1
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Andreu-Sánchez S, Ripoll-Cladellas A, Culinscaia A, Bulut O, Bourgonje AR, Netea MG, Lansdorp P, Aubert G, Bonder MJ, Franke L, Vogl T, van der Wijst MG, Melé M, Van Baarle D, Fu J, Zhernakova A. Antibody signatures against viruses and microbiome reflect past and chronic exposures and associate with aging and inflammation. iScience 2024; 27:109981. [PMID: 38868191 PMCID: PMC11167443 DOI: 10.1016/j.isci.2024.109981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/16/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Encounters with pathogens and other molecules can imprint long-lasting effects on our immune system, influencing future physiological outcomes. Given the wide range of microbes to which humans are exposed, their collective impact on health is not fully understood. To explore relations between exposures and biological aging and inflammation, we profiled an antibody-binding repertoire against 2,815 microbial, viral, and environmental peptides in a population cohort of 1,443 participants. Utilizing antibody-binding as a proxy for past exposures, we investigated their impact on biological aging, cell composition, and inflammation. Immune response against cytomegalovirus (CMV), rhinovirus, and gut bacteria relates with telomere length. Single-cell expression measurements identified an effect of CMV infection on the transcriptional landscape of subpopulations of CD8 and CD4 T-cells. This examination of the relationship between microbial exposures and biological aging and inflammation highlights a role for chronic infections (CMV and Epstein-Barr virus) and common pathogens (rhinoviruses and adenovirus C).
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Affiliation(s)
- Sergio Andreu-Sánchez
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Aida Ripoll-Cladellas
- Life Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Catalonia, Spain
| | - Anna Culinscaia
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ozlem Bulut
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboudumc, Nijmegen, the Netherlands
| | - Arno R. Bourgonje
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- The Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Mihai G. Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboudumc, Nijmegen, the Netherlands
- Department for Immunology & Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - Peter Lansdorp
- Terry Fox Laboratory, British Columbia Cancer Research Center, Vancouver, BC, Canada
- Departments of Hematology and Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Geraldine Aubert
- Terry Fox Laboratory, British Columbia Cancer Research Center, Vancouver, BC, Canada
- Repeat Diagnostics Inc, Vancouver, BC, Canada
| | - Marc Jan Bonder
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Thomas Vogl
- Center for Cancer Research, Medical University of Vienna, Wien, Austria
| | - Monique G.P. van der Wijst
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marta Melé
- Life Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Catalonia, Spain
| | - Debbie Van Baarle
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Jingyuan Fu
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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2
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Lei H. Quantitative and Longitudinal Assessment of Systemic Innate Immunity in Health and Disease Using a 2D Gene Model. Biomedicines 2024; 12:969. [PMID: 38790931 PMCID: PMC11117654 DOI: 10.3390/biomedicines12050969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Dysregulation of innate immunity is deeply involved in infectious and autoimmune diseases. For a better understanding of pathogenesis and improved management of these diseases, it is of vital importance to implement convenient monitoring of systemic innate immunity. Built upon our previous works on the host transcriptional response to infection in peripheral blood, we proposed a 2D gene model for the simultaneous assessment of two major components of systemic innate immunity, including VirSig as the signature of the host response to viral infection and BacSig as the signature of the host response to bacterial infection. The revelation of dysregulation in innate immunity by this 2D gene model was demonstrated with a wide variety of transcriptome datasets. In acute infection, distinctive patterns of VirSig and BacSig activation were observed in viral and bacterial infection. In comparison, both signatures were restricted to a defined range in the vast majority of healthy adults, regardless of age. In addition, BacSig showed significant elevation during pregnancy and an upward trend during development. In tuberculosis (TB), elevation of BacSig and VirSig was observed in a significant portion of active TB patients, and abnormal BacSig was also associated with a longer treatment course. In cystic fibrosis (CF), abnormal BacSig was observed in a subset of patients, and no overall change in BacSig abnormality was observed after the drug treatment. In systemic sclerosis-associated interstitial lung disease (SSc-ILD), significant elevation of VirSig and BacSig was observed in some patients, and treatment with a drug led to the further deviation of BacSig from the control level. In systemic lupus erythematosus (SLE), positivity for the anti-Ro autoantibody was associated with significant elevation of VirSig in SLE patients, and the additive effect of VirSig/BacSig activation was also observed in SLE patients during pregnancy. Overall, these data demonstrated that the 2D gene model can be used to assess systemic innate immunity in health and disease, with the potential clinical applications including patient stratification, prescription of antibiotics, understanding of pathogenesis, and longitudinal monitoring of treatment response.
