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Genomic Characterization of Escherichia coli Isolates Belonging to a New Hybrid aEPEC/ExPEC Pathotype O153:H10-A-ST10 eae-beta1 Occurred in Meat, Poultry, Wildlife and Human Diarrheagenic Samples (2020)

Díaz-Jiménez, Dafne ; García-Meniño, Isidro ; Herrera, Alexandra ; [et al.]

Molecular Diversity Preservation International

In: Antibiotics. 2020; 9(4): 192. Published 2020 Apr 17. doi: 10.3390/antibiotics9040192.

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Publication Date: 2020-04-17

Description: Different surveillance studies (2005–2015) in northwest Spain revealed the presence of eae-positive isolates of Escherichia coli O153:H10 in meat for human consumption, poultry farm, wildlife and human diarrheagenic samples. The aim of this study was to explore the genetic and genomic relatedness between human and animal/meat isolates, as well as the mechanism of its persistence. We also wanted to know whether it was a geographically restricted lineage, or whether it was also reported elsewhere. Conventional typing showed that 32 isolates were O153:H10-A-ST10 fimH54, fimAvMT78, traT and eae-beta1. Amongst these, 21 were CTX-M-32 or SHV-12 producers. The PFGE XbaI-macrorestriction comparison showed high similarity (〉85%). The plasmidome analysis revealed a stable combination of IncF (F2:A-:B-), IncI1 (STunknown) and IncX1 plasmid types, together with non-conjugative Col-like plasmids. The core genome investigation based on the cgMLST scheme from EnteroBase proved close relatedness between isolates of human and animal origin. Our results demonstrate that a hybrid MDR aEPEC/ExPEC of the clonal group O153:H10-A-ST10 (CH11-54) is circulating in our region within different hosts, including wildlife. It seems implicated in human diarrhea via meat transmission, and in the spreading of ESBL genes (mainly of CTX-M-32 type). We found genomic evidence of a related hybrid aEPEC/ExPEC in at least one other country.

Electronic ISSN: 2079-6382

Topics: Chemistry and Pharmacology , Medicine

Published by Molecular Diversity Preservation International

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High Prevalence and Diversity of Cephalosporin-Resistant Enterobacteriaceae Including Extraintestinal Pathogenic E. coli CC648 Lineage in Rural and Urban Dogs in Northwest Spain (2020)

Abreu-Salinas, Fátima ; Díaz-Jiménez, Dafne ; García-Meniño, Isidro ; [et al.]

Molecular Diversity Preservation International

In: Antibiotics. 2020; 9(8): 468. Published 2020 Aug 01. doi: 10.3390/antibiotics9080468.

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Publication Date: 2020-08-01

Description: The aim of this work was to assess the prevalence of extended spectrum-β-lactamase (ESBL)- and carbapenemase-producing Enterobacteriaceae in fecal samples recovered from rural and urban healthy dogs in Northwest Spain (Galicia) to identify potential high-risk clones and to molecularly characterize positive isolates regarding the genes coding for ESBL/pAmpC resistance and virulence. Thirty-five (19.6%) out of 179 dogs were positive for cephalosporin-resistant Enterobacteriaceae, including Escherichiacoli and Klebsiella pneumoniae (39 and three isolates, respectively). All the isolates were multidrug resistant, with high rates of resistance to different drugs, including ciprofloxacin (71.4%). A wide diversity of ESBL/pAmpC enzymes, as well as E. coli phylogroups (A, B1, C, D, E, F and clade I) were found. The eight isolates (20.5%) found to conform to the ExPEC status, belonged to clones O1:H45-clade I-ST770 (CH11-552), O18:H11-A-ST93-CC168 (CH11-neg), O23:H16-B1-ST453-CC86 (CH6-31), and O83:H42-F-ST1485-CC648 (CH231-58), with the latter also complying the uropathogenic (UPEC) status. The three K. pneumoniae recovered produced CTX-M-15 and belonged to the ST307, a clone previously reported in human clinical isolates. Our study highlights the potential role of both rural and urban dogs as a reservoir of high-risk Enterobacteriaceae clones, such as the CC648 of E. coli and antimicrobial resistance traits. Within a One-Health approach, their surveillance should be a priority in the fight against antimicrobial resistance.

Electronic ISSN: 2079-6382

Topics: Chemistry and Pharmacology , Medicine

Published by Molecular Diversity Preservation International

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Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research (2020)

Giacobbe, Daniele Roberto ; Mora, Sara ; Giacomini, Mauro ; [et al.]

