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Suhre K. Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions. Cell Genom 2024; 4:100506. [PMID: 38412862 PMCID: PMC10943581 DOI: 10.1016/j.xgen.2024.100506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/25/2023] [Accepted: 01/26/2024] [Indexed: 02/29/2024]
Abstract
Protein quantitative trait loci (pQTLs) are an invaluable source of information for drug target development because they provide genetic evidence to support protein function, suggest relationships between cis- and trans-associated proteins, and link proteins to disease endpoints. Using Olink proteomics data for 1,463 proteins measured in over 54,000 samples of the UK Biobank, we identified 4,248 associations with 2,821 ratios between protein levels (rQTLs). rQTLs were 7.6-fold enriched in known protein-protein interactions, suggesting that their ratios reflect biological links between the implicated proteins. Conducting a GWAS on ratios increased the number of discovered genetic signals by 24.7%. The approach can identify novel loci of clinical relevance, support causal gene identification, and reveal complex networks of interacting proteins. Taken together, our study adds significant value to the genetic insights that can be derived from the UKB proteomics data and motivates the wider use of ratios in large-scale GWAS.
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Affiliation(s)
- Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
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Ratter-Rieck JM, Shi M, Suhre K, Prehn C, Adamski J, Rathmann W, Thorand B, Roden M, Peters A, Wang-Sattler R, Herder C. Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study. BMJ Open Diabetes Res Care 2024; 12:e003865. [PMID: 38442989 DOI: 10.1136/bmjdrc-2023-003865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/01/2024] [Indexed: 03/07/2024] Open
Abstract
INTRODUCTION Circulating omentin levels have been positively associated with insulin sensitivity. Although a role for adiponectin in this relationship has been suggested, underlying mechanisms remain elusive. In order to reveal the relationship between omentin and systemic metabolism, this study aimed to investigate associations of serum concentrations of omentin and metabolites. RESEARCH DESIGN AND METHODS This study is based on 1124 participants aged 61-82 years from the population-based KORA (Cooperative Health Research in the Region of Augsburg) F4 Study, for whom both serum omentin levels and metabolite concentration profiles were available. Associations were assessed with five multivariable regression models, which were stepwise adjusted for multiple potential confounders, including age, sex, body mass index, waist-to-hip ratio, lifestyle markers (physical activity, smoking behavior and alcohol consumption), serum adiponectin levels, high-density lipoprotein cholesterol, use of lipid-lowering or anti-inflammatory medication, history of myocardial infarction and stroke, homeostasis model assessment 2 of insulin resistance, diabetes status, and use of oral glucose-lowering medication and insulin. RESULTS Omentin levels significantly associated with multiple metabolites including amino acids, acylcarnitines, and lipids (eg, sphingomyelins and phosphatidylcholines (PCs)). Positive associations for several PCs, such as diacyl (PC aa C32:1) and alkyl-alkyl (PC ae C32:2), were significant in models 1-4, whereas those with hydroxytetradecenoylcarnitine (C14:1-OH) were significant in all five models. Omentin concentrations were negatively associated with several metabolite ratios, such as the valine-to-PC ae C32:2 and the serine-to-PC ae C32:2 ratios in most models. CONCLUSIONS Our results suggest that omentin may influence insulin sensitivity and diabetes risk by changing systemic lipid metabolism, but further mechanistic studies investigating effects of omentin on metabolism of insulin-sensitive tissues are needed.
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Affiliation(s)
- Jacqueline M Ratter-Rieck
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
| | - Mengya Shi
- TUM School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Partner Neuherberg, Neuherberg, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Wolfgang Rathmann
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Barbara Thorand
- German Center for Diabetes Research, Partner Neuherberg, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Annette Peters
- German Center for Diabetes Research, Partner Neuherberg, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Rui Wang-Sattler
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Partner Neuherberg, Neuherberg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Arnold M, Buyukozkan M, Doraiswamy PM, Nho K, Wu T, Gudnason V, Launer LJ, Wang-Sattler R, Adamski J, De Jager PL, Ertekin-Taner N, Bennett DA, Saykin AJ, Peters A, Suhre K, Kaddurah-Daouk R, Kastenmüller G, Krumsiek J. Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease. medRxiv 2024:2024.01.23.23297820. [PMID: 38313266 PMCID: PMC10836119 DOI: 10.1101/2024.01.23.23297820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.
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Affiliation(s)
- Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mustafa Buyukozkan
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - P. Murali Doraiswamy
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tong Wu
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, Maryland
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | | | | | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Taub Institute, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY, USA
| | | | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; IBE, Medical Faculty, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Diabetes Research (DZD e.V.), Munich, Germany; German Center for Cardiovascular Disease (DZHK e.V.), Munich Heart Alliance, Munich, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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Salinas T, Li C, Snopkowski C, Sharma VK, Dadhania DM, Suhre K, Muthukumar T, Suthanthiran M. Urinary Cell Gene Signature of Acute Rejection in Kidney Allografts. medRxiv 2023:2023.12.18.23300165. [PMID: 38196644 PMCID: PMC10775338 DOI: 10.1101/2023.12.18.23300165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Introduction A kidney allograft biopsy may display acute T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or concurrent TCMR + ABMR (MR). Development of noninvasive biomarkers diagnostic of all three types of acute rejection is a useful addition to the diagnostic armamentarium. Methods We developed customized RT-qPCR assays and measured urinary cell mRNA copy number in 145 biopsy-matched urine samples from 126 kidney allograft recipients and calculated urinary cell three-gene signature score from log 10 -transformed values for the 18S-normalized CD3E mRNA, 18S-normalized CXCL10 mRNA and 18S rRNA. We determined whether the signature score in biopsy-matched urine specimens discriminates biopsies without rejection (NR, n=50) from biopsies displaying TCMR (n=47), ABMR (n=28) or MR (n=20). Results Urinary cell three-gene signature discriminated TCMR, ABMR or MR biopsies from NR biopsies (P <0.0001, One-way ANOVA). Dunnett's multiple comparisons test yielded P<0.0001 for NR vs. TCMR; P <0.001 for NR vs. ABMR; and P <0.0001 for NR vs. MR. By bootstrap resampling, optimism-corrected area under the receiver operating characteristic curve (AUC) was 0.749 (bias-corrected 95% confidence interval [CI], 0.638 to 0.840) for NR vs. TCMR (P<0.0001); 0.780 (95% CI, 0.656 to 0.878) for NR vs. ABMR (P<0.0001); and 0.857 (95% CI, 0.727 to 0.947) for NR vs. MR (P<0.0001). All three rejection biopsy categories were distinguished from NR biopsies with similar accuracy (all AUC comparisons P>0.05). Conclusion Urinary cell three-gene signature score may serve as a universal diagnostic signature of acute rejection due to TCMR, ABMR or MR in human kidney allografts with similar performance characteristics.
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Younuskunju S, Mohamoud YA, Mathew LS, Mayer KFX, Suhre K, Malek JA. Genome-wide association of dry (Tamar) date palm fruit color. Plant Genome 2023; 16:e20373. [PMID: 37621134 DOI: 10.1002/tpg2.20373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 06/05/2023] [Accepted: 07/03/2023] [Indexed: 08/26/2023]
Abstract
Date palm (Phoenix dactylifera) fruit (dates) are an economically and culturally significant crop in the Middle East and North Africa. There are hundreds of different commercial cultivars producing dates with distinctive shapes, colors, and sizes. Genetic studies of some date palm traits have been performed, including sex determination, sugar content, and fresh fruit color. In this study, we used genome sequences and image data of 199 dry dates (Tamar) collected from 14 countries to identify genetic loci associated with the color of this fruit stage. Here, we find loci across multiple linkage groups (LG) associated with dry fruit color phenotype. We recover both the previously identified VIRESCENS (VIR) genotype associated with fresh fruit yellow or red color and new associations with the lightness and darkness of dry fruit. This study will add resolution to our understanding of date color phenotype, especially at the most commercially important Tamar stage.
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Affiliation(s)
- Shameem Younuskunju
- Genomics Laboratory, Weill Cornell Medicine-Qatar, Doha, Qatar
- School of Life Sciences, Technical University of Munich, Munich, Germany
| | | | - Lisa S Mathew
- Clinical Genomics Laboratory, Sidra Medicine, Doha, Qatar
| | - Klaus F X Mayer
- School of Life Sciences, Technical University of Munich, Munich, Germany
- Plant Genome and Systems Biology, Helmholtz Center Munich, Munich, Germany
| | - Karsten Suhre
- Department of Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Joel A Malek
- Genomics Laboratory, Weill Cornell Medicine-Qatar, Doha, Qatar
- Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
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Goudswaard LJ, Smith ML, Hughes DA, Taylor R, Lean M, Sattar N, Welsh P, McConnachie A, Blazeby JM, Rogers CA, Suhre K, Zaghlool SB, Hers I, Timpson NJ, Corbin LJ. Using trials of caloric restriction and bariatric surgery to explore the effects of body mass index on the circulating proteome. Sci Rep 2023; 13:21077. [PMID: 38030643 PMCID: PMC10686974 DOI: 10.1038/s41598-023-47030-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/08/2023] [Indexed: 12/01/2023] Open
Abstract
Thousands of proteins circulate in the bloodstream; identifying those which associate with weight and intervention-induced weight loss may help explain mechanisms of diseases associated with adiposity. We aimed to identify consistent protein signatures of weight loss across independent studies capturing changes in body mass index (BMI). We analysed proteomic data from studies implementing caloric restriction (Diabetes Remission Clinical trial) and bariatric surgery (By-Band-Sleeve), using SomaLogic and Olink Explore1536 technologies, respectively. Linear mixed models were used to estimate the effect of the interventions on circulating proteins. Twenty-three proteins were altered in a consistent direction after both bariatric surgery and caloric restriction, suggesting that these proteins are modulated by weight change, independent of intervention type. We also integrated Mendelian randomisation (MR) estimates of the effect of BMI on proteins measured by SomaLogic from a UK blood donor cohort as a third line of causal evidence. These MR estimates provided further corroborative evidence for a role of BMI in regulating the levels of six proteins including alcohol dehydrogenase-4, nogo receptor and interleukin-1 receptor antagonist protein. These results indicate the importance of triangulation in interrogating causal relationships; further study into the role of proteins modulated by weight in disease is now warranted.
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Affiliation(s)
- Lucy J Goudswaard
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- MRC Integrative Epidemiology Unit, Bristol, UK.
- Physiology, Pharmacology & Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK.
| | - Madeleine L Smith
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - David A Hughes
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - Roy Taylor
- Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, NE4 5PL, UK
| | - Michael Lean
- Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G31 2ER, UK
| | - Naveed Sattar
- School of Cardiovascular and Medical Science, University of Glasgow, Glasgow, G12 8TA, UK
| | - Paul Welsh
- School of Cardiovascular and Medical Science, University of Glasgow, Glasgow, G12 8TA, UK
| | - Alex McConnachie
- Robertson Centre for Biostatistics, School of Health and Wellbeing, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Jane M Blazeby
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Chris A Rogers
- Bristol Medical School, Bristol Trials Centre, University of Bristol, Bristol, BS8 1NU, UK
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Shaza B Zaghlool
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Ingeborg Hers
- Physiology, Pharmacology & Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK
| | - Nicholas J Timpson
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - Laura J Corbin
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
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Belkadi A, Thareja G, Khan A, Stephan N, Zaghlool S, Halama A, Ahmed AA, Mohamoud YA, Malek J, Suhre K, Malik RA. Retinal nerve fibre layer thinning and corneal nerve loss in patients with Bardet-Biedl syndrome. BMC Med Genomics 2023; 16:301. [PMID: 37996899 PMCID: PMC10666305 DOI: 10.1186/s12920-023-01739-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Bardet-Biedl syndrome (BBS) is an autosomal recessive, genetically heterogeneous, pleiotropic disorder caused by variants in genes involved in the function of the primary cilium. We have harnessed genomics to identify BBS and ophthalmic technologies to describe novel features of BBS. CASE PRESENTATION A patient with an unclear diagnosis of syndromic type 2 diabetes mellitus, another affected sibling and unaffected siblings and parents were sequenced using DNA extracted from saliva samples. Corneal confocal microscopy (CCM) and retinal spectral domain optical coherence tomography (SD-OCT) were used to identify novel ophthalmic features in these patients. The two affected individuals had a homozygous variant in C8orf37 (p.Trp185*). SD-OCT and CCM demonstrated a marked and patchy reduction in the retinal nerve fiber layer thickness and loss of corneal nerve fibers, respectively. CONCLUSION This report highlights the use of ophthalmic imaging to identify novel retinal and corneal abnormalities that extend the phenotype of BBS in a patient with syndromic type 2 diabetes.
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Affiliation(s)
- Aziz Belkadi
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Gaurav Thareja
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Adnan Khan
- Faculty of Health Sciences, Khyber Medical University, Peshawar, Pakistan
| | - Nisha Stephan
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Shaza Zaghlool
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Anna Halama
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | | | - Yasmin A Mohamoud
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Joel Malek
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Karsten Suhre
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Rayaz A Malik
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar.
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Gadd DA, Hillary RF, McCartney DL, Zaghlool SB, Stevenson AJ, Cheng Y, Fawns-Ritchie C, Nangle C, Campbell A, Flaig R, Harris SE, Walker RM, Shi L, Tucker-Drob EM, Gieger C, Peters A, Waldenberger M, Graumann J, McRae AF, Deary IJ, Porteous DJ, Hayward C, Visscher PM, Cox SR, Evans KL, McIntosh AM, Suhre K, Marioni RE. Correction: Epigenetic scores for the circulating proteome as tools for disease prediction. eLife 2023; 12:e94481. [PMID: 37982710 PMCID: PMC10659577 DOI: 10.7554/elife.94481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023] Open
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Costeira R, Evangelista L, Wilson R, Yan X, Hellbach F, Sinke L, Christiansen C, Villicaña S, Masachs OM, Tsai PC, Mangino M, Menni C, Berry SE, Beekman M, van Heemst D, Slagboom PE, Heijmans BT, Suhre K, Kastenmüller G, Gieger C, Peters A, Small KS, Linseisen J, Waldenberger M, Bell JT. Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome. Clin Epigenetics 2023; 15:166. [PMID: 37858220 PMCID: PMC10588110 DOI: 10.1186/s13148-023-01578-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants. RESULTS Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals. CONCLUSION Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome.
