Genome scale metabolic model driven strategy to delineate host response to Mycobacterium tuberculosis infection

Abstract

We analyze high throughput proteomics data reflecting the response of the Mϕ-like THP1 cell line to Mycobacterium tuberculosis (M. tuberculosis) infection. M. tuberculosis's engagement with the host's metabolic pathways is a known strategy employed by the pathogen to shift the balance in its favour. Our study revisits this strategy through the integration of the temporal proteomics data in the genome-scale metabolic model (GSMM) giving context-specific GSMMs. THP1 cells were infected with H37Ra, H37Rv, BND433 and JAL2287 strains of M. tuberculosis and the host response was studied at 6, 18, 30 and 42 hours after infection. We have developed a modified flux balance analysis (FBA), which does not use an objective function, to find the fluxes of metabolic reactions in different strains and stages of infection and have revealed different functional modules. Hence, we have established a method of rewiring using GSMMs to explore potential strategies to change the flux state of virulent M. tuberculosis infected macrophages as against their avirulent counterparts. Our methodology gives a correlation between different flux states, the extent of which was interpreted as the extent of rewiring. The accuracy of the results from the proposed methodology was validated with gene knockout experimental data. We found that more than one reaction has to be rewired simultaneously to alter virulent to an avirulent response. The identified modules showed influence across the investigated strains and time points suggesting that these reactions could be therapeutically targeted. This novel methodology is now available for use in other systems.