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Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice.

ABSTRACT: The outcome of an encounter with Mycobacterium tuberculosis (Mtb) depends on the pathogen's ability to adapt to the variable immune pressures exerted by the host. Understanding this interplay has proven difficult, largely because experimentally tractable animal models do not recapitulate the heterogeneity of tuberculosis disease. We leveraged the genetically diverse Collaborative Cross (CC) mouse panel in conjunction with a library of Mtb mutants to create a resource for associating bacterial genetic requirements with host genetics and immunity. We report that CC strains vary dramatically in their susceptibility to infection and produce qualitatively distinct immune states. Global analysis of Mtb transposon mutant fitness (TnSeq) across the CC panel revealed that many virulence pathways are only required in specific host microenvironments, identifying a large fraction of the pathogen's genome that has been maintained to ensure fitness in a diverse population. Both immunological and bacterial traits can be associated with genetic variants distributed across the mouse genome, making the CC a unique population for identifying specific host-pathogen genetic interactions that influence pathogenesis.

SUBMITTER: Smith CM

PROVIDER: S-EPMC8846590 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice.

Smith Clare M CM Baker Richard E RE Proulx Megan K MK Mishra Bibhuti B BB Long Jarukit E JE Park Sae Woong SW Lee Ha-Na HN Kiritsy Michael C MC Bellerose Michelle M MM Olive Andrew J AJ Murphy Kenan C KC Papavinasasundaram Kadamba K Boehm Frederick J FJ Reames Charlotte J CJ Meade Rachel K RK Hampton Brea K BK Linnertz Colton L CL Shaw Ginger D GD Hock Pablo P Bell Timothy A TA Ehrt Sabine S Schnappinger Dirk D Pardo-Manuel de Villena Fernando F Ferris Martin T MT Ioerger Thomas R TR Sassetti Christopher M CM

eLife 20220203

The outcome of an encounter with Mycobacterium tuberculosis (Mtb) depends on the pathogen's ability to adapt to the variable immune pressures exerted by the host. Understanding this interplay has proven difficult, largely because experimentally tractable animal models do not recapitulate the heterogeneity of tuberculosis disease. We leveraged the genetically diverse Collaborative Cross (CC) mouse panel in conjunction with a library of Mtb mutants to create a resource for ass ...[more]

PMID: 35112666

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Project description:Mycobacterium tuberculosis (M.tb.) infection leads to over 1.5 million deaths annually, despite widespread vaccination with BCG at birth. Causes for the ongoing tuberculosis endemic are complex and include the failure of BCG to protect many against progressive pulmonary disease. Host genetics is one of the known factors implicated in susceptibility to primary tuberculosis, but less is known about the role that host genetics plays in controlling host responses to vaccination against M.tb. Here, we addressed this gap by utilizing Diversity Outbred (DO) mice as a small animal model to query genetic drivers of vaccine-induced protection against M.tb. DO mice are a highly genetically and phenotypically diverse outbred population that is well suited for fine genetic mapping. Similar to outcomes in people, our previous studies demonstrated that DO mice have a wide range of disease outcomes following BCG vaccination and M.tb. challenge. In the current study, we used a large population of BCG-vaccinated/M.tb.-challenged mice to perform quantitative trait loci mapping of complex infection traits; these included lung and spleen M.tb. burdens, as well as lung cytokines measured at necropsy. We found sixteen chromosomal loci associated with complex infection traits and cytokine production. QTL associated with bacterial burdens included a region encoding major histocompatibility antigens that are known to affect susceptibility to tuberculosis, supporting validity of the approach. Most of the other QTL represent novel associations with immune responses to M.tb. and novel pathways of cytokine regulation. Most importantly, we discovered that protection induced by BCG is a multigenic trait, in which genetic loci harboring functionally-distinct candidate genes influence different aspects of immune responses that are crucial collectively for successful protection. These data provide exciting new avenues to explore and exploit in developing new vaccines against M.tb.

Project description:Pulmonary tuberculosis (TB) is caused by Mycobacterium tuberculosis in susceptible humans. Here, we infected Diversity Outbred (DO) mice with ∼100 bacilli by aerosol to model responses in a highly heterogeneous population. Following infection, 'supersusceptible', 'susceptible' and 'resistant' phenotypes emerged. TB disease (reduced survival, weight loss, high bacterial load) correlated strongly with neutrophils, neutrophil chemokines, tumor necrosis factor (TNF) and cell death. By contrast, immune cytokines were weak correlates of disease. We next applied statistical and machine learning approaches to our dataset of cytokines and chemokines from lungs and blood. Six molecules from the lung: TNF, CXCL1, CXCL2, CXCL5, interferon-γ (IFN-γ), interleukin 12 (IL-12); and two molecules from blood - IL-2 and TNF - were identified as being important by applying both statistical and machine learning methods. Using molecular features to generate tree classifiers, CXCL1, CXCL2 and CXCL5 distinguished four classes (supersusceptible, susceptible, resistant and non-infected) from each other with approximately 77% accuracy using completely independent experimental data. By contrast, models based on other molecules were less accurate. Low to no IFN-γ, IL-12, IL-2 and IL-10 successfully discriminated non-infected mice from infected mice but failed to discriminate disease status amongst supersusceptible, susceptible and resistant M.-tuberculosis-infected DO mice. Additional analyses identified CXCL1 as a promising peripheral biomarker of disease and of CXCL1 production in the lungs. From these results, we conclude that: (1) DO mice respond variably to M. tuberculosis infection and will be useful to identify pathways involving necrosis and neutrophils; (2) data from DO mice is suited for machine learning methods to build, validate and test models with independent data based solely on molecular biomarkers; (3) low levels of immunological cytokines best indicate a lack of exposure to M. tuberculosis but cannot distinguish infection from disease.

Dataset Information

Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice.

Publications

Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice.

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