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Strosahl J, Ye K, Pazdro R. Novel insights into the pleiotropic health effects of growth differentiation factor 11 gained from genome-wide association studies in population biobanks. BMC Genomics 2024; 25:837. [PMID: 39237910 PMCID: PMC11378601 DOI: 10.1186/s12864-024-10710-7] [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: 03/07/2024] [Accepted: 08/14/2024] [Indexed: 09/07/2024] Open
Abstract
BACKGROUND Growth differentiation factor 11 (GDF11) is a member of the transforming growth factor-β (TGF-β) superfamily that has gained considerable attention over the last decade for its observed ability to reverse age-related deterioration of multiple tissues, including the heart. Yet as many researchers have struggled to confirm the cardioprotective and anti-aging effects of GDF11, the topic has grown increasingly controversial, and the field has reached an impasse. We postulated that a clearer understanding of GDF11 could be gained by investigating its health effects at the population level. METHODS AND RESULTS We employed a comprehensive strategy to interrogate results from genome-wide association studies in population Biobanks. Interestingly, phenome-wide association studies (PheWAS) of GDF11 tissue-specific cis-eQTLs revealed associations with asthma, immune function, lung function, and thyroid phenotypes. Furthermore, PheWAS of GDF11 genetic variants confirmed these results, revealing similar associations with asthma, immune function, lung function, and thyroid health. To complement these findings, we mined results from transcriptome-wide association studies, which uncovered associations between predicted tissue-specific GDF11 expression and the same health effects identified from PheWAS analyses. CONCLUSIONS In this study, we report novel relationships between GDF11 and disease, namely asthma and hypothyroidism, in contrast to its formerly assumed role as a rejuvenating factor in basic aging and cardiovascular health. We propose that these associations are mediated through the involvement of GDF11 in inflammatory signaling pathways. Taken together, these findings provide new insights into the health effects of GDF11 at the population level and warrant future studies investigating the role of GDF11 in these specific health conditions.
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Affiliation(s)
- Jessica Strosahl
- Department of Nutritional Sciences, University of Georgia, 305 Sanford Drive, Athens, GA, 30602, USA
| | - Kaixiong Ye
- Department of Genetics, University of Georgia, Athens, GA, 30602, USA
- Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA
| | - Robert Pazdro
- Department of Nutritional Sciences, University of Georgia, 305 Sanford Drive, Athens, GA, 30602, USA.
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2
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Breeyear JH, Mitchell SL, Nealon CL, Hellwege JN, Charest B, Khakharia A, Halladay CW, Yang J, Garriga GA, Wilson OD, Basnet TB, Hung AM, Reaven PD, Meigs JB, Rhee MK, Sun Y, Lynch MG, Sobrin L, Brantley MA, Sun YV, Wilson PW, Iyengar SK, Peachey NS, Phillips LS, Edwards TL, Giri A. Development of electronic health record based algorithms to identify individuals with diabetic retinopathy. J Am Med Inform Assoc 2024:ocae213. [PMID: 39158361 DOI: 10.1093/jamia/ocae213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 07/17/2024] [Accepted: 07/30/2024] [Indexed: 08/20/2024] Open
Abstract
OBJECTIVES To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs). MATERIALS AND METHODS We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet 1 of the following 3 criteria: (1) 2 or more dates with any DR ICD-9/10 code documented in the EHR, (2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or (3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology examination. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology examination. RESULTS The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.91 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV = 0.94; NPV = 0.86) and lower in MGB (PPV = 0.84; NPV = 0.76). In comparison, the algorithm for DR implemented in Phenome-wide association study (PheWAS) in VUMC yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62 000 DR cases with genetic data including 14 549 African Americans and 6209 Hispanics with DR. CONCLUSIONS/DISCUSSION We demonstrate the robustness of the algorithms at 3 separate healthcare centers, with a minimum PPV of 0.84 and substantially improved NPV than existing automated methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.
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Affiliation(s)
- Joseph H Breeyear
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
| | - Sabrina L Mitchell
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Cari L Nealon
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, OH 44106, United States
| | - Jacklyn N Hellwege
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, United States
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02111, United States
| | - Anjali Khakharia
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Department of Medicine and Geriatrics, Emory University School of Medicine, Atlanta, GA 30307, United States
| | | | - Janine Yang
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, MA 02114, United States
| | - Gustavo A Garriga
- Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Otis D Wilson
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Til B Basnet
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, United States
- Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Adriana M Hung
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Peter D Reaven
- Phoenix VA Health Care System, Phoenix, AZ 85012, United States
- College of Medicine, University of Arizona, Phoenix, AZ 85721, United States
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Mary K Rhee
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30307, United States
| | - Yang Sun
- Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, United States
| | - Mary G Lynch
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
| | - Lucia Sobrin
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, MA 02114, United States
| | - Milam A Brantley
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, United States
| | - Yan V Sun
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30307, United States
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30307, United States
| | - Peter W Wilson
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30307, United States
| | - Sudha K Iyengar
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH 44106, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, United States
| | - Neal S Peachey
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH 44106, United States
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44106, United States
- Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, United States
| | - Lawrence S Phillips
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30307, United States
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
| | - Ayush Giri
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, United States
- Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
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Delabays B, Trajanoska K, Walonoski J, Mooser V. Cardiovascular Pharmacogenetics: From Discovery of Genetic Association to Clinical Adoption of Derived Test. Pharmacol Rev 2024; 76:791-827. [PMID: 39122647 DOI: 10.1124/pharmrev.123.000750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 04/24/2024] [Accepted: 05/28/2024] [Indexed: 08/12/2024] Open
Abstract
Recent breakthroughs in human genetics and in information technologies have markedly expanded our understanding at the molecular level of the response to drugs, i.e., pharmacogenetics (PGx), across therapy areas. This review is restricted to PGx for cardiovascular (CV) drugs. First, we examined the PGx information in the labels approved by regulatory agencies in Europe, Japan, and North America and related recommendations from expert panels. Out of 221 marketed CV drugs, 36 had PGx information in their labels approved by one or more agencies. The level of annotations and recommendations varied markedly between agencies and expert panels. Clopidogrel is the only CV drug with consistent PGx recommendation (i.e., "actionable"). This situation prompted us to dissect the steps from discovery of a PGx association to clinical translation. We found 101 genome-wide association studies that investigated the response to CV drugs or drug classes. These studies reported significant associations for 48 PGx traits mapping to 306 genes. Six of these 306 genes are mentioned in the corresponding PGx labels or recommendations for CV drugs. Genomic analyses also highlighted the wide between-population differences in risk allele frequencies and the individual load of actionable PGx variants. Given the high attrition rate and the long road to clinical translation, additional work is warranted to identify and validate PGx variants for more CV drugs across diverse populations and to demonstrate the utility of PGx testing. To that end, pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond. SIGNIFICANCE STATEMENT: Despite spectacular breakthroughs in human molecular genetics and information technologies, consistent evidence supporting PGx testing in the cardiovascular area is limited to a few drugs. Additional work is warranted to discover and validate new PGx markers and demonstrate their utility. Pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond.
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Affiliation(s)
- Benoît Delabays
- Canada Excellence Research Chair in Genomic Medicine, Victor Phillip Dahdaleh Institute of Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada (B.D., K.T., V.M.); and Medeloop Inc., Palo Alto, California, and Montreal, QC, Canada (J.W.)
| | - Katerina Trajanoska
- Canada Excellence Research Chair in Genomic Medicine, Victor Phillip Dahdaleh Institute of Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada (B.D., K.T., V.M.); and Medeloop Inc., Palo Alto, California, and Montreal, QC, Canada (J.W.)
| | - Joshua Walonoski
- Canada Excellence Research Chair in Genomic Medicine, Victor Phillip Dahdaleh Institute of Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada (B.D., K.T., V.M.); and Medeloop Inc., Palo Alto, California, and Montreal, QC, Canada (J.W.)
| | - Vincent Mooser
- Canada Excellence Research Chair in Genomic Medicine, Victor Phillip Dahdaleh Institute of Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada (B.D., K.T., V.M.); and Medeloop Inc., Palo Alto, California, and Montreal, QC, Canada (J.W.)
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4
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Qi G, Chhetri SB, Ray D, Dutta D, Battle A, Bhattacharjee S, Chatterjee N. Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants. Nat Commun 2024; 15:6985. [PMID: 39143063 PMCID: PMC11324957 DOI: 10.1038/s41467-024-51075-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/29/2024] [Indexed: 08/16/2024] Open
Abstract
Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value < 2.2 × 10 - 16 ). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Surya B Chhetri
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Diptavo Dutta
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Samsiddhi Bhattacharjee
- Biotechnology Research and Innovation Council-National Institute of Biomedical Genomics (BRIC-NIBMG), Kalyani, India.
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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5
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Sookoian S, Rotman Y, Valenti L. Genetics of Metabolic Dysfunction-associated Steatotic Liver Disease: The State of Art Update. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00690-6. [PMID: 39094912 DOI: 10.1016/j.cgh.2024.05.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/18/2024] [Accepted: 05/28/2024] [Indexed: 08/04/2024]
Abstract
Recent advances in the genetics of metabolic dysfunction-associated steatotic liver disease (MASLD) are gradually revealing the mechanisms underlying the heterogeneity of the disease and have shown promising results in patient stratification. Genetic characterization of the disease has been rapidly developed using genome-wide association studies, exome-wide association studies, phenome-wide association studies, and whole exome sequencing. These advances have been powered by the increase in computational power, the development of new analytical algorithms, including some based on artificial intelligence, and the recruitment of large and well-phenotyped cohorts. This review presents an update on genetic studies that emphasize new biological insights from next-generation sequencing approaches. Additionally, we discuss innovative methods for discovering new genetic loci for MASLD, including rare variants. To comprehensively manage MASLD, it is important to stratify risks. Therefore, we present an update on phenome-wide association study associations, including extreme phenotypes. Additionally, we discuss whether polygenic risk scores and targeted sequencing are ready for clinical use. With particular focus on precision medicine, we introduce concepts such as the interplay between genetics and the environment in modulating genetic risk with lifestyle or standard therapies. A special chapter is dedicated to gene-based therapeutics. The limitations of approved pharmacological approaches are discussed, and the potential of gene-related mechanisms in therapeutic development is reviewed, including the decision to perform genetic testing in patients with MASLD.
