Comprehensive Evolutionary Analysis of Complete Epstein-Barr Virus Genomes from Argentina and Other Geographies.

The sequence variability of the Epstein-Barr virus has been extensively studied throughout previous years in isolates from various geographic regions and consequent variations at both genetic and genomic levels have been described. However, isolates from South America were underrepresented in these studies. Here, we sequenced 15 complete EBV genomes that we analyzed together with publicly available raw NGS data for 199 EBV isolates from other parts of the globe by means of a custom-built bioinformatic pipeline. The phylogenetic relations of the genomes, the geographic structure and variability of the data set, and the evolution rates for the whole genome and each gene were assessed. The present work contributes to overcoming the scarcity of complete EBV genomes from South America and is the most comprehensive geography-related variability study, which involved determining the actual contribution of each EBV gene to the geographic segregation of the entire genome. Moreover, to the best of our knowledge, we established for the first time the evolution rate for the entire EBV genome based on a host-virus codivergence-independent assumption and assessed their evolution rates on a gene-by-gene basis, which were related to the encoded protein function. Considering the evolution of dsDNA viruses with a codivergence-independent approach may lay the basis for future research on EBV evolution. The exhaustive bioinformatic analysis performed on this new dataset allowed us to draw a novel set of conclusions regarding the genome evolution of EBV.

Recombination rates along the entire Epstein Barr virus genome display a highly heterogeneous landscape.

Epstein Barr virus (EBV) has a large DNA genome assumed to be stable, but also subject to mutational processes such as nucleotide substitution and recombination, the latter explored to a lesser extent. Moreover, differences in the extent of recombination events across herpes sub-families were recently reported. Given the relevance of recombination in viral evolution and its possible impact in pathogenesis, we aimed to fully characterize and quantify its extension in all available EBV complete genome by assessing global and local recombination rate values (⍴/bp). Our results provide the first EBV recombination map based on recombination rates assessment, both at a global and gene by gene level, where the mean value for the entire genome was 0.035 (HPDI 0.020-0.062) ⍴/bp. We quantified how this evolutionary process changes along the EBV genome, and proved it to be non-homogeneous, since regulatory regions depicted the lowest recombination rate values while repetitive regions the highest signal. Moreover, GC content rich regions seem not to be linked to high recombination rates as previously reported. At an intragenic level, four genes (EBNA3C, EBNA3B, BRRF2 and BBLF2-BBLF3) presented a recombination rate above genome average. We specifically quantified the signal strength among different recombination-initiators previously described features and concluded that those which elicited the greatest amount of changes in ⍴/bp, TGGAG and CCCAG, were two well characterized recombination inducing motifs in eukaryotic cells. Strikingly, although TGGAG was not the most frequently detected DNA motif across the EBV genome (697 hits), it still induced a significantly greater proportion of initiation events (0.025 events/hits) than other more represented motifs, p-value = 0.04; one tailed proportion test. Present results support the idea that diversity and evolution of herpesviruses are impacted by mechanisms, such as recombination, which extends beyond the usual consideration of point mutations.

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.

Mining the modular structure of protein interaction networks.

BACKGROUND: Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. METHODOLOGY: We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera's cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. RESULTS: As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge.