Mycobacterium tuberculosis whole genome sequencing and protein structure modelling provides insights into anti-tuberculosis drug resistance. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Phelan, Jody; Coll, Francesc; McNerney, Ruth; Ascher, David; Pires, Douglas; Furnham, Nick; Coeck, Nele; Hill-Cawthorne, Grant; Nair, Mridul; Mallard, Kim; Ramsay, Andrew; Campino, Susana; Hibberd, Martin; Pain, Arnab; Rigouts, Leen; Clark, Taane
• Is Part Of: BMC medicine. Volume 14:Issue 1(2016)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-12
• Subjects: Tuberculosis -- Drug resistance -- Genomics -- Protein structural modelling -- Association study -- Convergent evolution; Medicine -- Periodicals; 610.5
• DOI: 10.1186/s12916-016-0575-9
• Languages: English
• ISSNs: 1741-7015

Abstract

Abstract Background Combating the spread of drug resistant tuberculosis is a global health priority. Whole genome association studies are being applied to identify genetic determinants of resistance to anti-tuberculosis drugs. Protein structure and interaction modelling are used to understand the functional effects of putative mutations and provide insight into the molecular mechanisms leading to resistance. Methods To investigate the potential utility of these approaches, we analysed the genomes of 144Mycobacterium tuberculosis clinical isolates from The Special Programme for Research and Training in Tropical Diseases (TDR) collection sourced from 20 countries in four continents. A genome-wide approach was applied to 127 isolates to identify polymorphisms associated with minimum inhibitory concentrations for first-line anti-tuberculosis drugs. In addition, the effect of identified candidate mutations on protein stability and interactions was assessed quantitatively with well-established computational methods. Results The analysis revealed that mutations in the genesrpoB (rifampicin), katG (isoniazid), inhA -promoter (isoniazid), rpsL (streptomycin) andembB (ethambutol) were responsible for the majority of resistance observed. A subset of the mutations identified inrpoB andkatG were predicted to affect protein stability. Further, a strong direct correlation was observed between the minimum inhibitory concentration values and the distance of the mutated residues in the three-dimensional structures ofrpoB andkatG to their respective drugs binding sites. Conclusions Using the TDR resource, we demonstrate the usefulness of whole genome association and convergent evolution approaches to detect known and potentially novel mutations associated with drug resistance. Further, protein structural modelling could provide a means of predicting the impact of polymorphisms on drug efficacy in the absence of phenotypic data. These approaches could ultimately lead to novel resistance mutations to improve the design of tuberculosis control measures, such as diagnostics, and inform patient management.