Salmonella Infantis is a poultry-adapted Salmonella enterica serovar that is increasingly reported in broilers and is also regularly identified among human salmonellosis cases. An emerging S. Infantis mega-plasmid (pESI), carrying fitness, virulence and antimicrobial resistance genes, is also increasingly found. We investigated the prevalence, genetic characteristics and risk factors for (pESI-carrying) S. Infantis in broilers. Faecal samples from 379 broiler flocks (in 198 farms with ≥3000 birds) in the Netherlands were tested. A questionnaire about farm characteristics was also administered. Sampling was performed in July 2018-May 2019, three weeks before slaughter. Fourteen flocks (in 10 farms) were S. Infantis-positive, resulting in a 3.7 % flock-level and 5.1 % farm-level prevalence. Based on multi-locus sequence typing (MLST), all isolates belonged to sequence type 32. All but one isolate carried a pESI-like mega-plasmid. Core-genome MLST showed considerable heterogeneity among the isolates, even within the same farm, with a few small clusters detected. The typical pESI-borne multi-resistance pattern to aminoglycosides, sulphonamide and tetracycline (93 %), as well as trimethoprim (71 %), was found. Additionally, resistance to (fluoro)quinolones based on gyrA gene mutations was detected. S. Infantis was found more often in flocks using salinomycin as coccidiostat, where flock thinning was applied or litter quality was poor, whereas employing external cleaning companies, wheat in feed, and vaccination against infectious bronchitis, were protective. Suggestive evidence for vertical transmission from hatcheries was found. A heterogeneous (pESI-carrying) S. Infantis population has established itself in Dutch broiler flocks, calling for further monitoring of its spread and a comprehensive appraisal of control options.

Background: Shigella spp. and entero-invasive E. coli (EIEC) use the same invasive mechanism to cause diarrheal diseases. Public health regulations apply only to Shigella spp. infections, but are hampered by the lack of simple methods to distinguish them from EIEC. In the last decades, molecular methods for detecting Shigella spp. and EIEC were implemented in medical microbiological laboratories (MMLs). However, shigellosis cases identified with molecular techniques alone are not notifiable in most countries. Our study investigates the impact of EIEC versus Shigella spp. infections and molecular diagnosed shigellosis versus culture confirmed shigellosis for re-examination of the rationale for the current public health regulations. Methods: In this multicenter cross-sectional study, fecal samples of patients suspected for gastro-enteritis, referred to 15 MMLs in the Netherlands, were screened by PCR for Shigella spp. or EIEC. Samples were cultured to discriminate between the two pathogens. We compared risk factors, symptoms, severity of disease, secondary infections and socio-economic consequences for (i) culture-confirmed Shigella spp. versus culture-confirmed EIEC cases (ii) culture positive versus PCR positive only shigellosis cases. Results: In 2016–2017, 777 PCR positive fecal samples with patient data were included, 254 of these were culture-confirmed shigellosis cases and 32 were culture-confirmed EIEC cases. EIEC cases were more likely to report ingestion of contaminated food and were less likely to be men who have sex with men (MSM). Both pathogens were shown to cause serious disease although differences in specific symptoms were observed. Culture-negative but PCR positive cases were more likely report travel or ingestion of contaminated food and were less likely to be MSM than culture-positive cases. Culture-negative cases were more likely to suffer from multiple symptoms. No differences in degree of secondary infections were observed between Shigella spp. and EIEC, and culture-negative and culture-positive cases. Conclusions: No convincing evidence was found to support the current guidelines that employs different measures based on species or detection method. Therefore, culture and molecular detection methods for Shigella spp. and EIEC should be considered equivalent for case definition and public health regulations regarding shigellosis. Differences were found regarding risks factors, indicating that different prevention strategies may be required.

Background: We investigated the association of symptoms and disease severity of shigellosis patients with genetic determinants of infecting Shigella and entero-invasive Escherichia coli (EIEC), because determinants that predict disease outcome per individual patient could be used to prioritize control measures. For this purpose, genome wide association studies (GWAS) were performed using presence or absence of single genes, combinations of genes, and k-mers. All genetic variants were derived from draft genome sequences of isolates from a multicenter cross-sectional study conducted in the Netherlands during 2016 and 2017. Clinical data of patients consisting of binary/dichotomous representation of symptoms and their calculated severity scores were also available from this study. To verify the suitability of the methods used, the genetic differences between the genera Shigella and Escherichia were used as control. Results: The isolates obtained were representative of the population structure encountered in other Western European countries. No association was found between single genes or combinations of genes and separate symptoms or disease severity scores. Our benchmark characteristic, genus, resulted in eight associated genes and > 3,000,000 k-mers, indicating adequate performance of the algorithms used. Conclusions: To conclude, using several microbial GWAS methods, genetic variants in Shigella spp. and EIEC that can predict specific symptoms or a more severe course of disease were not identified, suggesting that disease severity of shigellosis is dependent on other factors than the genetic variation of the infecting bacteria. Specific genes or gene fragments of isolates from patients are unsuitable to predict outcomes and cannot be used for development, prioritization and optimization of guidelines for control measures of shigellosis or infections with EIEC.

Shigella spp. and E. coli are closely related and cannot be distinguished using matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS) with commercially available databases. Here, three alternative approaches using MALDI-TOF MS to identify and distinguish Shigella spp., E. coli, and its pathotype EIEC were explored and evaluated using spectra of 456 Shigella spp., 42 E. coli, and 61 EIEC isolates. Identification with a custom-made database resulted in >94% Shigella identified at the genus level and >91% S. sonnei and S. flexneri at the species level, but the distinction of S. dysenteriae, S. boydii, and E. coli was poor. With biomarker assignment, 98% S. sonnei isolates were correctly identified, although specificity was low. Discriminating markers for S. dysenteriae, S. boydii, and E. coli were not assigned at all. Classification models using machine learning correctly identified Shigella in 96% of isolates, but most E. coli isolates were also assigned to Shigella. None of the proposed alternative approaches were suitable for clinical diagnostics for identifying Shigella spp., E. coli, and EIEC, reflecting their relatedness and taxonomical classification. We suggest the use of MALDI-TOF MS for the identification of the Shigella spp./E. coli complex, but other tests should be used for distinction.

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.