1
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Tahir UA, Katz DH, Avila-Pachecho J, Bick AG, Pampana A, Robbins JM, Yu Z, Chen ZZ, Benson MD, Cruz DE, Ngo D, Deng S, Shi X, Zheng S, Eisman AS, Farrell L, Hall ME, Correa A, Tracy RP, Durda P, Taylor KD, Liu Y, Johnson WC, Guo X, Yao J, Chen YDI, Manichaikul AW, Ruberg FL, Blaner WS, Jain D, Bouchard C, Sarzynski MA, Rich SS, Rotter JI, Wang TJ, Wilson JG, Clish CB, Natarajan P, Gerszten RE. Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals. Nat Commun 2022; 13:4923. [PMID: 35995766 PMCID: PMC9395431 DOI: 10.1038/s41467-022-32275-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 07/25/2022] [Indexed: 01/27/2023] Open
Abstract
Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.
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Affiliation(s)
- Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | | | | | - Akhil Pampana
- Broad Institute of Harvard and MIT, Cambridge, MA, US
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Zhi Yu
- Broad Institute of Harvard and MIT, Cambridge, MA, US
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Aaron S Eisman
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Michael E Hall
- University of Mississippi Medical Center, Jackson, MS, US
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS, US
| | - Russell P Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, US
| | - Peter Durda
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, US
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Yongmei Liu
- Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, US
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, US
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, US
- Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, US
| | - Frederick L Ruberg
- Section of Cardiovascular Medicine, Boston University School of Medicine and Boston Medical Center, Boston, MA, US
| | | | - Deepti Jain
- University of Washington, Seattle, Washington, US
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, US
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, SC, US
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, US
- Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, US
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Thomas J Wang
- Department of Medicine, UT Southwestern Medical Center, Dallas, TX, US
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, US
| | - Pradeep Natarajan
- Broad Institute of Harvard and MIT, Cambridge, MA, US
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, US
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US.
- Broad Institute of Harvard and MIT, Cambridge, MA, US.
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2
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Beuchel C, Dittrich J, Pott J, Henger S, Beutner F, Isermann B, Loeffler M, Thiery J, Ceglarek U, Scholz M. Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure. Metabolites 2022; 12:metabo12030216. [PMID: 35323659 PMCID: PMC8949022 DOI: 10.3390/metabo12030216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/15/2022] [Accepted: 02/25/2022] [Indexed: 02/04/2023] Open
Abstract
A variety of atherosclerosis and cardiovascular disease (ASCVD) phenotypes are tightly linked to changes in the cardiac energy metabolism that can lead to a loss of metabolic flexibility and to unfavorable clinical outcomes. We conducted an association analysis of 31 ASCVD phenotypes and 97 whole blood amino acids, acylcarnitines and derived ratios in the LIFE-Adult (n = 9646) and LIFE-Heart (n = 5860) studies, respectively. In addition to hundreds of significant associations, a total of 62 associations of six phenotypes were found in both studies. Positive associations of various amino acids and a range of acylcarnitines with decreasing cardiovascular health indicate disruptions in mitochondrial, as well as peroxisomal fatty acid oxidation. We complemented our metabolite association analyses with whole blood and peripheral blood mononuclear cell (PBMC) gene-expression analyses of fatty acid oxidation and ketone-body metabolism related genes. This revealed several differential expressions for the heart failure biomarker N-terminal prohormone of brain natriuretic peptide (NT-proBNP) in peripheral blood mononuclear cell (PBMC) gene expression. Finally, we constructed and compared three prediction models of significant stenosis in the LIFE-Heart study using (1) traditional risk factors only, (2) the metabolite panel only and (3) a combined model. Area under the receiver operating characteristic curve (AUC) comparison of these three models shows an improved prediction accuracy for the combined metabolite and classical risk factor model (AUC = 0.78, 95%-CI: 0.76–0.80). In conclusion, we improved our understanding of metabolic implications of ASCVD phenotypes by observing associations with metabolite concentrations and gene expression of the mitochondrial and peroxisomal fatty acid oxidation. Additionally, we demonstrated the predictive potential of the metabolite profile to improve classification of patients with significant stenosis.
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Affiliation(s)
- Carl Beuchel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- Correspondence: (C.B.); (U.C.); (M.S.)
| | - Julia Dittrich
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.D.); (B.I.); (J.T.)
| | - Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
| | - Sylvia Henger
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
| | | | - Berend Isermann
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.D.); (B.I.); (J.T.)
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
| | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.D.); (B.I.); (J.T.)
- Faculty of Medicine, Christian-Albrecht University of Kiel, 24118 Kiel, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.D.); (B.I.); (J.T.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
- Correspondence: (C.B.); (U.C.); (M.S.)
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
- IFB AdiposityDiseases, University Hospital Leipzig, 04103 Leipzig, Germany
- Correspondence: (C.B.); (U.C.); (M.S.)
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3
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Verification of immunology-related genetic associations in BPD supports ABCA3 and five other genes. Pediatr Res 2022; 92:190-198. [PMID: 34465876 PMCID: PMC9411063 DOI: 10.1038/s41390-021-01689-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 07/08/2021] [Accepted: 07/19/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Inflammatory processes are key drivers of bronchopulmonary dysplasia (BPD), a chronic lung disease in preterm infants. In a large sample, we verify previously reported associations of genetic variants of immunology-related genes with BPD. METHODS Preterm infants with a gestational age ≤32 weeks from PROGRESS and the German Neonatal Network (GNN) were included. Through a consensus case/control definition, 278 BPD cases and 670 controls were identified. We identified 49 immunity-related genes and 55 single-nucleotide polymorphisms (SNPs) previously associated with BPD through a comprehensive literature survey. Additionally, a quantitative genetic association analysis regarding oxygen supplements, mechanical ventilation, and continuous positive air pressure (CPAP) was performed. RESULTS Five candidate SNPs were nominally associated with BPD-related phenotypes with effect directions not conflicting the original studies: rs11265269-CRP, rs1427793-NUAK1, rs2229569-SELL, rs1883617-VNN2, and rs4148913-CHST3. Four of these genes are involved in cell adhesion. Extending our analysis to all well-imputed SNPs of all candidate genes, the strongest association was rs45538638-ABCA3 with CPAP (p = 4.9 × 10-7, FDR = 0.004), an ABC transporter involved in surfactant formation. CONCLUSIONS Most of the previously reported associations could not be replicated. We found additional support for SNPs in CRP, NUAK1, SELL, VNN2, and ABCA3. Larger studies and meta-analyses are required to corroborate these findings. IMPACT Larger cohort for improved statistical power to detect genetic associations with bronchopulmonary dysplasia (BPD). Most of the previously reported genetic associations with BPD could not be replicated in this larger study. Among investigated immunological relevant candidate genes, additional support was found for variants in genes CRP, NUAK1, SELL, VNN2, and CHST3, four of them related to cell adhesion. rs45538638 is a novel candidate SNP in reported candidate gene ABC-transporter ABCA3. Results help to prioritize molecular candidate pathomechanisms in follow-up studies.
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4
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Catanese S, Beuchel CF, Sawall T, Lordick F, Brauer R, Scholz M, Ceglarek U, Hacker UT. Biomarkers related to fatty acid oxidative capacity are predictive for continued weight loss in cachectic cancer patients. J Cachexia Sarcopenia Muscle 2021; 12:2101-2110. [PMID: 34636159 PMCID: PMC8718041 DOI: 10.1002/jcsm.12817] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/06/2021] [Accepted: 09/07/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Cachexia is characterized by a negative protein and energy balance leading to loss of adipose tissue and muscle mass. Cancer cachexia negatively impacts treatment tolerability and prognosis. Supportive interventions should be initiated as early as possible. Biomarkers for early prediction of continuing weight loss during the course of disease are currently lacking. METHODS In this pilot, observational, cross-sectional, case-control study, cachectic cancer patients undergoing systemic first-line cancer treatment were matched 2:1 with healthy controls according to age, gender and body mass index. Alterations in amino acid and energy metabolism, as indicated by acylcarnitine levels, were analysed using mass spectrometry in plasma samples (PS) and dried blood specimen (DBS). Welch's two-sample t-test was used for comparative analysis of metabolites between cancer patients and healthy matched controls and to identify the metabolomic profiles related to weight loss across different time points. A linear regression model was applied to correlate weight loss and single metabolites as predictor variables. Finally, metabolite pathway enrichment analyses were performed. RESULTS Eighteen cases (14 male and 4 female) and 36 paired controls were enrolled. There was a good correlation between baseline PS and DBS of healthy controls for the levels of most amino acids but not for acylcarnitine. Amino acid levels related to cancer metabolism were significantly altered in cancer patients compared with controls in both DBS and PS for arginine, citrulline, histidine and ornithine and in DBS only for asparagine, glutamine, methylhistidine, methionine, ornithine, serine, threonine and leucine/isoleucine. Metabolite enrichment analysis in PS of cancer patients revealed histidine metabolism activation (P = 0.0025). Baseline acylcarnitine analysis in DBS was indicative for alterations of the mitochondrial carnitine shuttle, related to β-oxidation: The ratio palmitoylcarnitine/acylcarnitine (Q2) and the ratio palmitoylcarnitine + octadecenoylcarnitine/acylcarnitine (Q3) were predictive for early weight loss (P < 0.0001) and weight loss during follow-up. Activation of tryptophan metabolism (P = 0.035) in DBS and PS and activation of serine/glycine metabolism (P = 0.017) in PS were also related to early weight loss and across successive time points. CONCLUSIONS We found alterations in amino acid levels most likely attributable to cancer metabolism itself in cancer patients compared with controls. Baseline DBS represent a valuable analyte to study energy metabolism related to cancer cachexia. Acylcarnitine patterns (Q2, Q3) predicted further weight loss in cachectic cancer patients undergoing systemic therapy, and pathway analyses indicated involvement of the serine/glycine and the tryptophan pathway in this condition. Validation in larger cohorts is warranted.
