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Ma W, Tang W, Kwok JS, Tong AH, Lo CW, Chu AT, Chung BH. A review on trends in development and translation of omics signatures in cancer. Comput Struct Biotechnol J 2024; 23:954-971. [PMID: 38385061 PMCID: PMC10879706 DOI: 10.1016/j.csbj.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/23/2024] Open
Abstract
The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.
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Affiliation(s)
- Wei Ma
- Hong Kong Genome Institute, Hong Kong, China
| | - Wenshu Tang
- Hong Kong Genome Institute, Hong Kong, China
| | | | | | | | | | - Brian H.Y. Chung
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hong Kong Genome Project
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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2
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Tachino J, Togami Y, Matsumoto H, Matsubara T, Seno S, Ogura H, Oda J. Plasma proteomics profile-based comparison of torso versus brain injury: A prospective cohort study. J Trauma Acute Care Surg 2024; 97:557-565. [PMID: 38595266 DOI: 10.1097/ta.0000000000004356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
BACKGROUND Trauma-related deaths and posttraumatic sequelae are a global health concern, necessitating a deeper understanding of the pathophysiology to advance trauma therapy. Proteomics offers insights into identifying and analyzing plasma proteins associated with trauma and inflammatory conditions; however, current proteomic methods have limitations in accurately measuring low-abundance plasma proteins. This study compared plasma proteomics profiles of patients from different acute trauma subgroups to identify new therapeutic targets and devise better strategies for personalized medicine. METHODS This prospective observational single-center cohort study was conducted between August 2020 and September 2021 in the intensive care unit of Osaka University Hospital in Japan. Enrolling 59 consecutive patients with blunt trauma, we meticulously analyzed plasma proteomics profiles in participants with torso or head trauma, comparing them with those of controls (mild trauma). Using the Olink Explore 3072 instrument (Olink Proteomics AB, Uppsala, Sweden), we identified five endotypes (α-ε) via unsupervised hierarchical clustering. RESULTS The median time from injury to blood collection was 47 minutes [interquartile range, 36-64 minutes]. The torso trauma subgroup exhibited 26 unique proteins with significantly altered expression, while the head trauma subgroup showed 68 unique proteins with no overlap between the two. The identified endotypes included α (torso trauma, n = 8), β (young patients with brain injury, n = 5), γ (severe brain injury postsurgery, n = 8), δ (torso or brain trauma with mild hyperfibrinolysis, n = 18), and ε (minor trauma, n = 20). Patients with torso trauma showed changes in blood pressure, smooth muscle adaptation, hypermetabolism, and hypoxemia. Patients with traumatic brain injury had dysregulated blood coagulation and altered nerves regeneration and differentiation. CONCLUSION This study identified unique plasma protein expression patterns in patients with torso trauma and traumatic brain injury, helping categorize five distinct endotypes. Our findings may offer new insights for clinicians, highlighting potential strategies for personalized medicine and improved trauma-related care. LEVEL OF EVIDENCE Prognostic and Epidemiological; Level III.
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Affiliation(s)
- Jotaro Tachino
- From the Department of Traumatology and Acute Critical Medicine (J.T., Y.T., H.M., T.M., H.O., J.O.), Osaka University Graduate School of Medicine; and Department of Bioinformatic Engineering (S.S.), Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
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3
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Carrasco-Zanini J, Wheeler E, Uluvar B, Kerrison N, Koprulu M, Wareham NJ, Pietzner M, Langenberg C. Mapping biological influences on the human plasma proteome beyond the genome. Nat Metab 2024:10.1038/s42255-024-01133-5. [PMID: 39327534 DOI: 10.1038/s42255-024-01133-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 08/23/2024] [Indexed: 09/28/2024]
Abstract
Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10-77% of the variation in plasma (median 19.88%, interquartile range 14.01-31.09%), independent of technical factors (median 2.48%, interquartile range 0.78-6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein-disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.
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Affiliation(s)
- Julia Carrasco-Zanini
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Burulça Uluvar
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Nicola Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Maik Pietzner
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
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4
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Wang QS, Hasegawa T, Namkoong H, Saiki R, Edahiro R, Sonehara K, Tanaka H, Azekawa S, Chubachi S, Takahashi Y, Sakaue S, Namba S, Yamamoto K, Shiraishi Y, Chiba K, Tanaka H, Makishima H, Nannya Y, Zhang Z, Tsujikawa R, Koike R, Takano T, Ishii M, Kimura A, Inoue F, Kanai T, Fukunaga K, Ogawa S, Imoto S, Miyano S, Okada Y. Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance. Nat Genet 2024:10.1038/s41588-024-01896-3. [PMID: 39317738 DOI: 10.1038/s41588-024-01896-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 08/06/2024] [Indexed: 09/26/2024]
Abstract
Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs.
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Affiliation(s)
- Qingbo S Wang
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
| | - Takanori Hasegawa
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan.
| | - Ryunosuke Saiki
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyuto Sonehara
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | | | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Children's Health and Genetics, Division of Health Science, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuichi Shiraishi
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Kenichi Chiba
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Hiroko Tanaka
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hideki Makishima
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Yasuhito Nannya
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Zicong Zhang
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Rika Tsujikawa
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Ryuji Koike
- Health Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, Japan
| | - Tomomi Takano
- Laboratory of Veterinary Infectious Disease, Department of Veterinary Medicine, Kitasato University, Tokyo, Japan
| | - Makoto Ishii
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Fumitaka Inoue
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yukinori Okada
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan.
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5
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Greenland P, Segal MR, McNeil RB, Parker CB, Pemberton VL, Grobman WA, Silver RM, Simhan HN, Saade GR, Ganz P, Mehta P, Catov JM, Bairey Merz CN, Varagic J, Khan SS, Parry S, Reddy UM, Mercer BM, Wapner RJ, Haas DM. Large-Scale Proteomics in Early Pregnancy and Hypertensive Disorders of Pregnancy. JAMA Cardiol 2024; 9:791-799. [PMID: 38958943 PMCID: PMC11223045 DOI: 10.1001/jamacardio.2024.1621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/29/2024] [Indexed: 07/04/2024]
Abstract
Importance There is no consensus regarding the best method for prediction of hypertensive disorders of pregnancy (HDP), including gestational hypertension and preeclampsia. Objective To determine predictive ability in early pregnancy of large-scale proteomics for prediction of HDP. Design, Setting, and Participants This was a nested case-control study, conducted in 2022 to 2023, using clinical data and plasma samples collected between 2010 and 2013 during the first trimester, with follow-up until pregnancy outcome. This multicenter observational study took place at 8 academic medical centers in the US. Nulliparous individuals during first-trimester clinical visits were included. Participants with HDP were selected as cases; controls were selected from those who delivered at or after 37 weeks without any HDP, preterm birth, or small-for-gestational-age infant. Age, self-reported race and ethnicity, body mass index, diabetes, health insurance, and fetal sex were available covariates. Exposures Proteomics using an aptamer-based assay that included 6481 unique human proteins was performed on stored plasma. Covariates were used in predictive models. Main Outcomes and Measures Prediction models were developed using the elastic net, and analyses were performed on a randomly partitioned training dataset comprising 80% of study participants, with the remaining 20% used as an independent testing dataset. Primary measure of predictive performance was area under the receiver operating characteristic curve (AUC). Results This study included 753 HDP cases and 1097 controls with a mean (SD) age of 26.9 (5.5) years. Maternal race and ethnicity were 51 Asian (2.8%), 275 non-Hispanic Black (14.9%), 275 Hispanic (14.9%), 1161 non-Hispanic White (62.8% ), and 88 recorded as other (4.8%), which included those who did not identify according to these designations. The elastic net model, allowing for forced inclusion of prespecified covariates, was used to adjust protein-based models for clinical and demographic variables. Under this approach, no proteins were selected to augment the clinical and demographic covariates. The predictive performance of the resulting model was modest, with a training set AUC of 0.64 (95% CI, 0.61-0.67) and a test set AUC of 0.62 (95% CI, 0.56-0.68). Further adjustment for study site yielded only minimal changes in AUCs. Conclusions and Relevance In this case-control study with detailed clinical data and stored plasma samples available in the first trimester, an aptamer-based proteomics panel did not meaningfully add to predictive utility over and above clinical and demographic factors that are routinely available.
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Affiliation(s)
- Philip Greenland
- Departments of Medicine and Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Mark R. Segal
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | | | | | - Victoria L. Pemberton
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - William A. Grobman
- Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Now with Department of Obstetrics and Gynecology, The Ohio State University, Columbus
| | - Robert M. Silver
- Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City
| | - Hyagriv N. Simhan
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - George R. Saade
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology at UTMB Health, Galveston, Texas
- Now with Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk
| | - Peter Ganz
- Department of Medicine, Zuckerberg San Francisco General Hospital and University of California, San Francisco
| | - Priya Mehta
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Janet M. Catov
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh and Magee-Women’s Research Institute, Pittsburgh, Pennsylvania
| | - C. Noel Bairey Merz
- Barbra Streisand Women’s Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jasmina Varagic
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Sadiya S. Khan
- Division of Cardiology, Department of Medicine and Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Samuel Parry
- Department of Obstetrics and Gynecology, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Uma M. Reddy
- Maternal & Fetal Medicine, Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, New York
| | - Brian M. Mercer
- Department of Obstetrics & Gynecology, Case Western Reserve University—The MetroHealth System, Cleveland, Ohio
| | - Ronald J. Wapner
- Clinical Genetics and Genomics, Maternal & Fetal Medicine, Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, New York
| | - David M. Haas
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis
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Salman O, Zhao L, Cohen JB, Dib MJ, Azzo JD, Gan S, Richards AM, Pourmussa B, Doughty R, Javaheri A, Mann DL, Rietzschel E, Zhao M, Wang Z, Ebert C, van Empel V, Kammerhoff K, Maranville J, Gogain J, Dennis J, Schafer PH, Seiffert D, Gordon DA, Ramirez-Valle F, Cappola TP, Chirinos JA. Proteomic Correlates and Prognostic Significance of Kidney Injury in Heart Failure With Preserved Ejection Fraction. J Am Heart Assoc 2024; 13:e033660. [PMID: 39206761 DOI: 10.1161/jaha.123.033660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 05/15/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Kidney disease is common in heart failure with preserved ejection fraction (HFpEF). However, the biologic correlates and prognostic significance of kidney injury (KI), in HFpEF, beyond the estimated glomerular filtration rate (eGFR), are unclear. METHODS AND RESULTS Using baseline plasma samples from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) trial, we measured the following KI biomarkers: cystatin-C, fatty acid-binding protein-3, Beta-2 microglobulin, neutrophil gelatinase-associated lipocalin, and kidney-injury molecule-1. Factor analysis was used to extract the common variability underlying these biomarkers. We assessed the relationship between the KI-factor score and the risk of death or HF-related hospital admission in models adjusted for the Meta-Analysis Global Group in Chronic Heart Failure risk score and eGFR. We also assessed the relationship between the KI factor score and ~5000 plasma proteins, followed by pathway analysis. We validated our findings among HFpEF participants in the Penn Heart Failure Study. KI was associated with the risk of death or HF-related hospital admission independent of the Meta-Analysis Global Group in Chronic Heart Failure risk score and eGFR. Both the risk score and eGFR were no longer associated with death or HF-related hospital admission after adjusting for the KI factor score. KI was predominantly associated with proteins and biologic pathways related to complement activation, inflammation, fibrosis, and cholesterol homeostasis. KI was associated with 140 proteins, which reproduced across cohorts. Findings regarding biologic associations and the prognostic significance of KI were also reproduced in the validation cohort. CONCLUSIONS KI is associated with adverse outcomes in HFpEF independent of baseline eGFR. Patients with HFpEF and KI exhibit a plasma proteomic signature indicative of complement activation, inflammation, fibrosis, and impaired cholesterol homeostasis.
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Affiliation(s)
- Oday Salman
- Hospital of the University of Pennsylvania Philadelphia PA USA
| | - Lei Zhao
- Bristol Myers Squibb Company Princeton NJ USA
| | - Jordana B Cohen
- Hospital of the University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
| | - Marie Joe Dib
- Hospital of the University of Pennsylvania Philadelphia PA USA
| | - Joe David Azzo
- Hospital of the University of Pennsylvania Philadelphia PA USA
| | - Sushrima Gan
- Hospital of the University of Pennsylvania Philadelphia PA USA
| | - A Mark Richards
- Cardiovascular Research Institute National University of Singapore Singapore
- Christchurch Heart Institute University of Otago New Zealand
| | - Bianca Pourmussa
- University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
| | | | - Ali Javaheri
- Washington University School of Medicine St. Louis MO USA
| | - Douglas L Mann
- Washington University School of Medicine St. Louis MO USA
| | - Ernst Rietzschel
- Department of Cardiovascular Diseases Ghent University and Ghent University Hospital Ghent Belgium
| | - Manyun Zhao
- Hospital of the University of Pennsylvania Philadelphia PA USA
| | | | | | - Vanessa van Empel
- Department of Cardiology Maastricht University Medical Center Maastricht The Netherlands
| | | | | | | | | | | | | | | | | | - Thomas P Cappola
- Hospital of the University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
| | - Julio A Chirinos
- Hospital of the University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
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7
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Beutgen VM, Shinkevich V, Pörschke J, Meena C, Steitz AM, Pogge von Strandmann E, Graumann J, Gómez-Serrano M. Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology. Mol Cell Proteomics 2024; 23:100830. [PMID: 39147028 PMCID: PMC11417252 DOI: 10.1016/j.mcpro.2024.100830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 07/20/2024] [Accepted: 08/12/2024] [Indexed: 08/17/2024] Open
Abstract
The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.
