An Evolutionary Model of Progression to AIDS

Evolutionary implications of SARS-CoV-2 vaccination for the future design of vaccination strategies

Once the first SARS-CoV-2 vaccine became available, mass vaccination was the main pillar of the public health response to the COVID-19 pandemic. It was very effective in reducing hospitalizations and deaths. Here, we discuss the possibility that mass vaccination might accelerate SARS-CoV-2 evolution in antibody-binding regions compared to natural infection at the population level. Using the evidence of strong genetic variation in antibody-binding regions and taking advantage of the similarity between the envelope proteins of SARS-CoV-2 and influenza, we assume that immune selection pressure acting on these regions of the two viruses is similar. We discuss the consequences of this assumption for SARS-CoV-2 evolution in light of mathematical models developed previously for influenza. We further outline the implications of this phenomenon, if our assumptions are confirmed, for the future design of SARS-CoV-2 vaccination strategies.

A role for CD4 + helper cells in HIV control and progression

OBJECTIVE

It remains unclear why HIV persists in most untreated individuals, and why a small minority of individuals can control the virus, either spontaneously or after an early treatment. Striking differences have been discovered between patient cohorts in CD4 + T-cell avidity but not in CD8 + T-cell avidity. The present work has the aim to explain the diverse outcome of infection and identify the key virological and immunological parameters predicting the outcome.

DESIGN AND METHOD

A mathematical model informed by these experiments and taking into account the details of HIV virology is developed.

RESULTS

The model predicts an arms race between viral dissemination and the proliferation of HIV-specific CD4 + helper cells leading to one of two states: a low-viremia state (controller) or a high-viremia state (progressor). Helper CD4 + cells with a higher avidity favor virus control. The parameter segregating spontaneous and posttreatment controllers is the infectivity difference between activated and resting CD4 + T cells. The model is shown to have a better connection to experiment than a previous model based on T-cell 'exhaustion'.

CONCLUSION

Using the model informed by patient data, the timing of antiretroviral therapy can be optimized.