Abstract Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects. Introduction Tuberculosis (TB), an airborne infectious disease caused by the bacterium Mycobacterium tuberculosis (MTB), is an ongoing global health crisis [[28]1,[29]2] resulting in over 9 million illnesses and 1.5 million deaths each year [[30]3]. In contrast, nontuberculous mycobacterial (NTM) disease, caused by phylogenetically related environmental mycobacteria [[31]4], has emerged as an increasingly prevalent infectious disease, particularly over the last two decades [[32]5–[33]8]. Although exposure to TB is common in certain regions of the world, a relatively small proportion of exposed people progress to develop active pulmonary disease. As an example, one third of the world’s population is latently infected with MTB, but only 10% of those individuals will ever progress to become ill with active TB. Similarly, many individuals come into contact with NTM through soil or municipal water sources [[34]4], but few develop pulmonary NTM disease. Certain clinical conditions, including immunodeficiencies and individuals with compromised lungs, increase susceptibility, but most TB and NTM disease occur in otherwise healthy people [[35]3,[36]9]. We hypothesized that a systems biology approach would help reveal critical human pathways involved in mycobacterial susceptibility, and help elucidate why some individuals progress to active disease while most do not. Accumulating evidence suggests that host genetic factors influence the susceptibility to MTB and NTM infection. Research studies utilizing twin design [[37]10,[38]11], linkage analysis [[39]12,[40]13], candidate gene association [[41]14–[42]17], genome-wide association analysis [[43]18–[44]21], and fine mapping studies [[45]22] have implicated numerous human genetic markers as contributing factors to the susceptibility of MTB infection. Although fewer studies have examined the human genetic contribution to NTM susceptibility, familial clustering of pulmonary NTM [[46]23] and candidate gene association studies have implicated certain genetic factors [[47]24–[48]26] and support the hypothesis of a genetic predisposition to NTM in some individuals. In this study, we examine genes critical to the human response to TB and NTM infection, as well as highlight enriched biological pathways and networks that may play a critical role in mycobacterial susceptibility. We explore whether there is any commonality between TB and NTM susceptibility genes and the functional implications of these shared genes and pathways. Shared susceptibility genes or pathways may suggest related mechanisms for the response or control of TB and NTM disease. Furthermore, we examined the resulting networks in order to identify drugs and nutrients with potential immunomodulatory or anti-mycobacterial effects. Materials and Methods Data sources & gene selection We identified genes associated with TB and NTM utilizing three publically available databases: the Online Mendelian Inheritance in Man (OMIM) [[49]27, [50]28] database, the Comparative Toxicogenomics Database (CTD) [[51]29], and the Human Genome Epidemiology encyclopedia (HuGE Navigator) [[52]30]. The Online Mendelian Inheritance in Man (OMIM) database [[53]27] is considered to be the best curated resource of genotype-phenotype relationships [[54]28]. The Comparative Toxicogenomics Database (CTD) [[55]29] curates relationships between chemicals, genes and human diseases, and is unique because it integrates chemical and gene/protein-disease relationships with the goal of understanding the effects of environmental chemicals on human health. In CTD, disease-gene associations are reported as curated or inferred. We selected only curated associations due to a higher confidence than inferred associations. Lastly, we used the Human Genome Epidemiology encyclopedia (HuGE Navigator) [[56]30] which mines the scientific literature on human gene-disease associations and maintains a comprehensive database of population-based epidemiologic studies of human genes [[57]31]. We selected these databases because of their unique approach, breadth, and depth to cataloguing human disease-gene associations. TB key word search 1. For searches of OMIM, we used the key words: “Mycobacterium tuberculosis, susceptibility to” which resulted in 11 genes associated with Mycobacterium tuberculosis susceptibility or protection. 2. For searches of CTD, we used the key words “Mycobacterium tuberculosis, susceptibility to infection by”, which resulted in 15 genes associated with Mycobacterium tuberculosis susceptibility. 3. For searches of the HuGE Navigator, we searched using key words “mycobacterium infections”. Tuberculosis was defined by disease phenotypes, such as, “Tuberculosis, Gastrointestinal”, “Tuberculosis, Pleural”, or “Tuberculosis, Pulmonary”. We therefore chose to use “Tuberculosis, Pulmonary” as our search term, since our focus for TB and NTM disease in this study is related to lung disease. We excluded genes that have been implicated in hepatotoxicity and other adverse reactions, rather than susceptibility to infection. None of these excluded genes were later identified in the network analyses. In the HuGE database, we found 154 genes that were associated with pulmonary tuberculosis. We further refined this list and selected only genes with at least 3 references. This restricted our list to 42 genes that were