Reduced polymorphism in domains involved in protein-protein interactions

The effect of gene overlapping on the rate of RNA virus evolution

Gene overlapping is widely employed by RNA viruses to generate genetic novelty while retaining a small genome size. However, gene overlapping also increases the deleterious effect of mutations as they affect more than one gene, thereby reducing the evolutionary rate of RNA viruses and hence their adaptive capacity. Although there is general agreement on the benefits of gene overlapping as a mechanism of genomic compression for rapidly evolving organisms, its effect on the pace of RNA virus evolution remains a source of debate. To address this issue, we collected sequence data from 117 instances of gene overlapping across 19 families, 30 genera, and 55 species of RNA viruses. On these data, we analyzed how genetic distances, selective pressures, and the distribution of RNA secondary structures and conserved protein functional domains vary between overlapping (OV) and nonoverlapping (NOV) regions. We show that gene overlapping generally results in a decrease in the rate of RNA virus evolution through a reduction in the frequency of synonymous mutations. However, this effect is less pronounced in genes with a terminal rather than an internal gene overlap, which might result from a greater proportion of protein functional conserved domains in NOV than in OV regions, in turn reducing the number of nonsynonymous mutations in the former. Overall, our analyses clarify the role of gene overlapping as a modulator of the evolutionary rates exhibited by RNA viruses and shed light on the factors that shape the genetic diversity of this important group of pathogens.

The study of protein–protein interactions in bacteria

Many of the functions fulfilled by proteins in the cell require specific protein–protein interactions (PPI). During the last decade, the use of high-throughput experimental technologies, primarily based on the yeast 2-hybrid system, generated extensive data currently located in public databases. This information has been used to build interaction networks for different species. Unfortunately, due to the nature of the yeast 2-hybrid system, these databases contain many false positives and negatives, thus they require purging. A method for confirming these PPI is to test them using a technique that operates in vivo and detects binary PPI. This article comprises an overview of the study of PPI and describes the main techniques that have been used to identify bacterial PPI, prioritizing those that can be used for their verification, and it also mentions a number of PPI that have been identified or confirmed using these methods.