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.
References
Abbadi SH, Sameaa GA, Morlock G, Cooksey RC (2009) Molecular identification of mutations associated with anti-tuberculosis drug resistance among strains of mycobacterium tuberculosis. Int J Infect Dis 13(6):673–678. doi:S1201-9712(08)01731-1[pii]10.1016/j.ijid.2008.10.006
Alekshun MN, Levy SB (2007) Molecular mechanisms of antibacterial multidrug resistance. Cell 128(6):1037–1050. doi:S0092-8674(07)00311-X[pii]10.1016/j.cell.2007.03.004
Argyrou A, Jin L, Siconilfi-Baez L, Angeletti RH, Blanchard JS (2006) Proteome-wide profiling of isoniazid targets in mycobacterium tuberculosis. Biochemistry 45(47):13947–13953. doi:10.1021/bi061874m
Assenov Y, Ramirez F, Schelhorn S-E, Lengauer T, Albrecht M (2008) Computing topological parameters of biological networks. Bioinformatics 24(2):282–284. doi:10.1093/bioinformatics/btm554
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–U115. doi:10.1038/nrg1272
Barradell LB, Fitton A (1995) Artesunate: a review of its pharmacology and therapeutic efficacy in the treatment of malaria. Drugs 50(4):714–741
Boshoff HI, Myers TG, Copp BR, McNeil MR, Wilson MA, Barry CE 3rd (2004) The transcriptional responses of mycobacterium tuberculosis to inhibitors of metabolism: novel insights into drug mechanisms of action. J Biol Chem 279(38):40174–40184. doi:10.1074/jbc.M406796200M406796200[pii]
Cabusora L, Sutton E, Fulmer A, Forst CV (2005) Differential network expression during drug and stress response. Bioinformatics 21(12):2898–2905. doi:bti440[pii]10.1093/bioinformatics/bti440
Card GL, Peterson NA, Smith CA, Rupp B, Schick BM, Baker EN (2005) The crystal structure of rv1347c, a putative antibiotic resistance protein from mycobacterium tuberculosis, reveals a gcn5-related fold and suggests an alternative function in siderophore biosynthesis. J Biol Chem 280(14):13978–13986. doi:M413904200[pii]10.1074/jbc.M413904200
Chen P, Gearhart J, Protopopova M, Einck L, Nacy CA (2006) Synergistic interactions of sq109, a new ethylene diamine, with front-line antitubercular drugs in vitro. J Antimicrob Chemother 58(2):332–337. doi:dkl227[pii]10.1093/jac/dkl227
Chopra I, Brennan P (1997) Molecular action of anti-mycobacterial agents. Tuber Lung Dis 78(2):89–98
Colangeli R, Helb D, Vilcheze C, Hazbon MH, Lee CG, Safi H, Sayers B, Sardone I, Jones MB, Fleischmann RD, Peterson SN, Jacobs WR, Jr., Alland D (2007) Transcriptional regulation of multi-drug tolerance and antibiotic-induced responses by the histone-like protein lsr2 in m. Tuberculosis. PLoS Pathog 3 (6):e87. doi:06-PLPA-RA-0540[pii]10.1371/journal.ppat.0030087
Coros A, DeConno E, Derbyshire KM (2008) Is6110, a mycobacterium tuberculosis complex-specific insertion sequence, is also present in the genome of mycobacterium smegmatis, suggestive of lateral gene transfer among mycobacterial species. J Bacteriol 190(9):3408–3410. doi:JB.00009-08[pii]10.1128/JB.00009-08
de Steenwinkel JE, de Knegt GJ, ten Kate MT, van Belkum A, Verbrugh HA, Kremer K, van Soolingen D, Bakker-Woudenberg IA (2010) Time-kill kinetics of anti-tuberculosis drugs, and emergence of resistance, in relation to metabolic activity of mycobacterium tuberculosis. J Antimicrob Chemother 65(12):2582–2589. doi:dkq374[pii]10.1093/jac/dkq374
Freeman L (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41
