Targeted next-generation sequencing to diagnose drug-resistant tuberculosis: a systematic review and meta-analysis.

BACKGROUND: Targeted next-generation sequencing (NGS) can rapidly and simultaneously detect mutations associated with resistance to tuberculosis drugs across multiple gene targets. The use of targeted NGS to diagnose drug-resistant tuberculosis, as described in publicly available data, has not been comprehensively reviewed. We aimed to identify targeted NGS assays that diagnose drug-resistant tuberculosis, determine how widely this technology has been used, and assess the diagnostic accuracy of these assays. METHODS: In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Library, Web of Science Core Collection, Global Index Medicus, Google Scholar, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform for published and unpublished reports on targeted NGS for drug-resistant tuberculosis from Jan 1, 2005, to Oct 14, 2022, with updates to our search in Embase and Google Scholar until Feb 13, 2024. Studies eligible for the systematic review described targeted NGS approaches to predict drug resistance in Mycobacterium tuberculosis infections using primary samples, reference strain collections, or cultured isolates from individuals with presumed or confirmed tuberculosis. Our search had no limitations on study type or language, although only reports in English, German, and French were screened for eligibility. For the meta-analysis, we included test accuracy studies that used any reference standard, and we assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes for the meta-analysis were sensitivity and specificity of targeted NGS to diagnose drug-resistant tuberculosis compared to phenotypic and genotypic drug susceptibility testing. We used a Bayesian bivariate model to generate summary receiver operating characteristic plots and diagnostic accuracy measures, overall and stratified by drug and sample type. This study is registered with PROSPERO, CRD42022368707. FINDINGS: We identified and screened 2920 reports, of which 124 were eligible for our systematic review, including 37 review articles and 87 reports of studies collecting samples for targeted NGS. Sequencing was mainly done in the USA (14 [16%] of 87), western Europe (ten [11%]), India (ten [11%]), and China (nine [10%]). We included 24 test accuracy studies in the meta-analysis, in which 23 different tuberculosis drugs or drug groups were assessed, covering first-line drugs, injectable drugs, and fluoroquinolones and predominantly comparing targeted NGS with phenotypic drug susceptibility testing. The combined sensitivity of targeted NGS across all drugs was 94·1% (95% credible interval [CrI] 90·9-96·3) and specificity was 98·1% (97·0-98·9). Sensitivity for individual drugs ranged from 76·5% (52·5-92·3) for capreomycin to 99·1% (98·3-99·7) for rifampicin; specificity ranged from 93·1% (88·0-96·3) for ethambutol to 99·4% (98·3-99·8) for amikacin. Diagnostic accuracy was similar for primary clinical samples and culture isolates overall and for rifampicin, isoniazid, ethambutol, streptomycin, and fluoroquinolones, and similar after excluding studies at high risk of bias (overall sensitivity 95·2% [95% CrI 91·7-97·1] and specificity 98·6% [97·4-99·3]). INTERPRETATION: Targeted NGS is highly sensitive and specific for detecting drug resistance across panels of tuberculosis drugs and can be performed directly on clinical samples. There is a paucity of data on performance for some currently recommended drugs. The barriers preventing the use of targeted NGS to diagnose drug-resistant tuberculosis in high-burden countries need to be addressed. FUNDING: National Institutes of Allergy and Infectious Diseases and Swiss National Science Foundation.

Population-Based Severe Acute Respiratory Syndrome Coronavirus 2 Whole-Genome Sequencing and Contact Tracing During the Coronavirus Disease 2019 Pandemic in Switzerland.

BACKGROUND: Testing and contact tracing (CT) can interrupt transmission chains of SARS-CoV-2. Whole-genome sequencing (WGS) can potentially strengthen these investigations and provide insights on transmission. METHODS: We included all laboratory-confirmed COVID-19 cases diagnosed between 4 June and 26 July 2021, in a Swiss canton. We defined CT clusters based on epidemiological links reported in the CT data and genomic clusters as sequences with no single-nucleotide polymorphism (SNP) differences between any 2 pairs of sequences being compared. We assessed the agreement between CT clusters and genomic clusters. RESULTS: Of 359 COVID-19 cases, 213 were sequenced. Overall, agreement between CT and genomic clusters was low (Cohen's κ = 0.13). Of 24 CT clusters with ≥2 sequenced samples, 9 (37.5%) were also linked based on genomic sequencing but in 4 of these, WGS found additional cases in other CT clusters. Household was most often reported source of infection (n = 101 [28.1%]) and home addresses coincided well with CT clusters: In 44 of 54 CT clusters containing ≥2 cases (81.5%), all cases in the cluster had the same reported home address. However, only a quarter of household transmission was confirmed by WGS (6 of 26 genomic clusters [23.1%]). A sensitivity analysis using ≤1-SNP differences to define genomic clusters resulted in similar results. CONCLUSIONS: WGS data supplemented epidemiological CT data, supported the detection of potential additional clusters missed by CT, and identified misclassified transmissions and sources of infection. Household transmission was overestimated by CT.

RDP5: a computer program for analyzing recombination in, and removing signals of recombination from, nucleotide sequence datasets.

For the past 20 years, the recombination detection program (RDP) project has focused on the development of a fast, flexible, and easy to use Windows-based recombination analysis tool. Whereas previous versions of this tool have relied on considerable user-mediated verification of detected recombination events, the latest iteration, RDP5, is automated enough that it can be integrated within analysis pipelines and run without any user input. The main innovation enabling this degree of automation is the implementation of statistical tests to identify recombination signals that could be attributable to evolutionary processes other than recombination. The additional analysis time required for these tests has been offset by algorithmic improvements throughout the program such that, relative to RDP4, RDP5 will still run up to five times faster and be capable of analyzing alignments containing twice as many sequences (up to 5000) that are five times longer (up to 50 million sites). For users wanting to remove signals of recombination from their datasets before using them for downstream phylogenetics-based molecular evolution analyses, RDP5 can disassemble detected recombinant sequences into their constituent parts and output a variety of different recombination-free datasets in an array of different alignment formats. For users that are interested in exploring the recombination history of their datasets, all the manual verification, data management and data visualization components of RDP5 have been extensively updated to minimize the amount of time needed by users to individually verify and refine the program's interpretation of each of the individual recombination events that it detects.