BackgroundPatients with Mycobacterium tuberculosis isolates sharing identical DNA fingerprint patterns can be epidemiologically linked. However, municipal health services in the Netherlands are able to confirm an epidemiological link in only around 23% of the patients with isolates clustered by the conventional variable number of tandem repeat (VNTR) genotyping. This research aims to investigate whether whole genome sequencing (WGS) is a more reliable predictor of epidemiological links between tuberculosis patients than VNTR genotyping.MethodsVNTR genotyping and WGS were performed in parallel on all Mycobacterium tuberculosis complex isolates received at the Netherlands National Institute for Public Health and the Environment in 2016. Isolates were clustered by VNTR when they shared identical 24-loci VNTR patterns; isolates were assigned to a WGS cluster when the pair-wise genetic distance was ≤ 12 single nucleotide polymorphisms (SNPs). Cluster investigation was performed by municipal health services on all isolates clustered by VNTR in 2016. The proportion of epidemiological links identified among patients clustered by either method was calculated.ResultsIn total, 535 isolates were genotyped, of which 25% (134/535) were clustered by VNTR and 14% (76/535) by WGS; the concordance between both typing methods was 86%. The proportion of epidemiological links among WGS clustered cases (57%) was twice as common than among VNTR clustered cases (31%).ConclusionWhen WGS was applied, the number of clustered isolates was halved, while all epidemiologically linked cases remained clustered. WGS is therefore a more reliable tool to predict epidemiological links between tuberculosis cases than VNTR genotyping and will allow more efficient transmission tracing, as epidemiological investigations based on false clustering can be avoided.

The clinical phenotype of zoonotic tuberculosis and its contribution to the global burden of disease are poorly understood and probably underestimated. This shortcoming is partly because of the inability of currently available laboratory and in silico tools to accurately identify all subspecies of the Mycobacterium tuberculosis complex (MTBC). We present SNPs to Identify TB (SNP-IT), a single-nucleotide polymorphism–based tool to identify all members of MTBC, including animal clades. By applying SNP-IT to a collection of clinical genomes from a UK reference laboratory, we detected an unexpectedly high number of M. orygis isolates. M. orygis is seen at a similar rate to M. bovis , yet M. orygis cases have not been previously described in the United Kingdom. From an international perspective, it is possible that M. orygis is an underestimated zoonosis. Accurate identification will enable study of the clinical phenotype, host range, and transmission mechanisms of all subspecies of MTBC in greater detail.

BackgroundWhole genome sequencing (WGS) is a reliable tool for studying tuberculosis (TB) transmission. WGS data are usually processed by custom-built analysis pipelines with little standardisation between them.AimTo compare the impact of variability of several WGS analysis pipelines used internationally to detect epidemiologically linked TB cases.MethodsFrom the Netherlands, 535 Mycobacterium tuberculosis complex (MTBC) strains from 2016 were included. Epidemiological information obtained from municipal health services was available for all mycobacterial interspersed repeat unit-variable number of tandem repeat (MIRU-VNTR) clustered cases. WGS data was analysed using five different pipelines: one core genome multilocus sequence typing (cgMLST) approach and four single nucleotide polymorphism (SNP)-based pipelines developed in Oxford, United Kingdom; Borstel, Germany; Bilthoven, the Netherlands and Copenhagen, Denmark. WGS clusters were defined using a maximum pairwise distance of 12 SNPs/alleles.ResultsThe cgMLST approach and Oxford pipeline clustered all epidemiologically linked cases, however, in the other three SNP-based pipelines one epidemiological link was missed due to insufficient coverage. In general, the genetic distances varied between pipelines, reflecting different clustering rates: the cgMLST approach clustered 92 cases, followed by 84, 83, 83 and 82 cases in the SNP-based pipelines from Copenhagen, Oxford, Borstel and Bilthoven respectively.ConclusionConcordance in ruling out epidemiological links was high between pipelines, which is an important step in the international validation of WGS data analysis. To increase accuracy in identifying TB transmission clusters, standardisation of crucial WGS criteria and creation of a reference database of representative MTBC sequences would be advisable.

Background Drug-susceptibility testing (DST) of Mycobacterium tuberculosis complex (MTBC) isolates by the Mycobacteria Growth Indicator Tube (MGIT) approach is the most widely applied reference standard. However, the use of WGS is increasing in many developed countries to detect resistance and predict susceptibility. We investigated the reliability of WGS in predicting drug susceptibility, and analysed the discrepancies between WGS and MGIT against the first-line drugs rifampicin, isoniazid, ethambutol and pyrazinamide. Methods DST by MGIT and WGS was performed on MTBC isolates received in 2016/2017. Nine genes and/or their promotor regions were investigated for resistance-associated mutations: rpoB, katG, fabG1, ahpC, inhA, embA, embB, pncA and rpsA. Isolates that were discrepant in their MGIT/WGS results and a control group with concordant results were retested in the MGIT, at the critical concentration and a lower concentration, and incubated for up to 45 days after the control tube became positive in the MGIT. Results In total, 1136 isolates were included, of which 1121 were routine MTBC isolates from the Netherlands. The negative predictive value of WGS was ≥99.3% for all four first-line antibiotics. The majority of discrepancies for isoniazid and ethambutol were explained by growth at the lower concentrations, and for rifampicin by prolonged incubation in the MGIT, both indicating low-level resistance. Conclusions Applying WGS in a country like the Netherlands, with a low TB incidence and low prevalence of resistance, can reduce the need for phenotypic DST for ∼90% of isolates and accurately detect mutations associated with low-level resistance, often missed in conventional DST.

In many countries, Mycobacterium tuberculosis isolates are routinely subjected to variable-number tandem-repeat (VNTR) typing to investigate M. tuberculosis transmission. Unexpectedly, cross-border clusters were identified among African refugees in the Netherlands and Denmark, although transmission in those countries was unlikely. Whole-genome sequencing (WGS) was applied to analyze transmission in depth and to assess the precision of VNTR typing. WGS was applied to 40 M. tuberculosis isolates from refugees in the Netherlands and Denmark (most of whom were from the Horn of Africa) that shared the exact same VNTR profile. Cluster investigations were undertaken to identify in-country epidemiological links. Combining WGS results for the isolates (all members of the central Asian strain [CAS]/Delhi genotype), from both European countries, an average genetic distance of 80 single-nucleotide polymorphisms (SNPs) (maximum, 153 SNPs) was observed. The few pairs of isolates with confirmed epidemiological links, except for one pair, had a maximum distance of 12 SNPs. WGS divided this refugee cluster into several subclusters of patients from the same country of origin. Although the M. tuberculosis cases, mainly originating from African countries, shared the exact same VNTR profile, most were clearly distinguished by WGS. The average genetic distance in this specific VNTR cluster was 2 times greater than that in other VNTR clusters. Thus, identical VNTR profiles did not represent recent direct M. tuberculosis transmission for this group of patients. It appears that either these strains from Africa are extremely conserved genetically or there is ongoing transmission of this genotype among refugees on their long migration routes from Africa to Europe.