Host sequence motifs shared by HIV predict response to antiretroviral therapy

The HIV viral genome mutates at a high rate and poses a significant long term health risk even in the presence of combination antiretroviral therapy. Current methods for predicting a patient's response to therapy rely on site-directed mutagenesis experiments and in-vitro resistance assays.

In this bioinformatics study we treat response to antiretroviral therapy as a two-body problem consisting of contributions from both the host and pathogen proteomes. We set out to identify potential responders based on the presence or absence of host protein/DNA motifs on the HIV sequence.

Results: An alignment of thousands of HIV-1 sequences attested to extensive variation in nucleotide sequence but also showed conservation of eukaryotic short linear motifs on the protein coding regions.

The reduction in viral load of patients in the Stanford HIV Drug Resistance Database exhibited a bimodal distribution after 24 weeks of antiretroviral therapy, with 2,000 copies/ml cutoff. Similarly, patients allocated into responder/non-responder categories based on consistent viral load reduction during a 24 week period showed clear separation.

In both cases of phenotype identification, a set of features composed of short linear motifs in the reverse transcriptase region of HIV sequence accurately predicted a patient's response to therapy. Motifs that overlapped resistance sites were highly predictive of responder identification in single drug regimens but these features lost importance in defining responders in multi drug therapies.

Conclusions: HIV sequence mutates in a way that preserves peptide sequence motifs that are also found in the human proteome.

The presence and absence of such motifs at specific regions of the HIV sequence is highly predictive of response to therapy. These motifs are well established in bioinformatics databases and do not require identification via in-vitro mutation experiments.

Author: William DampierPerry EvansLyle UngarAydin Tozeren
Credits/Source: BMC Medical Genomics 2009, 2:47

Published on: 2009-07-23

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