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Affiliation(s)
- Silvia Catanese
- Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany.,Department of Oncology, University Hospital of Pisa, Pisa, Italy
| | - Carl Friedrich Beuchel
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Medical Faculty of the University Leipzig, Leipzig, Germany
| | | | - Florian Lordick
- Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany
| | - Rommy Brauer
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University Medical Center, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Medical Faculty of the University Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University Medical Center, Leipzig, Germany
| | - Ulrich T Hacker
- Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany
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5
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Sönmez Flitman R, Khalili B, Kutalik Z, Rueedi R, Brümmer A, Bergmann S. Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine. J Proteome Res 2021; 20:5103-5114. [PMID: 34699229 PMCID: PMC9286311 DOI: 10.1021/acs.jproteome.1c00585] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
![]()
Gene products can
affect the concentrations of small molecules
(aka “metabolites”), and conversely, some metabolites
can modulate the concentrations of gene transcripts. While many specific
instances of this interplay have been revealed, a global approach
to systematically uncover human gene-metabolite interactions is still
lacking. We performed a metabolome- and transcriptome-wide association
study to identify genes influencing the human metabolome using untargeted
metabolome features, extracted from 1H nuclear magnetic
resonance spectroscopy (NMR) of urine samples, and gene expression
levels, quantified from RNA-Seq of lymphoblastoid cell lines (LCL)
from 555 healthy individuals. We identified 20 study-wide significant
associations corresponding to 15 genes, of which 5 associations (with
2 genes) were confirmed with follow-up NMR data. Using metabomatching,
we identified the metabolites corresponding to metabolome features
associated with the genes, namely, N-acetylated compounds with ALMS1 and trimethylamine (TMA) with HPS1. Finally, Mendelian randomization analysis supported a potential
causal link between the expression of genes in both the ALMS1- and HPS1-loci and their associated metabolite
concentrations. In the case of HPS1, we additionally
observed that TMA concentration likely exhibits a reverse causal effect
on HPS1 expression levels, indicating a negative
feedback loop. Our study highlights how the integration of metabolomics,
gene expression, and genetic data can pinpoint causal genes modulating
metabolite concentrations.
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Affiliation(s)
- Reyhan Sönmez Flitman
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Bita Khalili
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Zoltan Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Anneke Brümmer
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.,Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7700, South Africa
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6
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Ye M, Lin Y, Pan S, Wang ZW, Zhu X. Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis. Front Oncol 2021; 11:745808. [PMID: 34631583 PMCID: PMC8497990 DOI: 10.3389/fonc.2021.745808] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/08/2021] [Indexed: 12/29/2022] Open
Abstract
Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.
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Affiliation(s)
| | | | | | - Zhi-wei Wang
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xueqiong Zhu
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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7
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Chu X, Jaeger M, Beumer J, Bakker OB, Aguirre-Gamboa R, Oosting M, Smeekens SP, Moorlag S, Mourits VP, Koeken VACM, de Bree C, Jansen T, Mathews IT, Dao K, Najhawan M, Watrous JD, Joosten I, Sharma S, Koenen HJPM, Withoff S, Jonkers IH, Netea-Maier RT, Xavier RJ, Franke L, Xu CJ, Joosten LAB, Sanna S, Jain M, Kumar V, Clevers H, Wijmenga C, Netea MG, Li Y. Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. Genome Biol 2021; 22:198. [PMID: 34229738 PMCID: PMC8259168 DOI: 10.1186/s13059-021-02413-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Recent studies highlight the role of metabolites in immune diseases, but it remains unknown how much of this effect is driven by genetic and non-genetic host factors. RESULT We systematically investigate circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity are deeply measured and whose genetics are profiled. Our data reveal that several major metabolic pathways, including the alanine/glutamate pathway and the arachidonic acid pathway, have a strong impact on cytokine production in response to ex vivo stimulation. We also examine the genetic regulation of metabolites associated with immune phenotypes through genome-wide association analysis and identify 29 significant loci, including eight novel independent loci. Of these, one locus (rs174584-FADS2) associated with arachidonic acid metabolism is causally associated with Crohn's disease, suggesting it is a potential therapeutic target. CONCLUSION This study provides a comprehensive map of the integration between the blood metabolome and immune phenotypes, reveals novel genetic factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.
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Affiliation(s)
- Xiaojing Chu
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Martin Jaeger
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Joep Beumer
- Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, 3584, CT, Utrecht, the Netherlands
| | - Olivier B Bakker
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Raul Aguirre-Gamboa
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Marije Oosting
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Sanne P Smeekens
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Simone Moorlag
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Vera P Mourits
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Valerie A C M Koeken
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Charlotte de Bree
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Trees Jansen
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Ian T Mathews
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
- La Jolla Institute, La Jolla, CA, USA
| | - Khoi Dao
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Mahan Najhawan
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Jeramie D Watrous
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Irma Joosten
- Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, 6525, GA, Nijmegen, the Netherlands
| | | | - Hans J P M Koenen
- Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, 6525, GA, Nijmegen, the Netherlands
| | - Sebo Withoff
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Iris H Jonkers
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Romana T Netea-Maier
- Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard University, Cambridge, MA, 02142, USA
- Center for Computational and Integrative Biology and Gastrointestinal Unit, Massachusetts General Hospital, Harvard School of Medicine, Boston, MA, 02114, USA
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Cheng-Jian Xu
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Leo A B Joosten
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Serena Sanna
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Mohit Jain
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Vinod Kumar
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Hans Clevers
- Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, 3584, CT, Utrecht, the Netherlands
- Oncode Institute, Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands.
- Department of Immunology, University of Oslo, Oslo University Hospital, Rikshospitalet, 0372, Oslo, Norway.
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
- Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, 53115, Bonn, Germany.
| | - Yang Li
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands.
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany.
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany.
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
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8
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Beutner F, Ritter C, Scholz M, Teren A, Holdt LM, Teupser D, Becker S, Thiele H, Gielen S, Thiery J, Ceglarek U. A metabolomic approach to identify the link between sports activity and atheroprotection. Eur J Prev Cardiol 2020; 29:436-444. [PMID: 33624084 DOI: 10.1093/eurjpc/zwaa122] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 09/15/2020] [Accepted: 11/10/2020] [Indexed: 12/11/2022]
Abstract
AIMS Physical activity (PA) is a mainstay of cardiovascular prevention. This study aimed to identify metabolic mediators of PA that protect against the development of atherosclerosis. METHODS AND RESULTS A total of 2160 participants in the LIFE heart study were analysed with data on PA and vascular phenotyping. In a targeted metabolomic approach, 61 metabolites (amino acids and acylcarnitines) were measured using liquid chromatography-tandem mass spectrometry. We investigated the interactions between PA, metabolites and markers of atherosclerosis in order to uncover possible mediation effects. Intended sports activity, but no daily PA, was associated with a lower degree of atherosclerosis, odds ratio (OR) for total atherosclerotic burden of 0.76 (95% confidence interval 0.62-0.94), carotid artery plaque OR 0.79 (0.66-0.96), and peripheral artery disease OR 0.74 (0.56-0.98). Twelve amino acids, free carnitine, five acylcarnitines were associated with sports activity. Of these, eight metabolites were also associated with the degree of atherosclerosis. In the mediation analyses, a cluster of amino acids (arginine, glutamine, pipecolic acid, taurine) were considered as possible mediators of atheroprotection. In contrast, a group of members of the carnitine metabolism (free carnitine, acetyl carnitine, octadecenoyl carnitine) were associated with inactivity and higher atherosclerotic burden. CONCLUSION Our metabolomic approach, which is integrated into a mediation model, provides transformative insights into the complex metabolic processes involved in atheroprotection. Metabolites with antioxidant and endothelial active properties are believed to be possible mediators of atheroprotection. The metabolomic mediation approach can support the understanding of complex diseases in order to identify targets for prevention and therapy.
