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Weinisch P, Raffler J, Römisch-Margl W, Arnold M, Mohney RP, Rist MJ, Prehn C, Skurk T, Hauner H, Daniel H, Suhre K, Kastenmüller G. The HuMet Repository: Watching human metabolism at work. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.550079. [PMID: 37609175 PMCID: PMC10441358 DOI: 10.1101/2023.08.08.550079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.
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Affiliation(s)
- Patrick Weinisch
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Werner Römisch-Margl
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | | | - Manuela J. Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Skurk
- ZIEL Institute for Food and Health, Core Facility Human Studies, Technical University of Munich, Freising, Germany
- Else Kröner Fresenius Center of Nutritional Medicine, Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Hans Hauner
- Else Kröner Fresenius Center of Nutritional Medicine, Department of Food and Nutrition, Technical University of Munich, Freising, Germany
- Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Hannelore Daniel
- Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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India Aldana S, Valvi D, Joshi A, Lucchini RG, Placidi D, Petrick L, Horton M, Niedzwiecki M, Colicino E. Salivary Metabolomic Signatures and Body Mass Index in Italian Adolescents: A Pilot Study. J Endocr Soc 2023; 7:bvad091. [PMID: 37457847 PMCID: PMC10341611 DOI: 10.1210/jendso/bvad091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Indexed: 07/18/2023] Open
Abstract
Context Obesity surveillance is scarce in adolescents, and little is known on whether salivary metabolomics data, emerging minimally invasive biomarkers, can characterize metabolic patterns associated with overweight or obesity in adolescents. Objective This pilot study aims to identify the salivary molecular signatures associated with body mass index (BMI) in Italian adolescents. Methods Saliva samples and BMI were collected in a subset of n = 74 young adolescents enrolled in the Public Health Impact of Metal Exposure study (2007-2014). A total of 217 untargeted metabolites were identified using liquid chromatography-high resolution mass spectrometry. Robust linear regression was used to cross-sectionally determine associations between metabolomic signatures and sex-specific BMI-for-age z-scores (z-BMI). Results Nearly 35% of the adolescents (median age: 12 years; 51% females) were either obese or overweight. A higher z-BMI was observed in males compared to females (P = .02). One nucleoside (deoxyadenosine) and 2 lipids (18:0-18:2 phosphatidylcholine and dipalmitoyl-phosphoethanolamine) were negatively related to z-BMI (P < .05), whereas 2 benzenoids (3-hydroxyanthranilic acid and a phthalate metabolite) were positively associated with z-BMI (P < .05). In males, several metabolites including deoxyadenosine, as well as deoxycarnitine, hyodeoxycholic acid, N-methylglutamic acid, bisphenol P, and trigonelline were downregulated, while 3 metabolites (3-hydroxyanthranilic acid, theobromine/theophylline/paraxanthine, and alanine) were upregulated in relation to z-BMI (P < .05). In females, deoxyadenosine and dipalmitoyl-phosphoethanolamine were negatively associated with z-BMI while deoxycarnitine and a phthalate metabolite were positively associated (P < .05). A single energy-related pathway was enriched in the identified associations in females (carnitine synthesis, P = .04). Conclusion Salivary metabolites involved in nucleotide, lipid, and energy metabolism were primarily altered in relation to BMI in adolescents.
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Affiliation(s)
- Sandra India Aldana
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Damaskini Valvi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Anu Joshi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Roberto G Lucchini
- Department of Medical Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, Italy
- Department of Environmental Health Sciences, School of Public Health, Florida International University, Miami, FL 33199, USA
| | - Donatella Placidi
- Department of Medical Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, Italy
| | - Lauren Petrick
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Megan Horton
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Megan Niedzwiecki
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Elena Colicino
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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Wei W, Li S, Li P, Yu K, Fan G, Wang Y, Zhao F, Zhang X, Feng X, Shi G, Zhang W, Song G, Dan W, Wang F, Zhang Y, Li X, Wang D, Zhang W, Pei J, Wang X, Zhao Z. QTL analysis of important agronomic traits and metabolites in foxtail millet ( Setaria italica) by RIL population and widely targeted metabolome. FRONTIERS IN PLANT SCIENCE 2023; 13:1035906. [PMID: 36704173 PMCID: PMC9872001 DOI: 10.3389/fpls.2022.1035906] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 12/19/2022] [Indexed: 06/18/2023]
Abstract
As a bridge between genome and phenotype, metabolome is closely related to plant growth and development. However, the research on the combination of genome, metabolome and multiple agronomic traits in foxtail millet (Setaria italica) is insufficient. Here, based on the linkage analysis of 3,452 metabolites via with high-quality genetic linkage maps, we detected a total of 1,049 metabolic quantitative trait loci (mQTLs) distributed in 11 hotspots, and 28 metabolite-related candidate genes were mined from 14 mQTLs. In addition, 136 single-environment phenotypic QTL (pQTLs) related to 63 phenotypes were identified by linkage analysis, and there were 12 hotspots on these pQTLs. We futher dissected 39 candidate genes related to agronomic traits through metabolite-phenotype correlation and gene function analysis, including Sd1 semidwarf gene, which can affect plant height by regulating GA synthesis. Combined correlation network and QTL analysis, we found that flavonoid-lignin pathway maybe closely related to plant architecture and yield in foxtail millet. For example, the correlation coefficient between apigenin 7-rutinoside and stem diameter reached 0.98, and they were co-located at 41.33-44.15 Mb of chromosome 5, further gene function analysis revealed that 5 flavonoid pathway genes, as well as Sd1, were located in this interval . Therefore, the correlation and co-localization between flavonoid-lignins and plant architecture may be due to the close linkage of their regulatory genes in millet. Besides, we also found that a combination of genomic and metabolomic for BLUP analysis can better predict plant agronomic traits than genomic or metabolomic data, independently. In conclusion, the combined analysis of mQTL and pQTL in millet have linked genetic, metabolic and agronomic traits, and is of great significance for metabolite-related molecular assisted breeding.
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Affiliation(s)
- Wei Wei
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Shuangdong Li
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Peiyu Li
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Kuohai Yu
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Guangyu Fan
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Yixiang Wang
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Fang Zhao
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Xiaolei Zhang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Xiaolei Feng
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Gaolei Shi
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Weiqin Zhang
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Guoliang Song
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Wenhan Dan
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Feng Wang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Yali Zhang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Xinru Li
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Dequan Wang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Wenying Zhang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Jingjing Pei
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Xiaoming Wang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Zhihai Zhao
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
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4
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An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics. Sci Rep 2022; 12:11867. [PMID: 35831456 PMCID: PMC9278322 DOI: 10.1038/s41598-022-16123-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/05/2022] [Indexed: 12/15/2022] Open
Abstract
The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.