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Affiliation(s)
- Hongxing Lei
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China; ; Tel.: +86-010-8409-7276
- Cunji Medical School, University of Chinese Academy of Sciences, Beijing 101408, China
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3
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Sun JY, Li C, Du FY. Adenylate cyclase activates the cAMP signalling pathway to enhance platelet-rich plasma-treated Achilles tendon disease, a theoretical bioinformatics-based study. World J Orthop 2024; 15:192-200. [PMID: 38464349 PMCID: PMC10921184 DOI: 10.5312/wjo.v15.i2.192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/29/2023] [Accepted: 01/16/2024] [Indexed: 02/07/2024] Open
Abstract
The effectiveness of platelet-rich plasma (PRP) for the treatment of Achilles tendon disorders still needs to be evaluated through a series of prospective studies, but genomic analysis can reveal the existence of complementary PRP treatment options. Based on the 96 platelet activation-related genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we performed Gene Ontology functional enrichment analysis and KEGG enrichment analysis, pathway correlation analysis, and enrichment mapping to determine the enrichment results of the gene set enrichment analysis and found that the cAMP signalling pathway may be the key to enhancing the effectiveness of PRP treatment. The cAMP signalling pathway interacts with the Rap1 signalling pathway and cGMP-PKG signalling pathway to mediate the entire pathophysiological process of Achilles tendon disease. Moreover, ADCY1-9 may be the key to the activation of the cAMP signalling network. Further based on the data in the Gene Expression Omnibus database, it was found that ADCY4 and ADCY7 may be the players that play a major role, associated with the STAT4-ADCY4-LAMA5 axis and the GRbeta-ADCY7-SEMA3C axis, which is expected to be a complementary target for enhancing the efficacy of PRP in the treatment of Achilles tendon disease.
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Affiliation(s)
- Jing-Yi Sun
- College of Basic Medical Science, Qilu Medical University, Zibo 255300, Shandong Province, China
| | - Cai Li
- College of Basic Medical Science, Qilu Medical University, Zibo 255300, Shandong Province, China
| | - Feng-Ying Du
- Department of Gastroenterological Surgery, Shandong Provincial Hospital of Shandong First Medical University, Jinan 250021, Shandong Province, China
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4
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Boltz T, Schwarz T, Bot M, Hou K, Caggiano C, Lapinska S, Duan C, Boks MP, Kahn RS, Zaitlen N, Pasaniuc B, Ophoff R. Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders. Am J Hum Genet 2024; 111:323-337. [PMID: 38306997 PMCID: PMC10870131 DOI: 10.1016/j.ajhg.2023.12.018] [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: 06/05/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 02/04/2024] Open
Abstract
Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.
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Affiliation(s)
- Toni Boltz
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Tommer Schwarz
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Merel Bot
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Christa Caggiano
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Sandra Lapinska
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Chenda Duan
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Marco P Boks
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Rene S Kahn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - Noah Zaitlen
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Department of Neurology, University of California Los Angeles, Los Angeles, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Roel Ophoff
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA; Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands.
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5
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Kasela S, Aguet F, Kim-Hellmuth S, Brown BC, Nachun DC, Tracy RP, Durda P, Liu Y, Taylor KD, Johnson WC, Van Den Berg D, Gabriel S, Gupta N, Smith JD, Blackwell TW, Rotter JI, Ardlie KG, Manichaikul A, Rich SS, Barr RG, Lappalainen T. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. Am J Hum Genet 2024; 111:133-149. [PMID: 38181730 PMCID: PMC10806864 DOI: 10.1016/j.ajhg.2023.11.013] [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: 06/22/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024] Open
Abstract
Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.
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Affiliation(s)
- Silva Kasela
- New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
| | | | - Sarah Kim-Hellmuth
- New York Genome Center, New York, NY, USA; Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany; Computational Health Center, Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Brielin C Brown
- New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA
| | - Daniel C Nachun
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Russell P Tracy
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Peter Durda
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David Van Den Berg
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua D Smith
- Northwest Genomics Center, University of Washington, Seattle, WA, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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6
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Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. J Genet Genomics 2023; 50:925-933. [PMID: 37207929 PMCID: PMC10656365 DOI: 10.1016/j.jgg.2023.05.003] [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: 02/21/2023] [Revised: 05/02/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
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Affiliation(s)
- Jingfei Zhang
- Information Systems and Operations Management, Emory University, Atlanta, GA 30322, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 208034, USA.
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7
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Renganaath K, Albert FW. Trans -eQTL hotspots shape complex traits by modulating cellular states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.14.567054. [PMID: 38014174 PMCID: PMC10680915 DOI: 10.1101/2023.11.14.567054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae . We discovered thousands of genetic correlations between gene expression and growth, suggesting functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans -acting regulatory loci. Trans -acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans -acting regulatory hotspots shape complex traits by altering cellular states.
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8
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Gupta MK, Peng H, Li Y, Xu CJ. The role of DNA methylation in personalized medicine for immune-related diseases. Pharmacol Ther 2023; 250:108508. [PMID: 37567513 DOI: 10.1016/j.pharmthera.2023.108508] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Epigenetics functions as a bridge between host genetic & environmental factors, aiding in human health and diseases. Many immune-related diseases, including infectious and allergic diseases, have been linked to epigenetic mechanisms, particularly DNA methylation. In this review, we summarized an updated overview of DNA methylation and its importance in personalized medicine, and demonstrated that DNA methylation has excellent potential for disease prevention, diagnosis, and treatment in a personalized manner. The future implications and limitations of the DNA methylation study have also been well-discussed.
