Dataset Information

Co-evolution networks of HIV/HCV are modular with direct association to structure and function.

ABSTRACT: Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins. Specifically, by introducing a robust statistical method based on sparse principal component analysis, we identify near-disjoint sets of collectively-correlated residues (sectors) having mostly a one-to-one association to largely distinct structural or functional domains. This suggests that the distinct phenotypic properties of HIV/HCV proteins often give rise to quasi-independent modes of evolution, with each mode involving a sparse and localized network of mutational interactions. Moreover, individual inferred sectors of HIV are shown to carry immunological significance, providing insight for guiding targeted vaccine strategies.

SUBMITTER: Quadeer AA

PROVIDER: S-EPMC6145588 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Co-evolution networks of HIV/HCV are modular with direct association to structure and function.

Quadeer Ahmed Abdul AA Morales-Jimenez David D McKay Matthew R MR

PLoS computational biology 20180907 9

Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tig ...[more]

PMID: 30192744

Similar Datasets

Project description:Network modules are used for diverse purposes, ranging from delineation of biogeographical provinces to the study of biotic interactions. We assess spatial scaling effects on modular structure, using a multi-step process to compare fish co-occurrence networks at three nested scales. We first detect modules with simulated annealing and use spatial clustering tests (interspecific distances among species' range centroids) to determine if modules consist of species with broadly overlapping ranges; strong spatial clustering may reflect environmental filtering, while absence of spatial clustering may reflect positive interspecific relationships (commensalism or mutualism). We then use non-hierarchical, multivariate cluster analysis as an alternative method to identify fish subgroups, we repeat spatial clustering tests for the multivariate clusters, then compare spatial clustering results among modules and clusters. Next, we compare species lists within modules and clusters, and estimate congruence as the proportion of species assigned to the same groups by the two methods. Finally, we use a well-documented nest associate complex (fishes that deposit eggs in the gravel nests of a common host) to assess whether strong within-group associations may, in fact, reflect positive interspecific relationships. At each scale, 2-4 network modules were detected but a consistent relationship between scale and the number of modules was not observed. Significant spatial clustering was detected at all scales for network modules and multivariate clusters but was less prevalent at smaller scales. Congruence between modules and clusters was always < 90% and generally decreased as the number of groups increased. At all scales, the complete nest associate complex was completely preserved within a single network module, but not within a single multivariate cluster. Collectively, our results suggest that network modules are promising tools for studying positive interactions and that smaller scales may be preferable in this research.

Dataset Information

Co-evolution networks of HIV/HCV are modular with direct association to structure and function.

Publications

Co-evolution networks of HIV/HCV are modular with direct association to structure and function.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets