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Wang XY, Ren CX, Fan QW, Xu YP, Wang LW, Mao ZL, Cai XZ. Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to Sclerotinia sclerotiorum. Int J Mol Sci 2024; 25:6932. [PMID: 39000053 PMCID: PMC11240920 DOI: 10.3390/ijms25136932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 06/20/2024] [Accepted: 06/20/2024] [Indexed: 07/14/2024] Open
Abstract
Sclerotinia sclerotiorum (Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor genes. Genome-wide identification of genes involved in QDR to Ss is yet to be conducted. In this study, we integrated several assays including genome-wide association study (GWAS), multi-omics co-localization, and machine learning prediction to identify, on a genome-wide scale, genes involved in the oilseed rape QDR to Ss. Employing GWAS and multi-omics co-localization, we identified seven resistance-associated loci (RALs) associated with oilseed rape resistance to Ss. Furthermore, we developed a machine learning algorithm and named it Integrative Multi-Omics Analysis and Machine Learning for Target Gene Prediction (iMAP), which integrates multi-omics data to rapidly predict disease resistance-related genes within a broad chromosomal region. Through iMAP based on the identified RALs, we revealed multiple calcium signaling genes related to the QDR to Ss. Population-level analysis of selective sweeps and haplotypes of variants confirmed the positive selection of the predicted calcium signaling genes during evolution. Overall, this study has developed an algorithm that integrates multi-omics data and machine learning methods, providing a powerful tool for predicting target genes associated with specific traits. Furthermore, it makes a basis for further understanding the role and mechanisms of calcium signaling genes in the QDR to Ss.
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Affiliation(s)
- Xin-Yao Wang
- Key Laboratory of Biology and Ecological Control of Crop Pathogens and Insects of Zhejiang Province, Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; (X.-Y.W.); (C.-X.R.); (Q.-W.F.); (L.-W.W.); (Z.-L.M.)
| | - Chun-Xiu Ren
- Key Laboratory of Biology and Ecological Control of Crop Pathogens and Insects of Zhejiang Province, Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; (X.-Y.W.); (C.-X.R.); (Q.-W.F.); (L.-W.W.); (Z.-L.M.)
| | - Qing-Wen Fan
- Key Laboratory of Biology and Ecological Control of Crop Pathogens and Insects of Zhejiang Province, Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; (X.-Y.W.); (C.-X.R.); (Q.-W.F.); (L.-W.W.); (Z.-L.M.)
| | - You-Ping Xu
- Centre of Analysis and Measurement, Zhejiang University, 866 Yu Hang Tang Road, Hangzhou 310058, China;
| | - Lu-Wen Wang
- Key Laboratory of Biology and Ecological Control of Crop Pathogens and Insects of Zhejiang Province, Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; (X.-Y.W.); (C.-X.R.); (Q.-W.F.); (L.-W.W.); (Z.-L.M.)
| | - Zhou-Lu Mao
- Key Laboratory of Biology and Ecological Control of Crop Pathogens and Insects of Zhejiang Province, Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; (X.-Y.W.); (C.-X.R.); (Q.-W.F.); (L.-W.W.); (Z.-L.M.)
| | - Xin-Zhong Cai
- Key Laboratory of Biology and Ecological Control of Crop Pathogens and Insects of Zhejiang Province, Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; (X.-Y.W.); (C.-X.R.); (Q.-W.F.); (L.-W.W.); (Z.-L.M.)
- Hainan Institute, Zhejiang University, Sanya 572025, China
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2
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Elgedawy GA, Samir M, Elabd NS, Elsaid HH, Enar M, Salem RH, Montaser BA, AboShabaan HS, Seddik RM, El-Askaeri SM, Omar MM, Helal ML. Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning. PLoS One 2024; 19:e0302977. [PMID: 38814977 PMCID: PMC11139268 DOI: 10.1371/journal.pone.0302977] [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: 12/13/2023] [Accepted: 04/15/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity. METHODS Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models. RESULTS The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness. CONCLUSIONS Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.
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Affiliation(s)
- Gamalat A. Elgedawy
- Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Mohamed Samir
- Faculty of Veterinary Medicine, Department of Zoonoses, Zagazig University, Zagazig, Egypt
| | - Naglaa S. Elabd
- Faculty of Medicine, Department of Tropical Medicine, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Hala H. Elsaid
- Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Mohamed Enar
- Al Mahala Elkobra Fever Hospital, Al Mahala Elkobra, Egypt
| | - Radwa H. Salem
- Department of Clinical Microbiology and Immunology, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Belal A. Montaser
- Faculty of Medicine, Department of Clinical Pathology, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Hind S. AboShabaan
- Ph.D. of Biochemistry, National Liver Institute Hospital, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Randa M. Seddik
- Faculty of Medicine, Department of Tropical Medicine, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Shimaa M. El-Askaeri
- Department of Clinical Microbiology and Immunology, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Marwa M. Omar
- Faculty of Medicine, Department of Clinical Pathology, Menoufia University, Shebin El-Kom, Menoufia, Egypt
| | - Marwa L. Helal
- Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt
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3
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Suhre K. Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions. CELL GENOMICS 2024; 4:100506. [PMID: 38412862 PMCID: PMC10943581 DOI: 10.1016/j.xgen.2024.100506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/25/2023] [Accepted: 01/26/2024] [Indexed: 02/29/2024]
Abstract
Protein quantitative trait loci (pQTLs) are an invaluable source of information for drug target development because they provide genetic evidence to support protein function, suggest relationships between cis- and trans-associated proteins, and link proteins to disease endpoints. Using Olink proteomics data for 1,463 proteins measured in over 54,000 samples of the UK Biobank, we identified 4,248 associations with 2,821 ratios between protein levels (rQTLs). rQTLs were 7.6-fold enriched in known protein-protein interactions, suggesting that their ratios reflect biological links between the implicated proteins. Conducting a GWAS on ratios increased the number of discovered genetic signals by 24.7%. The approach can identify novel loci of clinical relevance, support causal gene identification, and reveal complex networks of interacting proteins. Taken together, our study adds significant value to the genetic insights that can be derived from the UKB proteomics data and motivates the wider use of ratios in large-scale GWAS.
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Affiliation(s)
- Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
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Allayee H, Farber CR, Seldin MM, Williams EG, James DE, Lusis AJ. Systems genetics approaches for understanding complex traits with relevance for human disease. eLife 2023; 12:e91004. [PMID: 37962168 PMCID: PMC10645424 DOI: 10.7554/elife.91004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023] Open
Abstract
Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.
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Affiliation(s)
- Hooman Allayee
- Departments of Population & Public Health Sciences, University of Southern CaliforniaLos AngelesUnited States
- Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Charles R Farber
- Center for Public Health Genomics, University of Virginia School of MedicineCharlottesvilleUnited States
- Departments of Biochemistry & Molecular Genetics, University of Virginia School of MedicineCharlottesvilleUnited States
- Public Health Sciences, University of Virginia School of MedicineCharlottesvilleUnited States
| | - Marcus M Seldin
- Department of Biological Chemistry, University of California, IrvineIrvineUnited States
| | - Evan Graehl Williams
- Luxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourgLuxembourg
| | - David E James
- School of Life and Environmental Sciences, University of SydneyCamperdownAustralia
- Faculty of Medicine and Health, University of SydneyCamperdownAustralia
- Charles Perkins Centre, University of SydneyCamperdownAustralia
| | - Aldons J Lusis
- Departments of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Medicine, University of California, Los AngelesLos AngelesUnited States
- Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLALos AngelesUnited States
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5
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Beuchel C, Dittrich J, Becker S, Kirsten H, Tönjes A, Kovacs P, Stumvoll M, Loeffler M, Teren A, Thiery J, Isermann B, Ceglarek U, Scholz M. An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index. J Mol Med (Berl) 2023; 101:1305-1321. [PMID: 37672078 PMCID: PMC10560167 DOI: 10.1007/s00109-023-02362-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 08/07/2023] [Accepted: 08/15/2023] [Indexed: 09/07/2023]
Abstract
Investigating the cross talk of different omics layers is crucial to understand molecular pathomechanisms of metabolic diseases like obesity. Here, we present a large-scale association meta-analysis of genome-wide whole blood and peripheral blood mononuclear cell (PBMC) gene expressions profiled with Illumina HT12v4 microarrays and metabolite measurements from dried blood spots (DBS) characterized by targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) in three large German cohort studies with up to 7706 samples. We found 37,295 associations comprising 72 amino acids (AA) and acylcarnitine (AC) metabolites (including ratios) and 8579 transcripts. We applied this catalogue of associations to investigate the impact of associating transcript-metabolite pairs on body mass index (BMI) as an example metabolic trait. This is achieved by conducting a comprehensive mediation analysis considering metabolites as mediators of gene expression effects and vice versa. We discovered large mediation networks comprising 27,023 potential mediation effects within 20,507 transcript-metabolite pairs. Resulting networks of highly connected (hub) transcripts and metabolites were leveraged to gain mechanistic insights into metabolic signaling pathways. In conclusion, here, we present the largest available multi-omics integration of genome-wide transcriptome data and metabolite data of amino acid and fatty acid metabolism and further leverage these findings to characterize potential mediation effects towards BMI proposing candidate mechanisms of obesity and related metabolic diseases. KEY MESSAGES: Thousands of associations of 72 amino acid and acylcarnitine metabolites and 8579 genes expand the knowledge of metabolome-transcriptome associations. A mediation analysis of effects on body mass index revealed large mediation networks of thousands of obesity-related gene-metabolite pairs. Highly connected, potentially mediating hub genes and metabolites enabled insight into obesity and related metabolic disease pathomechanisms.
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Affiliation(s)
- Carl Beuchel
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
| | - Julia Dittrich
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
| | - Susen Becker
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
- Department of Forensic Toxicology, Institute of Legal Medicine, University Leipzig, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Anke Tönjes
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University Hospital Leipzig, Leipzig, Germany
| | - Peter Kovacs
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University Hospital Leipzig, Leipzig, Germany
- Deutsches Zentrum für Diabetesforschung, Neuherberg, Germany
| | - Michael Stumvoll
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University Hospital Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | | | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Berend Isermann
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany.
