Rationale: The major human genes regulating M. tuberculosis (Mtb)-induced immune responses and tuberculosis (TB) susceptibility are poorly understood. Although IL-12 and IL-10 are critical for TB pathogenesis, the genetic factors that regulate their expression are unknown. CNBP, REL, and BHLHE40 are master regulators of IL-12 and IL-10 signaling. Objectives: To determine whether common human genetic variation in CNBP, REL and BHLHE40 is associated with IL-12 and IL-10 expression, adaptive immune responses to mycobacteria, and susceptibility to TB. Methods and Main Measurements: We characterized the association between common variants in CNBP, REL, and BHLHE40 and innate immune responses in dendritic cells and monocyte-derived macrophages (MDM), BCG-specific T cell responses, and susceptibility to pediatric and adult TB. Results: SNP BHLHE40 rs4496464 was associated with increased BHLHE40 expression in MDMs and increased IL-10 from both peripheral blood dendritic cells and MDMs after LPS and TB whole cell lysate stimulation. SNP BHLHE40 rs11130215, in linkage disequilibrium with rs4496464, was associated with increased BCG-specific IL2+CD4+ T cell responses and decreased risk for pediatric TB in South Africa. SNPs REL rs842634 and CNBP rs11709852 were associated with increased IL-12 production from dendritic cells, and SNP REL rs842618, in linkage disequilibrium with rs842634, was associated with increased risk for TB meningitis. Conclusions: Genetic variation in CNBP, REL, and BHLHE40 is associated with IL-12 and IL-10 cytokine response and TB clinical outcomes. Common human genetic regulation of well-defined intermediate cellular traits provides insights into mechanisms of TB pathogenesis.

Eukaryotic gene expression regulation involves thousands of distal regulatory elements. Understanding the quantitative contribution of individual enhancers to gene expression is critical for assessing the role of disease-associated genetic risk variants. Yet, we lack the ability to accurately link genes with their distal regulatory elements. To address this, we used 3D enhancer-promoter (E-P) associations identified using split-pool recognition of interactions by tag extension (SPRITE) to build a predictive model of gene expression. Our model dramatically outperforms models using genomic proximity and can be used to determine the quantitative impact of enhancer loss on gene expression in different genetic backgrounds. We show that genes that form stable E-P hubs have less cell-to-cell variability in gene expression. Finally, we identified transcription factors that regulate stimulation-dependent E-P interactions. Together, our results provide a framework for understanding quantitative contributions of E-P interactions and associated genetic variants to gene expression.