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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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2
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Wang B, Hou L, Yang W, Men X, Qi K, Xu Z, Wu W. Construction of a co-expression network affecting intramuscular fat content and meat color redness based on transcriptome analysis. Front Genet 2024; 15:1351429. [PMID: 38415055 PMCID: PMC10897757 DOI: 10.3389/fgene.2024.1351429] [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: 12/06/2023] [Accepted: 01/26/2024] [Indexed: 02/29/2024] Open
Abstract
Introduction: Intramuscular fat content (IFC) and meat color are vital indicators of pork quality. Methods: A significant positive correlation between IFC and redness of meat color (CIE a* value) indicates that these two traits are likely to be regulated by shared molecular pathways.To identify candidate genes, hub genes, and signaling pathways that regulate these two traits, we measured the IFC and CIE a* value in 147 hybrid pigs, and selected individuls with extreme phenotypes for transcriptome analysis. Results: The results revealed 485 and 394 overlapping differentially expressed genes (DEGs), using the DESeq2, limma, and edgeR packages, affecting the IFC and CIE a* value, respectively. Weighted gene co-expression network analysis (WGCNA) identified four modules significantly correlated with the IFC and CIE a* value. Moreover, we integrated functional enrichment analysis results based on DEGs, GSEA, and WGCNA conditions to identify candidate genes, and identified 47 and 53 candidate genes affecting the IFC and CIE a* value, respectively. The protein protein interaction (PPI) network analysis of candidate genes showed that 5 and 13 hub genes affect the IFC and CIE a* value, respectively. These genes mainly participate in various pathways related to lipid metabolism and redox reactions. Notably, four crucial hub genes (MYC, SOX9, CEBPB, and PPAGRC1A) were shared for these two traits. Discussion and conclusion: After functional annotation of these four hub genes, we hypothesized that the SOX9/CEBPB/PPARGC1A axis could co-regulate lipid metabolism and the myoglobin redox response. Further research on these hub genes, especially the SOX9/CEBPB/PPARGC1A axis, will help to understand the molecular mechanism of the co-regulation of the IFC and CIE a* value, which will provide a theoretical basis for improving pork quality.
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Affiliation(s)
- Binbin Wang
- Institute of Animal Husbandry and Veterinary, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Liming Hou
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Wen Yang
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Xiaoming Men
- Institute of Animal Husbandry and Veterinary, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Keke Qi
- Institute of Animal Husbandry and Veterinary, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Ziwei Xu
- Institute of Animal Husbandry and Veterinary, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Wangjun Wu
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
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Zhou M, Tamburini I, Van C, Molendijk J, Nguyen CM, Chang IYY, Johnson C, Velez LM, Cheon Y, Yeo R, Bae H, Le J, Larson N, Pulido R, Nascimento-Filho CHV, Jang C, Marazzi I, Justice J, Pannunzio N, Hevener AL, Sparks L, Kershaw EE, Nicholas D, Parker BL, Masri S, Seldin MM. Leveraging inter-individual transcriptional correlation structure to infer discrete signaling mechanisms across metabolic tissues. eLife 2024; 12:RP88863. [PMID: 38224289 PMCID: PMC10945578 DOI: 10.7554/elife.88863] [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] [Indexed: 01/16/2024] Open
Abstract
Inter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by 'brute force' surveys of all genes within RNA-sequencing measures across tissues within a population. Expanding on this intuition, we reasoned that parallel strategies could be used to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of gene variation between individuals. Thus, comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling. Here, we surveyed gene-gene correlation structure across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or high-fat/high-sucrose (HFHS) diet. Variation of genes such as FGF21, ADIPOQ, GCG, and IL6 showed enrichments which recapitulate experimental observations. Further, similar analyses were applied to explore both within-tissue signaling mechanisms (liver PCSK9) and genes encoding enzymes producing metabolites (adipose PNPLA2), where inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference of tissue-specific variation in relationships with metabolic traits. We refer to this resource as gene-derived correlations across tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables querying of any gene in any tissue to find correlated patterns of genes, cell types, pathways, and network architectures across metabolic organs.
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Affiliation(s)
- Mingqi Zhou
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Ian Tamburini
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Cassandra Van
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Jeffrey Molendijk
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia
| | - Christy M Nguyen
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | | | - Casey Johnson
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Leandro M Velez
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Youngseo Cheon
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Reichelle Yeo
- Translational Research Institute, AdventHealthOrlandoUnited States
| | - Hosung Bae
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Johnny Le
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Natalie Larson
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Ron Pulido
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Carlos HV Nascimento-Filho
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Cholsoon Jang
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Ivan Marazzi
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Jamie Justice
- Veterans Administration Greater Los Angeles Healthcare System, Geriatric Research Education and Clinical Center (GRECC)Los AngelesUnited States
| | - Nicholas Pannunzio
- Divison of Hematology/Oncology, Department of Medicine, UC Irvine HealthIrvineUnited States
| | - Andrea L Hevener
- Department of Medicine, Division of Endocrinology, Diabetes, and Hypertension, David Geffen School of Medicine at UCLALos AngelesUnited States
- Iris Cantor-UCLA Women’s Health Research Center, David Geffen School of Medicine at UCLALos AngelesUnited States
| | - Lauren Sparks
- Translational Research Institute, AdventHealthOrlandoUnited States
| | - Erin E Kershaw
- Division of Endocrinology, Department of Medicine, University of PittsburgPittsburghUnited States
| | - Dequina Nicholas
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
- Department of Molecular Biology and Biochemistry, School of Biological Sciences, University of California IrvineIrvineUnited States
| | - Benjamin L Parker
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia
| | - Selma Masri
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Marcus M Seldin
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
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Sakkers TR, Mokry M, Civelek M, Erdmann J, Pasterkamp G, Diez Benavente E, den Ruijter HM. Sex differences in the genetic and molecular mechanisms of coronary artery disease. Atherosclerosis 2023; 384:117279. [PMID: 37805337 DOI: 10.1016/j.atherosclerosis.2023.117279] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/09/2023] [Accepted: 09/01/2023] [Indexed: 10/09/2023]
Abstract
Sex differences in coronary artery disease (CAD) presentation, risk factors and prognosis have been widely studied. Similarly, studies on atherosclerosis have shown prominent sex differences in plaque biology. Our understanding of the underlying genetic and molecular mechanisms that drive these differences remains fragmented and largely understudied. Through reviewing genetic and epigenetic studies, we identified more than 40 sex-differential candidate genes (13 within known CAD loci) that may explain, at least in part, sex differences in vascular remodeling, lipid metabolism and endothelial dysfunction. Studies with transcriptomic and single-cell RNA sequencing data from atherosclerotic plaques highlight potential sex differences in smooth muscle cell and endothelial cell biology. Especially, phenotypic switching of smooth muscle cells seems to play a crucial role in female atherosclerosis. This matches the known sex differences in atherosclerotic phenotypes, with men being more prone to lipid-rich plaques, while women are more likely to develop fibrous plaques with endothelial dysfunction. To unravel the complex mechanisms that drive sex differences in CAD, increased statistical power and adjustments to study designs and analysis strategies are required. This entails increasing inclusion rates of women, performing well-defined sex-stratified analyses and the integration of multi-omics data.
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Affiliation(s)
- Tim R Sakkers
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Michal Mokry
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Mete Civelek
- Center for Public Health Genomics, University of Virginia, 1335 Lee St, Charlottesville, VA, 22908, USA; Department of Biomedical Engineering, University of Virginia, 351 McCormick Road, Charlottesville, VA, 22904, USA
| | - Jeanette Erdmann
- Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Gerard Pasterkamp
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Ernest Diez Benavente
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Hester M den Ruijter
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands.
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5
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Huang J, Ji X. Never a dull enzyme, RNA polymerase II. Transcription 2023; 14:49-67. [PMID: 37132022 PMCID: PMC10353340 DOI: 10.1080/21541264.2023.2208023] [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: 02/10/2023] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 05/04/2023] Open
Abstract
RNA polymerase II (Pol II) is composed of 12 subunits that collaborate to synthesize mRNA within the nucleus. Pol II is widely recognized as a passive holoenzyme, with the molecular functions of its subunits largely ignored. Recent studies employing auxin-inducible degron (AID) and multi-omics techniques have revealed that the functional diversity of Pol II is achieved through the differential contributions of its subunits to various transcriptional and post-transcriptional processes. By regulating these processes in a coordinated manner through its subunits, Pol II can optimize its activity for diverse biological functions. Here, we review recent progress in understanding Pol II subunits and their dysregulation in diseases, Pol II heterogeneity, Pol II clusters and the regulatory roles of RNA polymerases.
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Affiliation(s)
- Jie Huang
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Xiong Ji
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
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6
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Michoel T, Zhang JD. Causal inference in drug discovery and development. Drug Discov Today 2023; 28:103737. [PMID: 37591410 DOI: 10.1016/j.drudis.2023.103737] [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: 09/11/2022] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 08/19/2023]
Abstract
To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.
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Affiliation(s)
- Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Postboks 7803, 5020 Bergen, Norway
| | - Jitao David Zhang
- Pharma Early Research and Development, Roche Innovation Centre Basel, F. Hoffmann-La Roche, Grenzacherstrasse 124, 4070 Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Spiegelgasse 1, 4051 Basel, Switzerland.
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7
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Malik MA, Faraone SV, Michoel T, Haavik J. Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders. Pharmacol Ther 2023; 250:108530. [PMID: 37708996 DOI: 10.1016/j.pharmthera.2023.108530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/30/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.
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Affiliation(s)
- Muhammad Ammar Malik
- Computational Biology Unit, Department of Informatics, University of Bergen, PO BOX 7803, 5020 Bergen, Norway
| | - Stephen V Faraone
- Department of Psychiatry, Norton College of Medicine at SUNY Upstate Medical University, 13210, NY, USA
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, PO BOX 7803, 5020 Bergen, Norway
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, PO BOX 7804, 5020 Bergen, Norway; Bergen Center for Brain Plasticity, Division of Psychiatry, Haukeland University Hospital, PO BOX 1400, 5021 Bergen, Norway.