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6
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Eldjarn GH, Ferkingstad E, Lund SH, Helgason H, Magnusson OT, Gunnarsdottir K, Olafsdottir TA, Halldorsson BV, Olason PI, Zink F, Gudjonsson SA, Sveinbjornsson G, Magnusson MI, Helgason A, Oddsson A, Halldorsson GH, Magnusson MK, Saevarsdottir S, Eiriksdottir T, Masson G, Stefansson H, Jonsdottir I, Holm H, Rafnar T, Melsted P, Saemundsdottir J, Norddahl GL, Thorleifsson G, Ulfarsson MO, Gudbjartsson DF, Thorsteinsdottir U, Sulem P, Stefansson K. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature 2023; 622:348-358. [PMID: 37794188 PMCID: PMC10567571 DOI: 10.1038/s41586-023-06563-x] [Citation(s) in RCA: 49] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 08/22/2023] [Indexed: 10/06/2023]
Abstract
High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.
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Affiliation(s)
| | | | - Sigrun H Lund
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hannes Helgason
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Bjarni V Halldorsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | | | | | | | | | | | - Agnar Helgason
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Department of Anthropology, University of Iceland, Reykjavik, Iceland
| | | | | | - Magnus K Magnusson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Saedis Saevarsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Ingileif Jonsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | | | - Pall Melsted
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Kari Stefansson
- deCODE Genetics/Amgen, Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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Leliefeld HHJ, Debruyne FMJ, Reisman Y. The post-finasteride syndrome: possible etiological mechanisms and symptoms. Int J Impot Res 2023:10.1038/s41443-023-00759-5. [PMID: 37697052 DOI: 10.1038/s41443-023-00759-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/13/2023]
Abstract
Finasteride and dutasteride, synthetic 5α-reductase inhibitors (5ARIs) are recommended in many guidelines for the treatment of benign prostatic hyperplasia/lower urinary tract symptoms and alopecia despite a variety of side effects like sexual, neurological, psychiatric, endocrinological, metabolic and ophthalmological dysfunctions and the increased incidence of high grade prostate cancer. The sexual side effects are common during the use of the drug but in a small subgroup of patients, they can persist after stopping the drug. This so-called post-finasteride syndrome has serious implications for the quality of life without a clear etiology or therapy. Three types of 5α-reductases are present in many organs in- and outside the brain where they can be blocked by the two 5ARIs. There is increasing evidence that 5ARIs not only inhibit the conversion of testosterone to 5α-dihydrotestosterone (DHT) in the prostate and the scalp but also in many other tissues. The lipophilic 5ARIs can pass the blood-brain barrier and might block many other neurosteroids in the brain with changes in the neurochemistry and impaired neurogenesis. Further research and therapeutic innovations are urgently needed that might cure or relieve these side effects. More awareness is needed for physicians to outweigh these health risks against the benefits of 5ARIs.
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Affiliation(s)
- Herman H J Leliefeld
- Andros Clinics The Netherlands, Wilhelminapark 12, 3581 NC, Utrecht, The Netherlands.
| | - Frans M J Debruyne
- Andros Clinics The Netherlands, Mr. E.N. van Kleffenstraat 5, 6842 CV, Arnhem, The Netherlands
| | - Yakov Reisman
- Flare-Health, Oosteinderweg 348, 1432 BE, Aalsmeer, The Netherlands
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Oh SW, Imran M, Kim EH, Park SY, Lee SG, Park HM, Jung JW, Ryu TH. Approach strategies and application of metabolomics to biotechnology in plants. FRONTIERS IN PLANT SCIENCE 2023; 14:1192235. [PMID: 37636096 PMCID: PMC10451086 DOI: 10.3389/fpls.2023.1192235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023]
Abstract
Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.
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Chang L, Zhou G, Xia J. mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite-Phenotype Associations. Metabolites 2023; 13:826. [PMID: 37512533 PMCID: PMC10384390 DOI: 10.3390/metabo13070826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/23/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites, genes and phenotypes for hypothesis generation. It has become clear that identifying potential causal relationships between metabolites and phenotypes, as well as providing deep functional insights, are crucial for further downstream applications. Here, we introduce mGWAS-Explorer 2.0 to support the causal analysis between >4000 metabolites and various phenotypes. The results can be interpreted within the context of semantic triples and molecular quantitative trait loci (QTL) data. The underlying R package is released for reproducible analysis. Using two case studies, we demonstrate that mGWAS-Explorer 2.0 is able to detect potential causal relationships between arachidonic acid and Crohn's disease, as well as between glycine and coronary heart disease.
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Affiliation(s)
- Le Chang
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, QC H9X 3V9, Canada
| | - Jianguo Xia
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Institute of Parasitology, McGill University, Montreal, QC H9X 3V9, Canada
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10
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Mogilenko DA, Sergushichev A, Artyomov MN. Systems Immunology Approaches to Metabolism. Annu Rev Immunol 2023; 41:317-342. [PMID: 37126419 DOI: 10.1146/annurev-immunol-101220-031513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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11
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Yang M, Xu J, Zhang F, Luo P, Xu K, Feng R, Xu P. Large-Scale Genetic Correlation Analysis between Spondyloarthritis and Human Blood Metabolites. J Clin Med 2023; 12:jcm12031201. [PMID: 36769847 PMCID: PMC9917834 DOI: 10.3390/jcm12031201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/17/2023] [Accepted: 01/22/2023] [Indexed: 02/05/2023] Open
Abstract
The aim was to study the genetic correlation and causal relationship between spondyloarthritis (SpA) and blood metabolites based on the large-scale genome-wide association study (GWAS) summary data. The GWAS summary data (3966 SpA and 448,298 control cases) of SpA were from the UK Biobank, and the GWAS summary data (486 blood metabolites) of human blood metabolites were from a published study. First, the genetic correlation between SpA and blood metabolites was analyzed by linkage disequilibrium score (LDSC) regression. Next, we used Mendelian randomization (MR) analysis to perform access causal relationship between SpA and blood metabolites. Random effects inverse variance weighted (IVW) was the main analysis method, and the MR Egger, weighted median, simple mode, and weighted mode were supplementary methods. The MR analysis results were dominated by the random effects IVW. The Cochran's Q statistic (MR-IVW) and Rucker's Q statistic (MR Egger) were used to check heterogeneity. MR Egger and MR pleiotropy residual sum and outlier (MR-PRESSO) were used to check horizontal pleiotropy. The MR-PRESSO was also used to check outliers. The "leave-one-out" analysis was used to assess whether the MR analysis results were affected by a single SNP and thus test the robustness of the MR results. Finally, we identified seven blood metabolites that are genetically related to SpA: X-10395 (correlation coefficient = -0.546, p = 0.025), pantothenate (correlation coefficient = -0.565, p = 0.038), caprylate (correlation coefficient = -0.333, p = 0.037), pelargonate (correlation coefficient = -0.339, p = 0.047), X-11317 (correlation coefficient = -0.350, p = 0.038), X-12510 (correlation coefficient = -0.399, p = 0.034), and X-13859 (Correlation coefficient = -0.458, p = 0.015). Among them, X-10395 had a positive genetic causal relationship with SpA (p = 0.014, OR = 1.011). The blood metabolites that have genetic correlation and causal relationship with SpA found in this study provide a new idea for the study of the pathogenesis of SpA and the determination of diagnostic indicators.
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Affiliation(s)
- Mingyi Yang
- Department of Joint Surgery, HongHui Hospital, Xi’an Jiaotong University, Xi’an 710054, China
| | - Jiawen Xu
- Orthopedic Research Institute, Department of Orthopedics, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China
| | - Pan Luo
- Department of Joint Surgery, HongHui Hospital, Xi’an Jiaotong University, Xi’an 710054, China
| | - Ke Xu
- Department of Joint Surgery, HongHui Hospital, Xi’an Jiaotong University, Xi’an 710054, China
| | - Ruoyang Feng
- Department of Joint Surgery, HongHui Hospital, Xi’an Jiaotong University, Xi’an 710054, China
| | - Peng Xu
- Department of Joint Surgery, HongHui Hospital, Xi’an Jiaotong University, Xi’an 710054, China
- Correspondence:
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12
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Wildman E, Mickiewicz B, Vogel HJ, Thompson GC. Metabolomics in pediatric lower respiratory tract infections and sepsis: a literature review. Pediatr Res 2023; 93:492-502. [PMID: 35778499 PMCID: PMC9247944 DOI: 10.1038/s41390-022-02162-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/19/2022] [Accepted: 05/23/2022] [Indexed: 11/09/2022]
Abstract
Lower respiratory tract infections (LRTIs) are a leading cause of morbidity and mortality in children. The ability of healthcare providers to diagnose and prognose LRTIs in the pediatric population remains a challenge, as children can present with similar clinical features regardless of the underlying pathogen or ultimate severity. Metabolomics, the large-scale analysis of metabolites and metabolic pathways offers new tools and insights that may aid in diagnosing and predicting the outcomes of LRTIs in children. This review highlights the latest literature on the clinical utility of metabolomics in providing care for children with bronchiolitis, pneumonia, COVID-19, and sepsis. IMPACT: This article summarizes current metabolomics approaches to diagnosing and predicting the course of pediatric lower respiratory infections. This article highlights the limitations to current metabolomics research and highlights future directions for the field.
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Affiliation(s)
- Emily Wildman
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Beata Mickiewicz
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Hans J Vogel
- Bio-NMR Centre, Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Graham C Thompson
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,Department of Emergency Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
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13
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Iwasaki T, Kamatani Y, Sonomura K, Kawaguchi S, Kawaguchi T, Takahashi M, Ohmura K, Sato TA, Matsuda F. Genetic influences on human blood metabolites in the Japanese population. iScience 2023; 26:105738. [PMID: 36582826 PMCID: PMC9792902 DOI: 10.1016/j.isci.2022.105738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 10/08/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
An increase in ethnic diversity in genetic studies has the potential to provide unprecedented insights into how genetic variations influence human phenotypes. In this study, we conducted a quantitative trait locus (QTL) analysis of 121 metabolites measured using gas chromatography-mass spectrometry with plasma samples from 4,888 Japanese individuals. We found 60 metabolite-gene associations, of which 13 have not been previously reported. Meta-analyses with another Japanese and a European study identified six and two additional unreported loci, respectively. Genetic variants influencing metabolite levels were more enriched in protein-coding regions than in the regulatory regions while being associated with the risk of various diseases. Finally, we identified a signature of strong negative selection for uric acid ( S ˆ = -1.53, p = 6.2 × 10-18). Our study expanded the knowledge of genetic influences on human blood metabolites, providing valuable insights into their physiological, pathological, and selective properties.
