The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis. However, molecular diagnostics to date have focused largely on first-line drugs and predicting susceptibilities in a binary manner (classifying strains as either susceptible or resistant). Here, we used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration (MIC) in 15,211 Mycobacterium tuberculosis clinical isolates from 23 countries across five continents. We identified 492 unique MIC-elevating variants across 13 drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms.
Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach / Barilar, I.; Battaglia, S.; Borroni, E.; Brandao, A. P.; Brankin, A.; Cabibbe, A. M.; Carter, J.; Chetty, D.; Cirillo, D. M.; Claxton, P.; Clifton, D. A.; Cohen, T.; Coronel, J.; Crook, D. W.; Dreyer, V.; Earle, S. G.; Escuyer, V.; Ferrazoli, L.; Fowler, P. W.; Gao, G. F.; Gardy, J.; Gharbia, S.; Ghisi, K. T.; Ghodousi, A.; Gibertoni Cruz, A. L.; Grandjean, L.; Grazian, C.; Groenheit, R.; Guthrie, J. L.; He, W.; Hoffmann, H.; Hoosdally, S. J.; Hunt, M.; Iqbal, Z.; Ismail, N. A.; Jarrett, L.; Joseph, L.; Jou, R.; Kambli, P.; Khot, R.; Knaggs, J.; Koch, A.; Kohlerschmidt, D.; Kouchaki, S.; Lachapelle, A. S.; Lalvani, A.; Lapierre, S. G.; Laurenson, I. F.; Letcher, B.; Lin, W. -H.; Liu, C.; Liu, D.; Malone, K. M.; Mandal, A.; Mansjo, M.; Calisto Matias, D. V. L.; Meintjes, G.; De Freitas Mendes, F.; Merker, M.; Mihalic, M.; Millard, J.; Miotto, P.; Mistry, N.; Moore, D.; Musser, K. A.; Ngcamu, D.; Nhung, H. N.; Niemann, S.; Nilgiriwala, K. S.; Nimmo, C.; O'Donnell, M.; Okozi, N.; Oliveira, R. S.; Omar, S. V.; Paton, N.; Peto, T. E. A.; Pinhata, J. M. W.; Plesnik, S.; Puyen, Z. M.; Rabodoarivelo, M. S.; Rakotosamimanana, N.; Rancoita, P. M. V.; Rathod, P.; Robinson, E. R.; Rodger, G.; Rodrigues, C.; Rodwell, T. C.; Roohi, A.; Santos-Lazaro, D.; Shah, S.; Smith, G.; Kohl, T. A.; Solano, W.; Spitaleri, A.; Steyn, A. J. C.; Supply, P.; Surve, U.; Tahseen, S.; Thuong, N. T. T.; Thwaites, G.; Todt, K.; Trovato, A.; Utpatel, C.; Van Rie, A.; Vijay, S.; Walker, A. S.; Walker, T. M.; Warren, R.; Werngren, J.; Wijkander, M.; Wilkinson, R. J.; Wilson, D. J.; Wintringer, P.; Xiao, Y. -X.; Yang, Y.; Yanlin, Z.; Yao, S. -Y.; Zhu, B.. - In: NATURE COMMUNICATIONS. - ISSN 2041-1723. - 15:1(2024). [10.1038/s41467-023-44325-5]
Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach
Battaglia S.;Rancoita P. M. V.;
2024-01-01
Abstract
The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis. However, molecular diagnostics to date have focused largely on first-line drugs and predicting susceptibilities in a binary manner (classifying strains as either susceptible or resistant). Here, we used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration (MIC) in 15,211 Mycobacterium tuberculosis clinical isolates from 23 countries across five continents. We identified 492 unique MIC-elevating variants across 13 drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
