Markov Field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques

Abstract

Multi-modal biological datasets provide rich information from diverse scales or facets of complex biological systems that can be analyzed to highlight the critical multi-scale interactions underlying specific biological phenomena. However, identifying the most vital associations among features and outputs can be beset by a high degree of spurious connections due to indirect effects of various immune features propagating through an unmapped biological network. Here, we applied a probabilistic graphical modeling approach, Markov Fields, to empirically dissect the most direct associations correlations between features from a public multi-modal dataset (antibody titers, antibody-dependent functions, cytokines, cytometry) from macaques undergoing intravenous BCG vaccination—a promising vaccine strategy against the major public health threat tuberculosis. This yielded a network influence model that interprets the assemblage of multi-scale paths by which vaccine effects propagate through the immune network to eventually protect against tuberculosis infection. Importantly, our modeling shows that the vast majority of apparent correlations between features arise from indirect effects relating distant immune features. For a test of predictive capability, we conducted experimental depletion of B cells during BCG IV vaccination in macaques--which did not reduce BCG IV-mediated protection against tuberculosis— and validated that our Markov Field model can predict systems-wide modulation of numerous features across the immune system in response to this perturbation. Finally, we applied our model to discern perturbations in the network that could have strong effects on IV-BCG efficacy. All together, we have demonstrated that probabilistic graphical modeling can increase interpretability and predictive value of multi-modal datasets for identifying new disease treatment targets.SummaryMulti-modal datasets provide rich information that can help identify critical multi-scale interactions underlying biological systems. However, identifying associations between features and outputs can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. We applied a probabilistic graphical modeling approach, Markov Fields, to dissect correlations between immune features in a public multi-modal dataset (systems serology, cytokines, cytometry) of macaques undergoing intravenous BCG vaccination against tuberculosis. This yielded an interaction network that interprets network paths underlying vaccine efficacy, and shows how correlations between features often arise indirectly. We next conducted experimental depletion of B cells during vaccination in macaques—which did not reduce protection against tuberculosis—to validate our Markov Field model’s predictions of network-wide shifts post-depletion. Finally, we highlight immune changes predicted to strongly affect intravenous BCG vaccine efficacy, showing that probabilistic graphical models increase the interpretability of multi-modal datasets for identifying new disease targets.