Speakers
Description
Systemic HIV infection is typically established following mucosal exposure, but the earliest events stretching from viral dissemination from those mucosal tissues to body-wide (systemic) infection remain incompletely defined. Using tissue-level SIV RNA data from rhesus macaques (an animal model for HIV) we broadly aim to map paths of infection through tissues as infection is established, to better inform HIV prevention strategies. We will discuss how we transformed our data into a cohort-wide map of viral occupancy and co-occurrence across organs and lymphatic compartments. We converted tissue measurements into presence/absence, and introduced a probabilistic framework using a Naive Bayes classifier to estimate posterior probabilities of SIV tissue spread through time, yielding an interpretable, directed dissemination network. Building on the network, we implement Monte Carlo random walk simulations to explore likely dissemination pathways and quantify mean first-passage step counts to plasma (systemic infection). Probabilistic maps reveal an SIV tissue-hierarchy leading to systemic infection that identifies specific lymphatic tissues, and the spleen, as dominant conduits. Thus we generate hypotheses on modes of establishment of systemic HIV infection. This work also demonstrates how machine learning and network simulation can transform novel biological data into quantitative models of within-host disease spread.
