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Schlosser P, Surapaneni AL, Borisov O, Schmidt IM, Zhou L, Anderson A, Deo R, Dubin R, Ganz P, He J, Kimmel PL, Li H, Nelson RG, Porter AC, Rahman M, Rincon-Choles H, Shah V, Unruh ML, Vasan RS, Zheng Z, Feldman HI, Waikar SS, Köttgen A, Rhee EP, Coresh J, Grams ME. Association of Integrated Proteomic and Metabolomic Modules with Risk of Kidney Disease Progression. J Am Soc Nephrol 2024; 35:923-935. [PMID: 38640019 PMCID: PMC11230725 DOI: 10.1681/asn.0000000000000343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/01/2024] [Indexed: 04/21/2024] Open
Abstract
Key Points Integrated analysis of proteome and metabolome identifies modules associated with CKD progression and kidney failure. Ephrin transmembrane proteins and podocyte-expressed CRIM1 and NPNT emerged as central components and warrant experimental and clinical investigation. Background Proteins and metabolites play crucial roles in various biological functions and are frequently interconnected through enzymatic or transport processes. Methods We present an integrated analysis of 4091 proteins and 630 metabolites in the Chronic Renal Insufficiency Cohort study (N =1708; average follow-up for kidney failure, 9.5 years, with 537 events). Proteins and metabolites were integrated using an unsupervised clustering method, and we assessed associations between clusters and CKD progression and kidney failure using Cox proportional hazards models. Analyses were adjusted for demographics and risk factors, including the eGFR and urine protein–creatinine ratio. Associations were identified in a discovery sample (random two thirds, n =1139) and then evaluated in a replication sample (one third, n =569). Results We identified 139 modules of correlated proteins and metabolites, which were represented by their principal components. Modules and principal component loadings were projected onto the replication sample, which demonstrated a consistent network structure. Two modules, representing a total of 236 proteins and 82 metabolites, were robustly associated with both CKD progression and kidney failure in both discovery and validation samples. Using gene set enrichment, several transmembrane-related terms were identified as overrepresented in these modules. Transmembrane–ephrin receptor activity displayed the largest odds (odds ratio=13.2, P value = 5.5×10−5). A module containing CRIM1 and NPNT expressed in podocytes demonstrated particularly strong associations with kidney failure (P value = 2.6×10−5). Conclusions This study demonstrates that integration of the proteome and metabolome can identify functions of pathophysiologic importance in kidney disease.
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Affiliation(s)
- Pascal Schlosser
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Aditya L. Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
| | - Oleg Borisov
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Insa M. Schmidt
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Amanda Anderson
- Department of Epidemiology, Tulane University, New Orleans, Louisiana
| | - Rajat Deo
- Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ruth Dubin
- Division of Nephrology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Peter Ganz
- Division of Cardiology, University of California, San Francisco, San Francisco, California
| | - Jiang He
- Department of Epidemiology, Tulane University, New Orleans, Louisiana
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Robert G. Nelson
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
- Research Division, Joslin Diabetes Center, Boston, Massachusetts
| | - Anna C. Porter
- Renal Service, Wellington Regional Hospital, Wellington, New Zealand
| | - Mahboob Rahman
- Department of Kidney Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | | | - Vallabh Shah
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Mark L. Unruh
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Ramachandran S. Vasan
- University of Texas Health Sciences Center, San Antonio, Texas
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Harold I. Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sushrut S. Waikar
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Eugene P. Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Optimal Aging Institute, Departments of Population Health and Medicine, NYU Grossman School of Medicine, New York, New York
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
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Peng H, Liu X, Ieong CA, Tou T, Tsai T, Zhu H, Liu Z, Liu P. A Metabolomics study of metabolites associated with the glomerular filtration rate. BMC Nephrol 2023; 24:105. [PMID: 37085754 PMCID: PMC10122376 DOI: 10.1186/s12882-023-03147-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 03/31/2023] [Indexed: 04/23/2023] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates. METHODS An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The 145 samples were divided into four groups based on the patient's measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software. RESULTS A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function. CONCLUSIONS This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function.
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Affiliation(s)
- Hongquan Peng
- Department of Nephrology, Kiang Wu Hospital, Macau, China.
| | - Xun Liu
- Department of Nephrology, The Third Affiliated Hospital of Sun Yat-sen University, Guang Zhou, China
| | - Chiwa Ao Ieong
- Department of Nephrology, Kiang Wu Hospital, Macau, China
| | - Tou Tou
- Department of Nephrology, Kiang Wu Hospital, Macau, China
| | - Tsungyang Tsai
- Department of Nephrology, Kiang Wu Hospital, Macau, China
| | - Haibin Zhu
- Department of Statistics and Data Science, School of Economics, Jinan University, Guangzhou, China
| | - Zhi Liu
- Department of Mathematics, University of Macau, Macau, China
| | - Peijia Liu
- Department of Nephrology, The Third Affiliated Hospital of Sun Yat-sen University, Guang Zhou, China
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Horie T, Takagi W, Aburatani N, Yamazaki M, Inokuchi M, Tachizawa M, Okubo K, Ohtani-Kaneko R, Tokunaga K, Wong MKS, Hyodo S. Segment-Dependent Gene Expression Profiling of the Cartilaginous Fish Nephron Using Laser Microdissection for Functional Characterization of Nephron at Segment Levels. Zoolog Sci 2023; 40:91-104. [PMID: 37042689 DOI: 10.2108/zs220092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/21/2022] [Indexed: 01/18/2023]
Abstract
For adaptation to a high salinity marine environment, cartilaginous fishes have evolved a ureosmotic strategy. They have a highly elaborate "four-loop nephron" in the kidney, which is considered to be important for reabsorption of urea from the glomerular filtrate to maintain a high concentration of urea in the body. However, the function and regulation, generally, of the "four-loop nephron" are still largely unknown due to the complicated configuration of the nephron and its many subdivided segments. Laser microdissection (LMD) followed by RNA-sequencing (RNA-seq) analysis is a powerful technique to obtain segment-dependent gene expression profiles. In the present study, using the kidney of cloudy catshark, Scyliorhinus torazame, we tested several formaldehyde-free and formaldehyde-based fixatives to optimize the fixation methods. Fixation by 1% neutral buffered formalin for 15 min resulted in sufficient RNA and structural integrities, which allowed LMD clipping of specific nephron segments and subsequent RNA-seq analysis. RNA-seq from the LMD samples of the second-loop, the fourth-loop, and the five tubular segments in the bundle zone revealed a number of specific membrane transporter genes that can characterize each segment. Among them, we examined expressions of the Na + -coupled cotransporters abundantly expressed in the second loop samples. Although the proximal II segment of the second loop is known for the elimination of excess solutes, the present results imply that the PII segment is also crucial for reabsorption of valuable solutes. Looking ahead to future studies, the segment-dependent gene expression profiling will be a powerful technique for unraveling the renal mechanisms and regulation in euryhaline elasmobranchs.