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Affiliation(s)
- Manoj Kumar Gupta
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany; TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - He Peng
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany; TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Yang Li
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany; TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany; Department of Internal Medicine and Radboud Institute for Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Cheng-Jian Xu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany; TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany; Department of Internal Medicine and Radboud Institute for Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
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9
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Gaulton KJ, Preissl S, Ren B. Interpreting non-coding disease-associated human variants using single-cell epigenomics. Nat Rev Genet 2023; 24:516-534. [PMID: 37161089 PMCID: PMC10629587 DOI: 10.1038/s41576-023-00598-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.
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Affiliation(s)
- Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego School of Medicine, La Jolla, CA, USA.
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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10
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Kasela S, Aguet F, Kim-Hellmuth S, Brown BC, Nachun DC, Tracy RP, Durda P, Liu Y, Taylor KD, Craig Johnson W, Berg DVD, Gabriel S, Gupta N, Smith JD, Blackwell TW, Rotter JI, Ardlie KG, Manichaikul A, Rich SS, Graham Barr R, Lappalainen T. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.26.546528. [PMID: 37425716 PMCID: PMC10326995 DOI: 10.1101/2023.06.26.546528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.
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Affiliation(s)
- Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Sarah Kim-Hellmuth
- New York Genome Center, New York, NY, USA
- Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital LMU Munich, Munich, Germany
- Computational Health Center, Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Brielin C. Brown
- New York Genome Center, New York, NY, USA
- Data Science Institute, Columbia University, New York, NY, USA
| | | | - Russell P. Tracy
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Peter Durda
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kent D. Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David Van Den Berg
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua D. Smith
- Northwest Genomic Center, University of Washington, Seattle, WA, USA
| | - Thomas W. Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Ani Manichaikul
- Center for Public health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S. Rich
- Center for Public health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R. Graham Barr
- Epidemiology and Medicine, Columbia University Medical Center, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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11
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Darolti I, Mank JE. Sex-biased gene expression at single-cell resolution: cause and consequence of sexual dimorphism. Evol Lett 2023; 7:148-156. [PMID: 37251587 PMCID: PMC10210449 DOI: 10.1093/evlett/qrad013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/08/2023] [Accepted: 04/06/2023] [Indexed: 05/31/2023] Open
Abstract
Gene expression differences between males and females are thought to be key for the evolution of sexual dimorphism, and sex-biased genes are often used to study the molecular footprint of sex-specific selection. However, gene expression is often measured from complex aggregations of diverse cell types, making it difficult to distinguish between sex differences in expression that are due to regulatory rewiring within similar cell types and those that are simply a consequence of developmental differences in cell-type abundance. To determine the role of regulatory versus developmental differences underlying sex-biased gene expression, we use single-cell transcriptomic data from multiple somatic and reproductive tissues of male and female guppies, a species that exhibits extensive phenotypic sexual dimorphism. Our analysis of gene expression at single-cell resolution demonstrates that nonisometric scaling between the cell populations within each tissue and heterogeneity in cell-type abundance between the sexes can influence inferred patterns of sex-biased gene expression by increasing both the false-positive and false-negative rates. Moreover, we show that, at the bulk level, the subset of sex-biased genes that are the product of sex differences in cell-type abundance can significantly confound patterns of coding-sequence evolution. Taken together, our results offer a unique insight into the effects of allometry and cellular heterogeneity on perceived patterns of sex-biased gene expression and highlight the power of single-cell RNA-sequencing in distinguishing between sex-biased genes that are the result of regulatory change and those that stem from sex differences in cell-type abundance, and hence are a consequence rather than a cause of sexual dimorphism.
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Affiliation(s)
- Iulia Darolti
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Judith E Mank
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
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12
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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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13
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Li S, Schmid KT, de Vries DH, Korshevniuk M, Losert C, Oelen R, van Blokland IV, Groot HE, Swertz MA, van der Harst P, Westra HJ, van der Wijst MGP, Heinig M, Franke L. Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data. Genome Biol 2023; 24:80. [PMID: 37072791 PMCID: PMC10111756 DOI: 10.1186/s13059-023-02897-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 03/16/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. RESULTS We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. CONCLUSION Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.
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Affiliation(s)
- Shuang Li
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Katharina T Schmid
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany
| | - Dylan H de Vries
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Maryna Korshevniuk
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Corinna Losert
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany
| | - Roy Oelen
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Irene V van Blokland
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Hilde E Groot
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Morris A Swertz
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Harm-Jan Westra
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Matthias Heinig
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany.
- Munich Heart Alliance, DZHK (German Center for Cardiovascular Research), Munich, Germany.
| | - Lude Franke
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands.
