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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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Ye Z, Zhang Y, Zhang Y, Yang S, He P, Liu M, Zhou C, Gan X, Huang Y, Xiang H, Hou FF, Qin X. Large-Scale Proteomics Improve Prediction of Chronic Kidney Disease in People With Diabetes. Diabetes Care 2024; 47:1757-1763. [PMID: 39042512 DOI: 10.2337/dc24-0290] [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] [Received: 02/10/2024] [Accepted: 07/02/2024] [Indexed: 07/25/2024]
Abstract
OBJECTIVE To develop and validate a protein risk score for predicting chronic kidney disease (CKD) in patients with diabetes and compare its predictive performance with a validated clinical risk model (CKD Prediction Consortium [CKD-PC]) and CKD polygenic risk score. RESEARCH DESIGN AND METHODS This cohort study included 2,094 patients with diabetes who had proteomics and genetic information and no history of CKD at baseline from the UK Biobank Pharma Proteomics Project. Based on nearly 3,000 plasma proteins, a CKD protein risk score including 11 proteins was constructed in the training set (including 1,047 participants; 117 CKD events). RESULTS The median follow-up duration was 12.1 years. In the test set (including 1,047 participants; 112 CKD events), the CKD protein risk score was positively associated with incident CKD (per SD increment; hazard ratio 1.78; 95% CI 1.44, 2.20). Compared with the basic model (age + sex + race, C-index, 0.627; 95% CI 0.578, 0.675), the CKD protein risk score (C-index increase 0.122; 95% CI 0.071, 0.177), and the CKD-PC risk factors (C-index increase 0.175; 95% CI 0.126, 0.217) significantly improved the prediction performance of incident CKD, but the CKD polygenic risk score (C-index increase 0.007; 95% CI -0.016, 0.025) had no significant improvement. Adding the CKD protein risk score into the CKD-PC risk factors had the largest C-index of 0.825 (C-index from 0.802 to 0.825; difference 0.023; 95% CI 0.006, 0.044), and significantly improved the continuous 10-year net reclassification (0.199; 95% CI 0.059, 0.299) and 10-year integrated discrimination index (0.041; 95% CI 0.007, 0.083). CONCLUSIONS Adding the CKD protein risk score to a validated clinical risk model significantly improved the discrimination and reclassification of CKD risk in patients with diabetes.
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Affiliation(s)
- Ziliang Ye
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Yuanyuan Zhang
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Yanjun Zhang
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Sisi Yang
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Panpan He
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Mengyi Liu
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Chun Zhou
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Xiaoqin Gan
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Yu Huang
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Hao Xiang
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Fan Fan Hou
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Xianhui Qin
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
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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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Gadd DA, Smith HM, Mullin D, Chybowska O, Hillary RF, Kimenai DM, Bernabeu E, Cheng Y, Fawns-Ritchie C, Campbell A, Page D, Taylor A, Corley J, Del C Valdés-Hernández M, Maniega SM, Bastin ME, Wardlaw JM, Walker RM, Evans KL, McIntosh AM, Hayward C, Russ TC, Harris SE, Welsh P, Sattar N, Cox SR, McCartney DL, Marioni RE. DNAm scores for serum GDF15 and NT-proBNP levels associate with a range of traits affecting the body and brain. Clin Epigenetics 2024; 16:124. [PMID: 39256775 PMCID: PMC11389299 DOI: 10.1186/s13148-024-01734-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 08/23/2024] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND Plasma growth differentiation factor 15 (GDF15) and N-terminal proB-type natriuretic peptide (NT-proBNP) are cardiovascular biomarkers that associate with a range of diseases. Epigenetic scores (EpiScores) for GDF15 and NT-proBNP may provide new routes for risk stratification. RESULTS In the Generation Scotland cohort (N ≥ 16,963), GDF15 levels were associated with incident dementia, ischaemic stroke and type 2 diabetes, whereas NT-proBNP levels were associated with incident ischaemic heart disease, ischaemic stroke and type 2 diabetes (all PFDR < 0.05). Bayesian epigenome-wide association studies (EWAS) identified 12 and 4 DNA methylation (DNAm) CpG sites associated (Posterior Inclusion Probability [PIP] > 95%) with levels of GDF15 and NT-proBNP, respectively. EpiScores for GDF15 and NT-proBNP were trained in a subset of the population. The GDF15 EpiScore replicated protein associations with incident dementia, type 2 diabetes and ischaemic stroke in the Generation Scotland test set (hazard ratios (HR) range 1.36-1.41, PFDR < 0.05). The EpiScore for NT-proBNP replicated the protein association with type 2 diabetes, but failed to replicate an association with ischaemic stroke. EpiScores explained comparable variance in protein levels across both the Generation Scotland test set and the external LBC1936 test cohort (R2 range of 5.7-12.2%). In LBC1936, both EpiScores were associated with indicators of poorer brain health. Neither EpiScore was associated with incident dementia in the LBC1936 population. CONCLUSIONS EpiScores for serum levels of GDF15 and Nt-proBNP associate with body and brain health traits. These EpiScores are provided as potential tools for disease risk stratification.
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Affiliation(s)
- Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Hannah M Smith
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Donncha Mullin
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Ola Chybowska
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Dorien M Kimenai
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Elena Bernabeu
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Chloe Fawns-Ritchie
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Division of Psychology, University of Dundee, Dundee, Scotland, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Danielle Page
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Adele Taylor
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Janie Corley
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Maria Del C Valdés-Hernández
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences, Edinburgh Imaging and UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Susana Muñoz Maniega
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences, Edinburgh Imaging and UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences, Edinburgh Imaging and UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences, Edinburgh Imaging and UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
- Neurovascular Imaging Research Core, UCLA, Los Angeles, CA, USA
| | - Rosie M Walker
- Centre for Clinical Brain Sciences, Edinburgh Imaging and UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
- School of Psychology, University of Exeter, Perry Road, Exeter, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Tom C Russ
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Sarah E Harris
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Paul Welsh
- School of Cardiovascular and Metabolic Health, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, 126 University Place, Glasgow, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, 126 University Place, Glasgow, UK
| | - Simon R Cox
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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6
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Argentieri MA, Xiao S, Bennett D, Winchester L, Nevado-Holgado AJ, Ghose U, Albukhari A, Yao P, Mazidi M, Lv J, Millwood I, Fry H, Rodosthenous RS, Partanen J, Zheng Z, Kurki M, Daly MJ, Palotie A, Adams CJ, Li L, Clarke R, Amin N, Chen Z, van Duijn CM. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Nat Med 2024; 30:2450-2460. [PMID: 39117878 PMCID: PMC11405266 DOI: 10.1038/s41591-024-03164-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 06/27/2024] [Indexed: 08/10/2024]
Abstract
Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity and mortality. Here we developed a proteomic age clock in the UK Biobank (n = 45,441) using a proteomic platform comprising 2,897 plasma proteins and explored its utility to predict major disease morbidity and mortality in diverse populations. We identified 204 proteins that accurately predict chronological age (Pearson r = 0.94) and found that proteomic aging was associated with the incidence of 18 major chronic diseases (including diseases of the heart, liver, kidney and lung, diabetes, neurodegeneration and cancer), as well as with multimorbidity and all-cause mortality risk. Proteomic aging was also associated with age-related measures of biological, physical and cognitive function, including telomere length, frailty index and reaction time. Proteins contributing most substantially to the proteomic age clock are involved in numerous biological functions, including extracellular matrix interactions, immune response and inflammation, hormone regulation and reproduction, neuronal structure and function and development and differentiation. In a validation study involving biobanks in China (n = 3,977) and Finland (n = 1,990), the proteomic age clock showed similar age prediction accuracy (Pearson r = 0.92 and r = 0.94, respectively) compared to its performance in the UK Biobank. Our results demonstrate that proteomic aging involves proteins spanning multiple functional categories and can be used to predict age-related functional status, multimorbidity and mortality risk across geographically and genetically diverse populations.
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Affiliation(s)
- M Austin Argentieri
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA, USA.
| | - Sihao Xiao
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
| | - Derrick Bennett
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Laura Winchester
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Alejo J Nevado-Holgado
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Upamanyu Ghose
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ashwag Albukhari
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Pang Yao
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mohsen Mazidi
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Iona Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hannah Fry
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Zhili Zheng
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mitja Kurki
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Cassandra J Adams
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Robert Clarke
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Najaf Amin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cornelia M van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia.
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7
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Tambets R, Kolde A, Kolberg P, Love MI, Alasoo K. Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization. HGG ADVANCES 2024; 5:100348. [PMID: 39210598 PMCID: PMC11416642 DOI: 10.1016/j.xhgg.2024.100348] [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: 02/02/2024] [Revised: 08/27/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024] Open
Abstract
Identifying causal genes underlying genome-wide association studies (GWASs) is a fundamental problem in human genetics. Although colocalization with gene expression quantitative trait loci (eQTLs) is often used to prioritize GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analyzed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalog datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalization methods (coloc.susie, coloc.abf, and CLPP) and five Mendelian randomization (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalization methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalization method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalizing QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance-weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritizing novel targets requires triangulation of evidence from multiple sources.
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Affiliation(s)
- Ralf Tambets
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Anastassia Kolde
- Institute of Genomics, University of Tartu, Tartu, Estonia; Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Peep Kolberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Michael I Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
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8
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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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9
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Lopez LN, Durbin-Johnson B, Vargas CR, Ruzinski J, Goodling A, Mehrotra R, Vaisar T, Rocke DM, Afkarian M. Comparative Analysis of Protein Quantification by the SomaScan Assay versus Orthogonal Methods in Urine from People with Diabetic Kidney Disease. J Proteome Res 2024; 23:2598-2607. [PMID: 38965919 DOI: 10.1021/acs.jproteome.4c00322] [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] [Indexed: 07/06/2024]
Abstract
To our knowledge, calibration curves or other validations for thousands of SomaScan aptamers are not publicly available. Moreover, the abundance of urine proteins obtained from these assays is not routinely validated with orthogonal methods (OMs). We report an in-depth comparison of SomaScan readout for 23 proteins in urine samples from patients with diabetic kidney disease (n = 118) vs OMs, including liquid chromatography-targeted mass spectrometry (LC-MS), ELISA, and nephelometry. Pearson correlation between urine abundance of the 23 proteins from SomaScan 3.2 vs OMs ranged from -0.58 to 0.86, with a median (interquartile ratio, [IQR]) of 0.49 (0.18, 0.53). In multivariable linear regression, the SomaScan readout for 6 of the 23 examined proteins (26%) was most strongly associated with the OM-derived abundance of the same (target) protein. For 3 of 23 (13%), the SomaScan and OM-derived abundance of each protein were significantly associated, but the SomaScan readout was more strongly associated with OM-derived abundance of one or more "off-target" proteins. For the remaining 14 proteins (61%), the SomaScan readouts were not significantly associated with the OM-derived abundance of the targeted proteins. In 6 of the latest group, the SomaScan readout was not associated with urine abundance of any of the 23 quantified proteins. To sum, over half of the SomaScan results could not be confirmed by independent orthogonal methods.