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Affiliation(s)
- Takeshi Iwasaki
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan.,Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Yoichiro Kamatani
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Kazuhiro Sonomura
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan.,Life Science Research Center, Shimadzu Corporation, Kyoto 604-8511, Japan
| | - Shuji Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Meiko Takahashi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Taka-Aki Sato
- Life Science Research Center, Shimadzu Corporation, Kyoto 604-8511, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
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14
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Chen Y, Lonergan S, Lim KS, Cheng J, Putz AM, Dyck MK, Canada P, Fortin F, Harding JCS, Plastow GS, Dekkers JCM. Plasma protein levels of young healthy pigs as indicators of disease resilience. J Anim Sci 2023; 101:6987177. [PMID: 36638126 PMCID: PMC9977353 DOI: 10.1093/jas/skad014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 01/11/2023] [Indexed: 01/14/2023] Open
Abstract
Selection for disease resilience, which refers to the ability of an animal to maintain performance when exposed to disease, can reduce the impact of infectious diseases. However, direct selection for disease resilience is challenging because nucleus herds must maintain a high health status. A possible solution is indirect selection of indicators of disease resilience. To search for such indicators, we conducted phenotypic and genetic quantitative analyses of the abundances of 377 proteins in plasma samples from 912 young and visually healthy pigs and their relationships with performance and subsequent disease resilience after natural exposure to a polymicrobial disease challenge. Abundances of 100 proteins were significantly heritable (false discovery rate (FDR) <0.10). The abundance of some proteins was or tended to be genetically correlated (rg) with disease resilience, including complement system proteins (rg = -0.24, FDR = 0.001) and IgG heavy chain proteins (rg = -0.68, FDR = 0.22). Gene set enrichment analyses (FDR < 0.2) based on phenotypic and genetic associations of protein abundances with subsequent disease resilience revealed many pathways related to the immune system that were unfavorably associated with subsequent disease resilience, especially the innate immune system. It was not possible to determine whether the observed levels of these proteins reflected baseline levels in these young and visually healthy pigs or were the result of a response to environmental disturbances that the pigs were exposed to before sample collection. Nevertheless, results show that, under these conditions, the abundance of proteins in some immune-related pathways can be used as phenotypic and genetic predictors of disease resilience and have the potential for use in pig breeding and management.
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Affiliation(s)
- Yulu Chen
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Steven Lonergan
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Kyu-Sang Lim
- Department of Animal Science, Iowa State University, Ames, IA, USA,Department of Animal Resources Science, Kongju National University, Yesan, Republic of Korea
| | - Jian Cheng
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Austin M Putz
- Department of Animal Science, Iowa State University, Ames, IA, USA,Hendrix Genetics, Swine Business Unit, Boxmeer, The Netherlands
| | - Michael K Dyck
- Department of Agriculture, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - PigGen Canada
- PigGen Canada Research Consortium, Guelph, Ontario, Canada
| | - Frederic Fortin
- Centre de Développement du Porc du Québec Inc., Québec City, Canada
| | - John C S Harding
- Department of Large Animal Clinical Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Graham S Plastow
- Department of Agriculture, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
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15
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Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, Cerani A, Liang KYH, Yoshiji S, Willett JDS, Su CY, Raina P, Greenwood CMT, Farjoun Y, Forgetta V, Langenberg C, Zhou S, Ohlsson C, Richards JB. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet 2023; 55:44-53. [PMID: 36635386 PMCID: PMC7614162 DOI: 10.1038/s41588-022-01270-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 87.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/18/2022] [Indexed: 01/14/2023]
Abstract
Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.
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Affiliation(s)
- Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | | | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Guillaume Butler-Laporte
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Agustin Cerani
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Kevin Y H Liang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Satoshi Yoshiji
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Julian Daniel Sunday Willett
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- McGill Genome Centre, McGill University, Montreal, Quebec, Canada
| | - Chen-Yang Su
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Parminder Raina
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada
| | - Yossi Farjoun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Fulcrum Genomics, Boulder, CO, USA
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Claes Ohlsson
- Sahlgrenska Osteoporosis Centre, Centre of Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- 5 Prime Sciences Inc, Montreal, Quebec, Canada.
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Twin Research, King's College London, London, UK.
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16
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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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17
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Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, Li C, Smith RF, Wittemans LBL, Bomba L, Menni C, Zierer J, Rossi N, Sheridan PA, Watkins NA, Mangino M, Hysi PG, Di Angelantonio E, Falchi M, Spector TD, Soranzo N, Michelotti GA, Arlt W, Lotta LA, Denaxas S, Hemingway H, Gamazon ER, Howson JMM, Wood AM, Danesh J, Wareham NJ, Kastenmüller G, Fauman EB, Suhre K, Butterworth AS, Langenberg C. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med 2022; 28:2321-2332. [PMID: 36357675 PMCID: PMC9671801 DOI: 10.1038/s41591-022-02046-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/16/2022] [Indexed: 11/12/2022]
Abstract
Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.
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Affiliation(s)
- Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Rebecca F Smith
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Lorenzo Bomba
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jonas Zierer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Niccolò Rossi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | | | | | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | | | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall & MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Joanna M M Howson
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Angela M Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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18
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Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. SCIENCE ADVANCES 2022; 8:eadd6155. [PMID: 36260671 PMCID: PMC9581477 DOI: 10.1126/sciadv.add6155] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.
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Affiliation(s)
- Daniel J. Panyard
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (D.J.P.); (M.P.S.)
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (D.J.P.); (M.P.S.)
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19
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Yin X, Bose D, Kwon A, Hanks SC, Jackson AU, Stringham HM, Welch R, Oravilahti A, Fernandes Silva L, Locke AE, Fuchsberger C, Service SK, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Morrison J, Ripatti S, Palotie A, Freimer NB, Collins FS, Mohlke KL, Scott LJ, Fauman EB, Burant C, Boehnke M, Laakso M, Wen X. Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk. Am J Hum Genet 2022; 109:1727-1741. [PMID: 36055244 PMCID: PMC9606383 DOI: 10.1016/j.ajhg.2022.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/09/2022] [Indexed: 01/25/2023] Open
Abstract
Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.
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Affiliation(s)
- Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Debraj Bose
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Annie Kwon
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Sarah C Hanks
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anniina Oravilahti
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Institute for Biomedicine, Eurac Research, Bolzano 39100, Italy
| | - Susan K Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Michael R Erdos
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lori L Bonnycastle
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland; Center for Medicine and Clinical Research, Kuopio University Hospital, Kuopio 70210, Finland
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA; Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110, USA; Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Ira M Hall
- Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT 06510, USA
| | - Jean Morrison
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00290, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00290, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Francis S Collins
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Charles Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland.
| | - Xiaoquan Wen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
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20
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Tan VY, Timpson NJ. The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology. Annu Rev Genomics Hum Genet 2022; 23:569-589. [PMID: 35508184 DOI: 10.1146/annurev-genom-121321-093606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.
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Affiliation(s)
- Vanessa Y Tan
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
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21
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Whole Exome Sequencing Enhanced Imputation Identifies 85 Metabolite Associations in the Alpine CHRIS Cohort. Metabolites 2022; 12:metabo12070604. [PMID: 35888728 PMCID: PMC9320943 DOI: 10.3390/metabo12070604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/24/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with >5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriched ETFDH stop gain variant p.Trp286Ter (rs1235904433-hexanoylcarnitine) and the MCCC2 stop lost variant p.Ter564GlnextTer3 (rs751970792-carnitine) have been found in patients with glutaric acidemia type II and 3-methylcrotonylglycinuria, respectively, but the loci have not been associated with the respective metabolites in a genome-wide association study (GWAS) previously. We further identified three gene-trait associations, where multiple rare variants contribute to the signal. These results not only provide further evidence for previously described associations, but also describe novel genes and mechanisms for diseases and disease-related traits.
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22
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Brial F, Hedjazi L, Sonomura K, Al Hageh C, Zalloua P, Matsuda F, Gauguier D. Genetic Architecture of Untargeted Lipidomics in Cardiometabolic-Disease Patients Combines Strong Polygenic Control and Pleiotropy. Metabolites 2022; 12:metabo12070596. [PMID: 35888720 PMCID: PMC9322850 DOI: 10.3390/metabo12070596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 02/01/2023] Open
Abstract
Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography−high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10−8) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism.
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Affiliation(s)
- Francois Brial
- Center for Genomic Medicine, Graduate School of Medicine Kyoto University, Kyoto 606-8501, Japan; (F.B.); (F.M.)
- INSERM UMR 1124, Université Paris Cité, 45 rue des Saint-Pères, 75006 Paris, France
| | | | - Kazuhiro Sonomura
- Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto 606-8501, Japan;
| | - Cynthia Al Hageh
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 17666, United Arab Emirates; (C.A.H.); (P.Z.)
| | - Pierre Zalloua
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 17666, United Arab Emirates; (C.A.H.); (P.Z.)
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Graduate School of Medicine Kyoto University, Kyoto 606-8501, Japan; (F.B.); (F.M.)
- McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, QC H3A 0G1, Canada
| | - Dominique Gauguier
- Center for Genomic Medicine, Graduate School of Medicine Kyoto University, Kyoto 606-8501, Japan; (F.B.); (F.M.)