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Affiliation(s)
- Frank Beutner
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany.,Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, 04289 Leipzig, Germany
| | - Christian Ritter
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany
| | - Markus Scholz
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany.,Institute of Medical Informatics, Statistics and Epidemiology, University Leipzig, Leipzig, Germany
| | - Andrej Teren
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany.,Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, 04289 Leipzig, Germany
| | - Lesca Miriam Holdt
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Daniel Teupser
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Susen Becker
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, 04289 Leipzig, Germany
| | - Stephan Gielen
- Department of Cardiology, Angiology and Intensive Care, Klinikum Lippe, Detmold, Germany
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- LIFE Research Center for Civilization Diseases, University Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, University Hospital Leipzig, Leipzig, Germany
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9
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Naureen Z, Miggiano GAD, Aquilanti B, Velluti V, Matera G, Gagliardi L, Zulian A, Romanelli R, Bertelli M. Genetic test for the prescription of diets in support of physical activity. ACTA BIO-MEDICA : ATENEI PARMENSIS 2020; 91:e2020011. [PMID: 33170161 PMCID: PMC8023120 DOI: 10.23750/abm.v91i13-s.10584] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 09/17/2020] [Indexed: 01/03/2023]
Abstract
Owing to the fields of nutrigenetics and nutrigenomics today we can think of devising approaches to optimize health, delay onset of diseases and reduce its severity according to our genetic blue print. However this requires a deep understanding of nutritional impact on expression of genes that may result in a specific phenotype. The extensive research and observational studies during last two decades reporting interactions between genes, diet and physical activity suggest a cross talk between various genetic and environmental factors and lifestyle interventions. Although considerable efforts have been made in unraveling the mechanisms of gene-diet interactions the scientific evidences behind developing commercial genetic tests for providing personalized nutrition recommendations are still scarce. In this scenario the current mini-review aims to provide useful insights into salient feature of nutrition based genetic research and its commercial application and the ethical issue and concerns related to its outcome.
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Affiliation(s)
- Zakira Naureen
- Department of Biological Sciences and Chemistry, College of Arts and Sciences, University of Nizwa, Nizwa, Oman.
| | | | - Barbara Aquilanti
- UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Valeria Velluti
- UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Giuseppina Matera
- UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Lucilla Gagliardi
- UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | | | | | - Matteo Bertelli
- MAGI'S LAB, Rovereto (TN), Italy; MAGI EUREGIO, Bolzano, Italy; EBTNA-LAB, Rovereto (TN), Italy.
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10
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Scholz M, Henger S, Beutner F, Teren A, Baber R, Willenberg A, Ceglarek U, Pott J, Burkhardt R, Thiery J. Cohort Profile: The Leipzig Research Center for Civilization Diseases–Heart Study (LIFE-Heart). Int J Epidemiol 2020; 49:1439-1440h. [DOI: 10.1093/ije/dyaa075] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Markus Scholz
- Institute for Medical Informatics, Statistic and Epidemiology, University of Leipzig, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Sylvia Henger
- Institute for Medical Informatics, Statistic and Epidemiology, University of Leipzig, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Frank Beutner
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Andrej Teren
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Ronny Baber
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
| | - Anja Willenberg
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
| | - Janne Pott
- Institute for Medical Informatics, Statistic and Epidemiology, University of Leipzig, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Ralph Burkhardt
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Joachim Thiery
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
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11
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Chen Y, Zhang T, Wu L, Huang Y, Mao Z, Zhan Z, Chen W, Dai F, Cao W, Cao Y, Liu S, Cai Z, Tang L. Metabolism and Toxicity of Emodin: Genome-Wide Association Studies Reveal Hepatocyte Nuclear Factor 4α Regulates UGT2B7 and Emodin Glucuronidation. Chem Res Toxicol 2020; 33:1798-1808. [PMID: 32538071 DOI: 10.1021/acs.chemrestox.0c00047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Emodin is the main toxic component in Chinese medicinal herbs such as rhubarb. Our previous studies demonstrated that genetic polymorphisms of UDP-glucuronosyltransferase 2B7 (UGT2B7) had an effect on the glucuronidation and detoxification of emodin. This study aimed to reveal the transcriptional regulation mechanism of UGT2B7 on emodin glucuronidation and its effect on toxicity. Emodin glucuronic activity and genome and transcriptome data were obtained from 36 clinical human kidney tissues. The genome-wide association studies (GWAS) identified that four single nucleotide polymorphisms (SNPs) (rs6093966, rs2868094, rs2071197, and rs6073433), which were located on the hepatocyte nuclear factor 4α (HNF4A) gene, were significantly associated with the emodin glucuronidation (p < 0.05). Notably, rs2071197 was significantly associated with the gene expression of HNF4A and UGT2B7 and the glucuronidation of emodin. The gene expression of HNF4A showed a high correlation with UGT2B7 (R2 = 0.721, p = 5.83 × 10-11). The luciferase activity was increased 7.68-fold in 293T cells and 2.03-fold in HepG2 cells, confirming a significant transcriptional activation of UGT2B7 promoter by HNF4A. The knockdown of HNF4A in HepG2 cells (36.6%) led to a significant decrease of UGT2B7 (19.8%) and higher cytotoxicity (p < 0.05). The overexpression of HNF4A in HepG2 cells (31.2%) led to a significant increase of UGT2B7 (24.4%) and improved cell viability (p < 0.05). Besides, HNF4A and UGT2B7 were both decreased in HepG2 cells and rats after treatment with emodin. In conclusion, emodin used long term or in high doses could inhibit the expression of HNF4A, thereby reducing the expression of UGT2B7 and causing hepatotoxicity.
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Affiliation(s)
- Yulian Chen
- State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Southern Medical University, Guangzhou 510515, China.,Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tao Zhang
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lili Wu
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yilin Huang
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zhihao Mao
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zhikun Zhan
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Weizhong Chen
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Fahong Dai
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Wenyu Cao
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yong Cao
- Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, College of Food Science, South China Agricultural University, Guangzhou, Guangdong 510642, China
| | - Shuwen Liu
- State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Southern Medical University, Guangzhou 510515, China.,Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zheng Cai
- Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lan Tang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Southern Medical University, Guangzhou 510515, China.,Biopharmaceutics, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
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12
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Hsu YHH, Astley CM, Cole JB, Vedantam S, Mercader JM, Metspalu A, Fischer K, Fortney K, Morgen EK, Gonzalez C, Gonzalez ME, Esko T, Hirschhorn JN. Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome. Int J Obes (Lond) 2020; 44:1596-1606. [PMID: 32467615 PMCID: PMC7332400 DOI: 10.1038/s41366-020-0603-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 04/07/2020] [Accepted: 05/14/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals. METHODS We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS. RESULTS We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms. CONCLUSIONS These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.
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Affiliation(s)
- Yu-Han H Hsu
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Christina M Astley
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Joanne B Cole
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sailaja Vedantam
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Josep M Mercader
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Krista Fischer
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | | | | | - Clicerio Gonzalez
- Instituto Nacional de Salud Publica, Cuernavaca, Morelos, Mexico
- Centro de Estudios en Diabetes, Mexico City, Mexico
| | - Maria E Gonzalez
- Instituto Nacional de Salud Publica, Cuernavaca, Morelos, Mexico
- Centro de Estudios en Diabetes, Mexico City, Mexico
| | - Tonu Esko
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Joel N Hirschhorn
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA.
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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13
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Ranea-Robles P, Yu C, van Vlies N, Vaz FM, Houten SM. Slc22a5 haploinsufficiency does not aggravate the phenotype of the long-chain acyl-CoA dehydrogenase KO mouse. J Inherit Metab Dis 2020; 43:486-495. [PMID: 31845336 PMCID: PMC7205564 DOI: 10.1002/jimd.12204] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/22/2019] [Accepted: 12/11/2019] [Indexed: 01/10/2023]
Abstract
Secondary carnitine deficiency is commonly observed in inherited metabolic diseases characterised by the accumulation of acylcarnitines such as mitochondrial fatty acid oxidation (FAO) disorders. It is currently unclear if carnitine deficiency and/or acylcarnitine accumulation play a role in the pathophysiology of FAO disorders. The long-chain acyl-CoA dehydrogenase (LCAD) KO mouse is a model for long-chain FAO disorders and is characterised by decreased levels of tissue and plasma free carnitine. Tissue levels of carnitine are controlled by SLC22A5, the plasmalemmal carnitine transporter. Here, we have further decreased carnitine availability in the LCAD KO mouse through a genetic intervention by introducing one defective Slc22a5 allele (jvs). Slc22a5 haploinsufficiency decreased free carnitine levels in liver, kidney, and heart of LCAD KO animals. The resulting decrease in the tissue long-chain acylcarnitines levels had a similar magnitude as the decrease in free carnitine. Levels of cardiac deoxycarnitine, a carnitine biosynthesis intermediate, were elevated due to Slc22a5 haploinsufficiency in LCAD KO mice. A similar increase in heart and muscle deoxycarnitine was observed in an independent experiment using Slc22a5jvs/jvs mice. Cardiac hypertrophy, fasting-induced hypoglycemia and increased liver weight, the major phenotypes of the LCAD KO mouse, were not affected by Slc22a5 haploinsufficiency. This may suggest that secondary carnitine deficiency does not play a major role in the pathophysiology of these phenotypes. Similarly, our data do not support a major role for toxicity of long-chain acylcarnitines in the phenotype of the LCAD KO mouse.
