Elucidating sex-specific human tuberculosis infection mechanisms from single-sex animal models

Tuberculosis has long been reported at a higher rate in adult male patients than in female patients. Historically, researchers have attributed this to reporting biases and sociocultural gender roles. With increasing evidence of biologically regulated sex-specific immune responses across diseases, the paradigm of behaviorally driven sex-differences in tuberculosis infection must be revisited. Further complicating this landscape, tuberculosis presents along a phenotypic range from latent tuberculosis (LTBI) to active tuberculosis (ATB). Improving our understanding of the progression from LTBI to ATB and its interactions with sex may inform better vaccine design and therapeutics. Unfortunately, most tuberculosis animal models exhibit critical discrepancies with human disease. Further, it is particularly challenging to study sex-specific differences in tuberculosis using mice, as most research is conducted with female mice due to their more cost-effective upkeep in BSL3 spaces. To overcome species-specific limitations, the Lauffenburger Lab has developed a modeling framework, termed translatable components regression (TransComp-R), for integrating animal- and human disease-related molecular data to identify novel mechanisms predictive of human disease phenotypes. Here, we adapt TransComp-R to investigate whether sex-specific transcriptional signatures of human TB can be elucidated from a single sex mouse model. To do this, we utilize data from a study by Moreira-Teixeira et al. (Nat Immunol, 2020). Our cross-species model built on female mouse data can better discriminate between human male ATB and LTBI than in females. Interestingly, human female ATB patients share a transcriptional signature more similar to LTBI patients of either sex than to male ATB patients. We aim to leverage these murine correlates of sex-specific human infection in our interpretation of past and future female mouse experiments to maximize the information we acquire about the human immune response from preclinical studies. Ultimately, we seek to utilize mouse data to further our mechanistic understanding of sex-specific differences in human infection.