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Kostecka A, Kalamon N, Skoniecka A, Koczkowska M, Skowron PM, Piotrowski A, Pikuła M. Adipose-derived mesenchymal stromal cells in clinical trials: Insights from single-cell studies. Life Sci 2024; 351:122761. [PMID: 38866216 DOI: 10.1016/j.lfs.2024.122761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/15/2024] [Accepted: 05/27/2024] [Indexed: 06/14/2024]
Abstract
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.
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Affiliation(s)
- Anna Kostecka
- Faculty of Pharmacy, Medical University of Gdansk, Gdansk, Poland; 3P - Medicine Laboratory, Medical University of Gdansk, Gdansk, Poland.
| | - Natalia Kalamon
- Faculty of Pharmacy, Medical University of Gdansk, Gdansk, Poland.
| | - Aneta Skoniecka
- Laboratory of Tissue Engineering and Regenerative Medicine, Division of Embryology, Faculty of Medicine, Medical University of Gdansk, Dębinki 1, 80-211 Gdańsk, Poland.
| | - Magdalena Koczkowska
- Faculty of Pharmacy, Medical University of Gdansk, Gdansk, Poland; 3P - Medicine Laboratory, Medical University of Gdansk, Gdansk, Poland.
| | - Piotr M Skowron
- Department of Molecular Biotechnology, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.
| | - Arkadiusz Piotrowski
- Faculty of Pharmacy, Medical University of Gdansk, Gdansk, Poland; 3P - Medicine Laboratory, Medical University of Gdansk, Gdansk, Poland.
| | - Michał Pikuła
- Laboratory of Tissue Engineering and Regenerative Medicine, Division of Embryology, Faculty of Medicine, Medical University of Gdansk, Dębinki 1, 80-211 Gdańsk, Poland.
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2
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Sorek G, Haim Y, Chalifa-Caspi V, Lazarescu O, Ziv-Agam M, Hagemann T, Nono Nankam PA, Blüher M, Liberty IF, Dukhno O, Kukeev I, Yeger-Lotem E, Rudich A, Levin L. sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human adipose tissues. iScience 2024; 27:110368. [PMID: 39071890 PMCID: PMC11277759 DOI: 10.1016/j.isci.2024.110368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/27/2024] [Accepted: 06/21/2024] [Indexed: 07/30/2024] Open
Abstract
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.
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Affiliation(s)
- Gil Sorek
- Bioinformatics Core Facility, llse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Yulia Haim
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Vered Chalifa-Caspi
- Bioinformatics Core Facility, llse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Or Lazarescu
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Maya Ziv-Agam
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Tobias Hagemann
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Pamela Arielle Nono Nankam
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Matthias Blüher
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Idit F. Liberty
- Soroka University Medical Center, and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Oleg Dukhno
- Soroka University Medical Center, and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ivan Kukeev
- Soroka University Medical Center, and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Assaf Rudich
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Liron Levin
- Bioinformatics Core Facility, llse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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Cisternino F, Ometto S, Chatterjee S, Giacopuzzi E, Levine AP, Glastonbury CA. Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types. Nat Commun 2024; 15:5906. [PMID: 39003292 PMCID: PMC11246527 DOI: 10.1038/s41467-024-50317-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 07/04/2024] [Indexed: 07/15/2024] Open
Abstract
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.
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Affiliation(s)
| | - Sara Ometto
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy
| | | | | | - Adam P Levine
- Research Department of Pathology, University College London, London, UK
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4
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Li L, Feldman BJ. White adipocytes in subcutaneous fat depots require KLF15 for maintenance in preclinical models. J Clin Invest 2024; 134:e172360. [PMID: 38949025 PMCID: PMC11213504 DOI: 10.1172/jci172360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 05/10/2024] [Indexed: 07/02/2024] Open
Abstract
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.
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Affiliation(s)
- Liang Li
- Department of Pediatrics, University of California, San Francisco (UCSF) School of Medicine, San Francisco, California, USA
| | - Brian J. Feldman
- Department of Pediatrics, University of California, San Francisco (UCSF) School of Medicine, San Francisco, California, USA
- Nutrition and Obesity Research Center, UCSF, San Francisco, California, USA
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5
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Behtaj D, Ghorbani A, Eslamian G, Malekpour Alamdari N, Abbasi M, Zand H, Shakery A, Shimi G, Sohouli MH, Fazeli Taherian S. Ex vivo Anti-Senescence Activity of N-Acetylcysteine in Visceral Adipose Tissue of Obese Volunteers. Obes Facts 2024; 17:355-363. [PMID: 38718763 PMCID: PMC11299969 DOI: 10.1159/000539255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/06/2024] [Indexed: 08/07/2024] Open
Abstract
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.
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Affiliation(s)
- Diba Behtaj
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arman Ghorbani
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghazaleh Eslamian
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nasser Malekpour Alamdari
- School of Medicine, Department of General Surgery, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Abbasi
- School of Medicine, Department of General Surgery, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Zand
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azam Shakery
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghazaleh Shimi
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Hasan Sohouli
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sepideh Fazeli Taherian
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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6
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Alemany M. The Metabolic Syndrome, a Human Disease. Int J Mol Sci 2024; 25:2251. [PMID: 38396928 PMCID: PMC10888680 DOI: 10.3390/ijms25042251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024] Open
Abstract
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.
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Affiliation(s)
- Marià Alemany
- Faculty of Biology, Universitat de Barcelona, 08028 Barcelona, Catalonia, Spain
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7
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Kar A, Alvarez M, Garske KM, Huang H, Lee SHT, Deal M, Das SS, Koka A, Jamal Z, Mohlke KL, Laakso M, Heinonen S, Pietiläinen KH, Pajukanta P. 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. Genome Med 2024; 16:19. [PMID: 38297378 PMCID: PMC10829214 DOI: 10.1186/s13073-024-01291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/19/2024] [Indexed: 02/02/2024] Open
Abstract
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.
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Affiliation(s)
- Asha Kar
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Huiling Huang
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA
| | - Seung Hyuk T Lee
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Milena Deal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Sankha Subhra Das
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Amogha Koka
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Zoeb Jamal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HealthyWeightHub, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA.