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14
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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: 42] [Impact Index Per Article: 42.0] [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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15
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Zhang J, Zhao H. eQTL Studies: from Bulk Tissues to Single Cells. ARXIV 2023:arXiv:2302.11662v1. [PMID: 36866231 PMCID: PMC9980190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies to date have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detections of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
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Affiliation(s)
- Jingfei Zhang
- Information Systems and Operations Management, Emory University
| | - Hongyu Zhao
- Department of Biostatistics, Yale University
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16
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Qi C, Berg M, Chu X, Timens W, Kole T, van den Berge M, Xu CJ, Koppelman GH, Nawijn MC, Li Y. Cell-type eQTL deconvolution of bronchial epithelium through integration of single-cell and bulk RNA-seq. Allergy 2022; 77:3663-3666. [PMID: 35708915 DOI: 10.1111/all.15410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/28/2022] [Accepted: 06/07/2022] [Indexed: 01/28/2023]
Affiliation(s)
- Cancan Qi
- Department of Pediatric Pulmonology and Pediatric Allergology, University of Groningen, University Medical Center Groningen, Beatrix Children's Hospital, University of Groningen, Groningen, The Netherlands.,University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.,Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.,Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.,Centre for Individualized Infection Medicine (CiiM), a Joint Venture Between Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany
| | - Marijn Berg
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.,Department of Pathology & Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Xiaojing Chu
- Centre for Individualized Infection Medicine (CiiM), a Joint Venture Between Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany.,Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,TWINCORE, Centre for Experimental and Clinical Infection Research, a Joint Venture Between Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany
| | - Wim Timens
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.,Department of Pathology & Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Tessa Kole
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.,Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Maarten van den Berge
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.,Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Cheng-Jian Xu
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.,Centre for Individualized Infection Medicine (CiiM), a Joint Venture Between Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany.,TWINCORE, Centre for Experimental and Clinical Infection Research, a Joint Venture Between Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany.,Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, University of Groningen, University Medical Center Groningen, Beatrix Children's Hospital, University of Groningen, Groningen, The Netherlands.,University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
| | - Martijn C Nawijn
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.,Department of Pathology & Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Yang Li
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.,Centre for Individualized Infection Medicine (CiiM), a Joint Venture Between Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany.,TWINCORE, Centre for Experimental and Clinical Infection Research, a Joint Venture Between Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany
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17
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Cano-Gamez E, Burnham KL, Goh C, Allcock A, Malick ZH, Overend L, Kwok A, Smith DA, Peters-Sengers H, Antcliffe D. An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression. Sci Transl Med 2022; 14:eabq4433. [PMID: 36322631 PMCID: PMC7613832 DOI: 10.1126/scitranslmed.abq4433] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.
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Affiliation(s)
- Eddie Cano-Gamez
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK,Wellcome Sanger Institute, Wellcome Genome Campus; Cambridge, CB10 1SA, UK
| | - Katie L Burnham
- Wellcome Sanger Institute, Wellcome Genome Campus; Cambridge, CB10 1SA, UK
| | - Cyndi Goh
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK,The Jenner Institute, University of Oxford; Oxford, OX3 7DQ, UK
| | - Alice Allcock
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK
| | - Zunaira H. Malick
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK
| | - Lauren Overend
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK
| | - Andrew Kwok
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK
| | - David A. Smith
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK,Chinese Academy of Medical Science Oxford Institute, University of Oxford; Oxford, OX3 7BN, UK
| | - Hessel Peters-Sengers
- Centre for Experimental and Molecular Medicine, Amsterdam University Medical Centers, University of Amsterdam; 1100 DD Amsterdam Southeast, Netherlands,Department of Epidemiology and Data Science, Amsterdam Public Health, Amsterdam University Medical Centers, University of Amsterdam, 1100 DD Amsterdam Southeast, Netherlands,The Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Centers, 1100 DD Amsterdam Southeast, Netherlands
| | - David Antcliffe
- Division of Anaesthesia, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London; London, SW7 2AZ, UK
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18
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Porcu E, Claringbould A, Weihs A, Lepik K, Richardson TG, Völker U, Santoni FA, Teumer A, Franke L, Reymond A, Kutalik Z. Limited evidence for blood eQTLs in human sexual dimorphism. Genome Med 2022; 14:89. [PMID: 35953856 PMCID: PMC9373355 DOI: 10.1186/s13073-022-01088-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/14/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits. METHODS To explore this scenario, we combined sex-specific whole blood RNA-seq eQTL data from 3447 European individuals included in BIOS Consortium and GWAS data from UK Biobank. Next, to test the presence of sex-biased causal effect of gene expression on complex traits, we performed sex-specific transcriptome-wide Mendelian randomization (TWMR) analyses on the two most sexually dimorphic traits, waist-to-hip ratio (WHR) and testosterone levels. Finally, we performed power analysis to calculate the GWAS sample size needed to observe sex-specific trait associations driven by sex-biased eQTLs. RESULTS Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-biased cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-biased eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-biased trait-associations. In addition, we observed that sex-specific causal effects of gene expression on complex traits are not driven by sex-specific eQTLs. Power analyses using real eQTL- and causal-effect sizes showed that millions of samples would be necessary to observe sex-biased trait associations that are fully driven by sex-biased cis-eQTLs. Compensatory effects may further hamper their detection. CONCLUSIONS Our results suggest that sex-specific eQTLs in whole blood do not translate to detectable sex-specific trait associations of complex diseases, and vice versa that the observed sex-specific trait associations cannot be explained by sex-specific eQTLs.
