Systems-level computational translation of mouse and human transcriptomics reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection

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Krista M. Pullen, Ryan Finethy, Seung-Hyun B. Ko, Charlotte J. Reames, Christopher M. Sassetti, Douglas A. Lauffenburger

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Abstract:Numerous studies have identified similarities in blood transcriptomic signatures of mouse tuberculosis (TB) models and human disease phenotypes, such as type 1 interferon (IFN) production and innate immune cell activation, yet the pathophysiology observed in murine infection does not recapitulate some of the hallmarks of human TB disease. In order to leverage mouse models for the development of more effective TB treatments, we may need to elucidate additional mechanisms of seemingly lower importance in the mouse studies but potentially greater importance in human disease context. To address this critical animal-to-human gap, we applied a systems biology machine learning framework, Translatable Components Regression, which identifies axes of variation in the preclinical study that correspond with numerous biological pathways and processes and estimates their relative contributions to TB disease state in humans. Prominent among the pathways most predictive of human TB phenotype, beyond the previously established common signatures, is the infection-induced Unfolded Protein Response (UPR). To validate that this mechanism can be uncovered even in the murine context, wherein it has not previously been a major avenue of investigation, we show experimentally that this cellular stress pathway controls a variety of immune-related functions in Mtb-infected mouse macrophages. Thus, our work here demonstrates how systems-level computational models enhance the value of animal studies for purpose of elucidating complex human pathophysiology.

SEEK ID: https://fairdomhub.org/studies/1340

Investigation: IMPAcTB

Projects: Hi-IMPAcTB

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