular-disease endpoints (e.g., coronary artery disease: odds ratio = 1.05, 95% confidence interval = 1.04-1.07, p = 4.67 × 10^-14). In contrast, our analyses suggested that the adipose tissue variants might predominantly be responsible for the underlying relationship between BMI and measures of cardiac function, such as left ventricular stroke volume (beta = 0.21, 95% confidence interval = 0.09-0.32, p = 6.43 × 10^-4).

Extracellular vesicles in obesity and its associated inflammation

Obesity is characterized by low-grade, chronic inflammation, which promotes insulin resistance and diabetes. Obesity can lead to the development and progression of many autoimmune diseases, including inflammatory bowel disease, psoriasis, psoriatic arthritis, rheumatoid arthritis, thyroid autoimmunity, and type 1 diabetes mellitus (T1DM). These diseases result from an alteration of self-tolerance by promoting pro-inflammatory immune response by lowering numbers of regulatory T cells (T_regs), increasing Th1 and Th17 immune responses, and inflammatory cytokine production. Therefore, understanding the immunological changes that lead to this low-grade inflammatory milieu becomes crucial for the development of therapies that suppress the risk of autoimmune diseases and other immunological conditions. Cells generate extracellular vesicles (EVs) to eliminate cellular waste as well as communicating the adjacent and distant cells through exchanging the components (genetic material [DNA or RNA], lipids, and proteins) between them. Immune cells and adipocytes from individuals with obesity and a high basal metabolic index (BMI) produce also release exosomes (EXOs) and microvesicles (MVs), which are collectively called EVs. These EVs play a crucial role in the development of autoimmune diseases. The current review discusses the immunological dysregulation that leads to inflammation, inflammatory diseases associated with obesity, and the role played by EXOs and MVs in the induction and progression of this devastating conditi8on.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Assigning Co-Regulated Human Genes and Regulatory Gene Clusters

Elucidating the role of genetic variation in the regulation of gene expression is key to understanding the pathobiology of complex diseases which, in consequence, is crucial in devising targeted treatment options. Expression quantitative trait locus (eQTL) analysis correlates a genetic variant with the strength of gene expression, thus defining thousands of regulated genes in a multitude of human cell types and tissues. Some eQTL may not act independently of each other but instead may be regulated in a coordinated fashion by seemingly independent genetic variants. To address this issue, we combined the approaches of eQTL analysis and colocalization studies. Gene expression was determined in datasets comprising 49 tissues from the Genotype-Tissue Expression (GTEx) project. From about 33,000 regulated genes, over 14,000 were found to be co-regulated in pairs and were assembled across all tissues to almost 15,000 unique clusters containing up to nine regulated genes affected by the same eQTL signal. The distance of co-regulated eGenes was, on average, 112 kilobase pairs. Of 713 genes known to express clinical symptoms upon haploinsufficiency, 231 (32.4%) are part of at least one of the identified clusters. This calls for caution should treatment approaches aim at an upregulation of a haploinsufficient gene. In conclusion, we present an unbiased approach to identifying co-regulated genes in and across multiple tissues. Knowledge of such common effects is crucial to appreciate implications on biological pathways involved, specifically when a treatment option targets a co-regulated disease gene.

Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments

The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.

Cell-type deconvolution analysis identifies cancer-associated myofibroblast component as a poor prognostic factor in multiple cancer types

Cancer-associated fibroblasts (CAFs) constitute a prominent component of the tumor microenvironment and play critical roles in cancer progression and drug resistance. Although recent studies indicate CAFs may consist of several CAF subtypes, the breadth of CAF heterogeneity and functional roles of CAF subtypes in cancer progression remain unclear. In this study, we implemented a cell-type deconvolutional approach to comprehensively characterize cell-type alternations across 18 cancer types from The Cancer Genome Atlas (TCGA). Pan-cancer survival analysis using deconvoluted CAF subtypes revealed myofibroblastic CAF (myCAF) composition as a poor prognostic factor in nine cancer types. Patients with higher myCAF compositions tend to have worse response to six antineoplastic drugs predicted by a lncRNA-based Elastic Net prediction model (LENP). In addition, integrative mutational analysis identified 14 and 413 genes associated with the differentiation degree of myCAF and inflammatory CAF (iCAF), respectively, with significant enrichment of genes involved in fibroblast and extracellular matrix (ECM)-related pathways. In summary, our findings systematically illustrated the complex roles of CAF subtypes in patient prognosis and drug response, and identified putative driver genes in CAF-subtype differentiation. These results provided novel therapeutic perspectives for targeting CAF subtypes in tumor microenvironment and arranging treatment scheme based on the CAF compositions in different cancer types.

