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Zhou M, Tamburini I, Van C, Molendijk J, Nguyen CM, Chang IYY, Johnson C, Velez LM, Cheon Y, Yeo R, Bae H, Le J, Larson N, Pulido R, Nascimento-Filho CHV, Jang C, Marazzi I, Justice J, Pannunzio N, Hevener AL, Sparks L, Kershaw EE, Nicholas D, Parker BL, Masri S, Seldin MM. Leveraging inter-individual transcriptional correlation structure to infer discrete signaling mechanisms across metabolic tissues. eLife 2024; 12:RP88863. [PMID: 38224289 PMCID: PMC10945578 DOI: 10.7554/elife.88863] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
Inter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by 'brute force' surveys of all genes within RNA-sequencing measures across tissues within a population. Expanding on this intuition, we reasoned that parallel strategies could be used to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of gene variation between individuals. Thus, comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling. Here, we surveyed gene-gene correlation structure across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or high-fat/high-sucrose (HFHS) diet. Variation of genes such as FGF21, ADIPOQ, GCG, and IL6 showed enrichments which recapitulate experimental observations. Further, similar analyses were applied to explore both within-tissue signaling mechanisms (liver PCSK9) and genes encoding enzymes producing metabolites (adipose PNPLA2), where inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference of tissue-specific variation in relationships with metabolic traits. We refer to this resource as gene-derived correlations across tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables querying of any gene in any tissue to find correlated patterns of genes, cell types, pathways, and network architectures across metabolic organs.
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Affiliation(s)
- Mingqi Zhou
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Ian Tamburini
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Cassandra Van
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Jeffrey Molendijk
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia
| | - Christy M Nguyen
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | | | - Casey Johnson
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Leandro M Velez
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Youngseo Cheon
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Reichelle Yeo
- Translational Research Institute, AdventHealthOrlandoUnited States
| | - Hosung Bae
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Johnny Le
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Natalie Larson
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Ron Pulido
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Carlos HV Nascimento-Filho
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Cholsoon Jang
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Ivan Marazzi
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Jamie Justice
- Veterans Administration Greater Los Angeles Healthcare System, Geriatric Research Education and Clinical Center (GRECC)Los AngelesUnited States
| | - Nicholas Pannunzio
- Divison of Hematology/Oncology, Department of Medicine, UC Irvine HealthIrvineUnited States
| | - Andrea L Hevener
- Department of Medicine, Division of Endocrinology, Diabetes, and Hypertension, David Geffen School of Medicine at UCLALos AngelesUnited States
- Iris Cantor-UCLA Women’s Health Research Center, David Geffen School of Medicine at UCLALos AngelesUnited States
| | - Lauren Sparks
- Translational Research Institute, AdventHealthOrlandoUnited States
| | - Erin E Kershaw
- Division of Endocrinology, Department of Medicine, University of PittsburgPittsburghUnited States
| | - Dequina Nicholas
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
- Department of Molecular Biology and Biochemistry, School of Biological Sciences, University of California IrvineIrvineUnited States
| | - Benjamin L Parker
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia
| | - Selma Masri
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
| | - Marcus M Seldin
- Department of Biological Chemistry, UC IrvineIrvineUnited States
- Center for Epigenetics and Metabolism, UC IrvineIrvineUnited States
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Aharoni A, Goodacre R, Fernie AR. Plant and microbial sciences as key drivers in the development of metabolomics research. Proc Natl Acad Sci U S A 2023; 120:e2217383120. [PMID: 36930598 PMCID: PMC10041103 DOI: 10.1073/pnas.2217383120] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.
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Affiliation(s)
- Asaph Aharoni
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot76100, Israel
| | - Royston Goodacre
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, LiverpoolL69 7BE, UK
| | - Alisdair R. Fernie
- Max-Planck-Institute for Molecular Plant Physiology, Potsdam14476, Germany
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Lee IH, Smith MR, Yazdani A, Sandhu S, Walker DI, Mandl KD, Jones DP, Kong SW. Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort. Hum Genomics 2022; 16:67. [PMID: 36482414 PMCID: PMC9730628 DOI: 10.1186/s40246-022-00440-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. RESULTS We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (< 0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10-12 (= 5 × 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. CONCLUSION Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.
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Affiliation(s)
- In-Hee Lee
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA
| | - Matthew Ryan Smith
- grid.189967.80000 0001 0941 6502Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30602 USA ,grid.414026.50000 0004 0419 4084Atlanta Department of Veterans Affairs Medical Center, Decatur, GA 30033 USA
| | - Azam Yazdani
- grid.38142.3c000000041936754XCenter of Perioperative Genetics and Genomics, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Sumiti Sandhu
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA
| | - Douglas I. Walker
- grid.59734.3c0000 0001 0670 2351Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Kenneth D. Mandl
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDepartment of Pediatrics, Harvard Medical School, Boston, MA 02115 USA
| | - Dean P. Jones
- grid.189967.80000 0001 0941 6502Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30602 USA
| | - Sek Won Kong
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Pediatrics, Harvard Medical School, Boston, MA 02115 USA
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4
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Moore A, Busch MP, Dziewulska K, Francis RO, Hod EA, Zimring JC, D’Alessandro A, Page GP. Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors. J Biol Chem 2022; 298:102706. [PMID: 36395887 PMCID: PMC9763692 DOI: 10.1016/j.jbc.2022.102706] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
The red blood cell (RBC)-Omics study, part of the larger NHLBI-funded Recipient Epidemiology and Donor Evaluation Study (REDS-III), aims to understand the genetic contribution to blood donor RBC characteristics. Previous work identified donor demographic, behavioral, genetic, and metabolic underpinnings to blood donation, storage, and (to a lesser extent) transfusion outcomes, but none have yet linked the genetic and metabolic bodies of work. We performed a genome-wide association (GWA) analysis using RBC-Omics study participants with generated untargeted metabolomics data to identify metabolite quantitative trait loci in RBCs. We performed GWA analyses of 382 metabolites in 243 individuals imputed using the 1000 Genomes Project phase 3 all-ancestry reference panel. Analyses were conducted using ProbABEL and adjusted for sex, age, donation center, number of whole blood donations in the past 2 years, and first 10 principal components of ancestry. Our results identified 423 independent genetic loci associated with 132 metabolites (p < 5×10-8). Potentially novel locus-metabolite associations were identified for the region encoding heme transporter FLVCR1 and choline and for lysophosphatidylcholine acetyltransferase LPCAT3 and lysophosphatidylserine 16.0, 18.0, 18.1, and 18.2; these associations are supported by published rare disease and mouse studies. We also confirmed previous metabolite GWA results for associations, including N(6)-methyl-L-lysine and protein PYROXD2 and various carnitines and transporter SLC22A16. Association between pyruvate levels and G6PD polymorphisms was validated in an independent cohort and novel murine models of G6PD deficiency (African and Mediterranean variants). We demonstrate that it is possible to perform metabolomics-scale GWA analyses with a modest, trans-ancestry sample size.
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Affiliation(s)
- Amy Moore
- Division of Biostatistics and Epidemiology, RTI International, Atlanta, Georgia, USA
| | | | - Karolina Dziewulska
- Department of Pathology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Richard O. Francis
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, USA
| | - Eldad A. Hod
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, USA
| | - James C. Zimring
- Department of Pathology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Angelo D’Alessandro
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, USA,Department of Biochemistry and Molecular Genetics, University of Colorado Denver – Anschutz Medical Campus, Aurora, CO, USA,For correspondence: Grier P. Page; Angelo D’Alessandro
| | - Grier P. Page
- Division of Biostatistics and Epidemiology, RTI International, Atlanta, Georgia, USA,For correspondence: Grier P. Page; Angelo D’Alessandro
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Bykova M, Hou Y, Eng C, Cheng F. Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes. Hum Mol Genet 2022; 31:R105-R113. [PMID: 36018824 PMCID: PMC9989738 DOI: 10.1093/hmg/ddac208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.
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Affiliation(s)
- Marina Bykova
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
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6
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Resurreccion EP, Fong KW. The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer. Metabolites 2022; 12:metabo12060488. [PMID: 35736421 PMCID: PMC9230859 DOI: 10.3390/metabo12060488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 02/06/2023] Open
Abstract
Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.
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Affiliation(s)
- Eleazer P. Resurreccion
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA;
| | - Ka-wing Fong
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA;
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Correspondence: ; Tel.: +1-859-562-3455
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7
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Van Bergen NJ, Hock DH, Spencer L, Massey S, Stait T, Stark Z, Lunke S, Roesley A, Peters H, Lee JY, Le Fevre A, Heath O, Mignone C, Yang JYM, Ryan MM, D’Arcy C, Nash M, Smith S, Caruana NJ, Thorburn DR, Stroud DA, White SM, Christodoulou J, Brown NJ. Biallelic Variants in PYROXD2 Cause a Severe Infantile Metabolic Disorder Affecting Mitochondrial Function. Int J Mol Sci 2022; 23:ijms23020986. [PMID: 35055180 PMCID: PMC8777681 DOI: 10.3390/ijms23020986] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 12/04/2022] Open
Abstract
Pyridine Nucleotide-Disulfide Oxidoreductase Domain 2 (PYROXD2; previously called YueF) is a mitochondrial inner membrane/matrix-residing protein and is reported to regulate mitochondrial function. The clinical importance of PYROXD2 has been unclear, and little is known of the protein’s precise biological function. In the present paper, we report biallelic variants in PYROXD2 identified by genome sequencing in a patient with suspected mitochondrial disease. The child presented with acute neurological deterioration, unresponsive episodes, and extreme metabolic acidosis, and received rapid genomic testing. He died shortly after. Magnetic resonance imaging (MRI) brain imaging showed changes resembling Leigh syndrome, one of the more common childhood mitochondrial neurological diseases. Functional studies in patient fibroblasts showed a heightened sensitivity to mitochondrial metabolic stress and increased mitochondrial superoxide levels. Quantitative proteomic analysis demonstrated decreased levels of subunits of the mitochondrial respiratory chain complex I, and both the small and large subunits of the mitochondrial ribosome, suggesting a mitoribosomal defect. Our findings support the critical role of PYROXD2 in human cells, and suggest that the biallelic PYROXD2 variants are associated with mitochondrial dysfunction, and can plausibly explain the child’s clinical presentation.
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Affiliation(s)
- Nicole J. Van Bergen
- Brain and Mitochondrial Research Group, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (L.S.); (S.M.); (T.S.); (D.R.T.); (D.A.S.)
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Correspondence: (N.J.V.B.); (J.C.); (N.J.B.)
| | - Daniella H. Hock
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC 3010, Australia; (D.H.H.); (N.J.C.)
| | - Lucy Spencer
- Brain and Mitochondrial Research Group, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (L.S.); (S.M.); (T.S.); (D.R.T.); (D.A.S.)
| | - Sean Massey
- Brain and Mitochondrial Research Group, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (L.S.); (S.M.); (T.S.); (D.R.T.); (D.A.S.)
| | - Tegan Stait
- Brain and Mitochondrial Research Group, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (L.S.); (S.M.); (T.S.); (D.R.T.); (D.A.S.)
| | - Zornitza Stark
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (A.R.); (A.L.F.)
- Australian Genomics Health Alliance, Parkville, VIC 3052, Australia
| | - Sebastian Lunke
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Department of Pathology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Ain Roesley
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (A.R.); (A.L.F.)
| | - Heidi Peters
- Department of Metabolic Medicine, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (H.P.); (O.H.)
| | - Joy Yaplito Lee
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Department of Metabolic Medicine, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (H.P.); (O.H.)
| | - Anna Le Fevre
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (A.R.); (A.L.F.)
| | - Oliver Heath
- Department of Metabolic Medicine, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (H.P.); (O.H.)
| | - Cristina Mignone
- Medical Imaging Department, Royal Children’s Hospital, Parkville, VIC 3052, Australia;
| | - Joseph Yuan-Mou Yang
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Department of Neurosurgery, Neuroscience Advanced Clinical Imaging Service (NACIS), The Royal Children’s Hospital, Parkville, VIC 3052, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
- Neuroscience Research, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
| | - Monique M. Ryan
- Neurology Department, Royal Children’s Hospital, Parkville, VIC 3052, Australia;
| | - Colleen D’Arcy
- Anatomical Pathology Department, Royal Children’s Hospital, Parkville, VIC 3052, Australia;
| | - Margot Nash
- General Medicine, Royal Children’s Hospital, Parkville, VIC 3052, Australia;
| | - Sile Smith
- Paediatric Intensive Care Unit, Royal Children’s Hospital, Parkville, VIC 3052, Australia;
| | - Nikeisha J. Caruana
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC 3010, Australia; (D.H.H.); (N.J.C.)
- Institute for Health and Sport (iHeS), Victoria University, Footscray, VIC 3011, Australia
| | - David R. Thorburn
- Brain and Mitochondrial Research Group, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (L.S.); (S.M.); (T.S.); (D.R.T.); (D.A.S.)
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (A.R.); (A.L.F.)
| | - David A. Stroud
- Brain and Mitochondrial Research Group, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (L.S.); (S.M.); (T.S.); (D.R.T.); (D.A.S.)
