Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis
Artikel i vetenskaplig tidskrift, 2022
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.
gut and oral metagenomics
multiomics analysis
metabolic dysfunction-associated fatty liver disease
proteomics
systems biology
systems medicine
metabolomics
Författare
Mujdat Zeybel
NHS Foundation Trust
Koç Üniversitesi
University of Nottingham
Muhammad Arif
National Institute on Alcohol Abuse and Alcoholism
Kungliga Tekniska Högskolan (KTH)
Xiangyu Li
Kungliga Tekniska Högskolan (KTH)
Ozlem Altay
Kungliga Tekniska Högskolan (KTH)
Hong Yang
Kungliga Tekniska Högskolan (KTH)
Mengnan Shi
Kungliga Tekniska Högskolan (KTH)
Murat Akyildiz
Koç Üniversitesi
Burcin Saglam
Koç Üniversitesi
Mehmet Gokhan Gonenli
Koç Üniversitesi
Buket Yigit
Koç Üniversitesi
Burge Ulukan
Koç Üniversitesi
Dilek Ural
Koç Üniversitesi
Saeed Shoaie
King's College London
Kungliga Tekniska Högskolan (KTH)
Hasan Turkez
Atatürk Üniversitesi
Jens B Nielsen
Chalmers, Biologi och bioteknik, Systembiologi
C. Zhang
Zhengzhou University
Kungliga Tekniska Högskolan (KTH)
Mathias Uhlen
Kungliga Tekniska Högskolan (KTH)
Jan Borén
Sahlgrenska universitetssjukhuset
Adil Mardinoglu
Kungliga Tekniska Högskolan (KTH)
King's College London
Advanced Science
2198-3844 (ISSN) 21983844 (eISSN)
Vol. 9 11 2104373Ämneskategorier (SSIF 2011)
Farmaceutisk vetenskap
Medicinsk genetik
Bioinformatik och systembiologi
DOI
10.1002/advs.202104373
PubMed
35128832