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8
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Bankier S, Wang L, Crawford A, Morgan RA, Ruusalepp A, Andrew R, Björkegren JLM, Walker BR, Michoel T. Plasma cortisol-linked gene networks in hepatic and adipose tissues implicate corticosteroid-binding globulin in modulating tissue glucocorticoid action and cardiovascular risk. Front Endocrinol (Lausanne) 2023; 14:1186252. [PMID: 37745713 PMCID: PMC10513085 DOI: 10.3389/fendo.2023.1186252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/14/2023] [Indexed: 09/26/2023] Open
Abstract
Genome-wide association meta-analysis (GWAMA) by the Cortisol Network (CORNET) consortium identified genetic variants spanning the SERPINA6/SERPINA1 locus on chromosome 14 associated with morning plasma cortisol, cardiovascular disease (CVD), and SERPINA6 mRNA expression encoding corticosteroid-binding globulin (CBG) in the liver. These and other findings indicate that higher plasma cortisol levels are causally associated with CVD; however, the mechanisms by which variations in CBG lead to CVD are undetermined. Using genomic and transcriptomic data from The Stockholm Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET) study, we identified plasma cortisol-linked single-nucleotide polymorphisms (SNPs) that are trans-associated with genes from seven different vascular and metabolic tissues, finding the highest representation of trans-genes in the liver, subcutaneous fat, and visceral abdominal fat, [false discovery rate (FDR) = 15%]. We identified a subset of cortisol-associated trans-genes that are putatively regulated by the glucocorticoid receptor (GR), the primary transcription factor activated by cortisol. Using causal inference, we identified GR-regulated trans-genes that are responsible for the regulation of tissue-specific gene networks. Cis-expression Quantitative Trait Loci (eQTLs) were used as genetic instruments for identification of pairwise causal relationships from which gene networks could be reconstructed. Gene networks were identified in the liver, subcutaneous fat, and visceral abdominal fat, including a high confidence gene network specific to subcutaneous adipose (FDR = 10%) under the regulation of the interferon regulatory transcription factor, IRF2. These data identify a plausible pathway through which variation in the liver CBG production perturbs cortisol-regulated gene networks in peripheral tissues and thereby promote CVD.
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Affiliation(s)
- Sean Bankier
- University/BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
- Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom
| | - Lingfei Wang
- Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew Crawford
- University/BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Ruth A. Morgan
- University/BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
- SRUC, The Roslin Institute, Edinburgh, United Kingdom
| | - Arno Ruusalepp
- Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia
- Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, Estonia
- Clinical Gene Networks AB, Stockholm, Sweden
| | - Ruth Andrew
- University/BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Johan L. M. Björkegren
- Clinical Gene Networks AB, Stockholm, Sweden
- Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
- Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Brian R. Walker
- University/BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
- Clinical and Translational Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
- Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom
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Amadori L, Calcagno C, Fernandez DM, Koplev S, Fernandez N, Kaur R, Mury P, Khan NS, Sajja S, Shamailova R, Cyr Y, Jeon M, Hill CA, Chong PS, Naidu S, Sakurai K, Ghotbi AA, Soler R, Eberhardt N, Rahman A, Faries P, Moore KJ, Fayad ZA, Ma’ayan A, Giannarelli C. Systems immunology-based drug repurposing framework to target inflammation in atherosclerosis. NATURE CARDIOVASCULAR RESEARCH 2023; 2:550-571. [PMID: 37771373 PMCID: PMC10538622 DOI: 10.1038/s44161-023-00278-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 04/28/2023] [Indexed: 09/30/2023]
Abstract
The development of new immunotherapies to treat the inflammatory mechanisms that sustain atherosclerotic cardiovascular disease (ASCVD) is urgently needed. Herein, we present a path to drug repurposing to identify immunotherapies for ASCVD. The integration of time-of-flight mass cytometry and RNA sequencing identified unique inflammatory signatures in peripheral blood mononuclear cells stimulated with ASCVD plasma. By comparing these inflammatory signatures to large-scale gene expression data from the LINCS L1000 dataset, we identified drugs that could reverse this inflammatory response. Ex vivo screens, using human samples, showed that saracatinib-a phase 2a-ready SRC and ABL inhibitor-reversed the inflammatory responses induced by ASCVD plasma. In Apoe-/- mice, saracatinib reduced atherosclerosis progression by reprogramming reparative macrophages. In a rabbit model of advanced atherosclerosis, saracatinib reduced plaque inflammation measured by [18F] fluorodeoxyglucose positron emission tomography-magnetic resonance imaging. Here we show a systems immunology-driven drug repurposing with a preclinical validation strategy to aid the development of cardiovascular immunotherapies.
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Affiliation(s)
- Letizia Amadori
- Department of Medicine, Division of Cardiology, NYU Cardiovascular Research Center, New York, NY USA
- The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Claudia Calcagno
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Dawn M. Fernandez
- Department of Medicine, Division of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Simon Koplev
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Nicolas Fernandez
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Ravneet Kaur
- Department of Medicine, Division of Cardiology, NYU Cardiovascular Research Center, New York, NY USA
| | - Pauline Mury
- Department of Medicine, Division of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Nayaab S Khan
- Department of Medicine, Division of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Swathy Sajja
- Department of Medicine, Division of Cardiology, NYU Cardiovascular Research Center, New York, NY USA
| | - Roza Shamailova
- Department of Medicine, Division of Cardiology, NYU Cardiovascular Research Center, New York, NY USA
| | - Yannick Cyr
- Department of Medicine, Division of Cardiology, NYU Cardiovascular Research Center, New York, NY USA
| | - Minji Jeon
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Christopher A. Hill
- Department of Medicine, Division of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Peik Sean Chong
- The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Sonum Naidu
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Ken Sakurai
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Adam Ali Ghotbi
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Raphael Soler
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Natalia Eberhardt
- Department of Medicine, Division of Cardiology, NYU Cardiovascular Research Center, New York, NY USA
| | - Adeeb Rahman
- The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Peter Faries
- Department of Surgery, Vascular Division, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Kathryn J. Moore
- Department of Medicine, Division of Cardiology, NYU Cardiovascular Research Center, New York, NY USA
| | - Zahi A. Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Avi Ma’ayan
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Chiara Giannarelli
- Department of Medicine, Division of Cardiology, NYU Cardiovascular Research Center, New York, NY USA
- Department of Medicine, Division of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Pathology; NYU Grossman School of Medicine, NYU Langone Health, New York, NY USA
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10
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López Rodríguez M, Arasu UT, Kaikkonen MU. Exploring the genetic basis of coronary artery disease using functional genomics. Atherosclerosis 2023; 374:87-98. [PMID: 36801133 DOI: 10.1016/j.atherosclerosis.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/05/2023]
Abstract
Genome-wide Association Studies (GWAS) have identified more than 300 loci associated with coronary artery disease (CAD), defining the genetic risk map of the disease. However, the translation of the association signals into biological-pathophysiological mechanisms constitute a major challenge. Through a group of examples of studies focused on CAD, we discuss the rationale, basic principles and outcomes of the main methodologies implemented to prioritize and characterize causal variants and their target genes. Additionally, we highlight the strategies as well as the current methods that integrate association and functional genomics data to dissect the cellular specificity underlying the complexity of disease mechanisms. Despite the limitations of existing approaches, the increasing knowledge generated through functional studies helps interpret GWAS maps and opens novel avenues for the clinical usability of association data.
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Affiliation(s)
- Maykel López Rodríguez
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland; Department of Pathology and Laboratory Medicine, University of California, UCLA, Los Angeles, USA.
| | - Uma Thanigai Arasu
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland
| | - Minna U Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland.
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11
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Khan SU, Saeed S, Alsuhaibani AM, Fatima S, Ur Rehman K, Zaman U, Ullah M, Refati MS, Lu K. Advances and Challenges for GWAS Analysis in Cardiac Diseases: A Focus on Coronary Artery Disease (CAD). Curr Probl Cardiol 2023:101821. [PMID: 37211304 DOI: 10.1016/j.cpcardiol.2023.101821] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 05/16/2023] [Indexed: 05/23/2023]
Abstract
The achievement of genome-wide association studies (GWAS) has rapidly progressed our understanding of the etiology of coronary artery disease (CAD). It unlocks new strategies to strengthen the stalling of CAD drug development. In this review, we highlighted the recent drawbacks, mainly pointing out those involved in identifying causal genes and interpreting the connections between disease pathology and risk variants. We also benchmark the novel insights into the biological mechanism behind the disease primarily based on outcomes of GWAS. Furthermore, we also shed light on the successful discovery of novel treatment targets by introducing various layers of "omics" data and applying systems genetics strategies. Lastly, we discuss in-depth the significance of precision medicine that is helpful to improve through GWAS analysis in cardiovascular research.
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Affiliation(s)
- Shahid Ullah Khan
- Integrative Science Center of Germplasm Creation in Western China (CHONGQING) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Beibei, Chongqing 400715, China; Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing 400715, China; Women Medical and Dental College, Khyber Medical University, Peshawar, KPK, Pakistan
| | - Sumbul Saeed
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University, Nathan, QLD, 4111, Australia
| | - Amnah Mohammed Alsuhaibani
- Department of Physical Sport Science, College of Education, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Sumaya Fatima
- Research Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050011, China
| | - Khalil Ur Rehman
- Institute of Chemical Sciences, Gomal University, Dera Ismail Khan 29050, Pakistan
| | - Umber Zaman
- Institute of Chemical Sciences, Gomal University, Dera Ismail Khan 29050, Pakistan
| | - Muneeb Ullah
- Department of Pharmacy, Kohat University of Science and Technology, 26000, KPK, Pakistan
| | - Moamen S Refati
- Department of Chemistry, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Kun Lu
- Integrative Science Center of Germplasm Creation in Western China (CHONGQING) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Beibei, Chongqing 400715, China; Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing 400715, China.
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12
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Wong D, Auguste G, Cardenas CLL, Turner AW, Chen Y, Song Y, Ma L, Perry RN, Aherrahrou R, Kuppusamy M, Yang C, Mosquera JV, Dube CJ, Khan MD, Palmore M, Kalra JK, Kavousi M, Peyser PA, Matic L, Hedin U, Manichaikul A, Sonkusare SK, Civelek M, Kovacic JC, Björkegren JL, Malhotra R, Miller CL. FHL5 Controls Vascular Disease-Associated Gene Programs in Smooth Muscle Cells. Circ Res 2023; 132:1144-1161. [PMID: 37017084 PMCID: PMC10147587 DOI: 10.1161/circresaha.122.321692] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 03/21/2023] [Indexed: 04/06/2023]
Abstract
BACKGROUND Genome-wide association studies have identified hundreds of loci associated with common vascular diseases, such as coronary artery disease, myocardial infarction, and hypertension. However, the lack of mechanistic insights for many GWAS loci limits their translation into the clinic. Among these loci with unknown functions is UFL1-four-and-a-half LIM (LIN-11, Isl-1, MEC-3) domain 5 (FHL5; chr6q16.1), which reached genome-wide significance in a recent coronary artery disease/ myocardial infarction GWAS meta-analysis. UFL1-FHL5 is also associated with several vascular diseases, consistent with the widespread pleiotropy observed for GWAS loci. METHODS We apply a multimodal approach leveraging statistical fine-mapping, epigenomic profiling, and ex vivo analysis of human coronary artery tissues to implicate FHL5 as the top candidate causal gene. We unravel the molecular mechanisms of the cross-phenotype genetic associations through in vitro functional analyses and epigenomic profiling experiments in coronary artery smooth muscle cells. RESULTS We prioritized FHL5 as the top candidate causal gene at the UFL1-FHL5 locus through expression quantitative trait locus colocalization methods. FHL5 gene expression was enriched in the smooth muscle cells and pericyte population in human artery tissues with coexpression network analyses supporting a functional role in regulating smooth muscle cell contraction. Unexpectedly, under procalcifying conditions, FHL5 overexpression promoted vascular calcification and dysregulated processes related to extracellular matrix organization and calcium handling. Lastly, by mapping FHL5 binding sites and inferring FHL5 target gene function using artery tissue gene regulatory network analyses, we highlight regulatory interactions between FHL5 and downstream coronary artery disease/myocardial infarction loci, such as FOXL1 and FN1 that have roles in vascular remodeling. CONCLUSIONS Taken together, these studies provide mechanistic insights into the pleiotropic genetic associations of UFL1-FHL5. We show that FHL5 mediates vascular disease risk through transcriptional regulation of downstream vascular remodeling gene programs. These transacting mechanisms may explain a portion of the heritable risk for complex vascular diseases.