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA.
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, USA.
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8
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Brotman SM, Oravilahti A, Rosen JD, Alvarez M, Heinonen S, van der Kolk BW, Fernandes Silva L, Perrin HJ, Vadlamudi S, Pylant C, Deochand S, Basta PV, Valone JM, Narain MN, Stringham HM, Boehnke M, Kuusisto J, Love MI, Pietiläinen KH, Pajukanta P, Laakso M, Mohlke KL. Cell-Type Composition Affects Adipose Gene Expression Associations With Cardiometabolic Traits. Diabetes 2023; 72:1707-1718. [PMID: 37647564 PMCID: PMC10588284 DOI: 10.2337/db23-0365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023]
Abstract
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
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Affiliation(s)
- Sarah M. Brotman
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
| | - Anniina Oravilahti
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jonathan D. Rosen
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Birgitta W. van der Kolk
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Hannah J. Perrin
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
| | | | - Cortney Pylant
- Department of Epidemiology, The University of North Carolina, Chapel Hill, NC
| | - Sonia Deochand
- Department of Epidemiology, The University of North Carolina, Chapel Hill, NC
| | - Patricia V. Basta
- Department of Epidemiology, The University of North Carolina, Chapel Hill, NC
| | - Jordan M. Valone
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
- UNC Neuroscience Center, The University of North Carolina, Chapel Hill, NC
| | - Morgan N. Narain
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
- Curriculum of Toxicology and Environmental Medicine, The University of North Carolina, Chapel Hill, NC
| | - Heather M. Stringham
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Michael I. Love
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
- Department of Biostatistics, The University of North Carolina, Chapel Hill, NC
| | - Kirsi H. Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HealthyWeightHub, Endocrinology, Abdominal Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA
- Institute for Precision Health, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Karen L. Mohlke
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
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9
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Richardson TG, Leyden GM, Davey Smith G. Time-varying and tissue-dependent effects of adiposity on leptin levels: A Mendelian randomization study. eLife 2023; 12:e84646. [PMID: 37878001 PMCID: PMC10599655 DOI: 10.7554/elife.84646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 10/08/2023] [Indexed: 10/26/2023] Open
Abstract
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).
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Affiliation(s)
- Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield GroveBristolUnited Kingdom
| | - Genevieve M Leyden
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield GroveBristolUnited Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield GroveBristolUnited Kingdom
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10
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Costeira R, Evangelista L, Wilson R, Yan X, Hellbach F, Sinke L, Christiansen C, Villicaña S, Masachs OM, Tsai PC, Mangino M, Menni C, Berry SE, Beekman M, van Heemst D, Slagboom PE, Heijmans BT, Suhre K, Kastenmüller G, Gieger C, Peters A, Small KS, Linseisen J, Waldenberger M, Bell JT. Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome. Clin Epigenetics 2023; 15:166. [PMID: 37858220 PMCID: PMC10588110 DOI: 10.1186/s13148-023-01578-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
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.
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Affiliation(s)
- Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
| | - Laila Evangelista
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Rory Wilson
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Xinyu Yan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Fabian Hellbach
- Epidemiology, Medical Faculty, University Augsburg, University Hospital Augsburg, 86156, Augsburg, Germany
| | - Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Olatz M Masachs
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
- Department of Biomedical Sciences, Chang Gung University, Taoyuan City, Taiwan
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Chang Gung Memorial Hospital, Taoyuan City, Taiwan
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, SE1 9NH, UK
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Annette Peters
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, 81377, Munich, Germany
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Jakob Linseisen
- Epidemiology, Medical Faculty, University Augsburg, University Hospital Augsburg, 86156, Augsburg, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, 81377, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
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11
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Ahmad S, Drag MH, Mohamad Salleh S, Cai Z, Nielsen MO. Gene coexpression network analysis reveals perirenal adipose tissue as an important target of prenatal malnutrition in sheep. Physiol Genomics 2023; 55:392-413. [PMID: 37458462 PMCID: PMC10642927 DOI: 10.1152/physiolgenomics.00128.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 06/28/2023] [Accepted: 07/03/2023] [Indexed: 08/24/2023] Open
Abstract
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.
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Affiliation(s)
- Sharmila Ahmad
- Department of Animal Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
- Research Unit of Nutrition, Department of Animal and Veterinary Sciences, Aarhus University, Tjele, Denmark
| | - Markus Hodal Drag
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen Zoo, Frederiksberg, Denmark
| | - Suraya Mohamad Salleh
- Department of Animal Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
- Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Zexi Cai
- Centre for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Mette Olaf Nielsen
- Research Unit of Nutrition, Department of Animal and Veterinary Sciences, Aarhus University, Tjele, Denmark
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12
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Wang J, Lu L, Zheng S, Wang D, Jin L, Zhang Q, Li M, Zhang Z. DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301058. [PMID: 37515382 PMCID: PMC10520690 DOI: 10.1002/advs.202301058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/06/2023] [Indexed: 07/30/2023]
Abstract
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.
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Affiliation(s)
- Junmei Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Lu Lu
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Shiqi Zheng
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Danyang Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
- Sars‐Fang Centre & MOE Key Laboratory of Marine Genetics and BreedingCollege of Marine Life SciencesOcean University of ChinaQingdao266100China
| | - Long Jin
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Qing Zhang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
| | - Mingzhou Li
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Zhihua Zhang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
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13
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Kleeman SO, Thakir TM, Demestichas B, Mourikis N, Loiero D, Ferrer M, Bankier S, Riazat-Kesh YJ, Lee H, Chantzichristos D, Regan C, Preall J, Sinha S, Rosin N, Yipp B, de Almeida LG, Biernaskie J, Dufour A, Tober-Lau P, Ruusalepp A, Bjorkegren JL, Ralser M, Kurth F, Demichev V, Heywood T, Gao Q, Johannsson G, Koelzer VH, Walker BR, Meyer HV, Janowitz T. Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy. CELL GENOMICS 2023; 3:100347. [PMID: 37601967 PMCID: PMC10435381 DOI: 10.1016/j.xgen.2023.100347] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 03/23/2023] [Accepted: 05/30/2023] [Indexed: 08/22/2023]
Abstract
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.
