SYSTEMS AND NETWORK BIOLOGY ANALYSIS COMBINED WITH MACHINE LEARNING IDENTIFIES KEY IMMUNE RESPONSE PROFILES AND POTENTIAL CORRELATES OF PROTECTION FOR THE M72/AS01E TUBERCULOSIS VACCINE
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Tuberculosis claims around 1.5 million lives annually. The M72/AS01E vaccine candidate is an innovative effort demonstrating a 50% reduction in the incidence of active TB in adults. However, optimization and effective immunization strategies against TB depends heavily on precise identification of specific molecular signatures active in vaccine protection. In this study, we employed weighted gene co-expression network analysis, machine learning and network biology to investigate the gene expression patterns of peripheral blood mononuclear cells, identifying transcriptomic markers of vaccine protection. Our comprehensive exploration of publicly available gene expression dataset comprising samples from subjects vaccinated twice with 10μg of M72/AS01E vaccine one day post-second dose (D31) and one week post-second dose (D37) in a Phase IIA clinical trial revealed intense induction of multiple gene modules, indicative of acute/immediate immune response at D31 that subsided by D37. Thirty-one hub genes with significant elevation/correlation with immune protection were identified significantly mediating key events in immunity to TB. The more selective profile at D37 involved additional adaptive immunity pathways including Th1/Th2/Th17 differentiation, T cell receptor and cytokine signaling. The functional relevance of these biomarkers in predicting vaccine response was further analyzed using the random forest classifier demonstrating high accuracy in distinguishing between vaccinated and non-vaccinated samples. Additionally, the study pinpointed a miRNAs-transcription factors (TF)-target regulatory network excavating key TF, miRNA, mRNAs mediating vaccine protection. Our results provided new insights into immune protection of M72/AS01E vaccine meriting further study aiming to advance its optimization and informing development of future TB vaccines.