A pig BodyMap transcriptome reveals diverse tissue physiologies and evolutionary dynamics of transcription

A comprehensive transcriptomic survey of pigs can provide a mechanistic understanding of tissue specialization processes underlying economically valuable traits and accelerate their use as a biomedical model. Here we characterize four transcript types (lncRNAs, TUCPs, miRNAs, and circRNAs) and protein-coding genes in 31 adult pig tissues and two cell lines. We uncover the transcriptomic variability among 47 skeletal muscles, and six adipose depots linked to their different origins, metabolism, cell composition, physical activity, and mitochondrial pathways. We perform comparative analysis of the transcriptomes of seven tissues from pigs and nine other vertebrates to reveal that evolutionary divergence in transcription potentially contributes to lineage-specific biology. Long-range promoter–enhancer interaction analysis in subcutaneous adipose tissues across species suggests evolutionarily stable transcription patterns likely attributable to redundant enhancers buffering gene expression patterns against perturbations, thereby conferring robustness during speciation. This study can facilitate adoption of the pig as a biomedical model for human biology and disease and uncovers the molecular bases of valuable traits.

A comprehensive transcriptomic survey of the pig could enable mechanistic understanding of tissue specialization and accelerate its use as a biomedical model. Here the authors characterize four distinct transcript types in 31 adult pig tissues to dissect their distinct structural and transcriptional features and uncover transcriptomic variability related to tissue physiology.

AKR1C2 and AKR1C3 expression in adipose tissue: Association with body fat distribution and regulatory variants

BACKGROUND

Changes in androgen dynamics within adipose tissue have been proposed as modulators of body fat accumulation. In this context, AKR1C2 likely plays a significant role by inactivating 5α-dihydrotestosterone.

AIM

To characterize AKR1C2 expression patterns across adipose depots and cell populations and to provide insight into the link with body fat distribution and genetic regulation.

METHODS

We used RNA sequencing data from severely obese patients to assess patterns of AKR1C2 and AKR1C3 expression in abdominal adipose tissue depots and cell fractions. We additionally used data from 856 women to assess AKR1C2 heritability and to link its expression in adipose tissue with body fat distribution. Further, we used public resources to study AKR1C2 genetic regulation as well as reference epigenome data for regulatory element profiling and functional interpretation of genetic data.

RESULTS

We found that mature adipocytes and adipocyte-committed adipocyte progenitor cells (APCs) had enriched expression of AKR1C2. We found adipose tissue AKR1C2 and AKR1C3 expression to be significantly and positively associated with percentage trunk fat mass in women. We identified strong genetic regulation of AKR1C2 by rs28571848 and rs34477787 located on the binding sites of two nuclear transcription factors, namely retinoid acid-related orphan receptor alpha and the glucocorticoid receptor.

CONCLUSION

We confirm the link between AKR1C2, adipogenic differentiation and adipose tissue distribution. We provide insight into genetic regulation of AKR1C2 by identifying regulatory variants mapping to binding sites for the glucocorticoid receptor and retinoid acid-related orphan receptor alpha which may in part mediate the effect of AKR1C2 expression on body fat distribution.

Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects

BACKGROUND

Smoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG-sites in enhancers.

METHOD

A smoking-methylation meta-analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population-based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans-ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989-1991 and 1100 African-American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA).

RESULTS

Altogether 952 CpG-sites in 500 genes were differentially methylated between smokers and never smokers after Bonferroni correction. There were 526 novel smoking-associated CpG-sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta-analysis of the American Indian and African-American samples. Novel CpG sites mapped both to genes containing previously identified smoking-methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7. Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking-associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results.

CONCLUSION

This study identified novel smoking-associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking-related disease risk.

Cellular Heterogeneity in Adipose Tissues

Adipose tissue depots in distinct anatomical locations mediate key aspects of metabolism, including energy storage, nutrient release, and thermogenesis. Although adipocytes make up more than 90% of adipose tissue volume, they represent less than 50% of its cellular content. Here, I review recent advances in genetic lineage tracing and transcriptomics that reveal the identities of the heterogeneous cell populations constituting mouse and human adipose tissues. In addition to mature adipocytes and their progenitors, these include endothelial and various immune cell types that together orchestrate adipose tissue development and functions. One salient finding is the identification of progenitor subtypes that can modulate adipogenic capacity through paracrine mechanisms. Another is the description of fate trajectories of monocyte/macrophages, which can respond maladaptively to nutritional and thermogenic stimuli, leading to metabolic disease. These studies have generated an extraordinary source of publicly available data that can be leveraged to explore commonalities and differences among experimental models, providing new insights into adipose tissues and their role in metabolic disease.

Cell lineage-specific methylome and genome alterations in gout

In this study, we examined data from 69 gout patients and 1,455 non-gout controls using a MethylationEPIC BeadChip assay and Illumina HiSeq platform to identify lineage-specific epigenetic alterations and associated genetic factors that contributed to gouty inflammation. Cell lineage-specific differentially methylated sites were identified using CellDMC after adjusting for sex, age, alcohol drinking, smoking status, and smoking history (total pack-years). Different cell lineages displayed distinct differential methylation. Ingenuity Pathway Analysis and NetworkAnalyst indicated that many differential methylated sites were associated with interleukin-1β expression in monocytes. On the UCSC Genome Browser and WashU Epigenome Browser, metabolic trait, cis-methylation quantitative trait loci, genetic, and functional annotation analyses identified nine methylation loci located in interleukin-1β-regulating genes (PRKCZ, CIDEC, VDAC1, CPT1A, BIRC2, BRCA1, STK11, and NLRP12) that were associated specifically with gouty inflammation. All nine sites mapped to active regulatory elements in monocytes. MoLoTool and ReMap analyses indicated that the nine methylation loci overlapped with binding sites of several transcription factors that regulated interleukin-1β production and gouty inflammation. Decreases in PRKCZ and STK11 methylation were also associated with higher numbers of first-degree relatives who also had gout. The gouty-inflammation specific methylome and genome alterations could potentially aid in the identification of novel therapeutic targets.

