Reconstruction and analysis of transcriptome regulatory network of Methanobrevibacter ruminantium M1

Abstract

Global regulatory network modeling is a great concern of addressing metabolic complexity especially of a non-model organism to prioritize novel drug and vaccine targets. In this study, a knowledge-based bottom-up approach was used to reconstruct a transcriptional regulatory network of Methanobrevibacter ruminantium M1, one of the predominant hydrogenotrophic methanogens, contributing enteric methane emission in ruminants. The network robustness of the resulted model was validated with topological parameters and indicated its stability, consistency, and biological reliability. Each regulon-associated gene was assessed with gene ontology terms and literature support for functional confidence to infer its transcriptional regulatory mechanisms. The inferred genome-wide regulatory network comprises 164 predicted regulons associated with 235 coding genes. The results of this study have identified six DNA-binding motifs in 204 promoter regions interacting with 194 genes. It also described the metabolic gene regulation could be controlled either directly or indirectly under the respective promoter site in the regulatory network. As shown by our analysis, we proposed regulatory mechanisms of 7 important regulons such as PhoB (mru_0135), PrrA (mru_0526), NhaR (mru_0780), SigB (32-mer) (mru_1470), CmbR (mru_1544), MetR (mru_1915) and PpsR (mru_2036) at different environmental niche. Our intensive knowledge about the transcriptional regulatory mechanisms of this organism provides an insight into the understanding of rumen lifestyle and growth physiology in the ruminants.