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC 3010, Australia; (D.H.H.); (N.J.C.)
| | - Susan M. White
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (A.R.); (A.L.F.)
| | - John Christodoulou
- Brain and Mitochondrial Research Group, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC 3052, Australia; (L.S.); (S.M.); (T.S.); (D.R.T.); (D.A.S.)
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (A.R.); (A.L.F.)
- Discipline of Child and Adolescent Health, University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence: (N.J.V.B.); (J.C.); (N.J.B.)
| | - Natasha J. Brown
- Department of Paediatrics, University of Melbourne, Parkville, VIC 3010, Australia; (Z.S.); (S.L.); (J.Y.L.); (J.Y.-M.Y.); (S.M.W.)
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (A.R.); (A.L.F.)
- Correspondence: (N.J.V.B.); (J.C.); (N.J.B.)
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8
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Li-Gao R, Hughes DA, van Klinken JB, de Mutsert R, Rosendaal FR, Mook-Kanamori DO, Timpson NJ, Willems van Dijk K. Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism. Diabetes 2021; 70:2932-2946. [PMID: 34610981 DOI: 10.2337/db21-0397] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022]
Abstract
Humans spend the greater part of the day in a postprandial state. However, the genetic basis of postprandial blood measures is relatively uncharted territory. We examined the genetics of variation in concentrations of postprandial metabolites (t = 150 min) in response to a liquid mixed meal through genome-wide association studies (GWAS) performed in the Netherlands Epidemiology of Obesity (NEO) study (n = 5,705). The metabolite response GWAS identified an association between glucose change and rs10830963:G in the melatonin receptor 1B (β [SE] -0.23 [0.03], P = 2.15 × 10-19). In addition, the ANKRD55 locus led by rs458741:C showed strong associations with extremely large VLDL (XXLVLDL) particle response (XXLVLDL total cholesterol: β [SE] 0.17 [0.03], P = 5.76 × 10-10; XXLVLDL cholesterol ester: β [SE] 0.17 [0.03], P = 9.74 × 10-10), which also revealed strong associations with body composition and diabetes in the UK Biobank (P < 5 × 10-8). Furthermore, the associations between XXLVLDL response and insulinogenic index, HOMA-β, Matsuda insulin sensitivity index, and HbA1c in the NEO study implied the role of chylomicron synthesis in diabetes (with false discovery rate-corrected q <0.05). To conclude, genetic studies of metabolomics change after a liquid meal illuminate novel pathways for glucose and lipid metabolism. Further studies are warranted to corroborate biological pathways of the ANKRD55 locus underlying diabetes.
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Affiliation(s)
- Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - David A Hughes
- Medical Research Council Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, U.K
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Jan B van Klinken
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, U.K
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
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9
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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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10
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Yoo T, Joo SK, Kim HJ, Kim HY, Sim H, Lee J, Kim HH, Jung S, Lee Y, Jamialahmadi O, Romeo S, Jeong WI, Hwang GS, Kang KW, Kim JW, Kim W, Choi M. Disease-specific eQTL screening reveals an anti-fibrotic effect of AGXT2 in non-alcoholic fatty liver disease. J Hepatol 2021; 75:514-523. [PMID: 33892010 DOI: 10.1016/j.jhep.2021.04.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 03/22/2021] [Accepted: 04/07/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND & AIMS Non-alcoholic fatty liver disease (NAFLD) poses an increasing clinical burden. Genome-wide association studies have revealed a limited contribution of genomic variants to the disease, requiring alternative but robust approaches to identify disease-associated variants and genes. We carried out a disease-specific expression quantitative trait loci (eQTL) screen to identify novel genetic factors that specifically act on NAFLD progression on the basis of genotype. METHODS We recruited 125 Korean patients (83 with biopsy-proven NAFLD and 42 without NAFLD) and performed eQTL analyses using 21,272 transcripts and 3,234,941 genotyped and imputed single nucleotide polymorphisms. We then selected eQTLs that were detected only in the NAFLD group, but not in the control group (i.e., NAFLD-eQTLs). An additional cohort of 162 Korean individuals with NAFLD was used for replication. The function of the selected eQTL toward NAFLD development was validated using HepG2, primary hepatocytes and NAFLD mouse models. RESULTS The NAFLD-specific eQTL screening yielded 242 loci. Among them, AGXT2, encoding alanine-glyoxylate aminotransferase 2, displayed decreased expression in patients with NAFLD homozygous for the non-reference allele of rs2291702, compared to no-NAFLD individuals with the same genotype (p = 4.79 × 10-6). This change was replicated in an additional 162 individuals, yielding a combined p value of 8.05 × 10-8 from a total of 245 patients with NAFLD and 42 controls. Knockdown of AGXT2 induced palmitate-overloaded hepatocyte death by increasing endoplasmic reticulum stress, and exacerbated NAFLD diet-induced liver fibrosis in mice, while overexpression of AGXT2 attenuated liver fibrosis and steatosis. CONCLUSIONS We identified a new molecular role for AGXT2 in NAFLD. Our overall approach will serve as an efficient tool for uncovering novel genetic factors that contribute to liver steatosis and fibrosis in patients with NAFLD. LAY SUMMARY Elucidating causal genes for non-alcoholic fatty liver disease (NAFLD) has been challenging due to limited tissue availability and the polygenic nature of the disease. Using liver and blood samples from 125 Korean individuals (83 with NAFLD and 42 without NAFLD), we devised a new analytic method to identify causal genes. Among the candidates, we found that AGXT2-rs2291702 protects against liver fibrosis in a genotype-dependent manner with the potential for therapeutic interventions. Our approach enables the discovery of causal genes that act on the basis of genotype.
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Affiliation(s)
- Taekyeong Yoo
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sae Kyung Joo
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea
| | - Hyo Jung Kim
- Department of Biochemistry and Molecular Biology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyun Young Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyungtai Sim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jieun Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea
| | - Hee-Hoon Kim
- Laboratory of Liver Research, Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Sunhee Jung
- Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, Republic of Korea
| | - Youngha Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Oveis Jamialahmadi
- Salhgrenska Academy, Institute of Medicine, Department of Molecular and Clinical Medicine, University of Gothenburg, Sweden
| | - Stefano Romeo
- Salhgrenska Academy, Institute of Medicine, Department of Molecular and Clinical Medicine, University of Gothenburg, Sweden; Sahlgrenska University Hospital, Cardiology Department, Sweden; Department of Medical and Clinical Science, Clinical Nutrition Unit, University Magna Graecia, Catanzaro, Italy
| | - Won-Il Jeong
- Laboratory of Liver Research, Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Geum-Sook Hwang
- Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, Republic of Korea; Department of Chemistry & Nanoscience, Ewha Womans University, Seoul, Republic of Korea
| | - Keon Wook Kang
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jae Woo Kim
- Department of Biochemistry and Molecular Biology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Won Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea.
| | - Murim Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.
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11
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Luo S, Feofanova EV, Tin A, Tung S, Rhee EP, Coresh J, Arking DE, Surapaneni A, Schlosser P, Li Y, Köttgen A, Yu B, Grams ME. Genome-wide association study of serum metabolites in the African American Study of Kidney Disease and Hypertension. Kidney Int 2021; 100:430-439. [PMID: 33838163 PMCID: PMC8583323 DOI: 10.1016/j.kint.2021.03.026] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 02/26/2021] [Accepted: 03/11/2021] [Indexed: 01/23/2023]
Abstract
The genome-wide association study (GWAS) is a powerful means to study genetic determinants of disease traits and generate insights into disease pathophysiology. To date, few GWAS of circulating metabolite levels have been performed in African Americans with chronic kidney disease. Hypothesizing that novel genetic-metabolite associations may be identified in a unique population of African Americans with a lower glomerular filtration rate (GFR), we conducted a GWAS of 652 serum metabolites in 619 participants (mean measured glomerular filtration rate 45 mL/min/1.73m2) in the African American Study of Kidney Disease and Hypertension, a clinical trial of blood pressure lowering and antihypertensive medication in African Americans with chronic kidney disease. We identified 42 significant variant metabolite associations. Twenty associations had been previously identified in published GWAS, and eleven novel associations were replicated in a separate cohort of 818 African Americans with genetic and metabolomic data from the Atherosclerosis Risk in Communities Study. The replicated novel variant-metabolite associations comprised eight metabolites and eleven distinct genomic loci. Nine of the replicated associations represented clear enzyme-metabolite interactions, with high expression in the kidneys as well as the liver. Three loci (ACY1, ACY3, and NAT8) were associated with a common pool of metabolites, acetylated amino acids, but with different individual affinities. Thus, extensive metabolite profiling in an African American population with chronic kidney disease aided identification of novel genome-wide metabolite associations, providing clues about substrate specificity and the key roles of enzymes in modulating systemic levels of metabolites.
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Affiliation(s)
- Shengyuan Luo
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
| | - Elena V Feofanova
- Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sarah Tung
- Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Eugene P Rhee
- Division of Nephrology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Yong Li
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Bing Yu
- Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA; Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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12
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Jendoubi T. Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer. Metabolites 2021; 11:184. [PMID: 33801081 PMCID: PMC8003953 DOI: 10.3390/metabo11030184] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.
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Affiliation(s)
- Takoua Jendoubi
- Department of Statistical Science, University College London, London WC1E 6BT, UK
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13
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Tabassum R, Ripatti S. Integrating lipidomics and genomics: emerging tools to understand cardiovascular diseases. Cell Mol Life Sci 2021; 78:2565-2584. [PMID: 33449144 PMCID: PMC8004487 DOI: 10.1007/s00018-020-03715-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/09/2020] [Accepted: 11/16/2020] [Indexed: 02/07/2023]
Abstract
Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.
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Affiliation(s)
- Rubina Tabassum
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, PO Box 20, 00014, Helsinki, Finland.
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, PO Box 20, 00014, Helsinki, Finland.
- Department of Public Health, Clinicum, University of Helsinki, Helsinki, Finland.
- Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
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14
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Ziegler D, Strom A, Straßburger K, Knebel B, Bönhof GJ, Kotzka J, Szendroedi J, Roden M. Association of cardiac autonomic dysfunction with higher levels of plasma lipid metabolites in recent-onset type 2 diabetes. Diabetologia 2021; 64:458-468. [PMID: 33084971 PMCID: PMC7801358 DOI: 10.1007/s00125-020-05310-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 09/08/2020] [Indexed: 12/14/2022]
Abstract
AIMS/HYPOTHESIS Emerging evidence suggests that in addition to hyperglycaemia, dyslipidaemia could represent a contributing pathogenetic factor to diabetic neuropathy, while obesity and insulin resistance play a role in the development of diabetic cardiac autonomic neuropathy (CAN) characterised by reduced heart rate variability (HRV), particularly in type 2 diabetes. We hypothesised that distinct lipid metabolites are associated with diminished HRV in recent-onset type 2 diabetes rather than type 1 diabetes. METHODS We analysed 127 plasma lipid metabolites (11 acylcarnitines, 39 NEFA, 12 sphingomyelins (SMs), 56 phosphatidylcholines and nine lysophosphatidylcholines) using MS in participants from the German Diabetes Study baseline cohort recently diagnosed with type 1 (n = 100) and type 2 diabetes (n = 206). Four time-domain HRV indices (number of normal-to-normal (NN) intervals >50 ms divided by the number of all NN intervals [pNN50]; root mean square of successive differences [RMSSD]; SD of NN intervals [SDNN]; and SD of differences between adjacent NN intervals) and three frequency-domain HRV indices (very-low-frequency [VLF], low-frequency [LF] and high-frequency [HF] power spectrum) were computed from NN intervals recorded during a 3 h hyperinsulinaemic-euglycaemic clamp at baseline and in subsets of participants with type 1 (n = 60) and type 2 diabetes (n = 95) after 5 years. RESULTS In participants with type 2 diabetes, after Bonferroni correction and rigorous adjustment, SDNN was inversely associated with higher levels of diacyl-phosphatidylcholine (PCaa) C32:0, PCaa C34:1, acyl-alkyl-phosphatidylcholine (PCae) C36:0, SM C16:0 and SM C16:1. SD of differences between NN intervals was inversely associated with PCaa C32:0, PCaa C34:1, PCaa C34:2, PCae C36:0 and SM C16:1, and RMSSD with PCae C36:0. For VLF power, inverse associations were found with PCaa C30:0, PCaa C32:0, PCaa C32:1, PCaa C34:2 and SM C16:1, and for LF power inverse associations were found with PCaa C32:0 and SM C16:1 (r = -0.242 to r = -0.349; p ≤ 0.0005 for all correlations). In contrast, no associations of lipid metabolites with measures of cardiac autonomic function were noted in participants recently diagnosed with type 1 diabetes. After 5 years, HRV declined due to ageing rather than diabetes, whereby prediction analyses for lipid metabolites were hampered. CONCLUSIONS/INTERPRETATION Higher plasma levels of specific lipid metabolites are closely linked to cardiac autonomic dysfunction in recent-onset type 2 diabetes but not type 1 diabetes, suggesting a role for perturbed lipid metabolism in the early development of CAN in type 2 diabetes. Graphical abstract.
