Environmental and anthropogenic factors associated with the likelihood of detecting Salmonella in agricultural watersheds

Integrative genome-centric metagenomics for surface water surveillance: Elucidating microbiomes, antimicrobial resistance, and their associations

Surface water ecosystems are intimately intertwined with anthropogenic activities and have significant public health implications as primary sources of irrigation water in agricultural production. Our extensive metagenomic analysis examined 404 surface water samples from four different geological regions in Chile and Brazil, spanning irrigation canals (n = 135), rivers (n = 121), creeks (n = 74), reservoirs (n = 66), and ponds (n = 8). Overall, 50.25 % of the surface water samples contained at least one of the pathogenic or contaminant bacterial genera (Salmonella: 29.21 %; Listeria: 6.19 %; Escherichia: 35.64 %). Furthermore, a total of 1,582 antimicrobial resistance (AMR) gene clusters encoding resistance to 25 antimicrobial classes were identified, with samples from Brazil exhibiting an elevated AMR burden. Samples from stagnant water sources were characterized by dominant Cyanobacteriota populations, resulting in significantly reduced biodiversity and more uniform community compositions. A significant association between taxonomic composition and the resistome was supported by a Procrustes analysis (p < 0.001). Notably, regional signatures were observed regarding the taxonomic and resistome profiles, as samples from the same region clustered together on both ordinates. Additionally, network analysis illuminated the intricate links between taxonomy and AMR at the contig level. Our deep sequencing efforts not only mapped the microbial landscape but also expanded the genomic catalog with newly characterized metagenome-assembled genomes (MAGs), boosting the classification of reads by 12.85 %. In conclusion, this study underscores the value of metagenomic approaches in surveillance of surface waters, enhancing our understanding of microbial and AMR dynamics with far-reaching public health and ecological ramifications.

A multicenter genomic epidemiological investigation in Brazil, Chile, and Mexico reveals the diversity and persistence of Salmonella populations in surface waters

This study examined the diversity and persistence of Salmonella in the surface waters of agricultural regions of Brazil, Chile, and Mexico. Research groups (three in 2019-2020 and five in 2021-2022) conducted a long-term survey of surface water across 5-8 months annually (n = 30 monthly). On-site, each team filtered 10-L water samples with modified Moore Swabs to capture Salmonella, which were then isolated and identified using conventional microbiological techniques. Salmonella isolates were sequenced on Illumina platforms. Salmonella was present in 1,493/3,291 water samples (45.8%), with varying isolation rates across countries and years. Newport, Infantis, and Typhimurium were the most frequent among the 128 different serovars. Notably, 22 serovars were found in all three countries, representing almost half of the 1,911 different isolates collected. The resistome comprised 72 antimicrobial resistance (AMR) genes and six point mutations in three genes. At least one AMR determinant was observed in 33.8% (646/1,911) of the isolates, of which 47.4% (306/646) were potentially multidrug resistant. Phylogeny based on core genome multilocus sequence typing (cgMLST) showed that most isolates clustered according to sequence type and country of origin. Only 14 cgMLST multi-country clusters were detected among the 275 clusters. However, further analysis confirmed that close genetic relatedness occurred mostly among isolates from the same country, with three exceptions. Interestingly, isolates closely related phylogenetically were recovered over multiple years within the same country, indicating the persistence of certain Salmonella in those areas. In conclusion, surface waters in these regions are consistently contaminated with diverse Salmonella, including strains that persist over time.IMPORTANCESalmonella is a leading foodborne pathogen responsible for millions of illnesses, hospitalizations, and deaths annually. Although Salmonella-contaminated water has now been recognized as an important contamination source in the agrifood chain, there is a lack of knowledge on the global occurrence and diversity of Salmonella in surface water. Moreover, there has been insufficient research on Salmonella in surface waters from Latin American countries that are major producers and exporters of agricultural products. Incorporating genetic profiling of Salmonella isolates from underrepresented regions, such as Latin America, enhances our understanding of the pathogen's ecology, evolution, antimicrobial resistance, and pathogenicity. Moreover, leveraging genomic data derived from pathogens isolated from diverse geographical areas is critical for assessing the potential public health risk posed by the pathogen and expediting investigations of foodborne outbreaks. Ultimately, global efforts contribute significantly to reducing the incidence of foodborne infections.