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Affiliation(s)
- Silvia Sookoian
- Clinical and Molecular Hepatology, Translational Health Research Center (CENITRES), Maimónides University, Buenos Aires, Argentina; Faculty of Health Science, Maimónides University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
| | - Yaron Rotman
- Liver & Energy Metabolism Section, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Luca Valenti
- Precision Medicine - Department of Transfusion Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
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6
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Manipur I, Reales G, Sul JH, Shin MK, Longerich S, Cortes A, Wallace C. CoPheScan: phenome-wide association studies accounting for linkage disequilibrium. Nat Commun 2024; 15:5862. [PMID: 38997278 PMCID: PMC11245513 DOI: 10.1038/s41467-024-49990-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 06/25/2024] [Indexed: 07/14/2024] Open
Abstract
Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS is restricted by an inability to distinguish confounding due to linkage disequilibrium (LD) from true pleiotropy. Here we describe CoPheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systematic exploration of causal associations while simultaneously addressing LD confounding. We demonstrate its performance through simulation, showing considerably better control of false positive rates than a conventional approach not accounting for LD. We used CoPheScan to perform PheWAS of protein-truncating variants and fine-mapped variants from disease and pQTL studies, in 2275 disease phenotypes from the UK Biobank. Our results identify the complexity of known pleiotropic genes such as APOE, and suggest a new causal role for TGM3 in skin cancer.
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Affiliation(s)
- Ichcha Manipur
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK.
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK.
| | - Guillermo Reales
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
| | | | | | | | - Adrian Cortes
- Human Genetics and Genomics, GSK, Heidelberg, 69117, Germany
| | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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7
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Chen Y, Wang G, Chen J, Wang C, Dong X, Chang HM, Yuan S, Zhao Y, Mu L. Genetic and Epigenetic Landscape for Drug Development in Polycystic Ovary Syndrome. Endocr Rev 2024; 45:437-459. [PMID: 38298137 DOI: 10.1210/endrev/bnae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024]
Abstract
The treatment of polycystic ovary syndrome (PCOS) faces challenges as all known treatments are merely symptomatic. The US Food and Drug Administration has not approved any drug specifically for treating PCOS. As the significance of genetics and epigenetics rises in drug development, their pivotal insights have greatly enhanced the efficacy and success of drug target discovery and validation, offering promise for guiding the advancement of PCOS treatments. In this context, we outline the genetic and epigenetic advancement in PCOS, which provide novel insights into the pathogenesis of this complex disease. We also delve into the prospective method for harnessing genetic and epigenetic strategies to identify potential drug targets and ensure target safety. Additionally, we shed light on the preliminary evidence and distinctive challenges associated with gene and epigenetic therapies in the context of PCOS.
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Affiliation(s)
- Yi Chen
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- The First School of Medicine, Wenzhou Medical University, Wenzhou 325035, China
| | - Guiquan Wang
- Department of Reproductive Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen 361003, China
- Xiamen Key Laboratory of Reproduction and Genetics, Xiamen University, Xiamen 361023, China
| | - Jingqiao Chen
- The First School of Medicine, Wenzhou Medical University, Wenzhou 325035, China
| | - Congying Wang
- The Department of Cardiology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang 322000, China
| | - Xi Dong
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Hsun-Ming Chang
- Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung 40400, Taiwan
| | - Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm 171 65, Sweden
| | - Yue Zhao
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
- National Clinical Research Center for Obstetrics and Gynecology, Beijing 100007, China
- Key Laboratory of Assisted Reproduction, Ministry of Education, Peking University, Beijing 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Peking University, Beijing 100191, China
| | - Liangshan Mu
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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8
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Zhao H, Zhou Y, Wang Z, Zhang X, Chen L, Hong Z. Plasma proteins and psoriatic arthritis: a proteome-wide Mendelian randomization study. Front Immunol 2024; 15:1417564. [PMID: 39026678 PMCID: PMC11254630 DOI: 10.3389/fimmu.2024.1417564] [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: 04/15/2024] [Accepted: 06/21/2024] [Indexed: 07/20/2024] Open
Abstract
Background Previous epidemiological studies have identified a correlation between serum protein levels and Psoriatic Arthritis (PsA). However, the precise nature of this relationship remains uncertain. Therefore, our objective was to assess whether circulating levels of 2,923 plasma proteins are associated with the risk of PsA, utilizing the Mendelian randomization (MR) approach. Methods Two-sample MR analysis was performed to assess the causal impact of proteins on PsA risk. Exposure data for plasma proteins were sourced from a genome-wide association study (GWAS) conducted within the UK Biobank Pharma Proteomics Project, which encompassed 2,923 unique plasma proteins. The outcome data for PsA were sourced from the FinnGen study, a large-scale genomics initiative, comprising 3,537 cases and 262,844 controls. Additionally, colocalization analysis, Phenome-wide MR analysis, and candidate drug prediction were employed to identify potential causal circulating proteins and novel drug targets. Results We thoroughly assessed the association between 1,837 plasma proteins and PsA risk, identifying seven proteins associated with PsA risk. An inverse association of Interleukin-10 (IL-10) with PsA risk was observed [odds ratio (OR)=0.45, 95% confidence interval (CI), 0.28 to 0.70, P FDR=0.072]. Additionally, Apolipoprotein F (APOF) has a positive effect on PsA risk (OR=2.08, 95% CI, 1.51 to 2.86, P FDR=0.005). Subsequently, we found strong evidence indicating that IL-10 and APOF were colocalized with PsA associations (PP.H4 = 0.834 for IL-10 and PP.H4 = 0.900 for APOF). Phenome-wide association analysis suggested that these two proteins may have dual effects on other clinical traits (P FDR<0.1). Conclusion This study identified 7 plasma proteins associated with PsA risk, particularly IL-10 and APOF, which offer new insights into its etiology. Further studies are needed to assess the utility and effectiveness of these candidate proteins.
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Affiliation(s)
- Heran Zhao
- Department of Orthopaedics, The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Third Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yi Zhou
- Graduate School, Nanjing University of Chinese Medicine, Nanjing, China
| | - Ziyan Wang
- Graduate School, Nanjing University of Chinese Medicine, Nanjing, China
| | - Xuan Zhang
- College of Orthopedics and Traumatology, Guangxi University of Chinese Medicine, Nanning, China
| | - Leilei Chen
- Department of Orthopaedics, The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Third Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhinan Hong
- Department of Orthopaedics, The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Third Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
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9
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Gong W, Fu Y, Wu BS, Du J, Yang L, Zhang YR, Chen SD, Kang J, Mao Y, Dong Q, Tan L, Feng J, Cheng W, Yu JT. Whole-exome sequencing identifies protein-coding variants associated with brain iron in 29,828 individuals. Nat Commun 2024; 15:5540. [PMID: 38956042 PMCID: PMC11219919 DOI: 10.1038/s41467-024-49702-2] [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: 04/12/2024] [Accepted: 06/16/2024] [Indexed: 07/04/2024] Open
Abstract
Iron plays a fundamental role in multiple brain disorders. However, the genetic underpinnings of brain iron and its implications for these disorders are still lacking. Here, we conduct an exome-wide association analysis of brain iron, measured by quantitative susceptibility mapping technique, across 26 brain regions among 26,789 UK Biobank participants. We find 36 genes linked to brain iron, with 29 not being previously reported, and 16 of them can be replicated in an independent dataset with 3,039 subjects. Many of these genes are involved in iron transport and homeostasis, such as FTH1 and MLX. Several genes, while not previously connected to brain iron, are associated with iron-related brain disorders like Parkinson's (STAB1, KCNA10), Alzheimer's (SHANK1), and depression (GFAP). Mendelian randomization analysis reveals six causal relationships from regional brain iron to brain disorders, such as from the hippocampus to depression and from the substantia nigra to Parkinson's. These insights advance our understanding of the genetic architecture of brain iron and offer potential therapeutic targets for brain disorders.
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Affiliation(s)
- Weikang Gong
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, 266071, Qingdao, China
| | - Bang-Sheng Wu
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
| | - Liu Yang
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - JuJiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 200433, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, 200433, Shanghai, China
| | - Ying Mao
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Qiang Dong
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, 266071, Qingdao, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 200433, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, 200433, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Cheng
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 200433, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, 200433, Shanghai, China.
| | - Jin-Tai Yu
- School of Data Science, Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
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10
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Dalal T, Patel CJ. PYPE: A pipeline for phenome-wide association and Mendelian randomization in investigator-driven biobank scale analysis. PATTERNS (NEW YORK, N.Y.) 2024; 5:100982. [PMID: 39005490 PMCID: PMC11240175 DOI: 10.1016/j.patter.2024.100982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/30/2023] [Accepted: 04/08/2024] [Indexed: 07/16/2024]
Abstract
Phenome-wide association studies (PheWASs) serve as a way of documenting the relationship between genotypes and multiple phenotypes, helping to uncover unexplored genotype-phenotype associations (known as pleiotropy). Secondly, Mendelian randomization (MR) can be harnessed to make causal statements about a pair of phenotypes by comparing their genetic architecture. Thus, approaches that automate both PheWASs and MR can enhance biobank-scale analyses, circumventing the need for multiple tools by providing a comprehensive, end-to-end tool to drive scientific discovery. To this end, we present PYPE, a Python pipeline for running, visualizing, and interpreting PheWASs. PYPE utilizes input genotype or phenotype files to automatically estimate associations between the chosen independent variables and phenotypes. PYPE can also produce a variety of visualizations and can be used to identify nearby genes and functional consequences of significant associations. Finally, PYPE can identify possible causal relationships between phenotypes using MR under a variety of causal effect modeling scenarios.
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Affiliation(s)
- Taykhoom Dalal
- Harvard Medical School Department of Biomedical Informatics, Boston, MA 02115, USA
| | - Chirag J Patel
- Harvard Medical School Department of Biomedical Informatics, Boston, MA 02115, USA
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11
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Li Q, Song Q, Chen Z, Choi J, Moreno V, Ping J, Wen W, Li C, Shu X, Yan J, Shu XO, Cai Q, Long J, Huyghe JR, Pai R, Gruber SB, Casey G, Wang X, Toriola AT, Li L, Singh B, Lau KS, Zhou L, Wu C, Peters U, Zheng W, Long Q, Yin Z, Guo X. Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24308170. [PMID: 38853880 PMCID: PMC11160851 DOI: 10.1101/2024.05.29.24308170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.
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12
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Minikel EV, Painter JL, Dong CC, Nelson MR. Refining the impact of genetic evidence on clinical success. Nature 2024; 629:624-629. [PMID: 38632401 PMCID: PMC11096124 DOI: 10.1038/s41586-024-07316-0] [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: 07/05/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024]
Abstract
The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.
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Affiliation(s)
| | - Jeffery L Painter
- JiveCast, Raleigh, NC, USA
- GlaxoSmithKline, Research Triangle Park, NC, USA
| | | | - Matthew R Nelson
- Deerfield Management Company LP, New York, NY, USA.
- Genscience LLC, New York, NY, USA.