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Affiliation(s)
- Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
| | - Laila Evangelista
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Rory Wilson
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Xinyu Yan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Fabian Hellbach
- Epidemiology, Medical Faculty, University Augsburg, University Hospital Augsburg, 86156, Augsburg, Germany
| | - Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Olatz M Masachs
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
- Department of Biomedical Sciences, Chang Gung University, Taoyuan City, Taiwan
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Chang Gung Memorial Hospital, Taoyuan City, Taiwan
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, SE1 9NH, UK
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Annette Peters
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, 81377, Munich, Germany
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Jakob Linseisen
- Epidemiology, Medical Faculty, University Augsburg, University Hospital Augsburg, 86156, Augsburg, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, 81377, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
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11
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Nader N, Zarif L, Halama A, Yadav S, Dib M, Attarwala N, Chen Q, Suhre K, Gross SS, Machaca K. Progesterone induces meiosis through two obligate co-receptors with PLA2 activity. bioRxiv 2023:2023.09.09.556646. [PMID: 37905030 PMCID: PMC10614741 DOI: 10.1101/2023.09.09.556646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
The steroid hormone progesterone (P4) regulates multiple aspects of reproductive and metabolic physiology. Classical P4 signaling operates through nuclear receptors that regulate transcription. In addition, P4 signals through membrane P4 receptors (mPRs) in a rapid nongenomic modality. Despite the established physiological importance of P4 nongenomic signaling, its detailed signal transduction remains elusive. Here, using Xenopus oocyte maturation as a well-established physiological readout of nongenomic P4 signaling, we identify the lipid hydrolase ABHD2 (α/β hydrolase domain-containing protein 2) as an essential mPRβ co-receptor to trigger meiosis. We show using functional assays coupled to unbiased and targeted cell-based lipidomics that ABHD2 possesses a phospholipase A2 (PLA2) activity that requires both P4 and mPRβ. This PLA2 activity bifurcates P4 signaling by inducing mPRβ clathrin-dependent endocytosis and producing lipid messengers that are G-protein coupled receptors agonists. Therefore, P4 drives meiosis by inducing the ABHD2 PLA2 activity that requires both mPRβ and ABHD2 as obligate co-receptors.
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Affiliation(s)
- Nancy Nader
- Calcium Signaling Group, Research Department, Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Lubna Zarif
- Calcium Signaling Group, Research Department, Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Research Department, Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Sharan Yadav
- Calcium Signaling Group, Research Department, Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Maya Dib
- Research Department, Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Nabeel Attarwala
- Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA
- Biological Sciences division, University of Chicago, Chicago, IL, USA
| | - Qiuying Chen
- Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Research Department, Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Steven S. Gross
- Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA
| | - Khaled Machaca
- Calcium Signaling Group, Research Department, Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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12
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Shrine N, Izquierdo AG, Chen J, Packer R, Hall RJ, Guyatt AL, Batini C, Thompson RJ, Pavuluri C, Malik V, Hobbs BD, Moll M, Kim W, Tal-Singer R, Bakke P, Fawcett KA, John C, Coley K, Piga NN, Pozarickij A, Lin K, Millwood IY, Chen Z, Li L, Wijnant SRA, Lahousse L, Brusselle G, Uitterlinden AG, Manichaikul A, Oelsner EC, Rich SS, Barr RG, Kerr SM, Vitart V, Brown MR, Wielscher M, Imboden M, Jeong A, Bartz TM, Gharib SA, Flexeder C, Karrasch S, Gieger C, Peters A, Stubbe B, Hu X, Ortega VE, Meyers DA, Bleecker ER, Gabriel SB, Gupta N, Smith AV, Luan J, Zhao JH, Hansen AF, Langhammer A, Willer C, Bhatta L, Porteous D, Smith BH, Campbell A, Sofer T, Lee J, Daviglus ML, Yu B, Lim E, Xu H, O'Connor GT, Thareja G, Albagha OME, Suhre K, Granell R, Faquih TO, Hiemstra PS, Slats AM, Mullin BH, Hui J, James A, Beilby J, Patasova K, Hysi P, Koskela JT, Wyss AB, Jin J, Sikdar S, Lee M, May-Wilson S, Pirastu N, Kentistou KA, Joshi PK, Timmers PRHJ, Williams AT, Free RC, Wang X, Morrison JL, Gilliland FD, Chen Z, Wang CA, Foong RE, Harris SE, Taylor A, Redmond P, Cook JP, Mahajan A, Lind L, Palviainen T, Lehtimäki T, Raitakari OT, Kaprio J, Rantanen T, Pietiläinen KH, Cox SR, Pennell CE, Hall GL, Gauderman WJ, Brightling C, Wilson JF, Vasankari T, Laitinen T, Salomaa V, Mook-Kanamori DO, Timpson NJ, Zeggini E, Dupuis J, Hayward C, Brumpton B, Langenberg C, Weiss S, Homuth G, Schmidt CO, Probst-Hensch N, Jarvelin MR, Morrison AC, Polasek O, Rudan I, Lee JH, Sayers I, Rawlins EL, Dudbridge F, Silverman EK, Strachan DP, Walters RG, Morris AP, London SJ, Cho MH, Wain LV, Hall IP, Tobin MD. Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk. Nat Genet 2023; 55:1778-1779. [PMID: 37749248 PMCID: PMC10562210 DOI: 10.1038/s41588-023-01531-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Affiliation(s)
- Nick Shrine
- Department of Population Health Sciences, University of Leicester, Leicester, UK.
| | - Abril G Izquierdo
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Jing Chen
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Richard Packer
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Robert J Hall
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Anna L Guyatt
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Chiara Batini
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Rebecca J Thompson
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Chandan Pavuluri
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Vidhi Malik
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian D Hobbs
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Per Bakke
- Department of Clinical Science, Unversity of Bergen, Bergen, Norway
| | - Katherine A Fawcett
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Catherine John
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Noemi Nicole Piga
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Alfred Pozarickij
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Sara R A Wijnant
- Department of Respiratory Diseases, Ghent Universital Hospital, Ghent, Belgium
- Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Lies Lahousse
- Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Guy Brusselle
- Department of Respiratory Diseases, Ghent Universital Hospital, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Andre G Uitterlinden
- Department of Internal Medicine, Eramus Medical Center, Rotterdam, The Netherlands
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Elizabeth C Oelsner
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Shona M Kerr
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Matthias Wielscher
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Medea Imboden
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Ayoung Jeong
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Departments of Medicine and Biostatistics, University of Washington, Seattle, WA, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Claudia Flexeder
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Stefan Karrasch
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - 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
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig Maximilian University, Munich, Germany
| | - Beate Stubbe
- Department of Internal Medicine B-Cardiology, Intensive Care, Pulmonary Medicine and Infectious Diseases, University Medicine Greifswald, Greifswald, Germany
| | - Xiaowei Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Victor E Ortega
- Division of Respiratory Medicine, Department of Internal Medicine, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA
| | | | | | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Albert Vernon Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Jing-Hua Zhao
- Department of Public and Primary Care, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Arnulf Langhammer
- HUNT Research Centre, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Cristen Willer
- Division of Cardiology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - David Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Elise Lim
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Hanfei Xu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - George T O'Connor
- Pulmonary Center, School of Medicine, Boston University, Boston, MA, USA
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Omar M E Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
- Center for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA
| | - Raquel Granell
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tariq O Faquih
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Pieter S Hiemstra
- Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands
| | - Annelies M Slats
- Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands
| | - Benjamin H Mullin
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Jennie Hui
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
- School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
- PathWest Laboratory Medicine of WA, Nedlands, Western Australia, Australia
| | - Alan James
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
| | - Karina Patasova
- Department of Twin Research and Genetic Epidemiology, King's College London School of Medicine, London, UK
- Division of Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Pirro Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London School of Medicine, London, UK
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Jukka T Koskela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Annah B Wyss
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | | | - Sinjini Sikdar
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, USA
| | - Mikyeong Lee
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Sebastian May-Wilson
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Katherine A Kentistou
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Paul R H J Timmers
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Alexander T Williams
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Robert C Free
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Xueyang Wang
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - John L Morrison
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Frank D Gilliland
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Carol A Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Rachel E Foong
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, Perth, Western Australia, Australia
- School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Sarah E Harris
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Adele Taylor
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland
| | - Taina Rantanen
- Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Obesity and Abdominal Centers, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Simon R Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Department of Maternity and Gynaecology, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - Graham L Hall
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, Perth, Western Australia, Australia
- School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - W James Gauderman
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chris Brightling
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Infection, Inflammation and Immunity, Institute for Lung Health, University of Leicester, Leicester, UK
| | - James F Wilson
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Tuula Vasankari
- FILHA-Finnish Lung Health Association, Helsinki, Finland
- Department of Respiratory Diseases and Allergology, University of Turku, Turku, Finland
| | - Tarja Laitinen
- Administration Center, Tampere University Hospital and University of Tampere, Tampere, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- ALSPAC, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleftheria Zeggini
- Wellcome Sanger Institute, Cambridge, UK
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ben Brumpton
- HUNT Research Centre, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Carsten Oliver Schmidt
- Institute for Community Medicine, SHIP-Clinical Epidemiological Research, University Medicine Greifswald, Greifswald, Germany
| | - Nicole Probst-Hensch
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Marjo-Riitta Jarvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ozren Polasek
- School of Medicine, University of Split, Split, Croatia
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Joo-Hyeon Lee
- Jeffrey Cheah Biomedical Centre, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Ian Sayers
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Emma L Rawlins
- Wellcome Trust-CRUK Gurdon Institute and Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Frank Dudbridge
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Edwin K Silverman
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David P Strachan
- Population Health Research Institute, St George's University of London, London, UK
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Louise V Wain
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Ian P Hall
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Martin D Tobin
- Department of Population Health Sciences, University of Leicester, Leicester, UK.
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK.
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13
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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: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Hendi NN, Al-Sarraj Y, Ismail Umlai UK, Suhre K, Nemer G, Albagha O. Genetic determinants of Vitamin D deficiency in the Middle Eastern Qatari population: a genome-wide association study. Front Nutr 2023; 10:1242257. [PMID: 37841410 PMCID: PMC10570512 DOI: 10.3389/fnut.2023.1242257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 09/15/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction Epidemiological studies have consistently revealed that Vitamin D deficiency is most prevalent in Middle Eastern countries. However, research on the impact of genetic loci and polygenic models related to Vitamin D has primarily focused on European populations. Methods We conducted the first genome-wide association study to identify genetic determinants of Vitamin D levels in Middle Easterners using a whole genome sequencing approach in 6,047 subjects from the Qatar Biobank (QBB) project. We performed a GWAS meta-analysis, combining the QBB cohort with recent European GWAS data from the UK Biobank (involving 345,923 individuals). Additionally, we evaluated the performance of European-derived polygenic risk scores using UK Biobank data in the QBB cohort. Results Our study identified an association between a variant in a known locus for the group-specific component gene (GC), specifically rs2298850 (p-value = 1.71 × 10-08, Beta = -0.1285), and Vitamin D levels. Furthermore, our GWAS meta-analysis identified two novel variants at a known locus on chromosome 11, rs67609747 and rs1945603, that reached the GWAS significance threshold. Notably, we observed a moderately high heritability of Vitamin D, estimated at 18%, compared to Europeans. Despite the lower predictive performance of Vitamin D levels in Qataris compared to Europeans, the European-derived polygenic risk scores exhibited significant links to Vitamin D deficiency risk within the QBB cohort. Conclusion This novel study reveals the genetic architecture contributing to Vitamin D deficiency in the Qatari population, emphasizing the genetic heterogeneity across different populations.
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Affiliation(s)
- Nagham Nafiz Hendi
- Division of Biological and Biomedical Sciences, College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Yasser Al-Sarraj
- Division of Genomics and Translational Biomedicine, College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
- Qatar Genome Program (QGP), Qatar Foundation Research, Development and Innovation, Qatar Foundation (QF), Doha, Qatar
| | - Umm-Kulthum Ismail Umlai
- Division of Genomics and Translational Biomedicine, College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Georges Nemer
- Division of Genomics and Translational Biomedicine, College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Omar Albagha
- Division of Genomics and Translational Biomedicine, College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
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15
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van der Spek A, Stewart ID, Kühnel B, Pietzner M, Alshehri T, Gauß F, Hysi PG, MahmoudianDehkordi S, Heinken A, Luik AI, Ladwig KH, Kastenmüller G, Menni C, Hertel J, Ikram MA, de Mutsert R, Suhre K, Gieger C, Strauch K, Völzke H, Meitinger T, Mangino M, Flaquer A, Waldenberger M, Peters A, Thiele I, Kaddurah-Daouk R, Dunlop BW, Rosendaal FR, Wareham NJ, Spector TD, Kunze S, Grabe HJ, Mook-Kanamori DO, Langenberg C, van Duijn CM, Amin N. Circulating metabolites modulated by diet are associated with depression. Mol Psychiatry 2023; 28:3874-3887. [PMID: 37495887 PMCID: PMC10730409 DOI: 10.1038/s41380-023-02180-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023]
Abstract
Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.
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Affiliation(s)
- Ashley van der Spek
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- SkylineDx B.V., Rotterdam, The Netherlands
| | | | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Tahani Alshehri
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Friederike Gauß
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Str, 17475, Greifswald, Germany
| | - Pirro G Hysi
- Department of Twins Research and Genetic Epidemiology, Kings College London, London, UK
| | | | - Almut Heinken
- School of Medicine, University of Galway, University Road, Galway, Ireland
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy, France
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Karl-Heinz Ladwig
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Department of Psychosomatic Medicine and Psychotherapy, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), D-85764, Neuherberg, Germany
| | - Cristina Menni
- Department of Twins Research and Genetic Epidemiology, Kings College London, London, UK
| | - Johannes Hertel
- School of Medicine, University of Galway, University Road, Galway, Ireland
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Ellernholzstrasse 1-2, 17489, Greifswald, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO, 24144, Doha, Qatar
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), D-85764, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Henry Völzke
- Institute of Community Medicine, University Medicine Greifswald, Walter-Rathenau Str. 48, 17475, Greifswald, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Massimo Mangino
- Department of Twins Research and Genetic Epidemiology, Kings College London, London, UK
| | - Antonia Flaquer
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Ludwig-Maximilians-Universität München, IBE-Chair of Epidemiology, Munich, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, University Road, Galway, Ireland
- Division of Microbiology, University of Galway, Galway, Ireland
- APC Microbiome, Ireland, Ireland
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, US
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Tim D Spector
- Department of Twins Research and Genetic Epidemiology, Kings College London, London, UK
| | - Sonja Kunze
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
| | - Hans Jörgen Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Ellernholzstrasse 1-2, 17489, Greifswald, Germany
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, OX3 7LF, Oxford, UK
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
- Nuffield Department of Population Health, University of Oxford, OX3 7LF, Oxford, UK.