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Affiliation(s)
- Doris Wong
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
| | - Gaëlle Auguste
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Christian L. Lino Cardenas
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Adam W. Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Yixuan Chen
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Yipei Song
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - R. Noah Perry
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Redouane Aherrahrou
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Maniselvan Kuppusamy
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
| | - Chaojie Yang
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Jose Verdezoto Mosquera
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Collin J. Dube
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Mohammad Daud Khan
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Meredith Palmore
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Jaspreet K. Kalra
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, The Netherlands
| | | | - Ljubica Matic
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Ulf Hedin
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Ani Manichaikul
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Swapnil K. Sonkusare
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Mete Civelek
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Jason C. Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- St. Vincent’s Clinical School, University of New South Wales, Sydney, Australia
| | - Johan L.M. Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
- Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Rajeev Malhotra
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Clint L. Miller
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
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13
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Kumar T, Sethuraman R, Mitra S, Ravindran B, Narayanan M. MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication. PLoS Comput Biol 2023; 19:e1011022. [PMID: 37093889 PMCID: PMC10159362 DOI: 10.1371/journal.pcbi.1011022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 05/04/2023] [Accepted: 03/12/2023] [Indexed: 04/25/2023] Open
Abstract
With the evolution of multicellularity, communication among cells in different tissues and organs became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide screens for genes and other biomolecules mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the "influence" of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and performs well on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimer's disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like "Multi-tissue-expanded Gene Ontology" analysis, can enable whole-body yet molecular-level systems investigations in humans.
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Affiliation(s)
- Tarun Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | | | - Sanga Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Balaraman Ravindran
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | - Manikandan Narayanan
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
- Multiscale Digital Neuroanatomy (MDN), IIT Madras, Chennai, India
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14
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Scarpa JR, Elemento O. Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review. Br J Anaesth 2023:S0007-0912(23)00125-3. [PMID: 37055274 DOI: 10.1016/j.bja.2023.03.006] [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: 11/11/2022] [Revised: 02/21/2023] [Accepted: 03/04/2023] [Indexed: 04/15/2023] Open
Abstract
Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.
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Affiliation(s)
- Joseph R Scarpa
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA.
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA
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15
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Wang RS, Maron BA, Loscalzo J. Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases. Arterioscler Thromb Vasc Biol 2023; 43:493-503. [PMID: 36794589 PMCID: PMC10038904 DOI: 10.1161/atvbaha.122.318731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023]
Abstract
Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.
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Affiliation(s)
- Rui-Sheng Wang
- Division of Cardiovascular Medicine
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Joseph Loscalzo
- Division of Cardiovascular Medicine
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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16
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Jurrjens AW, Seldin MM, Giles C, Meikle PJ, Drew BG, Calkin AC. The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases. eLife 2023; 12:e86139. [PMID: 37000167 PMCID: PMC10065800 DOI: 10.7554/elife.86139] [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: 01/16/2023] [Accepted: 03/15/2023] [Indexed: 04/01/2023] Open
Abstract
Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
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Affiliation(s)
- Aaron W Jurrjens
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
| | - Marcus M Seldin
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, United States
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Brian G Drew
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Anna C Calkin
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
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17
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Zhang J, Sheng H, Pan C, Wang S, Yang M, Hu C, Wei D, Wang Y, Ma Y. Identification of key genes in bovine muscle development by co-expression analysis. PeerJ 2023; 11:e15093. [PMID: 37070092 PMCID: PMC10105563 DOI: 10.7717/peerj.15093] [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: 11/25/2022] [Accepted: 02/27/2023] [Indexed: 04/19/2023] Open
Abstract
Background Skeletal muscle is not only an important tissue involved in exercise and metabolism, but also an important part of livestock and poultry meat products. Its growth and development determines the output and quality of meat to a certain extent, and has an important impact on the economic benefits of animal husbandry. Skeletal muscle development is a complex regulatory network process, and its molecular mechanism needs to be further studied. Method We used a weighted co-expression network (WGCNA) and single gene set enrichment analysis (GSEA) to study the RNA-seq data set of bovine tissue differential expression analysis, and the core genes and functional enrichment pathways closely related to muscle tissue development were screened. Finally, the accuracy of the analysis results was verified by tissue expression profile detection and bovine skeletal muscle satellite cell differentiation model in vitro (BSMSCs). Results In this study, Atp2a1, Tmod4, Lmod3, Ryr1 and Mybpc2 were identified as marker genes in muscle tissue, which are mainly involved in glycolysis/gluconeogenesis, AMPK pathway and insulin pathway. The assay results showed that these five genes were highly expressed in muscle tissue and positively correlated with the differentiation of bovine BSMSCs. Conclusions In this study, several muscle tissue characteristic genes were excavated, which may play an important role in muscle development and provide new insights for bovine molecular genetic breeding.
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Affiliation(s)
| | | | | | | | | | | | | | - Yachun Wang
- China Agricultural University, Beijing, China
| | - Yun Ma
- Ningxia University, Yinchuan, China
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18
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Mauersberger C, Sager HB, Wobst J, Dang TA, Lambrecht L, Koplev S, Stroth M, Bettaga N, Schlossmann J, Wunder F, Friebe A, Björkegren JLM, Dietz L, Maas SL, van der Vorst EPC, Sandner P, Soehnlein O, Schunkert H, Kessler T. Loss of soluble guanylyl cyclase in platelets contributes to atherosclerotic plaque formation and vascular inflammation. NATURE CARDIOVASCULAR RESEARCH 2022; 1:1174-1186. [PMID: 37484062 PMCID: PMC10361702 DOI: 10.1038/s44161-022-00175-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 10/27/2022] [Indexed: 07/25/2023]
Abstract
Variants in genes encoding the soluble guanylyl cyclase (sGC) in platelets are associated with coronary artery disease (CAD) risk. Here, by using histology, flow cytometry and intravital microscopy, we show that functional loss of sGC in platelets of atherosclerosis-prone Ldlr-/- mice contributes to atherosclerotic plaque formation, particularly via increasing in vivo leukocyte adhesion to atherosclerotic lesions. In vitro experiments revealed that supernatant from activated platelets lacking sGC promotes leukocyte adhesion to endothelial cells (ECs) by activating ECs. Profiling of platelet-released cytokines indicated that reduced platelet angiopoietin-1 release by sGC-depleted platelets, which was validated in isolated human platelets from carriers of GUCY1A1 risk alleles, enhances leukocyte adhesion to ECs. I mp or ta ntly, p ha rm ac ol ogical sGC stimulation increased platelet angiopoietin-1 release in vitro and reduced leukocyte recruitment and atherosclerotic plaque formation in atherosclerosis-prone Ldlr-/- mice. Therefore, pharmacological sGC stimulation might represent a potential therapeutic strategy to prevent and treat CAD.
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Affiliation(s)
- Carina Mauersberger
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
- These authors contributed equally: Carina Mauersberger, Hendrik B. Sager
| | - Hendrik B. Sager
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
- These authors contributed equally: Carina Mauersberger, Hendrik B. Sager
| | - Jana Wobst
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
| | - Tan An Dang
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
| | - Laura Lambrecht
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
| | - Simon Koplev
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Marlène Stroth
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
| | - Noomen Bettaga
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
| | - Jens Schlossmann
- Department of Pharmacology and Toxicology, University of Regensburg, Regensburg, Germany
| | - Frank Wunder
- Bayer AG, R&D Pharmaceuticals, Wuppertal, Germany
| | - Andreas Friebe
- Institute of Physiology, Julius Maximilian University of Würzburg, Würzburg, Germany
| | - Johan L. M. Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Neo, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, Estonia
| | - Lisa Dietz
- Bayer AG, R&D Pharmaceuticals, Wuppertal, Germany
| | - Sanne L. Maas
- Institute for Molecular Cardiovascular Research and Interdisciplinary Centre for Clinical Research, Rhine-Westphalia Technical University of Aachen, Aachen, Germany
| | - Emiel P. C. van der Vorst
- Institute for Molecular Cardiovascular Research and Interdisciplinary Centre for Clinical Research, Rhine-Westphalia Technical University of Aachen, Aachen, Germany
- Institute for Cardiovascular Prevention, Ludwig Maximilian University of Munich, Munich, Germany
| | | | - Oliver Soehnlein
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
- Institute for Cardiovascular Prevention, Ludwig Maximilian University of Munich, Munich, Germany
- Institute for Experimental Pathology, University of Münster, Münster, Germany
- Department of Physiology and Pharmacology and Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Heribert Schunkert
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
- These authors jointly supervised this work: Heribert Schunkert, Thorsten Kessler
| | - Thorsten Kessler
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research, Munich Heart Alliance, Munich, Germany
- These authors jointly supervised this work: Heribert Schunkert, Thorsten Kessler
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19
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Zhang J, Cheng H, Di Narzo A, Zhu Y, Shan M, Zhang Z, Shao X, Chen J, Wang C, Hao K. Within- and cross-tissue gene regulations were disrupted by PM 2.5 nitrate exposure and associated with respiratory functions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:157977. [PMID: 35964746 DOI: 10.1016/j.scitotenv.2022.157977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 07/25/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pathogenesis of complex diseases often involves multiple organs/tissue-types. To date, the PM2.5 exposure's toxic effects and induced disease risks were not studied at multi-tissue level. METHODS C57BL/6 mice (n = 40) were exposed to PM2.5 NO3- and clean air, respectively, and afterwards assessed respiratory functions and transcriptome in relevant tissues: blood and lung. We constructed within- and cross-tissue gene regulation networks and identified network modules associated with exposure and respiratory functions. RESULTS PM2.5 NO3- exposure elevated naïve B cells proportion in blood (p = 0.0028). Among the 6000 highest expressed genes in blood, 18.8 % (1133 genes) were altered by exposure at p ≤ 0.05 level, among which 763 genes were also associated with respiratory function (enrichment folds = 7.63, p = 2.7E-189). The exposure disrupted blood genes were primarily in the immunoregulation pathways. Both within- and cross-tissue gene network modules were perturbed by exposure and associated with respiratory function. An immunodeficiency related cross-tissue module of 555 genes was affected by exposure (p = 0.0023) and strongly correlated with FEV0.05/FVC (r = 0.61 and p = 3E-5). CONCLUSIONS This study aims to fill in a major knowledge gap and investigated the effect of PM2.5 exposure simultaneously in multiple tissues. We provided novel evidence that PM2.5 NO3- exposure profoundly perturbed within- and cross-tissue gene regulations, and highlighted their roles in the etiology of respiratory decline.
