251
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Elhadad MA, Jonasson C, Huth C, Wilson R, Gieger C, Matias P, Grallert H, Graumann J, Gailus-Durner V, Rathmann W, von Toerne C, Hauck SM, Koenig W, Sinner MF, Oprea TI, Suhre K, Thorand B, Hveem K, Peters A, Waldenberger M. Deciphering the Plasma Proteome of Type 2 Diabetes. Diabetes 2020; 69:2766-2778. [PMID: 32928870 PMCID: PMC7679779 DOI: 10.2337/db20-0296] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 08/28/2020] [Indexed: 12/17/2022]
Abstract
With an estimated prevalence of 463 million affected, type 2 diabetes represents a major challenge to health care systems worldwide. Analyzing the plasma proteomes of individuals with type 2 diabetes may illuminate hitherto unknown functional mechanisms underlying disease pathology. We assessed the associations between type 2 diabetes and >1,000 plasma proteins in the Cooperative Health Research in the Region of Augsburg (KORA) F4 cohort (n = 993, 110 cases), with subsequent replication in the third wave of the Nord-Trøndelag Health Study (HUNT3) cohort (n = 940, 149 cases). We computed logistic regression models adjusted for age, sex, BMI, smoking status, and hypertension. Additionally, we investigated associations with incident type 2 diabetes and performed two-sample bidirectional Mendelian randomization (MR) analysis to prioritize our results. Association analysis of prevalent type 2 diabetes revealed 24 replicated proteins, of which 8 are novel. Proteins showing association with incident type 2 diabetes were aminoacylase-1, growth hormone receptor, and insulin-like growth factor-binding protein 2. Aminoacylase-1 was associated with both prevalent and incident type 2 diabetes. MR analysis yielded nominally significant causal effects of type 2 diabetes on cathepsin Z and rennin, both known to have roles in the pathophysiological pathways of cardiovascular disease, and of sex hormone-binding globulin on type 2 diabetes. In conclusion, our high-throughput proteomics study replicated previously reported type 2 diabetes-protein associations and identified new candidate proteins possibly involved in the pathogenesis of type 2 diabetes.
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Affiliation(s)
- Mohamed A Elhadad
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Christian Jonasson
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health, Norwegian University of Science and Technology, Levanger, Norway
| | - Cornelia Huth
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Rory Wilson
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Pamela Matias
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Johannes Graumann
- Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
- The German Centre for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Valerie Gailus-Durner
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christine von Toerne
- Research Unit Protein Science, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Stefanie M Hauck
- Research Unit Protein Science, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Koenig
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Deutsches Herzzentrum München, Technische Universitat München, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Moritz F Sinner
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Medicine I, University Hospital Munich, Ludwig Maximilian University, Munich, Germany
| | - Tudor I Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, University of New Mexico School of Medicine, Albuquerque, NM
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Education City, Doha, Qatar
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health, Norwegian University of Science and Technology, Levanger, Norway
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Medical Information Sciences, Biometry and Epidemiology, Ludwig Maximilian University, Munich, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
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252
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Brugger M, Becker-Dettling F, Brunet T, Strom T, Meitinger T, Lurz E, Borggraefe I, Wagner M. A homozygous truncating variant in CCDC186 in an individual with epileptic encephalopathy. Ann Clin Transl Neurol 2020; 8:278-283. [PMID: 33259146 PMCID: PMC7818090 DOI: 10.1002/acn3.51260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/02/2020] [Accepted: 11/10/2020] [Indexed: 12/13/2022] Open
Abstract
Coiled-Coil Domain Containing Protein 186 (CCDC186) is hypothesized to play an important role in the biogenesis of dense-core vesicles in neurons and endocrine cells. Biallelic loss-of-function variants in the encoding gene CCDC186 have been suggested as a candidate gene for a neurodevelopmental phenotype, but only one patient has been described so far. We report a second patient with a CCDC186-associated phenotype presenting with developmental delay, epileptic encephalopathy, and failure to thrive. Exome sequencing identified a homozygous loss-of-function variant in CCDC186 (NM_018017.2) c.767C> G; p.(Ser256Ter) thus providing further evidence to support CCDC186 as a new disease gene for an autosomal recessive neurodevelopmental disorder.
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Affiliation(s)
- Melanie Brugger
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Fiona Becker-Dettling
- Division of Pediatric Neurology, Developmental Medicine and Social Pediatrics, Department of Pediatrics, Dr. von Haunersches Childrens Hospital, Ludwig Maximilians University of Munich, Munich, Germany
| | - Theresa Brunet
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Tim Strom
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Eberhard Lurz
- Division of Pediatric Gastroenterology, Dr. von Haunersches Childrens Hospital, Ludwig Maximilians University of Munich, Munich, Germany
| | - Ingo Borggraefe
- Division of Pediatric Neurology, Developmental Medicine and Social Pediatrics, Department of Pediatrics, Dr. von Haunersches Childrens Hospital, Ludwig Maximilians University of Munich, Munich, Germany.,Comprensive Epilepsy Center Ludwig, Maximilians University of Munich, Munich, Germany
| | - Matias Wagner
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.,Institute of Neurogenomics, Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, Neuherberg, Germany
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253
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Brion C, Lutz SM, Albert FW. Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation. eLife 2020; 9:60645. [PMID: 33191917 PMCID: PMC7707838 DOI: 10.7554/elife.60645] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/14/2020] [Indexed: 01/27/2023] Open
Abstract
Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.
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Affiliation(s)
- Christian Brion
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Sheila M Lutz
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Frank Wolfgang Albert
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
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254
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Filbin MR, Mehta A, Schneider AM, Kays KR, Guess JR, Gentili M, Fenyves BG, Charland NC, Gonye ALK, Gushterova I, Khanna HK, LaSalle TJ, Lavin-Parsons KM, Lilly BM, Lodenstein CL, Manakongtreecheep K, Margolin JD, McKaig BN, Rojas-Lopez M, Russo BC, Sharma N, Tantivit J, Thomas MF, Gerszten RE, Heimberg GS, Hoover PJ, Lieb DJ, Lin B, Ngo D, Pelka K, Reyes M, Smillie CS, Waghray A, Wood TE, Zajac AS, Jennings LL, Grundberg I, Bhattacharyya RP, Parry BA, Villani AC, Sade-Feldman M, Hacohen N, Goldberg MB. Plasma proteomics reveals tissue-specific cell death and mediators of cell-cell interactions in severe COVID-19 patients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.11.02.365536. [PMID: 33173871 PMCID: PMC7654866 DOI: 10.1101/2020.11.02.365536] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
COVID-19 has caused over 1 million deaths globally, yet the cellular mechanisms underlying severe disease remain poorly understood. By analyzing several thousand plasma proteins in 306 COVID-19 patients and 78 symptomatic controls over serial timepoints using two complementary approaches, we uncover COVID-19 host immune and non-immune proteins not previously linked to this disease. Integration of plasma proteomics with nine published scRNAseq datasets shows that SARS-CoV-2 infection upregulates monocyte/macrophage, plasmablast, and T cell effector proteins. By comparing patients who died to severely ill patients who survived, we identify dynamic immunomodulatory and tissue-associated proteins associated with survival, providing insights into which host responses are beneficial and which are detrimental to survival. We identify intracellular death signatures from specific tissues and cell types, and by associating these with angiotensin converting enzyme 2 (ACE2) expression, we map tissue damage associated with severe disease and propose which damage results from direct viral infection rather than from indirect effects of illness. We find that disease severity in lung tissue is driven by myeloid cell phenotypes and cell-cell interactions with lung epithelial cells and T cells. Based on these results, we propose a model of immune and epithelial cell interactions that drive cell-type specific and tissue-specific damage in severe COVID-19.
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255
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Orwoll ES, Wiedrick J, Nielson CM, Jacobs J, Baker ES, Piehowski P, Petyuk V, Gao Y, Shi T, Smith RD, Bauer DC, Cummings SR, Lapidus J. Proteomic assessment of serum biomarkers of longevity in older men. Aging Cell 2020; 19:e13253. [PMID: 33078901 PMCID: PMC7681066 DOI: 10.1111/acel.13253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/30/2020] [Accepted: 08/30/2020] [Indexed: 12/28/2022] Open
Abstract
The biological bases of longevity are not well understood, and there are limited biomarkers for the prediction of long life. We used a high-throughput, discovery-based proteomics approach to identify serum peptides and proteins that were associated with the attainment of longevity in a longitudinal study of community-dwelling men age ≥65 years. Baseline serum in 1196 men were analyzed using liquid chromatography-ion mobility-mass spectrometry, and lifespan was determined during ~12 years of follow-up. Men who achieved longevity (≥90% expected survival) were compared to those who died earlier. Rigorous statistical methods that controlled for false positivity were utilized to identify 25 proteins that were associated with longevity. All these proteins were in lower abundance in long-lived men and included a variety involved in inflammation or complement activation. Lower levels of longevity-associated proteins were also associated with better health status, but as time to death shortened, levels of these proteins increased. Pathway analyses implicated a number of compounds as important upstream regulators of the proteins and implicated shared networks that underlie the observed associations with longevity. Overall, these results suggest that complex pathways, prominently including inflammation, are linked to the likelihood of attaining longevity. This work may serve to identify novel biomarkers for longevity and to understand the biology underlying lifespan.
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Affiliation(s)
| | | | | | - Jon Jacobs
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Erin S. Baker
- Department of Chemistry North Carolina State University Raleigh NC USA
| | - Paul Piehowski
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Vladislav Petyuk
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Yuqian Gao
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Tujin Shi
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Richard D. Smith
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Douglas C. Bauer
- Departments of Medicine and Epidemiology & Biostatistics University of California San Francisco CA USA
| | - Steven R. Cummings
- California Pacific Medical Center Research Institute San Francisco CA USA
| | - Jodi Lapidus
- Oregon Health & Science University Portland OR USA
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256
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Chirinos JA, Cohen JB, Zhao L, Hanff T, Sweitzer N, Fang J, Corrales-Medina V, Anmar R, Morley M, Zamani P, Bhattacharya P, Brandimarto J, Jia Y, Basso MD, Wang Z, Ebert C, Ramirez-Valle F, Schafer PH, Seiffert D, Gordon DA, Cappola T. Clinical and Proteomic Correlates of Plasma ACE2 (Angiotensin-Converting Enzyme 2) in Human Heart Failure. Hypertension 2020; 76:1526-1536. [PMID: 32981365 PMCID: PMC10681288 DOI: 10.1161/hypertensionaha.120.15829] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
ACE2 (angiotensin-converting enzyme 2) is a key component of the renin-angiotensin-aldosterone system. Yet, little is known about the clinical and biologic correlates of circulating ACE2 levels in humans. We assessed the clinical and proteomic correlates of plasma (soluble) ACE2 protein levels in human heart failure. We measured plasma ACE2 using a modified aptamer assay among PHFS (Penn Heart Failure Study) participants (n=2248). We performed an association study of ACE2 against ≈5000 other plasma proteins measured with the SomaScan platform. Plasma ACE2 was not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 was associated with older age, male sex, diabetes mellitus, a lower estimated glomerular filtration rate, worse New York Heart Association class, a history of coronary artery bypass surgery, and higher pro-BNP (pro-B-type natriuretic peptide) levels. Plasma ACE2 exhibited associations with 1011 other plasma proteins. In pathway overrepresentation analyses, top canonical pathways associated with plasma ACE2 included clathrin-mediated endocytosis signaling, actin cytoskeleton signaling, mechanisms of viral exit from host cells, EIF2 (eukaryotic initiation factor 2) signaling, and the protein ubiquitination pathway. In conclusion, in humans with heart failure, plasma ACE2 is associated with various clinical factors known to be associated with severe coronavirus disease 2019 (COVID-19), including older age, male sex, and diabetes mellitus, but is not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 protein levels are prominently associated with multiple cellular pathways involved in cellular endocytosis, exocytosis, and intracellular protein trafficking. Whether these have a causal relationship with ACE2 or are relevant to novel coronavirus-2 infection remains to be assessed in future studies.