A Systemic Review on Fitness and Survival of Salmonella in Dynamic Environment and Conceivable Ways of Its Mitigation

Gastroenteritis caused by non-typhoidal Salmonella still prevails resulting in several recent outbreaks affecting many people worldwide. The presence of invasive non-typhoidal Salmonella is exemplified by several characteristic symptoms and their severity relies on prominent risk factors. The persistence of this pathogen can be attributed to its broad host range, complex pathogenicity and virulence and adeptness in survival under challenging conditions inside the host. Moreover, a peculiar aid of the ever-changing climatic conditions grants this organism with remarkable potential to survive within the environment. Abusive use of antibiotics for the treatment of gastroenteritis has led to the emergence of multiple drug resistance, making the infections difficult to treat. This review emphasizes the importance of early detection of Salmonella, along with strategies for accomplishing it, as well as exploring alternative treatment approaches. The exceptional characteristics exhibited by Salmonella, like strategies of infection, persistence, and survival parallelly with multiple drug resistance, make this pathogen a prominent concern to human health.

Unveiling the genomic landscape of Salmonella enterica serotypes Typhimurium, Newport, and Infantis in Latin American surface waters: a comparative analysis

Surface waters are considered ecological habitats where Salmonella enterica can persist and disseminate to fresh produce production systems. This study aimed to explore the genomic profiles of S. enterica serotypes Typhimurium, Newport, and Infantis from surface waters in Chile, Mexico, and Brazil collected between 2019 and 2022. We analyzed the whole genomes of 106 S. Typhimurium, 161 S. Newport, and 113 S. Infantis isolates. Our phylogenetic analysis exhibited distinct groupings of isolates by their respective countries except for a notable case involving a Chilean S. Newport isolate closely related to two Mexican isolates, showing 4 and 13 single nucleotide polymorphisms of difference, respectively. The patterns of the most frequently detected antimicrobial resistance genes varied across countries and serotypes. A strong correlation existed between integron carriage and genotypic multidrug resistance (MDR) across serotypes in Chile and Mexico (R > 0.90, P < 0.01), while integron(s) were not detected in any of the Brazilian isolates. By contrast, we did not identify any strong correlation between plasmid carriage and genotypic MDR across diverse countries and serotypes.IMPORTANCEUnveiling the genomic landscape of S. enterica in Latin American surface waters is pivotal for ensuring public health. This investigation sheds light on the intricate genomic diversity of S. enterica in surface waters across Chile, Mexico, and Brazil. Our research also addresses critical knowledge gaps, pioneering a comprehensive understanding of surface waters as a reservoir for multidrug-resistant S. enterica. By integrating our understanding of integron carriage as biomarkers into broader MDR control strategies, we can also work toward targeted interventions that mitigate the emergence and dissemination of MDR in S. enterica in surface waters. Given its potential implications for food safety, this study emphasizes the critical need for informed policies and collaborative initiatives to address the risks associated with S. enterica in surface waters.

Microbiological hazard identification in river waters used for recreational activities

Pathogenic bacteria, viruses, and parasites can cause waterborne disease outbreaks. The study of coastal water quality contributes to identifying potential risks to human health and to improving water management practices. The Río de la Plata River, a wide estuary in South America, is used for recreational activities, as a water source for consumption and as a site for sewage discharges. In the present study, as the first step of a quantitative microbial risk assessment of the coastal water quality of this river, a descriptive study was performed to identify the microbial pathogens prevalent in its waters and in the sewage discharged into the river. Two sites, representing two different potential risk scenarios, were chosen: a heavily polluted beach and an apparently safe beach. Conductivity and fecal contamination indicators including enterococci, Escherichia coli, F + RNA bacteriophages, and human polyomaviruses showed high levels. Regarding enterococci, differences between sites were significant (p-values <0.001). 93.3% and 56.5% of the apparently safe beach exceeded the recreational water limits for E. coli and enterococci. Regarding pathogens, diarrheagenic E. coli, Salmonella, and noroviruses were detected with different frequencies between sites. The parasites Cryptosporidium spp. and Giardia duodenalis were frequently detected in both sites. The results regarding viral, bacterial, and parasitic pathogens, even without correlation with conventional indicators, showed the importance of monitoring a variety of microorganisms to determine water quality more reliably and accurately, and to facilitate further studies of health risk assessment. The taxonomic description of microbial pathogens in river waters allow identifying the microorganisms that infect the population living on its shores but also pathogens not previously reported by the clinical surveillance system.