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13
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Nguyen TQ, Kerley CI, Key AP, Maxwell-Horn AC, Wells QS, Neul JL, Cutting LE, Landman BA. Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records. JOURNAL OF INTELLECTUAL DISABILITY RESEARCH : JIDR 2024; 68:491-511. [PMID: 38303157 PMCID: PMC11023778 DOI: 10.1111/jir.13124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 11/20/2023] [Accepted: 12/27/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND Individuals with Down syndrome (DS) have a heightened risk for various co-occurring health conditions, including congenital heart disease (CHD). In this two-part study, electronic medical records (EMRs) were leveraged to examine co-occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2). METHODS De-identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one-by-two sex and age matched with samples of case-controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome-disease association study (PheDAS) strategy was employed to reveal co-occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case-controls. Data were included in predictive models and assessed which model-based health conditions, when more prevalent, would increase the likelihood of surgical intervention. RESULTS In Study 1, relative to case-controls and those with other IDDs, co-occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co-occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non-rheumatic aortic valve disorders and heart failure (circulatory); acid-base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention. CONCLUSIONS Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision-care development.
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Affiliation(s)
- T Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Peabody College of Education and Human Development, Vanderbilt University, Nashville, TN, USA
| | - C I Kerley
- School of Engineering, Vanderbilt University, Nashville, TN, USA
| | - A P Key
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Speech and Hearing Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - A C Maxwell-Horn
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Q S Wells
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - J L Neul
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L E Cutting
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Peabody College of Education and Human Development, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - B A Landman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- School of Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
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14
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Abou-Karam R, Cheng F, Gady S, Fahed AC. The Role of Genetics in Advancing Cardiometabolic Drug Development. Curr Atheroscler Rep 2024; 26:153-162. [PMID: 38451435 DOI: 10.1007/s11883-024-01195-6] [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] [Accepted: 02/22/2024] [Indexed: 03/08/2024]
Abstract
PURPOSE OF REVIEW The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. RECENT FINDINGS Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.
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Affiliation(s)
- Roukoz Abou-Karam
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fangzhou Cheng
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shoshana Gady
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akl C Fahed
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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15
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Foreman AL, Warth B, Hessel EVS, Price EJ, Schymanski EL, Cantelli G, Parkinson H, Hecht H, Klánová J, Vlaanderen J, Hilscherova K, Vrijheid M, Vineis P, Araujo R, Barouki R, Vermeulen R, Lanone S, Brunak S, Sebert S, Karjalainen T. Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:7256-7269. [PMID: 38641325 PMCID: PMC11064223 DOI: 10.1021/acs.est.3c07961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/21/2024]
Abstract
Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.
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Affiliation(s)
- Amy L. Foreman
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Benedikt Warth
- Department
of Food Chemistry and Toxicology, University
of Vienna, 1090 Vienna, Austria
| | - Ellen V. S. Hessel
- National
Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands
| | - Elliott J. Price
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Emma L. Schymanski
- Luxembourg
Centre for Systems Biomedicine, University
of Luxembourg, 6 avenue
du Swing, L-4367 Belvaux, Luxembourg
| | - Gaia Cantelli
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Helen Parkinson
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Helge Hecht
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Jana Klánová
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Jelle Vlaanderen
- Institute
for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Heidelberglaan 8 3584 CS Utrecht, The Netherlands
| | - Klara Hilscherova
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Martine Vrijheid
- Institute
for Global Health (ISGlobal), Barcelona
Biomedical Research Park (PRBB), Doctor Aiguader, 88, 08003 Barcelona, Spain
- Universitat
Pompeu Fabra, Carrer
de la Mercè, 12, Ciutat Vella, 08002 Barcelona, Spain
- Centro de Investigación Biomédica en Red
Epidemiología
y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5. Pebellón 11, Planta 0, 28029 Madrid, Spain
| | - Paolo Vineis
- Department
of Epidemiology and Biostatistics, School of Public Health, Imperial College, London SW7 2AZ, U.K.
| | - Rita Araujo
- European Commission, DG Research and Innovation, Sq. Frère-Orban 8, 1000 Bruxelles, Belgium
| | | | - Roel Vermeulen
- Institute
for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Heidelberglaan 8 3584 CS Utrecht, The Netherlands
| | - Sophie Lanone
- Univ Paris Est Creteil, INSERM, IMRB, F-94010 Creteil, France
| | - Søren Brunak
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Blegdamsvej 3B, 2200 København, Denmark
| | - Sylvain Sebert
- Research
Unit of Population Health, University of
Oulu, P.O. Box 8000, FI-90014 Oulu, Finland
| | - Tuomo Karjalainen
- European Commission, DG Research and Innovation, Sq. Frère-Orban 8, 1000 Bruxelles, Belgium
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16
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Wang X, Liu M, Nogues IE, Chen T, Xiong X, Bonzel CL, Zhang H, Hong C, Xia Y, Dahal K, Costa L, Cui J, Gaziano JM, Kim SC, Ho YL, Cho K, Cai T, Liao KP. Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy. Sci Rep 2024; 14:8021. [PMID: 38580710 PMCID: PMC10997791 DOI: 10.1038/s41598-024-54063-3] [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: 01/13/2023] [Accepted: 02/08/2024] [Indexed: 04/07/2024] Open
Abstract
The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.
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Affiliation(s)
- Xuan Wang
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Molei Liu
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Tony Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Xin Xiong
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Clara-Lea Bonzel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Harrison Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Chuan Hong
- Department of Biostatistics, Duke University, Durham, NC, USA
| | - Yin Xia
- Department of Statistics and Data Science, Fudan University, Shanghai, China
| | - Kumar Dahal
- Department of Biostatistics, Duke University, Durham, NC, USA
| | - Lauren Costa
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Jing Cui
- Department of Biostatistics, Duke University, Durham, NC, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
| | - Seoyoung C Kim
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
| | - Tianxi Cai
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Katherine P Liao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA.
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA.
- Rheumatology Section, VA Boston Healthcare System, Boston, USA.
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17
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Takundwa MM, Thimiri Govinda Raj DB. Novel strategies for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:9-21. [PMID: 38789188 DOI: 10.1016/bs.pmbts.2024.03.021] [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: 05/26/2024]
Abstract
Synthetic biology, precision medicine, and nanobiotechnology are the three main emerging areas that drive translational innovation toward commercialization. There are several strategies used in precision medicine and drug repurposing is one of the key approaches as it addresses the challenges in drug discovery (high cost and time). Here, we provide a perspective on various new approaches to drug repurposing for cancer precision medicine. We report here our optimized wound healing methodology that can be used to validate drug sensitivity and drug repurposing. Using HeLa as our benchmark, we demonstrated that the assay can be applied to identify drugs that limit cell proliferation. From a future perspective, this assay can be expanded to ex vivo culturing of solid tumors in 2D culture and leukemia in 3D culture.
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Affiliation(s)
- Mutsa Monica Takundwa
- Synthetic Nanobiotechnology and Biomachines, Synthetic Biology and Precision Medicine Centre, Future Production Chemicals Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - Deepak B Thimiri Govinda Raj
- Synthetic Nanobiotechnology and Biomachines, Synthetic Biology and Precision Medicine Centre, Future Production Chemicals Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa.
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18
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Jadhav V, Vaishnaw A, Fitzgerald K, Maier MA. RNA interference in the era of nucleic acid therapeutics. Nat Biotechnol 2024; 42:394-405. [PMID: 38409587 DOI: 10.1038/s41587-023-02105-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 12/15/2023] [Indexed: 02/28/2024]
Abstract
Two decades of research on RNA interference (RNAi) have transformed a breakthrough discovery in biology into a robust platform for a new class of medicines that modulate mRNA expression. Here we provide an overview of the trajectory of small-interfering RNA (siRNA) drug development, including the first approval in 2018 of a liver-targeted siRNA interference (RNAi) therapeutic in lipid nanoparticles and subsequent approvals of five more RNAi drugs, which used metabolically stable siRNAs combined with N-acetylgalactosamine ligands for conjugate-based liver delivery. We also consider the remaining challenges in the field, such as delivery to muscle, brain and other extrahepatic organs. Today's RNAi therapeutics exhibit high specificity, potency and durability, and are transitioning from applications in rare diseases to widespread, chronic conditions.
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Affiliation(s)
- Vasant Jadhav
- Research & Development, Alnylam Pharmaceuticals, Cambridge, MA, USA.
| | - Akshay Vaishnaw
- Research & Development, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Kevin Fitzgerald
- Research & Development, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Martin A Maier
- Research & Development, Alnylam Pharmaceuticals, Cambridge, MA, USA.
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19
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Cho SB. Comorbidity Genes of Alzheimer's Disease and Type 2 Diabetes Associated with Memory and Cognitive Function. Int J Mol Sci 2024; 25:2211. [PMID: 38396891 PMCID: PMC10889845 DOI: 10.3390/ijms25042211] [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: 01/02/2024] [Revised: 02/02/2024] [Accepted: 02/10/2024] [Indexed: 02/25/2024] Open
Abstract
Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction network identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed and relevant evidence summarized. Gene information will be useful in the discovery of biomarkers and the identification of tentative therapeutic targets for AD and T2DM.
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Affiliation(s)
- Seong Beom Cho
- Department of Biomedical Informatics, College of Medicine, Gachon University, 38-13, Dokgeom-ro 3 Street, Namdon-gu, Incheon 21565, Republic of Korea
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20
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Wang L, Lu Y, Li D, Zhou Y, Yu L, Mesa Eguiagaray I, Campbell H, Li X, Theodoratou E. The landscape of the methodology in drug repurposing using human genomic data: a systematic review. Brief Bioinform 2024; 25:bbad527. [PMID: 38279645 PMCID: PMC10818097 DOI: 10.1093/bib/bbad527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/24/2023] [Accepted: 12/19/2023] [Indexed: 01/28/2024] Open
Abstract
The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.
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Affiliation(s)
- Lijuan Wang
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Lu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Doudou Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yajing Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lili Yu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ines Mesa Eguiagaray
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Xue Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, The University of Edinburgh MRC Institute of Genetics and Cancer, Edinburgh, UK
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21
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Clancy J, Ritari J, Vaittinen E, Arvas M, Tammi S, Koskela S, Partanen J. Blood donor biobank as a resource in personalised biomedical genetic research. Eur J Hum Genet 2024:10.1038/s41431-023-01528-0. [PMID: 38212662 DOI: 10.1038/s41431-023-01528-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 10/14/2023] [Accepted: 12/19/2023] [Indexed: 01/13/2024] Open
Abstract
Health questionnaires and donation criteria result in accumulation of highly selected individuals in a blood donor population. To understand better the usefulness of a blood donor-based biobank in personalised disease-associated genetic studies, and for possible personalised blood donation policies, we evaluated the occurrence and distributions of common and rare disease-associated genetic variants in Finnish Blood Service Biobank. We analysed among 31,880 blood donors the occurrence and geographical distribution of (i) 53 rare Finnish-enriched disease-associated variants, (ii) mutations assumed to influence blood donation: four Bernard-Soulier syndrome and two hemochromatosis mutations, (iii) type I diabetes risk genotype HLA-DQ2/DQ8. In addition, we analysed the level of consanguinity in Blood Service Biobank. 80.3% of blood donors carried at least one (range 0-9 per donor) of the rare variants, many in homozygous form, as well. Donors carrying multiple rare variants were enriched in Eastern Finland. Haemochromatosis mutation HFE C282Y homozygosity was 43.8% higher than expected, whereas mutations leading to Bernard-Soulier thrombocytopenia were rare. The frequency of HLA-DQ2/DQ8 genotype was slightly lower than expected. First-degree consanguinity was higher in Blood Service Biobank than in the general population. We demonstrate that despite donor selection, the Blood Service Biobank is a valuable resource for personalised medical research and for genotype-selected samples from unaffected individuals. The geographical genetic substructure of Finland enables efficient recruitment of donors carrying rare variants. Furthermore, we show that blood donor biobank material can be utilised for personalised blood donation policies.