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Affiliation(s)
- Sam O. Kleeman
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | | | - Dominik Loiero
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Miriam Ferrer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Sean Bankier
- BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | | | - Hassal Lee
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Dimitrios Chantzichristos
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Endocrinology Diabetes and Metabolism, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Claire Regan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Sarthak Sinha
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Nicole Rosin
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Bryan Yipp
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luiz G.N. de Almeida
- Department of Biochemistry and Molecular Biology and Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada
| | - Jeff Biernaskie
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
- Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Antoine Dufour
- Department of Biochemistry and Molecular Biology and Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada
| | | | - Arno Ruusalepp
- Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia
| | - Johan L.M. Bjorkegren
- Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Markus Ralser
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Florian Kurth
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Todd Heywood
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Qing Gao
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Gudmundur Johannsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Endocrinology Diabetes and Metabolism, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Viktor H. Koelzer
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Oncology and Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Brian R. Walker
- BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
- Translational & Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Tobias Janowitz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Cancer Institute, Northwell Health, New Hyde Park, NY, USA
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14
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Rouskas K, Katsareli EA, Amerikanou C, Dimopoulos AC, Glentis S, Kalantzi A, Skoulakis A, Panousis N, Ongen H, Bielser D, Planchon A, Romano L, Harokopos V, Reczko M, Moulos P, Griniatsos I, Diamantis T, Dermitzakis ET, Ragoussis J, Dedoussis G, Dimas AS. Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population. BMC Genomics 2023; 24:442. [PMID: 37543566 PMCID: PMC10403965 DOI: 10.1186/s12864-023-09532-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023] Open
Abstract
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.
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Affiliation(s)
- Konstantinos Rouskas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, Greece
| | - Efthymia A Katsareli
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Charalampia Amerikanou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Alexandros C Dimopoulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Hellenic Naval Academy, Hatzikyriakou Avenue, Pireaus, Greece
| | - Stavros Glentis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Pediatric Hematology/Oncology Unit (POHemU), First Department of Pediatrics, University of Athens, Aghia Sophia Children's Hospital, Athens, Greece
| | - Alexandra Kalantzi
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Anargyros Skoulakis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | | | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Swiss Institute of Bioinformatics, University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Deborah Bielser
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Alexandra Planchon
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Vaggelis Harokopos
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Martin Reczko
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Panagiotis Moulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Center of New Biotechnologies & Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Griniatsos
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Theodoros Diamantis
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
- Department of Bioengineering, McGill University, Montréal, QC, Canada
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece.
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15
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
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.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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16
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Liu W, Deng W, Chen M, Dong Z, Zhu B, Yu Z, Tang D, Sauler M, Lin C, Wain LV, Cho MH, Kaminski N, Zhao H. A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. PLoS Genet 2023; 19:e1010825. [PMID: 37523391 PMCID: PMC10414598 DOI: 10.1371/journal.pgen.1010825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 08/10/2023] [Accepted: 06/12/2023] [Indexed: 08/02/2023] Open
Abstract
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.
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Affiliation(s)
- Wei Liu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Wenxuan Deng
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Ming Chen
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Zihan Dong
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Biqing Zhu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Zhaolong Yu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Daiwei Tang
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Maor Sauler
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Chen Lin
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Louise V. Wain
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
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17
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Song L, Sun X, Qi T, Yang J. Mixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectory. NATURE COMPUTATIONAL SCIENCE 2023; 3:630-643. [PMID: 38177744 PMCID: PMC10766563 DOI: 10.1038/s43588-023-00487-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/13/2023] [Indexed: 01/06/2024]
Abstract
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.
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Affiliation(s)
- Liyang Song
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Xiwei Sun
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Ting Qi
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
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18
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Jin L, Wang D, Zhang J, Liu P, Wang Y, Lin Y, Liu C, Han Z, Long K, Li D, Jiang Y, Li G, Zhang Y, Bai J, Li X, Li J, Lu L, Kong F, Wang X, Li H, Huang Z, Ma J, Fan X, Shen L, Zhu L, Jiang Y, Tang G, Feng B, Zeng B, Ge L, Li X, Tang Q, Zhang Z, Li M. Dynamic chromatin architecture of the porcine adipose tissues with weight gain and loss. Nat Commun 2023; 14:3457. [PMID: 37308492 DOI: 10.1038/s41467-023-39191-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
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.
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Affiliation(s)
- Long Jin
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Danyang Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
- School of Life Science, University of Chinese Academy of Sciences, 100049, Beijing, China
- Sars-Fang Centre and MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, 266100, China
| | - Jiaman Zhang
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Pengliang Liu
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yujie Wang
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yu Lin
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Can Liu
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Ziyin Han
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Molecular Design and Precise Breeding Key Laboratory of Guangdong Province, School of Life Science and Engineering, Foshan University, Foshan, 528225, China
| | - Keren Long
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Diyan Li
- School of Pharmacy, Chengdu University, Chengdu, 610106, China
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Guisen Li
- Institute of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Yu Zhang
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jingyi Bai
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Xiaokai Li
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jing Li
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Lu Lu
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Fanli Kong
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Xun Wang
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Hua Li
- Animal Molecular Design and Precise Breeding Key Laboratory of Guangdong Province, School of Life Science and Engineering, Foshan University, Foshan, 528225, China
| | - Zhiqing Huang
- Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jideng Ma
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Xiaolan Fan
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Linyuan Shen
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Li Zhu
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yanzhi Jiang
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
| | - Guoqing Tang
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Bin Feng
- Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, 611130, China
| | - Bo Zeng
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Ya'an Digital Economy Operation Company, Ya'an, 625014, China
| | - Liangpeng Ge
- Pig Industry Sciences Key Laboratory of Ministry of Agriculture and Rural Affairs, Chongqing Academy of Animal Sciences, Chongqing, 402460, China
| | - Xuewei Li
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Qianzi Tang
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China
| | - Zhihua Zhang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China.
