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

Molecular Systems Biology · 22 authors, 10 centres

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TL;DR

This study evaluated the concordance of five omics data types (metagenomics, metatranscriptomics, metaproteomics, metabolomics, and 16S amplicon sequencing) in profiling the taxonomic composition and functional response of a defined synthetic gut microbial community under drug perturbation in a controlled in vitro experiment.

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Researchers analyzed a defined synthetic gut microbial community under perturbations by three drugs (chlorpromazine, metformin, and niclosamide) using five omics methods in a highly controlled in vitro experiment. The central result was high concordance between all omics data types for estimating community taxonomic composition, with a particularly strong correlation between metagenomics and metatranscriptomics (ρ = 0.92). Metabolomics revealed differences in drug degradation and linked oligosaccharide abundance to changes in Bacteroidota under chlorpromazine treatment. Chlorpromazine was the only drug causing a large disturbance, depleting Bacteroidota species, while metformin and niclosamide had minimal effects, suggesting community-level protection. Upregulation of genes/proteins related to protein aggregation and RND-type efflux pumps was observed in response to chlorpromazine. Limitations include the use of a synthetic community, extracellular metabolomics only, and ambiguous ion annotations in metabolomics data. Implications support using multi-omics integration for comprehensive microbiome studies, though functional correlations were lower than taxonomic.

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research methods

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ORIGINAL PAPER

PMC10495815

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

Molecular Systems Biology

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ABSTRACT

Abstract 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. Keywords: metabolomics, metagenomics, metaproteomics, metatranscriptomics, microbiology Subject Categories: Microbiology, Virology & Host Pathogen Interaction; Proteomics

CITATION EXPORT

Sander Wuyts, Renato Alves, Maria Zimmermann‐Kogadeeva et al.. Consistency across multi‐omics layers in a drug‐perturbed gut microbial community. Molecular Systems Biology. (2023). https://doi.org/10.15252/msb.202311525

AUTHORS

22 · 10 CENTRES

Sander Wuyts, Renato Alves, Maria Zimmermann‐Kogadeeva, Suguru Nishijima, Sonja Blasche, Marja Driessen, Philipp E Geyer, Rajna Hercog, Ece Kartal, Lisa Maier, Johannes B Müller, Sarela Garcia Santamarina, Thomas Sebastian B Schmidt, Daniel C Sevin, Anja Telzerow, Peter V Treit, Tobias Wenzel, Athanasios Typas, Kiran R Patil, Matthias Mann, Michael Kuhn, Peer Bork

1 European Molecular Biology Laboratory, Heidelberg, Germany
2 Medical Research Council Toxicology Unit, Cambridge, UK
3 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
4 Cellzome, GlaxoSmithKline R&D, Heidelberg, Germany
5 Proteomics Program, NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
6 Max Delbrück Centre for Molecular Medicine, Berlin, Germany
7 Yonsei Frontier Lab (YFL), Yonsei University, Seoul, South Korea
8 Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
9 Present address: MOSTMICRO Unit, Instituto de Tecnologia Quimica e Biologica Universidade Nova de Lisboa Oeiras Portugal
10 Present address: Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences Pontificia Universidad Catolica de Chile Santiago Chile