Gillespie SH (2001) Antibiotic resistance in the absence of selective pressure. Int J Antimicrob Agents 17(3):171–176. doi:S0924-8579(00)00340-X[pii]
Gillespie SH (2002) Evolution of drug resistance in mycobacterium tuberculosis: clinical and molecular perspective. Antimicrob Agents Chemother 46(2):267–274
Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826. doi:10.1073/pnas.12265379999/12/7821[pii]
Gottesman MM, Fojo T, Bates SE (2002) Multidrug resistance in cancer: role of atp-dependent transporters. Nat Rev Cancer 2(1):48–58. doi:10.1038/nrc706
Gupta AK, Katoch VM, Chauhan DS, Sharma R, Singh M, Venkatesan K, Sharma VD (2009) Microarray analysis of efflux pump genes in multidrug-resistant mycobacterium tuberculosis during stress induced by common anti-tuberculous drugs. Microb Drug Resist 16(1):21–28. doi:10.1089/mdr.2009.0054
Gupta AK, Reddy VP, Lavania M, Chauhan DS, Venkatesan K, Sharma VD, Tyagi AK, Katoch VM (2010) Jefa (rv2459), a drug efflux gene in mycobacterium tuberculosis confers resistance to isoniazid and ethambutol. Indian J Med Res 132:176–188
Hegde SS, Vetting MW, Roderick SL, Mitchenall LA, Maxwell A, Takiff HE, Blanchard JS (2005) A fluoroquinolone resistance protein from mycobacterium tuberculosis that mimics DNA. Science 308(5727):1480–1483. doi:308/5727/1480[pii]10.1126/science.1110699
Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks T, Julien P, Roth A, Simonovic M, Bork P, von Mering C (2009) String 8–a global view on proteins and their functional interactions in 630 organisms. Nucl Acids Res 37(supp 1):D412–D416. doi:10.1093/nar/gkn760
Kurland CG, Canback B, Berg OG (2003) Horizontal gene transfer: a critical view. Proc Natl Acad Sci USA 100(17):9658–9662. doi:10.1073/pnas.16328701001632870100[pii]
Liu B, Pop M (2009) Ardb–antibiotic resistance genes database. Nucleic Acids Res 37 (Database issue): D443-447. doi:gkn656 [pii] 10.1093/nar/gkn656
Ma Z, Lienhardt C, McIlleron H, Nunn AJ, Wang X (2010) Global tuberculosis drug development pipeline: the need and the reality. Lancet 375(9731):2100–2109. doi:S0140-6736(10)60395-9[pii]10.1016/S0140-6736(10)60359-9
McKeegan KS, Borges-Walmsley MI, Walmsley AR (2002) Microbial and viral drug resistance mechanisms. Trends Microbiol 10(10 Suppl):S8–S14. doi:S0966842X02024290[pii]
Nathanson E, Gupta R, Huamani P, Leimane V, Pasechnikov AD, Tupasi TE, Vink K, Jaramillo E, Espinal MA (2004) Adverse events in the treatment of multidrug-resistant tuberculosis: results from the dots-plus initiative. Int J Tuberc Lung Dis 8(11):1382–1384
Nguyen L, Thompson CJ (2006) Foundations of antibiotic resistance in bacterial physiology: the mycobacterial paradigm. Trends Microbiol 14(7):304–312. doi:S0966-842X(06)00125-9[pii]10.1016/j.tim.2006.05.005
O’Sullivan DM, Hinds J, Butcher PD, Gillespie SH, McHugh TD (2008) Mycobacterium tuberculosis DNA repair in response to subinhibitory concentrations of ciprofloxacin. J Antimicrob Chemother 62(6):1199–1202. doi:dkn387[pii]10.1093/jac/dkn387
Ouellet H, Podust LM, de Montellano PR (2008) Mycobacterium tuberculosis cyp130: crystal structure, biophysical characterization, and interactions with antifungal azole drugs. J Biol Chem 283(8):5069–5080. doi:M708734200[pii]10.1074/jbc.M708734200