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Affiliation(s)
- Lauren N Lopez
- Division of Nephrology, Department of Medicine, University of California, Davis, California 95616, United States
| | - Blythe Durbin-Johnson
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, California 95616, United States
| | - Chenoa R Vargas
- Division of Nephrology, Department of Medicine, University of California, Davis, California 95616, United States
| | - John Ruzinski
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington 98195, United States
| | - Anne Goodling
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington 98195, United States
| | - Rajnish Mehrotra
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington 98195, United States
| | - Tomas Vaisar
- Diabetes Institute, Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, University of Washington, Seattle,Washington98195,United States
| | - David M Rocke
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, California 95616, United States
| | - Maryam Afkarian
- Division of Nephrology, Department of Medicine, University of California, Davis, California 95616, United States
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10
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Gadd DA, Hillary RF, Kuncheva Z, Mangelis T, Cheng Y, Dissanayake M, Admanit R, Gagnon J, Lin T, Ferber KL, Runz H, Foley CN, Marioni RE, Sun BB. Blood protein assessment of leading incident diseases and mortality in the UK Biobank. NATURE AGING 2024; 4:939-948. [PMID: 38987645 PMCID: PMC11257969 DOI: 10.1038/s43587-024-00655-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/22/2024] [Indexed: 07/12/2024]
Abstract
The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.
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Affiliation(s)
- Danni A Gadd
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Zhana Kuncheva
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Tasos Mangelis
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Manju Dissanayake
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Romi Admanit
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Jake Gagnon
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Tinchi Lin
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Christopher N Foley
- Optima Partners, Edinburgh, UK.
- Bayes Centre, University of Edinburgh, Edinburgh, UK.
| | - Riccardo E Marioni
- Optima Partners, Edinburgh, UK.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Benjamin B Sun
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
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11
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Plaza-Florido A, Olvera-Rojas M, Alcantara JMA, Radom-Aizik S, Ortega FB. Targeted proteomics involved in cardiovascular health and heart rate variability in children with overweight/obesity. Am J Hum Biol 2024:e24113. [PMID: 38864311 DOI: 10.1002/ajhb.24113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Children with overweight/obesity often exhibit alterations in their plasma protein profiles and reduced heart rate variability (HRV). Plasma proteomics is at the forefront of identifying biomarkers for various clinical conditions. We aimed to examine the association between plasma-targeted proteomics involved in cardiovascular health and resting vagal-related HRV parameters in children with overweight/obesity. METHODS Forty-four children with overweight/obesity (10.2 ± 1.1 years old; 52% boys) participated in the study. Olink's technology was used to quantify 92 proteins involved in cardiovascular health. HRV was measured using a heart rate monitor (Polar RS800CX). Four resting vagal-related HRV parameters were derived in time- and frequency-domain. RESULTS Eight proteins (KIM1, IgG Fc receptor II-b, IDUA, BOC, IL1RL2, TNFRSF11A, VSIG2, and TF) were associated with at least one out of the four vagal-related HRV parameters (β values ranging from -0.188 to 0.288; all p < .05), while KIM1, IDUA, and BOC associated with ≥ three vagal-related HRV parameters. Multiple hypothesis testing corrections did not reach statistical significance (false discovery rate [FDR >0.05]). CONCLUSION Plasma-targeted proteomics suggested novel biomarkers for resting vagal-related HRV parameters in children with overweight/obesity. Future studies using larger cohorts and longitudinal designs should confirm our findings and their potential clinical implications.
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Affiliation(s)
- Abel Plaza-Florido
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Pediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California at Irvine, Irvine, California, USA
| | - Marcos Olvera-Rojas
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Juan M A Alcantara
- Institute for Innovation & Sustainable Food Chain Development, Department of Health Sciences, Public University of Navarre, Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Shlomit Radom-Aizik
- Pediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California at Irvine, Irvine, California, USA
| | - Francisco B Ortega
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
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12
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Lindell RB, Sayed S, Campos JS, Knight M, Mauracher AA, Hay CA, Conrey PE, Fitzgerald JC, Yehya N, Famularo ST, Arroyo T, Tustin R, Fazelinia H, Behrens EM, Teachey DT, Freeman AF, Bergerson JRE, Holland SM, Leiding JW, Weiss SL, Hall MW, Zuppa AF, Taylor DM, Feng R, Wherry EJ, Meyer NJ, Henrickson SE. Dysregulated STAT3 signaling and T cell immunometabolic dysfunction define a targetable, high mortality subphenotype of critically ill children. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.11.24308709. [PMID: 38946991 PMCID: PMC11213094 DOI: 10.1101/2024.06.11.24308709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Sepsis is the leading cause of death of hospitalized children worldwide. Despite the established link between immune dysregulation and mortality in pediatric sepsis, it remains unclear which host immune factors contribute causally to adverse sepsis outcomes. Identifying modifiable pathobiology is an essential first step to successful translation of biologic insights into precision therapeutics. We designed a prospective, longitudinal cohort study of 88 critically ill pediatric patients with multiple organ dysfunction syndrome (MODS), including patients with and without sepsis, to define subphenotypes associated with targetable mechanisms of immune dysregulation. We first assessed plasma proteomic profiles and identified shared features of immune dysregulation in MODS patients with and without sepsis. We then employed consensus clustering to define three subphenotypes based on protein expression at disease onset and identified a strong association between subphenotype and clinical outcome. We next identified differences in immune cell frequency and activation state by MODS subphenotype and determined the association between hyperinflammatory pathway activation and cellular immunophenotype. Using single cell transcriptomics, we demonstrated STAT3 hyperactivation in lymphocytes from the sickest MODS subgroup and then identified an association between STAT3 hyperactivation and T cell immunometabolic dysregulation. Finally, we compared proteomics findings between patients with MODS and patients with inborn errors of immunity that amplify cytokine signaling pathways to further assess the impact of STAT3 hyperactivation in the most severe patients with MODS. Overall, these results identify a potentially pathologic and targetable role for STAT3 hyperactivation in a subset of pediatric patients with MODS who have high severity of illness and poor prognosis.
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13
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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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14
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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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15
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Akenroye A, Nopsopon T, Hacker JJ, Laidlaw TM. Ratio of plasma IL-13/TNF- ∝ and CXCL10/CCL17 predicts mepolizumab and omalizumab response in asthma better than eosinophil count or immunoglobulin E level. Sci Rep 2024; 14:10404. [PMID: 38710930 PMCID: PMC11074109 DOI: 10.1038/s41598-024-60864-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
To date, most studies to identify biomarkers associated with response to the anti-interleukin 5 agent, mepolizumab, and to the anti-immunoglobulin E agent, omalizumab have focused on clinically available biomarkers, such as the peripheral blood eosinophil counts (BEC) and total immunoglobulin E (IgE). However, these biomarkers often have low predictive accuracy, with many patients with eosinophilic or allergic asthma failing to demonstrate clinical response to mepolizumab or omalizumab respectively. In this study, we evaluated the association of baseline pre-biologic plasma levels of 26 cytokines and chemokines, including T-helper 1 (Th1)-, Th2-, Th17-related cytokines, and their ratios with subsequent clinical response to mepolizumab or omalizumab. We defined clinical response as a reduction in the baseline annual exacerbation rate by half or more over the one-year period following initiation of the biologic. Baseline levels of plasma IL-13 were differentially elevated in responders versus non-responders to mepolizumab and plasma CXCL10 levels were differentially elevated in responders to omalizumab. The ratio of IL-13/TNF-α had the best sensitivity and specificity in predicting response to mepolizumab and CXCL10/CCL17 to omalizumab, and these performed better as predictive biomarkers of response than BEC and IgE. Cytokines and chemokines associated with airway eosinophilia, allergic inflammation, or Th2 inflammation, such as IL-13 and CXCL10, may be better predictors of clinical response to mepolizumab and omalizumab, than IL-5 or IgE, the targets of mepolizumab and omalizumab.
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Affiliation(s)
- Ayobami Akenroye
- Division of Allergy and Clinical Immunology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, MA, 02115, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Tanawin Nopsopon
- Division of Allergy and Clinical Immunology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Jonathan J Hacker
- Division of Allergy and Clinical Immunology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Tanya M Laidlaw
- Division of Allergy and Clinical Immunology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, MA, 02115, USA
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16
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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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17
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Manda V, Pavelka J, Lau E. Proteomics applications in next generation induced pluripotent stem cell models. Expert Rev Proteomics 2024; 21:217-228. [PMID: 38511670 PMCID: PMC11065590 DOI: 10.1080/14789450.2024.2334033] [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: 06/14/2023] [Accepted: 03/08/2024] [Indexed: 03/22/2024]
Abstract
INTRODUCTION Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations. AREAS COVERED We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems. EXPERT OPINIONS We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.
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Affiliation(s)
- Vyshnavi Manda
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Jay Pavelka
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Edward Lau
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
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18
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Pietzner M, Uluvar B, Kolnes KJ, Jeppesen PB, Frivold SV, Skattebo Ø, Johansen EI, Skålhegg BS, Wojtaszewski JFP, Kolnes AJ, Yeo GSH, O'Rahilly S, Jensen J, Langenberg C. Systemic proteome adaptions to 7-day complete caloric restriction in humans. Nat Metab 2024; 6:764-777. [PMID: 38429390 DOI: 10.1038/s42255-024-01008-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/01/2024] [Indexed: 03/03/2024]
Abstract
Surviving long periods without food has shaped human evolution. In ancient and modern societies, prolonged fasting was/is practiced by billions of people globally for religious purposes, used to treat diseases such as epilepsy, and recently gained popularity as weight loss intervention, but we still have a very limited understanding of the systemic adaptions in humans to extreme caloric restriction of different durations. Here we show that a 7-day water-only fast leads to an average weight loss of 5.7 kg (±0.8 kg) among 12 volunteers (5 women, 7 men). We demonstrate nine distinct proteomic response profiles, with systemic changes evident only after 3 days of complete calorie restriction based on in-depth characterization of the temporal trajectories of ~3,000 plasma proteins measured before, daily during, and after fasting. The multi-organ response to complete caloric restriction shows distinct effects of fasting duration and weight loss and is remarkably conserved across volunteers with >1,000 significantly responding proteins. The fasting signature is strongly enriched for extracellular matrix proteins from various body sites, demonstrating profound non-metabolic adaptions, including extreme changes in the brain-specific extracellular matrix protein tenascin-R. Using proteogenomic approaches, we estimate the health consequences for 212 proteins that change during fasting across ~500 outcomes and identified putative beneficial (SWAP70 and rheumatoid arthritis or HYOU1 and heart disease), as well as adverse effects. Our results advance our understanding of prolonged fasting in humans beyond a merely energy-centric adaptions towards a systemic response that can inform targeted therapeutic modulation.
