Bioinformatics Core

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PROJECT SUMMARY/ABSTRACT (Bioinformatics Core) The development, evaluation and implementation of HIV cure strategies will depend critically on our understanding of the “rebound-competent” HIV reservoir, and our capacity to determine when this reservoir is reduced to the point that viral recrudescence is unlikely in the near-term, justifying cessation of antiretroviral therapy (ART). Increasing evidence suggests that the active HIV reservoir during ART likely contributes to viral recrudescence following ART interruption, and the nature of HIV expression varies dramatically in vivo with respect to time, anatomic compartment, and target cell lineage. In this P01, several cutting-edge, high- dimensional technologies will be applied to a broad range of clinical samples to characterize diverse, active HIV reservoir subsets and their immunological and inflammatory impact in unprecedented detail. These technologies include: HIV transcription profiling, the intact proviral DNA assay (IPDA), intact viral RNA assay (IVRA), single provirus and HIV transcript sequencing, single-cell RNA-seq (scRNA-seq), Cytometry by time of flight (CyTOF), and nanoString GeoMX digital spatial profiling (DSP) generating high-resolution transcriptomic and proteomic data from tissues. Our massive and complex datasets will require extensive and sophisticated data management, integration, and bioinformatic and biostatical analyses to ensure that clinically relevant biological insights are distilled from our experiments. The Bioinformatics Core, staffed by seasoned investigators with specific expertise in the analysis of high-dimensional data to study HIV pathogenesis and persistence, will perform these critical functions within our P01 project. We will pursue three Specific Aims in the Bioinformatics Core: In Aim 1, we will compile, curate, and disseminate data generated by all three P01 projects, building a searchable, relational database for our investigative team and submitting data to relevant public repositories to maximize data accessibility. In Aim 2, we will apply ensemble machine learning methods to high-dimensional data generated in all three P01 projects to identify molecular and immunologic signatures of diverse active HIV reservoir subsets. In Aim 3, we will apply advanced knowledge discovery methods to data generated from analytical treatment interruption (ATI) samples (Project #3) to reveal predictors and HIV reservoir signatures of viral rebound following ART cessation. The Bioinformatics Core will play a central role in ensuring successful implementation of our P01 objectives to advance the HIV cure agenda.