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Affiliation(s)
- Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Center for Primary Care and Public Health, Lausanne, Switzerland.
| | - Annique Claringbould
- University Medical Centre Groningen, Groningen, the Netherlands.,Structural and Computational Biology Unit, European Molecular Biology Laboratories (EMBL), Heidelberg, Germany
| | - Antoine Weihs
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Kaido Lepik
- Institute of Computer Science, University of Tartu, Tartu, Estonia.,Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, OX3 7DQ, UK
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Federico A Santoni
- Endocrine, Diabetes, and Metabolism Service, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Alexander Teumer
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Lude Franke
- University Medical Centre Groningen, Groningen, the Netherlands
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Center for Primary Care and Public Health, Lausanne, Switzerland. .,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
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19
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Detecting signatures of selection on gene expression. Nat Ecol Evol 2022; 6:1035-1045. [PMID: 35551249 DOI: 10.1038/s41559-022-01761-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 04/01/2022] [Indexed: 12/15/2022]
Abstract
A substantial amount of phenotypic diversity results from changes in gene expression levels and patterns. Understanding how the transcriptome evolves is therefore a key priority in identifying mechanisms of adaptive change. However, in contrast to powerful models of sequence evolution, we lack a consensus model of gene expression evolution. Furthermore, recent work has shown that many of the comparative approaches used to study gene expression are subject to biases that can lead to false signatures of selection. Here we first outline the main approaches for describing expression evolution and their inherent biases. Next, we bridge the gap between the fields of phylogenetic comparative methods and transcriptomics to reinforce the main pitfalls of inferring selection on expression patterns and use simulation studies to show that shifts in tissue composition can heavily bias inferences of selection. We close by highlighting the multi-dimensional nature of transcriptional variation and identifying major unanswered questions in disentangling how selection acts on the transcriptome.
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20
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Flynn E, Lappalainen T. Functional Characterization of Genetic Variant Effects on Expression. Annu Rev Biomed Data Sci 2022; 5:119-139. [PMID: 35483347 DOI: 10.1146/annurev-biodatasci-122120-010010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Elise Flynn
- New York Genome Center, New York, NY, USA; , .,Department of Systems Biology, Columbia University, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; , .,Department of Systems Biology, Columbia University, New York, NY, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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21
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Bossini-Castillo L, Glinos DA, Kunowska N, Golda G, Lamikanra AA, Spitzer M, Soskic B, Cano-Gamez E, Smyth DJ, Cattermole C, Alasoo K, Mann A, Kundu K, Lorenc A, Soranzo N, Dunham I, Roberts DJ, Trynka G. Immune disease variants modulate gene expression in regulatory CD4 + T cells. CELL GENOMICS 2022; 2:None. [PMID: 35591976 PMCID: PMC9010307 DOI: 10.1016/j.xgen.2022.100117] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 11/02/2021] [Accepted: 03/15/2022] [Indexed: 12/30/2022]
Abstract
Identifying cellular functions dysregulated by disease-associated variants could implicate novel pathways for drug targeting or modulation in cell therapies. However, follow-up studies can be challenging if disease-relevant cell types are difficult to sample. Variants associated with immune diseases point toward the role of CD4+ regulatory T cells (Treg cells). We mapped genetic regulation (quantitative trait loci [QTL]) of gene expression and chromatin activity in Treg cells, and we identified 133 colocalizing loci with immune disease variants. Colocalizations of immune disease genome-wide association study (GWAS) variants with expression QTLs (eQTLs) controlling the expression of CD28 and STAT5A, involved in Treg cell activation and interleukin-2 (IL-2) signaling, support the contribution of Treg cells to the pathobiology of immune diseases. Finally, we identified seven known drug targets suitable for drug repurposing and suggested 63 targets with drug tractability evidence among the GWAS signals that colocalized with Treg cell QTLs. Our study is the first in-depth characterization of immune disease variant effects on Treg cell gene expression modulation and dysregulation of Treg cell function.
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Affiliation(s)
| | - Dafni A. Glinos
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- New York Genome Center, New York, NY, USA
| | - Natalia Kunowska
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Gosia Golda
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Abigail A. Lamikanra
- NHS Blood and Transplant, Oxford, UK
- BRC Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Michaela Spitzer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Blagoje Soskic
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Eddie Cano-Gamez
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Deborah J. Smyth
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | | | - Kaur Alasoo
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Alice Mann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Kousik Kundu
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Anna Lorenc
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Nicole Soranzo
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Ian Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - David J. Roberts
- NHS Blood and Transplant, Oxford, UK
- BRC Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Gosia Trynka
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
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22
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Robinson EL, Baker AH, Brittan M, McCracken I, Condorelli G, Emanueli C, Srivastava PK, Gaetano C, Thum T, Vanhaverbeke M, Angione C, Heymans S, Devaux Y, Pedrazzini T, Martelli F. Dissecting the transcriptome in cardiovascular disease. Cardiovasc Res 2022; 118:1004-1019. [PMID: 33757121 PMCID: PMC8930073 DOI: 10.1093/cvr/cvab117] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 03/22/2021] [Indexed: 12/12/2022] Open
Abstract
The human transcriptome comprises a complex network of coding and non-coding RNAs implicated in a myriad of biological functions. Non-coding RNAs exhibit highly organized spatial and temporal expression patterns and are emerging as critical regulators of differentiation, homeostasis, and pathological states, including in the cardiovascular system. This review defines the current knowledge gaps, unmet methodological needs, and describes the challenges in dissecting and understanding the role and regulation of the non-coding transcriptome in cardiovascular disease. These challenges include poor annotation of the non-coding genome, determination of the cellular distribution of transcripts, assessment of the role of RNA processing and identification of cell-type specific changes in cardiovascular physiology and disease. We highlight similarities and differences in the hurdles associated with the analysis of the non-coding and protein-coding transcriptomes. In addition, we discuss how the lack of consensus and absence of standardized methods affect reproducibility of data. These shortcomings should be defeated in order to make significant scientific progress and foster the development of clinically applicable non-coding RNA-based therapeutic strategies to lessen the burden of cardiovascular disease.