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Affiliation(s)
- Takashi Horie
- Laboratory of Physiology, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba 277-8564, Japan
| | - Wataru Takagi
- Laboratory of Physiology, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba 277-8564, Japan
| | - Naotaka Aburatani
- Laboratory of Physiology, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba 277-8564, Japan
| | - Manabu Yamazaki
- Laboratory of Physiology, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba 277-8564, Japan
| | - Mayu Inokuchi
- Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan
| | - Masaya Tachizawa
- Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan
| | - Kataaki Okubo
- Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan
| | | | - Kotaro Tokunaga
- Ibaraki Prefectural Oarai Aquarium, Oarai, Ibaraki 311-1301, Japan
| | - Marty Kwok-Sing Wong
- Laboratory of Physiology, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba 277-8564, Japan
| | - Susumu Hyodo
- Laboratory of Physiology, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba 277-8564, Japan
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Zhou L, Surapaneni A, Rhee EP, Yu B, Boerwinkle E, Coresh J, Grams ME, Schlosser P. Integrated proteomic and metabolomic modules identified as biomarkers of mortality in the Atherosclerosis Risk in Communities study and the African American Study of Kidney Disease and Hypertension. Hum Genomics 2022; 16:53. [PMID: 36329547 PMCID: PMC9635174 DOI: 10.1186/s40246-022-00425-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Proteins and metabolites are essential for many biological functions and often linked through enzymatic or transport reactions. Individual molecules have been associated with all-cause mortality. Many of these are correlated and might jointly represent pathways or endophenotypes involved in diseases. RESULTS We present an integrated analysis of proteomics and metabolomics via a local dimensionality reduction clustering method. We identified 224 modules of correlated proteins and metabolites in the Atherosclerosis Risk in Communities (ARIC) study, a general population cohort of older adults (N = 4046, mean age 75.7, mean eGFR 65). Many of the modules displayed strong cross-sectional associations with demographic and clinical characteristics. In comprehensively adjusted analyses, including fasting plasma glucose, history of cardiovascular disease, systolic blood pressure and kidney function among others, 60 modules were associated with mortality. We transferred the network structure to the African American Study of Kidney Disease and Hypertension (AASK) (N = 694, mean age 54.5, mean mGFR 46) and identified mortality associated modules relevant in this disease specific cohort. The four mortality modules relevant in both the general population and CKD were all a combination of proteins and metabolites and were related to diabetes / insulin secretion, cardiovascular disease and kidney function. Key components of these modules included N-terminal (NT)-pro hormone BNP (NT-proBNP), Sushi, Von Willebrand Factor Type A, EGF And Pentraxin (SVEP1), and several kallikrein proteases. CONCLUSION Through integrated biomarkers of the proteome and metabolome we identified functions of (patho-) physiologic importance related to diabetes, cardiovascular disease and kidney function.
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Affiliation(s)
- Linda Zhou
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument St., Baltimore, MD, 21287, USA
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument St., Baltimore, MD, 21287, USA
| | - Eugene P Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument St., Baltimore, MD, 21287, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument St., Baltimore, MD, 21287, USA.,Division of Precision Medicine, Department of Medicine, New York University, New York, NY, USA
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument St., Baltimore, MD, 21287, USA.
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5
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Identification of Metabolite Markers Associated with Kidney Function. J Immunol Res 2022; 2022:6190333. [PMID: 35928631 PMCID: PMC9345691 DOI: 10.1155/2022/6190333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/10/2022] [Accepted: 07/08/2022] [Indexed: 11/25/2022] Open
Abstract
Background Chronic kidney disease (CKD) is a global public health problem. Identifying new biomarkers that can be used to calculate the glomerular filtration rate (GFR) would greatly improve the diagnosis and understanding of CKD at the molecular level. A metabolomics study of blood samples derived from patients with widely divergent glomerular filtration rates could potentially discover small molecule metabolites associated with varying kidney function. Methods Using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), serum was analyzed from 53 participants with a spectrum of measured GFR (by iohexol plasma clearance) ranging from normal to severe renal insufficiency. An untargeted metabolomics assay (N ¼ 214) was conducted at the Calibra-Metabolon Joint Laboratory. Results From a large number of metabolomics-derived metabolites, the top 30 metabolites correlated to increasing renal insufficiency according to mGFR were selected by the random forest method. Significant differences in metabolite profiles with increasing stages of CKD were observed. Combining candidate lists from six other unique statistical analyses, six novel, potential metabolites that were reproducibly strongly associated with mGFR were selected, including erythronate, gulonate, C-glycosyltryptophan, N-acetylserine, N6-carbamoylthreonyladenosine, and pseudouridine. In addition, hydroxyasparagine were strongly associated with mGFR and CKD, which were unique to this study. Conclusions Global metabolite profiling of serum yielded potentially valuable biomarkers of different stages of CKD. Additionally, these potential biomarkers might provide insight into the underlying pathophysiologic processes that contribute to the progression of CKD as well as improve GFR estimation.