Genomic characterisation of the population structure and antibiotic resistance of Salmonella enterica serovar Infantis in Chile, 2009-2022

Background

Multidrug-resistant (MDR) Salmonella Infantis has disseminated worldwide, mainly linked to the consumption of poultry products. Evidence shows dissemination of this pathogen in Chile; however, studies are primarily limited to phenotypic data or involve few isolates. As human cases of Salmonella Infantis infections have substantially increased in recent years, this study aimed to characterise the genomic epidemiology and antimicrobial-resistance profiles of isolates obtained from different sources, aiming to inform effective surveillance and control measures.

Methods

We sequenced 396 Salmonella Infantis genomes and analysed them with all publicly available genomes of this pathogen from Chile (440 genomes in total), representing isolates from environmental, food, animal, and human sources obtained from 2009 to 2022. Based on bioinformatic and phenotypic methods, we assessed the population structure, dissemination among different niches, and antimicrobial resistance (AMR) profiles of Salmonella Infantis in the country.

Findings

The genomic and phylogenetic analyses showed that Salmonella Infantis from Chile comprised several clusters of highly related isolates dominated by sequence type 32. The HC20_343 cluster grouped an important proportion of all isolates. This was the only cluster associated with pESI-like megaplasmids, and up to 12 acquired AMR genes/mutations predicted to result in an MDR phenotype. Accordingly, antimicrobial-susceptibility testing revealed a strong concordance between the AMR genetic determinants and their matching phenotypic expression, indicating that a significant proportion of HC20_343 isolates produce extended-spectrum β-lactamases and have intermediate fluoroquinolone resistance. HC20_343 Salmonella Infantis were spread among environmental, animal, food, and human niches, showing a close relationship between isolates from different years and sources, and a low intra-source genomic diversity.

Interpretation

Our findings show a widespread dissemination of MDR Salmonella Infantis from the HC20_343 cluster in Chile. The high proportion of isolates with resistance to first-line antibiotics and the evidence of active transmission between the environment, animals, food, and humans highlight the urgency of improved surveillance and control measures in the country. As HC20_343 isolates predominate in the Americas, our results suggest a high prevalence of ESBL-producing Salmonella Infantis with intermediate fluoroquinolone resistance in the continent.

Funding

Partially supported by the Food and Drug Administration (FDA) of the U.S. Department of Health and Human Services as part of an award, FDU001818, with 30% percent funded by FDA/HHS; and by Agencia de Investigación y Desarrollo de Chile (ANID) through FONDECYT de Postdoctorado Folio 3230796 and Folio 3210317, FONDECYT Regular Folio 1231082, and ANID-Millennium Science Initiative Program-ICN2021_044.

One Health Perspectives on Food Safety in Minimally Processed Vegetables and Fruits: From Farm to Fork

While food markets and food production chains are experiencing exponential growth, global attention to food safety is steadily increasing. This is particularly crucial for ready-to-eat products such as fresh-cut salads and fruits, as these items are consumed raw without prior heat treatment, making the presence of pathogenic microorganisms quite frequent. Moreover, many studies on foodborne illnesses associated with these foods often overlook the transmission links from the initial contamination source. The prevention and control of the dissemination of foodborne pathogens should be approached holistically, involving agricultural production, processing, transport, food production, and extending to final consumption, all while adopting a One Health perspective. In this context, our objective is to compile available information on the challenges related to microbiological contamination in minimally handled fruits and vegetables. This includes major reported outbreaks, specific bacterial strains, and associated statistics throughout the production chain. We address the sources of contamination at each stage, along with issues related to food manipulation and disinfection. Additionally, we provide potential solutions to promote a healthier approach to fresh-cut fruits and vegetables. This information will be valuable for both researchers and food producers, particularly those focused on ensuring food safety and quality.

Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams

The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.

Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density

Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R² =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.