Integrating multiple transcriptome-based methods for drug repurposing in tuberculosis
Tuberculosis (TB) remains the leading cause of infectious disease mortality worldwide, killing over one million people annually. Rising antibiotic resistance has added urgency to the need for host-directed therapeutics (HDTs) that modulate host immune responses alongside directly targeting the pathogen. Repurposing FDA-approved drugs is particularly attractive for this purpose because their safety profiles are already well established, substantially reducing development time and cost. Transcriptomic methods have successfully identified repurposable therapeutics for TB based on connectivity mapping, which identifies drugs that reverse disease gene expression patterns. However, these applications are limited to a small subset of data belonging to a specific data platform and a few connectivity methods. Expanding beyond these constrained settings introduces substantial challenges, including dataset heterogeneity across transcriptomics platforms and biological conditions, uncertainty about optimal scoring methods, and the lack of systematic approaches to identify robust disease signatures. We developed a computational workflow that integrates 28 TB gene expression signatures and multiple connectivity scoring methods to capture dominant TB signals regardless of variation in microarray and RNAseq platforms, cell types, and infection conditions. We systematically identified 64 FDA-approved drugs as promising TB host-directed therapeutics. These high-confidence drug candidates include known HDTs such as statins (rosuvastatin, fluvastatin, lovastatin) and tamoxifen, recently validated in experimental TB models. Our prioritized candidate drugs reveal enrichment for therapeutically TB-relevant mechanisms, e.g., cholesterol metabolism inhibition and immune modulation pathways. Network analysis of disease-drug interactions identified 12 key bridging genes (including IL-8, CXCR2) that represent potential novel druggable targets for TB host-directed therapy. This work establishes transcriptome-based connectivity mapping as a viable approach for systematic HDT discovery in bacterial infections and provides a robust computational framework applicable to other infectious diseases. Our findings offer immediate opportunities for experimental validation of prioritized drug candidates and mechanistic investigation of identified druggable targets in TB pathogenesis.