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16
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Weinisch P, Raffler J, Römisch-Margl W, Arnold M, Mohney RP, Rist MJ, Prehn C, Skurk T, Hauner H, Daniel H, Suhre K, Kastenmüller G. The HuMet Repository: Watching human metabolism at work. bioRxiv 2023:2023.08.08.550079. [PMID: 37609175 PMCID: PMC10441358 DOI: 10.1101/2023.08.08.550079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.
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Affiliation(s)
- Patrick Weinisch
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Werner Römisch-Margl
- 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
| | | | - Manuela J. Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Skurk
- ZIEL Institute for Food and Health, Core Facility Human Studies, Technical University of Munich, Freising, Germany
- Else Kröner Fresenius Center of Nutritional Medicine, Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Hans Hauner
- Else Kröner Fresenius Center of Nutritional Medicine, Department of Food and Nutrition, Technical University of Munich, Freising, Germany
- Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Hannelore Daniel
- Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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17
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Carland C, Png G, Malarstig A, Kho PF, Gustafsson S, Michaelsson K, Lind L, Tsafantakis E, Karaleftheri M, Dedoussis G, Ramisch A, Macdonald-Dunlop E, Klaric L, Joshi PK, Chen Y, Björck HM, Eriksson P, Carrasco-Zanini J, Wheeler E, Suhre K, Gilly A, Zeggini E, Viñuela A, Dermitzakis ET, Wilson JF, Langenberg C, Thareja G, Halama A, Schmidt F, Zanetti D, Assimes T. Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases. Clin Proteomics 2023; 20:31. [PMID: 37550624 PMCID: PMC10405520 DOI: 10.1186/s12014-023-09421-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/17/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. METHODS We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. RESULTS We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). CONCLUSION Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.
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Affiliation(s)
- Corinne Carland
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Grace Png
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Anders Malarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
| | - Pik Fang Kho
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford Cardiovascular Institute, Palo Alto, CA, USA
| | - Stefan Gustafsson
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Karl Michaelsson
- Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Lars Lind
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | | | | | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | - Anna Ramisch
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva Medical School, Geneva, Switzerland
| | - Erin Macdonald-Dunlop
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Yan Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Hanna M Björck
- Cardiovascular Medicine, Medicine, Karolinska Institute, Stockholm, Sweden
| | - Per Eriksson
- Cardiovascular Medicine, Medicine, Karolinska Institute, Stockholm, Sweden
| | | | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Karsten Suhre
- Bioinformatics Core, Cornell Medicine - Qatar Research, Doha, Qatar
| | - Arthur Gilly
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | - Ana Viñuela
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva Medical School, Geneva, Switzerland
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Gaurav Thareja
- Bioinformatics Core, Cornell Medicine - Qatar Research, Doha, Qatar
| | - Anna Halama
- Bioinformatics Core, Cornell Medicine - Qatar Research, Doha, Qatar
| | - Frank Schmidt
- Proteomics Core, Research, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Daniela Zanetti
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford Cardiovascular Institute, Palo Alto, CA, USA
| | - Themistocles Assimes
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford Cardiovascular Institute, Palo Alto, CA, USA.
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18
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Ngo D, Pratte KA, Flexeder C, Petersen H, Dang H, Ma Y, Keyes MJ, Gao Y, Deng S, Peterson BD, Farrell LA, Bhambhani VM, Palacios C, Quadir J, Gillenwater L, Xu H, Emson C, Gieger C, Suhre K, Graumann J, Jain D, Conomos MP, Tracy RP, Guo X, Liu Y, Johnson WC, Cornell E, Durda P, Taylor KD, Papanicolaou GJ, Rich SS, Rotter JI, Rennard SI, Curtis JL, Woodruff PG, Comellas AP, Silverman EK, Crapo JD, Larson MG, Vasan RS, Wang TJ, Correa A, Sims M, Wilson JG, Gerszten RE, O’Connor GT, Barr RG, Couper D, Dupuis J, Manichaikul A, O’Neal WK, Tesfaigzi Y, Schulz H, Bowler RP. Systemic Markers of Lung Function and Forced Expiratory Volume in 1 Second Decline across Diverse Cohorts. Ann Am Thorac Soc 2023; 20:1124-1135. [PMID: 37351609 PMCID: PMC10405603 DOI: 10.1513/annalsats.202210-857oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 06/13/2023] [Indexed: 06/24/2023] Open
Abstract
Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease characterized by airway obstruction and accelerated lung function decline. Our understanding of systemic protein biomarkers associated with COPD remains incomplete. Objectives: To determine what proteins and pathways are associated with impaired pulmonary function in a diverse population. Methods: We studied 6,722 participants across six cohort studies with both aptamer-based proteomic and spirometry data (4,566 predominantly White participants in a discovery analysis and 2,156 African American cohort participants in a validation). In linear regression models, we examined protein associations with baseline forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC). In linear mixed effects models, we investigated the associations of baseline protein levels with rate of FEV1 decline (ml/yr) in 2,777 participants with up to 7 years of follow-up spirometry. Results: We identified 254 proteins associated with FEV1 in our discovery analyses, with 80 proteins validated in the Jackson Heart Study. Novel validated protein associations include kallistatin serine protease inhibitor, growth differentiation factor 2, and tumor necrosis factor-like weak inducer of apoptosis (discovery β = 0.0561, Q = 4.05 × 10-10; β = 0.0421, Q = 1.12 × 10-3; and β = 0.0358, Q = 1.67 × 10-3, respectively). In longitudinal analyses within cohorts with follow-up spirometry, we identified 15 proteins associated with FEV1 decline (Q < 0.05), including elafin leukocyte elastase inhibitor and mucin-associated TFF2 (trefoil factor 2; β = -4.3 ml/yr, Q = 0.049; β = -6.1 ml/yr, Q = 0.032, respectively). Pathways and processes highlighted by our study include aberrant extracellular matrix remodeling, enhanced innate immune response, dysregulation of angiogenesis, and coagulation. Conclusions: In this study, we identify and validate novel biomarkers and pathways associated with lung function traits in a racially diverse population. In addition, we identify novel protein markers associated with FEV1 decline. Several protein findings are supported by previously reported genetic signals, highlighting the plausibility of certain biologic pathways. These novel proteins might represent markers for risk stratification, as well as novel molecular targets for treatment of COPD.
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Affiliation(s)
- Debby Ngo
- Cardiovascular Research Institute
- Division of Pulmonary, Critical Care, and Sleep Medicine, and
| | | | - Claudia Flexeder
- Institute of Epidemiology and
- Comprehensive Pneumology Center Munich (CPC-M) as member of the German Center for Lung Research (DZL), Munich, Germany
- Institute and Clinic for Occupational, Social, and Environmental Medicine, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Hans Petersen
- Lovelace Respiratory Research Institute, Albuquerque, New Mexico
| | - Hong Dang
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Yanlin Ma
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | | | - Yan Gao
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi; and
- Institute and Clinic for Occupational, Social, and Environmental Medicine, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | | | | | | | | | | | | | | | - Hanfei Xu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Claire Emson
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, Maryland
| | - Christian Gieger
- Institute of Epidemiology and
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine Qatar, Education City, Doha, Qatar
| | | | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Matthew P. Conomos
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Russell P. Tracy
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA (University of California, Los Angeles) Medical Center, Torrance, California
| | - Yongmei Liu
- Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, North Carolina
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Elaine Cornell
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont
| | - Peter Durda
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA (University of California, Los Angeles) Medical Center, Torrance, California
| | - George J. Papanicolaou
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA (University of California, Los Angeles) Medical Center, Torrance, California
| | - Steven I. Rennard
- Pulmonary, Critical Care, Allergy, and Sleep Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California
| | | | - Prescott G. Woodruff
- Pulmonary, Critical Care, Allergy, and Sleep Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California
| | | | | | | | - Martin G. Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts
| | - Ramachandran S. Vasan
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts
- Division of Preventive Medicine and
- Division of Cardiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Thomas J. Wang
- Department of Medicine, UT (University of Texas) Southwestern Medical Center, Dallas, Texas
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Adolfo Correa
- Jackson Heart Study, Department of Medicine, and
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi; and
| | - Mario Sims
- Jackson Heart Study, Department of Medicine, and
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi; and
| | - James G. Wilson
- Cardiovascular Research Institute
- Jackson Heart Study, Department of Medicine, and
| | - Robert E. Gerszten
- Cardiovascular Research Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - George T. O’Connor
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts
- Pulmonary Center, Department of Medicine, Boston University, Boston, Massachusetts
| | - R. Graham Barr
- Department of Medicine and
- Department of Epidemiology, Columbia University, New York, New York
| | - David Couper
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Wanda K. O’Neal
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Yohannes Tesfaigzi
- Lovelace Respiratory Research Institute, Albuquerque, New Mexico
- Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Holger Schulz
- Institute of Epidemiology and
- Comprehensive Pneumology Center Munich (CPC-M) as member of the German Center for Lung Research (DZL), Munich, Germany
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19
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Schmidt F, Abdesselem HB, Suhre K, Vaikath NN, Sohail MU, Al-Nesf M, Bensmail I, Mashod F, Sarwath H, Bernhardt J, Schaefer-Ramadan S, Tan TM, Morris PE, Schenck EJ, Price D, Mohamed-Ali V, Al-Maadheed M, Arredouani A, Decock J, Blackburn JM, Choi AMK, El-Agnaf OM. Auto-immunoproteomics analysis of COVID-19 ICU patients revealed increased levels of autoantibodies related to the male reproductive system. Front Physiol 2023; 14:1203723. [PMID: 37520825 PMCID: PMC10374950 DOI: 10.3389/fphys.2023.1203723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Background: Coronavirus disease (COVID-19) manifests many clinical symptoms, including an exacerbated immune response and cytokine storm. Autoantibodies in COVID-19 may have severe prodromal effects that are poorly understood. The interaction between these autoantibodies and self-antigens can result in systemic inflammation and organ dysfunction. However, the role of autoantibodies in COVID-19 complications has yet to be fully understood. Methods: The current investigation screened two independent cohorts of 97 COVID-19 patients [discovery (Disc) cohort from Qatar (case = 49 vs. control = 48) and replication (Rep) cohort from New York (case = 48 vs. control = 28)] utilizing high-throughput KoRectly Expressed (KREX) Immunome protein-array technology. Total IgG autoantibody responses were evaluated against 1,318 correctly folded and full-length human proteins. Samples were randomly applied on the precoated microarray slides for 2 h. Cy3-labeled secondary antibodies were used to detect IgG autoantibody response. Slides were scanned at a fixed gain setting using the Agilent fluorescence microarray scanner, generating a 16-bit TIFF file. Group comparisons were performed using a linear model and Fisher's exact test. Differentially expressed proteins were used for KEGG and WIKIpathway annotation to determine pathways in which the proteins of interest were significantly over-represented. Results and conclusion: Autoantibody responses to 57 proteins were significantly altered in the COVID-19 Disc cohort compared to healthy controls (p ≤ 0.05). The Rep cohort had altered autoantibody responses against 26 proteins compared to non-COVID-19 ICU patients who served as controls. Both cohorts showed substantial similarities (r 2 = 0.73) and exhibited higher autoantibody responses to numerous transcription factors, immunomodulatory proteins, and human disease markers. Analysis of the combined cohorts revealed elevated autoantibody responses against SPANXN4, STK25, ATF4, PRKD2, and CHMP3 proteins in COVID-19 patients. The sequences for SPANXN4 and STK25 were cross-validated using sequence alignment tools. ELISA and Western blot further verified the autoantigen-autoantibody response of SPANXN4. SPANXN4 is essential for spermiogenesis and male fertility, which may predict a potential role for this protein in COVID-19-associated male reproductive tract complications, and warrants further research.
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Affiliation(s)
- Frank Schmidt
- Proteomics Core, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Houari B. Abdesselem
- Proteomics Core Facility, Qatar Biomedical Research Institute (QBRI), Qatar Foundation, Hamad Bin Khalifa University (HBKU), Doha, Qatar
- Neurological Disorders Research Center, QBRI, HBKU, Qatar Foundation, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Nishant N. Vaikath
- Neurological Disorders Research Center, QBRI, HBKU, Qatar Foundation, Doha, Qatar
| | | | - Maryam Al-Nesf
- Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
- Center of Metabolism and Inflammation, Division of Medicine, University College London, London, United Kingdom
| | - Ilham Bensmail
- Proteomics Core Facility, Qatar Biomedical Research Institute (QBRI), Qatar Foundation, Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Fathima Mashod
- Proteomics Core, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Joerg Bernhardt
- Institute for Microbiology, University of Greifswald, Greifswald, Germany
| | | | - Ti-Myen Tan
- Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Sengenics Corporation, Damansara Heights, Kuala Lumpur, Malaysia
- Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Priscilla E. Morris
- Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Sengenics Corporation, Damansara Heights, Kuala Lumpur, Malaysia
- Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Edward J. Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York Presbyterian Hospital—Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - David Price
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York Presbyterian Hospital—Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - Vidya Mohamed-Ali
- Center of Metabolism and Inflammation, Division of Medicine, University College London, London, United Kingdom
- Anti-Doping Laboratory Qatar, Doha, Qatar
| | - Mohammed Al-Maadheed
- Center of Metabolism and Inflammation, Division of Medicine, University College London, London, United Kingdom
- Anti-Doping Laboratory Qatar, Doha, Qatar
| | - Abdelilah Arredouani
- Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
| | - Julie Decock
- College of Health and Life Sciences, Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
- Translational Cancer and Immunity Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
| | - Jonathan M. Blackburn
- Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Sengenics Corporation, Damansara Heights, Kuala Lumpur, Malaysia
- Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Augustine M. K. Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York Presbyterian Hospital—Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - Omar M. El-Agnaf
- Neurological Disorders Research Center, QBRI, HBKU, Qatar Foundation, Doha, Qatar
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20
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Shi M, Han S, Klier K, Fobo G, Montrone C, Yu S, Harada M, Henning AK, Friedrich N, Bahls M, Dörr M, Nauck M, Völzke H, Homuth G, Grabe HJ, Prehn C, Adamski J, Suhre K, Rathmann W, Ruepp A, Hertel J, Peters A, Wang-Sattler R. Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts. Cardiovasc Diabetol 2023; 22:141. [PMID: 37328862 PMCID: PMC10276453 DOI: 10.1186/s12933-023-01862-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/20/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. METHODS We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. RESULTS We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. CONCLUSION Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.