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Affiliation(s)
- Jonna Clancy
- Blood Service Biobank, Finnish Red Cross Blood Service, Vantaa, Finland.
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland.
| | - Jarmo Ritari
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | | | - Mikko Arvas
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Silja Tammi
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Satu Koskela
- Blood Service Biobank, Finnish Red Cross Blood Service, Vantaa, Finland
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Jukka Partanen
- Blood Service Biobank, Finnish Red Cross Blood Service, Vantaa, Finland
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
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22
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Li R, Benz L, Duan R, Denny JC, Hakonarson H, Mosley JD, Smoller JW, Wei WQ, Ritchie MD, Moore JH, Chen Y. mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301073. [PMID: 38260403 PMCID: PMC10802662 DOI: 10.1101/2024.01.09.24301073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.
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Affiliation(s)
- Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Luke Benz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health
| | - Hakon Hakonarson
- Division of Human Genetics, Children's Hospital of Philadelphia
- Center for Applied Genomics, Children's Hospital of Philadelphia
- Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
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23
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Cao R, Olawsky E, McFowland E, Marcotte E, Spector L, Yang T. Subset scanning for multi-trait analysis using GWAS summary statistics. Bioinformatics 2024; 40:btad777. [PMID: 38191683 PMCID: PMC11087659 DOI: 10.1093/bioinformatics/btad777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 01/10/2024] Open
Abstract
MOTIVATION Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge. RESULTS To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression. AVAILABILITY AND IMPLEMENTATION Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.
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Affiliation(s)
- Rui Cao
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
| | - Evan Olawsky
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
| | - Edward McFowland
- Technology and Operations Management, Harvard Business School, Harvard University, Boston, MA 02163, United States
| | - Erin Marcotte
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Logan Spector
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
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24
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Cao Q, Du X, Jiang XY, Tian Y, Gao CH, Liu ZY, Xu T, Tao XX, Lei M, Wang XQ, Ye LL, Duan DD. Phenome-wide association study and precision medicine of cardiovascular diseases in the post-COVID-19 era. Acta Pharmacol Sin 2023; 44:2347-2357. [PMID: 37532784 PMCID: PMC10692238 DOI: 10.1038/s41401-023-01119-1] [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: 04/07/2023] [Accepted: 05/29/2023] [Indexed: 08/04/2023] Open
Abstract
SARS-CoV-2 infection causes injuries of not only the lungs but also the heart and endothelial cells in vasculature of multiple organs, and induces systemic inflammation and immune over-reactions, which makes COVID-19 a disease phenome that simultaneously affects multiple systems. Cardiovascular diseases (CVD) are intrinsic risk and causative factors for severe COVID-19 comorbidities and death. The wide-spread infection and reinfection of SARS-CoV-2 variants and the long-COVID may become a new common threat to human health and propose unprecedented impact on the risk factors, pathophysiology, and pharmacology of many diseases including CVD for a long time. COVID-19 has highlighted the urgent demand for precision medicine which needs new knowledge network to innovate disease taxonomy for more precise diagnosis, therapy, and prevention of disease. A deeper understanding of CVD in the setting of COVID-19 phenome requires a paradigm shift from the current phenotypic study that focuses on the virus or individual symptoms to phenomics of COVID-19 that addresses the inter-connectedness of clinical phenotypes, i.e., clinical phenome. Here, we summarize the CVD manifestations in the full clinical spectrum of COVID-19, and the phenome-wide association study of CVD interrelated to COVID-19. We discuss the underlying biology for CVD in the COVID-19 phenome and the concept of precision medicine with new phenomic taxonomy that addresses the overall pathophysiological responses of the body to the SARS-CoV-2 infection. We also briefly discuss the unique taxonomy of disease as Zheng-hou patterns in traditional Chinese medicine, and their potential implications in precision medicine of CVD in the post-COVID-19 era.
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Affiliation(s)
- Qian Cao
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Xin Du
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Xiao-Yan Jiang
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Yuan Tian
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Chen-Hao Gao
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Zi-Yu Liu
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Ting Xu
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Xing-Xing Tao
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Ming Lei
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Xiao-Qiang Wang
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Lingyu Linda Ye
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China.
- Institute of Integrated Chinese and Western Medicine, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Autoimmune Diseases and Precision Medicie, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750001, China.
| | - Dayue Darrel Duan
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China.
- Institute of Integrated Chinese and Western Medicine, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Autoimmune Diseases and Precision Medicie, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750001, China.
- The Department of Pharmacology, University of Nevada Reno School of Medicine, Reno, NV, 89557, USA.
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25
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Ma L, Du Y, Ma C, Liu M. Association of HMGCR inhibition with rheumatoid arthritis: a Mendelian randomization and colocalization study. Front Endocrinol (Lausanne) 2023; 14:1272167. [PMID: 38047111 PMCID: PMC10691537 DOI: 10.3389/fendo.2023.1272167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/25/2023] [Indexed: 12/05/2023] Open
Abstract
Objective The objective of this study was to investigate the association between hydroxymethylglutaryl coenzyme A reductase (HMGCR) inhibition and rheumatoid arthritis (RA) using drug-target Mendelian randomization (MR) and genetic colocalization analyses. Methods Two sets of genetic instruments were employed to proxy HMGCR inhibitors: expression quantitative trait loci (eQTLs) of target genes from the eQTLGen Consortium and genetic variants associated with low-density lipoprotein cholesterol (LDL-C) levels with HMGCR locus from open genome-wide association studies (GWAS). Positive control analyses were conducted on type 2 diabetes and coronary heart disease, and multiple sensitivity analyses were performed. Results Genetically proxied expression of eQTL was associated with a lower risk of RA (OR=0.996, 95% CI =0.992-0.999, p= 0.032). Similarly, hydroxymethylglutaryl coenzyme A reductase (HMGCR)-mediated low-density lipoprotein cholesterol was negatively associated with risk of RA (OR=0.995, 95% CI =0.991-0.998, p= 0.007) in the inverse variance weighted (IVW) method. Colocalization analysis suggested a 74.6% posterior probability of sharing a causal variant within the SNPs locus (PH4 = 74.6%). A causal relationship also existed between HMGCR-mediated LDL and RA risk factors. The results were also confirmed by multiple sensitivity analyses. The results in positive control were consistent with the previous study. Conclusion Our study suggested that HMGCR inhibition was associated with an increased risk of RA while also highlighting an increased risk of current smoking and obesity. These findings contribute to a growing body of evidence regarding the adverse effects of HMGCR inhibition on RA risk, calling for further research on alternative approaches using HMGCR inhibitors in RA management.
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Affiliation(s)
- Li Ma
- Department of Endocrinology and Metabolism, Tianjin Medical University General Hospital, Tianjin, China
- Department of General Practice, Heze Municiple Hospital, Heze, Shandong, China
| | - Yufei Du
- Department of Endocrinology and Metabolism, Tianjin Medical University General Hospital, Tianjin, China
| | - Chao Ma
- Department of Urology, Heze Municiple Hospital, Heze, Shandong, China
| | - Ming Liu
- Department of Endocrinology and Metabolism, Tianjin Medical University General Hospital, Tianjin, China
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26
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Lin Y, Wang G, Li Y, Yang H, Zhao Y, Liu J, Mu L. Circulating Inflammatory Cytokines and Female Reproductive Diseases: A Mendelian Randomization Analysis. J Clin Endocrinol Metab 2023; 108:3154-3164. [PMID: 37350485 DOI: 10.1210/clinem/dgad376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/19/2023] [Accepted: 06/22/2023] [Indexed: 06/24/2023]
Abstract
CONTEXT Extensive studies have provided considerable evidence suggesting the role of inflammation in the development of female reproductive diseases. However, causality has not been established. OBJECTIVE To explore whether genetically determined circulating levels of cytokines are causally associated with female reproductive diseases and discover potential novel drug targets for these diseases. METHODS Instrumental variables (IVs) for 47 circulating cytokines were obtained from a genome-wide association study (GWAS) meta-analysis of 31 112 European individuals. Protein quantitative trait loci and expression quantitative trait loci close to genes served as our IVs. Summary data of 9 female reproductive diseases were mainly derived from GWAS meta-analysis of the UK biobank and FinnGen. We elevated the association using the Wald ratio or inverse variance-weighted Mendelian randomization (MR) with subsequent assessments for MR assumptions in several sensitivity and colocalization analyses. We consider a false discovery rate <0.05 as statistical significance in MR analyses. Replication studies were conducted for further validation, and phenome-wide association studies were designed to explore potential side effects. RESULTS Our results indicated that high levels of macrophage colony-stimulating factor (MCSF), growth-regulated oncogene-alpha (GROα), and soluble intercellular adhesion molecule-1 were associated with increased risks of endometriosis, female infertility, and pre-eclampsia, respectively. High platelet-derived growth factor-BB (PDGF-BB) levels that reduced the risk of ovarian aging were also supported. Replication analysis supported the relationship between GROα and female infertility, and between MCSF and endometriosis. CONCLUSION We identified 4 correlated pairs that implied potential protein drug targets. Notably, we preferred highlighting the value of PDGF-BB as a drug target for ovarian aging, and MCSF as a drug target for endometriosis.
