The evolutionary dynamics of functional modules and the extraordinary plasticity of regulons: the Escherichia coli perspective

The EcoCyc Database (2023)

EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

The Mycobacterial LexA/RecA-Independent DNA Damage Response Is Controlled by PafBC and the Pup-Proteasome System

Mycobacteria exhibit two DNA damage response pathways: the LexA/RecA-dependent SOS response and a LexA/RecA-independent pathway. Using a combination of transcriptomics and genome-wide binding site analysis, we demonstrate that PafBC (proteasome accessory factor B and C), encoded in the Pup-proteasome system (PPS) gene locus, is the transcriptional regulator of the predominant LexA/RecA-independent pathway. Comparison of the resulting PafBC regulon with the DNA damage response of Mycobacterium smegmatis reveals that the majority of induced DNA repair genes are upregulated by PafBC. We further demonstrate that RecA, a member of the PafBC regulon and principal regulator of the SOS response, is degraded by the PPS when DNA damage stress has been overcome. Our results suggest a model for the regulation of the mycobacterial DNA damage response that employs the concerted action of PafBC as master transcriptional activator and the PPS for removal of DNA repair proteins to maintain a temporally controlled stress response.

The EcoCyc Database

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

Mutational Pleiotropy and the Strength of Stabilizing Selection Within and Between Functional Modules of Gene Expression

Variational modules, sets of pleiotropically covarying traits, affect phenotypic evolution, and therefore are predicted to reflect functional modules, such that traits within a variational module also share a common function. Such an alignment of function and pleiotropy is expected to facilitate adaptation by reducing the deleterious effects of mutations, and by allowing coordinated evolution of functionally related sets of traits. Here, we adopt a high-dimensional quantitative genetic approach using a large number of gene expression traits in Drosophila serrata to test whether functional grouping, defined by gene ontology (GO terms), predicts variational modules. Mutational or standing genetic covariance was significantly greater than among randomly grouped sets of genes for 38% of our functional groups, indicating that GO terms can predict variational modularity to some extent. We estimated stabilizing selection acting on mutational covariance to test the prediction that functional pleiotropy would result in reduced deleterious effects of mutations within functional modules. Stabilizing selection within functional modules was weaker than that acting on randomly grouped sets of genes in only 23% of functional groups, indicating that functional alignment can reduce deleterious effects of pleiotropic mutation but typically does not. Our analyses also revealed the presence of variational modules that spanned multiple functions.

Inferring Functional Relationships from Conservation of Gene Order

Predicting functional associations using the Gene Neighbor Method depends on the simple idea that if genes are conserved next to each other in evolutionarily distant prokaryotes they might belong to a polycistronic transcription unit. The procedure presented in this chapter starts with the organization of the genes within genomes into pairs of adjacent genes. Then, the pairs of adjacent genes in a genome of interest are mapped to their corresponding orthologs in other, informative, genomes. The final step is to verify if the mapped orthologs are also pairs of adjacent genes in the informative genomes.

The power of operon rearrangements for predicting functional associations

In this mini-review I aim to make the case that operons might be the most powerful source for predicted associations among gene products. Such associations can help identify potential processes where the products of unannotated genes might play a role. The power of the operon for providing insight into functional associations stems from four features: (1) on average, around 60% of the genes in prokaryotes are associated into operons; (2) the functional associations between genes in operons tend to be highly conserved; (3) operons can be predicted with high accuracy by conservation of gene order and by the distances between adjacent genes in the same DNA strand; and (4) operons frequently reorganize, providing further insight into functional associations that would not be evident without these reorganization events.

