The complex interplay between host and pathogen determines if an individual controls infection or progresses to disease. While abundant evidence suggests that genetic diversity contributes to the variety of outcomes, the combined effect of variation in the host and pathogen remains unclear.  We developed a “dual-genome” system to unravel genetic interactions between Mycobacterium tuberculosis (Mtb)and its mammalian host that drive outcome to infection.  Host variation was modeled using a panel of ~100 mouse strains, including the Collaborative Cross (CC) and single-gene immunological knockouts. Bacterial variation was concurrently generated using saturated libraries of transposon mutants and panels of diverse Mtb clinical isolates.

The wide genotypic variation in the CC panel produced remarkably diverse phenotypes upon Mtb infection, ranging from extreme susceptibility to progressive clearance of the pathogen. Metrics of disease that are tightly linked in the typical C57BL/6 model such as bacterial burden, dissemination, weight loss and inflammation were genetically separable in the diverse mouse strains. We identified individual polymorphic host genome regions (QTLs) underlying lung and spleen bacterial load and host control of infection independently in the CC panel.

We additionally separated the clinical disease traits into intermediate phenotypes by determining the relative fitness of thousands of bacterial mutants in the mouse panels. Host QTLs underlying differential bacterial fitness modules were identified, many of which mapped to the same host region as the clinical disease metrics. Each interaction between host and pathogen locus was defined as a host-pathogen QTL (hpQTL) that controls a specific aspect of the bacterial microenvironment and collaboratively influences global susceptibility.

Overall, the strategy of using bacterial fitness profiles as reporters of the underlying host microenvironment is a sensitive and specific method for identifying disease-modifying host polymorphisms, demonstrating the power of a dual-genome systems genetics approach to understandthe fundamental drivers of susceptibility to infection.