Computational Systems Biology Lab

Emergence of drug resistant varieties of tuberculosis is posing a major threat to global tuberculosis eradication programmes. Although several approaches have been explored to counter resistance, there has been limited success due to a lack of understanding of how resistance emerges in bacteria upon drug treatment. A systems level analysis of the proteins involved is essential to gain insights into the routes required for emergence of drug resistance.

We derive a genome-scale protein-protein interaction network for Mycobacterium tuberculosis H37Rv from the STRING database, with proteins as nodes and interactions as edges. A set of proteins involved in both intrinsic and extrinsic drug resistance mechanisms are identified from literature. We then compute shortest paths from different drug targets to the set of resistance proteins in the protein-protein interactome, to derive a sub-network relevant to study emergence of drug resistance. The shortest paths are then scored and ranked based on a new scheme that considers (a) drug-induced gene upregulation data, from microarray experiments reported in literature, for the individual nodes and (b) edge-hubness, a network parameter which signifies centrality of a given edge in the network. High-scoring paths identified from this analysis indicate most plausible pathways for the emergence of drug resistance. Different targets appear to have different propensities for four drug resistance mechanisms. A new concept of ‘co-targets’ has been proposed to counter drug resistance, co-targets being defined as protein(s) that need to be simultaneously inhibited along with the intended target(s), to check emergence of resistance to a given drug.

The study leads to the identification of possible pathways for drug resistance, providing novel insights into the problem of resistance. Knowledge of important proteins in such pathways enables identification of appropriate ‘co-targets’, best examples being RecA, Rv0823c, Rv0892 and DnaE1, for drugs targeting the mycolic acid pathway. Insights obtained about the propensity of a drug to trigger resistance will be useful both for more careful identification of drug targets as well as to identify target-co-target pairs, both implementable in early stages of drug discovery itself. This approach is also inherently generic, likely to significantly impact drug discovery.

Original paper:

• K. Raman and N. Chandra, “Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance,” BMC Microbiology, vol. 8, p. 234, 2008.
  [bibtex]

  @article{Raman2008b,
    added-at = {2018-12-02T16:09:07.000+0100},
    author = {Raman, Karthik and Chandra, Nagasuma},
    biburl = {https://www.bibsonomy.org/bibtex/2dcbdf553bf832a4bc468f33f38125cab/karthikraman},
    comment = {10.1186/1471-2180-8-234},
    doi = {10.1186/1471-2180-8-234},
    interhash = {01ab5a3332335c3bbbb83113fb04bfec},
    intrahash = {dcbdf553bf832a4bc468f33f38125cab},
    journal = {BMC Microbiology},
    keywords = {mycobacteria myown},
    month = dec,
    pages = 234,
    posted-at = {2009-11-10 13:53:25},
    priority = {2},
    privnote = {10.1186/1471-2180-8-234},
    timestamp = {2019-02-10T16:15:28.000+0100},
    title = {Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance},
    url = {http://dx.doi.org/10.1186/1471-2180-8-234},
    volume = 8,
    year = 2008
  }