- School of Life Science, University of Chinese Academy of Sciences, 100049, Beijing, China.
| | - Mingzhou Li
- Livestock and Poultry Multi-omics Key Laboratory of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, 611130, China.
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19
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Little P, Liu S, Zhabotynsky V, Li Y, Lin DY, Sun W. A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data. Nat Commun 2023; 14:3030. [PMID: 37231002 PMCID: PMC10212972 DOI: 10.1038/s41467-023-38795-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
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.
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Affiliation(s)
- Paul Little
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Si Liu
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Vasyl Zhabotynsky
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wei Sun
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
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20
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McAllan L, Baranasic D, Villicaña S, Brown S, Zhang W, Lehne B, Adamo M, Jenkinson A, Elkalaawy M, Mohammadi B, Hashemi M, Fernandes N, Lambie N, Williams R, Christiansen C, Yang Y, Zudina L, Lagou V, Tan S, Castillo-Fernandez J, King JWD, Soong R, Elliott P, Scott J, Prokopenko I, Cebola I, Loh M, Lenhard B, Batterham RL, Bell JT, Chambers JC, Kooner JS, Scott WR. Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes. Nat Commun 2023; 14:2784. [PMID: 37188674 PMCID: PMC10185556 DOI: 10.1038/s41467-023-38439-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 05/03/2023] [Indexed: 05/17/2023] Open
Abstract
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.
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Affiliation(s)
- Liam McAllan
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Damir Baranasic
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Scarlett Brown
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Marco Adamo
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Andrew Jenkinson
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Mohamed Elkalaawy
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Borzoueh Mohammadi
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Majid Hashemi
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
| | - Nadia Fernandes
- Imperial BRC Genomics Facility, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Nathalie Lambie
- Imperial BRC Genomics Facility, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Richard Williams
- Imperial BRC Genomics Facility, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, UK
| | - Youwen Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- School of Cardiovascular and Metabolic Medicine and Sciences, James Black Centre, King's College London British Heart Foundation Centre of Excellence, 125 Coldharbour Lane, London, SE5 9NU, UK
| | - Liudmila Zudina
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
| | - Vasiliki Lagou
- Department of Microbiology and Immunology, Laboratory of Adaptive Immunity, KU Leuven, Leuven, Belgium
- VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
| | - Sili Tan
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
| | | | - James W D King
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Richie Soong
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- National Institute for Health Research Biomedical Research Centre, Imperial College London, London, UK
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
| | - Inga Prokopenko
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre Russian Academy of Sciences, Ufa, Russian Federation
| | - Inês Cebola
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Marie Loh
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos, Level 5, Singapore, 138648, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Boris Lenhard
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Rachel L Batterham
- UCLH Bariatric Centre for Weight Loss, Weight Management and Metabolic and Endocrine Surgery, University College London Hospitals, Ground Floor West Wing, 250 Euston Road, London, NW1 2PG, UK
- Centre for Obesity Research, Rayne Institute, Department of Medicine, University College, London, WC1E 6JJ, UK
- National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, W1T 7DN, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
| | - William R Scott
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.
- MRC London Institute of Medical Sciences, London, W12 0NN, UK.
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK.
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK.
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21
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Tan WX, Sim X, Khoo CM, Teo AKK. Prioritization of genes associated with type 2 diabetes mellitus for functional studies. Nat Rev Endocrinol 2023:10.1038/s41574-023-00836-1. [PMID: 37169822 DOI: 10.1038/s41574-023-00836-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 05/13/2023]
Abstract
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.
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Affiliation(s)
- Wei Xuan Tan
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Chin Meng Khoo
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Adrian K K Teo
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Precision Medicine Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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22
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Hao W, Zhao C, Li G, Wang H, Li T, Yan P, Wei S. Blue LED light induces cytotoxicity via ROS production and mitochondrial damage in bovine subcutaneous preadipocytes. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 322:121195. [PMID: 36736558 DOI: 10.1016/j.envpol.2023.121195] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/07/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Weiguang Hao
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Chongchong Zhao
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Guowen Li
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Hongzhuang Wang
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Tingting Li
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Peishi Yan
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Shengjuan Wei
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China.
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23
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de Klein N, Tsai EA, Vochteloo M, Baird D, Huang Y, Chen CY, van Dam S, Oelen R, Deelen P, Bakker OB, El Garwany O, Ouyang Z, Marshall EE, Zavodszky MI, van Rheenen W, Bakker MK, Veldink J, Gaunt TR, Runz H, Franke L, Westra HJ. Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. Nat Genet 2023; 55:377-388. [PMID: 36823318 PMCID: PMC10011140 DOI: 10.1038/s41588-023-01300-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 01/17/2023] [Indexed: 02/25/2023]
Abstract
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.
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Affiliation(s)
- Niek de Klein
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | - Ellen A Tsai
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Martijn Vochteloo
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Institute for Life Science and Technology, Hanze University of Applied Sciences, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Denis Baird
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Yunfeng Huang
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Sipko van Dam
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Ancora Health, Groningen, The Netherlands
| | - Roy Oelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Olivier B Bakker
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | - Omar El Garwany
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | | | - Eric E Marshall
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Maria I Zavodszky
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Wouter van Rheenen
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mark K Bakker
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jan Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Heiko Runz
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA.
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Groningen, The Netherlands.
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Groningen, The Netherlands.
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24
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Kelly DE, Ramdas S, Ma R, Rawlings-Goss RA, Grant GR, Ranciaro A, Hirbo JB, Beggs W, Yeager M, Chanock S, Nyambo TB, Omar SA, Woldemeskel D, Belay G, Li H, Brown CD, Tishkoff SA. The genetic and evolutionary basis of gene expression variation in East Africans. Genome Biol 2023; 24:35. [PMID: 36829244 PMCID: PMC9951478 DOI: 10.1186/s13059-023-02874-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
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.