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Affiliation(s)
- Yiting Lin
- Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Guiquan Wang
- Center for Reproductive Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Yan Li
- Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haiyan Yang
- Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yue Zhao
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, China
- Key Laboratory of Assisted Reproduction, Ministry of Education, Beijing, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China
| | - Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liangshan Mu
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai, China
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27
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Jeremian R, Xie P, Fotovati M, Lefrançois P, Litvinov IV. Gene-Environment Analyses in a UK Biobank Skin Cancer Cohort Identifies Important SNPs in DNA Repair Genes That May Help Prognosticate Disease Risk. Cancer Epidemiol Biomarkers Prev 2023; 32:1599-1607. [PMID: 37642678 PMCID: PMC10840669 DOI: 10.1158/1055-9965.epi-23-0545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/12/2023] [Accepted: 08/25/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Despite well-established relationships between sun exposure and skin cancer pathogenesis/progression, specific gene-environment interactions in at-risk individuals remain poorly-understood. METHODS We leveraged a UK Biobank cohort of basal cell carcinoma (BCC, n = 17,221), cutaneous squamous cell carcinoma (cSCC, n = 2,331), melanoma in situ (M-is, n = 1,158), invasive melanoma (M-inv, n = 3,798), and healthy controls (n = 448,164) to quantify the synergistic involvement of genetic and environmental factors influencing disease risk. We surveyed 8,798 SNPs from 190 DNA repair genes, and 11 demographic/behavioral risk factors. RESULTS Clinical analysis identified darker skin (RR = 0.01-0.65) and hair (RR = 0.27-0.63) colors as protective factors. Eleven SNPs were significantly associated with BCC, three of which were also associated with M-inv. Gene-environment analysis yielded 201 SNP-environment interactions across 90 genes (FDR-adjusted q < 0.05). SNPs from the FANCA gene showed interactions with at least one clinical factor in all cancer groups, of which three (rs9926296, rs3743860, rs2376883) showed interaction with nearly every factor in BCC and M-inv. CONCLUSIONS We identified novel risk factors for keratinocyte carcinomas and melanoma, highlighted the prognostic value of several FANCA alleles among individuals with a history of sunlamp use and childhood sunburns, and demonstrated the importance of combining genetic and clinical data in disease risk stratification. IMPACT This study revealed genome-wide associations with important implications for understanding skin cancer risk in the context of the rapidly-evolving field of precision medicine. Major individual factors (including sex, hair and skin color, and sun protection use) were significant mediators for all skin cancers, interacting with >200 SNPs across four skin cancer types.
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Affiliation(s)
- Richie Jeremian
- Faculty of Medicine and Health Sciences, McGill University
- Department of Medicine, Division of Dermatology, Research Institute of the McGill University Health Centre (RI-MUHC) Montreal, Quebec
| | - Pingxing Xie
- Faculty of Medicine and Health Sciences, McGill University
- Department of Medicine, Division of Dermatology, Research Institute of the McGill University Health Centre (RI-MUHC) Montreal, Quebec
| | - Misha Fotovati
- Faculty of Medicine and Health Sciences, McGill University
- Department of Medicine, Division of Dermatology, Lady Davis Institute (LDI), Jewish General Hospital, Montreal, Quebec
| | - Philippe Lefrançois
- Faculty of Medicine and Health Sciences, McGill University
- Department of Medicine, Division of Dermatology, Lady Davis Institute (LDI), Jewish General Hospital, Montreal, Quebec
| | - Ivan V. Litvinov
- Faculty of Medicine and Health Sciences, McGill University
- Department of Medicine, Division of Dermatology, Research Institute of the McGill University Health Centre (RI-MUHC) Montreal, Quebec
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28
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Pourteymour S, Drevon CA, Dalen KT, Norheim FA. Mechanisms Behind NAFLD: a System Genetics Perspective. Curr Atheroscler Rep 2023; 25:869-878. [PMID: 37812367 DOI: 10.1007/s11883-023-01158-3] [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] [Accepted: 09/19/2023] [Indexed: 10/10/2023]
Abstract
PURPOSE OF REVIEW To summarize the key factors contributing to the onset and progress of nonalcoholic fatty liver disease (NAFLD) and put them in a system genetics context. We particularly focus on how genetic regulation of hepatic lipids contributes to NAFLD. RECENT FINDINGS NAFLD is characterized by excessive accumulation of fat in the liver. This can progress to steatohepatitis (inflammation and hepatocyte injury) and eventually, cirrhosis. The severity of NAFLD is determined by a combination of factors including obesity, insulin resistance, and lipotoxic lipids, along with genetic susceptibility. Numerous studies have been conducted on large human cohorts and mouse panels, to identify key determinants in the genome, transcriptome, proteome, lipidome, microbiome and different environmental conditions contributing to NAFLD. We review common factors contributing to NAFLD and put them in a systems genetics context. In particular, we describe how genetic regulation of liver lipids contributes to NAFLD. The combination of an unhealthy lifestyle and genetic predisposition increases the likelihood of accumulating lipotoxic specie lipids that may be one of the driving forces behind developing severe forms of NAFLD.
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Affiliation(s)
- Shirin Pourteymour
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Blindern, PO Box 1046, 0317, Oslo, Norway
| | - Christian A Drevon
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Blindern, PO Box 1046, 0317, Oslo, Norway
- Vitas Ltd. Oslo Science Park, Oslo, Norway
| | - Knut Tomas Dalen
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Blindern, PO Box 1046, 0317, Oslo, Norway
| | - Frode A Norheim
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Blindern, PO Box 1046, 0317, Oslo, Norway.
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Hajjar S, Zhou X. pH sensing at the intersection of tissue homeostasis and inflammation. Trends Immunol 2023; 44:807-825. [PMID: 37714775 PMCID: PMC10543622 DOI: 10.1016/j.it.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 08/13/2023] [Accepted: 08/13/2023] [Indexed: 09/17/2023]
Abstract
pH is tightly maintained at cellular, tissue, and systemic levels, and altered pH - particularly in the acidic range - is associated with infection, injury, solid tumors, and physiological and pathological inflammation. However, how pH is sensed and regulated and how it influences immune responses remain poorly understood at the tissue level. Applying conceptual frameworks of homeostatic and inflammatory circuitries, we categorize cellular and tissue components engaged in pH regulation, drawing parallels from established cases in physiology. By expressing various intracellular (pHi) and extracellular pH (pHe)-sensing receptors, the immune system may integrate information on tissue and cellular states into the regulation of homeostatic and inflammatory programs. We introduce the novel concept of resistance and adaptation responses to rationalize pH-dependent immunomodulation intertwined with homeostatic equilibrium and inflammatory control. We discuss emerging challenges and opportunities in understanding the immunological roles of pH sensing, which might reveal new strategies to combat inflammation and restore tissue homeostasis.
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Affiliation(s)
- Stephanie Hajjar
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, 300 Longwood Ave, Boston, MA 02115, USA
| | - Xu Zhou
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, 300 Longwood Ave, Boston, MA 02115, USA.
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Lindén D, Romeo S. Therapeutic opportunities for the treatment of NASH with genetically validated targets. J Hepatol 2023; 79:1056-1064. [PMID: 37207913 DOI: 10.1016/j.jhep.2023.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/29/2023] [Accepted: 05/02/2023] [Indexed: 05/21/2023]
Abstract
The identification of genetic variants associated with fatty liver disease (FLD) from genome-wide association studies started in 2008 when single nucleotide polymorphisms in PNPLA3, the gene encoding patatin-like phospholipase domain-containing 3, were found to be associated with altered hepatic fat content. Since then, several genetic variants associated with protection from, or an increased risk of, FLD have been identified. The identification of these variants has provided insight into the metabolic pathways that cause FLD and enabled the identification of potential therapeutic targets. In this mini-review, we will examine the therapeutic opportunities derived from genetically validated targets in FLD, including oligonucleotide-based therapies targeting PNPLA3 and HSD17B13 that are currently being evaluated in clinical trials for the treatment of NASH (non-alcoholic steatohepatitis).
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Affiliation(s)
- Daniel Lindén
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden; Division of Endocrinology, Department of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Sweden.
| | - Stefano Romeo
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden; Clinical Nutrition Unit, Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy.
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Li X, Shen A, Zhao Y, Xia J. Mendelian Randomization Using the Druggable Genome Reveals Genetically Supported Drug Targets for Psychiatric Disorders. Schizophr Bull 2023; 49:1305-1315. [PMID: 37418754 PMCID: PMC10483453 DOI: 10.1093/schbul/sbad100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
BACKGROUND AND HYPOTHESIS Psychiatric disorders impose a huge health and economic burden on modern society. However, there is currently no proven completely effective treatment available, partly owing to the inefficiency of drug target identification and validation. We aim to identify therapeutic targets relevant to psychiatric disorders by conducting Mendelian randomization (MR) analysis. STUDY DESIGN We performed genome-wide MR analysis by integrating expression quantitative trait loci (eQTL) of 4479 actionable genes that encode druggable proteins and genetic summary statistics from genome-wide association studies of psychiatric disorders. After conducting colocalization analysis on the brain MR findings, we employed protein quantitative trait loci (pQTL) data as genetic proposed instruments for intersecting the colocalized genes to provide further genetic evidence. STUDY RESULTS By performing MR and colocalization analysis with eQTL genetic instruments, we obtained 31 promising drug targets for psychiatric disorders, including 21 significant genes for schizophrenia, 7 for bipolar disorder, 2 for depression, 1 for attention deficit and hyperactivity (ADHD) and none for autism spectrum disorder. Combining MR results using pQTL genetic instruments, we finally proposed 8 drug-targeting genes supported by the strongest MR evidence, including gene ACE, BTN3A3, HAPLN4, MAPK3 and NEK4 for schizophrenia, gene NEK4 and HAPLN4 for bipolar disorder, and gene TIE1 for ADHD. CONCLUSIONS Our findings with genetic support were more likely to be to succeed in clinical trials. In addition, our study prioritizes approved drug targets for the development of new therapies and provides critical drug reuse opportunities for psychiatric disorders.
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Affiliation(s)
- Xiaoyan Li
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Aotian Shen
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Yiran Zhao
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Junfeng Xia
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
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Lee MP, Dimos SF, Raffield LM, Wang Z, Ballou AF, Downie CG, Arehart CH, Correa A, de Vries PS, Du Z, Gignoux CR, Gordon-Larsen P, Guo X, Haessler J, Howard AG, Hu Y, Kassahun H, Kent ST, Lopez JAG, Monda KL, North KE, Peters U, Preuss MH, Rich SS, Rhodes SL, Yao J, Yarosh R, Tsai MY, Rotter JI, Kooperberg CL, Loos RJF, Ballantyne C, Avery CL, Graff M. Ancestral diversity in lipoprotein(a) studies helps address evidence gaps. Open Heart 2023; 10:e002382. [PMID: 37648373 PMCID: PMC10471864 DOI: 10.1136/openhrt-2023-002382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023] Open
Abstract
INTRODUCTION The independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in >1.5 billion individuals worldwide, but studies have prioritised European populations. METHODS Here, we examined how ancestrally diverse studies could clarify Lp(a)'s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations. RESULTS Fourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R2=15% in East Asians) to high (R2=50% in Europeans) accuracy. For all populations, PRS showed promise as a 'rule out' for elevated Lp(a) because certainty of assignment to the low-risk threshold was high (88.0%-99.9%) across PRS thresholds (80th-99th percentile). Causal effects of increased Lp(a) with increased glycated haemoglobin were estimated for Europeans (p value =1.4×10-6), although inverse effects in Africans and East Asians suggested the potential for heterogeneous causal effects. Finally, Hispanic/Latinos were the only population in which known associations with coronary atherosclerosis and ischaemic heart disease were identified in external testing of Lp(a) PRS phenotypic effects. CONCLUSIONS Our results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps.