- INSERM UMR 1124, Université Paris Cité, 45 rue des Saint-Pères, 75006 Paris, France
- McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, QC H3A 0G1, Canada
- Correspondence:
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23
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Costanzo M, Caterino M, Sotgiu G, Ruoppolo M, Franconi F, Campesi I. Sex differences in the human metabolome. Biol Sex Differ 2022; 13:30. [PMID: 35706042 PMCID: PMC9199320 DOI: 10.1186/s13293-022-00440-4] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 06/02/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The sexual dimorphism represents one of the triggers of the metabolic disparities between the organisms, advising about wild implications in research or diagnostics contexts. Despite the mounting recognition of the importance of sex consideration in the biomedical fields, the identification of male- and female-specific metabolic signatures has not been achieved. MAIN BODY This review pointed the focus on the metabolic differences related to the sex, evidenced by metabolomics studies performed on healthy populations, with the leading aim of understanding how the sex influences the baseline metabolome. The main shared signatures and the apparent dissimilarities between males and females were extracted and highlighted from the metabolome of the most commonly analyzed biological fluids, such as serum, plasma, and urine. Furthermore, the influence of age and the significant interactions between sex and age have been taken into account. CONCLUSIONS The recognition of sex patterns in human metabolomics has been defined in diverse biofluids. The detection of sex- and age-related differences in the metabolome of healthy individuals are helpful for translational applications from the bench to the bedside to set targeted diagnostic and prevention approaches in the context of personalized medicine.
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Affiliation(s)
- Michele Costanzo
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131, Naples, Italy. .,CEINGE - Biotecnologie Avanzate s.c.ar.l., 80145, Naples, Italy.
| | - Marianna Caterino
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131, Naples, Italy.,CEINGE - Biotecnologie Avanzate s.c.ar.l., 80145, Naples, Italy
| | - Giovanni Sotgiu
- Clinical Epidemiology and Medical Statistics Unit, Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100, Sassari, Italy
| | - Margherita Ruoppolo
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131, Naples, Italy.,CEINGE - Biotecnologie Avanzate s.c.ar.l., 80145, Naples, Italy
| | - Flavia Franconi
- Laboratory of Sex-Gender Medicine, National Institute of Biostructures and Biosystems, 07100, Sassari, Italy
| | - Ilaria Campesi
- Laboratory of Sex-Gender Medicine, National Institute of Biostructures and Biosystems, 07100, Sassari, Italy.,Department of Biomedical Sciences, University of Sassari, 07100, Sassari, Italy
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24
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mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights. Metabolites 2022; 12:metabo12060526. [PMID: 35736459 PMCID: PMC9230867 DOI: 10.3390/metabo12060526] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.
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25
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Bomba L, Walter K, Guo Q, Surendran P, Kundu K, Nongmaithem S, Karim MA, Stewart ID, Langenberg C, Danesh J, Di Angelantonio E, Roberts DJ, Ouwehand WH, Dunham I, Butterworth AS, Soranzo N. Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites. Am J Hum Genet 2022; 109:1038-1054. [PMID: 35568032 PMCID: PMC9247822 DOI: 10.1016/j.ajhg.2022.04.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 04/13/2022] [Indexed: 12/11/2022] Open
Abstract
Metabolite levels measured in the human population are endophenotypes for biological processes. We combined sequencing data for 3,924 (whole-exome sequencing, WES, discovery) and 2,805 (whole-genome sequencing, WGS, replication) donors from a prospective cohort of blood donors in England. We used multiple approaches to select and aggregate rare genetic variants (minor allele frequency [MAF] < 0.1%) in protein-coding regions and tested their associations with 995 metabolites measured in plasma by using ultra-high-performance liquid chromatography-tandem mass spectrometry. We identified 40 novel associations implicating rare coding variants (27 genes and 38 metabolites), of which 28 (15 genes and 28 metabolites) were replicated. We developed algorithms to prioritize putative driver variants at each locus and used mediation and Mendelian randomization analyses to test directionality at associations of metabolite and protein levels at the ACY1 locus. Overall, 66% of reported associations implicate gene targets of approved drugs or bioactive drug-like compounds, contributing to drug targets' validating efforts.
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Affiliation(s)
- Lorenzo Bomba
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Klaudia Walter
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Qi Guo
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge CB2 0AW, UK
| | - Kousik Kundu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge CB2 0AW, UK
| | - Suraj Nongmaithem
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Mohd Anisul Karim
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK; Computational Medicine, Berlin Institute of Health at Charité - Utniversitätsmedizin Berlin, Berlin 10117, Germany
| | - John Danesh
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK; Human Technopole, Palazzo Italia, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy
| | - David J Roberts
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; NHS Blood and Transplant-Oxford Centre, Level 2, John Radcliffe Hospital, Oxford OX3 9BQ, UK; Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9BQ, UK
| | - Willem H Ouwehand
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge CB2 0AW, UK
| | | | - Ian Dunham
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Nicole Soranzo
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge CB2 0AW, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; Human Technopole, Palazzo Italia, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy.
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26
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Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies. Metabolites 2022; 12:metabo12060512. [PMID: 35736446 PMCID: PMC9229972 DOI: 10.3390/metabo12060512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/27/2022] [Accepted: 05/27/2022] [Indexed: 02/04/2023] Open
Abstract
The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.
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Ratios of Acetaminophen Metabolites Identify New Loci of Pharmacogenetic Relevance in a Genome-Wide Association Study. Metabolites 2022; 12:metabo12060496. [PMID: 35736429 PMCID: PMC9228664 DOI: 10.3390/metabo12060496] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 11/17/2022] Open
Abstract
Genome-wide association studies (GWAS) with non-targeted metabolomics have identified many genetic loci of biomedical interest. However, metabolites with a high degree of missingness, such as drug metabolites and xenobiotics, are often excluded from such studies due to a lack of statistical power and higher uncertainty in their quantification. Here we propose ratios between related drug metabolites as GWAS phenotypes that can drastically increase power to detect genetic associations between pairs of biochemically related molecules. As a proof-of-concept we conducted a GWAS with 520 individuals from the Qatar Biobank for who at least five of the nine available acetaminophen metabolites have been detected. We identified compelling evidence for genetic variance in acetaminophen glucuronidation and methylation by UGT2A15 and COMT, respectively. Based on the metabolite ratio association profiles of these two loci we hypothesized the chemical structure of one of their products or substrates as being 3-methoxyacetaminophen, which we then confirmed experimentally. Taken together, our study suggests a novel approach to analyze metabolites with a high degree of missingness in a GWAS setting with ratios, and it also demonstrates how pharmacological pathways can be mapped out using non-targeted metabolomics measurements in large population-based studies.
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Large-scale genetic correlation scanning and causal association between deep vein thrombosis and human blood metabolites. Sci Rep 2022; 12:7888. [PMID: 35551264 PMCID: PMC9098636 DOI: 10.1038/s41598-022-12021-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 05/04/2022] [Indexed: 02/05/2023] Open
Abstract
Deep vein thrombosis (DVT) refers to the abnormal coagulation of blood in a deep vein. Recently, some studies have found that metabolites are related to the occurrence of DVT and may serve as new markers for the diagnosis of DVT. In this study, we used the GWAS summary dataset of blood metabolites and DVT to perform a large-scale genetic correlation scan of DVT and blood metabolites to explore the correlation between blood metabolites and DVT. We used GWAS summary data of DVT from the UK Biobank (UK Biobank fields: 20002) and GWAS summary data of blood metabolites from a previously published study (including 529 metabolites in plasma or serum from 7824 adults from two European population studies) for genetic correlation analysis. Then, we conducted a causal study between the screened blood metabolites and DVT by Mendelian randomization (MR) analysis. In the first stage, genetic correlation analysis identified 9 blood metabolites that demonstrated a suggestive association with DVT. These metabolites included Valine (correlation coefficient = 0.2440, P value = 0.0430), Carnitine (correlation coefficient = 0.1574, P value = 0.0146), Hydroxytryptophan (correlation coefficient = 0.2376, P value = 0.0360), and 1-stearoylglycerophosphoethanolamine (correlation coefficient = - 0.3850, P value = 0.0258). Then, based on the IVW MR model, we analysed the causal relationship between the screened blood metabolites and DVT and found that there was a suggestive causal relationship between Hydroxytryptophan (exposure) and DVT (outcome) (β = - 0.0378, se = 0.0163, P = 0.0204). Our study identified a set of candidate blood metabolites that showed a suggestive association with DVT. We hope that our findings will provide new insights into the pathogenesis and diagnosis of DVT in the future.
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Kanwore K, Kanwore K, Adzika GK, Abiola AA, Guo X, Kambey PA, Xia Y, Gao D. Cancer Metabolism: The Role of Immune Cells Epigenetic Alteration in Tumorigenesis, Progression, and Metastasis of Glioma. Front Immunol 2022; 13:831636. [PMID: 35392088 PMCID: PMC8980436 DOI: 10.3389/fimmu.2022.831636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/28/2022] [Indexed: 12/17/2022] Open
Abstract
Glioma is a type of brain and spinal cord tumor that begins in glial cells that support the nervous system neurons functions. Age, radiation exposure, and family background of glioma constitute are risk factors of glioma initiation. Gliomas are categorized on a scale of four grades according to their growth rate. Grades one and two grow slowly, while grades three and four grow faster. Glioblastoma is a grade four gliomas and the deadliest due to its aggressive nature (accelerated proliferation, invasion, and migration). As such, multiple therapeutic approaches are required to improve treatment outcomes. Recently, studies have implicated the significant roles of immune cells in tumorigenesis and the progression of glioma. The energy demands of gliomas alter their microenvironment quality, thereby inducing heterogeneity and plasticity change of stromal and immune cells via the PI3K/AKT/mTOR pathway, which ultimately results in epigenetic modifications that facilitates tumor growth. PI3K is utilized by many intracellular signaling pathways ensuring the proper functioning of the cell. The activation of PI3K/AKT/mTOR regulates the plasma membrane activities, contributing to the phosphorylation reaction necessary for transcription factors activities and oncogenes hyperactivation. The pleiotropic nature of PI3K/AKT/mTOR makes its activity unpredictable during altered cellular functions. Modification of cancer cell microenvironment affects many cell types, including immune cells that are the frontline cells involved in inflammatory cascades caused by cancer cells via high cytokines synthesis. Typically, the evasion of immunosurveillance by gliomas and their resistance to treatment has been attributed to epigenetic reprogramming of immune cells in the tumor microenvironment, which results from cancer metabolism. Hence, it is speculative that impeding cancer metabolism and/or circumventing the epigenetic alteration of immune cell functions in the tumor microenvironment might enhance treatment outcomes. Herein, from an oncological and immunological perspective, this review discusses the underlying pathomechanism of cell-cell interactions enhancing glioma initiation and metabolism activation and tumor microenvironment changes that affect epigenetic modifications in immune cells. Finally, prospects for therapeutic intervention were highlighted.