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Affiliation(s)
- Pablo Ranea-Robles
- Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Chunli Yu
- Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
- Mount Sinai Genomics, Inc., New York, New York
| | - Naomi van Vlies
- Institute for Translational Vaccinology, Bilthoven, The Netherlands
- Department of Clinical Chemistry, Amsterdam Gastroenterology & Metabolism, Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Frédéric M Vaz
- Department of Clinical Chemistry, Amsterdam Gastroenterology & Metabolism, Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Sander M Houten
- Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
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14
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Hirschel J, Vogel M, Baber R, Garten A, Beuchel C, Dietz Y, Dittrich J, Körner A, Kiess W, Ceglarek U. Relation of Whole Blood Amino Acid and Acylcarnitine Metabolome to Age, Sex, BMI, Puberty, and Metabolic Markers in Children and Adolescents. Metabolites 2020; 10:metabo10040149. [PMID: 32290284 PMCID: PMC7240971 DOI: 10.3390/metabo10040149] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 04/08/2020] [Indexed: 12/18/2022] Open
Abstract
Background: Changes in the metabolic fingerprint of blood during child growth and development are a largely under-investigated area of research. The examination of such aspects requires a cohort of healthy children and adolescents who have been subjected to deep phenotyping, including collection of biospecimens for metabolomic analysis. The present study considered whether amino acid (AA) and acylcarnitine (AC) concentrations are associated with age, sex, body mass index (BMI), and puberty during childhood and adolescence. It also investigated whether there are associations between amino acids (AAs) and acylcarnitines (ACs) and laboratory parameters of glucose and lipid metabolism, as well as liver, kidney, and thyroid parameters. Methods: A total of 3989 dried whole blood samples collected from 2191 healthy participants, aged 3 months to 18 years, from the LIFE Child cohort (Leipzig, Germany) were analyzed using liquid chromatography tandem mass spectrometry to detect levels of 23 AAs, 6 ACs, and free carnitine (C0). Age- and sex-related percentiles were estimated for each metabolite. In addition, correlations between laboratory parameters and levels of the selected AAs and ACs were calculated using hierarchical models. Results: Four different age-dependent profile types were identified for AAs and ACs. Investigating the association with puberty, we mainly identified peak metabolite levels at Tanner stages 2 to 3 in girls and stages 3 to 5 in boys. Significant correlations were observed between BMI standard deviation score (BMI-SDS) and certain metabolites, among them, branched-chain (leucine/isoleucine, valine) and aromatic (phenylalanine, tyrosine) amino acids. Most of the metabolites correlated significantly with absolute concentrations of glucose, glycated hemoglobin (HbA1c), triglycerides, cystatin C (CysC), and creatinine. After age adjustment, significant correlations were observed between most metabolites and CysC, as well as HbA1c. Conclusions: During childhood, several AA and AC levels are related to age, sex, BMI, and puberty. Moreover, our data verified known associations but also revealed new correlations between AAs/ACs and specific key markers of metabolic function.
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Affiliation(s)
- Josephin Hirschel
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Strasse 27, 04103 Leipzig, Germany; (J.H.); (M.V.); (R.B.); (Y.D.); (A.K.); (W.K.)
- Hospital for Children and Adolescents and Center for Pediatric Research (CPL), University of Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany;
| | - Mandy Vogel
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Strasse 27, 04103 Leipzig, Germany; (J.H.); (M.V.); (R.B.); (Y.D.); (A.K.); (W.K.)
- Hospital for Children and Adolescents and Center for Pediatric Research (CPL), University of Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany;
| | - Ronny Baber
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Strasse 27, 04103 Leipzig, Germany; (J.H.); (M.V.); (R.B.); (Y.D.); (A.K.); (W.K.)
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics (ILM), University Hospital Leipzig, Paul-List Str.13/15, 04103 Leipzig, Germany;
| | - Antje Garten
- Hospital for Children and Adolescents and Center for Pediatric Research (CPL), University of Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany;
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham B15 2TT, UK
| | - Carl Beuchel
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany;
| | - Yvonne Dietz
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Strasse 27, 04103 Leipzig, Germany; (J.H.); (M.V.); (R.B.); (Y.D.); (A.K.); (W.K.)
| | - Julia Dittrich
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics (ILM), University Hospital Leipzig, Paul-List Str.13/15, 04103 Leipzig, Germany;
| | - Antje Körner
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Strasse 27, 04103 Leipzig, Germany; (J.H.); (M.V.); (R.B.); (Y.D.); (A.K.); (W.K.)
- Hospital for Children and Adolescents and Center for Pediatric Research (CPL), University of Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany;
| | - Wieland Kiess
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Strasse 27, 04103 Leipzig, Germany; (J.H.); (M.V.); (R.B.); (Y.D.); (A.K.); (W.K.)
- Hospital for Children and Adolescents and Center for Pediatric Research (CPL), University of Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany;
| | - Uta Ceglarek
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Strasse 27, 04103 Leipzig, Germany; (J.H.); (M.V.); (R.B.); (Y.D.); (A.K.); (W.K.)
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics (ILM), University Hospital Leipzig, Paul-List Str.13/15, 04103 Leipzig, Germany;
- Correspondence:
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15
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Jacobi T, Massier L, Klöting N, Horn K, Schuch A, Ahnert P, Engel C, Löffler M, Burkhardt R, Thiery J, Tönjes A, Stumvoll M, Blüher M, Doxiadis I, Scholz M, Kovacs P. HLA Class II Allele Analyses Implicate Common Genetic Components in Type 1 and Non-Insulin-Treated Type 2 Diabetes. J Clin Endocrinol Metab 2020; 105:5715056. [PMID: 31974565 DOI: 10.1210/clinem/dgaa027] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/15/2020] [Indexed: 12/20/2022]
Abstract
CONTEXT Common genetic susceptibility may underlie the frequently observed co-occurrence of type 1 and type 2 diabetes in families. Given the role of HLA class II genes in the pathophysiology of type 1 diabetes, the aim of the present study was to test the association of high density imputed human leukocyte antigen (HLA) genotypes with type 2 diabetes. OBJECTIVES AND DESIGN Three cohorts (Ntotal = 10 413) from Leipzig, Germany were included in this study: LIFE-Adult (N = 4649), LIFE-Heart (N = 4815) and the Sorbs (N = 949) cohort. Detailed metabolic phenotyping and genome-wide single nucleotide polymorphism (SNP) data were available for all subjects. Using 1000 Genome imputation data, HLA genotypes were imputed on 4-digit level and association tests for type 2 diabetes, and related metabolic traits were conducted. RESULTS In a meta-analysis including all 3 cohorts, the absence of HLA-DRB5 was associated with increased risk of type 2 diabetes (P = 0.001). In contrast, HLA-DQB*06:02 and HLA-DQA*01:02 had a protective effect on type 2 diabetes (P = 0.005 and 0.003, respectively). Both alleles are part of the well-established type 1 diabetes protective haplotype DRB1*15:01~DQA1*01:02~DQB1*06:02, which was also associated with reduced risk of type 2 diabetes (OR 0.84; P = 0.005). On the contrary, the DRB1*07:01~DQA1*02:01~DQB1*03:03 was identified as a risk haplotype in non-insulin-treated diabetes (OR 1.37; P = 0.002). CONCLUSIONS Genetic variation in the HLA class II locus exerts risk and protective effects on non-insulin-treated type 2 diabetes. Our data suggest that the genetic architecture of type 1 diabetes and type 2 diabetes might share common components on the HLA class II locus.
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Affiliation(s)
- Thomas Jacobi
- University of Leipzig Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Lucas Massier
- University of Leipzig Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Nora Klöting
- University of Leipzig Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Alexander Schuch
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Peter Ahnert
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Markus Löffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Ralph Burkhardt
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Joachim Thiery
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine and Clinical Chemistry, University of Leipzig, Leipzig, Germany
| | - Anke Tönjes
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Michael Stumvoll
- University of Leipzig Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Matthias Blüher
- University of Leipzig Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Ilias Doxiadis
- Institute for Transfusion Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Markus Scholz
- University of Leipzig Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Peter Kovacs
- University of Leipzig Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
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16
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Guo B, Wu B. Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data. Bioinformatics 2020; 35:1366-1372. [PMID: 30239606 DOI: 10.1093/bioinformatics/bty811] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 08/29/2018] [Accepted: 09/18/2018] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data. RESULTS We build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis. AVAILABILITY AND IMPLEMENTATION We have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bin Guo
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Baolin Wu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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17
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Beuchel C, Becker S, Dittrich J, Kirsten H, Toenjes A, Stumvoll M, Loeffler M, Thiele H, Beutner F, Thiery J, Ceglarek U, Scholz M. Clinical and lifestyle related factors influencing whole blood metabolite levels - A comparative analysis of three large cohorts. Mol Metab 2019; 29:76-85. [PMID: 31668394 PMCID: PMC6734104 DOI: 10.1016/j.molmet.2019.08.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/13/2019] [Accepted: 08/13/2019] [Indexed: 12/31/2022] Open
Abstract
Objective Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels. Methods 63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses. Results Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies. Conclusions We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations. Amino-acids and acylcarnitines analyzed in three studies with >16,000 individuals. Develop a generic and adaptable bioinformatics workflow. Analysis of the impact of 29 clinical and life-style factors on blood metabolites. Analysis of network between factors and metabolites. Comparison of results between studies.
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Affiliation(s)
- Carl Beuchel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Susen Becker
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany; Department of Pediatric Surgery, University of Leipzig, Leipzig, Germany
| | - Julia Dittrich
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany; LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Anke Toenjes
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University Hospital Leipzig, Leipzig, Germany
| | - Michael Stumvoll
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University Hospital Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany; LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | | | | | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany; LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany; LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany; LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany; IFB Adiposity Diseases, University Hospital Leipzig, Leipzig, Germany.