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Affiliation(s)
- Moa P Lee
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sofia F Dimos
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Laura M Raffield
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anna F Ballou
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Carolina G Downie
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Christopher H Arehart
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Adolfo Correa
- Department of Population Health Science, The University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Paul S de Vries
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Zhaohui Du
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Christopher R Gignoux
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xiuqing Guo
- Department of Pediatrics, UCLA Medical Center, Los Angeles, California, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Annie Green Howard
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yao Hu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Helina Kassahun
- Global Development, Amgen Inc, Thousand Oaks, California, USA
| | - Shia T Kent
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | | | - Keri L Monda
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | - Kari E North
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stephen S Rich
- University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Shannon L Rhodes
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | - Jie Yao
- Department of Pediatrics, UCLA Medical Center, Los Angeles, California, USA
| | - Rina Yarosh
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine & Pathology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jerome I Rotter
- Department of Pediatrics, UCLA Medical Center, Los Angeles, California, USA
| | - Charles L Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Kobenhavn, Denmark
| | - Christie Ballantyne
- Department of Medicine, Section of Cardiology, Baylor College of Medicine, Houston, Texas, USA
| | - Christy L Avery
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mariaelisa Graff
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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He XY, Wu BS, Kuo K, Zhang W, Ma Q, Xiang ST, Li YZ, Wang ZY, Dong Q, Feng JF, Cheng W, Yu JT. Association between polygenic risk for Alzheimer's disease and brain structure in children and adults. Alzheimers Res Ther 2023; 15:109. [PMID: 37312172 DOI: 10.1186/s13195-023-01256-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND The correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages. METHODS This study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life. RESULTS Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD). CONCLUSIONS In conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zi-Yi Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China.
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
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Mantovani A, Pelusi S, Margarita S, Malvestiti F, Dell'Alma M, Bianco C, Ronzoni L, Prati D, Targher G, Valenti L. Adverse effect of PNPLA3 p.I148M genetic variant on kidney function in middle-aged individuals with metabolic dysfunction. Aliment Pharmacol Ther 2023; 57:1093-1102. [PMID: 36947711 DOI: 10.1111/apt.17477] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/01/2023] [Accepted: 03/09/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND The PNPLA3 p.I148M variant is the main genetic determinant of nonalcoholic fatty liver disease, and PNPLA3 silencing is being evaluated to treat this liver condition. Data suggest that the p.I148M variant predisposes to kidney damage, but the relative contribution to kidney function, compared to overall genetic susceptibility, is not defined. AIMS We aimed to assess the effect of PNPLA3 p.I148M on the estimated glomerular filtration rate (eGFR) in individuals with metabolic dysfunction. METHODS We included 1144 middle-aged individuals from the Liver-Bible-2022 cohort. Glomerular filtration rate (eGFR) was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation. The effect of PNPLA3 p.I148M on eGFRCKD-EPI levels was tested under additive genetic models adjusted for clinical predictors, ethnicity and a polygenic risk score of chronic kidney disease (PRS-CKD). In a subset of 144 individuals, we examined the effect of PNPLA3 p.I148M on eGFRCKD-EPI over a median follow-up of 17 months. RESULTS The p.I148M variant was associated with lower eGFRCKD-EPI levels (-1.24 mL/min/1.73 m2 per allele, 95% CI: -2.32 to -0.17; p = 0.023), independent of age, sex, height, waist circumference, systolic blood pressure, LDL-cholesterol, transaminases, fasting insulin, albuminuria, lipid-lowering drugs, ethnicity and PRS-CKD score. In the prospective evaluation, the p.I148M variant was independently associated with faster eGFRCKD-EPI decline (ΔeGFRCKD-EPI -3.57 mL/min/1.73 m2 per allele, 95% CI: -6.94 to -0.21; p = 0.037). CONCLUSIONS We found a detrimental impact of the PNPLA3 p.I148M variant on eGFRCKD-EPI levels in middle-aged individuals with metabolic dysfunction. This association was independent of established risk factors, ethnicity and genetic predisposition to CKD. PNPLA3 p.I148M silencing may protect against kidney damage progression in carriers.
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Affiliation(s)
- Alessandro Mantovani
- Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Serena Pelusi
- Precision Medicine Lab, Biological Resource Center - Department of Transfusion Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Sara Margarita
- Precision Medicine Lab, Biological Resource Center - Department of Transfusion Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Francesco Malvestiti
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Michela Dell'Alma
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Cristiana Bianco
- Precision Medicine Lab, Biological Resource Center - Department of Transfusion Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Luisa Ronzoni
- Precision Medicine Lab, Biological Resource Center - Department of Transfusion Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Daniele Prati
- Precision Medicine Lab, Biological Resource Center - Department of Transfusion Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giovanni Targher
- Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Luca Valenti
- Precision Medicine Lab, Biological Resource Center - Department of Transfusion Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
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Yuan S, Wang L, Zhang H, Xu F, Zhou X, Yu L, Sun J, Chen J, Ying H, Xu X, Yu Y, Spiliopoulou A, Shen X, Wilson J, Gill D, Theodoratou E, Larsson SC, Li X. Mendelian randomization and clinical trial evidence supports TYK2 inhibition as a therapeutic target for autoimmune diseases. EBioMedicine 2023; 89:104488. [PMID: 36842216 PMCID: PMC9988426 DOI: 10.1016/j.ebiom.2023.104488] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND To explore the associations of genetically proxied TYK2 inhibition with a wide range of disease outcomes and biomarkers to identify therapeutic repurposing opportunities, adverse effects, and biomarkers of efficacy. METHODS The loss-of-function missense variant rs34536443 in TYK2 gene was used as a genetic instrument to proxy the effect of TYK2 inhibition. A phenome-wide Mendelian randomization (MR) study was conducted to explore the associations of genetically-proxied TYK2 inhibition with 1473 disease outcomes in UK Biobank (N = 339,197). Identified associations were examined for replication in FinnGen (N = 260,405). We further performed tissue-specific gene expression MR, colocalization analyses, and MR with 247 blood biomarkers. A systematic review of randomized controlled trials (RCTs) on TYK2 inhibitor was performed to complement the genetic evidence. FINDINGS PheWAS-MR found that genetically-proxied TYK2 inhibition was associated with lower risk of a wide range of autoimmune diseases. The associations with hypothyroidism and psoriasis were confirmed in MR analysis of tissue-specific TYK2 gene expression and the associations with systemic lupus erythematosus, psoriasis, and rheumatoid arthritis were observed in colocalization analysis. There were nominal associations of genetically-proxied TYK2 inhibition with increased risk of prostate and breast cancer but not in tissue-specific expression MR or colocalization analyses. Thirty-seven blood biomarkers were associated with the TYK2 loss-of-function mutation. Evidence from RCTs confirmed the effectiveness of TYK2 inhibitors on plaque psoriasis and reported several adverse effects. INTERPRETATION This study supports TYK2 inhibitor as a potential treatment for psoriasis and several other autoimmune diseases. Increased pharmacovigilance is warranted in relation to the potential adverse effects. FUNDING None.
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Affiliation(s)
- Shuai Yuan
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Lijuan Wang
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Han Zhang
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Fengzhe Xu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Xuan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lili Yu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Sun
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie Chen
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haochao Ying
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaolin Xu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yongfu Yu
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
| | - Athina Spiliopoulou
- Centre for Public Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Xia Shen
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Jim Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
| | - Evropi Theodoratou
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
| | - Xue Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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36
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Carss KJ, Deaton AM, Del Rio-Espinola A, Diogo D, Fielden M, Kulkarni DA, Moggs J, Newham P, Nelson MR, Sistare FD, Ward LD, Yuan J. Using human genetics to improve safety assessment of therapeutics. Nat Rev Drug Discov 2023; 22:145-162. [PMID: 36261593 DOI: 10.1038/s41573-022-00561-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 02/07/2023]
Abstract
Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.
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Affiliation(s)
| | - Aimee M Deaton
- Amgen, Cambridge, MA, USA.,Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Alberto Del Rio-Espinola
- Novartis Institutes for BioMedical Research, Basel, Switzerland.,GentiBio Inc., Cambridge, MA, USA
| | | | - Mark Fielden
- Amgen, Thousand Oaks, MA, USA.,Kate Therapeutics, San Diego, CA, USA
| | | | - Jonathan Moggs
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | | | - Frank D Sistare
- Merck & Co., West Point, PA, USA.,315 Meadowmont Ln, Chapel Hill, NC, USA
| | - Lucas D Ward
- Amgen, Cambridge, MA, USA. .,Alnylam Pharmaceuticals, Cambridge, MA, USA.
| | - Jing Yuan
- Amgen, Cambridge, MA, USA.,Pfizer, Cambridge, MA, USA
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Abstract
A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.
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Affiliation(s)
- Kristi Krebs
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia;
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia;
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38
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Harlow CE, Patel VV, Waterworth DM, Wood AR, Beaumont RN, Ruth KS, Tyrrell J, Oguro-Ando A, Chu AY, Frayling TM. Genetically proxied therapeutic prolyl-hydroxylase inhibition and cardiovascular risk. Hum Mol Genet 2023; 32:496-505. [PMID: 36048866 PMCID: PMC9851745 DOI: 10.1093/hmg/ddac215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/05/2022] [Accepted: 08/22/2022] [Indexed: 01/24/2023] Open
Abstract
Prolyl hydroxylase (PHD) inhibitors are in clinical development for anaemia in chronic kidney disease. Epidemiological studies have reported conflicting results regarding safety of long-term therapeutic haemoglobin (Hgb) rises through PHD inhibition on risk of cardiovascular disease. Genetic variation in genes encoding PHDs can be used as partial proxies to investigate the potential effects of long-term Hgb rises. We used Mendelian randomization to investigate the effect of long-term Hgb level rises through genetically proxied PHD inhibition on coronary artery disease (CAD: 60 801 cases; 123 504 controls), myocardial infarction (MI: 42 561 cases; 123 504 controls) or stroke (40 585 cases; 406 111 controls). To further characterize long-term effects of Hgb level rises, we performed a phenome-wide association study (PheWAS) in up to 451 099 UK Biobank individuals. Genetically proxied therapeutic PHD inhibition, equivalent to a 1.00 g/dl increase in Hgb levels, was not associated (at P < 0.05) with increased odds of CAD; odd ratio (OR) [95% confidence intervals (CI)] = 1.06 (0.84, 1.35), MI [OR (95% CI) = 1.02 (0.79, 1.33)] or stroke [OR (95% CI) = 0.91 (0.66, 1.24)]. PheWAS revealed associations with blood related phenotypes consistent with EGLN's role, relevant kidney- and liver-related biomarkers like estimated glomerular filtration rate and microalbuminuria, and non-alcoholic fatty liver disease (Bonferroni-adjusted P < 5.42E-05) but these were not clinically meaningful. These findings suggest that long-term alterations in Hgb through PHD inhibition are unlikely to substantially increase cardiovascular disease risk; using large disease genome-wide association study data, we could exclude ORs of 1.35 for cardiovascular risk with a 1.00 g/dl increase in Hgb.
