@article {110791, title = {Evolutionary jumps in bacterial GC content.}, journal = {G3 (Bethesda)}, year = {2022}, month = {2022 May 17}, abstract = {

Genomic GC content is a fundamental molecular trait linked with many key genomic features such as codon and amino acid use. Across bacteria, GC content is surprisingly diverse and has been studied for many decades; yet its evolution remains incompletely understood. Since it is difficult to observe GC content evolve on laboratory time-scales, phylogenetic comparative approaches are instrumental; but this dimension is rarely studied systematically in the case of bacterial GC content. We applied phylogenetic comparative models to analyse GC content evolution in multiple bacterial groups across two major bacterial phyla. We find that GC content diversifies via a combination of gradual evolution and evolutionary "jumps". Surprisingly, unlike prior reports that solely focused on reductions in GC, we found a comparable number of jumps with both increased and decreased GC content. Overall, many of the identified jumps occur in lineages beyond the well-studied peculiar examples of endosymbiotic and AT-rich marine bacteria, and do not support the predicted role of oxygen dependence. Our analysis of rapid and large shifts in GC content thus identifies new clades and novel contexts to further understand the ecological and evolutionary drivers of this important genomic trait.

}, issn = {2160-1836}, doi = {10.1093/g3journal/jkac108}, author = {Mahajan, Saurabh and Agashe, Deepa} } @article {45309, title = {Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis.}, journal = {PLoS One}, volume = {13}, year = {2018}, month = {2018}, pages = {e0203311}, abstract = {

The genetic regulatory network (GRN) plays a key role in controlling the response of the cell to changes in the environment. Although the structure of GRNs has been the subject of many studies, their large scale structure in the light of feedbacks from the metabolic network (MN) has received relatively little attention. Here we study the causal structure of the GRNs, namely the chain of influence of one component on the other, taking into account feedback from the MN. First we consider the GRNs of E. coli and B. subtilis without feedback from MN and illustrate their causal structure. Next we augment the GRNs with feedback from their respective MNs by including (a) links from genes coding for enzymes to metabolites produced or consumed in reactions catalyzed by those enzymes and (b) links from metabolites to genes coding for transcription factors whose transcriptional activity the metabolites alter by binding to them. We find that the inclusion of feedback from MN into GRN significantly affects its causal structure, in particular the number of levels and relative positions of nodes in the hierarchy, and the number and size of the strongly connected components (SCCs). We then study the functional significance of the SCCs. For this we identify condition specific feedbacks from the MN into the GRN by retaining only those enzymes that are essential for growth in specific environmental conditions simulated via the technique of flux balance analysis (FBA). We find that the SCCs of the GRN augmented by these feedbacks can be ascribed specific functional roles in the organism. Our algorithmic approach thus reveals relatively autonomous subsystems with specific functionality, or regulatory modules in the organism. This automated approach could be useful in identifying biologically relevant modules in other organisms for which network data is available, but whose biology is less well studied.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0203311}, author = {Kumar, Santhust and Mahajan, Saurabh and Jain, Sanjay} } @article {29619, title = {Translational selection for speed is not sufficient to explain variation in bacterial codon usage bias.}, journal = {Genome Biol Evol}, year = {2018}, month = {2018 Jan 29}, abstract = {

Increasing growth rate across bacteria strengthens selection for faster translation, concomitantly increasing the total number of tRNA genes and codon usage bias (CUB: enrichment of specific synonymous codons in highly expressed genes). Typically, enriched codons are translated by tRNAs with higher gene copy numbers (GCN). A model of tRNA-CUB coevolution based on fast growth associated selection on translational speed recapitulates these patterns. A key untested implication of the coevolution model is that translational selection should favor higher tRNA GCN for more frequently used amino acids, potentially weakening the effect of growth associated selection on CUB. Surprisingly, we find that CUB saturates with increasing growth rate across γ-proteobacteria, even as the number of tRNA genes continues to increase. As predicted, amino acid specific tRNA GCN is positively correlated with the usage of corresponding amino acids, but there is no correlation between growth rate associated changes in CUB and amino acid usage. Instead, we find that some amino acids - Cysteine and those in the NNA/G codon family - show weak CUB that does not increase with growth rate, despite large variation in the corresponding tRNA GCN. We suggest that amino acid specific variation in CUB is not explained by tRNA GCN because GCN does not influence the difference between translation times of synonymous codons as expected. Thus, selection on translational speed alone cannot fully explain quantitative variation in overall or amino acid specific CUB, suggesting a significant role for other functional constraints and amino acid specific codon features.

}, issn = {1759-6653}, doi = {10.1093/gbe/evy018}, author = {Mahajan, Saurabh and Agashe, Deepa} }