Corynebacterium glutamicum Regulation beyond Transcription: Organizing Principles and Reconstruction of an Extended Regulatory Network Incorporating Regulations Mediated by Small RNA and Protein–Protein Interactions

Abstract

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by nondirected experiments, and protein–protein interactions with a direct effect on gene transcription; sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNA-mediated regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C.glutamicum regulatory network characterization.

System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks

“…These observations have been supported by analyzing historical snapshots of the E. coli GRN ( Escorcia-Rodriguez et al, 2020 ). Additionally, an assessment of the NDA predictions obtained by using three network models of the C. glutamicum GRN with different confidence degrees, including the addition of small RNAs, and an analysis of historical snapshots, have also confirmed these observations ( Escorcia-Rodriguez et al, 2021 ).…”

“…The NDA leverages the global structure of a regulatory network to define mathematical diagnostic criteria and an algorithm to identify these system elements by the controlled decomposition of a network ( Freyre-Gonzalez et al, 2008 ; Freyre-Gonzalez and Trevino-Quintanilla, 2010 ; Freyre-Gonzalez et al, 2012 ; Ibarra-Arellano et al, 2016 ; Freyre-Gonzalez and Tauch, 2017 ; Escorcia-Rodriguez et al, 2020 ; 2021 ). First, the κ -value is computed as the solution to the equation , where is the clustering coefficient distribution of a GRN as a function of the out-connectivity and is obtained by robust least-squares fitting.…”

“…One way to deal with this limitation is to transfer regulatory information from one organism to another ( Alkema et al, 2004 ). Nevertheless, this approach requires the organisms to be similar enough so the interactions are conserved ( McCue et al, 2002 ; Escorcia-Rodriguez et al, 2021 ), and prior regulatory information for the source organism is still required. Inferences based on gene expression data have also benefited from the integration of biological information.…”

“…Moreover, the incompleteness of the GRN gold standards hinders proper assessment of inferred networks as actual true positive interactions are incorrectly labeled as false positive if they are not part of the current gold standard. We can leverage the constrained space for structural properties found in biological GRNs ( Campos and Freyre-Gonzalez, 2019 ; Escorcia-Rodriguez et al, 2021 ) to verify if the inferred networks have similar properties.…”

Rhizobium etli CFN42 proteomes showed isoenzymes in free-living and symbiosis with a different transcriptional regulation inferred from a transcriptional regulatory network

“…The average clustering coefficient, maximum out connectivity, cluster coefficient R 2 C(k), and connectivity distribution R 2 P(k) were higher for the experimental than for inferred biological networks, implying that the inferred networks have an atypical very low modularity. As previously shown in several organisms ( Freyre-González et al 2008 ; 2012 ; Freyre-González and Tauch 2017 ; Escorcia-Rodríguez et al 2021 ), the Natural Decomposition Approach (NDA) reveals that bacterial regulatory networks shape a diamond-like, three-tier, hierarchy where global TFs govern modules, and the local response of these modules is integrated at the promoter level by intermodular genes, whereas modules are shaped by local TFs and structural genes ( Freyre-González et al 2022 ). An analysis of our predicted networks using the NDA showed a hierarchy only composed of global TF and basal machinery, where neither modules nor intermodular genes could be identified (data not shown).…”