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Affiliation(s)
- Jushan Zhang
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China; College of Environmental Science and Engineering, Tongji University, Shanghai, China
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Antonio Di Narzo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yujie Zhu
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | | | - Zhongyang Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiaowen Shao
- Department of Obstetrics and Gynecology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Jia Chen
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Changhui Wang
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Sema4, Stamford, CT, USA.
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20
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Foguet C, Xu Y, Ritchie SC, Lambert SA, Persyn E, Nath AP, Davenport EE, Roberts DJ, Paul DS, Di Angelantonio E, Danesh J, Butterworth AS, Yau C, Inouye M. Genetically personalised organ-specific metabolic models in health and disease. Nat Commun 2022; 13:7356. [PMID: 36446790 PMCID: PMC9708841 DOI: 10.1038/s41467-022-35017-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/15/2022] [Indexed: 11/30/2022] Open
Abstract
Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.
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Affiliation(s)
- Carles Foguet
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Elodie Persyn
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Artika P Nath
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | | | - David J Roberts
- BRC Haematology Theme, Radcliffe Department of Medicine, and NHSBT-Oxford, John Radcliffe Hospital, Oxford, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant, John Radcliffe Hospital, Oxford, UK
| | - Dirk S Paul
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Emanuele Di Angelantonio
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Science Centre, Human Technopole, Milan, Italy
| | - John Danesh
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - Adam S Butterworth
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - Christopher Yau
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, OX3 9DU, UK
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, NW1 2BE, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- The Alan Turing Institute, London, UK.
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21
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Huang T, Jiang G, Zhang Y, Lei Y, Liu S, Li H, Lu K. The RNA polymerase II subunit Rpb9 activates ATG1 transcription and autophagy. EMBO Rep 2022; 23:e54993. [PMID: 36102592 PMCID: PMC9638876 DOI: 10.15252/embr.202254993] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/23/2022] [Accepted: 08/26/2022] [Indexed: 08/01/2023] Open
Abstract
Macroautophagy/autophagy is a conserved process in eukaryotic cells that mediates the degradation and recycling of intracellular substrates. Proteins encoded by autophagy-related (ATG) genes are essentially involved in the autophagy process and must be tightly regulated in response to various circumstances, such as nutrient-rich and starvation conditions. However, crucial transcriptional activators of ATG genes have remained obscure. Here, we identify the RNA polymerase II subunit Rpb9 as an essential regulator of autophagy by a high-throughput screen of a Saccharomyces cerevisiae gene knockout library. Rpb9 plays a crucial and specific role in upregulating ATG1 transcription, and its deficiency decreases autophagic activities. Rpb9 promotes ATG1 transcription by binding to its promoter region, which is mediated by Gcn4. Furthermore, the function of Rpb9 in autophagy and its regulation of ATG1/ULK1 transcription are conserved in mammalian cells. Together, our results indicate that Rpb9 specifically activates ATG1 transcription and thus positively regulates the autophagy process.
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Affiliation(s)
- Ting Huang
- Department of Neurosurgery, State Key Laboratory of Biotherapy, West China HospitalSichuan UniversityChengduChina
| | - Gaoyue Jiang
- Department of Neurosurgery, State Key Laboratory of Biotherapy, West China HospitalSichuan UniversityChengduChina
| | - Yabin Zhang
- Department of Neurosurgery, State Key Laboratory of Biotherapy, West China HospitalSichuan UniversityChengduChina
| | - Yuqing Lei
- Department of Neurosurgery, State Key Laboratory of Biotherapy, West China HospitalSichuan UniversityChengduChina
| | - Shiyan Liu
- Department of Neurosurgery, State Key Laboratory of Biotherapy, West China HospitalSichuan UniversityChengduChina
| | - Huihui Li
- West China Second University HospitalSichuan UniversityChengduChina
| | - Kefeng Lu
- Department of Neurosurgery, State Key Laboratory of Biotherapy, West China HospitalSichuan UniversityChengduChina
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22
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Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome. Cells 2022; 11:cells11182867. [PMID: 36139449 PMCID: PMC9496853 DOI: 10.3390/cells11182867] [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: 07/19/2022] [Revised: 08/26/2022] [Accepted: 09/10/2022] [Indexed: 12/02/2022] Open
Abstract
Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.
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23
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Lu Z, Gopalan S, Yuan D, Conti DV, Pasaniuc B, Gusev A, Mancuso N. Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies. Am J Hum Genet 2022; 109:1388-1404. [PMID: 35931050 PMCID: PMC9388396 DOI: 10.1016/j.ajhg.2022.07.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
Transcriptome-wide association studies (TWASs) are a powerful approach to identify genes whose expression is associated with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium (LD) patterns and eQTL pleiotropy at genomic risk regions, which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample sizes across multiple metrics. We perform TWASs for 15 blood traits using genome-wide summary statistics (average nEA = 511 k, nAA = 13 k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries than SNP-level effects. Lastly, we perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries than they are to be ancestry specific. Using multiple lines of evidence to validate our findings, we find that gene sets produced by MA-FOCUS are more enriched in hematopoietic categories than alternative approaches (p = 2.36 × 10-15). Our work demonstrates that including and appropriately accounting for genetic diversity can drive more profound insights into the genetic architecture of complex traits.
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Affiliation(s)
- Zeyun Lu
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA,Corresponding author
| | - Shyamalika Gopalan
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA,Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
| | - Dong Yuan
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - David V. Conti
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA,Department of Human Genetics, 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,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Division of Population Sciences, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA, USA,Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA,Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA, USA
| | - Nicholas Mancuso
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA,Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA,Corresponding author
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24
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Sonawane AR, Aikawa E, Aikawa M. Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases. Front Cardiovasc Med 2022; 9:873582. [PMID: 35665246 PMCID: PMC9160390 DOI: 10.3389/fcvm.2022.873582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/19/2022] [Indexed: 01/18/2023] Open
Abstract
Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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25
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Turner AW, Hu SS, Mosquera JV, Ma WF, Hodonsky CJ, Wong D, Auguste G, Song Y, Sol-Church K, Farber E, Kundu S, Kundaje A, Lopez NG, Ma L, Ghosh SKB, Onengut-Gumuscu S, Ashley EA, Quertermous T, Finn AV, Leeper NJ, Kovacic JC, Björkgren JLM, Zang C, Miller CL. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat Genet 2022; 54:804-816. [PMID: 35590109 PMCID: PMC9203933 DOI: 10.1038/s41588-022-01069-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 03/31/2022] [Indexed: 12/24/2022]
Abstract
Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across cell types. Genome-wide association studies (GWAS) have identified over 200 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements (CREs). Here, we applied single-nucleus ATAC-seq to profile 28,316 nuclei across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell type-specific elements, transcription factors, and prioritized functional CAD risk variants. . We identified elements in smooth muscle cell (SMC) transition states (e.g. fibromyocytes) and functional variants predicted to alter SMC and macrophage-specific regulation of MRAS (3q22) and LIPA (10q23), respectively. We further nominated key driver transcription factors such as PRDM16 and TBX2. Together, this single nucleus atlas provides a critical step towards interpreting regulatory mechanisms across the continuum of CAD risk.
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Affiliation(s)
- Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Shengen Shawn Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jose Verdezoto Mosquera
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.,Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
| | - Wei Feng Ma
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.,Medical Scientist Training Program, University of Virginia, Charlottesville, VA, USA.,Department of Pathology, University of Virginia, Charlottesville, VA, USA
| | - Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.,Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
| | - Doris Wong
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.,Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.,Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
| | - Gaëlle Auguste
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Yipei Song
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Katia Sol-Church
- Department of Pathology, University of Virginia, Charlottesville, VA, USA.,Genome Analysis & Technology Core, University of Virginia, Charlottesville, VA, USA
| | - Emily Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.,Genome Sciences Laboratory, University of Virginia, Charlottesville, VA, USA
| | - Soumya Kundu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.,Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Nicolas G Lopez
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.,Genome Sciences Laboratory, University of Virginia, Charlottesville, VA, USA.,Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Euan A Ashley
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.,Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Thomas Quertermous
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | | | - Nicholas J Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Jason C Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Johan L M Björkgren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Chongzhi Zang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA. .,Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA. .,Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
| | - Clint L Miller
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA. .,Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA. .,Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA. .,Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
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26
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Yan S, Meng L, Guo X, Chen Z, Zhang Y, Li Y. Identification of ITGAX and CCR1 as potential biomarkers of atherosclerosis via Gene Set Enrichment Analysis. J Int Med Res 2022; 50:3000605211039480. [PMID: 35287505 PMCID: PMC8928411 DOI: 10.1177/03000605211039480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Objective Atherosclerosis (AS) is a life-threatening disease in aging populations worldwide. However, the molecular and gene regulation mechanisms of AS are still unclear. This study aimed to identify gene expression differences between atheroma plaques and normal tissues in humans. Methods The expression profiling dataset GSE43292 was obtained from the Gene Expression Omnibus (GEO) dataset. The differentially expressed genes (DEGs) were identified between the atheroma plaques and normal tissues via GEO2R, and functional annotation of the DEGs was performed by GSEA. STRING and MCODE plug-in of Cytoscape were used to construct a protein–protein interaction (PPI) network and analyze hub genes. Finally, quantitative polymerase chain reaction (qPCR) was performed to verify the hub genes. Results Overall, 134 DEGs were screened. Functional annotation demonstrated that these DEGs were mainly enriched in sphingolipid metabolism, apoptosis, lysosome, and more. Six hub genes were identified from the PPI network: ITGAX, CCR1, IL1RN, CXCL10, CD163, and MMP9. qPCR analysis suggested that the relative expression levels of the six hub genes were significantly higher in AS samples. Conclusions We used bioinformatics to identify six hub genes: ITGAX, CCR1, IL1RN, CXCL10, CD163, and MMP9. These hub genes are potential promising diagnostic and therapeutic targets for AS.