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Affiliation(s)
- Vanessa M Beutgen
- Institute of Translational Proteomics, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany; Core Facility Translational Proteomics, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany
| | - Veronika Shinkevich
- Institute of Pharmacology, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany
| | - Johanna Pörschke
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany
| | - Celina Meena
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany
| | - Anna M Steitz
- Translational Oncology Group, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany
| | - Elke Pogge von Strandmann
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany
| | - Johannes Graumann
- Institute of Translational Proteomics, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany; Core Facility Translational Proteomics, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany.
| | - María Gómez-Serrano
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany.
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8
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Ardissino M, Truong B, Slob EAW, Schuermans A, Yoshiji S, Morley AP, Burgess S, Ng FS, de Marvao A, Natarajan P, Nicolaides K, Gaziano L, Butterworth A, Honigberg MC. Proteome- and Transcriptome-Wide Genetic Analysis Identifies Biological Pathways and Candidate Drug Targets for Preeclampsia. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024:e004755. [PMID: 39119725 PMCID: PMC7616531 DOI: 10.1161/circgen.124.004755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality. However, the current understanding of its underlying biological pathways remains limited. METHODS In this study, we performed a cross-platform proteome- and transcriptome-wide genetic analysis aimed at evaluating the causal relevance of >2000 circulating proteins with preeclampsia, supported by data on the expression of over 15 000 genes across 36 tissues leveraging large-scale preeclampsia genetic association data from women of European ancestry. RESULTS We demonstrate genetic associations of 18 circulating proteins with preeclampsia (SULT1A1, SH2B3, SERPINE2, RGS18, PZP, NOTUM, METAP1, MANEA, jun-D, GDF15 [growth/differentiation factor 15], FGL1, FGF5, FES, APOBR, ANP, ALDH-E2, ADAMTS13, and 3MG), among which 11 were either directly or indirectly supported by gene expression data, 9 were supported by Bayesian colocalization analyses, and 5 (SERPINE2, PZP, FGF5, FES, and ANP) were supported by all lines of evidence examined. Protein interaction mapping identified potential shared biological pathways through natriuretic peptide signaling, blood pressure regulation, immune tolerance, and thrombin activity regulation. CONCLUSIONS This investigation identified multiple targetable proteins linked to cardiovascular, inflammatory, and coagulation pathways, with SERPINE2, PZP, FGF5, FES, and ANP identified as pivotal proteins with likely causal roles in the development of preeclampsia. The identification of these potential targets may guide the development of targeted therapies for preeclampsia.
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Affiliation(s)
- Maddalena Ardissino
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (M.A., L.G., A.B.)
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, United Kingdom. (M.A, A.B.)
- National Heart and Lung Institute, Imperial College London, United Kingdom. (M.A, F.S.N.)
- Medical Research Council, London Institute of Medical Sciences, Imperial College London, United Kingdom. (M.A.)
| | - Buu Truong
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, United Kingdom. (B.T., A.S., P.N., M.C.H.)
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston (B.T., A.S., P.N., M.C.H.)
| | - Eric A W Slob
- MRC Biostatistics Unit, University of Cambridge, United Kingdom. (E.A.W.S., S.B.)
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, the Netherlands. (E.A.W.S.)
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, the Netherlands. (E.A.W.S.)
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, the Netherlands. (E.A.W.S.)
| | - Art Schuermans
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, United Kingdom. (B.T., A.S., P.N., M.C.H.)
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston (B.T., A.S., P.N., M.C.H.)
- Faculty of Medicine, KU Leuven, Belgium (A.S.)
| | - Satoshi Yoshiji
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, United Kingdom. (S.Y.)
| | - Alec P Morley
- Department of Medicine, School of Clinical Medicine, University of Cambridge, United Kingdom. (A.P.M.)
- Gonville and Caius College, University of Cambridge, United Kingdom. (A.P.M.)
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, United Kingdom. (E.A.W.S., S.B.)
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, United Kingdom. (M.A, F.S.N.)
| | - Antonio de Marvao
- Department of Women and Children's Health, King's College Hospital, London, United Kingdom. (A.d.M., K.N.)
- British Heart Foundation Center of Research Excellence, School of Cardiovascular Medicine and Sciences, King's College Hospital, London, United Kingdom. (A.d.M.)
- Fetal Medicine Research Institute, King's College Hospital, London, United Kingdom. (A.d.M., K.N.)
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, United Kingdom. (B.T., A.S., P.N., M.C.H.)
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston (B.T., A.S., P.N., M.C.H.)
| | - Kypros Nicolaides
- Department of Women and Children's Health, King's College Hospital, London, United Kingdom. (A.d.M., K.N.)
- Fetal Medicine Research Institute, King's College Hospital, London, United Kingdom. (A.d.M., K.N.)
| | - Liam Gaziano
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (M.A., L.G., A.B.)
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Harvard Medical School, Boston. (L.G.)
| | - Adam Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (M.A., L.G., A.B.)
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, United Kingdom. (M.A, A.B.)
- British Heart Foundation Center of Research Excellence, University of Cambridge, United Kingdom. (A.B.)
- Health Data Research UK Cambridge, Wellcome Genome Campus, University of Cambridge, United Kingdom. (A.B.)
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, United Kingdom. (A.B.)
| | - Michael C Honigberg
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, United Kingdom. (B.T., A.S., P.N., M.C.H.)
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston (B.T., A.S., P.N., M.C.H.)
- Department of Medicine, Harvard Medical School, Boston. (M.C.H.)
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9
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Jiang MZ, Gaynor SM, Li X, Van Buren E, Stilp A, Buth E, Wang FF, Manansala R, Gogarten SM, Li Z, Polfus LM, Salimi S, Bis JC, Pankratz N, Yanek LR, Durda P, Tracy RP, Rich SS, Rotter JI, Mitchell BD, Lewis JP, Psaty BM, Pratte KA, Silverman EK, Kaplan RC, Avery C, North KE, Mathias RA, Faraday N, Lin H, Wang B, Carson AP, Norwood AF, Gibbs RA, Kooperberg C, Lundin J, Peters U, Dupuis J, Hou L, Fornage M, Benjamin EJ, Reiner AP, Bowler RP, Lin X, Auer PL, Raffield LM. Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium. Hum Mol Genet 2024; 33:1429-1441. [PMID: 38747556 PMCID: PMC11305684 DOI: 10.1093/hmg/ddae050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 01/31/2024] [Accepted: 03/11/2024] [Indexed: 05/28/2024] Open
Abstract
Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.
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Affiliation(s)
- Min-Zhi Jiang
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Sheila M Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, United States
- Regeneron Genetics Center, 777 Old Saw Mill River Road, Tarrytown, NY 10591, United States
| | - Xihao Li
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
- Department of Biostatistics, 135 Dauer Drive, 4115D McGavran-Greenberg Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Eric Van Buren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, United States
| | - Adrienne Stilp
- Department of Biostatistics, 4333 Brooklyn Ave NE, University of Washington, Seattle, WA 98105, United States
| | - Erin Buth
- Department of Biostatistics, 4333 Brooklyn Ave NE, University of Washington, Seattle, WA 98105, United States
| | - Fei Fei Wang
- Department of Biostatistics, 4333 Brooklyn Ave NE, University of Washington, Seattle, WA 98105, United States
| | - Regina Manansala
- Centre for Health Economics Research & Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO) WHO Collaborating Centre, University of Antwerp, Campus Drie Eiken - Building S; Universiteitsplein 1 2610 Antwerpen, Belgium
| | - Stephanie M Gogarten
- Department of Biostatistics, 4333 Brooklyn Ave NE, University of Washington, Seattle, WA 98105, United States
| | - Zilin Li
- School of Mathematics and Statistics, Northeast Normal University, 5268 Renmin Street, Changchun, JL 130024, China
| | - Linda M Polfus
- Advanced Analytics, Ambry Genetics, 1 Enterprise, Aliso Viejo, CA 92656, United States
| | - Shabnam Salimi
- Department of Epidemiology and Public Health, Division of Gerontology, University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD 21201, United States
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA 98195, United States
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, 420 Delaware Street SE, Minneapolis, MN 55455, United States
| | - Lisa R Yanek
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, 1830 E Monument St Rm 8024, Baltimore, MD 21287, United States
| | - Peter Durda
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT 05446, United States
| | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT 05446, United States
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, 200 Jeanette Lancaster Way, Charlottesville, VA 22903, United States
| | - 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, 1124 W. Carson Street, Torrance, CA 90502, United States
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD 21201, United States
| | - Joshua P Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD 21201, United States
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA 98195, United States
- Departments of Epidemiology and Health Systems and Population Health, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA 98101, United States
| | - Katherine A Pratte
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, 1400 Jackson Street, Denver, CO 80206, United States
| | - Edwin K Silverman
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, 137 East Franklin Street, Chapel Hill, NC 27599, United States
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, 137 East Franklin Street, Chapel Hill, NC 27599, United States
| | - Rasika A Mathias
- Department of Medicine, Allergy and Clinical Immunology, Johns Hopkins University School of Medicine, 5501 Hopkins Bayview Cir JHAAC Room 3B53, Baltimore, MD 21287, United States
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA 01655, United States
| | - Biqi Wang
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA 01655, United States
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS 39213, United States
| | - Arnita F Norwood
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS 39213, United States
| | - Richard A Gibbs
- Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, WA 98109, United States
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, WA 98109, United States
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, WA 98109, United States
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 2001 McGill College Avenue, Montreal, QC H3A 1G1, Canada
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118, United States
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N Lake Shore Drive, Chicago, IL 60611, United States
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street, Houston, TX 77030, United States
| | - Emelia J Benjamin
- Department of Medicine, Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, 72 East Newton Street, Boston, MA 02118, United States
- Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118, United States
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, 73 Mount Wayte Ave #2, Framingham, MA 01702, United States
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA 98105, United States
| | - Russell P Bowler
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, 1400 Jackson Street, Denver, CO 80206, United States
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, United States
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, United States
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
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10
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Logotheti S, Pavlopoulou A, Rudsari HK, Galow AM, Kafalı Y, Kyrodimos E, Giotakis AI, Marquardt S, Velalopoulou A, Verginadis II, Koumenis C, Stiewe T, Zoidakis J, Balasingham I, David R, Georgakilas AG. Intercellular pathways of cancer treatment-related cardiotoxicity and their therapeutic implications: the paradigm of radiotherapy. Pharmacol Ther 2024; 260:108670. [PMID: 38823489 DOI: 10.1016/j.pharmthera.2024.108670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 05/16/2024] [Accepted: 05/25/2024] [Indexed: 06/03/2024]
Abstract
Advances in cancer therapeutics have improved patient survival rates. However, cancer survivors may suffer from adverse events either at the time of therapy or later in life. Cardiovascular diseases (CVD) represent a clinically important, but mechanistically understudied complication, which interfere with the continuation of best-possible care, induce life-threatening risks, and/or lead to long-term morbidity. These concerns are exacerbated by the fact that targeted therapies and immunotherapies are frequently combined with radiotherapy, which induces durable inflammatory and immunogenic responses, thereby providing a fertile ground for the development of CVDs. Stressed and dying irradiated cells produce 'danger' signals including, but not limited to, major histocompatibility complexes, cell-adhesion molecules, proinflammatory cytokines, and damage-associated molecular patterns. These factors activate intercellular signaling pathways which have potentially detrimental effects on the heart tissue homeostasis. Herein, we present the clinical crosstalk between cancer and heart diseases, describe how it is potentiated by cancer therapies, and highlight the multifactorial nature of the underlying mechanisms. We particularly focus on radiotherapy, as a case known to often induce cardiovascular complications even decades after treatment. We provide evidence that the secretome of irradiated tumors entails factors that exert systemic, remote effects on the cardiac tissue, potentially predisposing it to CVDs. We suggest how diverse disciplines can utilize pertinent state-of-the-art methods in feasible experimental workflows, to shed light on the molecular mechanisms of radiotherapy-related cardiotoxicity at the organismal level and untangle the desirable immunogenic properties of cancer therapies from their detrimental effects on heart tissue. Results of such highly collaborative efforts hold promise to be translated to next-generation regimens that maximize tumor control, minimize cardiovascular complications, and support quality of life in cancer survivors.