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Affiliation(s)
- Dan Ziegler
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany.
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany.
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany.
| | - Alexander Strom
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Klaus Straßburger
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
| | - Birgit Knebel
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
| | - Gidon J Bönhof
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Jörg Kotzka
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
| | - Julia Szendroedi
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
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15
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Panyard DJ, Kim KM, Darst BF, Deming YK, Zhong X, Wu Y, Kang H, Carlsson CM, Johnson SC, Asthana S, Engelman CD, Lu Q. Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. Commun Biol 2021; 4:63. [PMID: 33437055 PMCID: PMC7803963 DOI: 10.1038/s42003-020-01583-z] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 12/09/2020] [Indexed: 02/07/2023] Open
Abstract
The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.
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Grants
- R01 AG037639 NIA NIH HHS
- UL1 TR000427 NCATS NIH HHS
- T15 LM007359 NLM NIH HHS
- T32 LM012413 NLM NIH HHS
- RF1 AG027161 NIA NIH HHS
- T32 AG000213 NIA NIH HHS
- P2C HD047873 NICHD NIH HHS
- UL1 TR002373 NCATS NIH HHS
- P30 AG062715 NIA NIH HHS
- P50 AG033514 NIA NIH HHS
- R01 AG027161 NIA NIH HHS
- R01 AG054047 NIA NIH HHS
- P30 AG017266 NIA NIH HHS
- R21 AG067092 NIA NIH HHS
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
- U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM)
- NSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences (DMS)
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)
- This research is supported by National Institutes of Health (NIH) grants R01AG27161 (Wisconsin Registry for Alzheimer Prevention: Biomarkers of Preclinical AD), R01AG054047 (Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer’s Disease), R21AG067092 (Identifying Metabolomic Risk Factors in Plasma and Cerebrospinal Fluid for Alzheimer’s Disease), R01AG037639 (White Matter Degeneration: Biomarkers in Preclinical Alzheimer’s Disease), P30AG017266 (Center for Demography of Health and Aging), and P50AG033514 and P30AG062715 (Wisconsin Alzheimer’s Disease Research Center Grant), the Helen Bader Foundation, Northwestern Mutual Foundation, Extendicare Foundation, State of Wisconsin, the Clinical and Translational Science Award (CTSA) program through the NIH National Center for Advancing Translational Sciences (NCATS) grant UL1TR000427, and the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. This research was supported in part by the Intramural Research Program of the National Institute on Aging. Computational resources were supported by a core grant to the Center for Demography and Ecology at the University of Wisconsin-Madison (P2CHD047873). Author DJP was supported by an NLM training grant to the Bio-Data Science Training Program (T32LM012413). Author BFD was supported by an NLM training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM 5T15LM007359). Author YKD was supported by a training grant from the National Institute on Aging (T32AG000213). Author HK was supported by National Science Foundation (NSF) grant DMS-1811414 (Theory and Methods for Inferring Causal Effects with Mendelian Randomization).
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Affiliation(s)
- Daniel J Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
| | - Kyeong Mo Kim
- Department of Biotechnology, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Burcu F Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA, 90033, USA
| | - Yuetiva K Deming
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
| | - Xiaoyuan Zhong
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA
| | - Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI, 53706, USA
| | - Cynthia M Carlsson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA.
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI, 53706, USA.
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16
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Saigusa D, Matsukawa N, Hishinuma E, Koshiba S. Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics. Drug Metab Pharmacokinet 2020; 37:100373. [PMID: 33631535 DOI: 10.1016/j.dmpk.2020.11.008] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 12/12/2022]
Abstract
Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.
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Affiliation(s)
- Daisuke Saigusa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Naomi Matsukawa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Eiji Hishinuma
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
| | - Seizo Koshiba
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan; Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
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17
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Michel M, Salvador C, Wiedemair V, Adam MG, Laser KT, Dubowy KO, Entenmann A, Karall D, Geiger R, Zlamy M, Scholl-Bürgi S. Method comparison of HPLC-ninhydrin-photometry and UHPLC-PITC-tandem mass spectrometry for serum amino acid analyses in patients with complex congenital heart disease and controls. Metabolomics 2020; 16:128. [PMID: 33319318 PMCID: PMC7736021 DOI: 10.1007/s11306-020-01741-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/28/2020] [Indexed: 11/17/2022]
Abstract
INTRODUCTION Metabolomics studies are not routine when quantifying amino acids (AA) in congenital heart disease (CHD). OBJECTIVES Comparative analysis of 24 AA in serum by traditional high-performance liquid chromatography (HPLC) based on ion exchange and ninhydrin derivatisation followed by photometry (PM) with ultra-high-performance liquid chromatography and phenylisothiocyanate derivatisation followed by tandem mass spectrometry (TMS); interpretation of findings in CHD patients and controls. METHODS PM: Sample analysis as above (total run time, ~ 119 min). TMS: Sample analysis by AbsoluteIDQ® p180 kit assay (BIOCRATES Life Sciences AG, Innsbruck, Austria), which employs PITC derivatisation; separation of analytes on a Waters Acquity UHPLC BEH18 C18 reversed-phase column, using water and acetonitrile with 0.1% formic acid as the mobile phases; and quantification on a Triple-Stage Quadrupole tandem mass spectrometer (Thermo Fisher Scientific, Waltham, MA) with electrospray ionisation in the presence of internal standards (total run time, ~ 8 min). Calculation of coefficients of variation (CV) (for precision), intra- and interday accuracies, limits of detection (LOD), limits of quantification (LOQ), and mean concentrations. RESULTS Both methods yielded acceptable results with regard to precision (CV < 10% PM, < 20% TMS), accuracies (< 10% PM, < 34% TMS), LOD, and LOQ. For both Fontan patients and controls AA concentrations differed significantly between methods, but patterns yielded overall were parallel. CONCLUSION Serum AA concentrations differ with analytical methods but both methods are suitable for AA pattern recognition. TMS is a time-saving alternative to traditional PM under physiological conditions as well as in patients with CHD. TRIAL REGISTRATION NUMBER ClinicalTrials.gov Identifier NCT03886935, date of registration March 27th, 2019 (retrospectively registered).
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Affiliation(s)
- Miriam Michel
- grid.5361.10000 0000 8853 2677Department of Pediatrics III, Division of Pediatric Cardiology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
- grid.5570.70000 0004 0490 981XCenter of Pediatric Cardiology and Congenital Heart Disease, Heart and Diabetes Center North Rhine-Westphalia, Ruhr-University of Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
| | - Christina Salvador
- grid.5361.10000 0000 8853 2677Department of Pediatrics I, Division of Pediatric Cardiology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Verena Wiedemair
- grid.5771.40000 0001 2151 8122Management Center Innsbruck, Department of Food Technologies, Maximilianstraße 2, 6020 Innsbruck, Austria
| | - Mark Gordian Adam
- grid.431833.e0000 0004 0521 4243BIOCRATES Life Sciences AG, Eduard-Bodem-Gasse 8, 6020 Innsbruck, Austria
| | - Kai Thorsten Laser
- grid.5570.70000 0004 0490 981XCenter of Pediatric Cardiology and Congenital Heart Disease, Heart and Diabetes Center North Rhine-Westphalia, Ruhr-University of Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
| | - Karl-Otto Dubowy
- grid.5570.70000 0004 0490 981XCenter of Pediatric Cardiology and Congenital Heart Disease, Heart and Diabetes Center North Rhine-Westphalia, Ruhr-University of Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
| | - Andreas Entenmann
- grid.5361.10000 0000 8853 2677Department of Pediatrics I, Division of Pediatric Cardiology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Daniela Karall
- grid.5361.10000 0000 8853 2677Department of Pediatrics I, Division of Pediatric Cardiology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Ralf Geiger
- grid.5361.10000 0000 8853 2677Department of Pediatrics III, Division of Pediatric Cardiology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Manuela Zlamy
- grid.5361.10000 0000 8853 2677Department of Pediatrics I, Division of Pediatric Cardiology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Sabine Scholl-Bürgi
- grid.5361.10000 0000 8853 2677Department of Pediatrics I, Division of Pediatric Cardiology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
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18
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Abstract
Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.
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19
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Calvo-Serra B, Maitre L, Lau CHE, Siskos AP, Gützkow KB, Andrušaitytė S, Casas M, Cadiou S, Chatzi L, González JR, Grazuleviciene R, McEachan R, Slama R, Vafeiadi M, Wright J, Coen M, Vrijheid M, Keun HC, Escaramís G, Bustamante M. Urinary metabolite quantitative trait loci in children and their interaction with dietary factors. Hum Mol Genet 2020; 29:3830-3844. [PMID: 33283231 DOI: 10.1093/hmg/ddaa257] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/26/2020] [Accepted: 11/30/2020] [Indexed: 11/14/2022] Open
Abstract
Human metabolism is influenced by genetic and environmental factors. Previous studies have identified over 23 loci associated with more than 26 urine metabolites levels in adults, which are known as urinary metabolite quantitative trait loci (metabQTLs). The aim of the present study is the identification for the first time of urinary metabQTLs in children and their interaction with dietary patterns. Association between genome-wide genotyping data and 44 urine metabolite levels measured by proton nuclear magnetic resonance spectroscopy was tested in 996 children from the Human Early Life Exposome project. Twelve statistically significant urine metabQTLs were identified, involving 11 unique loci and 10 different metabolites. Comparison with previous findings in adults revealed that six metabQTLs were already known, and one had been described in serum and three were involved the same locus as other reported metabQTLs but had different urinary metabolites. The remaining two metabQTLs represent novel urine metabolite-locus associations, which are reported for the first time in this study [single nucleotide polymorphism (SNP) rs12575496 for taurine, and the missense SNP rs2274870 for 3-hydroxyisobutyrate]. Moreover, it was found that urinary taurine levels were affected by the combined action of genetic variation and dietary patterns of meat intake as well as by the interaction of this SNP with beverage intake dietary patterns. Overall, we identified 12 urinary metabQTLs in children, including two novel associations. While a substantial part of the identified loci affected urinary metabolite levels both in children and in adults, the metabQTL for taurine seemed to be specific to children and interacted with dietary patterns.
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Affiliation(s)
- Beatriz Calvo-Serra
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Léa Maitre
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Chung-Ho E Lau
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Alexandros P Siskos
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK
| | - Kristine B Gützkow
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo 0213, Norway
| | - Sandra Andrušaitytė
- Department of Environmental Science, Vytautas Magnus University, Kaunas 44248, Lithuania
| | - Maribel Casas
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Solène Cadiou
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble 38000, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Juan R González
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Regina Grazuleviciene
- Department of Environmental Science, Vytautas Magnus University, Kaunas 44248, Lithuania
| | | | - Rémy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble 38000, France
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion 71003, Greece
| | - John Wright
- Bradford Institute for Health Research, Bradford BD9 6RJ, UK
| | - Murieann Coen
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB2 0RE, UK
| | - Martine Vrijheid
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Hector C Keun
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK
| | - Geòrgia Escaramís
- Departament de Biomedicina, Institut de Neurociències, Universitat de Barcelona (UB), Barcelona 08036, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
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20
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Gillenwater LA, Pratte KA, Hobbs BD, Cho MH, Zhuang Y, Halper-Stromberg E, Cruickshank-Quinn C, Reisdorph N, Petrache I, Labaki WW, O'Neal WK, Ortega VE, Jones DP, Uppal K, Jacobson S, Michelotti G, Wendt CH, Kechris KJ, Bowler RP. Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules. NETWORK AND SYSTEMS MEDICINE 2020; 3:159-181. [PMID: 33987620 PMCID: PMC8109053 DOI: 10.1089/nsm.2020.0009] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2020] [Indexed: 02/07/2023] Open
Abstract
Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV1]/forced vital capacity [FVC], and FEV1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.