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Affiliation(s)
- Maik Pietzner
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
| | - Burulça Uluvar
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kristoffer J Kolnes
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
| | - Per B Jeppesen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - S Victoria Frivold
- Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Øyvind Skattebo
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Egil I Johansen
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Bjørn S Skålhegg
- Department of Nutrition, Division for Molecular Nutrition, University of Oslo, Oslo, Norway
| | - Jørgen F P Wojtaszewski
- August Krogh Section for Molecular Physiology, Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Anders J Kolnes
- Section of Specialized Endocrinology, Department of Endocrinology, Oslo University Hospital, Oslo, Norway
| | - Giles S H Yeo
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Stephen O'Rahilly
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jørgen Jensen
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Claudia Langenberg
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
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19
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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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20
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Austin TR, Fink HA, Jalal DI, Törnqvist AE, Buzkova P, Barzilay JI, Lu T, Carbone L, Gabrielsen ME, Grahnemo L, Hveem K, Jonasson C, Kizer JR, Langhammer A, Mukamal KJ, Gerszten RE, Nethander M, Psaty BM, Robbins JA, Sun YV, Skogholt AH, Åsvold BO, Valderrabano RJ, Zheng J, Richards JB, Coward E, Ohlsson C. Large-scale circulating proteome association study (CPAS) meta-analysis identifies circulating proteins and pathways predicting incident hip fractures. J Bone Miner Res 2024; 39:139-149. [PMID: 38477735 PMCID: PMC11070286 DOI: 10.1093/jbmr/zjad011] [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: 08/21/2023] [Revised: 11/09/2023] [Accepted: 11/23/2023] [Indexed: 03/14/2024]
Abstract
Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. In an exploratory search of the underlying biology as reflected through the circulating proteome, we performed a comprehensive Circulating Proteome Association Study (CPAS) meta-analysis for incident hip fractures. Analyses included 6430 subjects from two prospective cohort studies (Cardiovascular Health Study and Trøndelag Health Study) with circulating proteomics data (aptamer-based 5 K SomaScan version 4.0 assay; 4979 aptamers). Associations between circulating protein levels and incident hip fractures were estimated for each cohort using age and sex-adjusted Cox regression models. Participants experienced 643 incident hip fractures. Compared with the individual studies, inverse-variance weighted meta-analyses yielded more statistically significant associations, identifying 23 aptamers associated with incident hip fractures (conservative Bonferroni correction 0.05/4979, P < 1.0 × 10-5). The aptamers most strongly associated with hip fracture risk corresponded to two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR. High levels of several inflammation-related proteins (CD14, CXCL12, MMP12, ITIH3) were also associated with increased hip fracture risk. Ingenuity pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. These analyses identified several circulating proteins and pathways consistently associated with incident hip fractures. These findings underscore the usefulness of the meta-analytic approach for comprehensive CPAS in a similar manner as has previously been observed for large-scale human genetic studies. Future studies should investigate the underlying biology of these potential novel drug targets.
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Affiliation(s)
- Thomas R Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98195, United States
| | - Howard A Fink
- Geriatric Research Education and Clinical Center, VA Health Care System, Minneapolis, MN, 56401, United States
| | - Diana I Jalal
- Division of Nephrology, Department of Internal Medicine, Carver College of Medicine, Iowa City, IA, 52242, United States
- Iowa City VA Medical Center, Iowa City, IA, 52246, United States
| | - Anna E Törnqvist
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, WA, 98115, United States
| | - Joshua I Barzilay
- Division of Endocrinology, Kaiser Permanente of Georgia, Atlanta, GA, 30339, United States
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, H3T 1E2, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, H3G 0B1, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, H3Y 2W4, Canada
| | - Laura Carbone
- Charlie Norwood VAMC, Augusta, GA, 30901, United States
- Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, 30912, United States
| | - Maiken E Gabrielsen
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Louise Grahnemo
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden
| | - Kristian Hveem
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, 7491, Trondheim, Norway
- HUNT Research Centre, NTNU, 7600, Levanger, Norway
| | - Christian Jonasson
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Jorge R Kizer
- Cardiology Section, San Francisco VA Health Care System, San Francisco, CA, 94121, United States
- Department of Medicine, Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, 94158, United States
| | - Arnulf Langhammer
- HUNT Research Centre, NTNU, 7600, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, 7600, Levanger, Norway
| | - Kenneth J Mukamal
- Department of Medicine, Beth Israel Deaconess Medical Center, Brookline, MA, 2446, United States
| | - Robert E Gerszten
- Department of Medicine, Beth Israel Deaconess Medical Center, Brookline, MA, 2446, United States
| | - Maria Nethander
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden
- Bioinformatics and Data Center, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98195, United States
- Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, 98195, United States
| | - John A Robbins
- Department of Medicine, University of California, Davis, CA, 95817, United States
| | - Yan V Sun
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, United States
| | - Anne Heidi Skogholt
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Bjørn Olav Åsvold
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, 7491, Trondheim, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, 7491, Trondheim, Norway
| | - Rodrigo J Valderrabano
- Research Program in Men’s Health, Aging and Metabolism, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, 2130, United States
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, Shanghai, 200025, China
- Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai National Clinical Research Center for Metabolic Diseases, Shanghai Digital Medicine Innovation Center, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, Shanghai, 200025, China
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Bristol, BS8 2BN, United Kingdom
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, H3T 1E2, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, H3Y 2W4, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
- Department of Medicine, McGill University, Montreal, Quebec, H4A 3J1, Canada
- Department of Twin Research, King’s College London, London, SE1 7EH, United Kingdom
| | - Eivind Coward
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden
- Department of Drug Treatment, Region Västra Götaland, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
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21
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He K, Baniasad M, Kwon H, Caval T, Xu G, Lebrilla C, Hommes DW, Bertozzi C. Decoding the glycoproteome: a new frontier for biomarker discovery in cancer. J Hematol Oncol 2024; 17:12. [PMID: 38515194 PMCID: PMC10958865 DOI: 10.1186/s13045-024-01532-x] [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: 12/02/2023] [Accepted: 03/04/2024] [Indexed: 03/23/2024] Open
Abstract
Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.
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Affiliation(s)
- Kai He
- James Comprehensive Cancer Center, The Ohio State University, Columbus, USA.
| | | | - Hyunwoo Kwon
- James Comprehensive Cancer Center, The Ohio State University, Columbus, USA
| | | | - Gege Xu
- InterVenn Biosciences, South San Francisco, USA
| | - Carlito Lebrilla
- Department of Biochemistry and Molecular Medicine, UC Davis Health, Sacramento, USA
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22
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Dorian D, Gustafson D, Quinn R, Bentley RF, Dorian P, Goodman JM, Fish JE, Connelly KA. Exercise-Dependent Modulation of Immunological Response Pathways in Endurance Athletes With and Without Atrial Fibrillation. J Am Heart Assoc 2024; 13:e033640. [PMID: 38497478 PMCID: PMC11009995 DOI: 10.1161/jaha.123.033640] [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: 11/20/2023] [Accepted: 01/12/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Atrial fibrillation (AF) is a common arrhythmia characterized by uncoordinated atrial electrical activity. Lone AF occurs in the absence of traditional risk factors and is frequently observed in male endurance athletes, who face a 2- to 5-fold higher risk of AF compared with healthy, moderately active males. Our understanding of how endurance exercise contributes to the pathophysiology of lone AF remains limited. This study aimed to characterize the circulating protein fluctuations during high-intensity exercise as well as explore potential biomarkers of exercise-associated AF. METHODS AND RESULTS A prospective cohort of 12 male endurance cyclists between the ages of 40 and 65 years, 6 of whom had a history of exercise-associated AF, were recruited to participate using a convenience sampling method. The circulating proteome was subsequently analyzed using multiplex immunoassays and aptamer-based proteomics before, during, and after an acute high-intensity endurance exercise bout to assess temporality and identify potential markers of AF. The endurance exercise bout resulted in significant alterations to proteins involved in immune modulation (eg, growth/differentiation factor 15), skeletal muscle metabolism (eg, α-actinin-2), cell death (eg, histones), and inflammation (eg, interleukin-6). Subjects with AF differed from those without, displaying modulation of proteins previously known to have associations with incident AF (eg, C-reactive protein, insulin-like growth factor-1, and angiopoietin-2), and also with proteins having no previous association (eg, tapasin-related protein and α2-Heremans-Schmid glycoprotein). CONCLUSIONS These findings provide insights into the proteomic response to acute intense exercise, provide mechanistic insights into the pathophysiology behind AF in athletes, and identify targets for future study and validation.
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Affiliation(s)
- David Dorian
- Department of Medicine, Division of CardiologyUniversity of TorontoTorontoOntarioCanada
| | - Dakota Gustafson
- Department of Laboratory Medicine & PathobiologyUniversity of TorontoTorontoOntarioCanada
- Toronto General Hospital Research InstituteUniversity Health NetworkTorontoOntarioCanada
- Faculty of Health SciencesQueen’s UniversityKingstonOntarioCanada
| | - Ryan Quinn
- Division of CardiologyLi Ka Shing Knowledge Institute of St. Michael’s HospitalTorontoOntarioCanada
| | - Robert F. Bentley
- Faculty of Kinesiology and Physical EducationUniversity of TorontoTorontoOntarioCanada
| | - Paul Dorian
- Department of Medicine, Division of CardiologyUniversity of TorontoTorontoOntarioCanada
- Division of CardiologyLi Ka Shing Knowledge Institute of St. Michael’s HospitalTorontoOntarioCanada
- Department of MedicineUniversity of TorontoTorontoOntarioCanada
- Keenan Research Centre for Biomedical ScienceSt Michael’s Hospital, University of TorontoTorontoOntarioCanada
- Department of PhysiologyUniversity of TorontoTorontoOntarioCanada
- Heart and Stroke Richard Lewar Centre for Research ExcellenceUniversity of TorontoTorontoOntarioCanada
| | - Jack M. Goodman
- Faculty of Kinesiology and Physical EducationUniversity of TorontoTorontoOntarioCanada
- Heart and Stroke Richard Lewar Centre for Research ExcellenceUniversity of TorontoTorontoOntarioCanada
- Division of CardiologySinai Health/University Health NetworkTorontoOntarioCanada
| | - Jason E. Fish
- Department of Laboratory Medicine & PathobiologyUniversity of TorontoTorontoOntarioCanada
- Toronto General Hospital Research InstituteUniversity Health NetworkTorontoOntarioCanada
- Peter Munk Cardiac CentreUniversity Health NetworkTorontoOntarioCanada
| | - Kim A. Connelly
- Department of Medicine, Division of CardiologyUniversity of TorontoTorontoOntarioCanada
- Division of CardiologyLi Ka Shing Knowledge Institute of St. Michael’s HospitalTorontoOntarioCanada
- Department of MedicineUniversity of TorontoTorontoOntarioCanada
- Keenan Research Centre for Biomedical ScienceSt Michael’s Hospital, University of TorontoTorontoOntarioCanada
- Department of PhysiologyUniversity of TorontoTorontoOntarioCanada
- Heart and Stroke Richard Lewar Centre for Research ExcellenceUniversity of TorontoTorontoOntarioCanada
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23
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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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24
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Li H, Du S, Dai J, Jiang Y, Li Z, Fan Q, Zhang Y, You D, Zhang R, Zhao Y, Christiani DC, Shen S, Chen F. Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer. iScience 2024; 27:108985. [PMID: 38333712 PMCID: PMC10850776 DOI: 10.1016/j.isci.2024.108985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/17/2023] [Accepted: 01/17/2024] [Indexed: 02/10/2024] Open
Abstract
Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.