Transcriptomic Changes of Murine Visceral Fat Exposed to Intermittent Hypoxia at Single Cell Resolution

Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA) and induces metabolic dysfunction manifesting as inflammation, increased lipolysis and insulin resistance in visceral white adipose tissues (vWAT). However, the cell types and their corresponding transcriptional pathways underlying these functional perturbations are unknown. Here, we applied single nucleus RNA sequencing (snRNA-seq) coupled with aggregate RNA-seq methods to evaluate the cellular heterogeneity in vWAT following IH exposures mimicking OSA. C57BL/6 male mice were exposed to IH and room air (RA) for 6 weeks, and nuclei from vWAT were isolated and processed for snRNA-seq followed by differential expressed gene (DEGs) analyses by cell type, along with gene ontology and canonical pathways enrichment tests of significance. IH induced significant transcriptional changes compared to RA across 14 different cell types identified in vWAT. We identified cell-specific signature markers, transcriptional networks, metabolic signaling pathways, and cellular subpopulation enrichment in vWAT. Globally, we also identify 298 common regulated genes across multiple cellular types that are associated with metabolic pathways. Deconvolution of cell types in vWAT using global RNA-seq revealed that distinct adipocytes appear to be differentially implicated in key aspects of metabolic dysfunction. Thus, the heterogeneity of vWAT and its response to IH at the cellular level provides important insights into the metabolic morbidity of OSA and may possibly translate into therapeutic targets.

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell-type composition. However, our understanding of this process is still largely incomplete owing to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell-type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain-associated traits revealed multiple novel trait–cell-type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.

Integrative Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation, and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans

Decline in insulin sensitivity due to dysfunction of adipose tissue (AT) is one of the earliest pathogenic events in type 2 diabetes. We hypothesize that differential DNA methylation (DNAm) controls insulin sensitivity and obesity by modulating transcript expression in AT. Integrating AT DNAm profiles with transcript profile data measured in a cohort of 230 African Americans (AAs) from the African American Genetics of Metabolism and Expression cohort, we performed cis-expression quantitative trait methylation (cis-eQTM) analysis to identify epigenetic regulatory loci for glucometabolic trait-associated transcripts. We identified significantly associated cytosine-guanine dinucleotide regions for 82 transcripts (false discovery rate [FDR]-P < 0.05). The strongest eQTM locus was observed for the proopiomelanocortin (POMC; ρ = -0.632, P = 4.70 × 10^-27) gene. Epigenome-wide association studies (EWAS) further identified 155, 46, and 168 cytosine-guanine dinucleotide regions associated (FDR-P < 0.05) with the Matsuda index, S_I, and BMI, respectively. Intersection of EWAS, transcript level to trait association, and eQTM results, followed by causal inference test identified significant eQTM loci for 23 genes that were also associated with Matsuda index, S_I, and/or BMI in EWAS. These associated genes include FERMT3, ITGAM, ITGAX, and POMC In summary, applying an integrative multiomics approach, our study provides evidence for DNAm-mediated regulation of gene expression at both previously identified and novel loci for many key AT transcripts influencing insulin resistance and obesity.

X chromosome dosage of histone demethylase KDM5C determines sex differences in adiposity

Males and females differ in body composition and fat distribution. Using a mouse model that segregates gonadal sex (ovaries and testes) from chromosomal sex (XX and XY), we showed that XX chromosome complement in combination with a high-fat diet led to enhanced weight gain in the presence of male or female gonads. We identified the genomic dosage of Kdm5c, an X chromosome gene that escapes X chromosome inactivation, as a determinant of the X chromosome effect on adiposity. Modulating Kdm5c gene dosage in XX female mice to levels that are normally present in males resulted in reduced body weight, fat content, and food intake to a degree similar to that seen with altering the entire X chromosome dosage. In cultured preadipocytes, the levels of KDM5C histone demethylase influenced chromatin accessibility (ATAC-Seq), gene expression (RNA-Seq), and adipocyte differentiation. Both in vitro and in vivo, Kdm5c dosage influenced gene expression involved in extracellular matrix remodeling, which is critical for adipocyte differentiation and adipose tissue expansion. In humans, adipose tissue KDM5C mRNA levels and KDM5C genetic variants were associated with body mass. These studies demonstrate that the sex-dependent dosage of Kdm5c contributes to male/female differences in adipocyte biology and highlight X-escape genes as a critical component of female physiology.