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Affiliation(s)
- Charli E Harlow
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Vickas V Patel
- GlaxoSmithKline, Collegeville, PA 19426, USA
- Spark Therapeutics, Inc., Philadelphia, PA 19104, USA
| | - Dawn M Waterworth
- GlaxoSmithKline, Collegeville, PA 19426, USA
- Immunology Translational Sciences, Janssen, Spring House, PA 19044, USA
| | - Andrew R Wood
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Robin N Beaumont
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Katherine S Ruth
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Jessica Tyrrell
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Asami Oguro-Ando
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | | | - Timothy M Frayling
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
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Genomic variants-driven drug repurposing for tuberculosis by utilizing the established bioinformatic-based approach. Biochem Biophys Rep 2022; 32:101334. [PMID: 36090591 PMCID: PMC9449755 DOI: 10.1016/j.bbrep.2022.101334] [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: 07/21/2022] [Revised: 08/08/2022] [Accepted: 08/22/2022] [Indexed: 11/21/2022] Open
Abstract
A major challenge in translating genomic variants of Tuberculosis (TB) into clinical implementation is to integrate the disease-associated variants and facilitate drug discovery through the concept of genomic-driven drug repurposing. Here, we utilized two established genomic databases, namely a Genome-Wide Association Study (GWAS) and a Phenome-Wide Association Study (PheWAS) to identify the genomic variants associated with TB disease and further utilize them for drug-targeted genes. We evaluated 3.425 genomic variants associated with TB disease which overlapped with 200 TB-associated genes. To prioritize the biological TB risk genes, we devised an in-silico pipeline and leveraged an established bioinformatics method based on six functional annotations (missense mutation, cis-eQTL, biological process, cellular component, molecular function, and KEGG molecular pathway analysis). Interestingly, based on the six functional annotations that we applied, we discovered that 14 biological TB risk genes are strongly linked to the deregulation of the biological TB risk genes. Hence, we demonstrated that 12 drug target genes overlapped with 40 drugs for other indications and further suggested that the drugs may be repurposed for the treatment of TB. We highlighted that CD44, CCR5, CXCR4, and C3 are highly promising proposed TB targets since they are connected to SELP and HLA-B, which are biological TB risk genes with high systemic scores on functional annotations. In sum, the current study shed light on the genomic variants involved in TB pathogenesis as the biological TB risk genes and provided empirical evidence that the genomics of TB may contribute to drug discovery. The feasibility of utilizing genomic variants to facilitate drug repurposing for Tuberculosis. Genomic information can be effectively used for drug discovery and treatment through genomic-based therapies. Findings from our research support the possibility of drug repurposing for Tuberculosis based on genomic variations.
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Lisboa RO, Sekula RF, Bezamat M, Deeley K, Santana-da-Silva LC, Vieira AR. Pain perception genes, asthma, and oral health: A reverse genetics study. PLoS One 2022; 17:e0277036. [PMID: 36395102 PMCID: PMC9671307 DOI: 10.1371/journal.pone.0277036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/18/2022] [Indexed: 11/19/2022] Open
Abstract
Pain is an experience of a subjective nature, interpreted in a personal way and according to an extensive palette of factors unique to each individual. Orofacial pain can be acute or chronic and it is usually the main reason for the patient to seek dental care. Pain perception varies widely among individuals. This variability is considered a mosaic of factors, which include biopsychosocial factors and genetic factors. Understanding these differences can be extremely beneficial for pain management in a personalized and more efficient way. We performed association studies to investigate phenotypes associated with genetic markers in pain-related genes in two groups of patients who received more or less anesthesia during dental treatment. The study group was comprised of 1289 individuals participating in the Dental Registry and DNA Repository Project (DRDR) of the University of Pittsburgh, with 900 participants in the group that received the most anesthesia and 389 constituting the comparison group that received less anesthesia. We tested 58 phenotypes and genotypic data of seven SNPs in genes that are associated with pain perception, pain modulation and response to drugs used in pain treatment: COMT (rs4818 and rs6269), GCH1 (rs3783641), DRD2 (rs6276), OPRM1 (rs1799971), SCN9A (rs6746030) and SCN10A (rs6795970). The analysis revealed a protective effect of rs1799971 on asthma in the total sample. rs3783641 was associated with salivary secretion disorders in females who received more anesthesia. rs1799971 was also associated with periodontitis in Whites who received less anesthesia. rs4818 was associated with disease and other tongue conditions in the group composed of Blacks who received less anesthesia. In conclusion, our study implicated variants in pain-related genes in asthma and oral phenotypes.
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Affiliation(s)
- Rosany O. Lisboa
- Laboratory of Inborn Errors of Metabolism, Institute of Biological Sciences, Federal University of Pará, Pará, Brazil
- Departments of Oral and Craniofacial Sciences, Pediatric Dentistry and Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Graduate Program in Oncology and Medical Sciences, Federal University of Pará, Pará, Brazil
| | - Raymond F. Sekula
- Department of Neurological Surgery, Columbia University Vagelos School of Medicine, New York, New York, United States of America
| | - Mariana Bezamat
- Departments of Oral and Craniofacial Sciences, Pediatric Dentistry and Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Kathleen Deeley
- Departments of Oral and Craniofacial Sciences, Pediatric Dentistry and Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Luiz Carlos Santana-da-Silva
- Laboratory of Inborn Errors of Metabolism, Institute of Biological Sciences, Federal University of Pará, Pará, Brazil
- Graduate Program in Oncology and Medical Sciences, Federal University of Pará, Pará, Brazil
| | - Alexandre R. Vieira
- Departments of Oral and Craniofacial Sciences, Pediatric Dentistry and Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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Lal JC, Mao C, Zhou Y, Gore-Panter SR, Rennison JH, Lovano BS, Castel L, Shin J, Gillinov AM, Smith JD, Barnard J, Van Wagoner DR, Luo Y, Cheng F, Chung MK. Transcriptomics-based network medicine approach identifies metformin as a repurposable drug for atrial fibrillation. Cell Rep Med 2022; 3:100749. [PMID: 36223777 PMCID: PMC9588904 DOI: 10.1016/j.xcrm.2022.100749] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/25/2022] [Accepted: 08/26/2022] [Indexed: 11/24/2022]
Abstract
Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.
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Affiliation(s)
- Jessica C. Lal
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave., NE5-305, Cleveland, OH 44195, USA,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Chengsheng Mao
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave., NE5-305, Cleveland, OH 44195, USA
| | - Shamone R. Gore-Panter
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA,Department of Biological, Geological, and Environmental Sciences, Cleveland State University, Cleveland, OH 44115, USA
| | - Julie H. Rennison
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Beth S. Lovano
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Laurie Castel
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jiyoung Shin
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - A. Marc Gillinov
- Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jonathan D. Smith
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA,Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - John Barnard
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - David R. Van Wagoner
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA,Corresponding author
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave., NE5-305, Cleveland, OH 44195, USA,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA,Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA,Corresponding author
| | - Mina K. Chung
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA,Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, 9500 Euclid Ave., J2-2, OH 44195, USA,Corresponding author
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The Role of Systems Biology in Deciphering Asthma Heterogeneity. LIFE (BASEL, SWITZERLAND) 2022; 12:life12101562. [PMID: 36294997 PMCID: PMC9605413 DOI: 10.3390/life12101562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/28/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022]
Abstract
Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.
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43
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Zhao J, Guo S, Schrodi SJ, He D. Trends in the Contribution of Genetic Susceptibility Loci to Hyperuricemia and Gout and Associated Novel Mechanisms. Front Cell Dev Biol 2022; 10:937855. [PMID: 35813212 PMCID: PMC9259951 DOI: 10.3389/fcell.2022.937855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/31/2022] [Indexed: 11/14/2022] Open
Abstract
Hyperuricemia and gout are complex diseases mediated by genetic, epigenetic, and environmental exposure interactions. The incidence and medical burden of gout, an inflammatory arthritis caused by hyperuricemia, increase every year, significantly increasing the disease burden. Genetic factors play an essential role in the development of hyperuricemia and gout. Currently, the search on disease-associated genetic variants through large-scale genome-wide scans has primarily improved our understanding of this disease. However, most genome-wide association studies (GWASs) still focus on the basic level, whereas the biological mechanisms underlying the association between genetic variants and the disease are still far from well understood. Therefore, we summarized the latest hyperuricemia- and gout-associated genetic loci identified in the Global Biobank Meta-analysis Initiative (GBMI) and elucidated the comprehensive potential molecular mechanisms underlying the effects of these gene variants in hyperuricemia and gout based on genetic perspectives, in terms of mechanisms affecting uric acid excretion and reabsorption, lipid metabolism, glucose metabolism, and nod-like receptor pyrin domain 3 (NLRP3) inflammasome and inflammatory pathways. Finally, we summarized the potential effect of genetic variants on disease prognosis and drug efficacy. In conclusion, we expect that this summary will increase our understanding of the pathogenesis of hyperuricemia and gout, provide a theoretical basis for the innovative development of new clinical treatment options, and enhance the capabilities of precision medicine for hyperuricemia and gout treatment.
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Affiliation(s)
- Jianan Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Shicheng Guo
- Computation and Informatics in Biology and Medicine, University of WI-Madison, Madison, WI, United States
- Department of Medical Genetics, School of Medicine and Public Health, University of WI-Madison, Madison, WI, United States
| | - Steven J. Schrodi
- Computation and Informatics in Biology and Medicine, University of WI-Madison, Madison, WI, United States
- Department of Medical Genetics, School of Medicine and Public Health, University of WI-Madison, Madison, WI, United States
| | - Dongyi He
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai, China
- Arthritis Institute of Integrated Traditional and Western Medicine, Shanghai Chinese Medicine Research Institute, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
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44
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Mizikovsky D, Naval Sanchez M, Nefzger CM, Cuellar Partida G, Palpant NJ. Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations. Nucleic Acids Res 2022; 50:e87. [PMID: 35716123 PMCID: PMC9410900 DOI: 10.1093/nar/gkac413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 04/28/2022] [Accepted: 05/09/2022] [Indexed: 12/28/2022] Open
Abstract
Genome wide association studies provide statistical measures of gene–trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein–protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis. We identify diverse gene networks with robust interactions but not associated with known biological processes. Analysis of independent disease traits shows that UnTANGLeD gene clusters are conserved across all complex traits, providing a simple and powerful framework to predict novel gene candidates and programs influencing orthogonal disease phenotypes. Collectively, this study demonstrates that gene programs co-ordinately orchestrating cell functions can be identified without reliance on prior knowledge, providing a method for use in functional annotation, hypothesis generation, machine learning and prediction algorithms, and the interpretation of diverse genomic data.