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Affiliation(s)
- Kouminin Kanwore
- Xuzhou Key Laboratory of Neurobiology, Department of Neurobiology, Xuzhou Medical University, Xuzhou, China.,Xuzhou Key Laboratory of Neurobiology, Department of Anatomy, Xuzhou Medical University, Xuzhou, China
| | - Konimpo Kanwore
- Faculty Mixed of Medicine and Pharmacy, Lomé-Togo, University of Lomé, Lomé, Togo
| | | | - Ayanlaja Abdulrahman Abiola
- Xuzhou Key Laboratory of Neurobiology, Department of Neurobiology, Xuzhou Medical University, Xuzhou, China.,Xuzhou Key Laboratory of Neurobiology, Department of Anatomy, Xuzhou Medical University, Xuzhou, China
| | - Xiaoxiao Guo
- Xuzhou Key Laboratory of Neurobiology, Department of Neurobiology, Xuzhou Medical University, Xuzhou, China.,Xuzhou Key Laboratory of Neurobiology, Department of Anatomy, Xuzhou Medical University, Xuzhou, China
| | - Piniel Alphayo Kambey
- Xuzhou Key Laboratory of Neurobiology, Department of Neurobiology, Xuzhou Medical University, Xuzhou, China.,Xuzhou Key Laboratory of Neurobiology, Department of Anatomy, Xuzhou Medical University, Xuzhou, China
| | - Ying Xia
- Xuzhou Key Laboratory of Neurobiology, Department of Neurobiology, Xuzhou Medical University, Xuzhou, China.,Xuzhou Key Laboratory of Neurobiology, Department of Anatomy, Xuzhou Medical University, Xuzhou, China
| | - Dianshuai Gao
- Xuzhou Key Laboratory of Neurobiology, Department of Neurobiology, Xuzhou Medical University, Xuzhou, China.,Xuzhou Key Laboratory of Neurobiology, Department of Anatomy, Xuzhou Medical University, Xuzhou, China
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Wells AE, Barrington WT, Dearth S, Milind N, Carter GW, Threadgill DW, Campagna SR, Voy BH. Independent and Interactive Effects of Genetic Background and Sex on Tissue Metabolomes of Adipose, Skeletal Muscle, and Liver in Mice. Metabolites 2022; 12:metabo12040337. [PMID: 35448524 PMCID: PMC9031494 DOI: 10.3390/metabo12040337] [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: 02/22/2022] [Revised: 04/03/2022] [Accepted: 04/06/2022] [Indexed: 12/10/2022] Open
Abstract
Genetics play an important role in the development of metabolic diseases. However, the relative influence of genetic variation on metabolism is not well defined, particularly in tissues, where metabolic dysfunction that leads to disease occurs. We used inbred strains of laboratory mice to evaluate the impact of genetic variation on the metabolomes of tissues that play central roles in metabolic diseases. We chose a set of four common inbred strains that have different levels of susceptibility to obesity, insulin resistance, and other common metabolic disorders. At the ages used, and under standard husbandry conditions, these lines are not overtly diseased. Using global metabolomics profiling, we evaluated water-soluble metabolites in liver, skeletal muscle, and adipose from A/J, C57BL/6J, FVB/NJ, and NOD/ShiLtJ mice fed a standard mouse chow diet. We included both males and females to assess the relative influence of strain, sex, and strain-by-sex interactions on metabolomes. The mice were also phenotyped for systems level traits related to metabolism and energy expenditure. Strain explained more variation in the metabolite profile than did sex or its interaction with strain across each of the tissues, especially in liver. Purine and pyrimidine metabolism and pathways related to amino acid metabolism were identified as pathways that discriminated strains across all three tissues. Based on the results from ANOVA, sex and sex-by-strain interaction had modest influence on metabolomes relative to strain, suggesting that the tissue metabolome remains largely stable across sexes consuming the same diet. Our data indicate that genetic variation exerts a fundamental influence on tissue metabolism.
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Affiliation(s)
- Ann E. Wells
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee-Knoxville, Knoxville, TN 37996, USA;
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA; (N.M.); (G.W.C.)
| | - William T. Barrington
- Department of Molecular and Cellular Medicine, Texas A&M Health Science Center, College Station, TX 77843, USA; (W.T.B.); (D.W.T.)
| | - Stephen Dearth
- Department of Chemistry, University of Tennessee-Knoxville, Knoxville, TN 37996, USA; (S.D.); (S.R.C.)
| | - Nikhil Milind
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA; (N.M.); (G.W.C.)
| | - Gregory W. Carter
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA; (N.M.); (G.W.C.)
| | - David W. Threadgill
- Department of Molecular and Cellular Medicine, Texas A&M Health Science Center, College Station, TX 77843, USA; (W.T.B.); (D.W.T.)
| | - Shawn R. Campagna
- Department of Chemistry, University of Tennessee-Knoxville, Knoxville, TN 37996, USA; (S.D.); (S.R.C.)
- Biological and Small Molecule Mass Spectrometry Core, University of Tennessee-Knoxville, Knoxville, TN 37996, USA
| | - Brynn H. Voy
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee-Knoxville, Knoxville, TN 37996, USA;
- Department of Animal Science, University of Tennessee-Knoxville, Knoxville, TN 37996, USA
- Correspondence:
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Yin X, Chan LS, Bose D, Jackson AU, VandeHaar P, Locke AE, Fuchsberger C, Stringham HM, Welch R, Yu K, Fernandes Silva L, Service SK, Zhang D, Hector EC, Young E, Ganel L, Das I, Abel H, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Wagner GR, Kang J, Morrison J, Burant CF, Collins FS, Ripatti S, Palotie A, Freimer NB, Mohlke KL, Scott LJ, Wen X, Fauman EB, Laakso M, Boehnke M. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat Commun 2022; 13:1644. [PMID: 35347128 PMCID: PMC8960770 DOI: 10.1038/s41467-022-29143-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/23/2022] [Indexed: 01/13/2023] Open
Abstract
Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.
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Affiliation(s)
- Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Lap Sum Chan
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Debraj Bose
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Peter VandeHaar
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Institute for Biomedicine, Eurac Research, Bolzano, 39100, Italy
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Ketian Yu
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland
| | - Susan K Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, 90024, USA
| | - Daiwei Zhang
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Emily C Hector
- Department of Statistics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Erica Young
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Liron Ganel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
| | - Indraniel Das
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
| | - Haley Abel
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Michael R Erdos
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lori L Bonnycastle
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland
- Center for Medicine and Clinical Research, Kuopio University Hospital, Kuopio, 70210, Finland
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO, 63110, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Ira M Hall
- Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT, 06510, USA
| | | | - Jian Kang
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Jean Morrison
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Francis S Collins
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290, Finland
- Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
- Broad Institute of MIT & Harvard, Cambridge, MA, 02142, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290, Finland
- Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, 90024, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, 02139, USA.
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland.
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA.
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Zhang C, Hansen MEB, Tishkoff SA. Advances in integrative African genomics. Trends Genet 2022; 38:152-168. [PMID: 34740451 PMCID: PMC8752515 DOI: 10.1016/j.tig.2021.09.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/16/2021] [Accepted: 09/28/2021] [Indexed: 12/16/2022]
Abstract
There has been a rapid increase in human genome sequencing in the past two decades, resulting in the identification of millions of previously unknown genetic variants. However, African populations are under-represented in sequencing efforts. Additional sequencing from diverse African populations and the construction of African-specific reference genomes is needed to better characterize the full spectrum of variation in humans. However, sequencing alone is insufficient to address the molecular and cellular mechanisms underlying variable phenotypes and disease risks. Determining functional consequences of genetic variation using multi-omics approaches is a fundamental post-genomic challenge. We discuss approaches to close the knowledge gaps about African genomic diversity and review advances in African integrative genomic studies and their implications for precision medicine.
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Affiliation(s)
- Chao Zhang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew E B Hansen
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah A Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Ghini V, Abuja PM, Polasek O, Kozera L, Laiho P, Anton G, Zins M, Klovins J, Metspalu A, Wichmann HE, Gieger C, Luchinat C, Zatloukal K, Turano P. Impact of the pre-examination phase on multicenter metabolomic studies. N Biotechnol 2022; 68:37-47. [PMID: 35066155 DOI: 10.1016/j.nbt.2022.01.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 01/23/2023]
Abstract
The development of metabolomics in clinical applications has been limited by the lack of validation in large multicenter studies. Large population cohorts and their biobanks are a valuable resource for acquiring insights into molecular disease mechanisms. Nevertheless, most of their collections are not tailored for metabolomics and have been created without specific attention to the pre-analytical requirements for high-quality metabolome assessment. Thus, comparing samples obtained by different pre-analytical procedures remains a major challenge. Here, 1H NMR-based analyses are used to demonstrate how human serum and plasma samples collected with different operating procedures within several large European cohort studies from the Biobanking and Biomolecular Resources Infrastructure - Large Prospective Cohorts (BBMRI-LPC) consortium can be easily revealed by supervised multivariate statistical analyses at the initial stages of the process, to avoid biases in the downstream analysis. The inter-biobank differences are discussed in terms of deviations from the validated CEN/TS 16945:2016 / ISO 23118:2021 norms. It clearly emerges that biobanks must adhere to the evidence-based guidelines in order to support wider-scale application of metabolomics in biomedicine, and that NMR spectroscopy is informative in comparing the quality of different sample sources in multi cohort/center studies.