Molecular Diversity Preservation International

In: Antibiotics. 2020; 9(2): 54. Published 2020 Jan 31. doi: 10.3390/antibiotics9020054.

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Publication Date: 2020-01-31

Description: The dissemination of multidrug-resistant Gram-negative bacteria (MDR-GNB) is associated with increased morbidity and mortality in several countries. Machine learning (ML) is a branch of artificial intelligence that consists of conferring on computers the ability to learn from data. In this narrative review, we discuss three existing examples of the application of ML algorithms for assessing three different types of risk: (i) the risk of developing a MDR-GNB infection, (ii) the risk of MDR-GNB etiology in patients with an already clinically evident infection, and (iii) the risk of anticipating the emergence of MDR in GNB through the misuse of antibiotics. In the next few years, we expect to witness an increasingly large number of research studies perfecting the application of ML techniques in the field of MDR-GNB infections. Very importantly, this cannot be separated from the availability of a continuously refined and updated ethical framework allowing an appropriate use of the large datasets of medical data needed to build efficient ML-based support systems that could be shared through appropriate standard infrastructures.

Electronic ISSN: 2079-6382

Topics: Chemistry and Pharmacology , Medicine

Published by Molecular Diversity Preservation International

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Artificial Intelligence to Get Insights of Multi-Drug Resistance Risk Factors during the First 48 Hours from ICU Admission (2021)

Mora-Jiménez, Inmaculada ; Tarancón-Rey, Jorge ; Álvarez-Rodríguez, Joaquín ; [et al.]

Molecular Diversity Preservation International

In: Antibiotics. 2021; 10(3): 239. Published 2021 Feb 27. doi: 10.3390/antibiotics10030239.

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Publication Date: 2021-02-27

Description: Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission. We considered clinical and demographic features, mechanical ventilation and the antibiotics taken by the patients during this time interval. Three feature selection strategies were applied to identify statistically significant differences between MDR and non-MDR patient episodes, ending up in 24 selected features. Among them, SAPS III and Apache II scores, the age and the department of origin were identified. Considering these features, we analyzed the potential of machine learning methods for predicting whether a patient will develop a MDR germ during the first 48 h from the ICU admission. Though the results presented here are just a first incursion into this problem, artificial intelligence approaches have a great impact in this scenario, especially when enriching the set of features from the electronic health records.

Electronic ISSN: 2079-6382

Topics: Chemistry and Pharmacology , Medicine

Published by Molecular Diversity Preservation International

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Comprehensive Statistical Evaluation of Etest®, UMIC®, MicroScan and Disc Diffusion versus Standard Broth Microdilution: Workflow for an Accurate Detection of Colistin-Resistant and Mcr-Positive E. coli (2020)

García-Meniño, Isidro ; Lumbreras, Pilar ; Valledor, Pablo ; [et al.]

Molecular Diversity Preservation International

In: Antibiotics. 2020; 9(12): 861. Published 2020 Dec 03. doi: 10.3390/antibiotics9120861.

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Publication Date: 2020-12-03

Description: Four colistin susceptibility testing methods were compared with the standard broth microdilution (BMD) in a collection of 75 colistin-susceptible and 75 mcr-positive E. coli, including ST131 isolates. Taking BMD as reference, all methods showed similar categorical agreement rates (CA) of circa 90%, and a low number of very major errors (VME) (0% for the MicroScan system and Etest®, 0.7% for UMIC®), except for the disc diffusion assay (breakpoint ≤ 11 mm), which yielded false-susceptible results for 8% of isolates. Of note is the number of mcr-positive isolates (17.3%) categorized as susceptible (≤2 mg/L) by the BMD method, but as resistant by the MicroScan system. ST131 mcr-positive E. coli were identified as colistin-resistant by all MIC-based methods. Our results show that applying the current clinical cut-off (〉2 mg/L), many mcr-positive E. coli remain undetected, while applying a threshold of 〉1 mg/L the sensitivity of detection increases significantly without loss of specificity. We propose two possible workflows, both starting with the MicroScan system, since it is automated and, importantly, it categorized all mcr-positive isolates as colistin-resistant. MicroScan should be followed by either BMD or MIC-based commercial methods for colistin resistance detection; or, alternatively, MicroScan, followed by PCR for the mcr screening.

Electronic ISSN: 2079-6382

Topics: Chemistry and Pharmacology , Medicine

Published by Molecular Diversity Preservation International

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