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Lee H, Lee B, Kim Y, Min S, Yang E, Lee S. Effects of Sodium Selenite Injection on Serum Metabolic Profiles in Women Diagnosed with Breast Cancer-Related Lymphedema-Secondary Analysis of a Randomized Placebo-Controlled Trial Using Global Metabolomics. Nutrients 2021; 13:nu13093253. [PMID: 34579131 PMCID: PMC8470409 DOI: 10.3390/nu13093253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/15/2021] [Accepted: 09/15/2021] [Indexed: 02/06/2023] Open
Abstract
In our previous study, intravenous (IV) injection of selenium alleviated breast cancer-related lymphedema (BCRL). This secondary analysis aimed to explore the metabolic effects of selenium on patients with BCRL. Serum samples of the selenium-treated (SE, n = 15) or the placebo-controlled (CTRL, n = 14) groups were analyzed by ultra-high-performance liquid chromatography with Q-Exactive Orbitrap tandem mass spectrometry (UHPLC-Q-Exactive Orbitrap/MS). The SE group showed a lower ratio of extracellular water to segmental water (ECW/SW) in the affected arm to ECW/SW in the unaffected arm (arm ECW/SW ratio) than the CTRL group. Metabolomics analysis showed a valid classification at 2-weeks and 107 differential metabolites were identified. Among them, the levels of corticosterone, LTB4-DMA, and PGE3—which are known anti-inflammatory compounds—were elevated in the SE group. Pathway analysis demonstrated that lipid metabolism (glycerophospholipid metabolism, steroid hormone biosynthesis, or arachidonic acid metabolism), nucleotide metabolism (pyrimidine or purine metabolism), and vitamin metabolism (pantothenate and CoA biosynthesis, vitamin B6 metabolism, ascorbate and aldarate metabolism) were altered in the SE group compared to the CTRL group. In addition, xanthurenic acid levels were negatively associated with whole blood selenium level (WBSe) and positively associated with the arm ECW/SW. In conclusion, selenium IV injection improved the arm ECW/SW ratio and altered the serum metabolic profiles in patients with BCRL, and improved the anti-inflammatory process in lipid, nucleotide and vitamin pathways, which might alleviate the symptoms of BCRL.
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Affiliation(s)
- Heeju Lee
- Department of Food and Nutrition, BK21 FOUR Project, College of Human Ecology, Yonsei University, Seoul 03722, Korea; (H.L.); (Y.K.); (S.M.)
| | - Bora Lee
- Graduate Program in Biomedical Engineering, College of Medicine, Yonsei University, Seoul 03722, Korea;
| | - Yeonhee Kim
- Department of Food and Nutrition, BK21 FOUR Project, College of Human Ecology, Yonsei University, Seoul 03722, Korea; (H.L.); (Y.K.); (S.M.)
| | - Sohyun Min
- Department of Food and Nutrition, BK21 FOUR Project, College of Human Ecology, Yonsei University, Seoul 03722, Korea; (H.L.); (Y.K.); (S.M.)
| | - Eunjoo Yang
- Department of Rehabilitation Medicine, College of Medicine, Seoul National University Bundang Hospital, Seoul National University, Seongnam 13620, Korea;
| | - Seungmin Lee
- Department of Food and Nutrition, BK21 FOUR Project, College of Human Ecology, Yonsei University, Seoul 03722, Korea; (H.L.); (Y.K.); (S.M.)
- Correspondence: ; Tel.: +82-2-2123-3118
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6
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Yamauchi T, Ochi D, Matsukawa N, Saigusa D, Ishikuro M, Obara T, Tsunemoto Y, Kumatani S, Yamashita R, Tanabe O, Minegishi N, Koshiba S, Metoki H, Kuriyama S, Yaegashi N, Yamamoto M, Nagasaki M, Hiyama S, Sugawara J. Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis. Sci Rep 2021; 11:17777. [PMID: 34493809 PMCID: PMC8423760 DOI: 10.1038/s41598-021-97342-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/25/2021] [Indexed: 02/07/2023] Open
Abstract
The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.
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Affiliation(s)
- Takafumi Yamauchi
- grid.419819.c0000 0001 2184 8682X-Tech Development Department, NTT DOCOMO, INC, 3-6 Hikarino-oka, Yokosuka, Kanagawa 239-8536 Japan
| | - Daisuke Ochi
- grid.419819.c0000 0001 2184 8682X-Tech Development Department, NTT DOCOMO, INC, 3-6 Hikarino-oka, Yokosuka, Kanagawa 239-8536 Japan
| | - Naomi Matsukawa
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan
| | - Daisuke Saigusa
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan
| | - Mami Ishikuro
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Graduate School of Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575 Japan
| | - Taku Obara
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Graduate School of Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575 Japan
| | - Yoshiki Tsunemoto
- grid.419819.c0000 0001 2184 8682X-Tech Development Department, NTT DOCOMO, INC, 3-6 Hikarino-oka, Yokosuka, Kanagawa 239-8536 Japan
| | - Satsuki Kumatani
- grid.419819.c0000 0001 2184 8682X-Tech Development Department, NTT DOCOMO, INC, 3-6 Hikarino-oka, Yokosuka, Kanagawa 239-8536 Japan
| | - Riu Yamashita
- grid.272242.30000 0001 2168 5385Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, 6-5-1 Kashiwanoha, Kashiwa, Chiba 277-8577 Japan
| | - Osamu Tanabe
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.418889.40000 0001 2198 115XRadiation Effects Research Foundation, 5-2 Hijiyama Park, Minami-ku, Hiroshima, 732-0815 Japan
| | - Naoko Minegishi
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan
| | - Seizo Koshiba
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.69566.3a0000 0001 2248 6943Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan
| | - Hirohito Metoki
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.412755.00000 0001 2166 7427Faculty of Medicine, Tohoku Medical Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, 981-0905 Japan
| | - Shinichi Kuriyama
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Graduate School of Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575 Japan ,grid.69566.3a0000 0001 2248 6943International Research Institute of Disaster Science, Tohoku University, Aramaki Aza-Aoba 468-1, Aoba-ku, Sendai, 980-8572 Japan
| | - Nobuo Yaegashi
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Graduate School of Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575 Japan ,grid.69566.3a0000 0001 2248 6943Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan
| | - Masayuki Yamamoto
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.69566.3a0000 0001 2248 6943Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan
| | - Masao Nagasaki
- grid.258799.80000 0004 0372 2033Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan ,grid.258799.80000 0004 0372 2033Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8507 Japan
| | - Satoshi Hiyama
- grid.419819.c0000 0001 2184 8682X-Tech Development Department, NTT DOCOMO, INC, 3-6 Hikarino-oka, Yokosuka, Kanagawa 239-8536 Japan
| | - Junichi Sugawara
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Graduate School of Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575 Japan
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7
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Eicher T, Kinnebrew G, Patt A, Spencer K, Ying K, Ma Q, Machiraju R, Mathé EA. Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources. Metabolites 2020; 10:E202. [PMID: 32429287 PMCID: PMC7281435 DOI: 10.3390/metabo10050202] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
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Affiliation(s)
- Tara Eicher
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
| | - Garrett Kinnebrew
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Bioinformatics Shared Resource Group, The Ohio State University, Columbus, OH 43210, USA
| | - Andrew Patt
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
| | - Kyle Spencer