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18
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Mondanelli G, Iacono A, Carvalho A, Orabona C, Volpi C, Pallotta MT, Matino D, Esposito S, Grohmann U. Amino acid metabolism as drug target in autoimmune diseases. Autoimmun Rev 2019; 18:334-348. [DOI: 10.1016/j.autrev.2019.02.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 10/30/2018] [Indexed: 12/14/2022]
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19
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Vogel H, Kamitz A, Hallahan N, Lebek S, Schallschmidt T, Jonas W, Jähnert M, Gottmann P, Zellner L, Kanzleiter T, Damen M, Altenhofen D, Burkhardt R, Renner S, Dahlhoff M, Wolf E, Müller TD, Blüher M, Joost HG, Chadt A, Al-Hasani H, Schürmann A. A collective diabetes cross in combination with a computational framework to dissect the genetics of human obesity and Type 2 diabetes. Hum Mol Genet 2019; 27:3099-3112. [PMID: 29893858 PMCID: PMC6097155 DOI: 10.1093/hmg/ddy217] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 05/29/2018] [Indexed: 12/16/2022] Open
Abstract
To explore the genetic determinants of obesity and Type 2 diabetes (T2D), the German Center for Diabetes Research (DZD) conducted crossbreedings of the obese and diabetes-prone New Zealand Obese mouse strain with four different lean strains (B6, DBA, C3H, 129P2) that vary in their susceptibility to develop T2D. Genome-wide linkage analyses localized more than 290 quantitative trait loci (QTL) for obesity, 190 QTL for diabetes-related traits and 100 QTL for plasma metabolites in the outcross populations. A computational framework was developed that allowed to refine critical regions and to nominate a small number of candidate genes by integrating reciprocal haplotype mapping and transcriptome data. The efficiency of the complex procedure was demonstrated for one obesity QTL. The genomic interval of 35 Mb with 502 annotated candidate genes was narrowed down to six candidates. Accordingly, congenic mice retained the obesity phenotype owing to an interval that contains three of the six candidate genes. Among these the phospholipase PLA2G4A exhibited an elevated expression in adipose tissue of obese human subjects and is therefore a critical regulator of the obesity locus. Together, our broad and complex approach demonstrates that combined- and comparative-cross analysis exhibits improved mapping resolution and represents a valid tool for the identification of disease genes.
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Affiliation(s)
- Heike Vogel
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Anne Kamitz
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Nicole Hallahan
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Sandra Lebek
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Medical Faculty, Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Heinrich Heine University, Düsseldorf D-40225, Germany
| | - Tanja Schallschmidt
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Medical Faculty, Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Heinrich Heine University, Düsseldorf D-40225, Germany
| | - Wenke Jonas
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Markus Jähnert
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Pascal Gottmann
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Lisa Zellner
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Timo Kanzleiter
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Mareike Damen
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Medical Faculty, Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Heinrich Heine University, Düsseldorf D-40225, Germany
| | - Delsi Altenhofen
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Medical Faculty, Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Heinrich Heine University, Düsseldorf D-40225, Germany
| | - Ralph Burkhardt
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig D-04303, Germany
| | - Simone Renner
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Chair for Molecular Animal Breeding and Biotechnology, Gene Center.,Department of Veterinary Sciences, Center for Innovative Medical Models (CiMM), LMU Munich, D-81377 Munich, Germany
| | - Maik Dahlhoff
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Chair for Molecular Animal Breeding and Biotechnology, Gene Center.,Department of Veterinary Sciences, Center for Innovative Medical Models (CiMM), LMU Munich, D-81377 Munich, Germany
| | - Eckhard Wolf
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Chair for Molecular Animal Breeding and Biotechnology, Gene Center.,Department of Veterinary Sciences, Center for Innovative Medical Models (CiMM), LMU Munich, D-81377 Munich, Germany
| | - Timo D Müller
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Institute for Diabetes and Obesity, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg D-85764, Germany.,Division of Metabolic Diseases, Department of Medicine, Technische Universität München, Munich D-80333, Germany
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig D-04103, Germany
| | - Hans-Georg Joost
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany
| | - Alexandra Chadt
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Medical Faculty, Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Heinrich Heine University, Düsseldorf D-40225, Germany
| | - Hadi Al-Hasani
- German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Medical Faculty, Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Heinrich Heine University, Düsseldorf D-40225, Germany
| | - Annette Schürmann
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.,Institute of Nutritional Science, University of Potsdam, Nuthetal D-14558, Germany
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20
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Li S, Gao D, Jiang Y. Function, Detection and Alteration of Acylcarnitine Metabolism in Hepatocellular Carcinoma. Metabolites 2019; 9:E36. [PMID: 30795537 PMCID: PMC6410233 DOI: 10.3390/metabo9020036] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/07/2019] [Accepted: 02/14/2019] [Indexed: 01/01/2023] Open
Abstract
Acylcarnitines play an essential role in regulating the balance of intracellular sugar and lipid metabolism. They serve as carriers to transport activated long-chain fatty acids into mitochondria for β-oxidation as a major source of energy for cell activities. The liver is the most important organ for endogenous carnitine synthesis and metabolism. Hepatocellular carcinoma (HCC), a primary malignancy of the live with poor prognosis, may strongly influence the level of acylcarnitines. In this paper, the function, detection and alteration of acylcarnitine metabolism in HCC were briefly reviewed. An overview was provided to introduce the metabolic roles of acylcarnitines involved in fatty acid β-oxidation. Then different analytical platforms and methodologies were also briefly summarised. The relationship between HCC and acylcarnitine metabolism was described. Many of the studies reported that short, medium and long-chain acylcarnitines were altered in HCC patients. These findings presented current evidence in support of acylcarnitines as new candidate biomarkers for studies on the pathogenesis and development of HCC. Finally we discussed the challenges and perspectives of exploiting acylcarnitine metabolism and its related metabolic pathways as a target for HCC diagnosis and prognosis.
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Affiliation(s)
- Shangfu Li
- State Key Laboratory of Chemical Oncogenomics, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
- National & Local United Engineering Lab for Personalized Anti-tumour Drugs, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
| | - Dan Gao
- State Key Laboratory of Chemical Oncogenomics, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
- National & Local United Engineering Lab for Personalized Anti-tumour Drugs, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
- Key Laboratory of Metabolomics at Shenzhen, Shenzhen 518055, China.
| | - Yuyang Jiang
- State Key Laboratory of Chemical Oncogenomics, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China.
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21
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Tönjes A, Scholz M, Krüger J, Krause K, Schleinitz D, Kirsten H, Gebhardt C, Marzi C, Grallert H, Ladenvall C, Heyne H, Laurila E, Kriebel J, Meisinger C, Rathmann W, Gieger C, Groop L, Prokopenko I, Isomaa B, Beutner F, Kratzsch J, Fischer-Rosinsky A, Pfeiffer A, Krohn K, Spranger J, Thiery J, Blüher M, Stumvoll M, Kovacs P. Genome-wide meta-analysis identifies novel determinants of circulating serum progranulin. Hum Mol Genet 2019; 27:546-558. [PMID: 29186428 DOI: 10.1093/hmg/ddx413] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 11/22/2017] [Indexed: 11/14/2022] Open
Abstract
Progranulin is a secreted protein with important functions in processes including immune and inflammatory response, metabolism and embryonic development. The present study aimed at identification of genetic factors determining progranulin concentrations. We conducted a genome-wide association meta-analysis for serum progranulin in three independent cohorts from Europe: Sorbs (N = 848) and KORA (N = 1628) from Germany and PPP-Botnia (N = 335) from Finland (total N = 2811). Single nucleotide polymorphisms (SNPs) associated with progranulin levels were replicated in two additional German cohorts: LIFE-Heart Study (Leipzig; N = 967) and Metabolic Syndrome Berlin Potsdam (Berlin cohort; N = 833). We measured mRNA expression of genes in peripheral blood mononuclear cells (PBMC) by micro-arrays and performed mRNA expression quantitative trait and expression-progranulin association studies to functionally substantiate identified loci. Finally, we conducted siRNA silencing experiments in vitro to validate potential candidate genes within the associated loci. Heritability of circulating progranulin levels was estimated at 31.8% and 26.1% in the Sorbs and LIFE-Heart cohort, respectively. SNPs at three loci reached study-wide significance (rs660240 in CELSR2-PSRC1-MYBPHL-SORT1, rs4747197 in CDH23-PSAP and rs5848 in GRN) explaining 19.4%/15.0% of the variance and 61%/57% of total heritability in the Sorbs/LIFE-Heart Study. The strongest evidence for association was at rs660240 (P = 5.75 × 10-50), which was also associated with mRNA expression of PSRC1 in PBMC (P = 1.51 × 10-21). Psrc1 knockdown in murine preadipocytes led to a consecutive 30% reduction in progranulin secretion. In conclusion, the present meta-GWAS combined with mRNA expression identified three loci associated with progranulin and supports the role of PSRC1 in the regulation of progranulin secretion.