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Affiliation(s)
- Stella Logotheti
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou, 15780, Athens, Greece; Biomedical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Athanasia Pavlopoulou
- Izmir Biomedicine and Genome Center, Izmir, Turkey; Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Turkey
| | | | - Anne-Marie Galow
- Institute for Genome Biology, Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany
| | - Yağmur Kafalı
- Izmir Biomedicine and Genome Center, Izmir, Turkey; Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Turkey
| | - Efthymios Kyrodimos
- First Department of Otorhinolaryngology, Head and Neck Surgery, Hippocrateion General Hospital Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Aris I Giotakis
- First Department of Otorhinolaryngology, Head and Neck Surgery, Hippocrateion General Hospital Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Stephan Marquardt
- Institute of Translational Medicine for Health Care Systems, Medical School Berlin, Hochschule Für Gesundheit Und Medizin, 14197 Berlin, Germany
| | - Anastasia Velalopoulou
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ioannis I Verginadis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Constantinos Koumenis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Thorsten Stiewe
- Institute of Molecular Oncology, Philipps-University, 35043 Marburg, Germany; German Center for Lung Research (DZL), Universities of Giessen and Marburg Lung Center (UGMLC), 35043 Marburg, Germany; Genomics Core Facility, Philipps-University, 35043 Marburg, Germany; Institute for Lung Health (ILH), Justus Liebig University, 35392 Giessen, Germany
| | - Jerome Zoidakis
- Department of Biotechnology, Biomedical Research Foundation, Academy of Athens, Athens, Greece; Department of Biology, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Robert David
- Department of Cardiac Surgery, Rostock University Medical Center, 18057 Rostock, Germany; Department of Life, Light & Matter, Interdisciplinary Faculty, Rostock University, 18059 Rostock, Germany
| | - Alexandros G Georgakilas
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou, 15780, Athens, Greece.
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11
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Thiele M, Villesen IF, Niu L, Johansen S, Sulek K, Nishijima S, Espen LV, Keller M, Israelsen M, Suvitaival T, Zawadzki AD, Juel HB, Brol MJ, Stinson SE, Huang Y, Silva MCA, Kuhn M, Anastasiadou E, Leeming DJ, Karsdal M, Matthijnssens J, Arumugam M, Dalgaard LT, Legido-Quigley C, Mann M, Trebicka J, Bork P, Jensen LJ, Hansen T, Krag A. Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases. J Hepatol 2024; 81:345-359. [PMID: 38552880 DOI: 10.1016/j.jhep.2024.03.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/16/2024] [Accepted: 03/19/2024] [Indexed: 07/26/2024]
Abstract
The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.
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Affiliation(s)
- Maja Thiele
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ida Falk Villesen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Stine Johansen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark
| | | | - Suguru Nishijima
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Lore Van Espen
- KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Laboratory of Viral Metagenomics, Leuven, Belgium
| | - Marisa Keller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Mads Israelsen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | | | - Helene Bæk Juel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Maximilian Joseph Brol
- Medizinische Klinik B (Gastroenterologie, Hepatologie, Endokrinologie, Klinische Infektiologie), Universitätsklinikum Münster Westfälische, Wilhelms-Universität Münster, Germany
| | - Sara Elizabeth Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Yun Huang
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Maria Camilla Alvarez Silva
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Diana Julie Leeming
- Fibrosis, Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Morten Karsdal
- Fibrosis, Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Jelle Matthijnssens
- KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Laboratory of Viral Metagenomics, Leuven, Belgium
| | - Manimozhiyan Arumugam
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Matthias Mann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Jonel Trebicka
- Medizinische Klinik B (Gastroenterologie, Hepatologie, Endokrinologie, Klinische Infektiologie), Universitätsklinikum Münster Westfälische, Wilhelms-Universität Münster, Germany
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Aleksander Krag
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark.
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12
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Reed ER, Chandler KB, Lopez P, Costello CE, Andersen SL, Perls TT, Li M, Bae H, Soerensen M, Monti S, Sebastiani P. Cross-platform proteomics signatures of extreme old age. GeroScience 2024:10.1007/s11357-024-01286-x. [PMID: 39048883 DOI: 10.1007/s11357-024-01286-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/10/2024] [Indexed: 07/27/2024] Open
Abstract
In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.
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Affiliation(s)
- Eric R Reed
- Data Intensive Study Center, Tufts University, Boston, MA, USA
| | - Kevin B Chandler
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Cellular and Molecular Medicine, Florida International University, Miami, FL, USA
| | - Prisma Lopez
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - Catherine E Costello
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Stacy L Andersen
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Thomas T Perls
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Mengze Li
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Harold Bae
- Biostatistics Program, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Mette Soerensen
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Stefano Monti
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Paola Sebastiani
- Data Intensive Study Center, Tufts University, Boston, MA, USA.
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
- Department of Medicine, School of Medicine, Tufts University, Boston, MA, USA.
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13
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Reed E, Sebastiani P. A Simple Strategy for Identifying Conserved Features across Non-independent Omics Studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568276. [PMID: 38045352 PMCID: PMC10690236 DOI: 10.1101/2023.11.22.568276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
False discovery is an ever-present concern in omics research, especially for burgeoning technologies with unvetted specificity of their biomolecular measurements, as such unknowns obscure the ability to characterize biologically informative features from studies performed with any single platform. Accordingly, performing replication studies of the same samples using different omics platforms is a viable strategy for identifying high-confidence molecular associations that are conserved across studies. However, an important caveat of replication studies that include the same samples is that they are inherently non-independent, leading to overestimating conservation if studies are treated otherwise. Strategies for accounting for such inter-study dependencies have been proposed for meta-analysis methods devised to increase statistical power to detect molecular associations in one or more studies. Still, they are not immediately suited for identifying conserved molecular associations across multiple studies. Here, we present a unifying strategy for performing inter-study conservation analysis as an alternative to meta-analysis strategies for aggregating summary statistical results of shared features across complementary studies while accounting for inter-study dependency. This method, which we call "adjusted maximum p-value" (AdjMaxP), is easy to implement with inter-study dependency and conservation estimated directly from the p-values from each study's molecular feature-level association testing results. Through simulation-based assessment, we demonstrate AdjMaxP's improved performance for accurately identifying conserved features over a related meta-analysis strategy for non-independent studies. AdjMaxP offers an easily implementable strategy for improving the precision of analyses for biomarker discovery from cross-platform omics study designs, thereby facilitating the adoption of such protocols for robust inference from emerging omics technologies.
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14
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Kraemer S, Schneider DJ, Paterson C, Perry D, Westacott MJ, Hagar Y, Katilius E, Lynch S, Russell TM, Johnson T, Astling DP, DeLisle RK, Cleveland J, Gold L, Drolet DW, Janjic N. Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome. J Proteome Res 2024. [PMID: 39038188 DOI: 10.1021/acs.jproteome.4c00411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.
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Affiliation(s)
- Stephan Kraemer
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Daniel J Schneider
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Clare Paterson
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Darryl Perry
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Matthew J Westacott
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Yolanda Hagar
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Evaldas Katilius
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Sean Lynch
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Theresa M Russell
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Ted Johnson
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - David P Astling
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Robert Kirk DeLisle
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Jason Cleveland
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Larry Gold
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Daniel W Drolet
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Nebojsa Janjic
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
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15
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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16
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Mckinnon K, Conole ELS, Vaher K, Hillary RF, Gadd DA, Binkowska J, Sullivan G, Stevenson AJ, Corrigan A, Murphy L, Whalley HC, Richardson H, Marioni RE, Cox SR, Boardman JP. Epigenetic scores derived in saliva are associated with gestational age at birth. Clin Epigenetics 2024; 16:84. [PMID: 38951914 PMCID: PMC11218140 DOI: 10.1186/s13148-024-01701-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 06/22/2024] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND Epigenetic scores (EpiScores), reflecting DNA methylation (DNAm)-based surrogates for complex traits, have been developed for multiple circulating proteins. EpiScores for pro-inflammatory proteins, such as C-reactive protein (DNAm CRP), are associated with brain health and cognition in adults and with inflammatory comorbidities of preterm birth in neonates. Social disadvantage can become embedded in child development through inflammation, and deprivation is overrepresented in preterm infants. We tested the hypotheses that preterm birth and socioeconomic status (SES) are associated with alterations in a set of EpiScores enriched for inflammation-associated proteins. RESULTS In total, 104 protein EpiScores were derived from saliva samples of 332 neonates born at gestational age (GA) 22.14 to 42.14 weeks. Saliva sampling was between 36.57 and 47.14 weeks. Forty-three (41%) EpiScores were associated with low GA at birth (standardised estimates |0.14 to 0.88|, Bonferroni-adjusted p-value < 8.3 × 10-3). These included EpiScores for chemokines, growth factors, proteins involved in neurogenesis and vascular development, cell membrane proteins and receptors, and other immune proteins. Three EpiScores were associated with SES, or the interaction between birth GA and SES: afamin, intercellular adhesion molecule 5, and hepatocyte growth factor-like protein (standardised estimates |0.06 to 0.13|, Bonferroni-adjusted p-value < 8.3 × 10-3). In a preterm subgroup (n = 217, median [range] GA 29.29 weeks [22.14 to 33.0 weeks]), SES-EpiScore associations did not remain statistically significant after adjustment for sepsis, bronchopulmonary dysplasia, necrotising enterocolitis, and histological chorioamnionitis. CONCLUSIONS Low birth GA is substantially associated with a set of EpiScores. The set was enriched for inflammatory proteins, providing new insights into immune dysregulation in preterm infants. SES had fewer associations with EpiScores; these tended to have small effect sizes and were not statistically significant after adjusting for inflammatory comorbidities. This suggests that inflammation is unlikely to be the primary axis through which SES becomes embedded in the development of preterm infants in the neonatal period.
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Affiliation(s)
- Katie Mckinnon
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Eleanor L S Conole
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Kadi Vaher
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Justyna Binkowska
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Gemma Sullivan
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Amy Corrigan
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Lee Murphy
- Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Hilary Richardson
- School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - James P Boardman
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK.
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
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17
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Perry AS, Zhang K, Murthy VL, Choi B, Zhao S, Gajjar P, Colangelo LA, Hou L, Rice MB, Carr JJ, Carson AP, Nigra AE, Vasan RS, Gerszten RE, Khan SS, Kalhan R, Nayor M, Shah RV. Proteomics, Human Environmental Exposure, and Cardiometabolic Risk. Circ Res 2024; 135:138-154. [PMID: 38662804 PMCID: PMC11189739 DOI: 10.1161/circresaha.124.324559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND The biological mechanisms linking environmental exposures with cardiovascular disease pathobiology are incompletely understood. We sought to identify circulating proteomic signatures of environmental exposures and examine their associations with cardiometabolic and respiratory disease in observational cohort studies. METHODS We tested the relations of >6500 circulating proteins with 29 environmental exposures across the built environment, green space, air pollution, temperature, and social vulnerability indicators in ≈3000 participants of the CARDIA study (Coronary Artery Risk Development in Young Adults) across 4 centers using penalized and ordinary linear regression. In >3500 participants from FHS (Framingham Heart Study) and JHS (Jackson Heart Study), we evaluated the prospective relations of proteomic signatures of the envirome with cardiovascular disease and mortality using Cox models. RESULTS Proteomic signatures of the envirome identified novel/established cardiovascular disease-relevant pathways including DNA damage, fibrosis, inflammation, and mitochondrial function. The proteomic signatures of the envirome were broadly related to cardiometabolic disease and respiratory phenotypes (eg, body mass index, lipids, and left ventricular mass) in CARDIA, with replication in FHS/JHS. A proteomic signature of social vulnerability was associated with a composite of cardiovascular disease/mortality (1428 events; FHS: hazard ratio, 1.16 [95% CI, 1.08-1.24]; P=1.77×10-5; JHS: hazard ratio, 1.25 [95% CI, 1.14-1.38]; P=6.38×10-6; hazard ratio expressed as per 1 SD increase in proteomic signature), robust to adjustment for known clinical risk factors. CONCLUSIONS Environmental exposures are related to an inflammatory-metabolic proteome, which identifies individuals with cardiometabolic disease and respiratory phenotypes and outcomes. Future work examining the dynamic impact of the environment on human cardiometabolic health is warranted.
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Affiliation(s)
- Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN (A.S.P., S.Z., J.J.C., R.V.S.)
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, (K.Z.)
| | | | - Bina Choi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA (B.C.)
| | - Shilin Zhao
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN (A.S.P., S.Z., J.J.C., R.V.S.)
| | - Priya Gajjar
- Cardiovascular Medicine Section, Department of Medicine (P.G.), Boston University School of Medicine, MA
| | - Laura A Colangelo
- Department of Preventive Medicine (L.A.C., L.H.), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Lifang Hou
- Department of Preventive Medicine (L.A.C., L.H.), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Mary B Rice
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA (M.B.R.)
| | - J Jeffrey Carr
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN (A.S.P., S.Z., J.J.C., R.V.S.)
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson (A.P.C.)
| | - Anne E Nigra
- Department of Environmental Health Science, Columbia University Mailman School of Public Health, New York, NY (A.E.N.)
| | - Ramachandran S Vasan
- School of Public Health, School of Medicine, University of Texas San Antonio (R.S.V.)
| | - Robert E Gerszten
- Broad Institute of Harvard and MIT, Cambridge, MA (R.E.G.)