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Nationwide Children’s Research Hospital, Columbus, OH 43210, USA
| | - Kevin Ying
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
| | - Raghu Machiraju
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Ewy A. Mathé
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
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8
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Associations between adipose tissue volume and small molecules in plasma and urine among asymptomatic subjects from the general population. Sci Rep 2020; 10:1487. [PMID: 32001750 PMCID: PMC6992585 DOI: 10.1038/s41598-020-58430-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 01/06/2020] [Indexed: 12/20/2022] Open
Abstract
Obesity is one of the major risk factor for cardiovascular and metabolic diseases. A disproportional accumulation of fat at visceral (VAT) compared to subcutaneous sites (SAT) has been suspected as a key detrimental event. We used non-targeted metabolomics profiling to reveal metabolic pathways associated with higher VAT or SAT amount among subjects free of metabolic diseases to identify possible contributing metabolic pathways. The study population comprised 491 subjects [mean (standard deviation): age 44.6 yrs (13.0), body mass index 25.4 kg/m² (3.6), 60.1% females] without diabetes, hypertension, dyslipidemia, the metabolic syndrome or impaired renal function. We associated MRI-derived fat amounts with mass spectrometry-derived metabolites in plasma and urine using linear regression models adjusting for major confounders. We tested for sex-specific effects using interactions terms and performed sensitivity analyses for the influence of insulin resistance on the results. VAT and SAT were significantly associated with 155 (101 urine) and 49 (29 urine) metabolites, respectively, of which 45 (27 urine) were common to both. Major metabolic pathways were branched-chain amino acid metabolism (partially independent of insulin resistance), surrogate markers of oxidative stress and gut microbial diversity, and cortisol metabolism. We observed a novel positive association between VAT and plasma levels of the potential pharmacological agent piperine. Sex-specific effects were only a few, e.g. the female-specific association between VAT and O-methylascorbate. In brief, higher VAT was associated with an unfavorable metabolite profile in a sample of healthy, mostly non-obese individuals from the general population and only few sex-specific associations became apparent.
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9
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Ismail IT, Showalter MR, Fiehn O. Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics. Metabolites 2019; 9:metabo9100242. [PMID: 31640247 PMCID: PMC6835511 DOI: 10.3390/metabo9100242] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 10/11/2019] [Accepted: 10/15/2019] [Indexed: 12/30/2022] Open
Abstract
Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.
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Affiliation(s)
- Israa T Ismail
- National Liver Institute, Menoufia University, Shebeen El Kom 55955, Egypt.
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
| | - Megan R Showalter
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
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10
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Playdon MC, Joshi AD, Tabung FK, Cheng S, Henglin M, Kim A, Lin T, van Roekel EH, Huang J, Krumsiek J, Wang Y, Mathé E, Temprosa M, Moore S, Chawes B, Eliassen AH, Gsur A, Gunter MJ, Harada S, Langenberg C, Oresic M, Perng W, Seow WJ, Zeleznik OA. Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS). Metabolites 2019; 9:E145. [PMID: 31319517 PMCID: PMC6681081 DOI: 10.3390/metabo9070145] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 06/28/2019] [Accepted: 07/04/2019] [Indexed: 12/13/2022] Open
Abstract
The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.
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Affiliation(s)
- Mary C Playdon
- Department of Nutrition and Integrative Physiology, College of Health, University of Utah, Salt Lake City, UT 84112, USA.
- Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA.
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Fred K Tabung
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH 43210, USA
- Division of Epidemiology, The Ohio State University College of Public Health, Columbus, OH 43210, USA
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Mir Henglin
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Andy Kim
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Tengda Lin
- Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Eline H van Roekel
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Jiaqi Huang
- Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA
| | - Ying Wang
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
| | - Ewy Mathé
- College of Medicine, Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| | - Marinella Temprosa
- Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Steven Moore
- Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 1165 Copenhagen, Denmark
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Claudia Langenberg
- MRC Epidemiology Unit, Public Health, University of Cambridge, Cambridge CB2 1 TN, UK
- The Francis Crick Institute, London NW1 1ST, UK
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku, 20500 Turku, Finland
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Wei Perng
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
- Life course epidemiology of adiposity and diabetes (LEAD) Center, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
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11
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Laíns I, Chung W, Kelly RS, Gil J, Marques M, Barreto P, Murta JN, Kim IK, Vavvas DG, Miller JB, Silva R, Lasky-Su J, Liang L, Miller JW, Husain D. Human Plasma Metabolomics in Age-Related Macular Degeneration: Meta-Analysis of Two Cohorts. Metabolites 2019; 9:E127. [PMID: 31269701 PMCID: PMC6680405 DOI: 10.3390/metabo9070127] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 06/27/2019] [Accepted: 06/28/2019] [Indexed: 01/01/2023] Open
Abstract
The pathogenesis of age-related macular degeneration (AMD), a leading cause of blindness worldwide, remains only partially understood. This has led to the current lack of accessible and reliable biofluid biomarkers for diagnosis and prognosis, and absence of treatments for dry AMD. This study aimed to assess the plasma metabolomic profiles of AMD and its severity stages with the ultimate goal of contributing to addressing these needs. We recruited two cohorts: Boston, United States (n = 196) and Coimbra, Portugal (n = 295). Fasting blood samples were analyzed using ultra-high performance liquid chromatography mass spectrometry. For each cohort, we compared plasma metabolites of AMD patients versus controls (logistic regression), and across disease stages (permutation-based cumulative logistic regression considering both eyes). Meta-analyses were then used to combine results from the two cohorts. Our results revealed that 28 metabolites differed significantly between AMD patients versus controls (false discovery rate (FDR) q-value: 4.1 × 10-2-1.8 × 10-5), and 67 across disease stages (FDR q-value: 4.5 × 10-2-1.7 × 10-4). Pathway analysis showed significant enrichment of glycerophospholipid, purine, taurine and hypotaurine, and nitrogen metabolism (p-value ≤ 0.04). In conclusion, our findings support that AMD patients present distinct plasma metabolomic profiles, which vary with disease severity. This work contributes to the understanding of AMD pathophysiology, and can be the basis of future biomarkers and precision medicine for this blinding condition.
