Details

Autor(en) / Beteiligte

• Lee, Ha Youn
• Giorgi, Elena E.
• Keele, Brandon F.
• Gaschen, Brian
• Athreya, Gayathri S.
• Salazar-Gonzalez, Jesus F.
• Pham, Kimmy T.
• Goepfert, Paul A.
• Michael Kilby, J.
• Saag, Michael S.
• Delwart, Eric L.
• Busch, Michael P.
• Hahn, Beatrice H.
• Shaw, George M.
• Korber, Bette T.
• Bhattacharya, Tanmoy
• Perelson, Alan S.

Titel

Modeling sequence evolution in acute HIV-1 infection

Ist Teil von

• Journal of theoretical biology, 2009-11, Vol.261 (2), p.341-360

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2009

Quelle

MEDLINE

Beschreibungen/Notizen

• We describe a mathematical model and Monte Carlo (MC) simulation of viral evolution during acute infection. We consider both synchronous and asynchronous processes of viral infection of new target cells. The model enables an assessment of the expected sequence diversity in new HIV-1 infections originating from a single transmitted viral strain, estimation of the most recent common ancestor (MRCA) of the transmitted viral lineage, and estimation of the time to coalesce back to the MRCA. We also calculate the probability of the MRCA being the transmitted virus or an evolved variant. Excluding insertions and deletions, we assume HIV-1 evolves by base substitution without selection pressure during the earliest phase of HIV-1 infection prior to the immune response. Unlike phylogenetic methods that follow a lineage backwards to coalescence, we compare the observed data to a model of the diversification of a viral population forward in time. To illustrate the application of these methods, we provide detailed comparisons of the model and simulations results to 306 envelope sequences obtained from eight newly infected subjects at a single time point. The data from 6 8 patients were in good agreement with model predictions, and hence compatible with a single-strain infection evolving under no selection pressure. The diversity of the samples from the other two patients was too great to be explained by the model, suggesting multiple HIV-1-strains were transmitted. The model can also be applied to longitudinal patient data to estimate within-host viral evolutionary parameters.