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Affiliation(s)
- Mengya Shi
- TUM School of Medicine, Technical University of Munich (TUM), Munich, Germany
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany
| | - Siyu Han
- TUM School of Medicine, Technical University of Munich (TUM), Munich, Germany
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany
| | - Kristin Klier
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Gisela Fobo
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Corinna Montrone
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Shixiang Yu
- TUM School of Medicine, Technical University of Munich (TUM), Munich, Germany
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Makoto Harada
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany
| | - Ann-Kristin Henning
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Martin Bahls
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Henry Völzke
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Diabetes Research (DZD), Partner Greifswald, Neuherberg, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine—Qatar, Education City—Qatar Foundation, Doha, Qatar
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), Partner Düsseldorf, Neuherberg, Germany
- Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Andreas Ruepp
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Hertel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Annette Peters
- German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, Ludwig Maximilian University of Munich (LMU), Munich, Germany
- Munich Heart Alliance, German Center for Cardiovascular Health (DZHK E.V., Partner-Site Munich), Munich, Germany
| | - Rui Wang-Sattler
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, Ludwig Maximilian University of Munich (LMU), Munich, Germany
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21
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Thareja G, Belkadi A, Arnold M, Albagha OME, Graumann J, Schmidt F, Grallert H, Peters A, Gieger C, Consortium TQGPR, Suhre K. Differences and commonalities in the genetic architecture of protein quantitative trait loci in European and Arab populations. Hum Mol Genet 2023; 32:907-916. [PMID: 36168886 PMCID: PMC9990988 DOI: 10.1093/hmg/ddac243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/19/2022] [Accepted: 09/22/2022] [Indexed: 11/12/2022] Open
Abstract
Polygenic scores (PGS) can identify individuals at risk of adverse health events and guide genetics-based personalized medicine. However, it is not clear how well PGS translate between different populations, limiting their application to well-studied ethnicities. Proteins are intermediate traits linking genetic predisposition and environmental factors to disease, with numerous blood circulating protein levels representing functional readouts of disease-related processes. We hypothesized that studying the genetic architecture of a comprehensive set of blood-circulating proteins between a European and an Arab population could shed fresh light on the translatability of PGS to understudied populations. We therefore conducted a genome-wide association study with whole-genome sequencing data using 1301 proteins measured on the SOMAscan aptamer-based affinity proteomics platform in 2935 samples of Qatar Biobank and evaluated the replication of protein quantitative traits (pQTLs) from European studies in an Arab population. Then, we investigated the colocalization of shared pQTL signals between the two populations. Finally, we compared the performance of protein PGS derived from a Caucasian population in a European and an Arab cohort. We found that the majority of shared pQTL signals (81.8%) colocalized between both populations. About one-third of the genetic protein heritability was explained by protein PGS derived from a European cohort, with protein PGS performing ~20% better in Europeans when compared to Arabs. Our results are relevant for the translation of PGS to non-Caucasian populations, as well as for future efforts to extend genetic research to understudied populations.
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Affiliation(s)
- Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar.,Department of Biophysics and Physiology, Weill Cornell Medicine, NY 10065, New York, USA
| | - Aziz Belkadi
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar.,Department of Biophysics and Physiology, Weill Cornell Medicine, NY 10065, New York, USA
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.,Department of Psychiatry and Behavioral Sciences, Duke University, NC 27710, USA
| | - Omar M E Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, 34110 Doha, Qatar.,Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, EH4 2XU, Edinburgh, UK
| | - Johannes Graumann
- Institute of Translational Proteomics, Department of Medicine, Philipps-Universität Marburg, Marburg, Germany
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, Neuherberg 85764, Germany
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, Neuherberg 85764, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.,Department of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, Neuherberg 85764, Germany
| | | | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar.,Department of Biophysics and Physiology, Weill Cornell Medicine, NY 10065, New York, USA
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22
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Shrine N, Izquierdo AG, Chen J, Packer R, Hall RJ, Guyatt AL, Batini C, Thompson RJ, Pavuluri C, Malik V, Hobbs BD, Moll M, Kim W, Tal-Singer R, Bakke P, Fawcett KA, John C, Coley K, Piga NN, Pozarickij A, Lin K, Millwood IY, Chen Z, Li L, Wijnant SRA, Lahousse L, Brusselle G, Uitterlinden AG, Manichaikul A, Oelsner EC, Rich SS, Barr RG, Kerr SM, Vitart V, Brown MR, Wielscher M, Imboden M, Jeong A, Bartz TM, Gharib SA, Flexeder C, Karrasch S, Gieger C, Peters A, Stubbe B, Hu X, Ortega VE, Meyers DA, Bleecker ER, Gabriel SB, Gupta N, Smith AV, Luan J, Zhao JH, Hansen AF, Langhammer A, Willer C, Bhatta L, Porteous D, Smith BH, Campbell A, Sofer T, Lee J, Daviglus ML, Yu B, Lim E, Xu H, O'Connor GT, Thareja G, Albagha OME, Suhre K, Granell R, Faquih TO, Hiemstra PS, Slats AM, Mullin BH, Hui J, James A, Beilby J, Patasova K, Hysi P, Koskela JT, Wyss AB, Jin J, Sikdar S, Lee M, May-Wilson S, Pirastu N, Kentistou KA, Joshi PK, Timmers PRHJ, Williams AT, Free RC, Wang X, Morrison JL, Gilliland FD, Chen Z, Wang CA, Foong RE, Harris SE, Taylor A, Redmond P, Cook JP, Mahajan A, Lind L, Palviainen T, Lehtimäki T, Raitakari OT, Kaprio J, Rantanen T, Pietiläinen KH, Cox SR, Pennell CE, Hall GL, Gauderman WJ, Brightling C, Wilson JF, Vasankari T, Laitinen T, Salomaa V, Mook-Kanamori DO, Timpson NJ, Zeggini E, Dupuis J, Hayward C, Brumpton B, Langenberg C, Weiss S, Homuth G, Schmidt CO, Probst-Hensch N, Jarvelin MR, Morrison AC, Polasek O, Rudan I, Lee JH, Sayers I, Rawlins EL, Dudbridge F, Silverman EK, Strachan DP, Walters RG, Morris AP, London SJ, Cho MH, Wain LV, Hall IP, Tobin MD. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk. Nat Genet 2023; 55:410-422. [PMID: 36914875 PMCID: PMC10011137 DOI: 10.1038/s41588-023-01314-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 01/25/2023] [Indexed: 03/16/2023]
Abstract
Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies.
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Affiliation(s)
- Nick Shrine
- Department of Population Health Sciences, University of Leicester, Leicester, UK.
| | - Abril G Izquierdo
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Jing Chen
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Richard Packer
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Robert J Hall
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Anna L Guyatt
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Chiara Batini
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Rebecca J Thompson
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Chandan Pavuluri
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Vidhi Malik
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian D Hobbs
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Per Bakke
- Department of Clinical Science, Unversity of Bergen, Bergen, Norway
| | - Katherine A Fawcett
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Catherine John
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Noemi Nicole Piga
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Alfred Pozarickij
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Sara R A Wijnant
- Department of Respiratory Diseases, Ghent Universital Hospital, Ghent, Belgium
- Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Lies Lahousse
- Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Guy Brusselle
- Department of Respiratory Diseases, Ghent Universital Hospital, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Andre G Uitterlinden
- Department of Internal Medicine, Eramus Medical Center, Rotterdam, The Netherlands
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Elizabeth C Oelsner
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Shona M Kerr
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Matthias Wielscher
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Medea Imboden
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Ayoung Jeong
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Departments of Medicine and Biostatistics, University of Washington, Seattle, WA, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Claudia Flexeder
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Stefan Karrasch
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - 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
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig Maximilian University, Munich, Germany
| | - Beate Stubbe
- Department of Internal Medicine B-Cardiology, Intensive Care, Pulmonary Medicine and Infectious Diseases, University Medicine Greifswald, Greifswald, Germany
| | - Xiaowei Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Victor E Ortega
- Division of Respiratory Medicine, Department of Internal Medicine, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA
| | | | | | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Albert Vernon Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Jing-Hua Zhao
- Department of Public and Primary Care, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Arnulf Langhammer
- HUNT Research Centre, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Cristen Willer
- Division of Cardiology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - David Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Elise Lim
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Hanfei Xu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - George T O'Connor
- Pulmonary Center, School of Medicine, Boston University, Boston, MA, USA
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Omar M E Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
- Center for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA
| | - Raquel Granell
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tariq O Faquih
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Pieter S Hiemstra
- Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands
| | - Annelies M Slats
- Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands
| | - Benjamin H Mullin
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Jennie Hui
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
- School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
- PathWest Laboratory Medicine of WA, Nedlands, Western Australia, Australia
| | - Alan James
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
| | - Karina Patasova
- Department of Twin Research and Genetic Epidemiology, King's College London School of Medicine, London, UK
- Division of Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Pirro Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London School of Medicine, London, UK
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Jukka T Koskela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Annah B Wyss
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | | | - Sinjini Sikdar
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, USA
| | - Mikyeong Lee
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Sebastian May-Wilson
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Katherine A Kentistou
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Paul R H J Timmers
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Alexander T Williams
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Robert C Free
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Xueyang Wang
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - John L Morrison
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Frank D Gilliland
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Carol A Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Rachel E Foong
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, Perth, Western Australia, Australia
- School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Sarah E Harris
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Adele Taylor
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland
| | - Taina Rantanen
- Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Obesity and Abdominal Centers, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Simon R Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Department of Maternity and Gynaecology, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - Graham L Hall
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, Perth, Western Australia, Australia
- School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - W James Gauderman
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chris Brightling
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Infection, Inflammation and Immunity, Institute for Lung Health, University of Leicester, Leicester, UK
| | - James F Wilson
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Tuula Vasankari
- FILHA-Finnish Lung Health Association, Helsinki, Finland
- Department of Respiratory Diseases and Allergology, University of Turku, Turku, Finland
| | - Tarja Laitinen
- Administration Center, Tampere University Hospital and University of Tampere, Tampere, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- ALSPAC, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleftheria Zeggini
- Wellcome Sanger Institute, Cambridge, UK
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ben Brumpton
- HUNT Research Centre, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Carsten Oliver Schmidt
- Institute for Community Medicine, SHIP-Clinical Epidemiological Research, University Medicine Greifswald, Greifswald, Germany
| | - Nicole Probst-Hensch
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Marjo-Riitta Jarvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ozren Polasek
- School of Medicine, University of Split, Split, Croatia
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Joo-Hyeon Lee
- Jeffrey Cheah Biomedical Centre, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Ian Sayers
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Emma L Rawlins
- Wellcome Trust-CRUK Gurdon Institute and Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Frank Dudbridge
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Edwin K Silverman
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David P Strachan
- Population Health Research Institute, St George's University of London, London, UK
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Louise V Wain
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Ian P Hall
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Martin D Tobin
- Department of Population Health Sciences, University of Leicester, Leicester, UK.
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK.
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23
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Batra R, Uni R, Akchurin OM, Alvarez-Mulett S, Gómez-Escobar LG, Patino E, Hoffman KL, Simmons W, Whalen W, Chetnik K, Buyukozkan M, Benedetti E, Suhre K, Schenck E, Cho SJ, Choi AMK, Schmidt F, Choi ME, Krumsiek J. Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS. Mol Med 2023; 29:13. [PMID: 36703108 PMCID: PMC9879238 DOI: 10.1186/s10020-023-00609-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/11/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. METHODS We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles. RESULTS The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. CONCLUSION In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.
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Affiliation(s)
- Richa Batra
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Rie Uni
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY USA
| | - Oleh M. Akchurin
- grid.5386.8000000041936877XDivision of Pediatric Nephrology, Department of Pediatrics, Weill Cornell Medicine, New York, NY USA ,grid.413734.60000 0000 8499 1112New York-Presbyterian Hospital, New York, NY USA
| | - Sergio Alvarez-Mulett
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Luis G. Gómez-Escobar
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Edwin Patino
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY USA
| | - Katherine L. Hoffman
- grid.5386.8000000041936877XDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY USA
| | - Will Simmons
- grid.5386.8000000041936877XDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY USA
| | - William Whalen
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Kelsey Chetnik
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Mustafa Buyukozkan
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Elisa Benedetti
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Karsten Suhre
- grid.418818.c0000 0001 0516 2170Bioinformatics Core, Weill Cornell Medicine –Qatar, Qatar Foundation, Doha, Qatar
| | - Edward Schenck
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Soo Jung Cho
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Augustine M. K. Choi
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine -Qatar, Qatar Foundation, Doha, Qatar.
| | - Mary E. Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA.