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Affiliation(s)
- Inês Laíns
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal
- Centro Hospitalar e Universitário de Coimbra, 3000 Coimbra, Portugal
- Association for Innovation and Biomedical Research on Light and Image, 3000 Coimbra, Portugal
| | - Wonil Chung
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Rachel S Kelly
- Systems Genetics and Genomics Unit, Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - João Gil
- Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal
| | - Marco Marques
- Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal
| | - Patrícia Barreto
- Association for Innovation and Biomedical Research on Light and Image, 3000 Coimbra, Portugal
| | - Joaquim N Murta
- Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal
- Centro Hospitalar e Universitário de Coimbra, 3000 Coimbra, Portugal
- Association for Innovation and Biomedical Research on Light and Image, 3000 Coimbra, Portugal
| | - Ivana K Kim
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Demetrios G Vavvas
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - John B Miller
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Rufino Silva
- Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal
- Centro Hospitalar e Universitário de Coimbra, 3000 Coimbra, Portugal
- Association for Innovation and Biomedical Research on Light and Image, 3000 Coimbra, Portugal
| | - Jessica Lasky-Su
- Systems Genetics and Genomics Unit, Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Liming Liang
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Joan W Miller
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Deeba Husain
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA.
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12
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Köttgen A, Raffler J, Sekula P, Kastenmüller G. Genome-Wide Association Studies of Metabolite Concentrations (mGWAS): Relevance for Nephrology. Semin Nephrol 2019; 38:151-174. [PMID: 29602398 DOI: 10.1016/j.semnephrol.2018.01.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Metabolites are small molecules that are intermediates or products of metabolism, many of which are freely filtered by the kidneys. In addition, the kidneys have a central role in metabolite anabolism and catabolism, as well as in active metabolite reabsorption and/or secretion during tubular passage. This review article illustrates how the coupling of genomics and metabolomics in genome-wide association analyses of metabolites can be used to illuminate mechanisms underlying human metabolism, with a special focus on insights relevant to nephrology. First, genetic susceptibility loci for reduced kidney function and chronic kidney disease (CKD) were reviewed systematically for their associations with metabolite concentrations in metabolomics studies of blood and urine. Second, kidney function and CKD-associated metabolites reported from observational studies were interrogated for metabolite-associated genetic variants to generate and discuss complementary insights. Finally, insights originating from the simultaneous study of both blood and urine or by modeling intermetabolite relationships are summarized. We also discuss methodologic questions related to the study of metabolite concentrations in urine as well as among CKD patients. In summary, genome-wide association analyses of metabolites using metabolite concentrations quantified from blood and/or urine are a promising avenue of research to illuminate physiological and pathophysiological functions of the kidney.
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Affiliation(s)
- Anna Köttgen
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
| | - Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Peggy Sekula
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
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13
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Oza VH, Aicher JK, Reed LK. Random Forest Analysis of Untargeted Metabolomics Data Suggests Increased Use of Omega Fatty Acid Oxidation Pathway in Drosophila Melanogaster Larvae Fed a Medium Chain Fatty Acid Rich High-Fat Diet. Metabolites 2018; 9:metabo9010005. [PMID: 30602659 PMCID: PMC6359074 DOI: 10.3390/metabo9010005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 12/27/2018] [Accepted: 12/27/2018] [Indexed: 12/27/2022] Open
Abstract
Obesity is a complex disease, shaped by both genetic and environmental factors such as diet. In this study, we use untargeted metabolomics and Drosophila melanogaster to model how diet and genotype shape the metabolome of obese phenotypes. We used 16 distinct outbred genotypes of Drosophila larvae raised on normal (ND) and high-fat (HFD) diets, to produce three distinct phenotypic classes; genotypes that stored more triglycerides on a ND relative to the HFD, genotypes that stored more triglycerides on a HFD relative to ND, and genotypes that showed no change in triglyceride storage on either of the two diets. Using untargeted metabolomics we characterized 350 metabolites: 270 with definitive chemical IDs and 80 that were chemically unidentified. Using random forests, we determined metabolites that were important in discriminating between the HFD and ND larvae as well as between the triglyceride phenotypic classes. We found that flies fed on a HFD showed evidence of an increased use of omega fatty acid oxidation pathway, an alternative to the more commonly used beta fatty acid oxidation pathway. Additionally, we observed no correlation between the triglyceride storage phenotype and free fatty acid levels (laurate, caprate, caprylate, caproate), indicating that the distinct metabolic profile of fatty acids in high-fat diet fed Drosophila larvae does not propagate into triglyceride storage differences. However, dipeptides did show moderate differences between the phenotypic classes. We fit Gaussian graphical models (GGMs) of the metabolic profiles for HFD and ND flies to characterize changes in metabolic network structure between the two diets, finding the HFD to have a greater number of edges indicating that metabolome varies more across samples on a HFD. Taken together, these results show that, in the context of obesity, metabolomic profiles under distinct dietary conditions may not be reliable predictors of phenotypic outcomes in a genetically diverse population.
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Affiliation(s)
- Vishal H Oza
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA.
| | - Joseph K Aicher
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA.
| | - Laura K Reed
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA.
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14
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Laíns I, Gantner M, Murinello S, Lasky-Su JA, Miller JW, Friedlander M, Husain D. Metabolomics in the study of retinal health and disease. Prog Retin Eye Res 2018; 69:57-79. [PMID: 30423446 DOI: 10.1016/j.preteyeres.2018.11.002] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 10/06/2018] [Accepted: 11/07/2018] [Indexed: 02/06/2023]
Abstract
Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.
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Affiliation(s)
- Inês Laíns
- Retina Service, Massachusetts Eye and Ear, Harvard Medical School, 243 Charles Street, Boston, MA, 02114, United States; Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal.
| | - Mari Gantner
- Lowy Medical Research Institute, La Jolla, CA, 92037, United States; Scripps Research Institute, La Jolla, CA, 92037, United States.
| | - Salome Murinello
- Lowy Medical Research Institute, La Jolla, CA, 92037, United States; Scripps Research Institute, La Jolla, CA, 92037, United States.
| | - Jessica A Lasky-Su
- Systems Genetics and Genomics Unit, Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, United States.
| | - Joan W Miller
- Retina Service, Massachusetts Eye and Ear, Harvard Medical School, 243 Charles Street, Boston, MA, 02114, United States.
| | - Martin Friedlander
- Lowy Medical Research Institute, La Jolla, CA, 92037, United States; Scripps Research Institute, La Jolla, CA, 92037, United States.
| | - Deeba Husain
- Retina Service, Massachusetts Eye and Ear, Harvard Medical School, 243 Charles Street, Boston, MA, 02114, United States.
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15
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Do KT, Wahl S, Raffler J, Molnos S, Laimighofer M, Adamski J, Suhre K, Strauch K, Peters A, Gieger C, Langenberg C, Stewart ID, Theis FJ, Grallert H, Kastenmüller G, Krumsiek J. Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies. Metabolomics 2018; 14:128. [PMID: 30830398 PMCID: PMC6153696 DOI: 10.1007/s11306-018-1420-2] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 08/24/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.