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Rhee EP, Surapaneni A, Zheng Z, Zhou L, Dutta D, Arking DE, Zhang J, Duong T, Chatterjee N, Luo S, Schlosser P, Mehta R, Waikar SS, Saraf SL, Kelly TN, Hamm LL, Rao PS, Mathew AV, Hsu CY, Parsa A, Vasan RS, Kimmel PL, Clish CB, Coresh J, Feldman HI, Grams ME. Trans-ethnic genome-wide association study of blood metabolites in the Chronic Renal Insufficiency Cohort (CRIC) study. Kidney Int 2022; 101:814-823. [PMID: 35120996 PMCID: PMC8940669 DOI: 10.1016/j.kint.2022.01.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/06/2021] [Accepted: 01/11/2022] [Indexed: 12/14/2022]
Abstract
Metabolomics genome wide association study (GWAS) help outline the genetic contribution to human metabolism. However, studies to date have focused on relatively healthy, population-based samples of White individuals. Here, we conducted a GWAS of 537 blood metabolites measured in the Chronic Renal Insufficiency Cohort (CRIC) Study, with separate analyses in 822 White and 687 Black study participants. Trans-ethnic meta-analysis was then applied to improve fine-mapping of potential causal variants. Mean estimated glomerular filtration rate was 44.4 and 41.5 mL/min/1.73m2 in the White and Black participants, respectively. There were 45 significant metabolite associations at 19 loci, including novel associations at PYROXD2, PHYHD1, FADS1-3, ACOT2, MYRF, FAAH, and LIPC. The strength of associations was unchanged in models additionally adjusted for estimated glomerular filtration rate and proteinuria, consistent with a direct biochemical effect of gene products on associated metabolites. At several loci, trans-ethnic meta-analysis, which leverages differences in linkage disequilibrium across populations, reduced the number and/or genomic interval spanned by potentially causal single nucleotide polymorphisms compared to fine-mapping in the White participant cohort alone. Across all validated associations, we found strong concordance in effect sizes of the potentially causal single nucleotide polymorphisms between White and Black study participants. Thus, our study identifies novel genetic determinants of blood metabolites in chronic kidney disease, demonstrates the value of diverse cohorts to improve causal inference in metabolomics GWAS, and underscores the shared genetic basis of metabolism across race.
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Affiliation(s)
- Eugene P Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachussetts, USA.
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Diptavo Dutta
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - ThuyVy Duong
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Shengyuan Luo
- Department of Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Rupal Mehta
- Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Sushrut S Waikar
- Section of Nephrology, Boston University School of Medicine, Boston Medical Center, Boston, Massachussetts, USA
| | - Santosh L Saraf
- Division of Hematology and Oncology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Lee L Hamm
- Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Panduranga S Rao
- Division of Nephrology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Anna V Mathew
- Division of Nephrology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Chi-Yuan Hsu
- Division of Nephrology, University of California, San Francisco School of Medicine, San Francisco, California, USA
| | - Afshin Parsa
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, Massachussetts, USA; Section of Cardiology, Department of Medicine, Boston University School of Medicine, Boston, Massachussetts, USA; Department of Epidemiology, Boston University School of Public Health, Boston, Massachussetts, USA
| | - Paul L Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachussetts, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Harold I Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA; Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Kim HR, Jin HS, Eom YB. Metabolite Genome-Wide Association Study for Indoleamine 2,3-Dioxygenase Activity Associated with Chronic Kidney Disease. Genes (Basel) 2021; 12:1905. [PMID: 34946851 PMCID: PMC8701662 DOI: 10.3390/genes12121905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 11/21/2022] Open
Abstract
Chronic kidney disease (CKD) causes progressive damage to kidney function with increased inflammation. This process contributes to complex amino acid changes. Indoleamine 2,3-dioxygenase (IDO) has been proposed as a new biomarker of CKD in previous studies. In our research, we performed a metabolite genome-wide association study (mGWAS) to identify common and rare variants associated with IDO activity in a Korean population. In addition, single-nucleotide polymorphisms (SNPs) selected through mGWAS were further analyzed for associations with the estimated glomerular filtration rate (eGFR) and CKD. A total of seven rare variants achieved the genome-wide significance threshold (p < 1 × 10-8). Among them, four genes (TNFRSF19, LOC105377444, LOC101928535, and FSTL5) associated with IDO activity showed statistically significant associations with eGFR and CKD. Most of these rare variants appeared specifically in an Asian geographic region. Furthermore, 15 common variants associated with IDO activity were detected in this study and five novel genes (RSU1, PDGFD, SNX25, LOC107984031, and UBASH3B) associated with CKD and eGFR were identified. This study discovered several loci for IDO activity via mGWAS and provided insight into the underlying mechanisms of CKD through association analysis with CKD. To the best of our knowledge, this is the first study to suggest a genetic link between IDO activity and CKD through comparative and integrated analysis.
