Antimicrobial Resistance of Salmonella Strains Isolated from Human, Wild Boar, and Environmental Samples in 2018-2020 in the Northwest of Italy

Shiga Toxin-Producing Escherichia coli Isolated from Wild Ruminants in Liguria, North-West Italy

Wildlife may represent an important source of infectious diseases for humans and other wild and domestic animals. Wild ruminants can harbour and transmit Shiga toxin-producing Escherichia coli (STEC) to humans, and some strains even carry important antimicrobial resistance. In this study, 289 livers of wild roe deer, fallow deer, red deer and chamois collected in Liguria, north-west Italy, from 2019 to 2023 were analysed. Overall, 44 STEC strains were isolated from 28 samples. The characterisation of serogroups showed the presence of O104, O113, O145 and O146 serogroups, although for 28 colonies, the serogroup could not be determined. The most prevalent Shiga toxin gene in isolated strains was Stx2, and more specifically the subtype Stx2b. The other retrieved subtypes were Stx1a, Stx1c, Stx1d and Stx2g. The isolated strains generally proved to be susceptible to the tested antimicrobials. However, multi-drug resistances against highly critical antimicrobials were found in one strain isolated from a roe deer. This study highlights the importance of wildlife monitoring in the context of a "One Health" approach.

Monitoring the long-term spatiotemporal transmission dynamics and ecological surveillance of multidrug-resistant Salmonella enterica serovar Goldcoast: A multicenter genomic epidemiology study

The emergence of multidrug-resistant Salmonella enterica serovar Goldcoast poses a significant threat to the effective treatment and control of salmonellosis within the ecological environment. Here, we conducted a genomic epidemiological study delineate the global dissemination scenarios of the multidrug-resistant S. Goldcoast originated from 11 countries for over 20 years. The population structure and evolutionary history of multidrug-resistant S. Goldcoast was investigated through phylogenomic and long-term spatiotemporal transmission dynamic analysis. ST358 and ST2529 are the predominant lineages of S. Goldcoast. Multidrug-resistant S. Goldcoast strains have mainly been identified in the ST358 lineage from human and the ST2529 lineage from livestock. ST358 S. Goldcoast was estimated to have emerged in the United Kingdom in 1969, and then spread to China, with both countries serve as centers for the global dissemination of the ST358 lineage. After its emergence and subsequent spread in Chinese clinical and environmental samples, occasional instances of this lineage have been reported in Canada, the United Kingdom, and Ireland. Clonal transmission of ST358 and ST2529 S. Goldcoast have occurred not only on an international and intercontinental scale but also among clinical, environmental and livestock samples. These data indicated that international circulation and local transmission of S. Goldcoast have occurred for over a decade. Continued surveillance of multidrug-resistant S. Goldcoast from a global "One Health" perspective is urgently needed to facilitate monitoring the spread of the antimicrobial resistant high-risk clones.

High levels of multidrug-resistant isolates of genetically similar Salmonella 1,4, [5],12:I:- from Brazil between 1983 and 2020

Introduction. Salmonella 1,4, [5],12:i:- strains with different antimicrobial resistance profiles have been associated with foodborne disease outbreaks in several countries. In Brazil, S. 1,4, [5],12:i:- was identified as one of the most prevalent serovars in São Paulo State during 2004-2020.Gap Statement. However, few studies have characterized this serovar in Brazil.Aim. This study aimed to determine the antimicrobial resistance profiles of S. 1,4, [5],12:i:- strains isolated from different sources in Southeast Brazil and compare their genetic diversity.Methodology. We analysed 113 S. 1,4, [5],12:i:- strains isolated from humans (n=99), animals (n=7), food (n=5) and the environment (n=2) between 1983 and 2020. Susceptibility testing against 13 antimicrobials was performed using the disc diffusion method for all the strains. Plasmid resistance genes and mutations in the quinolone resistance-determining regions were identified in phenotypically fluoroquinolone-resistant strains. Molecular typing was performed using enterobacterial repetitive intergenic consensus PCR (ERIC-PCR) for all strains and multilocus sequence typing (MLST) for 40 selected strains.Results. Of the 113 strains, 54.87 % were resistant to at least one antimicrobial. The highest resistance rates were observed against ampicillin (51.33 %), nalidixic acid (39.82 %) and tetracycline (38.05 %). Additionally, 39 (34.51 %) strains were classified as multidrug-resistant (MDR). Nine fluoroquinolone-resistant strains exhibited the gyrA mutation (Ser96→Tyr96) and contained the qnrB gene. The 113 strains were grouped into two clusters using ERIC-PCR, and most of strains were present in one cluster, with a genetic similarity of ≥80 %. Finally, 40 strains were typed as ST19 using MLST.Conclusion. The prevalence of MDR strains is alarming because antimicrobial treatment against these strains may lead to therapeutic failure. Furthermore, the ERIC-PCR and MLST results suggested that most strains belonged to one main cluster. Thus, a prevalent subtype of Salmonella 1,4, [5],12:i:- strains has probably been circulating among different sources in São Paulo, Brazil, over decades.

Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility

Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC are hindered by both computational and feature identification constraints. We propose an innovative methodology called the "Genome Feature Extractor Pipeline" that integrates traditional machine learning (random forest, RF) with deep learning models (multilayer perceptron (MLP) and DeepLift) for WGS-based MIC prediction. We used a dataset from the National Antimicrobial Resistance Monitoring System (NARMS), comprising 4500 assembled genomes of nontyphoidal Salmonella, each annotated with MIC metadata for 15 antibiotics. Our pipeline involves the batch downloading of annotated genomes, the determination of feature importance using RF, Gini-index-based selection of crucial 10-mers, and their expansion to 20-mers. This is followed by an MLP network, with four hidden layers of 1024 neurons each, to predict MIC values. Using DeepLift, key 20-mers and associated genes influencing MIC are identified. The 10 most significant 20-mers for each antibiotic are listed, showcasing our ability to discern genomic features affecting Salmonella MIC prediction with enhanced precision. The methodology replaces binary indicators with k-mer counts, offering a more nuanced analysis. The combination of RF and MLP addresses the limitations of the existing WGS approach, providing a robust and efficient method for predicting MIC values in Salmonella that could potentially be applied to other pathogens.