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Affiliation(s)
- Sheng Yan
- Department of Vascular Surgery, 117555Beijing Hospital, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science and Peking Union Medical College, Graduate School of Peking Union Medical College, Beijing, P.R. China
| | - Lingbing Meng
- Neurology Department, 117555Beijing Hospital, Beijing Hospital, National Center of Gerontology, No. 1 Dahua Road, Dong Dan, Beijing, P. R. China
| | - Xiaoyong Guo
- Internal Medicine Department, 12485Anhui Medical University, Anhui Medical University, Meishan Road, Hefei, Anhui, P. R. China
| | - Zuoguan Chen
- Department of Vascular Surgery, 117555Beijing Hospital, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science and Peking Union Medical College, Graduate School of Peking Union Medical College, Beijing, P.R. China
| | - Yuanmeng Zhang
- Internal Medicine Department, 154516Jinzhou Medical University, Jinzhou Medical University, No. 40, Section 3, Songpo Road, Linghe District, Jinzhou, Liaoning, P.R. China
| | - Yongjun Li
- Department of Vascular Surgery, 117555Beijing Hospital, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science and Peking Union Medical College, Graduate School of Peking Union Medical College, Beijing, P.R. China
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Vilne B, Sawant A, Rudaka I. Examining the Association between Mitochondrial Genome Variation and Coronary Artery Disease. Genes (Basel) 2022; 13:genes13030516. [PMID: 35328073 PMCID: PMC8953999 DOI: 10.3390/genes13030516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 12/04/2022] Open
Abstract
Large-scale genome-wide association studies have identified hundreds of single-nucleotide variants (SNVs) significantly associated with coronary artery disease (CAD). However, collectively, these explain <20% of the heritability. Hypothesis: Here, we hypothesize that mitochondrial (MT)-SNVs might present one potential source of this “missing heritability”. Methods: We analyzed 265 MT-SNVs in ~500,000 UK Biobank individuals, exploring two different CAD definitions: a more stringent (myocardial infarction and/or revascularization; HARD = 20,405), and a more inclusive (angina and chronic ischemic heart disease; SOFT = 34,782). Results: In HARD cases, the most significant (p < 0.05) associations were for m.295C>T (control region) and m.12612A>G (ND5), found more frequently in cases (OR = 1.05), potentially related to reduced cardiorespiratory fitness in response to exercise, as well as for m.12372G>A (ND5) and m.11467A>G (ND4), present more frequently in controls (OR = 0.97), previously associated with lower ROS production rate. In SOFT cases, four MT-SNVs survived multiple testing corrections (at FDR < 5%), all potentially conferring increased CAD risk. Of those, m.11251A>G (ND4) and m.15452C>A (CYB) have previously shown significant associations with body height. In line with this, we observed that CAD cases were slightly less physically active, and their average body height was ~2.00 cm lower compared to controls; both traits are known to be related to increased CAD risk. Gene-based tests identified CO2 associated with HARD/SOFT CAD, whereas ND3 and CYB associated with SOFT cases (p < 0.05), dysfunction of which has been related to MT oxidative stress, obesity/T2D (CO2), BMI (ND3), and angina/exercise intolerance (CYB). Finally, we observed that macro-haplogroup I was significantly (p < 0.05) more frequent in HARD cases vs. controls (3.35% vs. 3.08%), potentially associated with response to exercise. Conclusions: We found only spurious associations between MT genome variation and HARD/SOFT CAD and conclude that more MT-SNV data in even larger study cohorts may be needed to conclusively determine the role of MT DNA in CAD.
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Affiliation(s)
- Baiba Vilne
- Bioinformatics Lab, Rīga Stradiņš University, LV-1007 Riga, Latvia;
- Correspondence:
| | - Aniket Sawant
- Bioinformatics Lab, Rīga Stradiņš University, LV-1007 Riga, Latvia;
| | - Irina Rudaka
- Scientific Laboratory of Molecular Genetics, Rīga Stradiņš University, LV-1007 Riga, Latvia;
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28
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Hao K, Ermel R, Sukhavasi K, Cheng H, Ma L, Li L, Amadori L, Koplev S, Franzén O, d’Escamard V, Chandel N, Wolhuter K, Bryce NS, Venkata VRM, Miller CL, Ruusalepp A, Schunkert H, Björkegren JL, Kovacic JC. Integrative Prioritization of Causal Genes for Coronary Artery Disease. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003365. [PMID: 34961328 PMCID: PMC8847335 DOI: 10.1161/circgen.121.003365] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets. METHODS We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). RESULTS We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. CONCLUSIONS We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.
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Affiliation(s)
- Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Sema4, Stamford, CT
| | - Raili Ermel
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital, Tartu, Estonia
| | - Katyayani Sukhavasi
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital, Tartu, Estonia
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ling Li
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany,Center for Doctoral Studies in Informatics and its Applications, Dept of Informatics, Technische Universität München, Munich, Germany,Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Munich, Germany
| | - Letizia Amadori
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Current affiliation: New York University Cardiovascular Research Center, Department of Medicine, Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, New York University Langone Health, New York, NY
| | - Simon Koplev
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Oscar Franzén
- Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | - Valentina d’Escamard
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nirupama Chandel
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kathryn Wolhuter
- Victor Chang Cardiac Research Institute, Darlinghurst, Australia,University of New South Wales, Faculty of Medicine and Health, Sydney, Australia
| | - Nicole S. Bryce
- Victor Chang Cardiac Research Institute, Darlinghurst, Australia,University of New South Wales, Faculty of Medicine and Health, Sydney, Australia
| | | | - Clint L. Miller
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA
| | - Arno Ruusalepp
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital, Tartu, Estonia,Department of Cardiology, Institute of Clinical Medicine, Tartu University
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany,Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Munich, Germany
| | - Johan L.M. Björkegren
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | - Jason C. Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY,Victor Chang Cardiac Research Institute, Darlinghurst, Australia,St Vincent's Clinical School, University of New South Wales, Sydney, Australia
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29
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Choi S, Kim GS, Yang J, Cho H, Kang CY, Wang G. Controllable SiO x Nanorod Memristive Neuron for Probabilistic Bayesian Inference. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2104598. [PMID: 34618384 DOI: 10.1002/adma.202104598] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/06/2021] [Indexed: 06/13/2023]
Abstract
Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiOx nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate-and-fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiOx nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 1010 ) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self-resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10-2 ) and its robustness to the ProbAct variation.
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Affiliation(s)
- Sanghyeon Choi
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Gwang Su Kim
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Jehyeon Yang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Haein Cho
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Chong-Yun Kang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Integrative Energy Engineering, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
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30
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Koplev S, Seldin M, Sukhavasi K, Ermel R, Pang S, Zeng L, Bankier S, Di Narzo A, Cheng H, Meda V, Ma A, Talukdar H, Cohain A, Amadori L, Argmann C, Houten SM, Franzén O, Mocci G, Meelu OA, Ishikawa K, Whatling C, Jain A, Jain RK, Gan LM, Giannarelli C, Roussos P, Hao K, Schunkert H, Michoel T, Ruusalepp A, Schadt EE, Kovacic JC, Lusis AJ, Björkegren JLM. A mechanistic framework for cardiometabolic and coronary artery diseases. NATURE CARDIOVASCULAR RESEARCH 2022; 1:85-100. [PMID: 36276926 PMCID: PMC9583458 DOI: 10.1038/s44161-021-00009-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with (n = 600) and without (n = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver (n = 152). This axis was independently replicated in genetically diverse mouse strains and by injection of recombinant forms of adipose endocrine factors (EPDR1, FCN2, FSTL3 and LBP) that markedly altered blood lipid and glucose levels in mice. Altogether, the STARNET database and the associated GRN browser (http://starnet.mssm.edu) provide a multiorgan framework for exploration of the molecular interplay between cardiometabolic disorders and CAD.
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Affiliation(s)
- Simon Koplev
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marcus Seldin
- Departments of Medicine, Human Genetics and Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, CA, USA
| | - Katyayani Sukhavasi
- Department of Cardiac Surgery and the Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, Estonia
| | - Raili Ermel
- Department of Cardiac Surgery and the Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, Estonia
| | - Shichao Pang
- Deutsches Herzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Technische Universität München, DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Munich, Germany
| | - Lingyao Zeng
- Deutsches Herzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Technische Universität München, DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Munich, Germany
| | - Sean Bankier
- BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Antonio Di Narzo
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vamsidhar Meda
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Angela Ma
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Husain Talukdar
- Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | - Ariella Cohain
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Letizia Amadori
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- New York University Cardiovascular Research Center, Department of Medicine, Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, New York University Langone Health, New York, NY, USA
| | - Carmen Argmann
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sander M. Houten
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Oscar Franzén
- Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | - Giuseppe Mocci
- Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | - Omar A. Meelu
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kiyotake Ishikawa
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carl Whatling
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Anamika Jain
- Department of Cardiac Surgery and the Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, Estonia
| | - Rajeev Kumar Jain
- Department of Cardiac Surgery and the Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, Estonia
| | - Li-Ming Gan
- Early Clinical Development, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Chiara Giannarelli
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- New York University Cardiovascular Research Center, Department of Medicine, Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, New York University Langone Health, New York, NY, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Sema4, Stamford, CT, USA
| | - Heribert Schunkert
- Deutsches Herzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Technische Universität München, DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Munich, Germany
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Arno Ruusalepp
- Department of Cardiac Surgery and the Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, Estonia
- Clinical Gene Networks AB, Stockholm, Sweden
| | - Eric E. Schadt
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Sema4, Stamford, CT, USA
| | - Jason C. Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- St Vincent’s Clinical School, University of NSW, Sydney, New South Wales, Australia
| | - Aldon J. Lusis
- Departments of Medicine, Human Genetics and Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Johan L. M. Björkegren
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
- Clinical Gene Networks AB, Stockholm, Sweden
- Correspondence and requests for materials should be addressed to Johan L. M. Björkegren.
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31
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Bankier S, Michoel T. eQTLs as causal instruments for the reconstruction of hormone linked gene networks. Front Endocrinol (Lausanne) 2022; 13:949061. [PMID: 36060942 PMCID: PMC9428692 DOI: 10.3389/fendo.2022.949061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.