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Affiliation(s)
- Julio A. Chirinos
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jordana B. Cohen
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Lei Zhao
- Bristol Myers Squibb Company, Princeton, New Jersey, USA
| | - Thomas Hanff
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Nancy Sweitzer
- Sarver Heart Institute, University of Arizona, Tuscon, AZ
| | - James Fang
- University of Utah, Salt Lake City, Utah
| | | | - Ron Anmar
- Bristol Myers Squibb Company, Princeton, New Jersey, USA
| | - Michael Morley
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Payman Zamani
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Priyanka Bhattacharya
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jeff Brandimarto
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yi Jia
- SomaLogic Inc., Boulder, CO, USA
| | | | - Zhaoqing Wang
- Bristol Myers Squibb Company, Princeton, New Jersey, USA
| | | | | | | | | | | | - Thomas Cappola
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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257
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Sun C, Kovacs P, Guiu-Jurado E. Genetics of Obesity in East Asians. Front Genet 2020; 11:575049. [PMID: 33193685 PMCID: PMC7606890 DOI: 10.3389/fgene.2020.575049] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/17/2020] [Indexed: 12/31/2022] Open
Abstract
Obesity has become a public health problem worldwide. Compared with Europe, people in Asia tend to suffer from type 2 diabetes with a lower body mass index (BMI). Genome-wide association studies (GWASs) have identified over 750 loci associated with obesity. Although the majority of GWAS results were conducted in individuals of European ancestry, a recent GWAS in individuals of Asian ancestry has made a significant contribution to the identification of obesity susceptibility loci. Indeed, owing to the multifactorial character of obesity with a strong environmental component, the revealed loci may have distinct contributions in different ancestral genetic backgrounds and in different environments as presented through diet and exercise among other factors. Uncovering novel, yet unrevealed genes in non-European ancestries may further contribute to explaining the missing heritability for BMI. In this review, we aimed to summarize recent advances in obesity genetics in individuals of Asian ancestry. We therefore compared proposed mechanisms underlying susceptibility loci for obesity associated with individuals of European and Asian ancestries and discussed whether known genetic variants might explain ethnic differences in obesity risk. We further acknowledged that GWAS implemented in individuals of Asian ancestries have not only validated the potential role of previously specified obesity susceptibility loci but also exposed novel ones, which have been missed in the initial genetic studies in individuals of European ancestries. Thus, multi-ethnic studies have a great potential not only to contribute to a better understanding of the complex etiology of human obesity but also potentially of ethnic differences in the prevalence of obesity, which may ultimately pave new avenues in more targeted and personalized obesity treatments.
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Affiliation(s)
| | - Peter Kovacs
- Medical Department III – Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
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258
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Abstract
PURPOSE OF THE REVIEW Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D). RECENT FINDINGS Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.
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Affiliation(s)
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
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259
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Folkersen L, Gustafsson S, Wang Q, Hansen DH, Hedman ÅK, Schork A, Page K, Zhernakova DV, Wu Y, Peters J, Eriksson N, Bergen SE, Boutin TS, Bretherick AD, Enroth S, Kalnapenkis A, Gådin JR, Suur BE, Chen Y, Matic L, Gale JD, Lee J, Zhang W, Quazi A, Ala-Korpela M, Choi SH, Claringbould A, Danesh J, Davey Smith G, de Masi F, Elmståhl S, Engström G, Fauman E, Fernandez C, Franke L, Franks PW, Giedraitis V, Haley C, Hamsten A, Ingason A, Johansson Å, Joshi PK, Lind L, Lindgren CM, Lubitz S, Palmer T, Macdonald-Dunlop E, Magnusson M, Melander O, Michaelsson K, Morris AP, Mägi R, Nagle MW, Nilsson PM, Nilsson J, Orho-Melander M, Polasek O, Prins B, Pålsson E, Qi T, Sjögren M, Sundström J, Surendran P, Võsa U, Werge T, Wernersson R, Westra HJ, Yang J, Zhernakova A, Ärnlöv J, Fu J, Smith JG, Esko T, Hayward C, Gyllensten U, Landen M, Siegbahn A, Wilson JF, Wallentin L, Butterworth AS, Holmes MV, Ingelsson E, Mälarstig A. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat Metab 2020; 2:1135-1148. [PMID: 33067605 PMCID: PMC7611474 DOI: 10.1038/s42255-020-00287-2] [Citation(s) in RCA: 313] [Impact Index Per Article: 78.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 09/02/2020] [Indexed: 02/02/2023]
Abstract
Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.
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Affiliation(s)
- Lasse Folkersen
- Department of Medicine, Karolinska Institute, Solna, Sweden
- Danish National Genome Center, Copenhagen, Denmark
- SCALLOP consortium
| | - Stefan Gustafsson
- SCALLOP consortium
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Qin Wang
- SCALLOP consortium
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
| | | | - Åsa K Hedman
- Department of Medicine, Karolinska Institute, Solna, Sweden
- SCALLOP consortium
- Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Andrew Schork
- SCALLOP consortium
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region, Roskilde, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
| | - Karen Page
- SCALLOP consortium
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Daria V Zhernakova
- SCALLOP consortium
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Yang Wu
- SCALLOP consortium
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - James Peters
- SCALLOP consortium
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Immunology and Inflammation, Faculty of Medicine, Imperial College London, London, UK
| | - Niclas Eriksson
- SCALLOP consortium
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Sarah E Bergen
- SCALLOP consortium
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Thibaud S Boutin
- SCALLOP consortium
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
| | - Andrew D Bretherick
- SCALLOP consortium
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
| | - Stefan Enroth
- SCALLOP consortium
- Department of Immunology, Genetics, and Pathology, Biomedical Center, Science for Life Laboratory (SciLifeLab) Uppsala University, Uppsala, Sweden
| | - Anette Kalnapenkis
- SCALLOP consortium
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Jesper R Gådin
- Department of Medicine, Karolinska Institute, Solna, Sweden
- SCALLOP consortium
| | - Bianca E Suur
- SCALLOP consortium
- Department of Molecular Medicine and Surgery, Karolinska Institute, Solna, Sweden
| | - Yan Chen
- Department of Medicine, Karolinska Institute, Solna, Sweden
- SCALLOP consortium
| | - Ljubica Matic
- SCALLOP consortium
- Department of Molecular Medicine and Surgery, Karolinska Institute, Solna, Sweden
| | - Jeremy D Gale
- SCALLOP consortium
- Inflammation and Immunology Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Julie Lee
- SCALLOP consortium
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Weidong Zhang
- SCALLOP consortium
- Pfizer Global Product Development, Cambridge, MA, USA
| | - Amira Quazi
- SCALLOP consortium
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Mika Ala-Korpela
- SCALLOP consortium
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Seung Hoan Choi
- SCALLOP consortium
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Annique Claringbould
- SCALLOP consortium
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - John Danesh
- SCALLOP consortium
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - George Davey Smith
- SCALLOP consortium
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | - Sölve Elmståhl
- SCALLOP consortium
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Gunnar Engström
- SCALLOP consortium
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Eric Fauman
- SCALLOP consortium
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Celine Fernandez
- SCALLOP consortium
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Lude Franke
- SCALLOP consortium
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Paul W Franks
- SCALLOP consortium
- Department of Clinical Sciences, Lund University Diabetes Center, Malmö, Sweden
| | - Vilmantas Giedraitis
- SCALLOP consortium
- Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala, Sweden
| | - Chris Haley
- SCALLOP consortium
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
| | - Anders Hamsten
- Department of Medicine, Karolinska Institute, Solna, Sweden
- SCALLOP consortium
| | - Andres Ingason
- SCALLOP consortium
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region, Roskilde, Denmark
| | - Åsa Johansson
- SCALLOP consortium
- Department of Immunology, Genetics, and Pathology, Biomedical Center, Science for Life Laboratory (SciLifeLab) Uppsala University, Uppsala, Sweden
| | - Peter K Joshi
- SCALLOP consortium
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Lars Lind
- SCALLOP consortium
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Cecilia M Lindgren
- SCALLOP consortium
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Steven Lubitz
- SCALLOP consortium
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Tom Palmer
- SCALLOP consortium
- Department of Mathematics and Statistics, University of Lancaster, Lancaster, UK
| | - Erin Macdonald-Dunlop
- SCALLOP consortium
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Martin Magnusson
- SCALLOP consortium
- Department of Cardiology, Skåne University Hospital Malmö, Malmö, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- North-West University, Hypertension in Africa Research Team (HART), Potchefstroom, South Africa
| | - Olle Melander
- SCALLOP consortium
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Karl Michaelsson
- SCALLOP consortium
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Andrew P Morris
- SCALLOP consortium
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Reedik Mägi
- SCALLOP consortium
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Michael W Nagle
- SCALLOP consortium
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Peter M Nilsson
- SCALLOP consortium
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Jan Nilsson
- SCALLOP consortium
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Marju Orho-Melander
- SCALLOP consortium
- Department of Clinical Sciences, Clinical Research Center, Lund University, Malmö, Sweden
| | - Ozren Polasek
- SCALLOP consortium
- Faculty of Medicine, University of Split, Split, Croatia
| | - Bram Prins
- SCALLOP consortium
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Erik Pålsson
- SCALLOP consortium
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Ting Qi
- SCALLOP consortium
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Marketa Sjögren
- SCALLOP consortium
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Johan Sundström
- SCALLOP consortium
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Praveen Surendran
- SCALLOP consortium
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Urmo Võsa
- SCALLOP consortium
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Thomas Werge
- SCALLOP consortium
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region, Roskilde, Denmark
| | | | - Harm-Jan Westra
- SCALLOP consortium
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Jian Yang
- SCALLOP consortium
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Alexandra Zhernakova
- SCALLOP consortium
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Johan Ärnlöv
- SCALLOP consortium
- Department of Neurobiology, Care Sciences and Society (NVS) Division of Family Medicine and Primary Care, Karolinska Institute, Solna, Sweden
| | - Jingyuan Fu
- SCALLOP consortium
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Paediatrics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - J Gustav Smith
- SCALLOP consortium
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
| | - Tõnu Esko
- SCALLOP consortium
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Caroline Hayward
- SCALLOP consortium
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
| | - Ulf Gyllensten
- SCALLOP consortium
- Department of Immunology, Genetics, and Pathology, Biomedical Center, Science for Life Laboratory (SciLifeLab) Uppsala University, Uppsala, Sweden
| | - Mikael Landen
- SCALLOP consortium
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Agneta Siegbahn
- SCALLOP consortium
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
| | - James F Wilson
- SCALLOP consortium
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Lars Wallentin
- SCALLOP consortium
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Adam S Butterworth
- SCALLOP consortium
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Michael V Holmes
- SCALLOP consortium
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
| | - Erik Ingelsson
- SCALLOP consortium
- Department of Medicine, Division of Cardiovascular Medicine, Falk Cardiovascular Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Anders Mälarstig
- Department of Medicine, Karolinska Institute, Solna, Sweden.
- SCALLOP consortium, .
- Emerging Science & Innovation, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA.
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260
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Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, Gutteridge A, Erola P, Liu Y, Luo S, Robinson J, Richardson TG, Staley JR, Elsworth B, Burgess S, Sun BB, Danesh J, Runz H, Maranville JC, Martin HM, Yarmolinsky J, Laurin C, Holmes MV, Liu JZ, Estrada K, Santos R, McCarthy L, Waterworth D, Nelson MR, Smith GD, Butterworth AS, Hemani G, Scott RA, Gaunt TR. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet 2020; 52:1122-1131. [PMID: 32895551 PMCID: PMC7610464 DOI: 10.1038/s41588-020-0682-6] [Citation(s) in RCA: 270] [Impact Index Per Article: 67.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 07/24/2020] [Indexed: 01/23/2023]
Abstract
The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.
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Affiliation(s)
- Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.
- Proteome MR writing group, .
| | - Valeriia Haberland
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Denis Baird
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Venexia Walker
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Philip C Haycock
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Mark R Hurle
- Human Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Pau Erola
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Yi Liu
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Shan Luo
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, Hong Kong, China
| | - Jamie Robinson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - James R Staley
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Stephen Burgess
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Benjamin B Sun
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Heiko Runz
- Translational Biology, Biogen, Cambridge, MA, USA
| | - Joseph C Maranville
- Informatics and Predictive Sciences, Celgene Corporation, Cambridge, MA, USA
| | - Hannah M Martin
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Charles Laurin
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - Jimmy Z Liu
- Translational Biology, Biogen, Cambridge, MA, USA
| | | | - Rita Santos
- Functional Genomics, GlaxoSmithKline, Stevenage, Hertfordshire, UK
| | | | | | | | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Adam S Butterworth
- Proteome MR writing group
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Robert A Scott
- Proteome MR writing group, .
