Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli

Marciano, David C.; Wang, Chen; Hsu, Teng-Kuei; Bourquard, Thomas; Atri, Benu; Nehring, Ralf B.; Abel, Nicholas S.; Bowling, Elizabeth A.; Chen, Taylor J; Lurie, Pamela D.; Katsonis, Panagiotis; Rosenberg, Susan M.; Herman, Christophe; Lichtarge, Olivier

doi:10.1038/s41467-022-30889-1

Cited by 14 publications

(13 citation statements)

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“…The different classes of antibiotics used in this study against S. enterica have been tested in earlier studies ( Geornaras and von Holy, 2001 ; Jones et al, 2002 ; Muhammad et al, 2010 ; Chang et al, 2021 ). Studies in E. coli have found that resistance against antibiotics changes over the course of ALE studies ( Maeda et al, 2020 ; Marciano et al, 2022 ). E. coli strains sensitive to carbapenem were found to develop resistance against carbapenems by the 15th generation in another ALE study ( Geng et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Adaptive laboratory evolution of Salmonella enterica in acid stress

Ghoshal,

Bechtel,

Gibbons

et al. 2023

Front. Microbiol.

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IntroductionAdaptive laboratory evolution (ALE) studies play a crucial role in understanding the adaptation and evolution of different bacterial species. In this study, we have investigated the adaptation and evolution of Salmonella enterica serovar Enteritidis to acetic acid using ALE.Materials and methodsAcetic acid concentrations below the minimum inhibitory concentration (sub-MIC) were used. Four evolutionary lineages (EL), namely, EL1, EL2, EL3, and EL4, of S. Enteritidis were developed, each demonstrating varying levels of resistance to acetic acid.ResultsThe acetic acid MIC of EL1 remained constant at 27 mM throughout 70 days, while the MIC of EL2, EL3, and EL4 increased throughout the 70 days. EL4 was adapted to the highest concentration of acetic acid (30 mM) and demonstrated the highest increase in its MIC against acetic acid throughout the study, reaching an MIC of 35 mM on day 70. The growth rates of the evolved lineages increased over time and were dependent on the concentration of acetic acid used during the evolutionary process. EL4 had the greatest increase in growth rate, reaching 0.33 (h−1) after 70 days in the presence of 30 mM acetic acid as compared to EL1, which had a growth rate of 0.2 (h−1) after 70 days with no exposure to acetic acid. Long-term exposure to acetic acid led to an increased MIC of human antibiotics such as ciprofloxacin and meropenem against the S. enterica evolutionary lineages. The MIC of ciprofloxacin for EL1 stayed constant at 0.016 throughout the 70 days while that of EL4 increased to 0.047. Bacterial whole genome sequencing revealed single-nucleotide polymorphisms in the ELs in various genes known to be involved in S. enterica virulence, pathogenesis, and stress response including phoP, phoQ, and fhuA. We also observed genome deletions in some of the ELs as compared to the wild-type S. Enteritidis which may have contributed to the bacterial acid adaptation.DiscussionThis study highlights the potential for bacterial adaptation and evolution under environmental stress and underscores the importance of understanding the development of cross resistance to antibiotics in S. enterica populations. This study serves to enhance our understanding of the pathogenicity and survival strategies of S. enterica under acetic acid stress.

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Section: Discussionmentioning

confidence: 99%

Adaptive laboratory evolution of Salmonella enterica in acid stress

Ghoshal,

Bechtel,

Gibbons

et al. 2023

Front. Microbiol.

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“…Food-producing animals and their products have been identified as a source of resistance genes [ 23 ]. The resistance genes in E. coli are acquired through selective pressure, induction, or mutation [ 24 ]. Bacterial AMR genes can be spread horizontally and vertically to other bacteria, and they can also infiltrate the human food chain [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on the Occurrence of Antimicrobial‐Resistant Escherichia coli in Poultry and Poultry Environments in Bangladesh between 2010 and 2021

Islam

Hossain

Sobur

et al. 2023

BioMed Research International

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Antimicrobial resistance (AMR) is a significant public health issue in Bangladesh like many other developing countries where data on resistance trends are scarce. Moreover, the existence of multidrug-resistant (MDR) Escherichia coli exerts an ominous effect on the poultry sector. Therefore, the current systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was conducted to find out the AMR scenarios in E. coli isolates sourced from poultry and poultry environments in Bangladesh between 2010 and 2021. Following the PRISMA guidelines, a total of 17 published scientific articles were selected for this systematic review. This review revealed that 18 out of 64 districts in Bangladesh reported E. coli in poultry, having a higher prevalence (combined prevalence: 69.3%, 95% confidence interval, CI: 67.3-71%). Moreover, the prevalence ranged from 24.3% to 100%. This review found that E. coli isolates showed resistance to 14 antimicrobial classes and 45 different antimicrobial agents, including the last-line (reserve group) antibiotics and banned antimicrobial categories for the treatment of infections in agricultural animals. Phenotypic resistance of E. coli against penicillins and beta-lactamase inhibitors (20.2%-100%), cephalosporins (1.9%-100%), fluoroquinolones (5.98%-100%), aminoglycosides (6%-100%), tetracyclines (17.7%-100%), carbapenems (13.6%-72.7%), macrolides (11.8%-100%), polymyxins (7.9%-100%), phenicols (20%-97.2%), sulfa drugs (44.7%-100%), cephamycins (21.4%-48.8%), nitrofurans (21.4%-63.2%), monobactams (1.2%), and glycylcyclines (2.3%) was recorded in the last decades in Bangladesh. Also, 14 articles reported MDR E. coli in poultry, including a 100% MDR in nine articles and a 92.7% (95% CI: 91.2-94%) combined percentage of MDR E. coli isolates. Twenty-four different AMR genes encoding resistance to beta-lactams (blaTEM, blaCTX-M-1, blaCTX-M-2, blaCTX-M-9, blaOXA-1, blaOXA-47, blaSHV, and CITM), colistin (mcr1 and mcr3), fluoroquinolones (qnrB and qnrS), tetracyclines (tetA, tetB, and tetC), sulfonamides (sulI and sulII), trimethoprim (dfrA1), aminoglycosides (rmtB), streptomycin (aadA1), gentamicin (aac-3-IV), erythromycin (ereA), and chloramphenicol (catA1 and cmlA) were detected in E. coli isolates. The presence of MDR E. coli and their corresponding resistance genes in poultry and poultry environments is an alarming issue for all health communities in Bangladesh. We suggest a regular antimicrobial surveillance program with a strong One Health approach to lessen the hazardous effects of AMR E. coli in poultry industries in Bangladesh.

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“…The mechanism of antibiotics and drugs is not completely understood, particularly in the case of newly arising diseases [ 6 ]. Furthermore, with mutations and other changes, it makes it more difficult to understand these mechanisms [ 77 ]. Additionally, the resistance behavior changes from cell level to micro-organism community level.…”

Section: Ml/dl For Amr Prediction: Challenges and Towards Practical I...mentioning

confidence: 99%

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

2023

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Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.

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Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli

Cited by 14 publications

References 84 publications

Adaptive laboratory evolution of Salmonella enterica in acid stress

Adaptive laboratory evolution of Salmonella enterica in acid stress

A Systematic Review on the Occurrence of Antimicrobial‐Resistant Escherichia coli in Poultry and Poultry Environments in Bangladesh between 2010 and 2021

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

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