WGS-based retrospective analysis for surveillance of L. monocytogenes infections in Emilia-Romagna (Italy)
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB34036 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3515815_1.fastq.gz | Fastqsanger.gz | |||
| ERR3515815_2.fastq.gz | Fastqsanger.gz | |||
| ERR3522470_1.fastq.gz | Fastqsanger.gz | |||
| ERR3522470_2.fastq.gz | Fastqsanger.gz | |||
| ERR3522480_1.fastq.gz | Fastqsanger.gz |
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Project description:The population structure of human isolates of Listeria monocytogenes in Emilia-Romagna, Italy, from 2012 to 2018 was investigated with the aim of evaluating the presence of genomic clusters indicative of possible outbreaks, the proportion of cluster-associated vs. sporadic isolates and different methods and metrics of genomic analysis for use in routine surveillance. In the 2012-2018 period the notification rate of confirmed invasive cases in Emilia-Romagna was 0.91 per 100,000 population per year, more than twice the average rate of EU countries. Out of the total 283 cases, 268 (about 95%) isolates were typed through whole genome sequencing (WGS) for cluster detection with methods based on core-genome multi-locus sequence typing and single nucleotide polymorphisms. Between 66 and 72% of listeriosis cases belonged to genomic clusters which included up to 27 cases and lasted up to 5 years. This proportion of cluster-associated cases is higher than previously estimated in other European studies. Rarefaction analysis, performed by reducing both the number of consecutive years of surveillance considered and the proportion of isolates included in the analysis, suggested that the observed high proportion of cluster-associated cases can be ascribed to the long surveillance duration (7 years) and the high notification and typing rates of this study. Our findings show that a long temporal perspective and high surveillance intensity, intended as both exhaustiveness of the system to report cases and high WGS-typing rate, are critical for sensitive detection of possible outbreaks within a WGS-based surveillance of listeriosis. Furthermore, the power and complexity of WGS interpretation emerged from the integration of genomic and epidemiological information in the investigation of few past outbreaks within the study, indicating that the use of multiple approaches, including the analysis of the accessory genome, is needed to accurately elucidate the population dynamics of Listeria monocytogenes.
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