- Human Genetics, GlaxoSmithKline, Stevenage, UK.
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.
- Proteome MR writing group, .
- NIHR Bristol Biomedical Research Centre, Bristol, UK.
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261
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Eghbalnia HR, Wilfinger WW, Mackey K, Chomczynski P. Coordinated analysis of exon and intron data reveals novel differential gene expression changes. Sci Rep 2020; 10:15669. [PMID: 32973253 PMCID: PMC7515875 DOI: 10.1038/s41598-020-72482-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 08/24/2020] [Indexed: 12/14/2022] Open
Abstract
RNA-Seq expression analysis currently relies primarily upon exon expression data. The recognized role of introns during translation, and the presence of substantial RNA-Seq counts attributable to introns, provide the rationale for the simultaneous consideration of both exon and intron data. We describe here a method for the coordinated analysis of exon and intron data by investigating their relationship within individual genes and across samples, while taking into account changes in both variability and expression level. This coordinated analysis of exon and intron data offers strong evidence for significant differences that distinguish the profiles of the exon-only expression data from the combined exon and intron data. One advantage of our proposed method, called matched change characterization for exons and introns (MEI), is its straightforward applicability to existing archived data using small modifications to standard RNA-Seq pipelines. Using MEI, we demonstrate that when data are examined for changes in variability across control and case conditions, novel differential changes can be detected. Notably, when MEI criteria were employed in the analysis of an archived data set involving polyarthritic subjects, the number of differentially expressed genes was expanded by sevenfold. More importantly, the observed changes in exon and intron variability with statistically significant false discovery rates could be traced to specific immune pathway gene networks. The application of MEI analysis provides a strategy for incorporating the significance of exon and intron variability and further developing the role of using both exons and intron sequencing counts in studies of gene regulatory processes.
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Affiliation(s)
- Hamid R Eghbalnia
- University of Wisconsin-Madison, Madison, USA. .,University of Cincinnati, Cincinnati, USA.
| | | | - Karol Mackey
- Molecular Research Center, Inc., Cincinnati, USA
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262
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Genetics meets proteomics: perspectives for large population-based studies. Nat Rev Genet 2020; 22:19-37. [PMID: 32860016 DOI: 10.1038/s41576-020-0268-2] [Citation(s) in RCA: 163] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2020] [Indexed: 12/22/2022]
Abstract
Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.
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263
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Yue L, Zhang F, Sun R, Sun Y, Yuan C, Zhu Y, Guo T. Generating Proteomic Big Data for Precision Medicine. Proteomics 2020; 20:e1900358. [PMID: 32725921 DOI: 10.1002/pmic.201900358] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 07/13/2020] [Indexed: 12/23/2022]
Abstract
Here, the authors reason that the complexity of medical problems and proteome science might be tackled effectively with deep learning (DL) technology. However, deployment of DL for proteomics data requires the acquisition of data sets from a large number of samples. Based on the success of DL in medical imaging classification, proteome data from thousands of samples are arguably the minimal input for DL. Contemporary proteomics is turning high-throughput thanks to the rapid progresses of sample preparation and liquid chromatography mass spectrometry methods. In particular, data-independent acquisition now enables the generation of hundreds to thousands of quantitative proteome maps from clinical specimens in clinical cohorts with only limited sample amounts in clinical cohorts. Upheavals in the design of large-scale clinical proteomics studies might be required to generate proteomic big data and deploy DL to tackle complex medical problems.
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Affiliation(s)
- Liang Yue
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Fangfei Zhang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Rui Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Yaoting Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Chunhui Yuan
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Yi Zhu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Tiannan Guo
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
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264
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Gudmundsdottir V, Zaghlool SB, Emilsson V, Aspelund T, Ilkov M, Gudmundsson EF, Jonsson SM, Zilhão NR, Lamb JR, Suhre K, Jennings LL, Gudnason V. Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes. Diabetes 2020; 69:1843-1853. [PMID: 32385057 PMCID: PMC7372075 DOI: 10.2337/db19-1070] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 05/04/2020] [Indexed: 12/16/2022]
Abstract
The increasing prevalence of type 2 diabetes poses a major challenge to societies worldwide. Blood-based factors like serum proteins are in contact with every organ in the body to mediate global homeostasis and may thus directly regulate complex processes such as aging and the development of common chronic diseases. We applied a data-driven proteomics approach, measuring serum levels of 4,137 proteins in 5,438 elderly Icelanders, and identified 536 proteins associated with prevalent and/or incident type 2 diabetes. We validated a subset of the observed associations in an independent case-control study of type 2 diabetes. These protein associations provide novel biological insights into the molecular mechanisms that are dysregulated prior to and following the onset of type 2 diabetes and can be detected in serum. A bidirectional two-sample Mendelian randomization analysis indicated that serum changes of at least 23 proteins are downstream of the disease or its genetic liability, while 15 proteins were supported as having a causal role in type 2 diabetes.
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Affiliation(s)
- Valborg Gudmundsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Holtasmari 1, Kopavogur, Iceland
| | - Shaza B Zaghlool
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Valur Emilsson
- Icelandic Heart Association, Holtasmari 1, Kopavogur, Iceland
- Faculty of Pharmaceutical Sciences, University of Iceland, Reykjavik, Iceland
| | - Thor Aspelund
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Holtasmari 1, Kopavogur, Iceland
| | - Marjan Ilkov
- Icelandic Heart Association, Holtasmari 1, Kopavogur, Iceland
| | | | | | - Nuno R Zilhão
- Icelandic Heart Association, Holtasmari 1, Kopavogur, Iceland
| | | | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | | | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Holtasmari 1, Kopavogur, Iceland
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265
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Yang J, Brody EN, Murthy AC, Mehler RE, Weiss SJ, DeLisle RK, Ostroff R, Williams SA, Ganz P. Impact of Kidney Function on the Blood Proteome and on Protein Cardiovascular Risk Biomarkers in Patients With Stable Coronary Heart Disease. J Am Heart Assoc 2020; 9:e016463. [PMID: 32696702 PMCID: PMC7792282 DOI: 10.1161/jaha.120.016463] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Chronic kidney disease (CKD) confers increased cardiovascular risk, not fully explained by traditional factors. Proteins regulate biological processes and inform the risk of diseases. Thus, in 938 patients with stable coronary heart disease from the Heart and Soul cohort, we quantified 1054 plasma proteins using modified aptamers (SOMAscan) to: (1) discern how reduced glomerular filtration influences the circulating proteome, (2) learn of the importance of kidney function to the prognostic information contained in recently identified protein cardiovascular risk biomarkers, and (3) identify novel and even unique cardiovascular risk biomarkers among individuals with CKD. Methods and Results Plasma protein levels were correlated to estimated glomerular filtration rate (eGFR) using Spearman‐rank correlation coefficients. Cox proportional hazard models were used to estimate the association between individual protein levels and the risk of the cardiovascular outcome (first among myocardial infarction, stroke, heart failure hospitalization, or mortality). Seven hundred and nine (67.3%) plasma proteins correlated with eGFR at P<0.05 (ρ 0.06–0.74); 218 (20.7%) proteins correlated with eGFR moderately or strongly (ρ 0.2–0.74). Among the previously identified 196 protein cardiovascular biomarkers, just 87 remained prognostic after correction for eGFR. Among patients with CKD (eGFR <60 mL/min per 1.73 m2), we identified 21 protein cardiovascular risk biomarkers of which 8 are unique to CKD. Conclusions CKD broadly alters the composition of the circulating proteome. We describe protein biomarkers capable of predicting cardiovascular risk independently of glomerular filtration, and those that are prognostic of cardiovascular risk specifically in patients with CKD and even unique to patients with CKD.
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Affiliation(s)
- Joseph Yang
- Division of Cardiology Department of Medicine University of California, San Francisco CA.,Division of Cardiology Department of Medicine San Francisco Veterans Affairs Health Care System San Francisco CA
| | - Edward N Brody
- Cardiovascular Division Department of Medicine Hospital of the University of Pennsylvania Philadelphia PA
| | - Ashwin C Murthy
- Cardiovascular Division Department of Medicine Hospital of the University of Pennsylvania Philadelphia PA
| | | | | | | | | | | | - Peter Ganz
- Division of Cardiology Department of Medicine University of California, San Francisco CA.,Division of Cardiology Department of Medicine Zuckerberg San Francisco General Hospital San Francisco CA
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266
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Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Genome Med 2020; 12:60. [PMID: 32641083 PMCID: PMC7346642 DOI: 10.1186/s13073-020-00754-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/10/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets. METHODS In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). RESULTS We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn's disease. CONCLUSIONS Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.
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Affiliation(s)
- Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Daniel Trejo-Banos
- Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Athanasios Kousathanas
- Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Sarah E Harris
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Marion Patxot
- Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Sven Erik Ojavee
- Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Qian Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - David C Liewald
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at Austin, Austin, TX, 78712, USA
- Population Research Center, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Matthew R Robinson
- Institute of Science and Technology Austria, 3400, Klosterneuburg, Austria.
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
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267
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Xie J, Zhang L, Chen Z, Hu A, Liu S, Lu D, Xia Y, Qian J, Yang P, Shen H. Protein-protein correlations based variable dimension expansion algorithm for high efficient serum biomarker discovery. Anal Chim Acta 2020; 1119:25-34. [PMID: 32439051 DOI: 10.1016/j.aca.2020.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 04/06/2020] [Indexed: 01/04/2023]
Abstract
In this study, we constructed a high specific and efficient serum biomarker discovery pipeline. We utilized dysregulated proteins identified in primary tissue and potentially secreted into the blood as biomarker candidates. The scheduled multiple reaction monitoring method was performed to accurately quantify and verify these candidates directly in serum, thus circumventing the effects of high-abundance proteins. We then generated new variables through assigning values to protein-protein correlations to extend the dimensionality of the dataset (PPC-VDE), and the specificity of disease classification. We successfully applied this pipeline for biomarker discovery of dilated cardiomyopathy and achieved 88.6% accurate classification of dilated cardiomyopathy, ischemic cardiomyopathy and healthy controls with machine learning. This pipeline is straightforward for biomarker discovery in broad clinical field.
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Affiliation(s)
- Juanjuan Xie
- Minhang Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 201199, China
| | - Lei Zhang
- Minhang Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 201199, China
| | - Zhangwei Chen
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai, China
| | - Anqi Hu
- Minhang Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 201199, China
| | - Shanshan Liu
- Minhang Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 201199, China
| | - Danbo Lu
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai, China
| | - Yan Xia
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai, China
| | - Juying Qian
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai, China.
| | - Pengyuan Yang
- Minhang Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 201199, China; Department of Chemistry, Fudan University, Shanghai, 200032, China; Department of Systems Biology for Medicine and School of Basic Medical Sciences, Fudan University, Shanghai, China; NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, 200032, PR China.
| | - Huali Shen
- Minhang Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 201199, China; Department of Systems Biology for Medicine and School of Basic Medical Sciences, Fudan University, Shanghai, China; NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, 200032, PR China.
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268
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Guseh JS, Churchill TW, Yeri A, Lo C, Brown M, Houstis NE, Aragam KG, Lieberman DE, Rosenzweig A, Baggish AL. An expanded repertoire of intensity-dependent exercise-responsive plasma proteins tied to loci of human disease risk. Sci Rep 2020; 10:10831. [PMID: 32616758 PMCID: PMC7331669 DOI: 10.1038/s41598-020-67669-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 06/10/2020] [Indexed: 12/14/2022] Open
Abstract
Routine endurance exercise confers numerous health benefits, and high intensity exercise may accelerate and magnify many of these benefits. To date, explanatory molecular mechanisms and the influence of exercise intensity remain poorly understood. Circulating factors are hypothesized to transduce some of the systemic effects of exercise. We sought to examine the role of exercise and exercise intensity on the human plasma proteome. We employed an aptamer-based method to examine 1,305 plasma proteins in 12 participants before and after exercise at two physiologically defined intensities (moderate and high) to determine the proteomic response. We demonstrate that the human plasma proteome is responsive to acute exercise in an intensity-dependent manner with enrichment analysis suggesting functional biological differences between the moderate and high intensity doses. Through integration of available genetic data, we estimate the effects of acute exercise on exercise-associated traits and find proteomic responses that may contribute to observed clinical effects on coronary artery disease and blood pressure regulation. In sum, we provide supportive evidence that moderate and high intensity exercise elicit different signaling responses, that exercise may act in part non-cell autonomously through circulating plasma proteins, and that plasma protein dynamics can simulate some the beneficial and adverse effects of acute exercise.