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Affiliation(s)
- Kieu Trinh Do
- Institute of Computational Biology, Helmholtz-Zentrum München, Neuherberg, Germany
| | - Simone Wahl
- Institute of Epidemiology II, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz-Zentrum München, Neuherberg, Germany
| | - Sophie Molnos
- Institute of Epidemiology II, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Michael Laimighofer
- Institute of Computational Biology, Helmholtz-Zentrum München, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising, Germany
- German Center for Cardiovascular Disease Research (DZHK e.V.), Munich, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Doha, Qatar
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology II, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
| | - Christian Gieger
- Institute of Epidemiology II, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
| | | | | | - Fabian J Theis
- Institute of Computational Biology, Helmholtz-Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Garching, Germany
| | - Harald Grallert
- Institute of Epidemiology II, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Gabi Kastenmüller
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany.
- Institute of Bioinformatics and Systems Biology, Helmholtz-Zentrum München, Neuherberg, Germany.
| | - Jan Krumsiek
- Institute of Computational Biology, Helmholtz-Zentrum München, Neuherberg, Germany.
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany.
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, USA.
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Tebani A, Afonso C, Bekri S. Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery. J Inherit Metab Dis 2018; 41:393-406. [PMID: 28842777 PMCID: PMC5959951 DOI: 10.1007/s10545-017-0080-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 07/27/2017] [Accepted: 07/28/2017] [Indexed: 12/11/2022]
Abstract
This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Carlos Afonso
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France.
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France.
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Tebani A, Afonso C, Bekri S. Advances in metabolome information retrieval: turning chemistry into biology. Part I: analytical chemistry of the metabolome. J Inherit Metab Dis 2018; 41:379-391. [PMID: 28840392 PMCID: PMC5959978 DOI: 10.1007/s10545-017-0074-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 06/28/2017] [Accepted: 07/14/2017] [Indexed: 12/20/2022]
Abstract
Metabolites are small molecules produced by enzymatic reactions in a given organism. Metabolomics or metabolic phenotyping is a well-established omics aimed at comprehensively assessing metabolites in biological systems. These comprehensive analyses use analytical platforms, mainly nuclear magnetic resonance spectroscopy and mass spectrometry, along with associated separation methods to gather qualitative and quantitative data. Metabolomics holistically evaluates biological systems in an unbiased, data-driven approach that may ultimately support generation of hypotheses. The approach inherently allows the molecular characterization of a biological sample with regard to both internal (genetics) and environmental (exosome, microbiome) influences. Metabolomics workflows are based on whether the investigator knows a priori what kind of metabolites to assess. Thus, a targeted metabolomics approach is defined as a quantitative analysis (absolute concentrations are determined) or a semiquantitative analysis (relative intensities are determined) of a set of metabolites that are possibly linked to common chemical classes or a selected metabolic pathway. An untargeted metabolomics approach is a semiquantitative analysis of the largest possible number of metabolites contained in a biological sample. This is part I of a review intending to give an overview of the state of the art of major metabolic phenotyping technologies. Furthermore, their inherent analytical advantages and limits regarding experimental design, sample handling, standardization and workflow challenges are discussed.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Carlos Afonso
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France.
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France.
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Ladva CN, Golan R, Greenwald R, Yu T, Sarnat SE, Flanders WD, Uppal K, Walker DI, Tran V, Liang D, Jones DP, Sarnat JA. Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection. J Breath Res 2017; 12:016008. [PMID: 28808178 DOI: 10.1088/1752-7163/aa863c] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
INTRODUCTION Advances in the development of high-resolution metabolomics (HRM) have provided new opportunities for their use in characterizing exposures to environmental air pollutants and air pollution-related disease etiologies. Exposure assessment studies have considered blood, breath, and saliva as biological matrices suitable for measuring responses to air pollution exposures. The current study examines comparability among these three matrices using HRM and explores their potential for measuring mobile-source air toxics. METHODS Four participants provided saliva, exhaled breath concentrate (EBC), and plasma before and after a 2 h road traffic exposure. Samples were analyzed on a Thermo Scientific QExactive MS system in positive electrospray ionization mode and resolution of 70 000 full-width at half-maximum with C18 chromatography. Data were processed using an apLCMS and xMSanalyzer on the R statistical platform. RESULTS The analysis yielded 7110, 6019, and 7747 reproducible features in plasma, EBC, and saliva, respectively. Correlations were moderate-to-strong (R = 0.41-0.80) across all pairwise comparisons of feature intensity within profiles, with the strongest between EBC and saliva. The associations of mean intensities between matrix pairs were positive and significant, controlling for subject and sampling time effects. Six out of 20 features shared in all three matrices putatively matched a list of known mobile-source air toxics. CONCLUSIONS Plasma, saliva, and EBC have largely comparable metabolic profiles measurable through HRM. These matrices have the potential to be used in identification and measurement of exposures to mobile-source air toxics, though further, targeted study is needed.
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Affiliation(s)
- Chandresh Nanji Ladva
- Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, United States of America
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Benedetti E, Pučić-Baković M, Keser T, Wahl A, Hassinen A, Yang JY, Liu L, Trbojević-Akmačić I, Razdorov G, Štambuk J, Klarić L, Ugrina I, Selman MHJ, Wuhrer M, Rudan I, Polasek O, Hayward C, Grallert H, Strauch K, Peters A, Meitinger T, Gieger C, Vilaj M, Boons GJ, Moremen KW, Ovchinnikova T, Bovin N, Kellokumpu S, Theis FJ, Lauc G, Krumsiek J. Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway. Nat Commun 2017; 8:1483. [PMID: 29133956 PMCID: PMC5684356 DOI: 10.1038/s41467-017-01525-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Accepted: 09/25/2017] [Indexed: 12/19/2022] Open
Abstract
Immunoglobulin G (IgG) is a major effector molecule of the human immune response, and aberrations in IgG glycosylation are linked to various diseases. However, the molecular mechanisms underlying protein glycosylation are still poorly understood. We present a data-driven approach to infer reactions in the IgG glycosylation pathway using large-scale mass-spectrometry measurements. Gaussian graphical models are used to construct association networks from four cohorts. We find that glycan pairs with high partial correlations represent enzymatic reactions in the known glycosylation pathway, and then predict new biochemical reactions using a rule-based approach. Validation is performed using data from a GWAS and results from three in vitro experiments. We show that one predicted reaction is enzymatically feasible and that one rejected reaction does not occur in vitro. Moreover, in contrast to previous knowledge, enzymes involved in our predictions colocalize in the Golgi of two cell lines, further confirming the in silico predictions.