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Affiliation(s)
| | | | - Brian D. Hobbs
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Yonghua Zhuang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | | | - Nichole Reisdorph
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Irina Petrache
- National Jewish Health, Denver, Colorado, USA
- School of Medicine, University of Colorado, Aurora, Colorado, USA
| | - Wassim W. Labaki
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Wanda K. O'Neal
- Lung Institute/Cystic Fibrosis Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Victor E. Ortega
- Department of Internal Medicine, Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Dean P. Jones
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, and Critical Care Medicine, Emory School of Medicine, Atlanta, Georgia, USA
| | - Karan Uppal
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, and Critical Care Medicine, Emory School of Medicine, Atlanta, Georgia, USA
| | | | | | - Christine H. Wendt
- Department of Medicine, University of Minnesota and the VAMC, Minneapolis, Minnesota, USA
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Russell P. Bowler
- National Jewish Health, Denver, Colorado, USA
- School of Medicine, University of Colorado, Aurora, Colorado, USA
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21
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Li J, Akanno EC, Valente TS, Abo-Ismail M, Karisa BK, Wang Z, Plastow GS. Genomic Heritability and Genome-Wide Association Studies of Plasma Metabolites in Crossbred Beef Cattle. Front Genet 2020; 11:538600. [PMID: 33193612 PMCID: PMC7542097 DOI: 10.3389/fgene.2020.538600] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 09/01/2020] [Indexed: 11/16/2022] Open
Abstract
Metabolites, substrates or products of metabolic processes, are involved in many biological functions, such as energy metabolism, signaling, stimulatory and inhibitory effects on enzymes and immunological defense. Metabolomic phenotypes are influenced by combination of genetic and environmental effects allowing for metabolome-genome-wide association studies (mGWAS) as a powerful tool to investigate the relationship between these phenotypes and genetic variants. The objectives of this study were to estimate genomic heritability and perform mGWAS and in silico functional enrichment analyses for a set of plasma metabolites in Canadian crossbred beef cattle. Thirty-three plasma metabolites and 45,266 single nucleotide polymorphisms (SNPs) were available for 475 animals. Genomic heritability for all metabolites was estimated using genomic best linear unbiased prediction (GBLUP) including genomic breed composition as covariates in the model. A single-step GBLUP implemented in BLUPF90 programs was used to determine SNP P values and the proportion of genetic variance explained by SNP windows containing 10 consecutive SNPs. The top 10 SNP windows that explained the largest genetic variation for each metabolite were identified and mapped to detect corresponding candidate genes. Functional enrichment analyses were performed on metabolites and their candidate genes using the Ingenuity Pathway Analysis software. Eleven metabolites showed low to moderate heritability that ranged from 0.09 ± 0.15 to 0.36 ± 0.15, while heritability estimates for 22 metabolites were zero or negligible. This result indicates that while variations in 11 metabolites were due to genetic variants, the majority are largely influenced by environment. Three significant SNP associations were detected for betaine (rs109862186), L-alanine (rs81117935), and L-lactic acid (rs42009425) based on Bonferroni correction for multiple testing (family wise error rate <0.05). The SNP rs81117935 was found to be located within the Catenin Alpha 2 gene (CTNNA2) showing a possible association with the regulation of L-alanine concentration. Other candidate genes were identified based on additive genetic variance explained by SNP windows of 10 consecutive SNPs. The observed heritability estimates and the candidate genes and networks identified in this study will serve as baseline information for research into the utilization of plasma metabolites for genetic improvement of crossbred beef cattle.
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Affiliation(s)
- Jiyuan Li
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - Everestus C Akanno
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - Tiago S Valente
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada.,Department of Animal Science, Ethology and Animal Ecology Research Group, São Paulo State University, Jaboticabal, Brazil
| | - Mohammed Abo-Ismail
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada.,Department of Animal Science, College of Agriculture, Food and Environmental Sciences, California Polytechnic State University, San Luis Obispo, CA, United States
| | - Brian K Karisa
- Ministry of Agriculture and Forestry, Edmonton, AB, Canada
| | - Zhiquan Wang
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - Graham S Plastow
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
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22
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Sullivan KM, Susztak K. Unravelling the complex genetics of common kidney diseases: from variants to mechanisms. Nat Rev Nephrol 2020; 16:628-640. [PMID: 32514149 PMCID: PMC8014547 DOI: 10.1038/s41581-020-0298-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2020] [Indexed: 12/20/2022]
Abstract
Genome-wide association studies (GWAS) have identified hundreds of loci associated with kidney-related traits such as glomerular filtration rate, albuminuria, hypertension, electrolyte and metabolite levels. However, these impressive, large-scale mapping approaches have not always translated into an improved understanding of disease or development of novel therapeutics. GWAS have several important limitations. Nearly all disease-associated risk loci are located in the non-coding region of the genome and therefore, their target genes, affected cell types and regulatory mechanisms remain unknown. Genome-scale approaches can be used to identify associations between DNA sequence variants and changes in gene expression (quantified through bulk and single-cell methods), gene regulation and other molecular quantitative trait studies, such as chromatin accessibility, DNA methylation, protein expression and metabolite levels. Data obtained through these approaches, used in combination with robust computational methods, can deliver robust mechanistic inferences for translational exploitation. Understanding the genetic basis of common kidney diseases means having a comprehensive picture of the genes that have a causal role in disease development and progression, of the cells, tissues and organs in which these genes act to affect the disease, of the cellular pathways and mechanisms that drive disease, and of potential targets for disease prevention, detection and therapy.
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Affiliation(s)
- Katie Marie Sullivan
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Katalin Susztak
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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23
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Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet 2020; 52:167-176. [PMID: 31959995 DOI: 10.1038/s41588-019-0567-8] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 12/05/2019] [Indexed: 11/08/2022]
Abstract
The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research.
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24
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Al-Khelaifi F, Diboun I, Donati F, Botrè F, Abraham D, Hingorani A, Albagha O, Georgakopoulos C, Suhre K, Yousri NA, Elrayess MA. Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance. Sci Rep 2019; 9:19889. [PMID: 31882771 PMCID: PMC6934758 DOI: 10.1038/s41598-019-56496-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 12/12/2019] [Indexed: 01/10/2023] Open
Abstract
Genetic research of elite athletic performance has been hindered by the complex phenotype and the relatively small effect size of the identified genetic variants. The aims of this study were to identify genetic predisposition to elite athletic performance by investigating genetically-influenced metabolites that discriminate elite athletes from non-elite athletes and to identify those associated with endurance sports. By conducting a genome wide association study with high-resolution metabolomics profiling in 490 elite athletes, common variant metabolic quantitative trait loci (mQTLs) were identified and compared with previously identified mQTLs in non-elite athletes. Among the identified mQTLs, those associated with endurance metabolites were determined. Two novel genetic loci in FOLH1 and VNN1 are reported in association with N-acetyl-aspartyl-glutamate and Linoleoyl ethanolamide, respectively. When focusing on endurance metabolites, one novel mQTL linking androstenediol (3alpha, 17alpha) monosulfate and SULT2A1 was identified. Potential interactions between the novel identified mQTLs and exercise are highlighted. This is the first report of common variant mQTLs linked to elite athletic performance and endurance sports with potential applications in biomarker discovery in elite athletic candidates, non-conventional anti-doping analytical approaches and therapeutic strategies.
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Affiliation(s)
- Fatima Al-Khelaifi
- Anti Doping Laboratory Qatar, Sports City, Doha, Qatar.,Division of Medicine, University College London, London, NW3 2PF, United Kingdom
| | - Ilhame Diboun
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Francesco Donati
- Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Largo Giulio Onesti 1, 00197, Rome, Italy
| | - Francesco Botrè
- Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Largo Giulio Onesti 1, 00197, Rome, Italy
| | - David Abraham
- Division of Medicine, University College London, London, NW3 2PF, United Kingdom
| | - Aroon Hingorani
- UCL Institute of Cardiovascular Science, University College London, London, WC1E 6BT, United Kingdom
| | - Omar Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.,Center for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar-Foundation, P.O. Box 24144, Doha, Qatar
| | - Noha A Yousri
- Department of Genetic Medicine, Weill Cornell Medical College in Qatar, Qatar-Foundation, P.O. Box 24144, Doha, Qatar.,Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
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25
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Stevelink R, Pangilinan F, Jansen FE, Braun KPJ, Molloy AM, Brody LC, Koeleman BPC. Assessing the genetic association between vitamin B6 metabolism and genetic generalized epilepsy. Mol Genet Metab Rep 2019; 21:100518. [PMID: 31641590 PMCID: PMC6796782 DOI: 10.1016/j.ymgmr.2019.100518] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 09/07/2019] [Indexed: 01/23/2023] Open
Abstract
Altered vitamin B6 metabolism due to pathogenic variants in the gene PNPO causes early onset epileptic encephalopathy, which can be treated with high doses of vitamin B6. We recently reported that single nucleotide polymorphisms (SNPs) that influence PNPO expression in the brain are associated with genetic generalized epilepsy (GGE). However, it is not known whether any of these GGE-associated SNPs influence vitamin B6 metabolite levels. Such an influence would suggest that vitamin B6 could play a role in GGE therapy. Here, we performed genome-wide association studies (GWAS) to assess the influence of GGE associated genetic variants on measures of vitamin B6 metabolism in blood plasma in 2232 healthy individuals. We also asked if SNPs that influence vitamin B6 were associated with GGE in 3122 affected individuals and 20,244 controls. Our GWAS of vitamin B6 metabolites reproduced a previous association and found a novel genome-wide significant locus. The SNPs in these loci were not associated with GGE. We found that 84 GGE-associated SNPs influence expression levels of PNPO in the brain as well as in blood. However, these SNPs were not associated with vitamin B6 metabolism in plasma. By leveraging polygenic risk scoring (PRS), we found suggestive evidence of higher catabolism and lower levels of the active and transport forms of vitamin B6 in GGE, although these findings require further replication.
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Affiliation(s)
- Remi Stevelink
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Faith Pangilinan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
| | - Floor E Jansen
- Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kees P J Braun
- Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Anne M Molloy
- School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Lawrence C Brody
- National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
| | - Bobby P C Koeleman
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
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26
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Stautemas J, Van Kuilenburg ABP, Stroomer L, Vaz F, Blancquaert L, Lefevere FBD, Everaert I, Derave W. Acute Aerobic Exercise Leads to Increased Plasma Levels of R- and S-β-Aminoisobutyric Acid in Humans. Front Physiol 2019; 10:1240. [PMID: 31611815 PMCID: PMC6773837 DOI: 10.3389/fphys.2019.01240] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 09/10/2019] [Indexed: 12/29/2022] Open
Abstract
Recently, it was suggested that β-aminoisobutyric acid (BAIBA) is a myokine involved in browning of fat. However, there is no evidence for an acute effect of exercise supporting this statement and the metabolic distinct enantiomers of BAIBA were not taken into account. Concerning these enantiomers, there is at this point no consensus about resting concentrations of plasma R- and S-BAIBA. Additionally, a polymorphism of the alanine - glyoxylate aminotransferase 2 (AGXT2) gene (rs37369) is known to have a high impact on baseline levels of total BAIBA, but the effect on the enantiomers is unknown. Fifteen healthy recreationally active subjects, with different genotypes of rs37369, participated in a randomized crossover trial where they exercised for 1 h at 40% of Ppeak or remained at rest. Plasma samples were analyzed for R- and S-BAIBA using dual column HPLC-fluorescence. The plasma concentration of baseline R-BAIBA was 67 times higher compared to S-BAIBA (1734 ± 821 vs. 29.3 ± 7.8 nM). Exercise induced a 13 and 20% increase in R-BAIBA and S-BAIBA, respectively. The AGXT2 rs37369 genotype strongly affected baseline levels of R-BAIBA, but did not have an impact on baseline S-BAIBA. We demonstrate that BAIBA should not be treated as one molecule, given (1) the markedly uneven distribution of its enantiomers in human plasma favoring R-BAIBA, and (2) their different metabolic source, as evidenced by the AGXT2 polymorphism only affecting R-BAIBA. The proposed function in organ cross talk is supported by the current data and may apply to both enantiomers, but the tissue of origin remains unclear.
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Affiliation(s)
- Jan Stautemas
- Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
| | - André B P Van Kuilenburg
- Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Lida Stroomer
- Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Fred Vaz
- Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Laura Blancquaert
- Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
| | - Filip B D Lefevere
- Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
| | - Inge Everaert
- Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
| | - Wim Derave
- Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
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27
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Matejka K, Stückler F, Salomon M, Ensenauer R, Reischl E, Hoerburger L, Grallert H, Kastenmüller G, Peters A, Daniel H, Krumsiek J, Theis FJ, Hauner H, Laumen H. Dynamic modelling of an ACADS genotype in fatty acid oxidation - Application of cellular models for the analysis of common genetic variants. PLoS One 2019; 14:e0216110. [PMID: 31120904 PMCID: PMC6532850 DOI: 10.1371/journal.pone.0216110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 04/15/2019] [Indexed: 11/19/2022] Open
Abstract
Background Genome-wide association studies of common diseases or metabolite quantitative traits often identify common variants of small effect size, which may contribute to phenotypes by modulation of gene expression. Thus, there is growing demand for cellular models enabling to assess the impact of gene regulatory variants with moderate effects on gene expression. Mitochondrial fatty acid oxidation is an important energy metabolism pathway. Common noncoding acyl-CoA dehydrogenase short chain (ACADS) gene variants are associated with plasma C4-acylcarnitine levels and allele-specific modulation of ACADS expression may contribute to the observed phenotype. Methods and findings We assessed ACADS expression and intracellular acylcarnitine levels in human lymphoblastoid cell lines (LCL) genotyped for a common ACADS variant associated with plasma C4-acylcarnitine and found a significant genotype-dependent decrease of ACADS mRNA and protein. Next, we modelled gradual decrease of ACADS expression using a tetracycline-regulated shRNA-knockdown of ACADS in Huh7 hepatocytes, a cell line with high fatty acid oxidation-(FAO)-capacity. Assessing acylcarnitine flux in both models, we found increased C4-acylcarnitine levels with decreased ACADS expression levels. Moreover, assessing time-dependent changes of acylcarnitine levels in shRNA-hepatocytes with altered ACADS expression levels revealed an unexpected effect on long- and medium-chain fatty acid intermediates. Conclusions Both, genotyped LCL and regulated shRNA-knockdown are valuable tools to model moderate, gradual gene-regulatory effects of common variants on cellular phenotypes. Decreasing ACADS expression levels modulate short and surprisingly also long/medium chain acylcarnitines, and may contribute to increased plasma acylcarnitine levels.