The functional landscape bound to the transcription factors of Escherichia coli K-12

Motivated by the experimental evidences accumulated in the last ten years and based on information deposited in RegulonDB, literature look up, and sequence analysis, we analyze the repertoire of 304 DNA-binding Transcription factors (TFs) in Escherichia coli K-12. These regulators were grouped in 78 evolutionary families and are regulating almost half of the total genes in this bacterium. In structural terms, 60% of TFs are composed by two-domains, 30% are monodomain, and 10% three- and four-structural domains. As previously noticed, the most abundant DNA-binding domain corresponds to the winged helix-turn-helix, with few alternative DNA-binding structures, resembling the hypothesis of successful protein structures with the emergence of new ones at low scales. In summary, we identified and described the characteristics associated to the DNA-binding TF in E. coli K-12. We also identified twelve functional modules based on a co-regulated gene matrix. Finally, diverse regulons were predicted based on direct associations between the TFs and potential regulated genes. This analysis should increase our knowledge about the gene regulation in the bacterium E. coli K-12, and provide more additional clues for comprehensive modelling of transcriptional regulatory networks in other bacteria.

Predicting Functional Interactions Among Genes in Prokaryotes by Genomic Context

Genomic context methods for finding functions of unannotated genes were implemented very early after the publication of the first few prokaryotic genomes. The ideas behind these methods include gene fusions, conservation of gene adjacency, and the patters of co-occurrence of genes across available genomes. A later addition was the prediction of features related to functional organization, such as operons, stretches of genes co-transcribed into a single messenger RNA. The ideas behind these methods tend to be easy to understand, while the strategies for transforming those basic ideas into predictions can vary in complexity, mostly because genes whose products are known to functionally interact vary in the way they relate to those basic ideas. We present here a view of genomic context methods for predicting functional interactions, with simple examples of their implementation as compared and evaluated using genes whose products are known to functionally interact.

The loose evolutionary relationships between transcription factors and other gene products across prokaryotes

Background

Tests for the evolutionary conservation of associations between genes coding for transcription factors (TFs) and other genes have been limited to a few model organisms due to the lack of experimental information of functional associations in other organisms. We aimed at surmounting this limitation by using the most co-occurring gene pairs as proxies for the most conserved functional interactions available for each gene in a genome. We then used genes predicted to code for TFs to compare their most conserved interactions against the most conserved interactions for the rest of the genes within each prokaryotic genome available.

Results

We plotted profiles of phylogenetic profiles, p-cubic, to compare the maximally scoring interactions of TFs against those of other genes. In most prokaryotes, genes coding for TFs showed lower co-occurrences when compared to other genes. We also show that genes coding for TFs tend to have lower Codon Adaptation Indexes compared to other genes.

Conclusions

The co-occurrence tests suggest that transcriptional regulation evolves quickly in most, if not all, prokaryotes. The Codon Adaptation Index analyses suggest quick gene exchange and rewiring of transcriptional regulation across prokaryotes.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-0500-7-928) contains supplementary material, which is available to authorized users.

The EcoCyc Database

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review provides a detailed description of the data content of EcoCyc and of the procedures by which this content is generated.

Acetobixan, an inhibitor of cellulose synthesis identified by microbial bioprospecting

In plants, cellulose biosynthesis is an essential process for anisotropic growth and therefore is an ideal target for inhibition. Based on the documented utility of small-molecule inhibitors to dissect complex cellular processes we identified a cellulose biosynthesis inhibitor (CBI), named acetobixan, by bio-prospecting among compounds secreted by endophytic microorganisms. Acetobixan was identified using a drug-gene interaction screen to sift through hundreds of endophytic microbial secretions for one that caused synergistic reduction in root expansion of the leaky AtcesA6prc1-1 mutant. We then mined this microbial secretion for compounds that were differentially abundant compared with Bacilli that failed to mimic CBI action to isolate a lead pharmacophore. Analogs of this lead compound were screened for CBI activity, and the most potent analog was named acetobixan. In living Arabidopsis cells visualized by confocal microscopy, acetobixan treatment caused CESA particles localized at the plasma membrane (PM) to rapidly re-localize to cytoplasmic vesicles. Acetobixan inhibited 14C-Glc uptake into crystalline cellulose. Moreover, cortical microtubule dynamics were not disrupted by acetobixan, suggesting specific activity towards cellulose synthesis. Previous CBI resistant mutants such as ixr1-2, ixr2-1 or aegeus were not cross resistant to acetobixan indicating that acetobixan targets a different aspect of cellulose biosynthesis.