Sunlight Parameters Influence the Survival and Decline of Salmonella and Escherichia coli in Water

Application of machine learning for antibiotic resistance in water and wastewater: A systematic review

Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.

Antibiotic resistance associated lactic acid cross tolerance in Shiga-toxin producing E. coli

Introduction

The occurrence of antibiotic resistant (ABR) bacteria in foods is a growing public health challenge. We evaluated sanitizer cross-tolerance among ABR Escherichia coli (E. coli) O157:H7 and non-O157:H7 Shiga-toxin producing E. coli (STEC) serogroups. Sanitizer tolerance in STEC could be a public health concern as mitigation strategies against the pathogen might be compromised.

Methods

Resistance to ampicillin and streptomycin were evolved in E. coli serogroups: O157:H7 (H1730, and ATCC 43895), O121:H19 and O26:H11. Resistance to antibiotics was evolved chromosomally through incremental exposure to ampicillin (amp C) and streptomycin (strep C). Transformation using a plasmid was performed to confer resistance to ampicillin to generate amp P strep C.

Results

The minimum inhibitory concentration (MIC) of lactic acid for all strains evaluated was 0.375% v/v. Analysis of bacterial growth parameters in tryptic soy broth amended with 0.0625% v/v, 0.125% v/v, and 0.25% v/v (subMIC) lactic acid indicated that growth correlated positively with the lag phase duration, and negatively with both the maximum growth rate and change in population density for all strains evaluated except for the highly tolerant variant- O157:H7 amp P strep C. Strains O121 NR (non-ABR), O121 amp C, O121 amp P strep C, O157:H7 H1730 amp C and O157:H7 H1730 amp P strep C were not inactivated after exposure to 1% and 2.5% v/v lactic acid for 300 s. No recovery of cells was observed after the strains were exposed to 5% v/v lactic acid for 300 s. ABR strains O157:H7 H1730 amp C and O157: H7 H1730 amp P strep C demonstrated a high tolerance to lactic acid (P ≤ 0.05).

Conclusion

ABR in isolate E. coli O157: H7 H1730 may improve tolerance to lactic acid. Increased tolerance may be discerned by evaluating growth parameters of bacteria in presence of sub-MIC levels of lactic acid.

Influence of antibiotic-resistance and exudate on peroxyacetic acid tolerance in O157 and non-O157 Shiga toxin producing E. coli

Shiga toxin producing Escherichia coli (STEC) continues to cause foodborne outbreaks associated with beef and beef products despite consistent use of antimicrobial interventions. In this study, the influence of antibiotic resistance (ABR) in E. coli O157:H7 H1730, O157:H7 43,895, O121:H19 and O26:H11 on tolerance to peroxyacetic acid (PAA) was evaluated. Further, bactericidal concentrations of PAA in the presence of nutrient rich media (Tryptic Soy Broth, TSB and beef exudates) and nutrient deficient media (Sterile Deionized Water, SDW and Phosphate Buffered Saline, PBS) were evaluated for all bacterial strains. Antibiotic resistance to ampicillin (amp C), or ampicillin and streptomycin (amp P strep C) was generated in each bacterial strain through incremental exposure to the antibiotics or by plasmid transformation (n = 12 total strains). The mean bactericidal concentrations of PAA were higher (p ≤ 0.05) in nutrient rich media (205.55 ± 31.11 in beef exudate and 195.83 ± 25.00 ppm in TSB) than in nutrient deficient media (57.91 ± 11.97 ppm in SDW and 56.66 ± 9.56 ppm in PBS). Strain O157: H7 ampP strepC was the most tolerant to PAA (p ≤ 0.05). At 200 ppm in nutrient rich media and 60 ppm in nutrient deficient media, all bacterial strains declined in population to below the limit of detection. Analysis of the beef exudates indicated the presence of diverse amino acids that have been associated with acid tolerance. The results from this study indicate that beef exudates could contribute to acid tolerance and suggest that some STEC bacterial strains with certain ABR profiles might be more tolerant to PAA.