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Affiliation(s)
- Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig 04107, Germany.,LIFE Research Center, University of Leipzig, Leipzig 04103, Germany
| | - Jacqueline Krüger
- Leipzig University Medical Center, IFB AdiposityDiseases, University of Leipzig, Leipzig 04103, Germany
| | - Kerstin Krause
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany
| | - Dorit Schleinitz
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig 04107, Germany.,LIFE Research Center, University of Leipzig, Leipzig 04103, Germany
| | - Claudia Gebhardt
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany
| | - Carola Marzi
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Claes Ladenvall
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, CRC at Skåne University Hospital, Malmö 20502, Sweden
| | - Henrike Heyne
- Institute of Human Genetics, University of Leipzig, Leipzig 04103, Germany
| | - Esa Laurila
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, CRC at Skåne University Hospital, Malmö 20502, Sweden
| | - Jennifer Kriebel
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Christa Meisinger
- German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Wolfgang Rathmann
- German Diabetes Center, Institute of Biometrics and Epidemiology, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany
| | - Leif Groop
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, CRC at Skåne University Hospital, Malmö 20502, Sweden
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.,Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK.,Department of Genomics of Common Diseases, Imperial College London, London SW7 2AZ, UK
| | - Bo Isomaa
- Department of Social Services and Healthcare, Jakobstad 68601, Finland.,Folkhälsan Research Centre, Helsinki 00290, Finland
| | - Frank Beutner
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig 04103, Germany
| | - Jürgen Kratzsch
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig 04103, Germany
| | - Antje Fischer-Rosinsky
- Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin, Berlin 10117, Germany
| | - Andreas Pfeiffer
- Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin, Berlin 10117, Germany.,Department of Clinical Nutrition, German Institute of Human Nutrition, Nuthetal 14558, Germany
| | - Knut Krohn
- Interdisciplinary Centre for Clinical Research, University of Leipzig, Leipzig 04103, Germany
| | - Joachim Spranger
- Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin, Berlin 10117, Germany
| | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig 04103, Germany
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany.,Leipzig University Medical Center, IFB AdiposityDiseases, University of Leipzig, Leipzig 04103, Germany
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany.,Leipzig University Medical Center, IFB AdiposityDiseases, University of Leipzig, Leipzig 04103, Germany
| | - Peter Kovacs
- Leipzig University Medical Center, IFB AdiposityDiseases, University of Leipzig, Leipzig 04103, Germany
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22
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Lalem T, Zhang L, Scholz M, Burkhardt R, Saccheti V, Teren A, Thiery J, Devaux Y. Cyclin dependent kinase inhibitor 1 C is a female-specific marker of left ventricular function after acute myocardial infarction. Int J Cardiol 2019; 274:319-325. [DOI: 10.1016/j.ijcard.2018.07.042] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 07/03/2018] [Accepted: 07/06/2018] [Indexed: 12/12/2022]
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23
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Lu M, Seeve CM, Loopstra CA, Krutovsky KV. Exploring the genetic basis of gene transcript abundance and metabolite levels in loblolly pine (Pinus taeda L.) using association mapping and network construction. BMC Genet 2018; 19:100. [PMID: 30400815 PMCID: PMC6219081 DOI: 10.1186/s12863-018-0687-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 10/26/2018] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Identifying genetic variations that shape important complex traits is fundamental to the genetic improvement of important forest tree species, such as loblolly pine (Pinus taeda L.), which is one of the most commonly planted forest tree species in the southern U.S. Gene transcripts and metabolites are important regulatory intermediates that link genetic variations to higher-order complex traits such as wood development and drought response. A few prior studies have associated intermediate phenotypes including mRNA expression and metabolite levels with a limited number of molecular markers, but the identification of genetic variations that regulate intermediate phenotypes needs further investigation. RESULTS We identified 1841 single nucleotide polymorphisms (SNPs) associated with 191 gene expression mRNA phenotypes and 524 SNPs associated with 53 metabolite level phenotypes using 2.8 million exome-derived SNPs. The identified SNPs reside in genes with a wide variety of functions. We further integrated the identified SNPs and the associated expressed genes and metabolites into networks. We described the SNP-SNP interactions that significantly impacted the gene transcript abundance and metabolite level in the networks. Key loci and genes in the wood development and drought response networks were identified and analyzed. CONCLUSIONS This work provides new candidate genes for research on the genetic basis of gene expression and metabolism linked to wood development and drought response in loblolly pine and highlights the efficiency of using association-mapping-based networks to discover candidate genes with important roles in complex biological processes.
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Affiliation(s)
- Mengmeng Lu
- Department of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX, 77843-2138, USA.,Molecular and Environmental Plant Sciences Program, Texas A&M University, 2474 TAMU, College Station, TX, 77843-2474, USA.,Department of Biological Sciences, University of Calgary, 507 Campus Drive NW, Calgary, AB, T2N 4S8, Canada
| | | | - Carol A Loopstra
- Department of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX, 77843-2138, USA.,Molecular and Environmental Plant Sciences Program, Texas A&M University, 2474 TAMU, College Station, TX, 77843-2474, USA
| | - Konstantin V Krutovsky
- Department of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX, 77843-2138, USA. .,Molecular and Environmental Plant Sciences Program, Texas A&M University, 2474 TAMU, College Station, TX, 77843-2474, USA. .,Department of Forest Genetics and Forest Tree Breeding, Georg-August University of Göttingen, Büsgenweg 2, 37077, Göttingen, Germany. .,Laboratory of Population Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina Str. 3, Moscow, 119333, Russia. .,Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 50a/2 Akademgorodok, Krasnoyarsk, 660036, Russia.
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24
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Miyagawa T, Khor SS, Toyoda H, Kanbayashi T, Imanishi A, Sagawa Y, Kotorii N, Kotorii T, Ariyoshi Y, Hashizume Y, Ogi K, Hiejima H, Kamei Y, Hida A, Miyamoto M, Ikegami A, Wada Y, Takami M, Higashiyama Y, Miyake R, Kondo H, Fujimura Y, Tamura Y, Taniyama Y, Omata N, Tanaka Y, Moriya S, Furuya H, Kato M, Kawamura Y, Otowa T, Miyashita A, Kojima H, Saji H, Shimada M, Yamasaki M, Kobayashi T, Misawa R, Shigematsu Y, Kuwano R, Sasaki T, Ishigooka J, Wada Y, Tsuruta K, Chiba S, Tanaka F, Yamada N, Okawa M, Kuroda K, Kume K, Hirata K, Uchimura N, Shimizu T, Inoue Y, Honda Y, Mishima K, Honda M, Tokunaga K. A variant at 9q34.11 is associated with HLA-DQB1*06:02 negative essential hypersomnia. J Hum Genet 2018; 63:1259-1267. [PMID: 30266950 DOI: 10.1038/s10038-018-0518-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 08/29/2018] [Accepted: 09/13/2018] [Indexed: 12/13/2022]
Abstract
Essential hypersomnia (EHS) is a lifelong disorder characterized by excessive daytime sleepiness without cataplexy. EHS is associated with human leukocyte antigen (HLA)-DQB1*06:02, similar to narcolepsy with cataplexy (narcolepsy). Previous studies suggest that DQB1*06:02-positive and -negative EHS are different in terms of their clinical features and follow different pathological pathways. DQB1*06:02-positive EHS and narcolepsy share the same susceptibility genes. In the present study, we report a genome-wide association study with replication for DQB1*06:02-negative EHS (408 patients and 2247 healthy controls, all Japanese). One single-nucleotide polymorphism, rs10988217, which is located 15-kb upstream of carnitine O-acetyltransferase (CRAT), was significantly associated with DQB1*06:02-negative EHS (P = 7.5 × 10-9, odds ratio = 2.63). The risk allele of the disease-associated SNP was correlated with higher expression levels of CRAT in various tissues and cell types, including brain tissue. In addition, the risk allele was associated with levels of succinylcarnitine (P = 1.4 × 10-18) in human blood. The leading SNP in this region was the same in associations with both DQB1*06:02-negative EHS and succinylcarnitine levels. The results suggest that DQB1*06:02-negative EHS may be associated with an underlying dysfunction in energy metabolic pathways.