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (R.E.G.)
| | - Sadiya S Khan
- Division of Cardiology, Department of Medicine (S.S.K.), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Ravi Kalhan
- Division of Pulmonary and Critical Care Medicine, Department of Medicine (R.K.), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Matthew Nayor
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of Medicine (M.N.), Boston University School of Medicine, MA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN (A.S.P., S.Z., J.J.C., R.V.S.)
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18
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Plubell DL, Remes PM, Wu CC, Jacob CC, Merrihew GE, Hsu C, Shulman N, MacLean BX, Heil L, Poston K, Montine T, MacCoss MJ. Development of highly multiplex targeted proteomics assays in biofluids using the Stellar mass spectrometer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597431. [PMID: 38895256 PMCID: PMC11185584 DOI: 10.1101/2024.06.04.597431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The development of targeted assays that monitor biomedically relevant proteins is an important step in bridging discovery experiments to large scale clinical studies. Targeted assays are currently unable to scale to hundreds or thousands of targets. We demonstrate the generation of large-scale assays using a novel hybrid nominal mass instrument. The scale of these assays is achievable with the Stellar™ mass spectrometer through the accommodation of shifting retention times by real-time alignment, while being sensitive and fast enough to handle many concurrent targets. Assays were constructed using precursor information from gas-phase fractionated (GPF) data-independent acquisition (DIA). We demonstrate the ability to schedule methods from an orbitrap and linear ion trap acquired GPF DIA library and compare the quantification of a matrix-matched calibration curve from orbitrap DIA and linear ion trap parallel reaction monitoring (PRM). Two applications of these proposed workflows are shown with a cerebrospinal fluid (CSF) neurodegenerative disease protein PRM assay and with a Mag-Net enriched plasma extracellular vesicle (EV) protein survey PRM assay.
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Affiliation(s)
- Deanna L Plubell
- Department of Genome Sciences, University of Washington, Seattle WA
| | | | - Christine C Wu
- Department of Genome Sciences, University of Washington, Seattle WA
| | | | | | - Chris Hsu
- Department of Genome Sciences, University of Washington, Seattle WA
| | - Nick Shulman
- Department of Genome Sciences, University of Washington, Seattle WA
| | | | | | - Kathleen Poston
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto CA, USA
| | - Tom Montine
- Department of Pathology, Stanford University, Palo Alto CA, USA
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19
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Perry AS, Farber-Eger E, Gonzales T, Tanaka T, Robbins JM, Murthy VL, Stolze LK, Zhao S, Huang S, Colangelo LA, Deng S, Hou L, Lloyd-Jones DM, Walker KA, Ferrucci L, Watts EL, Barber JL, Rao P, Mi MY, Gabriel KP, Hornikel B, Sidney S, Houstis N, Lewis GD, Liu GY, Thyagarajan B, Khan SS, Choi B, Washko G, Kalhan R, Wareham N, Bouchard C, Sarzynski MA, Gerszten RE, Brage S, Wells QS, Nayor M, Shah RV. Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks. Nat Med 2024; 30:1711-1721. [PMID: 38834850 PMCID: PMC11186767 DOI: 10.1038/s41591-024-03039-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 04/30/2024] [Indexed: 06/06/2024]
Abstract
Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.
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Affiliation(s)
- Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Eric Farber-Eger
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Tomas Gonzales
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Toshiko Tanaka
- Longtidudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Jeremy M Robbins
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - Lindsey K Stolze
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shilin Zhao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shi Huang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laura A Colangelo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Shuliang Deng
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Keenan A Walker
- Multimodal Imaging of Neurodegenerative Disease (MIND) Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longtidudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Eleanor L Watts
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Jacob L Barber
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Prashant Rao
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michael Y Mi
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Kelley Pettee Gabriel
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Bjoern Hornikel
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Nicholas Houstis
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Gregory D Lewis
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Gabrielle Y Liu
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of California Davis, Sacramento, CA, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minnesota, MN, USA
| | - Sadiya S Khan
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Bina Choi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - George Washko
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Ravi Kalhan
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Nick Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina Columbia, Columbia, SC, USA
| | - Robert E Gerszten
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Quinn S Wells
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Matthew Nayor
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
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20
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Murthy VL, Mosley JD, Perry AS, Jacobs DR, Tanriverdi K, Zhao S, Sawicki KT, Carnethon M, Wilkins JT, Nayor M, Das S, Abel ED, Freedman JE, Clish CB, Shah RV. Metabolic liability for weight gain in early adulthood. Cell Rep Med 2024; 5:101548. [PMID: 38703763 PMCID: PMC11148768 DOI: 10.1016/j.xcrm.2024.101548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/27/2023] [Accepted: 04/10/2024] [Indexed: 05/06/2024]
Abstract
While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p = 7.5 × 10-19) gain in BMI over ≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.
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Affiliation(s)
- Venkatesh L Murthy
- Division of Cardiovascular Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Jonathan D Mosley
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kahraman Tanriverdi
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Shilin Zhao
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | | | | | - Matthew Nayor
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Saumya Das
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - E Dale Abel
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jane E Freedman
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
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21
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Turizo MJF, Patell R, Zwicker JI. Identifying novel biomarkers using proteomics to predict cancer-associated thrombosis. BLEEDING, THROMBOSIS AND VASCULAR BIOLOGY 2024; 3:120. [PMID: 38828226 PMCID: PMC11143428 DOI: 10.4081/btvb.2024.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/08/2024] [Indexed: 06/05/2024]
Abstract
Comprehensive protein analyses of plasma are made possible by high-throughput proteomic screens, which may help find new therapeutic targets and diagnostic biomarkers. Patients with cancer are frequently affected by venous thromboembolism (VTE). The limited predictive accuracy of current VTE risk assessment tools highlights the need for new, more targeted biomarkers. Although coagulation biomarkers for the diagnosis, prognosis, and treatment of VTE have been investigated, none of them have the necessary clinical validation or diagnostic accuracy. Proteomics holds the potential to uncover new biomarkers and thrombotic pathways that impact the risk of thrombosis. This review explores the fundamental methods used in proteomics and focuses on particular biomarkers found in VTE and cancer-associated thrombosis.
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Affiliation(s)
- Maria J Fernandez Turizo
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Rushad Patell
- Division of Medical Oncology and Hematology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Jeffrey I Zwicker
- Department of Medicine, Hematology Service, Memorial Sloan Kettering Cancer Center, New York, NY
- Weil Cornell Medical College, New York, NY, United States
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22
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Kurgan N, Kjærgaard Larsen J, Deshmukh AS. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 2024; 67:783-797. [PMID: 38345659 DOI: 10.1007/s00125-024-06097-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/20/2023] [Indexed: 03/21/2024]
Abstract
Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.
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Affiliation(s)
- Nigel Kurgan
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jeppe Kjærgaard Larsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Atul S Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
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23
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Royer P, Björnson E, Adiels M, Álvez MB, Fagerberg L, Bäckhed F, Uhlén M, Gummesson A, Bergström G. Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention. Am Heart J 2024; 271:55-67. [PMID: 38325523 DOI: 10.1016/j.ahj.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
BACKGROUND AND AIMS Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors. METHODS Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS. RESULTS The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors. CONCLUSIONS A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.
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Affiliation(s)
- Patrick Royer
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden; Department of Critical Care, University Hospital of Martinique, Fort-de-France, France
| | - Elias Björnson
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - Martin Adiels
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - María Bueno Álvez
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Bäckhed
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, 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
| | - Anders Gummesson
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Genetics and Genomics, Gothenburg, Sweden
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden.
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24
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Lee JS, Maher TM. Gazing into the Proteomic Crystal Ball: Predicting Survival in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 2024; 209:1056-1057. [PMID: 38117693 PMCID: PMC11092944 DOI: 10.1164/rccm.202311-2108ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/20/2023] [Indexed: 12/22/2023] Open
Affiliation(s)
- Joyce S Lee
- University of Colorado Denver Anschutz Medical Campus Aurora, Colorado
| | - Toby M Maher
- Keck School of Medicine University of Southern California Los Angeles, California
- National Heart and Lung Institute Imperial College London London, United Kingdom
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25
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Reed ER, Chandler KB, Lopez P, Costello CE, Andersen SL, Perls TT, Li M, Bae H, Soerensen M, Monti S, Sebastiani P. Cross-platform proteomics signatures of extreme old age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588876. [PMID: 38645061 PMCID: PMC11030369 DOI: 10.1101/2024.04.10.588876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.
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Affiliation(s)
- Eric R Reed
- Data Intensive Study Center, Tufts University, Boston, MA, USA
| | - Kevin B Chandler
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Cellular and Molecular Medicine, Florida International University, Miami, FL, USA
| | - Prisma Lopez
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - Catherine E Costello
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Stacy L Andersen
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Thomas T Perls
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Mengze Li
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Harold Bae
- Biostatistics Program, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Mette Soerensen
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Stefano Monti
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Paola Sebastiani
- Data Intensive Study Center, Tufts University, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Department of Medicine, School of Medicine, Tufts University, Boston, MA, USA
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26
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Abe K, Beer JC, Nguyen T, Ariyapala IS, Holmes TH, Feng W, Zhang B, Kuo D, Luo Y, Ma XJ, Maecker HT. Cross-Platform Comparison of Highly Sensitive Immunoassays for Inflammatory Markers in a COVID-19 Cohort. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:1244-1253. [PMID: 38334457 PMCID: PMC10948291 DOI: 10.4049/jimmunol.2300729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/19/2024] [Indexed: 02/10/2024]
Abstract
A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing Ab pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on human serum samples from the National Institutes of Health IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. First, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Second, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Third, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.
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Affiliation(s)
- Koji Abe
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305
| | | | - Tran Nguyen
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305
| | | | - Tyson H. Holmes
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305
| | - Wei Feng
- Alamar Biosciences, Inc., Fremont, CA 94538
| | | | - Dwight Kuo
- Alamar Biosciences, Inc., Fremont, CA 94538
| | - Yuling Luo
- Alamar Biosciences, Inc., Fremont, CA 94538
| | | | - Holden T. Maecker
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305
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Becker K, Sharma I, Slaven JE, Mosley AL, Doud EH, Malek S, Natoli RM. Proteomic Analyses of Plasma From Patients With Fracture-Related Infection Reveals Systemic Activation of the Complement and Coagulation Cascades. J Orthop Trauma 2024; 38:e111-e119. [PMID: 38117580 PMCID: PMC10922838 DOI: 10.1097/bot.0000000000002752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/16/2023] [Indexed: 12/22/2023]
Abstract
OBJECTIVES The objective of this study was to compare plasma proteomes of patients with confirmed fracture-related infections (FRIs) matched to noninfected controls using liquid chromatography-mass spectrometry. METHODS DESIGN This was a prospective case-control study. SETTING The study was conducted at a single, academic, Level 1 trauma center. PATIENT SELECTION CRITERIA Patients meeting confirmatory FRI criteria were matched to controls without infection based on fracture region, age, and time after surgery from June 2019 to January 2022. Tandem mass tag liquid chromatography-mass spectrometry analysis of patient plasma samples was performed. OUTCOME MEASURES AND COMPARISONS Protein abundance ratios in plasma for patients with FRI compared with those for matched controls without infection were calculated. RESULTS Twenty-seven patients meeting confirmatory FRI criteria were matched to 27 controls. Abundance ratios for more than 1000 proteins were measured in the 54 plasma samples. Seventy-three proteins were found to be increased or decreased in patients with FRI compared with those in matched controls (unadjusted t test P < 0.05). Thirty-two of these proteins were found in all 54 patient samples and underwent subsequent principal component analysis to reduce the dimensionality of the large proteomics dataset. A 3-component principal component analysis accounted for 45.7% of the variation in the dataset and had 88.9% specificity for the diagnosis of FRI. STRING protein-protein interaction network analysis of these 3 PCs revealed activation of the complement and coagulation cascades through the Reactome pathway database (false discovery rates <0.05). CONCLUSIONS Proteomic analyses of plasma from patients with FRI demonstrate systemic activation of the complement and coagulation cascades. Further investigation along these lines may help to better understand the systemic response to FRI and improve diagnostic strategies using proteomics. LEVEL OF EVIDENCE Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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Affiliation(s)
- Kevin Becker
- Department of Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, IN
| | - Ishani Sharma
- Department of Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, IN
| | - James E Slaven
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN
| | - Amber L Mosley
- Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Center for Proteome Analysis, Indiana University School of Medicine, Indianapolis, IN; and
| | - Emma H Doud
- Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Center for Proteome Analysis, Indiana University School of Medicine, Indianapolis, IN; and
| | - Sarah Malek
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN
| | - Roman M Natoli
- Department of Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, IN
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28
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Watson KT, Keller J, Spiro CM, Satz IB, Goncalves SV, Pankow H, Kosti I, Lehallier B, Sequeira A, Bunney WE, Rasgon NL, Schatzberg AF. Proteomic profiles of cytokines and chemokines in moderate to severe depression: Implications for comorbidities and biomarker discovery. Brain Behav Immun Health 2024; 36:100731. [PMID: 38435722 PMCID: PMC10906146 DOI: 10.1016/j.bbih.2024.100731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 03/05/2024] Open
Abstract
Objective This study assessed the proteomic profiles of cytokines and chemokines in individuals with moderate to severe depression, with or without comorbid medical disorders, compared to healthy controls. Two proteomic multiplex platforms were employed for this purpose. Metods An immunofluorescent multiplex platform and an aptamer-based method were used to evaluate 32 protein analytes from 153 individuals with moderate to severe major depressive disorder (MDD) and healthy controls (HCs). The study focused on determining the level of agreement between the two platforms and evaluating the ability of individual analytes and principal components (PCs) to differentiate between the MDD and HC groups. Additionally, the study investigated the relationship between PCs consisting of chemokines and cytokines and comorbid inflammatory and cardiometabolic diseases. Findings Analysis revealed a small or moderate correlation between 47% of the analytes measured by the two platforms. Two proteomic profiles were identified that differentiated individuals with moderate to severe MDD from HCs. High eotaxin, age, BMI, IP-10, or IL-10 characterized profile 1. This profile was associated with several cardiometabolic risk factors, including hypertension, hyperlipidemia, and type 2 diabetes. Profile 2 is characterized by higher age, BMI, interleukins, and a strong negative loading for eotaxin. This profile was associated with inflammation but not cardiometabolic risk factors. Conclusion This study provides further evidence that proteomic profiles can be used to identify potential biomarkers and pathways associated with MDD and comorbidities. Our findings suggest that MDD is associated with distinct profiles of proteins that are also associated with cardiometabolic risk factors, inflammation, and obesity. In particular, the chemokines eotaxin and IP-10 appear to play a role in the relationship between MDD and cardiometabolic risk factors. These findings suggest that a focus on the interplay between MDD and comorbidities may be useful in identifying potential targets for intervention and improving overall health outcomes.