Pethe K, Swenson DL, Alonso S, Anderson J, Wang C, Russell DG (2004) Isolation of mycobacterium tuberculosis mutants defective in the arrest of phagosome maturation. Proc Natl Acad Sci USA 101(37):13642–13647. doi:10.1073/pnas.04016571010401657101[pii]
Protopopova M, Hanrahan C, Nikonenko B, Samala R, Chen P, Gearhart J, Einck L, Nacy CA (2005) Identification of a new antitubercular drug candidate, sq109, from a combinatorial library of 1, 2-ethylenediamines. J Antimicrob Chemother 56(5):968–974. doi:dki319[pii]10.1093/jac/dki319
Raman K, Chandra N (2008) Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol 8:234. doi:1471-2180-8-234[pii]10.1186/1471-2180-8-234
Raman K, Yeturu K, Chandra N (2008) Targettb: a target identification pipeline for mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2:109. doi:1752-0509-2-109[pii]10.1186/1752-0509-2-109
Rawat R, Whitty A, Tonge PJ (2003) The isoniazid-nad adduct is a slow, tight-binding inhibitor of inha, the mycobacterium tuberculosis enoyl reductase: adduct affinity and drug resistance. Proc Natl Acad Sci USA 100(24):13881–13886. doi:10.1073/pnas.22358481002235848100[pii]
Sandgren A, Strong M, Muthukrishnan P, Weiner BK, Church GM, Murray MB (2009) Tuberculosis drug resistance mutation database. PLoS Med 6 (2):e2. doi:08-PLME-HIA-2556 [pii] 10.1371/journal.pmed.1000002
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. doi:10.1101/gr.123930313/11/2498[pii]
Shenoi S, Friedland G (2009) Extensively drug-resistant tuberculosis: a new face to an old pathogen. Annu Rev Med 60:307–320. doi:10.1146/annurev.med.60.053107.103955
Silva PE, Bigi F, Santangelo MP, Romano MI, Martin C, Cataldi A, Ainsa JA (2001) Characterization of p55, a multidrug efflux pump in mycobacterium bovis and mycobacterium tuberculosis. Antimicrob Agents Chemother 45(3):800–804. doi:10.1128/AAC.45.3.800-804.2001
Simon JW, Richard AS, Ken L, Laurent K, Robert CR, Gurdyal SB (2004) The use of microarray analysis to determine the gene expression profiles of mycobacterium tuberculosis in response to anti-bacterial compounds. Tuberculosis (Edinburgh, Scotland) 84 (3):263–274
Spies FS, da Silva PE, Ribeiro MO, Rossetti ML, Zaha A (2008) Identification of mutations related to streptomycin resistance in clinical isolates of mycobacterium tuberculosis and possible involvement of efflux mechanism. Antimicrob Agents Chemother 52(8):2947–2949. doi:AAC.01570-07[pii]10.1128/AAC.01570-07
Verkhedkar KD, Raman K, Chandra NR, Vishveshwara S (2007) Metabolome based reaction graphs of m. Tuberculosis and m. Leprae: a comparative network analysis. PLoS One 2 (9):e881. doi:10.1371/journal.pone.0000881
WHO (2010) Multidrug and extensively drug-resistant tb (m/xdr-tb): 2010 global report on surveillance and response
Wright GD (2007) The antibiotic resistome: the nexus of chemical and genetic diversity. Nat Rev Microbiol 5(3):175–186. doi:nrmicro1614[pii]10.1038/nrmicro1614
Zhang Y (2005) The magic bullets and tuberculosis drug targets. Annu Rev Pharmacol Toxicol 45:529–564
Zhang S-L, Qi H, Qiu D-L, Li D-X, Zhang J, Du C-M, Wang G-B, Yang Z-R, Sun Q (2007) Genotypic analysis of multidrug-resistant mycobacterium tuberculosis isolates recovered from central china. Biochem Genet 45(3):281–290. doi:10.1007/s10528-006-9074-6
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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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