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Affiliation(s)
- Taku Miyagawa
- Sleep Disorders Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan. .,Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Seik-Soon Khor
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hiromi Toyoda
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takashi Kanbayashi
- Department of Neuropsychiatry, Akita University School of Medicine, Akita, Japan.,International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Ibaraki, Japan
| | - Aya Imanishi
- Department of Neuropsychiatry, Akita University School of Medicine, Akita, Japan
| | - Yohei Sagawa
- Department of Neuropsychiatry, Akita University School of Medicine, Akita, Japan
| | - Nozomu Kotorii
- Department of Neuropsychiatry, Kurume University School of Medicine, Fukuoka, Japan.,Kotorii Isahaya Hospital, Nagasaki, Japan
| | | | | | - Yuji Hashizume
- Department of Neuropsychiatry, Kurume University School of Medicine, Fukuoka, Japan
| | - Kimihiro Ogi
- Department of Neuropsychiatry, Kurume University School of Medicine, Fukuoka, Japan
| | - Hiroshi Hiejima
- Department of Neuropsychiatry, Kurume University School of Medicine, Fukuoka, Japan
| | - Yuichi Kamei
- Department of Laboratory Medicine, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Akiko Hida
- Department of Sleep-Wake Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | | | | | - Yamato Wada
- Department of Psychiatry, Hannan Hospital, Osaka, Japan
| | - Masanori Takami
- Department of Psychiatry, Shiga University of Medical Science, Shiga, Japan
| | - Yuichi Higashiyama
- Department of Neurology and Stroke Medicine, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Ryoko Miyake
- Department of Neurology and Stroke Medicine, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Hideaki Kondo
- Center for Sleep Medicine, Saiseikai Nagasaki Hospital, Nagasaki, Japan
| | - Yota Fujimura
- Department of Psychiatry and Neurology, Asahikawa Medical University, Hokkaido, Japan.,Department of Psychiatry, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan
| | - Yoshiyuki Tamura
- Department of Psychiatry and Neurology, Asahikawa Medical University, Hokkaido, Japan
| | - Yukari Taniyama
- Department of Neurology, Junwakai Memorial Hospital, Miyazaki, Japan
| | - Naoto Omata
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Yuji Tanaka
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Shunpei Moriya
- Department of Psychiatry, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Hirokazu Furuya
- Department of Neurology, Neuro-Muscular Center, National Omuta Hospital, Fukuoka, Japan.,Department of Neurology, Kochi Medical School, Kochi University, Kochi, Japan
| | - Mitsuhiro Kato
- Department of Pediatrics, Yamagata University Faculty of Medicine, Yamagata, Japan.,Department of Pediatrics, Showa University School of Medicine, Tokyo, Japan
| | - Yoshiya Kawamura
- Department of Psychiatry, Shonan Kamakura General Hospital, Kanagawa, Japan
| | - Takeshi Otowa
- Graduate School of Clinical Psychology, Teikyo Heisei University Major of Professional Clinical Psychology, Tokyo, Japan
| | - Akinori Miyashita
- Department of Molecular Genetics, Center for Bioresources, Brain Research Institute, Niigata University, Niigata, Japan
| | | | | | - Mihoko Shimada
- Sleep Disorders Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan.,Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Maria Yamasaki
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takumi Kobayashi
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Department of Clinical Laboratory Medicine, Faculty of Health Science Technology, Bunkyo Gakuin University, Tokyo, Japan
| | - Rumi Misawa
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Department of Clinical Laboratory Medicine, Faculty of Health Science Technology, Bunkyo Gakuin University, Tokyo, Japan
| | - Yosuke Shigematsu
- Department of Pediatrics, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Ryozo Kuwano
- Department of Molecular Genetics, Center for Bioresources, Brain Research Institute, Niigata University, Niigata, Japan
| | - Tsukasa Sasaki
- Department of Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo, Japan
| | | | - Yuji Wada
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Kazuhito Tsuruta
- Department of Neurology, Junwakai Memorial Hospital, Miyazaki, Japan
| | - Shigeru Chiba
- Department of Psychiatry and Neurology, Asahikawa Medical University, Hokkaido, Japan
| | - Fumiaki Tanaka
- Department of Neurology and Stroke Medicine, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Naoto Yamada
- Department of Psychiatry, Shiga University of Medical Science, Shiga, Japan
| | - Masako Okawa
- Department of Sleep Medicine, Shiga University of Medical Science, Shiga, Japan.,Japan Foundation for Neuroscience and Mental Health, Tokyo, Japan.,Department of Somnology, Tokyo Medical University, Tokyo, Japan
| | - Kenji Kuroda
- Department of Psychiatry, Hannan Hospital, Osaka, Japan
| | - Kazuhiko Kume
- Sleep Center, Kuwamizu Hospital, Kumamoto, Japan.,Department of Stem Cell Biology, Institute of Molecular Genetics and Embryology, Kumamoto University, Kumamoto, Japan.,Department of Neuropharmacology, Graduate School of Pharmaceutical Sciences, Nagoya City University, Aichi, Japan
| | - Koichi Hirata
- Department of Neurology, Dokkyo Medical University, Tochigi, Japan
| | - Naohisa Uchimura
- Department of Neuropsychiatry, Kurume University School of Medicine, Fukuoka, Japan
| | - Tetsuo Shimizu
- Department of Neuropsychiatry, Akita University School of Medicine, Akita, Japan.,International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Ibaraki, Japan
| | - Yuichi Inoue
- Department of Somnology, Tokyo Medical University, Tokyo, Japan.,Yoyogi Sleep Disorder Center, Tokyo, Japan
| | - Yutaka Honda
- Seiwa Hospital, Neuropsychiatric Research Institute, Tokyo, Japan
| | - Kazuo Mishima
- Department of Neuropsychiatry, Akita University School of Medicine, Akita, Japan.,International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Ibaraki, Japan.,Department of Sleep-Wake Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Makoto Honda
- Sleep Disorders Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan.,Seiwa Hospital, Neuropsychiatric Research Institute, Tokyo, Japan
| | - Katsushi Tokunaga
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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25
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Richardson TG, Haycock PC, Zheng J, Timpson NJ, Gaunt TR, Davey Smith G, Relton CL, Hemani G. Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease. Hum Mol Genet 2018; 27:3293-3304. [PMID: 29893838 PMCID: PMC6121186 DOI: 10.1093/hmg/ddy210] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 04/10/2018] [Accepted: 04/29/2018] [Indexed: 12/21/2022] Open
Abstract
We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39 × 10-08). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits. These associations showed a high rate of replication in the BIOS QTL and UK Biobank datasets for 14 selected traits, as 101 of the attempted 128 associations survived multiple testing corrections (P < 3.91 × 10-04). Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 306 of the 348 refined meQTL associations also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in prioritizing candidate loci where DNA methylation may influence traits and help develop mechanistic insight into the aetiology of complex disease.
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Affiliation(s)
- Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Philip C Haycock
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
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26
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Heinze-Deml C, McWilliams B, Meinshausen N. Preserving privacy between features in distributed estimation. Stat (Int Stat Inst) 2018. [DOI: 10.1002/sta4.189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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27
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Li Y, Sekula P, Wuttke M, Wahrheit J, Hausknecht B, Schultheiss UT, Gronwald W, Schlosser P, Tucci S, Ekici AB, Spiekerkoetter U, Kronenberg F, Eckardt KU, Oefner PJ, Köttgen A. Genome-Wide Association Studies of Metabolites in Patients with CKD Identify Multiple Loci and Illuminate Tubular Transport Mechanisms. J Am Soc Nephrol 2018; 29:1513-1524. [PMID: 29545352 DOI: 10.1681/asn.2017101099] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 02/09/2018] [Indexed: 12/24/2022] Open
Abstract
Background The kidneys have a central role in the generation, turnover, transport, and excretion of metabolites, and these functions can be altered in CKD. Genetic studies of metabolite concentrations can identify proteins performing these functions.Methods We conducted genome-wide association studies and aggregate rare variant tests of the concentrations of 139 serum metabolites and 41 urine metabolites, as well as their pairwise ratios and fractional excretions in up to 1168 patients with CKD.Results After correction for multiple testing, genome-wide significant associations were detected for 25 serum metabolites, two urine metabolites, and 259 serum and 14 urinary metabolite ratios. These included associations already known from population-based studies. Additional findings included an association for the uremic toxin putrescine and variants upstream of an enzyme catalyzing the oxidative deamination of polyamines (AOC1, P-min=2.4×10-12), a relatively high carrier frequency (2%) for rare deleterious missense variants in ACADM that are collectively associated with serum ratios of medium-chain acylcarnitines (P-burden=6.6×10-16), and associations of a common variant in SLC7A9 with several ratios of lysine to neutral amino acids in urine, including the lysine/glutamine ratio (P=2.2×10-23). The associations of this SLC7A9 variant with ratios of lysine to specific neutral amino acids were much stronger than the association with lysine concentration alone. This finding is consistent with SLC7A9 functioning as an exchanger of urinary cationic amino acids against specific intracellular neutral amino acids at the apical membrane of proximal tubular cells.Conclusions Metabolomic indices of specific kidney functions in genetic studies may provide insight into human renal physiology.
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Affiliation(s)
- Yong Li
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology, and Medical Bioinformatics, and
| | - Peggy Sekula
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology, and Medical Bioinformatics, and
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology, and Medical Bioinformatics, and
| | - Judith Wahrheit
- BIOCRATES Life Sciences Aktiengesellschaft, Innsbruck, Austria
| | | | - Ulla T Schultheiss
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology, and Medical Bioinformatics, and
| | - Wolfram Gronwald
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany; and
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology, and Medical Bioinformatics, and
| | - Sara Tucci
- Department of General Pediatrics, Center for Pediatrics and Adolescent Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Arif B Ekici
- Insitute of Human Genetics, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Ute Spiekerkoetter
- Department of General Pediatrics, Center for Pediatrics and Adolescent Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Peter J Oefner
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany; and
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology, and Medical Bioinformatics, and
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28
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Shahin MH, Gong Y, Frye RF, Rotroff DM, Beitelshees AL, Baillie RA, Chapman AB, Gums JG, Turner ST, Boerwinkle E, Motsinger-Reif A, Fiehn O, Cooper-DeHoff RM, Han X, Kaddurah-Daouk R, Johnson JA. Sphingolipid Metabolic Pathway Impacts Thiazide Diuretics Blood Pressure Response: Insights From Genomics, Metabolomics, and Lipidomics. J Am Heart Assoc 2017; 7:e006656. [PMID: 29288159 PMCID: PMC5778957 DOI: 10.1161/jaha.117.006656] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 11/01/2017] [Indexed: 01/04/2023]
Abstract
BACKGROUND Although hydrochlorothiazide (HCTZ) is a well-established first-line antihypertensive in the United States, <50% of HCTZ treated patients achieve blood pressure (BP) control. Thus, identifying biomarkers that could predict the BP response to HCTZ is critically important. In this study, we utilized metabolomics, genomics, and lipidomics to identify novel pathways and biomarkers associated with HCTZ BP response. METHODS AND RESULTS First, we conducted a pathway analysis for 13 metabolites we recently identified to be significantly associated with HCTZ BP response. From this analysis, we found the sphingolipid metabolic pathway as the most significant pathway (P=5.8E-05). Testing 78 variants, within 14 genes involved in the sphingolipid metabolic canonical pathway, with the BP response to HCTZ identified variant rs6078905, within the SPTLC3 gene, as a novel biomarker significantly associated with the BP response to HCTZ in whites (n=228). We found that rs6078905 C-allele carriers had a better BP response to HCTZ versus noncarriers (∆SBP/∆DBP: -11.4/-6.9 versus -6.8/-3.5 mm Hg; ∆SBP P=6.7E-04; ∆DBP P=4.8E-04). Additionally, in blacks (n=148), we found genetic signals in the SPTLC3 genomic region significantly associated with the BP response to HCTZ (P<0.05). Last, we observed that rs6078905 significantly affects the baseline level of 4 sphingomyelins (N24:2, N24:3, N16:1, and N22:1; false discovery rate <0.05), from which N24:2 sphingomyelin has a significant correlation with both HCTZ DBP-response (r=-0.42; P=7E-03) and SBP-response (r=-0.36; P=2E-02). CONCLUSIONS This study provides insight into potential pharmacometabolomic and genetic mechanisms underlying HCTZ BP response and suggests that SPTLC3 is a potential determinant of the BP response to HCTZ. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT00246519.