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Affiliation(s)
- Veronica Ghini
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy
| | - Peter M Abuja
- Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, A-8010, Graz, Austria
| | - Ozren Polasek
- Department for Large Population Studies, University of Split, Šoltanska 2, HR-21000, Split, Croatia; Gen-info Ltd, Ružmarinka ul. 17, 10000, Zagreb, Croatia
| | - Lukasz Kozera
- BBMRI-ERIC, Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Päivi Laiho
- Institute for Molecular Medicine Finland, National Institute for Health and Welfare, THL, University of Helsinki, 00290, Helsinki, Finland
| | - Gabriele Anton
- Molecular Epidemiology, Helmholtz-Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Marie Zins
- Population-based Epidemiological Cohorts Unit-UMS 11, Inserm, 16 Avenue Paul Vaillant Couturier, 94800, Villejuif, France
| | - Janis Klovins
- Latvian Biomedical Research and Study Centre, Rātsupītes iela 1, Kurzemes rajons, Rīga, LV-1067, Latvia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
| | - H-Erich Wichmann
- Institute of Epidemiology, Helmholtz Center Munich, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Center Munich, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy
| | - Kurt Zatloukal
- Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, A-8010, Graz, Austria.
| | - Paola Turano
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy.
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Hoskins JW, Chung CC, O’Brien A, Zhong J, Connelly K, Collins I, Shi J, Amundadottir LT. Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals. PLoS Comput Biol 2021; 17:e1009563. [PMID: 34793442 PMCID: PMC8639061 DOI: 10.1371/journal.pcbi.1009563] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 12/02/2021] [Accepted: 10/15/2021] [Indexed: 12/12/2022] Open
Abstract
Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator's target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals.
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Affiliation(s)
- Jason W. Hoskins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (JWH); (LTA)
| | - Charles C. Chung
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Cancer Genome Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Aidan O’Brien
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jun Zhong
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Katelyn Connelly
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Irene Collins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Laufey T. Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (JWH); (LTA)
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35
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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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36
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亀田 雅, 近藤 祥. [Metabolites for frailty biomarkers]. Nihon Ronen Igakkai Zasshi 2021; 58:333-340. [PMID: 34483155 DOI: 10.3143/geriatrics.58.333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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37
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Reduced uremic metabolites are prominent feature of sarcopenia, distinct from antioxidative markers for frailty. Aging (Albany NY) 2021; 13:20915-20934. [PMID: 34492634 PMCID: PMC8457568 DOI: 10.18632/aging.203498] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/24/2021] [Indexed: 12/19/2022]
Abstract
Due to global aging, frailty and sarcopenia are increasing. Sarcopenia is defined as loss of volume and strength of skeletal muscle in elderlies, while frailty involves multiple domains of aging-related dysfunction, impaired cognition, hypomobility, and decreased social activity. However, little is known about the metabolic basis of sarcopenia, either shared with or discrete from frailty. Here we analyzed comprehensive metabolomic data of human blood in relation to sarcopenia, previously collected from 19 elderly participants in our frailty study. Among 131 metabolites, we identified 22 sarcopenia markers, distinct from 15 frailty markers, mainly including antioxidants, although sarcopenia overlaps clinically with physical frailty. Notably, 21 metabolites that decline in sarcopenia or low SMI are uremic compounds that increase in kidney dysfunction. These comprise TCA cycle, urea cycle, nitrogen, and methylated metabolites. Sarcopenia markers imply a close link between muscle and kidney function, while frailty markers define a state vulnerable to oxidative stress.
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38
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Fadason T, Farrow S, Gokuladhas S, Golovina E, Nyaga D, O'Sullivan JM, Schierding W. Assigning function to SNPs: Considerations when interpreting genetic variation. Semin Cell Dev Biol 2021; 121:135-142. [PMID: 34446357 DOI: 10.1016/j.semcdb.2021.08.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 08/12/2021] [Indexed: 12/26/2022]
Abstract
Assigning function to single nucleotide polymorphisms (SNPs) to understand the mechanisms that link genetic and phenotypic variation and disease is an area of intensive research that is necessary to contribute to the continuing development of precision medicine. However, despite the apparent simplicity that is captured in the name SNP - 'single nucleotide' changes are not easy to functionally characterize. This complexity arises from multiple features of the genome including the fact that function is development and environment specific. As such, we are often fooled by our terminology and underlying assumptions that there is a single function for a SNP. Here we discuss some of what is known about SNPs, their functions and how we can go about characterizing them.
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Affiliation(s)
- Tayaza Fadason
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Sophie Farrow
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | | | - Evgeniia Golovina
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Denis Nyaga
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand; Garvan Institute of Medical Research, Sydney, New South Wales, Australia; MRC Lifecourse Epidemiology Unit, University of Southampton, United Kingdom.
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
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39
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Chu X, Jaeger M, Beumer J, Bakker OB, Aguirre-Gamboa R, Oosting M, Smeekens SP, Moorlag S, Mourits VP, Koeken VACM, de Bree C, Jansen T, Mathews IT, Dao K, Najhawan M, Watrous JD, Joosten I, Sharma S, Koenen HJPM, Withoff S, Jonkers IH, Netea-Maier RT, Xavier RJ, Franke L, Xu CJ, Joosten LAB, Sanna S, Jain M, Kumar V, Clevers H, Wijmenga C, Netea MG, Li Y. Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. Genome Biol 2021; 22:198. [PMID: 34229738 PMCID: PMC8259168 DOI: 10.1186/s13059-021-02413-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Recent studies highlight the role of metabolites in immune diseases, but it remains unknown how much of this effect is driven by genetic and non-genetic host factors. RESULT We systematically investigate circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity are deeply measured and whose genetics are profiled. Our data reveal that several major metabolic pathways, including the alanine/glutamate pathway and the arachidonic acid pathway, have a strong impact on cytokine production in response to ex vivo stimulation. We also examine the genetic regulation of metabolites associated with immune phenotypes through genome-wide association analysis and identify 29 significant loci, including eight novel independent loci. Of these, one locus (rs174584-FADS2) associated with arachidonic acid metabolism is causally associated with Crohn's disease, suggesting it is a potential therapeutic target. CONCLUSION This study provides a comprehensive map of the integration between the blood metabolome and immune phenotypes, reveals novel genetic factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.
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Affiliation(s)
- Xiaojing Chu
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Martin Jaeger
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Joep Beumer
- Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, 3584, CT, Utrecht, the Netherlands
| | - Olivier B Bakker
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Raul Aguirre-Gamboa
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Marije Oosting
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Sanne P Smeekens
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Simone Moorlag
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Vera P Mourits
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Valerie A C M Koeken
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Charlotte de Bree
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Trees Jansen
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Ian T Mathews
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
- La Jolla Institute, La Jolla, CA, USA
| | - Khoi Dao
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Mahan Najhawan
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Jeramie D Watrous
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Irma Joosten
- Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, 6525, GA, Nijmegen, the Netherlands
| | | | - Hans J P M Koenen
- Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, 6525, GA, Nijmegen, the Netherlands
| | - Sebo Withoff
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Iris H Jonkers
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Romana T Netea-Maier
- Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard University, Cambridge, MA, 02142, USA
- Center for Computational and Integrative Biology and Gastrointestinal Unit, Massachusetts General Hospital, Harvard School of Medicine, Boston, MA, 02114, USA
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Cheng-Jian Xu
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Leo A B Joosten
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Serena Sanna
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Mohit Jain
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Vinod Kumar
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Hans Clevers
- Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, 3584, CT, Utrecht, the Netherlands
- Oncode Institute, Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands.
- Department of Immunology, University of Oslo, Oslo University Hospital, Rikshospitalet, 0372, Oslo, Norway.
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
- Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, 53115, Bonn, Germany.
| | - Yang Li
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands.
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany.
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany.
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
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40
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Whole Blood Metabolomics in Aging Research. Int J Mol Sci 2020; 22:ijms22010175. [PMID: 33375345 PMCID: PMC7796096 DOI: 10.3390/ijms22010175] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 12/25/2020] [Accepted: 12/25/2020] [Indexed: 02/08/2023] Open
Abstract
Diversity is observed in the wave of global aging because it is a complex biological process exhibiting individual variability. To assess aging physiologically, markers for biological aging are required in addition to the calendar age. From a metabolic perspective, the aging hypothesis includes the mitochondrial hypothesis and the calorie restriction (CR) hypothesis. In experimental models, several compounds or metabolites exert similar lifespan-extending effects, like CR. However, little is known about whether these metabolic modulations are applicable to human longevity, as human aging is greatly affected by a variety of factors, including lifestyle, genetic or epigenetic factors, exposure to stress, diet, and social environment. A comprehensive analysis of the human blood metabolome captures complex changes with individual differences. Moreover, a non-targeted analysis of the whole blood metabolome discloses unexpected aspects of human biology. By using such approaches, markers for aging or aging-relevant conditions were identified. This information should prove valuable for future diagnosis or clinical interventions in diseases relevant to aging.
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41
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Calvo-Serra B, Maitre L, Lau CHE, Siskos AP, Gützkow KB, Andrušaitytė S, Casas M, Cadiou S, Chatzi L, González JR, Grazuleviciene R, McEachan R, Slama R, Vafeiadi M, Wright J, Coen M, Vrijheid M, Keun HC, Escaramís G, Bustamante M. Urinary metabolite quantitative trait loci in children and their interaction with dietary factors. Hum Mol Genet 2020; 29:3830-3844. [PMID: 33283231 DOI: 10.1093/hmg/ddaa257] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/26/2020] [Accepted: 11/30/2020] [Indexed: 11/14/2022] Open
Abstract
Human metabolism is influenced by genetic and environmental factors. Previous studies have identified over 23 loci associated with more than 26 urine metabolites levels in adults, which are known as urinary metabolite quantitative trait loci (metabQTLs). The aim of the present study is the identification for the first time of urinary metabQTLs in children and their interaction with dietary patterns. Association between genome-wide genotyping data and 44 urine metabolite levels measured by proton nuclear magnetic resonance spectroscopy was tested in 996 children from the Human Early Life Exposome project. Twelve statistically significant urine metabQTLs were identified, involving 11 unique loci and 10 different metabolites. Comparison with previous findings in adults revealed that six metabQTLs were already known, and one had been described in serum and three were involved the same locus as other reported metabQTLs but had different urinary metabolites. The remaining two metabQTLs represent novel urine metabolite-locus associations, which are reported for the first time in this study [single nucleotide polymorphism (SNP) rs12575496 for taurine, and the missense SNP rs2274870 for 3-hydroxyisobutyrate]. Moreover, it was found that urinary taurine levels were affected by the combined action of genetic variation and dietary patterns of meat intake as well as by the interaction of this SNP with beverage intake dietary patterns. Overall, we identified 12 urinary metabQTLs in children, including two novel associations. While a substantial part of the identified loci affected urinary metabolite levels both in children and in adults, the metabQTL for taurine seemed to be specific to children and interacted with dietary patterns.