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24
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Pakula PD, Halama A, Al-Dous EK, Johnson SJ, Filho SA, Suhre K, Vinardell T. Characterization of exercise-induced hemolysis in endurance horses. Front Vet Sci 2023; 10:1115776. [PMID: 37180073 PMCID: PMC10174325 DOI: 10.3389/fvets.2023.1115776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 04/03/2023] [Indexed: 05/15/2023] Open
Abstract
Exercise-induced hemolysis occurs as the result of intense physical exercise and is caused by metabolic and mechanical factors including repeated muscle contractions leading to capillary vessels compression, vasoconstriction of internal organs and foot strike among others. We hypothesized that exercise-induced hemolysis occurred in endurance racehorses and its severity was associated with the intensity of exercise. To provide further insight into the hemolysis of endurance horses, the aim of the study was to deployed a strategy for small molecules (metabolites) profiling, beyond standard molecular methods. The study included 47 Arabian endurance horses competing for either 80, 100, or 120 km distances. Blood plasma was collected before and after the competition and analyzed macroscopically, by ELISA and non-targeted metabolomics with liquid chromatography-mass spectrometry. A significant increase in all hemolysis parameters was observed after the race, and an association was found between the measured parameters, average speed, and distance completed. Levels of hemolysis markers were highest in horses eliminated for metabolic reasons in comparison to finishers and horses eliminated for lameness (gait abnormality), which may suggest a connection between the intensity of exercise, metabolic challenges, and hemolysis. Utilization of omics methods alongside conventional methods revealed a broader insight into the exercise-induced hemolysis process by displaying, apart from commonly measured hemoglobin and haptoglobin, levels of hemoglobin degradation metabolites. Obtained results emphasized the importance of respecting horse limitations in regard to speed and distance which, if underestimated, may lead to severe damages.
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Affiliation(s)
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Eman K. Al-Dous
- Equine Veterinary Medical Center, Qatar Foundation, Doha, Qatar
| | | | | | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Tatiana Vinardell
- Equine Veterinary Medical Center, Qatar Foundation, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
- *Correspondence: Tatiana Vinardell,
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25
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Elashi AA, Toor SM, Diboun I, Al-Sarraj Y, Taheri S, Suhre K, Abou-Samra AB, Albagha OME. The Genetic Spectrum of Maturity-Onset Diabetes of the Young (MODY) in Qatar, a Population-Based Study. Int J Mol Sci 2022; 24:ijms24010130. [PMID: 36613572 PMCID: PMC9820507 DOI: 10.3390/ijms24010130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Maturity-onset diabetes of the young (MODY) is a rare monogenic form of diabetes mellitus. In this study, we estimated the prevalence and genetic spectrum of MODY in the Middle Eastern population of Qatar using whole-genome sequencing (WGS) of 14,364 subjects from the population-based Qatar biobank (QBB) cohort. We focused our investigations on 14 previously identified genes ascribed to the cause of MODY and two potentially novel MODY-causing genes, RFX6 and NKX6-1. Genetic variations within the 16 MODY-related genes were assessed for their pathogenicity to identify disease-causing mutations. Analysis of QBB phenotype data revealed 72 subjects (0.5%) with type 1 diabetes, 2915 subjects (20.3%) with type 2 diabetes and 11,377 (79.2%) without diabetes. We identified 22 mutations in 67 subjects that were previously reported in the Human Genetic Mutation Database (HGMD) as disease-causing (DM) or likely disease causing (DM?) for MODY. We also identified 28 potentially novel MODY-causing mutations, predicted to be among the top 1% most deleterious mutations in the human genome, which showed complete (100%) disease penetrance in 34 subjects. Overall, we estimated that MODY accounts for around 2.2-3.4% of diabetes patients in Qatar. This is the first population-based study to determine the genetic spectrum and estimate the prevalence of MODY in the Middle East. Further research to characterize the newly identified mutations is warranted.
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Affiliation(s)
- Asma A. Elashi
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Salman M. Toor
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Ilhame Diboun
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Medical and Population Genomics Lab, Sidra Medicine, Doha P.O. Box 26999, Qatar
| | - Yasser Al-Sarraj
- Qatar Genome Program (QGP), Qatar Foundation Research, Development and Innovation, Qatar Foundation (QF), Doha P.O. Box 5825, Qatar
| | - Shahrad Taheri
- Qatar Metabolic Institute, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha P.O. Box 24144, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Omar M. E. Albagha
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Correspondence: ; Tel.: +974-4454-2974
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26
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Zaghlool SB, Halama A, Stephan N, Gudmundsdottir V, Gudnason V, Jennings LL, Thangam M, Ahlqvist E, Malik RA, Albagha OME, Abou‑Samra AB, Suhre K. Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population. Nat Commun 2022; 13:7121. [PMID: 36402758 PMCID: PMC9675829 DOI: 10.1038/s41467-022-34754-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 11/07/2022] [Indexed: 11/20/2022] Open
Abstract
Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.
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Affiliation(s)
- Shaza B. Zaghlool
- grid.416973.e0000 0004 0582 4340Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Anna Halama
- grid.416973.e0000 0004 0582 4340Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Nisha Stephan
- grid.416973.e0000 0004 0582 4340Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Valborg Gudmundsdottir
- grid.14013.370000 0004 0640 0021Faculty of Medicine, University of Iceland, Reykjavik, Iceland ,grid.420802.c0000 0000 9458 5898Icelandic Heart Association, Kopavogur, Iceland
| | - Vilmundur Gudnason
- grid.14013.370000 0004 0640 0021Faculty of Medicine, University of Iceland, Reykjavik, Iceland ,grid.420802.c0000 0000 9458 5898Icelandic Heart Association, Kopavogur, Iceland
| | - Lori L. Jennings
- grid.418424.f0000 0004 0439 2056Novartis Institutes for Biomedical Research, Cambridge, MA USA
| | - Manonanthini Thangam
- grid.4514.40000 0001 0930 2361Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Emma Ahlqvist
- grid.4514.40000 0001 0930 2361Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rayaz A. Malik
- grid.416973.e0000 0004 0582 4340Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Omar M. E. Albagha
- grid.452146.00000 0004 1789 3191College of Health and Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar ,grid.4305.20000 0004 1936 7988Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Abdul Badi Abou‑Samra
- grid.413548.f0000 0004 0571 546XQatar Metabolic Institute, Hamad Medical Corporation, Doha, Qatar
| | - Karsten Suhre
- grid.416973.e0000 0004 0582 4340Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
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27
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Belkadi A, Thareja G, Abbaszadeh F, Badii R, Fauman E, Albagha OM, Suhre K. Identification of PCSK9-like human gene knockouts using metabolomics, proteomics, and whole-genome sequencing in a consanguineous population. Cell Genom 2022; 3:100218. [PMID: 36777185 PMCID: PMC9903797 DOI: 10.1016/j.xgen.2022.100218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 07/16/2022] [Accepted: 10/25/2022] [Indexed: 11/17/2022]
Abstract
Natural human knockouts of genes associated with desirable outcomes, such as PCSK9 with low levels of LDL-cholesterol, can lead to the discovery of new drug targets and treatments. Rare loss-of-function variants are more likely to be found in the homozygous state in consanguineous populations, and deep molecular phenotyping of blood samples from homozygous carriers can help to discriminate between silent and functional variants. Here, we combined whole-genome sequencing with proteomics and metabolomics for 2,935 individuals from the Qatar Biobank (QBB) to evaluate the power of this approach for finding genes of clinical and pharmaceutical interest. As proof-of-concept, we identified a homozygous carrier of a very rare PCSK9 variant with extremely low circulating PCSK9 levels and low LDL. Our study demonstrates that the chances of finding such variants are about 168 times higher in QBB compared with GnomAD and emphasizes the potential of consanguineous populations for drug discovery.
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Affiliation(s)
- Aziz Belkadi
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA
| | | | | | | | - Omar M.E. Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar,Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA,Corresponding author
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28
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Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, Li C, Smith RF, Wittemans LBL, Bomba L, Menni C, Zierer J, Rossi N, Sheridan PA, Watkins NA, Mangino M, Hysi PG, Di Angelantonio E, Falchi M, Spector TD, Soranzo N, Michelotti GA, Arlt W, Lotta LA, Denaxas S, Hemingway H, Gamazon ER, Howson JMM, Wood AM, Danesh J, Wareham NJ, Kastenmüller G, Fauman EB, Suhre K, Butterworth AS, Langenberg C. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med 2022; 28:2321-2332. [PMID: 36357675 PMCID: PMC9671801 DOI: 10.1038/s41591-022-02046-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/16/2022] [Indexed: 11/12/2022]
Abstract
Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.
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Affiliation(s)
- 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 Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Rebecca F Smith
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Lorenzo Bomba
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jonas Zierer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Niccolò Rossi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | | | | | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Emanuele Di Angelantonio
- 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 Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | | | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall & MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Joanna M M Howson
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Angela M Wood
- 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 Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - John Danesh
- 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 Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Adam S Butterworth
- 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 Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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Louca P, Tran TQB, Toit CD, Christofidou P, Spector TD, Mangino M, Suhre K, Padmanabhan S, Menni C. Machine learning integration of multimodal data identifies key features of blood pressure regulation. EBioMedicine 2022; 84:104243. [PMID: 36084617 PMCID: PMC9463529 DOI: 10.1016/j.ebiom.2022.104243] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/02/2022] [Accepted: 08/11/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP). METHODS We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB). FINDINGS Our model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts. INTERPRETATION We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies. FUNDING Chronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.
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Affiliation(s)
- Panayiotis Louca
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Tran Quoc Bao Tran
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Clea du Toit
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Paraskevi Christofidou
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom; NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, SE1 9RT, United Kingdom
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Sandosh Padmanabhan
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom.
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom.
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30
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Weinisch P, Fiamoncini J, Schranner D, Raffler J, Skurk T, Rist MJ, Römisch-Margl W, Prehn C, Adamski J, Hauner H, Daniel H, Suhre K, Kastenmüller G. Dynamic patterns of postprandial metabolic responses to three dietary challenges. Front Nutr 2022; 9:933526. [PMID: 36211489 PMCID: PMC9540193 DOI: 10.3389/fnut.2022.933526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.
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Affiliation(s)
- Patrick Weinisch
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jarlei Fiamoncini
- Food Research Center – FoRC, Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Daniela Schranner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Thomas Skurk
- Core Facility Human Studies, ZIEL Institute for Food and Health, Technical University of Munich, Freising, Germany
- Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Manuela J. Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Werner Römisch-Margl
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Hans Hauner
- Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Hannelore Daniel
- Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- *Correspondence: Gabi Kastenmüller
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31
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Ahmed L, Al-Najjar Y, Cramer ERA, Thareja G, Suhre K, Chen KC. Development and characterization of microsatellite primers for Triops granarius (Branchiopoda: Notostraca) using MiSeq technology. Mol Biol Rep 2022; 49:10121-10125. [PMID: 36057875 PMCID: PMC9515016 DOI: 10.1007/s11033-022-07804-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/15/2022] [Indexed: 12/03/2022]
Abstract
Background Next-generation sequencing technology has allowed for the rapid development of microsatellites, neutral polymorphic markers that can be used for the analysis of population structure. Methods and Results In this study, we performed whole-genome sequencing using the Illumina MiSeq system and de novo assembly to design microsatellite primers for Triops granarius populations in Qatar. The developed microsatellites are suitable for future studies of genetic structuring among geographically isolated freshwater pools. A total of 23 different primer pairs produced typical microsatellite results, with each pair successfully amplified in up to 40 individuals. Only five of the loci produced a significant departure from Hardy-Weinberg equilibrium. Conclusions Some of the underlying mechanisms regarding the few loci that deviated from HWE may be further investigated to determine the source of deviation. As T. granarius is the most widely distributed species of the family, the development of these molecular markers would be useful for conducting population genetics and biogeographical studies broadly.
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Affiliation(s)
- Lina Ahmed
- Department of Medical Education Education City, Qatar Foundation, Weill Cornell Medicine - Qatar, 24144, Doha, Qatar
| | - Yousef Al-Najjar
- Department of Medical Education Education City, Qatar Foundation, Weill Cornell Medicine - Qatar, 24144, Doha, Qatar
| | - Emily R A Cramer
- Natural History Museum, University of Oslo, Sars Gate 1, 0562, Oslo, Norway
| | - Gaurav Thareja
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar Education City, Qatar Foundation, P.O. Box 24144, Doha, Qatar
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar Education City, Qatar Foundation, P.O. Box 24144, Doha, Qatar
| | - Kuei-Chiu Chen
- Department of Premedical Education, Weill Cornell Medicine-Qatar Education City, Qatar Foundation, P.O. Box 24144, Doha, Qatar.
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32
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Batra R, Whalen W, Alvarez-Mulett S, Gomez-Escobar LG, Hoffman KL, Simmons W, Harrington J, Chetnik K, Buyukozkan M, Benedetti E, Choi ME, Suhre K, Schenck E, Choi AMK, Schmidt F, Cho SJ, Krumsiek J. Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS. PLoS Pathog 2022; 18:e1010819. [PMID: 36121875 PMCID: PMC9484674 DOI: 10.1371/journal.ppat.1010819] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/19/2022] [Indexed: 12/06/2022] Open
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions. METHODS AND FINDINGS In this study, we compared COVID-19 ARDS (n = 43) and bacterial sepsis-induced (non-COVID-19) ARDS (n = 24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within- ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation. CONCLUSION We present a first comprehensive molecular characterization of differences between two ARDS etiologies-COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - William Whalen
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Sergio Alvarez-Mulett
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Luis G. Gomez-Escobar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Katherine L. Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, New York, United States of America
| | - Will Simmons
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, New York, United States of America
| | - John Harrington
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Mustafa Buyukozkan
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Elisa Benedetti
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Mary E. Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, New York, United States of America
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine–Qatar, Qatar Foundation, Doha, Qatar
| | - Edward Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Augustine M. K. Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine–Qatar, Qatar Foundation, Doha, Qatar
| | - Soo Jung Cho
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
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33
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Fiamoncini J, Rist MJ, Frommherz L, Giesbertz P, Pfrang B, Kremer W, Huber F, Kastenmüller G, Skurk T, Hauner H, Suhre K, Daniel H, Kulling SE. Dynamics and determinants of human plasma bile acid profiles during dietary challenges. Front Nutr 2022; 9:932937. [PMID: 35967802 PMCID: PMC9366195 DOI: 10.3389/fnut.2022.932937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/01/2022] [Indexed: 11/30/2022] Open
Abstract
In recent years, bile acids (BA) have received great interest due to their pleiotropic biological activity and the presence of plasma membrane-bound and nuclear receptors. Moreover, BA in blood have been identified by metabolite screening approaches as biomarkers that are associated with various diseases and even with a human longevity phenotype. With the growing interest in the microbiota contribution to the health-disease trajectory, BA that undergo deconjugation and other modifications by bacteria in the large intestine have become a prime target as a microbiome diversity modifier. We here profiled BA by a quantitative and a semiquantitative approach in 15 healthy and phenotypically very similar young individuals for over a 36-h fasting period, an oral glucose tolerance test (OGTT), and an oral lipid tolerance test (OLTT). We demonstrate a remarkable heterogeneity of the responses and describe the different dynamics of the plasma changes that likely originate from different routes by which BA enters the peripheral blood, and that may represent a direct secretion from the liver into the blood and a route that reaches the blood as a spill-over after passing from the gallbladder through the intestine and the portal system. We discuss the finding that an individual transport process involved in the passage of BA could be a critical determinant in the kinetics of plasma appearance and the overall phenotypic variability found.