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Affiliation(s)
- Mohamed H Shahin
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL
| | - Yan Gong
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL
| | - Reginald F Frye
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL
| | - Daniel M Rotroff
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC
| | | | | | | | - John G Gums
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL
| | | | - Eric Boerwinkle
- Human Genetics Center and Institute for Molecular Medicine, University of Texas Health Science Center, Houston, TX
| | | | - Oliver Fiehn
- Genome Center, University of California at Davis, CA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi-Arabia
| | - Rhonda M Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL
| | - Xianlin Han
- Sanford-Burnham Medical Research Institute, Orlando, FL
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioural Sciences and Department of Medicine, Duke University, Durham, NC
| | - Julie A Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL
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29
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Jiang T, Wang Y, Zhu M, Wang Y, Huang M, Jin G, Guo X, Sha J, Dai J, Hu Z. Transcriptome-wide association study revealed two novel genes associated with nonobstructive azoospermia in a Chinese population. Fertil Steril 2017; 108:1056-1062.e4. [PMID: 29202958 DOI: 10.1016/j.fertnstert.2017.09.023] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2017] [Revised: 09/20/2017] [Accepted: 09/20/2017] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To investigate the associations between genetically cis-regulated gene expression levels and nonobstructive azoospermia (NOA) susceptibility. DESIGN Transcriptome-wide association study (TWAS). SETTING Medical university. INTERVENTIONS None. MAIN OUTCOME MEASURE(S) The cis-hg2 values for each gene were estimated with GCTA software. The effect sizes of cis-single-nucleotide polymorphisms (SNPs) on gene expression were measured using GEMMA software. Gene expression levels were entered into our existing NOA GWAS cohort using GEMMA software. The TWAS P-values were calculated using logistic regression models. RESULT(S) Expression levels of 1,296 cis-heritable genes were entered into our existing NOA GWAS data. The TWAS results identified two novel genes as statistically significantly associated with NOA susceptibility: PILRA and ZNF676. In addition, 6p21.32, previously reported in NOA GWAS, was further validated to be a susceptible region to NOA risk. CONCLUSION(S) Analysis with TWAS provides fruitful targets for follow-up functional studies.
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Affiliation(s)
- Tingting Jiang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, People's Republic of China; Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Yuzhuo Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Yifeng Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, People's Republic of China; Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Mingtao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Guangfu Jin
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, People's Republic of China; Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Xuejiang Guo
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, People's Republic of China
| | - Jiahao Sha
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, People's Republic of China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Zhibin Hu
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, People's Republic of China; Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.
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30
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Buchmann N, Scholz M, Lill CM, Burkhardt R, Eckardt R, Norman K, Loeffler M, Bertram L, Thiery J, Steinhagen-Thiessen E, Demuth I. Association between lipoprotein(a) level and type 2 diabetes: no evidence for a causal role of lipoprotein(a) and insulin. Acta Diabetol 2017; 54:1031-1038. [PMID: 28866807 DOI: 10.1007/s00592-017-1036-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/02/2017] [Indexed: 12/22/2022]
Abstract
AIMS Inverse relationships have been described between the largely genetically determined levels of serum/plasma lipoprotein(a) [Lp(a)], type 2 diabetes (T2D) and fasting insulin. Here, we aimed to evaluate the nature of these relationships with respect to causality. METHODS We tested whether we could replicate the recent negative findings on causality between Lp(a) and T2D by employing the Mendelian randomization (MR) approach using cross-sectional data from three independent cohorts, Berlin Aging Study II (BASE-II; n = 2012), LIFE-Adult (n = 3281) and LIFE-Heart (n = 2816). Next, we explored another frequently discussed hypothesis in this context: Increasing insulin levels during the course of T2D disease development inhibits hepatic Lp(a) synthesis and thereby might explain the inverse Lp(a)-T2D association. We used two fasting insulin-associated variants, rs780094 and rs10195252, as instrumental variables in MR analysis of n = 4937 individuals from BASE-II and LIFE-Adult. We further investigated causality of the association between fasting insulin and Lp(a) by combined MR analysis of 12 additional SNPs in LIFE-Adult. RESULTS While an Lp(a)-T2D association was observed in the combined analysis (meta-effect of OR [95% CI] = 0.91 [0.87-0.96] per quintile, p = 1.3x10-4), we found no evidence of causality in the Lp(a)-T2D association (p = 0.29, fixed effect model) when using the variant rs10455872 as the instrumental variable in the MR analyses. Likewise, no evidence of a causal effect of insulin on Lp(a) levels was found. CONCLUSIONS While these results await confirmation in larger cohorts, the nature of the inverse Lp(a)-T2D association remains to be elucidated.
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Affiliation(s)
- Nikolaus Buchmann
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
- LIFE Leipzig Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Christina M Lill
- Genetic and Molecular Epidemiology Group, Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany
| | - Ralph Burkhardt
- LIFE Leipzig Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital, Leipzig, Germany
| | - Rahel Eckardt
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Kristina Norman
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
- Research Group on Geriatrics, Charité - Universitätsmedizin Berlin, Reinickendorfer Str. 61, 13347, Berlin, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
- LIFE Leipzig Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany
- School of Public Health, Faculty of Medicine, Imperial College, London, UK
| | - Joachim Thiery
- LIFE Leipzig Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital, Leipzig, Germany
| | - Elisabeth Steinhagen-Thiessen
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Ilja Demuth
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany.
- Institute of Medical and Human Genetics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353 , Berlin, Germany.
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31
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Richardson TG, Zheng J, Davey Smith G, Timpson NJ, Gaunt TR, Relton CL, Hemani G. Mendelian Randomization Analysis Identifies CpG Sites as Putative Mediators for Genetic Influences on Cardiovascular Disease Risk. Am J Hum Genet 2017; 101:590-602. [PMID: 28985495 PMCID: PMC5630190 DOI: 10.1016/j.ajhg.2017.09.003] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 09/06/2017] [Indexed: 01/10/2023] Open
Abstract
The extent to which genetic influences on cardiovascular disease risk are mediated by changes in DNA methylation levels has not been systematically explored. We developed an analytical framework that integrates genetic fine mapping and Mendelian randomization with epigenome-wide association studies to evaluate the causal relationships between methylation levels and 14 cardiovascular disease traits. We identified ten genetic loci known to influence proximal DNA methylation which were also associated with cardiovascular traits after multiple-testing correction. Bivariate fine mapping provided evidence that the individual variants responsible for the observed effects on cardiovascular traits at the ADCY3 and ADIPOQ loci were potentially mediated through changes in DNA methylation, although we highlight that we are unable to reliably separate causality from horizontal pleiotropy. Estimates of causal effects were replicated with results from large-scale consortia. Genetic variants and CpG sites identified in this study were enriched for histone mark peaks in relevant tissue types and gene promoter regions. Integrating our results with expression quantitative trait loci data, we provide evidence that variation at these regulatory regions is likely to also influence gene expression levels at these loci.
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Affiliation(s)
- Tom G Richardson
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
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32
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Heianza Y, Qi L. Gene-Diet Interaction and Precision Nutrition in Obesity. Int J Mol Sci 2017; 18:ijms18040787. [PMID: 28387720 PMCID: PMC5412371 DOI: 10.3390/ijms18040787] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Revised: 03/30/2017] [Accepted: 04/03/2017] [Indexed: 02/06/2023] Open
Abstract
The rapid rise of obesity during the past decades has coincided with a profound shift of our living environment, including unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Growing studies have found that changes in adiposity and metabolic response to low-calorie weight loss diets might be modified by genetic variants related to obesity, metabolic status and preference to nutrients. This review summarized data from recent studies of gene-diet interactions, and discussed integration of research of metabolomics and gut microbiome, as well as potential application of the findings in precision nutrition.
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Affiliation(s)
- Yoriko Heianza
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
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