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Affiliation(s)
- Elisa Benedetti
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | | | - Toma Keser
- Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia
| | - Annika Wahl
- Institute of Epidemiology 2, Research Unit Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Institute of Epidemiology 2, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Antti Hassinen
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, FI-90014 Oulu, Finland
| | - Jeong-Yeh Yang
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602 USA
| | - Lin Liu
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602 USA
| | | | | | - Jerko Štambuk
- Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia
| | - Lucija Klarić
- Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, EH8 9AG Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Ivo Ugrina
- Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia
- Faculty of Science, University of Split, 21000 Split, Croatia
- Intellomics Ltd., 10000 Zagreb, Croatia
| | | | - Manfred Wuhrer
- Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, EH8 9AG Edinburgh, UK
| | - Ozren Polasek
- University of Split School of Medicine, 21000 Split, Croatia
- Gen-info Ltd., 10000 Zagreb, Croatia
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Harald Grallert
- Institute of Epidemiology 2, Research Unit Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Institute of Epidemiology 2, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- German Center for Diabetes Research (DZD), 40225 Düsseldorf, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians Universität, 81577 Munich, Germany
| | - Annette Peters
- Institute of Epidemiology 2, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Christian Gieger
- Institute of Epidemiology 2, Research Unit Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Institute of Epidemiology 2, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Marija Vilaj
- Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia
| | - Geert-Jan Boons
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602 USA
- Department of Chemical Biology and Drug Discovery, Utrecht Institute for Pharmaceutical Sciences, and Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CG Utrecht, The Netherlands
| | - Kelley W. Moremen
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602 USA
| | - Tatiana Ovchinnikova
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Nicolai Bovin
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Sakari Kellokumpu
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, FI-90014 Oulu, Finland
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Department of Mathematics, Technical University Munich, 85748 Garching bei München, Germany
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia
| | - Jan Krumsiek
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- German Center for Diabetes Research (DZD), 40225 Düsseldorf, Germany
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Breier M, Wahl S, Prehn C, Ferrari U, Sacco V, Weise M, Grallert H, Adamski J, Lechner A. Immediate reduction of serum citrulline but no change of steroid profile after initiation of metformin in individuals with type 2 diabetes. J Steroid Biochem Mol Biol 2017; 174:114-119. [PMID: 28801099 DOI: 10.1016/j.jsbmb.2017.08.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 06/30/2017] [Accepted: 08/07/2017] [Indexed: 12/29/2022]
Abstract
Metformin is the most important first-line treatment for type 2 diabetes mellitus (T2DM) but its exact mode of action remains unknown. In this study, we used targeted metabolomics to gain new insights into the metabolic effects of metformin in humans with T2DM. We also examined changes in the serum steroid hormone profile. We quantified 167 serum metabolites and 19 steroid hormones using liquid chromatography-tandem mass spectrometry at three time points in individuals with previously untreated T2DM: before the start of metformin therapy (time point A), after the first dose (B) and after short-term therapy for 4-6 weeks (C). For metabolite analysis, we split the study cohort into a discovery and a replication study of 88 and 45 subjects, respectively. The statistical analysis was done using linear mixed-effects models. Among the metabolites quantified, citrulline showed the most pronounced changes. Compared to its baseline serum concentration, citrulline was reduced by 17% after the first dose of metformin (p=1.34E-07) and by 24% after short-term therapy (p=2.84E-08) in the discovery study. These results were confirmed in the replication study. The only other metabolite significantly changed after correction for multiple testing was PC ae C36:4 between baseline and 4-6 weeks. The serum steroid hormone profile showed no significant changes after metformin intake. In summary, we observed an immediate and sustained reduction of serum citrulline by metformin in humans. This may be relevant for some of the wanted or unwanted effects of the drug.
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Affiliation(s)
- Michaela Breier
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology II, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany; Clinical Cooperation Group Subclassification of Type 2 Diabetes, Helmholtz Zentrum Muenchen, Munich, Germany.
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology II, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany.
| | - Cornelia Prehn
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Uta Ferrari
- German Center for Diabetes Research, Neuherberg, Germany; Clinical Cooperation Group Subclassification of Type 2 Diabetes, Helmholtz Zentrum Muenchen, Munich, Germany; Medizinische Klinik und Poliklinik IV, Diabetes Research Group, Klinikum der Universitaet Muenchen, Munich, Germany.
| | - Vanessa Sacco
- German Center for Diabetes Research, Neuherberg, Germany; Clinical Cooperation Group Subclassification of Type 2 Diabetes, Helmholtz Zentrum Muenchen, Munich, Germany; Medizinische Klinik und Poliklinik IV, Diabetes Research Group, Klinikum der Universitaet Muenchen, Munich, Germany.
| | - Michaela Weise
- German Center for Diabetes Research, Neuherberg, Germany; Clinical Cooperation Group Subclassification of Type 2 Diabetes, Helmholtz Zentrum Muenchen, Munich, Germany; Medizinische Klinik und Poliklinik IV, Diabetes Research Group, Klinikum der Universitaet Muenchen, Munich, Germany.
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology II, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany; Clinical Cooperation Group Subclassification of Type 2 Diabetes, Helmholtz Zentrum Muenchen, Munich, Germany.
| | - Jerzy Adamski
- German Center for Diabetes Research, Neuherberg, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany; Chair for Experimental Genetics, Technical University of Munich, Freising-Weihenstephan, Germany.
| | - Andreas Lechner
- German Center for Diabetes Research, Neuherberg, Germany; Clinical Cooperation Group Subclassification of Type 2 Diabetes, Helmholtz Zentrum Muenchen, Munich, Germany; Medizinische Klinik und Poliklinik IV, Diabetes Research Group, Klinikum der Universitaet Muenchen, Munich, Germany.
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Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations. NPJ Syst Biol Appl 2017; 3:28. [PMID: 28948040 PMCID: PMC5608949 DOI: 10.1038/s41540-017-0029-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 08/18/2017] [Accepted: 08/25/2017] [Indexed: 12/27/2022] Open
Abstract
The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered. Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field.
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23
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Rist MJ, Roth A, Frommherz L, Weinert CH, Krüger R, Merz B, Bunzel D, Mack C, Egert B, Bub A, Görling B, Tzvetkova P, Luy B, Hoffmann I, Kulling SE, Watzl B. Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study. PLoS One 2017; 12:e0183228. [PMID: 28813537 PMCID: PMC5558977 DOI: 10.1371/journal.pone.0183228] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 08/01/2017] [Indexed: 12/15/2022] Open
Abstract
Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles. In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18–80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms. Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy. The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.