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Affiliation(s)
- Hongru Li
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Sha Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Jinglan Dai
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yunke Jiang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Zaiming Li
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qihan Fan
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yixin Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Dongfang You
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
| | - Ruyang Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing 211166, China
| | - Yang Zhao
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing 211166, China
| | - David C. Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
- Pulmonary and Critical Care Division, Massachusetts General Hospital, Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Sipeng Shen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing 211166, China
| | - Feng Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
- China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
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25
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Suhre K, Venkataraman GR, Guturu H, Halama A, Stephan N, Thareja G, Sarwath H, Motamedchaboki K, Donovan MKR, Siddiqui A, Batzoglou S, Schmidt F. Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping. Nat Commun 2024; 15:989. [PMID: 38307861 PMCID: PMC10837160 DOI: 10.1038/s41467-024-45233-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 01/16/2024] [Indexed: 02/04/2024] Open
Abstract
Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.
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Affiliation(s)
- Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144, Doha, Qatar.
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA.
| | | | | | - Anna Halama
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144, Doha, Qatar
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Nisha Stephan
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144, Doha, Qatar
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144, Doha, Qatar
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, 24144, Doha, Qatar
| | | | | | - Asim Siddiqui
- Seer, Inc., Redwood City, Redwood City, CA, 94065, USA
| | | | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, 24144, Doha, Qatar
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26
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Specht A, Kolosov G, Cederberg KLJ, Bueno F, Arrona-Palacios A, Pardilla-Delgado E, Ruiz-Herrera N, Zitting KM, Kramer A, Zeitzer JM, Czeisler CA, Duffy JF, Mignot E. Circadian protein expression patterns in healthy young adults. Sleep Health 2024; 10:S41-S51. [PMID: 38087675 PMCID: PMC11031319 DOI: 10.1016/j.sleh.2023.10.005] [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] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 04/20/2024]
Abstract
OBJECTIVES To explore how the blood plasma proteome fluctuates across the 24-hour day and identify a subset of proteins that show endogenous circadian rhythmicity. METHODS Plasma samples from 17 healthy adults were collected hourly under controlled conditions designed to unmask endogenous circadian rhythmicity; in a subset of 8 participants, we also collected samples across a day on a typical sleep-wake schedule. A total of 6916 proteins were analyzed from each sample using the SomaScan aptamer-based multiplexed platform. We used differential rhythmicity analysis based on a cosinor model with mixed effects to identify a subset of proteins that showed circadian rhythmicity in their abundance. RESULTS One thousand and sixty-three (15%) proteins exhibited significant daily rhythmicity. Of those, 431 (6.2%) proteins displayed consistent endogenous circadian rhythms on both a sleep-wake schedule and under controlled conditions: it included both known and novel proteins. When models were fitted with two harmonics, an additional 259 (3.7%) proteins exhibited significant endogenous circadian rhythmicity, indicating that some rhythmic proteins cannot be solely captured by a simple sinusoidal model. Overall, we found that the largest number of proteins had their peak levels in the late afternoon/evening, with another smaller group peaking in the early morning. CONCLUSIONS This study reveals that hundreds of plasma proteins exhibit endogenous circadian rhythmicity in humans. Future analyses will likely reveal novel physiological pathways regulated by circadian clocks and pave the way for improved diagnosis and treatment for patients with circadian disorders and other pathologies. It will also advance efforts to include knowledge about time-of-day, thereby incorporating circadian medicine into personalized medicine.
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Affiliation(s)
- Adrien Specht
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, USA
| | - German Kolosov
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, USA
| | - Katie L J Cederberg
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, USA
| | - Flavia Bueno
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, USA
| | - Arturo Arrona-Palacios
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Enmanuelle Pardilla-Delgado
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Noelia Ruiz-Herrera
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Kirsi-Marja Zitting
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Achim Kramer
- Division of Chronobiology, Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jamie M Zeitzer
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, USA
| | - Charles A Czeisler
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeanne F Duffy
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA.
| | - Emmanuel Mignot
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, USA.
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27
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Yarmolinsky J, Robinson JW, Mariosa D, Karhunen V, Huang J, Dimou N, Murphy N, Burrows K, Bouras E, Smith-Byrne K, Lewis SJ, Galesloot TE, Kiemeney LA, Vermeulen S, Martin P, Albanes D, Hou L, Newcomb PA, White E, Wolk A, Wu AH, Le Marchand L, Phipps AI, Buchanan DD, Zhao SS, Gill D, Chanock SJ, Purdue MP, Davey Smith G, Brennan P, Herzig KH, Järvelin MR, Amos CI, Hung RJ, Dehghan A, Johansson M, Gunter MJ, Tsilidis KK, Martin RM. Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis. EBioMedicine 2024; 100:104991. [PMID: 38301482 PMCID: PMC10844944 DOI: 10.1016/j.ebiom.2024.104991] [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: 09/21/2023] [Revised: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Tumour-promoting inflammation is a "hallmark" of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear. METHODS We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis-Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,294 cancer cases and up to 1,238,345 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P < 5.0 × 10-8) cis-acting SNPs (i.e., in or ±250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r2 < 0.10). Effect estimates were generated using inverse-variance weighted random-effects models and standard errors were inflated to account for weak LD between variants with reference to the 1000 Genomes Phase 3 CEU panel. A false discovery rate (FDR)-corrected P-value ("q-value") <0.05 was used as a threshold to define "strong evidence" to support associations and 0.05 ≤ q-value < 0.20 to define "suggestive evidence". A colocalisation posterior probability (PPH4) >70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. Findings were replicated in the FinnGen study and then pooled using meta-analysis. FINDINGS We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR: 1.19, 95% CI: 1.10-1.29, q-value = 0.033, PPH4 = 84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR: 1.42, 95% CI: 1.20-1.69, q-value = 0.055, PPH4 = 73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR: 0.66, 95% CI: 0.53-0.81, q-value = 0.067, PPH4 = 81.8%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR: 0.92, 95% CI: 0.88-0.97, q-value = 0.15, PPH4 = 85.6%). These findings were replicated in pooled analyses with the FinnGen study. Though suggestive evidence was found to support an association of macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR: 2.46, 95% CI: 1.48-4.10, q-value = 0.072, PPH4 = 76.1%), this finding was not replicated when pooled with the FinnGen study. For 22 of 30 cancer outcomes examined, there was little evidence (q-value ≥0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk. INTERPRETATION Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 4 circulating inflammatory markers in risk of 4 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated. FUNDING Cancer Research UK (C68933/A28534, C18281/A29019, PPRCPJT∖100005), World Cancer Research Fund (IIG_FULL_2020_022), National Institute for Health Research (NIHR202411, BRC-1215-20011), Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4), Academy of Finland Project 326291, European Union's Horizon 2020 grant agreement no. 848158 (EarlyCause), French National Cancer Institute (INCa SHSESP20, 2020-076), Versus Arthritis (21173, 21754, 21755), National Institutes of Health (U19 CA203654), National Cancer Institute (U19CA203654).
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Affiliation(s)
- James Yarmolinsky
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, UK.
| | - Jamie W Robinson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Daniela Mariosa
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Ville Karhunen
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland; Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Jian Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, UK; Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Karl Smith-Byrne
- The Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sarah J Lewis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | | | - Sita Vermeulen
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Paul Martin
- School of Biochemistry, Biomedical Sciences Building, University of Bristol, University Walk, Bristol, UK
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; School of Public Health, University of Washington, Seattle, WA, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Anna H Wu
- University of Southern California, Preventative Medicine, Los Angeles, CA, USA
| | - Loïc Le Marchand
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Daniel D Buchanan
- Colorectal Oncogenomic Group, Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia; Victorian Comprehensive Cancer Centre, University of Melbourne Centre for Cancer Research, Parkville, Victoria, Australia; Genetic Medicine and Family Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Sizheng Steven Zhao
- Centre for Epidemiology Versus Arthritis, Faculty of Biological Medicine and Health, University of Manchester, Manchester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, UK
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Mark P Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Paul Brennan
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Karl-Heinz Herzig
- Institute of Biomedicine, Medical Research Center and Oulu University Hospital, University of Oulu, Oulu, Finland; Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznan, Poland
| | - Marjo-Riitta Järvelin
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France; Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland; Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Chris I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, UK; Dementia Research Institute, Imperial College London, London, UK
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Marc J Gunter
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, UK; Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Kostas K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Richard M Martin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; University Hospitals Bristol and Weston NHS Foundation Trust, National Institute for Health Research Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
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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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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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Nakajima D, Konno R, Miyashita Y, Ishikawa M, Ohara O, Kawashima Y. Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins. Int J Mol Sci 2024; 25:1315. [PMID: 38279312 PMCID: PMC10816257 DOI: 10.3390/ijms25021315] [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: 12/27/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 01/28/2024] Open
Abstract
Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.
