Reconstructing and assessing corynebacterial gene regulatory networks

Abstract

The Corynebacterium genus is a very heterogeneous group of organisms of biotechnological, medical and veterinary relevance. Its diversity allows its organisms to live in a wide range of environments. The organisms must quickly adapt their gene expression machinery to acclimate to hostile environments. Transcriptional regulation plays a crucial role in this process; it allows bacteria to quickly change the set of genes expressed to cope with the environment's challenges. Understanding the mechanisms underlying transcription regulation is a crucial step in understanding how organisms can adapt and thrive. Several studies have focused on unraveling the transcriptional gene regulatory networks (GRNs) of bacterial organisms; however, our knowledge is limited to a few model organisms with experimentally verified GRNs. Note that Escherichia coli is the best-studied bacterial organism and yet it is estimated that our knowledge corresponds to less than 30% of regulatory interactions between transcription factors (TFs) and target genes (TGs). In this context, computational approaches allow researchers to unravel the GRNs of many organisms based on the evolutionary conservation of the model organisms’ networks. Taken into account the Corynebacterium genus, little is known regarding their transcriptional regulatory repertory. This genus is greatly relevant for medicine, veterinary and biotechnology, comprising pathogens that affect human and animal health as well as amino acid producer organisms. CoryneRegNet has been the reference database for corynebacterial transcriptional regulatory knowledge since 2006. Here, we extend the transcriptional regulatory knowledge of the Corynebacterium genus and assess the consistency of the Corynebacterium glutamicum GRN with its gene expression data. In the first research article, we present the seventh version of CoryneRegNet, which now holds transcriptional GRNs for 225 corynebacterial organisms, increasing by twenty times the number of organisms with known GRNs of this genus. This regulatory knowledge combined with literature research resulted in the first review article of the transcriptional GRN of Corynebacterium pseudotuberculosis, in which we present the known transcriptional mechanisms of this organism under osmotic, acid, iron-starvation and thermal stress. In the third research article, we assessed the consistency between gene expression data and the C. glutamicum GRN by applying a conservative sign consistency model. Our results show that the C. glutamicum GRN is not more consistent than random GRNs, suggesting that omics data concerning other regulatory elements, such as post-transcriptional and translational regulation, should be integrated in future GRN studies for this organism. We conclude that we have just begun to understand the Corynebacterium genus' regulatory landscape and that new layers of regulation should be integrated into the GRNs to reconstruct more reliable networks for this genus.