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Affiliation(s)
- Kathleen T. Watson
- Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA
| | - Jennifer Keller
- Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA
| | - Caleb M. Spiro
- Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA
| | - Isaac B. Satz
- Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA
| | - Samantha V. Goncalves
- Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA
| | - Heather Pankow
- Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA
| | | | | | - Adolfo Sequeira
- Department of Psychiatry & Human Behavior, University of California, Irvine, CA, USA
- School of Medicine, Irvine, CA, USA
| | - William E. Bunney
- Department of Psychiatry & Human Behavior, University of California, Irvine, CA, USA
- School of Medicine, Irvine, CA, USA
| | - Natalie L. Rasgon
- Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA
| | - Alan F. Schatzberg
- Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA
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29
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Lin L, Yu H, Xue Y, Wang L, Zhu P. Proteome-wide mendelian randomization investigates potential associations in heart failure and its etiology: emphasis on PCSK9. BMC Med Genomics 2024; 17:59. [PMID: 38383373 PMCID: PMC10882912 DOI: 10.1186/s12920-024-01826-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Heart failure (HF) is a prevalent clinical syndrome with diverse etiologies. It is crucial to identify novel therapeutic targets based on underlying causes. Here, we aimed to use proteome-wide Mendelian randomization (MR) analyses to identify the associations between genetically predicted elevated levels of circulating proteins and distinct HF outcomes, along with specific HF etiologies. METHODS Protein quantitative trait loci (pQTL) data for circulating proteins were sourced from the Atherosclerosis Risk in Communities (ARIC) study, encompassing 7,213 individuals and profiling 4,657 circulating proteins. Genetic associations for outcomes were obtained from the HERMES Consortium and the FinnGen Consortium. Colocalization analysis was employed to assess the impact of linkage disequilibrium on discovered relationships. For replication, two-sample MR was conducted utilizing independent pQTL data from the deCODE study. Multivariable MR (MVMR) and two-step MR were further conducted to investigate potential mediators. RESULTS Two proteins (PCSK9 and AIDA) exhibited associations with HF in patients with coronary heart disease (CHD), and four proteins (PCSK9, SWAP70, NCF1, and RELT) were related with HF in patients receiving antihypertensive medication. Among these associations, strong evidence from subsequent analyses supported the positive relationship between genetically predicted PCSK9 levels and the risk of HF in the context of CHD. Notably, MVMR analysis revealed that CHD and LDL-C did not exert a complete mediating effect in this relationship. Moreover, two-step MR results yielded valuable insights into the potential mediating proportions of CHD or LDL-C in this relationship. CONCLUSIONS Our findings provide robust evidence supporting the association between PCSK9 and concomitant HF and CHD. This association is partly elucidated by the influence of CHD or LDL-C, underscoring the imperative for additional validation of this connection and a thorough exploration of the mechanisms through which PCSK9 directly impacts ischemic HF.
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Affiliation(s)
- Lichao Lin
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, People's Republic of China
| | - Huizhen Yu
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, People's Republic of China
- Department of Geriatric Medicine, Fujian Provincial Hospital, Fuzhou, People's Republic of China
- Fujian Provincial Key Laboratory of Geriatrics, Fuzhou, People's Republic of China
- Fujian Provincial Center for Geriatrics, Fuzhou, People's Republic of China
- Department of Cardiology in South Branch, Fujian Provincial Hospital, Fuzhou, People's Republic of China
| | - Yan Xue
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, People's Republic of China
| | - Liman Wang
- Department of Pharmacy in South Branch, Fujian Provincial Hospital, Fuzhou, People's Republic of China
| | - Pengli Zhu
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, People's Republic of China.
- Department of Geriatric Medicine, Fujian Provincial Hospital, Fuzhou, People's Republic of China.
- Fujian Provincial Key Laboratory of Geriatrics, Fuzhou, People's Republic of China.
- Fujian Provincial Center for Geriatrics, Fuzhou, People's Republic of China.
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30
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Shah R, Zhong J, Massier L, Tanriverdi K, Hwang SJ, Haessler J, Nayor M, Zhao S, Perry AS, Wilkins JT, Shadyab AH, Manson JE, Martin L, Levy D, Kooperberg C, Freedman JE, Rydén M, Murthy VL. Targeted Proteomics Reveals Functional Targets for Early Diabetes Susceptibility in Young Adults. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004192. [PMID: 38323454 PMCID: PMC10940209 DOI: 10.1161/circgen.123.004192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 11/05/2023] [Indexed: 02/08/2024]
Abstract
BACKGROUND The circulating proteome may encode early pathways of diabetes susceptibility in young adults for surveillance and intervention. Here, we define proteomic correlates of tissue phenotypes and diabetes in young adults. METHODS We used penalized models and principal components analysis to generate parsimonious proteomic signatures of diabetes susceptibility based on phenotypes and on diabetes diagnosis across 184 proteins in >2000 young adults in the CARDIA (Coronary Artery Risk Development in Young Adults study; mean age, 32 years; 44% women; 43% Black; mean body mass index, 25.6±4.9 kg/m2), with validation against diabetes in >1800 individuals in the FHS (Framingham Heart Study) and WHI (Women's Health Initiative). RESULTS In 184 proteins in >2000 young adults in CARDIA, we identified 2 proteotypes of diabetes susceptibility-a proinflammatory fat proteotype (visceral fat, liver fat, inflammatory biomarkers) and a muscularity proteotype (muscle mass), linked to diabetes in CARDIA and WHI/FHS. These proteotypes specified broad mechanisms of early diabetes pathogenesis, including transorgan communication, hepatic and skeletal muscle stress responses, vascular inflammation and hemostasis, fibrosis, and renal injury. Using human adipose tissue single cell/nuclear RNA-seq, we demonstrate expression at transcriptional level for implicated proteins across adipocytes and nonadipocyte cell types (eg, fibroadipogenic precursors, immune and vascular cells). Using functional assays in human adipose tissue, we demonstrate the association of expression of genes encoding these implicated proteins with adipose tissue metabolism, inflammation, and insulin resistance. CONCLUSIONS A multifaceted discovery effort uniting proteomics, underlying clinical susceptibility phenotypes, and tissue expression patterns may uncover potentially novel functional biomarkers of early diabetes susceptibility in young adults for future mechanistic evaluation.
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Affiliation(s)
- Ravi Shah
- Vanderbilt Translational & Clinical Cardiovascular Research Center, Vanderbilt Univ, Nashville, TN
| | - Jiawei Zhong
- Dept of Medicine (H7), Karolinska Institutet, Stockholm, Sweden
| | - Lucas Massier
- Dept of Medicine (H7), Karolinska Institutet, Stockholm, Sweden
| | - Kahraman Tanriverdi
- Vanderbilt Translational & Clinical Cardiovascular Research Center, Vanderbilt Univ, Nashville, TN
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | | | - Matthew Nayor
- Sections of Preventive Medicine & Epidemiology & Cardiovascular Medicine, Dept of Medicine, Dept of Epidemiology, Boston University Schools of Medicine & Public Health, Boston, MA & Framingham Heart Study, Framingham, MA
| | | | - Andrew S. Perry
- Vanderbilt Translational & Clinical Cardiovascular Research Center, Vanderbilt Univ, Nashville, TN
| | | | - Aladdin H. Shadyab
- Herbert Wertheim School of Public Health & Human Longevity Science, Univ of California, San Diego, La Jolla, CA
| | - JoAnn E. Manson
- Dept of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Lisa Martin
- George Washington Univ School of Medicine & Health Sciences
| | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | | | - Jane E. Freedman
- Vanderbilt Translational & Clinical Cardiovascular Research Center, Vanderbilt Univ, Nashville, TN
| | - Mikael Rydén
- Dept of Medicine (H7), Karolinska Institutet, Stockholm, Sweden
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Kuku KO, Oyetoro R, Hashemian M, Livinski AA, Shearer JJ, Joo J, Psaty BM, Levy D, Ganz P, Roger VL. Proteomics for heart failure risk stratification: a systematic review. BMC Med 2024; 22:34. [PMID: 38273315 PMCID: PMC10809595 DOI: 10.1186/s12916-024-03249-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Heart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality. METHODS We searched four databases and two clinical trial registries for articles published from 2012 to 2023. HF proteomics studies measuring high numbers of proteins using aptamer or antibody-based affinity platforms on human plasma or serum with outcomes of all-cause or cardiovascular death were included. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. A third reviewer resolved conflicts. We assessed the risk of bias using the Risk Of Bias In Non-randomized Studies-of Exposure tool. RESULTS Out of 5131 unique articles identified, nine articles were included in the review. The nine studies were observational; three used the aptamer platform, and six used the antibody platform. We found considerable heterogeneity across studies in measurement panels, HF definitions, ejection fraction categorization, follow-up duration, and outcome definitions, and a lack of risk estimates for most protein associations. Hence, we proceeded with a systematic review rather than a meta-analysis. In two comparable aptamer studies in patients with HF with reduced ejection fraction, 21 proteins were identified in common for the association with all-cause death. Among these, one protein, WAP four-disulfide core domain protein 2 was also reported in an antibody study on HFrEF and for the association with CV death. We proposed standardized reporting criteria to facilitate the interpretation of future studies. CONCLUSIONS In this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.
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Affiliation(s)
- Kayode O Kuku
- Heart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rebecca Oyetoro
- Heart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maryam Hashemian
- Heart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alicia A Livinski
- Office of Research Services, Office of the Director, National Institutes of Health Library, National Institutes of Health, Bethesda, MD, USA
| | - Joseph J Shearer
- Heart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jungnam Joo
- Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Daniel Levy
- Laboratory for Cardiovascular Epidemiology and Genomics, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter Ganz
- Zuckerberg San Francisco General Hospital, University of California, San Francisco, San Francisco, CA, USA
| | - Véronique L Roger
- Heart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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32
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Duijvelaar E, Gisby J, Peters JE, Bogaard HJ, Aman J. Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients. Nat Commun 2024; 15:744. [PMID: 38272877 PMCID: PMC10811341 DOI: 10.1038/s41467-024-44986-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between viral cytopathic effects and a dysregulated host immune response. In critically ill patients, imatinib treatment demonstrated potential for reducing invasive ventilation duration and mortality. Here, we perform longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predict critical illness development. Next to dysregulation of inflammation, critical illness is characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuates protein perturbations associated with inflammation and extracellular matrix turnover. These proteomic alterations are contextualised using external pulmonary RNA-sequencing data of deceased COVID-19 patients and imatinib-treated Syrian hamsters. Together, we show that alveolar capillary barrier disruption in critical COVID-19 is reflected in the plasma proteome, and is attenuated with imatinib treatment. This study comprises a secondary analysis of both clinical data and plasma samples derived from a clinical trial that was registered with the EU Clinical Trials Register (EudraCT 2020-001236-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001236-10/NL ) and Netherlands Trial Register (NL8491, https://www.trialregister.nl/trial/8491 ).
