New insights into the epidemiology of Listeria monocytogenes - A cross-sectoral retrospective genomic analysis in the Netherlands (2010-2020).

Introduction: Listeriosis, caused by infection with Listeria monocytogenes (Lm), is a relatively rare but severe disease with one of the highest mortality rates among bacterial foodborne illnesses. A better understanding on the degree of Lm clustering, the temporal distribution of the clusters, and their association with the various food sources is expected to lead to improved source tracing and risk-based sampling. Methods: We investigated the genomic epidemiology of Lm in the Netherlands between 2010 and 2020 by analyzing whole-genome-sequencing (WGS) data of isolates from listerioss patients and food sources from nationwide integrated surveillance and monitoring. WGS data of 756 patient and 770 food/environmental isolates was assessed using core-genome multi-locus sequence typing (cgMLST) with Hamming distance as measure for pairwise distances. Associations of genotype with the epidemiological variables such as patient's age and gender, and systematic use of specific drugs were tested by multinomial logistic regressions. Genetic differentiation of the Lm within and between food categories was calculated based on allele frequencies at the 1701 cgMLST loci in each food category. Results: We confirmed previous results that some clonal complexes (CCs) are overrepresented among clinical isolates but could not identify any epidemiological risk factors. The main findings of this study include the observation of a very weak attribution of Lm types to food categories and a much better attribution to the producer level. In addition, we identified a high degree of temporal persistence of food, patient and mixed clusters, with more than half of the clusters spanning over more than 1 year and up to 10 years. Discussion: Taken together this would indicate that identifying persistent contamination in food production settings, and producers that process a wide variety of raw food produce, could significantly contribute to lowering the Lm disease burden.

Associations and recovery dynamics of the nasopharyngeal microbiota during influenza-like illness in the aging population.

Influenza-like illness (ILI), a disease caused by respiratory pathogens including influenza virus, is a major health concern in older adults. There is little information on changes and recovery dynamics of the nasopharyngeal (NP) microbiota of older adults associated with an ILI. Here, we compared the NP microbiota in older adults reporting (n = 240) or not (n = 157) ILI during the 2014-2015 influenza season at different times of the ILI event. A small but significant effect of the ILI was observed on the microbiota community composition and structure when compared to controls and samples collected at recovery. Corynebacterium was negatively associated with ILI and its abundance increased after recovery. Potential pathobionts such as Haemophilus, Porphyromonas and Gemella had higher abundances during acute-ILI. Stability and changes in the NP microbial community showed individual dynamics. Key core genera, Corynebacterium, Moraxella and Dolosigranulum exhibited higher inter-individual variability in acute-ILI, but showed comparable variability to controls after recovery. Participants in the ILI group with higher core microbiota abundances at the acute phase showed higher microbiota stability after recovery. Our findings demonstrate that acute-ILI is associated with alterations in the phylogenetic structure of the NP microbiota in older adults. The variation in the core microbiota suggests imbalances in the ecosystem, which could potentially play a role in the susceptibility and recovery of the NP microbiota after an ILI event.

Benchmarking laboratory processes to characterise low-biomass respiratory microbiota.

The low biomass of respiratory samples makes it difficult to accurately characterise the microbial community composition. PCR conditions and contaminating microbial DNA can alter the biological profile. The objective of this study was to benchmark the currently available laboratory protocols to accurately analyse the microbial community of low biomass samples. To study the effect of PCR conditions on the microbial community profile, we amplified the 16S rRNA gene of respiratory samples using various bacterial loads and different number of PCR cycles. Libraries were purified by gel electrophoresis or AMPure XP and sequenced by V2 or V3 MiSeq reagent kits by Illumina sequencing. The positive control was diluted in different solvents. PCR conditions had no significant influence on the microbial community profile of low biomass samples. Purification methods and MiSeq reagent kits provided nearly similar microbiota profiles (paired Bray-Curtis dissimilarity median: 0.03 and 0.05, respectively). While profiles of positive controls were significantly influenced by the type of dilution solvent, the theoretical profile of the Zymo mock was most accurately analysed when the Zymo mock was diluted in elution buffer (difference compared to the theoretical Zymo mock: 21.6% for elution buffer, 29.2% for Milli-Q, and 79.6% for DNA/RNA shield). Microbiota profiles of DNA blanks formed a distinct cluster compared to low biomass samples, demonstrating that low biomass samples can accurately be distinguished from DNA blanks. In summary, to accurately characterise the microbial community composition we recommend 1. amplification of the obtained microbial DNA with 30 PCR cycles, 2. purifying amplicon pools by two consecutive AMPure XP steps and 3. sequence the pooled amplicons by V3 MiSeq reagent kit. The benchmarked standardized laboratory workflow presented here ensures comparability of results within and between low biomass microbiome studies.

Sources and transmission routes of campylobacteriosis: A combined analysis of genome and exposure data.