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Affiliation(s)
- Derek E Kelly
- Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Shweta Ramdas
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rong Ma
- Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - Jibril B Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - William Beggs
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Meredith Yeager
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Institutes of Health, Rockville, MD, USA
| | - Thomas B Nyambo
- Department of Biochemistry, Kampala International University in Tanzania, Dar Es Salaam, Tanzania
| | - Sabah A Omar
- Center for Biotechnology Research and Development, Kenya Medical Research Institute, Nairobi, Kenya
| | - Dawit Woldemeskel
- Microbial Cellular and Molecular Biology Department, Addis Ababa University, Addis Ababa, Ethiopia
| | - Gurja Belay
- Microbial Cellular and Molecular Biology Department, Addis Ababa University, Addis Ababa, Ethiopia
| | - Hongzhe Li
- Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D Brown
- Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah A Tishkoff
- Genetics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Biology, University of Pennsylvania, Philadelphia, USA.
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25
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KIDD JOHN, RAULERSON CHELSEAK, MOHLKE KARENL, LIN DANYU. Mediation analysis of multiple mediators with incomplete omics data. Genet Epidemiol 2023; 47:61-77. [PMID: 36125445 PMCID: PMC10423053 DOI: 10.1002/gepi.22504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/29/2022] [Accepted: 08/16/2022] [Indexed: 02/01/2023]
Abstract
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.
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Affiliation(s)
- JOHN KIDD
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A
| | - CHELSEA K. RAULERSON
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, U.S.A
| | - KAREN L. MOHLKE
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, U.S.A
| | - DAN-YU LIN
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A
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26
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Clinical traits impacting human tissue transcriptomes. CELL GENOMICS 2023; 3:100245. [PMID: 36777182 PMCID: PMC9903658 DOI: 10.1016/j.xgen.2022.100245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
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.
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27
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Connally NJ, Nazeen S, Lee D, Shi H, Stamatoyannopoulos J, Chun S, Cotsapas C, Cassa CA, Sunyaev SR. The missing link between genetic association and regulatory function. eLife 2022; 11:74970. [PMID: 36515579 PMCID: PMC9842386 DOI: 10.7554/elife.74970] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
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.'
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Affiliation(s)
- Noah J Connally
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Sumaiya Nazeen
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Department of Neurology, Harvard Medical SchoolBostonUnited States
| | - Daniel Lee
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Huwenbo Shi
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
- Department of Epidemiology, Harvard T.H. Chan School of Public HealthBostonUnited States
| | | | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s HospitalBostonUnited States
| | - Chris Cotsapas
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
- Department of Neurology, Yale Medical SchoolNew HavenUnited States
- Department of Genetics, Yale Medical SchoolNew HavenUnited States
| | - Christopher A Cassa
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
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28
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Wiarda JE, Loving CL. Intraepithelial lymphocytes in the pig intestine: T cell and innate lymphoid cell contributions to intestinal barrier immunity. Front Immunol 2022; 13:1048708. [PMID: 36569897 PMCID: PMC9772029 DOI: 10.3389/fimmu.2022.1048708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Jayne E. Wiarda
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States,Immunobiology Graduate Program, Iowa State University, Ames, IA, United States,Department of Veterinary Microbiology and Preventative Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States
| | - Crystal L. Loving
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States,Immunobiology Graduate Program, Iowa State University, Ames, IA, United States,*Correspondence: Crystal L. Loving,
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29
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Patel S, Haider A, Alvarez-Guaita A, Bidault G, El-Sayed Moustafa JS, Guiu-Jurado E, Tadross JA, Warner J, Harrison J, Virtue S, Scurria F, Zvetkova I, Blüher M, Small KS, O'Rahilly S, Savage DB. Combined genetic deletion of GDF15 and FGF21 has modest effects on body weight, hepatic steatosis and insulin resistance in high fat fed mice. Mol Metab 2022; 65:101589. [PMID: 36064109 PMCID: PMC9486046 DOI: 10.1016/j.molmet.2022.101589] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/26/2022] [Accepted: 08/28/2022] [Indexed: 11/28/2022] Open
Abstract
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.
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Affiliation(s)
- Satish Patel
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK; MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Afreen Haider
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK; MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Anna Alvarez-Guaita
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Guillaume Bidault
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | | | - Esther Guiu-Jurado
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - John A Tadross
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK; East Midlands and East of England Genomic Laboratory Hub & Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - James Warner
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - James Harrison
- Department of Medicine, Division of Cardiovascular Medicine, University of Cambridge, Cambridge, UK
| | - Samuel Virtue
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Fabio Scurria
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Ilona Zvetkova
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Matthias Blüher
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103 Leipzig, Germany; Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, SE1 7EH, UK
| | - Stephen O'Rahilly
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK; MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - David B Savage
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK; MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
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30
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Konigorski S, Janke J, Patone G, Bergmann MM, Lippert C, Hübner N, Kaaks R, Boeing H, Pischon T. Identification of novel genes whose expression in adipose tissue affects body fat mass and distribution: an RNA-Seq and Mendelian Randomization study. Eur J Hum Genet 2022:10.1038/s41431-022-01161-3. [PMID: 35953519 DOI: 10.1038/s41431-022-01161-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/26/2022] [Accepted: 07/19/2022] [Indexed: 10/15/2022] Open
Abstract
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.
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Affiliation(s)
- Stefan Konigorski
- Digital Health & Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.
- Molecular Epidemiology Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Jürgen Janke
- Molecular Epidemiology Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Giannino Patone
- Genetics and Genomics of Cardiovascular Diseases Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Manuela M Bergmann
- German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany
| | - Christoph Lippert
- Digital Health & Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Norbert Hübner
- Genetics and Genomics of Cardiovascular Diseases Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- DZHK (German Center for Cardiovascular Research) partner site Berlin, Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Rudolf Kaaks
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Heiner Boeing
- German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- DZHK (German Center for Cardiovascular Research) partner site Berlin, Berlin, Germany.
- Charité Universitätsmedizin Berlin, Berlin, Germany.
- MDC Biobank, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany.
- BIH Biobank, Berlin Institute of Health, Berlin, Germany.