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Affiliation(s)
- Emma L Robinson
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Universiteitssingel 50, 6229 Maastricht University, Maastricht, The Netherlands
- The Division of Cardiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrew H Baker
- Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK
| | - Mairi Brittan
- Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK
| | - Ian McCracken
- Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK
| | - G Condorelli
- Humanitas Research Hospital, Humanitas University, Via Manzoni 113, Rozzano, MI 20089, Italy
| | - C Emanueli
- Imperial College, National Heart and Lung Institute, Hammersmith campus, Du Cane Road, London W12 0NN, UK
| | - P K Srivastava
- Imperial College, National Heart and Lung Institute, Hammersmith campus, Du Cane Road, London W12 0NN, UK
| | - C Gaetano
- Laboratorio di Epigenetica, Istituti Clinici Scientifici Maugeri IRCCS, Via Maugeri 4, Pavia 27100, Italy
| | - T Thum
- Hannover Medical School, Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Carl-Neuberg-Straße 1 30625 Hannover, Germany
| | - M Vanhaverbeke
- UZ Gasthuisberg Campus, KU Leuven, Herestraat 49 3000 Leuven, Belgium
| | - C Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, TS4 3BX, UK
| | - S Heymans
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Universiteitssingel 50, 6229 Maastricht University, Maastricht, The Netherlands
| | - Y Devaux
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg
| | - T Pedrazzini
- Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, 1011 Lausanne, Switzerland
| | - F Martelli
- Molecular Cardiology Laboratory, IRCCS-Policlinico San Donato, Piazza Edmondo Malan, 2, 20097 San Donato, Milan, Italy
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23
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Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease. Biochem Soc Trans 2021; 49:2091-2100. [PMID: 34581766 PMCID: PMC8589426 DOI: 10.1042/bst20210128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/25/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022]
Abstract
Parkinson's disease (PD) is the second most prevalent late-onset neurodegenerative disorder worldwide after Alzheimer's disease for which available drugs only deliver temporary symptomatic relief. Loss of dopaminergic neurons (DaNs) in the substantia nigra and intracellular alpha-synuclein inclusions are the main hallmarks of the disease but the events that cause this degeneration remain uncertain. Despite cell types other than DaNs such as astrocytes, microglia and oligodendrocytes have been recently associated with the pathogenesis of PD, we still lack an in-depth characterisation of PD-affected brain regions at cell-type resolution that could help our understanding of the disease mechanisms. Nevertheless, publicly available large-scale brain-specific genomic, transcriptomic and epigenomic datasets can be further exploited to extract different layers of cell type-specific biological information for the reconstruction of cell type-specific transcriptional regulatory networks. By intersecting disease risk variants within the networks, it may be possible to study the functional role of these risk variants and their combined effects at cell type- and pathway levels, that, in turn, can facilitate the identification of key regulators involved in disease progression, which are often potential therapeutic targets.
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24
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Scherer M, Gasparoni G, Rahmouni S, Shashkova T, Arnoux M, Louis E, Nostaeva A, Avalos D, Dermitzakis ET, Aulchenko YS, Lengauer T, Lyons PA, Georges M, Walter J. Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR. Epigenetics Chromatin 2021; 14:44. [PMID: 34530905 PMCID: PMC8444396 DOI: 10.1186/s13072-021-00415-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/02/2021] [Indexed: 12/18/2022] Open
Abstract
Background Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects. Results Here, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements. Conclusions Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation. Supplementary Information The online version contains supplementary material available at 10.1186/s13072-021-00415-6.
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Affiliation(s)
- Michael Scherer
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.,Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.,Graduate School of Computer Science, Saarland Informatics Campus, Saarbrücken, Germany.,Department of Bioinformatics and Genomics, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Gilles Gasparoni
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany
| | - Souad Rahmouni
- Unit of Animal Genomics, GIGA-Institute & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Tatiana Shashkova
- Kurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.,Research and Training Center on Bioinformatics, A.A. Kharkevich Institute for Information Transmission Problems, Moscow, Russia
| | - Marion Arnoux
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany
| | - Edouard Louis
- Department of Gastroenterology, Liège University Hospital, CHU Liège, Liège, Belgium
| | | | - Diana Avalos
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.,Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland.,Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.,Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland.,Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Yurii S Aulchenko
- Kurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.,Novosibirsk State University, Novosibirsk, Russia.,Moscow Institute of Physics and Technology (State University), Moscow, Russia.,PolyKnomics BV, 's-Hertogenbosch, The Netherlands
| | - Thomas Lengauer
- Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Paul A Lyons
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.,Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, CB2 0AW, UK
| | - Michel Georges
- Unit of Animal Genomics, GIGA-Institute & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Jörn Walter
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.