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Affiliation(s)
- Kerstin Matejka
- Chair of Nutritional Medicine, Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
- ZIEL-Research Center for Nutrition and Food Sciences, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Helmholtz Zentrum München, Neuherberg, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Technische Universität München, Freising-Weihenstephan, Germany
| | - Ferdinand Stückler
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | | | - Regina Ensenauer
- Research Center, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-Universität München, München, Germany
- Experimental Pediatrics and Metabolism, Department of General Pediatrics, Neonatology and Pediatric Cardiology, University Children’s Hospital, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Child Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Eva Reischl
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Lena Hoerburger
- Paediatric Nutritional Medicine, Else Kröner-Fresenius-Centre for Nutritional Medicine, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Helmholtz Zentrum München, Neuherberg, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Technische Universität München, Freising-Weihenstephan, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK-Munich partner site), Neuherberg, Germany
| | - Hannelore Daniel
- ZIEL-Research Center for Nutrition and Food Sciences, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
- Chair of Physiology of Human Nutrition, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
| | - Jan Krumsiek
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, United States of America
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Mathematical Science, Technische Universität München, Garching, Germany
- * E-mail: (FJT); (HL)
| | - Hans Hauner
- Chair of Nutritional Medicine, Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
- ZIEL-Research Center for Nutrition and Food Sciences, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Helmholtz Zentrum München, Neuherberg, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Technische Universität München, Freising-Weihenstephan, Germany
- Else Kröner-Fresenius-Center for Nutritional Medicine, Klinikum rechts der Isar, Technische Universität München, München, Germany
| | - Helmut Laumen
- Chair of Nutritional Medicine, Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
- ZIEL-Research Center for Nutrition and Food Sciences, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Helmholtz Zentrum München, Neuherberg, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Technische Universität München, Freising-Weihenstephan, Germany
- Paediatric Nutritional Medicine, Else Kröner-Fresenius-Centre for Nutritional Medicine, TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit Protein Science, Helmholtz Zentrum München, Neuherberg, Germany
- * E-mail: (FJT); (HL)
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Ponsuksili S, Trakooljul N, Hadlich F, Methling K, Lalk M, Murani E, Wimmers K. Genetic Regulation of Liver Metabolites and Transcripts Linking to Biochemical-Clinical Parameters. Front Genet 2019; 10:348. [PMID: 31057604 PMCID: PMC6478805 DOI: 10.3389/fgene.2019.00348] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 04/01/2019] [Indexed: 01/23/2023] Open
Abstract
Given the central metabolic role of the liver, hepatic metabolites and transcripts reflect the organismal physiological state. Biochemical-clinical plasma biomarkers, hepatic metabolites, transcripts, and single nucleotide polymorphism (SNP) genotypes of some 300 pigs were integrated by weighted correlation networks and genome-wide association analyses. Network-based approaches of transcriptomic and metabolomics data revealed linked of transcripts and metabolites of the pentose phosphate pathway (PPP). This finding was evidenced by using a NADP/NADPH assay and HDAC4 and G6PD transcript quantification with the latter coding for first limiting enzyme of this pathway and by RNAi knockdown experiments of HDAC4. Other transcripts including ARG2 and SLC22A7 showed link to amino acids and biomarkers. The amino acid metabolites were linked with transcripts of immune or acute phase response signaling, whereas the carbohydrate metabolites were highly enrich in cholesterol biosynthesis transcripts. Genome-wide association analyses revealed 180 metabolic quantitative trait loci (mQTL) (p < 10-4). Trans-4-hydroxy-L-proline (p = 6 × 10-9), being strongly correlated with plasma creatinine (CREA), showed strongest association with SNPs on chromosome 6 that had pleiotropic effects on PRODH2 expression as revealed by multivariate analysis. Consideration of shared marker association with biomarkers, metabolites, and transcripts revealed 144 SNPs associated with 44 metabolites and 69 transcripts that are correlated with each other, representing 176 mQTL and expression quantitative trait loci (eQTL). This is the first work to report genetic variants associated with liver metabolite and transcript levels as well as blood biochemical-clinical parameters in a healthy porcine model. The identified associations provide links between variation at the genome, transcriptome, and metabolome level molecules with clinically relevant phenotypes. This approach has the potential to detect novel biomarkers displaying individual variation and promoting predictive biology in medicine and animal breeding.
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Affiliation(s)
- Siriluck Ponsuksili
- Leibniz Institute for Farm Animal Biology (FBN), Institute for Genome Biology, Functional Genome Analysis Research Unit, Dummerstorf, Germany
| | - Nares Trakooljul
- Leibniz Institute for Farm Animal Biology (FBN), Institute for Genome Biology, Functional Genome Analysis Research Unit, Dummerstorf, Germany
| | - Frieder Hadlich
- Leibniz Institute for Farm Animal Biology (FBN), Institute for Genome Biology, Functional Genome Analysis Research Unit, Dummerstorf, Germany
| | - Karen Methling
- Institute for Biochemistry - Metabolomics, University of Greifswald, Greifswald, Germany
| | - Michael Lalk
- Institute for Biochemistry - Metabolomics, University of Greifswald, Greifswald, Germany
| | - Eduard Murani
- Leibniz Institute for Farm Animal Biology (FBN), Institute for Genome Biology, Functional Genome Analysis Research Unit, Dummerstorf, Germany
| | - Klaus Wimmers
- Leibniz Institute for Farm Animal Biology (FBN), Institute for Genome Biology, Functional Genome Analysis Research Unit, Dummerstorf, Germany.,Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, Germany
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29
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Gupta V, Saxena R, Walia GK, Agarwal T, Vats H, Dunn W, Relton C, Sovio U, Papageorghiou A, Davey Smith G, Khadgawat R, Sachdeva MP. Gestational route to healthy birth (GaRBH): protocol for an Indian prospective cohort study. BMJ Open 2019; 9:e025395. [PMID: 31048433 PMCID: PMC6501957 DOI: 10.1136/bmjopen-2018-025395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 10/17/2018] [Accepted: 03/12/2019] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Pregnancy is characterised by a high rate of metabolic shifts from early to late phases of gestation in order to meet the raised physiological and metabolic needs. This change in levels of metabolites is influenced by gestational weight gain (GWG), which is an important characteristic of healthy pregnancy. Inadequate/excessive GWG has short-term and long-term implications on maternal and child health. Exploration of gestational metabolism is required for understanding the quantitative changes in metabolite levels during the course of pregnancy. Therefore, our aim is to study trimester-specific variation in levels of metabolites in relation to GWG and its influence on fetal growth and newborn anthropometric traits at birth. METHODS AND ANALYSIS A prospective longitudinal study is planned (start date: February 2018; end date: March 2023) on pregnant women that are being recruited in the first trimester and followed in subsequent trimesters and at the time of delivery (total 3 follow-ups). The study is being conducted in a hospital located in Bikaner district (66% rural population), Rajasthan, India. The estimated sample size is of 1000 mother-offspring pairs. Information on gynaecological and obstetric history, socioeconomic position, diet, physical activity, tobacco and alcohol consumption, depression, anthropometric measurements and blood samples is being collected for metabolic assays in each trimester using standardised methods. Mixed effects regression models will be used to assess the role of gestational weight in influencing metabolite levels in each trimester. The association of maternal levels of metabolites with fetal growth, offspring's weight and body composition at birth will be investigated using regression modelling. ETHICS AND DISSEMINATION The study has been approved by the ethics committees of the Department of Anthropology, University of Delhi and Sardar Patel Medical College, Rajasthan. We are taking written informed consent after discussing the various aspects of the study with the participants in the local language.
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Affiliation(s)
- Vipin Gupta
- Department of Anthropology, University of Delhi, Delhi, India
| | - Ruchi Saxena
- Department of Obstetrics and Gynaecology, Sardar Patel Medical College, Bikaner, Rajasthan, India
| | | | | | - Harsh Vats
- Department of Anthropology, University of Delhi, Delhi, India
| | - Warwick Dunn
- School of Biosciences, Phenome Centre Birmingham and Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Caroline Relton
- MRC Integrative Epidemiology Unit and Bristol Medical School, University of Bristol, Bristol, UK
| | - Ulla Sovio
- Obstetrics and Gyneacology, University of Cambridge, Cambridge, UK
| | - Aris Papageorghiou
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit and Bristol Medical School, University of Bristol, Bristol, UK
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30
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Köttgen A, Raffler J, Sekula P, Kastenmüller G. Genome-Wide Association Studies of Metabolite Concentrations (mGWAS): Relevance for Nephrology. Semin Nephrol 2019; 38:151-174. [PMID: 29602398 DOI: 10.1016/j.semnephrol.2018.01.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Metabolites are small molecules that are intermediates or products of metabolism, many of which are freely filtered by the kidneys. In addition, the kidneys have a central role in metabolite anabolism and catabolism, as well as in active metabolite reabsorption and/or secretion during tubular passage. This review article illustrates how the coupling of genomics and metabolomics in genome-wide association analyses of metabolites can be used to illuminate mechanisms underlying human metabolism, with a special focus on insights relevant to nephrology. First, genetic susceptibility loci for reduced kidney function and chronic kidney disease (CKD) were reviewed systematically for their associations with metabolite concentrations in metabolomics studies of blood and urine. Second, kidney function and CKD-associated metabolites reported from observational studies were interrogated for metabolite-associated genetic variants to generate and discuss complementary insights. Finally, insights originating from the simultaneous study of both blood and urine or by modeling intermetabolite relationships are summarized. We also discuss methodologic questions related to the study of metabolite concentrations in urine as well as among CKD patients. In summary, genome-wide association analyses of metabolites using metabolite concentrations quantified from blood and/or urine are a promising avenue of research to illuminate physiological and pathophysiological functions of the kidney.
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Affiliation(s)
- Anna Köttgen
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
| | - Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Peggy Sekula
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
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31
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Peng L, Guo JC, Long L, Pan F, Zhao JM, Xu LY, Li EM. A Novel Clinical Six-Flavoprotein-Gene Signature Predicts Prognosis in Esophageal Squamous Cell Carcinoma. BIOMED RESEARCH INTERNATIONAL 2019; 2019:3869825. [PMID: 31815134 PMCID: PMC6878914 DOI: 10.1155/2019/3869825] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 08/23/2019] [Accepted: 10/04/2019] [Indexed: 02/05/2023]
Abstract
Flavoproteins and their interacting proteins play important roles in mitochondrial electron transport, fatty acid degradation, and redox regulation. However, their clinical significance and function in esophageal squamous cell carcinoma (ESCC) are little known. Here, using survival analysis and machine learning, we mined 179 patient expression profiles with ESCC in GSE53625 from the Gene Expression Omnibus (GEO) database and constructed a signature consisting of two flavoprotein genes (GPD2 and PYROXD2) and four flavoprotein interacting protein genes (CTTN, GGH, SRC, and SYNJ2BP). Kaplan-Meier analysis revealed the signature was significantly associated with the survival of ESCC patients (mean survival time: 26.77 months in the high-risk group vs. 54.97 months in the low-risk group, P < 0.001, n = 179), and time-dependent ROC analysis demonstrated that the six-gene signature had good predictive ability for six-year survival for ESCC (AUC = 0.86, 95% CI: 0.81-0.90). We then validated its prediction performance in an independent set by RT-PCR (mean survival: 15.73 months in the high-risk group vs. 21.1 months in the low-risk group, P=0.032, n = 121). Furthermore, RNAi-mediated knockdown of genes in the flavoprotein signature led to decreased proliferation and migration of ESCC cells. Taken together, CTTN, GGH, GPD2, PYROXD2, SRC, and SYNJ2BP have an important clinical significance for prognosis of ESCC patients, suggesting they are efficient prognostic markers and potential targets for ESCC therapy.
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Affiliation(s)
- Liu Peng
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, Guangdong, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Jin-Cheng Guo
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, Guangdong, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Lin Long
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, Guangdong, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Feng Pan
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, Guangdong, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Jian-Mei Zhao
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, Guangdong, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Li-Yan Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, Guangdong, China
- Institute of Oncologic Pathology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - En-Min Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, Guangdong, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
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Tremblay BL, Guénard F, Lamarche B, Pérusse L, Vohl MC. Familial resemblances in human plasma metabolites are attributable to both genetic and common environmental effects. Nutr Res 2018; 61:22-30. [PMID: 30683436 DOI: 10.1016/j.nutres.2018.10.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 09/26/2018] [Accepted: 10/05/2018] [Indexed: 01/06/2023]
Abstract
Metabolites are of great importance for understanding the pathogenesis of several diseases. Understanding the genetic contribution to metabolite concentrations may provide insights into mechanisms of complex diseases. Several studies have investigated heritability of metabolites but none investigated potential influences of genetic and environmental factors on the relationship between metabolites and cardiometabolic (CM) risk factors. Thus, we tested the hypothesis that both genetic and common environmental effects contribute to the variance of plasma metabolite concentrations and that shared genetic and environmental effects explain their phenotypic correlations with CM risk factors. To test this hypothesis, variance component method and bivariate genetic analysis were performed in a family-based sample of 48 French Canadians from 16 families. Familial resemblances were computed for all 147 detected metabolites and 9 (acetylornithine, acylcarnitine C9, arginine, phosphatidylcholine acyl-alkyl C36:4, serotonin, lysophosphatidylcholine acyl C20:4, citrulline, asymmetric dimethylarginine, phosphatidylcholine acyl-alkyl C36:5) showed a significant familial effect (55.7%, 18.7%, and 37.0% for maximal heritability, genetic heritability, and common environmental effect, respectively). Citrulline, phosphatidylcholine acyl-alkyl C36:4, phosphatidylcholine acyl-alkyl C36:5, and serotonin had significant phenotypic correlations with CM risk factors. Citrulline had a positive genetic correlation with apolipoprotein B100, while phosphatidylcholine acyl-alkyl C36:5 had a positive environmental correlation with total cholesterol. In conclusion, familial resemblances in metabolite concentrations were mainly attributable to common environmental effect when considering metabolites with a significant familial effect. Common genetic and environmental factors may also influence the relationship between metabolites and CM risk factors.