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Affiliation(s)
- Manuela J. Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
- * E-mail:
| | - Alexander Roth
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Lara Frommherz
- Department of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Christoph H. Weinert
- Department of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Ralf Krüger
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Benedikt Merz
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Diana Bunzel
- Department of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Carina Mack
- Department of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Björn Egert
- Department of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Achim Bub
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Benjamin Görling
- Institute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Pavleta Tzvetkova
- Institute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Burkhard Luy
- Institute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Ingrid Hoffmann
- Department of Nutrition Behaviour, Max Rubner-Institut, Max Rubner-Institut, Karlsruhe, Germany
| | - Sabine E. Kulling
- Department of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Bernhard Watzl
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
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Bekri S. The role of metabolomics in precision medicine. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016. [DOI: 10.1080/23808993.2016.1273067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76000, France
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, INSERM U1245, Rouen 76000, France
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Trexel J, Yoon GS, Keswani RK, McHugh C, Yeomans L, Vitvitsky V, Banerjee R, Sud S, Sun Y, Rosania GR, Stringer KA. Macrophage-Mediated Clofazimine Sequestration Is Accompanied by a Shift in Host Energy Metabolism. J Pharm Sci 2016; 106:1162-1174. [PMID: 28007559 DOI: 10.1016/j.xphs.2016.12.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 12/05/2016] [Accepted: 12/07/2016] [Indexed: 02/07/2023]
Abstract
Prolonged (8 weeks) oral administration of clofazimine results in a profound pharmacodynamic response-bioaccumulation in macrophages (including Kupffer cells) as intracellular crystal-like drug inclusions (CLDIs) with an associated increase in interleukin-1 receptor antagonist production. Notably, CLDI formation in Kupffer cells concomitantly occurs with the formation of macrophage-centric granulomas. Accordingly, we sought to understand the impact of these events on host metabolism using 1H-nuclear magnetic resonance metabolomics. Mice received a clofazimine or vehicle-enriched (sham) diet for at least 8 weeks. At 2 weeks, the antimicrobial activity of clofazimine was evident by changes in urine metabolites. From 2 to 8 weeks, there was a striking change in metabolite levels indicative of a reorientation of host energy metabolism paralleling the onset of CLDI and granuloma formation. This was evidenced by a progressive reduction in urine levels of metabolites involved in one-carbon metabolism with corresponding increases in whole blood, and changes in metabolites associated with lipid, nucleotide and amino acid metabolism, and glycolysis. Although clofazimine-fed mice ate more, they gained less weight than control mice. Together, these results indicate that macrophage sequestration of clofazimine as CLDIs and granuloma formation is accompanied by a profound metabolic disruption in energy homeostasis and one-carbon metabolism.
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Affiliation(s)
- Julie Trexel
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109; NMR Metabolomics Laboratory, University of Michigan, Ann Arbor, Michigan 48109
| | - Gi S Yoon
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109
| | - Rahul K Keswani
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109
| | - Cora McHugh
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109; NMR Metabolomics Laboratory, University of Michigan, Ann Arbor, Michigan 48109
| | - Larisa Yeomans
- NMR Metabolomics Laboratory, University of Michigan, Ann Arbor, Michigan 48109
| | - Victor Vitvitsky
- Department of Biological Chemistry, School of Medicine, University of Michigan, Ann Arbor, Michigan 48109
| | - Ruma Banerjee
- Department of Biological Chemistry, School of Medicine, University of Michigan, Ann Arbor, Michigan 48109
| | - Sudha Sud
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109
| | - Yihan Sun
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109; NMR Metabolomics Laboratory, University of Michigan, Ann Arbor, Michigan 48109
| | - Gus R Rosania
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109
| | - Kathleen A Stringer
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109; NMR Metabolomics Laboratory, University of Michigan, Ann Arbor, Michigan 48109; Center for Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, Michigan 48109.
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Adamski J. Key elements of metabolomics in the study of biomarkers of diabetes. Diabetologia 2016; 59:2497-2502. [PMID: 27714446 DOI: 10.1007/s00125-016-4044-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 04/27/2016] [Indexed: 12/21/2022]
Abstract
Metabolomics is instrumental in the analysis of disease mechanisms and biomarkers of disease. The human metabolome is influenced by genetics and environmental interactions and reveals characteristic signatures of disease. Population studies with metabolomics require special study designs and care needs to be taken with pre-analytics. Gas chromatography coupled to mass spectrometry, liquid chromatography coupled to mass spectrometry or NMR are popular techniques used for metabolomic analyses in human cohorts. Metabolomics has been successfully used in the biomarker search for disease prediction and progression, for analyses of drug action and for the development of companion diagnostics. Several metabolites or metabolite classes identified by metabolomics have gained much attention in the field of diabetes research in the search for early disease detection, differentiation of progressor types and compliance with medication. This review summarises a presentation given at the 'New approaches beyond genetics' symposium at the 2015 annual meeting of the EASD. It is accompanied by another review from this symposium by Bernd Mayer (DOI: 10.1007/s00125-016-4032-2 ) and an overview by the Session Chair, Leif Groop (DOI: 10.1007/s00125-016-4014-4 ).
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Affiliation(s)
- Jerzy Adamski
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Experimental Genetics, Genome Analysis Center, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany.
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Tebani A, Afonso C, Marret S, Bekri S. Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations. Int J Mol Sci 2016; 17:ijms17091555. [PMID: 27649151 PMCID: PMC5037827 DOI: 10.3390/ijms17091555] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 09/06/2016] [Accepted: 09/07/2016] [Indexed: 12/20/2022] Open
Abstract
The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76031 Rouen, France.
- Normandie University, UNIROUEN, INSERM, CHU Rouen, Laboratoire NeoVasc ERI28, 76000 Rouen, France.
- Normandie University, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000 Rouen, France.
| | - Carlos Afonso
- Normandie University, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000 Rouen, France.
| | - Stéphane Marret
- Normandie University, UNIROUEN, INSERM, CHU Rouen, Laboratoire NeoVasc ERI28, 76000 Rouen, France.
- Department of Neonatal Pediatrics, Intensive Care and Neuropediatrics, Rouen University Hospital, 76031 Rouen, France.
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, 76031 Rouen, France.
- Normandie University, UNIROUEN, INSERM, CHU Rouen, Laboratoire NeoVasc ERI28, 76000 Rouen, France.
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Smita S, Lange F, Wolkenhauer O, Köhling R. Deciphering hallmark processes of aging from interaction networks. Biochim Biophys Acta Gen Subj 2016; 1860:2706-15. [PMID: 27456767 DOI: 10.1016/j.bbagen.2016.07.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 07/18/2016] [Accepted: 07/20/2016] [Indexed: 12/18/2022]
Abstract
BACKGROUND Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death. SCOPE OF REVIEW The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors. MAJOR CONCLUSIONS Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging. GENERAL SIGNIFICANCE In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
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Affiliation(s)
- Suchi Smita
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany; Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
| | - Falko Lange
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany; Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa.
| | - Rüdiger Köhling
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
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Tebani A, Abily-Donval L, Afonso C, Marret S, Bekri S. Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era. Int J Mol Sci 2016; 17:ijms17071167. [PMID: 27447622 PMCID: PMC4964538 DOI: 10.3390/ijms17071167] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Revised: 07/12/2016] [Accepted: 07/15/2016] [Indexed: 12/29/2022] Open
Abstract
Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76031, France.