OBJECTIVES: To determine the contributions of several animal and environmental sources of human campylobacteriosis and identify source-specific risk factors. METHODS: 1417 Campylobacter jejuni/coli isolates from the Netherlands in 2017-2019 were whole-genome sequenced, including isolates from human cases (n = 280), chickens/turkeys (n = 238), laying hens (n = 56), cattle (n = 158), veal calves (n = 49), sheep/goats (n = 111), pigs (n = 110), dogs/cats (n = 100), wild birds (n = 62), and surface water (n = 253). Questionnaire-based exposure data was collected. Source attribution was performed using core-genome multilocus sequence typing. Risk factors were determined on the attribution estimates. RESULTS: Cases were mostly attributed to chickens/turkeys (48.2%), dogs/cats (18.0%), cattle (12.1%), and surface water (8.5%). Of the associations identified, never consuming chicken, as well as frequent chicken consumption, and rarely washing hands after touching raw meat, were risk factors for chicken/turkey-attributable infections. Consuming unpasteurized milk or barbecued beef increased the risk for cattle-attributable infections. Risk factors for infections attributable to environmental sources were open water swimming, contact with dog faeces, and consuming non-chicken/turkey avian meat like game birds. CONCLUSIONS: Poultry and cattle are the main livestock sources of campylobacteriosis, while pets and surface water are important non-livestock sources. Foodborne transmission is only partially consistent with the attributions, as frequency and alternative pathways of exposure are significant.

Optimization of Notification Criteria for Shiga Toxin-Producing Escherichia coli Surveillance, the Netherlands.

We describe the consequences of 2 major changes in notification criteria for Shiga toxin-producing Escherichia coli surveillance in the Netherlands. The change to reporting acute, more severe infections appears to be a good compromise between workload, redundancy, and public health relevance, provided isolates remain available for typing and sequencing.

A Multifactorial Approach for Surveillance of Shigella spp. and Entero-Invasive Escherichia coli Is Important for Detecting (Inter)national Clusters.

Shigella spp. and entero-invasive Escherichia coli (EIEC) can cause mild diarrhea to dysentery. In Netherlands, although shigellosis is a notifiable disease, there is no laboratory surveillance for Shigella spp. and EIEC in place. Consequently, the population structure for circulating Shigella spp. and EIEC isolates is not known. This study describes the phenotypic and serological characteristics, the phenotypic and genetic antimicrobial resistance (AMR) profiles, the virulence gene profiles, the classic multi-locus sequence types (MLST) and core genome (cg)MLST types, and the epidemiology of 414 Shigella spp. and EIEC isolates collected during a cross-sectional study in Netherlands in 2016 and 2017. S. sonnei (56%), S. flexneri (25%), and EIEC (15%) were detected predominantly in Netherlands, of which the EIEC isolates were most diverse according to their phenotypical profile, O-types, MLST types, and cgMLST clades. Virulence gene profiling showed that none of the isolates harbored Shiga toxin genes. Most S. flexneri and EIEC isolates possessed nearly all virulence genes examined, while these genes were only detected in approximately half of the S. sonnei isolates, probably due to loss of the large invasion plasmid upon subculturing. Phenotypical resistance correlated well with the resistant genotype, except for the genes involved in resistance to aminoglycosides. A substantial part of the characterized isolates was resistant to antimicrobials advised for treatment, i.e., 73% was phenotypically resistant to co-trimoxazole and 19% to ciprofloxacin. AMR was particularly observed in isolates from male patients who had sex with men (MSM) or from patients that had traveled to Asia. Furthermore, isolates related to international clusters were also circulating in Netherlands. Travel-related isolates formed clusters with isolates from patients without travel history, indicating their emergence into the Dutch population. In conclusion, laboratory surveillance using whole genome sequencing as high-resolution typing technique and for genetic characterization of isolates complements the current epidemiological surveillance, as the latter is not sufficient to detect all (inter)national clusters, emphasizing the importance of multifactorial public health approaches.

Evaluation of a Culture-Dependent Algorithm and a Molecular Algorithm for Identification of Shigella spp., Escherichia coli, and Enteroinvasive E. coli.

Identification of Shigella spp., Escherichia coli, and enteroinvasive E. coli (EIEC) is challenging because of their close relatedness. Distinction is vital, as infections with Shigella spp. are under surveillance of health authorities, in contrast to EIEC infections. In this study, a culture-dependent identification algorithm and a molecular identification algorithm were evaluated. Discrepancies between the two algorithms and original identification were assessed using whole-genome sequencing (WGS). After discrepancy analysis with the molecular algorithm, 100% of the evaluated isolates were identified in concordance with the original identification. However, the resolution for certain serotypes was lower than that of previously described methods and lower than that of the culture-dependent algorithm. Although the resolution of the culture-dependent algorithm is high, 100% of noninvasive E. coli, Shigella sonnei, and Shigella dysenteriae, 93% of Shigella boydii and EIEC, and 85% of Shigella flexneri isolates were identified in concordance with the original identification. Discrepancy analysis using WGS was able to confirm one of the used algorithms in four discrepant results. However, it failed to clarify three other discrepant results, as it added yet another identification. Both proposed algorithms performed well for the identification of Shigella spp. and EIEC isolates and are applicable in low-resource settings, in contrast to previously described methods that require WGS for daily diagnostics. Evaluation of the algorithms showed that both algorithms are capable of identifying Shigella species and EIEC isolates. The molecular algorithm is more applicable in clinical diagnostics for fast and accurate screening, while the culture-dependent algorithm is more suitable for reference laboratories to identify Shigella spp. and EIEC up to the serotype level.