@article{19c62c9d4faa493395706e4fa7b9d46a,
title = "Genome-scale metabolic modelling of the human gut microbiome reveals changes in the glyoxylate and dicarboxylate metabolism in metabolic disorders",
abstract = "The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders.",
keywords = "Metabolomics, Microbiome, Omics, Systems biology",
author = "Ceri Proffitt and Gholamreza Bidkhori and Sunjae Lee and Abdellah Tebani and Adil Mardinoglu and Mathias Uhlen and Moyes, {David L.} and Saeed Shoaie",
note = "Funding Information: This study was supported by Engineering and Physical Sciences Research Council (EPSRC) (EP/S001301/1), Biotechnology Biological Sciences Research Council (BBSRC) (BB/M009513/1). This study used the Swedish National Infrastructure for Computing at SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project SNIC 2020-5-222, SNIC 2019/3-226, SNIC 2020/6-153, SNIC 2021/5-248, SNIC 2021/6-242 and SNIC 2021/6-89, and King's College London computational infrastructure facility, Rosalind (https://rosalind.kcl.ac.uk) for high-performance computing. SS, CP, and DLM conceived the study idea. CP and GB reconstructed the GEMs and ran all the simulations and modeling. CP and SL analyzed the patient's metagenome data. MU, AT, and AM provided the metabolomics data. CP and GB made the figures. CP wrote and drafted the article. DLM, AM, MU, and SS provided critical feedback on the article. All authors read, edited, and reviewed the article. The authors declare no competing financial interests. Funding Information: This study was supported by Engineering and Physical Sciences Research Council (EPSRC) ( EP/S001301/1 ), Biotechnology Biological Sciences Research Council ( BBSRC ) ( BB/M009513/1 ). This study used the Swedish National Infrastructure for Computing at SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project SNIC 2020-5-222, SNIC 2019/3-226, SNIC 2020/6-153, SNIC 2021/5-248, SNIC 2021/6-242 and SNIC 2021/6-89, and King{\textquoteright}s College London computational infrastructure facility, Rosalind ( https://rosalind.kcl.ac.uk ) for high-performance computing. Publisher Copyright: {\textcopyright} 2022 The Authors",
year = "2022",
month = jul,
day = "15",
doi = "10.1016/j.isci.2022.104513",
language = "English",
volume = "25",
journal = "iScience",
issn = "2589-0042",
publisher = "Elsevier publishing company",
number = "7",
}
