Identifying transient and stable bacteria- metabolite interactions from longitudinal multi-omics data

Zhang D, Mullish BH, Wang J, Barker G, Chrysostomou D, Gao S, Chen L, McDonald JAK, Marchesi JR, Cheng L.
Preprint from
Research Square
13 May 2022


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 in vitro model of Clostridioides difficile 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) Ruminococcus gnavus and Ruminococcus luti could generate 3-ketocholanic acid (2) Blautia obeum 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.