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Affiliation(s)
- Hye-Rim Kim
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan 31538, Chungnam, Korea
| | - Hyun-Seok Jin
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan 31499, Chungnam, Korea
| | - Yong-Bin Eom
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan 31538, Chungnam, Korea
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan 31538, Chungnam, Korea
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Luo S, Feofanova EV, Tin A, Tung S, Rhee EP, Coresh J, Arking DE, Surapaneni A, Schlosser P, Li Y, Köttgen A, Yu B, Grams ME. Genome-wide association study of serum metabolites in the African American Study of Kidney Disease and Hypertension. Kidney Int 2021; 100:430-439. [PMID: 33838163 PMCID: PMC8583323 DOI: 10.1016/j.kint.2021.03.026] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 02/26/2021] [Accepted: 03/11/2021] [Indexed: 01/23/2023]
Abstract
The genome-wide association study (GWAS) is a powerful means to study genetic determinants of disease traits and generate insights into disease pathophysiology. To date, few GWAS of circulating metabolite levels have been performed in African Americans with chronic kidney disease. Hypothesizing that novel genetic-metabolite associations may be identified in a unique population of African Americans with a lower glomerular filtration rate (GFR), we conducted a GWAS of 652 serum metabolites in 619 participants (mean measured glomerular filtration rate 45 mL/min/1.73m2) in the African American Study of Kidney Disease and Hypertension, a clinical trial of blood pressure lowering and antihypertensive medication in African Americans with chronic kidney disease. We identified 42 significant variant metabolite associations. Twenty associations had been previously identified in published GWAS, and eleven novel associations were replicated in a separate cohort of 818 African Americans with genetic and metabolomic data from the Atherosclerosis Risk in Communities Study. The replicated novel variant-metabolite associations comprised eight metabolites and eleven distinct genomic loci. Nine of the replicated associations represented clear enzyme-metabolite interactions, with high expression in the kidneys as well as the liver. Three loci (ACY1, ACY3, and NAT8) were associated with a common pool of metabolites, acetylated amino acids, but with different individual affinities. Thus, extensive metabolite profiling in an African American population with chronic kidney disease aided identification of novel genome-wide metabolite associations, providing clues about substrate specificity and the key roles of enzymes in modulating systemic levels of metabolites.
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Affiliation(s)
- Shengyuan Luo
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
| | - Elena V Feofanova
- Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sarah Tung
- Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Eugene P Rhee
- Division of Nephrology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Yong Li
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Bing Yu
- Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA; Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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10
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Steinbrenner I, Schultheiss UT, Kotsis F, Schlosser P, Stockmann H, Mohney RP, Schmid M, Oefner PJ, Eckardt KU, Köttgen A, Sekula P. Urine Metabolite Levels, Adverse Kidney Outcomes, and Mortality in CKD Patients: A Metabolome-wide Association Study. Am J Kidney Dis 2021; 78:669-677.e1. [PMID: 33839201 DOI: 10.1053/j.ajkd.2021.01.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/22/2021] [Indexed: 01/01/2023]
Abstract
RATIONALE & OBJECTIVE Mechanisms underlying the variable course of disease progression in patients with chronic kidney disease (CKD) are incompletely understood. The aim of this study was to identify novel biomarkers of adverse kidney outcomes and overall mortality, which may offer insights into pathophysiologic mechanisms. STUDY DESIGN Metabolome-wide association study. SETTING & PARTICIPANTS 5,087 patients with CKD enrolled in the observational German Chronic Kidney Disease Study. EXPOSURES Measurements of 1,487 metabolites in urine. OUTCOMES End points of interest were time to kidney failure (KF), a combined end point of KF and acute kidney injury (KF+AKI), and overall mortality. ANALYTICAL APPROACH Statistical analysis was based on a discovery-replication design (ratio 2:1) and multivariable-adjusted Cox regression models. RESULTS After a median follow-up of 4 years, 362 patients died, 241 experienced KF, and 382 experienced KF+AKI. Overall, we identified 55 urine metabolites whose levels were significantly associated with adverse kidney outcomes and/or mortality. Higher levels of C-glycosyltryptophan were consistently associated with all 3 main end points (hazard ratios of 1.43 [95% CI, 1.27-1.61] for KF, 1.40 [95% CI, 1.27-1.55] for KF+AKI, and 1.47 [95% CI, 1.33-1.63] for death). Metabolites belonging to the phosphatidylcholine pathway showed significant enrichment. Members of this pathway contributed to the improvement of the prediction performance for KF observed when multiple metabolites were added to the well-established Kidney Failure Risk Equation. LIMITATIONS Findings among patients of European ancestry with CKD may not be generalizable to the general population. CONCLUSIONS Our comprehensive screen of the association between urine metabolite levels and adverse kidney outcomes and mortality identifies metabolites that predict KF and represents a valuable resource for future studies of biomarkers of CKD progression.
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Affiliation(s)
- Inga Steinbrenner
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg
| | - Ulla T Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg; Department of Medicine IV-Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg
| | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg; Department of Medicine IV-Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg
| | - Helena Stockmann
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin
| | | | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn
| | - Peter J Oefner
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin; Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen; Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg.
| | - Peggy Sekula
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg.
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11
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Iliou A, Mikros E, Karaman I, Elliott F, Griffin JL, Tzoulaki I, Elliott P. Metabolic phenotyping and cardiovascular disease: an overview of evidence from epidemiological settings. Heart 2021; 107:1123-1129. [PMID: 33608305 DOI: 10.1136/heartjnl-2019-315615] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/05/2021] [Accepted: 01/08/2021] [Indexed: 12/13/2022] Open
Abstract
Metabolomics, the comprehensive measurement of low-molecular-weight molecules in biological fluids used for metabolic phenotyping, has emerged as a promising tool to better understand pathways underlying cardiovascular disease (CVD) and to improve cardiovascular risk stratification. Here, we present the main methodologies for metabolic phenotyping, the methodological steps to analyse these data in epidemiological settings and the associated challenges. We discuss evidence from epidemiological studies linking metabolites to coronary heart disease and stroke. These studies indicate the systemic nature of CVD and identify associated metabolic pathways such as gut microbial cometabolism, branched-chain amino acids, glycerophospholipid and cholesterol metabolism, as well as activation of inflammatory processes. Integration of metabolomic with genomic data can provide new evidence for involved biochemical pathways and potential for causality using Mendelian randomisation. The clinical utility of metabolic biomarkers for cardiovascular risk stratification in healthy individuals has not yet been established. As sample sizes with high-dimensional molecular data increase in epidemiological settings, integration of metabolomic data across studies and platforms with other molecular data will lead to new understanding of the metabolic processes underlying CVD and contribute to identification of potentially novel preventive and pharmacological targets. Metabolic phenotyping offers a powerful tool in the characterisation of the molecular signatures of CVD, paving the way to new mechanistic understanding and therapies, as well as improving risk prediction of CVD patients. However, there are still challenges to face in order to contribute to clinically important improvements in CVD.
