Mechanistic Deconvolution of Tuberculosis Treatment Failure: A Multi-Omic and Causal Network Approach

Background: The global tuberculosis (TB) epidemic is increasingly characterized by 'recycled' cases—patients who fail treatment or relapse, fueling transmission and drug resistance. Current diagnostic tools are inadequate for predicting these unfavorable outcomes at the point of care. While blood transcriptomic signatures have been developed, they typically lack mechanistic resolution, serving as 'black box' indicators of generalized inflammation rather than revealing actionable pathology. Methods: We bridged this 'Resolution Gap' using a V2 Intelligence pipeline (combining Virtual Deconvolution and Causal Network Inference). We integrated public whole-blood transcriptomics (N=254) with Virtual Single-Cell Deconvolution and Physical Single-Cell Validation (PBMC3k Atlas). We further employed Causal Network Analysis to identify upstream regulatory hubs. Results: Our model predicted treatment failure with high accuracy (Mean ROC AUC=0.79 ± 0.04 SD; Range: 0.70-0.85). Validating across modalities, we confirmed that failure is strongly associated with a specific 'Neutrophil-High/T-cell-Low' immunophenotype, distinct from general inflammation. Conclusions: This study provides the first multi-omic, mechanistic map of TB treatment failure. We identify a specific neutrophil-associated pathology as the primary target for host-directed therapies, rigorously cross-validated across bulk and single-cell landscapes.