Citation

BibTex format

@unpublished{Zhang:2022:rs.3.rs-1619373/v1,
author = {Zhang, D and Mullish, BH and Wang, J and Barker, G and Chrysostomou, D and Gao, S and Chen, L and McDonald, JAK and Marchesi, JR and Cheng, L},
doi = {rs.3.rs-1619373/v1},
title = {Identifying transient and stable bacteria- metabolite interactions from longitudinal multi-omics data},
url = {http://dx.doi.org/10.21203/rs.3.rs-1619373/v1},
year = {2022}
}

RIS format (EndNote, RefMan)

TY  - UNPB
AB - <jats:title>Abstract</jats:title> <jats:p>Background Understanding the complex relationships between bacteria and metabolites in ecological systems are extremely important in studies of different microbiomes. Longitudinal multi-omics study is adopted to investigate interactions between bacteria and metabolites, by directly associating their longitudinal profiles. Since a bacteria/metabolite may involve in many different biological processes, the longitudinal profile is an average of different interactions. Therefore, direct association could only uncover the strongest interactions.Results Here we present a computational approach that can rebuild short- and long-term bacteria-metabolite interactions from longitudinal multi-omics datasets. For this task, we re-analyse data (both microbial sequencing and metabolomic analysis) from an <jats:italic>in vitro</jats:italic> model of <jats:italic>Clostridioides difficile</jats:italic> infection and faecal microbiota transplant, a disease state and mode of therapy in which perturbed microbiome-metabolome interactions (and their reversal) are well-established to be pertinent. By analysing such a dataset, we generated both a short-term and a long-term interaction network, which predicted many new interactions. Four new interactions were randomly selected to be validated. In batch culture experiments, we validated two of them: (1) <jats:italic>Ruminococcus gnavus</jats:italic> and <jats:italic>Ruminococcus luti</jats:italic> could generate 3-ketocholanic acid (2) <jats:italic>Blautia obeum</jats:italic> could consume succinate.Conclusions The deconvolution of the raw longitudinal signal into short- and long-term trends can help users to gain a deeper understanding of their data. This tool will be useful for high-throughput screening of microbe/metabolite/host interactions from a longitudinal multi-omics setting.</jats:p>
AU - Zhang,D
AU - Mullish,BH
AU - Wang,J
AU - Barker,G
AU - Chrysostomou,D
AU - Gao,S
AU - Chen,L
AU - McDonald,JAK
AU - Marchesi,JR
AU - Cheng,L
DO - rs.3.rs-1619373/v1
PY - 2022///
TI - Identifying transient and stable bacteria- metabolite interactions from longitudinal multi-omics data
UR - http://dx.doi.org/10.21203/rs.3.rs-1619373/v1
UR - http://hdl.handle.net/10044/1/96774
ER -