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Affiliation(s)
- Aikaterini Iliou
- Pharmacy, National and Kapodistrian University of Athens School of Health Sciences, Athens, Attica, Greece
| | - Emmanuel Mikros
- Pharmacy, National and Kapodistrian University of Athens School of Health Sciences, Athens, Attica, Greece
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Freya Elliott
- School of Medicine and Dentistry, Queen Mary University, London, UK
| | - Julian L Griffin
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.,Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece.,BHF Research Centre for Excellence, Faculty of Medicine, Imperial College London, London, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK .,BHF Research Centre for Excellence, Faculty of Medicine, Imperial College London, London, UK.,MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Imperial College Biomedical Research Centre, Imperial College London, London, UK
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12
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Cheng Y, Schlosser P, Hertel J, Sekula P, Oefner PJ, Spiekerkoetter U, Mielke J, Freitag DF, Schmidts M, Kronenberg F, Eckardt KU, Thiele I, Li Y, Köttgen A. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. Nat Commun 2021; 12:964. [PMID: 33574263 PMCID: PMC7878905 DOI: 10.1038/s41467-020-20877-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.
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Affiliation(s)
- Yurong Cheng
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany ,grid.5963.9Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Pascal Schlosser
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Johannes Hertel
- grid.6142.10000 0004 0488 0789School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland ,grid.5603.0University of Greifswald, University Medicine Greifswald, Department of Psychiatry and Psychotherapy, Greifswald, Germany
| | - Peggy Sekula
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Peter J. Oefner
- grid.7727.50000 0001 2190 5763Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Ute Spiekerkoetter
- grid.5963.9Department of General Pediatrics and Adolescent Medicine, Medical Center and Faculty of Medicine - University of Freiburg, Freiburg, Germany
| | - Johanna Mielke
- grid.420044.60000 0004 0374 4101Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Daniel F. Freitag
- grid.420044.60000 0004 0374 4101Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Miriam Schmidts
- grid.5963.9Department of General Pediatrics and Adolescent Medicine, Medical Center and Faculty of Medicine - University of Freiburg, Freiburg, Germany
| | | | - Florian Kronenberg
- grid.5361.10000 0000 8853 2677Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Kai-Uwe Eckardt
- grid.5330.50000 0001 2107 3311Department of Nephrology and Hypertension, University of Erlangen-Nürnberg, Erlangen, Germany ,grid.6363.00000 0001 2218 4662Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ines Thiele
- grid.6142.10000 0004 0488 0789School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland ,grid.6142.10000 0004 0488 0789Division of Microbiology, National University of Ireland, Galway, University Road, Galway, Ireland ,APC Microbiome Ireland, Galway, Ireland
| | - Yong Li
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany ,grid.5963.9CIBSS – Centre for Integrative Biological Signalling Studies, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
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13
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Shirai Y, Okada Y. Elucidation of disease etiology by trans-layer omics analysis. Inflamm Regen 2021; 41:6. [PMID: 33557957 PMCID: PMC7871538 DOI: 10.1186/s41232-021-00155-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/22/2021] [Indexed: 12/18/2022] Open
Abstract
To date, genome-wide association studies (GWASs) have successfully identified thousands of associations between genetic polymorphisms and human traits. However, the pathways between the associated genotype and phenotype are often poorly understood. The transcriptome, proteome, and metabolome, the omics, are positioned along the pathway and can provide useful information to translate from genotype to phenotype. This review shows useful data resources for connecting each omics and describes how they are combined into a cohesive analysis. Quantitative trait loci (QTL) are useful information for connecting the genome and other omics. QTL represent how much genetic variants have effects on other omics and give us clues to how GWAS risk SNPs affect biological mechanisms. Integration of each omics provides a robust analytical framework for estimating disease causality, discovering drug targets, and identifying disease-associated tissues. Technological advances and the rise of consortia and biobanks have facilitated the analyses of unprecedented data, improving both the quality and quantity of research. Proficient management of these valuable datasets allows discovering novel insights into the genetic background and etiology of complex human diseases and contributing to personalized medicine.
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Affiliation(s)
- Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan.
