Metabolic adaptions of Mycobacterium tuberculosis at diverse host-pathogen interfaces

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Metabolic adaptions of Mycobacterium tuberculosis at diverse host-pathogen interfaces ABSTRACT Defining the mechanisms through which Mycobacterium tuberculosis (Mtb) metabolites impact human tuberculosis (TB) disease, diagnostics and treatment will require the implementation of model animal systems that are both tractable and faithfully replicate the TB disease states observed in humans. To overcome the limited genetic and phenotypic diversity shown in standard C57BL/6J, here we propose to leverage the comprehensive panel of Collaborative Cross (CC) mice, a genetically diverse panel of recombinant inbred mice. We have previously shown that the CC encompasses a broad spectrum of TB disease traits and infection microenvironments, exceeding the phenotypic spectrum observed in classical inbred strains. Based on previous TnSeq studies and as part of the parent TBRU grant (“Metabolic determinants of Mtb virulence, vulnerability and variation”; 1U19AI162584-02), we have ranked Mtb metabolic genes that were linked to Mtb virulence or control lipid variation in Mtb strains that infect humans. Of 19 high value Mtb metabolic genes identified, only one controlled growth in the conventional C57BL/6J (BL6) mouse strain. However, more than half the mutants studied showed in vivo growth phenotypes when screened across CC mouse strains. Further, through study of several of the CC host genetic backgrounds in which individual bacterial genes do or do not control in vivo Mtb survival, we are able to start to define the host factors in control of Mtb response. The current application will now extend these studies to focus on a targeted bacterial lipid library of CRISPRi mutants in conjunction with the comprehensive panel of CC mice. By infecting 60 distinct CC genotypes with the targeted lipid CRISPRi library, the metabolic vulnerabilities of Mtb will be comprehensively defined in genetically diverse hosts. By using bacterial mutant abundance as a phenotypic trait, we will conduct quantitative trait loci (QTL) mapping across the CC panel to identify the host loci underlying the lipid requirements of Mtb infection. Altogether these dual host and pathogen approaches will identify the host-Mtb lipid interactions that can be targeted for tuberculosis treatment.