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Affiliation(s)
- J Sawalla Guseh
- Cardiovascular Research Center, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
- Cardiovascular Performance Program, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
| | - Timothy W Churchill
- Cardiovascular Performance Program, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
| | - Ashish Yeri
- Cardiovascular Research Center, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
| | - Claire Lo
- Cardiovascular Performance Program, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Marcel Brown
- Cardiovascular Performance Program, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
| | - Nicholas E Houstis
- Cardiovascular Research Center, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
| | - Krishna G Aragam
- Cardiovascular Research Center, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
| | - Daniel E Lieberman
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Anthony Rosenzweig
- Cardiovascular Research Center, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA.
| | - Aaron L Baggish
- Cardiovascular Performance Program, Division of Cardiology, Corrigan Minehan Heart Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA.
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269
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Evaluation of a novel blood microsampling device for clinical trial sample collection and protein biomarker analysis. Bioanalysis 2020; 12:919-935. [DOI: 10.4155/bio-2020-0063] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Aim: Evaluation of a novel microsampling device for its use in clinical sample collection and biomarker analysis. Methodology: Matching samples were collected from 16 healthy donors (ten females, six males; age 42 ± 20) via K2EDTA touch activated phlebotomy (TAP) device and phlebotomy. The protein profile differences between sampling groups was evaluated using aptamer-based proteomic assay SomaScan and selected ELISA. Conclusion: Somascan signal concordance between phlebotomy- and TAP-generated samples was studied and comparability of protein abundances between these blood sample collection methods was demonstrated. Statistically significant correlation in selected ELISA assays also confirmed the TAP device applicability to the quantitative analysis of protein biomarkers in clinical trials.
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270
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Dodig-Crnković T, Hong MG, Thomas CE, Häussler RS, Bendes A, Dale M, Edfors F, Forsström B, Magnusson PKE, Schuppe-Koistinen I, Odeberg J, Fagerberg L, Gummesson A, Bergström G, Uhlén M, Schwenk JM. Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling. EBioMedicine 2020; 57:102854. [PMID: 32629387 PMCID: PMC7334812 DOI: 10.1016/j.ebiom.2020.102854] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/01/2020] [Accepted: 06/09/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Precision medicine approaches aim to tackle diseases on an individual level through molecular profiling. Despite the growing knowledge about diseases and the reported diversity of molecular phenotypes, the descriptions of human health on an individual level have been far less elaborate. METHODS To provide insights into the longitudinal protein signatures of well-being, we profiled blood plasma collected over one year from 101 clinically healthy individuals using multiplexed antibody assays. After applying an antibody validation scheme, we utilized > 700 protein profiles for in-depth analyses of the individuals' short-term health trajectories. FINDINGS We found signatures of circulating proteomes to be highly individual-specific. Considering technical and longitudinal variability, we observed that 49% of the protein profiles were stable over one year. We also identified eight networks of proteins in which 11-242 proteins covaried over time. For each participant, there were unique protein profiles of which some could be explained by associations to genetic variants. INTERPRETATION This observational and non-interventional study identifyed noticeable diversity among clinically healthy subjects, and facets of individual-specific signatures emerged by monitoring the variability of the circulating proteomes over time. To enable more personal hence precise assessments of health states, longitudinal profiling of circulating proteomes can provide a valuable component for precision medicine approaches. FUNDING This work was supported by the Erling Persson Foundation, the Swedish Heart and Lung Foundation, the Knut and Alice Wallenberg Foundation, Science for Life Laboratory, and the Swedish Research Council.
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Affiliation(s)
- Tea Dodig-Crnković
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
| | - Mun-Gwan Hong
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
| | - Cecilia Engel Thomas
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
| | - Ragna S Häussler
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
| | - Annika Bendes
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
| | - Matilda Dale
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Björn Forsström
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 171 77, Sweden
| | - Ina Schuppe-Koistinen
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden; Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm 171 77, Sweden
| | - Jacob Odeberg
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden; Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Center (TREC), UiT the Arctic University of Norway, Tromsø 9010, Norway; Coagulation unit, Department of Hematology, Karolinska University Hospital, Stockholm 171 76, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
| | - Anders Gummesson
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg 413 45, Sweden; Region Västra Götaland, Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg 413 45, Sweden
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg 413 45, Sweden; Region Västra Götaland, Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg 413 45, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden.
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271
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Pietzner M, Wheeler E, Carrasco-Zanini J, Raffler J, Kerrison ND, Oerton E, Auyeung VP, Luan J, Finan C, Casas JP, Ostroff R, Williams SA, Kastenmüller G, Ralser M, Gamazon ER, Wareham NJ, Hingorani AD, Langenberg C. Genetic architecture of host proteins interacting with SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.07.01.182709. [PMID: 32637948 PMCID: PMC7337378 DOI: 10.1101/2020.07.01.182709] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid 'in silico' assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).
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Affiliation(s)
- Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Erin Oerton
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, UK
- UCL BHF Research Accelerator centre
| | - Juan P. Casas
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Markus Ralser
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Eric R. Gamazon
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, UK
- UCL BHF Research Accelerator centre
- Health Data Research UK, Institute of Health Informatics, University College London, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, UK
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272
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Zhong W, Gummesson A, Tebani A, Karlsson MJ, Hong MG, Schwenk JM, Edfors F, Bergström G, Fagerberg L, Uhlén M. Whole-genome sequence association analysis of blood proteins in a longitudinal wellness cohort. Genome Med 2020; 12:53. [PMID: 32576278 PMCID: PMC7310558 DOI: 10.1186/s13073-020-00755-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 06/11/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The human plasma proteome is important for many biological processes and targets for diagnostics and therapy. It is therefore of great interest to understand the interplay of genetic and environmental factors to determine the specific protein levels in individuals and to gain a deeper insight of the importance of genetic architecture related to the individual variability of plasma levels of proteins during adult life. METHODS We have combined whole-genome sequencing, multiplex plasma protein profiling, and extensive clinical phenotyping in a longitudinal 2-year wellness study of 101 healthy individuals with repeated sampling. Analyses of genetic and non-genetic associations related to the variability of blood levels of proteins in these individuals were performed. RESULTS The analyses showed that each individual has a unique protein profile, and we report on the intra-individual as well as inter-individual variation for 794 plasma proteins. A genome-wide association study (GWAS) using 7.3 million genetic variants identified by whole-genome sequencing revealed 144 independent variants across 107 proteins that showed strong association (P < 6 × 10-11) between genetics and the inter-individual variability on protein levels. Many proteins not reported before were identified (67 out of 107) with individual plasma level affected by genetics. Our longitudinal analysis further demonstrates that these levels are stable during the 2-year study period. The variability of protein profiles as a consequence of environmental factors was also analyzed with focus on the effects of weight loss and infections. CONCLUSIONS We show that the adult blood levels of many proteins are determined at birth by genetics, which is important for efforts aimed to understand the relationship between plasma proteome profiles and human biology and disease.
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Affiliation(s)
- Wen Zhong
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Anders Gummesson
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Abdellah Tebani
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Max J Karlsson
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Mun-Gwan Hong
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden.
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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273
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Emilsson V, Gudmundsson EF, Aspelund T, Jonsson BG, Gudjonsson A, Launer LJ, Lamb JR, Gudmundsdottir V, Jennings LL, Gudnason V. ACE2 levels are altered in comorbidities linked to severe outcome in COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.06.04.20122044. [PMID: 32511628 PMCID: PMC7276056 DOI: 10.1101/2020.06.04.20122044] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
AIMS Severity of outcome in COVID-19 is disproportionately higher among the obese, males, smokers, those suffering from hypertension, kidney disease, coronary heart disease (CHD) and/or type 2 diabetes (T2D). We examined if serum levels of ACE2, the cellular entry point for the coronavirus SARS-CoV-2, were altered in these high-risk groups. METHODS Associations of serum ACE2 levels to hypertension, T2D, obesity, CHD, smokers and males in a single center population-based study of 5457 Icelanders from the Age, Gene/Environment Susceptibility Reykjavik Study (AGES-RS) of the elderly (mean age 75+/-6 years). RESULTS Smokers, males, and individuals with T2D or obesity have altered serum levels of ACE2 that may influence productive infection of SARS-CoV-2 in these high-risk groups. CONCLUSION ACE2 levels are upregulated in some patient groups with comorbidities linked to COVID-19 and as such may have an emerging role as a circulating biomarker for severity of outcome in COVID-19.
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274
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Kibinge NK, Relton CL, Gaunt TR, Richardson TG. Characterizing the Causal Pathway for Genetic Variants Associated with Neurological Phenotypes Using Human Brain-Derived Proteome Data. Am J Hum Genet 2020; 106:885-892. [PMID: 32413284 PMCID: PMC7273531 DOI: 10.1016/j.ajhg.2020.04.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 04/06/2020] [Indexed: 01/09/2023] Open
Abstract
Leveraging high-dimensional molecular datasets can help us develop mechanistic insight into associations between genetic variants and complex traits. In this study, we integrated human proteome data derived from brain tissue to evaluate whether targeted proteins putatively mediate the effects of genetic variants on seven neurological phenotypes (Alzheimer disease, amyotrophic lateral sclerosis, depression, insomnia, intelligence, neuroticism, and schizophrenia). Applying the principles of Mendelian randomization (MR) systematically across the genome highlighted 43 effects between genetically predicted proteins derived from the dorsolateral prefrontal cortex and these outcomes. Furthermore, genetic colocalization provided evidence that the same causal variant at 12 of these loci was responsible for variation in both protein and neurological phenotype. This included genes such as DCC, which encodes the netrin-1 receptor and has an important role in the development of the nervous system (p = 4.29 × 10-11 with neuroticism), as well as SARM1, which has been previously implicated in axonal degeneration (p = 1.76 × 10-08 with amyotrophic lateral sclerosis). We additionally conducted a phenome-wide MR study for each of these 12 genes to assess potential pleiotropic effects on 700 complex traits and diseases. Our findings suggest that genes such as SNX32, which was initially associated with increased risk of Alzheimer disease, may potentially influence other complex traits in the opposite direction. In contrast, genes such as CTSH (which was also associated with Alzheimer disease) and SARM1 may make worthwhile therapeutic targets because they did not have genetically predicted effects on any of the other phenotypes after correcting for multiple testing.
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Affiliation(s)
- Nelson K Kibinge
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Caroline L Relton
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Tom R Gaunt
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Tom G Richardson
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom.
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275
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A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma. PLoS Comput Biol 2020; 16:e1007882. [PMID: 32492067 PMCID: PMC7295243 DOI: 10.1371/journal.pcbi.1007882] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 06/15/2020] [Accepted: 04/16/2020] [Indexed: 11/19/2022] Open
Abstract
Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses. Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637. Exploring the functional mechanisms between the genotype and disease endpoints in view of identifying innovative therapeutic targets has prompted molecular quantitative trait locus studies, which assess how genetic variants (single nucleotide polymorphisms, SNPs) affect intermediate gene (eQTL), protein (pQTL) or metabolite (mQTL) levels. However, conventional univariate screening approaches do not account for local dependencies and association structures shared by multiple molecular levels and markers. Conversely, the current joint modelling approaches are restricted to small datasets by computational constraints. We illustrate and exploit the advantages of our recently introduced Bayesian framework LOCUS in a fully multivariate pQTL study, with ≈300K tag SNPs (capturing information from 4M markers) and 100 − 1, 000 plasma protein levels measured by two distinct technologies. LOCUS identifies novel pQTLs that replicate in an independent cohort, confirms signals documented in studies 2 − 18 times larger, and detects more pQTLs than a conventional two-stage univariate analysis of our datasets. Moreover, some of these pQTLs might be of biomedical relevance and would therefore deserve dedicated investigation. Our extensive numerical experiments on these data and on simulated data demonstrate that the increased statistical power of LOCUS over standard approaches is largely attributable to its ability to exploit shared information across outcomes while efficiently accounting for the genetic correlation structures at a genome-wide level.