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Abstract
Inflammation is intimately involved at all stages of atherosclerosis and remains a substantial residual cardiovascular risk factor in optimally treated patients. The proof of concept that targeting inflammation reduces cardiovascular events in patients with a history of myocardial infarction has highlighted the urgent need to identify new immunotherapies to treat patients with atherosclerotic cardiovascular disease. Importantly, emerging data from new clinical trials show that successful immunotherapies for atherosclerosis need to be tailored to the specific immune alterations in distinct groups of patients. In this Review, we discuss how single-cell technologies - such as single-cell mass cytometry, single-cell RNA sequencing and cellular indexing of transcriptomes and epitopes by sequencing - are ideal for mapping the cellular and molecular composition of human atherosclerotic plaques and how these data can aid in the discovery of new precise immunotherapies. We also argue that single-cell data from studies in humans need to be rigorously validated in relevant experimental models, including rapidly emerging single-cell CRISPR screening technologies and mouse models of atherosclerosis. Finally, we discuss the importance of implementing single-cell immune monitoring tools in early phases of drug development to aid in the precise selection of the target patient population for data-driven translation into randomized clinical trials and the successful translation of new immunotherapies into the clinic.
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Affiliation(s)
- Dawn M Fernandez
- Department of Medicine, Division of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chiara Giannarelli
- Department of Medicine, Division of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- New York University Cardiovascular Research Center, New York University Langone Health, New York, NY, USA.
- Department of Pathology, New York University Grossman School of Medicine, New York University Langone Health, New York, NY, USA.
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33
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Tau activates microglia via the PQBP1-cGAS-STING pathway to promote brain inflammation. Nat Commun 2021; 12:6565. [PMID: 34782623 PMCID: PMC8592984 DOI: 10.1038/s41467-021-26851-2] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 10/23/2021] [Indexed: 12/15/2022] Open
Abstract
Brain inflammation generally accompanies and accelerates neurodegeneration. Here we report a microglial mechanism in which polyglutamine binding protein 1 (PQBP1) senses extrinsic tau 3R/4R proteins by direct interaction and triggers an innate immune response by activating a cyclic GMP-AMP synthase (cGAS)-Stimulator of interferon genes (STING) pathway. Tamoxifen-inducible and microglia-specific depletion of PQBP1 in primary culture in vitro and mouse brain in vivo shows that PQBP1 is essential for sensing-tau to induce nuclear translocation of nuclear factor κB (NFκB), NFκB-dependent transcription of inflammation genes, brain inflammation in vivo, and eventually mouse cognitive impairment. Collectively, PQBP1 is an intracellular receptor in the cGAS-STING pathway not only for cDNA of human immunodeficiency virus (HIV) but also for the transmissible neurodegenerative disease protein tau. This study characterises a mechanism of brain inflammation that is common to virus infection and neurodegenerative disorders.
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34
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Chen J, Cheong C, Lan L, Zhou X, Liu J, Lyu A, Cheung WK, Zhang L. DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data. Brief Bioinform 2021; 22:bbab325. [PMID: 34424948 PMCID: PMC8499812 DOI: 10.1093/bib/bbab325] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/12/2021] [Accepted: 07/26/2021] [Indexed: 01/11/2023] Open
Abstract
Single-cell RNA sequencing has enabled to capture the gene activities at single-cell resolution, thus allowing reconstruction of cell-type-specific gene regulatory networks (GRNs). The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity. In this paper, we represent the joint gene expression distribution of a gene pair as an image and propose a novel supervised deep neural network called DeepDRIM which utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene pair's neighborhood context, DeepDRIM can effectively eliminate the false positives caused by transitive gene-gene interactions. We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data. It achieves evidently better performance for the scRNA-seq data collected from eight cell lines. The simulated data show that DeepDRIM is robust to the dropout rate, the cell number and the size of the training data. We further applied DeepDRIM to the scRNA-seq gene expression of B cells from the bronchoalveolar lavage fluid of the patients with mild and severe coronavirus disease 2019. We focused on the cell-type-specific GRN alteration and observed targets of TFs that were differentially expressed between the two statuses to be enriched in lysosome, apoptosis, response to decreased oxygen level and microtubule, which had been proved to be associated with coronavirus infection.
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Affiliation(s)
- Jiaxing Chen
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - ChinWang Cheong
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - Liang Lan
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - Xin Zhou
- Department of Biomedical Engineering, Vanderbilt University, Vanderbilt Place Nashville, 37235, TN, USA
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - Aiping Lyu
- School of Chinese Medicine, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - William K Cheung
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - Lu Zhang
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
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Abstract
During the past decade, genome-wide association studies (GWAS) have transformed our understanding of many heritable traits. Three recent large-scale GWAS meta-analyses now further markedly expand the knowledge on coronary artery disease (CAD) genetics in doubling the number of loci with genome-wide significant signals. Here, we review the unprecedented discoveries of CAD GWAS on low-frequency variants, underrepresented populations, sex differences and integrated polygenic risk. We present the milestones of CAD GWAS and post-GWAS studies from 2007 to 2021, and the trend in identification of variants with smaller odds ratio by year due to the increasing sample size. We compile the 321 CAD loci discovered thus far and classify candidate genes as well as distinct functional pathways on the road to indepth biological investigation and identification of novel treatment targets. We draw attention to systems genetics in integrating these loci into gene regulatory networks within and across tissues. We review the traits, biomarkers and diseases scrutinized by Mendelian randomization studies for CAD. Finally, we discuss the potentials and concerns of polygenic scores in predicting CAD risk in patient care as well as future directions of GWAS and post-GWAS studies in the field of precision medicine.
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Affiliation(s)
- Zhifen Chen
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany.,Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Munich, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany.,Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Munich, Germany
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Jia Z, Yang F, Liu X, Zhang X, Hu W, Sheng Z. The n-butanol fraction of the Xiao-Chai-Hu decoction alleviates the endocrine disturbance in the liver of mice exposed to lead. JOURNAL OF ETHNOPHARMACOLOGY 2021; 279:114381. [PMID: 34197961 DOI: 10.1016/j.jep.2021.114381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/26/2021] [Accepted: 06/26/2021] [Indexed: 06/13/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Lead is a toxic heavy metal that causes health risks globally. However, the mechanism of endocrine poisoning and detoxification of lead poisoning, especially in the liver, still needs to be studied. Xiao-Chai-Hu decoction (XCHD) is regarded as an antidote and an anti-hepatotoxic traditional prescription that has been recorded in the pharmacopeia of the People's Republic of China. AIM OF THE STUDY The study aimed to probe the hepatoprotective activity of XCHD in the regulation of endocrine dysfunction in the liver and its molecular mechanism. MATERIALS AND METHODS The mice from the Institute of Cancer Research (ICR) were exposed to different concentrations of XCHD and lead. Then, serum biochemical indices and liver pathology were analyzed. The key differential genes were detected by qRT-PCR and Western blot. RESULTS According to the biochemical and histopathological analysis, XCHD-NBA was the most effective in attenuating lead-induced hepatotoxicity. From the transcriptome, we analyzed the key genes of XCHD-NBA in the regulation of lead toxicity, including Tubb2a, Stip1, Cyp4a12a, Cyp2c50, Ugt1a1, Cyp3a11, Cyp4a12b, Ahsa1, Cyp2c54, Tubb4b, Esr1, Hsp90aa1, Tuba1a, Tuba1c, and Hsph1. We also analyzed the main components of XCHD-NBA by LC-MS. Because of their extensive role in regulating the endocrine function, baicalin and glycyrrhizin were identified as the main active components of XCHD in regulating endocrine disorders caused by lead. CONCLUSIONS Lead can disturb the endocrine regulatory process of the liver, while XCHD-NBA alleviates lead-induced liver injury by regulating the endocrine regulatory process.
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Affiliation(s)
- Zheng Jia
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, PR China; Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, PR China
| | - Fan Yang
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, PR China
| | - Xiaoqing Liu
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, PR China
| | - Xiaomeng Zhang
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, PR China
| | - Wanjun Hu
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, PR China
| | - Zunlai Sheng
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, PR China; Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, PR China.
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37
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Usova EII, Alieva AS, Yakovlev AN, Alieva MS, Prokhorikhin AA, Konradi AO, Shlyakhto EV, Magni P, Catapano AL, Baragetti A. Integrative Analysis of Multi-Omics and Genetic Approaches-A New Level in Atherosclerotic Cardiovascular Risk Prediction. Biomolecules 2021; 11:1597. [PMID: 34827594 PMCID: PMC8615817 DOI: 10.3390/biom11111597] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 10/25/2021] [Accepted: 10/27/2021] [Indexed: 12/18/2022] Open
Abstract
Genetics and environmental and lifestyle factors deeply affect cardiovascular diseases, with atherosclerosis as the etiopathological factor (ACVD) and their early recognition can significantly contribute to an efficient prevention and treatment of the disease. Due to the vast number of these factors, only the novel "omic" approaches are surmised. In addition to genomics, which extended the effective therapeutic potential for complex and rarer diseases, the use of "omics" presents a step-forward that can be harnessed for more accurate ACVD prediction and risk assessment in larger populations. The analysis of these data by artificial intelligence (AI)/machine learning (ML) strategies makes is possible to decipher the large amount of data that derives from such techniques, in order to provide an unbiased assessment of pathophysiological correlations and to develop a better understanding of the molecular background of ACVD. The predictive models implementing data from these "omics", are based on consolidated AI best practices for classical ML and deep learning paradigms that employ methods (e.g., Integrative Network Fusion method, using an AI/ML supervised strategy and cross-validation) to validate the reproducibility of the results. Here, we highlight the proposed integrated approach for the prediction and diagnosis of ACVD with the presentation of the key elements of a joint scientific project of the University of Milan and the Almazov National Medical Research Centre.
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Affiliation(s)
- EIena I. Usova
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (E.I.U.); (A.N.Y.); (M.S.A.); (A.A.P.); (A.O.K.); (E.V.S.)
| | - Asiiat S. Alieva
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (E.I.U.); (A.N.Y.); (M.S.A.); (A.A.P.); (A.O.K.); (E.V.S.)
| | - Alexey N. Yakovlev
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (E.I.U.); (A.N.Y.); (M.S.A.); (A.A.P.); (A.O.K.); (E.V.S.)
| | - Madina S. Alieva
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (E.I.U.); (A.N.Y.); (M.S.A.); (A.A.P.); (A.O.K.); (E.V.S.)
| | - Alexey A. Prokhorikhin
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (E.I.U.); (A.N.Y.); (M.S.A.); (A.A.P.); (A.O.K.); (E.V.S.)
| | - Alexandra O. Konradi
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (E.I.U.); (A.N.Y.); (M.S.A.); (A.A.P.); (A.O.K.); (E.V.S.)
| | - Evgeny V. Shlyakhto
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (E.I.U.); (A.N.Y.); (M.S.A.); (A.A.P.); (A.O.K.); (E.V.S.)
| | - Paolo Magni
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, 20133 Milan, Italy; (A.L.C.); (A.B.)