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, Rouen 76000, France.
| | - Lenaig Abily-Donval
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Department of Neonatal Pediatrics and Intensive Care, Rouen University Hospital, Rouen 76031, France.
| | - Carlos Afonso
- Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, Rouen 76000, France.
| | - Stéphane Marret
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Department of Neonatal Pediatrics and Intensive Care, Rouen University Hospital, Rouen 76031, France.
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76031, France.
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
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Wallace MAG, Kormos TM, Pleil JD. Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART B, CRITICAL REVIEWS 2016; 19:380-409. [PMID: 27759495 PMCID: PMC6147038 DOI: 10.1080/10937404.2016.1215772] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Environmental health science aims to link environmental pollution sources to adverse health outcomes to develop effective exposure intervention strategies that reduce long-term disease risks. Over the past few decades, the public health community recognized that health risk is driven by interaction between the human genome and external environment. Now that the human genetic code has been sequenced, establishing this "G × E" (gene-environment) interaction requires a similar effort to decode the human exposome, which is the accumulation of an individual's environmental exposures and metabolic responses throughout the person's lifetime. The exposome is composed of endogenous and exogenous chemicals, many of which are measurable as biomarkers in blood, breath, and urine. Exposure to pollutants is assessed by analyzing biofluids for the pollutant itself or its metabolic products. New methods are being developed to use a subset of biomarkers, termed bioindicators, to demonstrate biological changes indicative of future adverse health effects. Typically, environmental biomarkers are assessed using noninvasive (excreted) media, such as breath and urine. Blood is often avoided for biomonitoring due to practical reasons such as medical personnel, infectious waste, or clinical setting, despite the fact that blood represents the central compartment that interacts with every living cell and is the most relevant biofluid for certain applications and analyses. The aims of this study were to (1) review the current use of blood samples in environmental health research, (2) briefly contrast blood with other biological media, and (3) propose additional applications for blood analysis in human exposure research.
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Affiliation(s)
- M Ariel Geer Wallace
- a Exposure Methods and Measurement Division, National Exposure Research Laboratory, Office of Research and Development , U.S. Environmental Protection Agency , Research Triangle Park , North Carolina , USA
| | | | - Joachim D Pleil
- a Exposure Methods and Measurement Division, National Exposure Research Laboratory, Office of Research and Development , U.S. Environmental Protection Agency , Research Triangle Park , North Carolina , USA
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The foodomics approach for discovering biomarkers of food consumption in nutrition studies. Curr Opin Food Sci 2015. [DOI: 10.1016/j.cofs.2015.07.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Yousri NA, Mook-Kanamori DO, Selim MMED, Takiddin AH, Al-Homsi H, Al-Mahmoud KAS, Karoly ED, Krumsiek J, Do KT, Neumaier U, Mook-Kanamori MJ, Rowe J, Chidiac OM, McKeon C, Al Muftah WA, Kader SA, Kastenmüller G, Suhre K. A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood and urine at different timescales of glycaemic control. Diabetologia 2015; 58:1855-67. [PMID: 26049400 PMCID: PMC4499109 DOI: 10.1007/s00125-015-3636-2] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 04/20/2015] [Indexed: 12/21/2022]
Abstract
AIMS/HYPOTHESIS Metabolomics has opened new avenues for studying metabolic alterations in type 2 diabetes. While many urine and blood metabolites have been associated individually with diabetes, a complete systems view analysis of metabolic dysregulations across multiple biofluids and over varying timescales of glycaemic control is still lacking. METHODS Here we report a broad metabolomics study in a clinical setting, covering 2,178 metabolite measures in saliva, blood plasma and urine from 188 individuals with diabetes and 181 controls of Arab and Asian descent. Using multivariate linear regression we identified metabolites associated with diabetes and markers of acute, short-term and long-term glycaemic control. RESULTS Ninety-four metabolite associations with diabetes were identified at a Bonferroni level of significance (p < 2.3 × 10(-5)), 16 of which have never been reported. Sixty-five of these diabetes-associated metabolites were associated with at least one marker of glycaemic control in the diabetes group. Using Gaussian graphical modelling, we constructed a metabolic network that links diabetes-associated metabolites from three biofluids across three different timescales of glycaemic control. CONCLUSIONS/INTERPRETATION Our study reveals a complex network of biochemical dysregulation involving metabolites from different pathways of diabetes pathology, and provides a reference framework for future diabetes studies with metabolic endpoints.
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Affiliation(s)
- Noha A. Yousri
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, PO Box 24144, Doha, Qatar
- Department of Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
| | - Dennis O. Mook-Kanamori
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, PO Box 24144, Doha, Qatar
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
- Department of Endocrinology, Leiden University Medical Centre, Leiden, the Netherlands
| | | | | | - Hala Al-Homsi
- Dermatology Department, Hamad Medical Corporation, Doha, Qatar
| | | | | | - Jan Krumsiek
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Kieu Thinh Do
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ulrich Neumaier
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Marjonneke J. Mook-Kanamori
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, PO Box 24144, Doha, Qatar
| | - Jillian Rowe
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, PO Box 24144, Doha, Qatar
| | - Omar M. Chidiac
- Clinical Research Core, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, Doha, Qatar
| | - Cindy McKeon
- Clinical Research Core, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, Doha, Qatar
| | - Wadha A. Al Muftah
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, PO Box 24144, Doha, Qatar
| | - Sara Abdul Kader
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, PO Box 24144, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environment Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation – Education City, PO Box 24144, Doha, Qatar
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Functional metabolomics: from biomarker discovery to metabolome reprogramming. Protein Cell 2015; 6:628-37. [PMID: 26135925 PMCID: PMC4537470 DOI: 10.1007/s13238-015-0185-x] [Citation(s) in RCA: 209] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Accepted: 05/28/2015] [Indexed: 12/14/2022] Open
Abstract
Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabolomics here. The striking feature on the similarity of the basic metabolic pathways and components among vastly different species makes the reprogramming metabolomics possible when the engineered metabolites play biological roles in cellular activity as a substrate of enzymes and a regulator to other molecules including proteins. The reprogramming metabolomics approach can be used to clarify metabolic mechanisms of responding to changed internal and external environmental factors and to establish a framework to develop targeted tools for dealing with the changes such as controlling and/or preventing infection with pathogens and enhancing host immunity against pathogens. This review introduces the current state and trends of discovery metabolomics and reprogramming metabolomics and highlights the importance of reprogramming metabolomics.
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