Back

Blind Uneven Proliferation of CD4+ T cells During Primary Infection Generates the Majority of the HIV Reservoir

Tettamanti Boshier, F. A.; Reeves, D. B.; Duke, E. R.; Swan, D. A.; Prlic, M.; Cardozo-Ojeda, E. F.; Schiffer, J.

2020-04-10 hiv aids
10.1101/2020.04.06.20053231 medRxiv
Show abstract

The HIV reservoir is a population of 1-10 million anatomically dispersed, latently infected memory CD4+ T cells in which an HIV DNA molecule is quiescently integrated into human chromosomal DNA. When antiretroviral therapy (ART) is stopped and HIV replication initiates in one of these cells, systemic viral spread resumes, rekindling progression to AIDS. Therefore, HIV latency prevents cure. The HIV reservoir contains clones: identical HIV sequences that are integrated within identical human chromosomal DNA locations. The presence of these clones demonstrates that proliferation of CD4+ T cells sustains infection despite ART. The reservoir has a precise structure consisting of a small number of large clones and a large number of small clones. However, the mechanisms leading to this structure have not been identified. We developed a mathematical model that recapitulates the profound depletion and brisk recovery of CD4+ T cells, reservoir creation, and viral load trajectory during primary HIV infection. We extended the model to simulate stochastically individual HIV reservoir clones and identified that uneven proliferation among clones during recovery from CD4+ lymphopaenia is sufficient to explain the observed clonal reservoir distribution. We project that within one month of infection 75-95% of reservoir cells are generated from cellular proliferation rather than denovo viral infection. Recent detection of HIV infected clones during the first 5 weeks of infection support our models predictions.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.