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

Abstract

A comparative proteomic study at 6 h of growth in minimal medium (MM) and bacteroids at 18 days of symbiosis of Rhizobium etli CFN42 with the Phaseolus vulgaris leguminous plant was performed. A gene ontology classification of proteins in MM and bacteroid, showed 31 and 10 pathways with higher or equal than 30 and 20% of proteins with respect to genome content per pathway, respectively. These pathways were for energy and environmental compound metabolism, contributing to understand how Rhizobium is adapted to the different conditions. Metabolic maps based on orthology of the protein profiles, showed 101 and 74 functional homologous proteins in the MM and bacteroid profiles, respectively, which were grouped in 34 different isoenzymes showing a great impact in metabolism by covering 60 metabolic pathways in MM and symbiosis. Taking advantage of co-expression of transcriptional regulators (TF’s) in the profiles, by selection of genes whose matrices were clustered with matrices of TF’s, Transcriptional Regulatory networks (TRN´s) were deduced by the first time for these metabolic stages. In these clustered TF-MM and clustered TF-bacteroid networks, containing 654 and 246 proteins, including 93 and 46 TFs, respectively, showing valuable information of the TF’s and their regulated genes with high stringency. Isoenzymes were specific for adaptation to the different conditions and a different transcriptional regulation for MM and bacteroid was deduced. The parameters of the TRNs of these expected biological networks and biological networks of E. coli and B. subtilis segregate from the random theoretical networks. These are useful data to design experiments on TF gene–target relationships for bases to construct a TRN.

RhizoBindingSites v2.0 is a bioinformatic database of DNA motifs potentially involved in transcriptional regulation deduced from sites of the itself genome

“…Homology between the TF matrices was expected because of the functional relationship observed in an Escherichia coli K-12 transcriptional regulatory network (Martínez-Antonio and Collado-Vides, 2003). In addition, the hierarchy of TFs of minimal medium growth and symbiotic proteomes from R. etli CFN42 has been shown (Taboada-Castro et al, 2022). These homologies were displayed in hierarchical dendrograms using the HCLUST algorithm (Castro-Mondragon et al, 2017).…”

“…We have previously shown that genes clustered with a TF or TFs are potentially functionally organized in an hypothetical regulon (h-regulon) (Taboada-Castro et al, 2022). Some clusters were analyzed in the application from RhizoBindingSites and RhizoBindingSites v2.0 (“Prediction of Regulatory Networks”), by pasting the TF genes of a cluster from Supplementary Table 2 to both boxes; as regulators and as targets, a medium restriction level of 1.0e-05 was selected.…”

“…The next step would be to propose genetic circuits to build a transcriptional regulatory network for SNF. A bioinformatics method to construct in silico transcriptional regulatory networks, comparing the free-life and symbiosis in the maximal nitrogen fixation stage from R. etli CFN42 with the bean plant Phaseolus vulgaris , has been shown (Taboada-Castro et al, 2022). In addition, a computational reconstruction of the transcriptional regulation of nitrogen fixation and signaling by oxygen in alphaproteobacteria was reported (Tsoy et al, 2016).…”

Improving gene regulatory network inference and assessment: The importance of using network structure

“…When no GS exists for the organism of interest, the structural assessment can be used along with other biological networks and random models to prove the prediction is structurally more similar to a biological network than random. We recently used this approach to assess network inferences for Rhizobium etli (Taboada-Castro et al, 2022).…”

Improving gene regulatory network inference and assessment: The importance of using network structure

“…When no GS exists for the organism of interest, the structural assessment can be used along with other biological networks and random models to prove the prediction is structurally more similar to a biological network than random. We recently used this approach to assess network inferences for Rhizobium etli (Taboada- Castro et al, 2022).…”