TY - JOUR

T1 - Consistency across multi-omics layers in a drug-perturbed gut microbial community

AU - Wuyts, Sander

AU - Alves, Renato

AU - Zimmermann-Kogadeeva, Maria

AU - Nishijima, Suguru

AU - Blasche, Sonja

AU - Driessen, Marja

AU - Geyer, Philipp E.

AU - Hercog, Rajna

AU - Kartal, Ece

AU - Maier, Lisa

AU - Müller, Johannes B.

AU - Garcia Santamarina, Sarela

AU - Schmidt, Thomas Sebastian B.

AU - Sevin, Daniel C.

AU - Telzerow, Anja

AU - Treit, Peter V.

AU - Wenzel, Tobias

AU - Typas, Athanasios

AU - Patil, Kiran R.

AU - Mann, Matthias

AU - Kuhn, Michael

AU - Bork, Peer

N1 - Publisher Copyright: © 2023 The Authors. Published under the terms of the CC BY 4.0 license.

PY - 2023

Y1 - 2023

N2 - Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

AB - Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

KW - metabolomics

KW - metagenomics

KW - metaproteomics

KW - metatranscriptomics

KW - microbiology

U2 - 10.15252/msb.202311525

DO - 10.15252/msb.202311525

M3 - Journal article

C2 - 37485738

AN - SCOPUS:85165510022

VL - 19

JO - Molecular Systems Biology

JF - Molecular Systems Biology

SN - 1744-4292

IS - 9

M1 - e11525

ER -