Cross species modeling reveals a role for the unfolded protein response in shaping the transcriptional reaction to Mycobacterium tuberculosis infection

Abstract

Numerous blood mRNA signatures have been developed to diagnose tuberculosis (TB) disease. The utility of these signatures in diverse populations depends on the inclusion of ubiquitously expressed features, such as type 1 interferon (IFN) production and innate immune cell activities. As a result, these signatures are generally insensitive to heterogeneous responses between individuals. Designing more effective therapies will require understanding the diverse mechanisms underlying pathogenesis by associating them with appropriate preclinical animal models. To address this critical animal-to-human gap, we applied a modeling framework, Translatable Components Regression, which is designed to account for biological heterogeneity by identifying multiple orthogonal axes of variation that are common to humans and animal models. Our framework was capable of distinguishing human active TB from latent TB infection using a model derived from murine data. This discrimination was based on differential expression of numerous biological pathways in addition to the common IFN and neutrophil signatures. Prominent among these predictive pathways was protein translation, which we show is a feature of the Mtb infection-induced Unfolded Protein Response (UPR) in macrophages. We show that this cellular stress pathway controls a variety of immune-related functions in Mtb-infected mouse macrophages, suggesting a possible causative role during the development of TB disease.