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276
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Raffield LM, Dang H, Pratte KA, Jacobson S, Gillenwater L, Ampleford E, Barjaktarevic I, Basta P, Clish CB, Comellas AP, Cornell E, Curtis JL, Doerschuk C, Durda P, Emson C, Freeman C, Guo X, Hastie AT, Hawkins GA, Herrera J, Johnson WC, Labaki WW, Liu Y, Masters B, Miller M, Ortega VE, Papanicolaou G, Peters S, Taylor KD, Rich SS, Rotter JI, Auer P, Reiner AP, Tracy RP, Ngo D, Gerszten RE, O’Neal WK, Bowler RP. Comparison of Proteomic Assessment Methods in Multiple Cohort Studies. Proteomics 2020; 20:e1900278. [PMID: 32386347 PMCID: PMC7425176 DOI: 10.1002/pmic.201900278] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 04/30/2020] [Indexed: 11/09/2022]
Abstract
Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from -0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies.
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Affiliation(s)
- Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Hong Dang
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Katherine A. Pratte
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health (NJH), Denver, CO
| | - Sean Jacobson
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health (NJH), Denver, CO
| | - Lucas Gillenwater
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health (NJH), Denver, CO
| | - Elizabeth Ampleford
- Department of Internal Medicine, Section for Pulmonary, Critical Care, Allergy & Immunology, School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Igor Barjaktarevic
- UCLA Division of Pulmonary and Critical Care, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA
| | - Patricia Basta
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Clary B. Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Elaine Cornell
- Department of Pathology & Laboratory Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT
| | - Jeffrey L. Curtis
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI
- VA Ann Arbor Healthcare System, Ann Arbor, MI
| | - Claire Doerschuk
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Peter Durda
- Department of Pathology & Laboratory Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT
| | - Claire Emson
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZenenca, Gaithersburg, MD
| | - Christine Freeman
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI
- VA Ann Arbor Healthcare System, Ann Arbor, MI
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Annette T. Hastie
- Department of Internal Medicine, Section for Pulmonary, Critical Care, Allergy & Immunology, School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Gregory A. Hawkins
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | | | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Wassim W. Labaki
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI
| | - Yongmei Liu
- Department of Epidemiology & Prevention, Wake Forest School of Medicine, Winston-Salem, NC
| | | | | | - Victor E. Ortega
- Department of Internal Medicine, Section for Pulmonary, Critical Care, Allergy & Immunology, School of Medicine, Wake Forest University, Winston-Salem, NC
| | - George Papanicolaou
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD
| | - Stephen Peters
- Department of Internal Medicine, Section for Pulmonary, Critical Care, Allergy & Immunology, School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Paul Auer
- Zilber School of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI
| | - Alex P. Reiner
- Fred Hutchinson Cancer Research Center, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Russell P. Tracy
- Department of Pathology & Laboratory Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT
- Department of Biochemistry, Larner College of Medicine at the University of Vermont, Burlington, VT
| | - Debby Ngo
- Division of Pulmonary, Sleep and Critical Care Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Wanda Kay O’Neal
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Russell P. Bowler
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health (NJH), Denver, CO
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277
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Gao Z, Ding R, Zhai X, Wang Y, Chen Y, Yang CX, Du ZQ. Common Gene Modules Identified for Chicken Adiposity by Network Construction and Comparison. Front Genet 2020; 11:537. [PMID: 32547600 PMCID: PMC7272656 DOI: 10.3389/fgene.2020.00537] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 05/04/2020] [Indexed: 12/12/2022] Open
Abstract
Excessive fat deposition can cause chicken health problem, and affect production efficiency by causing great economic losses to the industry. However, the molecular underpinnings of the complex adiposity trait remain elusive. In the current study, we constructed and compared the gene co-expression networks on four transcriptome profiling datasets, from two chicken lines under divergent selection for abdominal fat contents, in an attempt to dissect network compositions underlying adipose tissue growth and development. After functional enrichment analysis, nine network modules important to adipogenesis were discovered to be involved in lipid metabolism, PPAR and insulin signaling pathways, and contained hub genes related to adipogenesis, cell cycle, inflammation, and protein synthesis. Moreover, after additional functional annotation and network module comparisons, common sub-modules of similar functionality for chicken fat deposition were identified for different chicken lines, apart from modules specific to each chicken line. We further validated the lysosome pathway, and found TFEB and its downstream target genes showed similar expression patterns along with chicken preadipocyte differentiation. Our findings could provide novel insights into the genetic basis of complex adiposity traits, as well as human obesity and related metabolic diseases.
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Affiliation(s)
- Zhuoran Gao
- College of Animal Science, Yangtze University, Jingzhou, China.,College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Ran Ding
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Xiangyun Zhai
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Yuhao Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Yaofeng Chen
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Cai-Xia Yang
- College of Animal Science, Yangtze University, Jingzhou, China.,College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Zhi-Qiang Du
- College of Animal Science, Yangtze University, Jingzhou, China.,College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
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278
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Emilsson V, Gudmundsson EF, Aspelund T, Jonsson BG, Gudjonsson A, Launer LJ, Jennings LL, Gudmundsdottir V, Gudnason V. Antihypertensive medication uses and serum ACE2 levels: ACEIs/ARBs treatment does not raise serum levels of ACE2. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.05.21.20108738. [PMID: 32511473 PMCID: PMC7265694 DOI: 10.1101/2020.05.21.20108738] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Importance Recent reports have shown that hypertension is the most common comorbidity associated with mortality in the current coronavirus disease 2019 (COVID-19). This has been related to the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) as animal studies indicate that these medications increase levels of ACE2, the cellular entry point for the coronavirus SARS-CoV-2. This has prompted clinicians to recommend discontinuing ACEIs and ARBs. Objective To examine the effect of ACEIs or ARBs treatment on serum levels of ACE2 and other key enzymes in the renin-angiotensin system (RAS). Design Setting and Participants A single center population-based study of 5457 Icelanders from the Age, Gene/Environment Susceptibility Reykjavik Study (AGES-RS) of the elderly (mean age 75±6 years) stratified by ACEIs (N = 699) or ARBs (N = 753) treatment. Main Outcomes and Measures The AGES-RS study population was stratified by ACEIs and ARBs medication use and compared for age, body mass index (BMI) (kg/m2), hypertension and type 2 diabetes (T2D) as well as serum levels of renin, ACE and ACE2. Results While renin and ACE levels were significantly raised in serum of individuals on ACEIs or ARBs treatments, the ACE2 levels remained unaffected. Conclusions and Relevance Treatment with ACEIs or ARBs does not raise ACE2 levels in serum. Therefore, the present study does not support the proposed discontinuation of these medications among patients affected with COVID-19.
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Affiliation(s)
- Valur Emilsson
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | | | - Thor Aspelund
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Iceland
| | | | | | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, MD 20892-9205, USA
| | - Lori L. Jennings
- Novartis Institutes for Biomedical Research, 22 Windsor Street, Cambridge, MA 02139, USA
| | - Valborg Gudmundsdottir
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Vilmundur Gudnason
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
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279
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Ahmad S, Milan MDC, Hansson O, Demirkan A, Agustin R, Sáez ME, Giagtzoglou N, Cabrera-Socorro A, Bakker MHM, Ramirez A, Hankemeier T, Stomrud E, Mattsson-Carlgren N, Scheltens P, van der Flier WM, Ikram MA, Malarstig A, Teunissen CE, Amin N, van Duijn CM. CDH6 and HAGH protein levels in plasma associate with Alzheimer's disease in APOE ε4 carriers. Sci Rep 2020; 10:8233. [PMID: 32427856 PMCID: PMC7237496 DOI: 10.1038/s41598-020-65038-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 04/24/2020] [Indexed: 02/06/2023] Open
Abstract
Many Alzheimer’s disease (AD) genes including Apolipoprotein E (APOE) are found to be expressed in blood-derived macrophages and thus may alter blood protein levels. We measured 91 neuro-proteins in plasma from 316 participants of the Rotterdam Study (incident AD = 161) using Proximity Extension Ligation assay. We studied the association of plasma proteins with AD in the overall sample and stratified by APOE. Findings from the Rotterdam study were replicated in 186 AD patients of the BioFINDER study. We further evaluated the correlation of these protein biomarkers with total tau (t-tau), phosphorylated tau (p-tau) and amyloid-beta (Aβ) 42 levels in cerebrospinal fluid (CSF) in the Amsterdam Dementia Cohort (N = 441). Finally, we conducted a genome-wide association study (GWAS) to identify the genetic variants determining the blood levels of AD-associated proteins. Plasma levels of the proteins, CDH6 (β = 0.638, P = 3.33 × 10−4) and HAGH (β = 0.481, P = 7.20 × 10−4), were significantly elevated in APOE ε4 carrier AD patients. The findings in the Rotterdam Study were replicated in the BioFINDER study for both CDH6 (β = 1.365, P = 3.97 × 10−3) and HAGH proteins (β = 0.506, P = 9.31 × 10−7) when comparing cases and controls in APOE ε4 carriers. In the CSF, CDH6 levels were positively correlated with t-tau and p-tau in the total sample as well as in APOE ε4 stratum (P < 1 × 10−3). The HAGH protein was not detected in CSF. GWAS of plasma CDH6 protein levels showed significant association with a cis-regulatory locus (rs111283466, P = 1.92 × 10−9). CDH6 protein is implicated in cell adhesion and synaptogenesis while HAGH protein is related to the oxidative stress pathway. Our findings suggest that these pathways may be altered during presymptomatic AD and that CDH6 and HAGH may be new blood-based biomarkers.
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Affiliation(s)
- Shahzad Ahmad
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Marta Del Campo Milan
- Neurochemistry laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers (AUMC), Vrije Universiteit, Amsterdam, The Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ruiz Agustin
- Research Center and Memory clinic Fundació ACE. Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain.,CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Maria E Sáez
- Centro Andaluz de Estudios Bioinformáticos CAEBi, Sevilla, Spain
| | | | | | - Margot H M Bakker
- Discovery Research, AbbVie Deutschland GmbH & Co. KG, Knollstrasse, 67061, Ludwigshafen, Germany
| | - Alfredo Ramirez
- Department of Neurodegeneration and Geriatric Psychiatry, University of Bonn, 53127, Bonn, Germany.,Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, 50937, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
| | - Thomas Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Erik Stomrud
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Philip Scheltens
- Alzheimer center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, UMC, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, UMC, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Anders Malarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Pfizer Worldwide R&D, Stockholm, Sweden
| | - Charlotte E Teunissen
- Neurochemistry laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers (AUMC), Vrije Universiteit, Amsterdam, The Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands. .,Nuffield Department of Population Health, Oxford University, Oxford, UK.
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280
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Chirinos JA, Zhao L, Jia Y, Frej C, Adamo L, Mann D, Shewale SV, Millar JS, Rader DJ, French B, Brandimarto J, Margulies KB, Parks JS, Wang Z, Seiffert DA, Fang J, Sweitzer N, Chistoffersen C, Dahlbäck B, Car BD, Gordon DA, Cappola TP, Javaheri A. Reduced Apolipoprotein M and Adverse Outcomes Across the Spectrum of Human Heart Failure. Circulation 2020; 141:1463-1476. [PMID: 32237898 PMCID: PMC7200273 DOI: 10.1161/circulationaha.119.045323] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Apo (apolipoprotein) M mediates the physical interaction between high-density lipoprotein (HDL) particles and sphingosine-1-phosphate (S1P). Apo M exerts anti-inflammatory and cardioprotective effects in animal models. METHODS In a subset of PHFS (Penn Heart Failure Study) participants (n=297), we measured apo M by Enzyme-Linked ImmunoSorbent Assay (ELISA). We also measured total S1P by liquid chromatography-mass spectrometry and isolated HDL particles to test the association between apo M and HDL-associated S1P. We confirmed the relationship between apo M and outcomes using modified aptamer-based apo M measurements among 2170 adults in the PHFS and 2 independent cohorts: the Washington University Heart Failure Registry (n=173) and a subset of TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist Trial; n=218). Last, we examined the relationship between apo M and ≈5000 other proteins (SomaScan assay) to identify biological pathways associated with apo M in heart failure. RESULTS In the PHFS, apo M was inversely associated with the risk of death (standardized hazard ratio, 0.56 [95% CI, 0.51-0.61]; P<0.0001) and the composite of death/ventricular assist device implantation/heart transplantation (standardized hazard ratio, 0.62 [95% CI, 0.58-0.67]; P<0.0001). This relationship was independent of HDL cholesterol or apo AI levels. Apo M remained associated with death (hazard ratio, 0.78 [95% CI, 0.69-0.88]; P<0.0001) and the composite of death/ventricular assist device/heart transplantation (hazard ratio, 0.85 [95% CI, 0.76-0.94]; P=0.001) in models that adjusted for multiple confounders. This association was present in both heart failure with reduced and preserved ejection fraction and was replicated in the Washington University cohort and a cohort with heart failure with preserved ejection fraction only (TOPCAT). The S1P and apo M content of isolated HDL particles strongly correlated (R=0.81, P<0.0001). The top canonical pathways associated with apo M were inflammation (negative association), the coagulation system (negative association), and liver X receptor/retinoid X receptor activation (positive association). The relationship with inflammation was validated with multiple inflammatory markers measured with independent assays. CONCLUSIONS Reduced circulating apo M is independently associated with adverse outcomes across the spectrum of human heart failure. Further research is needed to assess whether the apo M/S1P axis is a suitable therapeutic target in heart failure.