- IRCCS Multimedica Hospital, Sesto San Giovanni, 20099 Milan, Italy
| | - Alberico L. Catapano
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, 20133 Milan, Italy; (A.L.C.); (A.B.)
- IRCCS Multimedica Hospital, Sesto San Giovanni, 20099 Milan, Italy
| | - Andrea Baragetti
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, 20133 Milan, Italy; (A.L.C.); (A.B.)
- IRCCS Multimedica Hospital, Sesto San Giovanni, 20099 Milan, Italy
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Lee T, Lee H. Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis. Biomedicines 2021; 9:biomedicines9111525. [PMID: 34829754 PMCID: PMC8614900 DOI: 10.3390/biomedicines9111525] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 01/09/2023] Open
Abstract
Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.
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Affiliation(s)
- Taesic Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
| | - Hyunju Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
- Correspondence: ; Tel.: +82-62-715-2213
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Patient Endothelial Colony-Forming Cells to Model Coronary Artery Disease Susceptibility and Unravel the Role of Dysregulated Mitochondrial Redox Signalling. Antioxidants (Basel) 2021; 10:antiox10101547. [PMID: 34679682 PMCID: PMC8532880 DOI: 10.3390/antiox10101547] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 09/25/2021] [Accepted: 09/27/2021] [Indexed: 01/02/2023] Open
Abstract
Mechanisms involved in the individual susceptibility to atherosclerotic coronary artery disease (CAD) beyond traditional risk factors are poorly understood. Here, we describe the utility of cultured patient-derived endothelial colony-forming cells (ECFCs) in examining novel mechanisms of CAD susceptibility, particularly the role of dysregulated redox signalling. ECFCs were selectively cultured from peripheral blood mononuclear cells from 828 patients from the BioHEART-CT cohort, each with corresponding demographic, clinical and CT coronary angiographic imaging data. Spontaneous growth occurred in 178 (21.5%) patients and was more common in patients with hypertension (OR 1.45 (95% CI 1.03-2.02), p = 0.031), and less likely in patients with obesity (OR 0.62 [95% CI 0.40-0.95], p = 0.027) or obstructive CAD (stenosis > 50%) (OR 0.60 [95% CI 0.38-0.95], p = 0.027). ECFCs from patients with CAD had higher mitochondrial production of superoxide (O2--MitoSOX assay). The latter was strongly correlated with the severity of CAD as measured by either coronary artery calcium score (R2 = 0.46; p = 0.0051) or Gensini Score (R2 = 0.67; p = 0.0002). Patient-derived ECFCs were successfully cultured in 3D culture pulsatile mini-vessels. Patient-derived ECFCs can provide a novel resource for discovering mechanisms of CAD disease susceptibility, particularly in relation to mitochondrial redox signalling.
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40
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Westerlund AM, Hawe JS, Heinig M, Schunkert H. Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence. Int J Mol Sci 2021; 22:10291. [PMID: 34638627 PMCID: PMC8508897 DOI: 10.3390/ijms221910291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.
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Affiliation(s)
- Annie M. Westerlund
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
- Institute of Computational Biology, HelmholtzZentrum München, Ingolstädter Landstrasse 1, 85764 Munich, Germany
| | - Johann S. Hawe
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
| | - Matthias Heinig
- Institute of Computational Biology, HelmholtzZentrum München, Ingolstädter Landstrasse 1, 85764 Munich, Germany
- Department of Informatics, Technical University Munich, Boltzmannstrasse 3, 85748 Garching, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
- Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Biedersteiner Strasse 29, 80802 Munich, Germany
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Xing K, Liu H, Zhang F, Liu Y, Shi Y, Ding X, Wang C. Identification of key genes affecting porcine fat deposition based on co-expression network analysis of weighted genes. J Anim Sci Biotechnol 2021; 12:100. [PMID: 34419151 PMCID: PMC8379819 DOI: 10.1186/s40104-021-00616-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 07/01/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fat deposition is an important economic consideration in pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers' choice of pork. Weighted gene co-expression network analysis (WGCNA) is effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low) and to identify the core genes in each of these modules. RESULTS We used RNA sequences generated in different pig tissues to construct a gene expression matrix consisting of 12,862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA and the number of genes in these modules ranged from 39 to 3,363. Four co-expression modules were significantly correlated with backfat thickness. A total of 16 genes (RAD9A, IGF2R, SCAP, TCAP, SMYD1, PFKM, DGAT1, GPS2, IGF1, MAPK8, FABP, FABP5, LEPR, UCP3, APOF, and FASN) were associated with fat deposition. CONCLUSIONS RAD9A, TCAP, SMYD1, PFKM, GPS2, and APOF were the key genes in the four modules based on the degree of gene connectivity. Combining these results with those from differential gene analysis, SMYD1 and PFKM were proposed as strong candidate genes for body size traits. This study explored the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms underlying porcine fat deposition.
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Affiliation(s)
- Kai Xing
- Animal Science and Technology College, Beijing University of Agriculture, Beijing, China
| | - Huatao Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Fengxia Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yibing Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yong Shi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiangdong Ding
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
| | - Chuduan Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
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Joshi A, Rienks M, Theofilatos K, Mayr M. Systems biology in cardiovascular disease: a multiomics approach. Nat Rev Cardiol 2021; 18:313-330. [PMID: 33340009 DOI: 10.1038/s41569-020-00477-1] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 12/13/2022]
Abstract
Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.
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Affiliation(s)
- Abhishek Joshi
- King's British Heart Foundation Centre, King's College London, London, UK
- Bart's Heart Centre, St. Bartholomew's Hospital, London, UK
| | - Marieke Rienks
- King's British Heart Foundation Centre, King's College London, London, UK
| | | | - Manuel Mayr
- King's British Heart Foundation Centre, King's College London, London, UK.
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Precision Medicine Approaches to Vascular Disease: JACC Focus Seminar 2/5. J Am Coll Cardiol 2021; 77:2531-2550. [PMID: 34016266 DOI: 10.1016/j.jacc.2021.04.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/31/2021] [Accepted: 04/02/2021] [Indexed: 12/16/2022]
Abstract
In this second of a 5-part Focus Seminar series, we focus on precision medicine in the context of vascular disease. The most common vascular disease worldwide is atherosclerosis, which is the primary cause of coronary artery disease, peripheral vascular disease, and a large proportion of strokes and other disorders. Atherosclerosis is a complex genetic disease that likely involves many hundreds to thousands of single nucleotide polymorphisms, each with a relatively modest effect for causing disease. Conversely, although less prevalent, there are many vascular disorders that typically involve only a single genetic change, but these changes can often have a profound effect that is sufficient to cause disease. These are termed "Mendelian vascular diseases," which include Marfan and Loeys-Dietz syndromes. Given the very different genetic basis of atherosclerosis versus Mendelian vascular diseases, this article was divided into 2 parts to cover the most promising precision medicine approaches for these disease types.
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44
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Hartman RJG, Owsiany K, Ma L, Koplev S, Hao K, Slenders L, Civelek M, Mokry M, Kovacic JC, Pasterkamp G, Owens G, Björkegren JLM, den Ruijter HM. Sex-Stratified Gene Regulatory Networks Reveal Female Key Driver Genes of Atherosclerosis Involved in Smooth Muscle Cell Phenotype Switching. Circulation 2021; 143:713-726. [PMID: 33499648 PMCID: PMC7930467 DOI: 10.1161/circulationaha.120.051231] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although sex differences in coronary artery disease are widely accepted with women developing more stable atherosclerosis than men, the underlying pathobiology of such differences remains largely unknown. In coronary artery disease, recent integrative systems biological studies have inferred gene regulatory networks (GRNs). Within these GRNs, key driver genes have shown great promise but have thus far been unidentified in women. METHODS We generated sex-specific GRNs of the atherosclerotic arterial wall in 160 women and age-matched men in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). We integrated the female GRNs with single-cell RNA-sequencing data of the human atherosclerotic plaque and single-cell RNA sequencing of advanced atherosclerotic lesions from wild type and Klf4 knockout atherosclerotic smooth muscle cell (SMC) lineage-tracing mice. RESULTS By comparing sex-specific GRNs, we observed clear sex differences in network activity within the atherosclerotic tissues. Genes more active in women were associated with mesenchymal cells and endothelial cells, whereas genes more active in men were associated with the immune system. We determined that key drivers of GRNs active in female coronary artery disease were predominantly found in (SMCs by single-cell sequencing of the human atherosclerotic plaques, and higher expressed in female plaque SMCs, as well. To study the functions of these female SMC key drivers in atherosclerosis, we examined single-cell RNA sequencing of advanced atherosclerotic lesions from wild type and Klf4 knockout atherosclerotic SMC lineage-tracing mice. The female key drivers were found to be expressed by phenotypically modulated SMCs and affected by Klf4, suggesting that sex differences in atherosclerosis involve phenotypic switching of plaque SMCs. CONCLUSIONS Our systems approach provides novel insights into molecular mechanisms that underlie sex differences in atherosclerosis. To discover sex-specific therapeutic targets for atherosclerosis, an increased emphasis on sex-stratified approaches in the analysis of multi-omics data sets is warranted.
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Affiliation(s)
- Robin J G Hartman
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, The Netherlands (R.J.G.H., M.M., H.M.d.R.)
| | - Katie Owsiany
- Robert M. Berne Cardiovascular Research Center (K.O., G.O.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia.,Department of Biochemistry and Molecular Genetics (K.O.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia
| | - Lijiang Ma
- University of Virginia-School of Medicine, Charlottesville. Department of Genetics and Genomic Sciences (L.M., S.K., K.H., J.L.M.B.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia
| | - Simon Koplev
- University of Virginia-School of Medicine, Charlottesville. Department of Genetics and Genomic Sciences (L.M., S.K., K.H., J.L.M.B.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia
| | - Ke Hao
- University of Virginia-School of Medicine, Charlottesville. Department of Genetics and Genomic Sciences (L.M., S.K., K.H., J.L.M.B.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia.,Icahn Institute of Genomics and Multiscale Biology (K.H., J.L.M.B.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia
| | - Lotte Slenders
- Central Diagnostics Laboratory, University Medical Center Utrecht, Utrecht University, The Netherlands (L.S., M.M., G.P.)
| | - Mete Civelek
- Center for Public Health Genomics, Department of Biomedical Engineering (M.C.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia
| | - Michal Mokry
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, The Netherlands (R.J.G.H., M.M., H.M.d.R.).,Central Diagnostics Laboratory, University Medical Center Utrecht, Utrecht University, The Netherlands (L.S., M.M., G.P.)
| | - Jason C Kovacic
- Icahn School of Medicine at Mount Sinai (J.C.K.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia.,St Vincent's Clinical School, University of NSW (J.C.K.)