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Affiliation(s)
- Dalia Mizikovsky
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Marina Naval Sanchez
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Christian M Nefzger
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | | | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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Manipulating Microbiota to Treat Atopic Dermatitis: Functions and Therapies. Pathogens 2022; 11:pathogens11060642. [PMID: 35745496 PMCID: PMC9228373 DOI: 10.3390/pathogens11060642] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 05/27/2022] [Accepted: 05/29/2022] [Indexed: 12/13/2022] Open
Abstract
Atopic dermatitis (AD) is a globally prevalent skin inflammation with a particular impact on children. Current therapies for AD are challenged by the limited armamentarium and the high heterogeneity of the disease. A novel promising therapeutic target for AD is the microbiota. Numerous studies have highlighted the involvement of the skin and gut microbiota in the pathogenesis of AD. The resident microbiota at these two epithelial tissues can modulate skin barrier functions and host immune responses, thus regulating AD progression. For example, the pathogenic roles of Staphylococcus aureus in the skin are well-established, making this bacterium an attractive target for AD treatment. Targeting the gut microbiota is another therapeutic strategy for AD. Multiple oral supplements with prebiotics, probiotics, postbiotics, and synbiotics have demonstrated promising efficacy in both AD prevention and treatment. In this review, we summarize the association of microbiota dysbiosis in both the skin and gut with AD, and the current knowledge of the functions of commensal microbiota in AD pathogenesis. Furthermore, we discuss the existing therapies in manipulating both the skin and gut commensal microbiota to prevent or treat AD. We also propose potential novel therapies based on the cutting-edge progress in this area.
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46
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Lee C, Lin J, Prokop A, Gopalakrishnan V, Hanna RN, Papa E, Freeman A, Patel S, Yu W, Huhn M, Sheikh AS, Tan K, Sellman BR, Cohen T, Mangion J, Khan FM, Gusev Y, Shameer K. StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit. Front Genet 2022; 13:868015. [PMID: 35711912 PMCID: PMC9197487 DOI: 10.3389/fgene.2022.868015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/29/2022] [Indexed: 01/26/2023] Open
Abstract
Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.
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Affiliation(s)
- Chiyun Lee
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Junxia Lin
- Georgetown University, Washington, DC, United States
| | | | | | - Richard N. Hanna
- Early Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Eliseo Papa
- Research Data and Analytics, R&D IT, AstraZeneca, Cambridge, United Kingdom
| | - Adrian Freeman
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Saleha Patel
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Wen Yu
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Monika Huhn
- Biometrics and Information Sciences, BioPharmaceuticals R&D, AstraZeneca, Mölndal, Sweden
| | - Abdul-Saboor Sheikh
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Keith Tan
- Neuroscience, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Bret R. Sellman
- Discovery Microbiome, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Taylor Cohen
- Discovery Microbiome, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Jonathan Mangion
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Faisal M. Khan
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Yuriy Gusev
- Georgetown University, Washington, DC, United States
| | - Khader Shameer
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States,*Correspondence: Khader Shameer,
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Dumancas GG, Harrison D, Rico JA, Zamora PRFC, Liwag AG, Villaruz JF, Guanzon MLVV, Ferraris HFD, Jalandoni PJB, Padernal WF, Villareal BNL, Maculada RA, Fernandez RMA, Villa FR, de Castro R. Are phenome-wide association studies feasible in a developing country? Trends Genet 2022; 38:885-888. [PMID: 35660028 DOI: 10.1016/j.tig.2022.05.003] [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: 03/07/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 11/18/2022]
Abstract
Phenome-wide association studies (PheWASs), a powerful approach that examines phenotypes associated with a genetic marker, have been used extensively in highly developed countries. Although there may be a clear need for PheWAS in a developing country such as the Philippines, limitations related to resources and practicality would make conducting them a challenge.
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Affiliation(s)
- Gerard G Dumancas
- Department of Chemistry, Loyola Science Center, The University of Scranton, Scranton, PA 18510, USA; Balik Scientist Program, Department of Science and Technology-Philippine Council for Health Research and Development, Bicutan, Taguig City, 1631, Philippines.
| | - Destiny Harrison
- Department of Mathematics and Physical Sciences, Louisiana State University - Alexandria, Alexandria, LA 71302, USA
| | - Jonathan Adam Rico
- Center for Informatics, University of San Agustin, Gen. Luna St, Iloilo City, 5000, Philippines
| | | | - Aretha G Liwag
- West Visayas State University, Luna St, Lapaz, Iloilo City, 5000, Philippines
| | - Joselito F Villaruz
- West Visayas State University, Luna St, Lapaz, Iloilo City, 5000, Philippines
| | - Ma Luz Vicenta V Guanzon
- Corazon Locsin Montelibano Memorial Regional Hospital, Lacson St, Bacolod, Negros Occidental, 6100, Philippines
| | - Hans Francis D Ferraris
- Corazon Locsin Montelibano Memorial Regional Hospital, Lacson St, Bacolod, Negros Occidental, 6100, Philippines
| | | | | | | | | | | | | | - Romulo de Castro
- Center for Informatics, University of San Agustin, Gen. Luna St, Iloilo City, 5000, Philippines; Balik Scientist Program, Department of Science and Technology-Philippine Council for Health Research and Development, Bicutan, Taguig City, 1631, Philippines
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Alharthi J, Gastaldelli A, Cua IH, Ghazinian H, Eslam M. Metabolic dysfunction-associated fatty liver disease: a year in review. Curr Opin Gastroenterol 2022; 38:251-260. [PMID: 35143431 DOI: 10.1097/mog.0000000000000823] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE OF REVIEW In 2020, a novel comprehensive redefinition of fatty liver disease was proposed by an international panel of experts. This review aims to explore current evidence regarding the impact of this new definition on the current understanding of the epidemiology, pathogenesis, diagnosis, and clinical trials for fatty liver disease. RECENT FINDINGS The effectiveness of metabolic dysfunction-associated fatty liver disease (MAFLD) was compared to the existing criteria for nonalcoholic fatty liver disease (NAFLD). Recent data robustly suggest the superior utility of MAFLD in identifying patients at high risk for metabolic dysfunction, the hepatic and extra-hepatic complications, as well as those who would benefit from genetic testing, including patients with concomitant liver diseases. This change in name and criteria also appears to have improved disease awareness among patients and physicians. SUMMARY The transformation in name and definition from NAFLD to MAFLD represents an important milestone, which indicates significant tangible progress towards a more inclusive, equitable, and patient-centred approach to addressing the profound challenges of this disease. Growing evidence has illustrated the broader and specific contexts that have tremendous potential for positively influencing the diagnosis and treatment. In addition, the momentum accompanying this name change has included widespread public attention to the unique burden of this previously underappreciated disease.
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Affiliation(s)
- Jawaher Alharthi
- Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Westmead, New South Wales, Australia
- Department of Biotechnology, Faculty of Science, Taif University, Taif, Saudi Arabia
| | | | - Ian Homer Cua
- Institute of Digestive and Liver Diseases, St. Luke's Medical Center, Global City, Philippines
| | - Hasmik Ghazinian
- Hepatology Department, National Centre of Infectious Diseases, Yerevan, Armenia
| | - Mohammed Eslam
- Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Westmead, New South Wales, Australia
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Portelli MA, Rakkar K, Hu S, Guo Y, Adcock IM, Sayers I. Translational Analysis of Moderate to Severe Asthma GWAS Signals Into Candidate Causal Genes and Their Functional, Tissue-Dependent and Disease-Related Associations. FRONTIERS IN ALLERGY 2022; 2:738741. [PMID: 35386986 PMCID: PMC8974692 DOI: 10.3389/falgy.2021.738741] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 09/06/2021] [Indexed: 12/23/2022] Open
Abstract
Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including MUC5AC and STAT6. Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both MUC5AC and SLC22A4 genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the HLA-DQA1/A2/B1/B2 gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found IL33 and IL18R1 to be key co-localization partners for other genes, predicted that CD274 forms co-expression relationships with 13 other genes, including the HLA-DQA1/A2/B1/B2 gene cluster and that MUC5AC and IL37 are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma.
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Affiliation(s)
- Michael A Portelli
- Centre for Respiratory Research, Translational Medical Sciences, School of Medicine, National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Kamini Rakkar
- Centre for Respiratory Research, Translational Medical Sciences, School of Medicine, National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Sile Hu
- Data Science Institute, Imperial College London, London, United Kingdom
| | - Yike Guo
- Data Science Institute, Imperial College London, London, United Kingdom
| | - Ian M Adcock
- The National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Ian Sayers
- Centre for Respiratory Research, Translational Medical Sciences, School of Medicine, National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
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Pirola CJ, Sookoian S. Metabolic dysfunction-associated fatty liver disease: advances in genetic and epigenetic implications. Curr Opin Lipidol 2022; 33:95-102. [PMID: 34966133 DOI: 10.1097/mol.0000000000000814] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW Fatty liver associated with metabolic dysfunction, also known under the acronym NAFLD (nonalcoholic fatty liver disease) is the leading global cause of chronic liver disease. In this review, we address the state of research on genetics and epigenetics of NAFLD with focus on key discoveries and conceptual advances over the past 2 years. RECENT FINDINGS The analysis of NAFLD-associated genetic variant effects on the whole-transcriptome, including quantitative trait loci (QTL) associated with gene expression (eQTL) or splicing (sQTL) may explain pleiotropic effects. Functional experiments on NAFLD-epigenetics, including profiling of liver chromatin accessibility quantitative trait loci (caQTL) show co-localization with numerous genome-wide association study signals linked to metabolic and cardiovascular traits. Novel studies provide insights into the modulation of the hepatic transcriptome and epigenome by tissue microbiotas. Genetic variation of components of the liver cellular respirasome may result in broad cellular and metabolic effects. Mitochondrial noncoding RNAs may regulate liver inflammation and fibrogenesis. RNA modifications as N6-methyladenosine may explain sex-specific differences in liver gene transcription linked to lipid traits. SUMMARY The latest developments in the field of NAFLD-genomics can be leveraged for identifying novel disease mechanisms and therapeutic targets that may prevent the morbidity and mortality associated with disease progression. VIDEO ABSTRACT http://links.lww.com/COL/A23.
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Affiliation(s)
- Carlos J Pirola
- Institute of Medical Research A Lanari, University of Buenos Aires, School of Medicine
- Department of Molecular Genetics and Biology of Complex Diseases, Institute of Medical Research (IDIM), National Scientific and Technical Research Council (CONICET) - University of Buenos Aires
| | - Silvia Sookoian
- Institute of Medical Research A Lanari, University of Buenos Aires, School of Medicine
- Department of Clinical and Molecular Hepatology, Institute of Medical Research (IDIM), National Scientific and Technical Research Council (CONICET) - University of Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
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