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El-Sayed Moustafa JS, Jackson AU, Brotman SM, Guan L, Villicaña S, Roberts AL, Zito A, Bonnycastle L, Erdos MR, Narisu N, Stringham HM, Welch R, Yan T, Lakka T, Parker S, Tuomilehto J, Seow J, Graham C, Huettner I, Acors S, Kouphou N, Wadge S, Duncan EL, Steves CJ, Doores KJ, Malim MH, Collins FS, Pajukanta P, Boehnke M, Koistinen HA, Laakso M, Falchi M, Bell JT, Scott LJ, Mohlke KL, Small KS. ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19. Int J Obes (Lond) 2022; 46:1478-1486. [PMID: 35589964 PMCID: PMC9119844 DOI: 10.1038/s41366-022-01136-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 04/21/2022] [Accepted: 04/28/2022] [Indexed: 01/14/2023]
Abstract
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.
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Affiliation(s)
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sarah M Brotman
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Li Guan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Amy L Roberts
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Antonino Zito
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Genetics, Harvard Medical School, Boston, MA, 02114, USA
| | - Lori Bonnycastle
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael R Erdos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Narisu Narisu
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Tingfen Yan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Timo Lakka
- Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio, Finland
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Stephen Parker
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jaakko Tuomilehto
- University of Helsinki and Department of Medicine, Helsinki University Hospital, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Jeffrey Seow
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Carl Graham
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Isabella Huettner
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Sam Acors
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Neophytos Kouphou
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Samuel Wadge
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Katie J Doores
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Michael H Malim
- Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Päivi Pajukanta
- Department of Human Genetics and Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Heikki A Koistinen
- University of Helsinki and Department of Medicine, Helsinki University Hospital, Helsinki, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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Christiansen C, Tomlinson M, Eliot M, Nilsson E, Costeira R, Xia Y, Villicaña S, Mompeo O, Wells P, Castillo-Fernandez J, Potier L, Vohl MC, Tchernof A, Moustafa JES, Menni C, Steves CJ, Kelsey K, Ling C, Grundberg E, Small KS, Bell JT. Adipose methylome integrative-omic analyses reveal genetic and dietary metabolic health drivers and insulin resistance classifiers. Genome Med 2022; 14:75. [PMID: 35843982 PMCID: PMC9290282 DOI: 10.1186/s13073-022-01077-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 06/21/2022] [Indexed: 12/05/2022] Open
Abstract
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.
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Affiliation(s)
- Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Max Tomlinson
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Melissa Eliot
- Department of Epidemiology, Brown University School of Public Health, Providence, R.I., USA
| | - Emma Nilsson
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden
| | - Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Yujing Xia
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Olatz Mompeo
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Philippa Wells
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Louis Potier
- Diabetology Department, Bichat Hospital, AP-HP, Université de Paris, Paris, France
| | - Marie-Claude Vohl
- Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada
| | - Andre Tchernof
- Québec Heart and Lung Institute, Université Laval, Québec, QC, Canada
| | | | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Karl Kelsey
- Department of Epidemiology, Brown University School of Public Health, Providence, R.I., USA
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden
| | - Elin Grundberg
- Genomic Medicine Center, Children's Mercy Research Institute, Children's Mercy Kansas City, Kansas City, MO, 64108, USA
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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A human adipose tissue cell-type transcriptome atlas. Cell Rep 2022; 40:111046. [PMID: 35830816 DOI: 10.1016/j.celrep.2022.111046] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 04/29/2022] [Accepted: 06/13/2022] [Indexed: 12/19/2022] Open
Abstract
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.
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Lluch A, Veiga SR, Latorre J, Moreno-Navarrete JM, Bonifaci N, Nguyen VD, Zhou Y, Horing M, Liebisch G, Olkkonen VM, Llobet-Navas D, Thomas G, Rodriguez-Barrueco R, Fernández-Real JM, Kozma SC, Ortega FJ. A compound directed against S6K1 hampers fat mass expansion and mitigates diet-induced hepatosteatosis. JCI Insight 2022; 7:150461. [PMID: 35737463 PMCID: PMC9431684 DOI: 10.1172/jci.insight.150461] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/15/2022] [Indexed: 11/24/2022] Open
Abstract
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.
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Affiliation(s)
- Aina Lluch
- Department of Diabetes, Endocrinology, and Nutrition (UDEN), Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Sonia R Veiga
- Department of Aging & Metabolism, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Spain
| | - Jèssica Latorre
- Department of Diabetes, Endocrinology, and Nutrition (UDEN), Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | | | - Núria Bonifaci
- Breast Cancer and Systems Biology Unit, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Spain
| | - Van Dien Nguyen
- Division of Infection and Immunity, Cardiff University School of Medicine, Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom
| | - You Zhou
- Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Marcus Horing
- Institute of Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, Regensburg, Germany
| | - Gerhard Liebisch
- Institute for Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, Regensburg, Germany
| | - Vesa M Olkkonen
- Biomedicum, Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - David Llobet-Navas
- Institute of Genetic Medicine, Newcastle University, Newastle, United Kingdom
| | - George Thomas
- Laboratory of Cancer Metabolism, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Spain
| | | | - José M Fernández-Real
- Department of Endocrinology, Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Sara C Kozma
- Laboratory of Cancer Metabolism, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Spain
| | - Francisco J Ortega
- Department of Diabetes, Endocrinology, and Nutrition (UDEN), Girona Biomedical Research Institute (IDIBGI), Girona, Spain
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Comprehensive evaluation of deconvolution methods for human brain gene expression. Nat Commun 2022; 13:1358. [PMID: 35292647 PMCID: PMC8924248 DOI: 10.1038/s41467-022-28655-4] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/28/2022] [Indexed: 11/08/2022] Open
Abstract
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.
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Leyden GM, Shapland CY, Davey Smith G, Sanderson E, Greenwood MP, Murphy D, Richardson TG. Harnessing tissue-specific genetic variation to dissect putative causal pathways between body mass index and cardiometabolic phenotypes. Am J Hum Genet 2022; 109:240-252. [PMID: 35090585 DOI: 10.1016/j.ajhg.2021.12.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/14/2021] [Indexed: 12/11/2022] Open
Abstract
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).
