@article{3ff584e090fc400bb640586b956b5759,
title = "Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis",
abstract = "Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background, and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants and diabetes, 56 heterogenous MAFLD patients are characterized by generating multiomics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. The dysbiosis in the oral and gut microbiome is explored and the host–microbiome interactions based on global metabolic and inflammatory processes are revealed. These multiomics data are integrated using the biological network and HS's key features are identified using multiomics data. HS is finally predicted using these key features and findings are validated in a follow-up cohort, where 22 subjects with varying degree of HS are characterized.",
keywords = "gut and oral metagenomics, metabolic dysfunction-associated fatty liver disease, metabolomics, multiomics analysis, proteomics, systems biology, systems medicine",
author = "Mujdat Zeybel and Muhammad Arif and Xiangyu Li and Ozlem Altay and Hong Yang and Mengnan Shi and Murat Akyildiz and Burcin Saglam and Gonenli, {Mehmet Gokhan} and Buket Yigit and Burge Ulukan and Dilek Ural and Saeed Shoaie and Hasan Turkez and Jens Nielsen and Cheng Zhang and Mathias Uhl{\'e}n and Jan Bor{\'e}n and Adil Mardinoglu",
note = "Funding Information: This work was financially supported by Knut and Alice Wallenberg Foundation and ScandiBio Therapeutics AB, Sweden. The authors thank the Plasma Profiling Facility team at SciLifeLab in Stockholm for generating the Olink proteomics data, Metabolon Inc. (Durham, USA) for the generation of metabolomics data, and NGI, Scilifelab for the generation of metagenomics data. The authors gratefully acknowledge the use of the services and facilities of the Ko{\c c} University Research Center for Translational Medicine (KUTTAM), equally funded by the Republic of Turkey Ministry of Development Research Infrastructure Support Program. Findings, opinions, or points of view expressed in this article do not necessarily represent the official position or policies of the Ministry of Development. A.M. and H.Y. acknowledge support from the PoLiMeR Innovative Training Network (Marie Sk{\l}odowska-Curie Grant Agreement No. 812616) which has received funding from the European Union's Horizon 2020 research and innovation program. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project sens2019031. Publisher Copyright: {\textcopyright} 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.",
year = "2022",
month = apr,
day = "14",
doi = "10.1002/advs.202104373",
language = "English",
volume = "9",
journal = "Advanced Science",
issn = "2198-3844",
publisher = "Wiley-VCH Verlag",
number = "11",
}
