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Protein–protein interaction networks suggest different targets have different propensities for triggering drug resistance

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Systems and Synthetic Biology

Abstract

Emergence of drug resistance is a major problem in the treatment of many diseases including tuberculosis. To tackle the problem from a wholistic perspective, it is essential to understand the molecular mechanisms by which bacteria acquire drug resistance using a systems approach. Availability of genome-scale data of expression profiles under different drug exposed conditions and protein–protein interactions, makes it feasible to reconstruct and analyze systems-level models. A number of proteins involved in different resistance mechanisms, referred to as the resistome are identified from literature. The interaction of the drug directly with the resistome is unable to explain most resistance processes adequately, including that of increased mutations in the target’s binding site. We recently hypothesized that some communication might exist from the drug environment to the resistome to trigger emergence of drug resistance. We report here a network based approach to identify most plausible paths of such communication in Mycobacterium tuberculosis. Networks capturing both structural and functional linkages among various proteins were weighted based on gene expression profiles upon exposure to specific drugs and betweenness centrality of the interactions. Our analysis suggests that different drug targets and hence different drugs could trigger the resistome to different extents and through different routes. The identified paths correlate well with the mechanisms known through experiment. Some examples of the top ranked hubs in multiple drug specific networks are PolA, FadD1, CydA, a monoxygenase and GltS, which could serve as co-targets, that could be inhibited in order to retard resistance related communication in the cell.

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Abbreviations

DR:

Drug resistant

MDR:

Multidrug resistant

XDR:

Extensively drug resistant

INH:

Isoniazid.

HGT:

horizontal gene transfer.

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Acknowledgments

We thank the Department of Biotechnology (DBT), Government of India and the DST Centre for Mathematical Biology (grant no. SR/S4/MS:419/07) at the Indian Institute of Science for the financial support. The use of facilities at the Bioinformatics Centre, Indian Institute of Science is also gratefully acknowledged.

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Correspondence to Nagasuma Chandra.

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Padiadpu, J., Vashisht, R. & Chandra, N. Protein–protein interaction networks suggest different targets have different propensities for triggering drug resistance. Syst Synth Biol 4, 311–322 (2010). https://doi.org/10.1007/s11693-011-9076-5

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  • DOI: https://doi.org/10.1007/s11693-011-9076-5

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