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14
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Luo S, Surapaneni A, Zheng Z, Rhee EP, Coresh J, Hung AM, Nadkarni GN, Yu B, Boerwinkle E, Tin A, Arking DE, Steinbrenner I, Schlosser P, Köttgen A, Grams ME. NAT8 Variants, N-Acetylated Amino Acids, and Progression of CKD. Clin J Am Soc Nephrol 2020; 16:37-47. [PMID: 33380473 PMCID: PMC7792648 DOI: 10.2215/cjn.08600520] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 11/04/2020] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND OBJECTIVES Genetic variants in NAT8, a liver- and kidney-specific acetyltransferase encoding gene, have been associated with eGFR and CKD in European populations. Higher circulating levels of two NAT8-associated metabolites, N-δ-acetylornithine and N-acetyl-1-methylhistidine, have been linked to lower eGFR and higher risk of incident CKD in the Black population. We aimed to expand upon prior studies to investigate associations between rs13538, a missense variant in NAT8, N-acetylated amino acids, and kidney failure in multiple, well-characterized cohorts. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We conducted analyses among participants with genetic and/or serum metabolomic data in the African American Study of Kidney Disease and Hypertension (AASK; n=962), the Atherosclerosis Risk in Communities (ARIC) study (n=1050), and BioMe, an electronic health record-linked biorepository (n=680). Separately, we evaluated associations between rs13538, urinary N-acetylated amino acids, and kidney failure in participants in the German CKD (GCKD) study (n=1624). RESULTS Of 31 N-acetylated amino acids evaluated, the circulating and urinary levels of 14 were associated with rs13538 (P<0.05/31). Higher circulating levels of five of these N-acetylated amino acids, namely, N-δ-acetylornithine, N-acetyl-1-methylhistidine, N-acetyl-3-methylhistidine, N-acetylhistidine, and N2,N5-diacetylornithine, were associated with kidney failure, after adjustment for confounders and combining results in meta-analysis (combined hazard ratios per two-fold higher amino acid levels: 1.48, 1.44, 1.21, 1.65, and 1.41, respectively; 95% confidence intervals: 1.21 to 1.81, 1.22 to 1.70, 1.08 to 1.37, 1.29 to 2.10, and 1.17 to 1.71, respectively; all P values <0.05/14). None of the urinary levels of these N-acetylated amino acids were associated with kidney failure in the GCKD study. CONCLUSIONS We demonstrate significant associations between an NAT8 gene variant and 14 N-acetylated amino acids, five of which had circulation levels that were associated with kidney failure.
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Affiliation(s)
- Shengyuan Luo
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Eugene P. Rhee
- Division of Nephrology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Adriana M. Hung
- Geriatric Research Education Clinical Center, Veteran Administration Tennessee Valley Health Care System, Nashville, Tennessee
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, Texas
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, Texas
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Dan E. Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Inga Steinbrenner
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
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15
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Calvo-Serra B, Maitre L, Lau CHE, Siskos AP, Gützkow KB, Andrušaitytė S, Casas M, Cadiou S, Chatzi L, González JR, Grazuleviciene R, McEachan R, Slama R, Vafeiadi M, Wright J, Coen M, Vrijheid M, Keun HC, Escaramís G, Bustamante M. Urinary metabolite quantitative trait loci in children and their interaction with dietary factors. Hum Mol Genet 2020; 29:3830-3844. [PMID: 33283231 DOI: 10.1093/hmg/ddaa257] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/26/2020] [Accepted: 11/30/2020] [Indexed: 11/14/2022] Open
Abstract
Human metabolism is influenced by genetic and environmental factors. Previous studies have identified over 23 loci associated with more than 26 urine metabolites levels in adults, which are known as urinary metabolite quantitative trait loci (metabQTLs). The aim of the present study is the identification for the first time of urinary metabQTLs in children and their interaction with dietary patterns. Association between genome-wide genotyping data and 44 urine metabolite levels measured by proton nuclear magnetic resonance spectroscopy was tested in 996 children from the Human Early Life Exposome project. Twelve statistically significant urine metabQTLs were identified, involving 11 unique loci and 10 different metabolites. Comparison with previous findings in adults revealed that six metabQTLs were already known, and one had been described in serum and three were involved the same locus as other reported metabQTLs but had different urinary metabolites. The remaining two metabQTLs represent novel urine metabolite-locus associations, which are reported for the first time in this study [single nucleotide polymorphism (SNP) rs12575496 for taurine, and the missense SNP rs2274870 for 3-hydroxyisobutyrate]. Moreover, it was found that urinary taurine levels were affected by the combined action of genetic variation and dietary patterns of meat intake as well as by the interaction of this SNP with beverage intake dietary patterns. Overall, we identified 12 urinary metabQTLs in children, including two novel associations. While a substantial part of the identified loci affected urinary metabolite levels both in children and in adults, the metabQTL for taurine seemed to be specific to children and interacted with dietary patterns.
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Affiliation(s)
- Beatriz Calvo-Serra
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Léa Maitre
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Chung-Ho E Lau
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Alexandros P Siskos
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK
| | - Kristine B Gützkow
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo 0213, Norway
| | - Sandra Andrušaitytė
- Department of Environmental Science, Vytautas Magnus University, Kaunas 44248, Lithuania
| | - Maribel Casas
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Solène Cadiou
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble 38000, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Juan R González
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Regina Grazuleviciene
- Department of Environmental Science, Vytautas Magnus University, Kaunas 44248, Lithuania
| | | | - Rémy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble 38000, France
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion 71003, Greece
| | - John Wright
- Bradford Institute for Health Research, Bradford BD9 6RJ, UK
| | - Murieann Coen
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB2 0RE, UK
| | - Martine Vrijheid
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Hector C Keun
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK
| | - Geòrgia Escaramís
- Departament de Biomedicina, Institut de Neurociències, Universitat de Barcelona (UB), Barcelona 08036, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
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16
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Cheng Y, Li Y, Benkowitz P, Lamina C, Köttgen A, Sekula P. The relationship between blood metabolites of the tryptophan pathway and kidney function: a bidirectional Mendelian randomization analysis. Sci Rep 2020; 10:12675. [PMID: 32728058 PMCID: PMC7391729 DOI: 10.1038/s41598-020-69559-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 07/14/2020] [Indexed: 02/07/2023] Open
Abstract
Blood metabolites of the tryptophan pathway were found to be associated with kidney function and disease in observational studies. In order to evaluate causal relationship and direction, we designed a study using a bidirectional Mendelian randomization approach. The analyses were based on published summary statistics with study sizes ranging from 1,960 to 133,413. After correction for multiple testing, results provided no evidence of an effect of metabolites of the tryptophan pathway on estimated glomerular filtration rate (eGFR). Conversely, lower eGFR was related to higher levels of four metabolites: C-glycosyltryptophan (effect estimate = − 0.16, 95% confidence interval [CI] (− 0.22; − 0.1); p = 9.2e−08), kynurenine (effect estimate = − 0.18, 95% CI (− 0.25; − 0.11); p = 1.1e−06), 3-indoxyl sulfate (effect estimate = − 0.25, 95% CI (− 0.4; − 0.11); p = 6.3e−04) and indole-3-lactate (effect estimate = − 0.26, 95% CI (− 0.38; − 0.13); p = 5.4e−05). Our study supports that lower eGFR causes higher blood metabolite levels of the tryptophan pathway including kynurenine, C-glycosyltryptophan, 3-indoxyl sulfate, and indole-3-lactate. These findings aid the notion that metabolites of the tryptophan pathway are a consequence rather than a cause of reduced eGFR. Further research is needed to specifically examine relationships with respect to chronic kidney disease (CKD) progression among patients with existing CKD.