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Kumar V, Kiran S, Kumar S, Singh UP. Extracellular vesicles in obesity and its associated inflammation. Int Rev Immunol 2022; 41:30-44. [PMID: 34423733 PMCID: PMC8770589 DOI: 10.1080/08830185.2021.1964497] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
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 (Tregs), 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.
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Affiliation(s)
- Vijay Kumar
- Department of Pharmaceutical Sciences, College of Pharmacy, The University of Tennessee Health Science Center, 881 Madison Avenue, Memphis, Tennessee, 38103 USA
| | - Sonia Kiran
- Department of Pharmaceutical Sciences, College of Pharmacy, The University of Tennessee Health Science Center, 881 Madison Avenue, Memphis, Tennessee, 38103 USA
| | - Santosh Kumar
- Department of Pharmaceutical Sciences, College of Pharmacy, The University of Tennessee Health Science Center, 881 Madison Avenue, Memphis, Tennessee, 38103 USA
| | - Udai P. Singh
- Department of Pharmaceutical Sciences, College of Pharmacy, The University of Tennessee Health Science Center, 881 Madison Avenue, Memphis, Tennessee, 38103 USA,Correspondence: Udai P Singh, Ph.D., Associate Professor, Department of Pharmaceutical Sciences, College of Pharmacy, 881 Madison Avenue, The University of Tennessee Health Science Center Memphis, TN, 38163 USA,
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
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.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Strunz T, Kellner M, Kiel C, Weber BHF. Assigning Co-Regulated Human Genes and Regulatory Gene Clusters. Cells 2021; 10:2395. [PMID: 34572044 PMCID: PMC8470523 DOI: 10.3390/cells10092395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 08/30/2021] [Accepted: 09/10/2021] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Tobias Strunz
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Martin Kellner
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Christina Kiel
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Bernhard H. F. Weber
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
- Institute of Clinical Human Genetics, University Hospital Regensburg, 93053 Regensburg, Germany
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40
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Sheng X, Guan Y, Ma Z, Wu J, Liu H, Qiu C, Vitale S, Miao Z, Seasock MJ, Palmer M, Shin MK, Duffin KL, Pullen SS, Edwards TL, Hellwege JN, Hung AM, Li M, Voight BF, Coffman TM, Brown CD, Susztak K. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat Genet 2021; 53:1322-1333. [PMID: 34385711 PMCID: PMC9338440 DOI: 10.1038/s41588-021-00909-9] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
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.
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Affiliation(s)
- Xin Sheng
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuting Guan
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ziyuan Ma
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Junnan Wu
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Hongbo Liu
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Chengxiang Qiu
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Vitale
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhen Miao
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew J Seasock
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Palmer
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | | | - Kevin L Duffin
- Eli Lilly and Company Lilly Corporate Center, Indianapolis, IN, USA
| | - Steven S Pullen
- Department of Cardiometabolic Diseases Research, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jacklyn N Hellwege
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adriana M Hung
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mingyao Li
- Department of Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Benjamin F Voight
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas M Coffman
- Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | | | - Katalin Susztak
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA.
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
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41
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Li B, Pei G, Yao J, Ding Q, Jia P, Zhao Z. Cell-type deconvolution analysis identifies cancer-associated myofibroblast component as a poor prognostic factor in multiple cancer types. Oncogene 2021; 40:4686-4694. [PMID: 34140640 DOI: 10.1038/s41388-021-01870-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 05/10/2021] [Accepted: 05/26/2021] [Indexed: 01/04/2023]
Abstract
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.
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Affiliation(s)
- Bingrui Li
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jun Yao
- Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Qingqing Ding
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
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42
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Jin L, Tang Q, Hu S, Chen Z, Zhou X, Zeng B, Wang Y, He M, Li Y, Gui L, Shen L, Long K, Ma J, Wang X, Chen Z, Jiang Y, Tang G, Zhu L, Liu F, Zhang B, Huang Z, Li G, Li D, Gladyshev VN, Yin J, Gu Y, Li X, Li M. A pig BodyMap transcriptome reveals diverse tissue physiologies and evolutionary dynamics of transcription. Nat Commun 2021; 12:3715. [PMID: 34140474 PMCID: PMC8211698 DOI: 10.1038/s41467-021-23560-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 05/04/2021] [Indexed: 12/13/2022] Open
Abstract
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.
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Affiliation(s)
- Long Jin
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Qianzi Tang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China.
| | - Silu Hu
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Zhongxu Chen
- Department of Life Science, Tcuni Inc., Chengdu, Sichuan, China
| | - Xuming Zhou
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Bo Zeng
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Yuhao Wang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Mengnan He
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Yan Li
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Lixuan Gui
- Department of Life Science, Tcuni Inc., Chengdu, Sichuan, China
| | - Linyuan Shen
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Keren Long
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Jideng Ma
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Xun Wang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Zhengli Chen
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Yanzhi Jiang
- College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China
| | - Guoqing Tang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Li Zhu
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Fei Liu
- Information and Educational Technology Center, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Bo Zhang
- Ya'an Digital Economy Operation Company, Ya'an, Sichuan, China
| | - Zhiqing Huang
- Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Guisen Li
- Renal Department and Nephrology Institute, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Diyan Li
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jingdong Yin
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yiren Gu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, Sichuan, China
| | - Xuewei Li
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Mingzhou Li
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China.
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Ostinelli G, Vijay J, Vohl MC, Grundberg E, Tchernof A. AKR1C2 and AKR1C3 expression in adipose tissue: Association with body fat distribution and regulatory variants. Mol Cell Endocrinol 2021; 527:111220. [PMID: 33675863 PMCID: PMC8052191 DOI: 10.1016/j.mce.2021.111220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/15/2021] [Accepted: 02/17/2021] [Indexed: 02/07/2023]
Abstract
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.
