MétaCan
Menu
Retour à la cohorte
Enregistrement W6920716987 · doi:10.60692/tvehq-yxr26

Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation

2022· article· en· W6920716987 sur OpenAlexaff

Notice bibliographique

RevueGreater South Information System · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Organismes subventionnairesnon disponible
Mots-clésNanopore sequencingGenotypingContext (archaeology)Mycobacterium tuberculosisSingle-nucleotide polymorphismNanoporeOutbreakGenotypeSNP

Résumé

récupéré en direct d'OpenAlex

Abstract Background Mycobacterium tuberculosis whole-genome sequencing (WGS) using Illumina technology has been widely adopted for genotypic drug susceptibility testing (DST) and outbreak investigation. Oxford Nanopore Technologies is reported to have higher error rates but has not been thoroughly evaluated for these applications. Methods We analyse 151 isolates from Madagascar, South Africa and England with phenotypic DST and matched Illumina and Nanopore data. Using PacBio assemblies, we select Nanopore filters for BCFtools (software) detection of single nucleotide polymorphisms (SNPs). We compare transmission clusters identified by Nanopore and the United Kingdom Health Security Agency Illumina pipeline (COMPASS). We compare Illumina and Nanopore WGS-based DST predictions using Mykrobe (software). Findings Nanopore/BCFtools identifies SNPs with median precision/recall of 99·5/90·2% compared with 99·6/91·9% for Illumina/COMPASS. Using a threshold of 12 SNPs for putative transmission clusters, Illumina identifies 98 isolates as unrelated and 53 as belonging to 19 distinct clusters (size range 2-7). Nanopore reproduces this distribution with addition of 5 singleton isolates to distinct clusters and merging of two cluster pairs. Illumina-based clusters are also replicated using a 5 SNP threshold. Clustering accuracy is maintained using mixed Illumina/Nanopore datasets. Genotyping resistance variants is highly concordant, with 0(4) discordant SNPs (indels) across 151 isolates genotyped at >3000 (60,000) SNPs (indels). Interpretation Illumina and Nanopore sequence data provide comparable cluster-identification and DST results. Funding Academy for Medical Sciences (SGL018\110), Oxford Wellcome Institutional Strategic Support Fund (ISSF TT17 4). Swiss South Africa Joint Research Award (Swiss national science Foundation and South African national research foundation). Research in context Evidence before this study Two key types of information can be obtained from laboratory testing of M. tuberculosis isolates to help directly guide public health interventions: drug susceptibility testing (DST) to guide therapy, and bacterial typing to enrich understanding of the epidemiology and guide interventions to mitigate transmission. DST is typically performed by the "gold standard" culture-based phenotyping method or nucleic acid amplification assays targeting specific resistance-conferring mutations. Studies over the last 7 years have shown that prediction of susceptibility profile using Illumina-technology genome sequence data is possible, and can be automated. In a key publication, the CRyPTIC consortium and UK 100,000 Genomes project evaluated the method on over 10,000 genomes including prospectively sampled isolates and showed that for first-line tuberculosis (TB) drugs (isoniazid, rifampicin, ethambutol, pyrazinamide) a pan-susceptibility profile is accurate enough to be used clinically. The genetic basis of resistance remains imperfectly understood for second-line TB drugs, in particular for new and repurposed drugs (bedaquiline, clofazimine, delamanid, linezolid). Prior work in the field of genotypic DST was heavily based on Illumina technology, which provides short (70-300 base pair) sequence reads of very high quality. Many different softwares (e.g. TBProfiler, Mykrobe, MTBseq, kvarq) have been designed for sequence analysis and genotypic DST. However, the increasingly used Nanopore sequencing platforms yield very different data with much longer sequence reads (frequently over 1kb) and higher error rates including systematic biases. To date, very limited evaluation of Nanopore-based drug susceptibility prediction has been performed using the only two compatible tools (Mykrobe (n=5 independent samples), TBProfiler (n=3 independent samples)). Molecular typing of M. tuberculosis allows lineage identification and detection of putative transmission clusters. In the last decade, multiple M. tuberculosis molecular epidemiology studies have shown how genomic information can complement traditional epidemiology in identifying person-to-person transmission clusters with a high level of resolution. Typically, the number of single nucleotide polymorphism (SNP) disagreements between genomes, or SNP distance, is calculated and single-linkage clustering is performed for genomes falling within retrospectively established transmission thresholds of either 5 or 12 SNPs. Just as with DST, these thresholds were established with Illumina sequencing data. The increased error rate in Nanopore sequencing is believed to lead to inflated SNP distances if standard genome analysis tools are used. Prior to this study it was unknown what impact on isolate-clustering this would incur. Added value of this study Full-scale adoption of genomic sequencing in tuberculosis reference laboratories has so far taken place in a limited number of settings - England, the Netherlands, and New York State - all using Illumina-based sequencing data. Building on current evidence, specific WHO technical guidance and diversification and democratisation of technology, sequencing is expected to be increasingly used in tuberculosis control globally. For the first time, our study offers 4 key deliverables intended to inform adoption of Nanopore technology as an alternative, or a complement, to Illumina. First: a systematic head-to-head comparison of Nanopore and Illumina data for M. tuberculosis drug susceptibility profiling and isolate clustering, including quantitative metrics for cluster precision and recall. Second: an assessment of the impact of mixed Illumina and Nanopore data on clustering which represents an increasingly common challenge. Third: an open-source software pipeline allowing research and reference laboratories to replicate our analytical approach. Fourth: a publicly available curated test set of 151 isolates, including matched Illumina and Nanopore sequence data, and (for a subset of seven isolates) high-quality PacBio assemblies, for method development and validation. Implications of all the available evidence Catalogues of drug resistance conferring mutations will keep improving, especially for new and repurposed drugs. Our data confirms that Illumina and Nanopore sequencing technologies can be used to identify those mutations equally accurately in M. tuberculosis . Bacterial molecular typing is constantly shown to support the understanding of disease transmission and tuberculosis control in new settings. The bioinformatics tools and filters we have developed, assessed, and made publicly available allow the use of Nanopore or mixed-technology data to appropriately cluster genetically related isolates. We provide a measure of the expected level of over-clustering associated with Nanopore technology. This study confirms that Illumina and Nanopore sequence data provide comparable DST results and isolate cluster-identification.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,106
Score d'incertitude au seuil0,591

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,065
Tête enseignante GPT0,267
Écart entre enseignants0,202 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueGreater South Information SystemMême sujetTuberculosis Research and EpidemiologyTravaux en français237 207