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Affiliation(s)
- Jarlei Fiamoncini
- Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, Food Research Center - FoRC, University of São Paulo, São Paulo, Brazil
| | - Manuela J Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - Lara Frommherz
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - Pieter Giesbertz
- Department of Nutritional Physiology, Technische Universität München, Freising-Weihenstephan, Germany
| | - Birgit Pfrang
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - Werner Kremer
- Biophysics I, Regensburg Center for Biochemistry, Universität Regensburg, Regensburg, Germany
| | - Fritz Huber
- Department of Nutritional Physiology, Technische Universität München, Freising-Weihenstephan, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Skurk
- Chair of Nutritional Medicine, Else Kroener-Fresenius-Centre for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hans Hauner
- Chair of Nutritional Medicine, Else Kroener-Fresenius-Centre for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Bioinformatics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Hannelore Daniel
- Department of Nutritional Physiology, Technische Universität München, Freising-Weihenstephan, Germany
| | - Sabine E Kulling
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
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34
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Batra R, Whalen W, Alvarez-Mulett S, Gómez-Escobar LG, Hoffman KL, Simmons W, Harrington J, Chetnik K, Buyukozkan M, Benedetti E, Choi ME, Suhre K, Schenck E, Choi AMK, Schmidt F, Cho SJ, Krumsiek J. Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS. medRxiv 2022:2022.05.16.22274587. [PMID: 35982655 PMCID: PMC9387161 DOI: 10.1101/2022.05.16.22274587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions. Methods and Findings In this study, we compared COVID-19 ARDS (n=43) and bacterial sepsis-induced (non-COVID-19) ARDS (n=24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within-ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation. Conclusion We present a first comprehensive molecular characterization of differences between two ARDS etiologies - COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - William Whalen
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sergio Alvarez-Mulett
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Luis G Gómez-Escobar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Katherine L Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - Will Simmons
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - John Harrington
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mustafa Buyukozkan
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Elisa Benedetti
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mary E Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Edward Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Augustine M K Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Soo Jung Cho
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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35
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Batra R, Uni R, Akchurin OM, Alvarez-Mulett S, Gómez-Escobar LG, Patino E, Hoffman KL, Simmons W, Chetnik K, Buyukozkan M, Benedetti E, Suhre K, Schenck E, Cho SJ, Choi AMK, Schmidt F, Choi ME, Krumsiek J. Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS. medRxiv 2022:2022.08.10.22277939. [PMID: 35982662 PMCID: PMC9387152 DOI: 10.1101/2022.08.10.22277939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. In this study, we performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). The comparison of these ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Rie Uni
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Oleh M Akchurin
- Department of Pediatrics, Division of Pediatric Nephrology, Weill Cornell Medicine, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Sergio Alvarez-Mulett
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Luis G Gómez-Escobar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Edwin Patino
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Katherine L Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - Will Simmons
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mustafa Buyukozkan
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Elisa Benedetti
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Edward Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Soo Jung Cho
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Augustine M K Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Mary E Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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36
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Buyukozkan M, Alvarez-Mulett S, Racanelli AC, Schmidt F, Batra R, Hoffman KL, Sarwath H, Engelke R, Gomez-Escobar L, Simmons W, Benedetti E, Chetnik K, Zhang G, Schenck E, Suhre K, Choi JJ, Zhao Z, Racine-Brzostek S, Yang HS, Choi ME, Choi AM, Cho SJ, Krumsiek J. Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19. iScience 2022; 25:104612. [PMID: 35756895 PMCID: PMC9212983 DOI: 10.1016/j.isci.2022.104612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 02/05/2022] [Accepted: 06/09/2022] [Indexed: 01/08/2023] Open
Abstract
The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83-0.93 in two independent datasets.
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Affiliation(s)
- Mustafa Buyukozkan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sergio Alvarez-Mulett
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alexandra C. Racanelli
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine – Qatar, Doha, Qatar
| | - Richa Batra
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Katherine L. Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine – Qatar, Doha, Qatar
| | - Rudolf Engelke
- Proteomics Core, Weill Cornell Medicine – Qatar, Doha, Qatar
| | - Luis Gomez-Escobar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Will Simmons
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - Elisa Benedetti
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Guoan Zhang
- Proteomics and Metabolomics Core Facility, Weill Cornell Medicine, New York, NY, USA
| | - Edward Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine – Qatar, Education City, Doha 24144, Qatar
| | - Justin J. Choi
- Department of Medicine, Division of General Internal Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Zhen Zhao
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | - He S. Yang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Mary E. Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Augustine M.K. Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Soo Jung Cho
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
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Gomari DP, Schweickart A, Cerchietti L, Paietta E, Fernandez H, Al-Amin H, Suhre K, Krumsiek J. Variational autoencoders learn transferrable representations of metabolomics data. Commun Biol 2022; 5:645. [PMID: 35773471 PMCID: PMC9246987 DOI: 10.1038/s42003-022-03579-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/10/2022] [Indexed: 01/14/2023] Open
Abstract
Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which demonstrated that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, acute myeloid leukemia, and schizophrenia and found significant correlations with clinical patient groups. Notably, the VAE representations showed stronger effects than latent dimensions derived by linear and non-linear principal component analysis. Taken together, we demonstrate that the VAE is a powerful method that learns biologically meaningful, nonlinear, and transferrable latent representations of metabolomics data.
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Affiliation(s)
- Daniel P. Gomari
- grid.4567.00000 0004 0483 2525Institute of Computational Biology, Helmholtz Center Munich—German Research Center for Environmental Health, 85764 Neuherberg, Germany ,grid.6936.a0000000123222966Technical University of Munich—School of Life Sciences, 85354 Freising, Germany ,grid.168010.e0000000419368956Department of Genetics, Stanford University School of Medicine, Stanford, CA USA
| | - Annalise Schweickart
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY 10021 USA
| | - Leandro Cerchietti
- grid.5386.8000000041936877XDepartment of Medicine, Hematology and Oncology Division, Weill Cornell Medicine, New York, 10065 NY USA
| | - Elisabeth Paietta
- grid.251993.50000000121791997Albert Einstein College of Medicine-Montefiore Medical Center, Bronx, NY USA
| | - Hugo Fernandez
- grid.489080.d0000 0004 0444 4637Moffitt Malignant Hematology & Cellular Therapy at Memorial Healthcare System, Pembroke Pines, FL USA
| | - Hassen Al-Amin
- grid.416973.e0000 0004 0582 4340Department of Psychiatry, Weill Cornell Medicine—Qatar, Education City, P.O. Box 24144, Doha, Qatar
| | - Karsten Suhre
- grid.416973.e0000 0004 0582 4340Department of Physiology and Biophysics, Weill Cornell Medical College—Qatar Education City, Doha, Qatar
| | - Jan Krumsiek
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY 10021 USA
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Louca P, Nogal A, Moskal A, Goulding NJ, Shipley MJ, Alkis T, Lindbohm JV, Hu J, Kifer D, Wang N, Chawes B, Rexrode KM, Ben-Shlomo Y, Kivimaki M, Murphy RA, Yu B, Gunter MJ, Suhre K, Lawlor DA, Mangino M, Menni C. Cross-Sectional Blood Metabolite Markers of Hypertension: A Multicohort Analysis of 44,306 Individuals from the COnsortium of METabolomics Studies. Metabolites 2022; 12:601. [PMID: 35888725 PMCID: PMC9324896 DOI: 10.3390/metabo12070601] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 05/27/2022] [Accepted: 06/09/2022] [Indexed: 12/30/2022] Open
Abstract
Hypertension is the main modifiable risk factor for cardiovascular morbidity and mortality but discovering molecular mechanisms for targeted treatment has been challenging. Here we investigate associations of blood metabolite markers with hypertension by integrating data from nine intercontinental cohorts from the COnsortium of METabolomics Studies. We included 44,306 individuals with circulating metabolites (up to 813). Metabolites were aligned and inverse normalised to allow intra-platform comparison. Logistic models adjusting for covariates were performed in each cohort and results were combined using random-effect inverse-variance meta-analyses adjusting for multiple testing. We further conducted canonical pathway analysis to investigate the pathways underlying the hypertension-associated metabolites. In 12,479 hypertensive cases and 31,827 controls without renal impairment, we identified 38 metabolites, associated with hypertension after adjusting for age, sex, body mass index, ethnicity, and multiple testing. Of these, 32 metabolite associations, predominantly lipid (steroids and fatty acyls) and organic acids (amino-, hydroxy-, and keto-acids) remained after further adjusting for comorbidities and dietary intake. Among the identified metabolites, 5 were novel, including 2 bile acids, 2 glycerophospholipids, and ketoleucine. Pathway analysis further implicates the role of the amino-acids, serine/glycine, and bile acids in hypertension regulation. In the largest cross-sectional hypertension-metabolomics study to date, we identify 32 circulating metabolites (of which 5 novel and 27 confirmed) that are potentially actionable targets for intervention. Further in-vivo studies are needed to identify their specific role in the aetiology or progression of hypertension.
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Affiliation(s)
- Panayiotis Louca
- Department of Twin Research, King’s College London, London SE1 7EH, UK; (P.L.); (A.N.); (M.M.)
| | - Ana Nogal
- Department of Twin Research, King’s College London, London SE1 7EH, UK; (P.L.); (A.N.); (M.M.)
| | - Aurélie Moskal
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (A.M.); (M.J.G.)
| | - Neil J. Goulding
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; (N.J.G.); (Y.B.-S.); (D.A.L.)
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Martin J. Shipley
- Department Epidemiology and Public Health, University College London, London WC1E 7HB, UK; (M.J.S.); (J.V.L.); (M.K.)
| | - Taryn Alkis
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA; (T.A.); (B.Y.)
| | - Joni V. Lindbohm
- Department Epidemiology and Public Health, University College London, London WC1E 7HB, UK; (M.J.S.); (J.V.L.); (M.K.)
- Clinicum, Department of Public Health, University of Helsinki, P.O. Box 20 Helsinki, Finland
| | - Jie Hu
- Division of Women’s Health, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (J.H.); (K.M.R.)
| | - Domagoj Kifer
- Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia;
| | - Ni Wang
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (N.W.); (B.C.)
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Bo Chawes
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (N.W.); (B.C.)
| | - Kathryn M. Rexrode
- Division of Women’s Health, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; (J.H.); (K.M.R.)
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; (N.J.G.); (Y.B.-S.); (D.A.L.)
- NIHR Applied Research Collaboration West, University Hospitals Bristol and Weston National Health Service Foundation Trust, Bristol BS1 2NT, UK
| | - Mika Kivimaki
- Department Epidemiology and Public Health, University College London, London WC1E 7HB, UK; (M.J.S.); (J.V.L.); (M.K.)
| | - Rachel A. Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada;
- Cancer Control Research, BC Cancer, Vancouver, BC V5Z 1G1, Canada
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA; (T.A.); (B.Y.)
| | - Marc J. Gunter
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (A.M.); (M.J.G.)
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha 24144, Qatar;
| | - Deborah A. Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; (N.J.G.); (Y.B.-S.); (D.A.L.)
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Bristol NIHR Biomedical Research Centre, University of Bristol, Bristol BS1 2NT, UK
| | - Massimo Mangino
- Department of Twin Research, King’s College London, London SE1 7EH, UK; (P.L.); (A.N.); (M.M.)
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London SE1 9RT, UK
| | - Cristina Menni
- Department of Twin Research, King’s College London, London SE1 7EH, UK; (P.L.); (A.N.); (M.M.)
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Suryawanshi H, Yang H, Lubetzky M, Morozov P, Lagman M, Thareja G, Alonso A, Li C, Snopkowski C, Belkadi A, Mueller FB, Lee JR, Dadhania DM, Salvatore SP, Seshan SV, Sharma VK, Suhre K, Suthanthiran M, Tuschl T, Muthukumar T. Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts. PLoS One 2022; 17:e0267704. [PMID: 35657798 PMCID: PMC9165878 DOI: 10.1371/journal.pone.0267704] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 04/13/2022] [Indexed: 02/06/2023] Open
Abstract
We tested the hypothesis that single-cell RNA-sequencing (scRNA-seq) analysis of human kidney allograft biopsies will reveal distinct cell types and states and yield insights to decipher the complex heterogeneity of alloimmune injury. We selected 3 biopsies of kidney cortex from 3 individuals for scRNA-seq and processed them fresh using an identical protocol on the 10x Chromium platform; (i) HK: native kidney biopsy from a living donor, (ii) AK1: allograft kidney with transplant glomerulopathy, tubulointerstitial fibrosis, and worsening graft function, and (iii) AK2: allograft kidney after successful treatment of active antibody-mediated rejection. We did not study T-cell-mediated rejections. We generated 7217 high-quality single cell transcriptomes. Taking advantage of the recipient-donor sex mismatches revealed by X and Y chromosome autosomal gene expression, we determined that in AK1 with fibrosis, 42 months after transplantation, more than half of the kidney allograft fibroblasts were recipient-derived and therefore likely migratory and graft infiltrative, whereas in AK2 without fibrosis, 84 months after transplantation, most fibroblasts were donor-organ-derived. Furthermore, AK1 was enriched for tubular progenitor cells overexpressing profibrotic extracellular matrix genes. AK2, eight months after successful treatment of rejection, contained plasmablast cells with high expression of immunoglobulins, endothelial cell elaboration of T cell chemoattractant cytokines, and persistent presence of cytotoxic T cells. In addition to these key findings, our analysis revealed unique cell types and states in the kidney. Altogether, single-cell transcriptomics yielded novel mechanistic insights, which could pave the way for individualizing the care of transplant recipients.