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Affiliation(s)
- Erik Duijvelaar
- Department of Pulmonary Medicine, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
| | - Jack Gisby
- Department of Immunology and Inflammation, Centre for Inflammatory Disease, Imperial College London, London, UK
| | - James E Peters
- Department of Immunology and Inflammation, Centre for Inflammatory Disease, Imperial College London, London, UK
| | - Harm Jan Bogaard
- Department of Pulmonary Medicine, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Jurjan Aman
- Department of Pulmonary Medicine, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
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Ben Yellin, Lahav C, Sela I, Yahalom G, Shoval SR, Elon Y, Fuller J, Harel M. Analytical validation of the PROphet test for treatment decision-making guidance in metastatic non-small cell lung cancer. J Pharm Biomed Anal 2024; 238:115803. [PMID: 37871417 DOI: 10.1016/j.jpba.2023.115803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/22/2023] [Accepted: 10/16/2023] [Indexed: 10/25/2023]
Abstract
The blood proteome, consisting of thousands of proteins engaged in various biological processes, acts as a valuable source of potential biomarkers for various medical applications. PROphet is a plasma proteomics-based test that serves as a decision-support tool for non-small cell lung cancer (NSCLC) patients, combining proteomic profiling using SomaScan technology and subsequent computational algorithm. PROphet was implemented as a laboratory developed test (LDT). Under the Clinical Laboratory Improvement Amendments (CLIA) and Commission on Office Laboratory Accreditation (COLA) regulations, prior to releasing patient test results, a clinical laboratory located in the United States employing an LDT must examine its performance characteristics with regard to analytical validity. This study describes the experimental and computational analytical validity of the PROphet test, as required by CLIA/COLA regulations. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9 % and 4.7 % for intra-plate and inter-plate examination, respectively, and the median accuracy rate between sites was 88 %. Computational precision exhibited a high accuracy rate, with 93 % of samples displaying complete concordance in results. A cross-platform comparison between SomaScan and other proteomics platforms yielded a median Spearman's rank correlation coefficient of 0.51, affirming the consistency and reliability of the SomaScan platform as used under the PROphet test. Our study presents a robust framework for evaluating the analytical validity of a platform that combines an experimental assay with subsequent computational algorithms. When applied to the PROphet test, strong analytical performance of the test was demonstrated.
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Affiliation(s)
- Ben Yellin
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel
| | - Coren Lahav
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel
| | - Itamar Sela
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel
| | - Galit Yahalom
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel
| | | | | | - James Fuller
- OncoHost Inc., 1110 SE Cary Parkway, Suite 205, Cary, NC 27518, USA
| | - Michal Harel
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel.
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Mengelkoch S, Gassen J, Lev-Ari S, Alley JC, Schüssler-Fiorenza Rose SM, Snyder MP, Slavich GM. Multi-omics in stress and health research: study designs that will drive the field forward. Stress 2024; 27:2321610. [PMID: 38425100 PMCID: PMC11216062 DOI: 10.1080/10253890.2024.2321610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals' stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.
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Affiliation(s)
- Summer Mengelkoch
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Jeffrey Gassen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Shahar Lev-Ari
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Health Promotion, Tel Aviv University, Tel Aviv, Israel
| | - Jenna C. Alley
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | | | | | - George M. Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
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35
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Szabo I, Badii M, Gaál IO, Szabo R, Sîrbe C, Humiță O, Joosten LAB, Crișan TO, Rednic S. Immune Profiling of Patients with Systemic Sclerosis through Targeted Proteomic Analysis. Int J Mol Sci 2023; 24:17601. [PMID: 38139427 PMCID: PMC10744051 DOI: 10.3390/ijms242417601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/11/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
High-throughput proteomic analysis could offer new insights into the pathogenesis of systemic sclerosis (SSc) and reveal non-invasive biomarkers for diagnosis and severity. This study aimed to assess the protein signature of patients with SSc compared to that of healthy volunteers, decipher various disease endotypes using circulating proteins, and determine the diagnostic performance of significantly expressed plasma analytes. We performed targeted proteomic profiling in a cohort of fifteen patients with SSc and eighteen controls using the Olink® (Olink Bioscience, Uppsala, Sweden)Target 96 Inflammation Panels. Seventeen upregulated proteins involved in angiogenesis, innate immunity, and co-stimulatory pathways discriminated between patients with SSc and healthy controls (HCs) and further classified them into two clusters, a low-inflammatory and a high-inflammatory endotype. Younger age, shorter disease duration, and lack of reflux esophagitis characterized patients in the low-inflammatory endotype. TNF, CXCL9, TNFRSF9, and CXCL10 positively correlated with disease progression, while the four-protein panel comprising TNF, CXCL9, CXCL10, and CX3CL1 showed high diagnostic performance. Collectively, this study identified a distinct inflammatory signature in patients with SSc that reflects a persistent T helper type 1 (Th 1) immune response irrespective of disease duration, while the multi-protein panel might improve early diagnosis in SSc.
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Affiliation(s)
- Iulia Szabo
- Department of Rheumatology, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (I.S.)
- Department of Rheumatology, County Emergency Hospital, 400347 Cluj-Napoca, Romania
| | - Medeea Badii
- Department of Medical Genetics, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Department of Internal Medicine, Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Ildikó O. Gaál
- Department of Medical Genetics, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Department of Internal Medicine, Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Robert Szabo
- 2nd Anesthesia Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Department of Anesthesia and Intensive Care, County Emergency Hospital, 400347 Cluj-Napoca, Romania
| | - Claudia Sîrbe
- 2nd Pediatric Discipline, Department of Mother and Child, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- 2nd Pediatric Clinic, Center of Expertise in Pediatric Liver Rare Disorders, Emergency Clinical Hospital for Children, 400177 Cluj-Napoca, Romania
| | - Oana Humiță
- Department of Rheumatology, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (I.S.)
| | - Leo A. B. Joosten
- Department of Medical Genetics, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Department of Internal Medicine, Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Tania O. Crișan
- Department of Medical Genetics, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Department of Internal Medicine, Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Simona Rednic
- Department of Rheumatology, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (I.S.)
- Department of Rheumatology, County Emergency Hospital, 400347 Cluj-Napoca, Romania
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36
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Oh HSH, Rutledge J, Nachun D, Pálovics R, Abiose O, Moran-Losada P, Channappa D, Urey DY, Kim K, Sung YJ, Wang L, Timsina J, Western D, Liu M, Kohlfeld P, Budde J, Wilson EN, Guen Y, Maurer TM, Haney M, Yang AC, He Z, Greicius MD, Andreasson KI, Sathyan S, Weiss EF, Milman S, Barzilai N, Cruchaga C, Wagner AD, Mormino E, Lehallier B, Henderson VW, Longo FM, Montgomery SB, Wyss-Coray T. Organ aging signatures in the plasma proteome track health and disease. Nature 2023; 624:164-172. [PMID: 38057571 PMCID: PMC10700136 DOI: 10.1038/s41586-023-06802-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 10/31/2023] [Indexed: 12/08/2023]
Abstract
Animal studies show aging varies between individuals as well as between organs within an individual1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.
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Affiliation(s)
- Hamilton Se-Hwee Oh
- Graduate Program in Stem Cell and Regenerative Medicine, Stanford University, Stanford, CA, USA
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Jarod Rutledge
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Graduate Program in Genetics, Stanford University, Stanford, CA, USA
| | - Daniel Nachun
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Róbert Pálovics
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Olamide Abiose
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Patricia Moran-Losada
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Divya Channappa
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Deniz Yagmur Urey
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, USA
| | - Kate Kim
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Dan Western
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Menghan Liu
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Pat Kohlfeld
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John Budde
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Edward N Wilson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Yann Guen
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Taylor M Maurer
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Haney
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Andrew C Yang
- Departments of Neurology and Anatomy, University of California San Francisco, San Francisco, CA, USA
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Bakar Aging Research Institute, University of California San Francisco, San Francisco, CA, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael D Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Katrin I Andreasson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Sanish Sathyan
- Departments of Medicine and Genetics, Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA
| | - Erica F Weiss
- Department of Neurology, Montefiore Medical Center, New York, NY, USA
| | - Sofiya Milman
- Departments of Medicine and Genetics, Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA
| | - Nir Barzilai
- Departments of Medicine and Genetics, Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Anthony D Wagner
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Elizabeth Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Benoit Lehallier
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Victor W Henderson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Frank M Longo
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Tony Wyss-Coray
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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Larsson SC, Höijer J, Sun J, Li X, Burgess S, Michaëlsson K. Genome-Wide Association and Two-Sample Mendelian Randomization Analyses of Plasma Ghrelin and Gastrointestinal Cancer Risk. Cancer Epidemiol Biomarkers Prev 2023; 32:1771-1776. [PMID: 37791980 PMCID: PMC10690139 DOI: 10.1158/1055-9965.epi-23-0757] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/23/2023] [Accepted: 10/02/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Observational studies have suggested that the gut hormone ghrelin is an early marker of future risk of developing gastrointestinal cancer. However, whether ghrelin is a causal risk factor remains unclear. We conducted a genome-wide association study (GWAS) of plasma ghrelin and used Mendelian randomization (MR) to investigate the possible causal association between ghrelin and gastrointestinal cancer risk. METHODS Genetic variants associated with plasma ghrelin were identified in a GWAS comprising 10,742 Swedish adults in the discovery (N = 6,259) and replication (N = 4,483) cohorts. The association between ghrelin and gastrointestinal cancer was examined through a two-sample MR analysis using the identified genetic variants as instruments and GWAS data from the UK Biobank, FinnGen, and a colorectal cancer consortium. RESULTS GWAS found associations between multiple genetic variants within ±200 kb of the GHRL gene and plasma ghrelin. A two-sample MR analysis revealed that genetically predicted higher plasma ghrelin levels were associated with a lower risk of gastrointestinal cancer in UK Biobank and in a meta-analysis of the UK Biobank and FinnGen studies. The combined OR per approximate doubling of genetically predicted plasma ghrelin was 0.91 (95% confidence interval, 0.85-0.99; P = 0.02). Colocalization analysis revealed limited evidence of shared causal variants for plasma ghrelin and gastrointestinal cancer at the GHRL locus (posterior probability H4 = 24.5%); however, this analysis was likely underpowered. CONCLUSIONS Our study provides evidence in support of a possible causal association between higher plasma ghrelin levels and a reduced risk of gastrointestinal cancer. IMPACT Elevated plasma ghrelin levels might reduce the risk of gastrointestinal cancer.
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Affiliation(s)
- Susanna C. Larsson
- Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Jonas Höijer
- Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Jing Sun
- Department of Big Data in Health Science, School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xue Li
- Department of Big Data in Health Science, School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Karl Michaëlsson
- Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
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Truby LK, Maamari D, Saha A, Farr M, Abdulrahim J, Billia F, Peltz M, Khush KK, Wang TJ. Towards Allograft Longevity: Leveraging Omics Technologies to Improve Heart Transplant Outcomes. Curr Heart Fail Rep 2023; 20:493-503. [PMID: 37966542 DOI: 10.1007/s11897-023-00631-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
PURPOSE OF REVIEW Heart transplantation (HT) remains the optimal therapy for patients living with end-stage heart disease. Despite recent improvements in peri-transplant management, the median survival after HT has remained relatively static, and complications of HT, including infection, rejection, and allograft dysfunction, continue to impact quality of life and long-term survival. RECENT FINDINGS Omics technologies are becoming increasingly accessible and can identify novel biomarkers for, and reveal the underlying biology of, several disease states. While some technologies, such as gene expression profiling (GEP) and donor-derived cell-free DNA (dd-cfDNA), are routinely used in the clinical care of HT recipients, a number of emerging platforms, including pharmacogenomics, proteomics, and metabolomics, hold great potential for identifying biomarkers to aid in the diagnosis and management of post-transplant complications. Omics-based assays can improve patient and allograft longevity by facilitating a personalized and precision approach to post-HT care. The following article is a contemporary review of the current and future opportunities to leverage omics technologies, including genomics, transcriptomics, proteomics, and metabolomics in the field of HT.
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Affiliation(s)
- Lauren K Truby
- University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA.
| | - Dimitri Maamari
- University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amit Saha
- University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA
| | - Maryjane Farr
- University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA
| | | | | | - Matthias Peltz
- University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA
| | - Kiran K Khush
- Stanford University Medical Center, Palo Alto, CA, USA
| | - Thomas J Wang
- University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA
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Lanktree MB, Perrot N, Smyth A, Chong M, Narula S, Shanmuganathan M, Kroezen Z, Britz-Mckibbin P, Berger M, Krepinsky JC, Pigeyre M, Yusuf S, Paré G. A novel multi-ancestry proteome-wide Mendelian randomization study implicates extracellular proteins, tubular cells, and fibroblasts in estimated glomerular filtration rate regulation. Kidney Int 2023; 104:1170-1184. [PMID: 37774922 DOI: 10.1016/j.kint.2023.08.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 08/15/2023] [Accepted: 08/25/2023] [Indexed: 10/01/2023]
Abstract
Estimated glomerular filtration rate (eGFR) impacts the concentration of plasma biomarkers confounding biomarker association studies of eGFR with reverse causation. To identify biomarkers causally associated with eGFR, we performed a proteome-wide Mendelian randomization study. Genetic variants nearby biomarker coding genes were tested for association with plasma concentration of 1,161 biomarkers in a multi-ancestry sample of 12,066 participants from the Prospective Urban and Rural Epidemiological (PURE) study. Using two-sample Mendelian randomization, individual variants' effects on biomarker concentration were correlated with their effects on eGFR and kidney traits from published genome-wide association studies (GWAS). Genetically altered concentrations of 22 biomarkers were associated with eGFR above a Bonferroni-corrected significance threshold. Five biomarkers were previously identified by GWAS (UMOD, FGF5, LGALS7, NINJ1, COL18A1). Nine biomarkers were within 1 Mb of the lead GWAS variant but the gene for the biomarker was unidentified as the candidate for the GWAS signal (INHBC, TNFRSF11A, TCN2, PXN1, PRTN3, PSMD9, TFPI, ITGB6, CA3). Single-cell transcriptomic data indicated the 22 biomarkers are expressed in kidney tubules, collecting duct, fibroblasts, and immune cells. Pathway analysis showed significant enrichment of identified biomarkers in the extracellular kidney parenchyma. Thus, using genetic regulators of biomarker concentration via proteome-wide Mendelian randomization, we identified 22 biomarkers that appear to causally impact eGFR in either a beneficial or adverse manner. The current study provides rationale for novel therapeutic targets for eGFR and emphasized a role for extracellular proteins produced by tubular cells and fibroblasts for impacting eGFR.
