Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs

Phelan, Jody E., O'Sullivan, Denise M., Machado, Diana, Ramos, Jorge, Oppong, Yaa E. A., Campino, Susana, O'Grady, Justin, McNerney, Ruth, Hibberd, Martin L., Viveiros, Miguel, Huggett, Jim F. and Clark, Taane G. (2019) Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Medicine, 11. ISSN 1756-994X

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Abstract

BACKGROUND: Mycobacterium tuberculosis resistance to anti-tuberculosis drugs is a major threat to global public health. Whole genome sequencing (WGS) is rapidly gaining traction as a diagnostic tool for clinical tuberculosis settings. To support this informatically, previous work led to the development of the widely used TBProfiler webtool, which predicts resistance to 14 drugs from WGS data. However, for accurate and rapid high throughput of samples in clinical or epidemiological settings, there is a need for a stand-alone tool and the ability to analyse data across multiple WGS platforms, including Oxford Nanopore MinION.   RESULTS: We present a new command line version of the TBProfiler webserver, which includes hetero-resistance calling and will facilitate the batch processing of samples. The TBProfiler database has been expanded to incorporate 178 new markers across 16 anti-tuberculosis drugs. The predictive performance of the mutation library has been assessed using > 17,000 clinical isolates with WGS and laboratory-based drug susceptibility testing (DST) data. An integrated MinION analysis pipeline was assessed by performing WGS on 34 replicates across 3 multi-drug resistant isolates with known resistance mutations. TBProfiler accuracy varied by individual drug. Assuming DST as the gold standard, sensitivities for detecting multi-drug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) were 94% (95%CI 93-95%) and 83% (95%CI 79-87%) with specificities of 98% (95%CI 98-99%) and 96% (95%CI 95-97%) respectively. Using MinION data, only one resistance mutation was missed by TBProfiler, involving an insertion in the tlyA gene coding for capreomycin resistance. When compared to alternative platforms (e.g. Mykrobe predictor TB, the CRyPTIC library), TBProfiler demonstrated superior predictive performance across first- and second-line drugs.   CONCLUSIONS: The new version of TBProfiler can rapidly and accurately predict anti-TB drug resistance profiles across large numbers of samples with WGS data. The computing architecture allows for the ability to modify the core bioinformatic pipelines and outputs, including the analysis of WGS data sourced from portable technologies. TBProfiler has the potential to be integrated into the point of care and WGS diagnostic environments, including in resource-poor settings.

Item Type:	Article
Additional Information:	Funding information: This work was supported by the UK National Measurement System and the European Metrology Programme for Innovation and Research (EMPIR) joint research project [HLT07] “AntiMicroResist” which has received funding from the EMPIR programme co-financed by the Participating States and the European Union’s Horizon 2020 research and innovation programme. JP Is supported by a Newton Institutional Links Grant (British Council. 261868591). TGC is funded by the Medical Research Council UK (Grant no. MR/M01360X/1, MR/N010469/1, MR/R025576/1, and MR/R020973/1) and BBSRC (Grant no. BB/R013063/1). SC is funded by Medical Research Council UK grants (MR/M01360X/1, MR/R025576/1, and MR/R020973/1). DM is supported by Fundação para a Ciência e a Tecnologia, Portugal (Grant no SFRH/BPD/100688/2014). JO’G is supported by the UK Antimicrobial Resistance Cross Council Initiative (MR/N013956/1) and Rosetrees Trust (A749). DM, JR and MV are thankful for the support of Grant GHTMUID/Multi/04413/2013 from Fundação para a Ciência e a Tecnologia, Portugal. These funding bodies did not have a role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Uncontrolled Keywords:	sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
Depositing User:	LivePure Connector
Date Deposited:	01 Jul 2019 08:30
Last Modified:	11 Nov 2024 00:50
URI:	https://ueaeprints.uea.ac.uk/id/eprint/71585
DOI:	10.1186/s13073-019-0650-x

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