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Affiliation(s)
- Julio A. Chirinos
- Perelman School of Medicine. University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania. Philadelphia, PA
| | - Lei Zhao
- Bristol-Myers Squibb Company, Lawrenceville, NJ
| | - Yi Jia
- SomaLogic Inc., Boulder, CO
| | | | - Luigi Adamo
- Washington University School of Medicine, St. Louis, MO
| | - Douglas Mann
- Washington University School of Medicine, St. Louis, MO
| | - Swapnil V. Shewale
- Perelman School of Medicine. University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania. Philadelphia, PA
| | - John S. Millar
- Perelman School of Medicine. University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania. Philadelphia, PA
| | - Daniel J. Rader
- Perelman School of Medicine. University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania. Philadelphia, PA
| | - Benjamin French
- Perelman School of Medicine. University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania. Philadelphia, PA
| | - Jeff Brandimarto
- Perelman School of Medicine. University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania. Philadelphia, PA
| | - Kenneth B. Margulies
- Perelman School of Medicine. University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania. Philadelphia, PA
| | - John S. Parks
- Dept. of Internal Medicine-Molecular Medicine, Wake Forest School of Medicine, Winston Salem, NC
| | | | | | - James Fang
- University of Utah. Salt Lake City, Utah
| | - Nancy Sweitzer
- Sarver Heart Institute, University of Arizona, Tuscon, AZ
| | - Christina Chistoffersen
- Dept. of Clinical Biochemistry, Rigshospitalet and Dept. of Biomedical Sciences, Copenhagen, Denmark
| | | | | | | | - Thomas P. Cappola
- Perelman School of Medicine. University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania. Philadelphia, PA
| | - Ali Javaheri
- Washington University School of Medicine, St. Louis, MO
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Nayor M, Short MI, Rasheed H, Lin H, Jonasson C, Yang Q, Hveem K, Felix JF, Morrison AC, Wild PS, Morley MP, Cappola TP, Benson MD, Ngo D, Sinha S, Keyes MJ, Shen D, Wang TJ, Larson MG, Brumpton BM, Gerszten RE, Omland T, Vasan RS. Aptamer-Based Proteomic Platform Identifies Novel Protein Predictors of Incident Heart Failure and Echocardiographic Traits. Circ Heart Fail 2020; 13:e006749. [PMID: 32408813 PMCID: PMC7236427 DOI: 10.1161/circheartfailure.119.006749] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND We used a large-scale, high-throughput DNA aptamer-based discovery proteomic platform to identify circulating biomarkers of cardiac remodeling and incident heart failure (HF) in community-dwelling individuals. METHODS We evaluated 1895 FHS (Framingham Heart Study) participants (age 55±10 years, 54% women) who underwent proteomic profiling and echocardiography. Plasma levels of 1305 proteins were related to echocardiographic traits and to incident HF using multivariable regression. Statistically significant protein-HF associations were replicated in the HUNT (Nord-Trøndelag Health) study (n=2497, age 63±10 years, 43% women), and results were meta-analyzed. Genetic variants associated with circulating protein levels (pQTLs) were related to echocardiographic traits in the EchoGen (n=30 201) and to incident HF in the CHARGE (n=20 926) consortia. RESULTS Seventeen proteins associated with echocardiographic traits in cross-sectional analyses (false discovery rate <0.10), and 8 of these proteins had pQTLs associated with echocardiographic traits in EchoGen (P<0.0007). In Cox models adjusted for clinical risk factors, 29 proteins demonstrated associations with incident HF in FHS (174 HF events, mean follow-up 19 [limits, 0.2-23.7] years). In meta-analyses of FHS and HUNT, 6 of these proteins were associated with incident HF (P<3.8×10-5; 3 with higher risk: NT-proBNP [N-terminal proB-type natriuretic peptide], TSP2 [thrombospondin-2], MBL [mannose-binding lectin]; and 3 with lower risk: ErbB1 [epidermal growth factor receptor], GDF-11/8 [growth differentiation factor-11/8], and RGMC [hemojuvelin]). For 5 of the 6 proteins, pQTLs were associated with echocardiographic traits (P<0.0006) in EchoGen, and for RGMC, a protein quantitative trait loci was associated with incident HF (P=0.001). CONCLUSIONS A large-scale proteomics approach identified new predictors of cardiac remodeling and incident HF. Future studies are warranted to elucidate how biological pathways represented by these proteins may mediate cardiac remodeling and HF risk and to assess if these proteins can improve HF risk prediction.
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Affiliation(s)
- Matthew Nayor
- Framingham Heart Study, Framingham, MA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Meghan I. Short
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX
| | - Humaira Rasheed
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
- MRC Integrative Epidemiology Unit, University of Bristol, UK
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA
| | - Christian Jonasson
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Kristian Hveem
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
| | - Janine F. Felix
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Alanna C. Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Philipp S. Wild
- Preventive Cardiology and Preventive Medicine, Center for Cardiology, and Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- DZHK (German Center for Cardiovascular Research), partner site RhineMain, Mainz, Germany
| | - Michael P. Morley
- The Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, PA
| | - Thomas P. Cappola
- The Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, PA
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, PA
| | - Mark D. Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | | | | | - Debby Ngo
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Sumita Sinha
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Michelle J. Keyes
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Thomas J. Wang
- Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN
| | - Martin G. Larson
- Framingham Heart Study, Framingham, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Ben M. Brumpton
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
- Clinic of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Norway
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Torbjørn Omland
- Department of Cardiology, Akershus University Hospital, Lørenskog, and Center for Heart Failure Research, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ramachandran S. Vasan
- Framingham Heart Study, Framingham, MA
- Sections of Preventive Medicine & Epidemiology, and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA
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Abstract
The plasma proteome is rich in information. It comprises proteins that are secreted or lost from cells as they respond to their local environment. Changes in the constitution of the plasma proteome offer a relatively non-invasive report on the health of tissues. This is particularly true of the lung in pulmonary hypertension, given the large surface area of the pulmonary vasculature in direct communication with blood. So far, this is relatively untapped; we have relied on proteins released from the heart, specifically brain natriuretic peptide and troponin, to inform clinical management. New technology allows the measurement of a larger number of proteins that cover a broad range of molecular pathways in a single small aliquot. The emerging data will yield more than just new biomarkers of pulmonary hypertension for clinical use. Integrated with genomics and with the help of new bioinformatic tools, the plasma proteome can provide insight into the causative drivers of pulmonary vascular disease and guide drug development.
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Affiliation(s)
- Martin Wilkins
- Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, United Kingdom
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283
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Harbaum L, Rhodes CJ, Otero-Núñez P, Wharton J, Wilkins MR. The application of 'omics' to pulmonary arterial hypertension. Br J Pharmacol 2020; 178:108-120. [PMID: 32201940 DOI: 10.1111/bph.15056] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/03/2020] [Accepted: 02/18/2020] [Indexed: 12/14/2022] Open
Abstract
Recent genome-wide analyses of rare and common sequence variations have brought greater clarity to the genetic architecture of pulmonary arterial hypertension and implicated novel genes in disease development. Transcriptional signatures have been reported in whole lung tissue, pulmonary vascular cells and peripheral circulating cells. High-throughput platforms for plasma proteomics and metabolomics have identified novel biomarkers associated with clinical outcomes and provided molecular instruments for risk assessment. There are methodological challenges to integrating these datasets, coupled to statistical power limitations inherent to the study of a rare disease, but the expectation is that this approach will reveal novel druggable targets and biomarkers that will open the way to personalized medicine. Here, we review the current state-of-the-art and future promise of 'omics' in the field of translational medicine in pulmonary arterial hypertension. LINKED ARTICLES: This article is part of a themed issue on Risk factors, comorbidities, and comedications in cardioprotection. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.1/issuetoc.
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Affiliation(s)
- Lars Harbaum
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Pablo Otero-Núñez
- National Heart and Lung Institute, Imperial College London, London, UK
| | - John Wharton
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Martin R Wilkins
- National Heart and Lung Institute, Imperial College London, London, UK
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284
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Shu X, Bao J, Wu L, Long J, Shu XO, Guo X, Yang Y, Michailidou K, Bolla MK, Wang Q, Dennis J, Andrulis IL, Castelao JE, Dörk T, Gago-Dominguez M, García-Closas M, Giles GG, Lophatananon A, Muir K, Olsson H, Rennert G, Saloustros E, Scott RJ, Southey MC, Pharoah PDP, Milne RL, Kraft P, Simard J, Easton DF, Zheng W. Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk. Int J Cancer 2020; 146:2130-2138. [PMID: 31265136 DOI: 10.1002/ijc.32542] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 04/18/2019] [Accepted: 05/02/2019] [Indexed: 12/27/2022]
Abstract
A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse-variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate <0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin-like growth factor receptor 1 and other membrane receptors (OR: 0.82-1.18, p values: 6.96 × 10-4 -3.28 × 10-8 ), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p < 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.
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Affiliation(s)
- Xiang Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Jiandong Bao
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Lang Wu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Yaohua Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jose E Castelao
- Oncology and Genetics Unit, Instituto de Investigacion Biomedica (IBI) Galicia Sur, Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS, Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
- Moores Cancer Center, University of California San Diego, La Jolla, CA
| | | | - Graham G Giles
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Artitaya Lophatananon
- Division of Health Sciences, Warwick Medical School, Warwick University, Coventry, United Kingdom
- Institute of Population Health, University of Manchester, Manchester, United Kingdom
| | - Kenneth Muir
- Division of Health Sciences, Warwick Medical School, Warwick University, Coventry, United Kingdom
- Institute of Population Health, University of Manchester, Manchester, United Kingdom
| | - Håkan Olsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Gadi Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | | | - Rodney J Scott
- Division of Molecular Medicine, Pathology North, John Hunter Hospital, Newcastle, NSW, Australia
- Discipline of Medical Genetics, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, Callaghan, NSW, Australia
| | - Melissa C Southey
- Department of Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Roger L Milne
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, QC, Canada
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
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285
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Storm CS, Kia DA, Almramhi M, Wood NW. Using Mendelian randomization to understand and develop treatments for neurodegenerative disease. Brain Commun 2020; 2:fcaa031. [PMID: 32954289 PMCID: PMC7425289 DOI: 10.1093/braincomms/fcaa031] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 02/07/2020] [Accepted: 02/13/2020] [Indexed: 12/13/2022] Open
Abstract
Common neurodegenerative diseases are thought to arise from a combination of environmental and genetic exposures. Mendelian randomization is a powerful way to leverage existing genetic data to investigate causal relationships between risk factors and disease. In recent years, Mendelian randomization has gathered considerable traction in neurodegenerative disease research, providing valuable insights into the aetiology of these conditions. This review aims to evaluate the impact of Mendelian randomization studies on translational medicine for neurodegenerative diseases, highlighting the advances made and challenges faced. We will first describe the fundamental principles and limitations of Mendelian randomization and then discuss the lessons from Mendelian randomization studies of environmental risk factors for neurodegeneration. We will illustrate how Mendelian randomization projects have used novel resources to study molecular pathways of neurodegenerative disease and discuss the emerging role of Mendelian randomization in drug development. Finally, we will conclude with our view of the future of Mendelian randomization in these conditions, underscoring unanswered questions in this field.