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Affiliation(s)
- Matthew B Lanktree
- Population Health Research Institute, Hamilton, Ontario, Canada; Division of Nephrology, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
| | - Nicolas Perrot
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Andrew Smyth
- Population Health Research Institute, Hamilton, Ontario, Canada; HRB Clinical Research Facility Galway, University of Galway, Galway, Ireland
| | - Michael Chong
- Population Health Research Institute, Hamilton, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sukrit Narula
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Meera Shanmuganathan
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Zachary Kroezen
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Philip Britz-Mckibbin
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada; Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Mario Berger
- Bayer AG, Pharmaceuticals Research & Development, Pharma Research Center, Wuppertal, Germany
| | - Joan C Krepinsky
- Division of Nephrology, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Marie Pigeyre
- Population Health Research Institute, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Salim Yusuf
- Population Health Research Institute, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Population Health Research Institute, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
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Schlosser P. Advancing proteomics in nephrology: unraveling causal pathways and therapeutic targets. Kidney Int 2023; 104:1059-1061. [PMID: 37981427 DOI: 10.1016/j.kint.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 10/03/2023] [Indexed: 11/21/2023]
Abstract
Proteomics has illuminated disease pathophysiology, unearthed novel biomarkers, and bolstered risk assessment strategies. In nephrology, observational analyses unveil biomarkers associated with adverse outcomes, whereas genetics offer insights into causal pathways. Mendelian randomization offers the potential to link the two, uncovering causal relationships between biomarkers and kidney function. Lanktree et al. demonstrate Mendelian randomization's utility in identifying additional proteins affecting kidney function and kidney disease progression.
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Affiliation(s)
- Pascal Schlosser
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
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Goudswaard LJ, Smith ML, Hughes DA, Taylor R, Lean M, Sattar N, Welsh P, McConnachie A, Blazeby JM, Rogers CA, Suhre K, Zaghlool SB, Hers I, Timpson NJ, Corbin LJ. Using trials of caloric restriction and bariatric surgery to explore the effects of body mass index on the circulating proteome. Sci Rep 2023; 13:21077. [PMID: 38030643 PMCID: PMC10686974 DOI: 10.1038/s41598-023-47030-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/08/2023] [Indexed: 12/01/2023] Open
Abstract
Thousands of proteins circulate in the bloodstream; identifying those which associate with weight and intervention-induced weight loss may help explain mechanisms of diseases associated with adiposity. We aimed to identify consistent protein signatures of weight loss across independent studies capturing changes in body mass index (BMI). We analysed proteomic data from studies implementing caloric restriction (Diabetes Remission Clinical trial) and bariatric surgery (By-Band-Sleeve), using SomaLogic and Olink Explore1536 technologies, respectively. Linear mixed models were used to estimate the effect of the interventions on circulating proteins. Twenty-three proteins were altered in a consistent direction after both bariatric surgery and caloric restriction, suggesting that these proteins are modulated by weight change, independent of intervention type. We also integrated Mendelian randomisation (MR) estimates of the effect of BMI on proteins measured by SomaLogic from a UK blood donor cohort as a third line of causal evidence. These MR estimates provided further corroborative evidence for a role of BMI in regulating the levels of six proteins including alcohol dehydrogenase-4, nogo receptor and interleukin-1 receptor antagonist protein. These results indicate the importance of triangulation in interrogating causal relationships; further study into the role of proteins modulated by weight in disease is now warranted.
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Affiliation(s)
- Lucy J Goudswaard
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- MRC Integrative Epidemiology Unit, Bristol, UK.
- Physiology, Pharmacology & Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK.
| | - Madeleine L Smith
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - David A Hughes
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - Roy Taylor
- Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, NE4 5PL, UK
| | - Michael Lean
- Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G31 2ER, UK
| | - Naveed Sattar
- School of Cardiovascular and Medical Science, University of Glasgow, Glasgow, G12 8TA, UK
| | - Paul Welsh
- School of Cardiovascular and Medical Science, University of Glasgow, Glasgow, G12 8TA, UK
| | - Alex McConnachie
- Robertson Centre for Biostatistics, School of Health and Wellbeing, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Jane M Blazeby
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Chris A Rogers
- Bristol Medical School, Bristol Trials Centre, University of Bristol, Bristol, BS8 1NU, UK
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Shaza B Zaghlool
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Ingeborg Hers
- Physiology, Pharmacology & Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK
| | - Nicholas J Timpson
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - Laura J Corbin
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
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Mälarstig A, Grassmann F, Dahl L, Dimitriou M, McLeod D, Gabrielson M, Smith-Byrne K, Thomas CE, Huang TH, Forsberg SKG, Eriksson P, Ulfstedt M, Johansson M, Sokolov AV, Schiöth HB, Hall P, Schwenk JM, Czene K, Hedman ÅK. Evaluation of circulating plasma proteins in breast cancer using Mendelian randomisation. Nat Commun 2023; 14:7680. [PMID: 37996402 PMCID: PMC10667261 DOI: 10.1038/s41467-023-43485-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.
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Affiliation(s)
- Anders Mälarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- Pfizer Worldwide Research Development and Medical, Stockholm, Sweden.
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute of Clinical Research and Systems Medicine, Health and Medical University, Potsdam, Germany
| | - Leo Dahl
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Marios Dimitriou
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pfizer Worldwide Research Development and Medical, Stockholm, Sweden
| | - Dianna McLeod
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cecilia E Thomas
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Tzu-Hsuan Huang
- Cancer Immunology Discovery, Pfizer Inc., San Diego, California, USA
| | | | | | | | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Aleksandr V Sokolov
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Åsa K Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pfizer Worldwide Research Development and Medical, Stockholm, Sweden
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Mazidi M, Wright N, Yao P, Kartsonaki C, Millwood IY, Fry H, Said S, Pozarickij A, Pei P, Chen Y, Avery D, Du H, Schmidt DV, Yang L, Lv J, Yu C, Chen J, Hill M, Holmes MV, Howson JMM, Peto R, Collins R, Bennett DA, Walters RG, Li L, Clarke R, Chen Z. Plasma Proteomics to Identify Drug Targets for Ischemic Heart Disease. J Am Coll Cardiol 2023; 82:1906-1920. [PMID: 37940228 PMCID: PMC10641761 DOI: 10.1016/j.jacc.2023.09.804] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/11/2023] [Accepted: 09/05/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Integrated analyses of plasma proteomic and genetic markers in prospective studies can clarify the causal relevance of proteins and discover novel targets for ischemic heart disease (IHD) and other diseases. OBJECTIVES The purpose of this study was to examine associations of proteomics and genetics data with IHD in population studies to discover novel preventive treatments. METHODS We conducted a nested case-cohort study in the China Kadoorie Biobank (CKB) involving 1,971 incident IHD cases and 2,001 subcohort participants who were genotyped and free of prior cardiovascular disease. We measured 1,463 proteins in the stored baseline samples using the OLINK EXPLORE panel. Cox regression yielded adjusted HRs for IHD associated with individual proteins after accounting for multiple testing. Moreover, cis-protein quantitative loci (pQTLs) identified for proteins in genome-wide association studies of CKB and of UK Biobank were used as instrumental variables in separate 2-sample Mendelian randomization (MR) studies involving global CARDIOGRAM+C4D consortium (210,842 IHD cases and 1,378,170 controls). RESULTS Overall 361 proteins were significantly associated at false discovery rate <0.05 with risk of IHD (349 positively, 12 inversely) in CKB, including N-terminal prohormone of brain natriuretic peptide and proprotein convertase subtilisin/kexin type 9. Of these 361 proteins, 212 had cis-pQTLs in CKB, and MR analyses of 198 variants in CARDIOGRAM+C4D identified 13 proteins that showed potentially causal associations with IHD. Independent MR analyses of 307 cis-pQTLs identified in Europeans replicated associations for 4 proteins (FURIN, proteinase-activated receptor-1, Asialoglycoprotein receptor-1, and matrix metalloproteinase-3). Further downstream analyses showed that FURIN, which is highly expressed in endothelial cells, is a potential novel target and matrix metalloproteinase-3 a potential repurposing target for IHD. CONCLUSIONS Integrated analyses of proteomic and genetic data in Chinese and European adults provided causal support for FURIN and multiple other proteins as potential novel drug targets for treatment of IHD.
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Affiliation(s)
- Mohsen Mazidi
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Neil Wright
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Pang Yao
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Christiana Kartsonaki
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Iona Y Millwood
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Hannah Fry
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Saredo Said
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Alfred Pozarickij
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Huaidong Du
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Dan Valle Schmidt
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Junshi Chen
- China National Center for Food Risk Assessment, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Michael V Holmes
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Richard Peto
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Rory Collins
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Derrick A Bennett
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robin G Walters
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Liming Li
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
| | - Zhengming Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
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Perry AS, Zhao S, Gajjar P, Murthy VL, Lehallier B, Miller P, Nair S, Neill C, Carr JJ, Fearon W, Kapadia S, Kumbhani D, Gillam L, Lindenfeld J, Farrell L, Marron MM, Tian Q, Newman AB, Murabito J, Gerszten RE, Nayor M, Elmariah S, Lindman BR, Shah R. Proteomic architecture of frailty across the spectrum of cardiovascular disease. Aging Cell 2023; 22:e13978. [PMID: 37731195 PMCID: PMC10652351 DOI: 10.1111/acel.13978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 09/22/2023] Open
Abstract
While frailty is a prominent risk factor in an aging population, the underlying biology of frailty is incompletely described. Here, we integrate 979 circulating proteins across a wide range of physiologies with 12 measures of frailty in a prospective discovery cohort of 809 individuals with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation. Our aim was to characterize the proteomic architecture of frailty in a highly susceptible population and study its relation to clinical outcome and systems-wide phenotypes to define potential novel, clinically relevant frailty biology. Proteomic signatures (specifically of physical function) were related to post-intervention outcome in AS, specifying pathways of innate immunity, cell growth/senescence, fibrosis/metabolism, and a host of proteins not widely described in human aging. In published cohorts, the "frailty proteome" displayed heterogeneous trajectories across age (20-100 years, age only explaining a small fraction of variance) and were associated with cardiac and non-cardiac phenotypes and outcomes across two broad validation cohorts (N > 35,000) over ≈2-3 decades. These findings suggest the importance of precision biomarkers of underlying multi-organ health status in age-related morbidity and frailty.
