ArticleJournal of clinical microbiology2024
Bridging the gap between molecular and genomic epidemiology in tuberculosis: inferring MIRU-VNTR patterns from genomic data.
Article in Journal of clinical microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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14 authors.
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Abstract
The transition from MIRU-VNTR-based epidemiology studies in tuberculosis (TB) to genomic epidemiology has transformed how we track transmission. However, short-read sequencing is poor at analyzing repetitive regions such as the MIRU-VNTR loci. This causes a gap between the new genomic data and the large amount of information stored in historical databases. Long-read sequencing could bridge this knowledge gap by allowing analysis of repetitive regions. However, the feasibility of extracting MIRU-VNTRs from long reads and linking them to historical data has not been evaluated. In our study, an IMPORTANCE: The transition from molecular epidemiology in tuberculosis (TB), based on the analysis of repetitive regions (VNTR-based genotyping), to genomic epidemiology transforms in the precision with which we track transmission. However, short-read sequencing, the most common method for performing genomic analysis, is poor at analyzing repetitive regions. This means that we face a gap between the new genomic data and the large amount of information stored in historical databases, which is also an obstacle to cross-national surveillance involving settings where only molecular data are available. Long-read sequencing could help bridge this knowledge gap by allowing analysis of repetitive regions. Our study demonstrates that MIRU-VNTR patterns can be successfully inferred from long-read sequences, allowing the correct assignment of new cases as clustered/orphan by linking new data extracted from genomic analysis to historical MIRU-VNTR databases. Our data may provide a starting point for bridging the knowledge gap between the molecular and genomic eras in tuberculosis epidemiology.
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