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25
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Chu X, Zhang B, Koeken VACM, Gupta MK, Li Y. Multi-Omics Approaches in Immunological Research. Front Immunol 2021; 12:668045. [PMID: 34177908 PMCID: PMC8226116 DOI: 10.3389/fimmu.2021.668045] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/28/2021] [Indexed: 12/14/2022] Open
Abstract
The immune system plays a vital role in health and disease, and is regulated through a complex interactive network of many different immune cells and mediators. To understand the complexity of the immune system, we propose to apply a multi-omics approach in immunological research. This review provides a complete overview of available methodological approaches for the different omics data layers relevant for immunological research, including genetics, epigenetics, transcriptomics, proteomics, metabolomics, and cellomics. Thereafter, we describe the various methods for data analysis as well as how to integrate different layers of omics data. Finally, we discuss the possible applications of multi-omics studies and opportunities they provide for understanding the complex regulatory networks as well as immune variation in various immune-related diseases.
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Affiliation(s)
- Xiaojing Chu
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Bowen Zhang
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Valerie A. C. M. Koeken
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Manoj Kumar Gupta
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Yang Li
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
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26
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Wang J, Lan Y, He L, Tang R, Li Y, Huang Y, Liang S, Gao Z, Price M, Yue B, He M, Guo T, Fan Z. Sex-specific gene expression in the blood of four primates. Genomics 2021; 113:2605-2613. [PMID: 34116169 DOI: 10.1016/j.ygeno.2021.06.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 05/03/2021] [Accepted: 06/06/2021] [Indexed: 10/21/2022]
Abstract
Blood is an important non-reproductive tissue, but little is known about the sex-specific gene expressions in the blood. Therefore, we investigated sex-specific gene expression differences in the blood tissues of four primates, rhesus macaques (Macaca mulatta), Tibetan macaques (M. thibetana), yellow baboons (Papio cynocephalus), and humans. We identified seven sex-specific differentially expressed genes (SDEGs) in each non-human primate and 31 SDEGs in humans. The four primates had only one common SDEG, MAP7D2. In humans, immune-related SDEGs were identified as up-regulated, but also down-regulated in females. We also found that most of the X-Y gene pairs had similar expression levels between species, except pair EIF1AY/EIF1AX. The expression level of X-Y gene pairs of rhesus and Tibetan macaques showed no significant differential expression levels, while humans had six significant XY-biased and three XX-biased X-Y gene pairs. Our observed sex differences in blood should increase understanding of sex differences in primate blood tissue.
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Affiliation(s)
- Jiao Wang
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China
| | - Yue Lan
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China
| | - Lewei He
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China
| | - Ruixiang Tang
- Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, College of Life Sciences, Sichuan University, Chengdu 610064, Sichuan, China
| | - Yuhui Li
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu 610052, Sichuan, China
| | - Yuan Huang
- Medical Laboratory Department of West China Fourth Hospital, Sichuan University, Chengdu 610000, Sichuan, China
| | - Shan Liang
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu 610052, Sichuan, China
| | - Zhan Gao
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu 610052, Sichuan, China
| | - Megan Price
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China.
| | - Bisong Yue
- Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, College of Life Sciences, Sichuan University, Chengdu 610064, Sichuan, China.
| | - Miao He
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu 610052, Sichuan, China.
| | - Tao Guo
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Zhenxin Fan
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China.
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27
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Targeting of the CD80/86 proinflammatory axis as a therapeutic strategy to prevent severe COVID-19. Sci Rep 2021; 11:11462. [PMID: 34075090 PMCID: PMC8169841 DOI: 10.1038/s41598-021-90797-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 05/12/2021] [Indexed: 02/04/2023] Open
Abstract
An excessive immune response known as cytokine storm is the hallmark of severe COVID-19. The cause of this cytokine rampage is yet not known. Based on recent epidemiological evidence, we hypothesized that CD80/86 signaling is essential for this hyperinflammation, and that blocking this proinflammatory axis could be an effective therapeutic approach to protect against severe COVID-19. Here we provide exploratory evidence that abatacept, a drug that blocks CD80/86 co-stimulation, produces changes at the systemic level that are highly antagonistic of the proinflammatory processes elicited by COVID-19. Using RNA-seq from blood samples from a longitudinal cohort of n = 38 rheumatic patients treated with abatacept, we determined the immunological processes that are significantly regulated by this treatment. We then analyzed available blood RNA-seq from two COVID19 patient cohorts, a very early cohort from the epicenter of the pandemic in China (n = 3 COVID-19 cases and n = 3 controls), and a recent and larger cohort from the USA (n = 49 severe and n = 51 mild COVD-19 patients). We found a highly significant antagonism between SARS-CoV-2 infection and COVID-19 severity with the systemic response to abatacept. Analysis of previous single-cell RNA-seq data from bronchoalveolar lavage fluid from mild and severe COVID-19 patients and controls, reinforce the implication of the CD80/86 proinflammatory axis. Our functional results further support abatacept as a candidate therapeutic approach to prevent severe COVID-19.