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, University Medical Center Utrecht, Utrecht University, The Netherlands (L.S., M.M., G.P.)
| | - Gary Owens
- Robert M. Berne Cardiovascular Research Center (K.O., G.O.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia
| | - Johan L M Björkegren
- University of Virginia-School of Medicine, Charlottesville. Department of Genetics and Genomic Sciences (L.M., S.K., K.H., J.L.M.B.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia.,Icahn Institute of Genomics and Multiscale Biology (K.H., J.L.M.B.), New York. Victor Chang Cardiac Research Institute, Darlinghurst, Australia.,Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden (J.L.M.B.)
| | - Hester M den Ruijter
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, The Netherlands (R.J.G.H., M.M., H.M.d.R.)
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Lamb JR, Jennings LL, Gudmundsdottir V, Gudnason V, Emilsson V. It's in Our Blood: A Glimpse of Personalized Medicine. Trends Mol Med 2021; 27:20-30. [PMID: 32988739 PMCID: PMC11082297 DOI: 10.1016/j.molmed.2020.09.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/05/2020] [Accepted: 09/02/2020] [Indexed: 01/24/2023]
Abstract
Recent advances in protein profiling technology has facilitated simultaneous measurement of thousands of proteins in large population studies, exposing the depth and complexity of the plasma and serum proteomes. This revealed that proteins in circulation were organized into regulatory modules under genetic control and closely associated with current and future common diseases. Unlike networks in solid tissues, serum protein networks comprise members synthesized across different tissues of the body. Genetic analysis reveals that this cross-tissue regulation of the serum proteome participates in systemic homeostasis and mirrors the global disease state of individuals. Here, we discuss how application of this information in routine clinical evaluations may transform the future practice of medicine.
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Affiliation(s)
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, Cambridge, MA 02139, USA
| | - Valborg Gudmundsdottir
- Icelandic Heart Association, IS-201 Kopavogur, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Vilmundur Gudnason
- Icelandic Heart Association, IS-201 Kopavogur, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Valur Emilsson
- Icelandic Heart Association, IS-201 Kopavogur, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland.
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The crosstalk between bone metabolism, lncRNAs, microRNAs and mRNAs in coronary artery calcification. Genomics 2020; 113:503-513. [PMID: 32971215 DOI: 10.1016/j.ygeno.2020.09.041] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/31/2020] [Accepted: 09/19/2020] [Indexed: 01/02/2023]
Abstract
The association between Coronary Artery Calcification (CAC) and osteoporosis has been reported but not fully understood. Therefore, using an original bioinformatic framework we analyzed transcriptomic profiles of 20 elderly women with high CAC score and 31 age- and sex-matching controls from São Paulo Ageing & Health study (SPAH). We integrated differentially expressed microRNA (miRNA) and long-noncoding RNA (lncRNA) interactions with coding genes associated with CAC, in the context of bone-metabolism genes mined from literature. Top non-coding regulators of bone metabolism in CAC included miRNA 497-5p/195 and 106a-5p, and lncRNA FAM197Y7. Top non-coding RNAs revealed significant interplay between genes regulating bone metabolism, vascularization-related processes, chromatin organization, prostaglandin and calcium co-signaling. Prostaglandin E2 receptor 3 (PTGER3), Fibroblasts Growth Factor Receptor 1 (FGFR1), and One Cut Homeobox 2 (ONECUT2) were identified as the most susceptible to regulation by the top non-coding RNAs. This study provides a flexible transcriptomic framework including non-coding regulation for biomarker-related studies.
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47
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Zhang YH, Pan X, Zeng T, Chen L, Huang T, Cai YD. Identifying the RNA signatures of coronary artery disease from combined lncRNA and mRNA expression profiles. Genomics 2020; 112:4945-4958. [PMID: 32919019 DOI: 10.1016/j.ygeno.2020.09.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 07/28/2020] [Accepted: 09/05/2020] [Indexed: 12/23/2022]
Abstract
Coronary artery disease (CAD) is the most common cardiovascular disease. CAD research has greatly progressed during the past decade. mRNA is a traditional and popular pipeline to investigate various disease, including CAD. Compared with mRNA, lncRNA has better stability and thus may serve as a better disease indicator in blood. Investigating potential CAD-related lncRNAs and mRNAs will greatly contribute to the diagnosis and treatment of CAD. In this study, a computational analysis was conducted on patients with CAD by using a comprehensive transcription dataset with combined mRNA and lncRNA expression data. Several machine learning algorithms, including feature selection methods and classification algorithms, were applied to screen for the most CAD-related RNA molecules. Decision rules were also reported to provide a quantitative description about the effect of these RNA molecules on CAD progression. These new findings (CAD-related RNA molecules and rules) can help understand mRNA and lncRNA expression levels in CAD.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai 200444, China; Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China.
| | - Tao Zeng
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China.
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48
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Orekhov AN, Ivanova EA, Markin AM, Nikiforov NG, Sobenin IA. Genetics of Arterial-Wall-Specific Mechanisms in Atherosclerosis: Focus on Mitochondrial Mutations. Curr Atheroscler Rep 2020; 22:54. [PMID: 32772280 DOI: 10.1007/s11883-020-00873-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
PURPOSE OF REVIEW Mutations in both nuclear and mitochondrial genes are associated with the development of atherosclerotic lesions in arteries and may provide a partial explanation to the focal nature of lesion distribution in the arterial wall. This review is aimed to discuss the genetic aspects of atherogenesis with a special focus on possible pro-atherogenic variants (mutations) of the nuclear and mitochondrial genomes that may be implicated in atherosclerosis development and progression. RECENT FINDINGS Mutations in the nuclear genes generally do not cause a phenotype restricted to a specific vascular wall cell and manifest themselves mostly at the organism level. Such mutations can act as important contributors to changes in lipid metabolism and modulate other risk factors of atherosclerosis. By contrast, mitochondrial DNA (mtDNA) mutations occurring locally in the arterial wall cells or in circulating immune cells may play a site-specific role in atherogenesis. The mosaic distribution of heteroplasmic mtDNA mutations in the arterial wall tissue may explain, at least to some extent, the locality and focality of atherosclerotic lesions distribution. The genetic mechanisms of atherogenesis include alterations of both nuclear and mitochondrial genomes. Altered lipid metabolism and inflammatory response of resident arterial wall and circulating immune cells may be related to mtDNA damage and defective mitophagy, which hinders clearance of dysfunctional mitochondria. Mutations of mtDNA can have mosaic distribution and locally affect functionality of endothelial and subendothelial intimal cells in the arterial wall contributing to atherosclerotic lesion development.
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Affiliation(s)
- Alexander N Orekhov
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, 8 Baltiiskaya Street, Moscow, Russia, 125315. .,Laboratory of Infection Pathology and Molecular Microecology, Institute of Human Morphology, 3 Tsyurupa Street, Moscow, Russia, 117418.
| | - Ekaterina A Ivanova
- Institute for Atherosclerosis Research, 2-1-207 Osennyaya Street, Moscow, Russia, 121609.
| | - Alexander M Markin
- Laboratory of Infection Pathology and Molecular Microecology, Institute of Human Morphology, 3 Tsyurupa Street, Moscow, Russia, 117418
| | - Nikita G Nikiforov
- Centre of Collective Usage, Institute of Gene Biology, Russian Academy of Sciences, 34/5 Vavilova Street, Moscow, Russia, 119334.,Laboratory of Medical Genetics, Institute of Experimental Cardiology, National Medical Research Center of Cardiology, 15A 3-rd Cherepkovskaya Street, Moscow, Russia, 121552
| | - Igor A Sobenin
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, 8 Baltiiskaya Street, Moscow, Russia, 125315.,Laboratory of Medical Genetics, Institute of Experimental Cardiology, National Medical Research Center of Cardiology, 15A 3-rd Cherepkovskaya Street, Moscow, Russia, 121552
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49
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Abstract
Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.
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Affiliation(s)
- Christoph D. Rau
- Departments of Anesthesiology, Medicine, Physiology
- Current address: Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC 27599
| | - Aldons J. Lusis
- Department of Human Genetics and Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Yibin Wang
- Departments of Anesthesiology, Medicine, Physiology
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50
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Markin AM, Sobenin IA, Grechko AV, Zhang D, Orekhov AN. Cellular Mechanisms of Human Atherogenesis: Focus on Chronification of Inflammation and Mitochondrial Mutations. Front Pharmacol 2020; 11:642. [PMID: 32528276 PMCID: PMC7247837 DOI: 10.3389/fphar.2020.00642] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/22/2020] [Indexed: 12/23/2022] Open
Abstract
Atherosclerosis is one of the most common diseases of the cardiovascular system that leads to the development of life-threatening conditions, such as heart attack and stroke. Arthrosclerosis affects various arteries in the human body, but is especially dangerous in the arteries alimenting heart and brain, aorta, and arteries of the lower limbs. By its pathophysiology, atherosclerosis is an inflammatory disease. During the pathological process, lesions of arterial intima in the form of focal thickening are observed, which form atherosclerotic plaques as the disease progresses further. Given the significance of atherosclerosis for the global health, the search for novel effective therapies is highly prioritized. However, despite the constant progress, our understanding of the mechanisms of atherogenesis is still incomplete. One of the remaining puzzles in atherosclerosis development is the focal distribution of atherosclerotic lesions in the arterial wall. It implies the existence of certain mosaicism within the tissue, with some areas more susceptible to disease development than others, which may prove to be important for novel therapy development. There are many hypotheses explaining this phenomenon, for example, the influence of viruses, and the spread in the endothelium of the vessel multinucleated giant endothelial cells. We suggest the local variations of the mitochondrial genome as a possible explanation of this mosaicism. In this review, we discuss the role of genetic variations in the nuclear and mitochondrial genomes that influence the development of atherosclerosis. Changes in the mitochondrial and nuclear genome have been identified as independent factors for the development of the disease, as well as potential diagnostic markers.
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Affiliation(s)
- Alexander M Markin
- Laboratory of Infection Pathology and Molecular Microecology, Institute of Human Morphology, Moscow, Russia
| | - Igor A Sobenin
- Laboratory of Medical Genetics, Institute of Experimental Cardiology, National Medical Research Center of Cardiology, Moscow, Russia.,Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Moscow, Russia
| | - Andrey V Grechko
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
| | - Dongwei Zhang
- Diabetes Research Centre, Traditional Chinese Medicine School, Beijing University of Chinese Medicine, Beijing, China
| | - Alexander N Orekhov
- Laboratory of Infection Pathology and Molecular Microecology, Institute of Human Morphology, Moscow, Russia.,Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Moscow, Russia
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