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Affiliation(s)
- Hemant Suryawanshi
- Laboratory of RNA Molecular Biology, The Rockefeller University, New York, NY, United States of America
- * E-mail: (HS); (TT); (TM)
| | - Hua Yang
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Michelle Lubetzky
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
- Department of Transplantation Medicine, New York Presbyterian Hospital-Weill Cornell Medical College, New York, NY, United States of America
| | - Pavel Morozov
- Laboratory of RNA Molecular Biology, The Rockefeller University, New York, NY, United States of America
| | - Mila Lagman
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Gaurav Thareja
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar
| | - Alicia Alonso
- Epigenomics Core Facility, Weill Cornell Medical College, New York, NY, United States of America
| | - Carol Li
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Catherine Snopkowski
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Aziz Belkadi
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar
| | - Franco B. Mueller
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - John R. Lee
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
- Department of Transplantation Medicine, New York Presbyterian Hospital-Weill Cornell Medical College, New York, NY, United States of America
| | - Darshana M. Dadhania
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
- Department of Transplantation Medicine, New York Presbyterian Hospital-Weill Cornell Medical College, New York, NY, United States of America
| | - Steven P. Salvatore
- Division of Renal Pathology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Surya V. Seshan
- Division of Renal Pathology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Vijay K. Sharma
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar
| | - Manikkam Suthanthiran
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
- Department of Transplantation Medicine, New York Presbyterian Hospital-Weill Cornell Medical College, New York, NY, United States of America
| | - Thomas Tuschl
- Laboratory of RNA Molecular Biology, The Rockefeller University, New York, NY, United States of America
- * E-mail: (HS); (TT); (TM)
| | - Thangamani Muthukumar
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY, United States of America
- Department of Transplantation Medicine, New York Presbyterian Hospital-Weill Cornell Medical College, New York, NY, United States of America
- * E-mail: (HS); (TT); (TM)
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40
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Suhre K, Stephan N, Zaghlool S, Triggle CR, Robinson RJ, Evans AM, Halama A. Matching Drug Metabolites from Non-Targeted Metabolomics to Self-Reported Medication in the Qatar Biobank Study. Metabolites 2022; 12:metabo12030249. [PMID: 35323692 PMCID: PMC8948833 DOI: 10.3390/metabo12030249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 11/30/2022] Open
Abstract
Modern metabolomics platforms are able to identify many drug-related metabolites in blood samples. Applied to population-based biobank studies, the detection of drug metabolites can then be used as a proxy for medication use or serve as a validation tool for questionnaire-based health assessments. However, it is not clear how well detection of drug metabolites in blood samples matches information on self-reported medication provided by study participants. Here, we curate free-text responses to a drug-usage questionnaire from 6000 participants of the Qatar Biobank (QBB) using standardized WHO Anatomical Therapeutic Chemical (ATC) Classification System codes and compare the occurrence of these ATC terms to the detection of drug-related metabolites in matching blood plasma samples from 2807 QBB participants for which we collected non-targeted metabolomics data. We found that the detection of 22 drug-related metabolites significantly associated with the self-reported use of the corresponding medication. Good agreement of self-reported medication with non-targeted metabolomics was observed, with self-reported drugs and their metabolites being detected in a same blood sample in 79.4% of the cases. On the other hand, only 29.5% of detected drug metabolites matched to self-reported medication. Possible explanations for differences include under-reporting of over-the-counter medications from the study participants, such as paracetamol, misannotation of low abundance metabolites, such as metformin, and inability of the current methods to detect them. Taken together, our study provides a broad real-world view of what to expect from large non-targeted metabolomics measurements in population-based biobank studies and indicates areas where further improvements can be made.
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Affiliation(s)
- Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar; (N.S.); (S.Z.); (A.H.)
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- Correspondence:
| | - Nisha Stephan
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar; (N.S.); (S.Z.); (A.H.)
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Shaza Zaghlool
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar; (N.S.); (S.Z.); (A.H.)
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Chris R. Triggle
- Departments of Medical Education and Pharmacology, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar;
| | | | - Anne M. Evans
- Metabolon Inc., Morrisville, NC 27560, USA; (R.J.R.); (A.M.E.)
| | - Anna Halama
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar; (N.S.); (S.Z.); (A.H.)
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
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41
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Mbarek H, Devadoss Gandhi G, Selvaraj S, Al-Muftah W, Badji R, Al-Sarraj Y, Saad C, Darwish D, Alvi M, Fadl T, Yasin H, Alkuwari F, Razali R, Aamer W, Abbaszadeh F, Ahmed I, Mokrab Y, Suhre K, Albagha O, Fakhro K, Badii R, Ismail SI, Althani A. Qatar Genome: Insights on Genomics from the Middle East. Hum Mutat 2022; 43:499-510. [PMID: 35112413 DOI: 10.1002/humu.24336] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/16/2021] [Accepted: 01/29/2022] [Indexed: 11/09/2022]
Abstract
Despite recent biomedical breakthroughs and large genomic studies growing momentum, the Middle Eastern population, home to over 400 million people, is under-represented in the human genome variation databases. Here we describe insights from phase 1 of the Qatar Genome Program with whole genome sequenced 6,047 individuals from Qatar. We identified more than 88 million variants of which 24 million are novel and 23 million are singletons. Consistent with the high consanguinity and founder effects in the region, we found that several rare deleterious variants were more common in the Qatari population while others seem to provide protection against diseases and have shaped the genetic architecture of adaptive phenotypes. These results highlight the value of our data as a resource to advance genetic studies in the Arab and neighbouring Middle Eastern populations and will significantly boost the current efforts to improve our understanding of global patterns of human variations, human history and genetic contributions to health and diseases in diverse populations. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hamdi Mbarek
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Geethanjali Devadoss Gandhi
- Department of Biomedical Sciences, College of Health Sciences, Qatar University.,College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
| | - Senthil Selvaraj
- Department of Biomedical Sciences, College of Health Sciences, Qatar University
| | - Wadha Al-Muftah
- Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Radja Badji
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Yasser Al-Sarraj
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar.,Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Chadi Saad
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Dima Darwish
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Muhammad Alvi
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Tasnim Fadl
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Heba Yasin
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Fatima Alkuwari
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Rozaimi Razali
- Department of Biomedical Sciences, College of Health Sciences, Qatar University
| | - Waleed Aamer
- Human Genetics Department, Sidra Medicine, Doha, Qatar
| | | | - Ikhlak Ahmed
- Sidra Medicine, Biomedical Informatics - Research Branch, Doha, Qatar
| | - Younes Mokrab
- Human Genetics Department, Sidra Medicine, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Omar Albagha
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar.,Center of Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Khalid Fakhro
- Department of Biomedical Sciences, College of Health Sciences, Qatar University
| | - Ramin Badii
- Molecular Genetics Laboratory, Hamad Medical Corporation, Doha, Qatar
| | | | - Asma Althani
- Qatar Genome Program, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar.,Biomedical Research Center, Qatar University, Doha, Qatar
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Yousri NA, Suhre K, Yassin E, Al-Shakaki A, Robay A, Elshafei M, Chidiac O, Hunt SC, Crystal RG, Fakhro KA. Metabolic and Metabo-Clinical Signatures of Type 2 Diabetes, Obesity, Retinopathy, and Dyslipidemia. Diabetes 2022; 71:184-205. [PMID: 34732537 PMCID: PMC8914294 DOI: 10.2337/db21-0490] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022]
Abstract
Macro- and microvascular complications of type 2 diabetes (T2D), obesity, and dyslipidemia share common metabolic pathways. In this study, using a total of 1,300 metabolites from 996 Qatari adults (57% with T2D) and 1,159 metabolites from an independent cohort of 2,618 individuals from the Qatar BioBank (11% with T2D), we identified 373 metabolites associated with T2D, obesity, retinopathy, dyslipidemia, and lipoprotein levels, 161 of which were novel. Novel metabolites included phospholipids, sphingolipids, lysolipids, fatty acids, dipeptides, and metabolites of the urea cycle and xanthine, steroid, and glutathione metabolism. The identified metabolites enrich pathways of oxidative stress, lipotoxicity, glucotoxicity, and proteolysis. Second, we identified 15 patterns we defined as "metabo-clinical signatures." These are clusters of patients with T2D who group together based on metabolite levels and reveal the same clustering in two or more clinical variables (obesity, LDL, HDL, triglycerides, and retinopathy). These signatures revealed metabolic pathways associated with different clinical patterns and identified patients with extreme (very high/low) clinical variables associated with extreme metabolite levels in specific pathways. Among our novel findings are the role of N-acetylmethionine in retinopathy in conjunction with dyslipidemia and the possible roles of N-acetylvaline and pyroglutamine in association with high cholesterol levels and kidney function.
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Affiliation(s)
- Noha A. Yousri
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
- Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
- Corresponding author: Noha A. Yousri,
| | - Karsten Suhre
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Esraa Yassin
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Amal Robay
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Omar Chidiac
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Steven C. Hunt
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Khalid A. Fakhro
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
- Translational Research, Sidra Medical and Research Center, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
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Suhre K, Sarwath H, Engelke R, Sohail MU, Cho SJ, Whalen W, Alvarez-Mulett S, Krumsiek J, Choi AMK, Schmidt F. Identification of Robust Protein Associations With COVID-19 Disease Based on Five Clinical Studies. Front Immunol 2022; 12:781100. [PMID: 35145507 PMCID: PMC8821526 DOI: 10.3389/fimmu.2021.781100] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/27/2021] [Indexed: 01/08/2023] Open
Abstract
Multiple studies have investigated the role of blood circulating proteins in COVID-19 disease using the Olink affinity proteomics platform. However, study inclusion criteria and sample collection conditions varied between studies, leading to sometimes incongruent associations. To identify the most robust protein markers of the disease and the underlying pathways that are relevant under all conditions, it is essential to identify proteins that replicate most widely. Here we combined the Olink proteomics profiles of two newly recruited COVID-19 studies (N=68 and N=98) with those of three previously published COVID-19 studies (N=383, N=83, N=57). For these studies, three Olink panels (Inflammation and Cardiovascular II & III) with 253 unique proteins were compared. Case/control analysis revealed thirteen proteins (CCL16, CCL7, CXCL10, CCL8, LGALS9, CXCL11, IL1RN, CCL2, CD274, IL6, IL18, MERTK, IFNγ, and IL18R1) that were differentially expressed in COVID-19 patients in all five studies. Except CCL16, which was higher in controls, all proteins were overexpressed in COVID-19 patients. Pathway analysis revealed concordant trends across all studies with pathways related to cytokine-cytokine interaction, IL18 signaling, fluid shear stress and rheumatoid arthritis. Our results reaffirm previous findings related to a COVID-19 cytokine storm syndrome. Cross-study robustness of COVID-19 specific protein expression profiles support the utility of affinity proteomics as a tool and for the identification of potential therapeutic targets.
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Affiliation(s)
- Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Rudolf Engelke
- Proteomics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Soo Jung Cho
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - William Whalen
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - Sergio Alvarez-Mulett
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Augustine M K Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
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Halama A, Suhre K. Advancing Cancer Treatment by Targeting Glutamine Metabolism—A Roadmap. Cancers (Basel) 2022; 14:cancers14030553. [PMID: 35158820 PMCID: PMC8833671 DOI: 10.3390/cancers14030553] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Dysregulated glutamine metabolism is one of the metabolic features evident in cancer cells when compared to normal cells. Cancer cells utilize glutamine for energy generation as well as the synthesis of other molecules that are critical for cancer growth and progression. Therefore, drugs targeting glutamine metabolism have been extensively investigated. However, inhibition of glutamine metabolism in cancer cells results in the activation of other metabolic pathways enabling cancer cells to survive. In this review, we summarize and discuss the targets in glutamine metabolism, which has been probed in the development of anticancer drugs in preclinical and clinical studies. We further discuss pathways activated in response to glutamine metabolism inhibition, enabling cancer cells to survive the challenge. Finally, we put into perspective combined treatment strategies targeting glutamine metabolism along with other pathways as potential treatment options. Abstract Tumor growth and metastasis strongly depend on adapted cell metabolism. Cancer cells adjust their metabolic program to their specific energy needs and in response to an often challenging tumor microenvironment. Glutamine metabolism is one of the metabolic pathways that can be successfully targeted in cancer treatment. The dependence of many hematological and solid tumors on glutamine is associated with mitochondrial glutaminase (GLS) activity that enables channeling of glutamine into the tricarboxylic acid (TCA) cycle, generation of ATP and NADPH, and regulation of glutathione homeostasis and reactive oxygen species (ROS). Small molecules that target glutamine metabolism through inhibition of GLS therefore simultaneously limit energy availability and increase oxidative stress. However, some cancers can reprogram their metabolism to evade this metabolic trap. Therefore, the effectiveness of treatment strategies that rely solely on glutamine inhibition is limited. In this review, we discuss the metabolic and molecular pathways that are linked to dysregulated glutamine metabolism in multiple cancer types. We further summarize and review current clinical trials of glutaminolysis inhibition in cancer patients. Finally, we put into perspective strategies that deploy a combined treatment targeting glutamine metabolism along with other molecular or metabolic pathways and discuss their potential for clinical applications.
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Gadd DA, Hillary RF, McCartney DL, Zaghlool SB, Stevenson AJ, Cheng Y, Fawns-Ritchie C, Nangle C, Campbell A, Flaig R, Harris SE, Walker RM, Shi L, Tucker-Drob EM, Gieger C, Peters A, Waldenberger M, Graumann J, McRae AF, Deary IJ, Porteous DJ, Hayward C, Visscher PM, Cox SR, Evans KL, McIntosh AM, Suhre K, Marioni RE. Epigenetic scores for the circulating proteome as tools for disease prediction. eLife 2022; 11:e71802. [PMID: 35023833 PMCID: PMC8880990 DOI: 10.7554/elife.71802] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/11/2022] [Indexed: 11/13/2022] Open
Abstract
Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.
