INTRODUCTION: In 2007, a new federal legislation in Belgium prohibited non-biosafety level 3 laboratories to process culture tubes suspected of containing mycobacteria. AIM: To present mycobacterial surveillance/diagnosis data from the Belgian National Reference Centre for mycobacteria (NRC) from 2007 to 2016. METHODS: This retrospective observational study investigated the numbers of analyses at the NRC and false positive cultures (interpreted as containing mycobacteria at referring clinical laboratories, but with no mycobacterial DNA detected by PCR in the NRC). We reviewed mycobacterial species identified and assessed trends over time of proportions of nontuberculous mycobacteria (NTM) vs Mycobacterium tuberculosis complex (MTBc), and false positive cultures vs NTM. RESULTS: From 2007 to 2016, analyses requests to the NRC doubled from 12.6 to 25.3 per 100,000 inhabitants. A small but significant increase occurred in NTM vs MTBc proportions, from 57.9% (587/1,014) to 60.3% (867/1,437) (p < 0.001). Although NTM infection notification is not mandatory in Belgium, we annually received up to 8.6 NTM per 100,000 inhabitants. M. avium predominated (ca 20% of NTM cultures), but M. intracellulare culture numbers rose significantly, from 13.0% (74/587) of NTM cultures in 2007 to 21.0% (178/867) in 2016 (RR: 1.05; 95% CI: 1.03–1.07). The number of false positive cultures also increased, reaching 43.3% (1,097/2,534) of all samples in 2016. CONCLUSION: We recommend inclusion of NTM in sentinel programmes. The large increase of false positive cultures is hypothesised to result from processing issues prior to arrival at the NRC, highlighting the importance of sample decontamination/transport and equipment calibration in peripheral laboratories.
International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.
International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.
International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.
International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.
International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.
Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole-genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep-sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and a specificity of 97.4%. 56 out of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol and ethionamide, and low-level rifampicin or isoniazid resistance mutations, all notoriously prone to phenotypic testing variability. Only two out of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and a specificity of 98.5/97.2/95.3%, respectively. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis or natural pyrazinamide resistance of globally rare "Mycobacterium canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 out of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.
BACKGROUND: The risk of tuberculosis outbreaks among people fleeing hardship for refuge in Europe is heightened. We describe the cross-border European response to an outbreak of multidrug-resistant tuberculosis among patients from the Horn of Africa and Sudan. METHODS: On April 29 and May 30, 2016, the Swiss and German National Mycobacterial Reference Laboratories independently triggered an outbreak investigation after four patients were diagnosed with multidrug-resistant tuberculosis. In this molecular epidemiological study, we prospectively defined outbreak cases with 24-locus mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) profiles; phenotypic resistance to isoniazid, rifampicin, ethambutol, pyrazinamide, and capreomycin; and corresponding drug resistance mutations. We whole-genome sequenced all Mycobacterium tuberculosis isolates and clustered them using a threshold of five single nucleotide polymorphisms (SNPs). We collated epidemiological data from host countries from the European Centre for Disease Prevention and Control. FINDINGS: Between Feb 12, 2016, and April 19, 2017, 29 patients were diagnosed with multidrug-resistant tuberculosis in seven European countries. All originated from the Horn of Africa or Sudan, with all isolates two SNPs or fewer apart. 22 (76%) patients reported their travel routes, with clear spatiotemporal overlap between routes. We identified a further 29 MIRU-VNTR-linked cases from the Horn of Africa that predated the outbreak, but all were more than five SNPs from the outbreak. However all 58 isolates shared a capreomycin resistance-associated tlyA mutation. INTERPRETATION: Our data suggest that source cases are linked to an M tuberculosis clone circulating in northern Somalia or Djibouti and that transmission probably occurred en route before arrival in Europe. We hypothesise that the shared mutation of tlyA is a drug resistance mutation and phylogenetic marker, the first of its kind in M tuberculosis sensu stricto. FUNDING: The Swiss Federal Office of Public Health, the University of Zurich, the Wellcome Trust, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), the Medical Research Council, BELTA-TBnet, the European Union, the German Center for Infection Research, and Leibniz Science Campus Evolutionary Medicine of the Lung (EvoLUNG).
13 Pages, 1 Figure, 4 tables. The authors' affiliations are listed in the Supplementary Appendix, available at NEJM.org. Supplementary Material, available at http://dx.doi.org/10.1056/NEJMoa1800474 ; BACKGROUND: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. METHODS: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. RESULTS: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted. CONCLUSIONS: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.). ; Supported by grants from the Bill and Melinda Gates Foundation (OPP1133541, to CRyPTIC, plus separate support to Dr. Rodwell), a Wellcome Trust/Newton Fund–MRC Collaborative Award (200205/Z/15/Z, to CRyPTIC), the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) and NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, the NIHR Biomedical Research Centre at Barts, the NIHR Biomedical Research Centre at Imperial, the NIHR and NHS England (to the 100,000 Genomes Project, which is managed by Genomics England, a wholly owned company of the U.K. Department of Health), the Wellcome Trust, the Medical Research Council, Public Health England, a grant from the National Science and Technology Key Program of China (2014ZX10003002), a grant from the National Basic Research program of China (2014CB744403), a grant from the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB29020000), a grant from the European Commission Seventh Framework Program (FP7/2007-2013, to Borstel under grant agreement 278864 in the framework of the Patho-NGen-Trace project), the German Center for Infection Research (to Borstel), Leibniz Science Campus Evolutionary Medicine of the Lung (EvoLUNG), the Belgian Ministry of Social Affairs (to the Belgian Reference Center for Tuberculosis and Mycobacteria from Bacterial Diseases Service through a fund within the Health Insurance System), the French governmental program "Investing for the Future" (to Genoscreen), a grant from the European Commission Seventh Framework Program (FP7/2007-2013, to Genoscreen under grant agreement 278864 in the framework of the Patho-NGen-Trace project), grants from the Drug Resistant Tuberculosis Fund (R015833003, to Dr. Chaiprasert), the Faculty of Medicine, Siriraj Hospital, Mahidol University (to Dr. Chaiprasert), a grant from the Ministry of Economy and Competitiveness (MINECO), Spain (SAF2016-77346-R, to Dr. Comas), a grant from the European Research Council (638553-TB-ACCELERATE, to Dr. Comas), a grant from the BC Centre for Disease Control Foundation for Population and Public Health (to Dr. Gardy), a grant from the British Colombia Lung Association (to Dr. Gardy), grants from the Wellcome Trust and the Royal Society (101237/Z/13/Z and 102541/A/13/Z, to Drs. Wilson and Iqbal [Sir Henry Dale Fellows]), a grant from the National University of Singapore Yong Loo Lin School of Medicine Aspiration Fund (NUHSRO/2014/069/AF-New Idea/04, to Drs. Ong and Teo), a European Commission Seventh Framework Program European Genetic Network (EUROGEN) grant (201483, to Dr. Drobniewski), and the National Institute of Allergy and Infectious Diseases, National Institutes of Health (to Dr. Rodwell). Dr. T. Walker is an NIHR Academic Clinical Lecturer, and Drs. Crook, Peto, and Caulfield are NIHR Senior Investigators. No potential conflict of interest relevant to this article was reported. Disclosure forms provided by the authors are available with the full text of this article at NEJM.org. We thank Stéphanie Duthoy, Carina Hahn, Alamdar Hussain, Yannick Laurent, Mathilde Mairey, Vanessa Mohr, and Mahmood Qadir for technical assistance and George F. Gao, Director of the Chinese Center for Disease Control and Prevention, for directing the Chinese grant and sequencing program ; Peer reviewed