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Affiliation(s)
- Daisuke Nakajima
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Ryo Konno
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Yasuomi Miyashita
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
- Department of Developmental Biology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8670, Chiba, Japan
| | - Masaki Ishikawa
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Osamu Ohara
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Yusuke Kawashima
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
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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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Chen TK, Estrella MM, Appel LJ, Surapaneni AL, Köttgen A, Obeid W, Parikh CR, Grams ME. Associations of Baseline and Longitudinal Serum Uromodulin With Kidney Failure and Mortality: Results From the African American Study of Kidney Disease and Hypertension (AASK) Trial. Am J Kidney Dis 2024; 83:71-78. [PMID: 37690632 DOI: 10.1053/j.ajkd.2023.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/07/2023] [Accepted: 05/26/2023] [Indexed: 09/12/2023]
Abstract
RATIONALE & OBJECTIVE Uromodulin (UMOD) is the most abundant protein found in urine and has emerged as a promising biomarker of tubule health. Circulating UMOD is also detectable, but at lower levels. We evaluated whether serum UMOD levels were associated with the risks of incident kidney failure with replacement therapy (KFRT) and mortality. STUDY DESIGN Prospective cohort. SETTING & PARTICIPANTS Participants in AASK (the African American Study of Kidney Disease and Hypertension) with available stored serum samples from the 0-, 12-, and 24-month visits for biomarker measurement. PREDICTORS Baseline log-transformed UMOD and change in UMOD over 2 years. OUTCOMES KFRT and mortality. ANALYTICAL APPROACH Cox proportional hazards and mixed-effects models. RESULTS Among 500 participants with baseline serum UMOD levels (mean age, 54y; 37% female), 161 KFRT events occurred during a median of 8.5 years. After adjusting for baseline demographic factors, clinical factors, glomerular filtration rate, log-transformed urine protein-creatinine ratio, and randomized treatment groups, a 50% lower baseline UMOD level was independently associated with a 35% higher risk of KFRT (adjusted HR, 1.35; 95% CI, 1.07-1.70). For annual UMOD change, each 1-standard deviation lower change was associated with a 67% higher risk of KFRT (adjusted HR, 1.67; 95% CI, 1.41-1.99). Baseline UMOD and UMOD change were not associated with mortality. UMOD levels declined more steeply for metoprolol versus ramipril (P<0.001) as well as for intensive versus standard blood pressure goals (P = 0.002). LIMITATIONS Small sample size and limited generalizability. CONCLUSIONS Lower UMOD levels at baseline and steeper declines in UMOD over time were associated with a higher risk of subsequent KFRT in a cohort of African American adults with chronic kidney disease and hypertension. PLAIN-LANGUAGE SUMMARY Prior studies of uromodulin (UMOD), the most abundant protein in urine, and kidney disease have focused primarily on urinary UMOD levels. The present study evaluated associations of serum UMOD levels with the risks of kidney failure with replacement therapy (KFRT) and mortality in a cohort of African American adults with hypertension and chronic kidney disease. It found that participants with lower levels of UMOD at baseline were more likely to experience KFRT even after accounting for baseline kidney measures. Similarly, participants who experienced steeper annual declines in UMOD also had a heightened risk of kidney failure. Neither baseline nor annual change in UMOD was associated with mortality. Serum UMOD is a promising biomarker of kidney health.
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Affiliation(s)
- Teresa K Chen
- Kidney Health Research Collaborative and Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, California; San Francisco VA Health Care System, San Francisco, California; Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Michelle M Estrella
- Kidney Health Research Collaborative and Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, California; San Francisco VA Health Care System, San Francisco, California
| | - Lawrence J Appel
- General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, Maryland; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Aditya L Surapaneni
- Department of Medicine, New York University Langone School of Medicine, New York, New York
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Wassim Obeid
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chirag R Parikh
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, Maryland; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Department of Medicine, New York University Langone School of Medicine, New York, New York
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Akenroye A, Nopsopon T, Cho L, Moll M, Weiss ST. Lower myostatin and higher MUC1 levels are associated with better response to mepolizumab and omalizumab in asthma: a protein-protein interaction analyses. Respir Res 2023; 24:305. [PMID: 38057814 PMCID: PMC10698971 DOI: 10.1186/s12931-023-02620-1] [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: 11/13/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023] Open
Abstract
INTRODUCTION Biomarkers are needed to inform the choice of biologic therapy in patients with asthma given the increasing number of biologics. We aimed to identify proteins associated with response to omalizumab and mepolizumab. METHODS Aptamer-based proteomic profiling (SomaScan) was used to assess 1437 proteins from 51 patients with moderate to severe asthma who received omalizumab (n = 29) or mepolizumab (n = 22). Response was defined as the change in asthma-related exacerbations in the 12 months following therapy initiation. All models were adjusted for age, sex, and pre-treatment exacerbation rate. Additionally, body mass index was included in the omalizumab model and eosinophil count in the mepolizumab model. We evaluated the association between molecular signatures and response using negative binomial regression correcting for the false discovery rate (FDR) and gene set enrichment analyses (GSEA) to identify associated pathways. RESULTS Over two-thirds of patients were female. The average age for omalizumab patients was 42 years and 57 years for mepolizumab. At baseline, the average exacerbation rate was 1.5/year for omalizumab and 2.4/year for mepolizumab. Lower levels of LOXL2 (unadjusted p: 1.93 × 10E-05, FDR-corrected: 0.028) and myostatin (unadjusted: 3.87 × 10E-05, FDR-corrected: 0.028) were associated with better response to mepolizumab. Higher levels of CD9 antigen (unadjusted: 5.30 × 10E-07, FDR-corrected: 0.0006) and MUC1 (unadjusted: 1.15 × 10E-06, FDR-corrected: 0.0006) were associated with better response to omalizumab, and LTB4R (unadjusted: 1.12 × 10E-06, FDR-corrected: 0.0006) with worse response. Protein-protein interaction network modeling showed an enrichment of the TNF- and NF-kB signaling pathways for patients treated with mepolizumab and multiple pathways involving MAPK, including the FcER1 pathway, for patients treated with omalizumab. CONCLUSIONS This study provides novel fundamental data on proteins associated with response to mepolizumab or omalizumab in severe asthma and warrants further validation as potential biomarkers for therapy selection.
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Affiliation(s)
- Ayobami Akenroye
- Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital, 60 Fenwood Road, BostonBoston, MA, 02115, USA.
| | - Tanawin Nopsopon
- Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Laura Cho
- Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Brigham and Women's Hospital, 60 Fenwood Road, BostonBoston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, 60 Fenwood Road, BostonBoston, MA, 02115, USA
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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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Tin A, Fohner AE, Yang Q, Brody JA, Davies G, Yao J, Liu D, Caro I, Lindbohm JV, Duggan MR, Meirelles O, Harris SE, Gudmundsdottir V, Taylor AM, Henry A, Beiser AS, Shojaie A, Coors A, Fitzpatrick AL, Langenberg C, Satizabal CL, Sitlani CM, Wheeler E, Tucker-Drob EM, Bressler J, Coresh J, Bis JC, Candia J, Jennings LL, Pietzner M, Lathrop M, Lopez OL, Redmond P, Gerszten RE, Rich SS, Heckbert SR, Austin TR, Hughes TM, Tanaka T, Emilsson V, Vasan RS, Guo X, Zhu Y, Tzourio C, Rotter JI, Walker KA, Ferrucci L, Kivimäki M, Breteler MMB, Cox SR, Debette S, Mosley TH, Gudnason VG, Launer LJ, Psaty BM, Seshadri S, Fornage M. Identification of circulating proteins associated with general cognitive function among middle-aged and older adults. Commun Biol 2023; 6:1117. [PMID: 37923804 PMCID: PMC10624811 DOI: 10.1038/s42003-023-05454-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/12/2023] [Indexed: 11/06/2023] Open
Abstract
Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p < 4.9E-5, n up to 7289). Of these, 45 were replicated using SomaScan data, and three were replicated using Olink data at Bonferroni-corrected significance. Enrichment analysis linked the proteins associated with general cognitive function to cell signaling pathways and synapse architecture. Mendelian randomization analysis implicated higher levels of NECTIN2, a protein mediating viral entry into neuronal cells, with higher Alzheimer's disease (AD) risk (p = 2.5E-26). Levels of 14 other protein measures were implicated as consequences of AD susceptibility (p < 2.0E-4). Proteins implicated as causes or consequences of AD susceptibility may provide new insight into the potential relationship between immunity and AD susceptibility as well as potential therapeutic targets.