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Affiliation(s)
- Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Shaza B Zaghlool
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education CityDohaQatar
- Computer Engineering Department, Virginia TechBlacksburgUnited States
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Chloe Fawns-Ritchie
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
| | - Cliff Nangle
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Robin Flaig
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Sarah E Harris
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
- Lothian Birth Cohorts, University of EdinburghEdinburghUnited Kingdom
| | - Rosie M Walker
- Centre for Clinical Brain Sciences, Chancellor’s Building, University of EdinburghEdinburghUnited Kingdom
| | - Liu Shi
- Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at AustinAustinUnited States
- Population Research Center, The University of Texas at AustinAustinUnited States
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart AllianceMunichGermany
- German Center for Diabetes Research (DZD)NeuherbergGermany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart AllianceMunichGermany
- German Center for Diabetes Research (DZD)NeuherbergGermany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart AllianceMunichGermany
| | - Johannes Graumann
- Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff InstituteBad NauheimGermany
- German Centre for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Max Planck Institute of Heart and Lung ResearchBad NauheimGermany
| | - Allan F McRae
- Institute for Molecular Bioscience, University of QueenslandBrisbaneAustralia
| | - Ian J Deary
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
- Lothian Birth Cohorts, University of EdinburghEdinburghUnited Kingdom
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Caroline Hayward
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of QueenslandBrisbaneAustralia
| | - Simon R Cox
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
- Lothian Birth Cohorts, University of EdinburghEdinburghUnited Kingdom
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh HospitalEdinburghUnited Kingdom
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education CityDohaQatar
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
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Han S, Huang J, Foppiano F, Prehn C, Adamski J, Suhre K, Li Y, Matullo G, Schliess F, Gieger C, Peters A, Wang-Sattler R. TIGER: technical variation elimination for metabolomics data using ensemble learning architecture. Brief Bioinform 2022; 23:6492643. [PMID: 34981111 PMCID: PMC8921617 DOI: 10.1093/bib/bbab535] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 12/24/2022] Open
Abstract
Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.
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Affiliation(s)
- Siyu Han
- School of Medicine, Technical University of Munich, Germany
| | | | | | - Cornelia Prehn
- Head of Metabolomics Lab at Metabolomics and Proteomics Core Facility, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH)
| | - Jerzy Adamski
- National University of Singapore, University of Ljubljana, Slovenia and Technical University of Munich, Germany
| | - Karsten Suhre
- Weill Cornell Medicine and director of the Bioinformatics Core, Qatar
| | - Ying Li
- College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Giuseppe Matullo
- Human Genetics and group leader of the Genomics Variation, Complex Diseases and Population Medicine Unit at the Turin University, Italy
| | - Freimut Schliess
- Director Science & Innovation at Profil Institut für Stoffwechselforschung (GmbH)
| | - Christian Gieger
- Research Unit of Molecular Epidemiology at the Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH)
| | | | - Rui Wang-Sattler
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH)
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Engelke R, Ouanes S, Ghuloum S, Chamali R, Kiwan N, Sarwath H, Schmidt F, Suhre K, Al-Amin H. Proteomic Analysis of Plasma Markers in Patients Maintained on Antipsychotics: Comparison to Patients Off Antipsychotics and Normal Controls. Front Psychiatry 2022; 13:809071. [PMID: 35546954 PMCID: PMC9081931 DOI: 10.3389/fpsyt.2022.809071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Schizophrenia (SZ) and bipolar disorder (BD) share many features: overlap in mood and psychotic symptoms, common genetic predisposition, treatment with antipsychotics (APs), and similar metabolic comorbidities. The pathophysiology of both is still not well defined, and no biomarkers can be used clinically for diagnosis and management. This study aimed to assess the plasma proteomics profile of patients with SZ and BD maintained on APs compared to those who had been off APs for 6 months and to healthy controls (HCs). METHODS We analyzed the data using functional enrichment, random forest modeling to identify potential biomarkers, and multivariate regression for the associations with metabolic abnormalities. RESULTS We identified several proteins known to play roles in the differentiation of the nervous system like NTRK2, CNTN1, ROBO2, and PLXNC1, which were downregulated in AP-free SZ and BD patients but were "normalized" in those on APs. Other proteins (like NCAM1 and TNFRSF17) were "normal" in AP-free patients but downregulated in patients on APs, suggesting that these changes are related to medication's effects. We found significant enrichment of proteins involved in neuronal plasticity, mainly in SZ patients on APs. Most of the proteins associated with metabolic abnormalities were more related to APs use than having SZ or BD. The biomarkers identification showed specific and sensitive results for schizophrenia, where two proteins (PRL and MRC2) produced adequate results. CONCLUSIONS Our results confirmed the utility of blood samples to identify protein signatures and mechanisms involved in the pathophysiology and treatment of SZ and BD.
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Affiliation(s)
- Rudolf Engelke
- Proteomics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Sami Ouanes
- Psychiatry Department, Hamad Medical Corporation, Doha, Qatar
| | - Suhaila Ghuloum
- Psychiatry Department, Hamad Medical Corporation, Doha, Qatar
| | - Rifka Chamali
- Psychiatry Department, Weill Cornell Medicine, Doha, Qatar
| | - Nancy Kiwan
- Psychiatry Department, Weill Cornell Medicine, Doha, Qatar
| | - Hina Sarwath
- Proteomics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Frank Schmidt
- Proteomics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Hassen Al-Amin
- Psychiatry Department, Weill Cornell Medicine, Doha, Qatar
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Yousri NA, Engelke R, Sarwath H, McKinlay RD, Simper SC, Adams TD, Schmidt F, Suhre K, Hunt SC. Proteome-wide associations with short- and long-term weight loss and regain after Roux-en-Y gastric bypass surgery. Obesity (Silver Spring) 2022; 30:129-141. [PMID: 34796696 PMCID: PMC8692443 DOI: 10.1002/oby.23303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/26/2021] [Accepted: 08/25/2021] [Indexed: 12/05/2022]
Abstract
OBJECTIVE Gastric bypass surgery results in long-term weight loss. Small studies have examined protein changes during rapid weight loss (up to 1 or 2 years post surgery). This study tested whether short-term changes were maintained after 12 years. METHODS A 12-year follow-up, protein-wide association study of 1,297 SomaLogic aptamer-based plasma proteins compared short- (2-year) and long-term (12-year) protein changes in 234 individuals who had gastric bypass surgery with 144 nonintervened individuals with severe obesity. RESULTS There were 51 replicated 12-year protein changes that differed between the surgery and nonsurgery groups. Adjusting for change in BMI, only 12 proteins remained significant, suggesting that BMI change was the primary reason for most protein changes and not non-BMI-related surgical effects. Protein changes were related to BMI changes during both weight-loss and weight-regain periods. The significant proteins were associated primarily with lipid, uric acid, or resting energy expenditure clinical variables and metabolic pathways. Eight protein changes were associated with 12-year diabetes remission, including apolipoprotein M, sex hormone binding globulin, and adiponectin (p < 3.5 × 10-5 ). CONCLUSIONS This study showed that most short-term postsurgical changes in proteins were maintained at 12 years. Systemic protection pathways, including inflammation, complement, lipid, and adipocyte pathways, were related to the long-term benefits of gastric bypass surgery.
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Affiliation(s)
- Noha A. Yousri
- Department of Genetic MedicineWeill Cornell MedicineDohaQatar
- Computer and Systems EngineeringAlexandria UniversityAlexandriaEgypt
| | | | | | | | | | - Ted D. Adams
- Intermountain Live Well CenterIntermountain HealthcareSalt Lake CityUtahUSA
- Department of Internal MedicineUniversity of UtahSalt Lake CityUtahUSA
| | - Frank Schmidt
- Proteomics CoreWeill Cornell MedicineDohaQatar
- Department of BiochemistryWeill Cornell MedicineDohaQatar
| | - Karsten Suhre
- Department of Physiology and BiophysicsWeill Cornell MedicineDohaQatar
| | - Steven C. Hunt
- Department of Genetic MedicineWeill Cornell MedicineDohaQatar
- Department of Internal MedicineUniversity of UtahSalt Lake CityUtahUSA
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Abdul Kader S, Dib S, Achkar IW, Thareja G, Suhre K, Rafii A, Halama A. Defining the landscape of metabolic dysregulations in cancer metastasis. Clin Exp Metastasis 2021; 39:345-362. [PMID: 34921655 PMCID: PMC8971193 DOI: 10.1007/s10585-021-10140-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 12/10/2021] [Indexed: 02/07/2023]
Abstract
Metastasis is the primary cause of cancer related deaths due to the limited number of efficient druggable targets. Signatures of dysregulated cancer metabolism could serve as a roadmap for the determination of new treatment strategies. However, the metabolic signatures of metastatic cells remain vastly elusive. Our aim was to determine metabolic dysregulations associated with high metastatic potential in breast cancer cell lines. We have selected 5 triple negative breast cancer (TNBC) cell lines including three with high metastatic potential (HMP) (MDA-MB-231, MDA-MB-436, MDA-MB-468) and two with low metastatic potential (LMP) (BT549, HCC1143). The normal epithelial breast cell line (hTERT-HME1) was also investigated. The untargeted metabolic profiling of cells and growth media was conducted and total of 479 metabolites were quantified. First we characterized metabolic features differentiating TNBC cell lines from normal cells as well as identified cell line specific metabolic fingerprints. Next, we determined 92 metabolites in cells and 22 in growth medium that display significant differences between LMP and HMP. The HMP cell lines had elevated level of molecules involved in glycolysis, TCA cycle and lipid metabolism. We identified metabolic advantages of cell lines with HMP beyond enhanced glycolysis by pinpointing the role of branched chain amino acids (BCAA) catabolism as well as molecules supporting coagulation and platelet activation as important contributors to the metastatic cascade. The landscape of metabolic dysregulations, characterized in our study, could serve as a roadmap for the identification of treatment strategies targeting cancer cells with enhanced metastatic potential.
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Affiliation(s)
- Sara Abdul Kader
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, 24144, Doha, Qatar
- University of Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Shaima Dib
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, 24144, Doha, Qatar
| | - Iman W Achkar
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, 24144, Doha, Qatar
| | - Gaurav Thareja
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, 24144, Doha, Qatar
- University of Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, 24144, Doha, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, New York, USA
| | - Arash Rafii
- Department of Genetic Medicine, Weill Cornell Medicine, New York, USA
- Genetic Intelligence Laboratory, Weill Cornell Medicine in Qatar, Qatar Foundation, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, 24144, Doha, Qatar.
- Department of Biophysics and Physiology, Weill Cornell Medicine, New York, USA.
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Esefeld M, Pastor A, de la Torre R, Barroso O, Aikin R, Sarwath H, Engelke R, Schmidt F, Suhre K. The Proteomic Signature of Recombinant Growth Hormone in Recreational Athletes. J Endocr Soc 2021; 5:bvab156. [PMID: 34765854 PMCID: PMC8577606 DOI: 10.1210/jendso/bvab156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE Administration of human growth hormone (hGH) is prohibited in competitive sport and its detection in an athlete's sample triggers an adverse analytical finding. However, the biological processes that are modulated by recombinant hGH are not well characterized and associated blood serum proteins may constitute new biomarkers for hGH misuse. METHODS Thirty-five recreational athletes were enrolled in a study to investigate the time- and dose-dependent response of serum protein levels to recombinant hGH administration. Participants were randomly assigned to 4 groups, receiving 1 of 3 different doses of recombinant hGH or a placebo. Bio samples were collected at 22 time points over a period of 13 weeks, starting 4 weeks before treatment, during 3 weeks of treatment, and at 6 weeks' follow-up. A total of 749 serum samples were analyzed for 1305 protein markers using the SOMAscan proteomics platform. RESULTS We identified 66 proteins that significantly associated with recombinant hGH administration and dosage, including well known hGH targets, such as IGF1, but also previously unknown hGH-related proteins (eg, protease inhibitors, WFIKKN1, and chemokines, CCL2). Network analysis revealed changes in specific biological pathways, mainly related to the immune system and glucose metabolism. CONCLUSION Our analysis suggests that hGH administration affects biological processes more strongly than previously acknowledged. Some of the proteins were dysregulated even after hGH treatment and could potentially be developed into biomarkers for hGH misuse. Moreover, our findings suggest new roles for hGH-associated proteins in the etiology of hGH-related diseases and may indicate new risks that may be associated with hGH misuse.
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Affiliation(s)
- Max Esefeld
- Proteomics Core, Weill Cornell Medicine–Qatar, Qatar Foundation–Education City, Doha, Qatar
- Department of Transfusion Medicine, Institute for Immunology and Transfusion Medicine, University Medicine Greifswald, 17487 Greifswald, Germany
| | - Antoni Pastor
- Integrative Pharmacology and Systems Neuroscience Research Group. Hospital del Mar Medical Research Institute (IMIM), 08009 Barcelona, Spain
- Spanish Biomedical Research Centre in Physiopathology of Obesity and Nutrition (CIBEROBN), 28029 Madrid, Spain
- University Pompeu Fabra (CEXS-UPF)
| | - Rafael de la Torre
- Integrative Pharmacology and Systems Neuroscience Research Group. Hospital del Mar Medical Research Institute (IMIM), 08009 Barcelona, Spain
- Spanish Biomedical Research Centre in Physiopathology of Obesity and Nutrition (CIBEROBN), 28029 Madrid, Spain
- University Pompeu Fabra (CEXS-UPF)
| | - Osquel Barroso
- World Anti-Doping Agency, Montreal, Quebec H4Z 1B7, Canada
| | - Reid Aikin
- World Anti-Doping Agency, Montreal, Quebec H4Z 1B7, Canada
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine–Qatar, Qatar Foundation–Education City, Doha, Qatar
| | - Rudolf Engelke
- Proteomics Core, Weill Cornell Medicine–Qatar, Qatar Foundation–Education City, Doha, Qatar
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine–Qatar, Qatar Foundation–Education City, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Qatar Foundation–Education City, Doha, Qatar
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