Serial evolutionary networks of within-patient HIV-1 sequences reveal patterns of evolution of X4 strains.
BACKGROUND
The HIV virus is known for its ability to exploit numerous genetic and evolutionary mechanisms to ensure its proliferation, among them, high replication, mutation and recombination rates. Sliding MinPD, a recently introduced computational method 1, was used to investigate the patterns of evolution of serially-sampled HIV-1 sequence data from eight patients with a special focus on the emergence of X4 strains. Unlike other phylogenetic methods, Sliding MinPD combines distance-based inference with a nonparametric bootstrap procedure and automated recombination detection to reconstruct the evolutionary history of longitudinal sequence data. We present serial evolutionary networks as a longitudinal representation of the mutational pathways of a viral population in a within-host environment. The longitudinal representation of the evolutionary networks was complemented with charts of clinical markers to facilitate correlation analysis between pertinent clinical information and the evolutionary relationships.


RESULTS
Analysis based on the predicted networks suggests the following:: significantly stronger recombination signals (p = 0.003) for the inferred ancestors of the X4 strains, recombination events between different lineages and recombination events between putative reservoir virus and those from a later population, an early star-like topology observed for four of the patients who died of AIDS. A significantly higher number of recombinants were predicted at sampling points that corresponded to peaks in the viral load levels (p = 0.0042).


CONCLUSIONS
Our results indicate that serial evolutionary networks of HIV sequences enable systematic statistical analysis of the implicit relations embedded in the topology of the structure and can greatly facilitate identification of patterns of evolution that can lead to specific hypotheses and new insights. The conclusions of applying our method to empirical HIV data support the conventional wisdom of the new generation HIV treatments, that in order to keep the virus in check, viral loads need to be suppressed to almost undetectable levels.
Buendia
Patricia
P
Department of Biology and Center for Computational Science, University of Miami, Coral Gables, FL 33146, USA. pbuendia@miami.edu
Narasimhan
Giri
G
D001483:Base Sequence; D030541:Databases, Genetic; D019143:Evolution, Molecular; D015497:HIV-1 / Q000145:classification* / Q000235:genetics* / Q000302:isolation & purification; D006801:Humans; D009154:Mutation; D010802:Phylogeny; D011995:Recombination, Genetic; D049490:Systems Biology