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Affiliation(s)
- Bénédicte L Tremblay
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada.
| | - Frédéric Guénard
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada.
| | - Benoît Lamarche
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada.
| | - Louis Pérusse
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada; CHU de Québec Research Center - Endocrinology and Nephrology, 2705 Laurier Blvd, Quebec City, QC, G1V 4G2, Canada.
| | - Marie-Claude Vohl
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada; CHU de Québec Research Center - Endocrinology and Nephrology, 2705 Laurier Blvd, Quebec City, QC, G1V 4G2, Canada.
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Caussy C, Hsu C, Lo MT, Liu A, Bettencourt R, Ajmera VH, Bassirian S, Hooker J, Sy E, Richards L, Schork N, Schnabl B, Brenner DA, Sirlin CB, Chen CH, Loomba R, for the Genetics of NAFLD in Twins Consortium. Link between gut-microbiome derived metabolite and shared gene-effects with hepatic steatosis and fibrosis in NAFLD. Hepatology 2018; 68:918-932. [PMID: 29572891 PMCID: PMC6151296 DOI: 10.1002/hep.29892] [Citation(s) in RCA: 151] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 02/21/2018] [Accepted: 03/20/2018] [Indexed: 12/21/2022]
Abstract
Previous studies have shown that gut-microbiome is associated with nonalcoholic fatty liver disease (NAFLD). We aimed to examine if serum metabolites, especially those derived from the gut-microbiome, have a shared gene-effect with hepatic steatosis and fibrosis. This is a cross-sectional analysis of a prospective discovery cohort including 156 well-characterized twins and families with untargeted metabolome profiling assessment. Hepatic steatosis was assessed using magnetic-resonance-imaging proton-density-fat-fraction (MRI-PDFF) and fibrosis using MR-elastography (MRE). A twin additive genetics and unique environment effects (AE) model was used to estimate the shared gene-effect between metabolites and hepatic steatosis and fibrosis. The findings were validated in an independent prospective validation cohort of 156 participants with biopsy-proven NAFLD including shotgun metagenomics sequencing assessment in a subgroup of the cohort. In the discovery cohort, 56 metabolites including 6 microbial metabolites had a significant shared gene-effect with both hepatic steatosis and fibrosis after adjustment for age, sex and ethnicity. In the validation cohort, 6 metabolites were associated with advanced fibrosis. Among them, only one microbial metabolite, 3-(4-hydroxyphenyl)lactate, remained consistent and statistically significantly associated with liver fibrosis in the discovery and validation cohort (fold-change of higher-MRE versus lower-MRE: 1.78, P < 0.001 and of advanced versus no advanced fibrosis: 1.26, P = 0.037, respectively). The share genetic determination of 3-(4-hydroxyphenyl)lactate with hepatic steatosis was RG :0.57,95%CI:0.27-0.80, P < 0.001 and with fibrosis was RG :0.54,95%CI:0.036-1, P = 0.036. Pathway reconstruction linked 3-(4-hydroxyphenyl)lactate to several human gut-microbiome species. In the validation cohort, 3-(4-hydroxyphenyl)lactate was significantly correlated with the abundance of several gut-microbiome species, belonging only to Firmicutes, Bacteroidetes and Proteobacteria phyla, previously reported as associated with advanced fibrosis. Conclusion: This proof of concept study provides evidence of a link between the gut-microbiome and 3-(4-hydroxyphenyl)lactate that shares gene-effect with hepatic steatosis and fibrosis. (Hepatology 2018).
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Affiliation(s)
- Cyrielle Caussy
- NAFLD Research Center, Department of Medicine, La Jolla, California
- Université Lyon 1, Hospices Civils de Lyon, Lyon, France
| | - Cynthia Hsu
- NAFLD Research Center, Department of Medicine, La Jolla, California
| | - Min-Tzu Lo
- Department of Radiology, University of California at San Diego, La Jolla, California
| | - Amy Liu
- NAFLD Research Center, Department of Medicine, La Jolla, California
| | | | - Veeral H. Ajmera
- NAFLD Research Center, Department of Medicine, La Jolla, California
| | - Shirin Bassirian
- NAFLD Research Center, Department of Medicine, La Jolla, California
| | - Jonathan Hooker
- Liver Imaging Group, Department of Radiology, University of California at San Diego, La Jolla, California
| | - Ethan Sy
- Liver Imaging Group, Department of Radiology, University of California at San Diego, La Jolla, California
| | - Lisa Richards
- NAFLD Research Center, Department of Medicine, La Jolla, California
| | - Nicholas Schork
- Human Biology, J. Craig Venter Institute, La Jolla, California
| | - Bernd Schnabl
- NAFLD Research Center, Department of Medicine, La Jolla, California
- Division of Gastroenterology, Department of Medicine, La Jolla, California
| | - David A. Brenner
- NAFLD Research Center, Department of Medicine, La Jolla, California
- Division of Gastroenterology, Department of Medicine, La Jolla, California
| | - Claude B. Sirlin
- Liver Imaging Group, Department of Radiology, University of California at San Diego, La Jolla, California
| | - Chi-Hua Chen
- Department of Radiology, University of California at San Diego, La Jolla, California
| | - Rohit Loomba
- NAFLD Research Center, Department of Medicine, La Jolla, California
- Division of Gastroenterology, Department of Medicine, La Jolla, California
- Division of Epidemiology, Department of Family and Preventive Medicine, University of California at San Diego, La Jolla, California
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Hebebrand J, Peters T, Schijven D, Hebebrand M, Grasemann C, Winkler TW, Heid IM, Antel J, Föcker M, Tegeler L, Brauner L, Adan RAH, Luykx JJ, Correll CU, König IR, Hinney A, Libuda L. The role of genetic variation of human metabolism for BMI, mental traits and mental disorders. Mol Metab 2018; 12:1-11. [PMID: 29673576 PMCID: PMC6001916 DOI: 10.1016/j.molmet.2018.03.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 03/23/2018] [Accepted: 03/29/2018] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE The aim was to assess whether loci associated with metabolic traits also have a significant role in BMI and mental traits/disorders METHODS: We first assessed the number of single nucleotide polymorphisms (SNPs) with genome-wide significance for human metabolism (NHGRI-EBI Catalog). These 516 SNPs (216 independent loci) were looked-up in genome-wide association studies for association with body mass index (BMI) and the mental traits/disorders educational attainment, neuroticism, schizophrenia, well-being, anxiety, depressive symptoms, major depressive disorder, autism-spectrum disorder, attention-deficit/hyperactivity disorder, Alzheimer's disease, bipolar disorder, aggressive behavior, and internalizing problems. A strict significance threshold of p < 6.92 × 10-6 was based on the correction for 516 SNPs and all 14 phenotypes, a second less conservative threshold (p < 9.69 × 10-5) on the correction for the 516 SNPs only. RESULTS 19 SNPs located in nine independent loci revealed p-values < 6.92 × 10-6; the less strict criterion was met by 41 SNPs in 24 independent loci. BMI and schizophrenia showed the most pronounced genetic overlap with human metabolism with three loci each meeting the strict significance threshold. Overall, genetic variation associated with estimated glomerular filtration rate showed up frequently; single metabolite SNPs were associated with more than one phenotype. Replications in independent samples were obtained for BMI and educational attainment. CONCLUSIONS Approximately 5-10% of the regions involved in the regulation of blood/urine metabolite levels seem to also play a role in BMI and mental traits/disorders and related phenotypes. If validated in metabolomic studies of the respective phenotypes, the associated blood/urine metabolites may enable novel preventive and therapeutic strategies.
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Affiliation(s)
- Johannes Hebebrand
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Triinu Peters
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Dick Schijven
- Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Moritz Hebebrand
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Corinna Grasemann
- Pediatric Endocrinology and Diabetology, Klinik für Kinderheilkunde II, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Thomas W Winkler
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Jochen Antel
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Manuel Föcker
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Lisa Tegeler
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Lena Brauner
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Roger A H Adan
- Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jurjen J Luykx
- Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Psychiatry, ZNA Hospitals, Antwerp, Belgium
| | - Christoph U Correll
- Division of Psychiatry Research, Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Inke R König
- Institute of Medical Biometry and Statistics, University of Luebeck, Luebeck, Schleswig-Holstein, Germany
| | - Anke Hinney
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Lars Libuda
- Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
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Laufer VA, Chen JY, Langefeld CD, Bridges SL. Integrative Approaches to Understanding the Pathogenic Role of Genetic Variation in Rheumatic Diseases. Rheum Dis Clin North Am 2018; 43:449-466. [PMID: 28711145 DOI: 10.1016/j.rdc.2017.04.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The use of high-throughput omics may help to understand the contribution of genetic variants to the pathogenesis of rheumatic diseases. We discuss the concept of missing heritability: that genetic variants do not explain the heritability of rheumatoid arthritis and related rheumatologic conditions. In addition to an overview of how integrative data analysis can lead to novel insights into mechanisms of rheumatic diseases, we describe statistical approaches to prioritizing genetic variants for future functional analyses. We illustrate how analyses of large datasets provide hope for improved approaches to the diagnosis, treatment, and prevention of rheumatic diseases.
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Affiliation(s)
- Vincent A Laufer
- Division of Clinical Immunology and Rheumatology, School of Medicine, University of Alabama at Birmingham, 1720 2nd Avenue South, SHEL 236, Birmingham, AL 35294-2182, USA
| | - Jake Y Chen
- The Informatics Institute, School of Medicine, University of Alabama at Birmingham, 1720 2nd Avenue South, THT 137, Birmingham, AL 35294-0006, USA
| | - Carl D Langefeld
- Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA; Public Health Genomics, Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA
| | - S Louis Bridges
- Division of Clinical Immunology and Rheumatology, School of Medicine, University of Alabama at Birmingham, 1720 2nd Avenue South, SHEL 178, Birmingham, AL 35294-2182, USA.
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36
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Obón-Santacana M, Vilardell M, Carreras A, Duran X, Velasco J, Galván-Femenía I, Alonso T, Puig L, Sumoy L, Duell EJ, Perucho M, Moreno V, de Cid R. GCAT|Genomes for life: a prospective cohort study of the genomes of Catalonia. BMJ Open 2018; 8:e018324. [PMID: 29593016 PMCID: PMC5875652 DOI: 10.1136/bmjopen-2017-018324] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
PURPOSE The prevalence of chronic non-communicable diseases (NCDs) is increasing worldwide. NCDs are the leading cause of both morbidity and mortality, and it is estimated that by 2030, they will be responsible for 80% of deaths across the world. The Genomes for Life (GCAT) project is a long-term prospective cohort study that was designed to integrate and assess the role of epidemiological, genomic and epigenomic factors in the development of major chronic diseases in Catalonia, a north-east region of Spain. PARTICIPANTS At the end of 2017, the GCAT Study will have recruited 20 000 participants aged 40-65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. The GCAT Study has access to the electronic health records of the Catalan Public Healthcare System. Participants will be followed biannually at least 20 years after recruitment. FINDINGS TO DATE Among all GCAT participants, 59.2% are women and 83.3% of the cohort identified themselves as Caucasian/white. More than half of the participants have higher education levels, 72.2% are current workers and 42.1% are classified as overweight (body mass index ≥25 and <30 kg/m2). We have genotyped 5459 participants, of which 5000 have metabolome data. Further, the whole genome of 808 participants will be sequenced by the end of 2017. FUTURE PLANS The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites.