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28
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Zhang Z, van Dijk F, de Klein N, van Gijn ME, Franke LH, Sinke RJ, Swertz MA, van der Velde KJ. Feasibility of predicting allele specific expression from DNA sequencing using machine learning. Sci Rep 2021; 11:10606. [PMID: 34012022 PMCID: PMC8134421 DOI: 10.1038/s41598-021-89904-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/04/2021] [Indexed: 11/09/2022] Open
Abstract
Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.
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Affiliation(s)
- Zhenhua Zhang
- Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.,Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Freerk van Dijk
- Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.,Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.,Prinses Maxima Center for Child Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands
| | - Niek de Klein
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Mariëlle E van Gijn
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Lude H Franke
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Richard J Sinke
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Morris A Swertz
- Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.,Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - K Joeri van der Velde
- Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands. .,Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
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Bhattacharya A, Hamilton AM, Troester MA, Love MI. DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing. Nucleic Acids Res 2021; 49:e48. [PMID: 33524140 PMCID: PMC8096278 DOI: 10.1093/nar/gkab031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/21/2020] [Accepted: 01/12/2021] [Indexed: 12/13/2022] Open
Abstract
Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA
| | - Alina M Hamilton
- Department of Pathology and Laboratory Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
| | - Melissa A Troester
- Department of Pathology and Laboratory Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
- Department of Epidemiology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
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Takeuchi F, Kato N. Nonlinear ridge regression improves cell-type-specific differential expression analysis. BMC Bioinformatics 2021; 22:141. [PMID: 33752591 PMCID: PMC7986289 DOI: 10.1186/s12859-021-03982-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 01/27/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity. RESULTS First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data. CONCLUSION Nonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas.
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Affiliation(s)
- Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine (NCGM), 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine (NCGM), 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
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31
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Yankovitz G, Cohn O, Bacharach E, Peshes-Yaloz N, Steuerman Y, Iraqi FA, Gat-Viks I. Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics. Genetics 2021; 217:6126814. [PMID: 33734353 DOI: 10.1093/genetics/iyab016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 01/12/2021] [Indexed: 11/12/2022] Open
Abstract
Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered.
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Affiliation(s)
- Gal Yankovitz
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Ofir Cohn
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Eran Bacharach
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Naama Peshes-Yaloz
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Yael Steuerman
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Irit Gat-Viks
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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Pellegrino-Coppola D, Claringbould A, Stutvoet M, Boomsma DI, Ikram MA, Slagboom PE, Westra HJ, Franke L. Correction for both common and rare cell types in blood is important to identify genes that correlate with age. BMC Genomics 2021; 22:184. [PMID: 33722199 PMCID: PMC7958454 DOI: 10.1186/s12864-020-07344-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 12/22/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. RESULTS Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10-6). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. CONCLUSIONS We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.
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Affiliation(s)
| | - Annique Claringbould
- Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands
| | - Maartje Stutvoet
- Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands
| | | | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Amsterdam Public Health research institute and Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands.
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33
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Koeken VACM, van Crevel R, Netea MG, Li Y. Resolving trained immunity with systems biology. Eur J Immunol 2021; 51:773-784. [PMID: 33570164 DOI: 10.1002/eji.202048882] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Indexed: 12/20/2022]
Abstract
Trained immunity is characterized by long-term functional reprogramming of innate immune cells following challenge with pathogens or microbial ligands during infection or vaccination. This cellular reprogramming leads to increased responsiveness upon restimulation, and is mediated through epigenetic and metabolic modifications. In this review, we describe how molecular mechanisms underlying trained immunity, for example, induced by β-glucan or Bacille Calmette-Guérin (BCG) vaccination, can be investigated by using and integrating different layers of information including genome, epigenome, transcriptome, proteome, metabolome, microbiome, immune cell phenotyping, and function. We also describe the most commonly used experimental and computational techniques. Finally, we provide a number of examples of how a systems biology approach was applied to study trained immunity to understand interindividual variation or the complex interplay between molecular layers. In conclusion, trained immunity represents an opportunity for regulating innate immune function, and understanding the complex interplay of mechanisms that mediate trained immunity might enable us to employ it as a clinical tool in the future.
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Affiliation(s)
- Valerie A C M Koeken
- Radboud Center for Infectious Diseases and Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), A Joint Venture Between The Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,TWINCORE, a Joint Venture Between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
| | - Reinout van Crevel
- Radboud Center for Infectious Diseases and Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mihai G Netea
- Radboud Center for Infectious Diseases and Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.,Quantitative Systems Biology, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - Yang Li
- Radboud Center for Infectious Diseases and Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), A Joint Venture Between The Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,TWINCORE, a Joint Venture Between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
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