Prevalence and Characterization of Beta-Lactam and Carbapenem-Resistant Bacteria Isolated from Organic Fresh Produce Retailed in Eastern Spain

Microbiome-based classification models for fresh produce safety and quality evaluation

Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k-mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food.

IMPORTANCE

Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k-mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.

Assessment of the presence of Acinetobacter spp. resistant to β-lactams in commercial ready-to-eat salad samples

Acinetobacter baumannii is a well-known nosocomial infection causing agent. However, other Acinetobacter spp. have also been implicated in cases of human infection. Additionally, these bacteria are known for the development of antibiotic resistance thus making the treatment of the infections they cause, challenging. Due to their relevance in clinical setups less attention has been paid to their presence in foods, and its relation with infection/dissemination routes. In the current study commercial Ready-To-Eat (RTE) salads were analyzed seeking for antibiotic resistant Acinetobacter spp. A preliminary screening allowed us to recover Gram-negative bacteria resistant to β - lactams using cefotaxime, third generation cephalosporins, as the selective agent, and this was followed by identification with CHROMagar™ Acinetobacter and 16S rDNA sequencing. Finally, the isolates identified as Acinetobacter spp. were reanalyzed by PCR to determine the presence of nine potential Extended Spectrum β Lactamases (ESBL). Two commercial RTE salad brands were included in the study (2 batches per brand and 8 samples of each batch making a total of 32 independent samples), and compared against an organic lettuce. High concentrations of β - lactam, resistant bacteria were found in all the samples tested (5 log CFU/g). Additionally, 209 isolates were phenotypically characterized on CHROMagar Acinetobacter. Finally, PCR analysis identified the presence of different ESBL genes, being positive for blaACC, blaSHV, blaDHA and blaVEB; out of these, blaACC was the most prevalent. None of the isolates screened were positive for more than one gene. To conclude, it is important to highlight the fact that pathogenic species within the genus Acinetobacter spp., other than A. baumannii, have been identified bearing resistance genes not typically associated to these microorganisms highlight the importance of continuous surveillance.

Resistance to β-lactams in Enterobacteriaceae isolated from vegetables: a review

Vegetables are crucial for a healthy human diet due to their abundance of essential macronutrients and micronutrients. However, there have been increased reports of antimicrobial-resistant Enterobacteriaceae isolated from vegetables. Enterobacteriaceae is a large group of Gram-negative bacteria that can act as commensals, intestinal pathogens, or opportunistic extraintestinal pathogens. Extraintestinal infections caused by Enterobacteriaceae are a clinical concern due to antimicrobial resistance (AMR). β-lactams have high efficacy against Gram-negative bacteria and low toxicity for eukaryotic cells. These antimicrobials are widely used in the treatment of Enterobacteriaceae extraintestinal infections. This review aimed to conduct a literature survey of the last five years (2018-2023) on the occurrence of β-lactam-resistant Enterobacteriaceae in vegetables. Research was carried out in PubMed, Web of Science, Scopus, ScienceDirect, and LILACS (Latin American and Caribbean Health Sciences Literature) databases. After a careful evaluation, thirty-seven articles were selected. β-lactam-resistant Enterobacteriaceae, including extended-spectrum β-lactamases (ESBLs)-producing, AmpC β-lactamases, and carbapenemases, have been isolated from a wide variety of vegetables. Vegetables are vectors of β-lactam-resistant Enterobacteriaceae, contributing to the dissemination of resistance mechanisms previously observed only in the hospital environment.

Contamination Levels and Phenotypic and Genomic Characterization of Antimicrobial Resistance in Escherichia coli Isolated from Fresh Salad Vegetables in the United Arab Emirates

Contaminated fresh produce has been identified as a vehicle for human foodborne illness. The present study investigated the counts, antimicrobial resistance profile, and genome-based characterization of Escherichia coli in 11 different types of fresh salad vegetable products (n = 400) sampled from retailers in Abu Dhabi and Dubai in the United Arab Emirates. E. coli was detected in 30% of the tested fresh salad vegetable items, with 26.5% of the samples having an unsatisfactory level (≥100 CFU/g) of E. coli, notably arugula and spinach. The study also assessed the effect of the variability in sample conditions on E. coli counts and found, based on negative binominal regression analysis, that samples from local produce had a significantly higher (p-value < 0.001) E. coli count than imported samples. The analysis also indicated that fresh salad vegetables from the soil-less farming system (e.g., hydroponic and aeroponic) had significantly (p-value < 0.001) fewer E. coli than those from traditional produce farming. The study also examined the antimicrobial resistance in E. coli (n = 145) recovered from fresh salad vegetables and found that isolates exhibited the highest phenotypic resistance toward ampicillin (20.68%), tetracycline (20%), and trimethoprim-sulfamethoxazole (10.35%). A total of 20 (13.79%) of the 145 E. coli isolates exhibited a multidrug-resistant phenotype, all from locally sourced leafy salad vegetables. The study further characterized 18 of the 20 multidrug-resistant E. coli isolates using whole-genome sequencing and found that the isolates had varying numbers of virulence-related genes, ranging from 8 to 25 per isolate. The frequently observed genes likely involved in extra-intestinal infection were CsgA, FimH, iss, and afaA. The β-lactamases gene bla_CTX-M-15 was prevalent in 50% (9/18) of the E. coli isolates identified from leafy salad vegetable samples. The study highlights the potential risk of foodborne illness and the likely spread of antimicrobial resistance and resistance genes associated with consuming leafy salad vegetables and emphasizes the importance of proper food safety practices, including proper storage and handling of fresh produce.