Multi-omic latent variable data integration reveals multicellular structure pathways associated with resistance to...

Understanding the mechanisms of early clearance of Mycobacterium tuberculosis (Mtb) may illuminate new therapeutic strategies for tuberculosis (TB). We previously found genetic, epigenetic, and transcriptomic signatures associated with resistance (resister, RSTR) to tuberculin skin test (TST)/interferon gamma release assay (IGRA) conversion among highly exposed TB contacts. We hypothesized that integration of these datasets with multi-omic latent factor methods would detect pathways differentiating RSTR patients from those with asymptomatic TB infection (TBI, also known as latent TB infection or LTBI) that were not detected in individual dataset analyses. We pre-filtered and scaled features with the largest change between TBI and RSTR groups for 126 patients with data in at least two of five data modalities: single nucleotide polymorphisms (SNP), monocyte RNAseq (baseline and Mtb-stimulated conditions), and monocyte epigenetics (methylation and ATAC-seq). Using multiomic latent factor analysis (MOFA), we generated ten latent factors on the subset of 33 patients with all five datasets available, four of which differed by RSTR status (FDR < 0.1). Factor 4 showed the greatest difference between RSTR and TBI groups (FDR < 0.001). Three additional latent factor integration methods also distinguished the RSTR and TBI groups and identified overlapping features with MOFA. Using pathway analysis and a cluster-based enrichment method, we identified functions associated with latent factors and found that MOFA Factors 2-4 include functions related to cell-cell adhesion, cell shape, and multicellular structure development. In summary, latent variable integration methods uncovered signatures associated with resistance to TST/IGRA conversion that were not detected by individual dataset analyses and included pathways associated with cellular interactions and multicellular structures.