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Affiliation(s)
- Catherine S Storm
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
| | - Demis A Kia
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
| | - Mona Almramhi
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
| | - Nicholas W Wood
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
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286
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Liu J, Li Y, Dai J, Lin B, Xiao C, Zhang X, Luo L, Wang T, Li X, Yu Y, Chen S, Wu L, Liu Y, Yu X, Qin X. Comprehensive Analyses of the Immunoglobulin Proteome for the Classification of Glomerular Diseases. J Proteome Res 2020; 19:1502-1512. [PMID: 32168457 DOI: 10.1021/acs.jproteome.9b00748] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jianhua Liu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Yang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jiayu Dai
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Baoxu Lin
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Chunying Xiao
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Xinpeng Zhang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Lin Luo
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Tingting Wang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Xiaoying Li
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Yao Yu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Shixiao Chen
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Lina Wu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Yong Liu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Xiaobo Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xiaosong Qin
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, China
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287
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Smith MA, Chiang CC, Zerrouki K, Rahman S, White WI, Streicher K, Rees WA, Schiffenbauer A, Rider LG, Miller FW, Manna Z, Hasni S, Kaplan MJ, Siegel R, Sinibaldi D, Sanjuan MA, Casey KA. Using the circulating proteome to assess type I interferon activity in systemic lupus erythematosus. Sci Rep 2020; 10:4462. [PMID: 32157125 PMCID: PMC7064569 DOI: 10.1038/s41598-020-60563-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 02/13/2020] [Indexed: 12/27/2022] Open
Abstract
Type I interferon (IFN) drives pathology in systemic lupus erythematosus (SLE) and can be tracked via IFN-inducible transcripts in blood. Here, we examined whether measurement of circulating proteins, which enter the bloodstream from inflamed tissues, also offers insight into global IFN activity. Using a novel protocol we generated 1,132 aptamer-based protein measurements from anti-dsDNApos SLE blood samples and derived an IFN protein signature (IFNPS) that approximates the IFN 21-gene signature (IFNGS). Of 82 patients with SLE, IFNPS was elevated for 89% of IFNGS-high patients (49/55) and 26% of IFNGS-low patients (7/27). IFNGS-high/IFNPS-high patients exhibited activated NK, CD4, and CD8 T cells, while IFNPS-high only patients did not. IFNPS correlated with global disease activity in lymphopenic and non-lymphopenic patients and decreased following type I IFN neutralisation with anifrolumab in the SLE phase IIb study, MUSE. In summary, we developed a protein signature that reflects IFNGS and identifies a new subset of patients with SLE who have IFN activity.
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Affiliation(s)
| | | | | | | | | | | | | | - Adam Schiffenbauer
- Environmental Autoimmunity Group, Clinical Research Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Lisa G Rider
- Environmental Autoimmunity Group, Clinical Research Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Frederick W Miller
- Environmental Autoimmunity Group, Clinical Research Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Zerai Manna
- Lupus Clinical Research Program, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Sarfaraz Hasni
- Lupus Clinical Research Program, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Mariana J Kaplan
- Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Disease, National Institutes of Health, Bethesda, MD, USA
| | - Richard Siegel
- Immunoregulation Section, Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
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288
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A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine. Trends Genet 2020; 36:318-336. [PMID: 32294413 DOI: 10.1016/j.tig.2020.01.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 02/07/2023]
Abstract
Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.
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289
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de Oliveira TM, de Lacerda JTJG, Leite GGF, Dias M, Mendes MA, Kassab P, E Silva CGS, Juliano MA, Forones NM. Label-free peptide quantification coupled with in silico mapping of proteases for identification of potential serum biomarkers in gastric adenocarcinoma patients. Clin Biochem 2020; 79:61-69. [PMID: 32097616 DOI: 10.1016/j.clinbiochem.2020.02.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 01/31/2020] [Accepted: 02/18/2020] [Indexed: 12/27/2022]
Abstract
OBJECTIVES We aimed to identify serum level variations in protein-derived peptides between patients diagnosed with gastric adenocarcinoma (GAC) and non-cancer persons (control) to detect the activity changes of proteases and explore the auxiliary diagnostic value in the context of GAC physiopathology. METHODS The label-free quantitative peptidome approach was applied to identify variants in serum levels of peptides that can differentiate GAC patients from the control group. Peptide sequences were submitted against Proteasix tool predicting proteases potentially involved in their generation. The activity change of proteases was subsequently estimated based on the peptides with significantly altered relative abundance. In turn, activity change prediction of proteases was correlated with relevant protease expression data from the literature. RESULTS A total of 191 peptide sequences generated by the cleavage of 36 precursor proteins were identified. Using the label-free quantification approach, 33 peptides were differentially quantified (adjusted fold change ≥ 1.5 and p-value < 0.05) in which 19 were up-regulated and 14 were down-regulated in GAC samples. Of these peptides, fibrinopeptide A was significantly decreased and its phosphorylated form ADpSGEGDFLAEGGGVR was upregulated in GAC samples. Activity change prediction yielded 10 proteases including 6 Matrix Metalloproteinases (MMPs), Thrombin, Plasmin, and kallikreins 4 and 14. Among predicted proteases in our analysis, MMP-7 was presented as a more promising biomarker associated with useful assays of clinical practice for GAC diagnosis. CONCLUSION Our experimental results demonstrate that the serum levels of peptides were significantly differentiated in GAC physiopathology. The hypotheses built on protease regulation could be used for further investigations to measure proteases and their activity levels that have been poorly studied for GAC diagnosis.
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Affiliation(s)
- Talita Mendes de Oliveira
- Department of Medicine, Division of Gastroenterology, Oncology Group, Federal University of São Paulo, São Paulo, SP, Brazil.
| | | | | | - Meriellen Dias
- Department of Chemical Engineering, University of São Paulo, São Paulo, SP, Brazil
| | - Maria Anita Mendes
- Department of Chemical Engineering, University of São Paulo, São Paulo, SP, Brazil
| | - Paulo Kassab
- Digestive Surgical Oncology Division, Santa Casa of São Paulo Medical School, São Paulo, SP, Brazil
| | | | | | - Nora Manoukian Forones
- Department of Medicine, Division of Gastroenterology, Oncology Group, Federal University of São Paulo, São Paulo, SP, Brazil
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290
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Greenbaum LE, Ukomadu C, Tchorz JS. Clinical translation of liver regeneration therapies: A conceptual road map. Biochem Pharmacol 2020; 175:113847. [PMID: 32035080 DOI: 10.1016/j.bcp.2020.113847] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/04/2020] [Indexed: 02/07/2023]
Abstract
The increasing incidence of severe liver diseases worldwide has resulted in a high demand for curative liver transplantation. Unfortunately, the need for transplants by far eclipses the availability of suitable grafts leaving many waitlisted patients to face liver failure and often death. Routine use of smaller grafts (for example left lobes, split livers) from living or deceased donors could increase the number of life-saving transplants but is often limited by the graft versus recipient weight ratio defining the safety margins that minimize the risk of small for size syndrome (SFSS). SFSS is a severe complication characterized by failure of a small liver graft to regenerate and occurs when a donor graft is insufficient to meet the metabolic demand of the recipient, leading to liver failure as a result of insufficient liver mass. SFSS is not limited to transplantation but can also occur in the setting of hepatic surgical resections, where life-saving large resections of tumors may be limited by concerns of post-surgical liver failure. There are, as yet no available pro-regenerative therapies to enable liver regrowth and thus prevent SFSS. However, there is optimism around targeting factors and pathways that have been identified as regulators of liver regeneration to induce regrowth in vivo and ex vivo for clinical use. In this commentary, we propose a roadmap for developing such pro-regenerative therapy and for bringing it into the clinic. We summarize the clinical indications, preclinical models, pro-regenerative pathways and safety considerations necessary for developing such a drug.
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Affiliation(s)
- Linda E Greenbaum
- Novartis Institutes for Biomedical Research, Novartis Pharma AG, East Hanover, NJ, United States.
| | - Chinweike Ukomadu
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Cambridge, MA, United States.
| | - Jan S Tchorz
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland.
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291
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Melamud E, Taylor DL, Sethi A, Cule M, Baryshnikova A, Saleheen D, van Bruggen N, FitzGerald GA. The promise and reality of therapeutic discovery from large cohorts. J Clin Invest 2020; 130:575-581. [PMID: 31929188 PMCID: PMC6994121 DOI: 10.1172/jci129196] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.
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Affiliation(s)
- Eugene Melamud
- Calico Life Sciences LLC, South San Francisco, California, USA
| | | | - Anurag Sethi
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, California, USA
| | | | | | | | - Garret A. FitzGerald
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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292
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Strawbridge RJ, Ward J, Bailey ME, Cullen B, Ferguson A, Graham N, Johnston KJ, Lyall LM, Pearsall R, Pell J, Shaw RJ, Tank R, Lyall DM, Smith DJ. Carotid Intima-Media Thickness: Novel Loci, Sex-Specific Effects, and Genetic Correlations With Obesity and Glucometabolic Traits in UK Biobank. Arterioscler Thromb Vasc Biol 2020; 40:446-461. [PMID: 31801372 PMCID: PMC6975521 DOI: 10.1161/atvbaha.119.313226] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 11/21/2019] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Atherosclerosis is the underlying cause of most cardiovascular disease, but mechanisms underlying atherosclerosis are incompletely understood. Ultrasound measurement of the carotid intima-media thickness (cIMT) can be used to measure vascular remodeling, which is indicative of atherosclerosis. Genome-wide association studies have identified many genetic loci associated with cIMT, but heterogeneity of measurements collected by many small cohorts have been a major limitation in these efforts. Here, we conducted genome-wide association analyses in UKB (UK Biobank; N=22 179), the largest single study with consistent cIMT measurements. Approach and Results: We used BOLT-LMM software to run linear regression of cIMT in UKB, adjusted for age, sex, and genotyping chip. In white British participants, we identified 5 novel loci associated with cIMT and replicated most previously reported loci. In the first sex-specific analyses of cIMT, we identified a locus on chromosome 5, associated with cIMT in women only and highlight VCAN as a good candidate gene at this locus. Genetic correlations with body mass index and glucometabolic traits were also observed. Two loci influenced risk of ischemic heart disease. CONCLUSIONS These findings replicate previously reported associations, highlight novel biology, and provide new directions for investigating the sex differences observed in cardiovascular disease presentation and progression.
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Affiliation(s)
- Rona J. Strawbridge
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Joey Ward
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Mark E.S. Bailey
- School of Life Sciences, College of Medical, Veterinary and Life Sciences (M.E.S.B., K.J.A.J.), University of Glasgow, United Kingdom
| | - Breda Cullen
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Amy Ferguson
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Nicholas Graham
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Keira J.A. Johnston
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
- School of Life Sciences, College of Medical, Veterinary and Life Sciences (M.E.S.B., K.J.A.J.), University of Glasgow, United Kingdom
- Division of Psychiatry, College of Medicine, University of Edinburgh, United Kingdom (K.J.A.J.)
| | - Laura M. Lyall
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Robert Pearsall
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Jill Pell
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Richard J. Shaw
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
- Health Data Research United Kingdom (R.J.S.)
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden (R.J.S.)
| | - Rachana Tank
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Donald M. Lyall
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Daniel J. Smith
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
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293
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Systemic factors as mediators of brain homeostasis, ageing and neurodegeneration. Nat Rev Neurosci 2020; 21:93-102. [PMID: 31913356 DOI: 10.1038/s41583-019-0255-9] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/04/2019] [Indexed: 02/08/2023]
Abstract
A rapidly ageing population and a limited therapeutic toolbox urgently necessitate new approaches to treat neurodegenerative diseases. Brain ageing, the key risk factor for neurodegeneration, involves complex cellular and molecular processes that eventually result in cognitive decline. Although cell-intrinsic defects in neurons and glia may partially explain this decline, cell-extrinsic changes in the systemic environment, mediated by blood, have recently been shown to contribute to brain dysfunction with age. Here, we review the current understanding of how systemic factors mediate brain ageing, how these factors are regulated and how we can translate these findings into therapies for neurodegenerative diseases.