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Affiliation(s)
- Yurong Cheng
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.,Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Yong Li
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Paula Benkowitz
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Claudia Lamina
- Department of Genetics and Pharmacology, Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Anna Köttgen
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Peggy Sekula
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.
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17
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Abstract
A recent metabolite genome-wide association study (mGWAS) investigated the relationship between genetic factors and the urine metabolome in kidney disease. The findings demonstrate that mGWAS hold promise for identifying novel genetic factors involved in adsorption, distribution, metabolism and excretion of metabolites and pharmaceuticals, as well as biomarkers for disease progression.
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Affiliation(s)
- Daniel Montemayor
- Center for Renal Precision Medicine, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Kumar Sharma
- Center for Renal Precision Medicine, University of Texas Health San Antonio, San Antonio, TX, USA.
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18
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Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet 2020; 52:167-176. [PMID: 31959995 DOI: 10.1038/s41588-019-0567-8] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 12/05/2019] [Indexed: 11/08/2022]
Abstract
The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research.
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19
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Abstract
Metabolomics has been increasingly applied to study renal and related cardiometabolic diseases, including diabetes and cardiovascular diseases. These studies span cross-sectional studies correlating metabolites with specific phenotypes, longitudinal studies to identify metabolite predictors of future disease, and physiologic/interventional studies to probe underlying causal relationships. This chapter provides a description of how metabolomic profiling is being used in these contexts, with an emphasis on study design considerations as a practical guide for investigators who are new to this area. Research in kidney diseases is underlined to illustrate key principles. The chapter concludes by discussing the future potential of metabolomics in the study of renal and cardiometabolic diseases.
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20
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Larijani B, Goodarzi P, Payab M, Alavi-Moghadam S, Rahim F, Bana N, Abedi M, Arabi M, Adibi H, Gilany K, Arjmand B. Metabolomics and Cell Therapy in Diabetes Mellitus. INTERNATIONAL JOURNAL OF MOLECULAR AND CELLULAR MEDICINE 2019; 8:41-48. [PMID: 32351908 PMCID: PMC7175613 DOI: 10.22088/ijmcm.bums.8.2.41] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 05/30/2019] [Indexed: 12/22/2022]
Abstract
Diabetes with a broad spectrum of complications has become a global epidemic metabolic disorder. Till now, several pharmaceutical and non-pharmaceutical therapeutic approaches were applied for its treatment. Cell-based therapies have become promising methods for diabetes treatment. Better understanding of diabetes pathogenesis and identification of its specific biomarkers along with evaluation of different treatments efficacy, can be possible by clarification of specific metabolic modifications during the diabetes progression. Subsequently, metabolomics technology can support this goal as an effective tool. The present review tried to show how metabolomics quantifications can be useful for diabetic monitoring before and after cell therapy. Cell therapy is an alternative approach to achieve diabetes treatments goals including insulin resistance amelioration, insulin independence reparation, and control of glycemia. OMICs approaches provide a comprehensive insight into the molecular mechanisms of cells features and functional mechanism of their genomics, transcriptomics, proteomics, and metabolomics profile which can be useful for their therapeutic application. As a modern technology for the detection and analysis of metabolites in biological samples, metabolomica can identify many of the metabolic and molecular pathways associated with diabetes and its following complications.