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Grants
- N01 HC095163 NHLBI NIH HHS
- RC2 HL102419 NHLBI NIH HHS
- HHSN268201500003C NHLBI NIH HHS
- UH3 NS100605 NINDS NIH HHS
- R01 HL103612 NHLBI NIH HHS
- 75N92020D00002 NHLBI NIH HHS
- U01 HL096812 NHLBI NIH HHS
- MC_UU_00006/1 Medical Research Council
- UF1 NS125513 NINDS NIH HHS
- 75N92020D00005 NHLBI NIH HHS
- N01AG12100 NIA NIH HHS
- N01HC95160 NHLBI NIH HHS
- R01 AG054076 NIA NIH HHS
- R01 HL120393 NHLBI NIH HHS
- BB/F019394/1 Biotechnology and Biological Sciences Research Council
- RF1 AG059421 NIA NIH HHS
- R01 HL131136 NHLBI NIH HHS
- N01 HC095168 NHLBI NIH HHS
- UL1 RR025005 NCRR NIH HHS
- R01 AG015928 NIA NIH HHS
- HHSN268201800004I NHLBI NIH HHS
- U01 HL080295 NHLBI NIH HHS
- N01HC95163 NHLBI NIH HHS
- N01 AG012100 NIA NIH HHS
- HHSN268201500001C NHLBI NIH HHS
- UL1 TR001079 NCATS NIH HHS
- N01 HC085082 NHLBI NIH HHS
- U01 HL096917 NHLBI NIH HHS
- R01 HL059367 NHLBI NIH HHS
- U01 HL130114 NHLBI NIH HHS
- HHSN268200800007C NHLBI NIH HHS
- R01 HL085251 NHLBI NIH HHS
- N01HC95169 NHLBI NIH HHS
- R01 NS087541 NINDS NIH HHS
- 75N92020D00001 NHLBI NIH HHS
- R01 HL086694 NHLBI NIH HHS
- R01 AG054628 NIA NIH HHS
- U01 HL096902 NHLBI NIH HHS
- R01 HL087652 NHLBI NIH HHS
- N01 HC095162 NHLBI NIH HHS
- U01 HG004402 NHGRI NIH HHS
- N01HC95164 NHLBI NIH HHS
- N01 HC085086 NHLBI NIH HHS
- N01HC55222 NHLBI NIH HHS
- R01 AG049607 NIA NIH HHS
- R01 AG065596 NIA NIH HHS
- N01 HC095165 NHLBI NIH HHS
- N01HC95162 NHLBI NIH HHS
- MR/R024227/1 Medical Research Council
- N01HC85086 NHLBI NIH HHS
- 75N92020D00003 NHLBI NIH HHS
- R01 HL105756 NHLBI NIH HHS
- N01HC95168 NHLBI NIH HHS
- N01 HC095169 NHLBI NIH HHS
- HHSN268201800003I NHLBI NIH HHS
- P30 DK063491 NIDDK NIH HHS
- HHSN268201800007I NHLBI NIH HHS
- HHSN268201700002C NHLBI NIH HHS
- R01 AG066524 NIA NIH HHS
- RF1 AG063507 NIA NIH HHS
- HHSN268201200036C NHLBI NIH HHS
- R01 HL144483 NHLBI NIH HHS
- HHSN268201800001C NHLBI NIH HHS
- HHSN268201700001I NHLBI NIH HHS
- R01 AG056477 NIA NIH HHS
- HHSN268201700004I NHLBI NIH HHS
- N01HC95165 NHLBI NIH HHS
- N01 HC095159 NHLBI NIH HHS
- U01 AG058589 NIA NIH HHS
- N01HC95159 NHLBI NIH HHS
- N01 HC095161 NHLBI NIH HHS
- HHSN268201500001I NHLBI NIH HHS
- HHSN271201200022C NIDA NIH HHS
- N01 HC025195 NHLBI NIH HHS
- N01HC95161 NHLBI NIH HHS
- UL1 TR001420 NCATS NIH HHS
- 75N92020D00004 NHLBI NIH HHS
- U01 HL096814 NHLBI NIH HHS
- P30 AG066509 NIA NIH HHS
- R01 HL132320 NHLBI NIH HHS
- 75N92020D00007 NHLBI NIH HHS
- P30 AG066546 NIA NIH HHS
- R01 AG033040 NIA NIH HHS
- MR/S011676/1 Medical Research Council
- U01 AG052409 NIA NIH HHS
- HHSN268201500003I NHLBI NIH HHS
- K01 AG071689 NIA NIH HHS
- 75N92021D00006 NHLBI NIH HHS
- R01 AG026307 NIA NIH HHS
- R01 AG020098 NIA NIH HHS
- HHSN268201700005C NHLBI NIH HHS
- HHSN268201700001C NHLBI NIH HHS
- N01HC85082 NHLBI NIH HHS
- HHSN268201700003C NHLBI NIH HHS
- N01 HC095166 NHLBI NIH HHS
- N01HC95167 NHLBI NIH HHS
- N01HC85083 NHLBI NIH HHS
- UH2 NS100605 NINDS NIH HHS
- N01HC25195 NHLBI NIH HHS
- 75N92019D00031 NHLBI NIH HHS
- U01 HL096899 NHLBI NIH HHS
- HHSN268201700004C NHLBI NIH HHS
- UL1 TR000040 NCATS NIH HHS
- HHSN268201700002I NHLBI NIH HHS
- HHSN268201700005I NHLBI NIH HHS
- P30 AG072947 NIA NIH HHS
- R01 AG025941 NIA NIH HHS
- Chief Scientist Office
- 75N92020D00006 NHLBI NIH HHS
- N01HC95166 NHLBI NIH HHS
- R01 AG023629 NIA NIH HHS
- R01 HL087641 NHLBI NIH HHS
- N01HC85079 NHLBI NIH HHS
- N01 HC085080 NHLBI NIH HHS
- UL1 TR001881 NCATS NIH HHS
- N01 HC095167 NHLBI NIH HHS
- HHSN268201800005I NHLBI NIH HHS
- N01HC85080 NHLBI NIH HHS
- HHSN268201700003I NHLBI NIH HHS
- HHSN268201800006I NHLBI NIH HHS
- N01 HC095164 NHLBI NIH HHS
- N01HC85081 NHLBI NIH HHS
- N01 HC095160 NHLBI NIH HHS
- The ARIC study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract numbers HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I and HHSN268201700005I), R01HL087641, R01HL059367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Funding was also supported by 5RC2HL102419, R01NS087541 and R01HL131136. Neurocognitive data were collected by U01 2U01HL096812, 2U01HL096814, 2U01HL096899, 2U01HL096902, 2U01HL096917 from the NIH (NHLBI, NINDS, NIA and NIDCD). Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. This Cardiovascular Heath Study (CHS) research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006; and NHLBI grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, R01HL085251, R01HL144483, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629, R01AG15928, and R01AG20098 from the National Institute on Aging (NIA). AEF is supported by K01AG071689. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195, HHSN268201500001I and 75N92019D00031). This work was also supported by grant R01AG063507, R01AG054076, R01AG049607, R01AG059421, R01AG033040, R01AG066524, P30AG066546, U01 AG052409, U01 AG058589 from from the National Institute on Aging and R01 AG017950, UH2/3 NS100605, UF1 NS125513 from National Institute of Neurological Disorders and Stroke and R01HL132320. AGES has been funded by NIA contracts N01-AG012100 and HSSN271201200022C, NIH Grant No. 1R01AG065596-01A1, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). M. R. Duggan, T. Tanaka, J. Candia, K. A. Walker, L. Ferrucci, L.J. Launer, O. Meirelles are funded by the National Institute on Aging Intramural Research Program. This study was funded, in part, by the National Institute on Aging Intramural Research Program. The Coronary Artery Risk Development in Young Adults Study (CARDIA) is supported by contracts HHSN268201800003I, HHSN268201800004I, HHSN268201800005I, HHSN268201800006I, and HHSN268201800007I from the National Heart, Lung, and Blood Institute (NHLBI). The LBC1921 was supported by the UK’s Biotechnology and Biological Sciences Research Council (BBSRC), The Royal Society, and The Chief Scientist Office of the Scottish Government. Genotyping was funded by the BBSRC (BB/F019394/1). LBC1936 is supported by the Biotechnology and Biological Sciences Research Council, and the Economic and Social Research Council [BB/W008793/1], Age UK (Disconnected Mind project), and the University of Edinburgh. Genotyping was funded by the BBSRC (BB/F019394/1). The Olink® Neurology Proteomics assay was supported by a National Institutes of Health (NIH) research grant R01AG054628. Phenotype harmonization, data management, sample-identity QC, and general study coordination, were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1), and TOPMed MESA Multi-Omics (HHSN2682015000031/HSN26800004). The MESA projects are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for the Multi-Ethnic Study of Atherosclerosis (MESA) projects are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1TR001881, DK063491, and R01HL105756. The Three City (3C) Study is conducted under a partnership agreement among the Institut National de la Santé et de la Recherche Médicale (INSERM), the University of Bordeaux, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l’Education Nationale (MGEN), Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Programme “Cohortes et collections de données biologiques.” Ilana Caro received a grant from the EUR digital public health. This PhD program is supported within the framework of the PIA3 (Investment for the future). Project reference 17-EURE-0019.
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Affiliation(s)
- Adrienne Tin
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA.
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Alison E Fohner
- Department of Epidemiology, University of Washington, Seattle, WA, USA.
- Institute for Public Health Genetics, University of Washington, Seattle, WA, USA.
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
| | - Qiong Yang
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Dan Liu
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Ilana Caro
- University of Bordeaux, Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, Bordeaux, France
| | - Joni V Lindbohm
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, The Klarman Cell Observatory, Cambridge, MA, USA
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Michael R Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Osorio Meirelles
- National Institute on Aging, National Institutes of Health, Laboratory of Epidemiology and Population Science, Bethesda, MD, USA
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Valborg Gudmundsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Adele M Taylor
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Albert Henry
- Institute of Cardiovascular Science, University of London, London, UK
| | - Alexa S Beiser
- Department of Biostatistics, Boston University, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Annabell Coors
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Annette L Fitzpatrick
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Departments of Family Medicine, University of Washington, Seattle, WA, USA
| | - Claudia Langenberg
- Precision Healthcare Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia L Satizabal
- Framingham Heart Study, Framingham, MA, USA
- Department of Population Health Sciences and Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Julián Candia
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, 22 Windsor Street, Cambridge, MA, USA
| | - Maik Pietzner
- Precision Healthcare Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Oscar L Lopez
- Departments of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Paul Redmond
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Robert E Gerszten
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Susan R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R Austin
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Timothy M Hughes
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Valur Emilsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- University of Texas School of Public Health in San Antonio, San Antonio, TX, USA
- University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yineng Zhu
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Christophe Tzourio
- University of Bordeaux, Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, Bordeaux, France
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Mika Kivimäki
- UCL Brain Sciences, University College London, London, UK
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Stephanie Debette
- University of Bordeaux, Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, Bordeaux, France
- Department of Neurology, Institute for Neurodegenerative Diseases, CHU de Bordeaux, Bordeaux, France
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Department of Population Health Sciences and Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Myriam Fornage
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
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36
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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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Wolf J, Rasmussen DK, Sun YJ, Vu JT, Wang E, Espinosa C, Bigini F, Chang RT, Montague AA, Tang PH, Mruthyunjaya P, Aghaeepour N, Dufour A, Bassuk AG, Mahajan VB. Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo. Cell 2023; 186:4868-4884.e12. [PMID: 37863056 PMCID: PMC10720485 DOI: 10.1016/j.cell.2023.09.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/26/2023] [Accepted: 09/13/2023] [Indexed: 10/22/2023]
Abstract
Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.
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Affiliation(s)
- Julian Wolf
- Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Ditte K Rasmussen
- Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA; Department of Biomedicine, Aarhus University, 8000 Aarhus C, Denmark
| | - Young Joo Sun
- Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Jennifer T Vu
- Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Elena Wang
- Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Fabio Bigini
- Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Robert T Chang
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Artis A Montague
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Peter H Tang
- Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN 55455, USA; Retina Consultants of Minnesota, Edina, MN 55435, USA
| | - Prithvi Mruthyunjaya
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Antoine Dufour
- Departments of Physiology and Pharmacology & Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Alexander G Bassuk
- Departments of Pediatrics and Neurology, The Iowa Neuroscience Institute (INI), University of Iowa, Iowa City, IA 52242, USA
| | - Vinit B Mahajan
- Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA.