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Affiliation(s)
- Beatriz Calvo-Serra
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Léa Maitre
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Chung-Ho E Lau
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Alexandros P Siskos
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK
| | - Kristine B Gützkow
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo 0213, Norway
| | - Sandra Andrušaitytė
- Department of Environmental Science, Vytautas Magnus University, Kaunas 44248, Lithuania
| | - Maribel Casas
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Solène Cadiou
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble 38000, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Juan R González
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Regina Grazuleviciene
- Department of Environmental Science, Vytautas Magnus University, Kaunas 44248, Lithuania
| | | | - Rémy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble 38000, France
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion 71003, Greece
| | - John Wright
- Bradford Institute for Health Research, Bradford BD9 6RJ, UK
| | - Murieann Coen
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB2 0RE, UK
| | - Martine Vrijheid
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Hector C Keun
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK.,Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK
| | - Geòrgia Escaramís
- Departament de Biomedicina, Institut de Neurociències, Universitat de Barcelona (UB), Barcelona 08036, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
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42
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Guo X, Sarup P, Jensen JD, Orabi J, Kristensen NH, Mulder FAA, Jahoor A, Jensen J. Genetic Variance of Metabolomic Features and Their Relationship With Malting Quality Traits in Spring Barley. FRONTIERS IN PLANT SCIENCE 2020; 11:575467. [PMID: 33193515 PMCID: PMC7604292 DOI: 10.3389/fpls.2020.575467] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 09/24/2020] [Indexed: 06/11/2023]
Abstract
Barley is the most common source for malt to be used in brewing beer and other alcoholic beverages. This involves converting the starch of barley into fermentable sugars a process that involves malting, that is germinating of the grains, and mashing, which is an enzymatic process. Numerous metabolic processes are involved in germination, where distinct and time-dependent alterations at the metabolite levels happen. In this study, 2,628 plots of 565 spring malting barley lines from Nordic Seed A/S were investigated. Phenotypic records were available for six malting quality (MQ) traits: filtering speed (FS), wort clearness (WCL), extract yield (EY), wort color (WCO), beta glucan (BG), and wort viscosity (WV). Each line had a set of dense genomic markers. In addition, 24,018 metabolomic features (MFs) were obtained for each sample from nuclear magnetic resonance (NMR) spectra for wort samples produced from each experimental plot. The genetic variation in the MFs was investigated using a univariate model, and the relationship between MFs and the MQ traits was studied using a bivariate model. Results showed that a total of 8,604 MFs had heritability estimates significantly larger than 0 and for all MQ traits, there were genetic correlations with up to 86.77% and phenotypic correlations with up to 90.07% of the significant heritable MFs. In conclusion, around one third of all MFs were significantly heritable, among which a considerable proportion had significant additive genetic and/or phenotypic correlations with the MQ traits (WCO, WV, and BG) in spring barley. The results from this study indicate that many of the MFs are heritable and MFs have great potential to be used in breeding barley for high MQ.
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Affiliation(s)
- Xiangyu Guo
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | | | | | | | | | - Frans A. A. Mulder
- Department of Chemistry and Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Aarhus, Denmark
| | - Ahmed Jahoor
- Nordic Seed A/S, Odder, Denmark
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
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43
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Linke V, Overmyer KA, Miller IJ, Brademan DR, Hutchins PD, Trujillo EA, Reddy TR, Russell JD, Cushing EM, Schueler KL, Stapleton DS, Rabaglia ME, Keller MP, Gatti DM, Keele GR, Pham D, Broman KW, Churchill GA, Attie AD, Coon JJ. A large-scale genome-lipid association map guides lipid identification. Nat Metab 2020; 2:1149-1162. [PMID: 32958938 PMCID: PMC7572687 DOI: 10.1038/s42255-020-00278-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 08/11/2020] [Indexed: 12/13/2022]
Abstract
Despite the crucial roles of lipids in metabolism, we are still at the early stages of comprehensively annotating lipid species and their genetic basis. Mass spectrometry-based discovery lipidomics offers the potential to globally survey lipids and their relative abundances in various biological samples. To discover the genetics of lipid features obtained through high-resolution liquid chromatography-tandem mass spectrometry, we analysed liver and plasma from 384 diversity outbred mice, and quantified 3,283 molecular features. These features were mapped to 5,622 lipid quantitative trait loci and compiled into a public web resource termed LipidGenie. The data are cross-referenced to the human genome and offer a bridge between genetic associations in humans and mice. Harnessing this resource, we used genome-lipid association data as an additional aid to identify a number of lipids, for example gangliosides through their association with B4galnt1, and found evidence for a group of sex-specific phosphatidylcholines through their shared locus. Finally, LipidGenie's ability to query either mass or gene-centric terms suggests acyl-chain-specific functions for proteins of the ABHD family.
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Affiliation(s)
- Vanessa Linke
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Katherine A Overmyer
- Morgridge Institute for Research, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ian J Miller
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Dain R Brademan
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Paul D Hutchins
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Edna A Trujillo
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Thiru R Reddy
- Morgridge Institute for Research, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Emily M Cushing
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Kathryn L Schueler
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Donald S Stapleton
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Mary E Rabaglia
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Duy Pham
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - Karl W Broman
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Alan D Attie
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
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Genetics meets proteomics: perspectives for large population-based studies. Nat Rev Genet 2020; 22:19-37. [PMID: 32860016 DOI: 10.1038/s41576-020-0268-2] [Citation(s) in RCA: 163] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2020] [Indexed: 12/22/2022]
Abstract
Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.
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45
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Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data. Twin Res Hum Genet 2020; 23:145-155. [PMID: 32635965 DOI: 10.1017/thg.2020.53] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.
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46
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McCombe PA, Garton FC, Katz M, Wray NR, Henderson RD. What do we know about the variability in survival of patients with amyotrophic lateral sclerosis? Expert Rev Neurother 2020; 20:921-941. [PMID: 32569484 DOI: 10.1080/14737175.2020.1785873] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION ALS is a fatal neurodegenerative disease. However, patients show variability in the length of survival after symptom onset. Understanding the mechanisms of long survival could lead to possible avenues for therapy. AREAS COVERED This review surveys the reported length of survival in ALS, the clinical features that predict survival in individual patients, and possible factors, particularly genetic factors, that could cause short or long survival. The authors also speculate on possible mechanisms. EXPERT OPINION a small number of known factors can explain some variability in ALS survival. However, other disease-modifying factors likely exist. Factors that alter motor neurone vulnerability and immune, metabolic, and muscle function could affect survival by modulating the disease process. Knowing these factors could lead to interventions to change the course of the disease. The authors suggest a broad approach is needed to quantify the proportion of variation survival attributable to genetic and non-genetic factors and to identify and estimate the effect size of specific factors. Studies of this nature could not only identify novel avenues for therapeutic research but also play an important role in clinical trial design and personalized medicine.
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Affiliation(s)
- Pamela A McCombe
- Centre for Clinical Research, The University of Queensland , Brisbane, Australia.,Department of Neurology, Royal Brisbane and Women's Hospital , Brisbane, Australia
| | - Fleur C Garton
- Institute for Molecular Biosciences, The University of Queensland , Brisbane, Australia
| | - Matthew Katz
- Department of Neurology, Royal Brisbane and Women's Hospital , Brisbane, Australia
| | - Naomi R Wray
- Institute for Molecular Biosciences, The University of Queensland , Brisbane, Australia.,Queensland Brain Institute, The University of Queensland , Brisbane, Australia
| | - Robert D Henderson
- Centre for Clinical Research, The University of Queensland , Brisbane, Australia
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47
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Al-Khelaifi F, Yousri NA, Diboun I, Semenova EA, Kostryukova ES, Kulemin NA, Borisov OV, Andryushchenko LB, Larin AK, Generozov EV, Miyamoto-Mikami E, Murakami H, Zempo H, Miyachi M, Takaragawa M, Kumagai H, Naito H, Fuku N, Abraham D, Hingorani A, Donati F, Botrè F, Georgakopoulos C, Suhre K, Ahmetov II, Albagha O, Elrayess MA. Genome-Wide Association Study Reveals a Novel Association Between MYBPC3 Gene Polymorphism, Endurance Athlete Status, Aerobic Capacity and Steroid Metabolism. Front Genet 2020; 11:595. [PMID: 32612638 PMCID: PMC7308547 DOI: 10.3389/fgene.2020.00595] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 05/15/2020] [Indexed: 11/13/2022] Open
Abstract
Background The genetic predisposition to elite athletic performance has been a controversial subject due to the underpowered studies and the small effect size of identified genetic variants. The aims of this study were to investigate the association of common single-nucleotide polymorphisms (SNPs) with endurance athlete status in a large cohort of elite European athletes using GWAS approach, followed by replication studies in Russian and Japanese elite athletes and functional validation using metabolomics analysis. Results The association of 476,728 SNPs of Illumina DrugCore Gene chip and endurance athlete status was investigated in 796 European international-level athletes (645 males, 151 females) by comparing allelic frequencies between athletes specialized in sports with high (n = 662) and low/moderate (n = 134) aerobic component. Replication of results was performed by comparing the frequencies of the most significant SNPs between 242 and 168 elite Russian high and low/moderate aerobic athletes, respectively, and between 60 elite Japanese endurance athletes and 406 controls. A meta-analysis has identified rs1052373 (GG homozygotes) in Myosin Binding Protein (MYBPC3; implicated in cardiac hypertrophic myopathy) gene to be associated with endurance athlete status (P = 1.43 × 10-8, odd ratio 2.2). Homozygotes carriers of rs1052373 G allele in Russian athletes had significantly greater VO2 max than carriers of the AA + AG (P = 0.005). Subsequent metabolomics analysis revealed several amino acids and lipids associated with rs1052373 G allele (1.82 × 10-05) including the testosterone precursor androstenediol (3beta,17beta) disulfate. Conclusions This is the first report of genome-wide significant SNP and related metabolites associated with elite athlete status. Further investigations of the functional relevance of the identified SNPs and metabolites in relation to enhanced athletic performance are warranted.