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Affiliation(s)
- Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical sciences, Tehran, Iran
| | - Parisa Goodarzi
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Moloud Payab
- Obesity and Eating Habits Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Sepideh Alavi-Moghadam
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Fakher Rahim
- Health Research Institute, Thalassemia and Hemoglobinopathies Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Nikoo Bana
- Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mina Abedi
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Arabi
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Adibi
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kambiz Gilany
- Department of Biomedical Sciences, University of Antwerp, Belgium.,Integrative Oncology Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
| | - Babak Arjmand
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
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21
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Wiesenhofer FM, Herzog R, Boehm M, Wagner A, Unterwurzacher M, Kasper DC, Alper SL, Vychytil A, Aufricht C, Kratochwill K. Targeted Metabolomic Profiling of Peritoneal Dialysis Effluents Shows Anti-oxidative Capacity of Alanyl-Glutamine. Front Physiol 2019; 9:1961. [PMID: 30719009 PMCID: PMC6348277 DOI: 10.3389/fphys.2018.01961] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 12/28/2018] [Indexed: 01/25/2023] Open
Abstract
Readily available peritoneal dialysis (PD) effluents from PD patients in the course of renal replacement therapy are a potentially rich source for molecular markers for predicting clinical outcome, monitoring the therapy, and therapeutic interventions. The complex clinical phenotype of PD patients might be reflected in the PD effluent metabolome. Metabolomic analysis of PD effluent might allow quantitative detection and assessment of candidate PD biomarkers for prognostication and therapeutic monitoring. We therefore subjected peritoneal equilibration test effluents from 20 stable PD patients, obtained in a randomized controlled trial (RCT) to evaluate cytoprotective effects of standard PD solution (3.86% glucose) supplemented with 8 mM alanyl-glutamine (AlaGln) to targeted metabolomics analysis. One hundred eighty eight pre-defined metabolites, including free amino acids, acylcarnitines, and glycerophospholipids, as well as custom metabolic indicators calculated from these metabolites were surveyed in a high-throughput assay requiring only 10 μl of PD effluent. Metabolite profiles of effluents from the cross-over trial were analyzed with respect to AlaGln status and clinical parameters such as duration of PD therapy and history of previous episodes of peritonitis. This targeted approach detected and quantified 184 small molecules in PD effluent, a larger number of detected metabolites than in all previous metabolomic studies in PD effluent combined. Metabolites were clustered within substance classes regarding concentrations after a 4-h dwell. PD effluent metabolic profiles were differentiated according to PD patient sub-populations, revealing novel changes in small molecule abundance during PD therapy. AlaGln supplementation of PD fluid altered levels of specific metabolites, including increases in alanine and glutamine but not glutamate, and reduced levels of small molecule indicators of oxidative stress, such as methionine sulfoxide. Our study represents the first application of targeted metabolomics to PD effluents. The observed metabolomic changes in PD effluent associated with AlaGln-supplementation during therapy suggested an anti-oxidant effect, and were consistent with the restoration of important stress and immune processes previously noted in the RCT. High-throughput detection of PD effluent metabolomic signatures and their alterations by therapeutic interventions offers new opportunities for metabolome-clinical correlation in PD and for prescription of personalized PD therapy.
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Affiliation(s)
- Florian M Wiesenhofer
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Rebecca Herzog
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Michael Boehm
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Anja Wagner
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Markus Unterwurzacher
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | | | - Seth L Alper
- Division of Nephrology and Vascular Biology Research Center, Beth Israel Deaconess Medical Center, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Andreas Vychytil
- Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Vienna, Austria
| | - Christoph Aufricht
- Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Klaus Kratochwill
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
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22
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Hurtado Del Pozo C, Garreta E, Izpisúa Belmonte JC, Montserrat N. Modeling epigenetic modifications in renal development and disease with organoids and genome editing. Dis Model Mech 2018; 11:dmm035048. [PMID: 30459215 PMCID: PMC6262817 DOI: 10.1242/dmm.035048] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Understanding epigenetic mechanisms is crucial to our comprehension of gene regulation in development and disease. In the past decades, different studies have shown the role of epigenetic modifications and modifiers in renal disease, especially during its progression towards chronic and end-stage renal disease. Thus, the identification of genetic variation associated with chronic kidney disease has resulted in better clinical management of patients. Despite the importance of these findings, the translation of genotype-phenotype data into gene-based medicine in chronic kidney disease populations still lacks faithful cellular or animal models that recapitulate the key aspects of the human kidney. The latest advances in the field of stem cells have shown that it is possible to emulate kidney development and function with organoids derived from human pluripotent stem cells. These have successfully recapitulated not only kidney differentiation, but also the specific phenotypical traits related to kidney function. The combination of this methodology with CRISPR/Cas9 genome editing has already helped researchers to model different genetic kidney disorders. Nowadays, CRISPR/Cas9-based approaches also allow epigenetic modifications, and thus represent an unprecedented tool for the screening of genetic variants, epigenetic modifications or even changes in chromatin structure that are altered in renal disease. In this Review, we discuss these technical advances in kidney modeling, and offer an overview of the role of epigenetic regulation in kidney development and disease.
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Affiliation(s)
- Carmen Hurtado Del Pozo
- Pluripotency for organ regeneration. Institute for Bioengineering of Catalonia (IBEC), the Barcelona Institute of Technology (BIST), 08028 Barcelona, Spain
| | - Elena Garreta
- Pluripotency for organ regeneration. Institute for Bioengineering of Catalonia (IBEC), the Barcelona Institute of Technology (BIST), 08028 Barcelona, Spain
| | | | - Nuria Montserrat
- Pluripotency for organ regeneration. Institute for Bioengineering of Catalonia (IBEC), the Barcelona Institute of Technology (BIST), 08028 Barcelona, Spain
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23
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Davies R. The metabolomic quest for a biomarker in chronic kidney disease. Clin Kidney J 2018; 11:694-703. [PMID: 30288265 PMCID: PMC6165760 DOI: 10.1093/ckj/sfy037] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 04/16/2018] [Indexed: 12/15/2022] Open
Abstract
Chronic kidney disease (CKD) is a growing burden on people and on healthcare for which the diagnostics are niether disease-specific nor indicative of progression. Biomarkers are sought to enable clinicians to offer more appropriate patient-centred treatments, which could come to fruition by using a metabolomics approach. This mini-review highlights the current literature of metabolomics and CKD, and suggests additional factors that need to be considered in this quest for a biomarker, namely the diet and the gut microbiome, for more meaningful advances to be made.
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Affiliation(s)
- Robert Davies
- School of Biomedical and Healthcare Sciences, University of Plymouth School of Biological Sciences, Plymouth, UK
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24
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Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage. J Pharm Biomed Anal 2018; 161:313-325. [PMID: 30195171 DOI: 10.1016/j.jpba.2018.08.046] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 08/22/2018] [Accepted: 08/23/2018] [Indexed: 12/16/2022]
Abstract
Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.
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