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Richardson TG, Leyden GM, Davey Smith G. Time-varying and tissue-dependent effects of adiposity on leptin levels: A Mendelian randomization study. eLife 2023; 12:e84646. [PMID: 37878001 PMCID: PMC10599655 DOI: 10.7554/elife.84646] [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: 11/02/2022] [Accepted: 10/08/2023] [Indexed: 10/26/2023] Open
Abstract
Background Findings from Mendelian randomization (MR) studies are conventionally interpreted as lifelong effects, which typically do not provide insight into the molecular mechanisms underlying the effect of an exposure on an outcome. In this study, we apply two recently developed MR approaches (known as 'lifecourse' and 'tissue-partitioned' MR) to investigate lifestage-specific effects and tissues of action in the relationship between adiposity and circulating leptin levels. Methods Genetic instruments for childhood and adult adiposity were incorporated into a multivariable MR (MVMR) framework to estimate lifestage-specific effects on leptin levels measured during early life (mean age: 10 y) in the Avon Longitudinal Study of Parents and Children and in adulthood (mean age: 55 y) using summary-level data from the deCODE Health study. This was followed by partitioning body mass index (BMI) instruments into those whose effects are putatively mediated by gene expression in either subcutaneous adipose or brain tissues, followed by using MVMR to simultaneously estimate their separate effects on childhood and adult leptin levels. Results There was strong evidence that childhood adiposity has a direct effect on leptin levels at age 10 y in the lifecourse (β = 1.10 SD change in leptin levels, 95% CI = 0.90-1.30, p=6 × 10-28), whereas evidence of an indirect effect was found on adulthood leptin along the causal pathway involving adulthood body size (β = 0.74, 95% CI = 0.62-0.86, p=1 × 10-33). Tissue-partitioned MR analyses provided evidence to suggest that BMI exerts its effect on leptin levels during both childhood and adulthood via brain tissue-mediated pathways (β = 0.79, 95% CI = 0.22-1.36, p=6 × 10-3 and β = 0.51, 95% CI = 0.32-0.69, p=1 × 10-7, respectively). Conclusions Our findings demonstrate the use of lifecourse MR to disentangle direct and indirect effects of early-life exposures on time-varying complex outcomes. Furthermore, by integrating tissue-specific data, we highlight the etiological importance of appetite regulation in the effect of adiposity on leptin levels. Funding This work was supported by the Integrative Epidemiology Unit, which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/1).
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Affiliation(s)
- Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield GroveBristolUnited Kingdom
| | - Genevieve M Leyden
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield GroveBristolUnited Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield GroveBristolUnited Kingdom
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Tsukita K, Sakamaki-Tsukita H, Kaiser S, Zhang L, Messa M, Serrano-Fernandez P, Takahashi R. High-Throughput CSF Proteomics and Machine Learning to Identify Proteomic Signatures for Parkinson Disease Development and Progression. Neurology 2023; 101:e1434-e1447. [PMID: 37586882 PMCID: PMC10573147 DOI: 10.1212/wnl.0000000000207725] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 05/30/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND AND OBJECTIVES This study aimed to identify CSF proteomic signatures characteristic of Parkinson disease (PD) and evaluate their clinical utility. METHODS This observational study used data from the Parkinson's Progression Markers Initiative (PPMI), which enrolled patients with PD, healthy controls (HCs), and non-PD participants carrying GBA1, LRRK2, and/or SNCA pathogenic variants (genetic prodromals) at international sites. Study participants were chosen from PPMI enrollees based on the availability of aptamer-based CSF proteomic data, quantifying 4,071 proteins, and classified as patients with PD without GBA1, LRRK2, and/or SNCA pathogenic variants (nongenetic PD), HCs, patients with PD carrying the aforementioned pathogenic variants (genetic PD), or genetic prodromals. Differentially expressed protein (DEP) analysis and the least absolute shrinkage and selection operator (LASSO) were applied to the data from nongenetic PD and HCs. Signatures characteristics of nongenetic PD were quantified as a PD proteomic score (PD-ProS), validated internally and then externally using data of 1,556 CSF proteins from the LRRK2 Cohort Consortium (LCC). We further tested the PD-ProS in genetic PD and genetic prodromals and examined associations with clinical progression. RESULTS Data from 279 patients with nongenetic PD (mean ± SD, age 62.0 ± 9.6 years; male 67.7%) and 141 HCs (age 60.5 ± 11.9 years; male 64.5%) were used for PD-ProS derivation. From 23 DEPs, LASSO determined weights of 14 DEPs for the PD-ProS (area under the curve [AUC] 0.83, 95% CI 0.78-0.87), validated in an independent internal validation cohort of 71 patients with nongenetic PD and 35 HCs (AUC 0.81, 95% CI 0.73-0.90). In the LCC, only 5 of the 14 DEPs were also measured. Notably, these 5 DEPs still distinguished 34 patients with nongenetic PD from 31 HCs with the same weights (AUC 0.75, 95% CI 0.63-0.87). Furthermore, the PD-ProS distinguished 258 patients with genetic PD from 365 genetic prodromals. Finally, regardless of genetic status, the PD-ProS independently predicted both cognitive and motor decline in PD (dementia, adjusted hazard ratio in the highest quintile [aHR-Q5] 2.8 [95% CI 1.6-5.0]; Hoehn and Yahr stage IV, aHR-Q5 2.1 [95% CI 1.1-4.0]). DISCUSSION By integrating high-throughput proteomics with machine learning, we identified PD-associated CSF proteomic signatures crucial for PD development and progression. TRIAL REGISTRATION INFORMATION ClinicalTrials.gov (NCT01176565). A link to the trial registry page is clinicaltrials.gov/ct2/show/NCT01141023. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that the CSF proteome contains clinically important information regarding the development and progression of Parkinson disease that can be deciphered by a combination of high-throughput proteomics and machine learning.
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Affiliation(s)
- Kazuto Tsukita
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA.
| | - Haruhi Sakamaki-Tsukita
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Sergio Kaiser
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Luqing Zhang
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Mirko Messa
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Pablo Serrano-Fernandez
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Ryosuke Takahashi
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
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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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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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Dang T, Fermin ASR, Machizawa MG. oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data. Front Neuroinform 2023; 17:1266713. [PMID: 37829329 PMCID: PMC10566623 DOI: 10.3389/fninf.2023.1266713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/08/2023] [Indexed: 10/14/2023] Open
Abstract
The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r, with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy.
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Affiliation(s)
- Tung Dang
- Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Alan S. R. Fermin
- Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Maro G. Machizawa
- Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan
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Ran Q, Tian H, Lin J, Wang H, Wang B, Chen Z, Song D, Gong C. Mesenchymal Stem Cell-Derived Exosomes: A Novel Approach to Diabetes-Associated Cognitive Impairment. J Inflamm Res 2023; 16:4213-4228. [PMID: 37753267 PMCID: PMC10519429 DOI: 10.2147/jir.s429532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023] Open
Abstract
The progression of diabetes frequently results in a myriad of neurological disorders, including ischemic stroke, depression, blood-brain barrier impairment, and cognitive dysfunction. Notably, diabetes-associated cognitive impairment, a prevalent comorbidity during the course of diabetes, progressively affects patients' cognitive abilities and may reciprocally influence diabetes management, thereby severely impacting patients' quality of life. Extracellular vesicles, particularly nanoscale exosomes, have garnered considerable attention in recent years. These exosomes carry and transfer various functional molecules, such as proteins, lipids, and diverse non-coding RNAs, serving as novel regulators and communicators in intercellular interactions. Of particular interest, mesenchymal stem cell-derived exosomes (MSC-Exos) have been reported to traverse the blood-brain barrier and ameliorate intracerebral pathologies. This review elucidates the role of MSC-Exos in diabetes-related cognitive impairment, with a focus on their applications as biomarkers, modulation of neuronal regeneration and synaptic plasticity, anti-inflammatory properties, antioxidative effects, and their involvement in regulating the functionality of β-amyloid proteins during the course of cognitive impairment. The immense therapeutic potential of MSC-Exos in the treatment of diabetes-induced cognitive dysfunction is emphasized.
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Affiliation(s)
- Qingsen Ran
- Department of Science and Education, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital), Shenzhen, Guangzhou Province, 518118, People’s Republic of China
| | - He Tian
- Department of Science and Education, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital), Shenzhen, Guangzhou Province, 518118, People’s Republic of China
| | - Jian Lin
- Department of Science and Education, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital), Shenzhen, Guangzhou Province, 518118, People’s Republic of China
| | - Han Wang
- Department of Gastroenterology, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin Province, 130021, People’s Republic of China
| | - Bo Wang
- Department of Science and Education, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital), Shenzhen, Guangzhou Province, 518118, People’s Republic of China
| | - Zhixin Chen
- Department of Science and Education, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital), Shenzhen, Guangzhou Province, 518118, People’s Republic of China
| | - Da Song
- Department of Science and Education, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital), Shenzhen, Guangzhou Province, 518118, People’s Republic of China
| | - Chunzhu Gong
- Department of Science and Education, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital), Shenzhen, Guangzhou Province, 518118, People’s Republic of China
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Yuan S, Xu F, Li X, Chen J, Zheng J, Mantzoros CS, Larsson SC. Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses. Cell Rep Med 2023; 4:101174. [PMID: 37652020 PMCID: PMC10518626 DOI: 10.1016/j.xcrm.2023.101174] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/16/2023] [Accepted: 08/06/2023] [Indexed: 09/02/2023]
Abstract
We conduct proteome-wide Mendelian randomization and colocalization analyses to decipher the associations of blood proteins with the risk of type 2 diabetes and diabetic complications. Genetic data on plasma proteome are obtained from 54,306 UK Biobank participants and 35,559 Icelanders. Summary-level data on type 2 diabetes are obtained from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis consortium) consortium (74,124 cases) and FinnGen study (33,043 cases). Data on 10 diabetic complications are obtained from FinnGen and corresponding studies. Among 1,886 proteins, genetically predicted levels of 47 plasma proteins are associated with type 2 diabetes. Eleven of these proteins have strong support of colocalization. Seventeen proteins are associated with at least one diabetic complication, although a few have colocalization support. HLA-DRA, AGER, HSPA1A, and HSPA1B are associated with most microvascular complications. This study reveals causal proteins for the onset of type 2 diabetes and diabetic complications, which enhances the understanding of molecular etiology and development of therapeutics.
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Affiliation(s)
- Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Fengzhe Xu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Xue Li
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie Chen
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Christos S Mantzoros
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Section of Endocrinology, VA Boston Healthcare System, Harvard Medical School, Boston, MA, USA
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
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Zhao JH, Stacey D, Eriksson N, Macdonald-Dunlop E, Hedman ÅK, Kalnapenkis A, Enroth S, Cozzetto D, Digby-Bell J, Marten J, Folkersen L, Herder C, Jonsson L, Bergen SE, Gieger C, Needham EJ, Surendran P, Paul DS, Polasek O, Thorand B, Grallert H, Roden M, Võsa U, Esko T, Hayward C, Johansson Å, Gyllensten U, Powell N, Hansson O, Mattsson-Carlgren N, Joshi PK, Danesh J, Padyukov L, Klareskog L, Landén M, Wilson JF, Siegbahn A, Wallentin L, Mälarstig A, Butterworth AS, Peters JE. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat Immunol 2023; 24:1540-1551. [PMID: 37563310 PMCID: PMC10457199 DOI: 10.1038/s41590-023-01588-w] [Citation(s) in RCA: 100] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/13/2023] [Indexed: 08/12/2023]
Abstract
Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.