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Affiliation(s)
- Fatima Al-Khelaifi
- Anti-Doping Laboratory Qatar, Doha, Qatar.,UCL-Medical School, London, United Kingdom
| | - Noha A Yousri
- Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Qatar-Foundation, Doha, Qatar.,Department of Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
| | - Ilhame Diboun
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Ekaterina A Semenova
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia.,Department of Biochemistry, Kazan Federal University, Kazan, Russia
| | - Elena S Kostryukova
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
| | - Nikolay A Kulemin
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
| | - Oleg V Borisov
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia.,Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | | | - Andrey K Larin
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
| | - Edward V Generozov
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
| | - Eri Miyamoto-Mikami
- Graduate School of Health and Sports Science, Juntendo University, Chiba, Japan
| | - Haruka Murakami
- Department of Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan
| | - Hirofumi Zempo
- Graduate School of Health and Sports Science, Juntendo University, Chiba, Japan.,Faculty of Health and Nutrition, Tokyo Seiei College, Tokyo, Japan
| | - Motohiko Miyachi
- Department of Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan
| | - Mizuki Takaragawa
- Graduate School of Health and Sports Science, Juntendo University, Chiba, Japan
| | - Hiroshi Kumagai
- Graduate School of Health and Sports Science, Juntendo University, Chiba, Japan.,Japanese Society for the Promotion of Science, Tokyo, Japan
| | - Hisashi Naito
- Graduate School of Health and Sports Science, Juntendo University, Chiba, Japan
| | - Noriyuki Fuku
- Graduate School of Health and Sports Science, Juntendo University, Chiba, Japan
| | | | | | - Francesco Donati
- Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Rome, Italy
| | - Francesco Botrè
- Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Rome, Italy
| | | | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Qatar-Foundation, Doha, Qatar
| | - Ildus I Ahmetov
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia.,Department of Physical Education, Plekhanov Russian University of Economics, Moscow, Russia.,Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom.,Laboratory of Molecular Genetics, Kazan State Medical University, Kazan, Russia
| | - Omar Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.,Center for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, United Kingdom
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Fadason T, Schierding W, Kolbenev N, Liu J, Ingram JR, O’Sullivan JM. Reconstructing the blood metabolome and genotype using long-range chromatin interactions. Metabol Open 2020; 6:100035. [PMID: 32812909 PMCID: PMC7424797 DOI: 10.1016/j.metop.2020.100035] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 03/15/2020] [Accepted: 03/16/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND -Maintenance of tight controls on circulating blood metabolites is crucial to normal, healthy tissue and organismal function. A number of single nucleotide polymorphisms (SNPs) have been associated with changes in the levels of blood metabolites. However, the impacts of the metabolite-associated SNPs are largely unknown because they fall within non-coding regions of the genome. OBJECTIVE -We aimed to identify genes and tissues that are linked to changes in circulating blood metabolites by characterizing genome-wide spatial regulatory interactions involving blood metabolite-associated SNPs. METHOD -We systematically integrated chromatin interaction (Hi-C), expression quantitative trait loci (eQTL), gene ontology, drug interaction, and literature-supported connections to deconvolute the genetic regulatory influences of 145 blood metabolite-associated SNPs. FINDINGS -We identified 577 genes that are regulated by 130 distal and proximal metabolite-associated SNPs across 48 different human tissues. The affected genes are enriched in categories that include metabolism, enzymes, plasma proteins, disease development, and potential drug targets. Our results suggest that regulatory interactions in other tissues contribute to the modulation of blood metabolites. CONCLUSIONS -The spatial SNP-gene-metabolite associations identified in this study expand on the list of genes and tissues that are influenced by metabolic-associated SNPs and improves our understanding of the molecular mechanisms underlying pathologic blood metabolite levels.
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Affiliation(s)
- Tayaza Fadason
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - William Schierding
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Nikolai Kolbenev
- The Department of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Jiamou Liu
- The Department of Computer Science, The University of Auckland, Auckland, New Zealand
| | | | - Justin M. O’Sullivan
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
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49
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Systems Biology Analysis Reveals Eight SLC22 Transporter Subgroups, Including OATs, OCTs, and OCTNs. Int J Mol Sci 2020; 21:ijms21051791. [PMID: 32150922 PMCID: PMC7084758 DOI: 10.3390/ijms21051791] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 03/02/2020] [Accepted: 03/03/2020] [Indexed: 02/07/2023] Open
Abstract
The SLC22 family of OATs, OCTs, and OCTNs is emerging as a central hub of endogenous physiology. Despite often being referred to as “drug” transporters, they facilitate the movement of metabolites and key signaling molecules. An in-depth reanalysis supports a reassignment of these proteins into eight functional subgroups, with four new subgroups arising from the previously defined OAT subclade: OATS1 (SLC22A6, SLC22A8, and SLC22A20), OATS2 (SLC22A7), OATS3 (SLC22A11, SLC22A12, and Slc22a22), and OATS4 (SLC22A9, SLC22A10, SLC22A24, and SLC22A25). We propose merging the OCTN (SLC22A4, SLC22A5, and Slc22a21) and OCT-related (SLC22A15 and SLC22A16) subclades into the OCTN/OCTN-related subgroup. Using data from GWAS, in vivo models, and in vitro assays, we developed an SLC22 transporter-metabolite network and similar subgroup networks, which suggest how multiple SLC22 transporters with mono-, oligo-, and multi-specific substrate specificity interact to regulate metabolites. Subgroup associations include: OATS1 with signaling molecules, uremic toxins, and odorants, OATS2 with cyclic nucleotides, OATS3 with uric acid, OATS4 with conjugated sex hormones, particularly etiocholanolone glucuronide, OCT with neurotransmitters, and OCTN/OCTN-related with ergothioneine and carnitine derivatives. Our data suggest that the SLC22 family can work among itself, as well as with other ADME genes, to optimize levels of numerous metabolites and signaling molecules, involved in organ crosstalk and inter-organismal communication, as proposed by the remote sensing and signaling theory.
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50
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Maruvada P, Lampe JW, Wishart DS, Barupal D, Chester DN, Dodd D, Djoumbou-Feunang Y, Dorrestein PC, Dragsted LO, Draper J, Duffy LC, Dwyer JT, Emenaker NJ, Fiehn O, Gerszten RE, B Hu F, Karp RW, Klurfeld DM, Laughlin MR, Little AR, Lynch CJ, Moore SC, Nicastro HL, O'Brien DM, Ordovás JM, Osganian SK, Playdon M, Prentice R, Raftery D, Reisdorph N, Roche HM, Ross SA, Sang S, Scalbert A, Srinivas PR, Zeisel SH. Perspective: Dietary Biomarkers of Intake and Exposure-Exploration with Omics Approaches. Adv Nutr 2020; 11:200-215. [PMID: 31386148 PMCID: PMC7442414 DOI: 10.1093/advances/nmz075] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research.
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Affiliation(s)
- Padma Maruvada
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Johanna W Lampe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - David S Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Dinesh Barupal
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, Davis, CA, USA
| | - Deirdra N Chester
- Division of Nutrition, Institute of Food Safety and Nutrition at the National Institute of Food and Agriculture, USDA, Washington, DC, USA
| | - Dylan Dodd
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yannick Djoumbou-Feunang
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Lars O Dragsted
- Department of Nutrition, Exercise, and Sports, Section of Preventive and Clinical Nutrition, University of Copenhagen, Copenhagen, Denmark
| | - John Draper
- Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom
| | - Linda C Duffy
- National Institutes of Health, National Center for Complementary and Integrative Health, Bethesda, MD, USA
| | - Johanna T Dwyer
- National Institutes of Health, Office of Dietary Supplements, Bethesda, MD, USA
| | - Nancy J Emenaker
- National Institutes of Health, National Cancer Institute, Rockville, MD, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, Davis, CA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Departments of Nutrition; Epidemiology and Statistics, Harvard TH Chan School of Public Health, Boston, MA, USA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Robert W Karp
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - David M Klurfeld
- Department of Nutrition, Food Safety/Quality, USDA—Agricultural Research Service, Beltsville, MD, USA
| | - Maren R Laughlin
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - A Roger Little
- National Institutes of Health, National Institute on Drug Abuse, Bethesda, MD, USA
| | - Christopher J Lynch
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Steven C Moore
- National Institutes of Health, National Cancer Institute, Rockville, MD, USA
| | - Holly L Nicastro
- National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Diane M O'Brien
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
| | - José M Ordovás
- Nutrition and Genomics Laboratory, Jean Mayer–USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Stavroula K Osganian
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Mary Playdon
- Department of Nutrition and Integrative Physiology, University of Utah and Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Ross Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - Daniel Raftery
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Medicine, University of Washington, Seattle, WA, USA
| | | | - Helen M Roche
- Nutrigenomics Research Group, School of Public Health, Physiotherapy and Sports Science, UCD Institute of Food and Health, Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland
| | - Sharon A Ross
- National Institutes of Health, National Cancer Institute, Rockville, MD, USA
| | - Shengmin Sang
- Laboratory for Functional Foods and Human Health, Center for Excellence in Post-Harvest Technologies, North Carolina A&T State University, North Carolina Research Campus, Nutrition Research Building, Kannapolis, NC, USA
| | - Augustin Scalbert
- International Agency for Research on Cancer, Nutrition and Metabolism Section, Biomarkers Group, Lyon, France
| | - Pothur R Srinivas
- National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Steven H Zeisel
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
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