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Affiliation(s)
- Giada Ostinelli
- Centre de Recherche de l'Institut Universitaire de Cardiologie et Pneumologie de Québec-Université Laval, 2725 Chemin Sainte-Foy, G1V 4G5, Québec City, Québec, Canada; École de Nutrition, Université Laval, 2425 Rue de l'Agriculture, G1V 0A6, Québec City, Québec, Canada
| | - Jinchu Vijay
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada
| | - Marie-Claude Vohl
- École de Nutrition, Université Laval, 2425 Rue de l'Agriculture, G1V 0A6, Québec City, Québec, Canada; Centre Nutrition, Santé et Societé (NUTRISS)-Insitut sur la Nutrition et les Aliments Fonctionnells (INAF), Université Laval, Québec City, Québec, Canada
| | - Elin Grundberg
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada; Children's Mercy Research Institute, Children's Mercy Kansas City, Kansas City, MO, USA.
| | - Andre Tchernof
- Centre de Recherche de l'Institut Universitaire de Cardiologie et Pneumologie de Québec-Université Laval, 2725 Chemin Sainte-Foy, G1V 4G5, Québec City, Québec, Canada; École de Nutrition, Université Laval, 2425 Rue de l'Agriculture, G1V 0A6, Québec City, Québec, Canada.
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44
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Christiansen C, Castillo-Fernandez JE, Domingo-Relloso A, Zhao W, El-Sayed Moustafa JS, Tsai PC, Maddock J, Haack K, Cole SA, Kardia SLR, Molokhia M, Suderman M, Power C, Relton C, Wong A, Kuh D, Goodman A, Small KS, Smith JA, Tellez-Plaza M, Navas-Acien A, Ploubidis GB, Hardy R, Bell JT. Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects. Clin Epigenetics 2021; 13:36. [PMID: 33593402 PMCID: PMC7888173 DOI: 10.1186/s13148-021-01018-4] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 01/24/2021] [Indexed: 12/13/2022] Open
Abstract
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.
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Affiliation(s)
- C Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - A Domingo-Relloso
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, USA
- Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Madrid, Spain
- Department of Statistics and Operative Research, University of Valencia, Valencia, Spain
| | - W Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, USA
| | - J S El-Sayed Moustafa
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - P-C Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan
- Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan
| | - J Maddock
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - K Haack
- Population Health Program, Texas Biomedical Research Institute, San Antonio, USA
| | - S A Cole
- Population Health Program, Texas Biomedical Research Institute, San Antonio, USA
| | - S L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, USA
| | - M Molokhia
- School of Population Health and Environmental Sciences, King's College London, London, UK
| | - M Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - C Power
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | - C Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | - A Wong
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - D Kuh
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - A Goodman
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - K S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - J A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, USA
| | - M Tellez-Plaza
- Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Madrid, Spain
| | - A Navas-Acien
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, USA
| | - G B Ploubidis
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - R Hardy
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - J T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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Abstract
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.
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Affiliation(s)
- Silvia Corvera
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA;
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46
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Cell lineage-specific methylome and genome alterations in gout. Aging (Albany NY) 2021; 13:3843-3865. [PMID: 33493135 PMCID: PMC7906142 DOI: 10.18632/aging.202353] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/05/2020] [Indexed: 12/14/2022]
Abstract
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.
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Transcriptomic Changes of Murine Visceral Fat Exposed to Intermittent Hypoxia at Single Cell Resolution. Int J Mol Sci 2020; 22:ijms22010261. [PMID: 33383883 PMCID: PMC7795619 DOI: 10.3390/ijms22010261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 11/22/2020] [Accepted: 12/24/2020] [Indexed: 12/12/2022] Open
Abstract
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.
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48
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Pei G, Wang YY, Simon LM, Dai Y, Zhao Z, Jia P. Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders. Genome Res 2020; 31:146-158. [PMID: 33272935 PMCID: PMC7849392 DOI: 10.1101/gr.265769.120] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 11/25/2020] [Indexed: 12/30/2022]
Abstract
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.
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Affiliation(s)
- Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
| | - Yin-Ying Wang
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
| | - Lukas M Simon
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas 77030, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
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49
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Sharma NK, Comeau ME, Montoya D, Pellegrini M, Howard TD, Langefeld CD, Das SK. Integrative Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation, and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans. Diabetes 2020; 69:2779-2793. [PMID: 32928872 PMCID: PMC7679782 DOI: 10.2337/db20-0117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 08/28/2020] [Indexed: 12/13/2022]
Abstract
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, SI, 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, SI, 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.
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Affiliation(s)
- Neeraj K Sharma
- Department of Internal Medicine, Section of Endocrinology and Metabolism, Wake Forest School of Medicine, Winston-Salem, NC
| | - Mary E Comeau
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Dennis Montoya
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA
| | - Matteo Pellegrini
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA
| | - Timothy D Howard
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Swapan K Das
- Department of Internal Medicine, Section of Endocrinology and Metabolism, Wake Forest School of Medicine, Winston-Salem, NC
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50
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Link JC, Wiese CB, Chen X, Avetisyan R, Ronquillo E, Ma F, Guo X, Yao J, Allison M, Chen YDI, Rotter JI, El -Sayed Moustafa JS, Small KS, Iwase S, Pellegrini M, Vergnes L, Arnold AP, Reue K. X chromosome dosage of histone demethylase KDM5C determines sex differences in adiposity. J Clin Invest 2020; 130:5688-5702. [PMID: 32701509 PMCID: PMC7598065 DOI: 10.1172/jci140223] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/16/2020] [Indexed: 12/29/2022] Open
Abstract
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.
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Affiliation(s)
| | | | - Xuqi Chen
- Integrative Biology and Physiology, and
| | | | | | - Feiyang Ma
- Molecular, Cellular and Developmental Biology, UCLA, Los Angeles, California, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Matthew Allison
- Division of Preventive Medicine, School of Medicine, UCSD, San Diego, California, USA
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | | | - Kerrin S. Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Shigeki Iwase
- Human Genetics, Medical School, University of Michigan, Ann Arbor, Michigan, USA
| | - Matteo Pellegrini
- Molecular, Cellular and Developmental Biology, UCLA, Los Angeles, California, USA
| | | | | | - Karen Reue
- Molecular Biology Institute
- Human Genetics, David Geffen School of Medicine
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