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Affiliation(s)
- Jing Hua Zhao
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Niclas Eriksson
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Erin Macdonald-Dunlop
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Åsa K Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Development and Medical, Pfizer Worldwide Research, Stockholm, Sweden
| | - Anette Kalnapenkis
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stefan Enroth
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, Uppsala, Sweden
| | - Domenico Cozzetto
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Jonathan Digby-Bell
- School of Immunology and Microbial Sciences, King's College London, London, UK
| | - Jonathan Marten
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Munich-Neuherberg, Germany
| | - Lina Jonsson
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Sarah E Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Elise J Needham
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
| | - Dirk S Paul
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Barbara Thorand
- German Center for Diabetes Research, Munich-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Harald Grallert
- German Center for Diabetes Research, Munich-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Munich-Neuherberg, Germany
| | - Urmo Võsa
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tonu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Åsa Johansson
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, Uppsala, Sweden
| | - Ulf Gyllensten
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, Uppsala, Sweden
| | - Nick Powell
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine (Solna), Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine (Solna), Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Agneta Siegbahn
- Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Lars Wallentin
- Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Anders Mälarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Development and Medical, Pfizer Worldwide Research, Stockholm, Sweden
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK.
| | - James E Peters
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- Department of Immunology and Inflammation, Imperial College London, London, UK.
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47
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Li Y, Porta-Pardo E, Tokheim C, Bailey MH, Yaron TM, Stathias V, Geffen Y, Imbach KJ, Cao S, Anand S, Akiyama Y, Liu W, Wyczalkowski MA, Song Y, Storrs EP, Wendl MC, Zhang W, Sibai M, Ruiz-Serra V, Liang WW, Terekhanova NV, Rodrigues FM, Clauser KR, Heiman DI, Zhang Q, Aguet F, Calinawan AP, Dhanasekaran SM, Birger C, Satpathy S, Zhou DC, Wang LB, Baral J, Johnson JL, Huntsman EM, Pugliese P, Colaprico A, Iavarone A, Chheda MG, Ricketts CJ, Fenyö D, Payne SH, Rodriguez H, Robles AI, Gillette MA, Kumar-Sinha C, Lazar AJ, Cantley LC, Getz G, Ding L. Pan-cancer proteogenomics connects oncogenic drivers to functional states. Cell 2023; 186:3921-3944.e25. [PMID: 37582357 DOI: 10.1016/j.cell.2023.07.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 12/30/2022] [Accepted: 07/10/2023] [Indexed: 08/17/2023]
Abstract
Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.
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Affiliation(s)
- Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Eduard Porta-Pardo
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Matthew H Bailey
- Department of Biology and Simmons Center for Cancer Research, Brigham Young University, Provo, UT 84602, USA
| | - Tomer M Yaron
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Vasileios Stathias
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Yifat Geffen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - Kathleen J Imbach
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Shankara Anand
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yo Akiyama
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yizhe Song
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Erik P Storrs
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Mustafa Sibai
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Victoria Ruiz-Serra
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Fernanda Martins Rodrigues
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David I Heiman
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Qing Zhang
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Francois Aguet
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Anna P Calinawan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Saravana M Dhanasekaran
- Michigan Center for Translational Pathology, Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chet Birger
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Liang-Bo Wang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jessika Baral
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Emily M Huntsman
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Pietro Pugliese
- Department of Science and Technology, University of Sannio, 82100 Benevento, Italy
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Antonio Iavarone
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Neurological Surgery, Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Christopher J Ricketts
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Chandan Kumar-Sinha
- Michigan Center for Translational Pathology, Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander J Lazar
- Departments of Pathology & Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Gad Getz
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA.
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Mi MY, Barber JL, Rao P, Farrell LA, Sarzynski MA, Bouchard C, Robbins JM, Gerszten RE. Plasma Proteomic Kinetics in Response to Acute Exercise. Mol Cell Proteomics 2023; 22:100601. [PMID: 37343698 PMCID: PMC10460691 DOI: 10.1016/j.mcpro.2023.100601] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/09/2023] [Accepted: 06/11/2023] [Indexed: 06/23/2023] Open
Abstract
Regular exercise has many favorable effects on human health, which may be mediated in part by the release of circulating bioactive factors during each bout of exercise. Limited data exist regarding the kinetic responses of plasma proteins during and after acute exercise. Proteomic profiling of 4163 proteins was performed using a large-scale, affinity-based platform in 75 middle-aged adults who were referred for treadmill exercise stress testing. Plasma proteins were quantified at baseline, peak exercise, and 1-h postexercise, and those with significant changes at both exercise timepoints were further examined for their associations with cardiometabolic traits and change with aerobic exercise training in the Health, Risk Factors, Exercise Training and Genetics Family Study, a 20-week exercise intervention study. A total of 765 proteins changed (false discovery rate < 0.05) at peak exercise compared to baseline, and 128 proteins changed (false discovery rate < 0.05) at 1-h postexercise. The 56 proteins that changed at both timepoints included midkine, brain-derived neurotrophic factor, metalloproteinase inhibitor 4, and coiled-coil domain-containing protein 126 and were enriched for secreted proteins. The majority had concordant direction of change at both timepoints. Across all proteins assayed, gene set enrichment analysis showed increased abundance of coagulation-related proteins at 1-h postexercise. Forty-five proteins were associated with at least one measure of adiposity, lipids, glucose homeostasis, or cardiorespiratory fitness in Health, Risk Factors, Exercise Training and Genetics Family Study, and 20 proteins changed with aerobic exercise training. We identified hundreds of novel proteins that change during acute exercise, most of which resolved by 1 h into recovery. Proteins with sustained changes during exercise and recovery may be of particular interest as circulating biomarkers and pathways for further investigation in cardiometabolic diseases. These data will contribute to a biochemical roadmap of acute exercise that will be publicly available for the entire scientific community.
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Affiliation(s)
- Michael Y Mi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
| | - Jacob L Barber
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Prashant Rao
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Laurie A Farrell
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Mark A Sarzynski
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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49
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Fan C, Gao Y, Sun Y. Integrated multiple-microarray analysis and mendelian randomization to identify novel targets involved in diabetic nephropathy. Front Endocrinol (Lausanne) 2023; 14:1191768. [PMID: 37492198 PMCID: PMC10363738 DOI: 10.3389/fendo.2023.1191768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023] Open
Abstract
Background Diabetic nephropathy (DN), which is the main cause of renal failure in end-stage renal disease, is becoming a common chronic renal disease worldwide. Mendelian randomization (MR) is a genetic tool that is widely used to minimize confounding and reverse causation when identifying the causal effects of complex traits. In this study, we conducted an integrated multiple microarray analysis and large-scale plasma proteome MR analysis to identify candidate biomarkers and evaluate the causal effects of prospective therapeutic targets in DN. Methods Five DN gene expression datasets were selected from the Gene Expression Omnibus. The robust rank aggregation (RRA) method was used to integrate differentially expressed genes (DEGs) of glomerular samples between patients with DN and controls, followed by functional enrichment analysis. Protein quantitative trait loci were incorporated from seven different proteomic genome-wide association studies, and genetic association data on DN were obtained from FinnGen (3676 cases and 283,456 controls) for two-sample MR analysis. External validation and clinical correlation were also conducted. Results A total of 82 DEGs (53 upregulated and 29 downregulated) were identified through RRA integrated analysis. The enriched Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathways of the DEGs were significantly enriched in neutrophil degranulation, neutrophil activation, proteoglycan binding, collagen binding, secretory granule lumen, gluconeogenesis, tricarboxylic acid cycle, and pentose phosphate pathways. MR analysis revealed that the genetically predicted levels of MHC class I polypeptide-related sequence B (MICB), granzyme A (GZMA), cathepsin S (CTSS), chloride intracellular channel protein 5, and ficolin-1 (FCN1) were causally associated with DN risk. Expression validation and clinical correlation analysis showed that MICB, GZMA, FCN1, and insulin-like growth factor 1 may participate in the development of DN, and carbonic anhydrase 2 and lipoprotein lipase may play protective roles in patients with DN. Conclusion Our integrated analysis identified novel biomarkers, including MICB and GZMA, which may help further understand the complicated mechanisms of DN and identify new target pathways for intervention.
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Affiliation(s)
- Chenyu Fan
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China
| | - Yuye Gao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery III, Peking University Cancer Hospital & Institute, Beijing, China
| | - Ying Sun
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu, China
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50
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Giontella A, Zagkos L, Geybels M, Larsson SC, Tzoulaki I, Mantzoros CS, Andersen B, Gill D, Cronjé HT. Renoprotective effects of genetically proxied fibroblast growth factor 21: Mendelian randomization, proteome-wide and metabolome-wide association study. Metabolism 2023:155616. [PMID: 37302695 DOI: 10.1016/j.metabol.2023.155616] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/04/2023] [Accepted: 06/02/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND Fibroblast growth factor 21 (FGF21) has demonstrated efficacy for reducing liver fat and reversing non-alcoholic steatohepatitis in phase 2 clinical trials. It is also postulated to have anti-fibrotic effects and therefore may be amenable to repurposing for the prevention and treatment of chronic kidney disease (CKD). METHODS We leverage a missense genetic variant, rs739320 in the FGF21 gene, that associates with magnetic resonance imaging-derived liver fat as a clinically validated and biologically plausible instrumental variable for studying the effects of FGF21 analogs. Performing Mendelian randomization, we ascertain associations between instrumented FGF21 and kidney phenotypes, cardiometabolic disease risk factors, as well as the circulating proteome (Somalogic, 4907 aptamers) and metabolome (Nightingale platform, 249 metabolites). RESULTS We report consistent renoprotective associations of genetically proxied FGF21 effect, including higher glomerular filtration rates (p = 1.9 × 10-4), higher urinary sodium excretion (p = 5.1 × 10-11), and lower urine albumin-creatinine ratio (p = 3.6 × 10-5). These favorable effects translated to lower CKD risk (odds ratio per rs739320 C-allele, 0.96; 95%CI, 0.94-0.98; p = 3.2 × 10-4). Genetically proxied FGF21 effect was also associated with lower fasting insulin, waist-to-hip ratio, blood pressure (systolic and diastolic BP, p < 1.0 × 10-07) and blood lipid (low-density lipoprotein cholesterol, triglycerides and apolipoprotein B, p < 6.5 × 10-24) profiles. The latter associations are replicated in our metabolome-wide association study. Proteomic perturbations associated with genetically predicted FGF21 effect were consistent with fibrosis reduction. CONCLUSION This study highlights the pleiotropic effects of genetically proxied FGF21 and supports a re-purposing opportunity for the treatment and prevention of kidney disease specifically. Further work is required to triangulate these findings, towards possible clinical development of FGF21 towards the treatment and prevention of kidney disease.
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Affiliation(s)
- Alice Giontella
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | | | - 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
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Division of Systems Biology, Biomedical Research Foundation of the Academy of Athens, Athens. Greece
| | - Christos S Mantzoros
- Department of Medicine, Boston VA Healthcare System and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | | | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark.
| | - Héléne T Cronjé
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark.
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