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294
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Zaghlool SB, Kühnel B, Elhadad MA, Kader S, Halama A, Thareja G, Engelke R, Sarwath H, Al-Dous EK, Mohamoud YA, Meitinger T, Wilson R, Strauch K, Peters A, Mook-Kanamori DO, Graumann J, Malek JA, Gieger C, Waldenberger M, Suhre K. Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits. Nat Commun 2020; 11:15. [PMID: 31900413 PMCID: PMC6941977 DOI: 10.1038/s41467-019-13831-w] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 11/28/2019] [Indexed: 12/14/2022] Open
Abstract
DNA methylation and blood circulating proteins have been associated with many complex disorders, but the underlying disease-causing mechanisms often remain unclear. Here, we report an epigenome-wide association study of 1123 proteins from 944 participants of the KORA population study and replication in a multi-ethnic cohort of 344 individuals. We identify 98 CpG-protein associations (pQTMs) at a stringent Bonferroni level of significance. Overlapping associations with transcriptomics, metabolomics, and clinical endpoints suggest implication of processes related to chronic low-grade inflammation, including a network involving methylation of NLRC5, a regulator of the inflammasome, and associated pQTMs implicating key proteins of the immune system, such as CD48, CD163, CXCL10, CXCL11, LAG3, FCGR3B, and B2M. Our study links DNA methylation to disease endpoints via intermediate proteomics phenotypes and identifies correlative networks that may eventually be targeted in a personalized approach of chronic low-grade inflammation.
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Affiliation(s)
- Shaza B Zaghlool
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
- Computer Engineering Department, Virginia Tech, Blacksburg, VA, USA
| | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
| | - Mohamed A Elhadad
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Sara Kader
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gaurav Thareja
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Rudolf Engelke
- Proteomics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Eman K Al-Dous
- Genomics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Thomas Meitinger
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Rory Wilson
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Johannes Graumann
- Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, Bad Nauheim, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Rhine-Main, Max Planck Institute of Heart and Lung Research, Bad Nauheim, Germany
| | - Joel A Malek
- Genomics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar.
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295
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Egerstedt A, Berntsson J, Smith ML, Gidlöf O, Nilsson R, Benson M, Wells QS, Celik S, Lejonberg C, Farrell L, Sinha S, Shen D, Lundgren J, Rådegran G, Ngo D, Engström G, Yang Q, Wang TJ, Gerszten RE, Smith JG. Profiling of the plasma proteome across different stages of human heart failure. Nat Commun 2019; 10:5830. [PMID: 31862877 PMCID: PMC6925199 DOI: 10.1038/s41467-019-13306-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 10/31/2019] [Indexed: 12/11/2022] Open
Abstract
Heart failure (HF) is a major public health problem characterized by inability of the heart to maintain sufficient output of blood. The systematic characterization of circulating proteins across different stages of HF may provide pathophysiological insights and identify therapeutic targets. Here we report application of aptamer-based proteomics to identify proteins associated with prospective HF incidence in a population-based cohort, implicating modulation of immunological, complement, coagulation, natriuretic and matrix remodeling pathways up to two decades prior to overt disease onset. We observe further divergence of these proteins from the general population in advanced HF, and regression after heart transplantation. By leveraging coronary sinus samples and transcriptomic tools, we describe likely cardiac and specific cellular origins for several of the proteins, including Nt-proBNP, thrombospondin-2, interleukin-18 receptor, gelsolin, and activated C5. Our findings provide a broad perspective on both cardiac and systemic factors associated with HF development.
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Affiliation(s)
- Anna Egerstedt
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - John Berntsson
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
- Cardiovascular Epidemiology, Clinical Sciences, Lund University, Malmö, Sweden
| | - Maya Landenhed Smith
- Department of Cardiothoracic Surgery, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
| | - Olof Gidlöf
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Roland Nilsson
- Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Mark Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Quinn S Wells
- Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN, USA
| | - Selvi Celik
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Carl Lejonberg
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Sumita Sinha
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Jakob Lundgren
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
- Department of Heart Failure and Valvular Disease, Skåne University Hospital, Lund, Sweden
| | - Göran Rådegran
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
- Department of Heart Failure and Valvular Disease, Skåne University Hospital, Lund, Sweden
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Gunnar Engström
- Cardiovascular Epidemiology, Clinical Sciences, Lund University, Malmö, Sweden
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Thomas J Wang
- Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - J Gustav Smith
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden.
- Department of Heart Failure and Valvular Disease, Skåne University Hospital, Lund, Sweden.
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA.
- Wallenberg Center for Molecular Medicine and Lund University Diabetes Center, Lund University, Lund, Sweden.
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296
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Abstract
Multiple interventions in the aging process have been discovered to extend the healthspan of model organisms. Both industry and academia are therefore exploring possible transformative molecules that target aging and age-associated diseases. In this overview, we summarize the presented talks and discussion points of the 5th Annual Aging and Drug Discovery Forum 2018 in Basel, Switzerland. Here academia and industry came together, to discuss the latest progress and issues in aging research. The meeting covered talks about the mechanistic cause of aging, how longevity signatures may be highly conserved, emerging biomarkers of aging, possible interventions in the aging process and the use of artificial intelligence for aging research and drug discovery. Importantly, a consensus is emerging both in industry and academia, that molecules able to intervene in the aging process may contain the potential to transform both societies and healthcare.
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297
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Omenn GS, Lane L, Overall CM, Corrales FJ, Schwenk JM, Paik YK, Van Eyk JE, Liu S, Pennington S, Snyder MP, Baker MS, Deutsch EW. Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project. J Proteome Res 2019; 18:4098-4107. [PMID: 31430157 PMCID: PMC6898754 DOI: 10.1021/acs.jproteome.9b00434] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The Human Proteome Project (HPP) annually reports on progress made throughout the field in credibly identifying and characterizing the complete human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2019-01-11 contains 17 694 proteins with strong protein-level evidence (PE1), compliant with HPP Guidelines for Interpretation of MS Data v2.1; these represent 89% of all 19 823 neXtProt predicted coding genes (all PE1,2,3,4 proteins), up from 17 470 one year earlier. Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), has been reduced from 2949 to 2129 since 2016 through efforts throughout the community, including the chromosome-centric HPP. PeptideAtlas is the source of uniformly reanalyzed raw mass spectrometry data for neXtProt; PeptideAtlas added 495 canonical proteins between 2018 and 2019, especially from studies designed to detect hard-to-identify proteins. Meanwhile, the Human Protein Atlas has released version 18.1 with immunohistochemical evidence of expression of 17 000 proteins and survival plots as part of the Pathology Atlas. Many investigators apply multiplexed SRM-targeted proteomics for quantitation of organ-specific popular proteins in studies of various human diseases. The 19 teams of the Biology and Disease-driven B/D-HPP published a total of 160 publications in 2018, bringing proteomics to a broad array of biomedical research.
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Affiliation(s)
- Gilbert S. Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, CMU, Michel-Servet 1, 1211 Geneva 4, Switzerland
| | - Christopher M. Overall
- Life Sciences Institute, Faculty of Dentistry, University of British Columbia, 2350 Health Sciences Mall, Room 4.401, Vancouver, British Columbia V6T 1Z3, Canada
| | | | - Jochen M. Schwenk
- Science for Life Laboratory, KTH Royal Institute of Technology, Tomtebodavägen 23A, 17165 Solna, Sweden
| | - Young-Ki Paik
- Yonsei Proteome Research Center, Yonsei University, Room 425, Building #114, 50 Yonsei-ro, Seodaemoon-ku, Seoul 120-749, South Korea
| | - Jennifer E. Van Eyk
- Advanced Clinical BioSystems Research Institute, Cedars Sinai Precision Biomarker Laboratories, Barbra Streisand Women’s Heart Center, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Siqi Liu
- BGI Group-Shenzhen, Yantian District, Shenzhen 518083, China
| | - Stephen Pennington
- School of Medicine, University College Dublin, Conway Institute Belfield, Dublin 4, Ireland
| | - Michael P. Snyder
- Department of Genetics, Stanford University, Alway Building, 300 Pasteur Drive and 3165 Porter Drive, Palo Alto, California 94304, United States
| | - Mark S. Baker
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences, Macquarie University, 75 Talavera Road, North Ryde, NSW 2109, Australia
| | - Eric W. Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
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298
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Undulating changes in human plasma proteome profiles across the lifespan. Nat Med 2019; 25:1843-1850. [PMID: 31806903 PMCID: PMC7062043 DOI: 10.1038/s41591-019-0673-2] [Citation(s) in RCA: 400] [Impact Index Per Article: 80.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 10/30/2019] [Indexed: 12/19/2022]
Abstract
Aging is a predominant risk factor for numerous chronic diseases that limit healthspan1. Mechanisms of aging are thus increasingly recognized as potential therapeutic targets. Blood from young mice reverses aspects of aging and disease across multiple tissues2–10, which supports a hypothesis that age-related molecular changes in blood could provide novel insights into age-related disease biology. We measured 2,925 plasma proteins from 4,263 young adults to nonagenarians (18–95 years old) and developed a novel bioinformatics approach, which uncovered marked non-linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh, and eighth decades of life reflected distinct biological pathways and revealed differential associations with the genome and proteome of age-related diseases and phenotypic traits. This new approach to the study of aging led to the identification of unexpected signatures and pathways, which might offer potential targets for age-related diseases.
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299
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300
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Sebastiani P, Monti S, Morris M, Gurinovich A, Toshiko T, Andersen SL, Sweigart B, Ferrucci L, Jennings LL, Glass DJ, Perls TT. A serum protein signature of APOE genotypes in centenarians. Aging Cell 2019; 18:e13023. [PMID: 31385390 PMCID: PMC6826130 DOI: 10.1111/acel.13023] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/07/2019] [Accepted: 07/07/2019] [Indexed: 12/30/2022] Open
Abstract
The discovery of treatments to prevent or delay dementia and Alzheimer's disease is a priority. The gene APOE is associated with cognitive change and late-onset Alzheimer's disease, and epidemiological studies have provided strong evidence that the e2 allele of APOE has a neuroprotective effect, it is associated with increased longevity and an extended healthy lifespan in centenarians. In this study, we correlated APOE genotype data of 222 participants of the New England Centenarian Study, including 75 centenarians, 82 centenarian offspring, and 65 controls, comprising 55 carriers of APOE e2 , with aptamer-based serum proteomics (SomaLogic technology) of 4,785 human proteins corresponding to 4,137 genes. We discovered a signature of 16 proteins that associated with different APOE genotypes and replicated the signature in three independent studies. We also show that the protein signature tracks with gene expression profiles in brains of late-onset Alzheimer's disease versus healthy controls. Finally, we show that seven of these proteins correlate with cognitive function patterns in longitudinally collected data. This analysis in particular suggests that Baculoviral IAP repeat containing two (BIRC2) is a novel biomarker of neuroprotection that associates with the neuroprotective allele of APOE. Therefore, targeting APOE e2 molecularly may preserve cognitive function.
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Affiliation(s)
- Paola Sebastiani
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusetts
| | - Stefano Monti
- Bioinformatics ProgramBoston UniversityBostonMassachusetts
- Division of Computational Biomedicine, Department of MedicineBoston University School of MedicineBostonMassachusetts
| | - Melody Morris
- Novartis Institutes for Biomedical ResearchCambridgeMassachusetts
| | - Anastasia Gurinovich
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusetts
- Bioinformatics ProgramBoston UniversityBostonMassachusetts
| | - Tanaka Toshiko
- Translational Gerontology BranchNational Institute on AgingBaltimoreMaryland
| | - Stacy L. Andersen
- Geriatrics Section, Department of Medicine, School of Medicine and Boston Medical CenterBoston UniversityBostonMA
| | - Benjamin Sweigart
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusetts
| | - Luigi Ferrucci
- Translational Gerontology BranchNational Institute on AgingBaltimoreMaryland
| | - Lori L. Jennings
- Novartis Institutes for Biomedical ResearchCambridgeMassachusetts
| | - David J. Glass
- Novartis Institutes for Biomedical ResearchCambridgeMassachusetts
| | - Thomas T. Perls
- Geriatrics Section, Department of Medicine, School of Medicine and Boston Medical CenterBoston UniversityBostonMA
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