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Affiliation(s)
- Andrew S. Perry
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Shilin Zhao
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Priya Gajjar
- Cardiovascular Medicine Section, Department of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | | | | | - Patricia Miller
- Department of Medicine, and Department of BiostatisticsBoston University School of MedicineBostonMassachusettsUSA
| | - Sangeeta Nair
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Colin Neill
- Department of Medicine, Division of Cardiovascular MedicineUniversity of Wisconsin Hospital and ClinicsMadisonWisconsinUSA
| | - J. Jeffrey Carr
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - William Fearon
- Department of Medicine, Division of CardiologyStanford Medical CenterPalo AltoCaliforniaUSA
| | - Samir Kapadia
- Department of Medicine, Division of CardiologyCleveland Clinic FoundationClevelandOhioUSA
| | - Dharam Kumbhani
- Department of Medicine, Division of CardiologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Linda Gillam
- Department of Cardiovascular MedicineMorristown Medical CenterMorristownNew JerseyUSA
| | - JoAnn Lindenfeld
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Laurie Farrell
- Broad Institute of Harvard and MITCambridgeMassachusettsUSA
| | - Megan M. Marron
- Department of Epidemiology, Graduate School of Public HealthUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Qu Tian
- National Institute on Aging, National Institutes of HealthBaltimoreMarylandUSA
| | - Anne B. Newman
- Department of Epidemiology, Graduate School of Public HealthUniversity of PittsburghPittsburghPennsylvaniaUSA
- Departments of Medicine and Clinical and Translational ScienceUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Joanne Murabito
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | - Robert E. Gerszten
- Broad Institute of Harvard and MITCambridgeMassachusettsUSA
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical SchoolBostonMassachusettsUSA
| | - Matthew Nayor
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | - Sammy Elmariah
- Department of Medicine, Division of CardiologyThe University of CaliforniaSan FranciscoCaliforniaUSA
| | - Brian R. Lindman
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Ravi Shah
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
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Cai Y, Franceschini N, Surapaneni A, Garrett ME, Tahir UA, Hsu L, Telen MJ, Yu B, Tang H, Li Y, Liu S, Gerszten RE, Coresh J, Manson JE, Wojcik GL, Kooperberg C, Auer PL, Foster MW, Grams ME, Ashley-Koch AE, Raffield LM, Reiner AP. Differences in the Circulating Proteome in Individuals with versus without Sickle Cell Trait. Clin J Am Soc Nephrol 2023; 18:1416-1425. [PMID: 37533140 PMCID: PMC10637465 DOI: 10.2215/cjn.0000000000000257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 07/26/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND Sickle cell trait affects approximately 8% of Black individuals in the United States, along with many other individuals with ancestry from malaria-endemic regions worldwide. While traditionally considered a benign condition, recent evidence suggests that sickle cell trait is associated with lower eGFR and higher risk of kidney diseases, including kidney failure. The mechanisms underlying these associations remain poorly understood. We used proteomic profiling to gain insight into the pathobiology of sickle cell trait. METHODS We measured proteomics ( N =1285 proteins assayed by Olink Explore) using baseline plasma samples from 592 Black participants with sickle cell trait and 1:1 age-matched Black participants without sickle cell trait from the prospective Women's Health Initiative cohort. Age-adjusted linear regression was used to assess the association between protein levels and sickle cell trait. RESULTS In age-adjusted models, 35 proteins were significantly associated with sickle cell trait after correction for multiple testing. Several of the sickle cell trait-protein associations were replicated in Black participants from two independent cohorts (Atherosclerosis Risk in Communities study and Jackson Heart Study) assayed using an orthogonal aptamer-based proteomic platform (SomaScan). Many of the validated sickle cell trait-associated proteins are known biomarkers of kidney function or injury ( e.g. , hepatitis A virus cellular receptor 1 [HAVCR1]/kidney injury molecule-1 [KIM-1], uromodulin [UMOD], ephrins), related to red cell physiology or hemolysis (erythropoietin [EPO], heme oxygenase 1 [HMOX1], and α -hemoglobin stabilizing protein) and/or inflammation (fractalkine, C-C motif chemokine ligand 2/monocyte chemoattractant protein-1 [MCP-1], and urokinase plasminogen activator surface receptor [PLAUR]). A protein risk score constructed from the top sickle cell trait-associated biomarkers was associated with incident kidney failure among those with sickle cell trait during Women's Health Initiative follow-up (odds ratio, 1.32; 95% confidence interval, 1.10 to 1.58). CONCLUSIONS We identified and replicated the association of sickle cell trait with a number of plasma proteins related to hemolysis, kidney injury, and inflammation.
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Affiliation(s)
- Yanwei Cai
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | - Aditya Surapaneni
- Department of Epidemiology and Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Melanie E. Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, North Carolina
| | - Usman A. Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Marilyn J. Telen
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, North Carolina
- Division of Hematology, Department of Medicine, Duke University Medical Center, Durham, North Carolina
| | - Bing Yu
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
| | - Simin Liu
- Center for Global Cardiometabolic Health, Departments of Epidemiology, Medicine, and Surgery, Brown University, Providence, Rhode Island
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Josef Coresh
- Department of Epidemiology and Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - JoAnn E. Manson
- Brigham and Women's Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Genevieve L. Wojcik
- Department of Epidemiology and Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Charles Kooperberg
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | - Paul L. Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Matthew W. Foster
- Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Duke University Medical Center, Durham, North Carolina
| | - Morgan E. Grams
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, New York
| | - Allison E. Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, North Carolina
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
| | - Alex P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington
- Department of Epidemiology, University of Washington, Seattle, Washington
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Abe K, Beer JC, Nguyen T, Ariyapala IS, Holmes TH, Feng W, Zhang B, Kuo D, Luo Y, Ma XJ, Maecker HT. Cross-platform comparison of highly-sensitive immunoassays for inflammatory markers in a COVID-19 cohort 1. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563866. [PMID: 37961126 PMCID: PMC10634816 DOI: 10.1101/2023.10.24.563866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing antibody pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on serum samples from the NIH IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. Firstly, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Secondly, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Thirdly, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.
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47
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Jin L, Wang F, Wang X, Harvey BP, Bi Y, Hu C, Cui B, Darcy AT, Maull JW, Phillips BR, Kim Y, Jenkins GJ, Sornasse TR, Tian Y. Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow. Proteomes 2023; 11:32. [PMID: 37873874 PMCID: PMC10594463 DOI: 10.3390/proteomes11040032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/28/2023] [Accepted: 10/02/2023] [Indexed: 10/25/2023] Open
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.
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Affiliation(s)
- Liang Jin
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Fei Wang
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Xue Wang
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Bohdan P. Harvey
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Yingtao Bi
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Chenqi Hu
- DMPK, Takeda Development Center Americas Inc., Cambridge, MA 02142, USA; (C.H.)
| | - Baoliang Cui
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Anhdao T. Darcy
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - John W. Maull
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Ben R. Phillips
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Youngjae Kim
- DMPK, Takeda Development Center Americas Inc., Cambridge, MA 02142, USA; (C.H.)
| | - Gary J. Jenkins
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Thierry R. Sornasse
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Yu Tian
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
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Virdee SS, Bashir NZ, Krstic M, Camilleri J, Grant MM, Cooper PR, Tomson PL. Periradicular tissue fluid-derived biomarkers for apical periodontitis: An in vitro methodological and in vivo cross-sectional study. Int Endod J 2023; 56:1222-1240. [PMID: 37464545 DOI: 10.1111/iej.13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/04/2023] [Accepted: 07/14/2023] [Indexed: 07/20/2023]
Abstract
BACKGROUND Periradicular tissue fluid (PTF) offers a source of diagnostic, prognostic and predictive biomarkers for endodontic disease. AIMS (1) To optimize basic parameters for PTF paper point sampling in vitro for subsequent in vivo application. (2) To compare proteomes of PTF from teeth with normal apical tissues (NAT) and asymptomatic apical periodontitis (AAP) using high-throughput panels. METHODOLOGY (1) To assess volume absorbance, paper points (n = 20) of multiple brands, sizes and sampling durations were inserted into PBS/1%BSA at several depths. Wetted lengths (mm) were measured against standard curves to determine volume absorbance (μL). To assess analyte recovery, paper points (n = 6) loaded with 2 μL recombinant IL-1β (15.6 ng/mL) were eluted into 250 μL: (i) PBS; (ii) PBS/1% BSA; (iii) PBS/0.1% Tween20; (iv) PBS/0.25 M NaCl. These then underwent: (i) vortexing; (ii) vortexing/centrifugation; (iii) centrifugation; (iv) incubation/vortexing/centrifugation. Sandwich-ELISAs determined analyte recovery (%) against positive controls. (2) Using optimized protocols, PTF was retrieved from permanent teeth with NAT or AAP after accessing root canals. Samples, normalized to total fluid volume (TFV), were analysed to determine proteomic profiles (pg/TFV) of NAT and AAP via O-link Target-48 panel. Correlations between AAP and diagnostic accuracy were explored using principal-component analysis (PCA) and area under receive-operating-characteristic curves (AUC [95% CI]), respectively. Statistical comparisons were made using Mann-Whitney U, anova and post hoc Bonferonni tests (α < .01). RESULTS (1) UnoDent's 'Classic' points facilitated maximum volume absorbance (p < .05), with no significant differences after 60 s (1.6 μL [1.30-1.73]), 1 mm depth and up to 40/0.02 (2.2 μL [1.98-2.20]). For elution, vortexing (89.3%) and PBS/1% BSA (86.9%) yielded the largest IL-1β recovery (p < .05). (2) 41 (NAT: 13; AAP: 31) PTF samples proceeded to analysis. The panel detected 18 analytes (CCL-2, -3, -4; CSF-1; CXCL-8, -9; HGF; IL-1β, -6, -17A, -18; MMP-1, -12; OLR-1; OSM; TNFSF-10, -12; VEGF-A) in ≥75% of AAP samples at statistically higher concentrations (p < .01). CXCL-8, IL-1β, OLR-1, OSM and TNFSF-12 were strongly correlated to AAP. 'Excellent' diagnostic performance was observed for TNFSF-12 (AUC: 0.94 [95% CI: 0.86-1.00]) and the PCA-derived cluster (AUC: 0.96 [95% CI: 0.89-1.00]). CONCLUSIONS Optimized PTF sampling parameters were identified in this study. When applied clinically, high-throughput proteomic analyses revealed complex interconnected networks of potential biomarkers. TNFSF-12 discriminated periradicular disease from health the greatest; however, clustering analytes further improved diagnostic accuracy. Additional independent investigations are required to validate these findings.
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Affiliation(s)
- Satnam S Virdee
- Institute of Clinical Sciences, School of Dentistry & Birmingham Dental Hospital, University of Birmingham, Birmingham, UK
| | | | - Milan Krstic
- Institute of Clinical Sciences, School of Dentistry & Birmingham Dental Hospital, University of Birmingham, Birmingham, UK
| | - Josette Camilleri
- Institute of Clinical Sciences, School of Dentistry & Birmingham Dental Hospital, University of Birmingham, Birmingham, UK
| | - Melissa M Grant
- Institute of Clinical Sciences, School of Dentistry & Birmingham Dental Hospital, University of Birmingham, Birmingham, UK
| | - Paul R Cooper
- Department of Oral Sciences, Faculty of Dentistry, University of Otago, Dunedin, New Zealand
| | - Phillip L Tomson
- Institute of Clinical Sciences, School of Dentistry & Birmingham Dental Hospital, University of Birmingham, Birmingham, UK
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49
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Eldjarn GH, Ferkingstad E, Lund SH, Helgason H, Magnusson OT, Gunnarsdottir K, Olafsdottir TA, Halldorsson BV, Olason PI, Zink F, Gudjonsson SA, Sveinbjornsson G, Magnusson MI, Helgason A, Oddsson A, Halldorsson GH, Magnusson MK, Saevarsdottir S, Eiriksdottir T, Masson G, Stefansson H, Jonsdottir I, Holm H, Rafnar T, Melsted P, Saemundsdottir J, Norddahl GL, Thorleifsson G, Ulfarsson MO, Gudbjartsson DF, Thorsteinsdottir U, Sulem P, Stefansson K. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature 2023; 622:348-358. [PMID: 37794188 PMCID: PMC10567571 DOI: 10.1038/s41586-023-06563-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 08/22/2023] [Indexed: 10/06/2023]
Abstract
High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.
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Affiliation(s)
| | | | - Sigrun H Lund
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hannes Helgason
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Bjarni V Halldorsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | | | | | | | | | | | - Agnar Helgason
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Department of Anthropology, University of Iceland, Reykjavik, Iceland
| | | | | | - Magnus K Magnusson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Saedis Saevarsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Ingileif Jonsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | | | - Pall Melsted
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Kari Stefansson
- deCODE Genetics/Amgen, Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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50
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Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, Burren OS, Davitte J, Ferber KL, Gillies CE, Hedman ÅK, Hu S, Lin T, Mikkilineni R, Pendergrass RK, Pickering C, Prins B, Baird D, Chen CY, Ward LD, Deaton AM, Welsh S, Willis CM, Lehner N, Arnold M, Wörheide MA, Suhre K, Kastenmüller G, Sethi A, Cule M, Raj A, Burkitt-Gray L, Melamud E, Black MH, Fauman EB, Howson JMM, Kang HM, McCarthy MI, Nioi P, Petrovski S, Scott RA, Smith EN, Szalma S, Waterworth DM, Mitnaul LJ, Szustakowski JD, Gibson BW, Miller MR, Whelan CD. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 2023; 622:329-338. [PMID: 37794186 PMCID: PMC10567551 DOI: 10.1038/s41586-023-06592-6] [Citation(s) in RCA: 130] [Impact Index Per Article: 130.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/31/2023] [Indexed: 10/06/2023]
Abstract
The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.
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Affiliation(s)
- Benjamin B Sun
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
| | - Joshua Chiou
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Matthew Traylor
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Tom G Richardson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- Genomic Sciences, GlaxoSmithKline, Stevenage, UK
| | | | | | - Chloe Robins
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Liping Hou
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | | | - Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen, Cambridge, MA, USA
| | | | - Åsa K Hedman
- External Science and Innovation Target Sciences, Worldwide Research, Development and Medical, Pfizer, Stockholm, Sweden
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Tinchi Lin
- Analytics and Data Sciences, Biogen, Cambridge, MA, USA
| | - Rajesh Mikkilineni
- Data Science Institute, Takeda Development Center Americas, Cambridge, MA, USA
| | | | | | - Bram Prins
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Denis Baird
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Lucas D Ward
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Aimee M Deaton
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | | | - Carissa M Willis
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Nick Lehner
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - Anil Raj
- Calico Life Sciences, San Francisco, CA, USA
| | | | | | - Mary Helen Black
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Joanna M M Howson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Paul Nioi
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | | | - Erin N Smith
- Takeda Development Center Americas, San Diego, CA, USA
| | - Sándor Szalma
- Takeda Development Center Americas, San Diego, CA, USA
| | | | | | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Christopher D Whelan
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
- Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
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