Cox Madison S, Dill-McFarland Kimberly A, Simmons Jason D, Benchek Penelope, Mayanja-Kizza Harriet, Boom W Henry, Stein Catherine M, Hawn Thomas R
Department of Medicine, University of Washington, Seattle, WA, USA.
Department of Population & Quantitative Health Sciences, Case Western Reserve School of Medicine, Cleveland, OH, USA.
BMC Genomics. 2025 Mar 18;26(1):265. doi: 10.1186/s12864-025-11407-1.
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.
了解结核分枝杆菌（Mtb）早期清除的机制可能会为结核病（TB）带来新的治疗策略。我们之前发现，在结核菌素皮肤试验（TST）/干扰素γ释放试验（IGRA）转换的高暴露TB接触者中，存在与耐药性（抵抗者，RSTR）相关的遗传、表观遗传和转录组特征。我们假设，将这些数据集与多组学潜在因子方法相结合，将能够检测出区分RSTR患者与无症状结核感染（TBI，也称为潜伏性结核感染或LTBI）患者的途径，而这些途径在单个数据集分析中并未被检测到。我们对126名患者的特征进行了预筛选和缩放，这些患者在五种数据模式中的至少两种模式下有数据：单核苷酸多态性（SNP）、单核细胞RNA测序（基线和Mtb刺激条件下）以及单核细胞表观遗传学（甲基化和ATAC测序），TBI组和RSTR组之间变化最大。使用多组学潜在因子分析（MOFA），我们在33名拥有所有五个数据集的患者子集中生成了十个潜在因子，其中四个因RSTR状态而异（FDR < 0.1）。因子4在RSTR组和TBI组之间表现出最大差异（FDR < 0.001）。另外三种潜在因子整合方法也区分了RSTR组和TBI组，并确定了与MOFA重叠的特征。通过通路分析和基于聚类的富集方法，我们确定了与潜在因子相关的功能，并发现MOFA因子2 - 4包括与细胞间粘附、细胞形状和多细胞结构发育相关的功能。总之，潜在变量整合方法揭示了与TST/IGRA转换耐药性相关的特征，这些特征在单个数据集分析中未被检测到，并且包括与细胞相互作用和多细胞结构相关的途径。