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Affiliation(s)
- Mireia Obón-Santacana
- Genomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
- Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO-IDIBELL), Hospitalet del Llobregat, Spain
| | - Mireia Vilardell
- Genomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
| | - Anna Carreras
- Genomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
| | - Xavier Duran
- Genomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
| | - Juan Velasco
- Genomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
| | - Iván Galván-Femenía
- Genomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
| | - Teresa Alonso
- Genomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
| | - Lluís Puig
- Banc de Sang i Teixits (BST), Barcelona, Spain
| | - Lauro Sumoy
- Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
| | - Eric J Duell
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO-IDIBELL), Hospitalet del Llobregat, Spain
| | - Manuel Perucho
- Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
| | - Victor Moreno
- Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO-IDIBELL), Hospitalet del Llobregat, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Hospitalet del Llobregat, Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Rafael de Cid
- Genomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
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Yousri NA, Fakhro KA, Robay A, Rodriguez-Flores JL, Mohney RP, Zeriri H, Odeh T, Kader SA, Aldous EK, Thareja G, Kumar M, Al-Shakaki A, Chidiac OM, Mohamoud YA, Mezey JG, Malek JA, Crystal RG, Suhre K. Whole-exome sequencing identifies common and rare variant metabolic QTLs in a Middle Eastern population. Nat Commun 2018; 9:333. [PMID: 29362361 PMCID: PMC5780481 DOI: 10.1038/s41467-017-01972-9] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 10/30/2017] [Indexed: 12/30/2022] Open
Abstract
Metabolomics-genome-wide association studies (mGWAS) have uncovered many metabolic quantitative trait loci (mQTLs) influencing human metabolic individuality, though predominantly in European cohorts. By combining whole-exome sequencing with a high-resolution metabolomics profiling for a highly consanguineous Middle Eastern population, we discover 21 common variant and 12 functional rare variant mQTLs, of which 45% are novel altogether. We fine-map 10 common variant mQTLs to new metabolite ratio associations, and 11 common variant mQTLs to putative protein-altering variants. This is the first work to report common and rare variant mQTLs linked to diseases and/or pharmacological targets in a consanguineous Arab cohort, with wide implications for precision medicine in the Middle East. Blood metabolites are influenced by a combination of genetic and environmental factors. Here, Yousri and colleagues perform a whole-exome sequencing study in combination with a metabolomics analysis to identify metabolic quantitative trait loci in a Middle Eastern population.
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Affiliation(s)
- Noha A Yousri
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar. .,Computer and Systems Engineering, Alexandria University, Alexandria, Egypt.
| | - Khalid A Fakhro
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar. .,Sidra Medical Research Center, Department of Human Genetics, PO Box 26999, Doha, Qatar.
| | - Amal Robay
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | | | | | - Hassina Zeriri
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Tala Odeh
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Sara Abdul Kader
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Eman K Aldous
- Genomics Core, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Gaurav Thareja
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Manish Kumar
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Alya Al-Shakaki
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Omar M Chidiac
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Yasmin A Mohamoud
- Genomics Core, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Jason G Mezey
- Genetic Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Joel A Malek
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar.,Genomics Core, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Ronald G Crystal
- Genetic Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Karsten Suhre
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar.
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Ebbels TMD, Rodriguez-Martinez A, Dumas ME, Keun HC. Advances in Computational Analysis of Metabolomic NMR Data. NMR-BASED METABOLOMICS 2018. [DOI: 10.1039/9781782627937-00310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.
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Affiliation(s)
- Timothy M. D. Ebbels
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Andrea Rodriguez-Martinez
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Marc-Emmanuel Dumas
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Hector C. Keun
- Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London W12 0NN UK
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Rueedi R, Mallol R, Raffler J, Lamparter D, Friedrich N, Vollenweider P, Waeber G, Kastenmüller G, Kutalik Z, Bergmann S. Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput Biol 2017; 13:e1005839. [PMID: 29194434 PMCID: PMC5711027 DOI: 10.1371/journal.pcbi.1005839] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 10/23/2017] [Indexed: 01/06/2023] Open
Abstract
A metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for association with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of associated features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant associations observed in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features associated with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic association can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 reference NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 associations, respectively. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.
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Affiliation(s)
- Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Roger Mallol
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - David Lamparter
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site, Greifswald, Germany
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Gérard Waeber
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- * E-mail:
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Abstract
Metabolomics is the newest addition to the "omics" disciplines and has shown rapid growth in its application to human health research because of fundamental advancements in measurement and analysis techniques. Metabolomics has unique and proven advantages in systems biology and biomarker discovery. The next generation of analysis techniques promises even richer and more complete analysis capabilities that will enable earlier clinical diagnosis, drug refinement, and personalized medicine. A review of current advancements in methodologies and statistical analysis that are enhancing and improving the performance of metabolomics is presented along with highlights of some recent successful applications.
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Affiliation(s)
- Eli Riekeberg
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
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Nicholson G, Holmes C. A note on statistical repeatability and study design for high-throughput assays. Stat Med 2017; 36:790-798. [PMID: 27882571 PMCID: PMC5299465 DOI: 10.1002/sim.7175] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 09/30/2016] [Accepted: 10/28/2016] [Indexed: 01/10/2023]
Abstract
Characterizing the technical precision of measurements is a necessary stage in the planning of experiments and in the formal sample size calculation for optimal design. Instruments that measure multiple analytes simultaneously, such as in high-throughput assays arising in biomedical research, pose particular challenges from a statistical perspective. The current most popular method for assessing precision of high-throughput assays is by scatterplotting data from technical replicates. Here, we question the statistical rationale of this approach from both an empirical and theoretical perspective, illustrating our discussion using four example data sets from different genomic platforms. We demonstrate that such scatterplots convey little statistical information of relevance and are potentially highly misleading. We present an alternative framework for assessing the precision of high-throughput assays and planning biomedical experiments. Our methods are based on repeatability-a long-established statistical quantity also known as the intraclass correlation coefficient. We provide guidance and software for estimation and visualization of repeatability of high-throughput assays, and for its incorporation into study design. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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Affiliation(s)
- George Nicholson
- Department of StatisticsUniversity of Oxford24‐29 St GilesOxfordOX1 3LBU.K.
| | - Chris Holmes
- Department of StatisticsUniversity of Oxford24‐29 St GilesOxfordOX1 3LBU.K.
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Guo Y, Hwang LD, Li J, Eades J, Yu CW, Mansfield C, Burdick-Will A, Chang X, Chen Y, Duke FF, Zhang J, Fakharzadeh S, Fennessey P, Keating BJ, Jiang H, Hakonarson H, Reed DR, Preti G. Genetic analysis of impaired trimethylamine metabolism using whole exome sequencing. BMC MEDICAL GENETICS 2017; 18:11. [PMID: 28196478 PMCID: PMC5310055 DOI: 10.1186/s12881-017-0369-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 01/17/2017] [Indexed: 12/27/2022]
Abstract
Background Trimethylaminuria (TMAU) is a genetic disorder whereby people cannot convert trimethylamine (TMA) to its oxidized form (TMAO), a process that requires the liver enzyme FMO3. Loss-of-function variants in the FMO3 gene are a known cause of TMAU. In addition to the inability to metabolize TMA precursors like choline, patients often emit a characteristic odor because while TMAO is odorless, TMA has a fishy smell. The Monell Chemical Senses Center is a research institute with a program to evaluate people with odor complaints for TMAU. Methods Here we evaluated ten subjects by (1) odor evaluation by a trained sensory panel, (2) analysis of their urine concentration of TMA relative to TMAO before and after choline ingestion, and (3) whole exome sequencing as well as subsequent variant analysis of all ten samples to investigate the genetics of TMAU. Results While all subjects reported they often emitted a fish-like odor, none had this malodor during sensory evaluation. However, all were impaired in their ability to produce >90% TMAO/TMA in their urine and thus met the criteria for TMAU. To probe for genetic causes, the exome of each subject was sequenced, and variants were filtered by genes with a known (FMO3) or expected effect on TMA metabolism function (other oxidoreductases). We filtered the remaining variants by allele frequency and predicated functional effects. We identified one subject that had a rare loss-of-function FMO3 variant and six with more common decreased-function variants. In other oxidoreductases genes, five subjects had four novel rare single-nucleotide polymorphisms as well as one rare insertion/deletion. Novel in this context means no investigators have previously linked these variants to TMAU although they are in dbSNP. Conclusions Thus, variants in genes other than FMO3 may cause TMAU and the genetic variants identified here serve as a starting point for future studies of impaired TMA metabolism. Electronic supplementary material The online version of this article (doi:10.1186/s12881-017-0369-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yiran Guo
- Center for Applied Genomics, the Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Res Cntr, Ste 1016H, Philadelphia, PA, 19104, USA.
| | - Liang-Dar Hwang
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | | | - Jason Eades
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Chung Wen Yu
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | - Corrine Mansfield
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | | | - Xiao Chang
- Center for Applied Genomics, the Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Res Cntr, Ste 1016H, Philadelphia, PA, 19104, USA
| | | | - Fujiko F Duke
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA
| | | | - Steven Fakharzadeh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Paul Fennessey
- University of Colorado Health Sciences Center, Denver, CO, USA
| | - Brendan J Keating
- Center for Applied Genomics, the Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Res Cntr, Ste 1016H, Philadelphia, PA, 19104, USA
| | - Hui Jiang
- BGI-Shenzhen, Shenzhen, 518083, China.,Shenzhen Key Laboratory of Genomics, Shenzhen, 518083, China.,The Guangdong Enterprise Key Laboratory of Human Disease Genomics, Shenzhen, 518083, China
| | - Hakon Hakonarson
- Center for Applied Genomics, the Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Res Cntr, Ste 1016H, Philadelphia, PA, 19104, USA
| | - Danielle R Reed
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA.
| | - George Preti
- Monell Chemical Senses Center, 3500 Market St, Philadelphia, PA, 19104, USA.,Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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44
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Siskos AP, Jain P, Römisch-Margl W, Bennett M, Achaintre D, Asad Y, Marney L, Richardson L, Koulman A, Griffin JL, Raynaud F, Scalbert A, Adamski J, Prehn C, Keun HC. Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma. Anal Chem 2017; 89:656-665. [PMID: 27959516 PMCID: PMC6317696 DOI: 10.1021/acs.analchem.6b02930] [Citation(s) in RCA: 187] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A critical question facing the field of metabolomics is whether data obtained from different centers can be effectively compared and combined. An important aspect of this is the interlaboratory precision (reproducibility) of the analytical protocols used. We analyzed human samples in six laboratories using different instrumentation but a common protocol (the AbsoluteIDQ p180 kit) for the measurement of 189 metabolites via liquid chromatography (LC) or flow injection analysis (FIA) coupled to tandem mass spectrometry (MS/MS). In spiked quality control (QC) samples 82% of metabolite measurements had an interlaboratory precision of <20%, while 83% of averaged individual laboratory measurements were accurate to within 20%. For 20 typical biological samples (serum and plasma from healthy individuals) the median interlaboratory coefficient of variation (CV) was 7.6%, with 85% of metabolites exhibiting a median interlaboratory CV of <20%. Precision was largely independent of the type of sample (serum or plasma) or the anticoagulant used but was reduced in a sample from a patient with dyslipidaemia. The median interlaboratory accuracy and precision of the assay for standard reference plasma (NIST SRM 1950) were 107% and 6.7%, respectively. Likely sources of irreproducibility were the near limit of detection (LOD) typical abundance of some metabolites and the degree of manual review and optimization of peak integration in the LC-MS/MS data after acquisition. Normalization to a reference material was crucial for the semi-quantitative FIA measurements. This is the first interlaboratory assessment of a widely used, targeted metabolomics assay illustrating the reproducibility of the protocol and how data generated on different instruments could be directly integrated in large-scale epidemiological studies.
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Affiliation(s)
| | - Pooja Jain
- Department of Surgery and Cancer, Imperial College London, W12 0NN, UK
| | - Werner Römisch-Margl
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Mark Bennett
- Department of Life Sciences, Imperial College London, SW7 2AZ, UK
| | - David Achaintre
- International Agency for Research on Cancer (IARC), Biomarkers Group, F-69372 Lyon, France
| | - Yasmin Asad
- The Institute of Cancer Research, ICR, Sutton, SM2 5NG, UK
| | - Luke Marney
- MRC Human Nutrition Research, Cambridge, CB1 9NL, UK
| | | | | | | | | | - Augustin Scalbert
- International Agency for Research on Cancer (IARC), Biomarkers Group, F-69372 Lyon, France
| | - Jerzy Adamski
- Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85354 Freising-Weihenstephan, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Cornelia Prehn
- Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Hector C. Keun
- Department of Surgery and Cancer, Imperial College London, W12 0NN, UK
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Luykx JJ, Olde Loohuis LM, Neeleman M, Strengman E, Bakker SC, Lentjes E, Borgdorff P, van Dongen EPA, Bruins P, Kahn RS, Horvath S, de Jong S, Ophoff RA. Peripheral blood gene expression profiles linked to monoamine metabolite levels in cerebrospinal fluid. Transl Psychiatry 2016; 6:e983. [PMID: 27959337 PMCID: PMC5290339 DOI: 10.1038/tp.2016.245] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Accepted: 10/15/2016] [Indexed: 01/07/2023] Open
Abstract
The blood-brain barrier separates circulating blood from the central nervous system (CNS). The scope of this barrier is not fully understood which limits our ability to relate biological measurements from peripheral to central phenotypes. For example, it is unknown to what extent gene expression levels in peripheral blood are reflective of CNS metabolism. In this study, we examine links between central monoamine metabolite levels and whole-blood gene expression to better understand the connection between peripheral systems and the CNS. To that end, we correlated the prime monoamine metabolites in cerebrospinal fluid (CSF) with whole-genome gene expression microarray data from blood (N=240 human subjects). We additionally applied gene-enrichment analysis and weighted gene co-expression network analyses (WGCNA) to identify modules of co-expressed genes in blood that may be involved with monoamine metabolite levels in CSF. Transcript levels of two genes were significantly associated with CSF serotonin metabolite levels after Bonferroni correction for multiple testing: THAP7 (P=2.8 × 10-8, β=0.08) and DDX6 (P=2.9 × 10-7, β=0.07). Differentially expressed genes were significantly enriched for genes expressed in the brain tissue (P=6.0 × 10-52). WGCNA revealed significant correlations between serotonin metabolism and hub genes with known functions in serotonin metabolism, for example, HTR2A and COMT. We conclude that gene expression levels in whole blood are associated with monoamine metabolite levels in the human CSF. Our results, including the strong enrichment of brain-expressed genes, illustrate that gene expression profiles in peripheral blood can be relevant for quantitative metabolic phenotypes in the CNS.
