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Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches

View ORCID ProfileJoshua J Carter, Timothy M Walker, A Sarah Walker, Michael G. Whitfield, Glenn P. Morlock, Timothy EA Peto, James E. Posey, View ORCID ProfileDerrick W Crook, View ORCID ProfilePhilip W Fowler
doi: https://doi.org/10.1101/518142
Joshua J Carter
1Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
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  • ORCID record for Joshua J Carter
Timothy M Walker
1Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
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A Sarah Walker
1Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
2National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
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Michael G. Whitfield
3Division of Molecular Biology and Human Genetics, SAMRC Centre for Tuberculosis Research, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
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Glenn P. Morlock
4Division of Tuberculosis Elimination, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
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Timothy EA Peto
1Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
2National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
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James E. Posey
4Division of Tuberculosis Elimination, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
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Derrick W Crook
1Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
2National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
5NIHR Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, UK
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Philip W Fowler
1Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
2National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
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  • For correspondence: philip.fowler@ndm.ox.ac.uk
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Summary

Pyrazinamide is one of four first-line antibiotics used to treat tuberculosis, however antibiotic susceptibility testing for pyrazinamide is problematic. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, an enzyme that converts pyrazinamide into its active form. We curated a derivation dataset of 291 non-redundant, missense amino acid mutations in pncA with associated high-confidence phenotypes from published studies and then trained three different machine learning models to predict pyrazinamide resistance based on sequence- and structure-based features of each missense mutation. The clinical performance of the models was estimated by predicting the binary pyrazinamide resistance phenotype of 2,292 clinical isolates harboring missense mutations in pncA. Overall, this work offers an approach to improve the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs such as bedaquiline.

Footnotes

  • ↵‡ on behalf of the “EXIT-RIF” investigators: Prof Robin M Warren, Prof Annelies van Rie, Prof Lesley Scott, Prof Wendy Stevens

  • Manuscript revised following unsuccessful review at journal.

  • https://github.com/carterjosh/PZA-machine-learner/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 18, 2019.
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Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches
Joshua J Carter, Timothy M Walker, A Sarah Walker, Michael G. Whitfield, Glenn P. Morlock, Timothy EA Peto, James E. Posey, Derrick W Crook, Philip W Fowler
bioRxiv 518142; doi: https://doi.org/10.1101/518142
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Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches
Joshua J Carter, Timothy M Walker, A Sarah Walker, Michael G. Whitfield, Glenn P. Morlock, Timothy EA Peto, James E. Posey, Derrick W Crook, Philip W Fowler
bioRxiv 518142; doi: https://doi.org/10.1101/518142

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