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Affiliation(s)
- J J Luykx
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands,Department of Translational Neuroscience Human Neurogenetics Unit, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands,Department of Psychiatry, ZNA Hospitals, Antwerp, Belgium
| | - L M Olde Loohuis
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - M Neeleman
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E Strengman
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - S C Bakker
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E Lentjes
- Department of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - P Borgdorff
- Department of Anesthesiology, Intensive Care and Pain Management, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - E P A van Dongen
- Department of Anesthesiology, Intensive Care and Pain Management, University Medical Center Utrecht, Utrecht, The Netherlands
| | - P Bruins
- Department of Anesthesiology, Intensive Care and Pain Management, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - S Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA,Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - S de Jong
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - R A Ophoff
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands,Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA. E-mail:
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46
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Gevi F, Zolla L, Gabriele S, Persico AM. Urinary metabolomics of young Italian autistic children supports abnormal tryptophan and purine metabolism. Mol Autism 2016; 7:47. [PMID: 27904735 PMCID: PMC5121959 DOI: 10.1186/s13229-016-0109-5] [Citation(s) in RCA: 166] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2015] [Accepted: 11/11/2016] [Indexed: 12/13/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is still diagnosed through behavioral observation, due to a lack of laboratory biomarkers, which could greatly aid clinicians in providing earlier and more reliable diagnoses. Metabolomics on human biofluids provides a sensitive tool to identify metabolite profiles potentially usable as biomarkers for ASD. Initial metabolomic studies, analyzing urines and plasma of ASD and control individuals, suggested that autistic patients may share some metabolic abnormalities, despite several inconsistencies stemming from differences in technology, ethnicity, age range, and definition of “control” status. Methods ASD-specific urinary metabolomic patterns were explored at an early age in 30 ASD children and 30 matched controls (age range 2–7, M:F = 22:8) using hydrophilic interaction chromatography (HILIC)-UHPLC and mass spectrometry, a highly sensitive, accurate, and unbiased approach. Metabolites were then subjected to multivariate statistical analysis and grouped by metabolic pathway. Results Urinary metabolites displaying the largest differences between young ASD and control children belonged to the tryptophan and purine metabolic pathways. Also, vitamin B6, riboflavin, phenylalanine-tyrosine-tryptophan biosynthesis, pantothenate and CoA, and pyrimidine metabolism differed significantly. ASD children preferentially transform tryptophan into xanthurenic acid and quinolinic acid (two catabolites of the kynurenine pathway), at the expense of kynurenic acid and especially of melatonin. Also, the gut microbiome contributes to altered tryptophan metabolism, yielding increased levels of indolyl 3-acetic acid and indolyl lactate. Conclusions The metabolic pathways most distinctive of young Italian autistic children largely overlap with those found in rodent models of ASD following maternal immune activation or genetic manipulations. These results are consistent with the proposal of a purine-driven cell danger response, accompanied by overproduction of epileptogenic and excitotoxic quinolinic acid, large reductions in melatonin synthesis, and gut dysbiosis. These metabolic abnormalities could underlie several comorbidities frequently associated to ASD, such as seizures, sleep disorders, and gastrointestinal symptoms, and could contribute to autism severity. Their diagnostic sensitivity, disease-specificity, and interethnic variability will merit further investigation. Electronic supplementary material The online version of this article (doi:10.1186/s13229-016-0109-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Federica Gevi
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Lello Zolla
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Stefano Gabriele
- Unit of Child and Adolescent Neuropsychiatry, Laboratory of Molecular Psychiatry and Neurogenetics, University Campus Bio-Medico, Rome, Italy
| | - Antonio M Persico
- Unit of Child and Adolescent Neuropsychiatry, Interdepartmental Program "Autism 0-90", "Gaetano Martino" University Hospital, University of Messina, Messina, Italy ; Mafalda Luce Center for Pervasive Developmental Disorders, Milan, Italy
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47
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Beatty PH, Klein MS, Fischer JJ, Lewis IA, Muench DG, Good AG. Understanding Plant Nitrogen Metabolism through Metabolomics and Computational Approaches. PLANTS 2016; 5:plants5040039. [PMID: 27735856 PMCID: PMC5198099 DOI: 10.3390/plants5040039] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 09/21/2016] [Accepted: 09/30/2016] [Indexed: 01/24/2023]
Abstract
A comprehensive understanding of plant metabolism could provide a direct mechanism for improving nitrogen use efficiency (NUE) in crops. One of the major barriers to achieving this outcome is our poor understanding of the complex metabolic networks, physiological factors, and signaling mechanisms that affect NUE in agricultural settings. However, an exciting collection of computational and experimental approaches has begun to elucidate whole-plant nitrogen usage and provides an avenue for connecting nitrogen-related phenotypes to genes. Herein, we describe how metabolomics, computational models of metabolism, and flux balance analysis have been harnessed to advance our understanding of plant nitrogen metabolism. We introduce a model describing the complex flow of nitrogen through crops in a real-world agricultural setting and describe how experimental metabolomics data, such as isotope labeling rates and analyses of nutrient uptake, can be used to refine these models. In summary, the metabolomics/computational approach offers an exciting mechanism for understanding NUE that may ultimately lead to more effective crop management and engineered plants with higher yields.
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Affiliation(s)
- Perrin H Beatty
- Department of Biological Sciences, University of Alberta, 85 Avenue NW, Edmonton, AB T6G 2E9, Canada.
| | - Matthias S Klein
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Jeffrey J Fischer
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Ian A Lewis
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Douglas G Muench
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Allen G Good
- Department of Biological Sciences, University of Alberta, 85 Avenue NW, Edmonton, AB T6G 2E9, Canada.
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48
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Dumas ME, Domange C, Calderari S, Martínez AR, Ayala R, Wilder SP, Suárez-Zamorano N, Collins SC, Wallis RH, Gu Q, Wang Y, Hue C, Otto GW, Argoud K, Navratil V, Mitchell SC, Lindon JC, Holmes E, Cazier JB, Nicholson JK, Gauguier D. Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series. Genome Med 2016; 8:101. [PMID: 27716393 PMCID: PMC5045612 DOI: 10.1186/s13073-016-0352-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 09/12/2016] [Indexed: 12/14/2022] Open
Abstract
Background The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus occurs through complex organ-specific cellular mechanisms and networks contributing to impaired insulin secretion and insulin resistance. Genome-wide gene expression profiling systems can dissect the genetic contributions to metabolome and transcriptome regulations. The integrative analysis of multiple gene expression traits and metabolic phenotypes (i.e., metabotypes) together with their underlying genetic regulation remains a challenge. Here, we introduce a systems genetics approach based on the topological analysis of a combined molecular network made of genes and metabolites identified through expression and metabotype quantitative trait locus mapping (i.e., eQTL and mQTL) to prioritise biological characterisation of candidate genes and traits. Methods We used systematic metabotyping by 1H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualize the shortest paths between metabolites and genes significantly associated with each genomic block. Results Despite strong genomic similarities (95–99 %) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting the metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific mQTLs and genome-wide eQTLs. Variation in key metabolites like glucose, succinate, lactate, or 3-hydroxybutyrate and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing the shortest path length drove prioritization of biological validations by gene silencing. Conclusions These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulation and to characterize novel functional roles for genes determining tissue-specific metabolism. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0352-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marc-Emmanuel Dumas
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK. .,Centre de Résonance Magnétique Nucléaire à Très Hauts Champs, 5 rue de la Doua, Villeurbanne, 69100, France. .,Metabometrix Ltd, Prince Consort Road, London, SW7 2BP, UK.
| | - Céline Domange
- Centre de Résonance Magnétique Nucléaire à Très Hauts Champs, 5 rue de la Doua, Villeurbanne, 69100, France.,UMR Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, Paris, 75005, France
| | - Sophie Calderari
- Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM, UMR_S 1138, Cordeliers Research Centre, Paris, 75006, France
| | - Andrea Rodríguez Martínez
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK
| | - Rafael Ayala
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK
| | - Steven P Wilder
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK
| | - Nicolas Suárez-Zamorano
- Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM, UMR_S 1138, Cordeliers Research Centre, Paris, 75006, France
| | - Stephan C Collins
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK
| | - Robert H Wallis
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK
| | - Quan Gu
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK.,MRC-University of Glasgow Centre for Virus Research, Glasgow, G61 1QH, UK
| | - Yulan Wang
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK.,Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, University of Chinese Academy of Sciences, Wuhan, 430071, China
| | - Christophe Hue
- UMR Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, Paris, 75005, France
| | - Georg W Otto
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK
| | - Karène Argoud
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK
| | - Vincent Navratil
- Centre de Résonance Magnétique Nucléaire à Très Hauts Champs, 5 rue de la Doua, Villeurbanne, 69100, France
| | | | - John C Lindon
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK
| | - Elaine Holmes
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK
| | - Jean-Baptiste Cazier
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK.,Centre for Computational Biology, University of Birmingham, Haworth Building, Birmingham, B15 2TT, UK
| | - Jeremy K Nicholson
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK
| | - Dominique Gauguier
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, UK. .,Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM, UMR_S 1138, Cordeliers Research Centre, Paris, 75006, France. .,The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK.
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49
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Everett JR. From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine. Front Pharmacol 2016; 7:297. [PMID: 27660611 PMCID: PMC5014868 DOI: 10.3389/fphar.2016.00297] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 08/23/2016] [Indexed: 01/08/2023] Open
Abstract
Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.
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Affiliation(s)
- Jeremy R Everett
- Medway Metabonomics Research Group, University of Greenwich Kent, UK
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50
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Knebel B, Strassburger K, Szendroedi J, Kotzka J, Scheer M, Nowotny B, Müssig K, Lehr S, Pacini G, Finner H, Klüppelholz B, Giani G, Al-Hasani H, Roden M. Specific Metabolic Profiles and Their Relationship to Insulin Resistance in Recent-Onset Type 1 and Type 2 Diabetes. J Clin Endocrinol Metab 2016; 101:2130-40. [PMID: 26829444 DOI: 10.1210/jc.2015-4133] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
CONTEXT Insulin resistance reflects the inadequate insulin-mediated use of metabolites and predicts type 2 diabetes (T2D) but is also frequently seen in long-standing type 1 diabetes (T1D) and represents a major cardiovascular risk factor. OBJECTIVE We hypothesized that plasma metabolome profiles allow the identification of unique and common early biomarkers of insulin resistance in both diabetes types. DESIGN, SETTING, AND PATIENTS Two hundred ninety-five plasma metabolites were analyzed by mass spectrometry from patients of the prospective observational German Diabetes Study with T2D (n = 244) or T1D (n = 127) and known diabetes duration of less than 1 year and glucose-tolerant persons (CON; n = 129). Abundance of metabolites was tested for association with insulin sensitivity as assessed by hyperinsulinemic-euglycemic clamps and related metabolic phenotypes. MAIN OUTCOMES MEASURES Sixty-two metabolites with phenotype-specific patterns were identified using age, sex, and body mass index as covariates. RESULTS Compared with CON, the metabolome of T2D and T1D showed similar alterations in various phosphatidylcholine species and amino acids. Only T2D exhibited differences in free fatty acids compared with CON. Pairwise comparison of metabolites revealed alterations of 28 and 49 metabolites in T1D and T2D, respectively, when compared with CON. Eleven metabolites allowed differentiation between both diabetes types and alanine, α-amino-adipic acid, isoleucin, and stearic acid showed an inverse association with insulin sensitivity in both T2D and T1D combined. CONCLUSION Metabolome analyses from recent-onset T2D and T1D patients enables identification of defined diabetes type-specific differences and detection of biomarkers of insulin sensitivity. These analyses may help to identify novel clinical subphenotypes diabetes.
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Affiliation(s)
- Birgit Knebel
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Klaus Strassburger
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Julia Szendroedi
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Jorg Kotzka
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Marsel Scheer
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Bettina Nowotny
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Karsten Müssig
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Stefan Lehr
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Giovanni Pacini
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Helmut Finner
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Birgit Klüppelholz
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Guido Giani
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Hadi Al-Hasani
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Michael Roden
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
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